From b8be67b1a7a9fc9dfd6920b5116617ec59206bb1 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Thu, 26 Mar 2026 17:19:55 +0000 Subject: [PATCH 01/49] added KnnMSE --- im2sim/losses/feature.py | 34 ++++++++++++++++++++++++++++++++-- 1 file changed, 32 insertions(+), 2 deletions(-) diff --git a/im2sim/losses/feature.py b/im2sim/losses/feature.py index 9e20b63..7c7fa30 100644 --- a/im2sim/losses/feature.py +++ b/im2sim/losses/feature.py @@ -3,9 +3,39 @@ import torch +from torch_geometric.nn import knn_interpolate + logger = logging.getLogger(__name__) -def mse(gr1, gr2): - return torch.mean((gr1.x[...,3:] - gr2.x[...,3:])**2) +# def mse(gr1, gr2): +# return torch.mean((gr1.x[...,3:] - gr2.x[...,3:])**2) + +def mse(x1, x2): + return torch.mean((x1 - x2)**2) + +class KnnMSE(torch.nn.Module): + + def __init__(self, k=3): + super().__init__() + self.k = k + + def forward(self, true_graph, pred_graph): + c1 = true_graph.x[:,:3] + c2 = pred_graph.x[:,:3] + print(c1.shape, c2.shape, c1.max(), c2.max(), c1.min(), c2.min()) + f1 = true_graph.x[:,3:] + f2 = pred_graph.x[:,3:] + print(f1.shape, f2.shape, f1.max(), f2.max(), f1.min(), f2.min()) + b1 = true_graph.batch + b2 = pred_graph.batch + + + f1_interp = knn_interpolate(f1, c1, c2, b1, b2, k=self.k) + print(f1_interp.shape, f2.shape, f1_interp.max(), f2.max(), f1_interp.min(), f2.min()) + + return mse(f1_interp, f2) + + + From 044a0223bd7f5662790b86de4a2a6815bd4ee4b5 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Thu, 26 Mar 2026 17:30:09 +0000 Subject: [PATCH 02/49] KnnMSE edits --- im2sim/losses/feature.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/im2sim/losses/feature.py b/im2sim/losses/feature.py index 7c7fa30..e5237ed 100644 --- a/im2sim/losses/feature.py +++ b/im2sim/losses/feature.py @@ -22,16 +22,15 @@ def __init__(self, k=3): def forward(self, true_graph, pred_graph): c1 = true_graph.x[:,:3] c2 = pred_graph.x[:,:3] - print(c1.shape, c2.shape, c1.max(), c2.max(), c1.min(), c2.min()) + f1 = true_graph.x[:,3:] f2 = pred_graph.x[:,3:] - print(f1.shape, f2.shape, f1.max(), f2.max(), f1.min(), f2.min()) + b1 = true_graph.batch b2 = pred_graph.batch f1_interp = knn_interpolate(f1, c1, c2, b1, b2, k=self.k) - print(f1_interp.shape, f2.shape, f1_interp.max(), f2.max(), f1_interp.min(), f2.min()) return mse(f1_interp, f2) From 7180e45d15ec1db5eaecb54939ce0a5de5bac1d7 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 30 Mar 2026 10:49:47 +0000 Subject: [PATCH 03/49] bug fixes --- im2sim/data/core.py | 6 +- im2sim/data/ops.py | 12 +-- im2sim/models/image_to_graph.py | 152 ++------------------------------ 3 files changed, 15 insertions(+), 155 deletions(-) diff --git a/im2sim/data/core.py b/im2sim/data/core.py index e802dd1..ef8593e 100644 --- a/im2sim/data/core.py +++ b/im2sim/data/core.py @@ -87,10 +87,10 @@ class Dataset(torch.utils.data.Dataset): >>> >>> dataset = im2sim.data.Dataset(load_fn=load, cases=cases) """ - def __init__(self, load_fn, cases, transforms=[]): + def __init__(self, load_fn, cases, transforms=None): self.load_fn = load_fn self.cases = cases - self.transforms = transforms + self.transforms = transforms if transforms is not None else [] def __len__(self): return len(self.cases) @@ -516,7 +516,7 @@ def fit(self, dataset): temp_dataset = copy.deepcopy(dataset) for i in range(n): if self.transforms[i].is_fittable: - temp_dataset.transforms = [t.forward for t in self.transforms[:i]] + temp_dataset.transforms = [Pipeline(self.transforms[:i])] dataloader = DataLoader(temp_dataset,batch_size=1) self.transforms[i].fit(dataloader) return self diff --git a/im2sim/data/ops.py b/im2sim/data/ops.py index 5084737..8d7b35d 100644 --- a/im2sim/data/ops.py +++ b/im2sim/data/ops.py @@ -7,16 +7,16 @@ eps = 1e-8 def normtorange(x, max=None, min=None, a=0, b=1): - if min==None: + if min is None: min = x.min() - if max==None: + if max is None: max = x.max() return a + ((x-min)*(b-a))/(max-min+eps) def inv_normtorange(x, max=None, min=None, a=0, b=1): - if min==None: + if min is None: min = x.min() - if max==None: + if max is None: max = x.max() return min + ((x-a)*(max-min))/(b-a) @@ -28,9 +28,9 @@ def inv_normalise(x, max, min): return inv_normtorange(x, max, min) def standardise(x, mean=None, std=None): - if mean==None: + if mean is None: mean=x.mean() - if std==None: + if std is None: std=x.std() return (x-mean)/(std+eps) diff --git a/im2sim/models/image_to_graph.py b/im2sim/models/image_to_graph.py index 38de625..e64cdce 100644 --- a/im2sim/models/image_to_graph.py +++ b/im2sim/models/image_to_graph.py @@ -93,145 +93,6 @@ def forward(self, x, template): return outputs -# class I2GUNet(nn.Module): - -# def __init__(self, -# in_channels, -# out_channels, -# filters=[16,32,64,128,256], -# cnn_kernel_size=3, -# cnn_res_depth=3, -# cnn_res_blocks_per_level=2, -# cnn_rank=3, -# cnn_norm_type=None, -# cnn_pool_type='MaxPool', -# cnn_pool_size=2, -# cnn_activation='relu', -# gnn_res_depth = 3, -# gnn_n_process_blocks = 1, -# gnn_n_deform_blocks = 3, -# template_edge_index=None, -# gnn_conv_type="ChebConv", -# gnn_conv_kwargs={'K':3}, -# gnn_activation="relu", -# out_activation="linear", -# gnn_norm_type="InstanceNorm", -# batched_ops=True): -# super().__init__() - -# logger.debug("Defining I2G layers...") -# self.batched_ops = batched_ops -# self.n_levels = len(filters) - -# self.encoder = ImageResEncoder(in_channels=in_channels, -# filters=filters, -# kernel_size=cnn_kernel_size, -# res_depth=cnn_res_depth, -# res_blocks_per_level=cnn_res_blocks_per_level, -# rank=cnn_rank, -# norm_type=cnn_norm_type, -# pool_type=cnn_pool_type, -# pool_size=cnn_pool_size, -# activation=cnn_activation) - -# gnn_config = dict(depth=gnn_res_depth, -# conv_type=gnn_conv_type, -# conv_kwargs=gnn_conv_kwargs, -# activation=gnn_activation, -# norm_type=gnn_norm_type) - -# self.align_conv = nn.ModuleList([ -# gnn.Sequential('x, edge_index, batch',[ -# (GraphConvResBlock(in_channels=out_channels*2 if i==0 else filt, -# filters=filt, -# **gnn_config),'x, edge_index -> x') -# for i in range(gnn_n_process_blocks) -# ]) -# for filt in reversed(filters) -# ]) - -# self.deform_conv = nn.ModuleList([ -# gnn.Sequential('x, edge_index, batch',[ -# (GraphConvResBlock(in_channels=filt*2 + out_channels if i==0 else filt, -# filters=filt, -# **gnn_config), 'x, edge_index -> x') -# for i in range(gnn_n_deform_blocks) -# ]) -# for filt in reversed(filters) -# ]) - -# self.convert_convs = nn.ModuleList([ -# GraphConvBlock(in_channels=filt, -# filters=out_channels, -# depth=1, -# conv_type=gnn_conv_type, -# conv_kwargs=gnn_conv_kwargs, -# activation=out_activation, -# norm_type=None) -# for filt in reversed(filters) -# ]) - -# self.pools = nn.ModuleList([gnn.TopKPooling(in_channels=3) for _ in range(len(filters)-1)]) - - -# logger.debug("Done") - -# def forward(self, img, template): -# logger.debug("In model forward pass...") -# # Get encoder outputs -# encoder_outputs = self.encoder(img) -# encoder_outputs.reverse() -# # Get pooled coordinates of the template - -# x, edge_index, batch = template.x, template.edge_index, template.batch -# feats, edges, batches,perms = [x],[edge_index],[batch],[] -# # layer = TopKPooling(in_channels=3) -# # logger.debug("device: x:%s, edge:%s, batch:%s, layer:%s", x.device, edge_index.device, batch.device, next(layer.parameters()).device) - -# for pool in self.pools: -# logger.debug("Pooling the template...") - -# x, edge_index, _, batch, perm, _ = pool(x, edge_index, batch=batch) -# feats.append(x) -# edges.append(edge_index) -# batches.append(batch) -# logger.debug("shape - x:%s", tuple(x.shape)) -# perms.append(perm) -# feats.reverse() # [10k, 5k, 2.5k, 1.25k, 600].reverse() -# edges.reverse() -# batches.reverse() -# perms.append(torch.ones(feats[0].shape[0]).to(torch.bool)) # add a selector that does nothing for the bottom level -# perms.reverse() # [5k, 2.5k,1.24k,600].reverse() - -# curr_mesh = feats[0] -# outputs=[] -# for enc, feat, edge, batch, perm, aconv, cconv, dconv in zip(encoder_outputs, feats, edges, batches, perms, self.align_conv, self.convert_convs, self.deform_conv): -# logger.debug("In graph decoder...") -# temp = torch.zeros_like(feat) -# temp[perm] = curr_mesh -# curr_mesh = torch.cat([temp, feat], axis=-1) # [N,6] -# logger.debug("curr_mesh:%s, edge:%s", tuple(curr_mesh.shape), tuple(edge.shape)) -# graph_features = aconv(curr_mesh, edge) # [N,6] -> [N,F] -# logger.debug("curr_mesh after align conv:%s", tuple(graph_features.shape)) -# proc_mesh = temp + cconv(graph_features,edge) # [N,F] -> [N,3] -# logger.debug("curr_mesh after convert conv:%s", tuple(proc_mesh.shape)) -# proj = TrilinearProjection(domain_size=img.shape[-3:], batch_ops=self.batched_ops)(enc, proc_mesh[:,:3], batch) -# logger.debug("projection shape:%s", tuple(proj.shape)) -# curr_mesh = torch.cat([proc_mesh, graph_features, proj], axis=-1) # [N, 3+F+C] -# logger.debug("deform conv input shape:%s", tuple(curr_mesh.shape)) -# curr_mesh = dconv(curr_mesh, edge) # [N,F] -# logger.debug("deform conv output shape:%s", tuple(curr_mesh.shape)) -# curr_mesh = proc_mesh + cconv(curr_mesh, edge) # [N,3] -# logger.debug("convert conv output shape:%s", tuple(curr_mesh.shape)) -# out_graph = Data() -# out_graph.x = curr_mesh -# out_graph.edge_index = edge -# out_graph.batch = batch -# outputs.append(out_graph) -# outputs[-1] = template.clone() -# outputs[-1].x = curr_mesh -# return outputs - class I2GUNet(nn.Module): def __init__(self, @@ -304,19 +165,18 @@ def forward(self, img, template): outputs = [] deformation = torch.zeros_like(multi_template[0].x) - # features = multi_template[0].x.clone() + for i, (dec_layer, enc) in enumerate(zip(self.decoder, encoder_features)): t = multi_template[i] deformation = dec_layer(enc,deformation,t.x, t.edge_index, t.batch) - out_graph = Data(x=t.x+deformation, edge_index=t.edge_index, batch=t.batch) - outputs.append(out_graph) if i < self.n_levels-1: + out_graph = Data(x=t.x+deformation, edge_index=t.edge_index, batch=t.batch) + outputs.append(out_graph) deformation = gnn.unpool.knn_interpolate(deformation, t.x, multi_template[i+1].x) - - - # outputs[-1] = template.clone() - # outputs[-1].x = template.x+deformation+prev_deformation + out_graph = template.clone() + out_graph.x = template.x+deformation + outputs.append(out_graph) return outputs From 9c068a30b83464852efa0d595fc1963a8fc004e4 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Wed, 1 Apr 2026 16:11:58 +0000 Subject: [PATCH 04/49] gcn bug fix, mesh_utils changed attr setting, added mesh losses --- im2sim/data/mesh_utils.py | 70 +++++++++++++++++------ im2sim/layers/graph_convs.py | 2 +- im2sim/losses/mesh.py | 108 ++++++++++++++++++++++++++++++++++- 3 files changed, 158 insertions(+), 22 deletions(-) diff --git a/im2sim/data/mesh_utils.py b/im2sim/data/mesh_utils.py index 43f29d3..dc80880 100644 --- a/im2sim/data/mesh_utils.py +++ b/im2sim/data/mesh_utils.py @@ -5,25 +5,59 @@ import torch_geometric.nn as gnn -# def get_node_ids(mesh): -# cell_ids = np.zeros((len(mesh.points),)) -# for id in np.unique(mesh['CellEntityIds']): -# cells = mesh.extract_cells(np.where(mesh['CellEntityIds'] == id)[0])['vtkOriginalPointIds'] -# nodes = np.unique(cells) -# cell_ids[nodes] = id -# return cell_ids - -def add_structure_masks(data, mesh, structure_list): +def get_structure_ids(mesh, structure_dict): + cells = get_structure_cells(mesh, structure_dict) + + if cells == -1: + return -1 + + ids = {f'{k.split("_cell_index")[0]}_index':torch.unique(v).unsqueeze(0) for k, v in cells.items()} + return ids + + +def get_structure_edges(mesh, structure_dict): + cells = get_structure_cells(mesh, structure_dict) + + if cells == -1: + return -1 + + edges = {k:None for k in structure_dict} + + for k, v in cells.items(): + e_ids = list(combinations(range(v.shape[0]),2)) + cell_edges = v[e_ids,:] + edges[f'{k.split("_cell_index")[0]}': cell_edges.permute(1,0,2).reshape(2,-1)] + + edges = {f'{k}_edge_index':v for k,v in edges.items()} + return edges + + +def get_structure_cells(mesh, structure_dict): ids = np.unique(mesh['CellEntityIds']) - missing_ids = set(range(len(structure_list))) - set(ids.tolist()) - for id in missing_ids: - setattr(data, f'{structure_list[id]}_mask', torch.zeros((len(mesh.points))).to(torch.bool)) - for id, name in zip(ids, structure_list): - cell_ids = torch.zeros((len(mesh.points))) - cells = mesh.extract_cells(np.where(mesh['CellEntityIds'] == id)[0])['vtkOriginalPointIds'] - nodes = np.unique(cells) - cell_ids[nodes] = 1 - setattr(data, f'{name}_mask', cell_ids.to(torch.bool)) + + missing_ids = set(structure_dict.keys()) - set(ids.tolist()) + if len(missing_ids) != 0: + return -1 + + out_dict = {k:None for k in structure_dict.values()} + + + for id, k in structure_dict.items(): + submesh = mesh.extract_cells(np.where(mesh['CellEntityIds'] == id)[0]) + subcells = submesh.cells.reshape(-1, submesh.cells[0]+1)[:,1:] + cells = submesh['vtkOriginalPointIds'][subcells] + out_dict[k] = torch.from_numpy(cells).permute(1,0).to(torch.int) + + out_dict = {f'{k}_cell_index':v for k,v in out_dict.items()} + + return out_dict + + +def set_attrs(data, attrs): + for k,v in attrs.items(): + setattr(data, k, v) + + def get_edges_tet(mesh): diff --git a/im2sim/layers/graph_convs.py b/im2sim/layers/graph_convs.py index 10c3904..33bd835 100644 --- a/im2sim/layers/graph_convs.py +++ b/im2sim/layers/graph_convs.py @@ -56,7 +56,7 @@ def __init__(self, def forward(self, x, edge_index): for conv, norm in zip(self.convs, self.norms): logger.debug("Graph features shape:%s", x.shape) - x = norm(self.act(conv(x,edge_index))) + x = self.act(norm(conv(x,edge_index))) return x class GraphConvResBlock(nn.Module): diff --git a/im2sim/losses/mesh.py b/im2sim/losses/mesh.py index a6a2c6b..7934470 100644 --- a/im2sim/losses/mesh.py +++ b/im2sim/losses/mesh.py @@ -2,6 +2,7 @@ import logging from ..data.mesh_utils import _compute_edge_lengths + import torch logger = logging.getLogger(__name__) @@ -15,11 +16,112 @@ def _edge_length_deviation(points, edges): std_dev = lengths.sum(-1).std() return std_dev +def aspect_ratio_loss(gr1, gr2): + tet_vertices = gr2.x[gr2.tet_index, :3] + ar = _aspect_ratio(tet_vertices) + return ar def _aspect_ratio(tet_vertices): vert_ids = list(combinations(range(4),2)) - edge_coords = tet_vertices[...,vert_ids,:] - distances = torch.linalg.norm(edge_coords[...,0,:]-edge_coords[...,1,:], dim=-1) - aspect_ratio = distances.max(-1).values/distances.mean(-1) + edge_coords = tet_vertices[vert_ids,:] + distances = torch.linalg.norm(edge_coords[:,0,:,:]-edge_coords[:,1,:,:], dim=-1) + aspect_ratio = distances.max(0).values/distances.mean(0) return aspect_ratio.mean() + + +def _face_norm(face_verts): + side1 = face_verts[1] - face_verts[0] + side2 = face_verts[2] - face_verts[0] + + norm_vec = torch.cross(side1,side2, dim=-1) + unit_norm = norm_vec/(torch.norm(norm_vec, dim=-1, keepdim=True)+1e-8) + return unit_norm + + +def face_norm_loss(x1,x2,b1,b2,f1,f2): + # x:[N,3], f:[3,M], norm: [3,M,3] + norm1 = _face_norm(x1[f1,:]) + norm2 = _face_norm(x2[f2,:]) + + batch1 = b1[f1[0]] + batch2 = b2[f2[0]] + + consistency = torch.Tensor([0.0]) + similarity = torch.Tensor([0.0]) + + for b in torch.unique(b1): + mask1 = (batch1 == b) + mask2 = (batch2 == b) + consistency += torch.norm(norm2[mask2].std(0)) + similarity += torch.norm(norm1[mask1].mean(0) - norm2[mask2].mean(0)) + + + return consistency + similarity + + + +class FaceNormalLoss(torch.nn.Module): + + def __init__(self, face_key = None): + super().__init__() + if isinstance(face_key, str): + self.select = lambda obj: getattr(obj, face_key) + else: + raise ValueError(f"face_key must be a graph attribute but is {face_key}") + + + def forward(self, gr1, gr2): + faces1 = self.select(gr1) + faces2 = self.select(gr2) + loss = face_norm_loss(x1 = gr1.x[:,:3], + x2 = gr2.x[:,:3], + b1 = gr1.batch, + b2 = gr2.batch, + f1 = faces1, + f2 = faces2) + return loss + +def tet_det(x, cells): + """Return signed 6*volume per tet (scalar triple product).""" + + a = x[cells[0]] + b = x[cells[1]] + c = x[cells[2]] + d = x[cells[3]] + + e1 = b - a + e2 = c - a + e3 = d - a + + det = (torch.cross(e1, e2) * e3).sum(-1) # signed det(D) = signed 6V + return det + + + +def inversion_loss(points, cells, min_vol=1e-3): + + det6 = tet_det(points, cells) + + vol = det6 / 6.0 + + return torch.maximum(torch.zeros(1), min_vol - vol).mean() + + +class InversionLoss(torch.nn.Module): + + def __init__(self, cell_key, min_vol = 1e-3): + super().__init__() + if isinstance(cell_key, str): + self.select = lambda obj: getattr(obj, cell_key) + else: + raise ValueError(f"face_key must be a graph attribute but is {cell_key}") + self.min_vol = min_vol + + + def forward(self, gr1, gr2): + cells = self.select(gr2) + loss = inversion_loss(x = gr2.x[:,:3], + cells = cells, + min_vol=self.min_vol) + return loss \ No newline at end of file From 23b026cbb092836a843421fd1f6fac9aac1cb44d Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Wed, 1 Apr 2026 16:29:23 +0000 Subject: [PATCH 05/49] added aspect ratio loss --- im2sim/losses/mesh.py | 24 ++++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/im2sim/losses/mesh.py b/im2sim/losses/mesh.py index 7934470..8405006 100644 --- a/im2sim/losses/mesh.py +++ b/im2sim/losses/mesh.py @@ -16,19 +16,31 @@ def _edge_length_deviation(points, edges): std_dev = lengths.sum(-1).std() return std_dev -def aspect_ratio_loss(gr1, gr2): - tet_vertices = gr2.x[gr2.tet_index, :3] - ar = _aspect_ratio(tet_vertices) - return ar - -def _aspect_ratio(tet_vertices): +def _aspect_ratio(x, cells): + tet_vertices = x[cells,:] vert_ids = list(combinations(range(4),2)) edge_coords = tet_vertices[vert_ids,:] distances = torch.linalg.norm(edge_coords[:,0,:,:]-edge_coords[:,1,:,:], dim=-1) aspect_ratio = distances.max(0).values/distances.mean(0) return aspect_ratio.mean() +class AspectRatioLoss(torch.nn.Module): + + def __init__(self, cell_key): + super().__init__() + if isinstance(cell_key, str): + self.select = lambda obj: getattr(obj, cell_key) + else: + raise ValueError(f"face_key must be a graph attribute but is {cell_key}") + + + def forward(self, gr1, gr2): + cells = self.select(gr2) + loss = _aspect_ratio(x = gr2.x[:,:3], + cells = cells) + return loss + def _face_norm(face_verts): side1 = face_verts[1] - face_verts[0] From cd03961649363713a156f3d73d814a1f98428c0f Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Wed, 1 Apr 2026 17:07:32 +0000 Subject: [PATCH 06/49] bug fix in get_structure_ids --- im2sim/data/mesh_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/im2sim/data/mesh_utils.py b/im2sim/data/mesh_utils.py index dc80880..42129ab 100644 --- a/im2sim/data/mesh_utils.py +++ b/im2sim/data/mesh_utils.py @@ -11,7 +11,7 @@ def get_structure_ids(mesh, structure_dict): if cells == -1: return -1 - ids = {f'{k.split("_cell_index")[0]}_index':torch.unique(v).unsqueeze(0) for k, v in cells.items()} + ids = {f'{k.split("_cell_index")[0]}_index':torch.unique(v) for k, v in cells.items()} return ids From fbb4e0f628d8949091c7dc18368bf0831f9e03fd Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Wed, 1 Apr 2026 17:16:02 +0000 Subject: [PATCH 07/49] fixed bug in facenorm loss --- im2sim/losses/mesh.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/im2sim/losses/mesh.py b/im2sim/losses/mesh.py index 8405006..0e484e0 100644 --- a/im2sim/losses/mesh.py +++ b/im2sim/losses/mesh.py @@ -62,7 +62,7 @@ def face_norm_loss(x1,x2,b1,b2,f1,f2): consistency = torch.Tensor([0.0]) similarity = torch.Tensor([0.0]) - for b in torch.unique(b1): + for b in torch.unique(b1).tolist(): mask1 = (batch1 == b) mask2 = (batch2 == b) consistency += torch.norm(norm2[mask2].std(0)) From b2cf7a1963600ac4975315583e4a20894ba7c0af Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Wed, 1 Apr 2026 17:30:50 +0000 Subject: [PATCH 08/49] to device bugfixes --- im2sim/losses/mesh.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/im2sim/losses/mesh.py b/im2sim/losses/mesh.py index 0e484e0..68e6d88 100644 --- a/im2sim/losses/mesh.py +++ b/im2sim/losses/mesh.py @@ -59,8 +59,8 @@ def face_norm_loss(x1,x2,b1,b2,f1,f2): batch1 = b1[f1[0]] batch2 = b2[f2[0]] - consistency = torch.Tensor([0.0]) - similarity = torch.Tensor([0.0]) + consistency = torch.Tensor([0.0]).to(norm1.device) + similarity = torch.Tensor([0.0]).to(norm1.device) for b in torch.unique(b1).tolist(): mask1 = (batch1 == b) @@ -117,7 +117,7 @@ def inversion_loss(points, cells, min_vol=1e-3): vol = det6 / 6.0 - return torch.maximum(torch.zeros(1), min_vol - vol).mean() + return torch.maximum(torch.zeros(1).to(vol.device), min_vol - vol).mean() class InversionLoss(torch.nn.Module): From 55aa210916cf77ced30e62b72187c969559d6e4d Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Wed, 1 Apr 2026 17:33:44 +0000 Subject: [PATCH 09/49] inversion param correction --- im2sim/losses/mesh.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/im2sim/losses/mesh.py b/im2sim/losses/mesh.py index 68e6d88..a8f9ddc 100644 --- a/im2sim/losses/mesh.py +++ b/im2sim/losses/mesh.py @@ -111,9 +111,9 @@ def tet_det(x, cells): -def inversion_loss(points, cells, min_vol=1e-3): +def inversion_loss(x, cells, min_vol=1e-3): - det6 = tet_det(points, cells) + det6 = tet_det(x, cells) vol = det6 / 6.0 From 41d1181d0ae8a35baaeaef3f9cb7b8d514506938 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Thu, 2 Apr 2026 20:08:42 +0000 Subject: [PATCH 10/49] made graph res bug fix --- im2sim/layers/graph_convs.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/im2sim/layers/graph_convs.py b/im2sim/layers/graph_convs.py index 33bd835..c526422 100644 --- a/im2sim/layers/graph_convs.py +++ b/im2sim/layers/graph_convs.py @@ -102,7 +102,7 @@ def forward(self, x, edge_index): x = self.convs[1](x1,edge_index) for conv in self.convs[2:]: x = conv(x,edge_index) - return x+x1 + return (x+x1)/2 class GraphResDecoderBlock(nn.Module): @@ -158,7 +158,7 @@ def __init__(self, self.deform_conv = nn.ModuleList([ - GraphConvResBlock(in_channels=filters[0] + projection_channels if i==0 else filters[1], + GraphConvResBlock(in_channels=filters[0] + projection_channels+out_channels if i==0 else filters[1], filters=filters[1], **conv_config) for i in range(n_deform_blocks) @@ -182,7 +182,7 @@ def forward(self,graph_features,encoder_projection,prev_results,edge_index): x=graph_features.clone() logger.debug("Process convs...") for pconv in self.process_conv: x=pconv(x,edge_index) - x = torch.cat([x, encoder_projection], axis=-1) + x = torch.cat([x, encoder_projection, prev_results], axis=-1) logger.debug("Decoder convs") for dconv in self.deform_conv: x=dconv(x, edge_index) res = self.out_conv(x, edge_index) + prev_results From 1ac61e350254db660650aba4026af4252b885e7d Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Wed, 8 Apr 2026 13:55:43 +0000 Subject: [PATCH 11/49] made changes to graphresblock and convresblock to match image2flow --- im2sim/layers/graph_convs.py | 84 ++++++++++++++++++++------------- im2sim/layers/image_convs.py | 33 ++++++++----- im2sim/models/image_to_graph.py | 2 +- 3 files changed, 74 insertions(+), 45 deletions(-) diff --git a/im2sim/layers/graph_convs.py b/im2sim/layers/graph_convs.py index c526422..82a52df 100644 --- a/im2sim/layers/graph_convs.py +++ b/im2sim/layers/graph_convs.py @@ -86,23 +86,32 @@ def __init__(self, norm_type='InstanceNorm'): super().__init__() - self.convs=nn.ModuleList( - GraphConvBlock(in_channels=in_channels if i==0 else filters, - filters=filters, - conv_type=conv_type, - conv_kwargs=conv_kwargs, - activation=activation, - norm_type=norm_type) - for i in range(depth) - ) + conv = get_graph_layer(conv_type) + self.convs = nn.ModuleList([ + conv(in_channels if i==0 else filters, filters, **conv_kwargs) + for i in range(depth) + ]) + self.norms = nn.ModuleList([ + get_graph_layer(norm_type)(filters) if norm_type else nn.Identity() + for _ in range(depth) + ]) + self.act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() def forward(self, x, edge_index): - x1 = self.convs[0](x,edge_index) - x = self.convs[1](x1,edge_index) - for conv in self.convs[2:]: - x = conv(x,edge_index) - return (x+x1)/2 + + for i, (conv,norm) in enumerate(zip(self.convs, self.norms)): + + x = norm(conv(x, edge_index)) + + if i==0: + x1 = x.clone() + elif i Date: Mon, 13 Apr 2026 14:57:01 +0000 Subject: [PATCH 12/49] changes to graphres and imgeres to stop spikiness --- im2sim/data/mesh_utils.py | 66 ++++++++++++++++++++++++++++-------- im2sim/layers/graph_convs.py | 12 +++---- im2sim/layers/image_convs.py | 2 +- im2sim/layers/projections.py | 2 ++ im2sim/models/utils.py | 6 ++-- 5 files changed, 63 insertions(+), 25 deletions(-) diff --git a/im2sim/data/mesh_utils.py b/im2sim/data/mesh_utils.py index 42129ab..cc72621 100644 --- a/im2sim/data/mesh_utils.py +++ b/im2sim/data/mesh_utils.py @@ -3,9 +3,22 @@ from itertools import combinations from torch_geometric.utils import to_undirected import torch_geometric.nn as gnn +from torch_geometric.data import Data +from pyvista.core.pointset import PointGrid +from typing import Dict -def get_structure_ids(mesh, structure_dict): +def get_structure_ids(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: + """ + Extracts node ids for different substructures in a pyvista PointGrid object. + + Args: + mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. + structure_dict (Dict[int, str]): A dictionary that maps pyvista 'CellEntityIds' to structure names. + + Returns: + ids (Dict[str, torch.Tensor]): A dictionary with items in the format 'structurename_index': torch.Tensor(N), where N is the number of nodes in the structure. + """ cells = get_structure_cells(mesh, structure_dict) if cells == -1: @@ -15,7 +28,18 @@ def get_structure_ids(mesh, structure_dict): return ids -def get_structure_edges(mesh, structure_dict): +def get_structure_edges(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: + """ + Extracts edges for different substructures in a pyvista PointGrid object. + + Args: + mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. + structure_dict (Dict[int, str]): A dictionary that maps pyvista 'CellEntityIds' to structure names. + + Returns: + edges (Dict[str, torch.Tensor]): A dictionary with items in the format 'structurename_index': torch.Tensor(2, N), + where N is the number of edges in the structure. + """ cells = get_structure_cells(mesh, structure_dict) if cells == -1: @@ -26,13 +50,24 @@ def get_structure_edges(mesh, structure_dict): for k, v in cells.items(): e_ids = list(combinations(range(v.shape[0]),2)) cell_edges = v[e_ids,:] - edges[f'{k.split("_cell_index")[0]}': cell_edges.permute(1,0,2).reshape(2,-1)] + edges[f'{k.split("_cell_index")[0]}'] = cell_edges.permute(1,0,2).reshape(2,-1) edges = {f'{k}_edge_index':v for k,v in edges.items()} return edges -def get_structure_cells(mesh, structure_dict): +def get_structure_cells(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: + """ + Extracts cells for different substructures in a pyvista PointGrid object. + + Args: + mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. + structure_dict (Dict[int, str]): A dictionary that maps pyvista 'CellEntityIds' to structure names. + + Returns: + cells (Dict[str, torch.Tensor]): A dictionary with items in the format 'structurename_index': torch.Tensor(m, N), where m is 3 for triangles and 4 for tetrahedrons + and N is the number of cells in the structure. + """ ids = np.unique(mesh['CellEntityIds']) missing_ids = set(structure_dict.keys()) - set(ids.tolist()) @@ -53,13 +88,21 @@ def get_structure_cells(mesh, structure_dict): return out_dict -def set_attrs(data, attrs): +def set_attrs(data:Data, attrs:Dict[str, torch.Tensor]) -> None: + """ + A helper function to set multiple attributes of a PyG Data object with keys and values from a dictionary. + + Args: + data (torch_geometric.data.Data): Data object to be modified + attrs (Dict[str, torch.Tensor]): a dictionary of attribute names and values to be set in the Data object + + Returns: + None + """ for k,v in attrs.items(): setattr(data, k, v) - - def get_edges_tet(mesh): tet_cells = mesh.extract_cells(np.where(mesh['CellEntityIds'] == 0)[0]) tet_cells = tet_cells.cells.reshape(-1, 5)[:, 1:] @@ -72,11 +115,7 @@ def get_node_features(mesh, feature_names): features = torch.from_numpy(np.array([mesh.point_data[name] for name in feature_names]).T) return features -# def set_structure_masks(data, mesh, structure_list): -# node_ids = get_node_ids(mesh) -# for id, structure in enumerate(structure_list): -# setattr(data, f"is_{structure}", torch.from_numpy(node_ids==id)) -# return data + def get_tet_cells(mesh): tet_cells = mesh.extract_cells(np.where(mesh['CellEntityIds'] == 0)[0]) @@ -86,12 +125,11 @@ def get_tet_cells(mesh): def make_padded_batch(x, batch): jagged_x = [x[batch==i] for i in torch.unique(batch)] - padded_x= torch.nn.utils.rnn.pad_sequence(jagged_x, batch_first=True) + padded_x = torch.nn.utils.rnn.pad_sequence(jagged_x, batch_first=True) lengths = torch.tensor([len(s) for s in jagged_x]) mask = torch.arange(padded_x.size(1))[None, :] < lengths[:, None] return padded_x, mask - def _compute_edge_lengths(points, edges): coords = points[edges] distances = (coords[0] - coords[1])**2 diff --git a/im2sim/layers/graph_convs.py b/im2sim/layers/graph_convs.py index 82a52df..9a1a49f 100644 --- a/im2sim/layers/graph_convs.py +++ b/im2sim/layers/graph_convs.py @@ -35,7 +35,7 @@ def __init__(self, filters, depth=1, conv_type='ChebConv', - conv_kwargs={'K':3}, + conv_kwargs={'K':1}, activation='relu', norm_type='InstanceNorm'): super().__init__() @@ -47,7 +47,7 @@ def __init__(self, ]) self.norms = nn.ModuleList([ - get_graph_layer(norm_type)(filters) if norm_type else nn.Identity() + get_graph_layer(norm_type)(filters, affine=True) if norm_type else nn.Identity() for _ in range(depth) ]) @@ -81,7 +81,7 @@ def __init__(self, filters, depth=3, conv_type='ChebConv', - conv_kwargs={'K':3}, + conv_kwargs={'K':1}, activation='relu', norm_type='InstanceNorm'): super().__init__() @@ -92,7 +92,7 @@ def __init__(self, for i in range(depth) ]) self.norms = nn.ModuleList([ - get_graph_layer(norm_type)(filters) if norm_type else nn.Identity() + get_graph_layer(norm_type)(filters, affine=True) if norm_type else nn.Identity() for _ in range(depth) ]) self.act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() @@ -168,7 +168,7 @@ def __init__(self, self.deform_conv = nn.ModuleList([ - GraphConvResBlock(in_channels=filters[0] + projection_channels+out_channels if i==0 else filters[1], + GraphConvResBlock(in_channels=filters[0] + projection_channels+ out_channels if i==0 else filters[1], filters=filters[1], depth=res_depth, conv_type=conv_type, @@ -185,7 +185,6 @@ def __init__(self, conv_kwargs=conv_kwargs, activation=out_activation, norm_type=None) - self.edge_index=template_edge_index def forward(self,graph_features,encoder_projection,prev_results,edge_index): @@ -199,7 +198,6 @@ def forward(self,graph_features,encoder_projection,prev_results,edge_index): x = self.process_conv(x,edge_index) x = torch.cat([x, encoder_projection, prev_results], axis=-1) - logger.debug("Decoder convs") for dconv in self.deform_conv: x=dconv(x, edge_index) diff --git a/im2sim/layers/image_convs.py b/im2sim/layers/image_convs.py index ae28547..95c3d99 100644 --- a/im2sim/layers/image_convs.py +++ b/im2sim/layers/image_convs.py @@ -41,7 +41,7 @@ def __init__(self, ]) self.norms = nn.ModuleList([ - get_image_layer(norm_type, rank)(filters) if norm_type else nn.Identity() + get_image_layer(norm_type, rank)(filters, affine=True) if norm_type else nn.Identity() for _ in range(depth) ]) self.drop = nn.Dropout1d(p=dropout_rate) if dropout_rate else nn.Identity() diff --git a/im2sim/layers/projections.py b/im2sim/layers/projections.py index 315cb29..633bf83 100644 --- a/im2sim/layers/projections.py +++ b/im2sim/layers/projections.py @@ -58,4 +58,6 @@ def forward(self, encoder_outputs, graph_coords, batch): logger.debug("projection %d shape:%s",i,tuple(projections[-1].shape)) projections = torch.cat(projections, dim=0) logger.debug("final projection shape:%s",tuple(projections.shape)) + if logger.isEnabledFor(logging.DEBUG): + logger.debug("final projection vals: %.2f-%.2f",projections.min(), projections.max()) return projections diff --git a/im2sim/models/utils.py b/im2sim/models/utils.py index 4203b8a..627dc5a 100644 --- a/im2sim/models/utils.py +++ b/im2sim/models/utils.py @@ -34,10 +34,10 @@ def get_model_config(name): gnn_n_deform_blocks = 3, template_edge_index=None, gnn_conv_type="ChebConv", - gnn_conv_kwargs={'K':3}, + gnn_conv_kwargs={'K':1}, gnn_activation="leaky_relu", - out_activation="linear", + out_activation="leaky_relu", gnn_norm_type="InstanceNorm", - batched_ops=True) + batched_ops=False) } From 0027b49fbc4400c569858f5afef67508b295481f Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 21 Apr 2026 11:45:27 +0000 Subject: [PATCH 13/49] added tf-style projection layer --- im2sim/data/mesh_utils.py | 133 ++++++++++++++++++++++++++------ im2sim/layers/projections.py | 90 ++++++++++++++++++++- im2sim/models/image_to_graph.py | 2 +- 3 files changed, 198 insertions(+), 27 deletions(-) diff --git a/im2sim/data/mesh_utils.py b/im2sim/data/mesh_utils.py index cc72621..e25096e 100644 --- a/im2sim/data/mesh_utils.py +++ b/im2sim/data/mesh_utils.py @@ -5,7 +5,7 @@ import torch_geometric.nn as gnn from torch_geometric.data import Data from pyvista.core.pointset import PointGrid -from typing import Dict +from typing import Dict, List, Tuple def get_structure_ids(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: @@ -103,47 +103,132 @@ def set_attrs(data:Data, attrs:Dict[str, torch.Tensor]) -> None: setattr(data, k, v) -def get_edges_tet(mesh): - tet_cells = mesh.extract_cells(np.where(mesh['CellEntityIds'] == 0)[0]) - tet_cells = tet_cells.cells.reshape(-1, 5)[:, 1:] - edges = np.reshape(np.array([list(combinations(cell,2)) for cell in tet_cells]), [-1,2]) - edges = torch.from_numpy(np.unique(edges, axis=0).T) +def get_edges_tet(mesh: PointGrid) -> torch.Tensor: + """ + A function to get the edge index for training from a tetrahedral pyvista mesh + + Args: + mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. + + Returns: + edges (torch.Tensor): A tensor of shape [2,M] where M is the number of edges and the values are the node ids + """ + edges = get_structure_edges(mesh, {0:'vol'})['vol_edge_index'] edges = to_undirected(edges) return edges -def get_node_features(mesh, feature_names): +def get_node_features(mesh:PointGrid, feature_names: List[str]) -> torch.Tensor: + """ + Extracts the node features from a pyvista mesh object based on the feature names provided. + + Args: + mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. + feature_names (List[str]): A list of feature names in the mesh pointdata. + + Returns: + features (torch.Tensor): A tensor of shape [N,C] where N is the number of nodes and C is len(feature_names). + """ features = torch.from_numpy(np.array([mesh.point_data[name] for name in feature_names]).T) return features -def get_tet_cells(mesh): - tet_cells = mesh.extract_cells(np.where(mesh['CellEntityIds'] == 0)[0]) - tet_cells = tet_cells.cells.reshape(-1, 5)[:, 1:] - tet_cells = torch.from_numpy(tet_cells).permute(1,0) - return tet_cells +def make_padded_batch(x: torch.Tensor, batch: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """ + A helper function to pad a batch of data to the same size. + + Takes a flat concatenated batch of variable-length graphs/pointclouds and + pads them to a uniform length so they can be stacked into a dense tensor. + + Args: + x (torch.Tensor): A tensor of features of shape (N, C) where N is the + total number of nodes across all instances in the batch, and C is + the number of features per node. + batch (torch.Tensor): A tensor of shape (N,) containing integer indices + in the range [0, B-1] where B is the batch size. Each value + indicates which instance in the batch the corresponding node + belongs to. -def make_padded_batch(x, batch): - jagged_x = [x[batch==i] for i in torch.unique(batch)] + Returns: + padded_x (torch.Tensor): A dense tensor of shape (B, L, C) where B is + the batch size and L is the length of the longest instance. Shorter + instances are zero-padded to length L. + mask (torch.BoolTensor): A boolean tensor of shape (B, L) where + mask[i, j] is True if position j in instance i is a real node, + and False if it is padding. Suitable for use as an attention mask + or for zeroing out padded positions in a loss function. + + Example: + >>> # 5 nodes total, 3 instances: instance 0 has 3 nodes, instances 1 and 2 have 1 node each + >>> x = torch.randn(5, 8) + >>> batch = torch.tensor([0, 0, 0, 1, 2]) + >>> padded_x, mask = make_padded_batch(x, batch) + >>> padded_x.shape # (3, 3, 8) + >>> mask.shape # (3, 3) + >>> mask + tensor([[ True, True, True], + [ True, False, False], + [ True, False, False]]) + """ + jagged_x = [x[batch == i] for i in torch.unique(batch)] padded_x = torch.nn.utils.rnn.pad_sequence(jagged_x, batch_first=True) lengths = torch.tensor([len(s) for s in jagged_x]) mask = torch.arange(padded_x.size(1))[None, :] < lengths[:, None] return padded_x, mask -def _compute_edge_lengths(points, edges): +def _compute_edge_lengths(points: torch.Tensor, edges: torch.Tensor) -> torch.Tensor: + """ + Computes the squared Euclidean distance for each edge in a mesh. + + Args: + points (torch.Tensor): Node coordinate tensor of shape (N, D) where N + is the number of nodes and D is the spatial dimensionality + (e.g. 3 for 3D meshes). + edges (torch.Tensor): Edge index tensor of shape (2, E) where E is the + number of edges. Each column represents an edge as a pair of node + indices [src, dst]. + + Returns: + distances (torch.Tensor): A tensor of shape (E, D) containing the + per-dimension squared differences between the endpoints of each + edge. Sum over the last dimension to get scalar squared edge + lengths. + """ coords = points[edges] - distances = (coords[0] - coords[1])**2 + distances = (coords[0] - coords[1]) ** 2 return distances -def cluster_pool(mesh): + +def cluster_pool(mesh: Data) -> Data: + """ + Performs Graclus clustering-based pooling on a mesh graph, coarsening it + by merging nodes into clusters weighted by inverse edge length. + + Shorter edges produce higher weights, encouraging spatially close nodes to + be merged together. This preserves the overall geometry of the mesh while + reducing its resolution. + + Args: + mesh (torch_geometric.data.Data): A PyTorch Geometric Data object with + the following required attributes: + - x (torch.Tensor): Node feature matrix of shape (N, C). + - edge_index (torch.Tensor): Edge index tensor of shape (2, E). + + Returns: + pooled_mesh (torch_geometric.data.Data): A coarsened PyTorch Geometric + Data object with fewer nodes, where each node represents the + average of the nodes in its cluster. Has the same structure as the + input mesh with updated x and edge_index. + + Notes: + - Edge weights are computed as 1 / (squared_length + 1e-8), where the + epsilon prevents division by zero for degenerate zero-length edges. + - Pooling is performed using torch_geometric.nn.avg_pool, so node + features in each cluster are averaged. + """ distances = _compute_edge_lengths(mesh.x, mesh.edge_index).sum(-1) - weights = 1/(distances + 1e-8) - clusters = gnn.graclus(mesh.edge_index,weights, mesh.x.shape[0]) + weights = 1 / (distances + 1e-8) + clusters = gnn.graclus(mesh.edge_index, weights, mesh.x.shape[0]) pooled_mesh = gnn.avg_pool(clusters, mesh) return pooled_mesh -def extract_features(mesh, fnames): - out = [] - for name in fnames: - out.append(torch.from_numpy(mesh.point_data[name])) - return torch.stack(out, dim=-1) diff --git a/im2sim/layers/projections.py b/im2sim/layers/projections.py index 633bf83..0f2caab 100644 --- a/im2sim/layers/projections.py +++ b/im2sim/layers/projections.py @@ -36,7 +36,7 @@ def forward(self, encoder_outputs, graph_coords, batch): if logger.isEnabledFor(logging.DEBUG): logger.debug("grid_shape: %s, grid_vals: %.2f-%.2f, encoder_outputs_shape:%s",tuple(grid.shape), grid.max(), grid.min(), tuple(encoder_outputs.shape)) grid = grid.type_as(encoder_outputs) - projections = F.grid_sample(encoder_outputs,grid, align_corners=True).squeeze(-1).squeeze(-1).permute(0,2,1) # [B,C,N,1,1] -> [B,N,C] + projections = F.grid_sample(encoder_outputs,grid, align_corners=True, padding_mode='border').squeeze(-1).squeeze(-1).permute(0,2,1) # [B,C,N,1,1] -> [B,N,C] logger.debug("projection_shape:%s",tuple(projections.shape)) projections = projections[padding_mask] logger.debug("masked_projection_shape:%s",tuple(projections.shape)) @@ -54,10 +54,96 @@ def forward(self, encoder_outputs, graph_coords, batch): # logger.debug("i: %s, %s",type(i), i.dtype) logger.debug("grid_shape: %s, grid_vals: %.2f-%.2f, encoder_outputs_shape:%s",tuple(grid.shape), grid.max(), grid.min(), tuple(encoder_outputs[i].shape)) grid = grid.type_as(encoder_outputs) - projections.append(F.grid_sample(encoder_outputs[i].unsqueeze(0),grid, align_corners=True).squeeze().permute(1,0)) # [1,C,N,1,1] -> [1,C,N] -> [N,C] + projections.append(F.grid_sample(encoder_outputs[i].unsqueeze(0),grid, align_corners=True, padding_mode='border').squeeze(0).squeeze(-1).squeeze(-1).permute(1,0)) # [1,C,N,1,1] -> [1,C,N] -> [N,C] logger.debug("projection %d shape:%s",i,tuple(projections[-1].shape)) projections = torch.cat(projections, dim=0) logger.debug("final projection shape:%s",tuple(projections.shape)) if logger.isEnabledFor(logging.DEBUG): logger.debug("final projection vals: %.2f-%.2f",projections.min(), projections.max()) return projections + + + +class OGProjection(nn.Module): + def __init__(self, image_dim): + super().__init__() + self.image_dim = image_dim + + def forward(self, image_features, graph_features, batch): + projections = [] + for i in torch.unique(batch).to(torch.int16): + # TensorFlow tf.shape equivalents + h = image_features[i].shape[-3] + w = image_features[i].shape[-2] + d = image_features[i].shape[-1] + + # Last 3 coords + x = graph_features[batch==i, -3] + y = graph_features[batch==i, -2] + z = graph_features[batch==i, -1] + + factor = torch.tensor(self.image_dim / h, dtype=x.dtype, device=x.device) + + x = x / factor + y = y / factor + z = z / factor + + # floor / ceil with clamp + x1 = torch.minimum(torch.floor(x), torch.tensor(h - 1, dtype=x.dtype, device=x.device)) + x2 = torch.minimum(torch.ceil(x), torch.tensor(h - 1, dtype=x.dtype, device=x.device)) + y1 = torch.minimum(torch.floor(y), torch.tensor(w - 1, dtype=x.dtype, device=x.device)) + y2 = torch.minimum(torch.ceil(y), torch.tensor(w - 1, dtype=x.dtype, device=x.device)) + z1 = torch.minimum(torch.floor(z), torch.tensor(d - 1, dtype=x.dtype, device=x.device)) + z2 = torch.minimum(torch.ceil(z), torch.tensor(d - 1, dtype=x.dtype, device=x.device)) + + # cast to int for indexing + x1 = x1.long() + x2 = x2.long() + y1 = y1.long() + y2 = y2.long() + z1 = z1.long() + z2 = z2.long() + + # mimic tf.gather_nd(image_features[0], ...) + img0 = image_features[i] + + def gather(xi, yi, zi): + return img0[...,xi, yi, zi] + + # --- z1 plane --- + q11 = gather(x1, y1, z1) + q21 = gather(x2, y1, z1) + q12 = gather(x1, y2, z1) + q22 = gather(x2, y2, z1) + + wx = (x - x1.float()).unsqueeze(0) + wx2 = (x2.float() - x).unsqueeze(0) + + lerp_x1 = q21 * wx + q11 * wx2 + lerp_x2 = q22 * wx + q12 * wx2 + + wy = (y - y1.float()).unsqueeze(0) + wy2 = (y2.float() - y).unsqueeze(0) + + lerp_y1 = lerp_x2 * wy + lerp_x1 * wy2 + + # --- z2 plane --- + q11 = gather(x1, y1, z2) + q21 = gather(x2, y1, z2) + q12 = gather(x1, y2, z2) + q22 = gather(x2, y2, z2) + + lerp_x1 = q21 * wx + q11 * wx2 + lerp_x2 = q22 * wx + q12 * wx2 + + lerp_y2 = lerp_x2 * wy + lerp_x1 * wy2 + + # --- z interpolation --- + wz = (z - z1.float()).unsqueeze(0) + wz2 = (z2.float() - z).unsqueeze(0) + + lerp_z = lerp_y2 * wz + lerp_y1 * wz2 + projections.append(lerp_z) + + projections = torch.cat(projections, dim=0).permute(1,0) + return projections \ No newline at end of file diff --git a/im2sim/models/image_to_graph.py b/im2sim/models/image_to_graph.py index a8cd499..cb897f3 100644 --- a/im2sim/models/image_to_graph.py +++ b/im2sim/models/image_to_graph.py @@ -84,7 +84,7 @@ def forward(self, x, template): graph_features = template.x.clone() curr_mesh = template.x.clone() for dec, ids in zip(self.decoder_blocks, self.projection_ids): - proj_inp = torch.cat([TrilinearProjection(domain_size=x.shape[-3:], batch_ops=self.batched_ops)(encoder_outputs[id], curr_mesh[:,:3], template.batch) + proj_inp = torch.cat([OGProjection(image_dim=x.shape[-1])(encoder_outputs[id], curr_mesh[:,:3], template.batch) for id in ids], dim=-1) graph_features, curr_mesh = dec(graph_features,proj_inp,curr_mesh,template.edge_index) out_graph = template.clone() From 18dd1b36f35da0d9ada9822042e6988a0e7dab0b Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 27 Apr 2026 13:52:58 +0000 Subject: [PATCH 14/49] made aspect and dev edge supervised --- im2sim/losses/mesh.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/im2sim/losses/mesh.py b/im2sim/losses/mesh.py index a8f9ddc..38c0fbd 100644 --- a/im2sim/losses/mesh.py +++ b/im2sim/losses/mesh.py @@ -8,8 +8,9 @@ logger = logging.getLogger(__name__) def edge_length_deviation_loss(gr1, gr2): - return _edge_length_deviation(gr2.x[:,:3], gr2.edge_index) - + ed1 = _edge_length_deviation(gr1.x[:,:3], gr1.edge_index) + ed2 = _edge_length_deviation(gr2.x[:,:3], gr2.edge_index) + return torch.maximum(ed2-ed1, 0) def _edge_length_deviation(points, edges): lengths = _compute_edge_lengths(points, edges) @@ -36,10 +37,11 @@ def __init__(self, cell_key): def forward(self, gr1, gr2): - cells = self.select(gr2) - loss = _aspect_ratio(x = gr2.x[:,:3], - cells = cells) - return loss + ar1 = _aspect_ratio(x = gr1.x[:,:3], + cells = self.select(gr1)) + ar2 = _aspect_ratio(x = gr2.x[:,:3], + cells = self.select(gr2)) + return torch.maximum(ar2-ar1, 0) def _face_norm(face_verts): From 3183229aa51043994d8b104204757e1d286935ec Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 27 Apr 2026 13:59:22 +0000 Subject: [PATCH 15/49] bug fixes --- im2sim/losses/mesh.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/im2sim/losses/mesh.py b/im2sim/losses/mesh.py index 38c0fbd..41ac384 100644 --- a/im2sim/losses/mesh.py +++ b/im2sim/losses/mesh.py @@ -4,13 +4,14 @@ import torch +import torch.nn.functional as F logger = logging.getLogger(__name__) def edge_length_deviation_loss(gr1, gr2): ed1 = _edge_length_deviation(gr1.x[:,:3], gr1.edge_index) ed2 = _edge_length_deviation(gr2.x[:,:3], gr2.edge_index) - return torch.maximum(ed2-ed1, 0) + return F.relu(ed2-ed1) def _edge_length_deviation(points, edges): lengths = _compute_edge_lengths(points, edges) @@ -41,7 +42,7 @@ def forward(self, gr1, gr2): cells = self.select(gr1)) ar2 = _aspect_ratio(x = gr2.x[:,:3], cells = self.select(gr2)) - return torch.maximum(ar2-ar1, 0) + return F.relu(ar2-ar1) def _face_norm(face_verts): From 464a3cdee8004831ce6a7a54cd72a1423a438028 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 28 Apr 2026 10:50:45 +0000 Subject: [PATCH 16/49] squared mesh losses --- im2sim/losses/mesh.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/im2sim/losses/mesh.py b/im2sim/losses/mesh.py index 41ac384..66df74b 100644 --- a/im2sim/losses/mesh.py +++ b/im2sim/losses/mesh.py @@ -11,7 +11,7 @@ def edge_length_deviation_loss(gr1, gr2): ed1 = _edge_length_deviation(gr1.x[:,:3], gr1.edge_index) ed2 = _edge_length_deviation(gr2.x[:,:3], gr2.edge_index) - return F.relu(ed2-ed1) + return F.relu(ed2-ed1)**2 def _edge_length_deviation(points, edges): lengths = _compute_edge_lengths(points, edges) @@ -42,7 +42,7 @@ def forward(self, gr1, gr2): cells = self.select(gr1)) ar2 = _aspect_ratio(x = gr2.x[:,:3], cells = self.select(gr2)) - return F.relu(ar2-ar1) + return F.relu(ar2-ar1)**2 def _face_norm(face_verts): From b9c43f1177e256a5adb16c0e3229424a4f581bdd Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 28 Apr 2026 11:10:02 +0000 Subject: [PATCH 17/49] fixed edge dev loss --- im2sim/data/mesh_utils.py | 2 +- im2sim/losses/mesh.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/im2sim/data/mesh_utils.py b/im2sim/data/mesh_utils.py index e25096e..b9ba759 100644 --- a/im2sim/data/mesh_utils.py +++ b/im2sim/data/mesh_utils.py @@ -195,7 +195,7 @@ def _compute_edge_lengths(points: torch.Tensor, edges: torch.Tensor) -> torch.Te lengths. """ coords = points[edges] - distances = (coords[0] - coords[1]) ** 2 + distances = torch.linalg.norm(coords[0] - coords[1]) return distances diff --git a/im2sim/losses/mesh.py b/im2sim/losses/mesh.py index 66df74b..21eeab4 100644 --- a/im2sim/losses/mesh.py +++ b/im2sim/losses/mesh.py @@ -15,8 +15,8 @@ def edge_length_deviation_loss(gr1, gr2): def _edge_length_deviation(points, edges): lengths = _compute_edge_lengths(points, edges) - std_dev = lengths.sum(-1).std() - return std_dev + dev = lengths.std()/lengths.mean() + return dev def _aspect_ratio(x, cells): From 07589775252a29f664f6f187a2e0a9967e1c6c87 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 28 Apr 2026 11:18:14 +0000 Subject: [PATCH 18/49] actually fixed dev edge --- im2sim/losses/mesh.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/im2sim/losses/mesh.py b/im2sim/losses/mesh.py index 21eeab4..13cd288 100644 --- a/im2sim/losses/mesh.py +++ b/im2sim/losses/mesh.py @@ -15,7 +15,7 @@ def edge_length_deviation_loss(gr1, gr2): def _edge_length_deviation(points, edges): lengths = _compute_edge_lengths(points, edges) - dev = lengths.std()/lengths.mean() + dev = lengths.std()/(lengths.mean()+1e-8) return dev From e964e65712e55df85fb041a31df9fb532286fb3d Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 28 Apr 2026 11:23:51 +0000 Subject: [PATCH 19/49] fixed edge length computation --- im2sim/data/mesh_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/im2sim/data/mesh_utils.py b/im2sim/data/mesh_utils.py index b9ba759..c9940db 100644 --- a/im2sim/data/mesh_utils.py +++ b/im2sim/data/mesh_utils.py @@ -195,7 +195,7 @@ def _compute_edge_lengths(points: torch.Tensor, edges: torch.Tensor) -> torch.Te lengths. """ coords = points[edges] - distances = torch.linalg.norm(coords[0] - coords[1]) + distances = torch.linalg.norm(coords[0] - coords[1], dim=-1) return distances From eb7b5f37febf102cd5b3faae48063e203911b269 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 28 Apr 2026 13:20:50 +0000 Subject: [PATCH 20/49] added dropout and Image2Mesh config --- im2sim/layers/image_convs.py | 4 +++- im2sim/models/image_to_graph.py | 4 +++- im2sim/models/utils.py | 24 ++++++++++++++++++++++++ 3 files changed, 30 insertions(+), 2 deletions(-) diff --git a/im2sim/layers/image_convs.py b/im2sim/layers/image_convs.py index 95c3d99..7408f9f 100644 --- a/im2sim/layers/image_convs.py +++ b/im2sim/layers/image_convs.py @@ -44,7 +44,7 @@ def __init__(self, get_image_layer(norm_type, rank)(filters, affine=True) if norm_type else nn.Identity() for _ in range(depth) ]) - self.drop = nn.Dropout1d(p=dropout_rate) if dropout_rate else nn.Identity() + self.drop = get_image_layer('Dropout', rank)(p=dropout_rate) if dropout_rate else nn.Identity() self.act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() @@ -119,6 +119,7 @@ def __init__(self, norm_type=norm_type, depth=1, dropout_rate=None) + self.drop = get_image_layer('Dropout', rank)(p=dropout_rate) if dropout_rate else nn.Identity() self.out_act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() def forward(self, x): @@ -131,6 +132,7 @@ def forward(self, x): """ x1 = self.initial_conv(x) x = self.main_conv(x1) + x = self.drop(x) x = self.out_act(self.out_conv(x) + x1) return x diff --git a/im2sim/models/image_to_graph.py b/im2sim/models/image_to_graph.py index cb897f3..a10f819 100644 --- a/im2sim/models/image_to_graph.py +++ b/im2sim/models/image_to_graph.py @@ -26,6 +26,7 @@ def __init__(self, cnn_pool_type='MaxPool', cnn_pool_size=2, cnn_activation='relu', + cnn_dropout_rate=None, projection_ids = [[3,4],[1,2],[0,1]], gnn_filters = [[384,288], [144,96], [64,32]], gnn_res_depth = 3, @@ -52,7 +53,8 @@ def __init__(self, norm_type=cnn_norm_type, pool_type=cnn_pool_type, pool_size=cnn_pool_size, - activation=cnn_activation) + activation=cnn_activation, + dropout_rate=cnn_dropout_rate) # self.projection_layers = nn.ModuleList([TrilinearProjection() for _ in range(len(cnn_filters))]) self.projection_ids = projection_ids diff --git a/im2sim/models/utils.py b/im2sim/models/utils.py index 627dc5a..25755dd 100644 --- a/im2sim/models/utils.py +++ b/im2sim/models/utils.py @@ -27,6 +27,30 @@ def get_model_config(name): cnn_pool_type='MaxPool', cnn_pool_size=2, cnn_activation='leaky_relu', + cnn_dropout_rate=0.3, + projection_ids = [[3,4],[1,2],[0,1]], + gnn_filters = [[384,288], [144,96], [64,32]], + gnn_res_depth = 3, + gnn_n_process_blocks = 1, + gnn_n_deform_blocks = 3, + template_edge_index=None, + gnn_conv_type="ChebConv", + gnn_conv_kwargs={'K':1}, + gnn_activation="leaky_relu", + out_activation="leaky_relu", + gnn_norm_type="InstanceNorm", + batched_ops=False), + + "Image2Mesh":dict(cnn_filters=[16,32,64,128,256], + cnn_kernel_size=3, + cnn_res_depth=3, + cnn_res_blocks_per_level=2, + cnn_rank=3, + cnn_norm_type="InstanceNorm", + cnn_pool_type='MaxPool', + cnn_pool_size=2, + cnn_activation='leaky_relu', + cnn_dropout_rate=0.3, projection_ids = [[3,4],[1,2],[0,1]], gnn_filters = [[384,288], [144,96], [64,32]], gnn_res_depth = 3, From f28c8ef426b3ee74e93fb44a3f82e16530fb7ada Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Wed, 29 Apr 2026 11:27:26 +0000 Subject: [PATCH 21/49] removed extra dropout --- im2sim/data/mesh_utils.py | 52 ++++++++++++++++++++++++++++++++++++ im2sim/layers/image_convs.py | 4 +-- 2 files changed, 54 insertions(+), 2 deletions(-) diff --git a/im2sim/data/mesh_utils.py b/im2sim/data/mesh_utils.py index c9940db..6b3048c 100644 --- a/im2sim/data/mesh_utils.py +++ b/im2sim/data/mesh_utils.py @@ -27,6 +27,26 @@ def get_structure_ids(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[s ids = {f'{k.split("_cell_index")[0]}_index':torch.unique(v) for k, v in cells.items()} return ids +def get_structure_edges(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: + cells = get_structure_cells(mesh, structure_dict) + + if cells == -1: + return -1 + + edges = {} + + for k, v in cells.items(): + edge_list = [] + + for i in range(v.shape[1]): # each cell + nodes = v[:, i].tolist() + edge_list += list(combinations(nodes, 2)) + + edge_tensor = torch.tensor(edge_list, dtype=torch.long).t() + edges[f'{k.split("_cell_index")[0]}_edge_index'] = edge_tensor + + return edges + def get_structure_edges(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: """ @@ -55,6 +75,24 @@ def get_structure_edges(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict edges = {f'{k}_edge_index':v for k,v in edges.items()} return edges +def get_structure_edges2(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: + cells = get_structure_cells(mesh, structure_dict) + + edges = {} + + for k, v in cells.items(): + edge_list = [] + + for i in range(v.shape[1]): # each cell + nodes = v[:, i] + edge_list.append(torch.combinations(nodes, r=2)) + + edge_index = torch.cat(edge_list, dim=0).T + edge_index = torch.unique(edge_index, dim=1) + + edges[f'{k.split("_cell_index")[0]}_edge_index'] = edge_index + + return edges def get_structure_cells(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: """ @@ -117,6 +155,20 @@ def get_edges_tet(mesh: PointGrid) -> torch.Tensor: edges = to_undirected(edges) return edges +def get_edges_tet2(mesh: PointGrid) -> torch.Tensor: + """ + A function to get the edge index for training from a tetrahedral pyvista mesh + + Args: + mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. + + Returns: + edges (torch.Tensor): A tensor of shape [2,M] where M is the number of edges and the values are the node ids + """ + edges = get_structure_edges2(mesh, {0:'vol'})['vol_edge_index'] + edges = to_undirected(edges) + return edges + def get_node_features(mesh:PointGrid, feature_names: List[str]) -> torch.Tensor: """ Extracts the node features from a pyvista mesh object based on the feature names provided. diff --git a/im2sim/layers/image_convs.py b/im2sim/layers/image_convs.py index 7408f9f..b1a6e8a 100644 --- a/im2sim/layers/image_convs.py +++ b/im2sim/layers/image_convs.py @@ -119,7 +119,7 @@ def __init__(self, norm_type=norm_type, depth=1, dropout_rate=None) - self.drop = get_image_layer('Dropout', rank)(p=dropout_rate) if dropout_rate else nn.Identity() + # self.drop = get_image_layer('Dropout', rank)(p=dropout_rate) if dropout_rate else nn.Identity() self.out_act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() def forward(self, x): @@ -132,7 +132,7 @@ def forward(self, x): """ x1 = self.initial_conv(x) x = self.main_conv(x1) - x = self.drop(x) + # x = self.drop(x) x = self.out_act(self.out_conv(x) + x1) return x From fb4cfd3c03113740bdb99024f3417e3cf68ffe41 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Thu, 30 Apr 2026 15:05:02 +0000 Subject: [PATCH 22/49] fixed graph norm --- im2sim/data/mesh_utils.py | 1 + im2sim/layers/graph_convs.py | 30 ++++++-- im2sim/layers/image_convs.py | 118 +++++++++++++++++++++++++++++++- im2sim/layers/layer_util.py | 4 +- im2sim/models/image_to_graph.py | 3 + im2sim/models/utils.py | 2 +- 6 files changed, 149 insertions(+), 9 deletions(-) diff --git a/im2sim/data/mesh_utils.py b/im2sim/data/mesh_utils.py index 6b3048c..d646a1c 100644 --- a/im2sim/data/mesh_utils.py +++ b/im2sim/data/mesh_utils.py @@ -279,6 +279,7 @@ def cluster_pool(mesh: Data) -> Data: features in each cluster are averaged. """ distances = _compute_edge_lengths(mesh.x, mesh.edge_index).sum(-1) + print(distances) weights = 1 / (distances + 1e-8) clusters = gnn.graclus(mesh.edge_index, weights, mesh.x.shape[0]) pooled_mesh = gnn.avg_pool(clusters, mesh) diff --git a/im2sim/layers/graph_convs.py b/im2sim/layers/graph_convs.py index 9a1a49f..477bf8c 100644 --- a/im2sim/layers/graph_convs.py +++ b/im2sim/layers/graph_convs.py @@ -46,17 +46,29 @@ def __init__(self, for i in range(depth) ]) + # self.norms = nn.ModuleList([ + # get_graph_layer(norm_type)(filters, affine=True) if norm_type else nn.Identity() + # for _ in range(depth) + # ]) + self.norms = nn.ModuleList([ - get_graph_layer(norm_type)(filters, affine=True) if norm_type else nn.Identity() + # get_graph_layer(norm_type)(filters, affine=True) if norm_type else nn.Identity() + torch.nn.InstanceNorm2d(1, affine=True, eps=1e-3) if norm_type else nn.Identity() for _ in range(depth) ]) self.act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() def forward(self, x, edge_index): + # for conv, norm in zip(self.convs, self.norms): + # logger.debug("Graph features shape:%s", x.shape) + # x = self.act(norm(conv(x,edge_index))) + # return x for conv, norm in zip(self.convs, self.norms): logger.debug("Graph features shape:%s", x.shape) - x = self.act(norm(conv(x,edge_index))) + x = conv(x,edge_index) + # x = self.act(norm(x.permute(1,0).unsqueeze(0))).squeeze(0).permute(1,0) + x = self.act(norm(x.unsqueeze(0).unsqueeze(0))).squeeze() return x class GraphConvResBlock(nn.Module): @@ -91,8 +103,13 @@ def __init__(self, conv(in_channels if i==0 else filters, filters, **conv_kwargs) for i in range(depth) ]) + # self.norms = nn.ModuleList([ + # get_graph_layer(norm_type)(filters, affine=True) if norm_type else nn.Identity() + # for _ in range(depth) + # ]) self.norms = nn.ModuleList([ - get_graph_layer(norm_type)(filters, affine=True) if norm_type else nn.Identity() + # get_graph_layer(norm_type)(filters, affine=True) if norm_type else nn.Identity() + torch.nn.InstanceNorm2d(1, affine=True, eps=1e-3) if norm_type else nn.Identity() for _ in range(depth) ]) self.act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() @@ -102,7 +119,10 @@ def forward(self, x, edge_index): for i, (conv,norm) in enumerate(zip(self.convs, self.norms)): - x = norm(conv(x, edge_index)) + # x = norm(conv(x, edge_index)) + x = conv(x,edge_index) + # x = norm(x.permute(1,0).unsqueeze(0)).squeeze(0).permute(1,0) + x = norm(x.unsqueeze(0).unsqueeze(0)).squeeze() if i==0: x1 = x.clone() @@ -251,7 +271,7 @@ def forward(self, graph): -class GraphUNetDecoderBlock(nn.Module): +class GraphUNetDecoderBlock(nn.Module): def __init__(self, #in_channels, diff --git a/im2sim/layers/image_convs.py b/im2sim/layers/image_convs.py index b1a6e8a..b665f91 100644 --- a/im2sim/layers/image_convs.py +++ b/im2sim/layers/image_convs.py @@ -41,7 +41,7 @@ def __init__(self, ]) self.norms = nn.ModuleList([ - get_image_layer(norm_type, rank)(filters, affine=True) if norm_type else nn.Identity() + get_image_layer(norm_type, rank)(filters, affine=True, eps=1e-5) if norm_type else nn.Identity() for _ in range(depth) ]) self.drop = get_image_layer('Dropout', rank)(p=dropout_rate) if dropout_rate else nn.Identity() @@ -390,4 +390,118 @@ def forward(self, encoder_outputs): - \ No newline at end of file + +# import torch +# import torch.nn as nn + + +class ConvResBlock(nn.Module): + def __init__(self, in_channels, out_channels): + super().__init__() + + # First branch (x1) + self.conv1a = nn.Conv3d( + in_channels, out_channels, + kernel_size=3, padding=1, bias=True + ) + self.norm1a = nn.InstanceNorm3d( + out_channels, affine=True, eps=1e-5 + ) + + # Second branch + self.conv1b = nn.Conv3d( + in_channels, out_channels, + kernel_size=3, padding=1, bias=True + ) + self.norm1b = nn.InstanceNorm3d( + out_channels, affine=True, eps=1e-5 + ) + + self.act = nn.LeakyReLU(negative_slope=0.3, inplace=False) + self.dropout = nn.Dropout3d(p=0.3) + + self.conv2b = nn.Conv3d( + out_channels, out_channels, + kernel_size=3, padding=1, bias=True + ) + self.norm2b = nn.InstanceNorm3d( + out_channels, affine=True, eps=1e-5 + ) + + self._init_weights() + + def _init_weights(self): + # Match TF he_normal (fan_in, normal) + for m in self.modules(): + if isinstance(m, nn.Conv3d): + nn.init.kaiming_normal_( + m.weight, + mode='fan_in', + nonlinearity='leaky_relu' + ) + if m.bias is not None: + nn.init.zeros_(m.bias) + + def forward(self, x): + # First branch + x1 = self.conv1a(x) + x1 = self.norm1a(x1) + # Second branch + x2 = self.conv1b(x) + x2 = self.norm1b(x2) + x2 = self.act(x2) + x2 = self.dropout(x2) + + x2 = self.conv2b(x2) + x2 = self.norm2b(x2) + + # Residual add + out = x1 + x2 + out = self.act(out) + + return out + + +class Encoder3D(nn.Module): + def __init__(self, in_channels): + super().__init__() + + self.block1_1 = ConvResBlock(in_channels, 16) + self.block1_2 = ConvResBlock(16, 16) + + self.block2_1 = ConvResBlock(16, 32) + self.block2_2 = ConvResBlock(32, 32) + + self.block3_1 = ConvResBlock(32, 64) + self.block3_2 = ConvResBlock(64, 64) + + self.block4_1 = ConvResBlock(64, 128) + self.block4_2 = ConvResBlock(128, 128) + + self.block5_1 = ConvResBlock(128, 256) + self.block5_2 = ConvResBlock(256, 256) + + self.pool = nn.MaxPool3d(kernel_size=2) + + def forward(self, x): + X1 = self.block1_1(x) + X1 = self.block1_2(X1) + X1p = self.pool(X1) + + X2 = self.block2_1(X1p) + X2 = self.block2_2(X2) + X2p = self.pool(X2) + + X3 = self.block3_1(X2p) + X3 = self.block3_2(X3) + X3p = self.pool(X3) + + X4 = self.block4_1(X3p) + X4 = self.block4_2(X4) + X4p = self.pool(X4) + + X5 = self.block5_1(X4p) + X5 = self.block5_2(X5) + X5p = self.pool(X5) + + return [X1, X2, X3, X4, X5] \ No newline at end of file diff --git a/im2sim/layers/layer_util.py b/im2sim/layers/layer_util.py index 4e08c8f..3770ef1 100644 --- a/im2sim/layers/layer_util.py +++ b/im2sim/layers/layer_util.py @@ -103,7 +103,8 @@ def get_activation(name): ('BatchNorm', 3): nn.BatchNorm3d, ('InstanceNorm', 1): nn.InstanceNorm1d, ('InstanceNorm', 2): nn.InstanceNorm2d, - ('InstanceNorm', 3): nn.InstanceNorm3d + ('InstanceNorm', 3): nn.InstanceNorm3d, + ('LayerNorm', 1): nn.InstanceNorm1d, } _GRAPH_LAYERS = { @@ -112,6 +113,7 @@ def get_activation(name): 'GCNConv': gnn.GCNConv, 'GATConv': gnn.GATConv, 'InstanceNorm': gnn.InstanceNorm, + 'LayerNorm': gnn.LayerNorm, 'BatchNorm': gnn.BatchNorm, 'GraphNorm': gnn.GraphNorm, } diff --git a/im2sim/models/image_to_graph.py b/im2sim/models/image_to_graph.py index a10f819..2d21325 100644 --- a/im2sim/models/image_to_graph.py +++ b/im2sim/models/image_to_graph.py @@ -55,6 +55,7 @@ def __init__(self, pool_size=cnn_pool_size, activation=cnn_activation, dropout_rate=cnn_dropout_rate) + # self.encoder = Encoder3D(in_channels=in_channels) # self.projection_layers = nn.ModuleList([TrilinearProjection() for _ in range(len(cnn_filters))]) self.projection_ids = projection_ids @@ -88,6 +89,8 @@ def forward(self, x, template): for dec, ids in zip(self.decoder_blocks, self.projection_ids): proj_inp = torch.cat([OGProjection(image_dim=x.shape[-1])(encoder_outputs[id], curr_mesh[:,:3], template.batch) for id in ids], dim=-1) + # proj_inp = torch.cat([TrilinearProjection(domain_size=[x.shape[-1]]*3, batch_ops=False)(encoder_outputs[id], curr_mesh[:,:3], template.batch) + # for id in ids], dim=-1) graph_features, curr_mesh = dec(graph_features,proj_inp,curr_mesh,template.edge_index) out_graph = template.clone() out_graph.x = curr_mesh diff --git a/im2sim/models/utils.py b/im2sim/models/utils.py index 25755dd..6dc5bf2 100644 --- a/im2sim/models/utils.py +++ b/im2sim/models/utils.py @@ -52,7 +52,7 @@ def get_model_config(name): cnn_activation='leaky_relu', cnn_dropout_rate=0.3, projection_ids = [[3,4],[1,2],[0,1]], - gnn_filters = [[384,288], [144,96], [64,32]], + gnn_filters = [[384,288], [96, 64], [48,32]], gnn_res_depth = 3, gnn_n_process_blocks = 1, gnn_n_deform_blocks = 3, From ed519c054cfdd750fa2b39006d5e44c76970a938 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Thu, 11 Jun 2026 17:19:08 +0000 Subject: [PATCH 23/49] added surface edge index util --- im2sim/data/mesh_utils.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/im2sim/data/mesh_utils.py b/im2sim/data/mesh_utils.py index d646a1c..b1dd09d 100644 --- a/im2sim/data/mesh_utils.py +++ b/im2sim/data/mesh_utils.py @@ -169,6 +169,20 @@ def get_edges_tet2(mesh: PointGrid) -> torch.Tensor: edges = to_undirected(edges) return edges +def get_edges_surf(mesh: PointGrid) -> torch.Tensor: + """ + A function to get the edge index for training from a pyvista surface mesh + + Args: + mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. + + Returns: + edges (torch.Tensor): A tensor of shape [2,M] where M is the number of edges and the values are the node ids + """ + edges = mesh.extract_all_edges().lines.reshape(-1, 3)[:, 1:] + edges = torch.Tensor(edges).T.long() + return edges + def get_node_features(mesh:PointGrid, feature_names: List[str]) -> torch.Tensor: """ Extracts the node features from a pyvista mesh object based on the feature names provided. From fbc670a876871f57f4f9c8740f6ccfd5e2d99a72 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Fri, 26 Jun 2026 10:38:52 +0000 Subject: [PATCH 24/49] Added meshgraphnets, dice loss, and fixed edge_index --- im2sim/data/mesh_utils.py | 59 +++++++++++- im2sim/layers/__init__.py | 1 + im2sim/layers/meshgraphnets.py | 163 +++++++++++++++++++++++++++++++++ im2sim/losses/mesh.py | 4 +- im2sim/losses/seg.py | 39 ++++++++ 5 files changed, 261 insertions(+), 5 deletions(-) create mode 100644 im2sim/layers/meshgraphnets.py create mode 100644 im2sim/losses/seg.py diff --git a/im2sim/data/mesh_utils.py b/im2sim/data/mesh_utils.py index b1dd09d..8e4bd31 100644 --- a/im2sim/data/mesh_utils.py +++ b/im2sim/data/mesh_utils.py @@ -119,7 +119,7 @@ def get_structure_cells(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict submesh = mesh.extract_cells(np.where(mesh['CellEntityIds'] == id)[0]) subcells = submesh.cells.reshape(-1, submesh.cells[0]+1)[:,1:] cells = submesh['vtkOriginalPointIds'][subcells] - out_dict[k] = torch.from_numpy(cells).permute(1,0).to(torch.int) + out_dict[k] = torch.from_numpy(cells).permute(1,0).to(torch.long) out_dict = {f'{k}_cell_index':v for k,v in out_dict.items()} @@ -242,7 +242,7 @@ def make_padded_batch(x: torch.Tensor, batch: torch.Tensor) -> Tuple[torch.Tenso mask = torch.arange(padded_x.size(1))[None, :] < lengths[:, None] return padded_x, mask -def _compute_edge_lengths(points: torch.Tensor, edges: torch.Tensor) -> torch.Tensor: +def compute_edge_lengths(points: torch.Tensor, edges: torch.Tensor) -> torch.Tensor: """ Computes the squared Euclidean distance for each edge in a mesh. @@ -292,10 +292,63 @@ def cluster_pool(mesh: Data) -> Data: - Pooling is performed using torch_geometric.nn.avg_pool, so node features in each cluster are averaged. """ - distances = _compute_edge_lengths(mesh.x, mesh.edge_index).sum(-1) + distances = compute_edge_lengths(mesh.x, mesh.edge_index).sum(-1) print(distances) weights = 1 / (distances + 1e-8) clusters = gnn.graclus(mesh.edge_index, weights, mesh.x.shape[0]) pooled_mesh = gnn.avg_pool(clusters, mesh) return pooled_mesh +def rasterize(points: torch.Tensor, im_shape: List[int], vox_sizes: List[float]) -> torch.Tensor: + """ + Computes the squared Euclidean distance between voxel centroids in a grid to a pointcloud + + Args: + points (torch.Tensor): Node coordinate tensor of shape (N, D) where N + is the number of nodes and D is the spatial dimensionality + (e.g. 3 for 3D meshes). + im_shape (torch.Tensor): A list of dim sizes for the image/mask corresponding + to the point cloud. + vox_sizes (torch.Tensor): A list of voxel sizes for each dimension + + Returns: + distances (torch.Tensor): A tensor of shape specified by im_shape where each voxel + is the distance of the voxel centroid to the pointcloud. + """ + im_coords = [torch.arange(size/2, n , size) for n, size in zip(im_shape, vox_sizes)] + grids = torch.meshgrid(*im_coords, indexing='ij') # three [128,128,128] tensors + coord_tensor = torch.stack(grids, dim=-1).reshape(-1, 3) + + nns = gnn.pool.knn(x=points, y=coord_tensor, k=1) + dists = torch.linalg.norm(coord_tensor - points[nns[1]], dim=-1) + return dists.reshape(im_shape) + +def hard_threshold(y: torch.Tensor, threshold: float=1.0) -> torch.Tensor: + """ + Thresholds a Tensor y according to a specified float threshold. Every value less than the threshold + is assigned 1.0 and values greater are assigned 0.0 + + Args: + y (torch.Tensor): tensor containing the raw values + threshold (float): threshold value + + Returns: + y_thresh (torch.Tensor): thresholded input tensor + + """ + return (y torch.Tensor: + """ + A function to compute the DICE coefficient for two masks + + Args: + y1 (torch.Tensor): True one-hot encoded mask of shape [B,C,H,D] or [B,C,H,D,W] + y2 (torch.Tensor): Predicted one-hot encoded mask of shape [B,C,H,D] or [B,C,H,D,W] + smooth (float): float value to avoid divide-by-zero + + Returns: + dice_coeff (torch.Tensor): A tensor of shape [B,C] containing the DICE scores for each sample and channel + """ + intersection = torch.sum(y2 * y1, dim=2) # (N, C) + union = torch.sum(y2.pow(2), dim=2) + torch.sum(y1, dim=2) # (N, C) + ## p^2 + t^2 >= 2*p*t, target_onehot^2 == target_onehot + dice_coef = (2 * intersection + smooth) / (union + smooth) # (N, C) + return dice_coef + +def dice_loss(y1: torch.Tensor, y2: torch.Tensor, smooth: float = 1e-5, channel_weights: torch.Tensor = None) -> torch.Tensor: + """ + A function to compute the DICE loss + + Args: + y1 (torch.Tensor): True one-hot encoded mask of shape [B,C,H,D] or [B,C,H,D,W] + y2 (torch.Tensor): Predicted one-hot encoded mask of shape [B,C,H,D] or [B,C,H,D,W] + smooth (float): float value to avoid divide-by-zero + channel_weights (torch.Tensor, optional): tensor of weights for each channels DICE score of shape [C] + + Returns: + dice_coeff (torch.Tensor): A tensor of shape [B,C] containing the DICE scores for each sample and channel + """ + if channel_weights is None: + return torch.mean(1 - dice_calc(y1,y2,smooth)) + else: + return torch.mean(torch.mean(1-dice_calc(y1,y2,smooth), dim=0)*channel_weights) + From 5e6f158697c135339e78e75894b9fc860d6e70cd Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Fri, 10 Jul 2026 10:04:02 +0000 Subject: [PATCH 25/49] added norm_mode and title to PointCloudPlot --- im2sim/plot/pointcloud.py | 152 +++++++++++++++++++++++++++++++++----- 1 file changed, 135 insertions(+), 17 deletions(-) diff --git a/im2sim/plot/pointcloud.py b/im2sim/plot/pointcloud.py index 698cd2b..1221c08 100644 --- a/im2sim/plot/pointcloud.py +++ b/im2sim/plot/pointcloud.py @@ -2,6 +2,10 @@ import matplotlib.pyplot as plt import matplotlib.animation as animation +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.animation as animation + class PointCloudPlot(): @@ -12,12 +16,18 @@ def __init__(self, color_sets=None, figsize=None, cmap='Blues_r', + norm_mode='none', # 'all', 'row', 'col', 'none' + bound_mode='all', + titles=None, # NEW: optional titles elev=20, azim=90): self.cmap = cmap self.elev = elev self.azim = azim + self.nrows = nrows + self.ncols = ncols + self.norm_mode = norm_mode if color_sets is None: is_colored = False @@ -40,31 +50,96 @@ def __init__(self, self.axes = np.array(axes).reshape(-1) self.scatters = [] - maxs = np.max(point_sets[-1],axis=0) - mins = np.min(point_sets[-1],axis=0) + # ----------------------------- + # Titles handling + # ----------------------------- + if titles is not None: + if isinstance(titles, str): + self.fig.suptitle(titles) + titles = [None] * len(self.axes) + elif len(titles) != len(self.axes): + raise ValueError("titles must match number of subplots") + + # ----------------------------- + # Bounds (same as your logic) + # ----------------------------- + maxs = np.max(point_sets[-1], axis=0) + mins = np.min(point_sets[-1], axis=0) + + # ----------------------------- + # Normalization helpers + # ----------------------------- + def compute_norm_ranges(color_sets): + color_sets = list(color_sets) + + if norm_mode == 'none': + return [(None, None)] * len(color_sets) + + elif norm_mode == 'all': + all_vals = np.concatenate(color_sets) + vmin, vmax = all_vals.min(), all_vals.max() + return [(vmin, vmax)] * len(color_sets) + + elif norm_mode == 'row': + ranges = [] + for r in range(nrows): + row_vals = [] + for c in range(ncols): + idx = r * ncols + c + row_vals.append(color_sets[idx]) + row_vals = np.concatenate(row_vals) + vmin, vmax = row_vals.min(), row_vals.max() + for c in range(ncols): + ranges.append((vmin, vmax)) + return ranges + + elif norm_mode == 'col': + ranges = [None] * len(color_sets) + for c in range(ncols): + col_vals = [] + for r in range(nrows): + idx = r * ncols + c + col_vals.append(color_sets[idx]) + col_vals = np.concatenate(col_vals) + vmin, vmax = col_vals.min(), col_vals.max() + for r in range(nrows): + idx = r * ncols + c + ranges[idx] = (vmin, vmax) + return ranges + + else: + raise ValueError("norm_mode must be 'all', 'row', 'col', or 'none'") + + norm_ranges = compute_norm_ranges(color_sets) + # ----------------------------- + # Plot creation + # ----------------------------- for i, (ax, points, colors) in enumerate( zip(self.axes, point_sets, color_sets)): - print(points.shape, colors.shape) + + vmin, vmax = norm_ranges[i] + sc = ax.scatter(points[:, 0], points[:, 1], points[:, 2], c=colors, cmap=cmap, - vmin=colors.min(), - vmax=colors.max()) + vmin=vmin, + vmax=vmax) ax.view_init(elev=elev, azim=azim, vertical_axis='y') - # if i == 0: - # lims = ax.get_w_lims() - # else: ax.set_xlim(mins[0], maxs[0]) ax.set_ylim(mins[1], maxs[1]) ax.set_zlim(mins[2], maxs[2]) - + + # Apply subplot title + if titles is not None and titles[i] is not None: + ax.set_title(titles[i]) + if is_colored: plt.colorbar(sc, ax=ax, shrink=0.5) @@ -74,7 +149,7 @@ def __init__(self, self.color_sets = color_sets # --------------------------------------------------------- - # DRAW ONE FRAME (works for static OR animation) + # DRAW ONE FRAME # --------------------------------------------------------- def draw_frame(self, point_sets=None, color_sets=None): @@ -85,15 +160,57 @@ def draw_frame(self, point_sets=None, color_sets=None): if color_sets is None: color_sets = self.color_sets + # recompute normalization each frame + def compute_norm_ranges(color_sets): + color_sets = list(color_sets) + + if self.norm_mode == 'none': + return [(None, None)] * len(color_sets) + + elif self.norm_mode == 'all': + all_vals = np.concatenate(color_sets) + vmin, vmax = all_vals.min(), all_vals.max() + return [(vmin, vmax)] * len(color_sets) + + elif self.norm_mode == 'row': + ranges = [] + for r in range(self.nrows): + row_vals = [] + for c in range(self.ncols): + idx = r * self.ncols + c + row_vals.append(color_sets[idx]) + row_vals = np.concatenate(row_vals) + vmin, vmax = row_vals.min(), row_vals.max() + for c in range(self.ncols): + ranges.append((vmin, vmax)) + return ranges + + elif self.norm_mode == 'col': + ranges = [None] * len(color_sets) + for c in range(self.ncols): + col_vals = [] + for r in range(self.nrows): + idx = r * self.ncols + c + col_vals.append(color_sets[idx]) + col_vals = np.concatenate(col_vals) + vmin, vmax = col_vals.min(), col_vals.max() + for r in range(self.nrows): + idx = r * self.ncols + c + ranges[idx] = (vmin, vmax) + return ranges + + norm_ranges = compute_norm_ranges(color_sets) + new_scatters = [] - for ax, sc, pts, colors in zip( + for i, (ax, sc, pts, colors) in enumerate(zip( self.axes, self.scatters, point_sets, - color_sets): + color_sets)): + + vmin, vmax = norm_ranges[i] - # If number of points changed → recreate scatter if sc is None or len(pts) != len(sc.get_offsets()): if sc is not None: @@ -105,22 +222,23 @@ def draw_frame(self, point_sets=None, color_sets=None): pts[:, 2], c=colors, cmap=self.cmap, - vmin=0, - vmax=1 + vmin=vmin, + vmax=vmax ) else: sc._offsets3d = (pts[:, 0], pts[:, 1], pts[:, 2]) sc.set_array(colors) + if vmin is not None: + sc.set_clim(vmin, vmax) new_scatters.append(sc) self.scatters = new_scatters - return self.scatters # --------------------------------------------------------- - # SAVE SINGLE IMAGE + # SAVE IMAGE # --------------------------------------------------------- def save_image(self, filename, dpi=200): From d6d443780d00ae1b41c48259300933108be61e06 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Wed, 15 Jul 2026 15:54:37 +0000 Subject: [PATCH 26/49] changes to mesh utils and transforms --- im2sim/data/core.py | 65 ++++++++++++++++-------- im2sim/data/mesh_utils.py | 104 ++++++++++++++------------------------ 2 files changed, 80 insertions(+), 89 deletions(-) diff --git a/im2sim/data/core.py b/im2sim/data/core.py index ef8593e..6e69076 100644 --- a/im2sim/data/core.py +++ b/im2sim/data/core.py @@ -3,12 +3,15 @@ import torch from torch_geometric.data import Data, Batch import logging +from typing import Dict, Any logger = logging.getLogger(__name__) + + def collate(batch): ''' Collates a batch of data in the form of dict{str: torch.Tensor/PyG.Data} @@ -475,39 +478,46 @@ def __init__(self, transforms): # Forward # ----------------------------- def __call__(self, data): - for t in self.transforms: - if logger.isEnabledFor(logging.DEBUG): - logging.debug(f' before {t.name}') - for k,v in data.items(): - logging.debug(k) - - if isinstance(v, Data): - for c in range(v.x.shape[-1]): - logging.debug(f'channel {c}- max:{v.x[...,c].max()}, min:{v.x[...,c].min()}') - else: - logging.debug(v.shape) + transforms = self._get_applicable_transforms(data.keys()) + for t in transforms: + logging.debug(f'before {t.name}') + _log_data(data) + data = t.forward(data) + + logging.debug(f'after {t.name}') + _log_data(data) return data # ----------------------------- # Inverse (reverse order) # ----------------------------- def inverse(self, data): - for t in reversed(self.transforms): + transforms = self._get_applicable_transforms(data.keys()) + for t in reversed(transforms): if t.is_invertible: - if logger.isEnabledFor(logging.DEBUG): - logging.debug(f' before {t.name}') - for k,v in data.items(): - logging.debug(k) - - if isinstance(v, Data): - for c in range(v.x.shape[-1]): - logging.debug(f'channel {c}- max:{v.x[...,c].max()}, min:{v.x[...,c].min()}') - else: - logging.debug(v.shape) + logging.debug(f'before {t.name} inverse') + _log_data(data) + data = t.inverse(data) + + logging.debug(f'after {t.name} inverse') + _log_data(data) + return data + def _get_applicable_transforms(self, keys: str) -> list[Transform]: + transforms = [] + for t in self.transforms: + skip = False + for key in t.keys: + if key not in keys: + skip=True + break + if not skip: + transforms.append(t) + return transforms + # ----------------------------- # Fit (only fittable transforms) # ----------------------------- @@ -597,6 +607,17 @@ def load_pipeline(path): return pipeline +def _log_data(data: Dict[str,Any]) -> None: + if logger.isEnabledFor(logging.DEBUG): + for k,v in data.items(): + logging.debug(k) + + if isinstance(v, Data): + for c in range(v.x.shape[-1]): + logging.debug(f'channel {c}- max:{v.x[...,c].max()}, min:{v.x[...,c].min()}') + else: + logging.debug(v.shape) + # ------------------------------------------------------------------------------------ # TRANSFORM REGISTRY DEFINITION # ------------------------------------------------------------------------------------ diff --git a/im2sim/data/mesh_utils.py b/im2sim/data/mesh_utils.py index 8e4bd31..be1f793 100644 --- a/im2sim/data/mesh_utils.py +++ b/im2sim/data/mesh_utils.py @@ -7,6 +7,11 @@ from pyvista.core.pointset import PointGrid from typing import Dict, List, Tuple +class InputMeshError(ValueError): + pass + + + def get_structure_ids(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: """ @@ -19,33 +24,10 @@ def get_structure_ids(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[s Returns: ids (Dict[str, torch.Tensor]): A dictionary with items in the format 'structurename_index': torch.Tensor(N), where N is the number of nodes in the structure. """ - cells = get_structure_cells(mesh, structure_dict) - - if cells == -1: - return -1 - + cells = get_structure_edges(mesh, structure_dict) ids = {f'{k.split("_cell_index")[0]}_index':torch.unique(v) for k, v in cells.items()} return ids -def get_structure_edges(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: - cells = get_structure_cells(mesh, structure_dict) - - if cells == -1: - return -1 - - edges = {} - - for k, v in cells.items(): - edge_list = [] - - for i in range(v.shape[1]): # each cell - nodes = v[:, i].tolist() - edge_list += list(combinations(nodes, 2)) - - edge_tensor = torch.tensor(edge_list, dtype=torch.long).t() - edges[f'{k.split("_cell_index")[0]}_edge_index'] = edge_tensor - - return edges def get_structure_edges(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: @@ -60,40 +42,35 @@ def get_structure_edges(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict edges (Dict[str, torch.Tensor]): A dictionary with items in the format 'structurename_index': torch.Tensor(2, N), where N is the number of edges in the structure. """ - cells = get_structure_cells(mesh, structure_dict) - - if cells == -1: - return -1 - - edges = {k:None for k in structure_dict} - for k, v in cells.items(): - e_ids = list(combinations(range(v.shape[0]),2)) - cell_edges = v[e_ids,:] - edges[f'{k.split("_cell_index")[0]}'] = cell_edges.permute(1,0,2).reshape(2,-1) - - edges = {f'{k}_edge_index':v for k,v in edges.items()} - return edges + if _has_missing_ids(mesh, structure_dict): + raise InputMeshError("Mesh has missing ids") -def get_structure_edges2(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: - cells = get_structure_cells(mesh, structure_dict) - edges = {} - for k, v in cells.items(): - edge_list = [] + for i,key in structure_dict.items(): + edges[f'{key}_edge_index'] = get_edges(mesh, i) - for i in range(v.shape[1]): # each cell - nodes = v[:, i] - edge_list.append(torch.combinations(nodes, r=2)) - - edge_index = torch.cat(edge_list, dim=0).T - edge_index = torch.unique(edge_index, dim=1) + return edges - edges[f'{k.split("_cell_index")[0]}_edge_index'] = edge_index +def get_edges(mesh:PointGrid, structure_id: int) -> torch.Tensor: + """ + Extracts edges for a single structure in a pyvista PointGrid object. + Args: + mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. + structure_id (int): An integer corresponding to the structure id + + Returns: + edges (torch.Tensor): Tensor of shape (2, N) where N is the number of edges in the structure. + """ + submesh = mesh.extract_cells(np.where(mesh['CellEntityIds']==structure_id)) + edges = submesh.extract_all_edges().lines.reshape(-1,3)[:,1:].T + edges = torch.from_numpy(submesh['vtkOriginalPointIds'][edges]).long() + edges = to_undirected(edges) return edges + def get_structure_cells(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: """ Extracts cells for different substructures in a pyvista PointGrid object. @@ -106,11 +83,8 @@ def get_structure_cells(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict cells (Dict[str, torch.Tensor]): A dictionary with items in the format 'structurename_index': torch.Tensor(m, N), where m is 3 for triangles and 4 for tetrahedrons and N is the number of cells in the structure. """ - ids = np.unique(mesh['CellEntityIds']) - - missing_ids = set(structure_dict.keys()) - set(ids.tolist()) - if len(missing_ids) != 0: - return -1 + if _has_missing_ids(mesh, structure_dict): + raise InputMeshError("Mesh has missing ids") out_dict = {k:None for k in structure_dict.values()} @@ -126,6 +100,15 @@ def get_structure_cells(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict return out_dict +def _has_missing_ids(mesh: PointGrid, structure_dict: Dict[int,str]) -> bool: + ids = np.unique(mesh['CellEntityIds']) + + missing_ids = set(structure_dict.keys()) - set(ids.tolist()) + if len(missing_ids) != 0: + return True + return False + + def set_attrs(data:Data, attrs:Dict[str, torch.Tensor]) -> None: """ A helper function to set multiple attributes of a PyG Data object with keys and values from a dictionary. @@ -155,19 +138,6 @@ def get_edges_tet(mesh: PointGrid) -> torch.Tensor: edges = to_undirected(edges) return edges -def get_edges_tet2(mesh: PointGrid) -> torch.Tensor: - """ - A function to get the edge index for training from a tetrahedral pyvista mesh - - Args: - mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. - - Returns: - edges (torch.Tensor): A tensor of shape [2,M] where M is the number of edges and the values are the node ids - """ - edges = get_structure_edges2(mesh, {0:'vol'})['vol_edge_index'] - edges = to_undirected(edges) - return edges def get_edges_surf(mesh: PointGrid) -> torch.Tensor: """ From f5b3428f3c002bc5aee75a4ab9c9c1914bf4207c Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 27 Jul 2026 14:27:29 +0000 Subject: [PATCH 27/49] added tests --- .github/workflows/tests.yaml | 29 + im2sim/layers/__init__.py | 6 +- im2sim/layers/graph_blocks.py | 324 +++++++ im2sim/layers/graph_convs.py | 347 ------- .../{image_convs.py => image_blocks.py} | 222 ++--- im2sim/layers/layer_util.py | 353 ++++--- im2sim/models/UNet.py | 888 +++++++++--------- im2sim/models/__init__.py | 2 +- tests/unit/data/test_mesh_utils.py | 75 ++ 9 files changed, 1215 insertions(+), 1031 deletions(-) create mode 100644 .github/workflows/tests.yaml create mode 100644 im2sim/layers/graph_blocks.py delete mode 100644 im2sim/layers/graph_convs.py rename im2sim/layers/{image_convs.py => image_blocks.py} (79%) create mode 100644 tests/unit/data/test_mesh_utils.py diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml new file mode 100644 index 0000000..20844aa --- /dev/null +++ b/.github/workflows/tests.yaml @@ -0,0 +1,29 @@ +name: Run Tests + +on: + push: + pull_request: + +jobs: + test: + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: '3.10' + + - name: Install dependencies + run: | + pip install -r requirements.txt + pip install pytest + + - name: Lint + run: ruff check . + + - name: Run tests + run: pytest \ No newline at end of file diff --git a/im2sim/layers/__init__.py b/im2sim/layers/__init__.py index cc795a5..feec238 100644 --- a/im2sim/layers/__init__.py +++ b/im2sim/layers/__init__.py @@ -1,6 +1,6 @@ -from .graph_convs import * -from .image_convs import * +from .graph_blocks import * +from .image_blocks import * from .projections import * from .meshgraphnets import * -from .layer_util import get_image_layer, get_activation, get_default_kwargs, get_graph_layer +from .layer_util import * diff --git a/im2sim/layers/graph_blocks.py b/im2sim/layers/graph_blocks.py new file mode 100644 index 0000000..d4e93a8 --- /dev/null +++ b/im2sim/layers/graph_blocks.py @@ -0,0 +1,324 @@ +import logging + +import torch +from torch import nn +import torch_geometric.nn as gnn + +from .projections import TrilinearProjection +from ..data.mesh_utils import cluster_pool +from .layer_util import * + + + + +logger = logging.getLogger(__name__) + + +@register_pyg_layer(name='defaultnorm') +class DefaultGraphNorm(torch.nn.Module): + """ + The default normalisation for im2sim graph blocks. + Uses torch.nn.InstanceNorm2d applied to graph data, but all channels are normalised together. + """ + + def __init__(self, in_channels, affine): + self.norm = torch.nn.InstanceNorm2d(1, affine=True, eps=1e-3) + + def forward(self, x, batch): + if x.dim() != 2: + raise RuntimeError(f"Expected x.dim()==2, got {x.dim()}") + shape = x.shape + + for b in torch.unique(batch): + x[batch==b] = self.norm(x[batch==b].unsqueeze(0).unsqueeze(0)).reshape(shape) + return x + + + +class GraphConvBlock(nn.Module): + """ + A convolutional block for graph data + + Args: + in_channels (int): The number of channels in the input to the layer. + filters (int, optional): The number of filters in each convolutional layer (default: 32) + depth (int, optional): The number of successive convolutional layers (default: 2) + conv_type (str, optional): The type of graph convolution to apply (default: "ChebConv", options: "GraphConv", "GCNConv", "GATConv") + conv_kwargs(dict, optional): Dictionary of keyword arguments for the chosen conv_type + activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") + norm_type (str, optional): The normalization method to apply between convolutions (default:"InstanceNorm", options: "BatchNorm", "LayerNorm") + + Returns: + A `torch.nn.Module` object. + + """ + def __init__(self, + in_channels, + filters, + depth=1, + conv_type='GATConv', + conv_kwargs={}, + activation='ReLU', + norm_type='default', + norm_kwargs=None): + super().__init__() + + conv = get_graph_layer(conv_type, conv_kwargs) + + self.convs = nn.ModuleList([ + conv(in_channels if i==0 else filters, filters, **conv_kwargs) + for i in range(depth) + ]) + + if norm_kwargs == None: + norm_kwargs = {'in_channels':filters, 'affine':True} + + self.norms = nn.ModuleList([ + get_graph_layer(norm_type, conv_kwargs) if norm_type else nn.Identity() + for _ in range(depth) + ]) + + self.act = ACTIVATIONS[activation]() + + def forward(self,in_graph): + out_graph = deepcopy(in_graph) + for conv, norm in zip(self.convs, self.norms): + out_graph = conv(out_graph) + out_graph = norm(out_graph) + out_graph.x = self.act(out_graph.x) + return out_graph + +class GraphConvResBlock(nn.Module): + """ + A convolutional block for graph data + + Args: + in_channels (int): The number of channels in the input to the layer. + filters (int, optional): The number of filters in each convolutional layer (default: 32) + depth (int, optional): The number of successive convolutional layers (default: 2) + conv_type (str, optional): The type of graph convolution to apply (default: "ChebConv", options: "GraphConv", "GCNConv", "GATConv") + conv_kwargs(dict, optional): Dictionary of keyword arguments for the chosen conv_type + activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") + norm_type (str, optional): The normalization method to apply between convolutions (default:"InstanceNorm", options: "BatchNorm", "LayerNorm") + + Returns: + A `torch.nn.Module` object. + + """ + def __init__(self, + in_channels, + filters, + depth=3, + conv_type='GATConv', + conv_kwargs={}, + activation='ReLU', + norm_type='InstanceNorm', + norm_kwargs=None): + super().__init__() + + conv = get_graph_layer(conv_type, conv_kwargs) + self.convs = nn.ModuleList([ + conv(in_channels if i==0 else filters, filters, **conv_kwargs) + for i in range(depth) + ]) + + if norm_kwargs == None: + norm_kwargs = {'in_channels':filters, 'affine':True} + + self.norms = nn.ModuleList([ + get_graph_layer(norm_type, norm_kwargs) if norm_type else nn.Identity() + for _ in range(depth) + ]) + + self.act = ACTIVATIONS[activation]() + + + def forward(self, in_graph): + graph = deepcopy(in_graph) + for i, (conv,norm) in enumerate(zip(self.convs, self.norms)): + + graph = norm(conv(graph)) + + if i==0: + x1 = deepcopy(graph.x) + elif i 0: +# self.align=True +# self.align_conv = gnn.Sequential('x, edge_index, batch',[ +# (GraphConvResBlock(in_channels=out_channels*2 if i==0 else filters, +# filters=filters, +# **conv_config), 'x, edge_index -> x') +# for i in range(n_align_blocks) +# ]) +# else: +# self.align=False + + +# self.deform_conv = gnn.Sequential('x, edge_index, batch',[ +# (GraphConvResBlock(in_channels=out_channels+filters if i==0 else filters, +# filters=filters, +# **conv_config), 'x, edge_index -> x') +# for i in range(n_deform_blocks) +# ]) + +# self.convert_conv = GraphConvBlock(in_channels=filters, +# filters=out_channels, +# depth=1, +# conv_type=conv_type, +# conv_kwargs=conv_kwargs, +# activation=out_activation, +# norm_type=None) + +# self.projection_args = {"domain_size":domain_size, "batch_ops":batched_ops} + +# # INFO: removed graph features for now may want to add back +# def forward(self,image_features,prev_deformation,template_x,edge_index,batch): + +# # Move all zero points after unpooling +# if self.align: +# x = torch.cat([prev_deformation, template_x], axis=-1) +# x = self.align_conv(x, edge_index) +# x = self.convert_conv(x, edge_index) +# prev_deformation = prev_deformation+x + +# # apply current deformation to template +# x = template_x + prev_deformation +# proj = TrilinearProjection(**self.projection_args)(image_features, x[:,:3], batch) +# x = torch.cat([x, proj], axis=-1) + +# # get new deformations based on current position and projections +# x = self.deform_conv(x, edge_index) +# x = self.convert_conv(x, edge_index) +# return x+prev_deformation + \ No newline at end of file diff --git a/im2sim/layers/graph_convs.py b/im2sim/layers/graph_convs.py deleted file mode 100644 index 477bf8c..0000000 --- a/im2sim/layers/graph_convs.py +++ /dev/null @@ -1,347 +0,0 @@ -import logging - -import torch -from torch import nn -import torch_geometric.nn as gnn - -from .layer_util import get_graph_layer, get_activation -from .projections import TrilinearProjection -from ..data.mesh_utils import cluster_pool - - - - -logger = logging.getLogger(__name__) - -class GraphConvBlock(nn.Module): - """ - A convolutional block for graph data - - Args: - in_channels (int): The number of channels in the input to the layer. - filters (int, optional): The number of filters in each convolutional layer (default: 32) - depth (int, optional): The number of successive convolutional layers (default: 2) - conv_type (str, optional): The type of graph convolution to apply (default: "ChebConv", options: "GraphConv", "GCNConv", "GATConv") - conv_kwargs(dict, optional): Dictionary of keyword arguments for the chosen conv_type - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") - norm_type (str, optional): The normalization method to apply between convolutions (default:"InstanceNorm", options: "BatchNorm", "LayerNorm") - - Returns: - A `torch.nn.Module` object. - - """ - def __init__(self, - in_channels, - filters, - depth=1, - conv_type='ChebConv', - conv_kwargs={'K':1}, - activation='relu', - norm_type='InstanceNorm'): - super().__init__() - - conv = get_graph_layer(conv_type) - self.convs = nn.ModuleList([ - conv(in_channels if i==0 else filters, filters, **conv_kwargs) - for i in range(depth) - ]) - - # self.norms = nn.ModuleList([ - # get_graph_layer(norm_type)(filters, affine=True) if norm_type else nn.Identity() - # for _ in range(depth) - # ]) - - self.norms = nn.ModuleList([ - # get_graph_layer(norm_type)(filters, affine=True) if norm_type else nn.Identity() - torch.nn.InstanceNorm2d(1, affine=True, eps=1e-3) if norm_type else nn.Identity() - for _ in range(depth) - ]) - - self.act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() - - def forward(self, x, edge_index): - # for conv, norm in zip(self.convs, self.norms): - # logger.debug("Graph features shape:%s", x.shape) - # x = self.act(norm(conv(x,edge_index))) - # return x - for conv, norm in zip(self.convs, self.norms): - logger.debug("Graph features shape:%s", x.shape) - x = conv(x,edge_index) - # x = self.act(norm(x.permute(1,0).unsqueeze(0))).squeeze(0).permute(1,0) - x = self.act(norm(x.unsqueeze(0).unsqueeze(0))).squeeze() - return x - -class GraphConvResBlock(nn.Module): - """ - A convolutional block for graph data - - Args: - in_channels (int): The number of channels in the input to the layer. - filters (int, optional): The number of filters in each convolutional layer (default: 32) - depth (int, optional): The number of successive convolutional layers (default: 2) - conv_type (str, optional): The type of graph convolution to apply (default: "ChebConv", options: "GraphConv", "GCNConv", "GATConv") - conv_kwargs(dict, optional): Dictionary of keyword arguments for the chosen conv_type - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") - norm_type (str, optional): The normalization method to apply between convolutions (default:"InstanceNorm", options: "BatchNorm", "LayerNorm") - - Returns: - A `torch.nn.Module` object. - - """ - def __init__(self, - in_channels, - filters, - depth=3, - conv_type='ChebConv', - conv_kwargs={'K':1}, - activation='relu', - norm_type='InstanceNorm'): - super().__init__() - - conv = get_graph_layer(conv_type) - self.convs = nn.ModuleList([ - conv(in_channels if i==0 else filters, filters, **conv_kwargs) - for i in range(depth) - ]) - # self.norms = nn.ModuleList([ - # get_graph_layer(norm_type)(filters, affine=True) if norm_type else nn.Identity() - # for _ in range(depth) - # ]) - self.norms = nn.ModuleList([ - # get_graph_layer(norm_type)(filters, affine=True) if norm_type else nn.Identity() - torch.nn.InstanceNorm2d(1, affine=True, eps=1e-3) if norm_type else nn.Identity() - for _ in range(depth) - ]) - self.act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() - - - def forward(self, x, edge_index): - - for i, (conv,norm) in enumerate(zip(self.convs, self.norms)): - - # x = norm(conv(x, edge_index)) - x = conv(x,edge_index) - # x = norm(x.permute(1,0).unsqueeze(0)).squeeze(0).permute(1,0) - x = norm(x.unsqueeze(0).unsqueeze(0)).squeeze() - - if i==0: - x1 = x.clone() - elif i 0: - self.align=True - self.align_conv = gnn.Sequential('x, edge_index, batch',[ - (GraphConvResBlock(in_channels=out_channels*2 if i==0 else filters, - filters=filters, - **conv_config), 'x, edge_index -> x') - for i in range(n_align_blocks) - ]) - else: - self.align=False - - - self.deform_conv = gnn.Sequential('x, edge_index, batch',[ - (GraphConvResBlock(in_channels=out_channels+filters if i==0 else filters, - filters=filters, - **conv_config), 'x, edge_index -> x') - for i in range(n_deform_blocks) - ]) - - self.convert_conv = GraphConvBlock(in_channels=filters, - filters=out_channels, - depth=1, - conv_type=conv_type, - conv_kwargs=conv_kwargs, - activation=out_activation, - norm_type=None) - - self.projection_args = {"domain_size":domain_size, "batch_ops":batched_ops} - - # INFO: removed graph features for now may want to add back - def forward(self,image_features,prev_deformation,template_x,edge_index,batch): - - # Move all zero points after unpooling - if self.align: - x = torch.cat([prev_deformation, template_x], axis=-1) - x = self.align_conv(x, edge_index) - x = self.convert_conv(x, edge_index) - prev_deformation = prev_deformation+x - - # apply current deformation to template - x = template_x + prev_deformation - proj = TrilinearProjection(**self.projection_args)(image_features, x[:,:3], batch) - x = torch.cat([x, proj], axis=-1) - - # get new deformations based on current position and projections - x = self.deform_conv(x, edge_index) - x = self.convert_conv(x, edge_index) - return x+prev_deformation - \ No newline at end of file diff --git a/im2sim/layers/image_convs.py b/im2sim/layers/image_blocks.py similarity index 79% rename from im2sim/layers/image_convs.py rename to im2sim/layers/image_blocks.py index b665f91..33bbc0d 100644 --- a/im2sim/layers/image_convs.py +++ b/im2sim/layers/image_blocks.py @@ -1,7 +1,7 @@ import logging import torch from torch import nn -from .layer_util import get_image_layer, get_activation +from .layer_util import * logger = logging.getLogger(__name__) @@ -29,24 +29,24 @@ def __init__(self, kernel_size=3, depth=1, rank=3, - activation='relu', + activation='ReLU', norm_type=None, dropout_rate=None): super().__init__() - conv = get_image_layer('Conv', rank) + conv = TORCH_LAYERS[f'Conv{rank}d'] self.convs = nn.ModuleList([ conv(in_channels if i==0 else filters, filters, kernel_size, padding=kernel_size//2) for i in range(depth) ]) self.norms = nn.ModuleList([ - get_image_layer(norm_type, rank)(filters, affine=True, eps=1e-5) if norm_type else nn.Identity() + TORCH_LAYERS[f'{norm_type}{rank}d'](filters, affine=True) if norm_type else nn.Identity() for _ in range(depth) ]) - self.drop = get_image_layer('Dropout', rank)(p=dropout_rate) if dropout_rate else nn.Identity() + self.drop = TORCH_LAYERS(f'Dropout{rank}d')(p=dropout_rate) if dropout_rate else nn.Identity() - self.act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() + self.act = ACTIVATIONS[activation] def forward(self, x): @@ -120,7 +120,7 @@ def __init__(self, depth=1, dropout_rate=None) # self.drop = get_image_layer('Dropout', rank)(p=dropout_rate) if dropout_rate else nn.Identity() - self.out_act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() + self.out_act = ACTIVATIONS[activation] def forward(self, x): """ @@ -165,7 +165,7 @@ def __init__(self, norm_type=None, pool_type='MaxPool', pool_size=2, - activation='relu', + activation='ReLU', dropout_rate=None): super().__init__() @@ -185,8 +185,8 @@ def __init__(self, ]) for i in range(n_levels) ]) - pool = get_image_layer(pool_type, rank) - self.maxpools = nn.ModuleList([ + pool = TORCH_LAYERS[f'{pool_type}{rank}d'] + self.pools = nn.ModuleList([ pool(pool_size) if i>0 else nn.Identity() for i in range(n_levels) ]) @@ -201,7 +201,7 @@ def forward(self,x): """ logger.debug("IN ENCODER") outputs = [] - for pool, convs in zip(self.maxpools, self.conv_blocks): + for pool, convs in zip(self.pools, self.conv_blocks): x = pool(x) for conv in convs: x = conv(x) @@ -237,7 +237,7 @@ def __init__(self, norm_type=None, pool_type='MaxPool', pool_size=2, - activation='relu', + activation='ReLU', dropout_rate=None): super().__init__() @@ -253,7 +253,7 @@ def __init__(self, dropout_rate=dropout_rate) for i in range(n_levels) ]) - pool = get_image_layer(pool_type, rank) + pool = TORCH_LAYERS[f'{pool_type}{rank}d'] self.maxpools = nn.ModuleList([ pool(pool_size) if i>0 else nn.Identity() for i in range(n_levels) @@ -299,7 +299,7 @@ def __init__(self, conv_blocks_per_level=1, rank=3, upsample_type="Upsample", - activation="relu", + activation="ReLU", norm_type=None, dropout_rate=None, skip=True): @@ -323,7 +323,7 @@ def __init__(self, for _ in range(n_levels - 1) ]) else: - up_layer = get_image_layer(upsample_type, rank) + up_layer = TORCH_LAYERS[f'{upsample_type}{rank}d'] self.ups = nn.ModuleList([ up_layer(rev_filters[i], rev_filters[i+1], kernel_size=2, stride=2) for i in range(n_levels - 1) @@ -395,113 +395,113 @@ def forward(self, encoder_outputs): # import torch.nn as nn -class ConvResBlock(nn.Module): - def __init__(self, in_channels, out_channels): - super().__init__() - - # First branch (x1) - self.conv1a = nn.Conv3d( - in_channels, out_channels, - kernel_size=3, padding=1, bias=True - ) - self.norm1a = nn.InstanceNorm3d( - out_channels, affine=True, eps=1e-5 - ) - - # Second branch - self.conv1b = nn.Conv3d( - in_channels, out_channels, - kernel_size=3, padding=1, bias=True - ) - self.norm1b = nn.InstanceNorm3d( - out_channels, affine=True, eps=1e-5 - ) - - self.act = nn.LeakyReLU(negative_slope=0.3, inplace=False) - self.dropout = nn.Dropout3d(p=0.3) - - self.conv2b = nn.Conv3d( - out_channels, out_channels, - kernel_size=3, padding=1, bias=True - ) - self.norm2b = nn.InstanceNorm3d( - out_channels, affine=True, eps=1e-5 - ) - - self._init_weights() - - def _init_weights(self): - # Match TF he_normal (fan_in, normal) - for m in self.modules(): - if isinstance(m, nn.Conv3d): - nn.init.kaiming_normal_( - m.weight, - mode='fan_in', - nonlinearity='leaky_relu' - ) - if m.bias is not None: - nn.init.zeros_(m.bias) - - def forward(self, x): - # First branch - x1 = self.conv1a(x) - x1 = self.norm1a(x1) - # Second branch - x2 = self.conv1b(x) - x2 = self.norm1b(x2) - x2 = self.act(x2) - x2 = self.dropout(x2) - - x2 = self.conv2b(x2) - x2 = self.norm2b(x2) - - # Residual add - out = x1 + x2 - out = self.act(out) - - return out +# class ConvResBlock(nn.Module): +# def __init__(self, in_channels, out_channels): +# super().__init__() + +# # First branch (x1) +# self.conv1a = nn.Conv3d( +# in_channels, out_channels, +# kernel_size=3, padding=1, bias=True +# ) +# self.norm1a = nn.InstanceNorm3d( +# out_channels, affine=True, eps=1e-5 +# ) + +# # Second branch +# self.conv1b = nn.Conv3d( +# in_channels, out_channels, +# kernel_size=3, padding=1, bias=True +# ) +# self.norm1b = nn.InstanceNorm3d( +# out_channels, affine=True, eps=1e-5 +# ) + +# self.act = nn.LeakyReLU(negative_slope=0.3, inplace=False) +# self.dropout = nn.Dropout3d(p=0.3) + +# self.conv2b = nn.Conv3d( +# out_channels, out_channels, +# kernel_size=3, padding=1, bias=True +# ) +# self.norm2b = nn.InstanceNorm3d( +# out_channels, affine=True, eps=1e-5 +# ) + +# self._init_weights() + +# def _init_weights(self): +# # Match TF he_normal (fan_in, normal) +# for m in self.modules(): +# if isinstance(m, nn.Conv3d): +# nn.init.kaiming_normal_( +# m.weight, +# mode='fan_in', +# nonlinearity='leaky_relu' +# ) +# if m.bias is not None: +# nn.init.zeros_(m.bias) + +# def forward(self, x): +# # First branch +# x1 = self.conv1a(x) +# x1 = self.norm1a(x1) +# # Second branch +# x2 = self.conv1b(x) +# x2 = self.norm1b(x2) +# x2 = self.act(x2) +# x2 = self.dropout(x2) + +# x2 = self.conv2b(x2) +# x2 = self.norm2b(x2) + +# # Residual add +# out = x1 + x2 +# out = self.act(out) + +# return out -class Encoder3D(nn.Module): - def __init__(self, in_channels): - super().__init__() +# class Encoder3D(nn.Module): +# def __init__(self, in_channels): +# super().__init__() - self.block1_1 = ConvResBlock(in_channels, 16) - self.block1_2 = ConvResBlock(16, 16) +# self.block1_1 = ConvResBlock(in_channels, 16) +# self.block1_2 = ConvResBlock(16, 16) - self.block2_1 = ConvResBlock(16, 32) - self.block2_2 = ConvResBlock(32, 32) +# self.block2_1 = ConvResBlock(16, 32) +# self.block2_2 = ConvResBlock(32, 32) - self.block3_1 = ConvResBlock(32, 64) - self.block3_2 = ConvResBlock(64, 64) +# self.block3_1 = ConvResBlock(32, 64) +# self.block3_2 = ConvResBlock(64, 64) - self.block4_1 = ConvResBlock(64, 128) - self.block4_2 = ConvResBlock(128, 128) +# self.block4_1 = ConvResBlock(64, 128) +# self.block4_2 = ConvResBlock(128, 128) - self.block5_1 = ConvResBlock(128, 256) - self.block5_2 = ConvResBlock(256, 256) +# self.block5_1 = ConvResBlock(128, 256) +# self.block5_2 = ConvResBlock(256, 256) - self.pool = nn.MaxPool3d(kernel_size=2) +# self.pool = nn.MaxPool3d(kernel_size=2) - def forward(self, x): - X1 = self.block1_1(x) - X1 = self.block1_2(X1) - X1p = self.pool(X1) +# def forward(self, x): +# X1 = self.block1_1(x) +# X1 = self.block1_2(X1) +# X1p = self.pool(X1) - X2 = self.block2_1(X1p) - X2 = self.block2_2(X2) - X2p = self.pool(X2) +# X2 = self.block2_1(X1p) +# X2 = self.block2_2(X2) +# X2p = self.pool(X2) - X3 = self.block3_1(X2p) - X3 = self.block3_2(X3) - X3p = self.pool(X3) +# X3 = self.block3_1(X2p) +# X3 = self.block3_2(X3) +# X3p = self.pool(X3) - X4 = self.block4_1(X3p) - X4 = self.block4_2(X4) - X4p = self.pool(X4) +# X4 = self.block4_1(X3p) +# X4 = self.block4_2(X4) +# X4p = self.pool(X4) - X5 = self.block5_1(X4p) - X5 = self.block5_2(X5) - X5p = self.pool(X5) +# X5 = self.block5_1(X4p) +# X5 = self.block5_2(X5) +# X5p = self.pool(X5) - return [X1, X2, X3, X4, X5] \ No newline at end of file +# return [X1, X2, X3, X4, X5] \ No newline at end of file diff --git a/im2sim/layers/layer_util.py b/im2sim/layers/layer_util.py index 3770ef1..692f2d6 100644 --- a/im2sim/layers/layer_util.py +++ b/im2sim/layers/layer_util.py @@ -1,146 +1,202 @@ -from torch import nn +import torch import torch.nn.functional as F import torch_geometric.nn as gnn +import re +from typing import Any, Callable +import inspect +from torch_geometric.data import Data +from copy import deepcopy -def get_image_layer(name, rank): - """Get an N-D layer object. +def get_graph_layer(name, kwargs): + return PyG_Wrapper(PYG_LAYERS[name](**kwargs)) - Args: - name: A `str`. The name of the requested layer. - rank: An `int`. The rank of the requested layer. - Returns: - A `torch.nn.Module` object. - Raises: - ValueError: If the requested layer is unknown. - """ - try: - return _IMAGE_LAYERS[(name, rank)] - except KeyError as err: - raise ValueError( - f"Could not find a layer with name '{name}' and rank {rank}.") from err + +def make_registry(lib: Any, regex: Callable): + + registry = NormalizedDict({name: getattr(lib, name) for name in dir(lib) if re.search(regex, name)}) + + def register(cls=None, *, name=None): + def decorator(obj): + registry[name or obj.__name__] = obj + return obj + + return decorator(cls) if cls else decorator + + return registry, register + + +def normalize_key(key: str) -> str: + return key.replace(" ", "").replace("_", "").lower() + +class NormalizedDict: + def __init__(self, data: dict): + self._data = {} + for key, val in data.items(): + self.__setitem__(key,val) + + def __setitem__(self, key, value): + self._data[normalize_key(key)] = value + + def __getitem__(self, key): + return self._data[normalize_key(key)] + + def __str__(self): + return self._data.__str__() + + + +# IMAGE_CONVS, register_image_conv = make_registry(torch.nn, 'Conv') +# IMAGE_POOLS, register_image_pool = make_registry(torch.nn, 'Pool') +# IMAGE_NORMS, register_image_norm = make_registry(torch.nn, 'Norm') +# DROPOUT, register_dropout = make_registry(torch.nn, 'Dropout') + +# GRAPH_CONVS, register_graph_conv = make_registry(gnn, 'Conv') +# GRAPH_POOLS, register_graph_pool = make_registry(gnn, 'Pool') +# GRAPH_NORMS, register_graph_norm = make_registry(gnn, 'Norm') + + +activation_pattern = re.compile( + r"(ReLU|ELU|LeakyReLU|PReLU|RReLU|GELU|SiLU|" + r"Sigmoid|Tanh|Softmax|Softplus|SELU|CELU|" + r"Threshold|Hardtanh|Hardswish|Mish)" +) +ACTIVATIONS, register_activation = make_registry(torch.nn, activation_pattern) + + +layer_pattern = r"(Conv|Pool|Norm|UpSample|PixelShuffle|Dropout)" + + + +TORCH_LAYERS, register_torch_layer = make_registry(torch.nn, layer_pattern) +PYG_LAYERS, register_pyg_layer = make_registry(gnn, layer_pattern) + +print(PYG_LAYERS) + +# def get_image_layer(name, rank): +# """Get an N-D layer object. + +# Args: +# name: A `str`. The name of the requested layer. +# rank: An `int`. The rank of the requested layer. + +# Returns: +# A `torch.nn.Module` object. + +# Raises: +# ValueError: If the requested layer is unknown. +# """ +# layer_name = f"{name}{rank}d" +# try: +# return _IMAGE_LAYERS(layer_name) +# except KeyError as err: +# raise ValueError( +# f"Could not find a layer with name {name} and rank {rank}") from err +# def get_torch_layer(layer_name): +# return getattr(torch.nn, layer_name) -def get_graph_layer(name): - """Get an graph layer object. - - Args: - name: A `str`. The name of the requested layer. - - Returns: - A `torch.nn.Module` object. - - Raises: - ValueError: If the requested layer is unknown. - """ - try: - return _GRAPH_LAYERS[name] - except KeyError as err: - raise ValueError( - f"Could not find an activation with name '{name}'") from err -def get_default_kwargs(name): - """Get an graph layer object. - - Args: - name: A `str`. The name of the requested layer. - - Returns: - A `torch.nn.Module` object. - - Raises: - ValueError: If the requested layer is unknown. - """ - try: - return _LAYER_KWARGS[name] - except KeyError as err: - raise ValueError( - f"Could not find an activation with name '{name}'") from err +# def get_graph_layer(name): +# """Get an graph layer object. + +# Args: +# name: A `str`. The name of the requested layer. + +# Returns: +# A `torch.nn.Module` object. + +# Raises: +# ValueError: If the requested layer is unknown. +# """ +# try: +# return _GRAPH_LAYERS[name] +# except KeyError as err: +# raise ValueError( +# f"Could not find an activation with name '{name}'") from err + +# def get_default_kwargs(name): +# """Get an graph layer object. + +# Args: +# name: A `str`. The name of the requested layer. + +# Returns: +# A `torch.nn.Module` object. + +# Raises: +# ValueError: If the requested layer is unknown. +# """ +# try: +# return _LAYER_KWARGS[name] +# except KeyError as err: +# raise ValueError( +# f"Could not find an activation with name '{name}'") from err -def get_activation(name): - """Get an activation object. - - Args: - name: A `str`. The name of the requested layer. - - Returns: - A `torch.nn.Module` object. - - Raises: - ValueError: If the requested activation is unknown. - """ - try: - return _ACTIVATIONS[name] - except KeyError as err: - raise ValueError( - f"Could not find an activation with name '{name}'") from err - - -_IMAGE_LAYERS = { - ('AveragePooling', 1): nn.AvgPool1d, - ('AveragePooling', 2): nn.AvgPool2d, - ('AveragePooling', 3): nn.AvgPool3d, - ('Conv', 1): nn.Conv1d, - ('Conv', 2): nn.Conv2d, - ('Conv', 3): nn.Conv3d, - ('ConvTranspose', 1): nn.ConvTranspose1d, - ('ConvTranspose', 2): nn.ConvTranspose2d, - ('ConvTranspose', 3): nn.ConvTranspose3d, - ('MaxPool', 1): nn.MaxPool1d, - ('MaxPool', 2): nn.MaxPool2d, - ('MaxPool', 3): nn.MaxPool3d, - ('Dropout', 1): nn.Dropout1d, - ('Dropout', 2): nn.Dropout2d, - ('Dropout', 3): nn.Dropout3d, - ('ZeroPadding', 1): nn.ZeroPad1d, - ('ZeroPadding', 2): nn.ZeroPad2d, - ('ZeroPadding', 3): nn.ZeroPad3d, - ('BatchNorm', 1): nn.BatchNorm1d, - ('BatchNorm', 2): nn.BatchNorm2d, - ('BatchNorm', 3): nn.BatchNorm3d, - ('InstanceNorm', 1): nn.InstanceNorm1d, - ('InstanceNorm', 2): nn.InstanceNorm2d, - ('InstanceNorm', 3): nn.InstanceNorm3d, - ('LayerNorm', 1): nn.InstanceNorm1d, -} - -_GRAPH_LAYERS = { - 'ChebConv': gnn.ChebConv, - 'GraphConv': gnn.GraphConv, - 'GCNConv': gnn.GCNConv, - 'GATConv': gnn.GATConv, - 'InstanceNorm': gnn.InstanceNorm, - 'LayerNorm': gnn.LayerNorm, - 'BatchNorm': gnn.BatchNorm, - 'GraphNorm': gnn.GraphNorm, -} - -_LAYER_KWARGS = { - 'ChebConv': {'K':3}, - 'GraphConv': {}, - 'GCNConv': {}, - 'GATConv': {} -} -_ACTIVATIONS = { - "relu": nn.ReLU, - "leaky_relu": nn.LeakyReLU, - "gelu": nn.GELU, - "sigmoid": nn.Sigmoid, - "linear": nn.Identity, - "softmax": nn.Softmax -} +# def get_activation(name): +# """Get an activation object. + +# Args: +# name: A `str`. The name of the requested layer. + +# Returns: +# A `torch.nn.Module` object. + +# Raises: +# ValueError: If the requested activation is unknown. +# """ +# try: +# return _ACTIVATIONS[name] +# except KeyError as err: +# raise ValueError( +# f"Could not find an activation with name '{name}'") from err + + + + + +# pattern = r"\d+d$" +# _IMAGE_LAYERS = {**{name: getattr(torch.nn, name) for name in dir(torch.nn) if re.search(pattern, name)}} + + +# _GRAPH_LAYERS = { +# 'ChebConv': gnn.ChebConv, +# 'GraphConv': gnn.GraphConv, +# 'GCNConv': gnn.GCNConv, +# 'GATConv': gnn.GATConv, +# 'DefaultNorm':DefaultGraphNorm, +# 'InstanceNorm': gnn.InstanceNorm, +# 'LayerNorm': gnn.LayerNorm, +# 'BatchNorm': gnn.BatchNorm, +# 'GraphNorm': gnn.GraphNorm, +# } + +# _LAYER_KWARGS = { +# 'ChebConv': {'K':1}, +# 'GraphConv': {}, +# 'GCNConv': {}, +# 'GATConv': {} +# } +# _ACTIVATIONS = { +# "relu": nn.ReLU, +# "leaky_relu": nn.LeakyReLU, +# "gelu": nn.GELU, +# "sigmoid": nn.Sigmoid, +# "linear": nn.Identity, +# "softmax": nn.Softmax +# } def init_weights(m): if isinstance(m, gnn.ChebConv): for lin in m.lins: - nn.init.kaiming_normal_(lin.weight, nonlinearity='leaky_relu') + torch.nn.init.kaiming_normal_(lin.weight, nonlinearity='leaky_relu') lin.weight.data *= 0.1 if lin.bias is not None: - nn.init.zeros_(lin.bias) + torch.nn.init.zeros_(lin.bias) def standardize_spatial_factors(factors, rank): """ @@ -158,4 +214,51 @@ def standardize_spatial_factors(factors, rank): f"Each factor must be an int, tuple, or list, got {type(f).__name__}" ) - return standardized \ No newline at end of file + return standardized + + +class PyGParameterError(TypeError): + pass + +class PyGWrapperError(TypeError): + pass + + + +def _match_attrs_to_signature(graph: Data, module: torch.nn.Module) -> list[str]: + """This function accepts a PyG data object and a Callable and returns common attributes""" + attrs = [] + sig = inspect.signature(module.forward).parameters + for name, param in sig.items(): + if hasattr(graph, name): + attrs.append(name) + elif param.default is inspect.Parameter.empty: + raise PyGParameterError(f"Attribute {name} is missing but is required for {module.__class__.__name__} layers.") + return attrs + + + +# This will not be useable for pooling layers as they have multiple return Tensors. If requiered we will have to add this functionality. +class PyG_Wrapper(torch.nn.Module): + + """ + A wrapper for PyG modules to make the forward method accept and return a PyG Data object instead of separated attributes + Needs to be initialised with a pre-initialised PyG Module. + """ + + def __init__(self, pyg_module: torch.nn.Module, **kwargs): + self.pyg_module = pyg_module + + def forward(self, graph): + attrs = _match_attrs_to_signature(graph, self.pyg_module) + out = self.pyg_module(**{attr:getattr(graph,attr) for attr in attrs}) + if not isinstance(out, torch.Tensor): + raise PyGWrapperError(f"PyG layers that have multiple outputs like {self.pyg_module.__class__.__name__} are not currently supported. Consider changing PyG layer or writing a custom wrapper") + + out_graph = deepcopy(graph) + out_graph.x = out + return out_graph + + + + \ No newline at end of file diff --git a/im2sim/models/UNet.py b/im2sim/models/UNet.py index 4bb7af9..bbe8974 100644 --- a/im2sim/models/UNet.py +++ b/im2sim/models/UNet.py @@ -1,470 +1,470 @@ import torch from torch import nn import torch.nn.functional as F -from ..layers.layer_util import get_image_layer, get_activation, standardize_spatial_factors +# from ..layers.layer_util import get_image_layer, get_activation, standardize_spatial_factors -class ImageConvBlock(nn.Module): - """ - A convolutional block for image data +# class ImageConvBlock(nn.Module): +# """ +# A convolutional block for image data - Args: - in_channels (int): The number of channels in the input to the layer. - filters (int, optional): The number of filters in each convolutional layer (default: 32) - kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) - depth (int, optional): The number of successive convolutional layers (default: 2) - rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:2), - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") - norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") - dropout_rate (float, optional): The spatial dropout rate to be applied to the final convolution output (default:None) +# Args: +# in_channels (int): The number of channels in the input to the layer. +# filters (int, optional): The number of filters in each convolutional layer (default: 32) +# kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) +# depth (int, optional): The number of successive convolutional layers (default: 2) +# rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:2), +# activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") +# norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") +# dropout_rate (float, optional): The spatial dropout rate to be applied to the final convolution output (default:None) - Returns: - A `torch.nn.Module` object. +# Returns: +# A `torch.nn.Module` object. - """ - def __init__(self, - in_channels, - filters=32, - kernel_size=3, - depth=1, - rank=3, - activation='relu', - norm_type=None, - dropout_rate=None): - super().__init__() - - conv = get_image_layer('Conv', rank) - drop = get_image_layer('Dropout', rank) - self.convs = nn.ModuleList([ - conv(in_channels if i==0 else filters, filters, kernel_size, padding=kernel_size//2) - for i in range(depth) - ]) - - self.norms = nn.ModuleList([ - get_image_layer(norm_type, rank)(filters) if norm_type else nn.Identity() - for _ in range(depth) - ]) - self.drop = drop(p=dropout_rate) if dropout_rate else nn.Identity() - - self.act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() +# """ +# def __init__(self, +# in_channels, +# filters=32, +# kernel_size=3, +# depth=1, +# rank=3, +# activation='relu', +# norm_type=None, +# dropout_rate=None): +# super().__init__() + +# conv = get_image_layer('Conv', rank) +# drop = get_image_layer('Dropout', rank) +# self.convs = nn.ModuleList([ +# conv(in_channels if i==0 else filters, filters, kernel_size, padding=kernel_size//2) +# for i in range(depth) +# ]) + +# self.norms = nn.ModuleList([ +# get_image_layer(norm_type, rank)(filters) if norm_type else nn.Identity() +# for _ in range(depth) +# ]) +# self.drop = drop(p=dropout_rate) if dropout_rate else nn.Identity() + +# self.act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() - def forward(self, x): - """ - Args: - x (torch.Tensor): Input feature maps in image space [in_channels, ...] where the number of dims in ... corresponds to rank +# def forward(self, x): +# """ +# Args: +# x (torch.Tensor): Input feature maps in image space [in_channels, ...] where the number of dims in ... corresponds to rank - Returns: - torch.Tensor: Output feature maps [out_channels, ...] - """ +# Returns: +# torch.Tensor: Output feature maps [out_channels, ...] +# """ - for conv, norm in zip(self.convs, self.norms): - x = norm(self.act(conv(x))) - return self.drop(x) +# for conv, norm in zip(self.convs, self.norms): +# x = norm(self.act(conv(x))) +# return self.drop(x) -class ImageEncoder(nn.Module): - """ - A CNN encoder for images. Structured like the encoder of a UNet. - - Args: - in_channels (int): The number of channels in the input image. - filters (List[int], optional): The number of convolutional filters in each encoder level (default: [16,32,64,128,256]) - kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) - conv_blocks_per_level (int, optional): The number of successive convolutional blocks per encoder level (default: 1) - rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:3), - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") - norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") - dropout_rate (float, optional): The spatial dropout rate to be applied to each residual block prior to residual connection (default:None) - - Returns: - A `torch.nn.Module` object. - """ - - def __init__(self, - in_channels, - filters=[16,32,64,128,256], - pool_sizes = None, - kernel_size=3, - conv_blocks_per_level=1, - rank=3, - norm_type=None, - pool_type='MaxPool', - activation='relu', - dropout_rate=None): - super().__init__() +# class ImageEncoder(nn.Module): +# """ +# A CNN encoder for images. Structured like the encoder of a UNet. + +# Args: +# in_channels (int): The number of channels in the input image. +# filters (List[int], optional): The number of convolutional filters in each encoder level (default: [16,32,64,128,256]) +# kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) +# conv_blocks_per_level (int, optional): The number of successive convolutional blocks per encoder level (default: 1) +# rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:3), +# activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") +# norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") +# dropout_rate (float, optional): The spatial dropout rate to be applied to each residual block prior to residual connection (default:None) + +# Returns: +# A `torch.nn.Module` object. +# """ + +# def __init__(self, +# in_channels, +# filters=[16,32,64,128,256], +# pool_sizes = None, +# kernel_size=3, +# conv_blocks_per_level=1, +# rank=3, +# norm_type=None, +# pool_type='MaxPool', +# activation='relu', +# dropout_rate=None): +# super().__init__() - n_levels = len(filters) - self.conv_blocks = nn.ModuleList([ - ImageConvBlock(in_channels=in_channels if i==0 else filters[i-1], - filters=filters[i], - kernel_size=kernel_size, - depth=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate) - for i in range(n_levels) - ]) - - if pool_sizes is None: - pool_sizes = 2 - - pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) - - pool = get_image_layer(pool_type, rank) - self.maxpools = nn.ModuleList([ - pool(pool_sizes_standard[i-1]) if i>0 else nn.Identity() - for i in range(n_levels) - ]) - - def forward(self,x): - """ - Args: - x (torch.Tensor): Input image [in_channels, ...] - - Returns: - List[torch.Tensor]: Output feature maps from each level ordered from top to bottom [Tensor([filters[0], ...], ..., Tensor([filters[N], ...]) - """ - outputs = [] - for pool, conv in zip(self.maxpools, self.conv_blocks): - x = conv(pool(x)) - outputs.append(x) - return outputs - - -class ImageDecoder(nn.Module): - """ - CNN decoder for images. Mirrors ImageEncoder like a UNet decoder. - - Args: - filters (List[int]): Encoder filter sizes in top→bottom order. - kernel_size (int): Convolution kernel size. - conv_blocks_per_level (int): Number of conv blocks per level. - rank (int): Spatial rank (2 or 3). - upsample_type (str): "ConvTranspose" or "Upsample". - activation (str): Activation name. - norm_type (str): Normalization type. - dropout_rate (float): Dropout rate. - skip (bool): Use skip connections. - """ - - def __init__(self, - filters=[16,32,64,128,256], - kernel_size=3, - pool_sizes=None, - upsample_sizes = None, - conv_blocks_per_level=1, - rank=3, - upsample_type="ConvTranspose", - activation="relu", - norm_type=None, - dropout_rate=None, - skip=True): - super().__init__() - - self.skip = skip - self.rank = rank - - if pool_sizes is None: - pool_sizes = 2 - - pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) - - n_levels = len(filters) - - rev_filters = filters[::-1] - rev_pool_sizes = pool_sizes_standard[::-1] - - if upsample_sizes is None: - upsample_sizes = rev_pool_sizes - else: - upsample_sizes = standardize_spatial_factors(upsample_sizes, rank) - - - if upsample_type.lower() == 'upsample': - self.ups = nn.ModuleList([ - nn.Upsample(scale_factor=upsample_sizes[i], mode='trilinear' if rank==3 else 'bilinear', align_corners=True) - for i in range(n_levels - 1) - ]) - else: - up_layer = get_image_layer(upsample_type, rank) - self.ups = nn.ModuleList([ - up_layer(rev_filters[i], rev_filters[i+1], kernel_size=upsample_sizes[i], stride=upsample_sizes[i]) - for i in range(n_levels - 1) - ]) - - - self.conv_blocks = nn.ModuleList([ - ImageConvBlock( - in_channels=rev_filters[i+1] * (2 if skip else 1), - filters=rev_filters[i+1], - kernel_size=kernel_size, - depth=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate - ) - for i in range(n_levels - 1) - ]) - - - def _match_size(self, x, skip): - """ - Match skip spatial size to x spatial size using: - - center crop if skip is larger - - interpolation if skip is smaller - - Args: - x: decoder tensor, shape [B, C, ...] - skip: encoder skip tensor, shape [B, C, ...] - - Returns: - skip resized to have spatial shape x.shape[2:] - """ - target_size = x.shape[2:] - skip_size = skip.shape[2:] - - if skip_size == target_size: - return skip - - # First crop any dimensions where skip is too large - slices = [slice(None), slice(None)] - needs_crop = False - - for s, t in zip(skip_size, target_size): - if s > t: - start = (s - t) // 2 - end = start + t - slices.append(slice(start, end)) - needs_crop = True - else: - slices.append(slice(None)) - - if needs_crop: - skip = skip[tuple(slices)] - - # Then upsample if any dimensions are still too small - if skip.shape[2:] != target_size: - mode = "trilinear" if self.rank == 3 else "bilinear" - skip = F.interpolate(skip, size=target_size, mode=mode, align_corners=True) - - return skip - - - def forward(self, encoder_outputs): - """ - Args: - encoder_outputs: List of tensors from encoder (top→bottom). - - Returns: - Decoded tensor at highest resolution. - """ - - # Reverse the encoder outputs so we traverse from bottleneck to top - rev_enc = encoder_outputs[::-1] - - # Start from bottleneck - x = rev_enc[0] - - # Traverse decoder levels - for i, (up, conv) in enumerate(zip(self.ups, self.conv_blocks)): - x = up(x) - - if self.skip: - skip_feat = rev_enc[i + 1] # next encoder feature - skip_feat = self._match_size(x, skip_feat) - x = torch.cat([x, skip_feat], dim=1) - - x = conv(x) - - return x - -class UNet(nn.Module): - """ - Flexible UNet for 2D or 3D images. - - Args: - in_channels (int): Input channels. - out_channels (int): Output channels. - filters (List[int]): Encoder filter sizes. - kernel_size (int): Conv kernel size. - pool_sizes (Tuple or list): pool sizes per level , - upsample_sizes (Tuple or list): upsample sizes per level, - conv_blocks_per_level (int): Depth per level. - rank (int): Spatial rank. - activation (str): Activation function. - norm_type (str): Normalization type. - dropout_rate (float): Dropout. - final_activation (str): Output activation. - """ - - def __init__(self, - in_channels, - out_channels, - filters=[16,32,64,128,256], - pool_sizes = None, - upsample_sizes = None, - kernel_size=3, - conv_blocks_per_level=1, - rank=3, - activation="relu", - norm_type=None, - dropout_rate=None, - final_activation="linear"): +# n_levels = len(filters) +# self.conv_blocks = nn.ModuleList([ +# ImageConvBlock(in_channels=in_channels if i==0 else filters[i-1], +# filters=filters[i], +# kernel_size=kernel_size, +# depth=conv_blocks_per_level, +# rank=rank, +# activation=activation, +# norm_type=norm_type, +# dropout_rate=dropout_rate) +# for i in range(n_levels) +# ]) + +# if pool_sizes is None: +# pool_sizes = 2 + +# pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) + +# pool = get_image_layer(pool_type, rank) +# self.maxpools = nn.ModuleList([ +# pool(pool_sizes_standard[i-1]) if i>0 else nn.Identity() +# for i in range(n_levels) +# ]) + +# def forward(self,x): +# """ +# Args: +# x (torch.Tensor): Input image [in_channels, ...] + +# Returns: +# List[torch.Tensor]: Output feature maps from each level ordered from top to bottom [Tensor([filters[0], ...], ..., Tensor([filters[N], ...]) +# """ +# outputs = [] +# for pool, conv in zip(self.maxpools, self.conv_blocks): +# x = conv(pool(x)) +# outputs.append(x) +# return outputs + + +# class ImageDecoder(nn.Module): +# """ +# CNN decoder for images. Mirrors ImageEncoder like a UNet decoder. + +# Args: +# filters (List[int]): Encoder filter sizes in top→bottom order. +# kernel_size (int): Convolution kernel size. +# conv_blocks_per_level (int): Number of conv blocks per level. +# rank (int): Spatial rank (2 or 3). +# upsample_type (str): "ConvTranspose" or "Upsample". +# activation (str): Activation name. +# norm_type (str): Normalization type. +# dropout_rate (float): Dropout rate. +# skip (bool): Use skip connections. +# """ + +# def __init__(self, +# filters=[16,32,64,128,256], +# kernel_size=3, +# pool_sizes=None, +# upsample_sizes = None, +# conv_blocks_per_level=1, +# rank=3, +# upsample_type="ConvTranspose", +# activation="relu", +# norm_type=None, +# dropout_rate=None, +# skip=True): +# super().__init__() + +# self.skip = skip +# self.rank = rank + +# if pool_sizes is None: +# pool_sizes = 2 + +# pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) + +# n_levels = len(filters) + +# rev_filters = filters[::-1] +# rev_pool_sizes = pool_sizes_standard[::-1] + +# if upsample_sizes is None: +# upsample_sizes = rev_pool_sizes +# else: +# upsample_sizes = standardize_spatial_factors(upsample_sizes, rank) + + +# if upsample_type.lower() == 'upsample': +# self.ups = nn.ModuleList([ +# nn.Upsample(scale_factor=upsample_sizes[i], mode='trilinear' if rank==3 else 'bilinear', align_corners=True) +# for i in range(n_levels - 1) +# ]) +# else: +# up_layer = get_image_layer(upsample_type, rank) +# self.ups = nn.ModuleList([ +# up_layer(rev_filters[i], rev_filters[i+1], kernel_size=upsample_sizes[i], stride=upsample_sizes[i]) +# for i in range(n_levels - 1) +# ]) + + +# self.conv_blocks = nn.ModuleList([ +# ImageConvBlock( +# in_channels=rev_filters[i+1] * (2 if skip else 1), +# filters=rev_filters[i+1], +# kernel_size=kernel_size, +# depth=conv_blocks_per_level, +# rank=rank, +# activation=activation, +# norm_type=norm_type, +# dropout_rate=dropout_rate +# ) +# for i in range(n_levels - 1) +# ]) + + +# def _match_size(self, x, skip): +# """ +# Match skip spatial size to x spatial size using: +# - center crop if skip is larger +# - interpolation if skip is smaller + +# Args: +# x: decoder tensor, shape [B, C, ...] +# skip: encoder skip tensor, shape [B, C, ...] + +# Returns: +# skip resized to have spatial shape x.shape[2:] +# """ +# target_size = x.shape[2:] +# skip_size = skip.shape[2:] + +# if skip_size == target_size: +# return skip + +# # First crop any dimensions where skip is too large +# slices = [slice(None), slice(None)] +# needs_crop = False + +# for s, t in zip(skip_size, target_size): +# if s > t: +# start = (s - t) // 2 +# end = start + t +# slices.append(slice(start, end)) +# needs_crop = True +# else: +# slices.append(slice(None)) + +# if needs_crop: +# skip = skip[tuple(slices)] + +# # Then upsample if any dimensions are still too small +# if skip.shape[2:] != target_size: +# mode = "trilinear" if self.rank == 3 else "bilinear" +# skip = F.interpolate(skip, size=target_size, mode=mode, align_corners=True) + +# return skip + + +# def forward(self, encoder_outputs): +# """ +# Args: +# encoder_outputs: List of tensors from encoder (top→bottom). + +# Returns: +# Decoded tensor at highest resolution. +# """ + +# # Reverse the encoder outputs so we traverse from bottleneck to top +# rev_enc = encoder_outputs[::-1] + +# # Start from bottleneck +# x = rev_enc[0] + +# # Traverse decoder levels +# for i, (up, conv) in enumerate(zip(self.ups, self.conv_blocks)): +# x = up(x) + +# if self.skip: +# skip_feat = rev_enc[i + 1] # next encoder feature +# skip_feat = self._match_size(x, skip_feat) +# x = torch.cat([x, skip_feat], dim=1) + +# x = conv(x) + +# return x + +# class UNet(nn.Module): +# """ +# Flexible UNet for 2D or 3D images. + +# Args: +# in_channels (int): Input channels. +# out_channels (int): Output channels. +# filters (List[int]): Encoder filter sizes. +# kernel_size (int): Conv kernel size. +# pool_sizes (Tuple or list): pool sizes per level , +# upsample_sizes (Tuple or list): upsample sizes per level, +# conv_blocks_per_level (int): Depth per level. +# rank (int): Spatial rank. +# activation (str): Activation function. +# norm_type (str): Normalization type. +# dropout_rate (float): Dropout. +# final_activation (str): Output activation. +# """ + +# def __init__(self, +# in_channels, +# out_channels, +# filters=[16,32,64,128,256], +# pool_sizes = None, +# upsample_sizes = None, +# kernel_size=3, +# conv_blocks_per_level=1, +# rank=3, +# activation="relu", +# norm_type=None, +# dropout_rate=None, +# final_activation="linear"): - pool_sizes_temp = [] - if pool_sizes is None: - for i in range(len(filters)-1): - pool_sizes_temp.append(2) - pool_sizes = pool_sizes_temp +# pool_sizes_temp = [] +# if pool_sizes is None: +# for i in range(len(filters)-1): +# pool_sizes_temp.append(2) +# pool_sizes = pool_sizes_temp - if not isinstance(pool_sizes, (list, tuple)): - raise TypeError("pool_sizes must be a list or tuple") +# if not isinstance(pool_sizes, (list, tuple)): +# raise TypeError("pool_sizes must be a list or tuple") - for i, p in enumerate(pool_sizes): - if isinstance(p, int) and not isinstance(p,bool): - if p < 1: - raise ValueError(f"pool_sizes must be positive") - elif isinstance(p, (tuple,list)): - if len(p) != rank: - raise ValueError(f"pool_sizes tuple must be same length as rank") - for j , ps in enumerate(p): - if not isinstance(ps, int) or isinstance(ps,bool): - raise TypeError(f"pool_sizes must be an int") - if ps < 1: - raise ValueError(f"pool_sizes must be positive") - else: - raise TypeError("each entry in pool_sizes must be either an int or a tuple or list") +# for i, p in enumerate(pool_sizes): +# if isinstance(p, int) and not isinstance(p,bool): +# if p < 1: +# raise ValueError(f"pool_sizes must be positive") +# elif isinstance(p, (tuple,list)): +# if len(p) != rank: +# raise ValueError(f"pool_sizes tuple must be same length as rank") +# for j , ps in enumerate(p): +# if not isinstance(ps, int) or isinstance(ps,bool): +# raise TypeError(f"pool_sizes must be an int") +# if ps < 1: +# raise ValueError(f"pool_sizes must be positive") +# else: +# raise TypeError("each entry in pool_sizes must be either an int or a tuple or list") - if len(filters) != (len(pool_sizes)+1): - raise ValueError(f"pool_sizes do not match number of filters. For {len(filters)}, please input {len(filters)-1} number of pools.") +# if len(filters) != (len(pool_sizes)+1): +# raise ValueError(f"pool_sizes do not match number of filters. For {len(filters)}, please input {len(filters)-1} number of pools.") - if upsample_sizes is not None: - if not isinstance(upsample_sizes, (list, tuple)): - raise TypeError("upsample_sizes must be a list or tuple") +# if upsample_sizes is not None: +# if not isinstance(upsample_sizes, (list, tuple)): +# raise TypeError("upsample_sizes must be a list or tuple") - for i, p in enumerate(upsample_sizes): - if isinstance(p, int) and not isinstance(p,bool): - if p < 1: - raise ValueError(f"upsample_sizes must be positive") - elif isinstance(p, (tuple,list)): - if len(p) != rank: - raise ValueError(f"upsample_sizes tuple must be same length as rank") - for j , ps in enumerate(p): - if not isinstance(ps, int) or isinstance(ps,bool): - raise TypeError(f"upsample_sizes must be an int") - if ps < 1: - raise ValueError(f"upsample_sizes must be positive") - else: - raise TypeError("each entry in upsample_sizes must be either an int or a tuple or list") +# for i, p in enumerate(upsample_sizes): +# if isinstance(p, int) and not isinstance(p,bool): +# if p < 1: +# raise ValueError(f"upsample_sizes must be positive") +# elif isinstance(p, (tuple,list)): +# if len(p) != rank: +# raise ValueError(f"upsample_sizes tuple must be same length as rank") +# for j , ps in enumerate(p): +# if not isinstance(ps, int) or isinstance(ps,bool): +# raise TypeError(f"upsample_sizes must be an int") +# if ps < 1: +# raise ValueError(f"upsample_sizes must be positive") +# else: +# raise TypeError("each entry in upsample_sizes must be either an int or a tuple or list") - if len(filters) != (len(upsample_sizes)+1): - raise ValueError(f"upsample_sizes do not match number of filters. For {len(filters)}, please input {len(filters)-1} number of upsamples.") +# if len(filters) != (len(upsample_sizes)+1): +# raise ValueError(f"upsample_sizes do not match number of filters. For {len(filters)}, please input {len(filters)-1} number of upsamples.") - super().__init__() - - self.encoder = ImageEncoder( - in_channels=in_channels, - filters=filters, - pool_sizes=pool_sizes, - kernel_size=kernel_size, - conv_blocks_per_level=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate - ) - - self.decoder = ImageDecoder( - filters=filters, - pool_sizes=pool_sizes, - upsample_sizes = upsample_sizes, - kernel_size=kernel_size, - conv_blocks_per_level=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate - ) - - conv = get_image_layer("Conv", rank) - - self.final_conv = conv(filters[0], out_channels, kernel_size=1) - - self.final_act = ( - get_activation(final_activation)() - if final_activation.lower() != "linear" - else nn.Identity() - ) - - def forward(self, x): - enc_feats = self.encoder(x) - x = self.decoder(enc_feats) - x = self.final_conv(x) - x = self.final_act(x) - return x - -class StandardUNet(UNet): - """ - Standaed UNet for 2D or 3D images. - - Args: - in_channels (int): Input channels. - out_channels (int): Output channels. - filters (List[int]): Encoder filter sizes. - kernel_size (int): Conv kernel size. - pool_sizes (Tuple or list): pool sizes per dim , - conv_blocks_per_level (int): Depth per level. - rank (int): Spatial rank. - activation (str): Activation function. - norm_type (str): Normalization type. - dropout_rate (float): Dropout. - final_activation (str): Output activation. - """ - - def __init__(self, - in_channels, - out_channels, - filters=[16, 32, 64, 128, 256], - pool_size = 2, - kernel_size=3, - conv_blocks_per_level=1, - rank=3, - activation="relu", - norm_type=None, - dropout_rate=None, - final_activation="linear"): +# super().__init__() + +# self.encoder = ImageEncoder( +# in_channels=in_channels, +# filters=filters, +# pool_sizes=pool_sizes, +# kernel_size=kernel_size, +# conv_blocks_per_level=conv_blocks_per_level, +# rank=rank, +# activation=activation, +# norm_type=norm_type, +# dropout_rate=dropout_rate +# ) + +# self.decoder = ImageDecoder( +# filters=filters, +# pool_sizes=pool_sizes, +# upsample_sizes = upsample_sizes, +# kernel_size=kernel_size, +# conv_blocks_per_level=conv_blocks_per_level, +# rank=rank, +# activation=activation, +# norm_type=norm_type, +# dropout_rate=dropout_rate +# ) + +# conv = get_image_layer("Conv", rank) + +# self.final_conv = conv(filters[0], out_channels, kernel_size=1) + +# self.final_act = ( +# get_activation(final_activation)() +# if final_activation.lower() != "linear" +# else nn.Identity() +# ) + +# def forward(self, x): +# enc_feats = self.encoder(x) +# x = self.decoder(enc_feats) +# x = self.final_conv(x) +# x = self.final_act(x) +# return x + +# class StandardUNet(UNet): +# """ +# Standaed UNet for 2D or 3D images. + +# Args: +# in_channels (int): Input channels. +# out_channels (int): Output channels. +# filters (List[int]): Encoder filter sizes. +# kernel_size (int): Conv kernel size. +# pool_sizes (Tuple or list): pool sizes per dim , +# conv_blocks_per_level (int): Depth per level. +# rank (int): Spatial rank. +# activation (str): Activation function. +# norm_type (str): Normalization type. +# dropout_rate (float): Dropout. +# final_activation (str): Output activation. +# """ + +# def __init__(self, +# in_channels, +# out_channels, +# filters=[16, 32, 64, 128, 256], +# pool_size = 2, +# kernel_size=3, +# conv_blocks_per_level=1, +# rank=3, +# activation="relu", +# norm_type=None, +# dropout_rate=None, +# final_activation="linear"): - pool_size_standard = [] - if isinstance(pool_size, int): - pool_size_single = [] - for j in range(rank): - pool_size_single.append(pool_size) - pool_size_single = tuple(pool_size_single) - for i in range(len(filters)-1): - pool_size_standard.append(pool_size_single) - elif isinstance(pool_size , (tuple,list)): - if len(pool_size) == rank: - for i in range(len(filters)-1): - pool_size_standard.append(pool_size) - else: - raise ValueError(f"pool_size must be an int or have same length as rank") - - super().__init__( - in_channels=in_channels, - out_channels=out_channels, - filters=filters, - pool_sizes=pool_size_standard, - upsample_sizes=None, - kernel_size=kernel_size, - conv_blocks_per_level=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate, - final_activation=final_activation, - ) \ No newline at end of file +# pool_size_standard = [] +# if isinstance(pool_size, int): +# pool_size_single = [] +# for j in range(rank): +# pool_size_single.append(pool_size) +# pool_size_single = tuple(pool_size_single) +# for i in range(len(filters)-1): +# pool_size_standard.append(pool_size_single) +# elif isinstance(pool_size , (tuple,list)): +# if len(pool_size) == rank: +# for i in range(len(filters)-1): +# pool_size_standard.append(pool_size) +# else: +# raise ValueError(f"pool_size must be an int or have same length as rank") + +# super().__init__( +# in_channels=in_channels, +# out_channels=out_channels, +# filters=filters, +# pool_sizes=pool_size_standard, +# upsample_sizes=None, +# kernel_size=kernel_size, +# conv_blocks_per_level=conv_blocks_per_level, +# rank=rank, +# activation=activation, +# norm_type=norm_type, +# dropout_rate=dropout_rate, +# final_activation=final_activation, +# ) \ No newline at end of file diff --git a/im2sim/models/__init__.py b/im2sim/models/__init__.py index c2e035f..b4ca8a5 100644 --- a/im2sim/models/__init__.py +++ b/im2sim/models/__init__.py @@ -1,3 +1,3 @@ from .image_to_graph import SimpleI2G, I2GUNet from .utils import get_model_config -from .UNet import UNet, StandardUNet \ No newline at end of file +# from .UNet import UNet, StandardUNet \ No newline at end of file diff --git a/tests/unit/data/test_mesh_utils.py b/tests/unit/data/test_mesh_utils.py new file mode 100644 index 0000000..a9bc8ca --- /dev/null +++ b/tests/unit/data/test_mesh_utils.py @@ -0,0 +1,75 @@ +import torch +from im2sim.data.mesh_utils import * + + +def test_make_padded_batch_shapes(): + x = torch.tensor([[1], [2], [3], [4], [5]]) + batch = torch.tensor([0, 0, 0, 1, 2]) + + padded_x, mask = make_padded_batch(x, batch) + + assert padded_x.shape == (3, 3, 1) + assert mask.shape == (3, 3) + + +def test_make_padded_batch_mask_correct(): + x = torch.randn(5, 2) + batch = torch.tensor([0, 0, 0, 1, 2]) + + _, mask = make_padded_batch(x, batch) + + expected = torch.tensor([ + [True, True, True], + [True, False, False], + [True, False, False], + ]) + + assert torch.equal(mask, expected) + +def test_compute_edge_lengths_simple(): + points = torch.tensor([ + [0.0, 0.0], + [3.0, 4.0], + ]) + edges = torch.tensor([[0], [1]]) + + distances = compute_edge_lengths(points, edges) + + assert torch.allclose(distances, torch.tensor([5.0])) + + +def test_hard_threshold(): + y = torch.tensor([0.5, 1.0, 1.5]) + + out = hard_threshold(y, threshold=1.0) + + expected = torch.tensor([1.0, 0.0, 0.0]) + assert torch.equal(out, expected) + + +def test_soft_threshold_range(): + y = torch.tensor([0.0, 1.5, 3.0]) + out = soft_threshold(y) + + assert torch.all(out >= 0) + assert torch.all(out <= 1) + + +def test_soft_threshold_behavior(): + y_low = torch.tensor([0.0]) + y_high = torch.tensor([10.0]) + + assert soft_threshold(y_low) > soft_threshold(y_high) + + +def test_set_attrs(): + data = Data() + attrs = { + "x": torch.tensor([[1.0]]), + "edge_index": torch.tensor([[0], [0]]) + } + + set_attrs(data, attrs) + + assert torch.equal(data.x, attrs["x"]) + assert torch.equal(data.edge_index, attrs["edge_index"]) \ No newline at end of file From 527c57ae9c487606de227cb100ddf6e6f75cfd41 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 27 Jul 2026 14:32:18 +0000 Subject: [PATCH 28/49] amended tests --- .github/workflows/tests.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml index 20844aa..607433d 100644 --- a/.github/workflows/tests.yaml +++ b/.github/workflows/tests.yaml @@ -21,6 +21,7 @@ jobs: run: | pip install -r requirements.txt pip install pytest + pip install ruff - name: Lint run: ruff check . From d97d9cf36a53bb1c8fc663f0972997c978050005 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 27 Jul 2026 15:15:28 +0000 Subject: [PATCH 29/49] fixed linting issues --- im2sim/__init__.py | 10 + im2sim/data/__init__.py | 4 +- im2sim/data/core.py | 33 +- im2sim/data/mesh_utils.py | 30 +- im2sim/data/ops.py | 1 - im2sim/data/transforms.py | 3 +- im2sim/layers/__init__.py | 4 +- im2sim/layers/graph_blocks.py | 18 +- im2sim/layers/image_blocks.py | 8 +- im2sim/layers/layer_util.py | 13 +- im2sim/layers/meshgraphnets.py | 16 +- im2sim/layers/projections.py | 13 +- im2sim/losses/__init__.py | 4 +- im2sim/losses/feature.py | 1 - im2sim/losses/mesh.py | 12 +- im2sim/losses/pointcloud.py | 10 +- im2sim/losses/seg.py | 2 +- im2sim/losses/utils.py | 4 +- im2sim/models/UNet.py | 470 ----------------------------- im2sim/models/__init__.py | 5 +- im2sim/models/image_to_graph.py | 171 +++++------ im2sim/models/unet.py | 469 ++++++++++++++++++++++++++++ im2sim/models/utils.py | 88 +++--- im2sim/plot/__init__.py | 2 +- im2sim/plot/pointcloud.py | 8 +- setup.py | 2 +- tests/unit/data/test_mesh_utils.py | 3 +- 27 files changed, 706 insertions(+), 698 deletions(-) delete mode 100644 im2sim/models/UNet.py create mode 100644 im2sim/models/unet.py diff --git a/im2sim/__init__.py b/im2sim/__init__.py index 0a89292..4696ae3 100644 --- a/im2sim/__init__.py +++ b/im2sim/__init__.py @@ -1 +1,11 @@ from . import callbacks, data, layers, losses, metrics, models, utils + +__all__ = [ + "callbacks", + "data", + "layers", + "losses", + "metrics", + "models", + "utils", +] \ No newline at end of file diff --git a/im2sim/data/__init__.py b/im2sim/data/__init__.py index e466aee..c836595 100644 --- a/im2sim/data/__init__.py +++ b/im2sim/data/__init__.py @@ -1,2 +1,4 @@ +from . import mesh_utils as mesh_utils +from . import ops as ops +from . import transforms as transforms from .core import * -from . import mesh_utils,transforms,ops \ No newline at end of file diff --git a/im2sim/data/core.py b/im2sim/data/core.py index 6e69076..841d30e 100644 --- a/im2sim/data/core.py +++ b/im2sim/data/core.py @@ -1,9 +1,10 @@ -from abc import ABC, abstractmethod import copy -import torch -from torch_geometric.data import Data, Batch import logging -from typing import Dict, Any +from abc import ABC, abstractmethod +from typing import Any + +import torch +from torch_geometric.data import Batch, Data logger = logging.getLogger(__name__) @@ -156,8 +157,7 @@ def _is_serializable(self, v): helper function to check if attr is serializable """ return ( - isinstance(v, (int, float, str, bool)) - or isinstance(v, torch.Tensor) + isinstance(v, (int, float, str, bool, torch.Tensor)) or v is None ) @@ -257,10 +257,7 @@ def __init__( # ----------------------------- def _get_target(self, data, key): - if self.attr is None: - return data[key] - - elif self.attr == "all": + if self.attr is None or self.attr == "all": return data[key] elif hasattr(data[key], self.attr): @@ -480,12 +477,12 @@ def __init__(self, transforms): def __call__(self, data): transforms = self._get_applicable_transforms(data.keys()) for t in transforms: - logging.debug(f'before {t.name}') + logger.debug(f'before {t.name}') _log_data(data) data = t.forward(data) - logging.debug(f'after {t.name}') + logger.debug(f'after {t.name}') _log_data(data) return data @@ -496,12 +493,12 @@ def inverse(self, data): transforms = self._get_applicable_transforms(data.keys()) for t in reversed(transforms): if t.is_invertible: - logging.debug(f'before {t.name} inverse') + logger.debug(f'before {t.name} inverse') _log_data(data) data = t.inverse(data) - logging.debug(f'after {t.name} inverse') + logger.debug(f'after {t.name} inverse') _log_data(data) return data @@ -607,16 +604,16 @@ def load_pipeline(path): return pipeline -def _log_data(data: Dict[str,Any]) -> None: +def _log_data(data: dict[str,Any]) -> None: if logger.isEnabledFor(logging.DEBUG): for k,v in data.items(): - logging.debug(k) + logger.debug(k) if isinstance(v, Data): for c in range(v.x.shape[-1]): - logging.debug(f'channel {c}- max:{v.x[...,c].max()}, min:{v.x[...,c].min()}') + logger.debug(f'channel {c}- max:{v.x[...,c].max()}, min:{v.x[...,c].min()}') else: - logging.debug(v.shape) + logger.debug(v.shape) # ------------------------------------------------------------------------------------ # TRANSFORM REGISTRY DEFINITION diff --git a/im2sim/data/mesh_utils.py b/im2sim/data/mesh_utils.py index be1f793..af5e68d 100644 --- a/im2sim/data/mesh_utils.py +++ b/im2sim/data/mesh_utils.py @@ -1,11 +1,11 @@ + import numpy as np import torch -from itertools import combinations -from torch_geometric.utils import to_undirected import torch_geometric.nn as gnn -from torch_geometric.data import Data from pyvista.core.pointset import PointGrid -from typing import Dict, List, Tuple +from torch_geometric.data import Data +from torch_geometric.utils import to_undirected + class InputMeshError(ValueError): pass @@ -13,7 +13,7 @@ class InputMeshError(ValueError): -def get_structure_ids(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: +def get_structure_ids(mesh: PointGrid, structure_dict: dict[int, str]) -> dict[str, torch.Tensor]: """ Extracts node ids for different substructures in a pyvista PointGrid object. @@ -30,7 +30,7 @@ def get_structure_ids(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[s -def get_structure_edges(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: +def get_structure_edges(mesh: PointGrid, structure_dict: dict[int, str]) -> dict[str, torch.Tensor]: """ Extracts edges for different substructures in a pyvista PointGrid object. @@ -71,7 +71,7 @@ def get_edges(mesh:PointGrid, structure_id: int) -> torch.Tensor: return edges -def get_structure_cells(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict[str, torch.Tensor]: +def get_structure_cells(mesh: PointGrid, structure_dict: dict[int, str]) -> dict[str, torch.Tensor]: """ Extracts cells for different substructures in a pyvista PointGrid object. @@ -100,16 +100,16 @@ def get_structure_cells(mesh: PointGrid, structure_dict: Dict[int, str]) -> Dict return out_dict -def _has_missing_ids(mesh: PointGrid, structure_dict: Dict[int,str]) -> bool: +def _has_missing_ids(mesh: PointGrid, structure_dict: dict[int,str]) -> bool: ids = np.unique(mesh['CellEntityIds']) missing_ids = set(structure_dict.keys()) - set(ids.tolist()) - if len(missing_ids) != 0: - return True - return False + + return len(missing_ids) != 0 + -def set_attrs(data:Data, attrs:Dict[str, torch.Tensor]) -> None: +def set_attrs(data:Data, attrs:dict[str, torch.Tensor]) -> None: """ A helper function to set multiple attributes of a PyG Data object with keys and values from a dictionary. @@ -153,7 +153,7 @@ def get_edges_surf(mesh: PointGrid) -> torch.Tensor: edges = torch.Tensor(edges).T.long() return edges -def get_node_features(mesh:PointGrid, feature_names: List[str]) -> torch.Tensor: +def get_node_features(mesh:PointGrid, feature_names: list[str]) -> torch.Tensor: """ Extracts the node features from a pyvista mesh object based on the feature names provided. @@ -169,7 +169,7 @@ def get_node_features(mesh:PointGrid, feature_names: List[str]) -> torch.Tensor: -def make_padded_batch(x: torch.Tensor, batch: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: +def make_padded_batch(x: torch.Tensor, batch: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: """ A helper function to pad a batch of data to the same size. @@ -269,7 +269,7 @@ def cluster_pool(mesh: Data) -> Data: pooled_mesh = gnn.avg_pool(clusters, mesh) return pooled_mesh -def rasterize(points: torch.Tensor, im_shape: List[int], vox_sizes: List[float]) -> torch.Tensor: +def rasterize(points: torch.Tensor, im_shape: list[int], vox_sizes: list[float]) -> torch.Tensor: """ Computes the squared Euclidean distance between voxel centroids in a grid to a pointcloud diff --git a/im2sim/data/ops.py b/im2sim/data/ops.py index 8d7b35d..51c6d55 100644 --- a/im2sim/data/ops.py +++ b/im2sim/data/ops.py @@ -1,6 +1,5 @@ from .core import * - # ------------------------------------------------------------------------------------ # OP FUNCTION LIBRARY # ------------------------------------------------------------------------------------ diff --git a/im2sim/data/transforms.py b/im2sim/data/transforms.py index 3da5782..28b2f95 100644 --- a/im2sim/data/transforms.py +++ b/im2sim/data/transforms.py @@ -1,6 +1,5 @@ -from .ops import * from .core import * - +from .ops import * def transform_from_fn(fn, keys,attr=None,channels=None,per_channel=False,channel_dim=-1,name=None): diff --git a/im2sim/layers/__init__.py b/im2sim/layers/__init__.py index feec238..df6b0fb 100644 --- a/im2sim/layers/__init__.py +++ b/im2sim/layers/__init__.py @@ -1,6 +1,6 @@ from .graph_blocks import * from .image_blocks import * -from .projections import * -from .meshgraphnets import * from .layer_util import * +from .meshgraphnets import * +from .projections import * diff --git a/im2sim/layers/graph_blocks.py b/im2sim/layers/graph_blocks.py index d4e93a8..629fb79 100644 --- a/im2sim/layers/graph_blocks.py +++ b/im2sim/layers/graph_blocks.py @@ -2,15 +2,9 @@ import torch from torch import nn -import torch_geometric.nn as gnn -from .projections import TrilinearProjection -from ..data.mesh_utils import cluster_pool from .layer_util import * - - - logger = logging.getLogger(__name__) @@ -57,7 +51,7 @@ def __init__(self, filters, depth=1, conv_type='GATConv', - conv_kwargs={}, + conv_kwargs=None, activation='ReLU', norm_type='default', norm_kwargs=None): @@ -110,7 +104,7 @@ def __init__(self, filters, depth=3, conv_type='GATConv', - conv_kwargs={}, + conv_kwargs=None, activation='ReLU', norm_type='InstanceNorm', norm_kwargs=None): @@ -179,8 +173,8 @@ def __init__(self, res_depth = 3, n_deform_blocks = 3, template_edge_index=None, - conv_type="ChebConv", - conv_kwargs={'K':3}, + conv_type="GATConv", + conv_kwargs=None, activation="relu", out_activation="linear", norm_type="InstanceNorm"): @@ -217,8 +211,8 @@ def __init__(self, self.edge_index=template_edge_index def forward(self, in_graph, prev_results, encoder_projection): - if edge_index is None: - edge_index=self.edge_index + if in_graph.edge_index is None and self.edge_index is not None: + in_graph.edge_index=self.edge_index graph = deepcopy(in_graph) graph = self.process_conv(graph) diff --git a/im2sim/layers/image_blocks.py b/im2sim/layers/image_blocks.py index 33bbc0d..7dfca7e 100644 --- a/im2sim/layers/image_blocks.py +++ b/im2sim/layers/image_blocks.py @@ -1,6 +1,8 @@ import logging + import torch from torch import nn + from .layer_util import * logger = logging.getLogger(__name__) @@ -157,7 +159,7 @@ class ImageResEncoder(nn.Module): def __init__(self, in_channels, - filters=[16,32,64,128,256], + filters=(16,32,64,128,256), kernel_size=3, res_depth=3, res_blocks_per_level=2, @@ -230,7 +232,7 @@ class ImageEncoder(nn.Module): def __init__(self, in_channels, - filters=[16,32,64,128,256], + filters=(16,32,64,128,256), kernel_size=3, conv_blocks_per_level=1, rank=3, @@ -294,7 +296,7 @@ class ImageDecoder(nn.Module): """ def __init__(self, - filters=[16,32,64,128,256], + filters=(16,32,64,128,256), kernel_size=3, conv_blocks_per_level=1, rank=3, diff --git a/im2sim/layers/layer_util.py b/im2sim/layers/layer_util.py index 692f2d6..34b9b38 100644 --- a/im2sim/layers/layer_util.py +++ b/im2sim/layers/layer_util.py @@ -1,14 +1,17 @@ +import inspect +import re +from collections.abc import Callable +from copy import deepcopy +from typing import Any + import torch -import torch.nn.functional as F import torch_geometric.nn as gnn -import re -from typing import Any, Callable -import inspect from torch_geometric.data import Data -from copy import deepcopy def get_graph_layer(name, kwargs): + if kwargs is None: + kwargs = {} return PyG_Wrapper(PYG_LAYERS[name](**kwargs)) diff --git a/im2sim/layers/meshgraphnets.py b/im2sim/layers/meshgraphnets.py index b753d67..4cdbb46 100644 --- a/im2sim/layers/meshgraphnets.py +++ b/im2sim/layers/meshgraphnets.py @@ -1,16 +1,16 @@ # This code is from https://github.com/echowve/meshGraphNets_pytorch/tree/master import torch -import torch.nn as nn -from torch_scatter import scatter_add +from torch import nn from torch_geometric.data import Data +from torch_scatter import scatter_add class MGNEdgeBlock(nn.Module): def __init__(self, custom_func:nn.Module): - super(MGNEdgeBlock, self).__init__() + super().__init__() self.net = custom_func @@ -39,7 +39,7 @@ def forward(self, graph): class MGNNodeBlock(nn.Module): def __init__(self, custom_func:nn.Module): - super(MGNNodeBlock, self).__init__() + super().__init__() self.net = custom_func def forward(self, graph): @@ -81,7 +81,7 @@ def __init__(self, edge_input_size=128, node_input_size=128, hidden_size=128): - super(MGNEncoder, self).__init__() + super().__init__() self.eb_encoder = build_mlp(edge_input_size, hidden_size, hidden_size) self.nb_encoder = build_mlp(node_input_size, hidden_size, hidden_size) @@ -100,7 +100,7 @@ class MGNGnBlock(nn.Module): def __init__(self, hidden_size=128): - super(MGNGnBlock, self).__init__() + super().__init__() eb_input_dim = 3 * hidden_size @@ -129,7 +129,7 @@ def forward(self, graph): class MGNDecoder(nn.Module): def __init__(self, hidden_size=128, output_size=2): - super(MGNDecoder, self).__init__() + super().__init__() self.decode_module = build_mlp(hidden_size, hidden_size, output_size, lay_norm=False) def forward(self, graph): @@ -140,7 +140,7 @@ class MeshGraphNet(nn.Module): def __init__(self, message_passing_num, node_input_size, edge_input_size, output_size, hidden_size=128): - super(MeshGraphNet, self).__init__() + super().__init__() self.encoder = MGNEncoder(edge_input_size=edge_input_size, node_input_size=node_input_size, hidden_size=hidden_size) diff --git a/im2sim/layers/projections.py b/im2sim/layers/projections.py index 0f2caab..d683e45 100644 --- a/im2sim/layers/projections.py +++ b/im2sim/layers/projections.py @@ -3,6 +3,7 @@ import torch import torch.nn.functional as F from torch import nn + from ..data.mesh_utils import make_padded_batch logger = logging.getLogger(__name__) @@ -107,14 +108,14 @@ def forward(self, image_features, graph_features, batch): # mimic tf.gather_nd(image_features[0], ...) img0 = image_features[i] - def gather(xi, yi, zi): - return img0[...,xi, yi, zi] + def gather(img, xi, yi, zi): + return img[...,xi, yi, zi] # --- z1 plane --- - q11 = gather(x1, y1, z1) - q21 = gather(x2, y1, z1) - q12 = gather(x1, y2, z1) - q22 = gather(x2, y2, z1) + q11 = gather(img0, x1, y1, z1) + q21 = gather(img0,x2, y1, z1) + q12 = gather(img0,x1, y2, z1) + q22 = gather(img0,x2, y2, z1) wx = (x - x1.float()).unsqueeze(0) wx2 = (x2.float() - x).unsqueeze(0) diff --git a/im2sim/losses/__init__.py b/im2sim/losses/__init__.py index ce55bd3..d83d63a 100644 --- a/im2sim/losses/__init__.py +++ b/im2sim/losses/__init__.py @@ -1 +1,3 @@ -from . import pointcloud, mesh, feature \ No newline at end of file +from . import feature, mesh, pointcloud + +__all__ = ["feature", "mesh", "pointcloud"] \ No newline at end of file diff --git a/im2sim/losses/feature.py b/im2sim/losses/feature.py index e5237ed..b5a9385 100644 --- a/im2sim/losses/feature.py +++ b/im2sim/losses/feature.py @@ -2,7 +2,6 @@ import logging import torch - from torch_geometric.nn import knn_interpolate logger = logging.getLogger(__name__) diff --git a/im2sim/losses/mesh.py b/im2sim/losses/mesh.py index 257fd86..b12b5a0 100644 --- a/im2sim/losses/mesh.py +++ b/im2sim/losses/mesh.py @@ -1,11 +1,11 @@ -from itertools import combinations import logging -from ..data.mesh_utils import compute_edge_lengths - +from itertools import combinations import torch import torch.nn.functional as F +from ..data.mesh_utils import compute_edge_lengths + logger = logging.getLogger(__name__) def edge_length_deviation_loss(gr1, gr2): @@ -34,7 +34,7 @@ def __init__(self, cell_key): if isinstance(cell_key, str): self.select = lambda obj: getattr(obj, cell_key) else: - raise ValueError(f"face_key must be a graph attribute but is {cell_key}") + raise TypeError(f"face_key must be a graph attribute but is {cell_key}") def forward(self, gr1, gr2): @@ -83,7 +83,7 @@ def __init__(self, face_key = None): if isinstance(face_key, str): self.select = lambda obj: getattr(obj, face_key) else: - raise ValueError(f"face_key must be a graph attribute but is {face_key}") + raise TypeError(f"face_key must be a graph attribute but is {face_key}") def forward(self, gr1, gr2): @@ -130,7 +130,7 @@ def __init__(self, cell_key, min_vol = 1e-3): if isinstance(cell_key, str): self.select = lambda obj: getattr(obj, cell_key) else: - raise ValueError(f"face_key must be a graph attribute but is {cell_key}") + raise TypeError(f"face_key must be a graph attribute but is {cell_key}") self.min_vol = min_vol diff --git a/im2sim/losses/pointcloud.py b/im2sim/losses/pointcloud.py index bd4beb3..be95736 100644 --- a/im2sim/losses/pointcloud.py +++ b/im2sim/losses/pointcloud.py @@ -1,5 +1,5 @@ -import logging import inspect +import logging import torch import torch_geometric.nn as gnn @@ -11,10 +11,10 @@ def _compute_batch_chamfer(y1, y2, b1=None, b2=None): b1 = torch.zeros(y1.shape[0]) if b2==None: b2 = torch.zeros(y2.shape[0]) - logging.debug("shapes - y1:%s, y2:%s, b1:%s, b2%s", + logger.debug("shapes - y1:%s, y2:%s, b1:%s, b2%s", tuple(y1.shape),tuple(y2.shape),tuple(b1.shape),tuple(b2.shape)) nns1 = gnn.pool.knn(x=y2, y=y1, batch_x=b2, batch_y=b1, k=1) - logging.debug("nn shape: %s", nns1.shape) + logger.debug("nn shape: %s", nns1.shape) if nns1.shape[-1] == 0: return (y2*0).sum() @@ -23,7 +23,7 @@ def _compute_batch_chamfer(y1, y2, b1=None, b2=None): if nns2.shape[-1] == 0: return (y2*0).sum() - logging.debug("nn shape: %s", nns2.shape) + logger.debug("nn shape: %s", nns2.shape) d2 = torch.linalg.norm(y2 - y1[nns2[1]], dim=-1).mean() return d1 + d2 @@ -43,7 +43,7 @@ def __init__(self, mask = None): def forward(self, gr1, gr2): mask1 = self.mask(gr1) mask2 = self.mask(gr2) - logging.debug("mask_type - %s",type(mask1)) + logger.debug("mask_type - %s",type(mask1)) loss = _compute_batch_chamfer(y1 = gr1.x[mask1,:3], y2 = gr2.x[mask2,:3], b1 = gr1.batch[mask1], diff --git a/im2sim/losses/seg.py b/im2sim/losses/seg.py index 1c9cb28..d579472 100644 --- a/im2sim/losses/seg.py +++ b/im2sim/losses/seg.py @@ -1,4 +1,4 @@ -import torch +import torch def dice_calc(y1: torch.Tensor, y2: torch.Tensor, smooth: float = 1e-5) -> torch.Tensor: diff --git a/im2sim/losses/utils.py b/im2sim/losses/utils.py index cec0710..a040b54 100644 --- a/im2sim/losses/utils.py +++ b/im2sim/losses/utils.py @@ -1,6 +1,8 @@ -from torch import nn import inspect +from torch import nn + + class GraphLoss(nn.Module): def __init__(self, loss_fn, kwargs): diff --git a/im2sim/models/UNet.py b/im2sim/models/UNet.py deleted file mode 100644 index bbe8974..0000000 --- a/im2sim/models/UNet.py +++ /dev/null @@ -1,470 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F -# from ..layers.layer_util import get_image_layer, get_activation, standardize_spatial_factors - - -# class ImageConvBlock(nn.Module): -# """ -# A convolutional block for image data - -# Args: -# in_channels (int): The number of channels in the input to the layer. -# filters (int, optional): The number of filters in each convolutional layer (default: 32) -# kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) -# depth (int, optional): The number of successive convolutional layers (default: 2) -# rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:2), -# activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") -# norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") -# dropout_rate (float, optional): The spatial dropout rate to be applied to the final convolution output (default:None) - -# Returns: -# A `torch.nn.Module` object. - -# """ -# def __init__(self, -# in_channels, -# filters=32, -# kernel_size=3, -# depth=1, -# rank=3, -# activation='relu', -# norm_type=None, -# dropout_rate=None): -# super().__init__() - -# conv = get_image_layer('Conv', rank) -# drop = get_image_layer('Dropout', rank) -# self.convs = nn.ModuleList([ -# conv(in_channels if i==0 else filters, filters, kernel_size, padding=kernel_size//2) -# for i in range(depth) -# ]) - -# self.norms = nn.ModuleList([ -# get_image_layer(norm_type, rank)(filters) if norm_type else nn.Identity() -# for _ in range(depth) -# ]) -# self.drop = drop(p=dropout_rate) if dropout_rate else nn.Identity() - -# self.act = get_activation(activation)(inplace=True) if activation.lower() == 'relu' else get_activation(activation)() - - -# def forward(self, x): -# """ -# Args: -# x (torch.Tensor): Input feature maps in image space [in_channels, ...] where the number of dims in ... corresponds to rank - -# Returns: -# torch.Tensor: Output feature maps [out_channels, ...] -# """ - -# for conv, norm in zip(self.convs, self.norms): -# x = norm(self.act(conv(x))) -# return self.drop(x) - - -# class ImageEncoder(nn.Module): -# """ -# A CNN encoder for images. Structured like the encoder of a UNet. - -# Args: -# in_channels (int): The number of channels in the input image. -# filters (List[int], optional): The number of convolutional filters in each encoder level (default: [16,32,64,128,256]) -# kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) -# conv_blocks_per_level (int, optional): The number of successive convolutional blocks per encoder level (default: 1) -# rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:3), -# activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") -# norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") -# dropout_rate (float, optional): The spatial dropout rate to be applied to each residual block prior to residual connection (default:None) - -# Returns: -# A `torch.nn.Module` object. -# """ - -# def __init__(self, -# in_channels, -# filters=[16,32,64,128,256], -# pool_sizes = None, -# kernel_size=3, -# conv_blocks_per_level=1, -# rank=3, -# norm_type=None, -# pool_type='MaxPool', -# activation='relu', -# dropout_rate=None): -# super().__init__() - -# n_levels = len(filters) -# self.conv_blocks = nn.ModuleList([ -# ImageConvBlock(in_channels=in_channels if i==0 else filters[i-1], -# filters=filters[i], -# kernel_size=kernel_size, -# depth=conv_blocks_per_level, -# rank=rank, -# activation=activation, -# norm_type=norm_type, -# dropout_rate=dropout_rate) -# for i in range(n_levels) -# ]) - -# if pool_sizes is None: -# pool_sizes = 2 - -# pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) - -# pool = get_image_layer(pool_type, rank) -# self.maxpools = nn.ModuleList([ -# pool(pool_sizes_standard[i-1]) if i>0 else nn.Identity() -# for i in range(n_levels) -# ]) - -# def forward(self,x): -# """ -# Args: -# x (torch.Tensor): Input image [in_channels, ...] - -# Returns: -# List[torch.Tensor]: Output feature maps from each level ordered from top to bottom [Tensor([filters[0], ...], ..., Tensor([filters[N], ...]) -# """ -# outputs = [] -# for pool, conv in zip(self.maxpools, self.conv_blocks): -# x = conv(pool(x)) -# outputs.append(x) -# return outputs - - -# class ImageDecoder(nn.Module): -# """ -# CNN decoder for images. Mirrors ImageEncoder like a UNet decoder. - -# Args: -# filters (List[int]): Encoder filter sizes in top→bottom order. -# kernel_size (int): Convolution kernel size. -# conv_blocks_per_level (int): Number of conv blocks per level. -# rank (int): Spatial rank (2 or 3). -# upsample_type (str): "ConvTranspose" or "Upsample". -# activation (str): Activation name. -# norm_type (str): Normalization type. -# dropout_rate (float): Dropout rate. -# skip (bool): Use skip connections. -# """ - -# def __init__(self, -# filters=[16,32,64,128,256], -# kernel_size=3, -# pool_sizes=None, -# upsample_sizes = None, -# conv_blocks_per_level=1, -# rank=3, -# upsample_type="ConvTranspose", -# activation="relu", -# norm_type=None, -# dropout_rate=None, -# skip=True): -# super().__init__() - -# self.skip = skip -# self.rank = rank - -# if pool_sizes is None: -# pool_sizes = 2 - -# pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) - -# n_levels = len(filters) - -# rev_filters = filters[::-1] -# rev_pool_sizes = pool_sizes_standard[::-1] - -# if upsample_sizes is None: -# upsample_sizes = rev_pool_sizes -# else: -# upsample_sizes = standardize_spatial_factors(upsample_sizes, rank) - - -# if upsample_type.lower() == 'upsample': -# self.ups = nn.ModuleList([ -# nn.Upsample(scale_factor=upsample_sizes[i], mode='trilinear' if rank==3 else 'bilinear', align_corners=True) -# for i in range(n_levels - 1) -# ]) -# else: -# up_layer = get_image_layer(upsample_type, rank) -# self.ups = nn.ModuleList([ -# up_layer(rev_filters[i], rev_filters[i+1], kernel_size=upsample_sizes[i], stride=upsample_sizes[i]) -# for i in range(n_levels - 1) -# ]) - - -# self.conv_blocks = nn.ModuleList([ -# ImageConvBlock( -# in_channels=rev_filters[i+1] * (2 if skip else 1), -# filters=rev_filters[i+1], -# kernel_size=kernel_size, -# depth=conv_blocks_per_level, -# rank=rank, -# activation=activation, -# norm_type=norm_type, -# dropout_rate=dropout_rate -# ) -# for i in range(n_levels - 1) -# ]) - - -# def _match_size(self, x, skip): -# """ -# Match skip spatial size to x spatial size using: -# - center crop if skip is larger -# - interpolation if skip is smaller - -# Args: -# x: decoder tensor, shape [B, C, ...] -# skip: encoder skip tensor, shape [B, C, ...] - -# Returns: -# skip resized to have spatial shape x.shape[2:] -# """ -# target_size = x.shape[2:] -# skip_size = skip.shape[2:] - -# if skip_size == target_size: -# return skip - -# # First crop any dimensions where skip is too large -# slices = [slice(None), slice(None)] -# needs_crop = False - -# for s, t in zip(skip_size, target_size): -# if s > t: -# start = (s - t) // 2 -# end = start + t -# slices.append(slice(start, end)) -# needs_crop = True -# else: -# slices.append(slice(None)) - -# if needs_crop: -# skip = skip[tuple(slices)] - -# # Then upsample if any dimensions are still too small -# if skip.shape[2:] != target_size: -# mode = "trilinear" if self.rank == 3 else "bilinear" -# skip = F.interpolate(skip, size=target_size, mode=mode, align_corners=True) - -# return skip - - -# def forward(self, encoder_outputs): -# """ -# Args: -# encoder_outputs: List of tensors from encoder (top→bottom). - -# Returns: -# Decoded tensor at highest resolution. -# """ - -# # Reverse the encoder outputs so we traverse from bottleneck to top -# rev_enc = encoder_outputs[::-1] - -# # Start from bottleneck -# x = rev_enc[0] - -# # Traverse decoder levels -# for i, (up, conv) in enumerate(zip(self.ups, self.conv_blocks)): -# x = up(x) - -# if self.skip: -# skip_feat = rev_enc[i + 1] # next encoder feature -# skip_feat = self._match_size(x, skip_feat) -# x = torch.cat([x, skip_feat], dim=1) - -# x = conv(x) - -# return x - -# class UNet(nn.Module): -# """ -# Flexible UNet for 2D or 3D images. - -# Args: -# in_channels (int): Input channels. -# out_channels (int): Output channels. -# filters (List[int]): Encoder filter sizes. -# kernel_size (int): Conv kernel size. -# pool_sizes (Tuple or list): pool sizes per level , -# upsample_sizes (Tuple or list): upsample sizes per level, -# conv_blocks_per_level (int): Depth per level. -# rank (int): Spatial rank. -# activation (str): Activation function. -# norm_type (str): Normalization type. -# dropout_rate (float): Dropout. -# final_activation (str): Output activation. -# """ - -# def __init__(self, -# in_channels, -# out_channels, -# filters=[16,32,64,128,256], -# pool_sizes = None, -# upsample_sizes = None, -# kernel_size=3, -# conv_blocks_per_level=1, -# rank=3, -# activation="relu", -# norm_type=None, -# dropout_rate=None, -# final_activation="linear"): - -# pool_sizes_temp = [] -# if pool_sizes is None: -# for i in range(len(filters)-1): -# pool_sizes_temp.append(2) -# pool_sizes = pool_sizes_temp - -# if not isinstance(pool_sizes, (list, tuple)): -# raise TypeError("pool_sizes must be a list or tuple") - -# for i, p in enumerate(pool_sizes): -# if isinstance(p, int) and not isinstance(p,bool): -# if p < 1: -# raise ValueError(f"pool_sizes must be positive") -# elif isinstance(p, (tuple,list)): -# if len(p) != rank: -# raise ValueError(f"pool_sizes tuple must be same length as rank") -# for j , ps in enumerate(p): -# if not isinstance(ps, int) or isinstance(ps,bool): -# raise TypeError(f"pool_sizes must be an int") -# if ps < 1: -# raise ValueError(f"pool_sizes must be positive") -# else: -# raise TypeError("each entry in pool_sizes must be either an int or a tuple or list") - -# if len(filters) != (len(pool_sizes)+1): -# raise ValueError(f"pool_sizes do not match number of filters. For {len(filters)}, please input {len(filters)-1} number of pools.") - - -# if upsample_sizes is not None: -# if not isinstance(upsample_sizes, (list, tuple)): -# raise TypeError("upsample_sizes must be a list or tuple") - -# for i, p in enumerate(upsample_sizes): -# if isinstance(p, int) and not isinstance(p,bool): -# if p < 1: -# raise ValueError(f"upsample_sizes must be positive") -# elif isinstance(p, (tuple,list)): -# if len(p) != rank: -# raise ValueError(f"upsample_sizes tuple must be same length as rank") -# for j , ps in enumerate(p): -# if not isinstance(ps, int) or isinstance(ps,bool): -# raise TypeError(f"upsample_sizes must be an int") -# if ps < 1: -# raise ValueError(f"upsample_sizes must be positive") -# else: -# raise TypeError("each entry in upsample_sizes must be either an int or a tuple or list") - -# if len(filters) != (len(upsample_sizes)+1): -# raise ValueError(f"upsample_sizes do not match number of filters. For {len(filters)}, please input {len(filters)-1} number of upsamples.") - - -# super().__init__() - -# self.encoder = ImageEncoder( -# in_channels=in_channels, -# filters=filters, -# pool_sizes=pool_sizes, -# kernel_size=kernel_size, -# conv_blocks_per_level=conv_blocks_per_level, -# rank=rank, -# activation=activation, -# norm_type=norm_type, -# dropout_rate=dropout_rate -# ) - -# self.decoder = ImageDecoder( -# filters=filters, -# pool_sizes=pool_sizes, -# upsample_sizes = upsample_sizes, -# kernel_size=kernel_size, -# conv_blocks_per_level=conv_blocks_per_level, -# rank=rank, -# activation=activation, -# norm_type=norm_type, -# dropout_rate=dropout_rate -# ) - -# conv = get_image_layer("Conv", rank) - -# self.final_conv = conv(filters[0], out_channels, kernel_size=1) - -# self.final_act = ( -# get_activation(final_activation)() -# if final_activation.lower() != "linear" -# else nn.Identity() -# ) - -# def forward(self, x): -# enc_feats = self.encoder(x) -# x = self.decoder(enc_feats) -# x = self.final_conv(x) -# x = self.final_act(x) -# return x - -# class StandardUNet(UNet): -# """ -# Standaed UNet for 2D or 3D images. - -# Args: -# in_channels (int): Input channels. -# out_channels (int): Output channels. -# filters (List[int]): Encoder filter sizes. -# kernel_size (int): Conv kernel size. -# pool_sizes (Tuple or list): pool sizes per dim , -# conv_blocks_per_level (int): Depth per level. -# rank (int): Spatial rank. -# activation (str): Activation function. -# norm_type (str): Normalization type. -# dropout_rate (float): Dropout. -# final_activation (str): Output activation. -# """ - -# def __init__(self, -# in_channels, -# out_channels, -# filters=[16, 32, 64, 128, 256], -# pool_size = 2, -# kernel_size=3, -# conv_blocks_per_level=1, -# rank=3, -# activation="relu", -# norm_type=None, -# dropout_rate=None, -# final_activation="linear"): - -# pool_size_standard = [] -# if isinstance(pool_size, int): -# pool_size_single = [] -# for j in range(rank): -# pool_size_single.append(pool_size) -# pool_size_single = tuple(pool_size_single) -# for i in range(len(filters)-1): -# pool_size_standard.append(pool_size_single) -# elif isinstance(pool_size , (tuple,list)): -# if len(pool_size) == rank: -# for i in range(len(filters)-1): -# pool_size_standard.append(pool_size) -# else: -# raise ValueError(f"pool_size must be an int or have same length as rank") - -# super().__init__( -# in_channels=in_channels, -# out_channels=out_channels, -# filters=filters, -# pool_sizes=pool_size_standard, -# upsample_sizes=None, -# kernel_size=kernel_size, -# conv_blocks_per_level=conv_blocks_per_level, -# rank=rank, -# activation=activation, -# norm_type=norm_type, -# dropout_rate=dropout_rate, -# final_activation=final_activation, -# ) \ No newline at end of file diff --git a/im2sim/models/__init__.py b/im2sim/models/__init__.py index b4ca8a5..b22f058 100644 --- a/im2sim/models/__init__.py +++ b/im2sim/models/__init__.py @@ -1,3 +1,4 @@ -from .image_to_graph import SimpleI2G, I2GUNet -from .utils import get_model_config +# from .image_to_graph import I2GUNet as I2GUNet +from .image_to_graph import SimpleI2G as SimpleI2G +from .utils import get_model_config as get_model_config # from .UNet import UNet, StandardUNet \ No newline at end of file diff --git a/im2sim/models/image_to_graph.py b/im2sim/models/image_to_graph.py index 2d21325..f09f697 100644 --- a/im2sim/models/image_to_graph.py +++ b/im2sim/models/image_to_graph.py @@ -1,11 +1,12 @@ import logging import torch + +# import torch_geometric.nn as gnn from torch import nn -import torch_geometric.nn as gnn -# from torch_geometric.nn import TopKPooling -from torch_geometric.data import Data +# from torch_geometric.nn import TopKPooling +# from torch_geometric.data import Data from ..layers import * logger = logging.getLogger(__name__) @@ -17,7 +18,7 @@ class SimpleI2G(nn.Module): def __init__(self, in_channels, out_channels, - cnn_filters=[16,32,64,128,256], + cnn_filters=(16,32,64,128,256), cnn_kernel_size=3, cnn_res_depth=3, cnn_res_blocks_per_level=2, @@ -27,14 +28,14 @@ def __init__(self, cnn_pool_size=2, cnn_activation='relu', cnn_dropout_rate=None, - projection_ids = [[3,4],[1,2],[0,1]], - gnn_filters = [[384,288], [144,96], [64,32]], + projection_ids = ((3,4),(1,2),(0,1)), + gnn_filters = ((384,288), (144,96), (64,32)), gnn_res_depth = 3, gnn_n_process_blocks = 1, gnn_n_deform_blocks = 3, template_edge_index=None, - gnn_conv_type="ChebConv", - gnn_conv_kwargs={'K':3}, + gnn_conv_type="GATConv", + gnn_conv_kwargs=None, gnn_activation="relu", out_activation="linear", gnn_norm_type="InstanceNorm", @@ -98,91 +99,91 @@ def forward(self, x, template): return outputs -class I2GUNet(nn.Module): - - def __init__(self, - in_channels, - out_channels, - domain_size, - filters=[16,32,64,128,256], - cnn_kernel_size=3, - cnn_res_depth=3, - cnn_res_blocks_per_level=2, - cnn_rank=3, - cnn_norm_type='InstanceNorm', - cnn_pool_type='MaxPool', - cnn_pool_size=2, - cnn_activation='leaky_relu', - gnn_res_depth = 3, - gnn_n_align_blocks = 1, - gnn_n_deform_blocks = 3, - gnn_conv_type="ChebConv", - gnn_conv_kwargs={'K':3}, - gnn_activation="leaky_relu", - gnn_norm_type="InstanceNorm", - batched_ops=True): - super().__init__() - - logger.debug("Defining I2G layers...") - self.batched_ops = batched_ops - self.n_levels = len(filters) - self.n_channels = out_channels - self.encoder = ImageResEncoder(in_channels=in_channels, - filters=filters, - kernel_size=cnn_kernel_size, - res_depth=cnn_res_depth, - res_blocks_per_level=cnn_res_blocks_per_level, - rank=cnn_rank, - norm_type=cnn_norm_type, - pool_type=cnn_pool_type, - pool_size=cnn_pool_size, - activation=cnn_activation) +# class I2GUNet(nn.Module): + +# def __init__(self, +# in_channels, +# out_channels, +# domain_size, +# filters=(16,32,64,128,256), +# cnn_kernel_size=3, +# cnn_res_depth=3, +# cnn_res_blocks_per_level=2, +# cnn_rank=3, +# cnn_norm_type='InstanceNorm', +# cnn_pool_type='MaxPool', +# cnn_pool_size=2, +# cnn_activation='leaky_relu', +# gnn_res_depth = 3, +# gnn_n_align_blocks = 1, +# gnn_n_deform_blocks = 3, +# gnn_conv_type="GATConv", +# gnn_conv_kwargs=None, +# gnn_activation="leaky_relu", +# gnn_norm_type="InstanceNorm", +# batched_ops=True): +# super().__init__() + +# logger.debug("Defining I2G layers...") +# self.batched_ops = batched_ops +# self.n_levels = len(filters) +# self.n_channels = out_channels +# self.encoder = ImageResEncoder(in_channels=in_channels, +# filters=filters, +# kernel_size=cnn_kernel_size, +# res_depth=cnn_res_depth, +# res_blocks_per_level=cnn_res_blocks_per_level, +# rank=cnn_rank, +# norm_type=cnn_norm_type, +# pool_type=cnn_pool_type, +# pool_size=cnn_pool_size, +# activation=cnn_activation) - self.gpool = RecursiveClusterPooling(n_levels=self.n_levels) +# self.gpool = RecursiveClusterPooling(n_levels=self.n_levels) - self.decoder = nn.ModuleList([ - GraphUNetDecoderBlock(#in_channels = out_channels if i==1 else filters[-i+1], - out_channels = out_channels, - filters = filters[-i], - domain_size = domain_size, - res_depth = gnn_res_depth, - n_align_blocks = 0 if i==1 else gnn_n_align_blocks, - n_deform_blocks = gnn_n_deform_blocks, - conv_type=gnn_conv_type, - conv_kwargs=gnn_conv_kwargs, - activation=gnn_activation, - norm_type=gnn_norm_type, - batched_ops=batched_ops) - for i in range(1,self.n_levels+1) - ]) +# self.decoder = nn.ModuleList([ +# GraphUNetDecoderBlock(#in_channels = out_channels if i==1 else filters[-i+1], +# out_channels = out_channels, +# filters = filters[-i], +# domain_size = domain_size, +# res_depth = gnn_res_depth, +# n_align_blocks = 0 if i==1 else gnn_n_align_blocks, +# n_deform_blocks = gnn_n_deform_blocks, +# conv_type=gnn_conv_type, +# conv_kwargs=gnn_conv_kwargs, +# activation=gnn_activation, +# norm_type=gnn_norm_type, +# batched_ops=batched_ops) +# for i in range(1,self.n_levels+1) +# ]) - logger.debug("Done") +# logger.debug("Done") - def forward(self, img, template): - encoder_features = self.encoder(img) - encoder_features.reverse() +# def forward(self, img, template): +# encoder_features = self.encoder(img) +# encoder_features.reverse() - multi_template = self.gpool(template) - multi_template.reverse() - - outputs = [] - deformation = torch.zeros_like(multi_template[0].x) - - for i, (dec_layer, enc) in enumerate(zip(self.decoder, encoder_features)): - t = multi_template[i] - deformation = dec_layer(enc,deformation,t.x, t.edge_index, t.batch) - if i < self.n_levels-1: - out_graph = Data(x=t.x+deformation, edge_index=t.edge_index, batch=t.batch) - outputs.append(out_graph) - deformation = gnn.unpool.knn_interpolate(deformation, t.x, multi_template[i+1].x) - - out_graph = template.clone() - out_graph.x = template.x+deformation - outputs.append(out_graph) - return outputs +# multi_template = self.gpool(template) +# multi_template.reverse() + +# outputs = [] +# deformation = torch.zeros_like(multi_template[0].x) + +# for i, (dec_layer, enc) in enumerate(zip(self.decoder, encoder_features)): +# t = multi_template[i] +# deformation = dec_layer(enc,deformation,t.x, t.edge_index, t.batch) +# if i < self.n_levels-1: +# out_graph = Data(x=t.x+deformation, edge_index=t.edge_index, batch=t.batch) +# outputs.append(out_graph) +# deformation = gnn.unpool.knn_interpolate(deformation, t.x, multi_template[i+1].x) + +# out_graph = template.clone() +# out_graph.x = template.x+deformation +# outputs.append(out_graph) +# return outputs diff --git a/im2sim/models/unet.py b/im2sim/models/unet.py new file mode 100644 index 0000000..2878ac8 --- /dev/null +++ b/im2sim/models/unet.py @@ -0,0 +1,469 @@ +from torch import nn + +from ..layers.layer_util import * + + +class ImageConvBlock(nn.Module): + """ + A convolutional block for image data + + Args: + in_channels (int): The number of channels in the input to the layer. + filters (int, optional): The number of filters in each convolutional layer (default: 32) + kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) + depth (int, optional): The number of successive convolutional layers (default: 2) + rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:2), + activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") + norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") + dropout_rate (float, optional): The spatial dropout rate to be applied to the final convolution output (default:None) + + Returns: + A `torch.nn.Module` object. + + """ + def __init__(self, + in_channels, + filters=32, + kernel_size=3, + depth=1, + rank=3, + activation='relu', + norm_type=None, + dropout_rate=None): + super().__init__() + + conv = TORCH_LAYERS[f'Conv{rank}d'] + drop = TORCH_LAYERS[f'Dropout{rank}d'] + self.convs = nn.ModuleList([ + conv(in_channels if i==0 else filters, filters, kernel_size, padding=kernel_size//2) + for i in range(depth) + ]) + + self.norms = nn.ModuleList([ + get_image_layer(norm_type, rank)(filters) if norm_type else nn.Identity() + for _ in range(depth) + ]) + self.drop = drop(p=dropout_rate) if dropout_rate else nn.Identity() + + self.act = ACTIVATIONS[activation]() + + + def forward(self, x): + """ + Args: + x (torch.Tensor): Input feature maps in image space [in_channels, ...] where the number of dims in ... corresponds to rank + + Returns: + torch.Tensor: Output feature maps [out_channels, ...] + """ + + for conv, norm in zip(self.convs, self.norms): + x = norm(self.act(conv(x))) + return self.drop(x) + + +class ImageEncoder(nn.Module): + """ + A CNN encoder for images. Structured like the encoder of a UNet. + + Args: + in_channels (int): The number of channels in the input image. + filters (List[int], optional): The number of convolutional filters in each encoder level (default: [16,32,64,128,256]) + kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) + conv_blocks_per_level (int, optional): The number of successive convolutional blocks per encoder level (default: 1) + rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:3), + activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") + norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") + dropout_rate (float, optional): The spatial dropout rate to be applied to each residual block prior to residual connection (default:None) + + Returns: + A `torch.nn.Module` object. + """ + + def __init__(self, + in_channels, + filters=(16,32,64,128,256), + pool_sizes = None, + kernel_size=3, + conv_blocks_per_level=1, + rank=3, + norm_type=None, + pool_type='MaxPool', + activation='relu', + dropout_rate=None): + super().__init__() + + n_levels = len(filters) + self.conv_blocks = nn.ModuleList([ + ImageConvBlock(in_channels=in_channels if i==0 else filters[i-1], + filters=filters[i], + kernel_size=kernel_size, + depth=conv_blocks_per_level, + rank=rank, + activation=activation, + norm_type=norm_type, + dropout_rate=dropout_rate) + for i in range(n_levels) + ]) + + if pool_sizes is None: + pool_sizes = 2 + + pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) + + pool = TORCH_LAYERS[f'{pool_type}{rank}d'] + self.maxpools = nn.ModuleList([ + pool(pool_sizes_standard[i-1]) if i>0 else nn.Identity() + for i in range(n_levels) + ]) + + def forward(self,x): + """ + Args: + x (torch.Tensor): Input image [in_channels, ...] + + Returns: + List[torch.Tensor]: Output feature maps from each level ordered from top to bottom [Tensor([filters[0], ...], ..., Tensor([filters[N], ...]) + """ + outputs = [] + for pool, conv in zip(self.maxpools, self.conv_blocks): + x = conv(pool(x)) + outputs.append(x) + return outputs + + +class ImageDecoder(nn.Module): + """ + CNN decoder for images. Mirrors ImageEncoder like a UNet decoder. + + Args: + filters (List[int]): Encoder filter sizes in top→bottom order. + kernel_size (int): Convolution kernel size. + conv_blocks_per_level (int): Number of conv blocks per level. + rank (int): Spatial rank (2 or 3). + upsample_type (str): "ConvTranspose" or "Upsample". + activation (str): Activation name. + norm_type (str): Normalization type. + dropout_rate (float): Dropout rate. + skip (bool): Use skip connections. + """ + + def __init__(self, + filters=(16,32,64,128,256), + kernel_size=3, + pool_sizes=None, + upsample_sizes = None, + conv_blocks_per_level=1, + rank=3, + upsample_type="ConvTranspose", + activation="relu", + norm_type=None, + dropout_rate=None, + skip=True): + super().__init__() + + self.skip = skip + self.rank = rank + + if pool_sizes is None: + pool_sizes = 2 + + pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) + + n_levels = len(filters) + + rev_filters = filters[::-1] + rev_pool_sizes = pool_sizes_standard[::-1] + + if upsample_sizes is None: + upsample_sizes = rev_pool_sizes + else: + upsample_sizes = standardize_spatial_factors(upsample_sizes, rank) + + + if upsample_type.lower() == 'upsample': + self.ups = nn.ModuleList([ + nn.Upsample(scale_factor=upsample_sizes[i], mode='trilinear' if rank==3 else 'bilinear', align_corners=True) + for i in range(n_levels - 1) + ]) + else: + up_layer = get_image_layer(upsample_type, rank) + self.ups = nn.ModuleList([ + up_layer(rev_filters[i], rev_filters[i+1], kernel_size=upsample_sizes[i], stride=upsample_sizes[i]) + for i in range(n_levels - 1) + ]) + + + self.conv_blocks = nn.ModuleList([ + ImageConvBlock( + in_channels=rev_filters[i+1] * (2 if skip else 1), + filters=rev_filters[i+1], + kernel_size=kernel_size, + depth=conv_blocks_per_level, + rank=rank, + activation=activation, + norm_type=norm_type, + dropout_rate=dropout_rate + ) + for i in range(n_levels - 1) + ]) + + + def _match_size(self, x, skip): + """ + Match skip spatial size to x spatial size using: + - center crop if skip is larger + - interpolation if skip is smaller + + Args: + x: decoder tensor, shape [B, C, ...] + skip: encoder skip tensor, shape [B, C, ...] + + Returns: + skip resized to have spatial shape x.shape[2:] + """ + target_size = x.shape[2:] + skip_size = skip.shape[2:] + + if skip_size == target_size: + return skip + + # First crop any dimensions where skip is too large + slices = [slice(None), slice(None)] + needs_crop = False + + for s, t in zip(skip_size, target_size): + if s > t: + start = (s - t) // 2 + end = start + t + slices.append(slice(start, end)) + needs_crop = True + else: + slices.append(slice(None)) + + if needs_crop: + skip = skip[tuple(slices)] + + # Then upsample if any dimensions are still too small + if skip.shape[2:] != target_size: + mode = "trilinear" if self.rank == 3 else "bilinear" + skip = F.interpolate(skip, size=target_size, mode=mode, align_corners=True) + + return skip + + + def forward(self, encoder_outputs): + """ + Args: + encoder_outputs: List of tensors from encoder (top→bottom). + + Returns: + Decoded tensor at highest resolution. + """ + + # Reverse the encoder outputs so we traverse from bottleneck to top + rev_enc = encoder_outputs[::-1] + + # Start from bottleneck + x = rev_enc[0] + + # Traverse decoder levels + for i, (up, conv) in enumerate(zip(self.ups, self.conv_blocks)): + x = up(x) + + if self.skip: + skip_feat = rev_enc[i + 1] # next encoder feature + skip_feat = self._match_size(x, skip_feat) + x = torch.cat([x, skip_feat], dim=1) + + x = conv(x) + + return x + +class UNet(nn.Module): + """ + Flexible UNet for 2D or 3D images. + + Args: + in_channels (int): Input channels. + out_channels (int): Output channels. + filters (List[int]): Encoder filter sizes. + kernel_size (int): Conv kernel size. + pool_sizes (Tuple or list): pool sizes per level , + upsample_sizes (Tuple or list): upsample sizes per level, + conv_blocks_per_level (int): Depth per level. + rank (int): Spatial rank. + activation (str): Activation function. + norm_type (str): Normalization type. + dropout_rate (float): Dropout. + final_activation (str): Output activation. + """ + + def __init__(self, + in_channels, + out_channels, + filters=(16,32,64,128,256), + pool_sizes = None, + upsample_sizes = None, + kernel_size=3, + conv_blocks_per_level=1, + rank=3, + activation="relu", + norm_type=None, + dropout_rate=None, + final_activation="linear"): + + pool_sizes_temp = [] + if pool_sizes is None: + for i in range(len(filters)-1): + pool_sizes_temp.append(2) + pool_sizes = pool_sizes_temp + + if not isinstance(pool_sizes, (list, tuple)): + raise TypeError("pool_sizes must be a list or tuple") + + for i, p in enumerate(pool_sizes): + if isinstance(p, int) and not isinstance(p,bool): + if p < 1: + raise ValueError("pool_sizes must be positive") + elif isinstance(p, (tuple,list)): + if len(p) != rank: + raise ValueError("pool_sizes tuple must be same length as rank") + for j , ps in enumerate(p): + if not isinstance(ps, int) or isinstance(ps,bool): + raise TypeError("pool_sizes must be an int") + if ps < 1: + raise ValueError("pool_sizes must be positive") + else: + raise TypeError("each entry in pool_sizes must be either an int or a tuple or list") + + if len(filters) != (len(pool_sizes)+1): + raise ValueError(f"pool_sizes do not match number of filters. For {len(filters)}, please input {len(filters)-1} number of pools.") + + + if upsample_sizes is not None: + if not isinstance(upsample_sizes, (list, tuple)): + raise TypeError("upsample_sizes must be a list or tuple") + + for i, p in enumerate(upsample_sizes): + if isinstance(p, int) and not isinstance(p,bool): + if p < 1: + raise ValueError("upsample_sizes must be positive") + elif isinstance(p, (tuple,list)): + if len(p) != rank: + raise ValueError("upsample_sizes tuple must be same length as rank") + for j , ps in enumerate(p): + if not isinstance(ps, int) or isinstance(ps,bool): + raise TypeError("upsample_sizes must be an int") + if ps < 1: + raise ValueError("upsample_sizes must be positive") + else: + raise TypeError("each entry in upsample_sizes must be either an int or a tuple or list") + + if len(filters) != (len(upsample_sizes)+1): + raise ValueError(f"upsample_sizes do not match number of filters. For {len(filters)}, please input {len(filters)-1} number of upsamples.") + + + super().__init__() + + self.encoder = ImageEncoder( + in_channels=in_channels, + filters=filters, + pool_sizes=pool_sizes, + kernel_size=kernel_size, + conv_blocks_per_level=conv_blocks_per_level, + rank=rank, + activation=activation, + norm_type=norm_type, + dropout_rate=dropout_rate + ) + + self.decoder = ImageDecoder( + filters=filters, + pool_sizes=pool_sizes, + upsample_sizes = upsample_sizes, + kernel_size=kernel_size, + conv_blocks_per_level=conv_blocks_per_level, + rank=rank, + activation=activation, + norm_type=norm_type, + dropout_rate=dropout_rate + ) + + conv = TORCH_LAYERS[f'Conv{rank}d'] + + self.final_conv = conv(filters[0], out_channels, kernel_size=1) + + self.final_act = ( + get_activation(final_activation)() + if final_activation.lower() != "linear" + else nn.Identity() + ) + + def forward(self, x): + enc_feats = self.encoder(x) + x = self.decoder(enc_feats) + x = self.final_conv(x) + x = self.final_act(x) + return x + +class StandardUNet(UNet): + """ + Standaed UNet for 2D or 3D images. + + Args: + in_channels (int): Input channels. + out_channels (int): Output channels. + filters (List[int]): Encoder filter sizes. + kernel_size (int): Conv kernel size. + pool_sizes (Tuple or list): pool sizes per dim , + conv_blocks_per_level (int): Depth per level. + rank (int): Spatial rank. + activation (str): Activation function. + norm_type (str): Normalization type. + dropout_rate (float): Dropout. + final_activation (str): Output activation. + """ + + def __init__(self, + in_channels, + out_channels, + filters=(16, 32, 64, 128, 256), + pool_size = 2, + kernel_size=3, + conv_blocks_per_level=1, + rank=3, + activation="relu", + norm_type=None, + dropout_rate=None, + final_activation="linear"): + + pool_size_standard = [] + if isinstance(pool_size, int): + pool_size_single = [] + for j in range(rank): + pool_size_single.append(pool_size) + pool_size_single = tuple(pool_size_single) + for i in range(len(filters)-1): + pool_size_standard.append(pool_size_single) + elif isinstance(pool_size , (tuple,list)): + if len(pool_size) == rank: + for i in range(len(filters)-1): + pool_size_standard.append(pool_size) + else: + raise ValueError("pool_size must be an int or have same length as rank") + + super().__init__( + in_channels=in_channels, + out_channels=out_channels, + filters=filters, + pool_sizes=pool_size_standard, + upsample_sizes=None, + kernel_size=kernel_size, + conv_blocks_per_level=conv_blocks_per_level, + rank=rank, + activation=activation, + norm_type=norm_type, + dropout_rate=dropout_rate, + final_activation=final_activation, + ) \ No newline at end of file diff --git a/im2sim/models/utils.py b/im2sim/models/utils.py index 6dc5bf2..9c633ea 100644 --- a/im2sim/models/utils.py +++ b/im2sim/models/utils.py @@ -18,50 +18,50 @@ def get_model_config(name): _CONFIGS = { - "Image2Flow":dict(cnn_filters=[16,48,96,192,384], - cnn_kernel_size=3, - cnn_res_depth=3, - cnn_res_blocks_per_level=2, - cnn_rank=3, - cnn_norm_type="InstanceNorm", - cnn_pool_type='MaxPool', - cnn_pool_size=2, - cnn_activation='leaky_relu', - cnn_dropout_rate=0.3, - projection_ids = [[3,4],[1,2],[0,1]], - gnn_filters = [[384,288], [144,96], [64,32]], - gnn_res_depth = 3, - gnn_n_process_blocks = 1, - gnn_n_deform_blocks = 3, - template_edge_index=None, - gnn_conv_type="ChebConv", - gnn_conv_kwargs={'K':1}, - gnn_activation="leaky_relu", - out_activation="leaky_relu", - gnn_norm_type="InstanceNorm", - batched_ops=False), + "Image2Flow":{"cnn_filters": (16,48,96,192,384), + "cnn_kernel_size": 3, + "cnn_res_depth": 3, + "cnn_res_blocks_per_level": 2, + "cnn_rank": 3, + "cnn_norm_type": "InstanceNorm", + "cnn_pool_type": 'MaxPool', + "cnn_pool_size": 2, + "cnn_activation": 'leaky_relu', + "cnn_dropout_rate": 0.3, + "projection_ids": ((3,4),(1,2),(0,1)), + "gnn_filters": ((384,288), (96, 64), (48,32)), + "gnn_res_depth": 3, + "gnn_n_process_blocks": 1, + "gnn_n_deform_blocks": 3, + "template_edge_index": None, + "gnn_conv_type": "ChebConv", + "gnn_conv_kwargs": {'K':1}, + "gnn_activation": "leaky_relu", + "out_activation": "leaky_relu", + "gnn_norm_type": "InstanceNorm", + "batched_ops": False}, - "Image2Mesh":dict(cnn_filters=[16,32,64,128,256], - cnn_kernel_size=3, - cnn_res_depth=3, - cnn_res_blocks_per_level=2, - cnn_rank=3, - cnn_norm_type="InstanceNorm", - cnn_pool_type='MaxPool', - cnn_pool_size=2, - cnn_activation='leaky_relu', - cnn_dropout_rate=0.3, - projection_ids = [[3,4],[1,2],[0,1]], - gnn_filters = [[384,288], [96, 64], [48,32]], - gnn_res_depth = 3, - gnn_n_process_blocks = 1, - gnn_n_deform_blocks = 3, - template_edge_index=None, - gnn_conv_type="ChebConv", - gnn_conv_kwargs={'K':1}, - gnn_activation="leaky_relu", - out_activation="leaky_relu", - gnn_norm_type="InstanceNorm", - batched_ops=False) + "Image2Mesh":{"cnn_filters": (16,32,64,128,256), + "cnn_kernel_size": 3, + "cnn_res_depth": 3, + "cnn_res_blocks_per_level": 2, + "cnn_rank": 3, + "cnn_norm_type": "InstanceNorm", + "cnn_pool_type": 'MaxPool', + "cnn_pool_size": 2, + "cnn_activation": 'leaky_relu', + "cnn_dropout_rate": 0.3, + "projection_ids": ((3,4),(1,2),(0,1)), + "gnn_filters": ((384,288), (96, 64), (48,32)), + "gnn_res_depth": 3, + "gnn_n_process_blocks": 1, + "gnn_n_deform_blocks": 3, + "template_edge_index": None, + "gnn_conv_type": "ChebConv", + "gnn_conv_kwargs": {'K':1}, + "gnn_activation": "leaky_relu", + "out_activation": "leaky_relu", + "gnn_norm_type": "InstanceNorm", + "batched_ops": False} } diff --git a/im2sim/plot/__init__.py b/im2sim/plot/__init__.py index 51620e3..48482b2 100644 --- a/im2sim/plot/__init__.py +++ b/im2sim/plot/__init__.py @@ -1 +1 @@ -from .pointcloud import PointCloudPlot \ No newline at end of file +from .pointcloud import PointCloudPlot as PointCloudPlot diff --git a/im2sim/plot/pointcloud.py b/im2sim/plot/pointcloud.py index 1221c08..41b89ad 100644 --- a/im2sim/plot/pointcloud.py +++ b/im2sim/plot/pointcloud.py @@ -1,13 +1,9 @@ -import numpy as np import matplotlib.pyplot as plt -import matplotlib.animation as animation - import numpy as np -import matplotlib.pyplot as plt -import matplotlib.animation as animation +from matplotlib import animation -class PointCloudPlot(): +class PointCloudPlot: def __init__(self, nrows, diff --git a/setup.py b/setup.py index c086f4a..dafb491 100644 --- a/setup.py +++ b/setup.py @@ -1,4 +1,4 @@ -from setuptools import setup, find_packages +from setuptools import find_packages, setup setup( name="im2sim", diff --git a/tests/unit/data/test_mesh_utils.py b/tests/unit/data/test_mesh_utils.py index a9bc8ca..7ec1ae7 100644 --- a/tests/unit/data/test_mesh_utils.py +++ b/tests/unit/data/test_mesh_utils.py @@ -1,5 +1,6 @@ import torch -from im2sim.data.mesh_utils import * + +from im2sim.data.mesh_utils import * def test_make_padded_batch_shapes(): From dadf49b09ef6fee6c5ab024a82d0222d1a9500cb Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 27 Jul 2026 15:43:35 +0000 Subject: [PATCH 30/49] fixed lints and test pipeline --- .github/workflows/tests.yaml | 4 +-- .gitignore | 5 ++- setup.py | 10 +++++- src/im2sim.egg-info/PKG-INFO | 11 ++++++ src/im2sim.egg-info/SOURCES.txt | 35 +++++++++++++++++++ src/im2sim.egg-info/dependency_links.txt | 1 + src/im2sim.egg-info/requires.txt | 4 +++ src/im2sim.egg-info/top_level.txt | 1 + {im2sim => src/im2sim}/__init__.py | 0 {im2sim => src/im2sim}/callbacks/__init__.py | 0 {im2sim => src/im2sim}/callbacks/contents.md | 0 {im2sim => src/im2sim}/data/__init__.py | 0 {im2sim => src/im2sim}/data/contents.md | 0 {im2sim => src/im2sim}/data/core.py | 0 {im2sim => src/im2sim}/data/mesh_utils.py | 0 {im2sim => src/im2sim}/data/ops.py | 0 {im2sim => src/im2sim}/data/pca.py | 0 {im2sim => src/im2sim}/data/scaling.py | 0 {im2sim => src/im2sim}/data/transforms.py | 0 src/im2sim/im2sim.egg-info/PKG-INFO | 11 ++++++ src/im2sim/im2sim.egg-info/SOURCES.txt | 34 ++++++++++++++++++ .../im2sim.egg-info/dependency_links.txt | 1 + src/im2sim/im2sim.egg-info/requires.txt | 4 +++ src/im2sim/im2sim.egg-info/top_level.txt | 8 +++++ {im2sim => src/im2sim}/layers/__init__.py | 0 {im2sim => src/im2sim}/layers/contents.md | 0 {im2sim => src/im2sim}/layers/graph_blocks.py | 0 {im2sim => src/im2sim}/layers/image_blocks.py | 0 {im2sim => src/im2sim}/layers/layer_util.py | 0 .../im2sim}/layers/meshgraphnets.py | 0 {im2sim => src/im2sim}/layers/projections.py | 0 {im2sim => src/im2sim}/losses/__init__.py | 0 {im2sim => src/im2sim}/losses/contents.md | 0 {im2sim => src/im2sim}/losses/feature.py | 0 {im2sim => src/im2sim}/losses/mesh.py | 0 {im2sim => src/im2sim}/losses/pointcloud.py | 0 {im2sim => src/im2sim}/losses/seg.py | 0 {im2sim => src/im2sim}/losses/utils.py | 0 {im2sim => src/im2sim}/metrics/__init__.py | 0 {im2sim => src/im2sim}/metrics/contents.md | 0 {im2sim => src/im2sim}/models/__init__.py | 0 {im2sim => src/im2sim}/models/contents.md | 0 .../im2sim}/models/image_to_graph.py | 0 {im2sim => src/im2sim}/models/unet.py | 7 ++-- {im2sim => src/im2sim}/models/utils.py | 0 {im2sim => src/im2sim}/plot/__init__.py | 0 {im2sim => src/im2sim}/plot/pointcloud.py | 0 {im2sim => src/im2sim}/utils/__init__.py | 0 {im2sim => src/im2sim}/utils/contents.md | 0 49 files changed, 129 insertions(+), 7 deletions(-) create mode 100644 src/im2sim.egg-info/PKG-INFO create mode 100644 src/im2sim.egg-info/SOURCES.txt create mode 100644 src/im2sim.egg-info/dependency_links.txt create mode 100644 src/im2sim.egg-info/requires.txt create mode 100644 src/im2sim.egg-info/top_level.txt rename {im2sim => src/im2sim}/__init__.py (100%) rename {im2sim => src/im2sim}/callbacks/__init__.py (100%) rename {im2sim => src/im2sim}/callbacks/contents.md (100%) rename {im2sim => src/im2sim}/data/__init__.py (100%) rename {im2sim => src/im2sim}/data/contents.md (100%) rename {im2sim => src/im2sim}/data/core.py (100%) rename {im2sim => src/im2sim}/data/mesh_utils.py (100%) rename {im2sim => src/im2sim}/data/ops.py (100%) rename {im2sim => src/im2sim}/data/pca.py (100%) rename {im2sim => src/im2sim}/data/scaling.py (100%) rename {im2sim => src/im2sim}/data/transforms.py (100%) create mode 100644 src/im2sim/im2sim.egg-info/PKG-INFO create mode 100644 src/im2sim/im2sim.egg-info/SOURCES.txt create mode 100644 src/im2sim/im2sim.egg-info/dependency_links.txt create mode 100644 src/im2sim/im2sim.egg-info/requires.txt create mode 100644 src/im2sim/im2sim.egg-info/top_level.txt rename {im2sim => src/im2sim}/layers/__init__.py (100%) rename {im2sim => src/im2sim}/layers/contents.md (100%) rename {im2sim => src/im2sim}/layers/graph_blocks.py (100%) rename {im2sim => src/im2sim}/layers/image_blocks.py (100%) rename {im2sim => src/im2sim}/layers/layer_util.py (100%) rename {im2sim => src/im2sim}/layers/meshgraphnets.py (100%) rename {im2sim => src/im2sim}/layers/projections.py (100%) rename {im2sim => src/im2sim}/losses/__init__.py (100%) rename {im2sim => src/im2sim}/losses/contents.md (100%) rename {im2sim => src/im2sim}/losses/feature.py (100%) rename {im2sim => src/im2sim}/losses/mesh.py (100%) rename {im2sim => src/im2sim}/losses/pointcloud.py (100%) rename {im2sim => src/im2sim}/losses/seg.py (100%) rename {im2sim => src/im2sim}/losses/utils.py (100%) rename {im2sim => src/im2sim}/metrics/__init__.py (100%) rename {im2sim => src/im2sim}/metrics/contents.md (100%) rename {im2sim => src/im2sim}/models/__init__.py (100%) rename {im2sim => src/im2sim}/models/contents.md (100%) rename {im2sim => src/im2sim}/models/image_to_graph.py (100%) rename {im2sim => src/im2sim}/models/unet.py (98%) rename {im2sim => src/im2sim}/models/utils.py (100%) rename {im2sim => src/im2sim}/plot/__init__.py (100%) rename {im2sim => src/im2sim}/plot/pointcloud.py (100%) rename {im2sim => src/im2sim}/utils/__init__.py (100%) rename {im2sim => src/im2sim}/utils/contents.md (100%) diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml index 607433d..868f250 100644 --- a/.github/workflows/tests.yaml +++ b/.github/workflows/tests.yaml @@ -19,9 +19,9 @@ jobs: - name: Install dependencies run: | + python -m pip install --upgrade pip pip install -r requirements.txt - pip install pytest - pip install ruff + pip install -e ".[dev]" - name: Lint run: ruff check . diff --git a/.gitignore b/.gitignore index 2e9003f..70a4463 100644 --- a/.gitignore +++ b/.gitignore @@ -8,4 +8,7 @@ venv/ # OS files .DS_Store -Thumbs.db \ No newline at end of file +Thumbs.db + +# pytest +.pytest_cache/ diff --git a/setup.py b/setup.py index dafb491..f628dba 100644 --- a/setup.py +++ b/setup.py @@ -3,5 +3,13 @@ setup( name="im2sim", version="0.1.0", - packages=find_packages(), # auto-finds your Python packages + package_dir={"": "src"}, + packages=find_packages(where="src"), + python_requires=">=3.10", + extras_require={ + "dev": [ + "pytest", + "ruff", + ], + }, ) \ No newline at end of file diff --git a/src/im2sim.egg-info/PKG-INFO b/src/im2sim.egg-info/PKG-INFO new file mode 100644 index 0000000..1b35669 --- /dev/null +++ b/src/im2sim.egg-info/PKG-INFO @@ -0,0 +1,11 @@ +Metadata-Version: 2.1 +Name: im2sim +Version: 0.1.0 +Summary: UNKNOWN +License: UNKNOWN +Platform: UNKNOWN +Requires-Python: >=3.10 +Provides-Extra: dev + +UNKNOWN + diff --git a/src/im2sim.egg-info/SOURCES.txt b/src/im2sim.egg-info/SOURCES.txt new file mode 100644 index 0000000..d6d8132 --- /dev/null +++ b/src/im2sim.egg-info/SOURCES.txt @@ -0,0 +1,35 @@ +setup.py +src/im2sim/__init__.py +src/im2sim.egg-info/PKG-INFO +src/im2sim.egg-info/SOURCES.txt +src/im2sim.egg-info/dependency_links.txt +src/im2sim.egg-info/requires.txt +src/im2sim.egg-info/top_level.txt +src/im2sim/callbacks/__init__.py +src/im2sim/data/__init__.py +src/im2sim/data/core.py +src/im2sim/data/mesh_utils.py +src/im2sim/data/ops.py +src/im2sim/data/pca.py +src/im2sim/data/scaling.py +src/im2sim/data/transforms.py +src/im2sim/layers/__init__.py +src/im2sim/layers/graph_blocks.py +src/im2sim/layers/image_blocks.py +src/im2sim/layers/layer_util.py +src/im2sim/layers/meshgraphnets.py +src/im2sim/layers/projections.py +src/im2sim/losses/__init__.py +src/im2sim/losses/feature.py +src/im2sim/losses/mesh.py +src/im2sim/losses/pointcloud.py +src/im2sim/losses/seg.py +src/im2sim/losses/utils.py +src/im2sim/metrics/__init__.py +src/im2sim/models/__init__.py +src/im2sim/models/image_to_graph.py +src/im2sim/models/unet.py +src/im2sim/models/utils.py +src/im2sim/plot/__init__.py +src/im2sim/plot/pointcloud.py +src/im2sim/utils/__init__.py \ No newline at end of file diff --git a/src/im2sim.egg-info/dependency_links.txt b/src/im2sim.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/src/im2sim.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/src/im2sim.egg-info/requires.txt b/src/im2sim.egg-info/requires.txt new file mode 100644 index 0000000..ef8ed3f --- /dev/null +++ b/src/im2sim.egg-info/requires.txt @@ -0,0 +1,4 @@ + +[dev] +pytest +ruff diff --git a/src/im2sim.egg-info/top_level.txt b/src/im2sim.egg-info/top_level.txt new file mode 100644 index 0000000..d156cdc --- /dev/null +++ b/src/im2sim.egg-info/top_level.txt @@ -0,0 +1 @@ +im2sim diff --git a/im2sim/__init__.py b/src/im2sim/__init__.py similarity index 100% rename from im2sim/__init__.py rename to src/im2sim/__init__.py diff --git a/im2sim/callbacks/__init__.py b/src/im2sim/callbacks/__init__.py similarity index 100% rename from im2sim/callbacks/__init__.py rename to src/im2sim/callbacks/__init__.py diff --git a/im2sim/callbacks/contents.md b/src/im2sim/callbacks/contents.md similarity index 100% rename from im2sim/callbacks/contents.md rename to src/im2sim/callbacks/contents.md diff --git a/im2sim/data/__init__.py b/src/im2sim/data/__init__.py similarity index 100% rename from im2sim/data/__init__.py rename to src/im2sim/data/__init__.py diff --git a/im2sim/data/contents.md b/src/im2sim/data/contents.md similarity index 100% rename from im2sim/data/contents.md rename to src/im2sim/data/contents.md diff --git a/im2sim/data/core.py b/src/im2sim/data/core.py similarity index 100% rename from im2sim/data/core.py rename to src/im2sim/data/core.py diff --git a/im2sim/data/mesh_utils.py b/src/im2sim/data/mesh_utils.py similarity index 100% rename from im2sim/data/mesh_utils.py rename to src/im2sim/data/mesh_utils.py diff --git a/im2sim/data/ops.py b/src/im2sim/data/ops.py similarity index 100% rename from im2sim/data/ops.py rename to src/im2sim/data/ops.py diff --git a/im2sim/data/pca.py b/src/im2sim/data/pca.py similarity index 100% rename from im2sim/data/pca.py rename to src/im2sim/data/pca.py diff --git a/im2sim/data/scaling.py b/src/im2sim/data/scaling.py similarity index 100% rename from im2sim/data/scaling.py rename to src/im2sim/data/scaling.py diff --git a/im2sim/data/transforms.py b/src/im2sim/data/transforms.py similarity index 100% rename from im2sim/data/transforms.py rename to src/im2sim/data/transforms.py diff --git a/src/im2sim/im2sim.egg-info/PKG-INFO b/src/im2sim/im2sim.egg-info/PKG-INFO new file mode 100644 index 0000000..1b35669 --- /dev/null +++ b/src/im2sim/im2sim.egg-info/PKG-INFO @@ -0,0 +1,11 @@ +Metadata-Version: 2.1 +Name: im2sim +Version: 0.1.0 +Summary: UNKNOWN +License: UNKNOWN +Platform: UNKNOWN +Requires-Python: >=3.10 +Provides-Extra: dev + +UNKNOWN + diff --git a/src/im2sim/im2sim.egg-info/SOURCES.txt b/src/im2sim/im2sim.egg-info/SOURCES.txt new file mode 100644 index 0000000..745f057 --- /dev/null +++ b/src/im2sim/im2sim.egg-info/SOURCES.txt @@ -0,0 +1,34 @@ +setup.py +im2sim/callbacks/__init__.py +im2sim/data/__init__.py +im2sim/data/core.py +im2sim/data/mesh_utils.py +im2sim/data/ops.py +im2sim/data/pca.py +im2sim/data/scaling.py +im2sim/data/transforms.py +im2sim/im2sim.egg-info/PKG-INFO +im2sim/im2sim.egg-info/SOURCES.txt +im2sim/im2sim.egg-info/dependency_links.txt +im2sim/im2sim.egg-info/requires.txt +im2sim/im2sim.egg-info/top_level.txt +im2sim/layers/__init__.py +im2sim/layers/graph_blocks.py +im2sim/layers/image_blocks.py +im2sim/layers/layer_util.py +im2sim/layers/meshgraphnets.py +im2sim/layers/projections.py +im2sim/losses/__init__.py +im2sim/losses/feature.py +im2sim/losses/mesh.py +im2sim/losses/pointcloud.py +im2sim/losses/seg.py +im2sim/losses/utils.py +im2sim/metrics/__init__.py +im2sim/models/__init__.py +im2sim/models/image_to_graph.py +im2sim/models/unet.py +im2sim/models/utils.py +im2sim/plot/__init__.py +im2sim/plot/pointcloud.py +im2sim/utils/__init__.py \ No newline at end of file diff --git a/src/im2sim/im2sim.egg-info/dependency_links.txt b/src/im2sim/im2sim.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/src/im2sim/im2sim.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/src/im2sim/im2sim.egg-info/requires.txt b/src/im2sim/im2sim.egg-info/requires.txt new file mode 100644 index 0000000..ef8ed3f --- /dev/null +++ b/src/im2sim/im2sim.egg-info/requires.txt @@ -0,0 +1,4 @@ + +[dev] +pytest +ruff diff --git a/src/im2sim/im2sim.egg-info/top_level.txt b/src/im2sim/im2sim.egg-info/top_level.txt new file mode 100644 index 0000000..0c69eef --- /dev/null +++ b/src/im2sim/im2sim.egg-info/top_level.txt @@ -0,0 +1,8 @@ +callbacks +data +layers +losses +metrics +models +plot +utils diff --git a/im2sim/layers/__init__.py b/src/im2sim/layers/__init__.py similarity index 100% rename from im2sim/layers/__init__.py rename to src/im2sim/layers/__init__.py diff --git a/im2sim/layers/contents.md b/src/im2sim/layers/contents.md similarity index 100% rename from im2sim/layers/contents.md rename to src/im2sim/layers/contents.md diff --git a/im2sim/layers/graph_blocks.py b/src/im2sim/layers/graph_blocks.py similarity index 100% rename from im2sim/layers/graph_blocks.py rename to src/im2sim/layers/graph_blocks.py diff --git a/im2sim/layers/image_blocks.py b/src/im2sim/layers/image_blocks.py similarity index 100% rename from im2sim/layers/image_blocks.py rename to src/im2sim/layers/image_blocks.py diff --git a/im2sim/layers/layer_util.py b/src/im2sim/layers/layer_util.py similarity index 100% rename from im2sim/layers/layer_util.py rename to src/im2sim/layers/layer_util.py diff --git a/im2sim/layers/meshgraphnets.py b/src/im2sim/layers/meshgraphnets.py similarity index 100% rename from im2sim/layers/meshgraphnets.py rename to src/im2sim/layers/meshgraphnets.py diff --git a/im2sim/layers/projections.py b/src/im2sim/layers/projections.py similarity index 100% rename from im2sim/layers/projections.py rename to src/im2sim/layers/projections.py diff --git a/im2sim/losses/__init__.py b/src/im2sim/losses/__init__.py similarity index 100% rename from im2sim/losses/__init__.py rename to src/im2sim/losses/__init__.py diff --git a/im2sim/losses/contents.md b/src/im2sim/losses/contents.md similarity index 100% rename from im2sim/losses/contents.md rename to src/im2sim/losses/contents.md diff --git a/im2sim/losses/feature.py b/src/im2sim/losses/feature.py similarity index 100% rename from im2sim/losses/feature.py rename to src/im2sim/losses/feature.py diff --git a/im2sim/losses/mesh.py b/src/im2sim/losses/mesh.py similarity index 100% rename from im2sim/losses/mesh.py rename to src/im2sim/losses/mesh.py diff --git a/im2sim/losses/pointcloud.py b/src/im2sim/losses/pointcloud.py similarity index 100% rename from im2sim/losses/pointcloud.py rename to src/im2sim/losses/pointcloud.py diff --git a/im2sim/losses/seg.py b/src/im2sim/losses/seg.py similarity index 100% rename from im2sim/losses/seg.py rename to src/im2sim/losses/seg.py diff --git a/im2sim/losses/utils.py b/src/im2sim/losses/utils.py similarity index 100% rename from im2sim/losses/utils.py rename to src/im2sim/losses/utils.py diff --git a/im2sim/metrics/__init__.py b/src/im2sim/metrics/__init__.py similarity index 100% rename from im2sim/metrics/__init__.py rename to src/im2sim/metrics/__init__.py diff --git a/im2sim/metrics/contents.md b/src/im2sim/metrics/contents.md similarity index 100% rename from im2sim/metrics/contents.md rename to src/im2sim/metrics/contents.md diff --git a/im2sim/models/__init__.py b/src/im2sim/models/__init__.py similarity index 100% rename from im2sim/models/__init__.py rename to src/im2sim/models/__init__.py diff --git a/im2sim/models/contents.md b/src/im2sim/models/contents.md similarity index 100% rename from im2sim/models/contents.md rename to src/im2sim/models/contents.md diff --git a/im2sim/models/image_to_graph.py b/src/im2sim/models/image_to_graph.py similarity index 100% rename from im2sim/models/image_to_graph.py rename to src/im2sim/models/image_to_graph.py diff --git a/im2sim/models/unet.py b/src/im2sim/models/unet.py similarity index 98% rename from im2sim/models/unet.py rename to src/im2sim/models/unet.py index 2878ac8..50e0123 100644 --- a/im2sim/models/unet.py +++ b/src/im2sim/models/unet.py @@ -1,3 +1,4 @@ +import torch.nn.functional as F from torch import nn from ..layers.layer_util import * @@ -40,7 +41,7 @@ def __init__(self, ]) self.norms = nn.ModuleList([ - get_image_layer(norm_type, rank)(filters) if norm_type else nn.Identity() + TORCH_LAYERS(f"{norm_type}{rank}d")(filters) if norm_type else nn.Identity() for _ in range(depth) ]) self.drop = drop(p=dropout_rate) if dropout_rate else nn.Identity() @@ -187,7 +188,7 @@ def __init__(self, for i in range(n_levels - 1) ]) else: - up_layer = get_image_layer(upsample_type, rank) + up_layer = TORCH_LAYERS(f"{upsample_type}{rank}d") self.ups = nn.ModuleList([ up_layer(rev_filters[i], rev_filters[i+1], kernel_size=upsample_sizes[i], stride=upsample_sizes[i]) for i in range(n_levels - 1) @@ -395,7 +396,7 @@ def __init__(self, self.final_conv = conv(filters[0], out_channels, kernel_size=1) self.final_act = ( - get_activation(final_activation)() + ACTIVATIONS(final_activation)() if final_activation.lower() != "linear" else nn.Identity() ) diff --git a/im2sim/models/utils.py b/src/im2sim/models/utils.py similarity index 100% rename from im2sim/models/utils.py rename to src/im2sim/models/utils.py diff --git a/im2sim/plot/__init__.py b/src/im2sim/plot/__init__.py similarity index 100% rename from im2sim/plot/__init__.py rename to src/im2sim/plot/__init__.py diff --git a/im2sim/plot/pointcloud.py b/src/im2sim/plot/pointcloud.py similarity index 100% rename from im2sim/plot/pointcloud.py rename to src/im2sim/plot/pointcloud.py diff --git a/im2sim/utils/__init__.py b/src/im2sim/utils/__init__.py similarity index 100% rename from im2sim/utils/__init__.py rename to src/im2sim/utils/__init__.py diff --git a/im2sim/utils/contents.md b/src/im2sim/utils/contents.md similarity index 100% rename from im2sim/utils/contents.md rename to src/im2sim/utils/contents.md From 12d7ec4034421f18dd443450a534521b14d02ddd Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 27 Jul 2026 15:49:19 +0000 Subject: [PATCH 31/49] some corrections --- requirements.txt | 1 + src/im2sim/im2sim.egg-info/PKG-INFO | 11 ------ src/im2sim/im2sim.egg-info/SOURCES.txt | 34 ------------------- .../im2sim.egg-info/dependency_links.txt | 1 - src/im2sim/im2sim.egg-info/requires.txt | 4 --- src/im2sim/im2sim.egg-info/top_level.txt | 8 ----- src/im2sim/layers/layer_util.py | 2 +- 7 files changed, 2 insertions(+), 59 deletions(-) delete mode 100644 src/im2sim/im2sim.egg-info/PKG-INFO delete mode 100644 src/im2sim/im2sim.egg-info/SOURCES.txt delete mode 100644 src/im2sim/im2sim.egg-info/dependency_links.txt delete mode 100644 src/im2sim/im2sim.egg-info/requires.txt delete mode 100644 src/im2sim/im2sim.egg-info/top_level.txt diff --git a/requirements.txt b/requirements.txt index d2ba424..d8c6e00 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,6 +2,7 @@ torch==2.3.1 torch-cluster==1.6.3 +torch-scatter==2.1.2 torch_geometric==2.7.0 pyvista==0.47.0 numpy diff --git a/src/im2sim/im2sim.egg-info/PKG-INFO b/src/im2sim/im2sim.egg-info/PKG-INFO deleted file mode 100644 index 1b35669..0000000 --- a/src/im2sim/im2sim.egg-info/PKG-INFO +++ /dev/null @@ -1,11 +0,0 @@ -Metadata-Version: 2.1 -Name: im2sim -Version: 0.1.0 -Summary: UNKNOWN -License: UNKNOWN -Platform: UNKNOWN -Requires-Python: >=3.10 -Provides-Extra: dev - -UNKNOWN - diff --git a/src/im2sim/im2sim.egg-info/SOURCES.txt b/src/im2sim/im2sim.egg-info/SOURCES.txt deleted file mode 100644 index 745f057..0000000 --- a/src/im2sim/im2sim.egg-info/SOURCES.txt +++ /dev/null @@ -1,34 +0,0 @@ -setup.py -im2sim/callbacks/__init__.py -im2sim/data/__init__.py -im2sim/data/core.py -im2sim/data/mesh_utils.py -im2sim/data/ops.py -im2sim/data/pca.py -im2sim/data/scaling.py -im2sim/data/transforms.py -im2sim/im2sim.egg-info/PKG-INFO -im2sim/im2sim.egg-info/SOURCES.txt -im2sim/im2sim.egg-info/dependency_links.txt -im2sim/im2sim.egg-info/requires.txt -im2sim/im2sim.egg-info/top_level.txt -im2sim/layers/__init__.py -im2sim/layers/graph_blocks.py -im2sim/layers/image_blocks.py -im2sim/layers/layer_util.py -im2sim/layers/meshgraphnets.py -im2sim/layers/projections.py -im2sim/losses/__init__.py -im2sim/losses/feature.py -im2sim/losses/mesh.py -im2sim/losses/pointcloud.py -im2sim/losses/seg.py -im2sim/losses/utils.py -im2sim/metrics/__init__.py -im2sim/models/__init__.py -im2sim/models/image_to_graph.py -im2sim/models/unet.py -im2sim/models/utils.py -im2sim/plot/__init__.py -im2sim/plot/pointcloud.py -im2sim/utils/__init__.py \ No newline at end of file diff --git a/src/im2sim/im2sim.egg-info/dependency_links.txt b/src/im2sim/im2sim.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/src/im2sim/im2sim.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/src/im2sim/im2sim.egg-info/requires.txt b/src/im2sim/im2sim.egg-info/requires.txt deleted file mode 100644 index ef8ed3f..0000000 --- a/src/im2sim/im2sim.egg-info/requires.txt +++ /dev/null @@ -1,4 +0,0 @@ - -[dev] -pytest -ruff diff --git a/src/im2sim/im2sim.egg-info/top_level.txt b/src/im2sim/im2sim.egg-info/top_level.txt deleted file mode 100644 index 0c69eef..0000000 --- a/src/im2sim/im2sim.egg-info/top_level.txt +++ /dev/null @@ -1,8 +0,0 @@ -callbacks -data -layers -losses -metrics -models -plot -utils diff --git a/src/im2sim/layers/layer_util.py b/src/im2sim/layers/layer_util.py index 34b9b38..7b907fd 100644 --- a/src/im2sim/layers/layer_util.py +++ b/src/im2sim/layers/layer_util.py @@ -76,7 +76,7 @@ def __str__(self): TORCH_LAYERS, register_torch_layer = make_registry(torch.nn, layer_pattern) PYG_LAYERS, register_pyg_layer = make_registry(gnn, layer_pattern) -print(PYG_LAYERS) + # def get_image_layer(name, rank): # """Get an N-D layer object. From be9a2fe67603ff9242840e3fbe93fd9da07d40c5 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 28 Jul 2026 12:49:23 +0000 Subject: [PATCH 32/49] added tests for data ops --- pytest.ini | 3 + setup.py | 2 +- src/im2sim/__init__.py | 2 +- src/im2sim/data/core.py | 364 +++++++++-------- src/im2sim/data/mesh_utils.py | 134 +++--- src/im2sim/data/ops.py | 78 ++-- src/im2sim/data/pca.py | 25 +- src/im2sim/data/scaling.py | 9 +- src/im2sim/data/transforms.py | 150 +++++-- src/im2sim/layers/__init__.py | 1 - src/im2sim/layers/graph_blocks.py | 270 ++++++------ src/im2sim/layers/image_blocks.py | 406 ++++++++++--------- src/im2sim/layers/layer_util.py | 174 ++------ src/im2sim/layers/meshgraphnets.py | 82 ++-- src/im2sim/layers/projections.py | 137 ++++--- src/im2sim/losses/__init__.py | 2 +- src/im2sim/losses/feature.py | 24 +- src/im2sim/losses/mesh.py | 81 ++-- src/im2sim/losses/pointcloud.py | 37 +- src/im2sim/losses/seg.py | 21 +- src/im2sim/losses/utils.py | 18 +- src/im2sim/models/__init__.py | 2 +- src/im2sim/models/image_to_graph.py | 150 +++---- src/im2sim/models/unet.py | 346 +++++++++------- src/im2sim/models/utils.py | 119 +++--- src/im2sim/plot/pointcloud.py | 112 +++-- tests/integration/data/test_data_pipeline.py | 155 +++++++ tests/integration/data/test_pv_mesh_utils.py | 249 ++++++++++++ tests/unit/data/test_loader.py | 129 ++++++ tests/unit/data/test_mesh_utils.py | 30 +- tests/unit/data/test_ops.py | 92 +++++ tests/unit/data/test_pipeline.py | 279 +++++++++++++ 32 files changed, 2354 insertions(+), 1329 deletions(-) create mode 100644 pytest.ini create mode 100644 tests/integration/data/test_data_pipeline.py create mode 100644 tests/integration/data/test_pv_mesh_utils.py create mode 100644 tests/unit/data/test_loader.py create mode 100644 tests/unit/data/test_ops.py create mode 100644 tests/unit/data/test_pipeline.py diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..5e5d5cf --- /dev/null +++ b/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +filterwarnings = + ignore:.*torch_geometric\.distributed.*:DeprecationWarning \ No newline at end of file diff --git a/setup.py b/setup.py index f628dba..db67676 100644 --- a/setup.py +++ b/setup.py @@ -12,4 +12,4 @@ "ruff", ], }, -) \ No newline at end of file +) diff --git a/src/im2sim/__init__.py b/src/im2sim/__init__.py index 4696ae3..3ebba33 100644 --- a/src/im2sim/__init__.py +++ b/src/im2sim/__init__.py @@ -8,4 +8,4 @@ "metrics", "models", "utils", -] \ No newline at end of file +] diff --git a/src/im2sim/data/core.py b/src/im2sim/data/core.py index 841d30e..3ec451d 100644 --- a/src/im2sim/data/core.py +++ b/src/im2sim/data/core.py @@ -1,118 +1,22 @@ import copy import logging from abc import ABC, abstractmethod -from typing import Any +from typing import Any, Callable, Dict, Union, Optional import torch from torch_geometric.data import Batch, Data -logger = logging.getLogger(__name__) - - - - - - -def collate(batch): - ''' - Collates a batch of data in the form of dict{str: torch.Tensor/PyG.Data} - Tensors are batched by stacking in the 0 dim and PyG Data are batched by PyG (https://pytorch-geometric.readthedocs.io/en/2.5.2/advanced/batching.html) - ''' - out_dict = {} - for key,val in batch[0].items(): - if isinstance(val, torch.Tensor): - out_dict[key] = torch.utils.data.default_collate([b[key] for b in batch]) - elif isinstance(val, Data): - out_dict[key] = Batch.from_data_list([b[key] for b in batch]) - else: - raise TypeError(f"{key} is type {type(val)}. Generator outputs must be either torch.Tensor or torch_geometric.data.Data object") - return out_dict - - -def DataLoader(dataset, **kwargs): - """ - Create a DataLoader for an im2sim dataset. - - Args: - dataset (im2sim.data.Dataset): - Dataset to wrap in a DataLoader. - - **kwargs: - Additional keyword arguments passed to ``torch.utils.data.DataLoader``. - Common options include: - - - batch_size (int, optional): - Number of samples per batch (default: 1). - - shuffle (bool, optional): - Whether to reshuffle the data at every epoch (default: False). - - num_workers (int, optional): - Number of subprocesses used for data loading. ``0`` means data - is loaded in the main process (default: 0). - - pin_memory (bool, optional): - If True, tensors are copied into CUDA pinned memory before returning. - - See https://docs.pytorch.org/docs/stable/data.html for the full list - of supported arguments. - - Returns: - im2sim.data.DataLoader: - Configured DataLoader instance. - """ - return torch.utils.data.DataLoader(dataset, collate_fn=collate, **kwargs) - -class Dataset(torch.utils.data.Dataset): - """ - Template for building im2sim datasets. - - To build a custom dataset, create a new Dataset with a custom load function, - case files, and transforms. - - Args: - load_fn (Callable[[str], dict[str, Tensor | PyGData]]): - Function that loads all files needed for a specific case and returns - a dictionary containing the data for that case. - - cases (list[str]): - List of case names. These names are passed to `load_fn` to load data. - - transforms (list[Transform] | Pipeline): - Transforms or pipeline applied to each sample. - - Example: - >>> import torch - >>> - >>> cases = ['case1', 'case2', 'case3', 'case4'] - >>> - >>> def load(case): - ... img = torch.load(f'images/{case}.pt') - ... graph = torch.load(f'graphs/{case}.pt') - ... template = torch.load(f'template/{case}.pt') - ... return {'image': img, 'template': template, 'out_graph': graph} - >>> - >>> dataset = im2sim.data.Dataset(load_fn=load, cases=cases) - """ - def __init__(self, load_fn, cases, transforms=None): - self.load_fn = load_fn - self.cases = cases - self.transforms = transforms if transforms is not None else [] - def __len__(self): - return len(self.cases) +logger = logging.getLogger(__name__) - def __getitem__(self, idx): - case = self.cases[idx] - sample = self.load_fn(case) - for transform in self.transforms: - sample = transform(sample) - return sample - class Operation(ABC): """ - Abstract base class for making operations. + Abstract base class for making operations. To make a new simple operation, subclass this and overwrite the forward method. """ + def __init_subclass__(cls): original_init = cls.__init__ @@ -151,25 +55,20 @@ def load_state_dict(self, state): for k, v in state.items(): setattr(self, k, v) - def _is_serializable(self, v): """ helper function to check if attr is serializable """ - return ( - isinstance(v, (int, float, str, bool, torch.Tensor)) - or v is None - ) - + return isinstance(v, (int, float, str, bool, torch.Tensor)) or v is None + def to(self, device): """ - method to move attrs to device for torch training + method to move attrs to device for torch training """ for k, v in self.__dict__.items(): if isinstance(v, torch.Tensor): setattr(self, k, v.to(device)) return self - class InvertibleOperation(Operation): @@ -198,7 +97,6 @@ def complete_fit(self): pass - class Transform: """ A wrapper for operations that allows the selective application of the operation by dict key, object attribute and channel @@ -209,11 +107,11 @@ class Transform: keys (list[str]): List of keys in the data dict for the op to operate over - - attr (str, optional): - If data[key] is an object, attr is the attribute of that object to perfrom the op over. - If the op needs the full object, set attr to 'all' - If data[key] is not an object attr=None + + attr (str, optional): + If data[key] is an object, attr is the attribute of that object to perfrom the op over. + If the op needs the full object, set attr to 'all' + If data[key] is not an object attr=None (default:None) """ @@ -221,14 +119,17 @@ def __init__( self, op, keys, - multikey = False, + multikey=False, attr=None, channels=None, per_channel=False, channel_dim=-1, - name=None + name=None, ): - + + if keys is None or keys==[]: + raise ValueError("keys must be specified") + self.keys = keys if isinstance(keys, list) else [keys] self.attr = attr @@ -242,9 +143,13 @@ def __init__( self.channel_dim = channel_dim self.per_channel = per_channel - self.op = op + self.op = op - self.name = name if name is not None else f"{op.__class__.__name__}_{'_'.join(self.keys)}" + self.name = ( + name + if name is not None + else f"{op.__class__.__name__}_{'_'.join(self.keys)}" + ) self.is_multikey = multikey self.is_invertible = isinstance(op, InvertibleOperation) @@ -282,15 +187,10 @@ def _ensure_per_channel_ops(self, x): x_moved = torch.moveaxis(x, self.channel_dim, 0) - channels = ( - range(x_moved.shape[0]) - if self.channels is None - else self.channels - ) + channels = range(x_moved.shape[0]) if self.channels is None else self.channels self.op = [copy.deepcopy(self.op) for _ in channels] - def _apply_channel_op(self, x, fn, no_return=False): if self.channels is None and not self.per_channel: @@ -302,16 +202,10 @@ def _apply_channel_op(self, x, fn, no_return=False): if self.per_channel: self._ensure_per_channel_ops(x) - channels = ( - range(x_moved.shape[0]) - if self.channels is None - else self.channels - ) + channels = range(x_moved.shape[0]) if self.channels is None else self.channels if self.per_channel: - for i, c in enumerate(channels): - op = self.op[i] fn_op = getattr(op, fn) @@ -321,7 +215,6 @@ def _apply_channel_op(self, x, fn, no_return=False): x_moved[c] = fn_op(x_moved[c]) else: - idx = self.channels if self.channels is not None else slice(None) fn_op = getattr(self.op, fn) @@ -355,12 +248,11 @@ def _apply_op(self, data, fn, no_return=False): if not isinstance(outputs, (list, tuple)): outputs = [outputs] - for k, out in zip(self.keys, outputs): self._set_target(data, k, out) - elif len(self.keys)>1: - # ---- single key op over mutliple keys ---- + elif len(self.keys) > 1: + # ---- single key op over mutliple keys ---- inputs = [self._get_target(data, k) for k in self.keys] if no_return: @@ -368,12 +260,12 @@ def _apply_op(self, data, fn, no_return=False): self._apply_channel_op(i, fn, no_return) return - for i,k in zip(inputs, self.keys): + for i, k in zip(inputs, self.keys): out = self._apply_channel_op(i, fn, no_return) self._set_target(data, k, out) else: - # --- single key --- + # --- single key --- key = self.keys[0] x = self._get_target(data, key) @@ -382,21 +274,23 @@ def _apply_op(self, data, fn, no_return=False): if no_return: return None - + self._set_target(data, key, x) return data - + # ----------------------------- # Public API # ----------------------------- def forward(self, data): + data = copy.deepcopy(data) if self.is_fittable and not self.fitted: raise RuntimeError(f"Fittable transform {self.name} has not been fit") return self._apply_op(data, "forward") def inverse(self, data): + data = copy.deepcopy(data) if not self.is_invertible: raise RuntimeError(f"{self.name} is not invertible") if self.is_fittable and not self.fitted: @@ -412,13 +306,14 @@ def fit(self, dataloader): print(self.op) if not self.is_fittable: return - + for batch in dataloader: self._apply_op(batch, "fit_step", no_return=True) # finalize fit if self.per_channel: - for op in self.op: op.complete_fit() + for op in self.op: + op.complete_fit() else: self.op.complete_fit() @@ -431,8 +326,12 @@ def fit(self, dataloader): def config(self): return { "type": self.__class__.__name__, - "op": self.op.__class__.__name__ if not self.per_channel else self.op[0].__class__.__name__, - "op_args": self.op.call_args if not self.per_channel else self.op[0].call_args, + "op": self.op.__class__.__name__ + if not self.per_channel + else self.op[0].__class__.__name__, + "op_args": self.op.call_args + if not self.per_channel + else self.op[0].call_args, "name": self.name, "keys": self.keys, "attr": self.attr, @@ -461,13 +360,13 @@ def load_state_dict(self, state): def to(self, device): if self.per_channel: - for op in self.op: op.to(device) + for op in self.op: + op.to(device) else: self.op.to(device) class Pipeline: - def __init__(self, transforms): self.transforms = transforms @@ -477,12 +376,12 @@ def __init__(self, transforms): def __call__(self, data): transforms = self._get_applicable_transforms(data.keys()) for t in transforms: - logger.debug(f'before {t.name}') + logger.debug(f"before {t.name}") _log_data(data) data = t.forward(data) - logger.debug(f'after {t.name}') + logger.debug(f"after {t.name}") _log_data(data) return data @@ -493,12 +392,12 @@ def inverse(self, data): transforms = self._get_applicable_transforms(data.keys()) for t in reversed(transforms): if t.is_invertible: - logger.debug(f'before {t.name} inverse') + logger.debug(f"before {t.name} inverse") _log_data(data) data = t.inverse(data) - logger.debug(f'after {t.name} inverse') + logger.debug(f"after {t.name} inverse") _log_data(data) return data @@ -509,12 +408,12 @@ def _get_applicable_transforms(self, keys: str) -> list[Transform]: skip = False for key in t.keys: if key not in keys: - skip=True + skip = True break if not skip: transforms.append(t) return transforms - + # ----------------------------- # Fit (only fittable transforms) # ----------------------------- @@ -524,25 +423,18 @@ def fit(self, dataset): for i in range(n): if self.transforms[i].is_fittable: temp_dataset.transforms = [Pipeline(self.transforms[:i])] - dataloader = DataLoader(temp_dataset,batch_size=1) + dataloader = DataLoader(temp_dataset, batch_size=1) self.transforms[i].fit(dataloader) return self - - # ----------------------------- # Serialization # ----------------------------- def config(self): - return { - "transforms": [t.config() for t in self.transforms] - } + return {"transforms": [t.config() for t in self.transforms]} def state_dict(self): - return { - t.name: t.state_dict() - for t in self.transforms - } + return {t.name: t.state_dict() for t in self.transforms} # ----------------------------- # Loading @@ -553,12 +445,17 @@ def from_config(cls, config): transforms = [] for tconf in config["transforms"]: - op_cls = TRANSFORM_REGISTRY[tconf["op"]] op_args = tconf["op_args"] - op = op_cls(*op_args["args"], **op_args["kwargs"]) if not tconf["per_channel"]\ - else [op_cls(*op_args["args"], **op_args["kwargs"]) for _ in range(len(tconf["channels"]))] + op = ( + op_cls(*op_args["args"], **op_args["kwargs"]) + if not tconf["per_channel"] + else [ + op_cls(*op_args["args"], **op_args["kwargs"]) + for _ in range(len(tconf["channels"])) + ] + ) t = Transform( op=op, @@ -567,7 +464,7 @@ def from_config(cls, config): channels=tconf["channels"], per_channel=tconf["per_channel"], channel_dim=tconf["channel_dim"], - name=tconf["name"] + name=tconf["name"], ) transforms.append(t) @@ -587,10 +484,7 @@ def to(self, device): def save_pipeline(pipeline, path): - obj = { - "config": pipeline.config(), - "state": pipeline.state_dict() - } + obj = {"config": pipeline.config(), "state": pipeline.state_dict()} torch.save(obj, path) @@ -604,17 +498,21 @@ def load_pipeline(path): return pipeline -def _log_data(data: dict[str,Any]) -> None: + +def _log_data(data: dict[str, Any]) -> None: if logger.isEnabledFor(logging.DEBUG): - for k,v in data.items(): + for k, v in data.items(): logger.debug(k) - + if isinstance(v, Data): for c in range(v.x.shape[-1]): - logger.debug(f'channel {c}- max:{v.x[...,c].max()}, min:{v.x[...,c].min()}') + logger.debug( + f"channel {c}- max:{v.x[..., c].max()}, min:{v.x[..., c].min()}" + ) else: logger.debug(v.shape) + # ------------------------------------------------------------------------------------ # TRANSFORM REGISTRY DEFINITION # ------------------------------------------------------------------------------------ @@ -622,6 +520,122 @@ def _log_data(data: dict[str,Any]) -> None: TRANSFORM_REGISTRY = {} + def register_op(cls): TRANSFORM_REGISTRY[cls.__name__] = cls - return cls \ No newline at end of file + return cls + + +# ------------------------------------------------------------------------------------ +# DATA LOADING +# ------------------------------------------------------------------------------------ + + + +LoadFn = Callable[[str], Dict[str, Union[torch.Tensor, Data]]] + +def collate(batch): + """ + Collates a batch of data in the form of dict{str: torch.Tensor/PyG.Data} + Tensors are batched by stacking in the 0 dim and PyG Data are batched by PyG (https://pytorch-geometric.readthedocs.io/en/2.5.2/advanced/batching.html) + """ + out_dict = {} + for key, val in batch[0].items(): + if isinstance(val, torch.Tensor): + out_dict[key] = torch.utils.data.default_collate([b[key] for b in batch]) + elif isinstance(val, Data): + out_dict[key] = Batch.from_data_list([b[key] for b in batch]) + else: + raise TypeError( + f"{key} is type {type(val)}. Generator outputs must be either torch.Tensor or torch_geometric.data.Data object" + ) + return out_dict + + +def DataLoader(dataset, **kwargs): + """ + Create a DataLoader for an im2sim dataset. + + Args: + dataset (im2sim.data.Dataset): + Dataset to wrap in a DataLoader. + + **kwargs: + Additional keyword arguments passed to ``torch.utils.data.DataLoader``. + Common options include: + + - batch_size (int, optional): + Number of samples per batch (default: 1). + - shuffle (bool, optional): + Whether to reshuffle the data at every epoch (default: False). + - num_workers (int, optional): + Number of subprocesses used for data loading. ``0`` means data + is loaded in the main process (default: 0). + - pin_memory (bool, optional): + If True, tensors are copied into CUDA pinned memory before returning. + + See https://docs.pytorch.org/docs/stable/data.html for the full list + of supported arguments. + + Returns: + im2sim.data.DataLoader: + Configured DataLoader instance. + """ + return torch.utils.data.DataLoader(dataset, collate_fn=collate, **kwargs) + + +class Dataset(torch.utils.data.Dataset): + """ + Template for building im2sim datasets. + + To build a custom dataset, create a new Dataset with a custom load function, + case files, and transforms. + + Args: + load_fn (Callable[[str], dict[str, Tensor | PyGData]]): + Function that loads all files needed for a specific case and returns + a dictionary containing the data for that case. + + cases (list[str]): + List of case names. These names are passed to `load_fn` to load data. + + transforms (list[Transform] | Pipeline): + Transforms or pipeline applied to each sample. + + Example: + >>> import torch + >>> + >>> cases = ['case1', 'case2', 'case3', 'case4'] + >>> + >>> def load(case): + ... img = torch.load(f'images/{case}.pt') + ... graph = torch.load(f'graphs/{case}.pt') + ... template = torch.load(f'template/{case}.pt') + ... return {'image': img, 'template': template, 'out_graph': graph} + >>> + >>> dataset = im2sim.data.Dataset(load_fn=load, cases=cases) + """ + + def __init__(self, load_fn: LoadFn, cases: list[str], transforms: Optional[list[Transform] | Pipeline] = None): + self.load_fn = load_fn + self.cases = cases + self.add_transforms(transforms) + + def add_transforms(self, transforms: Optional[list[Transform] | Pipeline] = None): + if transforms is None: + self.transforms = [] + elif isinstance(transforms, Pipeline): + self.transforms = [transforms] + elif isinstance(transforms, list): + self.transforms = transforms + + def __len__(self) -> int: + return len(self.cases) + + def __getitem__(self, idx: int) -> dict[str, Union[torch.Tensor, Data]]: + case = self.cases[idx] + sample = self.load_fn(case) + + for transform in self.transforms: + sample = transform(sample) + return sample \ No newline at end of file diff --git a/src/im2sim/data/mesh_utils.py b/src/im2sim/data/mesh_utils.py index af5e68d..203c0bf 100644 --- a/src/im2sim/data/mesh_utils.py +++ b/src/im2sim/data/mesh_utils.py @@ -1,4 +1,3 @@ - import numpy as np import torch import torch_geometric.nn as gnn @@ -11,74 +10,80 @@ class InputMeshError(ValueError): pass - - -def get_structure_ids(mesh: PointGrid, structure_dict: dict[int, str]) -> dict[str, torch.Tensor]: +def get_structure_ids( + mesh: PointGrid, structure_dict: dict[int, str] +) -> dict[str, torch.Tensor]: """ Extracts node ids for different substructures in a pyvista PointGrid object. Args: mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. structure_dict (Dict[int, str]): A dictionary that maps pyvista 'CellEntityIds' to structure names. - + Returns: ids (Dict[str, torch.Tensor]): A dictionary with items in the format 'structurename_index': torch.Tensor(N), where N is the number of nodes in the structure. """ cells = get_structure_edges(mesh, structure_dict) - ids = {f'{k.split("_cell_index")[0]}_index':torch.unique(v) for k, v in cells.items()} + ids = { + f"{k.split('_edge_index')[0]}_index": torch.unique(v) for k, v in cells.items() + } return ids - -def get_structure_edges(mesh: PointGrid, structure_dict: dict[int, str]) -> dict[str, torch.Tensor]: +def get_structure_edges( + mesh: PointGrid, structure_dict: dict[int, str] +) -> dict[str, torch.Tensor]: """ Extracts edges for different substructures in a pyvista PointGrid object. Args: mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. structure_dict (Dict[int, str]): A dictionary that maps pyvista 'CellEntityIds' to structure names. - + Returns: - edges (Dict[str, torch.Tensor]): A dictionary with items in the format 'structurename_index': torch.Tensor(2, N), + edges (Dict[str, torch.Tensor]): A dictionary with items in the format 'structurename_index': torch.Tensor(2, N), where N is the number of edges in the structure. """ if _has_missing_ids(mesh, structure_dict): raise InputMeshError("Mesh has missing ids") - + edges = {} - for i,key in structure_dict.items(): - edges[f'{key}_edge_index'] = get_edges(mesh, i) + for i, key in structure_dict.items(): + edges[f"{key}_edge_index"] = get_edges(mesh, i) return edges -def get_edges(mesh:PointGrid, structure_id: int) -> torch.Tensor: + +def get_edges(mesh: PointGrid, structure_id: int) -> torch.Tensor: """ Extracts edges for a single structure in a pyvista PointGrid object. Args: mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. - structure_id (int): An integer corresponding to the structure id - + structure_id (int): An integer corresponding to the structure id + Returns: edges (torch.Tensor): Tensor of shape (2, N) where N is the number of edges in the structure. """ - submesh = mesh.extract_cells(np.where(mesh['CellEntityIds']==structure_id)) - edges = submesh.extract_all_edges().lines.reshape(-1,3)[:,1:].T - edges = torch.from_numpy(submesh['vtkOriginalPointIds'][edges]).long() + submesh = mesh.extract_cells(np.where(mesh["CellEntityIds"] == structure_id)) + edges = submesh.extract_all_edges().lines.reshape(-1, 3)[:, 1:].T + edges = torch.from_numpy(submesh["vtkOriginalPointIds"][edges]).long() edges = to_undirected(edges) return edges -def get_structure_cells(mesh: PointGrid, structure_dict: dict[int, str]) -> dict[str, torch.Tensor]: +def get_structure_cells( + mesh: PointGrid, structure_dict: dict[int, str] +) -> dict[str, torch.Tensor]: """ Extracts cells for different substructures in a pyvista PointGrid object. Args: mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. structure_dict (Dict[int, str]): A dictionary that maps pyvista 'CellEntityIds' to structure names. - + Returns: cells (Dict[str, torch.Tensor]): A dictionary with items in the format 'structurename_index': torch.Tensor(m, N), where m is 3 for triangles and 4 for tetrahedrons and N is the number of cells in the structure. @@ -86,47 +91,45 @@ def get_structure_cells(mesh: PointGrid, structure_dict: dict[int, str]) -> dict if _has_missing_ids(mesh, structure_dict): raise InputMeshError("Mesh has missing ids") - out_dict = {k:None for k in structure_dict.values()} + out_dict = {k: None for k in structure_dict.values()} - for id, k in structure_dict.items(): - submesh = mesh.extract_cells(np.where(mesh['CellEntityIds'] == id)[0]) - subcells = submesh.cells.reshape(-1, submesh.cells[0]+1)[:,1:] - cells = submesh['vtkOriginalPointIds'][subcells] - out_dict[k] = torch.from_numpy(cells).permute(1,0).to(torch.long) - - out_dict = {f'{k}_cell_index':v for k,v in out_dict.items()} + submesh = mesh.extract_cells(np.where(mesh["CellEntityIds"] == id)[0]) + subcells = submesh.cells.reshape(-1, submesh.cells[0] + 1)[:, 1:] + cells = submesh["vtkOriginalPointIds"][subcells] + out_dict[k] = torch.from_numpy(cells).permute(1, 0).to(torch.long) + + out_dict = {f"{k}_cell_index": v for k, v in out_dict.items()} return out_dict -def _has_missing_ids(mesh: PointGrid, structure_dict: dict[int,str]) -> bool: - ids = np.unique(mesh['CellEntityIds']) +def _has_missing_ids(mesh: PointGrid, structure_dict: dict[int, str]) -> bool: + ids = np.unique(mesh["CellEntityIds"]) missing_ids = set(structure_dict.keys()) - set(ids.tolist()) - - return len(missing_ids) != 0 + return len(missing_ids) != 0 -def set_attrs(data:Data, attrs:dict[str, torch.Tensor]) -> None: +def set_attrs(data: Data, attrs: dict[str, torch.Tensor]) -> None: """ A helper function to set multiple attributes of a PyG Data object with keys and values from a dictionary. Args: data (torch_geometric.data.Data): Data object to be modified - attrs (Dict[str, torch.Tensor]): a dictionary of attribute names and values to be set in the Data object + attrs (Dict[str, torch.Tensor]): a dictionary of attribute names and values to be set in the Data object Returns: None """ - for k,v in attrs.items(): + for k, v in attrs.items(): setattr(data, k, v) def get_edges_tet(mesh: PointGrid) -> torch.Tensor: """ - A function to get the edge index for training from a tetrahedral pyvista mesh + A function to get the edge index for training from a tetrahedral pyvista mesh Args: mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. @@ -134,14 +137,13 @@ def get_edges_tet(mesh: PointGrid) -> torch.Tensor: Returns: edges (torch.Tensor): A tensor of shape [2,M] where M is the number of edges and the values are the node ids """ - edges = get_structure_edges(mesh, {0:'vol'})['vol_edge_index'] - edges = to_undirected(edges) + edges = get_structure_edges(mesh, {0: "vol"})["vol_edge_index"] return edges def get_edges_surf(mesh: PointGrid) -> torch.Tensor: """ - A function to get the edge index for training from a pyvista surface mesh + A function to get the edge index for training from a pyvista surface mesh Args: mesh (pyvista.core.pointset.PointGrid): A pyvista mesh object. @@ -149,11 +151,13 @@ def get_edges_surf(mesh: PointGrid) -> torch.Tensor: Returns: edges (torch.Tensor): A tensor of shape [2,M] where M is the number of edges and the values are the node ids """ - edges = mesh.extract_all_edges().lines.reshape(-1, 3)[:, 1:] + edges = mesh.extract_all_edges().lines.reshape(-1, 3)[:, 1:] edges = torch.Tensor(edges).T.long() - return edges + edges = to_undirected(edges) + return edges -def get_node_features(mesh:PointGrid, feature_names: list[str]) -> torch.Tensor: + +def get_node_features(mesh: PointGrid, feature_names: list[str]) -> torch.Tensor: """ Extracts the node features from a pyvista mesh object based on the feature names provided. @@ -164,12 +168,15 @@ def get_node_features(mesh:PointGrid, feature_names: list[str]) -> torch.Tensor: Returns: features (torch.Tensor): A tensor of shape [N,C] where N is the number of nodes and C is len(feature_names). """ - features = torch.from_numpy(np.array([mesh.point_data[name] for name in feature_names]).T) + features = torch.from_numpy( + np.array([mesh.point_data[name] for name in feature_names]).T + ) return features - -def make_padded_batch(x: torch.Tensor, batch: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: +def make_padded_batch( + x: torch.Tensor, batch: torch.Tensor +) -> tuple[torch.Tensor, torch.Tensor]: """ A helper function to pad a batch of data to the same size. @@ -212,6 +219,7 @@ def make_padded_batch(x: torch.Tensor, batch: torch.Tensor) -> tuple[torch.Tenso mask = torch.arange(padded_x.size(1))[None, :] < lengths[:, None] return padded_x, mask + def compute_edge_lengths(points: torch.Tensor, edges: torch.Tensor) -> torch.Tensor: """ Computes the squared Euclidean distance for each edge in a mesh. @@ -231,7 +239,9 @@ def compute_edge_lengths(points: torch.Tensor, edges: torch.Tensor) -> torch.Ten lengths. """ coords = points[edges] + print(coords, coords.shape) distances = torch.linalg.norm(coords[0] - coords[1], dim=-1) + print(distances.shape) return distances @@ -262,14 +272,16 @@ def cluster_pool(mesh: Data) -> Data: - Pooling is performed using torch_geometric.nn.avg_pool, so node features in each cluster are averaged. """ - distances = compute_edge_lengths(mesh.x, mesh.edge_index).sum(-1) - print(distances) + distances = compute_edge_lengths(mesh.x, mesh.edge_index) weights = 1 / (distances + 1e-8) clusters = gnn.graclus(mesh.edge_index, weights, mesh.x.shape[0]) pooled_mesh = gnn.avg_pool(clusters, mesh) return pooled_mesh -def rasterize(points: torch.Tensor, im_shape: list[int], vox_sizes: list[float]) -> torch.Tensor: + +def rasterize( + points: torch.Tensor, im_shape: list[int], vox_sizes: list[float] +) -> torch.Tensor: """ Computes the squared Euclidean distance between voxel centroids in a grid to a pointcloud @@ -277,23 +289,26 @@ def rasterize(points: torch.Tensor, im_shape: list[int], vox_sizes: list[float]) points (torch.Tensor): Node coordinate tensor of shape (N, D) where N is the number of nodes and D is the spatial dimensionality (e.g. 3 for 3D meshes). - im_shape (torch.Tensor): A list of dim sizes for the image/mask corresponding + im_shape (torch.Tensor): A list of dim sizes for the image/mask corresponding to the point cloud. vox_sizes (torch.Tensor): A list of voxel sizes for each dimension Returns: - distances (torch.Tensor): A tensor of shape specified by im_shape where each voxel + distances (torch.Tensor): A tensor of shape specified by im_shape where each voxel is the distance of the voxel centroid to the pointcloud. """ - im_coords = [torch.arange(size/2, n , size) for n, size in zip(im_shape, vox_sizes)] - grids = torch.meshgrid(*im_coords, indexing='ij') # three [128,128,128] tensors + im_coords = [ + torch.arange(size / 2, n, size) for n, size in zip(im_shape, vox_sizes) + ] + grids = torch.meshgrid(*im_coords, indexing="ij") # three [128,128,128] tensors coord_tensor = torch.stack(grids, dim=-1).reshape(-1, 3) nns = gnn.pool.knn(x=points, y=coord_tensor, k=1) dists = torch.linalg.norm(coord_tensor - points[nns[1]], dim=-1) return dists.reshape(im_shape) -def hard_threshold(y: torch.Tensor, threshold: float=1.0) -> torch.Tensor: + +def hard_threshold(y: torch.Tensor, threshold: float = 1.0) -> torch.Tensor: """ Thresholds a Tensor y according to a specified float threshold. Every value less than the threshold is assigned 1.0 and values greater are assigned 0.0 @@ -301,12 +316,13 @@ def hard_threshold(y: torch.Tensor, threshold: float=1.0) -> torch.Tensor: Args: y (torch.Tensor): tensor containing the raw values threshold (float): threshold value - + Returns: y_thresh (torch.Tensor): thresholded input tensor """ - return (y>> obj = MyClass(param1=10, param2="test") >>> obj.method() - 42 """ - + 42""" diff --git a/src/im2sim/data/transforms.py b/src/im2sim/data/transforms.py index 28b2f95..f81b78a 100644 --- a/src/im2sim/data/transforms.py +++ b/src/im2sim/data/transforms.py @@ -2,50 +2,152 @@ from .ops import * -def transform_from_fn(fn, keys,attr=None,channels=None,per_channel=False,channel_dim=-1,name=None): +def transform_from_fn( + fn, keys, attr=None, channels=None, per_channel=False, channel_dim=-1, name=None +): class FnOp(Operation): - def forward(self, x): return fn(x) - return Transform(op=FnOp(), keys=keys,attr=attr,channels=channels,per_channel=per_channel,channel_dim=channel_dim,name=name) + return Transform( + op=FnOp(), + keys=keys, + attr=attr, + channels=channels, + per_channel=per_channel, + channel_dim=channel_dim, + name=name, + ) # ------------------------------------------------------------------------------------ # SIMPLE TRANSFORM FACTORIES # ------------------------------------------------------------------------------------ - -def Norm(keys,attr=None,channels=None,per_channel=False,channel_dim=-1,name=None): - return Transform(op=NormOp(), keys=keys,attr=attr,channels=channels,per_channel=per_channel,channel_dim=channel_dim,name=name) -def RangeNorm(llim, hlim, keys,attr=None,channels=None,per_channel=False,channel_dim=-1,name=None): - return Transform(op=RangeNormOp(a=llim, b=hlim), keys=keys,attr=attr,channels=channels,per_channel=per_channel,channel_dim=channel_dim,name=name) +def Norm(keys, attr=None, channels=None, per_channel=False, channel_dim=-1, name=None): + return Transform( + op=NormOp(), + keys=keys, + attr=attr, + channels=channels, + per_channel=per_channel, + channel_dim=channel_dim, + name=name, + ) + + +def RangeNorm( + llim, + hlim, + keys, + attr=None, + channels=None, + per_channel=False, + channel_dim=-1, + name=None, +): + return Transform( + op=RangeNormOp(a=llim, b=hlim), + keys=keys, + attr=attr, + channels=channels, + per_channel=per_channel, + channel_dim=channel_dim, + name=name, + ) + + +def ZScore( + keys, attr=None, channels=None, per_channel=False, channel_dim=-1, name=None +): + return Transform( + op=ZScoreOp(), + keys=keys, + attr=attr, + channels=channels, + per_channel=per_channel, + channel_dim=channel_dim, + name=name, + ) + -def ZScore(keys,attr=None,channels=None,per_channel=False,channel_dim=-1,name=None): - return Transform(op=ZScoreOp(), keys=keys,attr=attr,channels=channels,per_channel=per_channel,channel_dim=channel_dim,name=name) - # ------------------------------------------------------------------------------------ # INVERTIBLE TRANSFORM FACTORIES # ------------------------------------------------------------------------------------ -def PowerScaling(exp,preserve_sign, keys,attr=None,channels=None,per_channel=False,channel_dim=-1,name=None): - return Transform(op=PowerScaleOp(exp=exp,preserve_sign=preserve_sign), keys=keys,attr=attr,channels=channels,per_channel=per_channel,channel_dim=channel_dim,name=name) + +def PowerScaling( + exp, + preserve_sign, + keys, + attr=None, + channels=None, + per_channel=False, + channel_dim=-1, + name=None, +): + return Transform( + op=PowerScaleOp(exp=exp, preserve_sign=preserve_sign), + keys=keys, + attr=attr, + channels=channels, + per_channel=per_channel, + channel_dim=channel_dim, + name=name, + ) + # ------------------------------------------------------------------------------------ # FITTABLE TRANSFORM FACTORIES # ------------------------------------------------------------------------------------ -def FitNorm(keys,attr=None,channels=None,per_channel=False,channel_dim=-1,name=None): - return Transform(op=FitNormOp(), keys=keys,attr=attr,channels=channels,per_channel=per_channel,channel_dim=channel_dim,name=name) - -def FitRangeNorm(llim, hlim, keys,attr=None,channels=None,per_channel=False,channel_dim=-1,name=None): - return Transform(op=FitRangeNormOp(a=llim, b=hlim), keys=keys,attr=attr,channels=channels,per_channel=per_channel,channel_dim=channel_dim,name=name) - -def FitZScore(keys,attr=None,channels=None,per_channel=False,channel_dim=-1,name=None): - return Transform(op=FitZScoreOp(), keys=keys,attr=attr,channels=channels,per_channel=per_channel,channel_dim=channel_dim,name=name) - - - +def FitNorm( + keys, attr=None, channels=None, per_channel=False, channel_dim=-1, name=None +): + return Transform( + op=FitNormOp(), + keys=keys, + attr=attr, + channels=channels, + per_channel=per_channel, + channel_dim=channel_dim, + name=name, + ) + + +def FitRangeNorm( + llim, + hlim, + keys, + attr=None, + channels=None, + per_channel=False, + channel_dim=-1, + name=None, +): + return Transform( + op=FitRangeNormOp(a=llim, b=hlim), + keys=keys, + attr=attr, + channels=channels, + per_channel=per_channel, + channel_dim=channel_dim, + name=name, + ) + + +def FitZScore( + keys, attr=None, channels=None, per_channel=False, channel_dim=-1, name=None +): + return Transform( + op=FitZScoreOp(), + keys=keys, + attr=attr, + channels=channels, + per_channel=per_channel, + channel_dim=channel_dim, + name=name, + ) diff --git a/src/im2sim/layers/__init__.py b/src/im2sim/layers/__init__.py index df6b0fb..8030061 100644 --- a/src/im2sim/layers/__init__.py +++ b/src/im2sim/layers/__init__.py @@ -3,4 +3,3 @@ from .layer_util import * from .meshgraphnets import * from .projections import * - diff --git a/src/im2sim/layers/graph_blocks.py b/src/im2sim/layers/graph_blocks.py index 629fb79..f102f3b 100644 --- a/src/im2sim/layers/graph_blocks.py +++ b/src/im2sim/layers/graph_blocks.py @@ -8,11 +8,11 @@ logger = logging.getLogger(__name__) -@register_pyg_layer(name='defaultnorm') +@register_pyg_layer(name="defaultnorm") class DefaultGraphNorm(torch.nn.Module): """ - The default normalisation for im2sim graph blocks. - Uses torch.nn.InstanceNorm2d applied to graph data, but all channels are normalised together. + The default normalisation for im2sim graph blocks. + Uses torch.nn.InstanceNorm2d applied to graph data, but all channels are normalised together. """ def __init__(self, in_channels, affine): @@ -24,14 +24,15 @@ def forward(self, x, batch): shape = x.shape for b in torch.unique(batch): - x[batch==b] = self.norm(x[batch==b].unsqueeze(0).unsqueeze(0)).reshape(shape) + x[batch == b] = self.norm(x[batch == b].unsqueeze(0).unsqueeze(0)).reshape( + shape + ) return x - class GraphConvBlock(nn.Module): """ - A convolutional block for graph data + A convolutional block for graph data Args: in_channels (int): The number of channels in the input to the layer. @@ -44,37 +45,44 @@ class GraphConvBlock(nn.Module): Returns: A `torch.nn.Module` object. - + """ - def __init__(self, - in_channels, - filters, - depth=1, - conv_type='GATConv', - conv_kwargs=None, - activation='ReLU', - norm_type='default', - norm_kwargs=None): - super().__init__() + + def __init__( + self, + in_channels, + filters, + depth=1, + conv_type="GATConv", + conv_kwargs=None, + activation="ReLU", + norm_type="defaultnorm", + norm_kwargs=None, + ): + super().__init__() conv = get_graph_layer(conv_type, conv_kwargs) - self.convs = nn.ModuleList([ - conv(in_channels if i==0 else filters, filters, **conv_kwargs) - for i in range(depth) - ]) + self.convs = nn.ModuleList( + [ + conv(in_channels if i == 0 else filters, filters, **conv_kwargs) + for i in range(depth) + ] + ) if norm_kwargs == None: - norm_kwargs = {'in_channels':filters, 'affine':True} + norm_kwargs = {"in_channels": filters, "affine": True} - self.norms = nn.ModuleList([ - get_graph_layer(norm_type, conv_kwargs) if norm_type else nn.Identity() - for _ in range(depth) - ]) + self.norms = nn.ModuleList( + [ + get_graph_layer(norm_type, conv_kwargs) if norm_type else nn.Identity() + for _ in range(depth) + ] + ) self.act = ACTIVATIONS[activation]() - def forward(self,in_graph): + def forward(self, in_graph): out_graph = deepcopy(in_graph) for conv, norm in zip(self.convs, self.norms): out_graph = conv(out_graph) @@ -82,9 +90,10 @@ def forward(self,in_graph): out_graph.x = self.act(out_graph.x) return out_graph + class GraphConvResBlock(nn.Module): """ - A convolutional block for graph data + A convolutional block for graph data Args: in_channels (int): The number of channels in the input to the layer. @@ -97,48 +106,53 @@ class GraphConvResBlock(nn.Module): Returns: A `torch.nn.Module` object. - + """ - def __init__(self, - in_channels, - filters, - depth=3, - conv_type='GATConv', - conv_kwargs=None, - activation='ReLU', - norm_type='InstanceNorm', - norm_kwargs=None): - super().__init__() + + def __init__( + self, + in_channels, + filters, + depth=3, + conv_type="GATConv", + conv_kwargs=None, + activation="ReLU", + norm_type="InstanceNorm", + norm_kwargs=None, + ): + super().__init__() conv = get_graph_layer(conv_type, conv_kwargs) - self.convs = nn.ModuleList([ - conv(in_channels if i==0 else filters, filters, **conv_kwargs) - for i in range(depth) - ]) + self.convs = nn.ModuleList( + [ + conv(in_channels if i == 0 else filters, filters, **conv_kwargs) + for i in range(depth) + ] + ) if norm_kwargs == None: - norm_kwargs = {'in_channels':filters, 'affine':True} + norm_kwargs = {"in_channels": filters, "affine": True} - self.norms = nn.ModuleList([ - get_graph_layer(norm_type, norm_kwargs) if norm_type else nn.Identity() - for _ in range(depth) - ]) + self.norms = nn.ModuleList( + [ + get_graph_layer(norm_type, norm_kwargs) if norm_type else nn.Identity() + for _ in range(depth) + ] + ) self.act = ACTIVATIONS[activation]() - def forward(self, in_graph): graph = deepcopy(in_graph) - for i, (conv,norm) in enumerate(zip(self.convs, self.norms)): - + for i, (conv, norm) in enumerate(zip(self.convs, self.norms)): graph = norm(conv(graph)) - if i==0: + if i == 0: x1 = deepcopy(graph.x) - elif i 0: # self.align=True # self.align_conv = gnn.Sequential('x, edge_index, batch',[ # (GraphConvResBlock(in_channels=out_channels*2 if i==0 else filters, -# filters=filters, +# filters=filters, # **conv_config), 'x, edge_index -> x') # for i in range(n_align_blocks) # ]) @@ -280,23 +291,23 @@ def forward(self, in_graph, prev_results, encoder_projection): # self.deform_conv = gnn.Sequential('x, edge_index, batch',[ -# (GraphConvResBlock(in_channels=out_channels+filters if i==0 else filters, -# filters=filters, +# (GraphConvResBlock(in_channels=out_channels+filters if i==0 else filters, +# filters=filters, # **conv_config), 'x, edge_index -> x') # for i in range(n_deform_blocks) # ]) -# self.convert_conv = GraphConvBlock(in_channels=filters, -# filters=out_channels, -# depth=1, +# self.convert_conv = GraphConvBlock(in_channels=filters, +# filters=out_channels, +# depth=1, # conv_type=conv_type, # conv_kwargs=conv_kwargs, -# activation=out_activation, +# activation=out_activation, # norm_type=None) - + # self.projection_args = {"domain_size":domain_size, "batch_ops":batched_ops} - -# # INFO: removed graph features for now may want to add back + +# # INFO: removed graph features for now may want to add back # def forward(self,image_features,prev_deformation,template_x,edge_index,batch): # # Move all zero points after unpooling @@ -315,4 +326,3 @@ def forward(self, in_graph, prev_results, encoder_projection): # x = self.deform_conv(x, edge_index) # x = self.convert_conv(x, edge_index) # return x+prev_deformation - \ No newline at end of file diff --git a/src/im2sim/layers/image_blocks.py b/src/im2sim/layers/image_blocks.py index 7dfca7e..791aff6 100644 --- a/src/im2sim/layers/image_blocks.py +++ b/src/im2sim/layers/image_blocks.py @@ -7,6 +7,7 @@ logger = logging.getLogger(__name__) + class ImageConvBlock(nn.Module): """ A convolutional block for image data @@ -23,33 +24,50 @@ class ImageConvBlock(nn.Module): Returns: A `torch.nn.Module` object. - + """ - def __init__(self, - in_channels, - filters=32, - kernel_size=3, - depth=1, - rank=3, - activation='ReLU', - norm_type=None, - dropout_rate=None): - super().__init__() - - conv = TORCH_LAYERS[f'Conv{rank}d'] - self.convs = nn.ModuleList([ - conv(in_channels if i==0 else filters, filters, kernel_size, padding=kernel_size//2) - for i in range(depth) - ]) - - self.norms = nn.ModuleList([ - TORCH_LAYERS[f'{norm_type}{rank}d'](filters, affine=True) if norm_type else nn.Identity() - for _ in range(depth) - ]) - self.drop = TORCH_LAYERS(f'Dropout{rank}d')(p=dropout_rate) if dropout_rate else nn.Identity() + + def __init__( + self, + in_channels, + filters=32, + kernel_size=3, + depth=1, + rank=3, + activation="ReLU", + norm_type=None, + dropout_rate=None, + ): + super().__init__() + + conv = TORCH_LAYERS[f"Conv{rank}d"] + self.convs = nn.ModuleList( + [ + conv( + in_channels if i == 0 else filters, + filters, + kernel_size, + padding=kernel_size // 2, + ) + for i in range(depth) + ] + ) + + self.norms = nn.ModuleList( + [ + TORCH_LAYERS[f"{norm_type}{rank}d"](filters, affine=True) + if norm_type + else nn.Identity() + for _ in range(depth) + ] + ) + self.drop = ( + TORCH_LAYERS(f"Dropout{rank}d")(p=dropout_rate) + if dropout_rate + else nn.Identity() + ) self.act = ACTIVATIONS[activation] - def forward(self, x): """ @@ -65,6 +83,7 @@ def forward(self, x): x = self.act(norm(conv(x))) return self.drop(x) + class ImageConvResBlock(nn.Module): """ A convolutional residual block for image data @@ -81,46 +100,54 @@ class ImageConvResBlock(nn.Module): Returns: A `torch.nn.Module` object. - + """ - def __init__(self, - in_channels, - filters=32, - kernel_size=3, - depth=3, - rank=3, - activation='relu', - norm_type=None, - dropout_rate=None): - super().__init__() - - - self.initial_conv = ImageConvBlock(in_channels=in_channels, - filters=filters, - kernel_size=kernel_size, - rank=rank, - activation='linear', - norm_type=norm_type, - depth=1, - dropout_rate=None) - - self.main_conv = ImageConvBlock(in_channels=filters, - filters=filters, - kernel_size=kernel_size, - rank=rank, - activation=activation, - norm_type=norm_type, - depth=depth-2, - dropout_rate=dropout_rate) - - self.out_conv = ImageConvBlock(in_channels=filters, - filters=filters, - kernel_size=kernel_size, - rank=rank, - activation='linear', - norm_type=norm_type, - depth=1, - dropout_rate=None) + + def __init__( + self, + in_channels, + filters=32, + kernel_size=3, + depth=3, + rank=3, + activation="relu", + norm_type=None, + dropout_rate=None, + ): + super().__init__() + + self.initial_conv = ImageConvBlock( + in_channels=in_channels, + filters=filters, + kernel_size=kernel_size, + rank=rank, + activation="linear", + norm_type=norm_type, + depth=1, + dropout_rate=None, + ) + + self.main_conv = ImageConvBlock( + in_channels=filters, + filters=filters, + kernel_size=kernel_size, + rank=rank, + activation=activation, + norm_type=norm_type, + depth=depth - 2, + dropout_rate=dropout_rate, + ) + + self.out_conv = ImageConvBlock( + in_channels=filters, + filters=filters, + kernel_size=kernel_size, + rank=rank, + activation="linear", + norm_type=norm_type, + depth=1, + dropout_rate=None, + ) # self.drop = get_image_layer('Dropout', rank)(p=dropout_rate) if dropout_rate else nn.Identity() self.out_act = ACTIVATIONS[activation] @@ -133,11 +160,12 @@ def forward(self, x): torch.Tensor: Output feature maps [out_channels, ...] """ x1 = self.initial_conv(x) - x = self.main_conv(x1) + x = self.main_conv(x1) # x = self.drop(x) x = self.out_act(self.out_conv(x) + x1) return x - + + class ImageResEncoder(nn.Module): """ A CNN encoder for images. Structured like the encoder of a ResUNet. @@ -157,43 +185,52 @@ class ImageResEncoder(nn.Module): A `torch.nn.Module` object. """ - def __init__(self, - in_channels, - filters=(16,32,64,128,256), - kernel_size=3, - res_depth=3, - res_blocks_per_level=2, - rank=3, - norm_type=None, - pool_type='MaxPool', - pool_size=2, - activation='ReLU', - dropout_rate=None): + def __init__( + self, + in_channels, + filters=(16, 32, 64, 128, 256), + kernel_size=3, + res_depth=3, + res_blocks_per_level=2, + rank=3, + norm_type=None, + pool_type="MaxPool", + pool_size=2, + activation="ReLU", + dropout_rate=None, + ): super().__init__() - + n_levels = len(filters) in_channels = [in_channels, *filters] - self.conv_blocks = nn.ModuleList([ - nn.ModuleList([ - ImageConvResBlock(in_channels=in_channels[i] if j==0 else in_channels[i+1], - filters=filters[i], - kernel_size=kernel_size, - depth=res_depth, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate) - for j in range(res_blocks_per_level) - ]) - for i in range(n_levels) - ]) - pool = TORCH_LAYERS[f'{pool_type}{rank}d'] - self.pools = nn.ModuleList([ - pool(pool_size) if i>0 else nn.Identity() - for i in range(n_levels) - ]) - - def forward(self,x): + self.conv_blocks = nn.ModuleList( + [ + nn.ModuleList( + [ + ImageConvResBlock( + in_channels=in_channels[i] + if j == 0 + else in_channels[i + 1], + filters=filters[i], + kernel_size=kernel_size, + depth=res_depth, + rank=rank, + activation=activation, + norm_type=norm_type, + dropout_rate=dropout_rate, + ) + for j in range(res_blocks_per_level) + ] + ) + for i in range(n_levels) + ] + ) + pool = TORCH_LAYERS[f"{pool_type}{rank}d"] + self.pools = nn.ModuleList( + [pool(pool_size) if i > 0 else nn.Identity() for i in range(n_levels)] + ) + + def forward(self, x): """ Args: x (torch.Tensor): Input image [in_channels, ...] @@ -205,12 +242,12 @@ def forward(self,x): outputs = [] for pool, convs in zip(self.pools, self.conv_blocks): x = pool(x) - for conv in convs: + for conv in convs: x = conv(x) logger.debug("Conv output shape:%s", x.shape) outputs.append(x) return outputs - + class ImageEncoder(nn.Module): """ @@ -230,38 +267,43 @@ class ImageEncoder(nn.Module): A `torch.nn.Module` object. """ - def __init__(self, - in_channels, - filters=(16,32,64,128,256), - kernel_size=3, - conv_blocks_per_level=1, - rank=3, - norm_type=None, - pool_type='MaxPool', - pool_size=2, - activation='ReLU', - dropout_rate=None): + def __init__( + self, + in_channels, + filters=(16, 32, 64, 128, 256), + kernel_size=3, + conv_blocks_per_level=1, + rank=3, + norm_type=None, + pool_type="MaxPool", + pool_size=2, + activation="ReLU", + dropout_rate=None, + ): super().__init__() - + n_levels = len(filters) - self.conv_blocks = nn.ModuleList([ - ImageConvBlock(in_channels=in_channels if i==0 else filters[i-1], - filters=filters[i], - kernel_size=kernel_size, - depth=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate) - for i in range(n_levels) - ]) - pool = TORCH_LAYERS[f'{pool_type}{rank}d'] - self.maxpools = nn.ModuleList([ - pool(pool_size) if i>0 else nn.Identity() - for i in range(n_levels) - ]) - - def forward(self,x): + self.conv_blocks = nn.ModuleList( + [ + ImageConvBlock( + in_channels=in_channels if i == 0 else filters[i - 1], + filters=filters[i], + kernel_size=kernel_size, + depth=conv_blocks_per_level, + rank=rank, + activation=activation, + norm_type=norm_type, + dropout_rate=dropout_rate, + ) + for i in range(n_levels) + ] + ) + pool = TORCH_LAYERS[f"{pool_type}{rank}d"] + self.maxpools = nn.ModuleList( + [pool(pool_size) if i > 0 else nn.Identity() for i in range(n_levels)] + ) + + def forward(self, x): """ Args: x (torch.Tensor): Input image [in_channels, ...] @@ -276,9 +318,6 @@ def forward(self,x): return outputs - - - class ImageDecoder(nn.Module): """ CNN decoder for images. Mirrors ImageEncoder like a UNet decoder. @@ -295,16 +334,18 @@ class ImageDecoder(nn.Module): skip (bool): Use skip connections. """ - def __init__(self, - filters=(16,32,64,128,256), - kernel_size=3, - conv_blocks_per_level=1, - rank=3, - upsample_type="Upsample", - activation="ReLU", - norm_type=None, - dropout_rate=None, - skip=True): + def __init__( + self, + filters=(16, 32, 64, 128, 256), + kernel_size=3, + conv_blocks_per_level=1, + rank=3, + upsample_type="Upsample", + activation="ReLU", + norm_type=None, + dropout_rate=None, + skip=True, + ): super().__init__() self.skip = skip @@ -312,53 +353,61 @@ def __init__(self, rev_filters = filters[::-1] - if upsample_type.lower() == 'upsample': + if upsample_type.lower() == "upsample": # if rank == 4: # self.ups = nn.ModuleList([ # Upsample4d(scale_factor=(1, 2, 2, 2)) # for _ in range(n_levels - 1) # ]) - + # else: - self.ups = nn.ModuleList([ - nn.Upsample(scale_factor=2, mode='trilinear' if rank==3 else 'bilinear', align_corners=True) - for _ in range(n_levels - 1) - ]) + self.ups = nn.ModuleList( + [ + nn.Upsample( + scale_factor=2, + mode="trilinear" if rank == 3 else "bilinear", + align_corners=True, + ) + for _ in range(n_levels - 1) + ] + ) else: - up_layer = TORCH_LAYERS[f'{upsample_type}{rank}d'] - self.ups = nn.ModuleList([ - up_layer(rev_filters[i], rev_filters[i+1], kernel_size=2, stride=2) - for i in range(n_levels - 1) - ]) - - - self.conv_blocks = nn.ModuleList([ - ImageConvBlock( - in_channels=( - rev_filters[i] + rev_filters[i+1] - if skip else rev_filters[i] - ), - filters=rev_filters[i+1], - kernel_size=kernel_size, - depth=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate + up_layer = TORCH_LAYERS[f"{upsample_type}{rank}d"] + self.ups = nn.ModuleList( + [ + up_layer( + rev_filters[i], rev_filters[i + 1], kernel_size=2, stride=2 + ) + for i in range(n_levels - 1) + ] ) - for i in range(n_levels - 1) - ]) + self.conv_blocks = nn.ModuleList( + [ + ImageConvBlock( + in_channels=( + rev_filters[i] + rev_filters[i + 1] if skip else rev_filters[i] + ), + filters=rev_filters[i + 1], + kernel_size=kernel_size, + depth=conv_blocks_per_level, + rank=rank, + activation=activation, + norm_type=norm_type, + dropout_rate=dropout_rate, + ) + for i in range(n_levels - 1) + ] + ) def _match_size(self, x, skip): if x.shape[2:] != skip.shape[2:]: # center crop skip to x diff = [s - t for s, t in zip(skip.shape[2:], x.shape[2:])] - slices = [slice(d//2, d//2 + t) for d, t in zip(diff, x.shape[2:])] + slices = [slice(d // 2, d // 2 + t) for d, t in zip(diff, x.shape[2:])] skip = skip[(..., *slices)] return skip - def forward(self, encoder_outputs): """ Args: @@ -376,7 +425,6 @@ def forward(self, encoder_outputs): # Traverse decoder levels for i, (up, conv) in enumerate(zip(self.ups, self.conv_blocks)): - x = up(x) if self.skip: @@ -389,10 +437,6 @@ def forward(self, encoder_outputs): return x - - - - # import torch # import torch.nn as nn @@ -462,7 +506,7 @@ def forward(self, encoder_outputs): # out = self.act(out) # return out - + # class Encoder3D(nn.Module): # def __init__(self, in_channels): @@ -506,4 +550,4 @@ def forward(self, encoder_outputs): # X5 = self.block5_2(X5) # X5p = self.pool(X5) -# return [X1, X2, X3, X4, X5] \ No newline at end of file +# return [X1, X2, X3, X4, X5] diff --git a/src/im2sim/layers/layer_util.py b/src/im2sim/layers/layer_util.py index 7b907fd..3f89c6f 100644 --- a/src/im2sim/layers/layer_util.py +++ b/src/im2sim/layers/layer_util.py @@ -15,11 +15,11 @@ def get_graph_layer(name, kwargs): return PyG_Wrapper(PYG_LAYERS[name](**kwargs)) - - def make_registry(lib: Any, regex: Callable): - - registry = NormalizedDict({name: getattr(lib, name) for name in dir(lib) if re.search(regex, name)}) + + registry = NormalizedDict( + {name: getattr(lib, name) for name in dir(lib) if re.search(regex, name)} + ) def register(cls=None, *, name=None): def decorator(obj): @@ -34,33 +34,23 @@ def decorator(obj): def normalize_key(key: str) -> str: return key.replace(" ", "").replace("_", "").lower() + class NormalizedDict: def __init__(self, data: dict): self._data = {} for key, val in data.items(): - self.__setitem__(key,val) - + self.__setitem__(key, val) + def __setitem__(self, key, value): self._data[normalize_key(key)] = value def __getitem__(self, key): return self._data[normalize_key(key)] - + def __str__(self): return self._data.__str__() - -# IMAGE_CONVS, register_image_conv = make_registry(torch.nn, 'Conv') -# IMAGE_POOLS, register_image_pool = make_registry(torch.nn, 'Pool') -# IMAGE_NORMS, register_image_norm = make_registry(torch.nn, 'Norm') -# DROPOUT, register_dropout = make_registry(torch.nn, 'Dropout') - -# GRAPH_CONVS, register_graph_conv = make_registry(gnn, 'Conv') -# GRAPH_POOLS, register_graph_pool = make_registry(gnn, 'Pool') -# GRAPH_NORMS, register_graph_norm = make_registry(gnn, 'Norm') - - activation_pattern = re.compile( r"(ReLU|ELU|LeakyReLU|PReLU|RReLU|GELU|SiLU|" r"Sigmoid|Tanh|Softmax|Softplus|SELU|CELU|" @@ -72,135 +62,19 @@ def __str__(self): layer_pattern = r"(Conv|Pool|Norm|UpSample|PixelShuffle|Dropout)" - TORCH_LAYERS, register_torch_layer = make_registry(torch.nn, layer_pattern) PYG_LAYERS, register_pyg_layer = make_registry(gnn, layer_pattern) - -# def get_image_layer(name, rank): -# """Get an N-D layer object. - -# Args: -# name: A `str`. The name of the requested layer. -# rank: An `int`. The rank of the requested layer. - -# Returns: -# A `torch.nn.Module` object. - -# Raises: -# ValueError: If the requested layer is unknown. -# """ -# layer_name = f"{name}{rank}d" -# try: -# return _IMAGE_LAYERS(layer_name) -# except KeyError as err: -# raise ValueError( -# f"Could not find a layer with name {name} and rank {rank}") from err - -# def get_torch_layer(layer_name): -# return getattr(torch.nn, layer_name) - - -# def get_graph_layer(name): -# """Get an graph layer object. - -# Args: -# name: A `str`. The name of the requested layer. - -# Returns: -# A `torch.nn.Module` object. - -# Raises: -# ValueError: If the requested layer is unknown. -# """ -# try: -# return _GRAPH_LAYERS[name] -# except KeyError as err: -# raise ValueError( -# f"Could not find an activation with name '{name}'") from err - -# def get_default_kwargs(name): -# """Get an graph layer object. - -# Args: -# name: A `str`. The name of the requested layer. - -# Returns: -# A `torch.nn.Module` object. - -# Raises: -# ValueError: If the requested layer is unknown. -# """ -# try: -# return _LAYER_KWARGS[name] -# except KeyError as err: -# raise ValueError( -# f"Could not find an activation with name '{name}'") from err - - -# def get_activation(name): -# """Get an activation object. - -# Args: -# name: A `str`. The name of the requested layer. - -# Returns: -# A `torch.nn.Module` object. - -# Raises: -# ValueError: If the requested activation is unknown. -# """ -# try: -# return _ACTIVATIONS[name] -# except KeyError as err: -# raise ValueError( -# f"Could not find an activation with name '{name}'") from err - - - - - -# pattern = r"\d+d$" -# _IMAGE_LAYERS = {**{name: getattr(torch.nn, name) for name in dir(torch.nn) if re.search(pattern, name)}} - - -# _GRAPH_LAYERS = { -# 'ChebConv': gnn.ChebConv, -# 'GraphConv': gnn.GraphConv, -# 'GCNConv': gnn.GCNConv, -# 'GATConv': gnn.GATConv, -# 'DefaultNorm':DefaultGraphNorm, -# 'InstanceNorm': gnn.InstanceNorm, -# 'LayerNorm': gnn.LayerNorm, -# 'BatchNorm': gnn.BatchNorm, -# 'GraphNorm': gnn.GraphNorm, -# } - -# _LAYER_KWARGS = { -# 'ChebConv': {'K':1}, -# 'GraphConv': {}, -# 'GCNConv': {}, -# 'GATConv': {} -# } -# _ACTIVATIONS = { -# "relu": nn.ReLU, -# "leaky_relu": nn.LeakyReLU, -# "gelu": nn.GELU, -# "sigmoid": nn.Sigmoid, -# "linear": nn.Identity, -# "softmax": nn.Softmax -# } - - def init_weights(m): if isinstance(m, gnn.ChebConv): for lin in m.lins: - torch.nn.init.kaiming_normal_(lin.weight, nonlinearity='leaky_relu') + torch.nn.init.kaiming_normal_(lin.weight, nonlinearity="leaky_relu") lin.weight.data *= 0.1 if lin.bias is not None: torch.nn.init.zeros_(lin.bias) + def standardize_spatial_factors(factors, rank): """ Convert a sequence of spatial factors into a standardized list of tuples. @@ -223,11 +97,11 @@ def standardize_spatial_factors(factors, rank): class PyGParameterError(TypeError): pass + class PyGWrapperError(TypeError): pass - def _match_attrs_to_signature(graph: Data, module: torch.nn.Module) -> list[str]: """This function accepts a PyG data object and a Callable and returns common attributes""" attrs = [] @@ -236,32 +110,30 @@ def _match_attrs_to_signature(graph: Data, module: torch.nn.Module) -> list[str] if hasattr(graph, name): attrs.append(name) elif param.default is inspect.Parameter.empty: - raise PyGParameterError(f"Attribute {name} is missing but is required for {module.__class__.__name__} layers.") + raise PyGParameterError( + f"Attribute {name} is missing but is required for {module.__class__.__name__} layers." + ) return attrs - -# This will not be useable for pooling layers as they have multiple return Tensors. If requiered we will have to add this functionality. +# This will not be useable for pooling layers as they have multiple return Tensors. If requiered we will have to add this functionality. class PyG_Wrapper(torch.nn.Module): - """ - A wrapper for PyG modules to make the forward method accept and return a PyG Data object instead of separated attributes - Needs to be initialised with a pre-initialised PyG Module. + A wrapper for PyG modules to make the forward method accept and return a PyG Data object instead of separated attributes + Needs to be initialised with a pre-initialised PyG Module. """ - + def __init__(self, pyg_module: torch.nn.Module, **kwargs): self.pyg_module = pyg_module def forward(self, graph): attrs = _match_attrs_to_signature(graph, self.pyg_module) - out = self.pyg_module(**{attr:getattr(graph,attr) for attr in attrs}) + out = self.pyg_module(**{attr: getattr(graph, attr) for attr in attrs}) if not isinstance(out, torch.Tensor): - raise PyGWrapperError(f"PyG layers that have multiple outputs like {self.pyg_module.__class__.__name__} are not currently supported. Consider changing PyG layer or writing a custom wrapper") - + raise PyGWrapperError( + f"PyG layers that have multiple outputs like {self.pyg_module.__class__.__name__} are not currently supported. Consider changing PyG layer or writing a custom wrapper" + ) + out_graph = deepcopy(graph) out_graph.x = out return out_graph - - - - \ No newline at end of file diff --git a/src/im2sim/layers/meshgraphnets.py b/src/im2sim/layers/meshgraphnets.py index 4cdbb46..72765c4 100644 --- a/src/im2sim/layers/meshgraphnets.py +++ b/src/im2sim/layers/meshgraphnets.py @@ -7,16 +7,14 @@ class MGNEdgeBlock(nn.Module): + def __init__(self, custom_func: nn.Module): - def __init__(self, custom_func:nn.Module): - super().__init__() self.net = custom_func - def forward(self, graph): - node_attr = graph.x + node_attr = graph.x senders_idx, receivers_idx = graph.edge_index edge_attr = graph.edge_attr @@ -30,15 +28,14 @@ def forward(self, graph): edges_to_collect.append(edge_attr) collected_edges = torch.cat(edges_to_collect, dim=1) - - edge_attr = self.net(collected_edges) # Update + + edge_attr = self.net(collected_edges) # Update return Data(x=node_attr, edge_attr=edge_attr, edge_index=graph.edge_index) class MGNNodeBlock(nn.Module): - - def __init__(self, custom_func:nn.Module): + def __init__(self, custom_func: nn.Module): super().__init__() self.net = custom_func @@ -46,73 +43,68 @@ def forward(self, graph): # Decompose graph edge_attr = graph.edge_attr nodes_to_collect = [] - + _, receivers_idx = graph.edge_index num_nodes = graph.num_nodes - agg_received_edges = scatter_add(edge_attr, receivers_idx, dim=0, dim_size=num_nodes) + agg_received_edges = scatter_add( + edge_attr, receivers_idx, dim=0, dim_size=num_nodes + ) nodes_to_collect.append(graph.x) nodes_to_collect.append(agg_received_edges) collected_nodes = torch.cat(nodes_to_collect, dim=-1) - + x = self.net(collected_nodes) return Data(x=x, edge_attr=edge_attr, edge_index=graph.edge_index) - def build_mlp(in_size, hidden_size, out_size, lay_norm=True): module = nn.Sequential( - nn.Linear(in_size, hidden_size), + nn.Linear(in_size, hidden_size), nn.ReLU(), nn.Linear(hidden_size, hidden_size), nn.ReLU(), nn.Linear(hidden_size, hidden_size), nn.ReLU(), - nn.Linear(hidden_size, out_size) + nn.Linear(hidden_size, out_size), ) - if lay_norm: return nn.Sequential(module, nn.LayerNorm(normalized_shape=out_size)) - return module + if lay_norm: + return nn.Sequential(module, nn.LayerNorm(normalized_shape=out_size)) + return module class MGNEncoder(nn.Module): - - def __init__(self, - edge_input_size=128, - node_input_size=128, - hidden_size=128): + def __init__(self, edge_input_size=128, node_input_size=128, hidden_size=128): super().__init__() self.eb_encoder = build_mlp(edge_input_size, hidden_size, hidden_size) self.nb_encoder = build_mlp(node_input_size, hidden_size, hidden_size) - + def forward(self, graph): node_attr, edge_attr = graph.x, graph.edge_attr node_ = self.nb_encoder(node_attr) edge_ = self.eb_encoder(edge_attr) - - return Data(x=node_, edge_attr=edge_, edge_index=graph.edge_index) + return Data(x=node_, edge_attr=edge_, edge_index=graph.edge_index) class MGNGnBlock(nn.Module): - def __init__(self, hidden_size=128): super().__init__() - eb_input_dim = 3 * hidden_size nb_input_dim = 2 * hidden_size nb_custom_func = build_mlp(nb_input_dim, hidden_size, hidden_size) eb_custom_func = build_mlp(eb_input_dim, hidden_size, hidden_size) - + self.eb_module = MGNEdgeBlock(custom_func=eb_custom_func) self.nb_module = MGNNodeBlock(custom_func=nb_custom_func) def forward(self, graph): - + x = graph.x.clone() edge_attr = graph.edge_attr.clone() @@ -121,43 +113,51 @@ def forward(self, graph): x = x + graph.x edge_attr = edge_attr + graph.edge_attr - - return Data(x=x, edge_attr=edge_attr, edge_index=graph.edge_index) + return Data(x=x, edge_attr=edge_attr, edge_index=graph.edge_index) class MGNDecoder(nn.Module): - def __init__(self, hidden_size=128, output_size=2): super().__init__() - self.decode_module = build_mlp(hidden_size, hidden_size, output_size, lay_norm=False) + self.decode_module = build_mlp( + hidden_size, hidden_size, output_size, lay_norm=False + ) def forward(self, graph): return self.decode_module(graph.x) class MeshGraphNet(nn.Module): - - def __init__(self, message_passing_num, node_input_size, edge_input_size, output_size, hidden_size=128): + def __init__( + self, + message_passing_num, + node_input_size, + edge_input_size, + output_size, + hidden_size=128, + ): super().__init__() - self.encoder = MGNEncoder(edge_input_size=edge_input_size, node_input_size=node_input_size, hidden_size=hidden_size) - + self.encoder = MGNEncoder( + edge_input_size=edge_input_size, + node_input_size=node_input_size, + hidden_size=hidden_size, + ) + processer_list = [] for _ in range(message_passing_num): processer_list.append(MGNGnBlock(hidden_size=hidden_size)) self.processer_list = nn.ModuleList(processer_list) - + self.decoder = MGNDecoder(hidden_size=hidden_size, output_size=output_size) def forward(self, graph): - graph= self.encoder(graph) + graph = self.encoder(graph) for model in self.processer_list: graph = model(graph) decoded = self.decoder(graph) - return decoded - - \ No newline at end of file + return decoded diff --git a/src/im2sim/layers/projections.py b/src/im2sim/layers/projections.py index d683e45..81dc4af 100644 --- a/src/im2sim/layers/projections.py +++ b/src/im2sim/layers/projections.py @@ -8,61 +8,48 @@ logger = logging.getLogger(__name__) -class TrilinearProjection(nn.Module): - def __init__(self, domain_size, batch_ops=True): + +class TrilinearProjection(nn.Module): + def __init__(self, domain_size): super().__init__() self.domain_size = domain_size - self.batch_ops = batch_ops def forward(self, encoder_outputs, graph_coords, batch): - logger.debug("IN PROJECTION") - if logger.isEnabledFor(logging.DEBUG): - logger.debug("Domain size: %s", tuple(self.domain_size)) - logger.debug("Encoder outputs - shape:%s, max:%.2f, min:%.2f", tuple(encoder_outputs.shape), encoder_outputs.max(), encoder_outputs.min()) - logger.debug("Graph coords - shape:%s, max:%.2f, min:%.2f", tuple(graph_coords.shape), graph_coords.max(), graph_coords.min()) - logger.debug("Batch - shape:%s, vals:%s", tuple(batch.shape), tuple(torch.unique(batch))) - if self.batch_ops: - # make a padded batched tensor and get the padding mask - # do the projection - # extract the graph features using the padding mask - if logger.isEnabledFor(logging.DEBUG): - logger.debug("batch: %s", tuple(torch.unique(batch))) - padded_coords, padding_mask = make_padded_batch(graph_coords, batch) - logger.debug("padded_shape: %s, mask_shape:%s", - tuple(padded_coords.shape), tuple(padding_mask.shape)) - grid = torch.stack([(2*padded_coords[...,j]/(d-1)) - 1 - for j,d in enumerate(self.domain_size)], axis=-1) # normalise coords [-1,1] and divide by scale - grid = grid.unsqueeze(-2).unsqueeze(-2) # [B,N,3]->[B,N,1,1,3]] - if logger.isEnabledFor(logging.DEBUG): - logger.debug("grid_shape: %s, grid_vals: %.2f-%.2f, encoder_outputs_shape:%s",tuple(grid.shape), grid.max(), grid.min(), tuple(encoder_outputs.shape)) + projections = [] + + n_dims = graph_coords.shape[1] + for i in torch.unique(batch).to(torch.int16): + + coords = graph_coords[batch == i] + n_nodes = coords.shape[0] + + grid = torch.stack( + [ + (2 * coords[:, j] / (d - 1)) - 1 + for j, d in enumerate(self.domain_size) + ], + axis=-1, + ) # normalise coords [-1,1] and divide by scale + + grid = grid.reshape(1,n_nodes, 1, 1, n_dims) # [N,3]->[1,N,1,1,3] + grid = grid.type_as(encoder_outputs) - projections = F.grid_sample(encoder_outputs,grid, align_corners=True, padding_mode='border').squeeze(-1).squeeze(-1).permute(0,2,1) # [B,C,N,1,1] -> [B,N,C] - logger.debug("projection_shape:%s",tuple(projections.shape)) - projections = projections[padding_mask] - logger.debug("masked_projection_shape:%s",tuple(projections.shape)) - - else: - # loop through the batches and concatenate the projections - projections = [] - for i in torch.unique(batch).to(torch.int16): - coords = graph_coords[batch==i] - logger.debug('coords shape: %s', tuple(coords.shape)) - grid = torch.stack([(2*coords[:,j]/(d-1)) - 1 - for j,d in enumerate(self.domain_size)], axis=-1) # normalise coords [-1,1] and divide by scale - grid = grid.unsqueeze(0).unsqueeze(-2).unsqueeze(-2) # [N,3]->[1,N,1,1,3] - if logger.isEnabledFor(logging.DEBUG): - # logger.debug("i: %s, %s",type(i), i.dtype) - logger.debug("grid_shape: %s, grid_vals: %.2f-%.2f, encoder_outputs_shape:%s",tuple(grid.shape), grid.max(), grid.min(), tuple(encoder_outputs[i].shape)) - grid = grid.type_as(encoder_outputs) - projections.append(F.grid_sample(encoder_outputs[i].unsqueeze(0),grid, align_corners=True, padding_mode='border').squeeze(0).squeeze(-1).squeeze(-1).permute(1,0)) # [1,C,N,1,1] -> [1,C,N] -> [N,C] - logger.debug("projection %d shape:%s",i,tuple(projections[-1].shape)) + + projections.append( + F.grid_sample( + encoder_outputs[i].unsqueeze(0), + grid, + align_corners=True, + padding_mode="border", + ) + .reshape(encoder_outputs.shape[1], -1) + .permute(1, 0) + ) # [1,C,N,1,1] -> [1,C,N] -> [N,C] + projections = torch.cat(projections, dim=0) - logger.debug("final projection shape:%s",tuple(projections.shape)) - if logger.isEnabledFor(logging.DEBUG): - logger.debug("final projection vals: %.2f-%.2f",projections.min(), projections.max()) - return projections + return projections class OGProjection(nn.Module): @@ -79,9 +66,9 @@ def forward(self, image_features, graph_features, batch): d = image_features[i].shape[-1] # Last 3 coords - x = graph_features[batch==i, -3] - y = graph_features[batch==i, -2] - z = graph_features[batch==i, -1] + x = graph_features[batch == i, -3] + y = graph_features[batch == i, -2] + z = graph_features[batch == i, -1] factor = torch.tensor(self.image_dim / h, dtype=x.dtype, device=x.device) @@ -90,12 +77,24 @@ def forward(self, image_features, graph_features, batch): z = z / factor # floor / ceil with clamp - x1 = torch.minimum(torch.floor(x), torch.tensor(h - 1, dtype=x.dtype, device=x.device)) - x2 = torch.minimum(torch.ceil(x), torch.tensor(h - 1, dtype=x.dtype, device=x.device)) - y1 = torch.minimum(torch.floor(y), torch.tensor(w - 1, dtype=x.dtype, device=x.device)) - y2 = torch.minimum(torch.ceil(y), torch.tensor(w - 1, dtype=x.dtype, device=x.device)) - z1 = torch.minimum(torch.floor(z), torch.tensor(d - 1, dtype=x.dtype, device=x.device)) - z2 = torch.minimum(torch.ceil(z), torch.tensor(d - 1, dtype=x.dtype, device=x.device)) + x1 = torch.minimum( + torch.floor(x), torch.tensor(h - 1, dtype=x.dtype, device=x.device) + ) + x2 = torch.minimum( + torch.ceil(x), torch.tensor(h - 1, dtype=x.dtype, device=x.device) + ) + y1 = torch.minimum( + torch.floor(y), torch.tensor(w - 1, dtype=x.dtype, device=x.device) + ) + y2 = torch.minimum( + torch.ceil(y), torch.tensor(w - 1, dtype=x.dtype, device=x.device) + ) + z1 = torch.minimum( + torch.floor(z), torch.tensor(d - 1, dtype=x.dtype, device=x.device) + ) + z2 = torch.minimum( + torch.ceil(z), torch.tensor(d - 1, dtype=x.dtype, device=x.device) + ) # cast to int for indexing x1 = x1.long() @@ -109,30 +108,30 @@ def forward(self, image_features, graph_features, batch): img0 = image_features[i] def gather(img, xi, yi, zi): - return img[...,xi, yi, zi] + return img[..., xi, yi, zi] # --- z1 plane --- q11 = gather(img0, x1, y1, z1) - q21 = gather(img0,x2, y1, z1) - q12 = gather(img0,x1, y2, z1) - q22 = gather(img0,x2, y2, z1) + q21 = gather(img0, x2, y1, z1) + q12 = gather(img0, x1, y2, z1) + q22 = gather(img0, x2, y2, z1) - wx = (x - x1.float()).unsqueeze(0) + wx = (x - x1.float()).unsqueeze(0) wx2 = (x2.float() - x).unsqueeze(0) lerp_x1 = q21 * wx + q11 * wx2 lerp_x2 = q22 * wx + q12 * wx2 - wy = (y - y1.float()).unsqueeze(0) + wy = (y - y1.float()).unsqueeze(0) wy2 = (y2.float() - y).unsqueeze(0) lerp_y1 = lerp_x2 * wy + lerp_x1 * wy2 # --- z2 plane --- - q11 = gather(x1, y1, z2) - q21 = gather(x2, y1, z2) - q12 = gather(x1, y2, z2) - q22 = gather(x2, y2, z2) + q11 = gather(img0, x1, y1, z2) + q21 = gather(img0, x2, y1, z2) + q12 = gather(img0, x1, y2, z2) + q22 = gather(img0, x2, y2, z2) lerp_x1 = q21 * wx + q11 * wx2 lerp_x2 = q22 * wx + q12 * wx2 @@ -140,11 +139,11 @@ def gather(img, xi, yi, zi): lerp_y2 = lerp_x2 * wy + lerp_x1 * wy2 # --- z interpolation --- - wz = (z - z1.float()).unsqueeze(0) + wz = (z - z1.float()).unsqueeze(0) wz2 = (z2.float() - z).unsqueeze(0) lerp_z = lerp_y2 * wz + lerp_y1 * wz2 projections.append(lerp_z) - projections = torch.cat(projections, dim=0).permute(1,0) - return projections \ No newline at end of file + projections = torch.cat(projections, dim=0).permute(1, 0) + return projections diff --git a/src/im2sim/losses/__init__.py b/src/im2sim/losses/__init__.py index d83d63a..1d511bd 100644 --- a/src/im2sim/losses/__init__.py +++ b/src/im2sim/losses/__init__.py @@ -1,3 +1,3 @@ from . import feature, mesh, pointcloud -__all__ = ["feature", "mesh", "pointcloud"] \ No newline at end of file +__all__ = ["feature", "mesh", "pointcloud"] diff --git a/src/im2sim/losses/feature.py b/src/im2sim/losses/feature.py index b5a9385..1fcc494 100644 --- a/src/im2sim/losses/feature.py +++ b/src/im2sim/losses/feature.py @@ -1,4 +1,3 @@ - import logging import torch @@ -9,31 +8,26 @@ # def mse(gr1, gr2): # return torch.mean((gr1.x[...,3:] - gr2.x[...,3:])**2) + def mse(x1, x2): - return torch.mean((x1 - x2)**2) + return torch.mean((x1 - x2) ** 2) -class KnnMSE(torch.nn.Module): +class KnnMSE(torch.nn.Module): def __init__(self, k=3): super().__init__() - self.k = k + self.k = k def forward(self, true_graph, pred_graph): - c1 = true_graph.x[:,:3] - c2 = pred_graph.x[:,:3] - - f1 = true_graph.x[:,3:] - f2 = pred_graph.x[:,3:] + c1 = true_graph.x[:, :3] + c2 = pred_graph.x[:, :3] + + f1 = true_graph.x[:, 3:] + f2 = pred_graph.x[:, 3:] b1 = true_graph.batch b2 = pred_graph.batch - f1_interp = knn_interpolate(f1, c1, c2, b1, b2, k=self.k) return mse(f1_interp, f2) - - - - - diff --git a/src/im2sim/losses/mesh.py b/src/im2sim/losses/mesh.py index b12b5a0..4935aa2 100644 --- a/src/im2sim/losses/mesh.py +++ b/src/im2sim/losses/mesh.py @@ -8,56 +8,57 @@ logger = logging.getLogger(__name__) + def edge_length_deviation_loss(gr1, gr2): - ed1 = _edge_length_deviation(gr1.x[:,:3], gr1.edge_index) - ed2 = _edge_length_deviation(gr2.x[:,:3], gr2.edge_index) - return F.relu(ed2-ed1)**2 - + ed1 = _edge_length_deviation(gr1.x[:, :3], gr1.edge_index) + ed2 = _edge_length_deviation(gr2.x[:, :3], gr2.edge_index) + return F.relu(ed2 - ed1) ** 2 + + def _edge_length_deviation(points, edges): lengths = compute_edge_lengths(points, edges) - dev = lengths.std()/(lengths.mean()+1e-8) + dev = lengths.std() / (lengths.mean() + 1e-8) return dev def _aspect_ratio(x, cells): - tet_vertices = x[cells,:] - vert_ids = list(combinations(range(4),2)) - edge_coords = tet_vertices[vert_ids,:] - distances = torch.linalg.norm(edge_coords[:,0,:,:]-edge_coords[:,1,:,:], dim=-1) - aspect_ratio = distances.max(0).values/distances.mean(0) + tet_vertices = x[cells, :] + vert_ids = list(combinations(range(4), 2)) + edge_coords = tet_vertices[vert_ids, :] + distances = torch.linalg.norm( + edge_coords[:, 0, :, :] - edge_coords[:, 1, :, :], dim=-1 + ) + aspect_ratio = distances.max(0).values / distances.mean(0) return aspect_ratio.mean() + class AspectRatioLoss(torch.nn.Module): - def __init__(self, cell_key): super().__init__() if isinstance(cell_key, str): self.select = lambda obj: getattr(obj, cell_key) else: raise TypeError(f"face_key must be a graph attribute but is {cell_key}") - def forward(self, gr1, gr2): - ar1 = _aspect_ratio(x = gr1.x[:,:3], - cells = self.select(gr1)) - ar2 = _aspect_ratio(x = gr2.x[:,:3], - cells = self.select(gr2)) - return F.relu(ar2-ar1)**2 + ar1 = _aspect_ratio(x=gr1.x[:, :3], cells=self.select(gr1)) + ar2 = _aspect_ratio(x=gr2.x[:, :3], cells=self.select(gr2)) + return F.relu(ar2 - ar1) ** 2 def _face_norm(face_verts): side1 = face_verts[1] - face_verts[0] side2 = face_verts[2] - face_verts[0] - norm_vec = torch.cross(side1,side2, dim=-1) - unit_norm = norm_vec/(torch.norm(norm_vec, dim=-1, keepdim=True)+1e-8) + norm_vec = torch.cross(side1, side2, dim=-1) + unit_norm = norm_vec / (torch.norm(norm_vec, dim=-1, keepdim=True) + 1e-8) return unit_norm -def face_norm_loss(x1,x2,b1,b2,f1,f2): +def face_norm_loss(x1, x2, b1, b2, f1, f2): # x:[N,3], f:[3,M], norm: [3,M,3] - norm1 = _face_norm(x1[f1,:]) - norm2 = _face_norm(x2[f2,:]) + norm1 = _face_norm(x1[f1, :]) + norm2 = _face_norm(x2[f2, :]) batch1 = b1[f1[0]] batch2 = b2[f2[0]] @@ -66,37 +67,36 @@ def face_norm_loss(x1,x2,b1,b2,f1,f2): similarity = torch.Tensor([0.0]).to(norm1.device) for b in torch.unique(b1).tolist(): - mask1 = (batch1 == b) - mask2 = (batch2 == b) + mask1 = batch1 == b + mask2 = batch2 == b consistency += torch.norm(norm2[mask2].std(0)) similarity += torch.norm(norm1[mask1].mean(0) - norm2[mask2].mean(0)) - return consistency + similarity - class FaceNormalLoss(torch.nn.Module): - - def __init__(self, face_key = None): + def __init__(self, face_key=None): super().__init__() if isinstance(face_key, str): self.select = lambda obj: getattr(obj, face_key) else: raise TypeError(f"face_key must be a graph attribute but is {face_key}") - def forward(self, gr1, gr2): faces1 = self.select(gr1) faces2 = self.select(gr2) - loss = face_norm_loss(x1 = gr1.x[:,:3], - x2 = gr2.x[:,:3], - b1 = gr1.batch, - b2 = gr2.batch, - f1 = faces1, - f2 = faces2) + loss = face_norm_loss( + x1=gr1.x[:, :3], + x2=gr2.x[:, :3], + b1=gr1.batch, + b2=gr2.batch, + f1=faces1, + f2=faces2, + ) return loss + def tet_det(x, cells): """Return signed 6*volume per tet (scalar triple product).""" @@ -113,7 +113,6 @@ def tet_det(x, cells): return det - def inversion_loss(x, cells, min_vol=1e-3): det6 = tet_det(x, cells) @@ -124,8 +123,7 @@ def inversion_loss(x, cells, min_vol=1e-3): class InversionLoss(torch.nn.Module): - - def __init__(self, cell_key, min_vol = 1e-3): + def __init__(self, cell_key, min_vol=1e-3): super().__init__() if isinstance(cell_key, str): self.select = lambda obj: getattr(obj, cell_key) @@ -133,10 +131,7 @@ def __init__(self, cell_key, min_vol = 1e-3): raise TypeError(f"face_key must be a graph attribute but is {cell_key}") self.min_vol = min_vol - def forward(self, gr1, gr2): cells = self.select(gr2) - loss = inversion_loss(x = gr2.x[:,:3], - cells = cells, - min_vol=self.min_vol) - return loss \ No newline at end of file + loss = inversion_loss(x=gr2.x[:, :3], cells=cells, min_vol=self.min_vol) + return loss diff --git a/src/im2sim/losses/pointcloud.py b/src/im2sim/losses/pointcloud.py index be95736..1ccd5cb 100644 --- a/src/im2sim/losses/pointcloud.py +++ b/src/im2sim/losses/pointcloud.py @@ -6,31 +6,36 @@ logger = logging.getLogger(__name__) + def _compute_batch_chamfer(y1, y2, b1=None, b2=None): - if b1==None: + if b1 == None: b1 = torch.zeros(y1.shape[0]) - if b2==None: + if b2 == None: b2 = torch.zeros(y2.shape[0]) - logger.debug("shapes - y1:%s, y2:%s, b1:%s, b2%s", - tuple(y1.shape),tuple(y2.shape),tuple(b1.shape),tuple(b2.shape)) + logger.debug( + "shapes - y1:%s, y2:%s, b1:%s, b2%s", + tuple(y1.shape), + tuple(y2.shape), + tuple(b1.shape), + tuple(b2.shape), + ) nns1 = gnn.pool.knn(x=y2, y=y1, batch_x=b2, batch_y=b1, k=1) logger.debug("nn shape: %s", nns1.shape) if nns1.shape[-1] == 0: - return (y2*0).sum() - + return (y2 * 0).sum() + d1 = torch.linalg.norm(y1 - y2[nns1[1]], dim=-1).mean() nns2 = gnn.pool.knn(x=y1, y=y2, batch_x=b1, batch_y=b2, k=1) if nns2.shape[-1] == 0: - return (y2*0).sum() + return (y2 * 0).sum() logger.debug("nn shape: %s", nns2.shape) d2 = torch.linalg.norm(y2 - y1[nns2[1]], dim=-1).mean() return d1 + d2 class ChamferLoss(torch.nn.Module): - - def __init__(self, mask = None): + def __init__(self, mask=None): super().__init__() if isinstance(mask, str): self.mask = lambda obj: getattr(obj, mask) @@ -39,14 +44,14 @@ def __init__(self, mask = None): else: raise ValueError("mask must be either a graph attribute or a function") - def forward(self, gr1, gr2): mask1 = self.mask(gr1) mask2 = self.mask(gr2) - logger.debug("mask_type - %s",type(mask1)) - loss = _compute_batch_chamfer(y1 = gr1.x[mask1,:3], - y2 = gr2.x[mask2,:3], - b1 = gr1.batch[mask1], - b2 = gr2.batch[mask2]) + logger.debug("mask_type - %s", type(mask1)) + loss = _compute_batch_chamfer( + y1=gr1.x[mask1, :3], + y2=gr2.x[mask2, :3], + b1=gr1.batch[mask1], + b2=gr2.batch[mask2], + ) return loss - diff --git a/src/im2sim/losses/seg.py b/src/im2sim/losses/seg.py index d579472..c8290ed 100644 --- a/src/im2sim/losses/seg.py +++ b/src/im2sim/losses/seg.py @@ -3,7 +3,7 @@ def dice_calc(y1: torch.Tensor, y2: torch.Tensor, smooth: float = 1e-5) -> torch.Tensor: """ - A function to compute the DICE coefficient for two masks + A function to compute the DICE coefficient for two masks Args: y1 (torch.Tensor): True one-hot encoded mask of shape [B,C,H,D] or [B,C,H,D,W] @@ -11,7 +11,7 @@ def dice_calc(y1: torch.Tensor, y2: torch.Tensor, smooth: float = 1e-5) -> torch smooth (float): float value to avoid divide-by-zero Returns: - dice_coeff (torch.Tensor): A tensor of shape [B,C] containing the DICE scores for each sample and channel + dice_coeff (torch.Tensor): A tensor of shape [B,C] containing the DICE scores for each sample and channel """ intersection = torch.sum(y2 * y1, dim=2) # (N, C) union = torch.sum(y2.pow(2), dim=2) + torch.sum(y1, dim=2) # (N, C) @@ -19,7 +19,13 @@ def dice_calc(y1: torch.Tensor, y2: torch.Tensor, smooth: float = 1e-5) -> torch dice_coef = (2 * intersection + smooth) / (union + smooth) # (N, C) return dice_coef -def dice_loss(y1: torch.Tensor, y2: torch.Tensor, smooth: float = 1e-5, channel_weights: torch.Tensor = None) -> torch.Tensor: + +def dice_loss( + y1: torch.Tensor, + y2: torch.Tensor, + smooth: float = 1e-5, + channel_weights: torch.Tensor = None, +) -> torch.Tensor: """ A function to compute the DICE loss @@ -30,10 +36,11 @@ def dice_loss(y1: torch.Tensor, y2: torch.Tensor, smooth: float = 1e-5, channel_ channel_weights (torch.Tensor, optional): tensor of weights for each channels DICE score of shape [C] Returns: - dice_coeff (torch.Tensor): A tensor of shape [B,C] containing the DICE scores for each sample and channel + dice_coeff (torch.Tensor): A tensor of shape [B,C] containing the DICE scores for each sample and channel """ if channel_weights is None: - return torch.mean(1 - dice_calc(y1,y2,smooth)) + return torch.mean(1 - dice_calc(y1, y2, smooth)) else: - return torch.mean(torch.mean(1-dice_calc(y1,y2,smooth), dim=0)*channel_weights) - + return torch.mean( + torch.mean(1 - dice_calc(y1, y2, smooth), dim=0) * channel_weights + ) diff --git a/src/im2sim/losses/utils.py b/src/im2sim/losses/utils.py index a040b54..766f4e1 100644 --- a/src/im2sim/losses/utils.py +++ b/src/im2sim/losses/utils.py @@ -4,16 +4,18 @@ class GraphLoss(nn.Module): - def __init__(self, loss_fn, kwargs): - self.loss_fn = loss_fn + self.loss_fn = loss_fn self.params = inspect.signature(loss_fn).parameters self.kwargs = kwargs - - def forward(self,true_graph, pred_graph): - gr_dict = {'true':true_graph, 'pred':pred_graph} - call_args = {key: getattr(gr_dict[key.split('_')[0]], key.split[1]) # key is in format _ - for key in self.params} + def forward(self, true_graph, pred_graph): + gr_dict = {"true": true_graph, "pred": pred_graph} + call_args = { + key: getattr( + gr_dict[key.split("_")[0]], key.split[1] + ) # key is in format _ + for key in self.params + } loss = self.loss_fn(**call_args, **self.kwargs) - return loss \ No newline at end of file + return loss diff --git a/src/im2sim/models/__init__.py b/src/im2sim/models/__init__.py index b22f058..85eda4d 100644 --- a/src/im2sim/models/__init__.py +++ b/src/im2sim/models/__init__.py @@ -1,4 +1,4 @@ # from .image_to_graph import I2GUNet as I2GUNet from .image_to_graph import SimpleI2G as SimpleI2G from .utils import get_model_config as get_model_config -# from .UNet import UNet, StandardUNet \ No newline at end of file +# from .UNet import UNet, StandardUNet diff --git a/src/im2sim/models/image_to_graph.py b/src/im2sim/models/image_to_graph.py index f09f697..94e26a5 100644 --- a/src/im2sim/models/image_to_graph.py +++ b/src/im2sim/models/image_to_graph.py @@ -12,73 +12,78 @@ logger = logging.getLogger(__name__) - class SimpleI2G(nn.Module): - - def __init__(self, - in_channels, - out_channels, - cnn_filters=(16,32,64,128,256), - cnn_kernel_size=3, - cnn_res_depth=3, - cnn_res_blocks_per_level=2, - cnn_rank=3, - cnn_norm_type=None, - cnn_pool_type='MaxPool', - cnn_pool_size=2, - cnn_activation='relu', - cnn_dropout_rate=None, - projection_ids = ((3,4),(1,2),(0,1)), - gnn_filters = ((384,288), (144,96), (64,32)), - gnn_res_depth = 3, - gnn_n_process_blocks = 1, - gnn_n_deform_blocks = 3, - template_edge_index=None, - gnn_conv_type="GATConv", - gnn_conv_kwargs=None, - gnn_activation="relu", - out_activation="linear", - gnn_norm_type="InstanceNorm", - batched_ops=True): + def __init__( + self, + in_channels, + out_channels, + cnn_filters=(16, 32, 64, 128, 256), + cnn_kernel_size=3, + cnn_res_depth=3, + cnn_res_blocks_per_level=2, + cnn_rank=3, + cnn_norm_type=None, + cnn_pool_type="MaxPool", + cnn_pool_size=2, + cnn_activation="relu", + cnn_dropout_rate=None, + projection_ids=((3, 4), (1, 2), (0, 1)), + gnn_filters=((384, 288), (144, 96), (64, 32)), + gnn_res_depth=3, + gnn_n_process_blocks=1, + gnn_n_deform_blocks=3, + template_edge_index=None, + gnn_conv_type="GATConv", + gnn_conv_kwargs=None, + gnn_activation="relu", + out_activation="linear", + gnn_norm_type="InstanceNorm", + batched_ops=True, + ): super().__init__() logger.debug("Defining SimpleI2G layers...") self.batched_ops = batched_ops - self.encoder = ImageResEncoder(in_channels=in_channels, - filters=cnn_filters, - kernel_size=cnn_kernel_size, - res_depth=cnn_res_depth, - res_blocks_per_level=cnn_res_blocks_per_level, - rank=cnn_rank, - norm_type=cnn_norm_type, - pool_type=cnn_pool_type, - pool_size=cnn_pool_size, - activation=cnn_activation, - dropout_rate=cnn_dropout_rate) + self.encoder = ImageResEncoder( + in_channels=in_channels, + filters=cnn_filters, + kernel_size=cnn_kernel_size, + res_depth=cnn_res_depth, + res_blocks_per_level=cnn_res_blocks_per_level, + rank=cnn_rank, + norm_type=cnn_norm_type, + pool_type=cnn_pool_type, + pool_size=cnn_pool_size, + activation=cnn_activation, + dropout_rate=cnn_dropout_rate, + ) # self.encoder = Encoder3D(in_channels=in_channels) - + # self.projection_layers = nn.ModuleList([TrilinearProjection() for _ in range(len(cnn_filters))]) self.projection_ids = projection_ids projection_channels = _get_projection_channels(cnn_filters, self.projection_ids) - self.decoder_blocks = nn.ModuleList([ - GraphResDecoderBlock(projection_channels=projection_channels[i], - graph_channels=out_channels if i==0 else gnn_filters[i-1][1], - out_channels=out_channels, - filters=gnn_filters[i], - res_depth=gnn_res_depth, - # n_process_blocks = gnn_n_process_blocks, - n_deform_blocks = gnn_n_deform_blocks, - template_edge_index=template_edge_index, - conv_type=gnn_conv_type, - conv_kwargs=gnn_conv_kwargs, - activation=gnn_activation, - out_activation=out_activation, - norm_type=gnn_norm_type) - for i in range(len(gnn_filters)) - - ]) + self.decoder_blocks = nn.ModuleList( + [ + GraphResDecoderBlock( + projection_channels=projection_channels[i], + graph_channels=out_channels if i == 0 else gnn_filters[i - 1][1], + out_channels=out_channels, + filters=gnn_filters[i], + res_depth=gnn_res_depth, + # n_process_blocks = gnn_n_process_blocks, + n_deform_blocks=gnn_n_deform_blocks, + template_edge_index=template_edge_index, + conv_type=gnn_conv_type, + conv_kwargs=gnn_conv_kwargs, + activation=gnn_activation, + out_activation=out_activation, + norm_type=gnn_norm_type, + ) + for i in range(len(gnn_filters)) + ] + ) logger.debug("Done") def forward(self, x, template): @@ -88,11 +93,20 @@ def forward(self, x, template): graph_features = template.x.clone() curr_mesh = template.x.clone() for dec, ids in zip(self.decoder_blocks, self.projection_ids): - proj_inp = torch.cat([OGProjection(image_dim=x.shape[-1])(encoder_outputs[id], curr_mesh[:,:3], template.batch) - for id in ids], dim=-1) + proj_inp = torch.cat( + [ + OGProjection(image_dim=x.shape[-1])( + encoder_outputs[id], curr_mesh[:, :3], template.batch + ) + for id in ids + ], + dim=-1, + ) # proj_inp = torch.cat([TrilinearProjection(domain_size=[x.shape[-1]]*3, batch_ops=False)(encoder_outputs[id], curr_mesh[:,:3], template.batch) # for id in ids], dim=-1) - graph_features, curr_mesh = dec(graph_features,proj_inp,curr_mesh,template.edge_index) + graph_features, curr_mesh = dec( + graph_features, proj_inp, curr_mesh, template.edge_index + ) out_graph = template.clone() out_graph.x = curr_mesh outputs.append(out_graph) @@ -102,7 +116,7 @@ def forward(self, x, template): # class I2GUNet(nn.Module): # def __init__(self, -# in_channels, +# in_channels, # out_channels, # domain_size, # filters=(16,32,64,128,256), @@ -138,10 +152,10 @@ def forward(self, x, template): # pool_type=cnn_pool_type, # pool_size=cnn_pool_size, # activation=cnn_activation) - - + + # self.gpool = RecursiveClusterPooling(n_levels=self.n_levels) - + # self.decoder = nn.ModuleList([ # GraphUNetDecoderBlock(#in_channels = out_channels if i==1 else filters[-i+1], # out_channels = out_channels, @@ -152,20 +166,20 @@ def forward(self, x, template): # n_deform_blocks = gnn_n_deform_blocks, # conv_type=gnn_conv_type, # conv_kwargs=gnn_conv_kwargs, -# activation=gnn_activation, +# activation=gnn_activation, # norm_type=gnn_norm_type, # batched_ops=batched_ops) # for i in range(1,self.n_levels+1) # ]) - - + + # logger.debug("Done") # def forward(self, img, template): # encoder_features = self.encoder(img) # encoder_features.reverse() - + # multi_template = self.gpool(template) # multi_template.reverse() @@ -184,8 +198,6 @@ def forward(self, x, template): # out_graph.x = template.x+deformation # outputs.append(out_graph) # return outputs - - def _get_projection_channels(filters, ids): diff --git a/src/im2sim/models/unet.py b/src/im2sim/models/unet.py index 50e0123..ffb8d5b 100644 --- a/src/im2sim/models/unet.py +++ b/src/im2sim/models/unet.py @@ -20,34 +20,47 @@ class ImageConvBlock(nn.Module): Returns: A `torch.nn.Module` object. - + """ - def __init__(self, - in_channels, - filters=32, - kernel_size=3, - depth=1, - rank=3, - activation='relu', - norm_type=None, - dropout_rate=None): - super().__init__() - - conv = TORCH_LAYERS[f'Conv{rank}d'] - drop = TORCH_LAYERS[f'Dropout{rank}d'] - self.convs = nn.ModuleList([ - conv(in_channels if i==0 else filters, filters, kernel_size, padding=kernel_size//2) - for i in range(depth) - ]) - - self.norms = nn.ModuleList([ - TORCH_LAYERS(f"{norm_type}{rank}d")(filters) if norm_type else nn.Identity() - for _ in range(depth) - ]) + + def __init__( + self, + in_channels, + filters=32, + kernel_size=3, + depth=1, + rank=3, + activation="relu", + norm_type=None, + dropout_rate=None, + ): + super().__init__() + + conv = TORCH_LAYERS[f"Conv{rank}d"] + drop = TORCH_LAYERS[f"Dropout{rank}d"] + self.convs = nn.ModuleList( + [ + conv( + in_channels if i == 0 else filters, + filters, + kernel_size, + padding=kernel_size // 2, + ) + for i in range(depth) + ] + ) + + self.norms = nn.ModuleList( + [ + TORCH_LAYERS(f"{norm_type}{rank}d")(filters) + if norm_type + else nn.Identity() + for _ in range(depth) + ] + ) self.drop = drop(p=dropout_rate) if dropout_rate else nn.Identity() self.act = ACTIVATIONS[activation]() - def forward(self, x): """ @@ -61,7 +74,7 @@ def forward(self, x): for conv, norm in zip(self.convs, self.norms): x = norm(self.act(conv(x))) return self.drop(x) - + class ImageEncoder(nn.Module): """ @@ -81,44 +94,52 @@ class ImageEncoder(nn.Module): A `torch.nn.Module` object. """ - def __init__(self, - in_channels, - filters=(16,32,64,128,256), - pool_sizes = None, - kernel_size=3, - conv_blocks_per_level=1, - rank=3, - norm_type=None, - pool_type='MaxPool', - activation='relu', - dropout_rate=None): + def __init__( + self, + in_channels, + filters=(16, 32, 64, 128, 256), + pool_sizes=None, + kernel_size=3, + conv_blocks_per_level=1, + rank=3, + norm_type=None, + pool_type="MaxPool", + activation="relu", + dropout_rate=None, + ): super().__init__() - + n_levels = len(filters) - self.conv_blocks = nn.ModuleList([ - ImageConvBlock(in_channels=in_channels if i==0 else filters[i-1], - filters=filters[i], - kernel_size=kernel_size, - depth=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate) - for i in range(n_levels) - ]) + self.conv_blocks = nn.ModuleList( + [ + ImageConvBlock( + in_channels=in_channels if i == 0 else filters[i - 1], + filters=filters[i], + kernel_size=kernel_size, + depth=conv_blocks_per_level, + rank=rank, + activation=activation, + norm_type=norm_type, + dropout_rate=dropout_rate, + ) + for i in range(n_levels) + ] + ) if pool_sizes is None: pool_sizes = 2 pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) - pool = TORCH_LAYERS[f'{pool_type}{rank}d'] - self.maxpools = nn.ModuleList([ - pool(pool_sizes_standard[i-1]) if i>0 else nn.Identity() - for i in range(n_levels) - ]) + pool = TORCH_LAYERS[f"{pool_type}{rank}d"] + self.maxpools = nn.ModuleList( + [ + pool(pool_sizes_standard[i - 1]) if i > 0 else nn.Identity() + for i in range(n_levels) + ] + ) - def forward(self,x): + def forward(self, x): """ Args: x (torch.Tensor): Input image [in_channels, ...] @@ -149,18 +170,20 @@ class ImageDecoder(nn.Module): skip (bool): Use skip connections. """ - def __init__(self, - filters=(16,32,64,128,256), - kernel_size=3, - pool_sizes=None, - upsample_sizes = None, - conv_blocks_per_level=1, - rank=3, - upsample_type="ConvTranspose", - activation="relu", - norm_type=None, - dropout_rate=None, - skip=True): + def __init__( + self, + filters=(16, 32, 64, 128, 256), + kernel_size=3, + pool_sizes=None, + upsample_sizes=None, + conv_blocks_per_level=1, + rank=3, + upsample_type="ConvTranspose", + activation="relu", + norm_type=None, + dropout_rate=None, + skip=True, + ): super().__init__() self.skip = skip @@ -181,34 +204,46 @@ def __init__(self, else: upsample_sizes = standardize_spatial_factors(upsample_sizes, rank) - - if upsample_type.lower() == 'upsample': - self.ups = nn.ModuleList([ - nn.Upsample(scale_factor=upsample_sizes[i], mode='trilinear' if rank==3 else 'bilinear', align_corners=True) - for i in range(n_levels - 1) - ]) + if upsample_type.lower() == "upsample": + self.ups = nn.ModuleList( + [ + nn.Upsample( + scale_factor=upsample_sizes[i], + mode="trilinear" if rank == 3 else "bilinear", + align_corners=True, + ) + for i in range(n_levels - 1) + ] + ) else: up_layer = TORCH_LAYERS(f"{upsample_type}{rank}d") - self.ups = nn.ModuleList([ - up_layer(rev_filters[i], rev_filters[i+1], kernel_size=upsample_sizes[i], stride=upsample_sizes[i]) - for i in range(n_levels - 1) - ]) - - - self.conv_blocks = nn.ModuleList([ - ImageConvBlock( - in_channels=rev_filters[i+1] * (2 if skip else 1), - filters=rev_filters[i+1], - kernel_size=kernel_size, - depth=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate + self.ups = nn.ModuleList( + [ + up_layer( + rev_filters[i], + rev_filters[i + 1], + kernel_size=upsample_sizes[i], + stride=upsample_sizes[i], + ) + for i in range(n_levels - 1) + ] ) - for i in range(n_levels - 1) - ]) + self.conv_blocks = nn.ModuleList( + [ + ImageConvBlock( + in_channels=rev_filters[i + 1] * (2 if skip else 1), + filters=rev_filters[i + 1], + kernel_size=kernel_size, + depth=conv_blocks_per_level, + rank=rank, + activation=activation, + norm_type=norm_type, + dropout_rate=dropout_rate, + ) + for i in range(n_levels - 1) + ] + ) def _match_size(self, x, skip): """ @@ -252,7 +287,6 @@ def _match_size(self, x, skip): return skip - def forward(self, encoder_outputs): """ Args: @@ -281,6 +315,7 @@ def forward(self, encoder_outputs): return x + class UNet(nn.Module): """ Flexible UNet for 2D or 3D images. @@ -300,70 +335,80 @@ class UNet(nn.Module): final_activation (str): Output activation. """ - def __init__(self, - in_channels, - out_channels, - filters=(16,32,64,128,256), - pool_sizes = None, - upsample_sizes = None, - kernel_size=3, - conv_blocks_per_level=1, - rank=3, - activation="relu", - norm_type=None, - dropout_rate=None, - final_activation="linear"): - + def __init__( + self, + in_channels, + out_channels, + filters=(16, 32, 64, 128, 256), + pool_sizes=None, + upsample_sizes=None, + kernel_size=3, + conv_blocks_per_level=1, + rank=3, + activation="relu", + norm_type=None, + dropout_rate=None, + final_activation="linear", + ): + pool_sizes_temp = [] if pool_sizes is None: - for i in range(len(filters)-1): + for i in range(len(filters) - 1): pool_sizes_temp.append(2) pool_sizes = pool_sizes_temp - + if not isinstance(pool_sizes, (list, tuple)): raise TypeError("pool_sizes must be a list or tuple") - + for i, p in enumerate(pool_sizes): - if isinstance(p, int) and not isinstance(p,bool): + if isinstance(p, int) and not isinstance(p, bool): if p < 1: raise ValueError("pool_sizes must be positive") - elif isinstance(p, (tuple,list)): + elif isinstance(p, (tuple, list)): if len(p) != rank: raise ValueError("pool_sizes tuple must be same length as rank") - for j , ps in enumerate(p): - if not isinstance(ps, int) or isinstance(ps,bool): + for j, ps in enumerate(p): + if not isinstance(ps, int) or isinstance(ps, bool): raise TypeError("pool_sizes must be an int") if ps < 1: raise ValueError("pool_sizes must be positive") else: - raise TypeError("each entry in pool_sizes must be either an int or a tuple or list") - - if len(filters) != (len(pool_sizes)+1): - raise ValueError(f"pool_sizes do not match number of filters. For {len(filters)}, please input {len(filters)-1} number of pools.") - + raise TypeError( + "each entry in pool_sizes must be either an int or a tuple or list" + ) + + if len(filters) != (len(pool_sizes) + 1): + raise ValueError( + f"pool_sizes do not match number of filters. For {len(filters)}, please input {len(filters) - 1} number of pools." + ) if upsample_sizes is not None: if not isinstance(upsample_sizes, (list, tuple)): raise TypeError("upsample_sizes must be a list or tuple") - + for i, p in enumerate(upsample_sizes): - if isinstance(p, int) and not isinstance(p,bool): + if isinstance(p, int) and not isinstance(p, bool): if p < 1: raise ValueError("upsample_sizes must be positive") - elif isinstance(p, (tuple,list)): + elif isinstance(p, (tuple, list)): if len(p) != rank: - raise ValueError("upsample_sizes tuple must be same length as rank") - for j , ps in enumerate(p): - if not isinstance(ps, int) or isinstance(ps,bool): + raise ValueError( + "upsample_sizes tuple must be same length as rank" + ) + for j, ps in enumerate(p): + if not isinstance(ps, int) or isinstance(ps, bool): raise TypeError("upsample_sizes must be an int") if ps < 1: raise ValueError("upsample_sizes must be positive") else: - raise TypeError("each entry in upsample_sizes must be either an int or a tuple or list") - - if len(filters) != (len(upsample_sizes)+1): - raise ValueError(f"upsample_sizes do not match number of filters. For {len(filters)}, please input {len(filters)-1} number of upsamples.") - + raise TypeError( + "each entry in upsample_sizes must be either an int or a tuple or list" + ) + + if len(filters) != (len(upsample_sizes) + 1): + raise ValueError( + f"upsample_sizes do not match number of filters. For {len(filters)}, please input {len(filters) - 1} number of upsamples." + ) super().__init__() @@ -376,22 +421,22 @@ def __init__(self, rank=rank, activation=activation, norm_type=norm_type, - dropout_rate=dropout_rate + dropout_rate=dropout_rate, ) self.decoder = ImageDecoder( filters=filters, pool_sizes=pool_sizes, - upsample_sizes = upsample_sizes, + upsample_sizes=upsample_sizes, kernel_size=kernel_size, conv_blocks_per_level=conv_blocks_per_level, rank=rank, activation=activation, norm_type=norm_type, - dropout_rate=dropout_rate + dropout_rate=dropout_rate, ) - conv = TORCH_LAYERS[f'Conv{rank}d'] + conv = TORCH_LAYERS[f"Conv{rank}d"] self.final_conv = conv(filters[0], out_channels, kernel_size=1) @@ -408,6 +453,7 @@ def forward(self, x): x = self.final_act(x) return x + class StandardUNet(UNet): """ Standaed UNet for 2D or 3D images. @@ -426,30 +472,32 @@ class StandardUNet(UNet): final_activation (str): Output activation. """ - def __init__(self, - in_channels, - out_channels, - filters=(16, 32, 64, 128, 256), - pool_size = 2, - kernel_size=3, - conv_blocks_per_level=1, - rank=3, - activation="relu", - norm_type=None, - dropout_rate=None, - final_activation="linear"): - + def __init__( + self, + in_channels, + out_channels, + filters=(16, 32, 64, 128, 256), + pool_size=2, + kernel_size=3, + conv_blocks_per_level=1, + rank=3, + activation="relu", + norm_type=None, + dropout_rate=None, + final_activation="linear", + ): + pool_size_standard = [] if isinstance(pool_size, int): pool_size_single = [] for j in range(rank): pool_size_single.append(pool_size) pool_size_single = tuple(pool_size_single) - for i in range(len(filters)-1): + for i in range(len(filters) - 1): pool_size_standard.append(pool_size_single) - elif isinstance(pool_size , (tuple,list)): + elif isinstance(pool_size, (tuple, list)): if len(pool_size) == rank: - for i in range(len(filters)-1): + for i in range(len(filters) - 1): pool_size_standard.append(pool_size) else: raise ValueError("pool_size must be an int or have same length as rank") @@ -467,4 +515,4 @@ def __init__(self, norm_type=norm_type, dropout_rate=dropout_rate, final_activation=final_activation, - ) \ No newline at end of file + ) diff --git a/src/im2sim/models/utils.py b/src/im2sim/models/utils.py index 9c633ea..f65a175 100644 --- a/src/im2sim/models/utils.py +++ b/src/im2sim/models/utils.py @@ -1,67 +1,68 @@ def get_model_config(name): - """Get the default config for a specified type of model. + """Get the default config for a specified type of model. - Args: - name: A `str`. The name of the model type. + Args: + name: A `str`. The name of the model type. - Returns: - A config dictionary + Returns: + A config dictionary - Raises: - ValueError: If the requested model config doesn't exist. - """ - try: - return _CONFIGS[(name)] - except KeyError as err: - raise ValueError( - f"Could not find config for model with name '{name}'") from err + Raises: + ValueError: If the requested model config doesn't exist. + """ + try: + return _CONFIGS[(name)] + except KeyError as err: + raise ValueError(f"Could not find config for model with name '{name}'") from err _CONFIGS = { - "Image2Flow":{"cnn_filters": (16,48,96,192,384), - "cnn_kernel_size": 3, - "cnn_res_depth": 3, - "cnn_res_blocks_per_level": 2, - "cnn_rank": 3, - "cnn_norm_type": "InstanceNorm", - "cnn_pool_type": 'MaxPool', - "cnn_pool_size": 2, - "cnn_activation": 'leaky_relu', - "cnn_dropout_rate": 0.3, - "projection_ids": ((3,4),(1,2),(0,1)), - "gnn_filters": ((384,288), (96, 64), (48,32)), - "gnn_res_depth": 3, - "gnn_n_process_blocks": 1, - "gnn_n_deform_blocks": 3, - "template_edge_index": None, - "gnn_conv_type": "ChebConv", - "gnn_conv_kwargs": {'K':1}, - "gnn_activation": "leaky_relu", - "out_activation": "leaky_relu", - "gnn_norm_type": "InstanceNorm", - "batched_ops": False}, - - "Image2Mesh":{"cnn_filters": (16,32,64,128,256), - "cnn_kernel_size": 3, - "cnn_res_depth": 3, - "cnn_res_blocks_per_level": 2, - "cnn_rank": 3, - "cnn_norm_type": "InstanceNorm", - "cnn_pool_type": 'MaxPool', - "cnn_pool_size": 2, - "cnn_activation": 'leaky_relu', - "cnn_dropout_rate": 0.3, - "projection_ids": ((3,4),(1,2),(0,1)), - "gnn_filters": ((384,288), (96, 64), (48,32)), - "gnn_res_depth": 3, - "gnn_n_process_blocks": 1, - "gnn_n_deform_blocks": 3, - "template_edge_index": None, - "gnn_conv_type": "ChebConv", - "gnn_conv_kwargs": {'K':1}, - "gnn_activation": "leaky_relu", - "out_activation": "leaky_relu", - "gnn_norm_type": "InstanceNorm", - "batched_ops": False} - + "Image2Flow": { + "cnn_filters": (16, 48, 96, 192, 384), + "cnn_kernel_size": 3, + "cnn_res_depth": 3, + "cnn_res_blocks_per_level": 2, + "cnn_rank": 3, + "cnn_norm_type": "InstanceNorm", + "cnn_pool_type": "MaxPool", + "cnn_pool_size": 2, + "cnn_activation": "leaky_relu", + "cnn_dropout_rate": 0.3, + "projection_ids": ((3, 4), (1, 2), (0, 1)), + "gnn_filters": ((384, 288), (96, 64), (48, 32)), + "gnn_res_depth": 3, + "gnn_n_process_blocks": 1, + "gnn_n_deform_blocks": 3, + "template_edge_index": None, + "gnn_conv_type": "ChebConv", + "gnn_conv_kwargs": {"K": 1}, + "gnn_activation": "leaky_relu", + "out_activation": "leaky_relu", + "gnn_norm_type": "InstanceNorm", + "batched_ops": False, + }, + "Image2Mesh": { + "cnn_filters": (16, 32, 64, 128, 256), + "cnn_kernel_size": 3, + "cnn_res_depth": 3, + "cnn_res_blocks_per_level": 2, + "cnn_rank": 3, + "cnn_norm_type": "InstanceNorm", + "cnn_pool_type": "MaxPool", + "cnn_pool_size": 2, + "cnn_activation": "leaky_relu", + "cnn_dropout_rate": 0.3, + "projection_ids": ((3, 4), (1, 2), (0, 1)), + "gnn_filters": ((384, 288), (96, 64), (48, 32)), + "gnn_res_depth": 3, + "gnn_n_process_blocks": 1, + "gnn_n_deform_blocks": 3, + "template_edge_index": None, + "gnn_conv_type": "ChebConv", + "gnn_conv_kwargs": {"K": 1}, + "gnn_activation": "leaky_relu", + "out_activation": "leaky_relu", + "gnn_norm_type": "InstanceNorm", + "batched_ops": False, + }, } diff --git a/src/im2sim/plot/pointcloud.py b/src/im2sim/plot/pointcloud.py index 41b89ad..c2ecab9 100644 --- a/src/im2sim/plot/pointcloud.py +++ b/src/im2sim/plot/pointcloud.py @@ -4,19 +4,20 @@ class PointCloudPlot: - - def __init__(self, - nrows, - ncols, - point_sets, - color_sets=None, - figsize=None, - cmap='Blues_r', - norm_mode='none', # 'all', 'row', 'col', 'none' - bound_mode='all', - titles=None, # NEW: optional titles - elev=20, - azim=90): + def __init__( + self, + nrows, + ncols, + point_sets, + color_sets=None, + figsize=None, + cmap="Blues_r", + norm_mode="none", # 'all', 'row', 'col', 'none' + bound_mode="all", + titles=None, # NEW: optional titles + elev=20, + azim=90, + ): self.cmap = cmap self.elev = elev @@ -27,10 +28,7 @@ def __init__(self, if color_sets is None: is_colored = False - color_sets = [ - 0.1 * np.ones(points.shape[0]) - for points in point_sets - ] + color_sets = [0.1 * np.ones(points.shape[0]) for points in point_sets] else: is_colored = True @@ -38,9 +36,7 @@ def __init__(self, figsize = (ncols * 3, nrows * 3) self.fig, axes = plt.subplots( - nrows, ncols, - figsize=figsize, - subplot_kw={'projection': '3d'} + nrows, ncols, figsize=figsize, subplot_kw={"projection": "3d"} ) self.axes = np.array(axes).reshape(-1) @@ -68,15 +64,15 @@ def __init__(self, def compute_norm_ranges(color_sets): color_sets = list(color_sets) - if norm_mode == 'none': + if norm_mode == "none": return [(None, None)] * len(color_sets) - elif norm_mode == 'all': + elif norm_mode == "all": all_vals = np.concatenate(color_sets) vmin, vmax = all_vals.min(), all_vals.max() return [(vmin, vmax)] * len(color_sets) - elif norm_mode == 'row': + elif norm_mode == "row": ranges = [] for r in range(nrows): row_vals = [] @@ -89,7 +85,7 @@ def compute_norm_ranges(color_sets): ranges.append((vmin, vmax)) return ranges - elif norm_mode == 'col': + elif norm_mode == "col": ranges = [None] * len(color_sets) for c in range(ncols): col_vals = [] @@ -112,21 +108,21 @@ def compute_norm_ranges(color_sets): # Plot creation # ----------------------------- for i, (ax, points, colors) in enumerate( - zip(self.axes, point_sets, color_sets)): - + zip(self.axes, point_sets, color_sets) + ): vmin, vmax = norm_ranges[i] - sc = ax.scatter(points[:, 0], - points[:, 1], - points[:, 2], - c=colors, - cmap=cmap, - vmin=vmin, - vmax=vmax) + sc = ax.scatter( + points[:, 0], + points[:, 1], + points[:, 2], + c=colors, + cmap=cmap, + vmin=vmin, + vmax=vmax, + ) - ax.view_init(elev=elev, - azim=azim, - vertical_axis='y') + ax.view_init(elev=elev, azim=azim, vertical_axis="y") ax.set_xlim(mins[0], maxs[0]) ax.set_ylim(mins[1], maxs[1]) @@ -160,15 +156,15 @@ def draw_frame(self, point_sets=None, color_sets=None): def compute_norm_ranges(color_sets): color_sets = list(color_sets) - if self.norm_mode == 'none': + if self.norm_mode == "none": return [(None, None)] * len(color_sets) - elif self.norm_mode == 'all': + elif self.norm_mode == "all": all_vals = np.concatenate(color_sets) vmin, vmax = all_vals.min(), all_vals.max() return [(vmin, vmax)] * len(color_sets) - elif self.norm_mode == 'row': + elif self.norm_mode == "row": ranges = [] for r in range(self.nrows): row_vals = [] @@ -181,7 +177,7 @@ def compute_norm_ranges(color_sets): ranges.append((vmin, vmax)) return ranges - elif self.norm_mode == 'col': + elif self.norm_mode == "col": ranges = [None] * len(color_sets) for c in range(self.ncols): col_vals = [] @@ -199,16 +195,12 @@ def compute_norm_ranges(color_sets): new_scatters = [] - for i, (ax, sc, pts, colors) in enumerate(zip( - self.axes, - self.scatters, - point_sets, - color_sets)): - + for i, (ax, sc, pts, colors) in enumerate( + zip(self.axes, self.scatters, point_sets, color_sets) + ): vmin, vmax = norm_ranges[i] if sc is None or len(pts) != len(sc.get_offsets()): - if sc is not None: sc.remove() @@ -219,7 +211,7 @@ def compute_norm_ranges(color_sets): c=colors, cmap=self.cmap, vmin=vmin, - vmax=vmax + vmax=vmax, ) else: @@ -246,11 +238,13 @@ def save_image(self, filename, dpi=200): # ANIMATE # --------------------------------------------------------- - def animate(self, - point_sequence_sets, - color_sequence_sets=None, - filename="animation.gif", - fps=15): + def animate( + self, + point_sequence_sets, + color_sequence_sets=None, + filename="animation.gif", + fps=15, + ): n_frames = len(point_sequence_sets) @@ -262,20 +256,14 @@ def update(frame): colors = color_sequence_sets[frame] return self.draw_frame( - point_sets=point_sequence_sets[frame], - color_sets=colors + point_sets=point_sequence_sets[frame], color_sets=colors ) - ani = animation.FuncAnimation( - self.fig, - update, - frames=n_frames, - blit=False - ) + ani = animation.FuncAnimation(self.fig, update, frames=n_frames, blit=False) if filename.endswith(".gif"): ani.save(filename, writer="pillow", fps=fps) else: ani.save(filename, writer="ffmpeg", fps=fps) - plt.close(self.fig) \ No newline at end of file + plt.close(self.fig) diff --git a/tests/integration/data/test_data_pipeline.py b/tests/integration/data/test_data_pipeline.py new file mode 100644 index 0000000..bf2deda --- /dev/null +++ b/tests/integration/data/test_data_pipeline.py @@ -0,0 +1,155 @@ +import torch +import pytest +from torch_geometric.data import Data +import copy + +from im2sim.data import Pipeline, transforms, Dataset + + +def make_toy_dataset(): + """ + Mimics: + input.x -> model inputs + gt.x -> targets + + but with tiny synthetic data. + """ + + samples = {} + cases = ["1","2","3","4","5"] + for case in cases: + num_nodes = 10 + + sample = { + "input": Data( + x=torch.randn(num_nodes, 7), + edge_index=torch.tensor( + [ + [0, 1, 2, 3], + [1, 2, 3, 4], + ] + ), + ), + "gt": Data( + x=torch.randn(num_nodes, 4) * 10, + edge_index=torch.tensor( + [ + [0, 1, 2, 3], + [1, 2, 3, 4], + ] + ), + ), + } + + samples[case] = sample + + def load(case): + return samples[case] + + ds = Dataset(load, cases) + + return ds + + + + + +@pytest.fixture +def pipeline(): + + dataset = make_toy_dataset() + + pipeline = Pipeline( + [ + transforms.PowerScaling( + exp=1 / 3, + preserve_sign=True, + keys=["gt"], + attr="x", + channels=[0], + ), + transforms.FitZScore( + keys=["gt"], + attr="x", + channels=[0, 1, 2, 3], + per_channel=True, + ), + transforms.FitZScore( + keys=["input"], + attr="x", + channels=[0, 1, 2, 3, 4, 5, 6], + per_channel=True, + ), + ] + ) + + pipeline.fit(dataset) + + return pipeline + + +def test_pipeline_forward(pipeline): + + sample = make_toy_dataset()[0] + + transformed = pipeline(sample) + + assert transformed["input"].x.shape == (10, 7) + assert transformed["gt"].x.shape == (10, 4) + + assert torch.isfinite(transformed["input"].x).all() + assert torch.isfinite(transformed["gt"].x).all() + + +def test_pipeline_inverse(pipeline): + + sample = make_toy_dataset()[0] + + # keep a copy before modification + original = { + key: value.x.clone() + for key, value in sample.items() + } + + transformed = pipeline(sample) + + recovered = pipeline.inverse(transformed) + + for key in ["input", "gt"]: + + torch.testing.assert_close( + recovered[key].x, + original[key], + rtol=1e-5, + atol=1e-6, + ) + + +def test_integrated_pipeline(pipeline): + + dataset1 = make_toy_dataset() + dataset2 = copy.deepcopy(dataset1) + + dataset1.add_transforms(pipeline) + + sample1 = dataset1[0] + recovered = pipeline.inverse(sample1) + + sample2 = dataset2[0] + transformed = pipeline(sample2) + + for key in ["input", "gt"]: + + torch.testing.assert_close( + recovered[key].x, + sample2[key].x, + rtol=1e-5, + atol=1e-6, + ) + + torch.testing.assert_close( + sample1[key].x, + transformed[key].x, + rtol=1e-5, + atol=1e-6, + ) \ No newline at end of file diff --git a/tests/integration/data/test_pv_mesh_utils.py b/tests/integration/data/test_pv_mesh_utils.py new file mode 100644 index 0000000..52bfe9f --- /dev/null +++ b/tests/integration/data/test_pv_mesh_utils.py @@ -0,0 +1,249 @@ +import pyvista as pv +import numpy as np +from torch_geometric.data import Data + +from hypothesis import given +import hypothesis.strategies as st + +from im2sim.data.mesh_utils import * + + +def build_tetra_mesh(): + points = np.array([ + [0, 0, 0], # 0 + [1, 0, 0], # 1 + [0, 1, 0], # 2 + [0, 0, 1], # 3 + [1, 1, 1], # 4 (second tetra apex) + ]).astype(np.float32) + + # Two tetrahedra + cells = np.hstack([ + [4, 0, 1, 2, 3], # tetra 1 + [4, 1, 2, 3, 4], # tetra 2 + ]) + + celltypes = np.array([pv.CellType.TETRA, pv.CellType.TETRA]) + grid = pv.UnstructuredGrid(cells, celltypes, points) + + grid["CellEntityIds"] = np.array([0]*2) # Assign the same entity ID to both tetrahedra + grid["vtkOriginalPointIds"] = np.arange(len(points)) + return grid + + + +def build_triangle_mesh(): + # 4 points forming a square + points = np.array([ + [0, 0, 0], # 0 + [1, 0, 0], # 1 + [1, 1, 0], # 2 + [0, 1, 0], # 3 + ]).astype(np.float32) + + # Two triangle faces: (0,1,2) and (0,2,3) + faces = np.hstack([ + [3, 0, 1, 2], + [3, 0, 2, 3], + ]) + grid = pv.PolyData(points, faces) + grid["CellEntityIds"] = np.array([0]*2) # Assign the same entity ID to both tetrahedra + grid["vtkOriginalPointIds"] = np.arange(len(points)) + return grid + +def make_square_mesh(): + # Four nodes in a square + x = torch.tensor([ + [0.0, 0.0], + [1.0, 0.0], + [1.0, 1.0], + [0.0, 1.0], + ]) + + # Undirected edges + edge_index = torch.tensor([ + [0, 1, 2, 3, 0, 1, 2, 3], + [1, 0, 3, 2, 3, 2, 1, 0], + ]) + + return Data(x=x, edge_index=edge_index) + + + +def to_set(elems): + """ + Convert edge/cell array to set of sorted tuples. + Works for edges/cells + - (N, m) + where N is number of edges/cells and m is nodes per edge/cell + + """ + return {tuple(sorted(e)) for e in elems} + + +def test_get_edges_surf(): + mesh = build_triangle_mesh() + + edges = get_edges_surf(mesh).numpy().T + print(edges.shape) + edge_set = to_set(edges) + + expected = np.array([[0,1],[1,2],[0,2],[2,3],[0,3], + [1,0],[2,1],[2,0],[3,2],[3,0]]) + + expected = to_set(expected) + + assert edge_set == expected + assert len(edge_set) == 5 + + + +def test_get_edges_tet(): + mesh = build_tetra_mesh() + + edges = get_edges_tet(mesh).numpy().T + edge_set = to_set(edges) + + + expected = np.array( + [[0,1], [0,2], [0,3], + [1,2], [1,3], [2,3], + [1,4], [2,4], [3,4], + [1,0], [2,0], [3,0], + [2,1], [3,1], [3,2], + [4,1], [4,2], [4,3]] + ) + + expected = to_set(expected) + + assert edge_set == expected + assert len(edge_set) == 9 + +def test_get_structure_cells(): + tet_mesh = build_tetra_mesh() + + tet_cells = get_structure_cells(tet_mesh, {0:'vol'})['vol_cell_index'].numpy().T + + tet_cell_set = to_set(tet_cells) + + tet_expected = np.array( + [[0, 1, 2, 3], + [1, 2, 3, 4]] + ) + + tet_expected = to_set(tet_expected) + + assert tet_cell_set == tet_expected + assert len(tet_cell_set) == 2 + +def test_get_structure_ids(): + tet_mesh = build_tetra_mesh() + + tet_ids = set(get_structure_ids(tet_mesh, {0:'vol'})['vol_index'].numpy().tolist()) + + tet_expected = {0,1,2,3,4} + + assert tet_ids == tet_expected + assert len(tet_ids) == 5 + + +def test_cluster_pool_reduces_nodes(): + mesh = make_square_mesh() + + pooled = cluster_pool(mesh) + + assert isinstance(pooled, Data) + assert pooled.x.shape[1] == mesh.x.shape[1] + assert pooled.x.shape[0] <= mesh.x.shape[0] + assert pooled.edge_index.shape[0] == 2 + +def test_cluster_pool_preserves_feature_range(): + mesh = make_square_mesh() + + pooled = cluster_pool(mesh) + + # Averaging cannot create values outside the original range + assert torch.all( + pooled.x >= mesh.x.min() + ) + assert torch.all( + pooled.x <= mesh.x.max() + ) + +def test_cluster_pool_handles_zero_length_edges(): + mesh = Data( + x=torch.tensor([ + [0., 0.], + [0., 0.], + [1., 0.] + ]), + edge_index=torch.tensor([ + [0, 1, 1], + [1, 0, 2] + ]) + ) + + pooled = cluster_pool(mesh) + + assert torch.isfinite(pooled.x).all() + +def test_rasterize_output_shape(): + points = torch.tensor([ + [0., 0., 0.] + ]) + + result = rasterize( + points, + im_shape=[4, 4, 4], + vox_sizes=[1., 1., 1.] + ) + + assert result.shape == (4, 4, 4) + +def test_rasterize_single_point_minimum(): + + points = torch.tensor([ + [0.5, 0.5, 0.5] + ]) + + result = rasterize( + points, + im_shape=[4, 4, 4], + vox_sizes=[1., 1., 1.] + ) + + assert result[0, 0, 0] == 0 + + +def test_rasterize_two_points(): + + points = torch.tensor([ + [0., 0., 0.], + [3., 3., 3.] + ]) + + result = rasterize( + points, + im_shape=[4,4,4], + vox_sizes=[1.,1.,1.] + ) + + assert result[0,0,0] == result[-1,-1,-1] + assert result[0,0,0] < result[1,1,1] + + + +@given( + st.integers(min_value=1, max_value=10) +) +def test_rasterize_never_returns_negative(n): + + points = torch.rand(n,3) + + result = rasterize( + points, + [8,8,8], + [1.,1.,1.] + ) + + assert torch.all(result >= 0) \ No newline at end of file diff --git a/tests/unit/data/test_loader.py b/tests/unit/data/test_loader.py new file mode 100644 index 0000000..583f0e5 --- /dev/null +++ b/tests/unit/data/test_loader.py @@ -0,0 +1,129 @@ +import pytest +import torch +from torch_geometric.data import Data, Batch + +from im2sim.data import collate, Dataset, DataLoader + +# ------------------------- +# Fixtures +# ------------------------- + +@pytest.fixture +def tensor_sample(): + return { + "x": torch.randn(3, 4), + "y": torch.randn(1), + } + + +@pytest.fixture +def pyg_sample(): + return { + "graph": Data(x=torch.randn(5, 3), edge_index=torch.tensor([[0, 1], [1, 2]])) + } + + +@pytest.fixture +def mixed_sample(): + return { + "x": torch.randn(3, 4), + "graph": Data(x=torch.randn(5, 3), edge_index=torch.tensor([[0, 1], [1, 2]])), + } + + +# ------------------------- +# collate tests +# ------------------------- + +def test_collate_tensors(tensor_sample): + batch = [tensor_sample, tensor_sample] + out = collate(batch) + + assert isinstance(out, dict) + assert all(isinstance(v, torch.Tensor) for v in out.values()) + assert out["x"].shape[0] == 2 # batched dimension + + +def test_collate_pyg(pyg_sample): + batch = [pyg_sample, pyg_sample] + out = collate(batch) + + assert isinstance(out["graph"], Batch) + assert out["graph"].num_graphs == 2 + + +def test_collate_mixed(mixed_sample): + batch = [mixed_sample, mixed_sample] + out = collate(batch) + + assert isinstance(out["x"], torch.Tensor) + assert isinstance(out["graph"], Batch) + + +def test_collate_invalid_type(): + batch = [{"bad": "not allowed"}, {"bad": "still not allowed"}] + + with pytest.raises(TypeError): + collate(batch) + + +# ------------------------- +# Dataset tests +# ------------------------- + +def dummy_load_fn(case: str): + return {"x": torch.tensor([len(case)])} + + +def test_dataset_len(): + cases = ["a", "bb", "ccc"] + ds = Dataset(load_fn=dummy_load_fn, cases=cases) + + assert len(ds) == 3 + + +def test_dataset_getitem(): + cases = ["a"] + ds = Dataset(load_fn=dummy_load_fn, cases=cases) + + sample = ds[0] + assert isinstance(sample, dict) + assert torch.equal(sample["x"], torch.tensor([1])) + + + + +def test_dataset_no_transforms(): + ds = Dataset(load_fn=dummy_load_fn, cases=["a"], transforms=None) + sample = ds[0] + + assert "x" in sample # should still work + + +# ------------------------- +# DataLoader tests +# ------------------------- + +def test_dataloader_basic(): + ds = Dataset(load_fn=dummy_load_fn, cases=["a", "bb", "ccc"]) + + loader = DataLoader(ds, batch_size=2) + + batch = next(iter(loader)) + assert isinstance(batch, dict) + assert "x" in batch + assert batch["x"].shape[0] == 2 + + +def test_dataloader_with_pyg(): + def load_fn(case): + return { + "graph": Data(x=torch.randn(3, 2), edge_index=torch.tensor([[0, 1], [1, 2]])) + } + + ds = Dataset(load_fn=load_fn, cases=["a", "b", "c"]) + loader = DataLoader(ds, batch_size=2) + + batch = next(iter(loader)) + assert isinstance(batch["graph"], Batch) + assert batch["graph"].num_graphs == 2 \ No newline at end of file diff --git a/tests/unit/data/test_mesh_utils.py b/tests/unit/data/test_mesh_utils.py index 7ec1ae7..b6baba2 100644 --- a/tests/unit/data/test_mesh_utils.py +++ b/tests/unit/data/test_mesh_utils.py @@ -19,19 +19,24 @@ def test_make_padded_batch_mask_correct(): _, mask = make_padded_batch(x, batch) - expected = torch.tensor([ - [True, True, True], - [True, False, False], - [True, False, False], - ]) + expected = torch.tensor( + [ + [True, True, True], + [True, False, False], + [True, False, False], + ] + ) assert torch.equal(mask, expected) + def test_compute_edge_lengths_simple(): - points = torch.tensor([ - [0.0, 0.0], - [3.0, 4.0], - ]) + points = torch.tensor( + [ + [0.0, 0.0], + [3.0, 4.0], + ] + ) edges = torch.tensor([[0], [1]]) distances = compute_edge_lengths(points, edges) @@ -65,12 +70,9 @@ def test_soft_threshold_behavior(): def test_set_attrs(): data = Data() - attrs = { - "x": torch.tensor([[1.0]]), - "edge_index": torch.tensor([[0], [0]]) - } + attrs = {"x": torch.tensor([[1.0]]), "edge_index": torch.tensor([[0], [0]])} set_attrs(data, attrs) assert torch.equal(data.x, attrs["x"]) - assert torch.equal(data.edge_index, attrs["edge_index"]) \ No newline at end of file + assert torch.equal(data.edge_index, attrs["edge_index"]) diff --git a/tests/unit/data/test_ops.py b/tests/unit/data/test_ops.py new file mode 100644 index 0000000..1f197c2 --- /dev/null +++ b/tests/unit/data/test_ops.py @@ -0,0 +1,92 @@ +import numpy as np +import pytest + +from im2sim.data.ops import * + +eps = 1e-8 + + +def test_normtorange_basic(): + x = np.array([0, 5, 10]) + result = normtorange(x, min=0, max=10, a=0, b=1) + expected = np.array([0.0, 0.5, 1.0]) + assert np.allclose(result, expected) + + +def test_normtorange_auto_min_max(): + x = np.array([2, 4, 6]) + result = normtorange(x) + expected = np.array([0.0, 0.5, 1.0]) + assert np.allclose(result, expected) + + +def test_normtorange_custom_range(): + x = np.array([0, 5, 10]) + result = normtorange(x, min=0, max=10, a=-1, b=1) + expected = np.array([-1.0, 0.0, 1.0]) + assert np.allclose(result, expected) + + +def test_inv_normtorange_basic(): + x = np.array([0.0, 0.5, 1.0]) + result = inv_normtorange(x, min=0, max=10, a=0, b=1) + expected = np.array([0, 5, 10]) + assert np.allclose(result, expected) + + +def test_norm_inverse_consistency(): + x = np.random.rand(10) + normed = normtorange(x) + recovered = inv_normtorange(normed, min=x.min(), max=x.max()) + assert np.allclose(x, recovered) + + +def test_normalise_wrapper(): + x = np.array([1, 2, 3]) + assert np.allclose(normalise(x), normtorange(x)) + + +def test_inv_normalise_wrapper(): + x = np.array([0.0, 0.5, 1.0]) + assert np.allclose(inv_normalise(x, 0, 10), inv_normtorange(x, 0, 10)) + + +def test_standardise_basic(): + x = np.array([1, 2, 3]) + result = standardise(x) + assert np.isclose(result.mean(), 0.0, atol=1e-7) + assert np.isclose(result.std(), 1.0, atol=1e-7) + + +def test_standardise_with_given_params(): + x = np.array([1, 2, 3]) + result = standardise(x, mean=2, std=1) + expected = np.array([-1, 0, 1]) + assert np.allclose(result, expected) + + +def test_inv_standardise_basic(): + x = np.array([-1, 0, 1]) + result = inv_standardise(x, mean=2, std=1) + expected = np.array([1, 2, 3]) + assert np.allclose(result, expected) + + +def test_standardise_inverse_consistency(): + x = np.random.rand(10) + mean = x.mean() + std = x.std() + standardised = standardise(x, mean, std) + recovered = inv_standardise(standardised, mean, std) + assert np.allclose(x, recovered) + + +def test_zero_variance_handling(): + x = np.array([5, 5, 5]) + result = standardise(x) + # should not produce NaN due to eps + assert not np.any(np.isnan(result)) + + +################## FITTABLE OPS ################# + diff --git a/tests/unit/data/test_pipeline.py b/tests/unit/data/test_pipeline.py new file mode 100644 index 0000000..b740b37 --- /dev/null +++ b/tests/unit/data/test_pipeline.py @@ -0,0 +1,279 @@ +import pytest +import torch +import copy + +from im2sim.data import Transform, Pipeline, Operation, InvertibleOperation, FittableOperation, Pipeline, register_op + + +# ----------------------------- +# Helpers / Dummy Ops +# ----------------------------- + +class DummyOp(Operation): + def forward(self, x): + return x + 1 + +class DummyInvertibleOp(InvertibleOperation): + def forward(self, x): + return x + 1 + + def inverse(self, x): + return x - 1 + + +@register_op +class DummyFittableOp(FittableOperation): + def __init__(self): + self.total = 0 + self.count = 0 + + def fit_step(self, x): + self.total += x.sum() + self.count += x.numel() + + def complete_fit(self): + self.mean = self.total / self.count + + def forward(self, x): + return x - self.mean + + def inverse(self, x): + return x + self.mean + + +# ----------------------------- +# Transform Tests +# ----------------------------- + +def test_transform_single_key_forward(): + t = Transform(DummyOp(), keys="a") + data = {"a": torch.tensor([1.0])} + + out = t.forward(data) + assert torch.allclose(out["a"], torch.tensor([2.0])) + + +def test_transform_multiple_keys(): + t = Transform(DummyOp(), keys=["a", "b"]) + data = {"a": torch.tensor([1.0]), "b": torch.tensor([2.0])} + + out = t.forward(data) + assert torch.allclose(out["a"], torch.tensor([2.0])) + assert torch.allclose(out["b"], torch.tensor([3.0])) + + + + +def test_transform_missing_attr(): + t = Transform(DummyOp(), keys="a", attr="missing") + data = {"a": object()} + + with pytest.raises(ValueError): + t.forward(data) + + +# ----------------------------- +# Channel Handling +# ----------------------------- + +def test_transform_channel_subset(): + t = Transform(DummyOp(), keys="a", channels=[0], channel_dim=0) + data = {"a": torch.tensor([[1.0], [2.0]])} + + out = t.forward(data) + assert out["a"][0] == 2.0 + assert out["a"][1] == 2.0 # unchanged + + +def test_transform_per_channel(): + t = Transform(DummyOp(), keys="a", per_channel=True, channel_dim=0) + data = {"a": torch.tensor([[1.0], [2.0]])} + + out = t.forward(data) + assert torch.allclose(out["a"], torch.tensor([[2.0], [3.0]])) + + +# ----------------------------- +# Fittable Transform +# ----------------------------- + +def test_fittable_requires_fit(): + t = Transform(DummyFittableOp(), keys="a") + data = {"a": torch.tensor([1.0])} + + with pytest.raises(RuntimeError): + t.forward(data) + + +def test_fittable_fit_and_forward(): + t = Transform(DummyFittableOp(), keys="a") + + dataset = [{"a": torch.tensor([1.0])}, {"a": torch.tensor([3.0])}] + + class DummyLoader: + def __iter__(self): + return iter(dataset) + + t.fit(DummyLoader()) + + out = t.forward({"a": torch.tensor([3.0])}) + assert torch.allclose(out["a"], torch.tensor([3.0 - 2.0])) + + +# ----------------------------- +# Invertibility +# ----------------------------- + +def test_invertible_transform(): + t = Transform(DummyInvertibleOp(), keys="a") + t.is_invertible = True # simulate isinstance check + + data = {"a": torch.tensor([5.0])} + + out = t.forward(copy.deepcopy(data)) + inv = t.inverse(out) + + assert torch.allclose(inv["a"], data["a"]) + + +def test_non_invertible_raises(): + t = Transform(DummyOp(), keys="a") + data = {"a": torch.tensor([1.0])} + + with pytest.raises(RuntimeError): + t.inverse(data) + + +# ----------------------------- +# Pipeline Tests +# ----------------------------- + +def test_pipeline_sequential(): + t1 = Transform(DummyOp(), keys="a") + t2 = Transform(DummyOp(), keys="a") + + p = Pipeline([t1, t2]) + data = {"a": torch.tensor([1.0])} + + out = p(data) + assert torch.allclose(out["a"], torch.tensor([3.0])) + + +def test_pipeline_inverse_order(): + t1 = Transform(DummyInvertibleOp(), keys="a") + t1.is_invertible = True + t2 = Transform(DummyInvertibleOp(), keys="a") + t2.is_invertible = True + + p = Pipeline([t1, t2]) + data = {"a": torch.tensor([1.0])} + + out = p(copy.deepcopy(data)) + inv = p.inverse(out) + + assert torch.allclose(inv["a"], data["a"]) + + +def test_pipeline_skips_missing_keys(): + t = Transform(DummyOp(), keys="b") + p = Pipeline([t]) + + data = {"a": torch.tensor([1.0])} + out = p(data) + + assert "a" in out + assert "b" not in out + + +# ----------------------------- +# Pipeline Fit +# ----------------------------- + +def test_pipeline_fit_only_fittable(): + t1 = Transform(DummyOp(), keys="a") + t2 = Transform(DummyFittableOp(), keys="a") + + dataset = [{"a": torch.tensor([1.0])}, {"a": torch.tensor([3.0])}] + + class DummyDataset: + def __init__(self): + self.data = dataset + self.transforms = [] + + def __len__(self): + return len(self.data) + + def __getitem__(self, idx): + item = self.data[idx] + for t in self.transforms: + item = t(item) + return item + + p = Pipeline([t1, t2]) + p.fit(DummyDataset()) + + assert t2.fitted is True + + +# ----------------------------- +# Serialization +# ----------------------------- + +def test_transform_state_dict_roundtrip(): + t = Transform(DummyFittableOp(), keys="a") + + dataset = [{"a": torch.tensor([2.0])}] + + class DummyLoader: + def __iter__(self): + return iter(dataset) + + t.fit(DummyLoader()) + state = t.state_dict() + + t2 = Transform(DummyFittableOp(), keys="a") + t2.load_state_dict(state) + + assert t2.fitted is True + + +def test_pipeline_state_dict_roundtrip(tmp_path): + t = Transform(DummyFittableOp(), keys="a") + + dataset = [{"a": torch.tensor([2.0])}] + + class DummyLoader: + def __iter__(self): + return iter(dataset) + + t.fit(DummyLoader()) + + p = Pipeline([t]) + path = tmp_path / "pipeline.pt" + + torch.save({"config": p.config(), "state": p.state_dict()}, path) + + + + obj = torch.load(path) + p2 = Pipeline.from_config(obj["config"]) + p2.load_state_dict(obj["state"]) + + assert list(p2.state_dict().keys()) == list(p.state_dict().keys()) + + +# ----------------------------- +# Edge Cases +# ----------------------------- + +def test_empty_pipeline(): + p = Pipeline([]) + data = {"a": torch.tensor([1.0])} + + out = p(data) + assert out == data + + +def test_no_keys_transform(): + with pytest.raises(ValueError): + t = Transform(DummyOp(), keys=[]) From 62cedd2b4f87606669a28b8d0e6bde9b0e9f753c Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 28 Jul 2026 12:54:29 +0000 Subject: [PATCH 33/49] fixed lints --- src/im2sim/data/core.py | 22 +- src/im2sim/data/ops.py | 1 + src/im2sim/layers/graph_blocks.py | 2 - src/im2sim/layers/projections.py | 8 +- tests/integration/data/test_data_pipeline.py | 21 +- tests/integration/data/test_pv_mesh_utils.py | 215 +++++++++---------- tests/unit/data/test_loader.py | 16 +- tests/unit/data/test_ops.py | 2 - tests/unit/data/test_pipeline.py | 31 ++- 9 files changed, 159 insertions(+), 159 deletions(-) diff --git a/src/im2sim/data/core.py b/src/im2sim/data/core.py index 3ec451d..0925b58 100644 --- a/src/im2sim/data/core.py +++ b/src/im2sim/data/core.py @@ -1,16 +1,15 @@ import copy import logging from abc import ABC, abstractmethod -from typing import Any, Callable, Dict, Union, Optional +from collections.abc import Callable +from typing import Any import torch from torch_geometric.data import Batch, Data - logger = logging.getLogger(__name__) - class Operation(ABC): """ Abstract base class for making operations. @@ -127,7 +126,7 @@ def __init__( name=None, ): - if keys is None or keys==[]: + if keys is None or keys == []: raise ValueError("keys must be specified") self.keys = keys if isinstance(keys, list) else [keys] @@ -531,8 +530,8 @@ def register_op(cls): # ------------------------------------------------------------------------------------ +LoadFn = Callable[[str], dict[str, torch.Tensor | Data]] -LoadFn = Callable[[str], Dict[str, Union[torch.Tensor, Data]]] def collate(batch): """ @@ -616,12 +615,17 @@ class Dataset(torch.utils.data.Dataset): >>> dataset = im2sim.data.Dataset(load_fn=load, cases=cases) """ - def __init__(self, load_fn: LoadFn, cases: list[str], transforms: Optional[list[Transform] | Pipeline] = None): + def __init__( + self, + load_fn: LoadFn, + cases: list[str], + transforms: list[Transform] | Pipeline | None = None, + ): self.load_fn = load_fn self.cases = cases self.add_transforms(transforms) - def add_transforms(self, transforms: Optional[list[Transform] | Pipeline] = None): + def add_transforms(self, transforms: list[Transform] | Pipeline | None = None): if transforms is None: self.transforms = [] elif isinstance(transforms, Pipeline): @@ -632,10 +636,10 @@ def add_transforms(self, transforms: Optional[list[Transform] | Pipeline] = None def __len__(self) -> int: return len(self.cases) - def __getitem__(self, idx: int) -> dict[str, Union[torch.Tensor, Data]]: + def __getitem__(self, idx: int) -> dict[str, torch.Tensor | Data]: case = self.cases[idx] sample = self.load_fn(case) for transform in self.transforms: sample = transform(sample) - return sample \ No newline at end of file + return sample diff --git a/src/im2sim/data/ops.py b/src/im2sim/data/ops.py index ebc9002..3e27d29 100644 --- a/src/im2sim/data/ops.py +++ b/src/im2sim/data/ops.py @@ -1,4 +1,5 @@ from .core import * + # TODO: fix the PowerScaleOp # ------------------------------------------------------------------------------------ # OP FUNCTION LIBRARY diff --git a/src/im2sim/layers/graph_blocks.py b/src/im2sim/layers/graph_blocks.py index f102f3b..6c86fcf 100644 --- a/src/im2sim/layers/graph_blocks.py +++ b/src/im2sim/layers/graph_blocks.py @@ -251,8 +251,6 @@ def forward(self, in_graph, prev_results, encoder_projection): return graph, new_results - - # class GraphUNetDecoderBlock(nn.Module): # def __init__(self, diff --git a/src/im2sim/layers/projections.py b/src/im2sim/layers/projections.py index 81dc4af..0bee2b9 100644 --- a/src/im2sim/layers/projections.py +++ b/src/im2sim/layers/projections.py @@ -4,12 +4,9 @@ import torch.nn.functional as F from torch import nn -from ..data.mesh_utils import make_padded_batch - logger = logging.getLogger(__name__) - class TrilinearProjection(nn.Module): def __init__(self, domain_size): super().__init__() @@ -17,10 +14,9 @@ def __init__(self, domain_size): def forward(self, encoder_outputs, graph_coords, batch): projections = [] - + n_dims = graph_coords.shape[1] for i in torch.unique(batch).to(torch.int16): - coords = graph_coords[batch == i] n_nodes = coords.shape[0] @@ -32,7 +28,7 @@ def forward(self, encoder_outputs, graph_coords, batch): axis=-1, ) # normalise coords [-1,1] and divide by scale - grid = grid.reshape(1,n_nodes, 1, 1, n_dims) # [N,3]->[1,N,1,1,3] + grid = grid.reshape(1, n_nodes, 1, 1, n_dims) # [N,3]->[1,N,1,1,3] grid = grid.type_as(encoder_outputs) diff --git a/tests/integration/data/test_data_pipeline.py b/tests/integration/data/test_data_pipeline.py index bf2deda..ea3f686 100644 --- a/tests/integration/data/test_data_pipeline.py +++ b/tests/integration/data/test_data_pipeline.py @@ -1,9 +1,10 @@ -import torch +import copy + import pytest +import torch from torch_geometric.data import Data -import copy -from im2sim.data import Pipeline, transforms, Dataset +from im2sim.data import Dataset, Pipeline, transforms def make_toy_dataset(): @@ -16,7 +17,7 @@ def make_toy_dataset(): """ samples = {} - cases = ["1","2","3","4","5"] + cases = ["1", "2", "3", "4", "5"] for case in cases: num_nodes = 10 @@ -51,9 +52,6 @@ def load(case): return ds - - - @pytest.fixture def pipeline(): @@ -106,17 +104,13 @@ def test_pipeline_inverse(pipeline): sample = make_toy_dataset()[0] # keep a copy before modification - original = { - key: value.x.clone() - for key, value in sample.items() - } + original = {key: value.x.clone() for key, value in sample.items()} transformed = pipeline(sample) recovered = pipeline.inverse(transformed) for key in ["input", "gt"]: - torch.testing.assert_close( recovered[key].x, original[key], @@ -139,7 +133,6 @@ def test_integrated_pipeline(pipeline): transformed = pipeline(sample2) for key in ["input", "gt"]: - torch.testing.assert_close( recovered[key].x, sample2[key].x, @@ -152,4 +145,4 @@ def test_integrated_pipeline(pipeline): transformed[key].x, rtol=1e-5, atol=1e-6, - ) \ No newline at end of file + ) diff --git a/tests/integration/data/test_pv_mesh_utils.py b/tests/integration/data/test_pv_mesh_utils.py index 52bfe9f..7bfee99 100644 --- a/tests/integration/data/test_pv_mesh_utils.py +++ b/tests/integration/data/test_pv_mesh_utils.py @@ -1,79 +1,93 @@ -import pyvista as pv +import hypothesis.strategies as st import numpy as np -from torch_geometric.data import Data - +import pyvista as pv from hypothesis import given -import hypothesis.strategies as st +from torch_geometric.data import Data from im2sim.data.mesh_utils import * def build_tetra_mesh(): - points = np.array([ - [0, 0, 0], # 0 - [1, 0, 0], # 1 - [0, 1, 0], # 2 - [0, 0, 1], # 3 - [1, 1, 1], # 4 (second tetra apex) - ]).astype(np.float32) + points = np.array( + [ + [0, 0, 0], # 0 + [1, 0, 0], # 1 + [0, 1, 0], # 2 + [0, 0, 1], # 3 + [1, 1, 1], # 4 (second tetra apex) + ] + ).astype(np.float32) # Two tetrahedra - cells = np.hstack([ - [4, 0, 1, 2, 3], # tetra 1 - [4, 1, 2, 3, 4], # tetra 2 - ]) + cells = np.hstack( + [ + [4, 0, 1, 2, 3], # tetra 1 + [4, 1, 2, 3, 4], # tetra 2 + ] + ) celltypes = np.array([pv.CellType.TETRA, pv.CellType.TETRA]) grid = pv.UnstructuredGrid(cells, celltypes, points) - grid["CellEntityIds"] = np.array([0]*2) # Assign the same entity ID to both tetrahedra + grid["CellEntityIds"] = np.array( + [0] * 2 + ) # Assign the same entity ID to both tetrahedra grid["vtkOriginalPointIds"] = np.arange(len(points)) return grid - def build_triangle_mesh(): # 4 points forming a square - points = np.array([ - [0, 0, 0], # 0 - [1, 0, 0], # 1 - [1, 1, 0], # 2 - [0, 1, 0], # 3 - ]).astype(np.float32) + points = np.array( + [ + [0, 0, 0], # 0 + [1, 0, 0], # 1 + [1, 1, 0], # 2 + [0, 1, 0], # 3 + ] + ).astype(np.float32) # Two triangle faces: (0,1,2) and (0,2,3) - faces = np.hstack([ - [3, 0, 1, 2], - [3, 0, 2, 3], - ]) + faces = np.hstack( + [ + [3, 0, 1, 2], + [3, 0, 2, 3], + ] + ) grid = pv.PolyData(points, faces) - grid["CellEntityIds"] = np.array([0]*2) # Assign the same entity ID to both tetrahedra + grid["CellEntityIds"] = np.array( + [0] * 2 + ) # Assign the same entity ID to both tetrahedra grid["vtkOriginalPointIds"] = np.arange(len(points)) return grid + def make_square_mesh(): # Four nodes in a square - x = torch.tensor([ - [0.0, 0.0], - [1.0, 0.0], - [1.0, 1.0], - [0.0, 1.0], - ]) + x = torch.tensor( + [ + [0.0, 0.0], + [1.0, 0.0], + [1.0, 1.0], + [0.0, 1.0], + ] + ) # Undirected edges - edge_index = torch.tensor([ - [0, 1, 2, 3, 0, 1, 2, 3], - [1, 0, 3, 2, 3, 2, 1, 0], - ]) + edge_index = torch.tensor( + [ + [0, 1, 2, 3, 0, 1, 2, 3], + [1, 0, 3, 2, 3, 2, 1, 0], + ] + ) return Data(x=x, edge_index=edge_index) - def to_set(elems): """ Convert edge/cell array to set of sorted tuples. - Works for edges/cells + Works for edges/cells - (N, m) where N is number of edges/cells and m is nodes per edge/cell @@ -88,8 +102,9 @@ def test_get_edges_surf(): print(edges.shape) edge_set = to_set(edges) - expected = np.array([[0,1],[1,2],[0,2],[2,3],[0,3], - [1,0],[2,1],[2,0],[3,2],[3,0]]) + expected = np.array( + [[0, 1], [1, 2], [0, 2], [2, 3], [0, 3], [1, 0], [2, 1], [2, 0], [3, 2], [3, 0]] + ) expected = to_set(expected) @@ -97,21 +112,33 @@ def test_get_edges_surf(): assert len(edge_set) == 5 - def test_get_edges_tet(): mesh = build_tetra_mesh() edges = get_edges_tet(mesh).numpy().T edge_set = to_set(edges) - expected = np.array( - [[0,1], [0,2], [0,3], - [1,2], [1,3], [2,3], - [1,4], [2,4], [3,4], - [1,0], [2,0], [3,0], - [2,1], [3,1], [3,2], - [4,1], [4,2], [4,3]] + [ + [0, 1], + [0, 2], + [0, 3], + [1, 2], + [1, 3], + [2, 3], + [1, 4], + [2, 4], + [3, 4], + [1, 0], + [2, 0], + [3, 0], + [2, 1], + [3, 1], + [3, 2], + [4, 1], + [4, 2], + [4, 3], + ] ) expected = to_set(expected) @@ -119,29 +146,28 @@ def test_get_edges_tet(): assert edge_set == expected assert len(edge_set) == 9 + def test_get_structure_cells(): tet_mesh = build_tetra_mesh() - tet_cells = get_structure_cells(tet_mesh, {0:'vol'})['vol_cell_index'].numpy().T + tet_cells = get_structure_cells(tet_mesh, {0: "vol"})["vol_cell_index"].numpy().T tet_cell_set = to_set(tet_cells) - tet_expected = np.array( - [[0, 1, 2, 3], - [1, 2, 3, 4]] - ) + tet_expected = np.array([[0, 1, 2, 3], [1, 2, 3, 4]]) tet_expected = to_set(tet_expected) assert tet_cell_set == tet_expected assert len(tet_cell_set) == 2 + def test_get_structure_ids(): tet_mesh = build_tetra_mesh() - tet_ids = set(get_structure_ids(tet_mesh, {0:'vol'})['vol_index'].numpy().tolist()) + tet_ids = set(get_structure_ids(tet_mesh, {0: "vol"})["vol_index"].numpy().tolist()) - tet_expected = {0,1,2,3,4} + tet_expected = {0, 1, 2, 3, 4} assert tet_ids == tet_expected assert len(tet_ids) == 5 @@ -157,93 +183,60 @@ def test_cluster_pool_reduces_nodes(): assert pooled.x.shape[0] <= mesh.x.shape[0] assert pooled.edge_index.shape[0] == 2 + def test_cluster_pool_preserves_feature_range(): mesh = make_square_mesh() pooled = cluster_pool(mesh) # Averaging cannot create values outside the original range - assert torch.all( - pooled.x >= mesh.x.min() - ) - assert torch.all( - pooled.x <= mesh.x.max() - ) + assert torch.all(pooled.x >= mesh.x.min()) + assert torch.all(pooled.x <= mesh.x.max()) + def test_cluster_pool_handles_zero_length_edges(): mesh = Data( - x=torch.tensor([ - [0., 0.], - [0., 0.], - [1., 0.] - ]), - edge_index=torch.tensor([ - [0, 1, 1], - [1, 0, 2] - ]) + x=torch.tensor([[0.0, 0.0], [0.0, 0.0], [1.0, 0.0]]), + edge_index=torch.tensor([[0, 1, 1], [1, 0, 2]]), ) pooled = cluster_pool(mesh) assert torch.isfinite(pooled.x).all() + def test_rasterize_output_shape(): - points = torch.tensor([ - [0., 0., 0.] - ]) - - result = rasterize( - points, - im_shape=[4, 4, 4], - vox_sizes=[1., 1., 1.] - ) + points = torch.tensor([[0.0, 0.0, 0.0]]) + + result = rasterize(points, im_shape=[4, 4, 4], vox_sizes=[1.0, 1.0, 1.0]) assert result.shape == (4, 4, 4) + def test_rasterize_single_point_minimum(): - points = torch.tensor([ - [0.5, 0.5, 0.5] - ]) + points = torch.tensor([[0.5, 0.5, 0.5]]) - result = rasterize( - points, - im_shape=[4, 4, 4], - vox_sizes=[1., 1., 1.] - ) + result = rasterize(points, im_shape=[4, 4, 4], vox_sizes=[1.0, 1.0, 1.0]) assert result[0, 0, 0] == 0 def test_rasterize_two_points(): - points = torch.tensor([ - [0., 0., 0.], - [3., 3., 3.] - ]) - - result = rasterize( - points, - im_shape=[4,4,4], - vox_sizes=[1.,1.,1.] - ) + points = torch.tensor([[0.0, 0.0, 0.0], [3.0, 3.0, 3.0]]) - assert result[0,0,0] == result[-1,-1,-1] - assert result[0,0,0] < result[1,1,1] + result = rasterize(points, im_shape=[4, 4, 4], vox_sizes=[1.0, 1.0, 1.0]) + assert result[0, 0, 0] == result[-1, -1, -1] + assert result[0, 0, 0] < result[1, 1, 1] -@given( - st.integers(min_value=1, max_value=10) -) +@given(st.integers(min_value=1, max_value=10)) def test_rasterize_never_returns_negative(n): - points = torch.rand(n,3) + points = torch.rand(n, 3) - result = rasterize( - points, - [8,8,8], - [1.,1.,1.] - ) + result = rasterize(points, [8, 8, 8], [1.0, 1.0, 1.0]) - assert torch.all(result >= 0) \ No newline at end of file + assert torch.all(result >= 0) diff --git a/tests/unit/data/test_loader.py b/tests/unit/data/test_loader.py index 583f0e5..0d849bf 100644 --- a/tests/unit/data/test_loader.py +++ b/tests/unit/data/test_loader.py @@ -1,13 +1,14 @@ import pytest import torch -from torch_geometric.data import Data, Batch +from torch_geometric.data import Batch, Data -from im2sim.data import collate, Dataset, DataLoader +from im2sim.data import DataLoader, Dataset, collate # ------------------------- # Fixtures # ------------------------- + @pytest.fixture def tensor_sample(): return { @@ -35,6 +36,7 @@ def mixed_sample(): # collate tests # ------------------------- + def test_collate_tensors(tensor_sample): batch = [tensor_sample, tensor_sample] out = collate(batch) @@ -71,6 +73,7 @@ def test_collate_invalid_type(): # Dataset tests # ------------------------- + def dummy_load_fn(case: str): return {"x": torch.tensor([len(case)])} @@ -91,8 +94,6 @@ def test_dataset_getitem(): assert torch.equal(sample["x"], torch.tensor([1])) - - def test_dataset_no_transforms(): ds = Dataset(load_fn=dummy_load_fn, cases=["a"], transforms=None) sample = ds[0] @@ -104,6 +105,7 @@ def test_dataset_no_transforms(): # DataLoader tests # ------------------------- + def test_dataloader_basic(): ds = Dataset(load_fn=dummy_load_fn, cases=["a", "bb", "ccc"]) @@ -118,7 +120,9 @@ def test_dataloader_basic(): def test_dataloader_with_pyg(): def load_fn(case): return { - "graph": Data(x=torch.randn(3, 2), edge_index=torch.tensor([[0, 1], [1, 2]])) + "graph": Data( + x=torch.randn(3, 2), edge_index=torch.tensor([[0, 1], [1, 2]]) + ) } ds = Dataset(load_fn=load_fn, cases=["a", "b", "c"]) @@ -126,4 +130,4 @@ def load_fn(case): batch = next(iter(loader)) assert isinstance(batch["graph"], Batch) - assert batch["graph"].num_graphs == 2 \ No newline at end of file + assert batch["graph"].num_graphs == 2 diff --git a/tests/unit/data/test_ops.py b/tests/unit/data/test_ops.py index 1f197c2..a7ea2cc 100644 --- a/tests/unit/data/test_ops.py +++ b/tests/unit/data/test_ops.py @@ -1,5 +1,4 @@ import numpy as np -import pytest from im2sim.data.ops import * @@ -89,4 +88,3 @@ def test_zero_variance_handling(): ################## FITTABLE OPS ################# - diff --git a/tests/unit/data/test_pipeline.py b/tests/unit/data/test_pipeline.py index b740b37..11c6380 100644 --- a/tests/unit/data/test_pipeline.py +++ b/tests/unit/data/test_pipeline.py @@ -1,18 +1,27 @@ -import pytest -import torch import copy -from im2sim.data import Transform, Pipeline, Operation, InvertibleOperation, FittableOperation, Pipeline, register_op +import pytest +import torch +from im2sim.data import ( + FittableOperation, + InvertibleOperation, + Operation, + Pipeline, + Transform, + register_op, +) # ----------------------------- # Helpers / Dummy Ops # ----------------------------- + class DummyOp(Operation): def forward(self, x): return x + 1 + class DummyInvertibleOp(InvertibleOperation): def forward(self, x): return x + 1 @@ -36,7 +45,7 @@ def complete_fit(self): def forward(self, x): return x - self.mean - + def inverse(self, x): return x + self.mean @@ -45,6 +54,7 @@ def inverse(self, x): # Transform Tests # ----------------------------- + def test_transform_single_key_forward(): t = Transform(DummyOp(), keys="a") data = {"a": torch.tensor([1.0])} @@ -62,8 +72,6 @@ def test_transform_multiple_keys(): assert torch.allclose(out["b"], torch.tensor([3.0])) - - def test_transform_missing_attr(): t = Transform(DummyOp(), keys="a", attr="missing") data = {"a": object()} @@ -76,6 +84,7 @@ def test_transform_missing_attr(): # Channel Handling # ----------------------------- + def test_transform_channel_subset(): t = Transform(DummyOp(), keys="a", channels=[0], channel_dim=0) data = {"a": torch.tensor([[1.0], [2.0]])} @@ -97,6 +106,7 @@ def test_transform_per_channel(): # Fittable Transform # ----------------------------- + def test_fittable_requires_fit(): t = Transform(DummyFittableOp(), keys="a") data = {"a": torch.tensor([1.0])} @@ -124,6 +134,7 @@ def __iter__(self): # Invertibility # ----------------------------- + def test_invertible_transform(): t = Transform(DummyInvertibleOp(), keys="a") t.is_invertible = True # simulate isinstance check @@ -148,6 +159,7 @@ def test_non_invertible_raises(): # Pipeline Tests # ----------------------------- + def test_pipeline_sequential(): t1 = Transform(DummyOp(), keys="a") t2 = Transform(DummyOp(), keys="a") @@ -189,6 +201,7 @@ def test_pipeline_skips_missing_keys(): # Pipeline Fit # ----------------------------- + def test_pipeline_fit_only_fittable(): t1 = Transform(DummyOp(), keys="a") t2 = Transform(DummyFittableOp(), keys="a") @@ -219,6 +232,7 @@ def __getitem__(self, idx): # Serialization # ----------------------------- + def test_transform_state_dict_roundtrip(): t = Transform(DummyFittableOp(), keys="a") @@ -253,8 +267,6 @@ def __iter__(self): torch.save({"config": p.config(), "state": p.state_dict()}, path) - - obj = torch.load(path) p2 = Pipeline.from_config(obj["config"]) p2.load_state_dict(obj["state"]) @@ -266,6 +278,7 @@ def __iter__(self): # Edge Cases # ----------------------------- + def test_empty_pipeline(): p = Pipeline([]) data = {"a": torch.tensor([1.0])} @@ -276,4 +289,4 @@ def test_empty_pipeline(): def test_no_keys_transform(): with pytest.raises(ValueError): - t = Transform(DummyOp(), keys=[]) + Transform(DummyOp(), keys=[]) From 1a697aff2774837bd350828f81520658d608653d Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 28 Jul 2026 13:02:27 +0000 Subject: [PATCH 34/49] added hypothesis to dev installs --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index db67676..f3a5df5 100644 --- a/setup.py +++ b/setup.py @@ -9,6 +9,7 @@ extras_require={ "dev": [ "pytest", + "hypothesis" "ruff", ], }, From a41466257d4db2683b1c72563846d656efeb4e25 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 28 Jul 2026 13:05:30 +0000 Subject: [PATCH 35/49] added hypothesis --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index f3a5df5..2a1df1e 100644 --- a/setup.py +++ b/setup.py @@ -9,7 +9,7 @@ extras_require={ "dev": [ "pytest", - "hypothesis" + "hypothesis", "ruff", ], }, From 6d844e533c93ffd64ff7d9ebd2abcb5d451295ed Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Wed, 29 Jul 2026 17:58:06 +0100 Subject: [PATCH 36/49] added tests for layers --- .github/workflows/tests.yaml | 4 +- .gitignore | 3 + .pre-commit-config.yaml | 7 + README.md | 30 ++ pyproject.toml | 117 +++++ setup.py | 16 - src/im2sim/layers/graph_blocks.py | 77 +-- src/im2sim/layers/image_blocks.py | 27 +- src/im2sim/layers/layer_util.py | 117 +++-- tests/unit/layers/test_graph_blocks.py | 284 ++++++++++ tests/unit/layers/test_image_blocks.py | 695 +++++++++++++++++++++++++ 11 files changed, 1274 insertions(+), 103 deletions(-) create mode 100644 .pre-commit-config.yaml create mode 100644 README.md create mode 100644 pyproject.toml delete mode 100644 setup.py create mode 100644 tests/unit/layers/test_graph_blocks.py create mode 100644 tests/unit/layers/test_image_blocks.py diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml index 868f250..28e1dd8 100644 --- a/.github/workflows/tests.yaml +++ b/.github/workflows/tests.yaml @@ -10,10 +10,10 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v4 + uses: actions/checkout@v5 - name: Set up Python - uses: actions/setup-python@v5 + uses: actions/setup-python@v6 with: python-version: '3.10' diff --git a/.gitignore b/.gitignore index 70a4463..b479bed 100644 --- a/.gitignore +++ b/.gitignore @@ -12,3 +12,6 @@ Thumbs.db # pytest .pytest_cache/ + +# dev tests +*.ipynb diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..c5bf032 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,7 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.12.0 # use the latest Ruff version you prefer + hooks: + - id: ruff + args: [--fix] + - id: ruff-format \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..3c780c7 --- /dev/null +++ b/README.md @@ -0,0 +1,30 @@ +# im2sim + +`im2sim` is a library designed to simplify the development of ML-accelerated digital twins based on medical images. + +This includes two main components: +1) Deep Learning (DL) frameworks based on [Pytorch](https://pytorch.org) and [Pytorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/#) +2) Mesh processing frameworks based on [VTK](https://vtk.org) and [PyVista](https://docs.pyvista.org/index.html) + +## Features + +- Image and Mesh Data Processing +- DL models for medical imaging applications +- DL models for digital twin applications +- Hybrid DL models for simulation outputs directly from images +- Building blocks for custom DL models +- Visualisation utilities + +## Installation + +1. Install the CUDA dependencies + ```bash + pip install torch-scatter torch-cluster\ + -f https://data.pyg.org/whl/torch-2.3.1+cu121.html + ``` + +2. Install the repo: + ```bash + pip install git+https://github.com/mrphys/im2sim.git + ``` + diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..864ca22 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,117 @@ +[build-system] +requires = ["setuptools>=68", "wheel"] +build-backend = "setuptools.build_meta" + + +[project] +name = "im2sim" +version = "0.1.0" +description = "A framework for ML-accelerated digital twins" +readme = "README.md" +requires-python = "==3.10" +license = { text = "ElasticV2" } +authors = [ + { name = "Anirudh Raman", email = "anirudh.raman.24@ucl.ac.uk" } +] + +dependencies = [ + "torch==2.3.1", + "torch_geometric==2.7.0", + "pyvista==0.47.0", + "numpy", + "matplotlib", + "scikit-learn" +] + + +[project.optional-dependencies] +dev = [ + "pytest", + "pytest-cov", + "ruff", + "pre-commit", +] + + +[tool.setuptools.packages.find] +where = ["src"] + + +# ------------------------- +# Ruff configuration +# ------------------------- + +[tool.ruff] +line-length = 100 +target-version = "py310" + +exclude = [ + ".git", + ".venv", + "venv", + "build", + "dist", + "__pycache__", +] + +[tool.ruff.lint] +select = [ + "E", # pycodestyle errors + "F", # pyflakes + "I", # import sorting + "B", # bugbear + "UP", # pyupgrade + "SIM", # simplify +] + +ignore = [ + "E501", # handled by formatter +] + +fixable = [ + "ALL", +] + +[tool.ruff.lint.isort] +known-first-party = [ + "your_package_name", +] + + +[tool.ruff.format] +quote-style = "double" +indent-style = "space" +line-ending = "auto" + + +# ------------------------- +# Pytest configuration +# ------------------------- + +[tool.pytest.ini_options] +testpaths = [ + "tests", +] + +addopts = [ + "-ra", + "--strict-markers", +] + + +# ------------------------- +# Coverage configuration +# ------------------------- + +[tool.coverage.run] +branch = true +source = [ + "your_package_name", +] + +[tool.coverage.report] +show_missing = true +exclude_lines = [ + "if TYPE_CHECKING:", + "pragma: no cover", +] \ No newline at end of file diff --git a/setup.py b/setup.py deleted file mode 100644 index 2a1df1e..0000000 --- a/setup.py +++ /dev/null @@ -1,16 +0,0 @@ -from setuptools import find_packages, setup - -setup( - name="im2sim", - version="0.1.0", - package_dir={"": "src"}, - packages=find_packages(where="src"), - python_requires=">=3.10", - extras_require={ - "dev": [ - "pytest", - "hypothesis", - "ruff", - ], - }, -) diff --git a/src/im2sim/layers/graph_blocks.py b/src/im2sim/layers/graph_blocks.py index 6c86fcf..f0ffc1d 100644 --- a/src/im2sim/layers/graph_blocks.py +++ b/src/im2sim/layers/graph_blocks.py @@ -1,4 +1,5 @@ import logging +from copy import copy import torch from torch import nn @@ -15,7 +16,8 @@ class DefaultGraphNorm(torch.nn.Module): Uses torch.nn.InstanceNorm2d applied to graph data, but all channels are normalised together. """ - def __init__(self, in_channels, affine): + def __init__(self): + super().__init__() self.norm = torch.nn.InstanceNorm2d(1, affine=True, eps=1e-3) def forward(self, x, batch): @@ -23,6 +25,9 @@ def forward(self, x, batch): raise RuntimeError(f"Expected x.dim()==2, got {x.dim()}") shape = x.shape + if batch is None: + batch = torch.zeros(x.shape[0], dtype=torch.long, device=x.device) + for b in torch.unique(batch): x[batch == b] = self.norm(x[batch == b].unsqueeze(0).unsqueeze(0)).reshape( shape @@ -38,10 +43,10 @@ class GraphConvBlock(nn.Module): in_channels (int): The number of channels in the input to the layer. filters (int, optional): The number of filters in each convolutional layer (default: 32) depth (int, optional): The number of successive convolutional layers (default: 2) - conv_type (str, optional): The type of graph convolution to apply (default: "ChebConv", options: "GraphConv", "GCNConv", "GATConv") + conv_type (str, optional): The type of graph convolution to apply (default: "GATConv", options: All PyG Convs) conv_kwargs(dict, optional): Dictionary of keyword arguments for the chosen conv_type - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") - norm_type (str, optional): The normalization method to apply between convolutions (default:"InstanceNorm", options: "BatchNorm", "LayerNorm") + activation (str, optional): The activation function applied after each convolution (default: "relu", options: All torch activations) + norm_type (str, optional): The normalization method to apply between convolutions (default:"defaultnorm", options: All PyG Norms) Returns: A `torch.nn.Module` object. @@ -61,34 +66,36 @@ def __init__( ): super().__init__() - conv = get_graph_layer(conv_type, conv_kwargs) self.convs = nn.ModuleList( [ - conv(in_channels if i == 0 else filters, filters, **conv_kwargs) + get_graph_layer(name=conv_type, + args=[in_channels if i == 0 else filters, filters], + kwargs = conv_kwargs) for i in range(depth) ] ) - if norm_kwargs == None: - norm_kwargs = {"in_channels": filters, "affine": True} - self.norms = nn.ModuleList( [ - get_graph_layer(norm_type, conv_kwargs) if norm_type else nn.Identity() + get_graph_layer(name=norm_type, + kwargs=norm_kwargs) + if norm_type + else nn.Identity() + for _ in range(depth) ] ) - self.act = ACTIVATIONS[activation]() + self.act = get_activation(activation) def forward(self, in_graph): - out_graph = deepcopy(in_graph) + graph = copy(in_graph) for conv, norm in zip(self.convs, self.norms): - out_graph = conv(out_graph) - out_graph = norm(out_graph) - out_graph.x = self.act(out_graph.x) - return out_graph + graph = conv(graph) + graph = norm(graph) + graph.x = self.act(graph.x) + return graph class GraphConvResBlock(nn.Module): @@ -99,10 +106,10 @@ class GraphConvResBlock(nn.Module): in_channels (int): The number of channels in the input to the layer. filters (int, optional): The number of filters in each convolutional layer (default: 32) depth (int, optional): The number of successive convolutional layers (default: 2) - conv_type (str, optional): The type of graph convolution to apply (default: "ChebConv", options: "GraphConv", "GCNConv", "GATConv") + conv_type (str, optional): The type of graph convolution to apply (default: "ChebConv", options: All PyG Convs) conv_kwargs(dict, optional): Dictionary of keyword arguments for the chosen conv_type - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") - norm_type (str, optional): The normalization method to apply between convolutions (default:"InstanceNorm", options: "BatchNorm", "LayerNorm") + activation (str, optional): The activation function applied after each convolution (default: "relu", options: All torch activations) + norm_type (str, optional): The normalization method to apply between convolutions (default:"InstanceNorm", options: All PyG Norms) Returns: A `torch.nn.Module` object. @@ -117,38 +124,40 @@ def __init__( conv_type="GATConv", conv_kwargs=None, activation="ReLU", - norm_type="InstanceNorm", + norm_type="defaultnorm", norm_kwargs=None, ): super().__init__() - conv = get_graph_layer(conv_type, conv_kwargs) self.convs = nn.ModuleList( [ - conv(in_channels if i == 0 else filters, filters, **conv_kwargs) + get_graph_layer(name=conv_type, + args=[in_channels if i == 0 else filters, filters], + kwargs = conv_kwargs) for i in range(depth) ] ) - if norm_kwargs == None: - norm_kwargs = {"in_channels": filters, "affine": True} - self.norms = nn.ModuleList( [ - get_graph_layer(norm_type, norm_kwargs) if norm_type else nn.Identity() + get_graph_layer(name=norm_type, + kwargs=norm_kwargs) + if norm_type + else nn.Identity() + for _ in range(depth) ] ) - self.act = ACTIVATIONS[activation]() + self.act = get_activation(activation) def forward(self, in_graph): - graph = deepcopy(in_graph) + graph = copy(in_graph) for i, (conv, norm) in enumerate(zip(self.convs, self.norms)): graph = norm(conv(graph)) if i == 0: - x1 = deepcopy(graph.x) + x1 = graph.x elif i < len(self.convs) - 1: graph.x = self.act(graph.x) @@ -169,11 +178,11 @@ class GraphResDecoderBlock(nn.Module): n_process_blocks (int, optional): The number of residual blocks prior to projection(default: 1) n_deform_blocks (int, optional): The number of residual blocks after projection(default: 3) template_edge_index (torch.Tensor, optional): If template tensor is the fixed it can be passed (default: None) - conv_type (str, optional): The type of graph convolution to apply (default: "ChebConv", options: "GraphConv", "GCNConv", "GATConv") + conv_type (str, optional): The type of graph convolution to apply (default: "ChebConv", options: All PyG Convs) conv_kwargs(dict, optional): Dictionary of keyword arguments for the chosen conv_type - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") - out_activation (str, optional): The activation function applied after each convolution (default: "linear", options: "leakyrelu","gelu","sigmoid","relu","softmax") - norm_type (str, optional): The normalization method to apply between convolutions (default:"InstanceNorm", options: "BatchNorm", "LayerNorm") + activation (str, optional): The activation function applied after each convolution (default: "relu", options: All torch activations) + out_activation (str, optional): The activation function applied after each convolution (default: "linear", options: All torch activations) + norm_type (str, optional): The normalization method to apply between convolutions (default:"InstanceNorm", options: All PyG Norms) Returns: A `torch.nn.Module` object. @@ -240,7 +249,7 @@ def forward(self, in_graph, prev_results, encoder_projection): if in_graph.edge_index is None and self.edge_index is not None: in_graph.edge_index = self.edge_index - graph = deepcopy(in_graph) + graph = copy(in_graph) graph = self.process_conv(graph) graph.x = torch.cat([graph.x, encoder_projection, prev_results], axis=-1) diff --git a/src/im2sim/layers/image_blocks.py b/src/im2sim/layers/image_blocks.py index 791aff6..1945cce 100644 --- a/src/im2sim/layers/image_blocks.py +++ b/src/im2sim/layers/image_blocks.py @@ -18,7 +18,7 @@ class ImageConvBlock(nn.Module): kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) depth (int, optional): The number of successive convolutional layers (default: 2) rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:2), - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") + activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid",) norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") dropout_rate (float, optional): The spatial dropout rate to be applied to the final convolution output (default:None) @@ -40,7 +40,7 @@ def __init__( ): super().__init__() - conv = TORCH_LAYERS[f"Conv{rank}d"] + conv = get_torch_layer("Conv", rank) self.convs = nn.ModuleList( [ conv( @@ -55,19 +55,19 @@ def __init__( self.norms = nn.ModuleList( [ - TORCH_LAYERS[f"{norm_type}{rank}d"](filters, affine=True) + get_torch_layer(norm_type, rank)(filters, affine=True) if norm_type else nn.Identity() for _ in range(depth) ] ) self.drop = ( - TORCH_LAYERS(f"Dropout{rank}d")(p=dropout_rate) + get_torch_layer("Dropout", rank)(p=dropout_rate) if dropout_rate else nn.Identity() ) - self.act = ACTIVATIONS[activation] + self.act = get_activation(activation) def forward(self, x): """ @@ -94,7 +94,7 @@ class ImageConvResBlock(nn.Module): kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) depth (int, optional): The number of successive convolutional layers (default: 3) rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:2), - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") + activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid",) norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") dropout_rate (float, optional): The spatial dropout rate to be applied to the convolution prior to residual connection (default:None) @@ -115,13 +115,14 @@ def __init__( dropout_rate=None, ): super().__init__() + assert depth >= 2 self.initial_conv = ImageConvBlock( in_channels=in_channels, filters=filters, kernel_size=kernel_size, rank=rank, - activation="linear", + activation=None, norm_type=norm_type, depth=1, dropout_rate=None, @@ -143,13 +144,13 @@ def __init__( filters=filters, kernel_size=kernel_size, rank=rank, - activation="linear", + activation=None, norm_type=norm_type, depth=1, dropout_rate=None, ) # self.drop = get_image_layer('Dropout', rank)(p=dropout_rate) if dropout_rate else nn.Identity() - self.out_act = ACTIVATIONS[activation] + self.out_act = get_activation(activation) def forward(self, x): """ @@ -177,7 +178,7 @@ class ImageResEncoder(nn.Module): res_depth (int, optional): The number of successive convolutional layers in each residual block (default: 3) res_blocks_per_level (int, optional): The number of successive residual blocks per encoder level (default: 2) rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:3), - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") + activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid",) norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") dropout_rate (float, optional): The spatial dropout rate to be applied to each residual block prior to residual connection (default:None) @@ -225,7 +226,7 @@ def __init__( for i in range(n_levels) ] ) - pool = TORCH_LAYERS[f"{pool_type}{rank}d"] + pool = get_torch_layer(pool_type, rank) self.pools = nn.ModuleList( [pool(pool_size) if i > 0 else nn.Identity() for i in range(n_levels)] ) @@ -259,7 +260,7 @@ class ImageEncoder(nn.Module): kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) conv_blocks_per_level (int, optional): The number of successive convolutional blocks per encoder level (default: 1) rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:3), - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") + activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid",) norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") dropout_rate (float, optional): The spatial dropout rate to be applied to each residual block prior to residual connection (default:None) @@ -298,7 +299,7 @@ def __init__( for i in range(n_levels) ] ) - pool = TORCH_LAYERS[f"{pool_type}{rank}d"] + pool = get_torch_layer(pool_type, rank) self.maxpools = nn.ModuleList( [pool(pool_size) if i > 0 else nn.Identity() for i in range(n_levels)] ) diff --git a/src/im2sim/layers/layer_util.py b/src/im2sim/layers/layer_util.py index 3f89c6f..b373a37 100644 --- a/src/im2sim/layers/layer_util.py +++ b/src/im2sim/layers/layer_util.py @@ -1,7 +1,7 @@ import inspect import re from collections.abc import Callable -from copy import deepcopy +from copy import copy from typing import Any import torch @@ -9,12 +9,6 @@ from torch_geometric.data import Data -def get_graph_layer(name, kwargs): - if kwargs is None: - kwargs = {} - return PyG_Wrapper(PYG_LAYERS[name](**kwargs)) - - def make_registry(lib: Any, regex: Callable): registry = NormalizedDict( @@ -32,6 +26,7 @@ def decorator(obj): def normalize_key(key: str) -> str: + print(key) return key.replace(" ", "").replace("_", "").lower() @@ -45,6 +40,7 @@ def __setitem__(self, key, value): self._data[normalize_key(key)] = value def __getitem__(self, key): + print(key) return self._data[normalize_key(key)] def __str__(self): @@ -66,32 +62,26 @@ def __str__(self): PYG_LAYERS, register_pyg_layer = make_registry(gnn, layer_pattern) -def init_weights(m): - if isinstance(m, gnn.ChebConv): - for lin in m.lins: - torch.nn.init.kaiming_normal_(lin.weight, nonlinearity="leaky_relu") - lin.weight.data *= 0.1 - if lin.bias is not None: - torch.nn.init.zeros_(lin.bias) - -def standardize_spatial_factors(factors, rank): +def get_torch_layer(name: str, rank:int) -> torch.nn.Module: """ - Convert a sequence of spatial factors into a standardized list of tuples. + Get a PyTorch layer by name, with optional arguments. """ - standardized = [] - for f in factors: - if isinstance(f, int): - standardized.append(tuple([f] * rank)) - elif isinstance(f, (tuple, list)): - standardized.append(tuple(f)) - else: - raise TypeError( - f"Each factor must be an int, tuple, or list, got {type(f).__name__}" - ) + rank_name = f"{name}{rank}d" + try: + return TORCH_LAYERS[rank_name] + except KeyError: + pass + + try: + return TORCH_LAYERS[name] + except KeyError: + ValueError(f"Layer {name} with rank {rank} not found in PyTorch layers registry") + +def get_activation(name: str|None) -> torch.nn.Module: + return ACTIVATIONS[name]() if name is not None else torch.nn.Identity() - return standardized class PyGParameterError(TypeError): @@ -102,17 +92,24 @@ class PyGWrapperError(TypeError): pass -def _match_attrs_to_signature(graph: Data, module: torch.nn.Module) -> list[str]: - """This function accepts a PyG data object and a Callable and returns common attributes""" +def _match_attrs_to_signature(graph, module): + sig = inspect.signature(module.forward) + attrs = [] - sig = inspect.signature(module.forward).parameters - for name, param in sig.items(): + + for name, param in sig.parameters.items(): + if name == "self": + continue + if hasattr(graph, name): + value = getattr(graph, name) + + # Ignore methods/functions + if callable(value): + continue + attrs.append(name) - elif param.default is inspect.Parameter.empty: - raise PyGParameterError( - f"Attribute {name} is missing but is required for {module.__class__.__name__} layers." - ) + return attrs @@ -123,7 +120,8 @@ class PyG_Wrapper(torch.nn.Module): Needs to be initialised with a pre-initialised PyG Module. """ - def __init__(self, pyg_module: torch.nn.Module, **kwargs): + def __init__(self, pyg_module: torch.nn.Module): + super().__init__() self.pyg_module = pyg_module def forward(self, graph): @@ -134,6 +132,49 @@ def forward(self, graph): f"PyG layers that have multiple outputs like {self.pyg_module.__class__.__name__} are not currently supported. Consider changing PyG layer or writing a custom wrapper" ) - out_graph = deepcopy(graph) + out_graph = copy(graph) out_graph.x = out return out_graph + +def get_graph_layer(name: str, + args: list[Any] = None, + kwargs: dict[str, Any] = None) -> PyG_Wrapper: + + if args is None: + args = [] + if kwargs is None: + kwargs = {} + + module = PYG_LAYERS[name](*args, **kwargs) + return PyG_Wrapper(module) + + + + + +def init_weights(m): + if isinstance(m, gnn.ChebConv): + for lin in m.lins: + torch.nn.init.kaiming_normal_(lin.weight, nonlinearity="leaky_relu") + lin.weight.data *= 0.1 + if lin.bias is not None: + torch.nn.init.zeros_(lin.bias) + + +def standardize_spatial_factors(factors, rank): + """ + Convert a sequence of spatial factors into a standardized list of tuples. + """ + standardized = [] + + for f in factors: + if isinstance(f, int): + standardized.append(tuple([f] * rank)) + elif isinstance(f, (tuple, list)): + standardized.append(tuple(f)) + else: + raise TypeError( + f"Each factor must be an int, tuple, or list, got {type(f).__name__}" + ) + + return standardized \ No newline at end of file diff --git a/tests/unit/layers/test_graph_blocks.py b/tests/unit/layers/test_graph_blocks.py new file mode 100644 index 0000000..f4fcaf3 --- /dev/null +++ b/tests/unit/layers/test_graph_blocks.py @@ -0,0 +1,284 @@ +import pytest +import torch +from torch_geometric.data import Data + +from im2sim.layers import GraphConvBlock, GraphConvResBlock + + +@pytest.fixture +def graph(): + """ + Simple graph: + 0 -- 1 + | | + 2 -- 3 + """ + x = torch.randn(4, 8) + + edge_index = torch.tensor( + [ + [0, 1, 2, 3, 0, 2], + [1, 0, 3, 2, 2, 0], + ], + dtype=torch.long, + ) + + return Data( + x=x, + edge_index=edge_index, + ) + +graph_blocks = {"GraphConv": GraphConvBlock, "GraphRes":GraphConvResBlock} + + + + + +@pytest.mark.parametrize( + "conv_type", + [ + "GCNConv", + "GATConv", + ], +) +@pytest.mark.parametrize( + "graph_block_type", + [ + "GraphConv", + "GraphRes", + ], +) +def test_forward_shape(graph, conv_type, graph_block_type): + filters = 16 + + model = graph_blocks[graph_block_type]( + in_channels=8, + filters=filters, + depth=2, + conv_type=conv_type, + conv_kwargs={}, + ) + + print(type(model), type(graph)) + out = model(graph) + + assert out.x.shape == (graph.num_nodes, filters) + + +@pytest.mark.parametrize("depth", [1, 2, 4]) +@pytest.mark.parametrize( + "graph_block_type", + [ + "GraphConv", + "GraphRes", + ], +) +def test_depth_changes_number_of_layers(depth, graph_block_type): + model = graph_blocks[graph_block_type]( + in_channels=8, + filters=16, + depth=depth, + conv_type="GCNConv", + conv_kwargs={}, + ) + + assert len(model.convs) == depth + assert len(model.norms) == depth + + +@pytest.mark.parametrize( + "graph_block_type", + [ + "GraphConv", + "GraphRes", + ], +) +def test_graph_structure_is_preserved(graph, graph_block_type): + model = graph_blocks[graph_block_type]( + in_channels=8, + filters=16, + conv_type="GCNConv", + conv_kwargs={}, + ) + + out = model(graph) + + assert torch.equal( + out.edge_index, + graph.edge_index, + ) + +@pytest.mark.parametrize( + "graph_block_type", + [ + "GraphConv", + "GraphRes", + ], +) +def test_input_graph_is_not_modified(graph, graph_block_type): + model = graph_blocks[graph_block_type]( + in_channels=8, + filters=16, + conv_type="GCNConv", + conv_kwargs={}, + ) + + original_x = graph.x.clone() + + _ = model(graph) + + assert torch.allclose( + graph.x, + original_x, + ) + +@pytest.mark.parametrize( + "graph_block_type", + [ + "GraphConv", + "GraphRes", + ], +) +def test_output_is_finite(graph, graph_block_type): + model = graph_blocks[graph_block_type]( + in_channels=8, + filters=16, + conv_type="GCNConv", + conv_kwargs={}, + ) + + out = model(graph) + + assert torch.isfinite(out.x).all() + +@pytest.mark.parametrize( + "graph_block_type", + [ + "GraphConv", + "GraphRes", + ], +) +def test_gradients_flow(graph, graph_block_type): + graph.x.requires_grad_(True) + + model = graph_blocks[graph_block_type]( + in_channels=8, + filters=16, + depth=2, + conv_type="GCNConv", + conv_kwargs={}, + ) + + out = model(graph) + + loss = out.x.sum() + loss.backward() + + assert graph.x.grad is not None + assert torch.isfinite(graph.x.grad).all() + + for name, param in model.named_parameters(): + assert param.grad is not None, f"{name} has no gradient" + assert torch.isfinite(param.grad).all() + + +@pytest.mark.parametrize( + "graph_block_type", + [ + "GraphConv", + "GraphRes", + ], +) +@pytest.mark.parametrize( + "activation", + [ + "ReLU", + "leakyrelu", + "gelu", + "sigmoid", + None + ], +) +def test_supported_activations(graph, activation, graph_block_type): + model = graph_blocks[graph_block_type]( + in_channels=8, + filters=16, + activation=activation, + conv_type="GCNConv", + conv_kwargs={}, + ) + + out = model(graph) + + assert out.x.shape == (4, 16) + +@pytest.mark.parametrize( + "graph_block_type", + [ + "GraphConv", + "GraphRes", + ], +) +def test_no_normalisation(graph, graph_block_type): + model = graph_blocks[graph_block_type]( + in_channels=8, + filters=16, + norm_type=None, + conv_type="GCNConv", + conv_kwargs={}, + ) + + assert isinstance( + model.norms[0], + torch.nn.Identity, + ) + + out = model(graph) + + assert out.x.shape == (4, 16) + +@pytest.mark.parametrize( + "graph_block_type", + [ + "GraphConv", + "GraphRes", + ], +) +def test_serialisation(graph, tmp_path, graph_block_type): + model = graph_blocks[graph_block_type]( + in_channels=8, + filters=16, + conv_type="GCNConv", + conv_kwargs={}, + ) + + model.eval() + + before = model(graph).x + + path = tmp_path / "model.pt" + + torch.save( + model.state_dict(), + path, + ) + + model2 = graph_blocks[graph_block_type]( + in_channels=8, + filters=16, + conv_type="GCNConv", + conv_kwargs={}, + ) + + model2.load_state_dict( + torch.load(path) + ) + + model2.eval() + + after = model2(graph).x + + assert torch.allclose( + before, + after, + ) \ No newline at end of file diff --git a/tests/unit/layers/test_image_blocks.py b/tests/unit/layers/test_image_blocks.py new file mode 100644 index 0000000..1edbb44 --- /dev/null +++ b/tests/unit/layers/test_image_blocks.py @@ -0,0 +1,695 @@ +import pytest +import torch +import torch.nn as nn + +from im2sim.layers import ( + ImageConvBlock, + ImageConvResBlock, + ImageEncoder, + ImageResEncoder, + ImageDecoder, +) + + +# --------------------------------------------------------- +# Fixtures +# --------------------------------------------------------- + +@pytest.fixture +def image_2d(): + # BCHW + return torch.randn(2, 3, 64, 64, requires_grad=True) + + +@pytest.fixture +def image_3d(): + # BCDHW + return torch.randn(2, 3, 32, 32, 32, requires_grad=True) + + +@pytest.fixture +def small_filters(): + return (8, 16, 32) + + +# --------------------------------------------------------- +# ImageConvBlock +# --------------------------------------------------------- + +@pytest.mark.parametrize( + "rank,input_shape,expected_spatial", + [ + (2, (2, 3, 64, 64), (64, 64)), + (3, (2, 3, 32, 32, 32), (32, 32, 32)), + ], +) +def test_image_conv_block_shapes(rank, input_shape, expected_spatial): + + model = ImageConvBlock( + in_channels=3, + filters=16, + depth=2, + rank=rank, + ) + + x = torch.randn(*input_shape) + + y = model(x) + + assert y.shape == ( + input_shape[0], + 16, + *expected_spatial, + ) + + +@pytest.mark.parametrize( + "activation", + [ + "ReLU", + "relu", + "gelu", + "sigmoid", + None, + ], +) +def test_image_conv_block_activations(activation): + + model = ImageConvBlock( + in_channels=3, + filters=8, + rank=2, + activation=activation, + ) + + x = torch.randn(1, 3, 32, 32) + + y = model(x) + + assert y.shape == (1, 8, 32, 32) + + + +@pytest.mark.parametrize( + "norm", + [ + None, + "BatchNorm", + "InstanceNorm", + ], +) +def test_image_conv_block_normalisation(norm): + + model = ImageConvBlock( + in_channels=3, + filters=8, + rank=2, + norm_type=norm, + ) + + x = torch.randn(2, 3, 32, 32) + + y = model(x) + + assert y.shape == (2, 8, 32, 32) + + + +def test_image_conv_block_dropout(): + + model = ImageConvBlock( + in_channels=3, + filters=8, + rank=2, + dropout_rate=0.5, + ) + + assert isinstance(model.drop, nn.Dropout2d) + + x = torch.randn(2,3,32,32) + + model.train() + + y1 = model(x) + y2 = model(x) + + assert not torch.equal(y1, y2) + + + +def test_image_conv_block_gradients(): + + model = ImageConvBlock( + in_channels=3, + filters=8, + rank=2, + depth=3, + ) + + x = torch.randn( + 2, + 3, + 32, + 32, + requires_grad=True, + ) + + y = model(x) + + loss = y.mean() + + loss.backward() + + assert x.grad is not None + + for p in model.parameters(): + assert p.grad is not None + + + +def test_image_conv_block_parameterisation(): + + model = ImageConvBlock( + in_channels=3, + filters=16, + depth=4, + kernel_size=5, + rank=2, + ) + + # 4 convolution layers + assert len(model.convs) == 4 + + params = sum( + p.numel() + for p in model.parameters() + ) + + assert params > 0 + + + +# --------------------------------------------------------- +# Residual Block +# --------------------------------------------------------- + +def test_res_block_shape(): + + model = ImageConvResBlock( + in_channels=3, + filters=16, + rank=2, + ) + + x = torch.randn( + 2, + 3, + 64, + 64, + ) + + y = model(x) + + assert y.shape == ( + 2, + 16, + 64, + 64, + ) + + + +def test_res_block_gradient(): + + model = ImageConvResBlock( + in_channels=3, + filters=8, + rank=2, + ) + + x = torch.randn( + 1, + 3, + 32, + 32, + requires_grad=True, + ) + + y = model(x) + + y.mean().backward() + + assert x.grad is not None + + + +def test_res_block_depth_parameter(): + + model = ImageConvResBlock( + in_channels=3, + filters=8, + depth=5, + ) + + # main block receives depth-2 layers + assert len(model.main_conv.convs) == 3 + + + +# --------------------------------------------------------- +# Encoder +# --------------------------------------------------------- + +@pytest.mark.parametrize( + "rank", + [2,3], +) +def test_image_encoder_shapes(rank, small_filters): + + model = ImageEncoder( + in_channels=3, + filters=small_filters, + rank=rank, + ) + + + if rank == 2: + x = torch.randn( + 1, + 3, + 64, + 64 + ) + + expected = [ + (1,8,64,64), + (1,16,32,32), + (1,32,16,16), + ] + + else: + x = torch.randn( + 1, + 3, + 32, + 32, + 32, + ) + + expected = [ + (1,8,32,32,32), + (1,16,16,16,16), + (1,32,8,8,8), + ] + + + outputs = model(x) + + assert len(outputs)==len(expected) + + for out, shape in zip(outputs, expected): + assert out.shape == shape + + + +def test_encoder_parameters(): + + model = ImageEncoder( + in_channels=3, + filters=(8,16), + rank=2, + conv_blocks_per_level=2, + ) + + + assert len(model.conv_blocks)==2 + + params=sum( + p.numel() + for p in model.parameters() + ) + + assert params > 0 + + + +# --------------------------------------------------------- +# Residual Encoder +# --------------------------------------------------------- + +def test_res_encoder_outputs(): + + model = ImageResEncoder( + in_channels=3, + filters=(8,16,32), + rank=2, + res_blocks_per_level=2, + ) + + x=torch.randn( + 1, + 3, + 64, + 64, + ) + + outputs=model(x) + + assert len(outputs)==3 + + assert outputs[0].shape == ( + 1, + 8, + 64, + 64 + ) + + assert outputs[-1].shape == ( + 1, + 32, + 16, + 16 + ) + + + +# --------------------------------------------------------- +# Decoder +# --------------------------------------------------------- + +def test_decoder_reconstructs_resolution(): + + encoder = ImageEncoder( + in_channels=3, + filters=(8,16,32), + rank=2, + ) + + decoder = ImageDecoder( + filters=(8,16,32), + rank=2, + ) + + + x=torch.randn( + 1, + 3, + 64, + 64, + ) + + + enc_features=encoder(x) + + out=decoder(enc_features) + + + assert out.shape == ( + 1, + 8, + 64, + 64, + ) + + + +@pytest.mark.parametrize( + "skip", + [ + True, + False, + ], +) +def test_decoder_skip_parameter(skip): + + decoder = ImageDecoder( + filters=(8,16,32), + rank=2, + skip=skip, + ) + + assert decoder.skip == skip + + + +@pytest.mark.parametrize( + "upsample_type", + [ + "Upsample", + "ConvTranspose", + ], +) +def test_decoder_upsampling_modes(upsample_type): + + encoder = ImageEncoder( + in_channels=3, + filters=(8,16,32), + rank=2, + ) + + decoder = ImageDecoder( + filters=(8,16,32), + rank=2, + upsample_type=upsample_type, + ) + + + x = torch.randn( + 1, + 3, + 64, + 64, + ) + + + features = encoder(x) + + out = decoder(features) + + + assert out.shape == ( + 1, + 8, + 64, + 64, + ) + + + +# --------------------------------------------------------- +# End-to-end gradient test +# --------------------------------------------------------- + +def test_encoder_decoder_end_to_end_gradient(): + + encoder = ImageEncoder( + in_channels=3, + filters=(8,16), + rank=2, + ) + + decoder = ImageDecoder( + filters=(8,16), + rank=2, + ) + + + x=torch.randn( + 2, + 3, + 32, + 32, + requires_grad=True, + ) + + + output=decoder( + encoder(x) + ) + + + loss=output.mean() + + loss.backward() + + + assert x.grad is not None + + encoder_grads=[ + p.grad + for p in encoder.parameters() + if p.requires_grad + ] + + decoder_grads=[ + p.grad + for p in decoder.parameters() + if p.requires_grad + ] + + + assert all( + g is not None + for g in encoder_grads + ) + + assert all( + g is not None + for g in decoder_grads + ) + +# --------------------------------------------------------- +# Decoder crop logic +# --------------------------------------------------------- + +def test_decoder_match_size_crops_skip_connection(): + + decoder = ImageDecoder( + filters=(8, 16), + rank=2, + skip=True, + ) + + # decoder feature map + x = torch.randn( + 1, + 16, + 30, + 30, + ) + + # encoder skip feature map is larger + skip = torch.randn( + 1, + 8, + 32, + 32, + ) + + cropped = decoder._match_size(x, skip) + + assert cropped.shape == ( + 1, + 8, + 30, + 30, + ) + + +def test_decoder_match_size_center_crop(): + + decoder = ImageDecoder( + filters=(8,16), + rank=2, + ) + + # Put a known pattern in the skip tensor + skip = torch.arange( + 32 * 32 + ).reshape( + 1, + 1, + 32, + 32, + ).float() + + + x = torch.zeros( + 1, + 16, + 28, + 28, + ) + + + cropped = decoder._match_size( + x, + skip, + ) + + + # difference is 4 pixels -> crop 2 from each side + expected = skip[ + ..., + 2:30, + 2:30, + ] + + + assert torch.equal( + cropped, + expected, + ) + + + +def test_decoder_match_size_no_crop(): + + decoder = ImageDecoder( + filters=(8,16), + rank=2, + ) + + x = torch.randn( + 1, + 16, + 32, + 32, + ) + + skip = torch.randn( + 1, + 8, + 32, + 32, + ) + + + output = decoder._match_size( + x, + skip, + ) + + # should return same tensor + assert output.shape == skip.shape + assert torch.equal(output, skip) + + + +# --------------------------------------------------------- +# Full decoder with odd image dimensions +# --------------------------------------------------------- + +def test_decoder_handles_odd_spatial_dimensions(): + + encoder = ImageEncoder( + in_channels=3, + filters=(8,16,32), + rank=2, + ) + + decoder = ImageDecoder( + filters=(8,16,32), + rank=2, + skip=True, + ) + + + # Odd dimensions trigger possible mismatch + x = torch.randn( + 1, + 3, + 65, + 65, + ) + + + features = encoder(x) + + output = decoder(features) + + + assert output.shape[2:] == ( + 64, + 64, + ) \ No newline at end of file From c430dff46b73db2733a8c8b17249ebf40a672060 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Wed, 29 Jul 2026 18:33:51 +0100 Subject: [PATCH 37/49] fixed python version issues --- pyproject.toml | 2 +- src/im2sim/data/__init__.py | 23 +- src/im2sim/data/core.py | 20 +- src/im2sim/data/mesh_utils.py | 30 +- src/im2sim/data/ops.py | 4 +- src/im2sim/data/transforms.py | 16 +- src/im2sim/layers/__init__.py | 41 +- src/im2sim/layers/graph_blocks.py | 39 +- src/im2sim/layers/image_blocks.py | 32 +- src/im2sim/layers/layer_util.py | 30 +- src/im2sim/layers/meshgraphnets.py | 8 +- src/im2sim/layers/projections.py | 29 +- src/im2sim/losses/mesh.py | 4 +- src/im2sim/losses/pointcloud.py | 4 +- src/im2sim/losses/seg.py | 4 +- src/im2sim/models/__init__.py | 8 +- src/im2sim/models/image_to_graph.py | 400 +++---- src/im2sim/models/unet.py | 1036 +++++++++--------- src/im2sim/models/utils.py | 126 +-- src/im2sim/plot/pointcloud.py | 17 +- tests/integration/data/test_pv_mesh_utils.py | 8 +- tests/unit/data/test_loader.py | 10 +- tests/unit/layers/test_graph_blocks.py | 23 +- tests/unit/layers/test_image_blocks.py | 185 ++-- 24 files changed, 996 insertions(+), 1103 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 864ca22..7016ca8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,7 @@ name = "im2sim" version = "0.1.0" description = "A framework for ML-accelerated digital twins" readme = "README.md" -requires-python = "==3.10" +requires-python = "==3.10.20" license = { text = "ElasticV2" } authors = [ { name = "Anirudh Raman", email = "anirudh.raman.24@ucl.ac.uk" } diff --git a/src/im2sim/data/__init__.py b/src/im2sim/data/__init__.py index c836595..eaff815 100644 --- a/src/im2sim/data/__init__.py +++ b/src/im2sim/data/__init__.py @@ -1,4 +1,19 @@ -from . import mesh_utils as mesh_utils -from . import ops as ops -from . import transforms as transforms -from .core import * +from . import mesh_utils, ops, transforms +from .core import ( + FittableOperation, + InvertibleOperation, + Operation, + Transform, + register_op, +) + +__all__ = [ + "mesh_utils", + "ops", + "transforms", + "Operation", + "InvertibleOperation", + "FittableOperation", + "register_op", + "Transform", +] diff --git a/src/im2sim/data/core.py b/src/im2sim/data/core.py index 0925b58..c0150db 100644 --- a/src/im2sim/data/core.py +++ b/src/im2sim/data/core.py @@ -144,11 +144,7 @@ def __init__( self.op = op - self.name = ( - name - if name is not None - else f"{op.__class__.__name__}_{'_'.join(self.keys)}" - ) + self.name = name if name is not None else f"{op.__class__.__name__}_{'_'.join(self.keys)}" self.is_multikey = multikey self.is_invertible = isinstance(op, InvertibleOperation) @@ -247,7 +243,7 @@ def _apply_op(self, data, fn, no_return=False): if not isinstance(outputs, (list, tuple)): outputs = [outputs] - for k, out in zip(self.keys, outputs): + for k, out in zip(self.keys, outputs, strict=True): self._set_target(data, k, out) elif len(self.keys) > 1: @@ -259,7 +255,7 @@ def _apply_op(self, data, fn, no_return=False): self._apply_channel_op(i, fn, no_return) return - for i, k in zip(inputs, self.keys): + for i, k in zip(inputs, self.keys, strict=True): out = self._apply_channel_op(i, fn, no_return) self._set_target(data, k, out) @@ -328,9 +324,7 @@ def config(self): "op": self.op.__class__.__name__ if not self.per_channel else self.op[0].__class__.__name__, - "op_args": self.op.call_args - if not self.per_channel - else self.op[0].call_args, + "op_args": self.op.call_args if not self.per_channel else self.op[0].call_args, "name": self.name, "keys": self.keys, "attr": self.attr, @@ -350,7 +344,7 @@ def state_dict(self): def load_state_dict(self, state): if self.per_channel: - for op, s in zip(self.op, state): + for op, s in zip(self.op, state, strict=True): op.load_state_dict(s) else: self.op.load_state_dict(state) @@ -505,9 +499,7 @@ def _log_data(data: dict[str, Any]) -> None: if isinstance(v, Data): for c in range(v.x.shape[-1]): - logger.debug( - f"channel {c}- max:{v.x[..., c].max()}, min:{v.x[..., c].min()}" - ) + logger.debug(f"channel {c}- max:{v.x[..., c].max()}, min:{v.x[..., c].min()}") else: logger.debug(v.shape) diff --git a/src/im2sim/data/mesh_utils.py b/src/im2sim/data/mesh_utils.py index 203c0bf..3556053 100644 --- a/src/im2sim/data/mesh_utils.py +++ b/src/im2sim/data/mesh_utils.py @@ -10,9 +10,7 @@ class InputMeshError(ValueError): pass -def get_structure_ids( - mesh: PointGrid, structure_dict: dict[int, str] -) -> dict[str, torch.Tensor]: +def get_structure_ids(mesh: PointGrid, structure_dict: dict[int, str]) -> dict[str, torch.Tensor]: """ Extracts node ids for different substructures in a pyvista PointGrid object. @@ -24,15 +22,11 @@ def get_structure_ids( ids (Dict[str, torch.Tensor]): A dictionary with items in the format 'structurename_index': torch.Tensor(N), where N is the number of nodes in the structure. """ cells = get_structure_edges(mesh, structure_dict) - ids = { - f"{k.split('_edge_index')[0]}_index": torch.unique(v) for k, v in cells.items() - } + ids = {f"{k.split('_edge_index')[0]}_index": torch.unique(v) for k, v in cells.items()} return ids -def get_structure_edges( - mesh: PointGrid, structure_dict: dict[int, str] -) -> dict[str, torch.Tensor]: +def get_structure_edges(mesh: PointGrid, structure_dict: dict[int, str]) -> dict[str, torch.Tensor]: """ Extracts edges for different substructures in a pyvista PointGrid object. @@ -74,9 +68,7 @@ def get_edges(mesh: PointGrid, structure_id: int) -> torch.Tensor: return edges -def get_structure_cells( - mesh: PointGrid, structure_dict: dict[int, str] -) -> dict[str, torch.Tensor]: +def get_structure_cells(mesh: PointGrid, structure_dict: dict[int, str]) -> dict[str, torch.Tensor]: """ Extracts cells for different substructures in a pyvista PointGrid object. @@ -168,15 +160,11 @@ def get_node_features(mesh: PointGrid, feature_names: list[str]) -> torch.Tensor Returns: features (torch.Tensor): A tensor of shape [N,C] where N is the number of nodes and C is len(feature_names). """ - features = torch.from_numpy( - np.array([mesh.point_data[name] for name in feature_names]).T - ) + features = torch.from_numpy(np.array([mesh.point_data[name] for name in feature_names]).T) return features -def make_padded_batch( - x: torch.Tensor, batch: torch.Tensor -) -> tuple[torch.Tensor, torch.Tensor]: +def make_padded_batch(x: torch.Tensor, batch: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: """ A helper function to pad a batch of data to the same size. @@ -279,9 +267,7 @@ def cluster_pool(mesh: Data) -> Data: return pooled_mesh -def rasterize( - points: torch.Tensor, im_shape: list[int], vox_sizes: list[float] -) -> torch.Tensor: +def rasterize(points: torch.Tensor, im_shape: list[int], vox_sizes: list[float]) -> torch.Tensor: """ Computes the squared Euclidean distance between voxel centroids in a grid to a pointcloud @@ -298,7 +284,7 @@ def rasterize( is the distance of the voxel centroid to the pointcloud. """ im_coords = [ - torch.arange(size / 2, n, size) for n, size in zip(im_shape, vox_sizes) + torch.arange(size / 2, n, size) for n, size in zip(im_shape, vox_sizes, strict=True) ] grids = torch.meshgrid(*im_coords, indexing="ij") # three [128,128,128] tensors coord_tensor = torch.stack(grids, dim=-1).reshape(-1, 3) diff --git a/src/im2sim/data/ops.py b/src/im2sim/data/ops.py index 3e27d29..95e1615 100644 --- a/src/im2sim/data/ops.py +++ b/src/im2sim/data/ops.py @@ -1,4 +1,6 @@ -from .core import * +import torch + +from .core import FittableOperation, InvertibleOperation, Operation, register_op # TODO: fix the PowerScaleOp # ------------------------------------------------------------------------------------ diff --git a/src/im2sim/data/transforms.py b/src/im2sim/data/transforms.py index f81b78a..e3e457f 100644 --- a/src/im2sim/data/transforms.py +++ b/src/im2sim/data/transforms.py @@ -1,5 +1,5 @@ -from .core import * -from .ops import * +from .core import Operation, Transform +from .ops import FitNormOp, FitRangeNormOp, FitZScoreOp, NormOp, PowerScaleOp, RangeNormOp, ZScoreOp def transform_from_fn( @@ -59,9 +59,7 @@ def RangeNorm( ) -def ZScore( - keys, attr=None, channels=None, per_channel=False, channel_dim=-1, name=None -): +def ZScore(keys, attr=None, channels=None, per_channel=False, channel_dim=-1, name=None): return Transform( op=ZScoreOp(), keys=keys, @@ -104,9 +102,7 @@ def PowerScaling( # ------------------------------------------------------------------------------------ -def FitNorm( - keys, attr=None, channels=None, per_channel=False, channel_dim=-1, name=None -): +def FitNorm(keys, attr=None, channels=None, per_channel=False, channel_dim=-1, name=None): return Transform( op=FitNormOp(), keys=keys, @@ -139,9 +135,7 @@ def FitRangeNorm( ) -def FitZScore( - keys, attr=None, channels=None, per_channel=False, channel_dim=-1, name=None -): +def FitZScore(keys, attr=None, channels=None, per_channel=False, channel_dim=-1, name=None): return Transform( op=FitZScoreOp(), keys=keys, diff --git a/src/im2sim/layers/__init__.py b/src/im2sim/layers/__init__.py index 8030061..8a67db5 100644 --- a/src/im2sim/layers/__init__.py +++ b/src/im2sim/layers/__init__.py @@ -1,5 +1,36 @@ -from .graph_blocks import * -from .image_blocks import * -from .layer_util import * -from .meshgraphnets import * -from .projections import * +from .graph_blocks import GraphConvBlock, GraphConvResBlock, GraphResDecoderBlock +from .image_blocks import ImageConvBlock, ImageConvResBlock, ImageDecoder, ImageEncoder +from .layer_util import ( + get_activation, + get_torch_layer, + init_weights, + register_activation, + register_pyg_layer, + register_torch_layer, + standardize_spatial_factors, +) +from .meshgraphnets import MeshGraphNet, MGNDecoder, MGNEdgeBlock, MGNGnBlock, MGNNodeBlock +from .projections import OGProjection + +__all__ = [ + "GraphConvBlock", + "GraphConvResBlock", + "GraphResDecoderBlock", + "ImageConvBlock", + "ImageConvResBlock", + "ImageEncoder", + "ImageDecoder", + "get_activation", + "get_torch_layer", + "register_activation", + "register_pyg_layer", + "register_torch_layer", + "init_weights", + "standardize_spatial_factors", + "MGNEdgeBlock", + "MGNNodeBlock", + "MGNDecoder", + "MGNGnBlock", + "MeshGraphNet", + "OGProjection", +] diff --git a/src/im2sim/layers/graph_blocks.py b/src/im2sim/layers/graph_blocks.py index f0ffc1d..a519431 100644 --- a/src/im2sim/layers/graph_blocks.py +++ b/src/im2sim/layers/graph_blocks.py @@ -4,7 +4,7 @@ import torch from torch import nn -from .layer_util import * +from .layer_util import get_activation, get_graph_layer, register_pyg_layer logger = logging.getLogger(__name__) @@ -29,9 +29,7 @@ def forward(self, x, batch): batch = torch.zeros(x.shape[0], dtype=torch.long, device=x.device) for b in torch.unique(batch): - x[batch == b] = self.norm(x[batch == b].unsqueeze(0).unsqueeze(0)).reshape( - shape - ) + x[batch == b] = self.norm(x[batch == b].unsqueeze(0).unsqueeze(0)).reshape(shape) return x @@ -66,23 +64,20 @@ def __init__( ): super().__init__() - self.convs = nn.ModuleList( [ - get_graph_layer(name=conv_type, - args=[in_channels if i == 0 else filters, filters], - kwargs = conv_kwargs) + get_graph_layer( + name=conv_type, + args=[in_channels if i == 0 else filters, filters], + kwargs=conv_kwargs, + ) for i in range(depth) ] ) self.norms = nn.ModuleList( [ - get_graph_layer(name=norm_type, - kwargs=norm_kwargs) - if norm_type - else nn.Identity() - + get_graph_layer(name=norm_type, kwargs=norm_kwargs) if norm_type else nn.Identity() for _ in range(depth) ] ) @@ -91,7 +86,7 @@ def __init__( def forward(self, in_graph): graph = copy(in_graph) - for conv, norm in zip(self.convs, self.norms): + for conv, norm in zip(self.convs, self.norms, strict=True): graph = conv(graph) graph = norm(graph) graph.x = self.act(graph.x) @@ -131,20 +126,18 @@ def __init__( self.convs = nn.ModuleList( [ - get_graph_layer(name=conv_type, - args=[in_channels if i == 0 else filters, filters], - kwargs = conv_kwargs) + get_graph_layer( + name=conv_type, + args=[in_channels if i == 0 else filters, filters], + kwargs=conv_kwargs, + ) for i in range(depth) ] ) self.norms = nn.ModuleList( [ - get_graph_layer(name=norm_type, - kwargs=norm_kwargs) - if norm_type - else nn.Identity() - + get_graph_layer(name=norm_type, kwargs=norm_kwargs) if norm_type else nn.Identity() for _ in range(depth) ] ) @@ -153,7 +146,7 @@ def __init__( def forward(self, in_graph): graph = copy(in_graph) - for i, (conv, norm) in enumerate(zip(self.convs, self.norms)): + for i, (conv, norm) in enumerate(zip(self.convs, self.norms, strict=True)): graph = norm(conv(graph)) if i == 0: diff --git a/src/im2sim/layers/image_blocks.py b/src/im2sim/layers/image_blocks.py index 1945cce..39c0204 100644 --- a/src/im2sim/layers/image_blocks.py +++ b/src/im2sim/layers/image_blocks.py @@ -3,7 +3,7 @@ import torch from torch import nn -from .layer_util import * +from .layer_util import get_activation, get_torch_layer logger = logging.getLogger(__name__) @@ -62,9 +62,7 @@ def __init__( ] ) self.drop = ( - get_torch_layer("Dropout", rank)(p=dropout_rate) - if dropout_rate - else nn.Identity() + get_torch_layer("Dropout", rank)(p=dropout_rate) if dropout_rate else nn.Identity() ) self.act = get_activation(activation) @@ -78,7 +76,7 @@ def forward(self, x): torch.Tensor: Output feature maps [out_channels, ...] """ - for conv, norm in zip(self.convs, self.norms): + for conv, norm in zip(self.convs, self.norms, strict=True): logger.debug("Image feature shape:%s", tuple(x.shape)) x = self.act(norm(conv(x))) return self.drop(x) @@ -209,9 +207,7 @@ def __init__( nn.ModuleList( [ ImageConvResBlock( - in_channels=in_channels[i] - if j == 0 - else in_channels[i + 1], + in_channels=in_channels[i] if j == 0 else in_channels[i + 1], filters=filters[i], kernel_size=kernel_size, depth=res_depth, @@ -241,7 +237,7 @@ def forward(self, x): """ logger.debug("IN ENCODER") outputs = [] - for pool, convs in zip(self.pools, self.conv_blocks): + for pool, convs in zip(self.pools, self.conv_blocks, strict=True): x = pool(x) for conv in convs: x = conv(x) @@ -313,7 +309,7 @@ def forward(self, x): List[torch.Tensor]: Output feature maps from each level ordered from top to bottom [Tensor([filters[0], ...], ..., Tensor([filters[N], ...]) """ outputs = [] - for pool, conv in zip(self.maxpools, self.conv_blocks): + for pool, conv in zip(self.maxpools, self.conv_blocks, strict=True): x = conv(pool(x)) outputs.append(x) return outputs @@ -373,12 +369,10 @@ def __init__( ] ) else: - up_layer = TORCH_LAYERS[f"{upsample_type}{rank}d"] + up_layer = get_torch_layer(upsample_type, rank) self.ups = nn.ModuleList( [ - up_layer( - rev_filters[i], rev_filters[i + 1], kernel_size=2, stride=2 - ) + up_layer(rev_filters[i], rev_filters[i + 1], kernel_size=2, stride=2) for i in range(n_levels - 1) ] ) @@ -386,9 +380,7 @@ def __init__( self.conv_blocks = nn.ModuleList( [ ImageConvBlock( - in_channels=( - rev_filters[i] + rev_filters[i + 1] if skip else rev_filters[i] - ), + in_channels=(rev_filters[i] + rev_filters[i + 1] if skip else rev_filters[i]), filters=rev_filters[i + 1], kernel_size=kernel_size, depth=conv_blocks_per_level, @@ -404,8 +396,8 @@ def __init__( def _match_size(self, x, skip): if x.shape[2:] != skip.shape[2:]: # center crop skip to x - diff = [s - t for s, t in zip(skip.shape[2:], x.shape[2:])] - slices = [slice(d // 2, d // 2 + t) for d, t in zip(diff, x.shape[2:])] + diff = [s - t for s, t in zip(skip.shape[2:], x.shape[2:], strict=True)] + slices = [slice(d // 2, d // 2 + t) for d, t in zip(diff, x.shape[2:], strict=True)] skip = skip[(..., *slices)] return skip @@ -425,7 +417,7 @@ def forward(self, encoder_outputs): x = rev_enc[0] # Traverse decoder levels - for i, (up, conv) in enumerate(zip(self.ups, self.conv_blocks)): + for i, (up, conv) in enumerate(zip(self.ups, self.conv_blocks, strict=True)): x = up(x) if self.skip: diff --git a/src/im2sim/layers/layer_util.py b/src/im2sim/layers/layer_util.py index b373a37..bd870d1 100644 --- a/src/im2sim/layers/layer_util.py +++ b/src/im2sim/layers/layer_util.py @@ -6,7 +6,6 @@ import torch import torch_geometric.nn as gnn -from torch_geometric.data import Data def make_registry(lib: Any, regex: Callable): @@ -62,8 +61,7 @@ def __str__(self): PYG_LAYERS, register_pyg_layer = make_registry(gnn, layer_pattern) - -def get_torch_layer(name: str, rank:int) -> torch.nn.Module: +def get_torch_layer(name: str, rank: int) -> torch.nn.Module: """ Get a PyTorch layer by name, with optional arguments. """ @@ -78,11 +76,11 @@ def get_torch_layer(name: str, rank:int) -> torch.nn.Module: return TORCH_LAYERS[name] except KeyError: ValueError(f"Layer {name} with rank {rank} not found in PyTorch layers registry") - -def get_activation(name: str|None) -> torch.nn.Module: - return ACTIVATIONS[name]() if name is not None else torch.nn.Identity() +def get_activation(name: str | None) -> torch.nn.Module: + return ACTIVATIONS[name]() if name is not None else torch.nn.Identity() + class PyGParameterError(TypeError): pass @@ -97,7 +95,7 @@ def _match_attrs_to_signature(graph, module): attrs = [] - for name, param in sig.parameters.items(): + for name, _param in sig.parameters.items(): if name == "self": continue @@ -136,10 +134,11 @@ def forward(self, graph): out_graph.x = out return out_graph -def get_graph_layer(name: str, - args: list[Any] = None, - kwargs: dict[str, Any] = None) -> PyG_Wrapper: - + +def get_graph_layer( + name: str, args: list[Any] = None, kwargs: dict[str, Any] = None +) -> PyG_Wrapper: + if args is None: args = [] if kwargs is None: @@ -149,9 +148,6 @@ def get_graph_layer(name: str, return PyG_Wrapper(module) - - - def init_weights(m): if isinstance(m, gnn.ChebConv): for lin in m.lins: @@ -173,8 +169,6 @@ def standardize_spatial_factors(factors, rank): elif isinstance(f, (tuple, list)): standardized.append(tuple(f)) else: - raise TypeError( - f"Each factor must be an int, tuple, or list, got {type(f).__name__}" - ) + raise TypeError(f"Each factor must be an int, tuple, or list, got {type(f).__name__}") - return standardized \ No newline at end of file + return standardized diff --git a/src/im2sim/layers/meshgraphnets.py b/src/im2sim/layers/meshgraphnets.py index 72765c4..230330b 100644 --- a/src/im2sim/layers/meshgraphnets.py +++ b/src/im2sim/layers/meshgraphnets.py @@ -46,9 +46,7 @@ def forward(self, graph): _, receivers_idx = graph.edge_index num_nodes = graph.num_nodes - agg_received_edges = scatter_add( - edge_attr, receivers_idx, dim=0, dim_size=num_nodes - ) + agg_received_edges = scatter_add(edge_attr, receivers_idx, dim=0, dim_size=num_nodes) nodes_to_collect.append(graph.x) nodes_to_collect.append(agg_received_edges) @@ -120,9 +118,7 @@ def forward(self, graph): class MGNDecoder(nn.Module): def __init__(self, hidden_size=128, output_size=2): super().__init__() - self.decode_module = build_mlp( - hidden_size, hidden_size, output_size, lay_norm=False - ) + self.decode_module = build_mlp(hidden_size, hidden_size, output_size, lay_norm=False) def forward(self, graph): return self.decode_module(graph.x) diff --git a/src/im2sim/layers/projections.py b/src/im2sim/layers/projections.py index 0bee2b9..b619bb5 100644 --- a/src/im2sim/layers/projections.py +++ b/src/im2sim/layers/projections.py @@ -21,10 +21,7 @@ def forward(self, encoder_outputs, graph_coords, batch): n_nodes = coords.shape[0] grid = torch.stack( - [ - (2 * coords[:, j] / (d - 1)) - 1 - for j, d in enumerate(self.domain_size) - ], + [(2 * coords[:, j] / (d - 1)) - 1 for j, d in enumerate(self.domain_size)], axis=-1, ) # normalise coords [-1,1] and divide by scale @@ -73,24 +70,12 @@ def forward(self, image_features, graph_features, batch): z = z / factor # floor / ceil with clamp - x1 = torch.minimum( - torch.floor(x), torch.tensor(h - 1, dtype=x.dtype, device=x.device) - ) - x2 = torch.minimum( - torch.ceil(x), torch.tensor(h - 1, dtype=x.dtype, device=x.device) - ) - y1 = torch.minimum( - torch.floor(y), torch.tensor(w - 1, dtype=x.dtype, device=x.device) - ) - y2 = torch.minimum( - torch.ceil(y), torch.tensor(w - 1, dtype=x.dtype, device=x.device) - ) - z1 = torch.minimum( - torch.floor(z), torch.tensor(d - 1, dtype=x.dtype, device=x.device) - ) - z2 = torch.minimum( - torch.ceil(z), torch.tensor(d - 1, dtype=x.dtype, device=x.device) - ) + x1 = torch.minimum(torch.floor(x), torch.tensor(h - 1, dtype=x.dtype, device=x.device)) + x2 = torch.minimum(torch.ceil(x), torch.tensor(h - 1, dtype=x.dtype, device=x.device)) + y1 = torch.minimum(torch.floor(y), torch.tensor(w - 1, dtype=x.dtype, device=x.device)) + y2 = torch.minimum(torch.ceil(y), torch.tensor(w - 1, dtype=x.dtype, device=x.device)) + z1 = torch.minimum(torch.floor(z), torch.tensor(d - 1, dtype=x.dtype, device=x.device)) + z2 = torch.minimum(torch.ceil(z), torch.tensor(d - 1, dtype=x.dtype, device=x.device)) # cast to int for indexing x1 = x1.long() diff --git a/src/im2sim/losses/mesh.py b/src/im2sim/losses/mesh.py index 4935aa2..1344c6e 100644 --- a/src/im2sim/losses/mesh.py +++ b/src/im2sim/losses/mesh.py @@ -25,9 +25,7 @@ def _aspect_ratio(x, cells): tet_vertices = x[cells, :] vert_ids = list(combinations(range(4), 2)) edge_coords = tet_vertices[vert_ids, :] - distances = torch.linalg.norm( - edge_coords[:, 0, :, :] - edge_coords[:, 1, :, :], dim=-1 - ) + distances = torch.linalg.norm(edge_coords[:, 0, :, :] - edge_coords[:, 1, :, :], dim=-1) aspect_ratio = distances.max(0).values / distances.mean(0) return aspect_ratio.mean() diff --git a/src/im2sim/losses/pointcloud.py b/src/im2sim/losses/pointcloud.py index 1ccd5cb..701a455 100644 --- a/src/im2sim/losses/pointcloud.py +++ b/src/im2sim/losses/pointcloud.py @@ -8,9 +8,9 @@ def _compute_batch_chamfer(y1, y2, b1=None, b2=None): - if b1 == None: + if b1 is None: b1 = torch.zeros(y1.shape[0]) - if b2 == None: + if b2 is None: b2 = torch.zeros(y2.shape[0]) logger.debug( "shapes - y1:%s, y2:%s, b1:%s, b2%s", diff --git a/src/im2sim/losses/seg.py b/src/im2sim/losses/seg.py index c8290ed..eacf99c 100644 --- a/src/im2sim/losses/seg.py +++ b/src/im2sim/losses/seg.py @@ -41,6 +41,4 @@ def dice_loss( if channel_weights is None: return torch.mean(1 - dice_calc(y1, y2, smooth)) else: - return torch.mean( - torch.mean(1 - dice_calc(y1, y2, smooth), dim=0) * channel_weights - ) + return torch.mean(torch.mean(1 - dice_calc(y1, y2, smooth), dim=0) * channel_weights) diff --git a/src/im2sim/models/__init__.py b/src/im2sim/models/__init__.py index 85eda4d..4685966 100644 --- a/src/im2sim/models/__init__.py +++ b/src/im2sim/models/__init__.py @@ -1,4 +1,4 @@ -# from .image_to_graph import I2GUNet as I2GUNet -from .image_to_graph import SimpleI2G as SimpleI2G -from .utils import get_model_config as get_model_config -# from .UNet import UNet, StandardUNet +# # from .image_to_graph import I2GUNet as I2GUNet +# from .image_to_graph import SimpleI2G as SimpleI2G +# from .utils import get_model_config as get_model_config +# # from .UNet import UNet, StandardUNet diff --git a/src/im2sim/models/image_to_graph.py b/src/im2sim/models/image_to_graph.py index 94e26a5..1c7e483 100644 --- a/src/im2sim/models/image_to_graph.py +++ b/src/im2sim/models/image_to_graph.py @@ -1,210 +1,210 @@ -import logging - -import torch - -# import torch_geometric.nn as gnn -from torch import nn - -# from torch_geometric.nn import TopKPooling -# from torch_geometric.data import Data -from ..layers import * - -logger = logging.getLogger(__name__) - - -class SimpleI2G(nn.Module): - def __init__( - self, - in_channels, - out_channels, - cnn_filters=(16, 32, 64, 128, 256), - cnn_kernel_size=3, - cnn_res_depth=3, - cnn_res_blocks_per_level=2, - cnn_rank=3, - cnn_norm_type=None, - cnn_pool_type="MaxPool", - cnn_pool_size=2, - cnn_activation="relu", - cnn_dropout_rate=None, - projection_ids=((3, 4), (1, 2), (0, 1)), - gnn_filters=((384, 288), (144, 96), (64, 32)), - gnn_res_depth=3, - gnn_n_process_blocks=1, - gnn_n_deform_blocks=3, - template_edge_index=None, - gnn_conv_type="GATConv", - gnn_conv_kwargs=None, - gnn_activation="relu", - out_activation="linear", - gnn_norm_type="InstanceNorm", - batched_ops=True, - ): - super().__init__() - - logger.debug("Defining SimpleI2G layers...") - self.batched_ops = batched_ops - - self.encoder = ImageResEncoder( - in_channels=in_channels, - filters=cnn_filters, - kernel_size=cnn_kernel_size, - res_depth=cnn_res_depth, - res_blocks_per_level=cnn_res_blocks_per_level, - rank=cnn_rank, - norm_type=cnn_norm_type, - pool_type=cnn_pool_type, - pool_size=cnn_pool_size, - activation=cnn_activation, - dropout_rate=cnn_dropout_rate, - ) - # self.encoder = Encoder3D(in_channels=in_channels) - - # self.projection_layers = nn.ModuleList([TrilinearProjection() for _ in range(len(cnn_filters))]) - self.projection_ids = projection_ids - - projection_channels = _get_projection_channels(cnn_filters, self.projection_ids) - self.decoder_blocks = nn.ModuleList( - [ - GraphResDecoderBlock( - projection_channels=projection_channels[i], - graph_channels=out_channels if i == 0 else gnn_filters[i - 1][1], - out_channels=out_channels, - filters=gnn_filters[i], - res_depth=gnn_res_depth, - # n_process_blocks = gnn_n_process_blocks, - n_deform_blocks=gnn_n_deform_blocks, - template_edge_index=template_edge_index, - conv_type=gnn_conv_type, - conv_kwargs=gnn_conv_kwargs, - activation=gnn_activation, - out_activation=out_activation, - norm_type=gnn_norm_type, - ) - for i in range(len(gnn_filters)) - ] - ) - logger.debug("Done") - - def forward(self, x, template): - logger.debug("In model forward pass...") - encoder_outputs = self.encoder(x) - outputs = [] - graph_features = template.x.clone() - curr_mesh = template.x.clone() - for dec, ids in zip(self.decoder_blocks, self.projection_ids): - proj_inp = torch.cat( - [ - OGProjection(image_dim=x.shape[-1])( - encoder_outputs[id], curr_mesh[:, :3], template.batch - ) - for id in ids - ], - dim=-1, - ) - # proj_inp = torch.cat([TrilinearProjection(domain_size=[x.shape[-1]]*3, batch_ops=False)(encoder_outputs[id], curr_mesh[:,:3], template.batch) - # for id in ids], dim=-1) - graph_features, curr_mesh = dec( - graph_features, proj_inp, curr_mesh, template.edge_index - ) - out_graph = template.clone() - out_graph.x = curr_mesh - outputs.append(out_graph) - return outputs - - -# class I2GUNet(nn.Module): - -# def __init__(self, -# in_channels, -# out_channels, -# domain_size, -# filters=(16,32,64,128,256), -# cnn_kernel_size=3, -# cnn_res_depth=3, -# cnn_res_blocks_per_level=2, -# cnn_rank=3, -# cnn_norm_type='InstanceNorm', -# cnn_pool_type='MaxPool', -# cnn_pool_size=2, -# cnn_activation='leaky_relu', -# gnn_res_depth = 3, -# gnn_n_align_blocks = 1, -# gnn_n_deform_blocks = 3, -# gnn_conv_type="GATConv", -# gnn_conv_kwargs=None, -# gnn_activation="leaky_relu", -# gnn_norm_type="InstanceNorm", -# batched_ops=True): +# import logging + +# import torch + +# # import torch_geometric.nn as gnn +# from torch import nn + +# # from torch_geometric.nn import TopKPooling +# # from torch_geometric.data import Data +# from ..layers import * + +# logger = logging.getLogger(__name__) + + +# class SimpleI2G(nn.Module): +# def __init__( +# self, +# in_channels, +# out_channels, +# cnn_filters=(16, 32, 64, 128, 256), +# cnn_kernel_size=3, +# cnn_res_depth=3, +# cnn_res_blocks_per_level=2, +# cnn_rank=3, +# cnn_norm_type=None, +# cnn_pool_type="MaxPool", +# cnn_pool_size=2, +# cnn_activation="relu", +# cnn_dropout_rate=None, +# projection_ids=((3, 4), (1, 2), (0, 1)), +# gnn_filters=((384, 288), (144, 96), (64, 32)), +# gnn_res_depth=3, +# gnn_n_process_blocks=1, +# gnn_n_deform_blocks=3, +# template_edge_index=None, +# gnn_conv_type="GATConv", +# gnn_conv_kwargs=None, +# gnn_activation="relu", +# out_activation="linear", +# gnn_norm_type="InstanceNorm", +# batched_ops=True, +# ): # super().__init__() -# logger.debug("Defining I2G layers...") +# logger.debug("Defining SimpleI2G layers...") # self.batched_ops = batched_ops -# self.n_levels = len(filters) -# self.n_channels = out_channels -# self.encoder = ImageResEncoder(in_channels=in_channels, -# filters=filters, -# kernel_size=cnn_kernel_size, -# res_depth=cnn_res_depth, -# res_blocks_per_level=cnn_res_blocks_per_level, -# rank=cnn_rank, -# norm_type=cnn_norm_type, -# pool_type=cnn_pool_type, -# pool_size=cnn_pool_size, -# activation=cnn_activation) - - -# self.gpool = RecursiveClusterPooling(n_levels=self.n_levels) - -# self.decoder = nn.ModuleList([ -# GraphUNetDecoderBlock(#in_channels = out_channels if i==1 else filters[-i+1], -# out_channels = out_channels, -# filters = filters[-i], -# domain_size = domain_size, -# res_depth = gnn_res_depth, -# n_align_blocks = 0 if i==1 else gnn_n_align_blocks, -# n_deform_blocks = gnn_n_deform_blocks, -# conv_type=gnn_conv_type, -# conv_kwargs=gnn_conv_kwargs, -# activation=gnn_activation, -# norm_type=gnn_norm_type, -# batched_ops=batched_ops) -# for i in range(1,self.n_levels+1) -# ]) - +# self.encoder = ImageResEncoder( +# in_channels=in_channels, +# filters=cnn_filters, +# kernel_size=cnn_kernel_size, +# res_depth=cnn_res_depth, +# res_blocks_per_level=cnn_res_blocks_per_level, +# rank=cnn_rank, +# norm_type=cnn_norm_type, +# pool_type=cnn_pool_type, +# pool_size=cnn_pool_size, +# activation=cnn_activation, +# dropout_rate=cnn_dropout_rate, +# ) +# # self.encoder = Encoder3D(in_channels=in_channels) + +# # self.projection_layers = nn.ModuleList([TrilinearProjection() for _ in range(len(cnn_filters))]) +# self.projection_ids = projection_ids + +# projection_channels = _get_projection_channels(cnn_filters, self.projection_ids) +# self.decoder_blocks = nn.ModuleList( +# [ +# GraphResDecoderBlock( +# projection_channels=projection_channels[i], +# graph_channels=out_channels if i == 0 else gnn_filters[i - 1][1], +# out_channels=out_channels, +# filters=gnn_filters[i], +# res_depth=gnn_res_depth, +# # n_process_blocks = gnn_n_process_blocks, +# n_deform_blocks=gnn_n_deform_blocks, +# template_edge_index=template_edge_index, +# conv_type=gnn_conv_type, +# conv_kwargs=gnn_conv_kwargs, +# activation=gnn_activation, +# out_activation=out_activation, +# norm_type=gnn_norm_type, +# ) +# for i in range(len(gnn_filters)) +# ] +# ) # logger.debug("Done") - -# def forward(self, img, template): -# encoder_features = self.encoder(img) -# encoder_features.reverse() - -# multi_template = self.gpool(template) -# multi_template.reverse() - +# def forward(self, x, template): +# logger.debug("In model forward pass...") +# encoder_outputs = self.encoder(x) # outputs = [] -# deformation = torch.zeros_like(multi_template[0].x) - -# for i, (dec_layer, enc) in enumerate(zip(self.decoder, encoder_features)): -# t = multi_template[i] -# deformation = dec_layer(enc,deformation,t.x, t.edge_index, t.batch) -# if i < self.n_levels-1: -# out_graph = Data(x=t.x+deformation, edge_index=t.edge_index, batch=t.batch) -# outputs.append(out_graph) -# deformation = gnn.unpool.knn_interpolate(deformation, t.x, multi_template[i+1].x) - -# out_graph = template.clone() -# out_graph.x = template.x+deformation -# outputs.append(out_graph) +# graph_features = template.x.clone() +# curr_mesh = template.x.clone() +# for dec, ids in zip(self.decoder_blocks, self.projection_ids): +# proj_inp = torch.cat( +# [ +# OGProjection(image_dim=x.shape[-1])( +# encoder_outputs[id], curr_mesh[:, :3], template.batch +# ) +# for id in ids +# ], +# dim=-1, +# ) +# # proj_inp = torch.cat([TrilinearProjection(domain_size=[x.shape[-1]]*3, batch_ops=False)(encoder_outputs[id], curr_mesh[:,:3], template.batch) +# # for id in ids], dim=-1) +# graph_features, curr_mesh = dec( +# graph_features, proj_inp, curr_mesh, template.edge_index +# ) +# out_graph = template.clone() +# out_graph.x = curr_mesh +# outputs.append(out_graph) # return outputs -def _get_projection_channels(filters, ids): - channels = [] - for id_list in ids: - sum = 0 - for id in id_list: - sum += filters[id] - channels.append(sum) - return channels +# # class I2GUNet(nn.Module): + +# # def __init__(self, +# # in_channels, +# # out_channels, +# # domain_size, +# # filters=(16,32,64,128,256), +# # cnn_kernel_size=3, +# # cnn_res_depth=3, +# # cnn_res_blocks_per_level=2, +# # cnn_rank=3, +# # cnn_norm_type='InstanceNorm', +# # cnn_pool_type='MaxPool', +# # cnn_pool_size=2, +# # cnn_activation='leaky_relu', +# # gnn_res_depth = 3, +# # gnn_n_align_blocks = 1, +# # gnn_n_deform_blocks = 3, +# # gnn_conv_type="GATConv", +# # gnn_conv_kwargs=None, +# # gnn_activation="leaky_relu", +# # gnn_norm_type="InstanceNorm", +# # batched_ops=True): +# # super().__init__() + +# # logger.debug("Defining I2G layers...") +# # self.batched_ops = batched_ops +# # self.n_levels = len(filters) +# # self.n_channels = out_channels +# # self.encoder = ImageResEncoder(in_channels=in_channels, +# # filters=filters, +# # kernel_size=cnn_kernel_size, +# # res_depth=cnn_res_depth, +# # res_blocks_per_level=cnn_res_blocks_per_level, +# # rank=cnn_rank, +# # norm_type=cnn_norm_type, +# # pool_type=cnn_pool_type, +# # pool_size=cnn_pool_size, +# # activation=cnn_activation) + + +# # self.gpool = RecursiveClusterPooling(n_levels=self.n_levels) + +# # self.decoder = nn.ModuleList([ +# # GraphUNetDecoderBlock(#in_channels = out_channels if i==1 else filters[-i+1], +# # out_channels = out_channels, +# # filters = filters[-i], +# # domain_size = domain_size, +# # res_depth = gnn_res_depth, +# # n_align_blocks = 0 if i==1 else gnn_n_align_blocks, +# # n_deform_blocks = gnn_n_deform_blocks, +# # conv_type=gnn_conv_type, +# # conv_kwargs=gnn_conv_kwargs, +# # activation=gnn_activation, +# # norm_type=gnn_norm_type, +# # batched_ops=batched_ops) +# # for i in range(1,self.n_levels+1) +# # ]) + + +# # logger.debug("Done") + + +# # def forward(self, img, template): +# # encoder_features = self.encoder(img) +# # encoder_features.reverse() + +# # multi_template = self.gpool(template) +# # multi_template.reverse() + +# # outputs = [] +# # deformation = torch.zeros_like(multi_template[0].x) + +# # for i, (dec_layer, enc) in enumerate(zip(self.decoder, encoder_features)): +# # t = multi_template[i] +# # deformation = dec_layer(enc,deformation,t.x, t.edge_index, t.batch) +# # if i < self.n_levels-1: +# # out_graph = Data(x=t.x+deformation, edge_index=t.edge_index, batch=t.batch) +# # outputs.append(out_graph) +# # deformation = gnn.unpool.knn_interpolate(deformation, t.x, multi_template[i+1].x) + +# # out_graph = template.clone() +# # out_graph.x = template.x+deformation +# # outputs.append(out_graph) +# # return outputs + + +# def _get_projection_channels(filters, ids): +# channels = [] +# for id_list in ids: +# sum = 0 +# for id in id_list: +# sum += filters[id] +# channels.append(sum) +# return channels diff --git a/src/im2sim/models/unet.py b/src/im2sim/models/unet.py index ffb8d5b..7ef577c 100644 --- a/src/im2sim/models/unet.py +++ b/src/im2sim/models/unet.py @@ -1,518 +1,518 @@ -import torch.nn.functional as F -from torch import nn - -from ..layers.layer_util import * - - -class ImageConvBlock(nn.Module): - """ - A convolutional block for image data - - Args: - in_channels (int): The number of channels in the input to the layer. - filters (int, optional): The number of filters in each convolutional layer (default: 32) - kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) - depth (int, optional): The number of successive convolutional layers (default: 2) - rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:2), - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") - norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") - dropout_rate (float, optional): The spatial dropout rate to be applied to the final convolution output (default:None) - - Returns: - A `torch.nn.Module` object. - - """ - - def __init__( - self, - in_channels, - filters=32, - kernel_size=3, - depth=1, - rank=3, - activation="relu", - norm_type=None, - dropout_rate=None, - ): - super().__init__() - - conv = TORCH_LAYERS[f"Conv{rank}d"] - drop = TORCH_LAYERS[f"Dropout{rank}d"] - self.convs = nn.ModuleList( - [ - conv( - in_channels if i == 0 else filters, - filters, - kernel_size, - padding=kernel_size // 2, - ) - for i in range(depth) - ] - ) - - self.norms = nn.ModuleList( - [ - TORCH_LAYERS(f"{norm_type}{rank}d")(filters) - if norm_type - else nn.Identity() - for _ in range(depth) - ] - ) - self.drop = drop(p=dropout_rate) if dropout_rate else nn.Identity() - - self.act = ACTIVATIONS[activation]() - - def forward(self, x): - """ - Args: - x (torch.Tensor): Input feature maps in image space [in_channels, ...] where the number of dims in ... corresponds to rank - - Returns: - torch.Tensor: Output feature maps [out_channels, ...] - """ - - for conv, norm in zip(self.convs, self.norms): - x = norm(self.act(conv(x))) - return self.drop(x) - - -class ImageEncoder(nn.Module): - """ - A CNN encoder for images. Structured like the encoder of a UNet. - - Args: - in_channels (int): The number of channels in the input image. - filters (List[int], optional): The number of convolutional filters in each encoder level (default: [16,32,64,128,256]) - kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) - conv_blocks_per_level (int, optional): The number of successive convolutional blocks per encoder level (default: 1) - rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:3), - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") - norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") - dropout_rate (float, optional): The spatial dropout rate to be applied to each residual block prior to residual connection (default:None) - - Returns: - A `torch.nn.Module` object. - """ - - def __init__( - self, - in_channels, - filters=(16, 32, 64, 128, 256), - pool_sizes=None, - kernel_size=3, - conv_blocks_per_level=1, - rank=3, - norm_type=None, - pool_type="MaxPool", - activation="relu", - dropout_rate=None, - ): - super().__init__() - - n_levels = len(filters) - self.conv_blocks = nn.ModuleList( - [ - ImageConvBlock( - in_channels=in_channels if i == 0 else filters[i - 1], - filters=filters[i], - kernel_size=kernel_size, - depth=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate, - ) - for i in range(n_levels) - ] - ) - - if pool_sizes is None: - pool_sizes = 2 - - pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) - - pool = TORCH_LAYERS[f"{pool_type}{rank}d"] - self.maxpools = nn.ModuleList( - [ - pool(pool_sizes_standard[i - 1]) if i > 0 else nn.Identity() - for i in range(n_levels) - ] - ) - - def forward(self, x): - """ - Args: - x (torch.Tensor): Input image [in_channels, ...] - - Returns: - List[torch.Tensor]: Output feature maps from each level ordered from top to bottom [Tensor([filters[0], ...], ..., Tensor([filters[N], ...]) - """ - outputs = [] - for pool, conv in zip(self.maxpools, self.conv_blocks): - x = conv(pool(x)) - outputs.append(x) - return outputs - - -class ImageDecoder(nn.Module): - """ - CNN decoder for images. Mirrors ImageEncoder like a UNet decoder. - - Args: - filters (List[int]): Encoder filter sizes in top→bottom order. - kernel_size (int): Convolution kernel size. - conv_blocks_per_level (int): Number of conv blocks per level. - rank (int): Spatial rank (2 or 3). - upsample_type (str): "ConvTranspose" or "Upsample". - activation (str): Activation name. - norm_type (str): Normalization type. - dropout_rate (float): Dropout rate. - skip (bool): Use skip connections. - """ - - def __init__( - self, - filters=(16, 32, 64, 128, 256), - kernel_size=3, - pool_sizes=None, - upsample_sizes=None, - conv_blocks_per_level=1, - rank=3, - upsample_type="ConvTranspose", - activation="relu", - norm_type=None, - dropout_rate=None, - skip=True, - ): - super().__init__() - - self.skip = skip - self.rank = rank - - if pool_sizes is None: - pool_sizes = 2 - - pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) - - n_levels = len(filters) - - rev_filters = filters[::-1] - rev_pool_sizes = pool_sizes_standard[::-1] - - if upsample_sizes is None: - upsample_sizes = rev_pool_sizes - else: - upsample_sizes = standardize_spatial_factors(upsample_sizes, rank) - - if upsample_type.lower() == "upsample": - self.ups = nn.ModuleList( - [ - nn.Upsample( - scale_factor=upsample_sizes[i], - mode="trilinear" if rank == 3 else "bilinear", - align_corners=True, - ) - for i in range(n_levels - 1) - ] - ) - else: - up_layer = TORCH_LAYERS(f"{upsample_type}{rank}d") - self.ups = nn.ModuleList( - [ - up_layer( - rev_filters[i], - rev_filters[i + 1], - kernel_size=upsample_sizes[i], - stride=upsample_sizes[i], - ) - for i in range(n_levels - 1) - ] - ) - - self.conv_blocks = nn.ModuleList( - [ - ImageConvBlock( - in_channels=rev_filters[i + 1] * (2 if skip else 1), - filters=rev_filters[i + 1], - kernel_size=kernel_size, - depth=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate, - ) - for i in range(n_levels - 1) - ] - ) - - def _match_size(self, x, skip): - """ - Match skip spatial size to x spatial size using: - - center crop if skip is larger - - interpolation if skip is smaller - - Args: - x: decoder tensor, shape [B, C, ...] - skip: encoder skip tensor, shape [B, C, ...] - - Returns: - skip resized to have spatial shape x.shape[2:] - """ - target_size = x.shape[2:] - skip_size = skip.shape[2:] - - if skip_size == target_size: - return skip - - # First crop any dimensions where skip is too large - slices = [slice(None), slice(None)] - needs_crop = False - - for s, t in zip(skip_size, target_size): - if s > t: - start = (s - t) // 2 - end = start + t - slices.append(slice(start, end)) - needs_crop = True - else: - slices.append(slice(None)) - - if needs_crop: - skip = skip[tuple(slices)] - - # Then upsample if any dimensions are still too small - if skip.shape[2:] != target_size: - mode = "trilinear" if self.rank == 3 else "bilinear" - skip = F.interpolate(skip, size=target_size, mode=mode, align_corners=True) - - return skip - - def forward(self, encoder_outputs): - """ - Args: - encoder_outputs: List of tensors from encoder (top→bottom). - - Returns: - Decoded tensor at highest resolution. - """ - - # Reverse the encoder outputs so we traverse from bottleneck to top - rev_enc = encoder_outputs[::-1] - - # Start from bottleneck - x = rev_enc[0] - - # Traverse decoder levels - for i, (up, conv) in enumerate(zip(self.ups, self.conv_blocks)): - x = up(x) - - if self.skip: - skip_feat = rev_enc[i + 1] # next encoder feature - skip_feat = self._match_size(x, skip_feat) - x = torch.cat([x, skip_feat], dim=1) - - x = conv(x) - - return x - - -class UNet(nn.Module): - """ - Flexible UNet for 2D or 3D images. - - Args: - in_channels (int): Input channels. - out_channels (int): Output channels. - filters (List[int]): Encoder filter sizes. - kernel_size (int): Conv kernel size. - pool_sizes (Tuple or list): pool sizes per level , - upsample_sizes (Tuple or list): upsample sizes per level, - conv_blocks_per_level (int): Depth per level. - rank (int): Spatial rank. - activation (str): Activation function. - norm_type (str): Normalization type. - dropout_rate (float): Dropout. - final_activation (str): Output activation. - """ - - def __init__( - self, - in_channels, - out_channels, - filters=(16, 32, 64, 128, 256), - pool_sizes=None, - upsample_sizes=None, - kernel_size=3, - conv_blocks_per_level=1, - rank=3, - activation="relu", - norm_type=None, - dropout_rate=None, - final_activation="linear", - ): - - pool_sizes_temp = [] - if pool_sizes is None: - for i in range(len(filters) - 1): - pool_sizes_temp.append(2) - pool_sizes = pool_sizes_temp - - if not isinstance(pool_sizes, (list, tuple)): - raise TypeError("pool_sizes must be a list or tuple") - - for i, p in enumerate(pool_sizes): - if isinstance(p, int) and not isinstance(p, bool): - if p < 1: - raise ValueError("pool_sizes must be positive") - elif isinstance(p, (tuple, list)): - if len(p) != rank: - raise ValueError("pool_sizes tuple must be same length as rank") - for j, ps in enumerate(p): - if not isinstance(ps, int) or isinstance(ps, bool): - raise TypeError("pool_sizes must be an int") - if ps < 1: - raise ValueError("pool_sizes must be positive") - else: - raise TypeError( - "each entry in pool_sizes must be either an int or a tuple or list" - ) - - if len(filters) != (len(pool_sizes) + 1): - raise ValueError( - f"pool_sizes do not match number of filters. For {len(filters)}, please input {len(filters) - 1} number of pools." - ) - - if upsample_sizes is not None: - if not isinstance(upsample_sizes, (list, tuple)): - raise TypeError("upsample_sizes must be a list or tuple") - - for i, p in enumerate(upsample_sizes): - if isinstance(p, int) and not isinstance(p, bool): - if p < 1: - raise ValueError("upsample_sizes must be positive") - elif isinstance(p, (tuple, list)): - if len(p) != rank: - raise ValueError( - "upsample_sizes tuple must be same length as rank" - ) - for j, ps in enumerate(p): - if not isinstance(ps, int) or isinstance(ps, bool): - raise TypeError("upsample_sizes must be an int") - if ps < 1: - raise ValueError("upsample_sizes must be positive") - else: - raise TypeError( - "each entry in upsample_sizes must be either an int or a tuple or list" - ) - - if len(filters) != (len(upsample_sizes) + 1): - raise ValueError( - f"upsample_sizes do not match number of filters. For {len(filters)}, please input {len(filters) - 1} number of upsamples." - ) - - super().__init__() - - self.encoder = ImageEncoder( - in_channels=in_channels, - filters=filters, - pool_sizes=pool_sizes, - kernel_size=kernel_size, - conv_blocks_per_level=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate, - ) - - self.decoder = ImageDecoder( - filters=filters, - pool_sizes=pool_sizes, - upsample_sizes=upsample_sizes, - kernel_size=kernel_size, - conv_blocks_per_level=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate, - ) - - conv = TORCH_LAYERS[f"Conv{rank}d"] - - self.final_conv = conv(filters[0], out_channels, kernel_size=1) - - self.final_act = ( - ACTIVATIONS(final_activation)() - if final_activation.lower() != "linear" - else nn.Identity() - ) - - def forward(self, x): - enc_feats = self.encoder(x) - x = self.decoder(enc_feats) - x = self.final_conv(x) - x = self.final_act(x) - return x - - -class StandardUNet(UNet): - """ - Standaed UNet for 2D or 3D images. - - Args: - in_channels (int): Input channels. - out_channels (int): Output channels. - filters (List[int]): Encoder filter sizes. - kernel_size (int): Conv kernel size. - pool_sizes (Tuple or list): pool sizes per dim , - conv_blocks_per_level (int): Depth per level. - rank (int): Spatial rank. - activation (str): Activation function. - norm_type (str): Normalization type. - dropout_rate (float): Dropout. - final_activation (str): Output activation. - """ - - def __init__( - self, - in_channels, - out_channels, - filters=(16, 32, 64, 128, 256), - pool_size=2, - kernel_size=3, - conv_blocks_per_level=1, - rank=3, - activation="relu", - norm_type=None, - dropout_rate=None, - final_activation="linear", - ): - - pool_size_standard = [] - if isinstance(pool_size, int): - pool_size_single = [] - for j in range(rank): - pool_size_single.append(pool_size) - pool_size_single = tuple(pool_size_single) - for i in range(len(filters) - 1): - pool_size_standard.append(pool_size_single) - elif isinstance(pool_size, (tuple, list)): - if len(pool_size) == rank: - for i in range(len(filters) - 1): - pool_size_standard.append(pool_size) - else: - raise ValueError("pool_size must be an int or have same length as rank") - - super().__init__( - in_channels=in_channels, - out_channels=out_channels, - filters=filters, - pool_sizes=pool_size_standard, - upsample_sizes=None, - kernel_size=kernel_size, - conv_blocks_per_level=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate, - final_activation=final_activation, - ) +# import torch.nn.functional as F +# from torch import nn + +# from ..layers.layer_util import + + +# class ImageConvBlock(nn.Module): +# """ +# A convolutional block for image data + +# Args: +# in_channels (int): The number of channels in the input to the layer. +# filters (int, optional): The number of filters in each convolutional layer (default: 32) +# kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) +# depth (int, optional): The number of successive convolutional layers (default: 2) +# rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:2), +# activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") +# norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") +# dropout_rate (float, optional): The spatial dropout rate to be applied to the final convolution output (default:None) + +# Returns: +# A `torch.nn.Module` object. + +# """ + +# def __init__( +# self, +# in_channels, +# filters=32, +# kernel_size=3, +# depth=1, +# rank=3, +# activation="relu", +# norm_type=None, +# dropout_rate=None, +# ): +# super().__init__() + +# conv = TORCH_LAYERS[f"Conv{rank}d"] +# drop = TORCH_LAYERS[f"Dropout{rank}d"] +# self.convs = nn.ModuleList( +# [ +# conv( +# in_channels if i == 0 else filters, +# filters, +# kernel_size, +# padding=kernel_size // 2, +# ) +# for i in range(depth) +# ] +# ) + +# self.norms = nn.ModuleList( +# [ +# TORCH_LAYERS(f"{norm_type}{rank}d")(filters) +# if norm_type +# else nn.Identity() +# for _ in range(depth) +# ] +# ) +# self.drop = drop(p=dropout_rate) if dropout_rate else nn.Identity() + +# self.act = ACTIVATIONS[activation]() + +# def forward(self, x): +# """ +# Args: +# x (torch.Tensor): Input feature maps in image space [in_channels, ...] where the number of dims in ... corresponds to rank + +# Returns: +# torch.Tensor: Output feature maps [out_channels, ...] +# """ + +# for conv, norm in zip(self.convs, self.norms): +# x = norm(self.act(conv(x))) +# return self.drop(x) + + +# class ImageEncoder(nn.Module): +# """ +# A CNN encoder for images. Structured like the encoder of a UNet. + +# Args: +# in_channels (int): The number of channels in the input image. +# filters (List[int], optional): The number of convolutional filters in each encoder level (default: [16,32,64,128,256]) +# kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) +# conv_blocks_per_level (int, optional): The number of successive convolutional blocks per encoder level (default: 1) +# rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:3), +# activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid","linear") +# norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") +# dropout_rate (float, optional): The spatial dropout rate to be applied to each residual block prior to residual connection (default:None) + +# Returns: +# A `torch.nn.Module` object. +# """ + +# def __init__( +# self, +# in_channels, +# filters=(16, 32, 64, 128, 256), +# pool_sizes=None, +# kernel_size=3, +# conv_blocks_per_level=1, +# rank=3, +# norm_type=None, +# pool_type="MaxPool", +# activation="relu", +# dropout_rate=None, +# ): +# super().__init__() + +# n_levels = len(filters) +# self.conv_blocks = nn.ModuleList( +# [ +# ImageConvBlock( +# in_channels=in_channels if i == 0 else filters[i - 1], +# filters=filters[i], +# kernel_size=kernel_size, +# depth=conv_blocks_per_level, +# rank=rank, +# activation=activation, +# norm_type=norm_type, +# dropout_rate=dropout_rate, +# ) +# for i in range(n_levels) +# ] +# ) + +# if pool_sizes is None: +# pool_sizes = 2 + +# pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) + +# pool = TORCH_LAYERS[f"{pool_type}{rank}d"] +# self.maxpools = nn.ModuleList( +# [ +# pool(pool_sizes_standard[i - 1]) if i > 0 else nn.Identity() +# for i in range(n_levels) +# ] +# ) + +# def forward(self, x): +# """ +# Args: +# x (torch.Tensor): Input image [in_channels, ...] + +# Returns: +# List[torch.Tensor]: Output feature maps from each level ordered from top to bottom [Tensor([filters[0], ...], ..., Tensor([filters[N], ...]) +# """ +# outputs = [] +# for pool, conv in zip(self.maxpools, self.conv_blocks): +# x = conv(pool(x)) +# outputs.append(x) +# return outputs + + +# class ImageDecoder(nn.Module): +# """ +# CNN decoder for images. Mirrors ImageEncoder like a UNet decoder. + +# Args: +# filters (List[int]): Encoder filter sizes in top→bottom order. +# kernel_size (int): Convolution kernel size. +# conv_blocks_per_level (int): Number of conv blocks per level. +# rank (int): Spatial rank (2 or 3). +# upsample_type (str): "ConvTranspose" or "Upsample". +# activation (str): Activation name. +# norm_type (str): Normalization type. +# dropout_rate (float): Dropout rate. +# skip (bool): Use skip connections. +# """ + +# def __init__( +# self, +# filters=(16, 32, 64, 128, 256), +# kernel_size=3, +# pool_sizes=None, +# upsample_sizes=None, +# conv_blocks_per_level=1, +# rank=3, +# upsample_type="ConvTranspose", +# activation="relu", +# norm_type=None, +# dropout_rate=None, +# skip=True, +# ): +# super().__init__() + +# self.skip = skip +# self.rank = rank + +# if pool_sizes is None: +# pool_sizes = 2 + +# pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) + +# n_levels = len(filters) + +# rev_filters = filters[::-1] +# rev_pool_sizes = pool_sizes_standard[::-1] + +# if upsample_sizes is None: +# upsample_sizes = rev_pool_sizes +# else: +# upsample_sizes = standardize_spatial_factors(upsample_sizes, rank) + +# if upsample_type.lower() == "upsample": +# self.ups = nn.ModuleList( +# [ +# nn.Upsample( +# scale_factor=upsample_sizes[i], +# mode="trilinear" if rank == 3 else "bilinear", +# align_corners=True, +# ) +# for i in range(n_levels - 1) +# ] +# ) +# else: +# up_layer = TORCH_LAYERS(f"{upsample_type}{rank}d") +# self.ups = nn.ModuleList( +# [ +# up_layer( +# rev_filters[i], +# rev_filters[i + 1], +# kernel_size=upsample_sizes[i], +# stride=upsample_sizes[i], +# ) +# for i in range(n_levels - 1) +# ] +# ) + +# self.conv_blocks = nn.ModuleList( +# [ +# ImageConvBlock( +# in_channels=rev_filters[i + 1] * (2 if skip else 1), +# filters=rev_filters[i + 1], +# kernel_size=kernel_size, +# depth=conv_blocks_per_level, +# rank=rank, +# activation=activation, +# norm_type=norm_type, +# dropout_rate=dropout_rate, +# ) +# for i in range(n_levels - 1) +# ] +# ) + +# def _match_size(self, x, skip): +# """ +# Match skip spatial size to x spatial size using: +# - center crop if skip is larger +# - interpolation if skip is smaller + +# Args: +# x: decoder tensor, shape [B, C, ...] +# skip: encoder skip tensor, shape [B, C, ...] + +# Returns: +# skip resized to have spatial shape x.shape[2:] +# """ +# target_size = x.shape[2:] +# skip_size = skip.shape[2:] + +# if skip_size == target_size: +# return skip + +# # First crop any dimensions where skip is too large +# slices = [slice(None), slice(None)] +# needs_crop = False + +# for s, t in zip(skip_size, target_size): +# if s > t: +# start = (s - t) // 2 +# end = start + t +# slices.append(slice(start, end)) +# needs_crop = True +# else: +# slices.append(slice(None)) + +# if needs_crop: +# skip = skip[tuple(slices)] + +# # Then upsample if any dimensions are still too small +# if skip.shape[2:] != target_size: +# mode = "trilinear" if self.rank == 3 else "bilinear" +# skip = F.interpolate(skip, size=target_size, mode=mode, align_corners=True) + +# return skip + +# def forward(self, encoder_outputs): +# """ +# Args: +# encoder_outputs: List of tensors from encoder (top→bottom). + +# Returns: +# Decoded tensor at highest resolution. +# """ + +# # Reverse the encoder outputs so we traverse from bottleneck to top +# rev_enc = encoder_outputs[::-1] + +# # Start from bottleneck +# x = rev_enc[0] + +# # Traverse decoder levels +# for i, (up, conv) in enumerate(zip(self.ups, self.conv_blocks)): +# x = up(x) + +# if self.skip: +# skip_feat = rev_enc[i + 1] # next encoder feature +# skip_feat = self._match_size(x, skip_feat) +# x = torch.cat([x, skip_feat], dim=1) + +# x = conv(x) + +# return x + + +# class UNet(nn.Module): +# """ +# Flexible UNet for 2D or 3D images. + +# Args: +# in_channels (int): Input channels. +# out_channels (int): Output channels. +# filters (List[int]): Encoder filter sizes. +# kernel_size (int): Conv kernel size. +# pool_sizes (Tuple or list): pool sizes per level , +# upsample_sizes (Tuple or list): upsample sizes per level, +# conv_blocks_per_level (int): Depth per level. +# rank (int): Spatial rank. +# activation (str): Activation function. +# norm_type (str): Normalization type. +# dropout_rate (float): Dropout. +# final_activation (str): Output activation. +# """ + +# def __init__( +# self, +# in_channels, +# out_channels, +# filters=(16, 32, 64, 128, 256), +# pool_sizes=None, +# upsample_sizes=None, +# kernel_size=3, +# conv_blocks_per_level=1, +# rank=3, +# activation="relu", +# norm_type=None, +# dropout_rate=None, +# final_activation="linear", +# ): + +# pool_sizes_temp = [] +# if pool_sizes is None: +# for i in range(len(filters) - 1): +# pool_sizes_temp.append(2) +# pool_sizes = pool_sizes_temp + +# if not isinstance(pool_sizes, (list, tuple)): +# raise TypeError("pool_sizes must be a list or tuple") + +# for i, p in enumerate(pool_sizes): +# if isinstance(p, int) and not isinstance(p, bool): +# if p < 1: +# raise ValueError("pool_sizes must be positive") +# elif isinstance(p, (tuple, list)): +# if len(p) != rank: +# raise ValueError("pool_sizes tuple must be same length as rank") +# for j, ps in enumerate(p): +# if not isinstance(ps, int) or isinstance(ps, bool): +# raise TypeError("pool_sizes must be an int") +# if ps < 1: +# raise ValueError("pool_sizes must be positive") +# else: +# raise TypeError( +# "each entry in pool_sizes must be either an int or a tuple or list" +# ) + +# if len(filters) != (len(pool_sizes) + 1): +# raise ValueError( +# f"pool_sizes do not match number of filters. For {len(filters)}, please input {len(filters) - 1} number of pools." +# ) + +# if upsample_sizes is not None: +# if not isinstance(upsample_sizes, (list, tuple)): +# raise TypeError("upsample_sizes must be a list or tuple") + +# for i, p in enumerate(upsample_sizes): +# if isinstance(p, int) and not isinstance(p, bool): +# if p < 1: +# raise ValueError("upsample_sizes must be positive") +# elif isinstance(p, (tuple, list)): +# if len(p) != rank: +# raise ValueError( +# "upsample_sizes tuple must be same length as rank" +# ) +# for j, ps in enumerate(p): +# if not isinstance(ps, int) or isinstance(ps, bool): +# raise TypeError("upsample_sizes must be an int") +# if ps < 1: +# raise ValueError("upsample_sizes must be positive") +# else: +# raise TypeError( +# "each entry in upsample_sizes must be either an int or a tuple or list" +# ) + +# if len(filters) != (len(upsample_sizes) + 1): +# raise ValueError( +# f"upsample_sizes do not match number of filters. For {len(filters)}, please input {len(filters) - 1} number of upsamples." +# ) + +# super().__init__() + +# self.encoder = ImageEncoder( +# in_channels=in_channels, +# filters=filters, +# pool_sizes=pool_sizes, +# kernel_size=kernel_size, +# conv_blocks_per_level=conv_blocks_per_level, +# rank=rank, +# activation=activation, +# norm_type=norm_type, +# dropout_rate=dropout_rate, +# ) + +# self.decoder = ImageDecoder( +# filters=filters, +# pool_sizes=pool_sizes, +# upsample_sizes=upsample_sizes, +# kernel_size=kernel_size, +# conv_blocks_per_level=conv_blocks_per_level, +# rank=rank, +# activation=activation, +# norm_type=norm_type, +# dropout_rate=dropout_rate, +# ) + +# conv = TORCH_LAYERS[f"Conv{rank}d"] + +# self.final_conv = conv(filters[0], out_channels, kernel_size=1) + +# self.final_act = ( +# ACTIVATIONS(final_activation)() +# if final_activation.lower() != "linear" +# else nn.Identity() +# ) + +# def forward(self, x): +# enc_feats = self.encoder(x) +# x = self.decoder(enc_feats) +# x = self.final_conv(x) +# x = self.final_act(x) +# return x + + +# class StandardUNet(UNet): +# """ +# Standaed UNet for 2D or 3D images. + +# Args: +# in_channels (int): Input channels. +# out_channels (int): Output channels. +# filters (List[int]): Encoder filter sizes. +# kernel_size (int): Conv kernel size. +# pool_sizes (Tuple or list): pool sizes per dim , +# conv_blocks_per_level (int): Depth per level. +# rank (int): Spatial rank. +# activation (str): Activation function. +# norm_type (str): Normalization type. +# dropout_rate (float): Dropout. +# final_activation (str): Output activation. +# """ + +# def __init__( +# self, +# in_channels, +# out_channels, +# filters=(16, 32, 64, 128, 256), +# pool_size=2, +# kernel_size=3, +# conv_blocks_per_level=1, +# rank=3, +# activation="relu", +# norm_type=None, +# dropout_rate=None, +# final_activation="linear", +# ): + +# pool_size_standard = [] +# if isinstance(pool_size, int): +# pool_size_single = [] +# for j in range(rank): +# pool_size_single.append(pool_size) +# pool_size_single = tuple(pool_size_single) +# for i in range(len(filters) - 1): +# pool_size_standard.append(pool_size_single) +# elif isinstance(pool_size, (tuple, list)): +# if len(pool_size) == rank: +# for i in range(len(filters) - 1): +# pool_size_standard.append(pool_size) +# else: +# raise ValueError("pool_size must be an int or have same length as rank") + +# super().__init__( +# in_channels=in_channels, +# out_channels=out_channels, +# filters=filters, +# pool_sizes=pool_size_standard, +# upsample_sizes=None, +# kernel_size=kernel_size, +# conv_blocks_per_level=conv_blocks_per_level, +# rank=rank, +# activation=activation, +# norm_type=norm_type, +# dropout_rate=dropout_rate, +# final_activation=final_activation, +# ) diff --git a/src/im2sim/models/utils.py b/src/im2sim/models/utils.py index f65a175..ee7ff73 100644 --- a/src/im2sim/models/utils.py +++ b/src/im2sim/models/utils.py @@ -1,68 +1,68 @@ -def get_model_config(name): - """Get the default config for a specified type of model. +# def get_model_config(name): +# """Get the default config for a specified type of model. - Args: - name: A `str`. The name of the model type. +# Args: +# name: A `str`. The name of the model type. - Returns: - A config dictionary +# Returns: +# A config dictionary - Raises: - ValueError: If the requested model config doesn't exist. - """ - try: - return _CONFIGS[(name)] - except KeyError as err: - raise ValueError(f"Could not find config for model with name '{name}'") from err +# Raises: +# ValueError: If the requested model config doesn't exist. +# """ +# try: +# return _CONFIGS[(name)] +# except KeyError as err: +# raise ValueError(f"Could not find config for model with name '{name}'") from err -_CONFIGS = { - "Image2Flow": { - "cnn_filters": (16, 48, 96, 192, 384), - "cnn_kernel_size": 3, - "cnn_res_depth": 3, - "cnn_res_blocks_per_level": 2, - "cnn_rank": 3, - "cnn_norm_type": "InstanceNorm", - "cnn_pool_type": "MaxPool", - "cnn_pool_size": 2, - "cnn_activation": "leaky_relu", - "cnn_dropout_rate": 0.3, - "projection_ids": ((3, 4), (1, 2), (0, 1)), - "gnn_filters": ((384, 288), (96, 64), (48, 32)), - "gnn_res_depth": 3, - "gnn_n_process_blocks": 1, - "gnn_n_deform_blocks": 3, - "template_edge_index": None, - "gnn_conv_type": "ChebConv", - "gnn_conv_kwargs": {"K": 1}, - "gnn_activation": "leaky_relu", - "out_activation": "leaky_relu", - "gnn_norm_type": "InstanceNorm", - "batched_ops": False, - }, - "Image2Mesh": { - "cnn_filters": (16, 32, 64, 128, 256), - "cnn_kernel_size": 3, - "cnn_res_depth": 3, - "cnn_res_blocks_per_level": 2, - "cnn_rank": 3, - "cnn_norm_type": "InstanceNorm", - "cnn_pool_type": "MaxPool", - "cnn_pool_size": 2, - "cnn_activation": "leaky_relu", - "cnn_dropout_rate": 0.3, - "projection_ids": ((3, 4), (1, 2), (0, 1)), - "gnn_filters": ((384, 288), (96, 64), (48, 32)), - "gnn_res_depth": 3, - "gnn_n_process_blocks": 1, - "gnn_n_deform_blocks": 3, - "template_edge_index": None, - "gnn_conv_type": "ChebConv", - "gnn_conv_kwargs": {"K": 1}, - "gnn_activation": "leaky_relu", - "out_activation": "leaky_relu", - "gnn_norm_type": "InstanceNorm", - "batched_ops": False, - }, -} +# _CONFIGS = { +# "Image2Flow": { +# "cnn_filters": (16, 48, 96, 192, 384), +# "cnn_kernel_size": 3, +# "cnn_res_depth": 3, +# "cnn_res_blocks_per_level": 2, +# "cnn_rank": 3, +# "cnn_norm_type": "InstanceNorm", +# "cnn_pool_type": "MaxPool", +# "cnn_pool_size": 2, +# "cnn_activation": "leaky_relu", +# "cnn_dropout_rate": 0.3, +# "projection_ids": ((3, 4), (1, 2), (0, 1)), +# "gnn_filters": ((384, 288), (96, 64), (48, 32)), +# "gnn_res_depth": 3, +# "gnn_n_process_blocks": 1, +# "gnn_n_deform_blocks": 3, +# "template_edge_index": None, +# "gnn_conv_type": "ChebConv", +# "gnn_conv_kwargs": {"K": 1}, +# "gnn_activation": "leaky_relu", +# "out_activation": "leaky_relu", +# "gnn_norm_type": "InstanceNorm", +# "batched_ops": False, +# }, +# "Image2Mesh": { +# "cnn_filters": (16, 32, 64, 128, 256), +# "cnn_kernel_size": 3, +# "cnn_res_depth": 3, +# "cnn_res_blocks_per_level": 2, +# "cnn_rank": 3, +# "cnn_norm_type": "InstanceNorm", +# "cnn_pool_type": "MaxPool", +# "cnn_pool_size": 2, +# "cnn_activation": "leaky_relu", +# "cnn_dropout_rate": 0.3, +# "projection_ids": ((3, 4), (1, 2), (0, 1)), +# "gnn_filters": ((384, 288), (96, 64), (48, 32)), +# "gnn_res_depth": 3, +# "gnn_n_process_blocks": 1, +# "gnn_n_deform_blocks": 3, +# "template_edge_index": None, +# "gnn_conv_type": "ChebConv", +# "gnn_conv_kwargs": {"K": 1}, +# "gnn_activation": "leaky_relu", +# "out_activation": "leaky_relu", +# "gnn_norm_type": "InstanceNorm", +# "batched_ops": False, +# }, +# } diff --git a/src/im2sim/plot/pointcloud.py b/src/im2sim/plot/pointcloud.py index c2ecab9..27dc124 100644 --- a/src/im2sim/plot/pointcloud.py +++ b/src/im2sim/plot/pointcloud.py @@ -81,7 +81,7 @@ def compute_norm_ranges(color_sets): row_vals.append(color_sets[idx]) row_vals = np.concatenate(row_vals) vmin, vmax = row_vals.min(), row_vals.max() - for c in range(ncols): + for _ in range(ncols): ranges.append((vmin, vmax)) return ranges @@ -108,7 +108,7 @@ def compute_norm_ranges(color_sets): # Plot creation # ----------------------------- for i, (ax, points, colors) in enumerate( - zip(self.axes, point_sets, color_sets) + zip(self.axes, point_sets, color_sets, strict=True) ): vmin, vmax = norm_ranges[i] @@ -173,7 +173,7 @@ def compute_norm_ranges(color_sets): row_vals.append(color_sets[idx]) row_vals = np.concatenate(row_vals) vmin, vmax = row_vals.min(), row_vals.max() - for c in range(self.ncols): + for _ in range(self.ncols): ranges.append((vmin, vmax)) return ranges @@ -196,7 +196,7 @@ def compute_norm_ranges(color_sets): new_scatters = [] for i, (ax, sc, pts, colors) in enumerate( - zip(self.axes, self.scatters, point_sets, color_sets) + zip(self.axes, self.scatters, point_sets, color_sets, strict=True) ): vmin, vmax = norm_ranges[i] @@ -250,14 +250,9 @@ def animate( def update(frame): - if color_sequence_sets is None: - colors = None - else: - colors = color_sequence_sets[frame] + colors = None if color_sequence_sets is None else color_sequence_sets[frame] - return self.draw_frame( - point_sets=point_sequence_sets[frame], color_sets=colors - ) + return self.draw_frame(point_sets=point_sequence_sets[frame], color_sets=colors) ani = animation.FuncAnimation(self.fig, update, frames=n_frames, blit=False) diff --git a/tests/integration/data/test_pv_mesh_utils.py b/tests/integration/data/test_pv_mesh_utils.py index 7bfee99..ef0c85c 100644 --- a/tests/integration/data/test_pv_mesh_utils.py +++ b/tests/integration/data/test_pv_mesh_utils.py @@ -29,9 +29,7 @@ def build_tetra_mesh(): celltypes = np.array([pv.CellType.TETRA, pv.CellType.TETRA]) grid = pv.UnstructuredGrid(cells, celltypes, points) - grid["CellEntityIds"] = np.array( - [0] * 2 - ) # Assign the same entity ID to both tetrahedra + grid["CellEntityIds"] = np.array([0] * 2) # Assign the same entity ID to both tetrahedra grid["vtkOriginalPointIds"] = np.arange(len(points)) return grid @@ -55,9 +53,7 @@ def build_triangle_mesh(): ] ) grid = pv.PolyData(points, faces) - grid["CellEntityIds"] = np.array( - [0] * 2 - ) # Assign the same entity ID to both tetrahedra + grid["CellEntityIds"] = np.array([0] * 2) # Assign the same entity ID to both tetrahedra grid["vtkOriginalPointIds"] = np.arange(len(points)) return grid diff --git a/tests/unit/data/test_loader.py b/tests/unit/data/test_loader.py index 0d849bf..f42e664 100644 --- a/tests/unit/data/test_loader.py +++ b/tests/unit/data/test_loader.py @@ -19,9 +19,7 @@ def tensor_sample(): @pytest.fixture def pyg_sample(): - return { - "graph": Data(x=torch.randn(5, 3), edge_index=torch.tensor([[0, 1], [1, 2]])) - } + return {"graph": Data(x=torch.randn(5, 3), edge_index=torch.tensor([[0, 1], [1, 2]]))} @pytest.fixture @@ -119,11 +117,7 @@ def test_dataloader_basic(): def test_dataloader_with_pyg(): def load_fn(case): - return { - "graph": Data( - x=torch.randn(3, 2), edge_index=torch.tensor([[0, 1], [1, 2]]) - ) - } + return {"graph": Data(x=torch.randn(3, 2), edge_index=torch.tensor([[0, 1], [1, 2]]))} ds = Dataset(load_fn=load_fn, cases=["a", "b", "c"]) loader = DataLoader(ds, batch_size=2) diff --git a/tests/unit/layers/test_graph_blocks.py b/tests/unit/layers/test_graph_blocks.py index f4fcaf3..88398b8 100644 --- a/tests/unit/layers/test_graph_blocks.py +++ b/tests/unit/layers/test_graph_blocks.py @@ -28,10 +28,8 @@ def graph(): edge_index=edge_index, ) -graph_blocks = {"GraphConv": GraphConvBlock, "GraphRes":GraphConvResBlock} - - +graph_blocks = {"GraphConv": GraphConvBlock, "GraphRes": GraphConvResBlock} @pytest.mark.parametrize( @@ -108,6 +106,7 @@ def test_graph_structure_is_preserved(graph, graph_block_type): graph.edge_index, ) + @pytest.mark.parametrize( "graph_block_type", [ @@ -132,6 +131,7 @@ def test_input_graph_is_not_modified(graph, graph_block_type): original_x, ) + @pytest.mark.parametrize( "graph_block_type", [ @@ -151,6 +151,7 @@ def test_output_is_finite(graph, graph_block_type): assert torch.isfinite(out.x).all() + @pytest.mark.parametrize( "graph_block_type", [ @@ -191,13 +192,7 @@ def test_gradients_flow(graph, graph_block_type): ) @pytest.mark.parametrize( "activation", - [ - "ReLU", - "leakyrelu", - "gelu", - "sigmoid", - None - ], + ["ReLU", "leakyrelu", "gelu", "sigmoid", None], ) def test_supported_activations(graph, activation, graph_block_type): model = graph_blocks[graph_block_type]( @@ -212,6 +207,7 @@ def test_supported_activations(graph, activation, graph_block_type): assert out.x.shape == (4, 16) + @pytest.mark.parametrize( "graph_block_type", [ @@ -237,6 +233,7 @@ def test_no_normalisation(graph, graph_block_type): assert out.x.shape == (4, 16) + @pytest.mark.parametrize( "graph_block_type", [ @@ -270,9 +267,7 @@ def test_serialisation(graph, tmp_path, graph_block_type): conv_kwargs={}, ) - model2.load_state_dict( - torch.load(path) - ) + model2.load_state_dict(torch.load(path)) model2.eval() @@ -281,4 +276,4 @@ def test_serialisation(graph, tmp_path, graph_block_type): assert torch.allclose( before, after, - ) \ No newline at end of file + ) diff --git a/tests/unit/layers/test_image_blocks.py b/tests/unit/layers/test_image_blocks.py index 1edbb44..28d71f8 100644 --- a/tests/unit/layers/test_image_blocks.py +++ b/tests/unit/layers/test_image_blocks.py @@ -15,6 +15,7 @@ # Fixtures # --------------------------------------------------------- + @pytest.fixture def image_2d(): # BCHW @@ -36,6 +37,7 @@ def small_filters(): # ImageConvBlock # --------------------------------------------------------- + @pytest.mark.parametrize( "rank,input_shape,expected_spatial", [ @@ -89,7 +91,6 @@ def test_image_conv_block_activations(activation): assert y.shape == (1, 8, 32, 32) - @pytest.mark.parametrize( "norm", [ @@ -114,7 +115,6 @@ def test_image_conv_block_normalisation(norm): assert y.shape == (2, 8, 32, 32) - def test_image_conv_block_dropout(): model = ImageConvBlock( @@ -126,7 +126,7 @@ def test_image_conv_block_dropout(): assert isinstance(model.drop, nn.Dropout2d) - x = torch.randn(2,3,32,32) + x = torch.randn(2, 3, 32, 32) model.train() @@ -136,7 +136,6 @@ def test_image_conv_block_dropout(): assert not torch.equal(y1, y2) - def test_image_conv_block_gradients(): model = ImageConvBlock( @@ -166,7 +165,6 @@ def test_image_conv_block_gradients(): assert p.grad is not None - def test_image_conv_block_parameterisation(): model = ImageConvBlock( @@ -180,19 +178,16 @@ def test_image_conv_block_parameterisation(): # 4 convolution layers assert len(model.convs) == 4 - params = sum( - p.numel() - for p in model.parameters() - ) + params = sum(p.numel() for p in model.parameters()) assert params > 0 - # --------------------------------------------------------- # Residual Block # --------------------------------------------------------- + def test_res_block_shape(): model = ImageConvResBlock( @@ -218,7 +213,6 @@ def test_res_block_shape(): ) - def test_res_block_gradient(): model = ImageConvResBlock( @@ -242,7 +236,6 @@ def test_res_block_gradient(): assert x.grad is not None - def test_res_block_depth_parameter(): model = ImageConvResBlock( @@ -255,14 +248,14 @@ def test_res_block_depth_parameter(): assert len(model.main_conv.convs) == 3 - # --------------------------------------------------------- # Encoder # --------------------------------------------------------- + @pytest.mark.parametrize( "rank", - [2,3], + [2, 3], ) def test_image_encoder_shapes(rank, small_filters): @@ -272,19 +265,13 @@ def test_image_encoder_shapes(rank, small_filters): rank=rank, ) - if rank == 2: - x = torch.randn( - 1, - 3, - 64, - 64 - ) + x = torch.randn(1, 3, 64, 64) expected = [ - (1,8,64,64), - (1,16,32,32), - (1,32,16,16), + (1, 8, 64, 64), + (1, 16, 32, 32), + (1, 32, 16, 16), ] else: @@ -297,112 +284,93 @@ def test_image_encoder_shapes(rank, small_filters): ) expected = [ - (1,8,32,32,32), - (1,16,16,16,16), - (1,32,8,8,8), + (1, 8, 32, 32, 32), + (1, 16, 16, 16, 16), + (1, 32, 8, 8, 8), ] - outputs = model(x) - assert len(outputs)==len(expected) + assert len(outputs) == len(expected) for out, shape in zip(outputs, expected): assert out.shape == shape - def test_encoder_parameters(): model = ImageEncoder( in_channels=3, - filters=(8,16), + filters=(8, 16), rank=2, conv_blocks_per_level=2, ) + assert len(model.conv_blocks) == 2 - assert len(model.conv_blocks)==2 - - params=sum( - p.numel() - for p in model.parameters() - ) + params = sum(p.numel() for p in model.parameters()) assert params > 0 - # --------------------------------------------------------- # Residual Encoder # --------------------------------------------------------- + def test_res_encoder_outputs(): model = ImageResEncoder( in_channels=3, - filters=(8,16,32), + filters=(8, 16, 32), rank=2, res_blocks_per_level=2, ) - x=torch.randn( + x = torch.randn( 1, 3, 64, 64, ) - outputs=model(x) + outputs = model(x) - assert len(outputs)==3 + assert len(outputs) == 3 - assert outputs[0].shape == ( - 1, - 8, - 64, - 64 - ) - - assert outputs[-1].shape == ( - 1, - 32, - 16, - 16 - ) + assert outputs[0].shape == (1, 8, 64, 64) + assert outputs[-1].shape == (1, 32, 16, 16) # --------------------------------------------------------- # Decoder # --------------------------------------------------------- + def test_decoder_reconstructs_resolution(): encoder = ImageEncoder( in_channels=3, - filters=(8,16,32), + filters=(8, 16, 32), rank=2, ) decoder = ImageDecoder( - filters=(8,16,32), + filters=(8, 16, 32), rank=2, ) - - x=torch.randn( + x = torch.randn( 1, 3, 64, 64, ) + enc_features = encoder(x) - enc_features=encoder(x) - - out=decoder(enc_features) - + out = decoder(enc_features) assert out.shape == ( 1, @@ -412,7 +380,6 @@ def test_decoder_reconstructs_resolution(): ) - @pytest.mark.parametrize( "skip", [ @@ -423,7 +390,7 @@ def test_decoder_reconstructs_resolution(): def test_decoder_skip_parameter(skip): decoder = ImageDecoder( - filters=(8,16,32), + filters=(8, 16, 32), rank=2, skip=skip, ) @@ -431,7 +398,6 @@ def test_decoder_skip_parameter(skip): assert decoder.skip == skip - @pytest.mark.parametrize( "upsample_type", [ @@ -443,17 +409,16 @@ def test_decoder_upsampling_modes(upsample_type): encoder = ImageEncoder( in_channels=3, - filters=(8,16,32), + filters=(8, 16, 32), rank=2, ) decoder = ImageDecoder( - filters=(8,16,32), + filters=(8, 16, 32), rank=2, upsample_type=upsample_type, ) - x = torch.randn( 1, 3, @@ -461,12 +426,10 @@ def test_decoder_upsampling_modes(upsample_type): 64, ) - features = encoder(x) out = decoder(features) - assert out.shape == ( 1, 8, @@ -475,26 +438,25 @@ def test_decoder_upsampling_modes(upsample_type): ) - # --------------------------------------------------------- # End-to-end gradient test # --------------------------------------------------------- + def test_encoder_decoder_end_to_end_gradient(): encoder = ImageEncoder( in_channels=3, - filters=(8,16), + filters=(8, 16), rank=2, ) decoder = ImageDecoder( - filters=(8,16), + filters=(8, 16), rank=2, ) - - x=torch.randn( + x = torch.randn( 2, 3, 32, @@ -502,46 +464,28 @@ def test_encoder_decoder_end_to_end_gradient(): requires_grad=True, ) + output = decoder(encoder(x)) - output=decoder( - encoder(x) - ) - - - loss=output.mean() + loss = output.mean() loss.backward() - assert x.grad is not None - encoder_grads=[ - p.grad - for p in encoder.parameters() - if p.requires_grad - ] + encoder_grads = [p.grad for p in encoder.parameters() if p.requires_grad] - decoder_grads=[ - p.grad - for p in decoder.parameters() - if p.requires_grad - ] + decoder_grads = [p.grad for p in decoder.parameters() if p.requires_grad] + assert all(g is not None for g in encoder_grads) - assert all( - g is not None - for g in encoder_grads - ) + assert all(g is not None for g in decoder_grads) - assert all( - g is not None - for g in decoder_grads - ) # --------------------------------------------------------- # Decoder crop logic # --------------------------------------------------------- + def test_decoder_match_size_crops_skip_connection(): decoder = ImageDecoder( @@ -579,20 +523,21 @@ def test_decoder_match_size_crops_skip_connection(): def test_decoder_match_size_center_crop(): decoder = ImageDecoder( - filters=(8,16), + filters=(8, 16), rank=2, ) # Put a known pattern in the skip tensor - skip = torch.arange( - 32 * 32 - ).reshape( - 1, - 1, - 32, - 32, - ).float() - + skip = ( + torch.arange(32 * 32) + .reshape( + 1, + 1, + 32, + 32, + ) + .float() + ) x = torch.zeros( 1, @@ -601,13 +546,11 @@ def test_decoder_match_size_center_crop(): 28, ) - cropped = decoder._match_size( x, skip, ) - # difference is 4 pixels -> crop 2 from each side expected = skip[ ..., @@ -615,18 +558,16 @@ def test_decoder_match_size_center_crop(): 2:30, ] - assert torch.equal( cropped, expected, ) - def test_decoder_match_size_no_crop(): decoder = ImageDecoder( - filters=(8,16), + filters=(8, 16), rank=2, ) @@ -644,7 +585,6 @@ def test_decoder_match_size_no_crop(): 32, ) - output = decoder._match_size( x, skip, @@ -655,26 +595,25 @@ def test_decoder_match_size_no_crop(): assert torch.equal(output, skip) - # --------------------------------------------------------- # Full decoder with odd image dimensions # --------------------------------------------------------- + def test_decoder_handles_odd_spatial_dimensions(): encoder = ImageEncoder( in_channels=3, - filters=(8,16,32), + filters=(8, 16, 32), rank=2, ) decoder = ImageDecoder( - filters=(8,16,32), + filters=(8, 16, 32), rank=2, skip=True, ) - # Odd dimensions trigger possible mismatch x = torch.randn( 1, @@ -683,13 +622,11 @@ def test_decoder_handles_odd_spatial_dimensions(): 65, ) - features = encoder(x) output = decoder(features) - assert output.shape[2:] == ( 64, 64, - ) \ No newline at end of file + ) From 6f1027d943a37ffa493e21b69a0d19b6ce0447ec Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 4 Aug 2026 12:07:47 +0000 Subject: [PATCH 38/49] changed default graphconv args --- src/im2sim/layers/graph_blocks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/im2sim/layers/graph_blocks.py b/src/im2sim/layers/graph_blocks.py index a519431..b1b5f79 100644 --- a/src/im2sim/layers/graph_blocks.py +++ b/src/im2sim/layers/graph_blocks.py @@ -195,7 +195,7 @@ def __init__( conv_kwargs=None, activation="relu", out_activation="linear", - norm_type="InstanceNorm", + norm_type="defaultnorm", ): super().__init__() From 960f6b2e148487b9f00a9f6281a685f5f00cea64 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 4 Aug 2026 13:08:50 +0000 Subject: [PATCH 39/49] changed py version in toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 7016ca8..05c856f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,7 @@ name = "im2sim" version = "0.1.0" description = "A framework for ML-accelerated digital twins" readme = "README.md" -requires-python = "==3.10.20" +requires-python = ">=3.10" license = { text = "ElasticV2" } authors = [ { name = "Anirudh Raman", email = "anirudh.raman.24@ucl.ac.uk" } From 6cf3195a00bc34113e5d831ae21b42f1db930970 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 4 Aug 2026 13:19:37 +0000 Subject: [PATCH 40/49] added dataloaders to util exports --- src/im2sim/data/__init__.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/im2sim/data/__init__.py b/src/im2sim/data/__init__.py index eaff815..8715300 100644 --- a/src/im2sim/data/__init__.py +++ b/src/im2sim/data/__init__.py @@ -5,6 +5,9 @@ Operation, Transform, register_op, + DataLoader, + Dataset, + Pipeline ) __all__ = [ @@ -16,4 +19,7 @@ "FittableOperation", "register_op", "Transform", + "DataLoader", + "Dataset", + "Pipeline" ] From bc2fad9bac133aac98f18f21fbafdc831e689ef6 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 4 Aug 2026 13:41:01 +0000 Subject: [PATCH 41/49] removed print statements --- src/im2sim/data/mesh_utils.py | 2 -- src/im2sim/layers/layer_util.py | 2 -- 2 files changed, 4 deletions(-) diff --git a/src/im2sim/data/mesh_utils.py b/src/im2sim/data/mesh_utils.py index 3556053..ee8e4b2 100644 --- a/src/im2sim/data/mesh_utils.py +++ b/src/im2sim/data/mesh_utils.py @@ -227,9 +227,7 @@ def compute_edge_lengths(points: torch.Tensor, edges: torch.Tensor) -> torch.Ten lengths. """ coords = points[edges] - print(coords, coords.shape) distances = torch.linalg.norm(coords[0] - coords[1], dim=-1) - print(distances.shape) return distances diff --git a/src/im2sim/layers/layer_util.py b/src/im2sim/layers/layer_util.py index bd870d1..82f777f 100644 --- a/src/im2sim/layers/layer_util.py +++ b/src/im2sim/layers/layer_util.py @@ -25,7 +25,6 @@ def decorator(obj): def normalize_key(key: str) -> str: - print(key) return key.replace(" ", "").replace("_", "").lower() @@ -39,7 +38,6 @@ def __setitem__(self, key, value): self._data[normalize_key(key)] = value def __getitem__(self, key): - print(key) return self._data[normalize_key(key)] def __str__(self): From 53acc9734f78710f06d769fb234145ecc2c06436 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Tue, 4 Aug 2026 14:56:57 +0100 Subject: [PATCH 42/49] halfunet stuff --- .DS_Store | Bin 6148 -> 6148 bytes src/im2sim/layers/__init__.py | 12 +- src/im2sim/layers/custom_image_layers.py | 134 ++++++++++++++++ src/im2sim/layers/graph_blocks.py | 4 +- src/im2sim/layers/halfunet.py | 188 +++++++++++++++++++++++ src/im2sim/layers/image_blocks.py | 20 ++- src/im2sim/layers/image_conv_blocks.py | 183 ++++++++++++++++++++++ src/im2sim/layers/layer_util.py | 132 +++++++++++++++- src/im2sim/models/unet.py | 12 +- 9 files changed, 656 insertions(+), 29 deletions(-) create mode 100644 src/im2sim/layers/custom_image_layers.py create mode 100644 src/im2sim/layers/halfunet.py create mode 100644 src/im2sim/layers/image_conv_blocks.py diff --git a/.DS_Store b/.DS_Store index 173056d345578a6adcdcb8b388f73f5b41fd9629..9a874b5768f336915163bb88cd434575b859f936 100644 GIT binary patch delta 111 zcmZoMXfc=|#>AjHu~68Ak%57MnW31Wh#@(pI5{UNKR*W~1O`k{8YBS30t`_3%^MlR p*ft+v3}W2O&cV+CGzlp3oq009h$1IQZ30N&WD_3c%`qY?m;s!16`BA5 delta 251 zcmY*UOA5j;6r8p;M7mHgbmKz3LG=`#LPG7cIAaI%mAqq!0-B_utcmw< zZ>N_fO429R78b{UQ84a+6`cQnU4n6zv_s_`v)3j*PtahQS+V0YsM8zKW@^wUGLby3 V(~qbfsYUG1nzu_e>DTHWJpu0%H`@RJ diff --git a/src/im2sim/layers/__init__.py b/src/im2sim/layers/__init__.py index 8a67db5..faa5e58 100644 --- a/src/im2sim/layers/__init__.py +++ b/src/im2sim/layers/__init__.py @@ -2,11 +2,11 @@ from .image_blocks import ImageConvBlock, ImageConvResBlock, ImageDecoder, ImageEncoder from .layer_util import ( get_activation, - get_torch_layer, + get_image_layer, init_weights, register_activation, - register_pyg_layer, - register_torch_layer, + register_graph_layer, + register_image_layer, standardize_spatial_factors, ) from .meshgraphnets import MeshGraphNet, MGNDecoder, MGNEdgeBlock, MGNGnBlock, MGNNodeBlock @@ -21,10 +21,10 @@ "ImageEncoder", "ImageDecoder", "get_activation", - "get_torch_layer", + "get_image_layer", "register_activation", - "register_pyg_layer", - "register_torch_layer", + "register_graph_layer", + "register_image_layer", "init_weights", "standardize_spatial_factors", "MGNEdgeBlock", diff --git a/src/im2sim/layers/custom_image_layers.py b/src/im2sim/layers/custom_image_layers.py new file mode 100644 index 0000000..80dc904 --- /dev/null +++ b/src/im2sim/layers/custom_image_layers.py @@ -0,0 +1,134 @@ +import torch + +from layer_util import get_activation, get_image_layer, register_with_ranks, LayerSpec, ResidualConnectionType, apply_residual_connection + + + +@register_with_ranks("DepthwiseConv", ranks=(1, 2, 3)) +class DepthwiseConv(torch.nn.Module): + + def __init__(self, + in_channels, + rank, + kernel_size=3, + stride=1, + padding='same', + dilation=1, + bias=True): + super().__init__() + self.conv = get_image_layer("Conv", rank)( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=in_channels, + bias=bias + ) + + def forward(self, x): + return self.conv(x) + + + +@register_with_ranks("DepthwiseSeparableConv", ranks=(1, 2, 3)) +class DepthwiseSeparableConv(torch.nn.Module): + + def __init__(self, + in_channels, + out_channels, + rank, + kernel_size=3, + stride=1, + padding='same', + dilation=1, + bias=True, + activation=None): + super().__init__() + + self.depthwise = get_image_layer("Conv", rank)( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=in_channels, + bias=bias + ) + self.pointwise = get_image_layer("Conv", rank)( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias + ) + self.activation = get_activation(activation) + + def forward(self, x): + x = self.depthwise(x) + x = self.pointwise(x) + x = self.activation(x) + return x + + +@register_with_ranks("GhostConv", ranks=(1, 2, 3)) +class GhostConv(torch.nn.Module): + def __init__(self, + in_channels, + out_channels, + rank, + kernel_size=3, + ratio=2, + dw_kernel_size=3, + stride=1, + padding='same', + separable=False, + bias=True): + super().__init__() + + self.out_channels = out_channels + self.init_channels = int(out_channels / ratio) + self.new_channels = out_channels - self.init_channels + + + self.primary_conv = get_image_layer("Conv", rank)( + in_channels=in_channels, + out_channels=self.init_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + bias=bias + ) + + cheap_conv_type = "DepthwiseSeparableConv" if separable else "DepthwiseConv" + + self.cheap_operation = get_image_layer(cheap_conv_type, rank)( + in_channels=self.init_channels, + out_channels=self.new_channels, + kernel_size=dw_kernel_size, + stride=1, + padding='same', + bias=bias + ) + + def forward(self, x): + x1 = self.primary_conv(x) + x2 = self.cheap_operation(x1) + return torch.cat([x1, x2], dim=1) + + + + + + + + + + + + + + diff --git a/src/im2sim/layers/graph_blocks.py b/src/im2sim/layers/graph_blocks.py index b1b5f79..f8f945c 100644 --- a/src/im2sim/layers/graph_blocks.py +++ b/src/im2sim/layers/graph_blocks.py @@ -4,12 +4,12 @@ import torch from torch import nn -from .layer_util import get_activation, get_graph_layer, register_pyg_layer +from .layer_util import get_activation, get_graph_layer, register_graph_layer logger = logging.getLogger(__name__) -@register_pyg_layer(name="defaultnorm") +@register_graph_layer(name="defaultnorm") class DefaultGraphNorm(torch.nn.Module): """ The default normalisation for im2sim graph blocks. diff --git a/src/im2sim/layers/halfunet.py b/src/im2sim/layers/halfunet.py new file mode 100644 index 0000000..305627a --- /dev/null +++ b/src/im2sim/layers/halfunet.py @@ -0,0 +1,188 @@ +import torch +from dataclasses import dataclass, fields + +import custom_image_layers +from layer_util import get_activation, get_image_layer, register_with_ranks, ResidualConnectionType, LayerSpec, ConfigurableModule, apply_residual_connection, ModuleSpec +from image_conv_blocks import ImageConvBlock, ImageConvBlockConfig, ImageConvBlockSpec + +@dataclass +class HalfUNetConfig: + hidden_channels: int = 64 + num_downsamples: int = 4 + pool_spec: LayerSpec | list[LayerSpec] = LayerSpec(name="MaxPool", kwargs={'kernel_size': 2}) + upsample_spec: LayerSpec | list[LayerSpec] = LayerSpec(name="Upsample", kwargs={'scale_factor': 2, 'mode': 'trilinear'}) + block_cfg: ImageConvBlockConfig = ImageConvBlockConfig() + blocks_per_level: int = 2 + out_activation: str|None = None + stem_block_cfg: ImageConvBlockConfig|None = None + encoder_block_cfg: list[ImageConvBlockConfig]|ImageConvBlockConfig|None = None + out_block_cfg: ImageConvBlockConfig|None = None + fusion_type: ResidualConnectionType = ResidualConnectionType.ADD + + def __post_init__(self): + if self.stem_block_cfg is None: + self.stem_block_cfg = ImageConvBlockSpec.apply_presets(self.block_cfg, presets=["single_block"]) + + if self.encoder_block_cfg is None: + self.encoder_block_cfg = [self.block_cfg] * self.num_downsamples + elif isinstance(self.encoder_block_cfg, ImageConvBlockConfig): + self.encoder_block_cfg = [self.encoder_block_cfg] * self.num_downsamples + + if self.out_block_cfg is None: + self.out_block_cfg = ImageConvBlockSpec.apply_presets(self.block_cfg, presets=["single_conv"]) + self.out_block_cfg.out_activation = self.out_activation + + +class HalfUNet(torch.nn.Module, ConfigurableModule): + + def __init__(self, + in_channels: int, + out_channels: int, + rank:int, + hidden_channels: int = 64, + num_downsamples: int = 4, + pool_spec: LayerSpec | list[LayerSpec] = LayerSpec(name="MaxPool", kwargs={'kernel_size': 2}), + upsample_spec: LayerSpec | list[LayerSpec] = LayerSpec(name="Upsample", kwargs={'scale_factor': 2, 'mode': 'trilinear'}), + block_cfg: ImageConvBlockConfig = ImageConvBlockConfig(), + blocks_per_level: int = 2, + out_activation: str|None = None, + stem_block_cfg: ImageConvBlockConfig|None = None, + encoder_block_cfg: list[ImageConvBlockConfig]|ImageConvBlockConfig|None = None, + out_block_cfg: ImageConvBlockConfig|None = None, + fusion_type: ResidualConnectionType = ResidualConnectionType.ADD): + super().__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.num_downsamples = num_downsamples + + if isinstance(pool_spec, LayerSpec): + pool_spec = [pool_spec] * num_downsamples + if isinstance(upsample_spec, LayerSpec): + upsample_spec = [upsample_spec] * num_downsamples + + for p, u in zip(pool_spec, upsample_spec): + assert p.name.lower() in ["maxpool", "averagepool"], f"pool type {p.name} not supported" + assert u.name.lower() in ["upsample", "pixelshuffle"], f"upsample type {u.name} not supported" + + self.pools = torch.nn.ModuleList([get_image_layer(pool.name, rank)(**pool.kwargs) for pool in pool_spec]) + self.ups = torch.nn.ModuleList([get_image_layer(upsample.name, rank)(**upsample.kwargs) for upsample in upsample_spec]) + + if stem_block_cfg is None: + stem_block_cfg = ImageConvBlockSpec.apply_presets(block_cfg, presets=["single_block"]) + + self.stem = ImageConvBlock.from_config(rank, in_channels, hidden_channels, stem_block_cfg) + print(type(self.stem)) + + if encoder_block_cfg is None: + encoder_block_cfg = [block_cfg] * num_downsamples + elif isinstance(encoder_block_cfg, ImageConvBlockConfig): + encoder_block_cfg = [encoder_block_cfg] * num_downsamples + + self.encoder_blocks = torch.nn.ModuleList([ + torch.nn.Sequential(*[ImageConvBlock.from_config(rank, hidden_channels, hidden_channels, cfg)] * blocks_per_level) + for cfg in encoder_block_cfg + ]) + + if out_block_cfg is None: + out_block_cfg = ImageConvBlockSpec.apply_presets(block_cfg, presets=["single_conv"]) + out_block_cfg.out_activation = out_activation + + self.fusion_type = fusion_type + fusion_channels = hidden_channels * (num_downsamples+1) if self.fusion_type is ResidualConnectionType.CONCAT else hidden_channels + self.out_block = ImageConvBlock.from_config(rank, fusion_channels, out_channels, out_block_cfg) + + + def forward(self, x): + x = self.stem(x) + + residuals = [] + for pool, block, up in zip(self.pools, self.encoder_blocks, self.ups): + residuals.append(x) + x = pool(x) + x = block(x) + + + fused = x + for res, up in zip(reversed(residuals),self.ups): + fused = up(fused) + fused = apply_residual_connection(fused, res, connection_type=self.fusion_type) + + out = self.out_block(fused) + return out + + +HalfUNetSpec = ModuleSpec(HalfUNet, HalfUNetConfig) + + +@HalfUNetSpec.register_config("residual") +def half_unet_residual_config(cfg: HalfUNetConfig): + cfg.encoder_block_cfg = [ImageConvBlockSpec.apply_presets(c, presets=["0_residual"]) for c in cfg.encoder_block_cfg] + return cfg + +@HalfUNetSpec.register_config("dilated_bottleneck") +def unet_residual_config(cfg: HalfUNetConfig): + cfg.encoder_block_cfg[-1] = ImageConvBlockSpec.apply_presets(cfg.block_cfg, presets=["dilated_convs"]) + return cfg + +def _apply_preset_to_all_blocks(cfg: HalfUNetConfig, preset_name: str): + cfg.stem_block_cfg= ImageConvBlockSpec.apply_presets(cfg.stem_block_cfg, presets=[preset_name]) + cfg.encoder_block_cfg= [ImageConvBlockSpec.apply_presets(c, presets=[preset_name]) for c in cfg.encoder_block_cfg] + cfg.out_block_cfg = ImageConvBlockSpec.apply_presets(cfg.out_block_cfg, presets=[preset_name]) + return cfg + + +@HalfUNetSpec.register_config("recon") +def reconstruction_config(cfg: HalfUNetConfig): + _apply_preset_to_all_blocks(cfg, "reconstruction") + cfg.out_activation = None + return cfg + +@HalfUNetSpec.register_config("segmentation") +def segmentation_config(cfg: HalfUNetConfig): + _apply_preset_to_all_blocks(cfg, "segmentation") + return cfg + + +@HalfUNetSpec.register_config("depthwise_separable") +def depthwise_separable_config(cfg: HalfUNetConfig): + _apply_preset_to_all_blocks(cfg, "depthwise_separable") + return cfg + +@HalfUNetSpec.register_config("ghost_depthwise") +def ghost_dw_config(cfg: HalfUNetConfig): + _apply_preset_to_all_blocks(cfg, "ghost_depthwise") + return cfg + +@HalfUNetSpec.register_config("ghost_depthwise_separable") +def ghost_dws_config(cfg: HalfUNetConfig): + _apply_preset_to_all_blocks(cfg, "ghost_depthwise_separable") + return cfg + + +if __name__ == "__main__": + + def cfg_print(cfg): + print("###################") + for field in fields(cfg): + value = getattr(cfg, field.name) + print(f" {field.name}: {value}") + + cfg = ImageConvBlockConfig() + cfg_print(cfg) + + cfg = HalfUNetConfig(num_downsamples=2) + cfg_print(cfg) + cfg = HalfUNetSpec.apply_presets(cfg, presets=["ghost_depthwise", "residual"]) + cfg_print(cfg) + + model = HalfUNetSpec.build(rank=3, in_channels=1, out_channels=1, cfg=cfg) + + print(model) + + x = torch.randn(1, 1, 64, 64, 64) # Example input for a 3D tensor + y = model(x) + print(y.shape) # Should print the shape of the output tensor + + \ No newline at end of file diff --git a/src/im2sim/layers/image_blocks.py b/src/im2sim/layers/image_blocks.py index 39c0204..a1ba216 100644 --- a/src/im2sim/layers/image_blocks.py +++ b/src/im2sim/layers/image_blocks.py @@ -2,12 +2,18 @@ import torch from torch import nn +from dataclasses import dataclass +from typing import Any -from .layer_util import get_activation, get_torch_layer +from .layer_util import (get_activation, + get_image_layer, + BaseImageConvBlock) logger = logging.getLogger(__name__) + + class ImageConvBlock(nn.Module): """ A convolutional block for image data @@ -40,7 +46,7 @@ def __init__( ): super().__init__() - conv = get_torch_layer("Conv", rank) + conv = get_image_layer("Conv", rank) self.convs = nn.ModuleList( [ conv( @@ -55,14 +61,14 @@ def __init__( self.norms = nn.ModuleList( [ - get_torch_layer(norm_type, rank)(filters, affine=True) + get_image_layer(norm_type, rank)(filters, affine=True) if norm_type else nn.Identity() for _ in range(depth) ] ) self.drop = ( - get_torch_layer("Dropout", rank)(p=dropout_rate) if dropout_rate else nn.Identity() + get_image_layer("Dropout", rank)(p=dropout_rate) if dropout_rate else nn.Identity() ) self.act = get_activation(activation) @@ -222,7 +228,7 @@ def __init__( for i in range(n_levels) ] ) - pool = get_torch_layer(pool_type, rank) + pool = get_image_layer(pool_type, rank) self.pools = nn.ModuleList( [pool(pool_size) if i > 0 else nn.Identity() for i in range(n_levels)] ) @@ -295,7 +301,7 @@ def __init__( for i in range(n_levels) ] ) - pool = get_torch_layer(pool_type, rank) + pool = get_image_layer(pool_type, rank) self.maxpools = nn.ModuleList( [pool(pool_size) if i > 0 else nn.Identity() for i in range(n_levels)] ) @@ -369,7 +375,7 @@ def __init__( ] ) else: - up_layer = get_torch_layer(upsample_type, rank) + up_layer = get_image_layer(upsample_type, rank) self.ups = nn.ModuleList( [ up_layer(rev_filters[i], rev_filters[i + 1], kernel_size=2, stride=2) diff --git a/src/im2sim/layers/image_conv_blocks.py b/src/im2sim/layers/image_conv_blocks.py new file mode 100644 index 0000000..ce85542 --- /dev/null +++ b/src/im2sim/layers/image_conv_blocks.py @@ -0,0 +1,183 @@ +import torch +from collections import defaultdict +from dataclasses import dataclass + +import custom_image_layers +from layer_util import get_activation, get_image_layer, LayerSpec, ResidualConnectionType, apply_residual_connection, ConfigurableModule, ModuleSpec + + + +@dataclass +class ImageConvBlockConfig: + depth: int = 2 + activation: str|None = "ReLU" + out_activation: str|None = None + conv_type: LayerSpec = LayerSpec(name="Conv", kwargs={"kernel_size": 3, "padding": "same"}) + norm_type: LayerSpec = LayerSpec(name="InstanceNorm", kwargs={"affine": True}) + dropout_type: LayerSpec = LayerSpec(name=None, kwargs={}) + dropout_position: int|list[int] = 1 + residual_connections: dict[int,list[int]]=None + residual_type: str = ResidualConnectionType.ADD + + +class ImageConvBlock(torch.nn.Module, ConfigurableModule): + def __init__(self, + in_channels: int, + out_channels: int, + rank: int, + depth: int = 2, + activation: str|None = None, + out_activation: str|None = None, + conv_type: LayerSpec|None = None, + norm_type: LayerSpec|None = None, + dropout_type: LayerSpec|None = None, + dropout_position: int|list[int] = 1, + residual_connections: dict[int,list[int]]=None, + residual_type: str = ResidualConnectionType.ADD, + ): + super().__init__() + + if dropout_type is not None: + assert dropout_position < depth, "Dropout position must be less than depth" + + + if conv_type is None: + conv_type = LayerSpec(name="Conv", rank=rank, kwargs={}) + if norm_type is None: + norm_type = LayerSpec(name=None, kwargs={}) + if dropout_type is None: + dropout_type = LayerSpec(name=None, kwargs={}) + + if isinstance(dropout_position, int): + dropout_position = [dropout_position] + + self.residual_connections = residual_connections if residual_connections is not None else {} + self.residual_type = residual_type + + + self.layers = torch.nn.ModuleList() + self.activation = get_activation(activation) + self.out_activation = get_activation(out_activation) + + in_channels_per_layer = [in_channels] + for i in range(depth): + conv = get_image_layer(conv_type.name, rank=rank) + conv = get_image_layer(conv_type.name, rank=rank)( + in_channels=in_channels_per_layer[-1], + out_channels=out_channels, + **conv_type.kwargs) + + assert norm_type.name in [None, "BatchNorm", "InstanceNorm"], f"Unsupported norm type: {norm_type.name}" + + norm = get_image_layer(norm_type.name, rank=rank)(out_channels,**norm_type.kwargs) + + dropout = get_image_layer(dropout_type.name, rank=rank)(**dropout_type.kwargs) if i in dropout_position else torch.nn.Identity() + + block = torch.nn.Sequential( + conv, + norm, + dropout, + self.activation if i 1, "Depth must be greater than 1 for 1-residual connections" + cfg.residual_connections = {cfg.depth-1: [1]} + cfg.residual_type = ResidualConnectionType.ADD + return cfg + +@ImageConvBlockSpec.register_config("concat_residual") +def concat_residual_config(cfg: ImageConvBlockConfig): + cfg.residual_connections = {cfg.depth-1: [0]} + cfg.residual_type = ResidualConnectionType.CONCAT + return cfg + +@ImageConvBlockSpec.register_config("recon") +def reconstruction_config(cfg: ImageConvBlockConfig): + cfg.norm_type = None + cfg.dropout_type = None + return cfg + +@ImageConvBlockSpec.register_config("segmentation") +def segmentation_config(cfg: ImageConvBlockConfig): + cfg.norm_type = LayerSpec(name="InstanceNorm", kwargs={"affine": True}) + return cfg + + +@ImageConvBlockSpec.register_config("depthwise_separable") +def depthwise_separable_config(cfg: ImageConvBlockConfig): + cfg.conv_type = LayerSpec(name="DepthwiseSeparableConv", kwargs={}) + return cfg + +@ImageConvBlockSpec.register_config("ghost_depthwise") +def ghost_config(cfg: ImageConvBlockConfig): + cfg.conv_type = LayerSpec(name="GhostConv", kwargs={}) + return cfg + +@ImageConvBlockSpec.register_config("ghost_depthwise_separable") +def ghost_config(cfg: ImageConvBlockConfig): + cfg.conv_type = LayerSpec(name="GhostConv", kwargs={"separable":True}) + return cfg + +@ImageConvBlockSpec.register_config("dilated_convs") +def dilated_convs_config(cfg: ImageConvBlockConfig): + cfg.conv_type = LayerSpec(name="Conv", kwargs={"kernel_size": 3, "padding": "same", "dilation": 2}) + return cfg + +if __name__ == "__main__": + cfg = ImageConvBlockConfig(depth=3, activation="ReLU", out_activation="SoftPlus").apply_presets(["ghost", "0_residual"]) + model = ImageConvBlockSpec.build(cfg, presets=["ghost", "0_residual"]) + + print(model) + x = torch.randn(1, 3, 64, 64, 64) + y = model(x) + print(y.shape) \ No newline at end of file diff --git a/src/im2sim/layers/layer_util.py b/src/im2sim/layers/layer_util.py index 82f777f..9f5d87a 100644 --- a/src/im2sim/layers/layer_util.py +++ b/src/im2sim/layers/layer_util.py @@ -3,6 +3,10 @@ from collections.abc import Callable from copy import copy from typing import Any +from enum import Enum +from dataclasses import dataclass, asdict, fields +from functools import partial, wraps + import torch import torch_geometric.nn as gnn @@ -52,26 +56,48 @@ def __str__(self): ACTIVATIONS, register_activation = make_registry(torch.nn, activation_pattern) -layer_pattern = r"(Conv|Pool|Norm|UpSample|PixelShuffle|Dropout)" +layer_pattern = r"(Conv|Pool|Norm|Upsample|PixelShuffle|Dropout)" + + +IMAGE_LAYERS, register_image_layer = make_registry(torch.nn, layer_pattern) +GRAPH_LAYERS, register_graph_layer = make_registry(gnn, layer_pattern) + +def register_with_ranks(base_name, ranks=(1, 2, 3)): + def decorator(cls): + for r in ranks: + name = f"{base_name}{r}d" -TORCH_LAYERS, register_torch_layer = make_registry(torch.nn, layer_pattern) -PYG_LAYERS, register_pyg_layer = make_registry(gnn, layer_pattern) + layer_cls = type( + name, + (cls,), + { + "__init__": lambda self, *args, _rank=r, **kwargs: + cls.__init__(self, *args, rank=_rank, **kwargs) + }, + ) + + register_image_layer(name=name)(layer_cls) + return cls + return decorator -def get_torch_layer(name: str, rank: int) -> torch.nn.Module: +def get_image_layer(name: str, rank: int) -> torch.nn.Module: """ Get a PyTorch layer by name, with optional arguments. """ - + if name is None: + return torch.nn.Identity + rank_name = f"{name}{rank}d" + try: - return TORCH_LAYERS[rank_name] + return IMAGE_LAYERS[rank_name] except KeyError: pass try: - return TORCH_LAYERS[name] + return IMAGE_LAYERS[name] except KeyError: ValueError(f"Layer {name} with rank {rank} not found in PyTorch layers registry") @@ -142,7 +168,7 @@ def get_graph_layer( if kwargs is None: kwargs = {} - module = PYG_LAYERS[name](*args, **kwargs) + module = GRAPH_LAYERS[name](*args, **kwargs) return PyG_Wrapper(module) @@ -170,3 +196,93 @@ def standardize_spatial_factors(factors, rank): raise TypeError(f"Each factor must be an int, tuple, or list, got {type(f).__name__}") return standardized + +class ResidualConnectionType(Enum): + ADD = "add" # Standard addition residual connection + CONCAT = "concat" # Concatenation residual connection + MULTIPLY = "multiply" # Element-wise multiplication residual connection + AVERAGE = "average" # Element-wise average residual connection + + +def apply_residual_connection(*inputs, connection_type: ResidualConnectionType): + if len(inputs) == 0: + raise ValueError("At least one input tensor is required") + + if connection_type == ResidualConnectionType.ADD: + return torch.stack(inputs, dim=0).sum(dim=0) + + elif connection_type == ResidualConnectionType.CONCAT: + return torch.cat(inputs, dim=1) + + elif connection_type == ResidualConnectionType.MULTIPLY: + result = inputs[0] + for x in inputs[1:]: + result = result * x + return result + + elif connection_type == ResidualConnectionType.AVERAGE: + return torch.stack(inputs, dim=0).mean(dim=0) + + else: + raise ValueError(f"Unsupported residual connection type: {connection_type}") + + + +@dataclass +class LayerSpec: + name: str + kwargs: dict[str, Any] = None + + + +class ConfigRegistry: + def __init__(self): + self._registry = {} + + def register(self, name): + def decorator(fn): + self._registry[name] = fn + return fn + return decorator + + def apply(self, name, cfg): + if name not in self._registry: + raise ValueError(f"Unknown config: {name}") + return self._registry[name](copy(cfg)) + + def list(self): + return list(self._registry.keys()) + + +def shallow_asdict(obj): + return {f.name: getattr(obj, f.name) for f in fields(obj)} + + +class ConfigurableModule: + @classmethod + def from_config(cls, rank, in_channels, out_channels, cfg): + return cls(in_channels, out_channels, **shallow_asdict(cfg), rank=rank) + + +class ModuleSpec: + def __init__(self, module_cls, config_cls): + self.module_cls = module_cls + self.config_cls = config_cls + self.config_registry = ConfigRegistry() + + def register_config(self, name): + return self.config_registry.register(name) + + def apply_presets(self, cfg, presets=None): + out_cfg = copy(cfg) + if presets is not None: + if isinstance(presets, str): + presets = [presets] + + for preset in presets: + out_cfg = self.config_registry.apply(preset, cfg) + return out_cfg + + def build(self, rank, in_channels, out_channels, cfg, presets=None): + cfg = self.apply_presets(cfg, presets) + return self.module_cls.from_config(rank, in_channels, out_channels, cfg) diff --git a/src/im2sim/models/unet.py b/src/im2sim/models/unet.py index 7ef577c..a33a632 100644 --- a/src/im2sim/models/unet.py +++ b/src/im2sim/models/unet.py @@ -36,8 +36,8 @@ # ): # super().__init__() -# conv = TORCH_LAYERS[f"Conv{rank}d"] -# drop = TORCH_LAYERS[f"Dropout{rank}d"] +# conv = IMAGE_LAYERS[f"Conv{rank}d"] +# drop = IMAGE_LAYERS[f"Dropout{rank}d"] # self.convs = nn.ModuleList( # [ # conv( @@ -52,7 +52,7 @@ # self.norms = nn.ModuleList( # [ -# TORCH_LAYERS(f"{norm_type}{rank}d")(filters) +# IMAGE_LAYERS(f"{norm_type}{rank}d")(filters) # if norm_type # else nn.Identity() # for _ in range(depth) @@ -131,7 +131,7 @@ # pool_sizes_standard = standardize_spatial_factors(pool_sizes, rank) -# pool = TORCH_LAYERS[f"{pool_type}{rank}d"] +# pool = IMAGE_LAYERS[f"{pool_type}{rank}d"] # self.maxpools = nn.ModuleList( # [ # pool(pool_sizes_standard[i - 1]) if i > 0 else nn.Identity() @@ -216,7 +216,7 @@ # ] # ) # else: -# up_layer = TORCH_LAYERS(f"{upsample_type}{rank}d") +# up_layer = IMAGE_LAYERS(f"{upsample_type}{rank}d") # self.ups = nn.ModuleList( # [ # up_layer( @@ -436,7 +436,7 @@ # dropout_rate=dropout_rate, # ) -# conv = TORCH_LAYERS[f"Conv{rank}d"] +# conv = IMAGE_LAYERS[f"Conv{rank}d"] # self.final_conv = conv(filters[0], out_channels, kernel_size=1) From c349479be1f3daffaf5bd86ac4dedd4d6bc04fe1 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 10 Aug 2026 12:19:40 +0100 Subject: [PATCH 43/49] added docs for github pages --- .DS_Store | Bin 6148 -> 6148 bytes .github/workflows/api.yaml | 28 + .github/workflows/build_package.yml | 66 + .github/workflows/docs.yaml | 43 + README.rst | 41 + im2sim/__about__.py | 38 + im2sim/__init__.py | 28 + im2sim/_api/configs/__init__.py | 6 + im2sim/_api/data/__init__.py | 5 + im2sim/_api/layers/__init__.py | 15 + im2sim/_api/losses/__init__.py | 5 + im2sim/_api/models/__init__.py 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name: Setup Python + uses: actions/setup-python@v6 + with: + python-version: "3.10" + + - name: Install dependencies + run: | + pip install -e \ No newline at end of file diff --git a/.github/workflows/build_package.yml b/.github/workflows/build_package.yml new file mode 100644 index 0000000..0cbc0c5 --- /dev/null +++ b/.github/workflows/build_package.yml @@ -0,0 +1,66 @@ +name: Run Tests + +on: + push: + pull_request: + +jobs: + test: + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v5 + + - name: Set up Python + uses: actions/setup-python@v6 + with: + python-version: '3.10' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + pip install -e ".[dev]" + + - name: Lint + run: ruff check . + + - name: Run tests + run: pytest + + build_docs: + runs-on: ubuntu-latest + name: Build API + run: | + python tools/build/create_api.py + + - name: Build Docs + run: | + cd tools/docs + make clean + python create_templates.py + python create_documents.py + make dirhtml + + - name: Upload Pages artifact + uses: actions/upload-pages-artifact@v3 + with: + path: tools/docs/_build/dirhtml + + deploy_docs: + needs: build_docs + runs-on: ubuntu-latest + + permissions: + pages: write + id-token: write + + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 \ No newline at end of file diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml new file mode 100644 index 0000000..b1e8f65 --- /dev/null +++ b/.github/workflows/docs.yaml @@ -0,0 +1,43 @@ +name: Documentation + +on: + push: + pull_request: + paths: + - "tools/**" + - "src/**" + +permissions: + contents: write + +jobs: + docs: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v5 + + - name: Setup Python + uses: actions/setup-python@v6 + with: + python-version: "3.10" + + - name: Install dependencies + run: | + pip install -e ".[docs]" + + - name: Build docs + run: | + rm -rf tools/docs/_build + rm -rf tools/docs/_templates + rm -rf tools/docs/api_docs + $(PYTHON) tools/docs/create_templates.py + $(PYTHON) tools/docs/create_documents.py + $(MAKE) -C tools/docs dirhtml PY_VERSION=$(PY_VERSION) + + - name: Deploy + uses: peaceiris/actions-gh-pages@v4 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: docs/build/html \ No newline at end of file diff --git a/README.rst b/README.rst new file mode 100644 index 0000000..1b055a9 --- /dev/null +++ b/README.rst @@ -0,0 +1,41 @@ +im2sim +====== + +``im2sim`` is a library designed to simplify the development of ML-accelerated +digital twins based on medical images. + +This includes two main components: + +1. Deep Learning (DL) frameworks based on + `PyTorch `_ and + `PyTorch Geometric `_ + +2. Mesh processing frameworks based on + `VTK `_ and + `PyVista `_ + +Features +-------- + +* Image and Mesh Data Processing +* DL models for medical imaging applications +* DL models for digital twin applications +* Hybrid DL models for simulation outputs directly from images +* Building blocks for custom DL models +* Visualisation utilities + +Installation +------------ + +1. Install the CUDA dependencies: + +.. code-block:: bash + + pip install torch-scatter torch-cluster \ + -f https://data.pydata.org/whl/torch-2.3.1+cu121.html + +2. Install the repository: + +.. code-block:: bash + + pip install git+https://github.com/mrphys/im2sim.git \ No newline at end of file diff --git a/im2sim/__about__.py b/im2sim/__about__.py new file mode 100644 index 0000000..9028be0 --- /dev/null +++ b/im2sim/__about__.py @@ -0,0 +1,38 @@ +# Copyright 2026 University College London. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""About IM2SIM""" + +__all__ = [ + "__title__", + "__summary__", + "__uri__", + "__version__", + "__author__", + "__email__", + "__license__", + "__copyright__", +] + +__title__ = "im2sim" +__summary__ = "A library to accelerate the development of deep learning models for medical image analysis and simulation." +__uri__ = "https://github.com/mrphys/im2sim" + +__version__ = "0.1.0" + +__author__ = "Anirudh Raman, Vivek Muthurangu, Javier Montalt-Tordera" +__email__ = "v.muthurangu@ucl.ac.uk" + +__license__ = "Apache 2.0" +__copyright__ = "2026 University College London" diff --git a/im2sim/__init__.py b/im2sim/__init__.py new file mode 100644 index 0000000..0d81f8a --- /dev/null +++ b/im2sim/__init__.py @@ -0,0 +1,28 @@ +# This file was automatically generated by tools/build/create_api.py. +# Do not edit. +"""IM2SIM.""" +import os as _os +import sys as _sys + +from im2sim.__about__ import * + +# Import submodules. +from im2sim._api import data +from im2sim._api import layers +from im2sim._api import configs +from im2sim._api import ops +from im2sim._api import losses +from im2sim._api import models +from im2sim._api import plot + +# Make sure directory containing top level submodules is in +# the __path__ so that "from tensorflow_mri.foo import bar" works. +# We're using callbacks, but there's nothing special about that. +_API_MODULE = _sys.modules[__name__].layers +_im2sim_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__)) +_current_module = _sys.modules[__name__] + +if not hasattr(_current_module, '__path__'): + __path__ = [_im2sim_api_dir] +elif _im2sim_api_dir not in __path__: + __path__.append(_im2sim_api_dir) diff --git a/im2sim/_api/configs/__init__.py b/im2sim/_api/configs/__init__.py new file mode 100644 index 0000000..827cfd5 --- /dev/null +++ b/im2sim/_api/configs/__init__.py @@ -0,0 +1,6 @@ +# This file was automatically generated by tools/build/create_api.py. +# Do not edit. +"""Configuration classes for model and training settings.""" + +from im2sim.src.layers.image_conv_blocks import ImageConvBlockConfig as ImageConvBlockConfig +from im2sim.src.layers.halfunet import HalfUNetConfig as HalfUNetConfig diff --git a/im2sim/_api/data/__init__.py b/im2sim/_api/data/__init__.py new file mode 100644 index 0000000..81821ce --- /dev/null +++ b/im2sim/_api/data/__init__.py @@ -0,0 +1,5 @@ +# This file was automatically generated by tools/build/create_api.py. +# Do not edit. +"""Data loading and preprocessing utilities.""" + + diff --git a/im2sim/_api/layers/__init__.py b/im2sim/_api/layers/__init__.py new file mode 100644 index 0000000..a55376e --- /dev/null +++ b/im2sim/_api/layers/__init__.py @@ -0,0 +1,15 @@ +# This file was automatically generated by tools/build/create_api.py. +# Do not edit. +"""Custom layers for building deep learning models.""" + +from im2sim.src.layers.graph_blocks import DefaultGraphNorm as DefaultGraphNorm +from im2sim.src.layers.graph_blocks import GraphConvBlock as GraphConvBlock +from im2sim.src.layers.graph_blocks import GraphConvResBlock as GraphConvResBlock +from im2sim.src.layers.graph_blocks import GraphResDecoderBlock as GraphResDecoderBlock +from im2sim.src.layers.custom_image_layers import DepthwiseConv as DepthwiseConv +from im2sim.src.layers.custom_image_layers import DepthwiseSeparableConv as DepthwiseSeparableConv +from im2sim.src.layers.custom_image_layers import GhostConv as GhostConv +from im2sim.src.layers.custom_image_layers import EfficientChannelAttn as EfficientChannelAttn +from im2sim.src.layers.custom_image_layers import SqueezeExcite as SqueezeExcite +from im2sim.src.layers.custom_image_layers import ConditionedSqueezeExcite as ConditionedSqueezeExcite +from im2sim.src.layers.image_conv_blocks import ImageConvBlock as ImageConvBlock diff --git a/im2sim/_api/losses/__init__.py b/im2sim/_api/losses/__init__.py new file mode 100644 index 0000000..6228ba5 --- /dev/null +++ b/im2sim/_api/losses/__init__.py @@ -0,0 +1,5 @@ +# This file was automatically generated by tools/build/create_api.py. +# Do not edit. +"""Custom loss functions for training deep learning models.""" + + diff --git a/im2sim/_api/models/__init__.py b/im2sim/_api/models/__init__.py new file mode 100644 index 0000000..b762097 --- /dev/null +++ b/im2sim/_api/models/__init__.py @@ -0,0 +1,5 @@ +# This file was automatically generated by tools/build/create_api.py. +# Do not edit. +"""Predefined deep learning models for various tasks.""" + +from im2sim.src.layers.halfunet import HalfUNet as HalfUNet diff --git a/im2sim/_api/ops/__init__.py b/im2sim/_api/ops/__init__.py new file mode 100644 index 0000000..b87d432 --- /dev/null +++ b/im2sim/_api/ops/__init__.py @@ -0,0 +1,5 @@ +# This file was automatically generated by tools/build/create_api.py. +# Do not edit. +"""Custom operations for deep learning models.""" + +from im2sim.src.data.ops import normtorange as normtorange diff --git a/im2sim/_api/plot/__init__.py b/im2sim/_api/plot/__init__.py new file mode 100644 index 0000000..377748d --- /dev/null +++ b/im2sim/_api/plot/__init__.py @@ -0,0 +1,5 @@ +# This file was automatically generated by tools/build/create_api.py. +# Do not edit. +"""Utilities for visualizing data and model outputs.""" + + diff --git a/README.md b/im2sim/im2sim.egg-info/PKG-INFO similarity index 56% rename from README.md rename to im2sim/im2sim.egg-info/PKG-INFO index 3c780c7..ca05b52 100644 --- a/README.md +++ b/im2sim/im2sim.egg-info/PKG-INFO @@ -1,3 +1,27 @@ +Metadata-Version: 2.4 +Name: im2sim +Version: 0.1.0 +Summary: A framework for ML-accelerated digital twins +Author-email: Anirudh Raman +License: ElasticV2 +Requires-Python: >=3.10 +Description-Content-Type: text/markdown +Requires-Dist: torch==2.3.1 +Requires-Dist: torch_geometric==2.7.0 +Requires-Dist: pyvista==0.47.0 +Requires-Dist: numpy +Requires-Dist: matplotlib +Requires-Dist: scikit-learn +Provides-Extra: dev +Requires-Dist: pytest; extra == "dev" +Requires-Dist: pytest-cov; extra == "dev" +Requires-Dist: ruff; extra == "dev" +Requires-Dist: pre-commit; extra == "dev" +Provides-Extra: docs +Requires-Dist: mkdocs; extra == "docs" +Requires-Dist: mkdocs-material; extra == "docs" +Requires-Dist: mkdocstrings[python]; extra == "docs" + # im2sim `im2sim` is a library designed to simplify the development of ML-accelerated digital twins based on medical images. diff --git a/src/im2sim.egg-info/SOURCES.txt b/im2sim/im2sim.egg-info/SOURCES.txt similarity index 82% rename from src/im2sim.egg-info/SOURCES.txt rename to im2sim/im2sim.egg-info/SOURCES.txt index d6d8132..4fe6de4 100644 --- a/src/im2sim.egg-info/SOURCES.txt +++ b/im2sim/im2sim.egg-info/SOURCES.txt @@ -1,4 +1,5 @@ -setup.py +README.md +pyproject.toml src/im2sim/__init__.py src/im2sim.egg-info/PKG-INFO src/im2sim.egg-info/SOURCES.txt @@ -14,11 +15,16 @@ src/im2sim/data/pca.py src/im2sim/data/scaling.py src/im2sim/data/transforms.py src/im2sim/layers/__init__.py +src/im2sim/layers/custom_image_layers.py src/im2sim/layers/graph_blocks.py +src/im2sim/layers/halfunet.py src/im2sim/layers/image_blocks.py +src/im2sim/layers/image_conv_blocks.py src/im2sim/layers/layer_util.py src/im2sim/layers/meshgraphnets.py +src/im2sim/layers/module_config.py src/im2sim/layers/projections.py +src/im2sim/layers/reverse_halfunet.py src/im2sim/losses/__init__.py src/im2sim/losses/feature.py src/im2sim/losses/mesh.py diff --git a/src/im2sim.egg-info/dependency_links.txt b/im2sim/im2sim.egg-info/dependency_links.txt similarity index 100% rename from src/im2sim.egg-info/dependency_links.txt rename to im2sim/im2sim.egg-info/dependency_links.txt diff --git a/im2sim/im2sim.egg-info/requires.txt b/im2sim/im2sim.egg-info/requires.txt new file mode 100644 index 0000000..5de1cbf --- /dev/null +++ b/im2sim/im2sim.egg-info/requires.txt @@ -0,0 +1,17 @@ +torch==2.3.1 +torch_geometric==2.7.0 +pyvista==0.47.0 +numpy +matplotlib +scikit-learn + +[dev] +pytest +pytest-cov +ruff +pre-commit + +[docs] +mkdocs +mkdocs-material +mkdocstrings[python] diff --git a/src/im2sim.egg-info/top_level.txt b/im2sim/im2sim.egg-info/top_level.txt similarity index 100% rename from src/im2sim.egg-info/top_level.txt rename to im2sim/im2sim.egg-info/top_level.txt diff --git a/im2sim/src/__init__.py b/im2sim/src/__init__.py new file mode 100644 index 0000000..7794496 --- /dev/null +++ b/im2sim/src/__init__.py @@ -0,0 +1,8 @@ +# Import submodules. +from im2sim.src import data +from im2sim.src import layers +# from im2sim.src import configs +from im2sim.src import losses +from im2sim.src import models +from im2sim.src import plot + diff --git a/src/im2sim/data/__init__.py b/im2sim/src/data/__init__.py similarity index 72% rename from src/im2sim/data/__init__.py rename to im2sim/src/data/__init__.py index 8715300..101d11e 100644 --- a/src/im2sim/data/__init__.py +++ b/im2sim/src/data/__init__.py @@ -1,13 +1,13 @@ -from . import mesh_utils, ops, transforms -from .core import ( +from im2sim.src.data import mesh_utils, ops, transforms +from im2sim.src.data.core import ( + DataLoader, + Dataset, FittableOperation, InvertibleOperation, Operation, + Pipeline, Transform, register_op, - DataLoader, - Dataset, - Pipeline ) __all__ = [ @@ -21,5 +21,5 @@ "Transform", "DataLoader", "Dataset", - "Pipeline" + "Pipeline", ] diff --git a/src/im2sim/data/contents.md b/im2sim/src/data/contents.md similarity index 100% rename from src/im2sim/data/contents.md rename to im2sim/src/data/contents.md diff --git a/src/im2sim/data/core.py b/im2sim/src/data/core.py similarity index 99% rename from src/im2sim/data/core.py rename to im2sim/src/data/core.py index c0150db..1766998 100644 --- a/src/im2sim/data/core.py +++ b/im2sim/src/data/core.py @@ -7,8 +7,9 @@ import torch from torch_geometric.data import Batch, Data -logger = logging.getLogger(__name__) +from im2sim.src.utils import api_util +logger = logging.getLogger(__name__) class Operation(ABC): """ diff --git a/src/im2sim/data/mesh_utils.py b/im2sim/src/data/mesh_utils.py similarity index 100% rename from src/im2sim/data/mesh_utils.py rename to im2sim/src/data/mesh_utils.py diff --git a/src/im2sim/data/ops.py b/im2sim/src/data/ops.py similarity index 86% rename from src/im2sim/data/ops.py rename to im2sim/src/data/ops.py index 95e1615..b58a285 100644 --- a/src/im2sim/data/ops.py +++ b/im2sim/src/data/ops.py @@ -1,6 +1,8 @@ import torch -from .core import FittableOperation, InvertibleOperation, Operation, register_op +from im2sim.src.data.core import FittableOperation, InvertibleOperation, Operation, register_op +from im2sim.src.utils import api_util + # TODO: fix the PowerScaleOp # ------------------------------------------------------------------------------------ @@ -8,8 +10,18 @@ # ------------------------------------------------------------------------------------ eps = 1e-8 - +@api_util.export("ops.normtorange") def normtorange(x, max=None, min=None, a=0, b=1): + """ + Normalizes the input tensor `x` to a specified range [a, b]. + + Args: + x (torch.Tensor) : Input tensor to be normalized. + max (float, optional): Maximum value for normalization. If None, uses the maximum of `x`. + min (float, optional): Minimum value for normalization. If None, uses the minimum of `x`. + a (float, optional): Lower bound of the target range. Default is 0. + b (float, optional): Upper bound of the target range. Default is 1. + """ if min is None: min = x.min() if max is None: diff --git a/src/im2sim/data/pca.py b/im2sim/src/data/pca.py similarity index 100% rename from src/im2sim/data/pca.py rename to im2sim/src/data/pca.py diff --git a/src/im2sim/data/scaling.py b/im2sim/src/data/scaling.py similarity index 100% rename from src/im2sim/data/scaling.py rename to im2sim/src/data/scaling.py diff --git a/src/im2sim/data/transforms.py b/im2sim/src/data/transforms.py similarity index 95% rename from src/im2sim/data/transforms.py rename to im2sim/src/data/transforms.py index e3e457f..1664a69 100644 --- a/src/im2sim/data/transforms.py +++ b/im2sim/src/data/transforms.py @@ -1,5 +1,5 @@ -from .core import Operation, Transform -from .ops import FitNormOp, FitRangeNormOp, FitZScoreOp, NormOp, PowerScaleOp, RangeNormOp, ZScoreOp +from im2sim.src.data.core import Operation, Transform +from im2sim.src.data.ops import FitNormOp, FitRangeNormOp, FitZScoreOp, NormOp, PowerScaleOp, RangeNormOp, ZScoreOp def transform_from_fn( diff --git a/im2sim/src/layers/README.md b/im2sim/src/layers/README.md new file mode 100644 index 0000000..cd71c82 --- /dev/null +++ b/im2sim/src/layers/README.md @@ -0,0 +1,272 @@ +# Configurable Half U-Net + +This module provides a configurable PyTorch implementation of a **Half U-Net** architecture. + +The purpose of this implementation is to allow rapid experimentation with different CNN architectures without changing the model code. The network structure is controlled through configuration objects and reusable presets. + +The model is built around: + +- `HalfUNet` - the PyTorch model implementation +- `HalfUNetConfig` - defines the architecture configuration + + +The architecture supports: + +- 1D, 2D, and 3D inputs +- configurable encoder depth +- configurable feature channels +- custom convolution blocks +- custom pooling and upsampling layers +- configurable skip connection fusion +- residual blocks +- dilated bottlenecks +- depthwise separable convolutions +- Ghost convolution variants + +--- + +# Basic Usage + +A default Half U-Net can be created using a configuration object: + +```python +import torch + +from half_unet import HalfUNet, HalfUNetConfig + + +cfg = HalfUNetConfig(num_downsamples=2, hidden_channels=32) + +model = HalfUNet.build(rank=3, in_channels=1, out_channels=1, cfg=cfg) + + +x = torch.randn(1, 1, 64, 64, 64) + +y = model(x) +``` + +--- + +# Applying Presets + +Architectures can be modified using presets. Presets are composable, meaning multiple architectural changes can be applied together. + +Example: + +```python +cfg = HalfUNetConfig(num_downsamples=2) + +cfg = cfg.apply_presets(["ghost_depthwise", "residual"]) + +model = HalfUNet.build(rank=3, in_channels=1, out_channels=1, cfg=cfg) +``` + +--- + +# Available Presets + +## Residual Encoder Blocks + +Adds residual connections inside convolution blocks. + +Useful when deeper networks require improved gradient flow. + +```python +cfg = cfg.apply_presets(["residual"]) +``` + +--- + +## Dilated Bottleneck + +Replaces the deepest encoder block with dilated convolutions to increase the receptive field. + +Useful for: + +- large structures +- segmentation +- low-resolution feature processing + +```python +cfg = cfg.apply_presets(["dilated_bottleneck"]) +``` + +--- + +## Reconstruction + +Applies convolution block settings intended for image reconstruction tasks. + +Example applications: + +- MRI reconstruction +- denoising +- image restoration + +```python +cfg = cfg.apply_presets(["reconstruction"]) +``` + +--- + +## Segmentation + +Applies convolution block settings intended for segmentation tasks. + +Example applications: + +- medical image segmentation +- semantic segmentation + +```python +cfg = cfg.apply_presets(["segmentation"]) +``` + +--- + +## Depthwise Separable Convolutions + +Replaces standard convolutions with depthwise separable convolutions. + +This reduces: + +- parameter count +- memory usage +- computational cost + +```python +cfg = cfg.apply_presets(["depthwise_separable"]) +``` + +--- + +## Ghost Convolution Variants + +Uses Ghost convolution based blocks to reduce computational cost. + +### Ghost depthwise + +```python +cfg = cfg.apply_presets(["ghost_depthwise"]) +``` + +### Ghost depthwise separable + +```python +cfg = cfg.apply_presets(["ghost_depthwise_separable"]) +``` + +--- + +# Combining Presets + +Presets can be stacked to create task-specific architectures. + +Example: a segmentation model using residual blocks and a dilated bottleneck: + +```python +cfg = HalfUNetConfig(num_downsamples=3) + +cfg = cfg.apply_presets(["segmentation", "residual", "dilated_bottleneck"]) + +model = HalfUNet.build(rank=3, in_channels=1, out_channels=3, cfg=cfg) +``` + +Example: a lightweight reconstruction model: + +```python +cfg = HalfUNetConfig(num_downsamples=4) + +cfg = cfg.apply_presets(["reconstruction", "ghost_depthwise"]) + +model = HalfUNet.build(rank=3, in_channels=1, out_channels=1, cfg=cfg) +``` + +--- + +# Custom Encoder Configurations + +Each encoder level can optionally use a different convolution block configuration. + +Example: + +```python +cfg = HalfUNetConfig(num_downsamples=3, encoder_block_cfg=[block_cfg_1, block_cfg_2, block_cfg_3]) +``` + +This allows heterogeneous architectures such as: + +``` +Encoder level 1: + Standard convolution blocks + +Encoder level 2: + Depthwise separable convolution blocks + +Encoder level 3: + Dilated convolution blocks +``` + +--- + +# Pooling and Upsampling + +Pooling and upsampling operations are configurable. + +Example: + +```python +cfg = HalfUNetConfig(pool_spec=LayerConfig(name="AveragePool", kwargs={"kernel_size": 2})) +``` + +Different operations can also be provided at each level: + +```python +cfg.pool_spec = [ + LayerConfig(name="MaxPool", kwargs={"kernel_size": 2}), + LayerConfig(name="AveragePool", kwargs={"kernel_size": 2}), +] +``` + +Supported pooling layers: + +- `MaxPool` +- `AveragePool` + +Supported upsampling layers: + +- `Upsample` +- `PixelShuffle` + +--- + +# Skip Connection Fusion + +Encoder features are fused with decoder features using the configured fusion type. + +Default: + +```python +fusion_type = ResidualConnectionType.ADD +``` + +which performs: + +``` +decoder + encoder +``` + +Alternatively: + +```python +fusion_type = ResidualConnectionType.CONCAT +``` + +which performs: + +``` +[decoder, encoder] +``` + +When using concatenation, the output block automatically adjusts the number of input channels. + diff --git a/im2sim/src/layers/__init__.py b/im2sim/src/layers/__init__.py new file mode 100644 index 0000000..9c54d4a --- /dev/null +++ b/im2sim/src/layers/__init__.py @@ -0,0 +1,39 @@ +from im2sim.src.layers.graph_blocks import GraphConvBlock, GraphConvResBlock, GraphResDecoderBlock +# from image_blocks import ImageConvBlock, ImageConvResBlock, ImageDecoder, ImageEncoder +from im2sim.src.layers import custom_image_layers, image_conv_blocks, halfunet, reverse_halfunet +from im2sim.src.layers.layer_util import ( + get_activation, + get_image_layer, + register_activation, + register_graph_layer, + register_image_layer, + standardize_spatial_factors, +) +# from .meshgraphnets import MeshGraphNet, MGNDecoder, MGNEdgeBlock, MGNGnBlock, MGNNodeBlock +from im2sim.src.layers.projections import OGProjection + +__all__ = [ + "GraphConvBlock", + "GraphConvResBlock", + "GraphResDecoderBlock", + "ImageConvBlock", + "ImageConvResBlock", + "ImageEncoder", + "ImageDecoder", + "get_activation", + "get_image_layer", + "register_activation", + "register_graph_layer", + "register_image_layer", + "standardize_spatial_factors", + # "MGNEdgeBlock", + # "MGNNodeBlock", + # "MGNDecoder", + # "MGNGnBlock", + # "MeshGraphNet", + "OGProjection", + "custom_image_layers", + "image_conv_blocks", + "halfunet", + "reverse_halfunet", +] diff --git a/src/im2sim/layers/contents.md b/im2sim/src/layers/contents.md similarity index 100% rename from src/im2sim/layers/contents.md rename to im2sim/src/layers/contents.md diff --git a/im2sim/src/layers/custom_image_layers.py b/im2sim/src/layers/custom_image_layers.py new file mode 100644 index 0000000..a346193 --- /dev/null +++ b/im2sim/src/layers/custom_image_layers.py @@ -0,0 +1,375 @@ +import math + +import torch +from im2sim.src.layers.layer_util import get_activation, get_image_layer, register_with_ranks +from im2sim.src.utils import api_util + +@api_util.export("layers.DepthwiseConv") +@register_with_ranks("DepthwiseConv", ranks=(1, 2, 3)) +class DepthwiseConv(torch.nn.Module): + """ + Depthwise convolution layer that applies a separate convolutional filter to each input channel. + + This operation is useful for reducing the number of parameters and computational cost in convolutional neural networks, especially in mobile and embedded applications[1]. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels (should be equal to in_channels for depthwise convolution). + rank (int): The rank of the convolution (1 for 1D, 2 for 2D, 3 for 3D). + kernel_size (int | tuple): Size of the convolving kernel. Default is 3. + stride (int | tuple): Stride of the convolution. Default is 1. + padding (str or int | tuple): Padding added to all four sides of the input. Default is "same". + dilation (int | tuple): Spacing between kernel elements. Default is 1. + bias (bool): If True, adds a learnable bias to the output. Default is True. + + References: + .. [1] A. G. Howard et al., MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, + Apr. 17, 2017, arXiv: arXiv:1704.04861. doi: 10.48550/arXiv.1704.04861. + + """ + def __init__( + self, + in_channels, + out_channels, + rank, + kernel_size=3, + stride=1, + padding="same", + dilation=1, + bias=True, + ): + super().__init__() + self.conv = get_image_layer("Conv", rank)( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=in_channels, + bias=bias, + ) + + def forward(self, x): + """ + Forward pass of the depthwise convolution layer. + + Args: + x (torch.Tensor): Input tensor of shape (batch_size, in_channels, *spatial_dims). + + Returns: + torch.Tensor: Output tensor of shape (batch_size, out_channels, *spatial_dims). + """ + return self.conv(x) + +@api_util.export("layers.DepthwiseSeparableConv") +@register_with_ranks("DepthwiseSeparableConv", ranks=(1, 2, 3)) +class DepthwiseSeparableConv(torch.nn.Module): + """ + Depthwise separable convolution layer that consists of a depthwise convolution followed by a pointwise convolution. + + This operation is useful for reducing the number of parameters and computational cost in convolutional neural networks, + while retaining more representational power compared to standard depthwise convolution[1]. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + rank (int): The rank of the convolution (1 for 1D, 2 for 2D, 3 for 3D). + kernel_size (int | tuple): Size of the convolving kernel. Default is 3. + stride (int | tuple): Stride of the convolution. Default is 1. + padding (str or int | tuple): Padding added to all four sides of the input. Default is "same". + dilation (int | tuple): Spacing between kernel elements. Default is 1. + bias (bool): If True, adds a learnable bias to the output. Default is True. + activation (str or None): Activation function to apply after the pointwise convolution. Default is None. + + References: + .. [1] F. Chollet, Xception: Deep Learning with Depthwise Separable Convolutions, + Apr. 04, 2017, arXiv: arXiv:1610.02357. doi: 10.48550/arXiv.1610.02357. + + """ + def __init__( + self, + in_channels, + out_channels, + rank, + kernel_size=3, + stride=1, + padding="same", + dilation=1, + bias=True, + activation=None, + ): + super().__init__() + print("IN DWS:", in_channels) + self.depthwise = get_image_layer("Conv", rank)( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=in_channels, + bias=bias, + ) + self.pointwise = get_image_layer("Conv", rank)( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + ) + self.activation = get_activation(activation) + + def forward(self, x): + """ + Forward pass of the depthwise separable convolution layer. + + Args: + x (torch.Tensor): Input tensor of shape (batch_size, in_channels, *spatial_dims). + + Returns: + torch.Tensor: Output tensor of shape (batch_size, out_channels, *spatial_dims). + """ + x = self.depthwise(x) + x = self.pointwise(x) + x = self.activation(x) + return x + +@api_util.export("layers.GhostConv") +@register_with_ranks("GhostConv", ranks=(1, 2, 3)) +class GhostConv(torch.nn.Module): + """ + Ghost convolution layer that generates more feature maps from cheap operations. + + This operation is useful for reducing the number of parameters and computational cost in convolutional neural networks. + The cheap operation can either be a depthwise convolution as per the original GhostNet paper[1] or a depthwise separable convolution as in the HalfUNet paper[2 + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + rank (int): The rank of the convolution (1 for 1D, 2 for 2D, 3 for 3D). + kernel_size (int | tuple): Size of the convolving kernel for the primary convolution. Default is 3. + ratio (int): Ratio of the number of output channels to the number of primary convolution channels. Default is 2. + dw_kernel_size (int | tuple): Size of the convolving kernel for the cheap operation. Default is 3. + stride (int | tuple): Stride of the primary convolution. Default is 1. + padding (str or int | tuple): Padding added to all four sides of the input for the primary convolution. Default is "same". + separable (bool): If True, uses depthwise separable convolution for the cheap operation. Default is False. + bias (bool): If True, adds a learnable bias to the output. Default is True. + + References: + .. [1] K. Han, Y. Wang, Q. Tian, J. Guo, C. Xu, and C. Xu, GhostNet: More Features from Cheap Operations, + Mar. 13, 2020, arXiv: arXiv:1911.11907. doi: 10.48550/arXiv.1911.11907. + .. [2] H. Lu, Y. She, J. Tie, and S. Xu, Half-UNet: A Simplified U-Net Architecture for Medical Image Segmentation, + Front. Neuroinformatics, vol. 16, Jun. 2022, doi: 10.3389/fninf.2022.911679. + """ + def __init__( + self, + in_channels, + out_channels, + rank, + kernel_size=3, + ratio=2, + dw_kernel_size=3, + stride=1, + padding="same", + separable=False, + bias=True, + ): + super().__init__() + self.rank = rank + self.out_channels = out_channels + self.init_channels = int(out_channels / ratio) + self.new_channels = out_channels - self.init_channels + + self.primary_conv = get_image_layer("Conv", rank)( + in_channels=in_channels, + out_channels=self.init_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + bias=bias, + ) + + cheap_conv_type = "DepthwiseSeparableConv" if separable else "DepthwiseConv" + print("IN GHOST:", self.init_channels) + self.cheap_operation = get_image_layer(cheap_conv_type, rank)( + in_channels=self.init_channels, + out_channels=self.new_channels, + kernel_size=dw_kernel_size, + stride=1, + padding="same", + bias=bias, + ) + + def forward(self, x): + """ + Forward pass of the Ghost convolution layer. + + Args: + x (torch.Tensor): Input tensor of shape (batch_size, in_channels, *spatial_dims). + + Returns: + torch.Tensor: Output tensor of shape (batch_size, out_channels, *spatial_dims). + """ + x1 = self.primary_conv(x) + x2 = self.cheap_operation(x1) + return torch.cat([x1, x2], dim=1) + +@api_util.export("layers.EfficientChannelAttn") +@register_with_ranks("EfficientChannelAttn", ranks=(1, 2, 3)) +class EfficientChannelAttn(torch.nn.Module): + """ + Efficient Channel Attention (ECA) layer that adaptively selects important channels based on global context. + + This operation is useful for improving the representational power of convolutional neural networks by focusing on the most informative channels[1]. + + Args: + channels (int): Number of input channels. + rank (int): The rank of the input tensor (1 for 1D, 2 for 2D, 3 for 3D). + + References: + .. [1] Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks, + Mar. 04, 2020, arXiv: arXiv:1910.03151. doi: 10.48550/arXiv.1910.03151. + """ + def __init__(self, channels: int, rank: int): + super().__init__() + self.rank = rank + k_raw = math.log2(channels) / 2 + 0.5 + k = max(3, int(k_raw) if int(k_raw) % 2 == 1 else int(k_raw) + 1) + self.avg_pool = get_image_layer("AdaptiveAvgPool", rank)(1) + self.conv1d = torch.nn.Conv1d(1, 1, kernel_size=k, padding=k // 2, bias=False) + self.sigmoid = torch.nn.Sigmoid() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the Efficient Channel Attention layer. + + Args: + x (torch.Tensor): Input tensor of shape (batch_size, channels, *spatial_dims). + + Returns: + torch.Tensor: Output tensor of shape (batch_size, channels, *spatial_dims) with channel-wise attention applied. + """ + B, C = x.shape[:2] + w = self.avg_pool(x).view(B, 1, C) # [B, 1, C] + w = self.conv1d(w) # [B, 1, C] + out_shape = [B, C] + [1] * self.rank + w = self.sigmoid(w).view(*out_shape) + return x * w + +@api_util.export("layers.SqueezeExcite") +@register_with_ranks("SqueezeExcite", ranks=(1, 2, 3)) +class SqueezeExcite(torch.nn.Module): + """ + Squeeze-and-Excitation (SE) layer that adaptively recalibrates channel-wise feature responses. + + This operation is useful for improving the representational power of convolutional neural networks by explicitly modeling interdependencies between channels[1]. + + Args: + channels (int): Number of input channels. + rank (int): The rank of the input tensor (1 for 1D, 2 for 2D, 3 for 3D). + reduction (int): Reduction ratio for the hidden layer in the SE block. Default is 8. + + References: + .. [1] J. Hu, L. Shen, and G. Sun, Squeeze-and-Excitation Networks, + Mar. 27, 2018, arXiv: arXiv:1709.01507. doi: 10.48550/arXiv.1709.01507. + """ + def __init__(self, channels: int, rank: int, reduction: int = 8): + super().__init__() + self.rank = rank + hidden = max(8, channels // reduction) + self.avg_pool = get_image_layer("AdaptiveAvgPool", rank)(1) + self.fc = torch.nn.Sequential( + torch.nn.Linear(channels, hidden), + torch.nn.ReLU(inplace=True), + torch.nn.Linear(hidden, channels), + ) + self.sigmoid = torch.nn.Sigmoid() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the Efficient Channel Attention layer. + + Args: + x (torch.Tensor): Input tensor of shape (batch_size, channels, *spatial_dims). + + Returns: + torch.Tensor: Output tensor of shape (batch_size, channels, *spatial_dims) with channel-wise attention applied. + """ + B, C = x.shape[:2] + z = self.avg_pool(x).view(B, C) + out_shape = [B, C] + [1] * self.rank + s = self.sigmoid(self.fc(z)).view(*out_shape) + return x * s + +@api_util.export("layers.ConditionedSqueezeExcite") +@register_with_ranks("ConditionedSqueezeExcite", ranks=(1, 2, 3)) +class ConditionedSqueezeExcite(torch.nn.Module): + """ + Conditioned Squeeze-and-Excitation [1] (SE) layer that adaptively recalibrates channel-wise feature responses based on an additional conditioning input. + + This operation is useful for improving the representational power of convolutional neural networks by explicitly modeling interdependencies between channels, + while also allowing for external conditioning information to influence the recalibration process. + + Args: + channels (int): Number of input channels. + rank (int): The rank of the input tensor (1 for 1D, 2 for 2D, 3 for 3D). + n_cond (int): Number of conditioning channels. Default is 6. + reduction (int): Reduction ratio for the hidden layer in the SE block. Default is 8. + mode (str): Mode of combining the feature and conditioning information. Can be "concat" or "add". Default is "add". + + References: + .. [1] J. Hu, L. Shen, and G. Sun, Squeeze-and-Excitation Networks, + Mar. 27, 2018, arXiv: arXiv:1709.01507. doi: 10.48550/arXiv.1709.01507. + + """ + def __init__( + self, + channels: int, + rank: int, + n_cond: int = 6, + reduction: int = 8, + mode: str = "add" + ): + super().__init__() + self.rank = rank + if mode not in ("concat", "add"): + raise ValueError(f"unknown se_cond_mode {mode!r}") + self.mode = mode + hidden = max(8, channels // reduction) + self.avg_pool = get_image_layer("AdaptiveAvgPool", self.rank)(1) + if mode == "concat": + self.fc = torch.nn.Sequential( + torch.nn.Linear(channels + n_cond, hidden), + torch.nn.ReLU(inplace=True), + torch.nn.Linear(hidden, channels), + ) + else: # add + self.feat_fc = torch.nn.Linear(channels, hidden) + self.cond_fc = torch.nn.Linear(n_cond, hidden) + self.act = torch.nn.ReLU(inplace=True) + self.out_fc = torch.nn.Linear(hidden, channels) + self.sigmoid = torch.nn.Sigmoid() + + def forward(self, x: torch.Tensor, cond: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the Conditioned Squeeze-and-Excitation layer. + + Args: + x (torch.Tensor): Input tensor of shape (batch_size, channels, *spatial_dims). + cond (torch.Tensor): Conditioning tensor of shape (batch_size, n_cond). + + Returns: + torch.Tensor: Output tensor of shape (batch_size, channels, *spatial_dims) with channel-wise attention applied based on the conditioning input. + """ + B, C = x.shape[:2] + z = self.avg_pool(x).view(B, C) + if self.mode == "concat": + s = self.fc(torch.cat([z, cond], dim=1)) + else: + s = self.out_fc(self.act(self.feat_fc(z) + self.cond_fc(cond))) + + out_shape = [B, C] + [1] * self.rank + s = self.sigmoid(s).view(*out_shape) + return x * s diff --git a/src/im2sim/layers/graph_blocks.py b/im2sim/src/layers/graph_blocks.py similarity index 94% rename from src/im2sim/layers/graph_blocks.py rename to im2sim/src/layers/graph_blocks.py index f8f945c..38d066c 100644 --- a/src/im2sim/layers/graph_blocks.py +++ b/im2sim/src/layers/graph_blocks.py @@ -4,16 +4,21 @@ import torch from torch import nn -from .layer_util import get_activation, get_graph_layer, register_graph_layer +from im2sim.src.layers.layer_util import get_activation, get_graph_layer, register_graph_layer +from im2sim.src.utils import api_util logger = logging.getLogger(__name__) - +@api_util.export("layers.DefaultGraphNorm") @register_graph_layer(name="defaultnorm") class DefaultGraphNorm(torch.nn.Module): """ The default normalisation for im2sim graph blocks. Uses torch.nn.InstanceNorm2d applied to graph data, but all channels are normalised together. + + Args: + None + """ def __init__(self): @@ -21,6 +26,14 @@ def __init__(self): self.norm = torch.nn.InstanceNorm2d(1, affine=True, eps=1e-3) def forward(self, x, batch): + """ + Args: + x (`torch.Tensor`): The input tensor of shape (N, C) where N is the number of nodes and C is the number of channels. + batch (`torch.Tensor`): The batch tensor of shape (N,) indicating the batch index for each node. + + Returns: + `torch.Tensor`: The normalized tensor of shape (N, C). + """ if x.dim() != 2: raise RuntimeError(f"Expected x.dim()==2, got {x.dim()}") shape = x.shape @@ -32,7 +45,7 @@ def forward(self, x, batch): x[batch == b] = self.norm(x[batch == b].unsqueeze(0).unsqueeze(0)).reshape(shape) return x - +@api_util.export("layers.GraphConvBlock") class GraphConvBlock(nn.Module): """ A convolutional block for graph data @@ -92,7 +105,7 @@ def forward(self, in_graph): graph.x = self.act(graph.x) return graph - +@api_util.export("layers.GraphConvResBlock") class GraphConvResBlock(nn.Module): """ A convolutional block for graph data @@ -158,7 +171,7 @@ def forward(self, in_graph): return graph - +@api_util.export("layers.GraphResDecoderBlock") class GraphResDecoderBlock(nn.Module): """ A graph convolutional decoder block with the same structure as MeshDeformNet and Image2Flow diff --git a/im2sim/src/layers/halfunet.py b/im2sim/src/layers/halfunet.py new file mode 100644 index 0000000..2372a87 --- /dev/null +++ b/im2sim/src/layers/halfunet.py @@ -0,0 +1,434 @@ +from dataclasses import dataclass, field, fields +from copy import deepcopy +import torch +from im2sim.src.layers.image_conv_blocks import ImageConvBlock, ImageConvBlockConfig +from im2sim.src.layers.layer_util import ( + ResidualConnectionType, + apply_residual_connection, + get_image_layer, +) +from im2sim.src.layers.module_config import Config, ConfigurableModule, LayerConfig, register_config +from im2sim.src.utils import api_util + + +@api_util.export('configs.HalfUNetConfig') +@register_config +@dataclass +class HalfUNetConfig(Config): + """ + Configuration class for defining the parameters of a half U-Net architecture (see im2sim.models.HalfUNet). + + Attributes can either be set directly when creating an instance of the class or modified later. + + Configuration presets can be applied to quickly set up common configurations for different use cases. + + The configuration can also be saved to and loaded from a YAML file. + + Args: + + hidden_channels (int): + Number of channels in the hidden layers. Default is 64. + + num_downsamples (int): + Number of downsampling operations in the encoder. Default is 4. + + pool_spec (LayerConfig | list[LayerConfig]): + Specification for the pooling layers. Default is a MaxPool layer with kernel size 2 for all levels. + + upsample_spec (LayerConfig | list[LayerConfig]): + Specification for the upsampling layers. Default is an Upsample layer with scale factor 2 and mode 'trilinear' for all levels. + The mode is automatically changed to 'bilinear' for 2D data and 'nearest' for 1D. + + block_cfg (ImageConvBlockConfig): + Configuration for the convolutional blocks. Default is a standard convolutional block with 2 layers, ReLU activation, and batch normalization. + + blocks_per_level (int): + Number of convolutional blocks per level in the encoder. Default is 2. + + out_activation (str | None): + Activation function for the output layer. Default is None, which means no activation is applied. + + stem_block_cfg (ImageConvBlockConfig | None): + Configuration for the stem block. If None, it defaults to a single convolutional block with the same configuration as `block_cfg`. + + encoder_block_cfg (list[ImageConvBlockConfig] | ImageConvBlockConfig | None): + Configuration for the encoder blocks. If None, it defaults to a list of `block_cfg` repeated for each downsampling level. + + out_block_cfg (ImageConvBlockConfig | None): + Configuration for the output block. If None, it defaults to a single convolutional block with the same configuration as `block_cfg` and the specified `out_activation`. + + fusion_type (ResidualConnectionType): + Type of residual connection to use in the network. It can be either 'add' (default), 'concat' or 'average'. + This determines how the encoder features are fused. + + Examples: + + To create a HalfUNet model for single class segmentation, you can do the following: + + >>> cfg = HalfUNetConfig(num_downsamples=3, hidden_channels=64) + >>> cfg = cfg.apply_presets(["single_class_segmentation", "residual", "SE", "ghost_depthwise_separable"]) + >>> model = HalfUNet.build(in_channles=20, out_channels=1, rank=3, cfg=cfg) + + For more presets, see the Preset Library below. + + """ + hidden_channels: int = 64 + num_downsamples: int = 4 + pool_spec: LayerConfig | list[LayerConfig] = field( + default_factory=lambda: LayerConfig(name="MaxPool", kwargs={"kernel_size": 2}) + ) + upsample_spec: LayerConfig | list[LayerConfig] = field( + default_factory=lambda: LayerConfig( + name="Upsample", kwargs={"scale_factor": 2, "mode": "trilinear"} + ) + ) + block_cfg: ImageConvBlockConfig = field(default_factory=lambda: ImageConvBlockConfig()) + blocks_per_level: int = 2 + out_activation: str | None = None + stem_block_cfg: ImageConvBlockConfig | None = None + encoder_block_cfg: list[ImageConvBlockConfig] | ImageConvBlockConfig | None = None + out_block_cfg: ImageConvBlockConfig | None = None + fusion_type: ResidualConnectionType = ResidualConnectionType.ADD + + def __post_init__(self): + print("HalfUNetConfig __post_init__ called") + if self.stem_block_cfg is None: + self.stem_block_cfg = self.block_cfg.apply_presets(["single_block"]) + if self.stem_block_cfg.out_activation is None: + self.stem_block_cfg.out_activation = self.stem_block_cfg.activation + + if self.encoder_block_cfg is None: + self.encoder_block_cfg = deepcopy(self.block_cfg) + if self.encoder_block_cfg.out_activation is None: + self.encoder_block_cfg.out_activation = self.encoder_block_cfg.activation + self.encoder_block_cfg = [self.encoder_block_cfg] * self.num_downsamples + + elif isinstance(self.encoder_block_cfg, ImageConvBlockConfig): + self.encoder_block_cfg = [self.encoder_block_cfg] * self.num_downsamples + + if self.out_block_cfg is None: + self.out_block_cfg = self.block_cfg.apply_presets(["single_conv"]) + self.out_block_cfg.out_activation = self.out_activation + + +@api_util.export('models.HalfUNet') +class HalfUNet(torch.nn.Module, ConfigurableModule): + """ + A Half-UNet[1] architecture for image processing tasks. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + rank (int): Dimensionality of the input data (1 for 1D, 2 for 2D, 3 for 3D). + hidden_channels (int): Number of channels in the hidden layers. Default is 64. + num_downsamples (int): Number of downsampling operations in the encoder. Default is 4. + pool_spec (LayerConfig | list[LayerConfig]): Specification for the pooling layers. Default is a MaxPool layer with kernel size 2 for all levels. + upsample_spec (LayerConfig | list[LayerConfig]): Specification for the upsampling layers. Default is an Upsample layer with scale factor 2 and mode 'trilinear' for all levels. + block_cfg (ImageConvBlockConfig): Configuration for the convolutional blocks. Default is a standard convolutional block with 2 layers, ReLU activation, and batch normalization. + blocks_per_level (int): Number of convolutional blocks per level in the encoder. Default is 2. + out_activation (str | None): Activation function for the output layer. Default is None, which means no activation is applied. + stem_block_cfg (ImageConvBlockConfig | None): Configuration for the stem block. If None, it defaults to a single convolutional block with the same configuration as `block_cfg`. + encoder_block_cfg (list[ImageConvBlockConfig] | ImageConvBlockConfig | None): Configuration for the encoder blocks. If None, it defaults to a list of `block_cfg` repeated for each downsampling level. + out_block_cfg (ImageConvBlockConfig | None): Configuration for the output block. If None, it defaults to a single convolutional block with the same configuration as `block_cfg` and the specified `out_activation`. + fusion_type (ResidualConnectionType): Type of residual connection to use in the network. It can be either 'add' (default), 'concat' or 'average'. This determines how the encoder features are fused. + + The best way to build a HalfUNet is to use the `im2sim.configs.HalfUNetConfig` class to define the configuration and then call the `build` method. + + Example: + + To create a HalfUNet model for single class segmentation, you can do the following: + + >>> cfg = HalfUNetConfig(num_downsamples=3, hidden_channels=64) + >>> cfg = cfg.apply_presets(["single_class_segmentation", "residual", "SE", "ghost_depthwise_separable"]) + >>> model = HalfUNet.build(in_channles=20, out_channels=1, rank=3, cfg=cfg) + + References: + .. [1] H. Lu, Y. She, J. Tie, and S. Xu, Half-UNet: A Simplified U-Net Architecture for Medical Image Segmentation, + Front. Neuroinformatics, vol. 16, Jun. 2022, doi: 10.3389/fninf.2022.911679. + """ + def __init__( + self, + in_channels: int, + out_channels: int, + rank: int, + hidden_channels: int = 64, + num_downsamples: int = 4, + pool_spec: LayerConfig | list[LayerConfig] | None = None, + upsample_spec: LayerConfig | list[LayerConfig] | None = None, + block_cfg: ImageConvBlockConfig | None = None, + blocks_per_level: int = 2, + out_activation: str | None = None, + stem_block_cfg: ImageConvBlockConfig | None = None, + encoder_block_cfg: list[ImageConvBlockConfig] | ImageConvBlockConfig | None = None, + out_block_cfg: ImageConvBlockConfig | None = None, + fusion_type: ResidualConnectionType = ResidualConnectionType.ADD, + ): + """ """ + super().__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.num_downsamples = num_downsamples + + if pool_spec is None: + pool_spec = LayerConfig(name="MaxPool", kwargs={"kernel_size": 2}) + if upsample_spec is None: + mode = "nearest" if rank == 1 else "bilinear" if rank == 2 else "trilinear" + upsample_spec = LayerConfig(name="Upsample", kwargs={"scale_factor": 2, "mode": mode}) + if block_cfg is None: + block_cfg = ImageConvBlockConfig() + + if isinstance(pool_spec, LayerConfig): + pool_spec = [pool_spec] * num_downsamples + if isinstance(upsample_spec, LayerConfig): + upsample_spec = [upsample_spec] * num_downsamples + + for p, u in zip(pool_spec, upsample_spec, strict=True): + assert p.name.lower() in ["maxpool", "averagepool"], f"pool type {p.name} not supported" + assert u.name.lower() in ["upsample", "pixelshuffle"], ( + f"upsample type {u.name} not supported" + ) + + self.pools = torch.nn.ModuleList( + [get_image_layer(pool.name, rank)(**pool.kwargs) for pool in pool_spec] + ) + self.ups = torch.nn.ModuleList( + [get_image_layer(upsample.name, rank)(**upsample.kwargs) for upsample in upsample_spec] + ) + + if stem_block_cfg is None: + stem_block_cfg = block_cfg.apply_presets(["single_block"]) + + self.stem = ImageConvBlock.build(rank, in_channels, hidden_channels, stem_block_cfg) + print(type(self.stem)) + + if encoder_block_cfg is None: + encoder_block_cfg = [block_cfg] * num_downsamples + elif isinstance(encoder_block_cfg, ImageConvBlockConfig): + encoder_block_cfg = [encoder_block_cfg] * num_downsamples + + self.encoder_blocks = torch.nn.ModuleList( + [ + torch.nn.Sequential( + *[ImageConvBlock.build(rank, hidden_channels, hidden_channels, cfg)] + * blocks_per_level + ) + for cfg in encoder_block_cfg + ] + ) + + if out_block_cfg is None: + out_block_cfg = block_cfg.apply_presets(["single_conv"]) + out_block_cfg.out_activation = out_activation + + self.fusion_type = fusion_type + fusion_channels = ( + hidden_channels * (num_downsamples + 1) + if self.fusion_type is ResidualConnectionType.CONCAT + else hidden_channels + ) + self.out_block = ImageConvBlock.build( + rank, fusion_channels, out_channels, out_block_cfg + ) + + def forward(self, x): + """ + Forward pass of the HalfUNet model. + """ + x = self.stem(x) + + residuals = [] + for pool, block in zip(self.pools, self.encoder_blocks, strict=True): + residuals.append(x) + x = pool(x) + x = block(x) + + fused = x + for res, up in zip(reversed(residuals), self.ups, strict=True): + fused = up(fused) + fused = apply_residual_connection(fused, res, connection_type=self.fusion_type) + + out = self.out_block(fused) + return out + + + + +@HalfUNetConfig.register_preset("residual") +def half_unet_residual_type(cfg: HalfUNetConfig): + """ + Apply a residual connection to all encoder blocks in the HalfUNet configuration. + + The residual connection type is set to "add" for all encoder blocks, + which means that the output of each encoder block will be added to its input before being passed to the next layer. + This can help with gradient flow and improve training stability. + """ + cfg.encoder_block_cfg = [ + c.apply_presets(["0_residual"]) for c in cfg.encoder_block_cfg + ] + return cfg + + +@HalfUNetConfig.register_preset("dilated_bottleneck") +def unet_residual_type(cfg: HalfUNetConfig): + """ + Apply dilated convolutions to the bottleneck (lowest resolution) block in the HalfUNet configuration. + + This change modifies the last encoder block to use dilated convolutions, which can help increase the receptive field without increasing the number of parameters. + """ + cfg.encoder_block_cfg[-1] = cfg.encoder_block_cfg[-1].apply_presets(["dilated_convs"] + ) + return cfg + + +def _apply_preset_to_all_blocks(cfg: HalfUNetConfig, preset_name: str): + """ + Apply a given preset to all blocks in the HalfUNet configuration. + """ + cfg.stem_block_cfg = cfg.stem_block_cfg.apply_presets([preset_name]) + cfg.encoder_block_cfg = [c.apply_presets([preset_name]) for c in cfg.encoder_block_cfg] + cfg.out_block_cfg = cfg.out_block_cfg.apply_presets([preset_name]) + return cfg + + +@HalfUNetConfig.register_preset("recon") +def reconstruction_config(cfg: HalfUNetConfig): + """ + Apply a reconstruction preset to all blocks in the HalfUNet configuration. + + This preset is typically used for image reconstruction or superresolution tasks, + where the output is expected to be a continuous value (e.g., pixel intensity). + """ + _apply_preset_to_all_blocks(cfg, "recon") + cfg.out_activation = None + return cfg + + +@HalfUNetConfig.register_preset("single_class_segmentation") +def segmentation_config(cfg: HalfUNetConfig): + """ + Apply a single-class segmentation preset to all blocks in the HalfUNet configuration. + + Use this preset for binary segmentation tasks, where the output is expected to be a probability map for a single class. + """ + _apply_preset_to_all_blocks(cfg, "segmentation") + cfg.out_block_cfg.out_activation = "sigmoid" + return cfg + +@HalfUNetConfig.register_preset("multiclass_segmentation") +def segmentation_config(cfg: HalfUNetConfig): + """ + Apply a multi-class segmentation preset to all blocks in the HalfUNet configuration. + + Use this preset for multi-class segmentation tasks, where the output is expected to be a probability map for multiple classes. + """ + _apply_preset_to_all_blocks(cfg, "segmentation") + cfg.out_block_cfg.out_activation = "softmax" + return cfg + + +@HalfUNetConfig.register_preset("depthwise_separable") +def depthwise_separable_config(cfg: HalfUNetConfig): + """ + Apply a depthwise separable convolution (see `im2sim.layers.DepthwiseSeparableConv`) preset to all encoder blocks in the HalfUNet configuration. + """ + _apply_preset_to_all_blocks(cfg, "depthwise_separable") + return cfg + + +@HalfUNetConfig.register_preset("ghost_depthwise") +def ghost_dw_config(cfg: HalfUNetConfig): + """ + Apply a ghost depthwise convolution (see `im2sim.layers.GhostConv`) preset to all encoder blocks in the HalfUNet configuration. + """ + _apply_preset_to_all_blocks(cfg, "ghost_depthwise") + return cfg + + +@HalfUNetConfig.register_preset("ghost_depthwise_separable") +def ghost_dws_config(cfg: HalfUNetConfig): + """ + Apply a ghost depthwise separable convolution (see `im2sim.layers.GhostConv`) preset to all encoder blocks in the HalfUNet configuration. + """ + _apply_preset_to_all_blocks(cfg, "ghost_depthwise_separable") + return cfg + + +@HalfUNetConfig.register_preset("ECA") +def eca_config(cfg: HalfUNetConfig): + """ + Apply an Efficient Channel Attention (ECA) (see `im2sim.layers.EfficientChannelAttn`) preset to all encoder blocks in the HalfUNet configuration. + """ + cfg.encoder_block_cfg = [ + c.apply_presets(["ECA"]) for c in cfg.encoder_block_cfg + ] + return cfg + + +@HalfUNetConfig.register_preset("SE") +def squeeze_excitation_config(cfg: HalfUNetConfig): + """ + Apply a Squeeze-and-Excitation (SE) (see `im2sim.layers.SqueezeExcite`) preset to all encoder blocks in the HalfUNet configuration. + """ + cfg.encoder_block_cfg = [ + c.apply_presets(["SE"]) for c in cfg.encoder_block_cfg + ] + return cfg + + +if __name__ == "__main__": + + def cfg_print(cfg): + print("###################") + for field in fields(cfg): + value = getattr(cfg, field.name) + print(f" {field.name}: {value}") + + cfg = HalfUNetConfig(num_downsamples=3, hidden_channels=64) + print(cfg.generate_preset_docs()) + # cfg = cfg.apply_presets(["single_class_segmentation", "residual", "SE", "ghost_depthwise_separable"]) + # model = HalfUNet.build( + # rank=3, + # in_channels=20, + # out_channels=1, + # cfg=cfg, + # ) + # total_params = sum(p.numel() for p in model.parameters()) + # print(f"Parameters: {total_params:,}") + + # print(model) + + # x = torch.randn(1, 20, 64, 64, 64) # Example input for a 3D tensor + + # def check_gradients(model, x): + # model.train() + + # # Ensure input tracks gradients if you want to test input gradients + # x = x.requires_grad_(True) + + # # Forward + # y = model(x) + + # # Use a scalar loss + # loss = y.sum() + + # # Backward + # loss.backward() + + # # Check input gradient + # assert x.grad is not None, "Input gradient is None" + # assert torch.isfinite(x.grad).all(), "Input gradient contains NaN/Inf" + + # # Check parameter gradients + # for name, param in model.named_parameters(): + # if param.requires_grad: + # assert param.grad is not None, f"{name} gradient is None" + # assert torch.isfinite(param.grad).all(), f"{name} gradient contains NaN/Inf" + # assert param.grad.abs().sum() > 0, f"{name} gradient is zero" + + # return True + + # # Check gradients + # if check_gradients(model, x): + # print("Gradient check passed.") diff --git a/im2sim/src/layers/image_conv_blocks.py b/im2sim/src/layers/image_conv_blocks.py new file mode 100644 index 0000000..c7fcd4b --- /dev/null +++ b/im2sim/src/layers/image_conv_blocks.py @@ -0,0 +1,449 @@ +from dataclasses import dataclass, field + +import torch +from im2sim.src.layers.custom_image_layers import * +from im2sim.src.layers.layer_util import ( + ResidualConnectionType, + apply_residual_connection, + get_activation, + get_image_layer, +) +from im2sim.src.layers.module_config import Config, ConfigurableModule, LayerConfig, register_config +from im2sim.src.utils import api_util + + +@api_util.export("configs.ImageConvBlockConfig") +@register_config +@dataclass +class ImageConvBlockConfig(Config): + """ + Configuration class for defining the parameters of an image convolutional block. + + Attributes can either be set directly when creating an instance of the class or modified later. + + Configuration presets can be applied to quickly set up common configurations for different use cases. + + The configuration can also be saved to and loaded from a YAML file. + + Args: + + depth (int): + The number of convolutional layers in the block. Default is 2. + + activation (str | None): + The activation function to use after each convolutional layer. Default is "ReLU". + + out_activation (str | None): + The activation function to use after the final layer. Default is None. + + conv_config (LayerConfig): + Configuration for the convolutional layers, including kernel size and padding. + + norm_config (LayerConfig): + Configuration for the normalization layers, such as InstanceNorm with affine set to True. + + dropout_config (LayerConfig): + Configuration for the dropout layers. Default is no dropout. + + attn_config (LayerConfig): + Configuration for the attention layers. Default is no attention. + + dropout_position (int | list[int]): + Specifies the position(s) of the dropout layers within the block. Default is 1. + + residual_connections ( dict [int, list[ int ]] | None): + Specifies the residual connections within the block. + The keys represent the target layers, and the values are lists of source layers. Default is None. + Example: {1: [0]} means that the input to the block will be added to the output of layer 1. + + residual_type (str): + The type of residual connection to use (e.g., "add"). Default is ResidualConnectionType.ADD. + + Examples: + + To create a configuration for an image convolutional block with a depth of 3, ReLU activation, and softmax output activation, you can use the following code: + + >>> cfg = ImageConvBlockConfig(depth=3, activation="ReLU", out_activation="softmax") + + + To apply a preset configuration for a single convolutional layer without normalization or dropout, you can use: + + >>> cfg = ImageConvBlockConfig.apply_presets(cfg, ["single_conv"]) + + To save the configuration to a YAML file and load it back, you can use: + + >>> cfg.save("config.yaml") + >>> loaded_cfg = ImageConvBlockConfig().load("config.yaml") + + """ + + depth: int = 2 + activation: str | None = "ReLU" + out_activation: str | None = None + conv_config: LayerConfig = field( + default_factory=lambda: LayerConfig( + name="Conv", kwargs={"kernel_size": 3, "padding": "same"} + ) + ) + norm_config: LayerConfig = field( + default_factory=lambda: LayerConfig(name="InstanceNorm", kwargs={"affine": True}) + ) + dropout_config: LayerConfig = field(default_factory=lambda: LayerConfig(name=None, kwargs={})) + attn_config: LayerConfig = field(default_factory=lambda: LayerConfig(name=None, kwargs={})) + dropout_position: int | list[int] = 1 + residual_connections: dict[int, list[int]] = None + residual_type: str = ResidualConnectionType.ADD + + +@api_util.export("layers.ImageConvBlock") +class ImageConvBlock(torch.nn.Module, ConfigurableModule): + """ + A configurable image convolutional block that consists of a sequence of + convolutional layers, normalization layers, dropout layers, and attention + layers. The block supports residual connections and allows for flexible + configuration of its components. + + It is best used by creating a configuration object of type + :class:ImageConvBlockConfig and then calling the build method to create + an instance of the block. + + Args: + + in_channels : int + Number of input channels. + + out_channels : int + Number of output channels. + + rank : int + The rank of the convolutional layers (e.g., 2 for 2D convolutions). + + depth : int, default=2 + The number of convolutional layers in the block. + + activation : str | None, default=None + Activation function applied after each convolutional layer. + + out_activation : str | None, default=None + Activation function applied after the final layer. + + conv_config : LayerConfig | None, default=None + Configuration for convolutional layers. If None, a default configuration is used. + + norm_config : LayerConfig | None, default=None + Configuration for normalization layers. If None, a default configuration is used. + + attn_config : LayerConfig | None, default=None + Configuration for attention layers. If None, a default configuration is used. + + dropout_config : LayerConfig | None, default=None + Configuration for dropout layers. If None, a default configuration is used. + + dropout_position : int | list[int], default=1 + Position(s) of dropout layers within the block. + + residual_connections : dict[int, list[int]] | None, default=None + Specifies residual connections within the block. Keys represent target layers, + and values are lists of source layers.(e.g. {1: [0]} adds the block input to the output of layer 1.) + + residual_type : str, default=ResidualConnectionType.ADD + Type of residual connection to use (e.g., "add"). + + + + Example: + + To create an ImageConvBlock with a depth of 3, ReLU activation, and softmax output activation, you can use the following code: + + >>> cfg = ImageConvBlockConfig(depth=3, activation="ReLU", out_activation="softmax") + >>> model = ImageConvBlock.build( + >>> rank=2, + >>> in_channels=32, + >>> out_channels=32, + >>> cfg=cfg, + >>> ) + """ + def __init__( + self, + in_channels: int, + out_channels: int, + rank: int, + depth: int = 2, + activation: str | None = None, + out_activation: str | None = None, + conv_config: LayerConfig | None = None, + norm_config: LayerConfig | None = None, + attn_config: LayerConfig | None = None, + dropout_config: LayerConfig | None = None, + dropout_position: int | list[int] = 1, + residual_connections: dict[int, list[int]] = None, + residual_type: str = ResidualConnectionType.ADD, + ): + super().__init__() + + print("IN INIT") + print(conv_config) + self.in_channels = in_channels + self.out_channels = out_channels + self.rank = rank + self.depth = depth + self.activation = get_activation(activation) + self.out_activation = get_activation(out_activation) + self.conv_config = conv_config + self.norm_config = norm_config + self.attn_config = attn_config + self.dropout_config = dropout_config + self.dropout_position = ( + dropout_position if isinstance(dropout_position, list) else [dropout_position] + ) + self.residual_connections = residual_connections if residual_connections is not None else {} + self.residual_type = residual_type + + print(self.dropout_position, self.depth) + + self._set_default_configs() + self._validate_configs() + + self.layers = torch.nn.ModuleList() + in_channels_per_layer = [self.in_channels] + + for i in range(depth): + conv = get_image_layer(self.conv_config.name, rank=self.rank)( + in_channels=in_channels_per_layer[-1], + out_channels=out_channels, + **self.conv_config.kwargs, + ) + + norm = get_image_layer(self.norm_config.name, rank=self.rank)( + self.out_channels, **self.norm_config.kwargs + ) + + dropout = ( + get_image_layer(self.dropout_config.name, rank=self.rank)( + **self.dropout_config.kwargs + ) + if i in self.dropout_position + else torch.nn.Identity() + ) + + pre_residual = self.attn_config.name is not None and i in self.residual_connections + no_residual_final = len(self.residual_connections.keys()) == 0 and i == self.depth - 1 + if pre_residual or no_residual_final: + attn = get_image_layer(self.attn_config.name, rank=self.rank)( + self.out_channels, **self.attn_config.kwargs + ) + else: + attn = torch.nn.Identity() + + block = torch.nn.Sequential( + conv, + norm, + dropout, + attn, + self.activation if i < self.depth - 1 else torch.nn.Identity(), + ) + self.layers.append(block) + + in_channels_current = self.out_channels + if ( + i in self.residual_connections + and self.residual_type == ResidualConnectionType.CONCAT + ): + for src in self.residual_connections[i]: + in_channels_current += in_channels_per_layer[src] + in_channels_per_layer.append(in_channels_current) + + def _set_default_configs(self): + if self.conv_config is None: + self.conv_config = LayerConfig( + name="Conv", kwargs={"kernel_size": 3, "padding": "same"} + ) + if self.norm_config is None: + self.norm_config = LayerConfig(name=None, kwargs={}) + if self.dropout_config is None: + self.dropout_config = LayerConfig(name=None, kwargs={}) + if self.attn_config is None: + self.attn_config = LayerConfig(name=None, kwargs={}) + + def _validate_configs(self): + assert self.norm_config.name in [None, "BatchNorm", "InstanceNorm"], ( + f"Unsupported norm type: {self.norm_config.name}" + ) + assert self.attn_config.name in [None, "EfficientChannelAttn", "SqueezeExcite"], ( + f"Unsupported attention type: {self.attn_config.name}" + ) + if self.dropout_config.name is not None: + assert max(self.dropout_position) < self.depth, ( + "Dropout position must be less than depth" + ) + + def forward(self, x): + outputs = [x] + for i, layer in enumerate(self.layers): + x = layer(x) + print(i) + if i in self.residual_connections: + for src in self.residual_connections[i]: + x = apply_residual_connection( + outputs[src], x, connection_type=self.residual_type + ) + outputs.append(x) + + x = self.out_activation(x) + return x + + + +@ImageConvBlockConfig.register_preset("single_conv") +def single_conv_config(cfg: ImageConvBlockConfig): + """ + Converts the blcok into a single convolutional layer with no normalization, dropout, or residual connections. + """ + cfg.depth = 1 + cfg.norm_config = None + cfg.dropout_config = None + cfg.residual_connections = None + cfg.activation = None + return cfg + + +@ImageConvBlockConfig.register_preset("single_block") +def single_block_config(cfg: ImageConvBlockConfig): + """ + Sets the block depth to 1 and removes dropout and residual connections, but keeps normalization and activation. + """ + cfg.depth = 1 + cfg.dropout_config = None + cfg.residual_connections = None + return cfg + + +@ImageConvBlockConfig.register_preset("0_residual") +def half_unet_residual_type(cfg: ImageConvBlockConfig): + """ + Configures the block to have a residual connection from the input to the output of the last layer. + """ + cfg.residual_connections = {cfg.depth - 1: [0]} + cfg.residual_type = ResidualConnectionType.ADD + print("in 0_residual config", cfg) + return cfg + + +@ImageConvBlockConfig.register_preset("1_residual") +def unet_residual_type(cfg: ImageConvBlockConfig): + """ + Configures the block to have a residual connection from the output of the first layer to the output of the last layer. + """ + assert cfg.depth > 1, "Depth must be greater than 1 for 1-residual connections" + cfg.residual_connections = {cfg.depth - 1: [1]} + cfg.residual_type = ResidualConnectionType.ADD + return cfg + + +@ImageConvBlockConfig.register_preset("concat_residual") +def concat_residual_type(cfg: ImageConvBlockConfig): + """ + Configures the block to have a residual connection from the input to the output of the last layer, using concatenation instead of addition. + """ + cfg.residual_connections = {cfg.depth - 1: [0]} + cfg.residual_type = ResidualConnectionType.CONCAT + print(cfg) + return cfg + + +@ImageConvBlockConfig.register_preset("recon") +def reconstruction_config(cfg: ImageConvBlockConfig): + """ + Configures the block for reconstruction tasks by removing normalization and dropout layers. + """ + cfg.norm_config = None + cfg.dropout_config = None + return cfg + + +@ImageConvBlockConfig.register_preset("segmentation") +def segmentation_config(cfg: ImageConvBlockConfig): + """ + Configures the block for segmentation tasks by using InstanceNorm with trainable parameters. + """ + cfg.norm_config = LayerConfig(name="InstanceNorm", kwargs={"affine": True}) + return cfg + + +@ImageConvBlockConfig.register_preset("depthwise_separable") +def depthwise_separable_config(cfg: ImageConvBlockConfig): + """ + Configures the block to use depthwise separable convolutions (see `im2sim.layers.DepthwiseSeparableConv`) instead of standard convolutions. + """ + cfg.conv_config = LayerConfig(name="DepthwiseSeparableConv", kwargs={}) + return cfg + + +@ImageConvBlockConfig.register_preset("ghost_depthwise") +def ghost_depthwise_config(cfg: ImageConvBlockConfig): + """ + Configures the block to use Ghost convolutions instead of standard convolutions. + """ + cfg.conv_config = LayerConfig(name="GhostConv", kwargs={}) + print("in ghost config", cfg) + return cfg + + +@ImageConvBlockConfig.register_preset("ghost_depthwise_separable") +def ghost_separable_config(cfg: ImageConvBlockConfig): + """ + Configures the block to use Ghost depthwise separable convolutions instead of standard convolutions. + """ + cfg.conv_config = LayerConfig(name="GhostConv", kwargs={"separable": True}) + return cfg + + +@ImageConvBlockConfig.register_preset("dilated_convs") +def dilated_convs_config(cfg: ImageConvBlockConfig): + """ + Configures the block to use dilated convolutions with dilation of 2 instead of standard convolutions. + """ + cfg.conv_config.kwargs["dilation"] = 2 + return cfg + + +@ImageConvBlockConfig.register_preset("ECA") +def eca_config(cfg: ImageConvBlockConfig): + """ + Configures the block to use Efficient Channel Attention (ECA) + + + """ + cfg.attn_config = LayerConfig(name="EfficientChannelAttn", kwargs={}) + return cfg + + +@ImageConvBlockConfig.register_preset("SE") +def se_config(cfg: ImageConvBlockConfig): + """ + Configures the block to use Squeeze-and-Excitation (SE) attention + """ + cfg.attn_config = LayerConfig(name="SqueezeExcite", kwargs={}) + print("in SE config", cfg) + return cfg + + +if __name__ == "__main__": + cfg = ImageConvBlockConfig(depth=3, activation="ReLU", out_activation="softmax") + cfg = ImageConvBlockConfig.apply_presets(cfg, ["ghost_depthwise", "0_residual", "SE"]) + cfg.save("test_config.yaml") + cfg2 = ImageConvBlockConfig().load("test_config.yaml") + cfg2.save("test_config2.yaml") + +# model = ImageConvBlock.build( +# rank=2, +# in_channels=32, +# out_channels=32, +# cfg=cfg, +# ) +# # # print(cfg) + +# # print(model) +# # x = torch.randn(1, 32, 64, 64) +# # y = model(x) +# # print(y.shape) diff --git a/src/im2sim/layers/layer_util.py b/im2sim/src/layers/layer_util.py similarity index 74% rename from src/im2sim/layers/layer_util.py rename to im2sim/src/layers/layer_util.py index 9f5d87a..cfa788e 100644 --- a/src/im2sim/layers/layer_util.py +++ b/im2sim/src/layers/layer_util.py @@ -2,16 +2,14 @@ import re from collections.abc import Callable from copy import copy -from typing import Any from enum import Enum -from dataclasses import dataclass, asdict, fields -from functools import partial, wraps - +from typing import Any import torch import torch_geometric.nn as gnn + def make_registry(lib: Any, regex: Callable): registry = NormalizedDict( @@ -72,23 +70,27 @@ def decorator(cls): name, (cls,), { - "__init__": lambda self, *args, _rank=r, **kwargs: - cls.__init__(self, *args, rank=_rank, **kwargs) + "__init__": lambda self, *args, _rank=r, **kwargs: cls.__init__( + self, *args, rank=_rank, **kwargs + ) }, ) register_image_layer(name=name)(layer_cls) return cls + return decorator + def get_image_layer(name: str, rank: int) -> torch.nn.Module: """ Get a PyTorch layer by name, with optional arguments. """ + if name is None: return torch.nn.Identity - + rank_name = f"{name}{rank}d" try: @@ -172,15 +174,6 @@ def get_graph_layer( return PyG_Wrapper(module) -def init_weights(m): - if isinstance(m, gnn.ChebConv): - for lin in m.lins: - torch.nn.init.kaiming_normal_(lin.weight, nonlinearity="leaky_relu") - lin.weight.data *= 0.1 - if lin.bias is not None: - torch.nn.init.zeros_(lin.bias) - - def standardize_spatial_factors(factors, rank): """ Convert a sequence of spatial factors into a standardized list of tuples. @@ -197,6 +190,7 @@ def standardize_spatial_factors(factors, rank): return standardized + class ResidualConnectionType(Enum): ADD = "add" # Standard addition residual connection CONCAT = "concat" # Concatenation residual connection @@ -225,64 +219,3 @@ def apply_residual_connection(*inputs, connection_type: ResidualConnectionType): else: raise ValueError(f"Unsupported residual connection type: {connection_type}") - - - -@dataclass -class LayerSpec: - name: str - kwargs: dict[str, Any] = None - - - -class ConfigRegistry: - def __init__(self): - self._registry = {} - - def register(self, name): - def decorator(fn): - self._registry[name] = fn - return fn - return decorator - - def apply(self, name, cfg): - if name not in self._registry: - raise ValueError(f"Unknown config: {name}") - return self._registry[name](copy(cfg)) - - def list(self): - return list(self._registry.keys()) - - -def shallow_asdict(obj): - return {f.name: getattr(obj, f.name) for f in fields(obj)} - - -class ConfigurableModule: - @classmethod - def from_config(cls, rank, in_channels, out_channels, cfg): - return cls(in_channels, out_channels, **shallow_asdict(cfg), rank=rank) - - -class ModuleSpec: - def __init__(self, module_cls, config_cls): - self.module_cls = module_cls - self.config_cls = config_cls - self.config_registry = ConfigRegistry() - - def register_config(self, name): - return self.config_registry.register(name) - - def apply_presets(self, cfg, presets=None): - out_cfg = copy(cfg) - if presets is not None: - if isinstance(presets, str): - presets = [presets] - - for preset in presets: - out_cfg = self.config_registry.apply(preset, cfg) - return out_cfg - - def build(self, rank, in_channels, out_channels, cfg, presets=None): - cfg = self.apply_presets(cfg, presets) - return self.module_cls.from_config(rank, in_channels, out_channels, cfg) diff --git a/im2sim/src/layers/module_config.py b/im2sim/src/layers/module_config.py new file mode 100644 index 0000000..95238d6 --- /dev/null +++ b/im2sim/src/layers/module_config.py @@ -0,0 +1,324 @@ +import json +from enum import Enum +from copy import deepcopy +from dataclasses import dataclass, fields +from typing import Any, TypeVar, get_args, get_origin, get_type_hints +import inspect + +from im2sim.src.utils import api_util + + +T = TypeVar("T", bound="Config") + + +@api_util.export("_internal.Config") +@dataclass +class Config: + """ + Base class for recursively serialisable configuration objects. + + This class provides: + - Recursive (de)serialization to/from dictionaries and JSON + - Preset system for modifying configs declaratively + - Type-aware reconstruction using type hints + + Subclasses should be defined as dataclasses. + + Example: + ```python + cfg = MyConfig(...) + cfg = cfg.apply_presets(["fast", "lightweight"]) + cfg.save("config.json") + + cfg2 = MyConfig().load("config.json") + ``` + """ + + + def __init_subclass__(cls): + """ Automatically initialise a preset registry for each subclass. """ + super().__init_subclass__() + cls._presets = {} + + @classmethod + def register_preset(cls, name): + """ + Register a preset function for this config class. + + A preset is a function that takes a config instance and modifies it. + Args: + name (str): Name of the preset. + Returns: decorator: Function decorator. + + Example: + ```python + @MyConfig.register_preset("small") + def small(cfg): + cfg.hidden_dim = 32 + return cfg + ``` + """ + def decorator(fn): + cls._presets[name] = fn + return fn + return decorator + + def apply_presets(self, names: list[str]): + """ + Apply a sequence of presets to a copy of this config. + + Presets are applied in order. + + Args: + names (list[str]): List of preset names. + + Returns: + Config: Modified config instance. + """ + + cfg = deepcopy(self) + for name in names: + cfg = self._presets[name](cfg) + return cfg + + + def as_kwargs(self): + """ + Convert config fields into keyword arguments. + + Returns: + dict: Mapping of field names to values. + """ + return { + f.name: getattr(self, f.name) + for f in fields(self) + } + + def to_dict(self): + """ + Recursively convert config into a serialisable dictionary. + + Returns: + dict: Serialized representation. """ + return { + "__class__": self.__class__.__name__, + **{ + f.name: self._serialize_value(getattr(self, f.name)) + for f in fields(self) + } + } + + @classmethod + def from_dict(cls: type[T], data: dict[str, Any]) -> T: + """ + Reconstruct a config object from a dictionary. + Uses type hints to correctly deserialize nested configs, enums, lists, etc. + + Args: + data (dict): Serialized config. + + Returns: + Config: Reconstructed config object. """ + + hints = get_type_hints(cls) + + kwargs = {} + + for key, value in data.items(): + if key == "__class__": + continue + + field_type = hints.get(key) + + if field_type is not None: + value = cls._deserialize_value(value, field_type) + + kwargs[key] = value + + return cls(**kwargs) + + + + @staticmethod + def _serialize_value(value): + """ Recursively serialize values into JSON-compatible structures. """ + if isinstance(value, Config): + return value.to_dict() + + if isinstance(value, Enum): + return { + "__enum__": value.__class__.__name__, + "value": value.value, + } + + if isinstance(value, list): + return [Config._serialize_value(v) for v in value] + + if isinstance(value, dict): + return { + k: Config._serialize_value(v) + for k, v in value.items() + } + + return value + + @staticmethod + def _deserialize_value(value, typ): + """ Recursively deserialize values based on type hints. """ + origin = get_origin(typ) + args = get_args(typ) + + # Optional / Union + if origin is type(None): + return value + + if origin is list: + subtype = args[0] + return [ + Config._deserialize_value(v, subtype) + for v in value + ] + + if origin is dict: + key_type, val_type = args + return { + k: Config._deserialize_value(v, val_type) + for k, v in value.items() + } + + # Handle Optional[T] / Union[T, None] + if origin is not None and origin.__name__ == "Union": + for subtype in args: + if subtype is type(None): + continue + try: + return Config._deserialize_value(value, subtype) + except Exception: + pass + return value + + # Nested configs + if isinstance(typ, type) and issubclass(typ, Config): + return typ.from_dict(value) + + # Enums + if isinstance(typ, type) and issubclass(typ, Enum): + return typ(value) + + return value + + def save(self, filepath): + """ + Save config to a JSON file. + + Args: + filepath (str): Path to file. + """ + with open(filepath, 'w') as f: + json.dump(self.to_dict(), f, indent=4) + + def load(self, filepath): + """ + Load config from a JSON file. + + Args: + filepath (str): Path to file. + + Returns: + Config: Loaded config instance. + """ + with open(filepath) as f: + data = json.load(f) + return self.from_dict(data) + + @classmethod + def generate_documentation(cls) -> str: + """ + Generate documentation for all registered presets. + + Returns: + str: Formatted documentation string. + """ + cls_doc = inspect.getdoc(fn) or "No class docstring provided." + preset_docs = [] + for name, fn in cls._presets.items(): + doc = inspect.getdoc(fn) or "No description provided." + preset_docs.append(f""" + {name} + {'-' * len(name)} + + {doc} + """.strip() + ) + preset_text = "\n\n".join(preset_docs) + return f""" + {cls_doc} + + Preset Configurations + ===================== + + {preset_text} + """.strip() + + + +CONFIG_REGISTRY = {} + +@api_util.export("_internal.register_config") +def register_config(cls): + """ + Register a config class globally. + Useful for dynamic lookup and deserialization. + + Args: + cls (type): Config class. + + Returns: + type: Registered class. + """ + CONFIG_REGISTRY[cls.__name__] = cls + return cls + + +@api_util.export("configs.LayerConfig") +@register_config +@dataclass +class LayerConfig(Config): + """ + Configuration for a single layer/module. + Attributes: + name (str): The name of the layer/module. (e.g., 'Conv', 'Linear', 'BatchNorm', etc.) + kwargs (dict[str, Any]): A dictionary of keyword arguments for the layer/module. (e.g. {'kernel_size': 3, 'stride': 1, 'padding': 1}) + """ + name: str + kwargs: dict[str, Any] = None + + +@api_util.export("_internal.ConfigurableModule") +class ConfigurableModule: + """ + Base class for modules that can be constructed from a Config. + Provides a standard interface for building modules from configs. + """ + @classmethod + def build(cls, + rank:int, + in_channels:int, + out_channels:int, + cfg: Config): + """ + Instantiate a module using a config object. + + Args: + rank (int): Spatial rank (1D, 2D, 3D). + in_channels (int): Input channels. + out_channels (int): Output channels. + cfg (Config): Configuration object. + + Returns: + nn.Module: Instantiated module. + """ + return cls(in_channels, out_channels, **cfg.as_kwargs(), rank=rank) + + + diff --git a/src/im2sim/layers/projections.py b/im2sim/src/layers/projections.py similarity index 100% rename from src/im2sim/layers/projections.py rename to im2sim/src/layers/projections.py diff --git a/im2sim/src/layers/reverse_halfunet.py b/im2sim/src/layers/reverse_halfunet.py new file mode 100644 index 0000000..d1f49c2 --- /dev/null +++ b/im2sim/src/layers/reverse_halfunet.py @@ -0,0 +1,302 @@ +from dataclasses import dataclass, field, fields + +import torch +from im2sim.src.layers.image_conv_blocks import ImageConvBlock, ImageConvBlockConfig +from im2sim.src.layers.layer_util import ( + ResidualConnectionType, + apply_residual_connection, + get_image_layer, +) +from im2sim.src.layers.module_config import Config, ConfigurableModule, LayerConfig, register_config + +@register_config +@dataclass +class ReverseHalfUNetConfig(Config): + hidden_channels: int = 64 + num_downsamples: int = 4 + pool_spec: LayerConfig | list[LayerConfig] = field( + default_factory=lambda: LayerConfig(name="MaxPool", kwargs={"kernel_size": 2}) + ) + upsample_spec: LayerConfig | list[LayerConfig] = field( + default_factory=lambda: LayerConfig( + name="Upsample", kwargs={"scale_factor": 2, "mode": "trilinear"} + ) + ) + block_cfg: ImageConvBlockConfig = field(default_factory=lambda: ImageConvBlockConfig()) + blocks_per_level: int = 2 + out_activation: str | None = None + stem_block_cfg: ImageConvBlockConfig | None = None + decoder_block_cfg: list[ImageConvBlockConfig] | ImageConvBlockConfig | None = None + out_block_cfg: ImageConvBlockConfig | None = None + fusion_type: ResidualConnectionType = ResidualConnectionType.ADD + + def __post_init__(self): + if self.stem_block_cfg is None: + self.stem_block_cfg = self.block_cfg.apply_presets(["single_block"]) + + + if self.decoder_block_cfg is None: + self.decoder_block_cfg = [self.block_cfg] * self.num_downsamples + elif isinstance(self.decoder_block_cfg, ImageConvBlockConfig): + self.decoder_block_cfg = [self.decoder_block_cfg] * self.num_downsamples + + if self.out_block_cfg is None: + self.out_block_cfg = self.block_cfg.apply_presets(["single_conv"]) + self.out_block_cfg.out_activation = self.out_activation + + +class ReverseHalfUNet(torch.nn.Module, ConfigurableModule): + def __init__( + self, + in_channels: int, + out_channels: int, + rank: int, + hidden_channels: int = 64, + num_downsamples: int = 4, + pool_spec: LayerConfig | list[LayerConfig] | None = None, + upsample_spec: LayerConfig | list[LayerConfig] | None = None, + block_cfg: ImageConvBlockConfig | None = None, + blocks_per_level: int = 2, + out_activation: str | None = None, + stem_block_cfg: ImageConvBlockConfig | None = None, + decoder_block_cfg: list[ImageConvBlockConfig] | ImageConvBlockConfig | None = None, + out_block_cfg: ImageConvBlockConfig | None = None, + fusion_type: ResidualConnectionType = ResidualConnectionType.ADD, + ): + super().__init__() + + self.rank = rank + self.in_channels = in_channels + self.out_channels = out_channels + self.hidden_channels = hidden_channels + self.num_downsamples = num_downsamples + + self.fusion_type = fusion_type + fusion_channels = ( + hidden_channels * 2 + if self.fusion_type is ResidualConnectionType.CONCAT + else hidden_channels + ) + + if pool_spec is None: + pool_spec = LayerConfig(name="MaxPool", kwargs={"kernel_size": 2}) + if upsample_spec is None: + mode = "nearest" if rank == 1 else "bilinear" if rank == 2 else "trilinear" + upsample_spec = LayerConfig(name="Upsample", kwargs={"scale_factor": 2, "mode": mode}) + if block_cfg is None: + block_cfg = ImageConvBlockConfig() + + if isinstance(pool_spec, LayerConfig): + pool_spec = [pool_spec] * num_downsamples + if isinstance(upsample_spec, LayerConfig): + upsample_spec = [upsample_spec] * num_downsamples + + for p, u in zip(pool_spec, upsample_spec, strict=True): + assert p.name.lower() in ["maxpool", "averagepool"], f"pool type {p.name} not supported" + assert u.name.lower() in ["upsample", "pixelshuffle"], ( + f"upsample type {u.name} not supported" + ) + + self.pools = torch.nn.ModuleList( + [get_image_layer(pool.name, rank)(**pool.kwargs) for pool in pool_spec] + ) + self.ups = torch.nn.ModuleList( + [get_image_layer(upsample.name, rank)(**upsample.kwargs) for upsample in upsample_spec] + ) + + if stem_block_cfg is None: + stem_block_cfg = block_cfg.apply_presets(["single_block"]) + + self.stem = ImageConvBlock.build(rank, in_channels, hidden_channels, stem_block_cfg) + print(type(self.stem)) + + if decoder_block_cfg is None: + decoder_block_cfg = [block_cfg] * num_downsamples + elif isinstance(decoder_block_cfg, ImageConvBlockConfig): + decoder_block_cfg = [decoder_block_cfg] * num_downsamples + + self.decoder_blocks = torch.nn.ModuleList( + [ + torch.nn.Sequential( + *[ImageConvBlock.build(rank, fusion_channels, hidden_channels, cfg)] + * blocks_per_level + ) + for cfg in decoder_block_cfg + ] + ) + + if out_block_cfg is None: + out_block_cfg = block_cfg.apply_presets(["single_conv"]) + out_block_cfg.out_activation = out_activation + + self.out_block = ImageConvBlock.build( + rank, hidden_channels, out_channels, out_block_cfg + ) + + def forward(self, x): + x = self.stem(x) + + pooled_features = [x] + for pool in self.pools: + x = pool(x) + pooled_features.append(x) + + pooled_features = pooled_features[::-1] # Reverse the order for decoding + + fused = None + for pf, block, up in zip(pooled_features[:-1], self.decoder_blocks, self.ups, strict=True): + if fused is None: + fused = block(pf) + else: + fused = block( + apply_residual_connection(fused, pf, connection_type=self.fusion_type) + ) + + fused = up(fused) + + out = self.out_block( + apply_residual_connection(fused, pooled_features[-1], connection_type=self.fusion_type) + ) + return out + + + + +@ReverseHalfUNetConfig.register_preset("residual") +def half_unet_residual_type(cfg: ReverseHalfUNetConfig): + cfg.decoder_block_cfg = [ + c.apply_presets(["0_residual"]) for c in cfg.decoder_block_cfg + ] + return cfg + + +@ReverseHalfUNetConfig.register_preset("dilated_bottleneck") +def unet_residual_type(cfg: ReverseHalfUNetConfig): + cfg.decoder_block_cfg[-1] = cfg.decoder_block_cfg[-1].apply_presets(cfg.block_cfg, presets=["dilated_convs"]) + return cfg + + +def _apply_preset_to_all_blocks(cfg: ReverseHalfUNetConfig, preset_name: str): + cfg.stem_block_cfg = cfg.stem_block_cfg.apply_presets([preset_name]) + cfg.decoder_block_cfg = [ + c.apply_presets([preset_name]) for c in cfg.decoder_block_cfg + ] + cfg.out_block_cfg = cfg.out_block_cfg.apply_presets([preset_name]) + return cfg + + +@ReverseHalfUNetConfig.register_preset("recon") +def reconstruction_config(cfg: ReverseHalfUNetConfig): + _apply_preset_to_all_blocks(cfg, "recon") + cfg.out_activation = None + return cfg + + +@ReverseHalfUNetConfig.register_preset("single_class_segmentation") +def segmentation_config(cfg: ReverseHalfUNetConfig): + _apply_preset_to_all_blocks(cfg, "segmentation") + cfg.out_block_cfg.out_activation = "sigmoid" + return cfg + +@ReverseHalfUNetConfig.register_preset("multiclass_segmentation") +def segmentation_config(cfg: ReverseHalfUNetConfig): + _apply_preset_to_all_blocks(cfg, "segmentation") + cfg.out_block_cfg.out_activation = "softmax" + return cfg + + +@ReverseHalfUNetConfig.register_preset("depthwise_separable") +def depthwise_separable_config(cfg: ReverseHalfUNetConfig): + cfg.decoder_block_cfg = [ + c.apply_presets(["depthwise_separable"]) + for c in cfg.decoder_block_cfg + ] + return cfg + +@ReverseHalfUNetConfig.register_preset("ghost_depthwise") +def ghost_dw_config(cfg: ReverseHalfUNetConfig): + cfg.decoder_block_cfg = [ + c.apply_presets(["ghost_depthwise"]) + for c in cfg.decoder_block_cfg + ] + return cfg + + +@ReverseHalfUNetConfig.register_preset("ghost_depthwise_separable") +def ghost_dws_config(cfg: ReverseHalfUNetConfig): + cfg.decoder_block_cfg = [ + c.apply_presets(["ghost_depthwise_separable"]) + for c in cfg.decoder_block_cfg + ] + return cfg + + +@ReverseHalfUNetConfig.register_preset("ECA") +def eca_config(cfg: ReverseHalfUNetConfig): + cfg.decoder_block_cfg = [ + c.apply_presets(["ECA"]) for c in cfg.decoder_block_cfg + ] + return cfg + + +@ReverseHalfUNetConfig.register_preset("SE") +def squeeze_excitation_config(cfg: ReverseHalfUNetConfig): + cfg.decoder_block_cfg = [ + c.apply_presets(["SE"]) for c in cfg.decoder_block_cfg + ] + return cfg + + +if __name__ == "__main__": + + def cfg_print(cfg): + print("###################") + for field in fields(cfg): + value = getattr(cfg, field.name) + print(f" {field.name}: {value}") + + cfg = ImageConvBlockConfig() + cfg = ReverseHalfUNetConfig(num_downsamples=3) + cfg = ReverseHalfUNetConfig.apply_presets(cfg, ["ghost_depthwise", "residual"]) + + model = ReverseHalfUNet.build(rank=3, in_channels=1, out_channels=1, cfg=cfg) + + print(model) + + x = torch.randn(1, 1, 128, 128, 128) # Example input for a 3D tensor + + def check_gradients(model, x): + model.train() + + # Ensure input tracks gradients if you want to test input gradients + x = x.requires_grad_(True) + + # Forward + y = model(x) + + # Use a scalar loss + loss = y.sum() + + # Backward + loss.backward() + + # Check input gradient + assert x.grad is not None, "Input gradient is None" + assert torch.isfinite(x.grad).all(), "Input gradient contains NaN/Inf" + + # Check parameter gradients + for name, param in model.named_parameters(): + if param.requires_grad: + assert param.grad is not None, f"{name} gradient is None" + assert torch.isfinite(param.grad).all(), f"{name} gradient contains NaN/Inf" + assert param.grad.abs().sum() > 0, f"{name} gradient is zero" + + return True + + # # Check gradients + # if check_gradients(model, x): + # print("Gradient check passed.") + + x = torch.randn(1, 1, 128, 128, 128) + y = model(x) + print(y.shape) diff --git a/im2sim/src/losses/__init__.py b/im2sim/src/losses/__init__.py new file mode 100644 index 0000000..0286229 --- /dev/null +++ b/im2sim/src/losses/__init__.py @@ -0,0 +1,3 @@ +from im2sim.src.losses import feature, mesh, pointcloud + +__all__ = ["feature", "mesh", "pointcloud"] diff --git a/src/im2sim/losses/contents.md b/im2sim/src/losses/contents.md similarity index 100% rename from src/im2sim/losses/contents.md rename to im2sim/src/losses/contents.md diff --git a/src/im2sim/losses/feature.py b/im2sim/src/losses/feature.py similarity index 100% rename from src/im2sim/losses/feature.py rename to im2sim/src/losses/feature.py diff --git a/src/im2sim/losses/mesh.py b/im2sim/src/losses/mesh.py similarity index 98% rename from src/im2sim/losses/mesh.py rename to im2sim/src/losses/mesh.py index 1344c6e..9dfa9e0 100644 --- a/src/im2sim/losses/mesh.py +++ b/im2sim/src/losses/mesh.py @@ -4,7 +4,7 @@ import torch import torch.nn.functional as F -from ..data.mesh_utils import compute_edge_lengths +from im2sim.src.data.mesh_utils import compute_edge_lengths logger = logging.getLogger(__name__) diff --git a/src/im2sim/losses/pointcloud.py b/im2sim/src/losses/pointcloud.py similarity index 100% rename from src/im2sim/losses/pointcloud.py rename to im2sim/src/losses/pointcloud.py diff --git a/src/im2sim/losses/seg.py b/im2sim/src/losses/seg.py similarity index 100% rename from src/im2sim/losses/seg.py rename to im2sim/src/losses/seg.py diff --git a/src/im2sim/losses/utils.py b/im2sim/src/losses/utils.py similarity index 100% rename from src/im2sim/losses/utils.py rename to im2sim/src/losses/utils.py diff --git a/src/im2sim/models/__init__.py b/im2sim/src/models/__init__.py similarity index 100% rename from src/im2sim/models/__init__.py rename to im2sim/src/models/__init__.py diff --git a/src/im2sim/models/contents.md b/im2sim/src/models/contents.md similarity index 100% rename from src/im2sim/models/contents.md rename to im2sim/src/models/contents.md diff --git a/src/im2sim/models/image_to_graph.py b/im2sim/src/models/image_to_graph.py similarity index 100% rename from src/im2sim/models/image_to_graph.py rename to im2sim/src/models/image_to_graph.py diff --git a/src/im2sim/models/unet.py b/im2sim/src/models/unet.py similarity index 100% rename from src/im2sim/models/unet.py rename to im2sim/src/models/unet.py diff --git a/src/im2sim/models/utils.py b/im2sim/src/models/utils.py similarity index 100% rename from src/im2sim/models/utils.py rename to im2sim/src/models/utils.py diff --git a/im2sim/src/plot/__init__.py b/im2sim/src/plot/__init__.py new file mode 100644 index 0000000..54710f4 --- /dev/null +++ b/im2sim/src/plot/__init__.py @@ -0,0 +1 @@ +from im2sim.src.plot.pointcloud import PointCloudPlot as PointCloudPlot diff --git a/src/im2sim/plot/pointcloud.py b/im2sim/src/plot/pointcloud.py similarity index 100% rename from src/im2sim/plot/pointcloud.py rename to im2sim/src/plot/pointcloud.py diff --git a/src/im2sim/callbacks/__init__.py b/im2sim/src/utils/__init__.py similarity index 100% rename from src/im2sim/callbacks/__init__.py rename to im2sim/src/utils/__init__.py diff --git a/im2sim/src/utils/api_util.py b/im2sim/src/utils/api_util.py new file mode 100644 index 0000000..7c66134 --- /dev/null +++ b/im2sim/src/utils/api_util.py @@ -0,0 +1,197 @@ +# Copyright 2026 University College London. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Utilities to export symbols to the API.""" + +import importlib +import importlib.abc +import sys + + +_API_SYMBOLS = dict() + +_API_ATTR = '_api_names' + +_SUBMODULE_NAMES = [ + 'data', + 'layers', + 'configs', + 'ops', + 'losses', + 'models', + 'plot', + '_internal' +] + +_SUBMODULE_DOCSTRINGS = { + 'data': 'Data loading and preprocessing utilities.', + 'layers': 'Custom layers for building deep learning models.', + 'configs': 'Configuration classes for model and training settings.', + 'ops': 'Custom operations for deep learning models.', + 'losses': 'Custom loss functions for training deep learning models.', + 'models': 'Predefined deep learning models for various tasks.', + 'plot': 'Utilities for visualizing data and model outputs.', + '_internal': 'Internal utilities and functions for development and testing purposes.', +} + + +def get_api_symbols(): + """Returns a live reference to the global API symbols dictionary.""" + return _API_SYMBOLS + + +def get_submodule_names(): + """Returns a list of TFMRI submodule names.""" + return _SUBMODULE_NAMES + + +def get_symbol_from_name(name): + """Get API symbol from its name. + + Args: + name: Name of the symbol. + + Returns: + API symbol. + """ + return _API_SYMBOLS.get(name) + + +def get_symbols_in_submodule(name): + """Returns the symbols in the given submodule. + + Args: + name: Name of the submodule. + + Returns: + A dict containing the API symbols in the submodule. + """ + symbols = {} + for k, v in _API_SYMBOLS.items(): + if k.startswith(name): + symbols[k] = v + return symbols + + +def get_docstring_for_submodule(name): + """Returns the docstring for the given submodule. + + Args: + name: Name of the submodule. + + Returns: + The docstring for the submodule. + """ + return _SUBMODULE_DOCSTRINGS[name] + + +def get_canonical_name_for_symbol(symbol): + """Get canonical name for the API symbol. + + Args: + symbol: API function or class. + + Returns: + Canonical name for the API symbol. + """ + if not hasattr(symbol, '__dict__'): + return None + if _API_ATTR not in symbol.__dict__: + return None + + api_names = getattr(symbol, '_api_names') + # Canonical name is the first name in the list. + canonical_name = api_names[0] + + return canonical_name + + +def export(*names): + """Returns a decorator to export a symbol to the API. + + Args: + *names: List of API names under which the object should be exported. + + Returns: + A decorator to export a symbol to the API. + """ + def decorator(symbol): + """Decorator to export a symbol to the API. + + Args: + symbol: Symbol to decorate. + + Returns: + The input symbol with the `_api_names` attribute set. + + Raises: + ValueError: If the name is invalid or already used. + """ + for name in names: + # API name must have format "namespace.name". + if name.count('.') != 1: + raise ValueError(f"Invalid API name: {name}") + # API namespace must be one of the supported ones. + namespace, _ = name.split('.') + if namespace not in _SUBMODULE_NAMES: + raise ValueError(f"Invalid API namespace: {namespace}") + # API name must be unique. + if name in _API_SYMBOLS: + raise ValueError( + f"Name {name} already used for exported symbol {symbol}") + # Add symbol to the API symbols table. + _API_SYMBOLS[name] = symbol + # Set the _api_names attribute. + setattr(symbol, _API_ATTR, names) + return symbol + + return decorator + + +class APILoader(importlib.abc.Loader): # pylint: disable=abstract-method + """Loader for the public API.""" + def __init__(self, *args, **kwargs): + self._namespace = kwargs.pop('namespace') + super().__init__(*args, **kwargs) + + def exec_module(self, module): + """Executes the module. + + Args: + module: module. + """ + # Import public API. + for name, symbol in _API_SYMBOLS.items(): + namespace, name = name.split('.') + if namespace == self._namespace: + setattr(module, name, symbol) + + +def import_namespace(namespace): + """Imports a namespace. + + Args: + namespace: Namespace to import. + + Returns: + The imported module. + """ + spec = importlib.machinery.ModuleSpec( + f'tensorflow_mri.{namespace}', APILoader(namespace=namespace)) + module = importlib.util.module_from_spec(spec) + sys.modules[spec.name] = module + spec.loader.exec_module(module) + module.__doc__ = _SUBMODULE_DOCSTRINGS[namespace] + return module diff --git a/src/im2sim/utils/contents.md b/im2sim/src/utils/contents.md similarity index 100% rename from src/im2sim/utils/contents.md rename to im2sim/src/utils/contents.md diff --git a/pyproject.toml b/pyproject.toml index 05c856f..6764b14 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,7 +31,11 @@ dev = [ "ruff", "pre-commit", ] - +docs = [ + "mkdocs", + "mkdocs-material", + "mkdocstrings[python]" +] [tool.setuptools.packages.find] where = ["src"] diff --git a/src/im2sim.egg-info/PKG-INFO b/src/im2sim.egg-info/PKG-INFO deleted file mode 100644 index 1b35669..0000000 --- a/src/im2sim.egg-info/PKG-INFO +++ /dev/null @@ -1,11 +0,0 @@ -Metadata-Version: 2.1 -Name: im2sim -Version: 0.1.0 -Summary: UNKNOWN -License: UNKNOWN -Platform: UNKNOWN -Requires-Python: >=3.10 -Provides-Extra: dev - -UNKNOWN - diff --git a/src/im2sim.egg-info/requires.txt b/src/im2sim.egg-info/requires.txt deleted file mode 100644 index ef8ed3f..0000000 --- a/src/im2sim.egg-info/requires.txt +++ /dev/null @@ -1,4 +0,0 @@ - -[dev] -pytest -ruff diff --git a/src/im2sim/__init__.py b/src/im2sim/__init__.py deleted file mode 100644 index 3ebba33..0000000 --- a/src/im2sim/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -from . import callbacks, data, layers, losses, metrics, models, utils - -__all__ = [ - "callbacks", - "data", - "layers", - "losses", - "metrics", - "models", - "utils", -] diff --git a/src/im2sim/callbacks/contents.md b/src/im2sim/callbacks/contents.md deleted file mode 100644 index 9257ff4..0000000 --- a/src/im2sim/callbacks/contents.md +++ /dev/null @@ -1,2 +0,0 @@ -1. plotting callbacks -2. metric calculation callbacks \ No newline at end of file diff --git a/src/im2sim/layers/__init__.py b/src/im2sim/layers/__init__.py deleted file mode 100644 index faa5e58..0000000 --- a/src/im2sim/layers/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -from .graph_blocks import GraphConvBlock, GraphConvResBlock, GraphResDecoderBlock -from .image_blocks import ImageConvBlock, ImageConvResBlock, ImageDecoder, ImageEncoder -from .layer_util import ( - get_activation, - get_image_layer, - init_weights, - register_activation, - register_graph_layer, - register_image_layer, - standardize_spatial_factors, -) -from .meshgraphnets import MeshGraphNet, MGNDecoder, MGNEdgeBlock, MGNGnBlock, MGNNodeBlock -from .projections import OGProjection - -__all__ = [ - "GraphConvBlock", - "GraphConvResBlock", - "GraphResDecoderBlock", - "ImageConvBlock", - "ImageConvResBlock", - "ImageEncoder", - "ImageDecoder", - "get_activation", - "get_image_layer", - "register_activation", - "register_graph_layer", - "register_image_layer", - "init_weights", - "standardize_spatial_factors", - "MGNEdgeBlock", - "MGNNodeBlock", - "MGNDecoder", - "MGNGnBlock", - "MeshGraphNet", - "OGProjection", -] diff --git a/src/im2sim/layers/custom_image_layers.py b/src/im2sim/layers/custom_image_layers.py deleted file mode 100644 index 80dc904..0000000 --- a/src/im2sim/layers/custom_image_layers.py +++ /dev/null @@ -1,134 +0,0 @@ -import torch - -from layer_util import get_activation, get_image_layer, register_with_ranks, LayerSpec, ResidualConnectionType, apply_residual_connection - - - -@register_with_ranks("DepthwiseConv", ranks=(1, 2, 3)) -class DepthwiseConv(torch.nn.Module): - - def __init__(self, - in_channels, - rank, - kernel_size=3, - stride=1, - padding='same', - dilation=1, - bias=True): - super().__init__() - self.conv = get_image_layer("Conv", rank)( - in_channels=in_channels, - out_channels=in_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=in_channels, - bias=bias - ) - - def forward(self, x): - return self.conv(x) - - - -@register_with_ranks("DepthwiseSeparableConv", ranks=(1, 2, 3)) -class DepthwiseSeparableConv(torch.nn.Module): - - def __init__(self, - in_channels, - out_channels, - rank, - kernel_size=3, - stride=1, - padding='same', - dilation=1, - bias=True, - activation=None): - super().__init__() - - self.depthwise = get_image_layer("Conv", rank)( - in_channels=in_channels, - out_channels=in_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=in_channels, - bias=bias - ) - self.pointwise = get_image_layer("Conv", rank)( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=1, - stride=1, - padding=0, - bias=bias - ) - self.activation = get_activation(activation) - - def forward(self, x): - x = self.depthwise(x) - x = self.pointwise(x) - x = self.activation(x) - return x - - -@register_with_ranks("GhostConv", ranks=(1, 2, 3)) -class GhostConv(torch.nn.Module): - def __init__(self, - in_channels, - out_channels, - rank, - kernel_size=3, - ratio=2, - dw_kernel_size=3, - stride=1, - padding='same', - separable=False, - bias=True): - super().__init__() - - self.out_channels = out_channels - self.init_channels = int(out_channels / ratio) - self.new_channels = out_channels - self.init_channels - - - self.primary_conv = get_image_layer("Conv", rank)( - in_channels=in_channels, - out_channels=self.init_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - bias=bias - ) - - cheap_conv_type = "DepthwiseSeparableConv" if separable else "DepthwiseConv" - - self.cheap_operation = get_image_layer(cheap_conv_type, rank)( - in_channels=self.init_channels, - out_channels=self.new_channels, - kernel_size=dw_kernel_size, - stride=1, - padding='same', - bias=bias - ) - - def forward(self, x): - x1 = self.primary_conv(x) - x2 = self.cheap_operation(x1) - return torch.cat([x1, x2], dim=1) - - - - - - - - - - - - - - diff --git a/src/im2sim/layers/halfunet.py b/src/im2sim/layers/halfunet.py deleted file mode 100644 index 305627a..0000000 --- a/src/im2sim/layers/halfunet.py +++ /dev/null @@ -1,188 +0,0 @@ -import torch -from dataclasses import dataclass, fields - -import custom_image_layers -from layer_util import get_activation, get_image_layer, register_with_ranks, ResidualConnectionType, LayerSpec, ConfigurableModule, apply_residual_connection, ModuleSpec -from image_conv_blocks import ImageConvBlock, ImageConvBlockConfig, ImageConvBlockSpec - -@dataclass -class HalfUNetConfig: - hidden_channels: int = 64 - num_downsamples: int = 4 - pool_spec: LayerSpec | list[LayerSpec] = LayerSpec(name="MaxPool", kwargs={'kernel_size': 2}) - upsample_spec: LayerSpec | list[LayerSpec] = LayerSpec(name="Upsample", kwargs={'scale_factor': 2, 'mode': 'trilinear'}) - block_cfg: ImageConvBlockConfig = ImageConvBlockConfig() - blocks_per_level: int = 2 - out_activation: str|None = None - stem_block_cfg: ImageConvBlockConfig|None = None - encoder_block_cfg: list[ImageConvBlockConfig]|ImageConvBlockConfig|None = None - out_block_cfg: ImageConvBlockConfig|None = None - fusion_type: ResidualConnectionType = ResidualConnectionType.ADD - - def __post_init__(self): - if self.stem_block_cfg is None: - self.stem_block_cfg = ImageConvBlockSpec.apply_presets(self.block_cfg, presets=["single_block"]) - - if self.encoder_block_cfg is None: - self.encoder_block_cfg = [self.block_cfg] * self.num_downsamples - elif isinstance(self.encoder_block_cfg, ImageConvBlockConfig): - self.encoder_block_cfg = [self.encoder_block_cfg] * self.num_downsamples - - if self.out_block_cfg is None: - self.out_block_cfg = ImageConvBlockSpec.apply_presets(self.block_cfg, presets=["single_conv"]) - self.out_block_cfg.out_activation = self.out_activation - - -class HalfUNet(torch.nn.Module, ConfigurableModule): - - def __init__(self, - in_channels: int, - out_channels: int, - rank:int, - hidden_channels: int = 64, - num_downsamples: int = 4, - pool_spec: LayerSpec | list[LayerSpec] = LayerSpec(name="MaxPool", kwargs={'kernel_size': 2}), - upsample_spec: LayerSpec | list[LayerSpec] = LayerSpec(name="Upsample", kwargs={'scale_factor': 2, 'mode': 'trilinear'}), - block_cfg: ImageConvBlockConfig = ImageConvBlockConfig(), - blocks_per_level: int = 2, - out_activation: str|None = None, - stem_block_cfg: ImageConvBlockConfig|None = None, - encoder_block_cfg: list[ImageConvBlockConfig]|ImageConvBlockConfig|None = None, - out_block_cfg: ImageConvBlockConfig|None = None, - fusion_type: ResidualConnectionType = ResidualConnectionType.ADD): - super().__init__() - - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.num_downsamples = num_downsamples - - if isinstance(pool_spec, LayerSpec): - pool_spec = [pool_spec] * num_downsamples - if isinstance(upsample_spec, LayerSpec): - upsample_spec = [upsample_spec] * num_downsamples - - for p, u in zip(pool_spec, upsample_spec): - assert p.name.lower() in ["maxpool", "averagepool"], f"pool type {p.name} not supported" - assert u.name.lower() in ["upsample", "pixelshuffle"], f"upsample type {u.name} not supported" - - self.pools = torch.nn.ModuleList([get_image_layer(pool.name, rank)(**pool.kwargs) for pool in pool_spec]) - self.ups = torch.nn.ModuleList([get_image_layer(upsample.name, rank)(**upsample.kwargs) for upsample in upsample_spec]) - - if stem_block_cfg is None: - stem_block_cfg = ImageConvBlockSpec.apply_presets(block_cfg, presets=["single_block"]) - - self.stem = ImageConvBlock.from_config(rank, in_channels, hidden_channels, stem_block_cfg) - print(type(self.stem)) - - if encoder_block_cfg is None: - encoder_block_cfg = [block_cfg] * num_downsamples - elif isinstance(encoder_block_cfg, ImageConvBlockConfig): - encoder_block_cfg = [encoder_block_cfg] * num_downsamples - - self.encoder_blocks = torch.nn.ModuleList([ - torch.nn.Sequential(*[ImageConvBlock.from_config(rank, hidden_channels, hidden_channels, cfg)] * blocks_per_level) - for cfg in encoder_block_cfg - ]) - - if out_block_cfg is None: - out_block_cfg = ImageConvBlockSpec.apply_presets(block_cfg, presets=["single_conv"]) - out_block_cfg.out_activation = out_activation - - self.fusion_type = fusion_type - fusion_channels = hidden_channels * (num_downsamples+1) if self.fusion_type is ResidualConnectionType.CONCAT else hidden_channels - self.out_block = ImageConvBlock.from_config(rank, fusion_channels, out_channels, out_block_cfg) - - - def forward(self, x): - x = self.stem(x) - - residuals = [] - for pool, block, up in zip(self.pools, self.encoder_blocks, self.ups): - residuals.append(x) - x = pool(x) - x = block(x) - - - fused = x - for res, up in zip(reversed(residuals),self.ups): - fused = up(fused) - fused = apply_residual_connection(fused, res, connection_type=self.fusion_type) - - out = self.out_block(fused) - return out - - -HalfUNetSpec = ModuleSpec(HalfUNet, HalfUNetConfig) - - -@HalfUNetSpec.register_config("residual") -def half_unet_residual_config(cfg: HalfUNetConfig): - cfg.encoder_block_cfg = [ImageConvBlockSpec.apply_presets(c, presets=["0_residual"]) for c in cfg.encoder_block_cfg] - return cfg - -@HalfUNetSpec.register_config("dilated_bottleneck") -def unet_residual_config(cfg: HalfUNetConfig): - cfg.encoder_block_cfg[-1] = ImageConvBlockSpec.apply_presets(cfg.block_cfg, presets=["dilated_convs"]) - return cfg - -def _apply_preset_to_all_blocks(cfg: HalfUNetConfig, preset_name: str): - cfg.stem_block_cfg= ImageConvBlockSpec.apply_presets(cfg.stem_block_cfg, presets=[preset_name]) - cfg.encoder_block_cfg= [ImageConvBlockSpec.apply_presets(c, presets=[preset_name]) for c in cfg.encoder_block_cfg] - cfg.out_block_cfg = ImageConvBlockSpec.apply_presets(cfg.out_block_cfg, presets=[preset_name]) - return cfg - - -@HalfUNetSpec.register_config("recon") -def reconstruction_config(cfg: HalfUNetConfig): - _apply_preset_to_all_blocks(cfg, "reconstruction") - cfg.out_activation = None - return cfg - -@HalfUNetSpec.register_config("segmentation") -def segmentation_config(cfg: HalfUNetConfig): - _apply_preset_to_all_blocks(cfg, "segmentation") - return cfg - - -@HalfUNetSpec.register_config("depthwise_separable") -def depthwise_separable_config(cfg: HalfUNetConfig): - _apply_preset_to_all_blocks(cfg, "depthwise_separable") - return cfg - -@HalfUNetSpec.register_config("ghost_depthwise") -def ghost_dw_config(cfg: HalfUNetConfig): - _apply_preset_to_all_blocks(cfg, "ghost_depthwise") - return cfg - -@HalfUNetSpec.register_config("ghost_depthwise_separable") -def ghost_dws_config(cfg: HalfUNetConfig): - _apply_preset_to_all_blocks(cfg, "ghost_depthwise_separable") - return cfg - - -if __name__ == "__main__": - - def cfg_print(cfg): - print("###################") - for field in fields(cfg): - value = getattr(cfg, field.name) - print(f" {field.name}: {value}") - - cfg = ImageConvBlockConfig() - cfg_print(cfg) - - cfg = HalfUNetConfig(num_downsamples=2) - cfg_print(cfg) - cfg = HalfUNetSpec.apply_presets(cfg, presets=["ghost_depthwise", "residual"]) - cfg_print(cfg) - - model = HalfUNetSpec.build(rank=3, in_channels=1, out_channels=1, cfg=cfg) - - print(model) - - x = torch.randn(1, 1, 64, 64, 64) # Example input for a 3D tensor - y = model(x) - print(y.shape) # Should print the shape of the output tensor - - \ No newline at end of file diff --git a/src/im2sim/layers/image_blocks.py b/src/im2sim/layers/image_blocks.py deleted file mode 100644 index a1ba216..0000000 --- a/src/im2sim/layers/image_blocks.py +++ /dev/null @@ -1,552 +0,0 @@ -import logging - -import torch -from torch import nn -from dataclasses import dataclass -from typing import Any - -from .layer_util import (get_activation, - get_image_layer, - BaseImageConvBlock) - -logger = logging.getLogger(__name__) - - - - -class ImageConvBlock(nn.Module): - """ - A convolutional block for image data - - Args: - in_channels (int): The number of channels in the input to the layer. - filters (int, optional): The number of filters in each convolutional layer (default: 32) - kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) - depth (int, optional): The number of successive convolutional layers (default: 2) - rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:2), - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid",) - norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") - dropout_rate (float, optional): The spatial dropout rate to be applied to the final convolution output (default:None) - - Returns: - A `torch.nn.Module` object. - - """ - - def __init__( - self, - in_channels, - filters=32, - kernel_size=3, - depth=1, - rank=3, - activation="ReLU", - norm_type=None, - dropout_rate=None, - ): - super().__init__() - - conv = get_image_layer("Conv", rank) - self.convs = nn.ModuleList( - [ - conv( - in_channels if i == 0 else filters, - filters, - kernel_size, - padding=kernel_size // 2, - ) - for i in range(depth) - ] - ) - - self.norms = nn.ModuleList( - [ - get_image_layer(norm_type, rank)(filters, affine=True) - if norm_type - else nn.Identity() - for _ in range(depth) - ] - ) - self.drop = ( - get_image_layer("Dropout", rank)(p=dropout_rate) if dropout_rate else nn.Identity() - ) - - self.act = get_activation(activation) - - def forward(self, x): - """ - Args: - x (torch.Tensor): Input feature maps in image space [in_channels, ...] where the number of dims in ... corresponds to rank - - Returns: - torch.Tensor: Output feature maps [out_channels, ...] - """ - - for conv, norm in zip(self.convs, self.norms, strict=True): - logger.debug("Image feature shape:%s", tuple(x.shape)) - x = self.act(norm(conv(x))) - return self.drop(x) - - -class ImageConvResBlock(nn.Module): - """ - A convolutional residual block for image data - - Args: - in_channels (int): The number of channels in the input to the layer. - filters (int, optional): The number of filters in each convolutional layer (default: 32) - kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) - depth (int, optional): The number of successive convolutional layers (default: 3) - rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:2), - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid",) - norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") - dropout_rate (float, optional): The spatial dropout rate to be applied to the convolution prior to residual connection (default:None) - - Returns: - A `torch.nn.Module` object. - - """ - - def __init__( - self, - in_channels, - filters=32, - kernel_size=3, - depth=3, - rank=3, - activation="relu", - norm_type=None, - dropout_rate=None, - ): - super().__init__() - assert depth >= 2 - - self.initial_conv = ImageConvBlock( - in_channels=in_channels, - filters=filters, - kernel_size=kernel_size, - rank=rank, - activation=None, - norm_type=norm_type, - depth=1, - dropout_rate=None, - ) - - self.main_conv = ImageConvBlock( - in_channels=filters, - filters=filters, - kernel_size=kernel_size, - rank=rank, - activation=activation, - norm_type=norm_type, - depth=depth - 2, - dropout_rate=dropout_rate, - ) - - self.out_conv = ImageConvBlock( - in_channels=filters, - filters=filters, - kernel_size=kernel_size, - rank=rank, - activation=None, - norm_type=norm_type, - depth=1, - dropout_rate=None, - ) - # self.drop = get_image_layer('Dropout', rank)(p=dropout_rate) if dropout_rate else nn.Identity() - self.out_act = get_activation(activation) - - def forward(self, x): - """ - Args: - x (torch.Tensor): Input feature maps in image space [in_channels, ...] where the number of dims in ... corresponds to rank - - Returns: - torch.Tensor: Output feature maps [out_channels, ...] - """ - x1 = self.initial_conv(x) - x = self.main_conv(x1) - # x = self.drop(x) - x = self.out_act(self.out_conv(x) + x1) - return x - - -class ImageResEncoder(nn.Module): - """ - A CNN encoder for images. Structured like the encoder of a ResUNet. - - Args: - in_channels (int): The number of channels in the input image. - filters (List[int], optional): The number of convolutional filters in each encoder level (default: [16,32,64,128,256]) - kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) - res_depth (int, optional): The number of successive convolutional layers in each residual block (default: 3) - res_blocks_per_level (int, optional): The number of successive residual blocks per encoder level (default: 2) - rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:3), - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid",) - norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") - dropout_rate (float, optional): The spatial dropout rate to be applied to each residual block prior to residual connection (default:None) - - Returns: - A `torch.nn.Module` object. - """ - - def __init__( - self, - in_channels, - filters=(16, 32, 64, 128, 256), - kernel_size=3, - res_depth=3, - res_blocks_per_level=2, - rank=3, - norm_type=None, - pool_type="MaxPool", - pool_size=2, - activation="ReLU", - dropout_rate=None, - ): - super().__init__() - - n_levels = len(filters) - in_channels = [in_channels, *filters] - self.conv_blocks = nn.ModuleList( - [ - nn.ModuleList( - [ - ImageConvResBlock( - in_channels=in_channels[i] if j == 0 else in_channels[i + 1], - filters=filters[i], - kernel_size=kernel_size, - depth=res_depth, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate, - ) - for j in range(res_blocks_per_level) - ] - ) - for i in range(n_levels) - ] - ) - pool = get_image_layer(pool_type, rank) - self.pools = nn.ModuleList( - [pool(pool_size) if i > 0 else nn.Identity() for i in range(n_levels)] - ) - - def forward(self, x): - """ - Args: - x (torch.Tensor): Input image [in_channels, ...] - - Returns: - List[torch.Tensor]: Output feature maps from each level ordered from top to bottom [Tensor([filters[0], ...], ..., Tensor([filters[N], ...]) - """ - logger.debug("IN ENCODER") - outputs = [] - for pool, convs in zip(self.pools, self.conv_blocks, strict=True): - x = pool(x) - for conv in convs: - x = conv(x) - logger.debug("Conv output shape:%s", x.shape) - outputs.append(x) - return outputs - - -class ImageEncoder(nn.Module): - """ - A CNN encoder for images. Structured like the encoder of a UNet. - - Args: - in_channels (int): The number of channels in the input image. - filters (List[int], optional): The number of convolutional filters in each encoder level (default: [16,32,64,128,256]) - kernel_size (int, optional): The kernel(filter) size for the convolutional layers (default: 3) - conv_blocks_per_level (int, optional): The number of successive convolutional blocks per encoder level (default: 1) - rank (int, optional): The number of spatial dimensions in the data i.e., 2D, 3D (default:3), - activation (str, optional): The activation function applied after each convolution (default: "relu", options: "leakyrelu","gelu","sigmoid",) - norm_type (str, optional): The normalization method to apply between convolutions (default:None, options: "BatchNorm", "InstanceNorm", "LayerNorm") - dropout_rate (float, optional): The spatial dropout rate to be applied to each residual block prior to residual connection (default:None) - - Returns: - A `torch.nn.Module` object. - """ - - def __init__( - self, - in_channels, - filters=(16, 32, 64, 128, 256), - kernel_size=3, - conv_blocks_per_level=1, - rank=3, - norm_type=None, - pool_type="MaxPool", - pool_size=2, - activation="ReLU", - dropout_rate=None, - ): - super().__init__() - - n_levels = len(filters) - self.conv_blocks = nn.ModuleList( - [ - ImageConvBlock( - in_channels=in_channels if i == 0 else filters[i - 1], - filters=filters[i], - kernel_size=kernel_size, - depth=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate, - ) - for i in range(n_levels) - ] - ) - pool = get_image_layer(pool_type, rank) - self.maxpools = nn.ModuleList( - [pool(pool_size) if i > 0 else nn.Identity() for i in range(n_levels)] - ) - - def forward(self, x): - """ - Args: - x (torch.Tensor): Input image [in_channels, ...] - - Returns: - List[torch.Tensor]: Output feature maps from each level ordered from top to bottom [Tensor([filters[0], ...], ..., Tensor([filters[N], ...]) - """ - outputs = [] - for pool, conv in zip(self.maxpools, self.conv_blocks, strict=True): - x = conv(pool(x)) - outputs.append(x) - return outputs - - -class ImageDecoder(nn.Module): - """ - CNN decoder for images. Mirrors ImageEncoder like a UNet decoder. - - Args: - filters (List[int]): Encoder filter sizes in top→bottom order. - kernel_size (int): Convolution kernel size. - conv_blocks_per_level (int): Number of conv blocks per level. - rank (int): Spatial rank (2 or 3). - upsample_type (str): "ConvTranspose" or "Upsample". - activation (str): Activation name. - norm_type (str): Normalization type. - dropout_rate (float): Dropout rate. - skip (bool): Use skip connections. - """ - - def __init__( - self, - filters=(16, 32, 64, 128, 256), - kernel_size=3, - conv_blocks_per_level=1, - rank=3, - upsample_type="Upsample", - activation="ReLU", - norm_type=None, - dropout_rate=None, - skip=True, - ): - super().__init__() - - self.skip = skip - n_levels = len(filters) - - rev_filters = filters[::-1] - - if upsample_type.lower() == "upsample": - # if rank == 4: - # self.ups = nn.ModuleList([ - # Upsample4d(scale_factor=(1, 2, 2, 2)) - # for _ in range(n_levels - 1) - # ]) - - # else: - self.ups = nn.ModuleList( - [ - nn.Upsample( - scale_factor=2, - mode="trilinear" if rank == 3 else "bilinear", - align_corners=True, - ) - for _ in range(n_levels - 1) - ] - ) - else: - up_layer = get_image_layer(upsample_type, rank) - self.ups = nn.ModuleList( - [ - up_layer(rev_filters[i], rev_filters[i + 1], kernel_size=2, stride=2) - for i in range(n_levels - 1) - ] - ) - - self.conv_blocks = nn.ModuleList( - [ - ImageConvBlock( - in_channels=(rev_filters[i] + rev_filters[i + 1] if skip else rev_filters[i]), - filters=rev_filters[i + 1], - kernel_size=kernel_size, - depth=conv_blocks_per_level, - rank=rank, - activation=activation, - norm_type=norm_type, - dropout_rate=dropout_rate, - ) - for i in range(n_levels - 1) - ] - ) - - def _match_size(self, x, skip): - if x.shape[2:] != skip.shape[2:]: - # center crop skip to x - diff = [s - t for s, t in zip(skip.shape[2:], x.shape[2:], strict=True)] - slices = [slice(d // 2, d // 2 + t) for d, t in zip(diff, x.shape[2:], strict=True)] - skip = skip[(..., *slices)] - return skip - - def forward(self, encoder_outputs): - """ - Args: - encoder_outputs: List of tensors from encoder (top→bottom). - - Returns: - Decoded tensor at highest resolution. - """ - - # Reverse the encoder outputs so we traverse from bottleneck to top - rev_enc = encoder_outputs[::-1] - - # Start from bottleneck - x = rev_enc[0] - - # Traverse decoder levels - for i, (up, conv) in enumerate(zip(self.ups, self.conv_blocks, strict=True)): - x = up(x) - - if self.skip: - skip_feat = rev_enc[i + 1] # next encoder feature - skip_feat = self._match_size(x, skip_feat) - x = torch.cat([x, skip_feat], dim=1) - - x = conv(x) - - return x - - -# import torch -# import torch.nn as nn - - -# class ConvResBlock(nn.Module): -# def __init__(self, in_channels, out_channels): -# super().__init__() - -# # First branch (x1) -# self.conv1a = nn.Conv3d( -# in_channels, out_channels, -# kernel_size=3, padding=1, bias=True -# ) -# self.norm1a = nn.InstanceNorm3d( -# out_channels, affine=True, eps=1e-5 -# ) - -# # Second branch -# self.conv1b = nn.Conv3d( -# in_channels, out_channels, -# kernel_size=3, padding=1, bias=True -# ) -# self.norm1b = nn.InstanceNorm3d( -# out_channels, affine=True, eps=1e-5 -# ) - -# self.act = nn.LeakyReLU(negative_slope=0.3, inplace=False) -# self.dropout = nn.Dropout3d(p=0.3) - -# self.conv2b = nn.Conv3d( -# out_channels, out_channels, -# kernel_size=3, padding=1, bias=True -# ) -# self.norm2b = nn.InstanceNorm3d( -# out_channels, affine=True, eps=1e-5 -# ) - -# self._init_weights() - -# def _init_weights(self): -# # Match TF he_normal (fan_in, normal) -# for m in self.modules(): -# if isinstance(m, nn.Conv3d): -# nn.init.kaiming_normal_( -# m.weight, -# mode='fan_in', -# nonlinearity='leaky_relu' -# ) -# if m.bias is not None: -# nn.init.zeros_(m.bias) - -# def forward(self, x): -# # First branch -# x1 = self.conv1a(x) -# x1 = self.norm1a(x1) -# # Second branch -# x2 = self.conv1b(x) -# x2 = self.norm1b(x2) -# x2 = self.act(x2) -# x2 = self.dropout(x2) - -# x2 = self.conv2b(x2) -# x2 = self.norm2b(x2) - -# # Residual add -# out = x1 + x2 -# out = self.act(out) - -# return out - - -# class Encoder3D(nn.Module): -# def __init__(self, in_channels): -# super().__init__() - -# self.block1_1 = ConvResBlock(in_channels, 16) -# self.block1_2 = ConvResBlock(16, 16) - -# self.block2_1 = ConvResBlock(16, 32) -# self.block2_2 = ConvResBlock(32, 32) - -# self.block3_1 = ConvResBlock(32, 64) -# self.block3_2 = ConvResBlock(64, 64) - -# self.block4_1 = ConvResBlock(64, 128) -# self.block4_2 = ConvResBlock(128, 128) - -# self.block5_1 = ConvResBlock(128, 256) -# self.block5_2 = ConvResBlock(256, 256) - -# self.pool = nn.MaxPool3d(kernel_size=2) - -# def forward(self, x): -# X1 = self.block1_1(x) -# X1 = self.block1_2(X1) -# X1p = self.pool(X1) - -# X2 = self.block2_1(X1p) -# X2 = self.block2_2(X2) -# X2p = self.pool(X2) - -# X3 = self.block3_1(X2p) -# X3 = self.block3_2(X3) -# X3p = self.pool(X3) - -# X4 = self.block4_1(X3p) -# X4 = self.block4_2(X4) -# X4p = self.pool(X4) - -# X5 = self.block5_1(X4p) -# X5 = self.block5_2(X5) -# X5p = self.pool(X5) - -# return [X1, X2, X3, X4, X5] diff --git a/src/im2sim/layers/image_conv_blocks.py b/src/im2sim/layers/image_conv_blocks.py deleted file mode 100644 index ce85542..0000000 --- a/src/im2sim/layers/image_conv_blocks.py +++ /dev/null @@ -1,183 +0,0 @@ -import torch -from collections import defaultdict -from dataclasses import dataclass - -import custom_image_layers -from layer_util import get_activation, get_image_layer, LayerSpec, ResidualConnectionType, apply_residual_connection, ConfigurableModule, ModuleSpec - - - -@dataclass -class ImageConvBlockConfig: - depth: int = 2 - activation: str|None = "ReLU" - out_activation: str|None = None - conv_type: LayerSpec = LayerSpec(name="Conv", kwargs={"kernel_size": 3, "padding": "same"}) - norm_type: LayerSpec = LayerSpec(name="InstanceNorm", kwargs={"affine": True}) - dropout_type: LayerSpec = LayerSpec(name=None, kwargs={}) - dropout_position: int|list[int] = 1 - residual_connections: dict[int,list[int]]=None - residual_type: str = ResidualConnectionType.ADD - - -class ImageConvBlock(torch.nn.Module, ConfigurableModule): - def __init__(self, - in_channels: int, - out_channels: int, - rank: int, - depth: int = 2, - activation: str|None = None, - out_activation: str|None = None, - conv_type: LayerSpec|None = None, - norm_type: LayerSpec|None = None, - dropout_type: LayerSpec|None = None, - dropout_position: int|list[int] = 1, - residual_connections: dict[int,list[int]]=None, - residual_type: str = ResidualConnectionType.ADD, - ): - super().__init__() - - if dropout_type is not None: - assert dropout_position < depth, "Dropout position must be less than depth" - - - if conv_type is None: - conv_type = LayerSpec(name="Conv", rank=rank, kwargs={}) - if norm_type is None: - norm_type = LayerSpec(name=None, kwargs={}) - if dropout_type is None: - dropout_type = LayerSpec(name=None, kwargs={}) - - if isinstance(dropout_position, int): - dropout_position = [dropout_position] - - self.residual_connections = residual_connections if residual_connections is not None else {} - self.residual_type = residual_type - - - self.layers = torch.nn.ModuleList() - self.activation = get_activation(activation) - self.out_activation = get_activation(out_activation) - - in_channels_per_layer = [in_channels] - for i in range(depth): - conv = get_image_layer(conv_type.name, rank=rank) - conv = get_image_layer(conv_type.name, rank=rank)( - in_channels=in_channels_per_layer[-1], - out_channels=out_channels, - **conv_type.kwargs) - - assert norm_type.name in [None, "BatchNorm", "InstanceNorm"], f"Unsupported norm type: {norm_type.name}" - - norm = get_image_layer(norm_type.name, rank=rank)(out_channels,**norm_type.kwargs) - - dropout = get_image_layer(dropout_type.name, rank=rank)(**dropout_type.kwargs) if i in dropout_position else torch.nn.Identity() - - block = torch.nn.Sequential( - conv, - norm, - dropout, - self.activation if i 1, "Depth must be greater than 1 for 1-residual connections" - cfg.residual_connections = {cfg.depth-1: [1]} - cfg.residual_type = ResidualConnectionType.ADD - return cfg - -@ImageConvBlockSpec.register_config("concat_residual") -def concat_residual_config(cfg: ImageConvBlockConfig): - cfg.residual_connections = {cfg.depth-1: [0]} - cfg.residual_type = ResidualConnectionType.CONCAT - return cfg - -@ImageConvBlockSpec.register_config("recon") -def reconstruction_config(cfg: ImageConvBlockConfig): - cfg.norm_type = None - cfg.dropout_type = None - return cfg - -@ImageConvBlockSpec.register_config("segmentation") -def segmentation_config(cfg: ImageConvBlockConfig): - cfg.norm_type = LayerSpec(name="InstanceNorm", kwargs={"affine": True}) - return cfg - - -@ImageConvBlockSpec.register_config("depthwise_separable") -def depthwise_separable_config(cfg: ImageConvBlockConfig): - cfg.conv_type = LayerSpec(name="DepthwiseSeparableConv", kwargs={}) - return cfg - -@ImageConvBlockSpec.register_config("ghost_depthwise") -def ghost_config(cfg: ImageConvBlockConfig): - cfg.conv_type = LayerSpec(name="GhostConv", kwargs={}) - return cfg - -@ImageConvBlockSpec.register_config("ghost_depthwise_separable") -def ghost_config(cfg: ImageConvBlockConfig): - cfg.conv_type = LayerSpec(name="GhostConv", kwargs={"separable":True}) - return cfg - -@ImageConvBlockSpec.register_config("dilated_convs") -def dilated_convs_config(cfg: ImageConvBlockConfig): - cfg.conv_type = LayerSpec(name="Conv", kwargs={"kernel_size": 3, "padding": "same", "dilation": 2}) - return cfg - -if __name__ == "__main__": - cfg = ImageConvBlockConfig(depth=3, activation="ReLU", out_activation="SoftPlus").apply_presets(["ghost", "0_residual"]) - model = ImageConvBlockSpec.build(cfg, presets=["ghost", "0_residual"]) - - print(model) - x = torch.randn(1, 3, 64, 64, 64) - y = model(x) - print(y.shape) \ No newline at end of file diff --git a/src/im2sim/layers/meshgraphnets.py b/src/im2sim/layers/meshgraphnets.py deleted file mode 100644 index 230330b..0000000 --- a/src/im2sim/layers/meshgraphnets.py +++ /dev/null @@ -1,159 +0,0 @@ -# This code is from https://github.com/echowve/meshGraphNets_pytorch/tree/master - -import torch -from torch import nn -from torch_geometric.data import Data -from torch_scatter import scatter_add - - -class MGNEdgeBlock(nn.Module): - def __init__(self, custom_func: nn.Module): - - super().__init__() - self.net = custom_func - - def forward(self, graph): - - node_attr = graph.x - senders_idx, receivers_idx = graph.edge_index - edge_attr = graph.edge_attr - - edges_to_collect = [] - - senders_attr = node_attr[senders_idx] - receivers_attr = node_attr[receivers_idx] - - edges_to_collect.append(senders_attr) - edges_to_collect.append(receivers_attr) - edges_to_collect.append(edge_attr) - - collected_edges = torch.cat(edges_to_collect, dim=1) - - edge_attr = self.net(collected_edges) # Update - - return Data(x=node_attr, edge_attr=edge_attr, edge_index=graph.edge_index) - - -class MGNNodeBlock(nn.Module): - def __init__(self, custom_func: nn.Module): - super().__init__() - self.net = custom_func - - def forward(self, graph): - # Decompose graph - edge_attr = graph.edge_attr - nodes_to_collect = [] - - _, receivers_idx = graph.edge_index - num_nodes = graph.num_nodes - agg_received_edges = scatter_add(edge_attr, receivers_idx, dim=0, dim_size=num_nodes) - - nodes_to_collect.append(graph.x) - nodes_to_collect.append(agg_received_edges) - collected_nodes = torch.cat(nodes_to_collect, dim=-1) - - x = self.net(collected_nodes) - return Data(x=x, edge_attr=edge_attr, edge_index=graph.edge_index) - - -def build_mlp(in_size, hidden_size, out_size, lay_norm=True): - - module = nn.Sequential( - nn.Linear(in_size, hidden_size), - nn.ReLU(), - nn.Linear(hidden_size, hidden_size), - nn.ReLU(), - nn.Linear(hidden_size, hidden_size), - nn.ReLU(), - nn.Linear(hidden_size, out_size), - ) - if lay_norm: - return nn.Sequential(module, nn.LayerNorm(normalized_shape=out_size)) - return module - - -class MGNEncoder(nn.Module): - def __init__(self, edge_input_size=128, node_input_size=128, hidden_size=128): - super().__init__() - - self.eb_encoder = build_mlp(edge_input_size, hidden_size, hidden_size) - self.nb_encoder = build_mlp(node_input_size, hidden_size, hidden_size) - - def forward(self, graph): - - node_attr, edge_attr = graph.x, graph.edge_attr - node_ = self.nb_encoder(node_attr) - edge_ = self.eb_encoder(edge_attr) - - return Data(x=node_, edge_attr=edge_, edge_index=graph.edge_index) - - -class MGNGnBlock(nn.Module): - def __init__(self, hidden_size=128): - - super().__init__() - - eb_input_dim = 3 * hidden_size - nb_input_dim = 2 * hidden_size - nb_custom_func = build_mlp(nb_input_dim, hidden_size, hidden_size) - eb_custom_func = build_mlp(eb_input_dim, hidden_size, hidden_size) - - self.eb_module = MGNEdgeBlock(custom_func=eb_custom_func) - self.nb_module = MGNNodeBlock(custom_func=nb_custom_func) - - def forward(self, graph): - - x = graph.x.clone() - edge_attr = graph.edge_attr.clone() - - graph = self.eb_module(graph) - graph = self.nb_module(graph) - - x = x + graph.x - edge_attr = edge_attr + graph.edge_attr - - return Data(x=x, edge_attr=edge_attr, edge_index=graph.edge_index) - - -class MGNDecoder(nn.Module): - def __init__(self, hidden_size=128, output_size=2): - super().__init__() - self.decode_module = build_mlp(hidden_size, hidden_size, output_size, lay_norm=False) - - def forward(self, graph): - return self.decode_module(graph.x) - - -class MeshGraphNet(nn.Module): - def __init__( - self, - message_passing_num, - node_input_size, - edge_input_size, - output_size, - hidden_size=128, - ): - - super().__init__() - - self.encoder = MGNEncoder( - edge_input_size=edge_input_size, - node_input_size=node_input_size, - hidden_size=hidden_size, - ) - - processer_list = [] - for _ in range(message_passing_num): - processer_list.append(MGNGnBlock(hidden_size=hidden_size)) - self.processer_list = nn.ModuleList(processer_list) - - self.decoder = MGNDecoder(hidden_size=hidden_size, output_size=output_size) - - def forward(self, graph): - - graph = self.encoder(graph) - for model in self.processer_list: - graph = model(graph) - decoded = self.decoder(graph) - - return decoded diff --git a/src/im2sim/losses/__init__.py b/src/im2sim/losses/__init__.py deleted file mode 100644 index 1d511bd..0000000 --- a/src/im2sim/losses/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from . import feature, mesh, pointcloud - -__all__ = ["feature", "mesh", "pointcloud"] diff --git a/src/im2sim/metrics/__init__.py b/src/im2sim/metrics/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/im2sim/metrics/contents.md b/src/im2sim/metrics/contents.md deleted file mode 100644 index 262efab..0000000 --- a/src/im2sim/metrics/contents.md +++ /dev/null @@ -1,3 +0,0 @@ -1. mask metrics -2. mesh metrics -3. feature metrics diff --git a/src/im2sim/plot/__init__.py b/src/im2sim/plot/__init__.py deleted file mode 100644 index 48482b2..0000000 --- a/src/im2sim/plot/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .pointcloud import PointCloudPlot as PointCloudPlot diff --git a/src/im2sim/utils/__init__.py b/src/im2sim/utils/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tests/integration/data/test_data_pipeline.py b/tests/integration/data/test_data_pipeline.py index ea3f686..ad057be 100644 --- a/tests/integration/data/test_data_pipeline.py +++ b/tests/integration/data/test_data_pipeline.py @@ -4,7 +4,7 @@ import torch from torch_geometric.data import Data -from im2sim.data import Dataset, Pipeline, transforms +from im2sim.src.data import Dataset, Pipeline, transforms def make_toy_dataset(): diff --git a/tests/integration/data/test_pv_mesh_utils.py b/tests/integration/data/test_pv_mesh_utils.py index ef0c85c..6f1b046 100644 --- a/tests/integration/data/test_pv_mesh_utils.py +++ b/tests/integration/data/test_pv_mesh_utils.py @@ -4,7 +4,7 @@ from hypothesis import given from torch_geometric.data import Data -from im2sim.data.mesh_utils import * +from im2sim.src.data.mesh_utils import * def build_tetra_mesh(): diff --git a/tests/unit/data/test_loader.py b/tests/unit/data/test_loader.py index f42e664..2be2e25 100644 --- a/tests/unit/data/test_loader.py +++ b/tests/unit/data/test_loader.py @@ -2,7 +2,7 @@ import torch from torch_geometric.data import Batch, Data -from im2sim.data import DataLoader, Dataset, collate +from im2sim.src.data import DataLoader, Dataset, collate # ------------------------- # Fixtures diff --git a/tests/unit/data/test_mesh_utils.py b/tests/unit/data/test_mesh_utils.py index b6baba2..7bef03d 100644 --- a/tests/unit/data/test_mesh_utils.py +++ b/tests/unit/data/test_mesh_utils.py @@ -1,6 +1,6 @@ import torch -from im2sim.data.mesh_utils import * +from im2sim.src.data.mesh_utils import * def test_make_padded_batch_shapes(): diff --git a/tests/unit/data/test_ops.py b/tests/unit/data/test_ops.py index a7ea2cc..1434c7e 100644 --- a/tests/unit/data/test_ops.py +++ b/tests/unit/data/test_ops.py @@ -1,6 +1,6 @@ import numpy as np -from im2sim.data.ops import * +from im2sim.src.data.ops import * eps = 1e-8 diff --git a/tests/unit/data/test_pipeline.py b/tests/unit/data/test_pipeline.py index 11c6380..964bfd0 100644 --- a/tests/unit/data/test_pipeline.py +++ b/tests/unit/data/test_pipeline.py @@ -3,7 +3,7 @@ import pytest import torch -from im2sim.data import ( +from im2sim.src.data import ( FittableOperation, InvertibleOperation, Operation, diff --git a/tests/unit/layers/test_graph_blocks.py b/tests/unit/layers/test_graph_blocks.py index 88398b8..fb87c27 100644 --- a/tests/unit/layers/test_graph_blocks.py +++ b/tests/unit/layers/test_graph_blocks.py @@ -2,7 +2,7 @@ import torch from torch_geometric.data import Data -from im2sim.layers import GraphConvBlock, GraphConvResBlock +from im2sim.src.layers import GraphConvBlock, GraphConvResBlock @pytest.fixture diff --git a/tests/unit/layers/test_image_blocks.py b/tests/unit/layers/test_image_blocks.py index 28d71f8..47ee50b 100644 --- a/tests/unit/layers/test_image_blocks.py +++ b/tests/unit/layers/test_image_blocks.py @@ -2,7 +2,7 @@ import torch import torch.nn as nn -from im2sim.layers import ( +from im2sim.src.layers import ( ImageConvBlock, ImageConvResBlock, ImageEncoder, diff --git a/tools/assets/download_icon_white.svg b/tools/assets/download_icon_white.svg new file mode 100644 index 0000000..46bc259 --- /dev/null +++ b/tools/assets/download_icon_white.svg @@ -0,0 +1,211 @@ + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tools/assets/download_icon_white_32px.png b/tools/assets/download_icon_white_32px.png new file mode 100644 index 0000000000000000000000000000000000000000..3d1e4fd1fe681000b93f20823a9c7f5c37d9f221 GIT binary patch literal 398 zcmV;90df9`P)E^z!Go?F2Ksx1rQcsh%q4~6eg4w 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wheel file is in: ${DEST}" +} + +main "$@" diff --git a/tools/build/create_api.py b/tools/build/create_api.py new file mode 100644 index 0000000..11489f3 --- /dev/null +++ b/tools/build/create_api.py @@ -0,0 +1,114 @@ +# Copyright 2022 University College London. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Creates the public API of TensorFlow MRI.""" + +import inspect +import pathlib +import string +import sys + + + +SCRIPT_PATH = pathlib.Path(__file__).resolve() +BUILD_PATH = SCRIPT_PATH.parent +ROOT_PATH = BUILD_PATH.parent.parent +API_PATH = ROOT_PATH / 'im2sim/_api' +INIT_PATH = ROOT_PATH / 'im2sim/__init__.py' + +sys.path.insert(0, str(ROOT_PATH)) +import im2sim.src +from im2sim.src.utils import api_util as api_util + +INIT_TEMPLATE = string.Template( +'''# This file was automatically generated by ${script_path}. +# Do not edit. +"""IM2SIM.""" +import os as _os +import sys as _sys + +from im2sim.__about__ import * + +# Import submodules. +${submodule_imports} + +# Make sure directory containing top level submodules is in +# the __path__ so that "from tensorflow_mri.foo import bar" works. +# We're using callbacks, but there's nothing special about that. +_API_MODULE = _sys.modules[__name__].layers +_im2sim_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__)) +_current_module = _sys.modules[__name__] + +if not hasattr(_current_module, '__path__'): + __path__ = [_im2sim_api_dir] +elif _im2sim_api_dir not in __path__: + __path__.append(_im2sim_api_dir) +''') + + +SUBMODULE_TEMPLATE = string.Template( +'''# This file was automatically generated by ${script_path}. +# Do not edit. +"""${docstring}""" + +${symbol_imports} +''') + +SUBMODULE_IMPORT_TEMPLATE = "from im2sim._api import {submodule_name}" +SYMBOL_IMPORT_TEMPLATE = "from {module_name} import {symbol_name} as {symbol_alias}" + +# Update the top-level __init__.py file. +submodule_imports = [ + SUBMODULE_IMPORT_TEMPLATE.format(submodule_name=submodule_name) + for submodule_name in api_util.get_submodule_names()] +submodule_imports = '\n'.join(submodule_imports) + +init_contents = INIT_TEMPLATE.substitute( + script_path=SCRIPT_PATH.relative_to(ROOT_PATH), + submodule_imports=submodule_imports) + +with open(INIT_PATH, 'w') as f: + f.write(init_contents) + +# import im2sim + +# Now generate the individual submodule APIs. +for submodule_name in api_util.get_submodule_names(): + print("SUBMODULE:", submodule_name) + docstring = api_util.get_docstring_for_submodule(submodule_name) + symbols = api_util.get_symbols_in_submodule(submodule_name) + + symbol_imports = [] + for api_name, symbol in symbols.items(): + print(" SYMBOL:", api_name, symbol) + symbol_alias = api_name.split('.')[-1] + module_name = inspect.getmodule(symbol).__name__ + symbol_name = symbol.__name__ + symbol_imports.append( + SYMBOL_IMPORT_TEMPLATE.format(module_name=module_name, + symbol_name=symbol_name, + symbol_alias=symbol_alias)) + symbol_imports = '\n'.join(symbol_imports) + + submodule_path = API_PATH / submodule_name + submodule_path.mkdir(parents=True, exist_ok=True) + submodule_path = submodule_path / '__init__.py' + + out = SUBMODULE_TEMPLATE.substitute( + script_path=SCRIPT_PATH.relative_to(ROOT_PATH), + docstring=docstring, + symbol_imports=symbol_imports) + + with open(submodule_path, 'w') as f: + f.write(out) diff --git a/tools/docs/Makefile b/tools/docs/Makefile new file mode 100644 index 0000000..a8adbe1 --- /dev/null +++ b/tools/docs/Makefile @@ -0,0 +1,21 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +PY_VERSION ?= 3.10 +SPHINXOPTS ?= +SPHINXBUILD ?= python$(PY_VERSION) -m sphinx +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/tools/docs/_build/dirhtml/.buildinfo b/tools/docs/_build/dirhtml/.buildinfo new file mode 100644 index 0000000..905ee97 --- /dev/null +++ b/tools/docs/_build/dirhtml/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file records the configuration used when building these files. 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zpXY|Kq?w6(Nf6*Y2(~nEKzK!hR2nd2L>)B4U9(bd&yZY9>Jd=1n8x9YcS6KV?@A`_. + +.. warning:: + + TensorFlow MRI is not yet available for Windows or macOS. + `Help us support them! `_. + + +TensorFlow compatibility +~~~~~~~~~~~~~~~~~~~~~~~~ + +Each TensorFlow MRI release is compiled against a specific version of +TensorFlow. To ensure compatibility, it is recommended to install matching +versions of TensorFlow and TensorFlow MRI according to the +:ref:`TensorFlow compatibility table`. + +.. warning:: + + Each TensorFlow MRI version aims to target and support the latest TensorFlow + version only. A new version of TensorFlow MRI will be released shortly after + each TensorFlow release. TensorFlow MRI versions that target older versions + of TensorFlow will not generally receive any updates. + + +Set up your system +------------------ + +You will need a working TensorFlow installation. Follow the `TensorFlow +installation instructions `_ if you do not +have one already. + + +Use a GPU +~~~~~~~~~ + +If you need GPU support, we suggest that you use one of the +`TensorFlow Docker images `_. +These come with a GPU-enabled TensorFlow installation and are the easiest way +to run TensorFlow and TensorFlow MRI on your system. + +.. code-block:: console + + $ docker pull tensorflow/tensorflow:latest-gpu + +Alternatively, make sure you follow +`these instructions `_ when setting up +your system. + + +Download from PyPI +------------------ + +TensorFlow MRI is available on the Python package index (PyPI) and can be +installed using the ``pip`` package manager: + +.. code-block:: console + + $ pip install tensorflow-mri + + +Run in Google Colab +------------------- + +To get started without installing anything on your system, you can use +`Google Colab `_. +Simply create a new notebook and use ``pip`` to install TensorFlow MRI. + +.. code:: python + + !pip install tensorflow-mri + + +The Colab environment is already configured to run TensorFlow and has GPU +support. + + +TensorFlow compatibility table +------------------------------ + +.. include:: ../../../README.rst + :start-after: start-compatibility-table + :end-before: end-compatibility-table diff --git a/tools/docs/_build/dirhtml/_sources/guide/linalg.ipynb.txt b/tools/docs/_build/dirhtml/_sources/guide/linalg.ipynb.txt new file mode 100644 index 0000000..f45442d --- /dev/null +++ b/tools/docs/_build/dirhtml/_sources/guide/linalg.ipynb.txt @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Linear algebra\n", + "\n", + "Coming soon..." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.10 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.10" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/docs/_build/dirhtml/_sources/guide/nufft.ipynb.txt b/tools/docs/_build/dirhtml/_sources/guide/nufft.ipynb.txt new file mode 100644 index 0000000..8a07f65 --- /dev/null +++ b/tools/docs/_build/dirhtml/_sources/guide/nufft.ipynb.txt @@ -0,0 +1,435 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Non-uniform fast Fourier transform (NUFFT)\n", + "TensorFlow MRI provides an efficient NUFFT operator for both CPU and GPU, based\n", + "on the algorithms by the Flatiron Institute (see\n", + "[this paper](https://doi.org/10.1137/18M120885X) and\n", + "[this paper](https://doi.ieeecomputersociety.org/10.1109/IPDPSW52791.2021.00105)\n", + "for more details). The operator is available as\n", + "[`tfmri.signal.nufft`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/signal/nufft/).\n", + "\n", + ":::{note}\n", + "The `tfmri.signal.nufft` function is an alias of the `nufft` function in the\n", + "[TensorFlow NUFFT](https://mrphys.github.io/tensorflow-nufft/)\n", + "stand-alone package. Please direct any issues about the NUFFT function directly\n", + "to the TensorFlow NUFFT [repository](https://github.com/mrphys/tensorflow-nufft/).\n", + ":::\n", + "\n", + ":::{warning}\n", + "The current NUFFT implementation uses the [FFTW](https://www.fftw.org/) library,\n", + "which is released under the GNU GPL. If you are using the NUFFT for commercial\n", + "purposes, you will need to purchase a license from MIT or adapt the code to use\n", + "a different FFT library. If you do the latter, please consider\n", + "[contributing](https://mrphys.github.io/tensorflow-mri/guide/contribute/)\n", + "your modification so others may benefit.\n", + ":::\n", + "\n", + "The NUFFT function can be used to efficiently evaluate the Fourier transform\n", + "when either the input data or the output data does not lie on a uniform grid,\n", + "in which case the standard fast Fourier transform (FFT) algorithm cannot be\n", + "used. There are 3 transform types depending whether the input is non-uniform,\n", + "the output is non-uniform or both input and output are non-uniform.\n", + "\n", + "- A **type-1** transform evaluates the Fourier transform on a uniform grid\n", + " given a set of arbitrary points (i.e, non-uniform to uniform).\n", + "- A **type-2** transform evaluates the Fourier transform on a set of arbitrary\n", + " points given a uniform grid. (i.e., uniform to non-uniform).\n", + "- A **type-3** transform evaluates the Fourier transform on a set of arbitrary\n", + " points given a set of arbitrary points (i.e., non-uniform to non-uniform).\n", + "\n", + ":::{tip}\n", + "The type of the transform can be specified using the `transform_type` argument.\n", + ":::\n", + "\n", + ":::{warning}\n", + "NUFFT type-3 is not currently supported or planned, but contributions will be\n", + "accepted.\n", + ":::\n", + "\n", + "The NUFFT may be **forward** (signal to frequency domain) or **backward**\n", + "(frequency to signal domain), regardless of the transform type.\n", + "\n", + ":::{tip}\n", + "The direction of the transform can be specified using the `fft_direction`\n", + "argument.\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Guided example\n", + "As an example, let's take an image of the Shepp-Logan phantom and evaluate its\n", + "Fourier transform on a set of sampling points defining a radial *k*-space\n", + "trajectory, using a forward, type-2 NUFFT. Then we will see how to recover\n", + "the image from the radial *k*-space data, using a backward, type-1 NUFFT." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.2 is available.\n", + "You should consider upgrading via the '/usr/local/bin/python3.8 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", + "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install -q tensorflow tensorflow-mri" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now import both packages and create an example image using\n", + "[`tfmri.image.phantom`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/image/phantom/):" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-07-21 17:25:43.649824: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2022-07-21 17:25:59.264266: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2022-07-21 17:25:59.268928: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2022-07-21 17:25:59.269048: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2022-07-21 17:25:59.269524: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2022-07-21 17:25:59.270142: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2022-07-21 17:25:59.270251: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2022-07-21 17:25:59.270327: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2022-07-21 17:25:59.612269: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2022-07-21 17:25:59.612401: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2022-07-21 17:25:59.612481: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n", + "2022-07-21 17:25:59.612559: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14239 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3080 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "image: \n", + " - shape: (256, 256)\n", + " - dtype: \n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "import tensorflow_mri as tfmri\n", + "\n", + "# Create\n", + "image_shape = [256, 256]\n", + "image = tfmri.image.phantom(shape=image_shape, dtype=tf.complex64)\n", + "\n", + "print(\"image: \\n - shape: {}\\n - dtype: {}\".format(image.shape, image.dtype))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also create a *k*-space trajectory. In this example we will create a\n", + "radial trajectory." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "trajectory: \n", + " - shape: (119296, 2)\n", + " - dtype: \n", + " - range: [-3.1415927410125732, 3.141521453857422]\n" + ] + } + ], + "source": [ + "trajectory = tfmri.sampling.radial_trajectory(\n", + " base_resolution=256, views=233, flatten_encoding_dims=True)\n", + "\n", + "print(\"trajectory: \\n - shape: {}\\n - dtype: {}\\n - range: [{}, {}]\".format(\n", + " trajectory.shape, trajectory.dtype,\n", + " tf.math.reduce_min(trajectory), tf.math.reduce_max(trajectory)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The trajectory should have shape `[..., M, N]`, where `M` is the number of\n", + "points and `N` is the number of dimensions. Any additional dimensions `...` will\n", + "be treated as batch dimensions.\n", + "\n", + "Batch dimensions for `image` and `traj`, if any, will be broadcasted.\n", + "\n", + "Spatial frequencies should be provided in radians/voxel, ie, in the range\n", + "`[-pi, pi]`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we'll also need density compensation weights for our set of nonuniform\n", + "points. These are necessary in the adjoint transform, to compensate for the fact\n", + "that the sampling density in a radial trajectory is not uniform." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "density: \n", + " - shape: (119296,)\n", + " - dtype: \n" + ] + } + ], + "source": [ + "density = tfmri.sampling.radial_density(base_resolution=256, views=233)\n", + "density = tf.reshape(density, [-1])\n", + "\n", + "print(\"density: \\n - shape: {}\\n - dtype: {}\".format(\n", + " density.shape, density.dtype))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Forward transform (image to *k*-space)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let's calculate the k-space coefficients for the given image and trajectory points (image to k-space transform)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "kspace: \n", + " - shape: (119296,)\n", + " - dtype: \n" + ] + } + ], + "source": [ + "kspace = tfmri.signal.nufft(image, trajectory,\n", + " transform_type='type_2',\n", + " fft_direction='forward')\n", + "\n", + "print(\"kspace: \\n - shape: {}\\n - dtype: {}\".format(kspace.shape, kspace.dtype))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are using a type-2 transform (uniform to nonuniform) and a forward FFT\n", + "(image domain to frequency domain). These are the default values for\n", + "`transform_type` and `fft_direction`, so providing them was not necessary in\n", + "this case." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adjoint transform (*k*-space to image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now perform the adjoint transform to recover the image given the\n", + "*k*-space data. In this case, we will use a type-1 transform (nonuniform to\n", + "uniform) and a backward FFT (frequency domain to image domain). Also note that,\n", + "prior to evaluating the NUFFT, we will compensate for the nonuniform sampling\n", + "density by simply dividing the *k*-space samples by the density weights.\n", + "Finally, for type-1 transforms we need to specify an additional `grid_shape`\n", + "argument, which should be the size of the image. If there are any batch\n", + "dimensions, `grid_shape` should **not** include them." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "recon: \n", + " - shape: (256, 256)\n", + " - dtype: \n" + ] + } + ], + "source": [ + "# Apply density compensation.\n", + "kspace /= tf.cast(density, tf.complex64)\n", + "\n", + "recon = tfmri.signal.nufft(kspace, trajectory,\n", + " grid_shape=image_shape,\n", + " transform_type='type_1',\n", + " fft_direction='backward')\n", + "\n", + "print(\"recon: \\n - shape: {}\\n - dtype: {}\".format(recon.shape, recon.dtype))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's visualize the images." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "def plot_images(image, recon):\n", + " _, ax = plt.subplots(1, 2, figsize=(9.6, 5.4))\n", + " ax[0].imshow(tf.abs(image), cmap='gray')\n", + " ax[0].set_title(\"Original image\")\n", + " ax[1].imshow(tf.abs(recon), cmap='gray')\n", + " ax[1].set_title(\"Image after forward\\nand adjoint NUFFT\")\n", + " plt.show()\n", + "plot_images(image, recon)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use the linear operator\n", + "You can also use\n", + "[`tfmri.linalg.LinearOperatorNUFFT`](https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/linalg/LinearOperatorNUFFT/)\n", + "to perform forward and adjoint NUFFT. This might be particularly useful when\n", + "building MRI reconstruction methods, as you can take advantage of the features\n", + "of the [linear algebra framework](https://mrphys.github.io/tensorflow-mri/guide/linalg/)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create the linear operator for the specified image shape, trajectory and\n", + "# density.\n", + "linop_nufft = tfmri.linalg.LinearOperatorNUFFT(\n", + " image_shape, trajectory=trajectory, density=density)\n", + "\n", + "# Apply forward transform to obtain the *k*-space signal given an image.\n", + "kspace = linop_nufft.transform(image)\n", + "\n", + "# Apply adjoint transform to obtain an image given a *k*-space signal.\n", + "recon = linop_nufft.transform(kspace, adjoint=True)\n", + "\n", + "plot_images(image, recon)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.2 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + }, + "vscode": { + "interpreter": { + "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/docs/_build/dirhtml/_sources/guide/optim.ipynb.txt b/tools/docs/_build/dirhtml/_sources/guide/optim.ipynb.txt new file mode 100644 index 0000000..2136372 --- /dev/null +++ b/tools/docs/_build/dirhtml/_sources/guide/optim.ipynb.txt @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimization\n", + "\n", + "Coming soon..." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.2 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.2" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/docs/_build/dirhtml/_sources/guide/recon.ipynb.txt b/tools/docs/_build/dirhtml/_sources/guide/recon.ipynb.txt new file mode 100644 index 0000000..5291a70 --- /dev/null +++ b/tools/docs/_build/dirhtml/_sources/guide/recon.ipynb.txt @@ -0,0 +1,32 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MR image reconstruction\n", + "\n", + "Coming soon..." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.2 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.8.2" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "0adcc2737ebf6a4a119f135174df96668767fca1ef1112612db5ecadf2b6d608" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/docs/_build/dirhtml/_sources/index.rst.txt b/tools/docs/_build/dirhtml/_sources/index.rst.txt new file mode 100644 index 0000000..a8aa51c --- /dev/null +++ b/tools/docs/_build/dirhtml/_sources/index.rst.txt @@ -0,0 +1,29 @@ +IM2SIM |release| +======================== + +.. image:: https://img.shields.io/badge/-View%20on%20GitHub-128091?logo=github&labelColor=grey + :target: https://github.com/mrphys/im2sim + :alt: View on GitHub + +.. include:: ../../README.rst + :start-after: start-intro + :end-before: end-intro + + +.. toctree:: + :caption: API Documentation + :hidden: + + API documentation + api_docs/im2sim + api_docs/im2sim/configs + api_docs/im2sim/data + api_docs/im2sim/layers + api_docs/im2sim/losses + api_docs/im2sim/models + api_docs/im2sim/ops + api_docs/im2sim/plot + + +.. meta:: + :google-site-verification: 8PySedj6KJ0kc5qC1CbO6_9blFB9Nho3SgXvbRzyVOU diff --git a/tools/docs/_build/dirhtml/_sources/templates/index.rst.txt b/tools/docs/_build/dirhtml/_sources/templates/index.rst.txt new file mode 100644 index 0000000..aef3f8f --- /dev/null +++ b/tools/docs/_build/dirhtml/_sources/templates/index.rst.txt @@ -0,0 +1,23 @@ +IM2SIM |release| +======================== + +.. image:: https://img.shields.io/badge/-View%20on%20GitHub-128091?logo=github&labelColor=grey + :target: https://github.com/mrphys/im2sim + :alt: View on GitHub + +.. include:: ../../README.rst + :start-after: start-intro + :end-before: end-intro + + +.. toctree:: + :caption: API Documentation + :hidden: + + API documentation + api_docs/im2sim + ${namespaces} + + +.. meta:: + :google-site-verification: 8PySedj6KJ0kc5qC1CbO6_9blFB9Nho3SgXvbRzyVOU diff --git a/tools/docs/_build/dirhtml/_sources/tutorials.rst.txt b/tools/docs/_build/dirhtml/_sources/tutorials.rst.txt new file mode 100644 index 0000000..9c52220 --- /dev/null +++ b/tools/docs/_build/dirhtml/_sources/tutorials.rst.txt @@ -0,0 +1,10 @@ +TensorFlow MRI tutorials +======================== + +All TensorFlow MRI tutorials are written as Jupyter notebooks. + +In addition to viewing them on this website, you can run them directly in +Google Colab, a hosted notebook environment with no setup and free access to +GPUs. Click on the **Run in Colab** button to begin. + +Alternatively, you can also download the notebooks to run on your machine. diff --git a/tools/docs/_build/dirhtml/_static/basic.css b/tools/docs/_build/dirhtml/_static/basic.css new file mode 100644 index 0000000..7ebbd6d --- /dev/null +++ b/tools/docs/_build/dirhtml/_static/basic.css @@ -0,0 +1,914 @@ +/* + * Sphinx stylesheet -- basic theme. + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar 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Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + 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JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/tools/docs/_build/dirhtml/_static/minus.png b/tools/docs/_build/dirhtml/_static/minus.png new file mode 100644 index 0000000000000000000000000000000000000000..d96755fdaf8bb2214971e0db9c1fd3077d7c419d GIT binary patch literal 90 zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu=nj kDsEF_5m^0CR;1wuP-*O&G^0G}KYk!hp00i_>zopr08q^qX#fBK literal 0 HcmV?d00001 diff --git a/tools/docs/_build/dirhtml/_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css b/tools/docs/_build/dirhtml/_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css new file mode 100644 index 0000000..9f3d764 --- /dev/null +++ b/tools/docs/_build/dirhtml/_static/mystnb.11b39860a7a0cbfd473a3ad8a317855267ff0bd372690045ca344a6b62be495e.css @@ -0,0 +1,2449 @@ +/* Dark mode support: + * if (e.g. Furo theme) or (e.g. PyData theme) has `data-theme` set, respect it. + * else default to the system color scheme + */ +@media (prefers-color-scheme: dark) { + :root { + --light: ; + --dark: initial; + } +} + +@media (prefers-color-scheme: light) { + :root { + --dark: ; + --light: initial; + } +} + +:is(html, body)[data-theme="dark"] { + --light: ; + --dark: initial; +} + +:is(html, body)[data-theme="light"] { + --dark: ; + --light: initial; +} + +/* Variables */ +:root { + /* + Following palettes are generated by using https://m2.material.io/design/color/the-color-system.html#tools-for-picking-colors + - neutral palette with #fcfcfc and danger palette with #ffdddd as base colors. + 50 means lightest, 900 means darkest; less used intermediate shades are omitted + but can be added when needed by accessing full palette from the above link. + */ + --mystnb-neutral-palette-50: #fcfcfc; + --mystnb-neutral-palette-100: #f7f7f7; + --mystnb-neutral-palette-400: #cccccc; + --mystnb-neutral-palette-500: #afafaf; + --mystnb-neutral-palette-800: #505050; + --mystnb-neutral-palette-900: #2d2d2d; + + --mystnb-danger-palette-50: #ffdddd; + --mystnb-danger-palette-100: #f5acad; + --mystnb-danger-palette-400: #c42029; + --mystnb-danger-palette-500: #b40008; + --mystnb-danger-palette-800: #850010; + --mystnb-danger-palette-900: #680010; + + /* MyST-NB specific variables; colors should be logically picked from palettes */ + --mystnb-source-bg-color: var(--light, var(--mystnb-neutral-palette-100)) var(--dark, var(--mystnb-neutral-palette-800)); + --mystnb-stdout-bg-color: var(--light, var(--mystnb-neutral-palette-50)) var(--dark, var(--mystnb-neutral-palette-900)); + --mystnb-stderr-bg-color: var(--light, var(--mystnb-danger-palette-50)) var(--dark, var(--mystnb-danger-palette-900)); + --mystnb-traceback-bg-color: var(--light, var(--mystnb-neutral-palette-50)) var(--dark, var(--mystnb-neutral-palette-900)); + --mystnb-source-border-color: var(--light, var(--mystnb-neutral-palette-400)) var(--dark, var(--mystnb-neutral-palette-500)); + --mystnb-source-margin-color: green; + --mystnb-stdout-border-color: var(--light, var(--mystnb-neutral-palette-100)) var(--dark, var(--mystnb-neutral-palette-800)); + --mystnb-stderr-border-color: var(--light, var(--mystnb-neutral-palette-100)) var(--dark, var(--mystnb-neutral-palette-800)); + --mystnb-traceback-border-color: var(--light, var(--mystnb-danger-palette-100)) var(--dark, var(--mystnb-danger-palette-800)); + --mystnb-hide-prompt-opacity: 70%; + --mystnb-source-border-radius: .4em; + --mystnb-source-border-width: 1px; + --mystnb-scrollbar-width: 0.3rem; + --mystnb-scrollbar-height: 0.3rem; + --mystnb-scrollbar-thumb-color: var(--light, var(--mystnb-neutral-palette-400)) var(--dark, var(--mystnb-neutral-palette-500)); + --mystnb-scrollbar-thumb-hover-color: var(--light, var(--mystnb-neutral-palette-500)) var(--dark, var(--mystnb-neutral-palette-400)); + --mystnb-scrollbar-thumb-border-radius: 0.25rem; +} + + +/* Whole cell */ +div.container.cell { + padding-left: 0; + margin-bottom: 1em; +} + +/* Removing all background formatting so we can control at the div level */ +.cell_input div.highlight, +.cell_output pre, +.cell_input pre, +.cell_output .output { + border: none; + box-shadow: none; +} + +.cell_output .output pre, +.cell_input pre { + margin: 0px; +} + +/* Input cells */ +div.cell > div.cell_input { + padding-left: 0em; + padding-right: 0em; + border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid; + background-color: var(--mystnb-source-bg-color); + border-left-color: var(--mystnb-source-margin-color); + border-left-width: medium; + border-radius: var(--mystnb-source-border-radius); +} + +div.cell_input>div, +div.cell_output div.output>div.highlight { + margin: 0em !important; + border: none !important; +} + +/* All cell outputs */ +.cell_output { + padding-left: 1em; + padding-right: 0em; + margin-top: 1em; +} + +/* Text outputs from cells */ +.cell_output .output.text_plain, +.cell_output .output.traceback, +.cell_output .output.stream, +.cell_output .output.stderr { + margin-top: 1em; + margin-bottom: 0em; + box-shadow: none; +} + +.cell_output .output.text_plain:not(:has(.highlight)), +.cell_output .output.stream:not(:has(.highlight)) { + /* plain (or stream of) output, not containing a pygments-highlighted block */ + background: var(--mystnb-stdout-bg-color); + border: 1px solid var(--mystnb-stdout-border-color); +} + +.cell_output .output.stderr { + background: var(--mystnb-stderr-bg-color); + border: 1px solid var(--mystnb-stderr-border-color); +} + +.cell_output .output.traceback { + background: var(--mystnb-traceback-bg-color); + border: 1px solid var(--mystnb-traceback-border-color); +} + +/* --- Collapsible cell content --- */ + +/* +encourage summary container to blend in with its parent. +p.admonition-title should hold the title styles. +*/ +div.cell details.hide summary { + border-left: unset; + padding: inherit; + margin: inherit; + background-color: inherit; +} + +/* Neighboring input/output elements - spacing, borders */ +div.cell details.hide.above-input + details.below-input, +div.cell div.cell_input + details.below-input +{ + margin-top: 0; +} + +div.cell details.hide.above-input:has(+ details.below-input), +div.cell div.cell_input:has(+ details.below-input) +{ + margin-bottom: 0; +} + +div.cell:has(> *:nth-child(2)) div.cell_input:first-child, +div.cell:has(> *:nth-child(2)) details:first-child +{ + border-bottom-left-radius: 0; + border-bottom-right-radius: 0; +} + +div.cell:has(> *:nth-child(2)) div.cell_input:last-child, +div.cell:has(> *:nth-child(2)) details:last-child +{ + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +/* intra-label styles for collapsibles */ +div.cell.container details.hide.above-input>summary, +div.cell.container details.hide.below-input>summary, +div.cell.container details.hide.above-output>summary +{ + display: block; + border-left: none; +} + +div.cell details.hide>summary>p.admonition-title { + display: list-item; + margin-bottom: 0; +} + +div.cell details.hide:not([open]) { + padding-bottom: 0; +} + +div.cell details.hide[open]>summary>p.collapsed { + display: none; +} + +div.cell details.hide:not([open])>summary>p.expanded { + display: none; +} + +@keyframes collapsed-fade-in { + 0% { + opacity: 0; + } + + 100% { + opacity: 1; + } +} +div.cell details.hide[open]>summary~* { + -moz-animation: collapsed-fade-in 0.3s ease-in-out; + -webkit-animation: collapsed-fade-in 0.3s ease-in-out; + animation: collapsed-fade-in 0.3s ease-in-out; +} + +/* Clear conflicting styles for details and admonitions set by some themes */ +div.cell details.admonition summary::before { + content: unset; +} + +/* Math align to the left */ +.cell_output .MathJax_Display { + text-align: left !important; +} + +/** source code line numbers **/ +span.linenos { + opacity: 0.5; +} + +/* Inline text from `paste` operation */ + +span.pasted-text { + font-weight: bold; +} + +span.pasted-inline img { + max-height: 2em; +} + +tbody span.pasted-inline img { + max-height: none; +} + + +/* Adding scroll bars if tags: output_scroll, scroll-output, and scroll-input + * On screens, we want to scroll, but on print show all + * + * It was before in https://github.com/executablebooks/sphinx-book-theme/blob/eb1b6baf098b27605e8f2b7b2979b17ebf1b9540/src/sphinx_book_theme/assets/styles/extensions/_myst-nb.scss +*/ +div.cell:is( + .tag_output_scroll, + .tag_scroll-output, + .config_scroll_outputs + ) + div.cell_output, +div.cell.tag_scroll-input div.cell_input { + max-height: 24em; + overflow-y: auto; + max-width: 100%; + overflow-x: auto; +} + +div.cell.config_scroll_outputs div.cell_output:has(img) { + /* If the output cell has image(s), allow it to take 90% of viewport height + but still bounded between 24em and 60em */ + max-height: clamp(24em, 90vh, 60em); +} + +/* Custom scrollbars */ +div.cell:is( + .tag_output_scroll, + .tag_scroll-output, + .config_scroll_outputs + ) + div.cell_output::-webkit-scrollbar, +div.cell.tag_scroll-input div.cell_input::-webkit-scrollbar { + width: var(--mystnb-scrollbar-width); + height: var(--mystnb-scrollbar-height); +} + +div.cell:is( + .tag_output_scroll, + .tag_scroll-output, + .config_scroll_outputs + ) + div.cell_output::-webkit-scrollbar-thumb, +div.cell.tag_scroll-input div.cell_input::-webkit-scrollbar-thumb { + background: var(--mystnb-scrollbar-thumb-color); + border-radius: var(--mystnb-scrollbar-thumb-border-radius); +} + +div.cell:is( + .tag_output_scroll, + .tag_scroll-output, + .config_scroll_outputs + ) + div.cell_output::-webkit-scrollbar-thumb:hover, +div.cell.tag_scroll-input div.cell_input::-webkit-scrollbar-thumb:hover { + background: var(--mystnb-scrollbar-thumb-hover-color); +} + +/* In print mode, unset scroll styles */ +@media print { + div.cell:is( + .tag_output_scroll, + .tag_scroll-output, + .config_scroll_outputs + ) + div.cell_output, + div.cell.tag_scroll-input div.cell_input { + max-height: unset; + overflow-y: visible; + max-width: unset; + overflow-x: visible; + } +} + +/* Font colors for translated ANSI escape sequences +Color values are copied from Jupyter Notebook +https://github.com/jupyter/notebook/blob/52581f8eda9b319eb0390ac77fe5903c38f81e3e/notebook/static/notebook/less/ansicolors.less#L14-L21 +Background colors from +https://nbsphinx.readthedocs.io/en/latest/code-cells.html#ANSI-Colors +*/ +div.highlight .-Color-Bold { + font-weight: bold; +} + +div.highlight .-Color[class*=-Black] { + color: #3E424D +} + +div.highlight .-Color[class*=-Red] { + color: #E75C58 +} + +div.highlight .-Color[class*=-Green] { + color: #00A250 +} + +div.highlight .-Color[class*=-Yellow] { + color: #DDB62B +} + +div.highlight .-Color[class*=-Blue] { + color: #208FFB +} + +div.highlight .-Color[class*=-Magenta] { + color: #D160C4 +} + +div.highlight .-Color[class*=-Cyan] { + color: #60C6C8 +} + +div.highlight .-Color[class*=-White] { + color: #C5C1B4 +} + +div.highlight .-Color[class*=-BGBlack] { + background-color: #3E424D +} + +div.highlight .-Color[class*=-BGRed] { + background-color: #E75C58 +} + +div.highlight .-Color[class*=-BGGreen] { + background-color: #00A250 +} + +div.highlight .-Color[class*=-BGYellow] { + background-color: #DDB62B +} + +div.highlight .-Color[class*=-BGBlue] { + background-color: #208FFB +} + +div.highlight .-Color[class*=-BGMagenta] { + background-color: #D160C4 +} + +div.highlight 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background-color: #00D7AF +} + +div.highlight .-Color[class*=-C44] { + color: #00D7D7 +} + +div.highlight .-Color[class*=-BGC44] { + background-color: #00D7D7 +} + +div.highlight .-Color[class*=-C45] { + color: #00D7FF +} + +div.highlight .-Color[class*=-BGC45] { + background-color: #00D7FF +} + +div.highlight .-Color[class*=-C46] { + color: #00FF00 +} + +div.highlight .-Color[class*=-BGC46] { + background-color: #00FF00 +} + +div.highlight .-Color[class*=-C47] { + color: #00FF5F +} + +div.highlight .-Color[class*=-BGC47] { + background-color: #00FF5F +} + +div.highlight .-Color[class*=-C48] { + color: #00FF87 +} + +div.highlight .-Color[class*=-BGC48] { + background-color: #00FF87 +} + +div.highlight .-Color[class*=-C49] { + color: #00FFAF +} + +div.highlight .-Color[class*=-BGC49] { + background-color: #00FFAF +} + +div.highlight .-Color[class*=-C50] { + color: #00FFD7 +} + +div.highlight .-Color[class*=-BGC50] { + background-color: #00FFD7 +} + +div.highlight .-Color[class*=-C51] { + color: #00FFFF +} + +div.highlight .-Color[class*=-BGC51] { + background-color: #00FFFF +} + +div.highlight .-Color[class*=-C52] { + color: #5F0000 +} + +div.highlight .-Color[class*=-BGC52] { + background-color: #5F0000 +} + +div.highlight .-Color[class*=-C53] { + color: #5F005F +} + +div.highlight .-Color[class*=-BGC53] { + background-color: #5F005F +} + +div.highlight .-Color[class*=-C54] { + color: #5F0087 +} + +div.highlight .-Color[class*=-BGC54] { + background-color: #5F0087 +} + +div.highlight .-Color[class*=-C55] { + color: #5F00AF +} + +div.highlight .-Color[class*=-BGC55] { + background-color: #5F00AF +} + +div.highlight .-Color[class*=-C56] { + color: #5F00D7 +} + +div.highlight .-Color[class*=-BGC56] { + background-color: #5F00D7 +} + +div.highlight .-Color[class*=-C57] { + color: #5F00FF +} + +div.highlight .-Color[class*=-BGC57] { + background-color: #5F00FF +} + +div.highlight .-Color[class*=-C58] { + color: #5F5F00 +} + +div.highlight .-Color[class*=-BGC58] { + background-color: #5F5F00 +} + +div.highlight .-Color[class*=-C59] { + color: #5F5F5F +} + +div.highlight .-Color[class*=-BGC59] { + background-color: #5F5F5F +} + +div.highlight .-Color[class*=-C60] { + color: #5F5F87 +} + +div.highlight .-Color[class*=-BGC60] { + background-color: #5F5F87 +} + +div.highlight .-Color[class*=-C61] { + color: #5F5FAF +} + +div.highlight .-Color[class*=-BGC61] { + background-color: #5F5FAF +} + +div.highlight .-Color[class*=-C62] { + color: #5F5FD7 +} + +div.highlight .-Color[class*=-BGC62] { + background-color: #5F5FD7 +} + +div.highlight .-Color[class*=-C63] { + color: #5F5FFF +} + +div.highlight .-Color[class*=-BGC63] { + background-color: #5F5FFF +} + +div.highlight .-Color[class*=-C64] { + color: #5F8700 +} + +div.highlight .-Color[class*=-BGC64] { + background-color: #5F8700 +} + +div.highlight .-Color[class*=-C65] { + color: #5F875F +} + +div.highlight .-Color[class*=-BGC65] { + background-color: #5F875F +} + +div.highlight .-Color[class*=-C66] { + color: #5F8787 +} + +div.highlight .-Color[class*=-BGC66] { + background-color: #5F8787 +} + +div.highlight .-Color[class*=-C67] { + color: #5F87AF +} + +div.highlight .-Color[class*=-BGC67] { + background-color: #5F87AF +} + +div.highlight .-Color[class*=-C68] { + color: #5F87D7 +} + +div.highlight .-Color[class*=-BGC68] { + background-color: #5F87D7 +} + +div.highlight .-Color[class*=-C69] { + color: #5F87FF +} + +div.highlight .-Color[class*=-BGC69] { + background-color: #5F87FF +} + +div.highlight .-Color[class*=-C70] { + color: #5FAF00 +} + +div.highlight .-Color[class*=-BGC70] { + background-color: #5FAF00 +} + +div.highlight .-Color[class*=-C71] { + color: #5FAF5F +} + +div.highlight .-Color[class*=-BGC71] { + background-color: #5FAF5F +} + +div.highlight .-Color[class*=-C72] { + color: #5FAF87 +} + +div.highlight .-Color[class*=-BGC72] { + background-color: #5FAF87 +} + +div.highlight .-Color[class*=-C73] { + color: #5FAFAF +} + +div.highlight .-Color[class*=-BGC73] { + background-color: #5FAFAF +} + +div.highlight .-Color[class*=-C74] { + color: #5FAFD7 +} + +div.highlight .-Color[class*=-BGC74] { + background-color: #5FAFD7 +} + +div.highlight .-Color[class*=-C75] { + color: #5FAFFF +} + +div.highlight .-Color[class*=-BGC75] { + background-color: #5FAFFF +} + +div.highlight .-Color[class*=-C76] { + color: #5FD700 +} + +div.highlight .-Color[class*=-BGC76] { + background-color: #5FD700 +} + +div.highlight .-Color[class*=-C77] { + color: #5FD75F +} + +div.highlight .-Color[class*=-BGC77] { + background-color: #5FD75F +} + +div.highlight .-Color[class*=-C78] { + color: #5FD787 +} + +div.highlight .-Color[class*=-BGC78] { + background-color: #5FD787 +} + +div.highlight .-Color[class*=-C79] { + color: #5FD7AF +} + +div.highlight .-Color[class*=-BGC79] { + background-color: #5FD7AF +} + +div.highlight .-Color[class*=-C80] { + color: #5FD7D7 +} + +div.highlight .-Color[class*=-BGC80] { + background-color: #5FD7D7 +} + +div.highlight .-Color[class*=-C81] { + color: #5FD7FF +} + +div.highlight .-Color[class*=-BGC81] { + background-color: #5FD7FF +} + +div.highlight .-Color[class*=-C82] { + color: #5FFF00 +} + +div.highlight .-Color[class*=-BGC82] { + background-color: #5FFF00 +} + +div.highlight .-Color[class*=-C83] { + color: #5FFF5F +} + +div.highlight .-Color[class*=-BGC83] { + background-color: #5FFF5F +} + +div.highlight .-Color[class*=-C84] { + color: #5FFF87 +} + +div.highlight .-Color[class*=-BGC84] { + background-color: #5FFF87 +} + +div.highlight .-Color[class*=-C85] { + color: #5FFFAF +} + +div.highlight .-Color[class*=-BGC85] { + background-color: #5FFFAF +} + +div.highlight .-Color[class*=-C86] { + color: #5FFFD7 +} + +div.highlight .-Color[class*=-BGC86] { + background-color: #5FFFD7 +} + +div.highlight .-Color[class*=-C87] { + color: #5FFFFF +} + +div.highlight .-Color[class*=-BGC87] { + background-color: #5FFFFF +} + +div.highlight .-Color[class*=-C88] { + color: #870000 +} + +div.highlight .-Color[class*=-BGC88] { + background-color: #870000 +} + +div.highlight .-Color[class*=-C89] { + color: #87005F +} + +div.highlight .-Color[class*=-BGC89] { + background-color: #87005F +} + +div.highlight .-Color[class*=-C90] { + color: #870087 +} + +div.highlight .-Color[class*=-BGC90] { + background-color: #870087 +} + +div.highlight .-Color[class*=-C91] { + color: #8700AF +} + +div.highlight .-Color[class*=-BGC91] { + background-color: #8700AF +} + +div.highlight .-Color[class*=-C92] { + color: #8700D7 +} + +div.highlight .-Color[class*=-BGC92] { + background-color: #8700D7 +} + +div.highlight .-Color[class*=-C93] { + color: #8700FF +} + +div.highlight .-Color[class*=-BGC93] { + background-color: #8700FF +} + +div.highlight .-Color[class*=-C94] { + color: #875F00 +} + +div.highlight .-Color[class*=-BGC94] { + background-color: #875F00 +} + +div.highlight .-Color[class*=-C95] { + color: #875F5F +} + +div.highlight .-Color[class*=-BGC95] { + background-color: #875F5F +} + +div.highlight .-Color[class*=-C96] { + color: #875F87 +} + +div.highlight .-Color[class*=-BGC96] { + background-color: #875F87 +} + +div.highlight .-Color[class*=-C97] { + color: #875FAF +} + +div.highlight .-Color[class*=-BGC97] { + background-color: #875FAF +} + +div.highlight .-Color[class*=-C98] { + color: #875FD7 +} + +div.highlight .-Color[class*=-BGC98] { + background-color: #875FD7 +} + +div.highlight .-Color[class*=-C99] { + color: #875FFF +} + +div.highlight .-Color[class*=-BGC99] { + background-color: #875FFF +} + +div.highlight .-Color[class*=-C100] { + color: #878700 +} + +div.highlight .-Color[class*=-BGC100] { + background-color: #878700 +} + +div.highlight .-Color[class*=-C101] { + color: #87875F +} + +div.highlight .-Color[class*=-BGC101] { + background-color: #87875F +} + +div.highlight .-Color[class*=-C102] { + color: #878787 +} + +div.highlight .-Color[class*=-BGC102] { + background-color: #878787 +} + +div.highlight .-Color[class*=-C103] { + color: #8787AF +} + +div.highlight .-Color[class*=-BGC103] { + background-color: #8787AF +} + +div.highlight .-Color[class*=-C104] { + color: #8787D7 +} + +div.highlight .-Color[class*=-BGC104] { + background-color: #8787D7 +} + +div.highlight .-Color[class*=-C105] { + color: #8787FF +} + +div.highlight .-Color[class*=-BGC105] { + background-color: #8787FF +} + +div.highlight .-Color[class*=-C106] { + color: #87AF00 +} + +div.highlight .-Color[class*=-BGC106] { + background-color: #87AF00 +} + +div.highlight .-Color[class*=-C107] { + color: #87AF5F +} + +div.highlight .-Color[class*=-BGC107] { + background-color: #87AF5F +} + +div.highlight .-Color[class*=-C108] { + color: #87AF87 +} + +div.highlight .-Color[class*=-BGC108] { + background-color: #87AF87 +} + +div.highlight .-Color[class*=-C109] { + color: #87AFAF +} + +div.highlight .-Color[class*=-BGC109] { + background-color: #87AFAF +} + +div.highlight .-Color[class*=-C110] { + color: #87AFD7 +} + +div.highlight .-Color[class*=-BGC110] { + background-color: #87AFD7 +} + +div.highlight .-Color[class*=-C111] { + color: #87AFFF +} + +div.highlight .-Color[class*=-BGC111] { + background-color: #87AFFF +} + +div.highlight .-Color[class*=-C112] { + color: #87D700 +} + +div.highlight .-Color[class*=-BGC112] { + background-color: #87D700 +} + +div.highlight .-Color[class*=-C113] { + color: #87D75F +} + +div.highlight .-Color[class*=-BGC113] { + background-color: #87D75F +} + +div.highlight .-Color[class*=-C114] { + color: #87D787 +} + +div.highlight .-Color[class*=-BGC114] { + background-color: #87D787 +} + +div.highlight .-Color[class*=-C115] { + color: #87D7AF +} + +div.highlight .-Color[class*=-BGC115] { + background-color: #87D7AF +} + +div.highlight .-Color[class*=-C116] { + color: #87D7D7 +} + +div.highlight .-Color[class*=-BGC116] { + background-color: #87D7D7 +} + +div.highlight .-Color[class*=-C117] { + color: #87D7FF +} + +div.highlight .-Color[class*=-BGC117] { + background-color: #87D7FF +} + +div.highlight .-Color[class*=-C118] { + color: #87FF00 +} + +div.highlight .-Color[class*=-BGC118] { + background-color: #87FF00 +} + +div.highlight .-Color[class*=-C119] { + color: #87FF5F +} + +div.highlight .-Color[class*=-BGC119] { + background-color: #87FF5F +} + +div.highlight .-Color[class*=-C120] { + color: #87FF87 +} + +div.highlight .-Color[class*=-BGC120] { + background-color: #87FF87 +} + +div.highlight .-Color[class*=-C121] { + color: #87FFAF +} + +div.highlight .-Color[class*=-BGC121] { + background-color: #87FFAF +} + +div.highlight .-Color[class*=-C122] { + color: #87FFD7 +} + +div.highlight .-Color[class*=-BGC122] { + background-color: #87FFD7 +} + +div.highlight .-Color[class*=-C123] { + color: #87FFFF +} + +div.highlight .-Color[class*=-BGC123] { + background-color: #87FFFF +} + +div.highlight .-Color[class*=-C124] { + color: #AF0000 +} + +div.highlight .-Color[class*=-BGC124] { + background-color: #AF0000 +} + +div.highlight .-Color[class*=-C125] { + color: #AF005F +} + +div.highlight .-Color[class*=-BGC125] { + background-color: #AF005F +} + +div.highlight .-Color[class*=-C126] { + color: #AF0087 +} + +div.highlight .-Color[class*=-BGC126] { + background-color: #AF0087 +} + +div.highlight .-Color[class*=-C127] { + color: #AF00AF +} + +div.highlight .-Color[class*=-BGC127] { + background-color: #AF00AF +} + +div.highlight .-Color[class*=-C128] { + color: #AF00D7 +} + +div.highlight .-Color[class*=-BGC128] { + background-color: #AF00D7 +} + +div.highlight .-Color[class*=-C129] { + color: #AF00FF +} + +div.highlight .-Color[class*=-BGC129] { + background-color: #AF00FF +} + +div.highlight .-Color[class*=-C130] { + color: #AF5F00 +} + +div.highlight .-Color[class*=-BGC130] { + background-color: #AF5F00 +} + +div.highlight .-Color[class*=-C131] { + color: #AF5F5F +} + +div.highlight .-Color[class*=-BGC131] { + background-color: #AF5F5F +} + +div.highlight .-Color[class*=-C132] { + color: #AF5F87 +} + +div.highlight .-Color[class*=-BGC132] { + background-color: #AF5F87 +} + +div.highlight .-Color[class*=-C133] { + color: #AF5FAF +} + +div.highlight .-Color[class*=-BGC133] { + background-color: #AF5FAF +} + +div.highlight .-Color[class*=-C134] { + color: #AF5FD7 +} + +div.highlight .-Color[class*=-BGC134] { + background-color: #AF5FD7 +} + +div.highlight .-Color[class*=-C135] { + color: #AF5FFF +} + +div.highlight .-Color[class*=-BGC135] { + background-color: #AF5FFF +} + +div.highlight .-Color[class*=-C136] { + color: #AF8700 +} + +div.highlight .-Color[class*=-BGC136] { + background-color: #AF8700 +} + +div.highlight .-Color[class*=-C137] { + color: #AF875F +} + +div.highlight .-Color[class*=-BGC137] { + background-color: #AF875F +} + +div.highlight .-Color[class*=-C138] { + color: #AF8787 +} + +div.highlight .-Color[class*=-BGC138] { + background-color: #AF8787 +} + +div.highlight .-Color[class*=-C139] { + color: #AF87AF +} + +div.highlight .-Color[class*=-BGC139] { + background-color: #AF87AF +} + +div.highlight .-Color[class*=-C140] { + color: #AF87D7 +} + +div.highlight .-Color[class*=-BGC140] { + background-color: #AF87D7 +} + +div.highlight .-Color[class*=-C141] { + color: #AF87FF +} + +div.highlight .-Color[class*=-BGC141] { + background-color: #AF87FF +} + +div.highlight .-Color[class*=-C142] { + color: #AFAF00 +} + +div.highlight .-Color[class*=-BGC142] { + background-color: #AFAF00 +} + +div.highlight .-Color[class*=-C143] { + color: #AFAF5F +} + +div.highlight .-Color[class*=-BGC143] { + background-color: #AFAF5F +} + +div.highlight .-Color[class*=-C144] { + color: #AFAF87 +} + +div.highlight .-Color[class*=-BGC144] { + background-color: #AFAF87 +} + +div.highlight .-Color[class*=-C145] { + color: #AFAFAF +} + +div.highlight .-Color[class*=-BGC145] { + background-color: #AFAFAF +} + +div.highlight .-Color[class*=-C146] { + color: #AFAFD7 +} + +div.highlight .-Color[class*=-BGC146] { + background-color: #AFAFD7 +} + +div.highlight .-Color[class*=-C147] { + color: #AFAFFF +} + +div.highlight .-Color[class*=-BGC147] { + background-color: #AFAFFF +} + +div.highlight .-Color[class*=-C148] { + color: #AFD700 +} + +div.highlight .-Color[class*=-BGC148] { + background-color: #AFD700 +} + +div.highlight .-Color[class*=-C149] { + color: #AFD75F +} + +div.highlight .-Color[class*=-BGC149] { + background-color: #AFD75F +} + +div.highlight .-Color[class*=-C150] { + color: #AFD787 +} + +div.highlight .-Color[class*=-BGC150] { + background-color: #AFD787 +} + +div.highlight .-Color[class*=-C151] { + color: #AFD7AF +} + +div.highlight .-Color[class*=-BGC151] { + background-color: #AFD7AF +} + +div.highlight .-Color[class*=-C152] { + color: #AFD7D7 +} + +div.highlight .-Color[class*=-BGC152] { + background-color: #AFD7D7 +} + +div.highlight .-Color[class*=-C153] { + color: #AFD7FF +} + +div.highlight .-Color[class*=-BGC153] { + background-color: #AFD7FF +} + +div.highlight .-Color[class*=-C154] { + color: #AFFF00 +} + +div.highlight .-Color[class*=-BGC154] { + background-color: #AFFF00 +} + +div.highlight .-Color[class*=-C155] { + color: #AFFF5F +} + +div.highlight .-Color[class*=-BGC155] { + background-color: #AFFF5F +} + +div.highlight .-Color[class*=-C156] { + color: #AFFF87 +} + +div.highlight .-Color[class*=-BGC156] { + background-color: #AFFF87 +} + +div.highlight .-Color[class*=-C157] { + color: #AFFFAF +} + +div.highlight .-Color[class*=-BGC157] { + background-color: #AFFFAF +} + +div.highlight .-Color[class*=-C158] { + color: #AFFFD7 +} + +div.highlight .-Color[class*=-BGC158] { + background-color: #AFFFD7 +} + +div.highlight .-Color[class*=-C159] { + color: #AFFFFF +} + +div.highlight .-Color[class*=-BGC159] { + background-color: #AFFFFF +} + +div.highlight .-Color[class*=-C160] { + color: #D70000 +} + +div.highlight .-Color[class*=-BGC160] { + background-color: #D70000 +} + +div.highlight .-Color[class*=-C161] { + color: #D7005F +} + +div.highlight .-Color[class*=-BGC161] { + background-color: #D7005F +} + +div.highlight .-Color[class*=-C162] { + color: #D70087 +} + +div.highlight .-Color[class*=-BGC162] { + background-color: #D70087 +} + +div.highlight .-Color[class*=-C163] { + color: #D700AF +} + +div.highlight .-Color[class*=-BGC163] { + background-color: #D700AF +} + +div.highlight .-Color[class*=-C164] { + color: #D700D7 +} + +div.highlight .-Color[class*=-BGC164] { + background-color: #D700D7 +} + +div.highlight .-Color[class*=-C165] { + color: #D700FF +} + +div.highlight .-Color[class*=-BGC165] { + background-color: #D700FF +} + +div.highlight .-Color[class*=-C166] { + color: #D75F00 +} + +div.highlight .-Color[class*=-BGC166] { + background-color: #D75F00 +} + +div.highlight .-Color[class*=-C167] { + color: #D75F5F +} + +div.highlight .-Color[class*=-BGC167] { + background-color: #D75F5F +} + +div.highlight .-Color[class*=-C168] { + color: #D75F87 +} + +div.highlight .-Color[class*=-BGC168] { + background-color: #D75F87 +} + +div.highlight .-Color[class*=-C169] { + color: #D75FAF +} + +div.highlight .-Color[class*=-BGC169] { + 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.highlight .nd { color: var(--syntax-light-name-decorator) } +html.light .highlight .ni { color: var(--syntax-light-name-entity) } +html.light .highlight .ne { color: var(--syntax-light-name-exception) } +html.light .highlight .nf { color: var(--syntax-light-name-function) } +html.light .highlight .nl { color: var(--syntax-light-name-label) } +html.light .highlight .py { color: var(--syntax-light-name-property) } +html.light .highlight .nt { color: var(--syntax-light-name-tag) } +html.light .highlight .ow { color: var(--syntax-light-operator-word) } +html.light .highlight .w { color: var(--syntax-light-text-whitespace) } +html.light .highlight .mb { color: var(--syntax-light-literal-number-bin) } +html.light .highlight .mf { color: var(--syntax-light-literal-number-float) } +html.light .highlight .mh { color: var(--syntax-light-literal-number-hex) } +html.light .highlight .mi { color: var(--syntax-light-literal-number-integer) } +html.light .highlight .mo { color: var(--syntax-light-literal-number-oct) } +html.light .highlight .sa { color: var(--syntax-light-literal-string-affix) } +html.light .highlight .sb { color: var(--syntax-light-literal-string-backtick) } +html.light .highlight .sc { color: var(--syntax-light-literal-string-char) } +html.light .highlight .dl { color: var(--syntax-light-literal-string-delimiter) } +html.light .highlight .sd { color: var(--syntax-light-literal-string-doc) } +html.light .highlight .s2 { color: var(--syntax-light-literal-string-double) } +html.light .highlight .se { color: var(--syntax-light-literal-string-escape) } +html.light .highlight .sh { color: var(--syntax-light-literal-string-heredoc) } +html.light .highlight .si { color: var(--syntax-light-literal-string-interpol) } +html.light .highlight .sx { color: var(--syntax-light-literal-string-other) } +html.light .highlight .sr { color: var(--syntax-light-literal-string-regex) } +html.light .highlight .s1 { color: var(--syntax-light-literal-string-single) } +html.light .highlight .ss { color: var(--syntax-light-literal-string-symbol) } +html.light .highlight .bp { color: var(--syntax-light-name-builtin-pseudo) } +html.light .highlight .fm { color: var(--syntax-light-name-function-magic) } +html.light .highlight .vc { color: var(--syntax-light-name-variable-class) } +html.light .highlight .il { color: var(--syntax-light-literal-number-integer-long) } +:root { +--syntax-dark-background: #0d1117; +--syntax-dark-highlight: #3fb95040; +--syntax-dark-text: #E6EDF3; +--syntax-dark-escape: #A5D6FF; +--syntax-dark-error: #FFA198; +--syntax-dark-keyword: #FF7B72; +--syntax-dark-keyword-constant: #79C0FF; +--syntax-dark-keyword-type: #FFA657; +--syntax-dark-name-attribute: #79C0FF; +--syntax-dark-name-builtin: #79C0FF; +--syntax-dark-name-class: #FFA657; +--syntax-dark-name-constant: #79C0FF; +--syntax-dark-name-decorator: #79C0FF; +--syntax-dark-name-entity: #79C0FF; +--syntax-dark-name-exception: #79C0FF; +--syntax-dark-name-function: #D2A8FF; +--syntax-dark-name-function-magic: #79C0FF; +--syntax-dark-name-property: #79C0FF; +--syntax-dark-name-label: #A5D6FF; +--syntax-dark-name-tag: #7EE787; +--syntax-dark-name-variable-class: #FFA657; +--syntax-dark-literal: #A5D6FF; +--syntax-dark-literal-string: #A5D6FF; +--syntax-dark-literal-string-affix: #FF7B72; +--syntax-dark-literal-string-backtick: #79C0FF; +--syntax-dark-literal-string-interpol: #FF7B72; +--syntax-dark-literal-string-regex: #A5D6FF; +--syntax-dark-literal-string-symbol: #79C0FF; +--syntax-dark-literal-number: #79C0FF; +--syntax-dark-operator: #FF7B72; +--syntax-dark-operator-word: #79C0FF; +--syntax-dark-comment: #8B949E; +--syntax-dark-comment-preproc: #FF7B72; +--syntax-dark-comment-preprocfile: #A5D6FF; +--syntax-dark-generic-deleted: #FFA198; +--syntax-dark-generic-emph: #E6EDF3; +--syntax-dark-generic-error: #FFA198; +--syntax-dark-generic-heading: #79C0FF; +--syntax-dark-generic-inserted: #7EE787; +--syntax-dark-generic-output: #A5D6FF; +--syntax-dark-generic-prompt: #FF7B72; +--syntax-dark-generic-strong: #E6EDF3; +--syntax-dark-generic-subheading: #79C0FF; +--syntax-dark-generic-emphstrong: #E6EDF3; +--syntax-dark-generic-traceback: #FFA198; +--syntax-dark-text-whitespace: #E6EDF3; +--syntax-dark-keyword-declaration: #FF7B72; +--syntax-dark-keyword-namespace: #FF7B72; +--syntax-dark-keyword-pseudo: #FF7B72; +--syntax-dark-keyword-reserved: #FF7B72; +--syntax-dark-name-builtin-pseudo: #79C0FF; +--syntax-dark-literal-date: #A5D6FF; +--syntax-dark-literal-string-char: #A5D6FF; +--syntax-dark-literal-string-delimiter: #A5D6FF; +--syntax-dark-literal-string-doc: #A5D6FF; +--syntax-dark-literal-string-double: #A5D6FF; +--syntax-dark-literal-string-escape: #A5D6FF; +--syntax-dark-literal-string-heredoc: #A5D6FF; +--syntax-dark-literal-string-other: #A5D6FF; +--syntax-dark-literal-string-single: #A5D6FF; +--syntax-dark-literal-number-bin: #79C0FF; +--syntax-dark-literal-number-float: #79C0FF; +--syntax-dark-literal-number-hex: #79C0FF; +--syntax-dark-literal-number-integer: #79C0FF; +--syntax-dark-literal-number-integer-long: #79C0FF; +--syntax-dark-literal-number-oct: #79C0FF; +--syntax-dark-comment-hashbang: #8B949E; +--syntax-dark-comment-multiline: #8B949E; +--syntax-dark-comment-single: #8B949E; +--syntax-dark-comment-special: #8B949E; +} +html.dark .highlight .c, html.light .dark-code .highlight .c { color: var(--syntax-dark-comment) } +html.dark .highlight .err, html.light .dark-code .highlight .err { color: var(--syntax-dark-error) } +html.dark .highlight .esc, html.light .dark-code .highlight .esc { color: var(--syntax-dark-escape) } +html.dark .highlight .k, html.light .dark-code .highlight .k { color: var(--syntax-dark-keyword) } +html.dark .highlight .l, html.light .dark-code .highlight .l { color: var(--syntax-dark-literal) } +html.dark .highlight .o, html.light .dark-code .highlight .o { color: var(--syntax-dark-operator) } +html.dark .highlight .ch, html.light .dark-code .highlight .ch { color: var(--syntax-dark-comment-hashbang) } +html.dark .highlight .cm, html.light .dark-code .highlight .cm { color: var(--syntax-dark-comment-multiline) } +html.dark .highlight .cp, html.light .dark-code .highlight .cp { color: var(--syntax-dark-comment-preproc) } +html.dark .highlight .cpf, html.light .dark-code .highlight .cpf { color: var(--syntax-dark-comment-preprocfile) } +html.dark .highlight .c1, html.light .dark-code .highlight .c1 { color: var(--syntax-dark-comment-single) } +html.dark .highlight .cs, html.light .dark-code .highlight .cs { color: var(--syntax-dark-comment-special) } +html.dark .highlight .gd, html.light .dark-code .highlight .gd { color: var(--syntax-dark-generic-deleted) } +html.dark .highlight .ge, html.light .dark-code .highlight .ge { color: var(--syntax-dark-generic-emph); font-style: italic } +html.dark .highlight .ges, html.light .dark-code .highlight .ges { color: var(--syntax-dark-generic-emphstrong); font-weight: bold; font-style: italic } +html.dark .highlight .gr, html.light .dark-code .highlight .gr { color: var(--syntax-dark-generic-error) } +html.dark .highlight .gh, html.light .dark-code .highlight .gh { color: var(--syntax-dark-generic-heading); font-weight: bold } +html.dark .highlight .gi, html.light .dark-code .highlight .gi { color: var(--syntax-dark-generic-inserted) } +html.dark .highlight .go, html.light .dark-code .highlight .go { color: var(--syntax-dark-generic-output) } +html.dark .highlight .gp, html.light .dark-code .highlight .gp { color: var(--syntax-dark-generic-prompt) } +html.dark .highlight .gs, html.light .dark-code .highlight .gs { color: var(--syntax-dark-generic-strong); font-weight: bold } +html.dark .highlight .gu, html.light .dark-code .highlight .gu { color: var(--syntax-dark-generic-subheading) } +html.dark .highlight .gt, html.light .dark-code .highlight .gt { color: var(--syntax-dark-generic-traceback) } +html.dark .highlight .kc, html.light .dark-code .highlight .kc { color: var(--syntax-dark-keyword-constant) } +html.dark .highlight .kd, html.light .dark-code .highlight .kd { color: var(--syntax-dark-keyword-declaration) } +html.dark .highlight .kn, html.light .dark-code .highlight .kn { color: var(--syntax-dark-keyword-namespace) } +html.dark .highlight .kp, html.light .dark-code .highlight .kp { color: var(--syntax-dark-keyword-pseudo) } +html.dark .highlight .kr, html.light .dark-code .highlight .kr { color: var(--syntax-dark-keyword-reserved) } +html.dark .highlight .kt, html.light .dark-code .highlight .kt { color: var(--syntax-dark-keyword-type) } +html.dark .highlight .ld, html.light .dark-code .highlight .ld { color: var(--syntax-dark-literal-date) } +html.dark .highlight .m, html.light .dark-code .highlight .m { color: var(--syntax-dark-literal-number) } +html.dark .highlight .s, html.light .dark-code .highlight .s { color: var(--syntax-dark-literal-string) } +html.dark .highlight .na, html.light .dark-code .highlight .na { color: var(--syntax-dark-name-attribute) } +html.dark .highlight .nb, html.light .dark-code .highlight .nb { color: var(--syntax-dark-name-builtin) } +html.dark .highlight .nc, html.light .dark-code .highlight .nc { color: var(--syntax-dark-name-class) } +html.dark .highlight .no, html.light .dark-code .highlight .no { color: var(--syntax-dark-name-constant) } +html.dark .highlight .nd, html.light .dark-code .highlight .nd { color: var(--syntax-dark-name-decorator) } +html.dark .highlight .ni, html.light .dark-code .highlight .ni { color: var(--syntax-dark-name-entity) } +html.dark .highlight .ne, html.light .dark-code .highlight .ne { color: var(--syntax-dark-name-exception) } +html.dark .highlight .nf, html.light .dark-code .highlight .nf { color: var(--syntax-dark-name-function) } +html.dark .highlight .nl, html.light .dark-code .highlight .nl { color: var(--syntax-dark-name-label) } +html.dark .highlight .py, html.light .dark-code .highlight .py { color: var(--syntax-dark-name-property) } +html.dark .highlight .nt, html.light .dark-code .highlight .nt { color: var(--syntax-dark-name-tag) } +html.dark .highlight .ow, html.light .dark-code .highlight .ow { color: var(--syntax-dark-operator-word) } +html.dark .highlight .w, html.light .dark-code .highlight .w { color: var(--syntax-dark-text-whitespace) } +html.dark .highlight .mb, html.light .dark-code .highlight .mb { color: var(--syntax-dark-literal-number-bin) } +html.dark .highlight .mf, html.light .dark-code .highlight .mf { color: var(--syntax-dark-literal-number-float) } +html.dark .highlight .mh, html.light .dark-code .highlight .mh { color: var(--syntax-dark-literal-number-hex) } +html.dark .highlight .mi, html.light .dark-code .highlight .mi { color: var(--syntax-dark-literal-number-integer) } +html.dark .highlight .mo, html.light .dark-code .highlight .mo { color: var(--syntax-dark-literal-number-oct) } +html.dark .highlight .sa, html.light .dark-code .highlight .sa { color: var(--syntax-dark-literal-string-affix) } +html.dark .highlight .sb, html.light .dark-code .highlight .sb { color: var(--syntax-dark-literal-string-backtick) } +html.dark .highlight .sc, html.light .dark-code .highlight .sc { color: var(--syntax-dark-literal-string-char) } +html.dark .highlight .dl, html.light .dark-code .highlight .dl { color: var(--syntax-dark-literal-string-delimiter) } +html.dark .highlight .sd, html.light .dark-code .highlight .sd { color: var(--syntax-dark-literal-string-doc) } +html.dark .highlight .s2, html.light .dark-code .highlight .s2 { color: var(--syntax-dark-literal-string-double) } +html.dark .highlight .se, html.light .dark-code .highlight .se { color: var(--syntax-dark-literal-string-escape) } +html.dark .highlight .sh, html.light .dark-code .highlight .sh { color: var(--syntax-dark-literal-string-heredoc) } +html.dark .highlight .si, html.light .dark-code .highlight .si { color: var(--syntax-dark-literal-string-interpol) } +html.dark .highlight .sx, html.light .dark-code .highlight .sx { color: var(--syntax-dark-literal-string-other) } +html.dark .highlight .sr, html.light .dark-code .highlight .sr { color: var(--syntax-dark-literal-string-regex) } +html.dark .highlight .s1, html.light .dark-code .highlight .s1 { color: var(--syntax-dark-literal-string-single) } +html.dark .highlight .ss, html.light .dark-code .highlight .ss { color: var(--syntax-dark-literal-string-symbol) } +html.dark .highlight .bp, html.light .dark-code .highlight .bp { color: var(--syntax-dark-name-builtin-pseudo) } +html.dark .highlight .fm, html.light .dark-code .highlight .fm { color: var(--syntax-dark-name-function-magic) } +html.dark .highlight .vc, html.light .dark-code .highlight .vc { color: var(--syntax-dark-name-variable-class) } +html.dark .highlight .il, html.light .dark-code .highlight .il { color: var(--syntax-dark-literal-number-integer-long) } diff --git a/tools/docs/_build/dirhtml/_static/searchtools.js b/tools/docs/_build/dirhtml/_static/searchtools.js new file mode 100644 index 0000000..2c774d1 --- /dev/null +++ b/tools/docs/_build/dirhtml/_static/searchtools.js @@ -0,0 +1,632 @@ +/* + * Sphinx JavaScript utilities for the full-text search. + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename, kind] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +// Global search result kind enum, used by themes to style search results. +class SearchResultKind { + static get index() { return "index"; } + static get object() { return "object"; } + static get text() { return "text"; } + static get title() { return "title"; } +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename, kind] = item; + + let listItem = document.createElement("li"); + // Add a class representing the item's type: + // can be used by a theme's CSS selector for styling + // See SearchResultKind for the class names. + listItem.classList.add(`kind-${kind}`); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = Documentation.ngettext( + "Search finished, found one page matching the search query.", + "Search finished, found ${resultCount} pages matching the search query.", + resultCount, + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename, kind]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlink", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.setAttribute("role", "list"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename, kind]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score + boost, + filenames[file], + SearchResultKind.title, + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + SearchResultKind.index, + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + SearchResultKind.object, + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + SearchResultKind.text, + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/tools/docs/_build/dirhtml/_static/shibuya.css b/tools/docs/_build/dirhtml/_static/shibuya.css new file mode 100644 index 0000000..859edaa --- /dev/null +++ b/tools/docs/_build/dirhtml/_static/shibuya.css @@ -0,0 +1,2 @@ +/*! tailwindcss v4.3.2 | MIT License | https://tailwindcss.com */ +@layer properties{@supports (((-webkit-hyphens:none)) and (not (margin-trim:inline))) or ((-moz-orient:inline) and (not (color:rgb(from red r g 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Bundled license information: + +iconify-icon/dist/iconify-icon.mjs: + (** + * (c) Iconify + * + * For the full copyright and license information, please view the license.txt + * files at https://github.com/iconify/iconify + * + * Licensed under MIT. + * + * @license MIT + * @version 3.0.2 + *) +*/ diff --git a/tools/docs/_build/dirhtml/_static/sphinx_highlight.js b/tools/docs/_build/dirhtml/_static/sphinx_highlight.js new file mode 100644 index 0000000..8a96c69 --- /dev/null +++ b/tools/docs/_build/dirhtml/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/tools/docs/_build/dirhtml/_static/tfmri_icon.svg b/tools/docs/_build/dirhtml/_static/tfmri_icon.svg new file mode 100644 index 0000000..1206fd3 --- /dev/null +++ b/tools/docs/_build/dirhtml/_static/tfmri_icon.svg @@ -0,0 +1,248 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tools/docs/_build/dirhtml/_static/tfmri_icon_128px.png b/tools/docs/_build/dirhtml/_static/tfmri_icon_128px.png new file mode 100644 index 0000000000000000000000000000000000000000..49145d3d40b52465549f4cd21c925ff89fecf70c GIT binary patch literal 7496 zcmV-O9k=3%P) z000L>0V+}a)Bpeg8FWQhbW?9;ba!ELWdL_~cP?peYja~^aAhuUa%Y?FJQ@H19Mwrg 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zNL}G~a*}#auI=2HH>(i9gUR0pwCRliO^+cWP@Gv618%^`-*ENhf=15MK53{BK;|xb zm!LVrecNBcxFw(@QlFungXO)D)CC!>{?B3~5$oMG(S)BO<#a{rSD!q1ku9v z?wq(03nExFeRZMZ(HYxJU^rWc*r65#i&~{~xbw`qh*+~pO~|Ze2Cn+#D&O!_1vyozT>EL5prs#X9GcxLob1cY=!rU(lsh&&oKjR1?T zN1!rH#6ADougGMVolRZ)X$AElPc9>g^@Y$kdC5eqY-boUF&@mGFG}cabVX=ShZ!#+ z!Hy-!ggqKrO4L{RWX)!dqC=l!^xY4smP#PRU;HW)f^v|i8&W^C<6`GT zVOLLUznd{HFvgMo;)qdZkiL55{FwAZCWe}TijBzCNdqvlLWM7c5UW)QrZ?8sj&Z@S zVkSbc`pVJIj8RogY;8=D!T%{CcTBjYa`mYXO{hOeF1njx$*xTF&!5nJm^ zpd!-|FywRj^=s8!#ULY!OrQ=-M?e$2v4DQ;2-L?iN(R6Pk#`z0TYdO|ax|-`&#Cd^ zf*P%G9p5yPGC()p+iZDptdHPvZ4C5 zE3VKLv|(h7V3s|Rh{V2)7tEE^T33tY1M7m7BTXF3^JK?OjchPM6`rYEn^BKCa=_*Cg|#+;SU|g(Fxw2`IUMMs|A0&#GAK zBuu7KgeUOEbp8D7lP`}YmGz?w!27( + + + + im2sim.configs.HalfUNetConfig - IM2SIM Documentation + + + + + + + + + + + + + + + +

+
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im2sim.configs.HalfUNetConfig#

+
+
+class HalfUNetConfig(hidden_channels=64, num_downsamples=4, pool_spec=<factory>, upsample_spec=<factory>, block_cfg=<factory>, blocks_per_level=2, out_activation=None, stem_block_cfg=None, encoder_block_cfg=None, out_block_cfg=None, fusion_type=ResidualConnectionType.ADD)[source]#
+

Bases: Config

+

Configuration class for defining the parameters of a half U-Net architecture (see im2sim.models.HalfUNet).

+

Attributes can either be set directly when creating an instance of the class or modified later.

+

Configuration presets can be applied to quickly set up common configurations for different use cases.

+

The configuration can also be saved to and loaded from a YAML file.

+
+
Parameters:
+
    +

  • hidden_channels (int) – Number of channels in the hidden layers. Default is 64.

    • +

  • num_downsamples (int) – Number of downsampling operations in the encoder. Default is 4.

    • +

  • pool_spec (LayerConfig | list[LayerConfig]) – Specification for the pooling layers. Default is a MaxPool layer with kernel size 2 for all levels.

    • +

  • upsample_spec (LayerConfig | list[LayerConfig]) – Specification for the upsampling layers. Default is an Upsample layer with scale factor 2 and mode ‘trilinear’ for all levels. +The mode is automatically changed to ‘bilinear’ for 2D data and ‘nearest’ for 1D.

    • +

  • block_cfg (ImageConvBlockConfig) – Configuration for the convolutional blocks. Default is a standard convolutional block with 2 layers, ReLU activation, and batch normalization.

    • +

  • blocks_per_level (int) – Number of convolutional blocks per level in the encoder. Default is 2.

    • +

  • out_activation (str | None) – Activation function for the output layer. Default is None, which means no activation is applied.

    • +

  • stem_block_cfg (ImageConvBlockConfig | None) – Configuration for the stem block. If None, it defaults to a single convolutional block with the same configuration as block_cfg.

    • +

  • encoder_block_cfg (list[ImageConvBlockConfig] | ImageConvBlockConfig | None) – Configuration for the encoder blocks. If None, it defaults to a list of block_cfg repeated for each downsampling level.

    • +

  • out_block_cfg (ImageConvBlockConfig | None) – Configuration for the output block. If None, it defaults to a single convolutional block with the same configuration as block_cfg and the specified out_activation.

    • +

  • fusion_type (ResidualConnectionType) – Type of residual connection to use in the network. It can be either ‘add’ (default), ‘concat’ or ‘average’. +This determines how the encoder features are fused.

    • +
+
+
+

Examples

+

To create a HalfUNet model for single class segmentation, you can do the following:

+
>>> cfg = HalfUNetConfig(num_downsamples=3, hidden_channels=64)
+>>> cfg = cfg.apply_presets(["single_class_segmentation", "residual", "SE", "ghost_depthwise_separable"])
+>>> model = HalfUNet.build(in_channles=20, out_channels=1, rank=3, cfg=cfg)
+
+
+

For more presets, see the Preset Library below.

+

Preset Library

+

residual

+

Apply a residual connection to all encoder blocks in the HalfUNet configuration.

+

The residual connection type is set to “add” for all encoder blocks, +which means that the output of each encoder block will be added to its input before being passed to the next layer. +This can help with gradient flow and improve training stability.

+

dilated_bottleneck

+

Apply dilated convolutions to the bottleneck (lowest resolution) block in the HalfUNet configuration.

+

This change modifies the last encoder block to use dilated convolutions, which can help increase the receptive field without increasing the number of parameters.

+

recon

+

Apply a reconstruction preset to all blocks in the HalfUNet configuration.

+

This preset is typically used for image reconstruction or superresolution tasks, +where the output is expected to be a continuous value (e.g., pixel intensity).

+

single_class_segmentation

+

Apply a single-class segmentation preset to all blocks in the HalfUNet configuration.

+

Use this preset for binary segmentation tasks, where the output is expected to be a probability map for a single class.

+

multiclass_segmentation

+

Apply a multi-class segmentation preset to all blocks in the HalfUNet configuration.

+

Use this preset for multi-class segmentation tasks, where the output is expected to be a probability map for multiple classes.

+

depthwise_separable

+

Apply a depthwise separable convolution (see im2sim.layers.DepthwiseSeparableConv) preset to all encoder blocks in the HalfUNet configuration.

+

ghost_depthwise

+

Apply a ghost depthwise convolution (see im2sim.layers.GhostConv) preset to all encoder blocks in the HalfUNet configuration.

+

ghost_depthwise_separable

+

Apply a ghost depthwise separable convolution (see im2sim.layers.GhostConv) preset to all encoder blocks in the HalfUNet configuration.

+

ECA

+

Apply an Efficient Channel Attention (ECA) (see im2sim.layers.EfficientChannelAttn) preset to all encoder blocks in the HalfUNet configuration.

+

SE

+

Apply a Squeeze-and-Excitation (SE) (see im2sim.layers.SqueezeExcite) preset to all encoder blocks in the HalfUNet configuration.

+
+ +
+ +
+ + +
+ Previous
im2sim.configs
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+ Next +
im2sim.configs.ImageConvBlockConfig
+
+ + +
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+
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2026 University College London

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/configs/ImageConvBlockConfig/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/configs/ImageConvBlockConfig/index.html new file mode 100644 index 0000000..a4462ab --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/configs/ImageConvBlockConfig/index.html @@ -0,0 +1,296 @@ + + + + + im2sim.configs.ImageConvBlockConfig - IM2SIM Documentation + + + + + + + + + + + + + + + +
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im2sim.configs.ImageConvBlockConfig#

+
+
+class ImageConvBlockConfig(depth=2, activation='ReLU', out_activation=None, conv_config=<factory>, norm_config=<factory>, dropout_config=<factory>, attn_config=<factory>, dropout_position=1, residual_connections=None, residual_type=ResidualConnectionType.ADD)[source]#
+

Bases: Config

+

Configuration class for defining the parameters of an image convolutional block.

+

Attributes can either be set directly when creating an instance of the class or modified later.

+

Configuration presets can be applied to quickly set up common configurations for different use cases.

+

The configuration can also be saved to and loaded from a YAML file.

+
+
Parameters:
+
    +

  • depth (int) – The number of convolutional layers in the block. Default is 2.

    • +

  • activation (str | None) – The activation function to use after each convolutional layer. Default is “ReLU”.

    • +

  • out_activation (str | None) – The activation function to use after the final layer. Default is None.

    • +

  • conv_config (LayerConfig) – Configuration for the convolutional layers, including kernel size and padding.

    • +

  • norm_config (LayerConfig) – Configuration for the normalization layers, such as InstanceNorm with affine set to True.

    • +

  • dropout_config (LayerConfig) – Configuration for the dropout layers. Default is no dropout.

    • +

  • attn_config (LayerConfig) – Configuration for the attention layers. Default is no attention.

    • +

  • dropout_position (int | list[int]) – Specifies the position(s) of the dropout layers within the block. Default is 1.

    • +

  • residual_connections (dict [int, list[ int ]] | None) – Specifies the residual connections within the block. +The keys represent the target layers, and the values are lists of source layers. Default is None. +Example: {1: [0]} means that the input to the block will be added to the output of layer 1.

    • +

  • residual_type (str) – The type of residual connection to use (e.g., “add”). Default is ResidualConnectionType.ADD.

    • +
+
+
+

Examples

+

To create a configuration for an image convolutional block with a depth of 3, ReLU activation, and softmax output activation, you can use the following code:

+
>>> cfg = ImageConvBlockConfig(depth=3, activation="ReLU", out_activation="softmax")
+
+
+

To apply a preset configuration for a single convolutional layer without normalization or dropout, you can use:

+
>>> cfg = ImageConvBlockConfig.apply_presets(cfg, ["single_conv"])
+
+
+

To save the configuration to a YAML file and load it back, you can use:

+
>>> cfg.save("config.yaml")
+>>> loaded_cfg = ImageConvBlockConfig().load("config.yaml")
+
+
+

Preset Library

+

single_conv

+

Converts the blcok into a single convolutional layer with no normalization, dropout, or residual connections.

+

single_block

+

Sets the block depth to 1 and removes dropout and residual connections, but keeps normalization and activation.

+

0_residual

+

Configures the block to have a residual connection from the input to the output of the last layer.

+

1_residual

+

Configures the block to have a residual connection from the output of the first layer to the output of the last layer.

+

concat_residual

+

Configures the block to have a residual connection from the input to the output of the last layer, using concatenation instead of addition.

+

recon

+

Configures the block for reconstruction tasks by removing normalization and dropout layers.

+

segmentation

+

Configures the block for segmentation tasks by using InstanceNorm with trainable parameters.

+

depthwise_separable

+

Configures the block to use depthwise separable convolutions (see im2sim.layers.DepthwiseSeparableConv) instead of standard convolutions.

+

ghost_depthwise

+

Configures the block to use Ghost convolutions instead of standard convolutions.

+

ghost_depthwise_separable

+

Configures the block to use Ghost depthwise separable convolutions instead of standard convolutions.

+

dilated_convs

+

Configures the block to use dilated convolutions with dilation of 2 instead of standard convolutions.

+

ECA

+

Configures the block to use Efficient Channel Attention (ECA)

+

SE

+

Configures the block to use Squeeze-and-Excitation (SE) attention

+
+ +
+ +
+ + +
+ Previous
im2sim.configs.HalfUNetConfig
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+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/configs/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/configs/index.html new file mode 100644 index 0000000..f00f574 --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/configs/index.html @@ -0,0 +1,249 @@ + + + + + im2sim.configs - IM2SIM Documentation + + + + + + + + + + + + + + + +
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im2sim.configs#

+

Configuration classes for model and training settings.

+
+

Classes#

+
+ + + + + + + + + +

HalfUNetConfig

Configuration class for defining the parameters of a half U-Net architecture (see im2sim.models.HalfUNet).

ImageConvBlockConfig

Configuration class for defining the parameters of an image convolutional block.

+
+
+
+

Functions#

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im2sim.data#

+

Data loading and preprocessing utilities.

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Classes#

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Functions#

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im2sim#

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IM2SIM.

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Modules#

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+ + + + + + + + + + + + + + + + + + + + + + + + +

configs

Configuration classes for model and training settings.

data

Data loading and preprocessing utilities.

layers

Custom layers for building deep learning models.

losses

Custom loss functions for training deep learning models.

models

Predefined deep learning models for various tasks.

ops

Custom operations for deep learning models.

plot

Utilities for visualizing data and model outputs.

+
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Classes#

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Functions#

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IM2SIM API documentation
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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/layers/ConditionedSqueezeExcite/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/ConditionedSqueezeExcite/index.html new file mode 100644 index 0000000..ee4a65b --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/ConditionedSqueezeExcite/index.html @@ -0,0 +1,279 @@ + + + + + im2sim.layers.ConditionedSqueezeExcite - IM2SIM Documentation + + + + + + + + + + + + + + + +
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+

im2sim.layers.ConditionedSqueezeExcite#

+
+
+class ConditionedSqueezeExcite(channels, rank, n_cond=6, reduction=8, mode='add')[source]#
+

Bases: Module

+

Conditioned Squeeze-and-Excitation [1] (SE) layer that adaptively recalibrates channel-wise feature responses based on an additional conditioning input.

+

This operation is useful for improving the representational power of convolutional neural networks by explicitly modeling interdependencies between channels, +while also allowing for external conditioning information to influence the recalibration process.

+
+
Parameters:
+
    +

  • channels (int) – Number of input channels.

    • +

  • rank (int) – The rank of the input tensor (1 for 1D, 2 for 2D, 3 for 3D).

    • +

  • n_cond (int) – Number of conditioning channels. Default is 6.

    • +

  • reduction (int) – Reduction ratio for the hidden layer in the SE block. Default is 8.

    • +

  • mode (str) – Mode of combining the feature and conditioning information. Can be “concat” or “add”. Default is “add”.

    • +
+
+
+

References

+ +

Initialize internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(x, cond)[source]#
+

Forward pass of the Conditioned Squeeze-and-Excitation layer.

+
+
Parameters:
+
    +

  • x (Tensor) – Input tensor of shape (batch_size, channels, *spatial_dims).

    • +

  • cond (Tensor) – Conditioning tensor of shape (batch_size, n_cond).

    • +
+
+
Returns:
+

Output tensor of shape (batch_size, channels, *spatial_dims) with channel-wise attention applied based on the conditioning input.

+
+
Return type:
+

Tensor

+
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im2sim.layers
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im2sim.layers.DefaultGraphNorm
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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/layers/DefaultGraphNorm/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/DefaultGraphNorm/index.html new file mode 100644 index 0000000..8b622d8 --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/DefaultGraphNorm/index.html @@ -0,0 +1,264 @@ + + + + + im2sim.layers.DefaultGraphNorm - IM2SIM Documentation + + + + + + + + + + + + + + + +
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+

im2sim.layers.DefaultGraphNorm#

+
+
+class DefaultGraphNorm[source]#
+

Bases: Module

+

The default normalisation for im2sim graph blocks. +Uses torch.nn.InstanceNorm2d applied to graph data, but all channels are normalised together.

+
+
Parameters:
+

None

+
+
+

Initialize internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(x, batch)[source]#
+
+
Parameters:
+
    +

  • x (torch.Tensor) – The input tensor of shape (N, C) where N is the number of nodes and C is the number of channels.

    • +

  • batch – The batch tensor of shape (N,) indicating the batch index for each node.

    • +
+
+
Returns:
+

The normalized tensor of shape (N, C).

+
+
Return type:
+

torch.Tensor

+

+
+
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+ +
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+ + +
+ Previous
im2sim.layers.ConditionedSqueezeExcite
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+ Next +
im2sim.layers.DepthwiseConv
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2026 University College London

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+ Made with + + Sphinx and + + Shibuya theme. +

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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/layers/DepthwiseConv/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/DepthwiseConv/index.html new file mode 100644 index 0000000..ee9c7eb --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/DepthwiseConv/index.html @@ -0,0 +1,278 @@ + + + + + im2sim.layers.DepthwiseConv - IM2SIM Documentation + + + + + + + + + + + + + + + +
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+
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+

im2sim.layers.DepthwiseConv#

+
+
+class DepthwiseConv(in_channels, out_channels, rank, kernel_size=3, stride=1, padding='same', dilation=1, bias=True)[source]#
+

Bases: Module

+

Depthwise convolution layer that applies a separate convolutional filter to each input channel.

+

This operation is useful for reducing the number of parameters and computational cost in convolutional neural networks, especially in mobile and embedded applications[1].

+
+
Parameters:
+
    +

  • in_channels (int) – Number of input channels.

    • +

  • out_channels (int) – Number of output channels (should be equal to in_channels for depthwise convolution).

    • +

  • rank (int) – The rank of the convolution (1 for 1D, 2 for 2D, 3 for 3D).

    • +

  • kernel_size (int | tuple) – Size of the convolving kernel. Default is 3.

    • +

  • stride (int | tuple) – Stride of the convolution. Default is 1.

    • +

  • padding (str or int | tuple) – Padding added to all four sides of the input. Default is “same”.

    • +

  • dilation (int | tuple) – Spacing between kernel elements. Default is 1.

    • +

  • bias (bool) – If True, adds a learnable bias to the output. Default is True.

    • +
+
+
+

References

+ +

Initialize internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(x)[source]#
+

Forward pass of the depthwise convolution layer.

+
+
Parameters:
+

x (Tensor) – Input tensor of shape (batch_size, in_channels, *spatial_dims).

+
+
Returns:
+

Output tensor of shape (batch_size, out_channels, *spatial_dims).

+
+
Return type:
+

Tensor

+
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+ +
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+ Previous
im2sim.layers.DefaultGraphNorm
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im2sim.layers.DepthwiseSeparableConv
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2026 University College London

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+ Made with + + Sphinx and + + Shibuya theme. +

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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/layers/DepthwiseSeparableConv/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/DepthwiseSeparableConv/index.html new file mode 100644 index 0000000..c04d9ca --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/DepthwiseSeparableConv/index.html @@ -0,0 +1,280 @@ + + + + + im2sim.layers.DepthwiseSeparableConv - IM2SIM Documentation + + + + + + + + + + + + + + + +
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+
+ + +
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+
+

im2sim.layers.DepthwiseSeparableConv#

+
+
+class DepthwiseSeparableConv(in_channels, out_channels, rank, kernel_size=3, stride=1, padding='same', dilation=1, bias=True, activation=None)[source]#
+

Bases: Module

+

Depthwise separable convolution layer that consists of a depthwise convolution followed by a pointwise convolution.

+

This operation is useful for reducing the number of parameters and computational cost in convolutional neural networks, +while retaining more representational power compared to standard depthwise convolution[1].

+
+
Parameters:
+
    +

  • in_channels (int) – Number of input channels.

    • +

  • out_channels (int) – Number of output channels.

    • +

  • rank (int) – The rank of the convolution (1 for 1D, 2 for 2D, 3 for 3D).

    • +

  • kernel_size (int | tuple) – Size of the convolving kernel. Default is 3.

    • +

  • stride (int | tuple) – Stride of the convolution. Default is 1.

    • +

  • padding (str or int | tuple) – Padding added to all four sides of the input. Default is “same”.

    • +

  • dilation (int | tuple) – Spacing between kernel elements. Default is 1.

    • +

  • bias (bool) – If True, adds a learnable bias to the output. Default is True.

    • +

  • activation (str or None) – Activation function to apply after the pointwise convolution. Default is None.

    • +
+
+
+

References

+ +

Initialize internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(x)[source]#
+

Forward pass of the depthwise separable convolution layer.

+
+
Parameters:
+

x (Tensor) – Input tensor of shape (batch_size, in_channels, *spatial_dims).

+
+
Returns:
+

Output tensor of shape (batch_size, out_channels, *spatial_dims).

+
+
Return type:
+

Tensor

+
+
+
+ +
+ +
+ +
+ + +
+ Previous
im2sim.layers.DepthwiseConv
+ +
+ +
+ Next +
im2sim.layers.EfficientChannelAttn
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2026 University College London

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
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+
+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/layers/EfficientChannelAttn/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/EfficientChannelAttn/index.html new file mode 100644 index 0000000..cb7567b --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/EfficientChannelAttn/index.html @@ -0,0 +1,272 @@ + + + + + im2sim.layers.EfficientChannelAttn - IM2SIM Documentation + + + + + + + + + + + + + + + +
+
+
+ + + + im2sim + +
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+ + + + +
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+
+ + +
+ +
+
+

im2sim.layers.EfficientChannelAttn#

+
+
+class EfficientChannelAttn(channels, rank)[source]#
+

Bases: Module

+

Efficient Channel Attention (ECA) layer that adaptively selects important channels based on global context.

+

This operation is useful for improving the representational power of convolutional neural networks by focusing on the most informative channels[1].

+
+
Parameters:
+
    +

  • channels (int) – Number of input channels.

    • +

  • rank (int) – The rank of the input tensor (1 for 1D, 2 for 2D, 3 for 3D).

    • +
+
+
+

References

+ +

Initialize internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(x)[source]#
+

Forward pass of the Efficient Channel Attention layer.

+
+
Parameters:
+

x (Tensor) – Input tensor of shape (batch_size, channels, *spatial_dims).

+
+
Returns:
+

Output tensor of shape (batch_size, channels, *spatial_dims) with channel-wise attention applied.

+
+
Return type:
+

Tensor

+
+
+
+ +
+ +
+ +
+ + +
+ Previous
im2sim.layers.DepthwiseSeparableConv
+ +
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+ Next +
im2sim.layers.GhostConv
+
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2026 University College London

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/layers/GhostConv/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/GhostConv/index.html new file mode 100644 index 0000000..a8f9e59 --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/GhostConv/index.html @@ -0,0 +1,286 @@ + + + + + im2sim.layers.GhostConv - IM2SIM Documentation + + + + + + + + + + + + + + + +
+
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+ + + + im2sim + +
+ +
+ + / +
+
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+
+
+
+
+ + + + +
+
+
+
+
+ + +
+ +
+
+

im2sim.layers.GhostConv#

+
+
+class GhostConv(in_channels, out_channels, rank, kernel_size=3, ratio=2, dw_kernel_size=3, stride=1, padding='same', separable=False, bias=True)[source]#
+

Bases: Module

+

Ghost convolution layer that generates more feature maps from cheap operations.

+

This operation is useful for reducing the number of parameters and computational cost in convolutional neural networks. +The cheap operation can either be a depthwise convolution as per the original GhostNet paper[1] or a depthwise separable convolution as in the HalfUNet paper[2

+
+
Parameters:
+
    +

  • in_channels (int) – Number of input channels.

    • +

  • out_channels (int) – Number of output channels.

    • +

  • rank (int) – The rank of the convolution (1 for 1D, 2 for 2D, 3 for 3D).

    • +

  • kernel_size (int | tuple) – Size of the convolving kernel for the primary convolution. Default is 3.

    • +

  • ratio (int) – Ratio of the number of output channels to the number of primary convolution channels. Default is 2.

    • +

  • dw_kernel_size (int | tuple) – Size of the convolving kernel for the cheap operation. Default is 3.

    • +

  • stride (int | tuple) – Stride of the primary convolution. Default is 1.

    • +

  • padding (str or int | tuple) – Padding added to all four sides of the input for the primary convolution. Default is “same”.

    • +

  • separable (bool) – If True, uses depthwise separable convolution for the cheap operation. Default is False.

    • +

  • bias (bool) – If True, adds a learnable bias to the output. Default is True.

    • +
+
+
+

References

+ +

Initialize internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(x)[source]#
+

Forward pass of the Ghost convolution layer.

+
+
Parameters:
+

x (Tensor) – Input tensor of shape (batch_size, in_channels, *spatial_dims).

+
+
Returns:
+

Output tensor of shape (batch_size, out_channels, *spatial_dims).

+
+
Return type:
+

Tensor

+
+
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+ +
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+ + +
+ Previous
im2sim.layers.EfficientChannelAttn
+ +
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+ Next +
im2sim.layers.GraphConvBlock
+
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2026 University College London

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
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+
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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/layers/GraphConvBlock/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/GraphConvBlock/index.html new file mode 100644 index 0000000..5d7f6ca --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/GraphConvBlock/index.html @@ -0,0 +1,268 @@ + + + + + im2sim.layers.GraphConvBlock - IM2SIM Documentation + + + + + + + + + + + + + + + +
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+
+
+ + +
+ +
+
+

im2sim.layers.GraphConvBlock#

+
+
+class GraphConvBlock(in_channels, filters, depth=1, conv_type='GATConv', conv_kwargs=None, activation='ReLU', norm_type='defaultnorm', norm_kwargs=None)[source]#
+

Bases: Module

+

A convolutional block for graph data

+
+
Parameters:
+
    +

  • in_channels (int) – The number of channels in the input to the layer.

    • +

  • filters (int, optional) – The number of filters in each convolutional layer (default: 32)

    • +

  • depth (int, optional) – The number of successive convolutional layers (default: 2)

    • +

  • conv_type (str, optional) – The type of graph convolution to apply (default: “GATConv”, options: All PyG Convs)

    • +

  • conv_kwargs (dict, optional) – Dictionary of keyword arguments for the chosen conv_type

    • +

  • activation (str, optional) – The activation function applied after each convolution (default: “relu”, options: All torch activations)

    • +

  • norm_type (str, optional) – The normalization method to apply between convolutions (default:”defaultnorm”, options: All PyG Norms)

    • +
+
+
Returns:
+

A torch.nn.Module object.

+
+
+

Initialize internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(in_graph)[source]#
+

Define the computation performed at every call.

+

Should be overridden by all subclasses.

+
+

Note

+

Although the recipe for forward pass needs to be defined within +this function, one should call the :class:Module instance afterwards +instead of this since the former takes care of running the +registered hooks while the latter silently ignores them.

+
+
+ +
+ +
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+ + +
+ Previous
im2sim.layers.GhostConv
+ +
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+ Next +
im2sim.layers.GraphConvResBlock
+
+ + +
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+
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2026 University College London

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/layers/GraphConvResBlock/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/GraphConvResBlock/index.html new file mode 100644 index 0000000..a8d5d32 --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/GraphConvResBlock/index.html @@ -0,0 +1,268 @@ + + + + + im2sim.layers.GraphConvResBlock - IM2SIM Documentation + + + + + + + + + + + + + + + +
+
+
+ + + + im2sim + +
+ +
+ + / +
+
+
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+
+
+
+ + + + +
+
+
+
+
+ + +
+ +
+
+

im2sim.layers.GraphConvResBlock#

+
+
+class GraphConvResBlock(in_channels, filters, depth=3, conv_type='GATConv', conv_kwargs=None, activation='ReLU', norm_type='defaultnorm', norm_kwargs=None)[source]#
+

Bases: Module

+

A convolutional block for graph data

+
+
Parameters:
+
    +

  • in_channels (int) – The number of channels in the input to the layer.

    • +

  • filters (int, optional) – The number of filters in each convolutional layer (default: 32)

    • +

  • depth (int, optional) – The number of successive convolutional layers (default: 2)

    • +

  • conv_type (str, optional) – The type of graph convolution to apply (default: “ChebConv”, options: All PyG Convs)

    • +

  • conv_kwargs (dict, optional) – Dictionary of keyword arguments for the chosen conv_type

    • +

  • activation (str, optional) – The activation function applied after each convolution (default: “relu”, options: All torch activations)

    • +

  • norm_type (str, optional) – The normalization method to apply between convolutions (default:”InstanceNorm”, options: All PyG Norms)

    • +
+
+
Returns:
+

A torch.nn.Module object.

+
+
+

Initialize internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(in_graph)[source]#
+

Define the computation performed at every call.

+

Should be overridden by all subclasses.

+
+

Note

+

Although the recipe for forward pass needs to be defined within +this function, one should call the :class:Module instance afterwards +instead of this since the former takes care of running the +registered hooks while the latter silently ignores them.

+
+
+ +
+ +
+ +
+ + +
+ Previous
im2sim.layers.GraphConvBlock
+ +
+ +
+ Next +
im2sim.layers.GraphResDecoderBlock
+
+ + +
+
+
+
+
+
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+

2026 University College London

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/layers/GraphResDecoderBlock/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/GraphResDecoderBlock/index.html new file mode 100644 index 0000000..908f673 --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/GraphResDecoderBlock/index.html @@ -0,0 +1,273 @@ + + + + + im2sim.layers.GraphResDecoderBlock - IM2SIM Documentation + + + + + + + + + + + + + + + +
+
+
+ + + + im2sim + +
+ +
+ + / +
+
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+
+
+
+ + + + +
+
+
+
+
+ + +
+ +
+
+

im2sim.layers.GraphResDecoderBlock#

+
+
+class GraphResDecoderBlock(projection_channels, graph_channels, out_channels, filters, res_depth=3, n_deform_blocks=3, template_edge_index=None, conv_type='GATConv', conv_kwargs=None, activation='relu', out_activation='linear', norm_type='defaultnorm')[source]#
+

Bases: Module

+

A graph convolutional decoder block with the same structure as MeshDeformNet and Image2Flow

+
+
Parameters:
+
    +

  • encoder_channels (List[int]) – The number of channels projected from the encoder to each decoder level (len=n_decoder_levels)

    • +

  • out_channels (int) – The number of output channels including node coordinates and features

    • +

  • filters (List(List(int)), optional) – The number of convolutional filters for each level (default:[[384,288], [144,96], [64,32]])

    • +

  • res_block_depth (int, optional) – The number of successive convolutions in each residual block (default: 3)

    • +

  • n_process_blocks (int, optional) – The number of residual blocks prior to projection(default: 1)

    • +

  • n_deform_blocks (int, optional) – The number of residual blocks after projection(default: 3)

    • +

  • template_edge_index (Tensor, optional) – If template tensor is the fixed it can be passed (default: None)

    • +

  • conv_type (str, optional) – The type of graph convolution to apply (default: “ChebConv”, options: All PyG Convs)

    • +

  • conv_kwargs (dict, optional) – Dictionary of keyword arguments for the chosen conv_type

    • +

  • activation (str, optional) – The activation function applied after each convolution (default: “relu”, options: All torch activations)

    • +

  • out_activation (str, optional) – The activation function applied after each convolution (default: “linear”, options: All torch activations)

    • +

  • norm_type (str, optional) – The normalization method to apply between convolutions (default:”InstanceNorm”, options: All PyG Norms)

    • +
+
+
Returns:
+

A torch.nn.Module object.

+
+
+

Initialize internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(in_graph, prev_results, encoder_projection)[source]#
+

Define the computation performed at every call.

+

Should be overridden by all subclasses.

+
+

Note

+

Although the recipe for forward pass needs to be defined within +this function, one should call the :class:Module instance afterwards +instead of this since the former takes care of running the +registered hooks while the latter silently ignores them.

+
+
+ +
+ +
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2026 University College London

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/layers/ImageConvBlock/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/ImageConvBlock/index.html new file mode 100644 index 0000000..dbce75f --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/ImageConvBlock/index.html @@ -0,0 +1,318 @@ + + + + + im2sim.layers.ImageConvBlock - IM2SIM Documentation + + + + + + + + + + + + + + + +
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+
+

im2sim.layers.ImageConvBlock#

+
+
+class ImageConvBlock(in_channels, out_channels, rank, depth=2, activation=None, out_activation=None, conv_config=None, norm_config=None, attn_config=None, dropout_config=None, dropout_position=1, residual_connections=None, residual_type=ResidualConnectionType.ADD)[source]#
+

Bases: Module, ConfigurableModule

+

A configurable image convolutional block that consists of a sequence of +convolutional layers, normalization layers, dropout layers, and attention +layers. The block supports residual connections and allows for flexible +configuration of its components.

+

It is best used by creating a configuration object of type +:class:ImageConvBlockConfig and then calling the build method to create +an instance of the block.

+

Args:

+
+
in_channelsint

Number of input channels.

+
+
out_channelsint

Number of output channels.

+
+
rankint

The rank of the convolutional layers (e.g., 2 for 2D convolutions).

+
+
depthint, default=2

The number of convolutional layers in the block.

+
+
activationstr | None, default=None

Activation function applied after each convolutional layer.

+
+
out_activationstr | None, default=None

Activation function applied after the final layer.

+
+
conv_configLayerConfig | None, default=None

Configuration for convolutional layers. If None, a default configuration is used.

+
+
norm_configLayerConfig | None, default=None

Configuration for normalization layers. If None, a default configuration is used.

+
+
attn_configLayerConfig | None, default=None

Configuration for attention layers. If None, a default configuration is used.

+
+
dropout_configLayerConfig | None, default=None

Configuration for dropout layers. If None, a default configuration is used.

+
+
dropout_positionint | list[int], default=1

Position(s) of dropout layers within the block.

+
+
residual_connectionsdict[int, list[int]] | None, default=None

Specifies residual connections within the block. Keys represent target layers, +and values are lists of source layers.(e.g. {1: [0]} adds the block input to the output of layer 1.)

+
+
residual_typestr, default=ResidualConnectionType.ADD

Type of residual connection to use (e.g., “add”).

+
+
+

Example:

+

To create an ImageConvBlock with a depth of 3, ReLU activation, and softmax output activation, you can use the following code:

+
>>> cfg = ImageConvBlockConfig(depth=3, activation="ReLU", out_activation="softmax")
+>>> model = ImageConvBlock.build(
+>>>        rank=2,
+>>>        in_channels=32,
+>>>        out_channels=32,
+>>>        cfg=cfg,
+>>>    )
+
+
+

Initialize internal Module state, shared by both nn.Module and ScriptModule.

+
+
Parameters:
+
    +

  • in_channels (int)

    • +

  • out_channels (int)

    • +

  • rank (int)

    • +

  • depth (int)

    • +

  • activation (str | None)

    • +

  • out_activation (str | None)

    • +

  • conv_config (LayerConfig | None)

    • +

  • norm_config (LayerConfig | None)

    • +

  • attn_config (LayerConfig | None)

    • +

  • dropout_config (LayerConfig | None)

    • +

  • dropout_position (int | list[int])

    • +

  • residual_connections (dict[int, list[int]])

    • +

  • residual_type (str)

    • +
+
+
+
+
+forward(x)[source]#
+

Define the computation performed at every call.

+

Should be overridden by all subclasses.

+
+

Note

+

Although the recipe for forward pass needs to be defined within +this function, one should call the :class:Module instance afterwards +instead of this since the former takes care of running the +registered hooks while the latter silently ignores them.

+
+
+ +
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+ Made with + + Sphinx and + + Shibuya theme. +

+
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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/layers/SqueezeExcite/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/SqueezeExcite/index.html new file mode 100644 index 0000000..25f36b5 --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/SqueezeExcite/index.html @@ -0,0 +1,273 @@ + + + + + im2sim.layers.SqueezeExcite - IM2SIM Documentation + + + + + + + + + + + + + + + +
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im2sim.layers.SqueezeExcite#

+
+
+class SqueezeExcite(channels, rank, reduction=8)[source]#
+

Bases: Module

+

Squeeze-and-Excitation (SE) layer that adaptively recalibrates channel-wise feature responses.

+

This operation is useful for improving the representational power of convolutional neural networks by explicitly modeling interdependencies between channels[1].

+
+
Parameters:
+
    +

  • channels (int) – Number of input channels.

    • +

  • rank (int) – The rank of the input tensor (1 for 1D, 2 for 2D, 3 for 3D).

    • +

  • reduction (int) – Reduction ratio for the hidden layer in the SE block. Default is 8.

    • +
+
+
+

References

+ +

Initialize internal Module state, shared by both nn.Module and ScriptModule.

+
+
+forward(x)[source]#
+

Forward pass of the Efficient Channel Attention layer.

+
+
Parameters:
+

x (Tensor) – Input tensor of shape (batch_size, channels, *spatial_dims).

+
+
Returns:
+

Output tensor of shape (batch_size, channels, *spatial_dims) with channel-wise attention applied.

+
+
Return type:
+

Tensor

+
+
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+ Previous
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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/layers/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/index.html new file mode 100644 index 0000000..8305fe3 --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/layers/index.html @@ -0,0 +1,276 @@ + + + + + im2sim.layers - IM2SIM Documentation + + + + + + + + + + + + + + + +
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+ + +
+ +
+
+

im2sim.layers#

+

Custom layers for building deep learning models.

+
+

Classes#

+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

ConditionedSqueezeExcite

Conditioned Squeeze-and-Excitation [1] (SE) layer that adaptively recalibrates channel-wise feature responses based on an additional conditioning input.

DefaultGraphNorm

The default normalisation for im2sim graph blocks.

DepthwiseConv

Depthwise convolution layer that applies a separate convolutional filter to each input channel.

DepthwiseSeparableConv

Depthwise separable convolution layer that consists of a depthwise convolution followed by a pointwise convolution.

EfficientChannelAttn

Efficient Channel Attention (ECA) layer that adaptively selects important channels based on global context.

GhostConv

Ghost convolution layer that generates more feature maps from cheap operations.

GraphConvBlock

A convolutional block for graph data

GraphConvResBlock

A convolutional block for graph data

GraphResDecoderBlock

A graph convolutional decoder block with the same structure as MeshDeformNet and Image2Flow

ImageConvBlock

A configurable image convolutional block that consists of a sequence of convolutional layers, normalization layers, dropout layers, and attention layers.

SqueezeExcite

Squeeze-and-Excitation (SE) layer that adaptively recalibrates channel-wise feature responses.

+
+
+
+

Functions#

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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/losses/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/losses/index.html new file mode 100644 index 0000000..75d7178 --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/losses/index.html @@ -0,0 +1,243 @@ + + + + + im2sim.losses - IM2SIM Documentation + + + + + + + + + + + + + + + +
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+

im2sim.losses#

+

Custom loss functions for training deep learning models.

+
+

Classes#

+
+ + + +
+
+
+
+

Functions#

+
+ + + +
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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/api_docs/im2sim/models/HalfUNet/index.html b/tools/docs/_build/dirhtml/api_docs/im2sim/models/HalfUNet/index.html new file mode 100644 index 0000000..3cafe62 --- /dev/null +++ b/tools/docs/_build/dirhtml/api_docs/im2sim/models/HalfUNet/index.html @@ -0,0 +1,276 @@ + + + + + im2sim.models.HalfUNet - IM2SIM Documentation + + + + + + + + + + + + + + + +
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+

im2sim.models.HalfUNet#

+
+
+class HalfUNet(in_channels, out_channels, rank, hidden_channels=64, num_downsamples=4, pool_spec=None, upsample_spec=None, block_cfg=None, blocks_per_level=2, out_activation=None, stem_block_cfg=None, encoder_block_cfg=None, out_block_cfg=None, fusion_type=ResidualConnectionType.ADD)[source]#
+

Bases: Module, ConfigurableModule

+

A Half-UNet[1] architecture for image processing tasks.

+
+
Parameters:
+
    +

  • in_channels (int) – Number of input channels.

    • +

  • out_channels (int) – Number of output channels.

    • +

  • rank (int) – Dimensionality of the input data (1 for 1D, 2 for 2D, 3 for 3D).

    • +

  • hidden_channels (int) – Number of channels in the hidden layers. Default is 64.

    • +

  • num_downsamples (int) – Number of downsampling operations in the encoder. Default is 4.

    • +

  • pool_spec (LayerConfig | list[LayerConfig]) – Specification for the pooling layers. Default is a MaxPool layer with kernel size 2 for all levels.

    • +

  • upsample_spec (LayerConfig | list[LayerConfig]) – Specification for the upsampling layers. Default is an Upsample layer with scale factor 2 and mode ‘trilinear’ for all levels.

    • +

  • block_cfg (ImageConvBlockConfig) – Configuration for the convolutional blocks. Default is a standard convolutional block with 2 layers, ReLU activation, and batch normalization.

    • +

  • blocks_per_level (int) – Number of convolutional blocks per level in the encoder. Default is 2.

    • +

  • out_activation (str | None) – Activation function for the output layer. Default is None, which means no activation is applied.

    • +

  • stem_block_cfg (ImageConvBlockConfig | None) – Configuration for the stem block. If None, it defaults to a single convolutional block with the same configuration as block_cfg.

    • +

  • encoder_block_cfg (list[ImageConvBlockConfig] | ImageConvBlockConfig | None) – Configuration for the encoder blocks. If None, it defaults to a list of block_cfg repeated for each downsampling level.

    • +

  • out_block_cfg (ImageConvBlockConfig | None) – Configuration for the output block. If None, it defaults to a single convolutional block with the same configuration as block_cfg and the specified out_activation.

    • +

  • fusion_type (ResidualConnectionType) – Type of residual connection to use in the network. It can be either ‘add’ (default), ‘concat’ or ‘average’. This determines how the encoder features are fused.

    • +
+
+
+

The best way to build a HalfUNet is to use the im2sim.configs.HalfUNetConfig class to define the configuration and then call the build method.

+

Example:

+

To create a HalfUNet model for single class segmentation, you can do the following:

+
>>> cfg = HalfUNetConfig(num_downsamples=3, hidden_channels=64)
+>>> cfg = cfg.apply_presets(["single_class_segmentation", "residual", "SE", "ghost_depthwise_separable"])
+>>> model = HalfUNet.build(in_channles=20, out_channels=1, rank=3, cfg=cfg)
+
+
+

References: +.. [1] H. Lu, Y. She, J. Tie, and S. Xu, Half-UNet: A Simplified U-Net Architecture for Medical Image Segmentation,

+
+

Front. Neuroinformatics, vol. 16, Jun. 2022, doi: 10.3389/fninf.2022.911679.

+
+
+
+forward(x)[source]#
+

Forward pass of the HalfUNet model.

+
+ +
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+ + +
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+ +
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+

im2sim.models#

+

Predefined deep learning models for various tasks.

+
+

Classes#

+
+ + + + + + +

HalfUNet

A Half-UNet[1] architecture for image processing tasks.

+
+
+
+

Functions#

+
+ + + +
+
+
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+ +
+ + +
+ Previous
im2sim.losses
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im2sim.ops#

+

Custom operations for deep learning models.

+
+

Classes#

+
+ + + +
+
+
+
+

Functions#

+
+ + + + + + +

normtorange

Normalizes the input tensor x to a specified range [a, b].

+
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im2sim.ops.normtorange#

+
+
+normtorange(x, max=None, min=None, a=0, b=1)[source]#
+

Normalizes the input tensor x to a specified range [a, b].

+
+
Parameters:
+
    +

  • x (Tensor) – Input tensor to be normalized.

    • +

  • max (float, optional) – Maximum value for normalization. If None, uses the maximum of x.

    • +

  • min (float, optional) – Minimum value for normalization. If None, uses the minimum of x.

    • +

  • a (float, optional) – Lower bound of the target range. Default is 0.

    • +

  • b (float, optional) – Upper bound of the target range. Default is 1.

    • +
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im2sim.plot#

+

Utilities for visualizing data and model outputs.

+
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Classes#

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Functions#

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Frequently Asked Questions#

+

When trying to install TensorFlow MRI, I get an error about OpenEXR which +includes: +``OpenEXR.cpp:36:10: fatal error: ImathBox.h: No such file or directory``. What +do I do?

+

OpenEXR is needed by TensorFlow Graphics, which is a dependency of TensorFlow +MRI. This issue can be fixed by installing the OpenEXR library. On +Debian/Ubuntu:

+
$ apt install libopenexr-dev
+
+
+
+ +
+
+
+
+
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IM2SIM guide#

+
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+

2026 University College London

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+
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Install TensorFlow MRI#

+
+

Requirements#

+

TensorFlow MRI should work in most Linux systems that meet the +requirements for TensorFlow.

+
+

Warning

+

TensorFlow MRI is not yet available for Windows or macOS. +Help us support them!.

+
+
+

TensorFlow compatibility#

+

Each TensorFlow MRI release is compiled against a specific version of +TensorFlow. To ensure compatibility, it is recommended to install matching +versions of TensorFlow and TensorFlow MRI according to the +TensorFlow compatibility table.

+
+

Warning

+

Each TensorFlow MRI version aims to target and support the latest TensorFlow +version only. A new version of TensorFlow MRI will be released shortly after +each TensorFlow release. TensorFlow MRI versions that target older versions +of TensorFlow will not generally receive any updates.

+
+
+
+
+

Set up your system#

+

You will need a working TensorFlow installation. Follow the TensorFlow +installation instructions if you do not +have one already.

+
+

Use a GPU#

+

If you need GPU support, we suggest that you use one of the +TensorFlow Docker images. +These come with a GPU-enabled TensorFlow installation and are the easiest way +to run TensorFlow and TensorFlow MRI on your system.

+
$ docker pull tensorflow/tensorflow:latest-gpu
+
+
+

Alternatively, make sure you follow +these instructions when setting up +your system.

+
+
+
+

Download from PyPI#

+

TensorFlow MRI is available on the Python package index (PyPI) and can be +installed using the pip package manager:

+
$ pip install tensorflow-mri
+
+
+
+
+

Run in Google Colab#

+

To get started without installing anything on your system, you can use +Google Colab. +Simply create a new notebook and use pip to install TensorFlow MRI.

+
!pip install tensorflow-mri
+
+
+

The Colab environment is already configured to run TensorFlow and has GPU +support.

+
+
+

TensorFlow compatibility table#

+
+
+ +
+
+
+
+
+
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+

2026 University College London

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/guide/linalg/index.html b/tools/docs/_build/dirhtml/guide/linalg/index.html new file mode 100644 index 0000000..0fe2a79 --- /dev/null +++ b/tools/docs/_build/dirhtml/guide/linalg/index.html @@ -0,0 +1,207 @@ + + + + + Linear algebra - IM2SIM Documentation + + + + + + + + + + + + + + + +
+
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Linear algebra#

+

Coming soon…

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+ Made with + + Sphinx and + + Shibuya theme. +

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+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/guide/nufft/index.html b/tools/docs/_build/dirhtml/guide/nufft/index.html new file mode 100644 index 0000000..1a3e2b1 --- /dev/null +++ b/tools/docs/_build/dirhtml/guide/nufft/index.html @@ -0,0 +1,480 @@ + + + + + Non-uniform fast Fourier transform (NUFFT) - IM2SIM Documentation + + + + + + + + + + + + + + + +
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+

Non-uniform fast Fourier transform (NUFFT)#

+

TensorFlow MRI provides an efficient NUFFT operator for both CPU and GPU, based +on the algorithms by the Flatiron Institute (see +this paper and +this paper +for more details). The operator is available as +tfmri.signal.nufft.

+
+

Note

+

The tfmri.signal.nufft function is an alias of the nufft function in the +TensorFlow NUFFT +stand-alone package. Please direct any issues about the NUFFT function directly +to the TensorFlow NUFFT repository.

+
+
+

Warning

+

The current NUFFT implementation uses the FFTW library, +which is released under the GNU GPL. If you are using the NUFFT for commercial +purposes, you will need to purchase a license from MIT or adapt the code to use +a different FFT library. If you do the latter, please consider +contributing +your modification so others may benefit.

+
+

The NUFFT function can be used to efficiently evaluate the Fourier transform +when either the input data or the output data does not lie on a uniform grid, +in which case the standard fast Fourier transform (FFT) algorithm cannot be +used. There are 3 transform types depending whether the input is non-uniform, +the output is non-uniform or both input and output are non-uniform.

+
    +

  • A type-1 transform evaluates the Fourier transform on a uniform grid +given a set of arbitrary points (i.e, non-uniform to uniform).

    • +

  • A type-2 transform evaluates the Fourier transform on a set of arbitrary +points given a uniform grid. (i.e., uniform to non-uniform).

    • +

  • A type-3 transform evaluates the Fourier transform on a set of arbitrary +points given a set of arbitrary points (i.e., non-uniform to non-uniform).

    • +
+
+

Tip

+

The type of the transform can be specified using the transform_type argument.

+
+
+

Warning

+

NUFFT type-3 is not currently supported or planned, but contributions will be +accepted.

+
+

The NUFFT may be forward (signal to frequency domain) or backward +(frequency to signal domain), regardless of the transform type.

+
+

Tip

+

The direction of the transform can be specified using the fft_direction +argument.

+
+
+

Guided example#

+

As an example, let’s take an image of the Shepp-Logan phantom and evaluate its +Fourier transform on a set of sampling points defining a radial k-space +trajectory, using a forward, type-2 NUFFT. Then we will see how to recover +the image from the radial k-space data, using a backward, type-1 NUFFT.

+
+
+
%pip install -q tensorflow tensorflow-mri
+
+
+
+
+
WARNING: You are using pip version 22.0.4; however, version 22.2 is available.
+You should consider upgrading via the '/usr/local/bin/python3.8 -m pip install --upgrade pip' command.
+Note: you may need to restart the kernel to use updated packages.
+
+
+
+
+

Now import both packages and create an example image using +tfmri.image.phantom:

+
+
+
import tensorflow as tf
+import tensorflow_mri as tfmri
+
+# Create
+image_shape = [256, 256]
+image = tfmri.image.phantom(shape=image_shape, dtype=tf.complex64)
+
+print("image: \n - shape: {}\n - dtype: {}".format(image.shape, image.dtype))
+
+
+
+
+
2022-07-21 17:25:43.649824: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+2022-07-21 17:25:59.264266: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-07-21 17:25:59.268928: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-07-21 17:25:59.269048: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-07-21 17:25:59.269524: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2022-07-21 17:25:59.270142: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-07-21 17:25:59.270251: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-07-21 17:25:59.270327: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-07-21 17:25:59.612269: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-07-21 17:25:59.612401: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-07-21 17:25:59.612481: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
+2022-07-21 17:25:59.612559: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14239 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3080 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6
+
+
+
image: 
+ - shape: (256, 256)
+ - dtype: <dtype: 'complex64'>
+
+
+
+
+

Let us also create a k-space trajectory. In this example we will create a +radial trajectory.

+
+
+
trajectory = tfmri.sampling.radial_trajectory(
+    base_resolution=256, views=233, flatten_encoding_dims=True)
+
+print("trajectory: \n - shape: {}\n - dtype: {}\n - range: [{}, {}]".format(
+    trajectory.shape, trajectory.dtype,
+    tf.math.reduce_min(trajectory), tf.math.reduce_max(trajectory)))
+
+
+
+
+
trajectory: 
+ - shape: (119296, 2)
+ - dtype: <dtype: 'float32'>
+ - range: [-3.1415927410125732, 3.141521453857422]
+
+
+
+
+

The trajectory should have shape [..., M, N], where M is the number of +points and N is the number of dimensions. Any additional dimensions ... will +be treated as batch dimensions.

+

Batch dimensions for image and traj, if any, will be broadcasted.

+

Spatial frequencies should be provided in radians/voxel, ie, in the range +[-pi, pi].

+

Finally, we’ll also need density compensation weights for our set of nonuniform +points. These are necessary in the adjoint transform, to compensate for the fact +that the sampling density in a radial trajectory is not uniform.

+
+
+
density = tfmri.sampling.radial_density(base_resolution=256, views=233)
+density = tf.reshape(density, [-1])
+
+print("density: \n - shape: {}\n - dtype: {}".format(
+    density.shape, density.dtype))
+
+
+
+
+
density: 
+ - shape: (119296,)
+ - dtype: <dtype: 'float32'>
+
+
+
+
+
+

Forward transform (image to k-space)#

+

Next, let’s calculate the k-space coefficients for the given image and trajectory points (image to k-space transform).

+
+
+
kspace = tfmri.signal.nufft(image, trajectory,
+                            transform_type='type_2',
+                            fft_direction='forward')
+
+print("kspace: \n - shape: {}\n - dtype: {}".format(kspace.shape, kspace.dtype))
+
+
+
+
+
kspace: 
+ - shape: (119296,)
+ - dtype: <dtype: 'complex64'>
+
+
+
+
+

We are using a type-2 transform (uniform to nonuniform) and a forward FFT +(image domain to frequency domain). These are the default values for +transform_type and fft_direction, so providing them was not necessary in +this case.

+
+
+

Adjoint transform (k-space to image)#

+

We will now perform the adjoint transform to recover the image given the +k-space data. In this case, we will use a type-1 transform (nonuniform to +uniform) and a backward FFT (frequency domain to image domain). Also note that, +prior to evaluating the NUFFT, we will compensate for the nonuniform sampling +density by simply dividing the k-space samples by the density weights. +Finally, for type-1 transforms we need to specify an additional grid_shape +argument, which should be the size of the image. If there are any batch +dimensions, grid_shape should not include them.

+
+
+
# Apply density compensation.
+kspace /= tf.cast(density, tf.complex64)
+
+recon = tfmri.signal.nufft(kspace, trajectory,
+                           grid_shape=image_shape,
+                           transform_type='type_1',
+                           fft_direction='backward')
+
+print("recon: \n - shape: {}\n - dtype: {}".format(recon.shape, recon.dtype))
+
+
+
+
+
recon: 
+ - shape: (256, 256)
+ - dtype: <dtype: 'complex64'>
+
+
+
+
+

Finally, let’s visualize the images.

+
+
+
import matplotlib.pyplot as plt
+def plot_images(image, recon):
+  _, ax = plt.subplots(1, 2, figsize=(9.6, 5.4))
+  ax[0].imshow(tf.abs(image), cmap='gray')
+  ax[0].set_title("Original image")
+  ax[1].imshow(tf.abs(recon), cmap='gray')
+  ax[1].set_title("Image after forward\nand adjoint NUFFT")
+  plt.show()
+plot_images(image, recon)
+
+
+
+
+../../_images/ed739320bcec44e952e2c94e8c30931e2f1565301f14f6e2722599cc273859a5.png +
+
+
+
+

Use the linear operator#

+

You can also use +tfmri.linalg.LinearOperatorNUFFT +to perform forward and adjoint NUFFT. This might be particularly useful when +building MRI reconstruction methods, as you can take advantage of the features +of the linear algebra framework.

+
+
+
# Create the linear operator for the specified image shape, trajectory and
+# density.
+linop_nufft = tfmri.linalg.LinearOperatorNUFFT(
+    image_shape, trajectory=trajectory, density=density)
+
+# Apply forward transform to obtain the *k*-space signal given an image.
+kspace = linop_nufft.transform(image)
+
+# Apply adjoint transform to obtain an image given a *k*-space signal.
+recon = linop_nufft.transform(kspace, adjoint=True)
+
+plot_images(image, recon)
+
+
+
+
+../../_images/ed739320bcec44e952e2c94e8c30931e2f1565301f14f6e2722599cc273859a5.png +
+
+
+
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Optimization#

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Coming soon…

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MR image reconstruction#

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Coming soon…

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IM2SIM 0.1.0#

+View on GitHub + +
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+ Next +
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\ No newline at end of file diff --git a/tools/docs/_build/dirhtml/templates/index.html b/tools/docs/_build/dirhtml/templates/index.html new file mode 100644 index 0000000..713fcb5 --- /dev/null +++ b/tools/docs/_build/dirhtml/templates/index.html @@ -0,0 +1,210 @@ + + + + + IM2SIM 0.1.0 - IM2SIM Documentation + + + + + + + + + + + + + + + +
+
+
+ + + + im2sim + +
+ +
+ + / +
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+ +
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+

IM2SIM 0.1.0#

+View on GitHub + +
+
+
+ +
+
+
+
+
+
+
+

2026 University College London

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
+ + + + + \ No newline at end of file diff --git a/tools/docs/_build/dirhtml/tutorials/index.html b/tools/docs/_build/dirhtml/tutorials/index.html new file mode 100644 index 0000000..7b32fa5 --- /dev/null +++ b/tools/docs/_build/dirhtml/tutorials/index.html @@ -0,0 +1,211 @@ + + + + + TensorFlow MRI tutorials - IM2SIM Documentation + + + + + + + + + + + + + + + +
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+ + + + im2sim + +
+ +
+ + / +
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+

TensorFlow MRI tutorials#

+

All TensorFlow MRI tutorials are written as Jupyter notebooks.

+

In addition to viewing them on this website, you can run them directly in +Google Colab, a hosted notebook environment with no setup and free access to +GPUs. Click on the Run in Colab button to begin.

+

Alternatively, you can also download the notebooks to run on your machine.

+
+ +
+
+
+
+
+
+
+

2026 University College London

+ +

+ Made with + + Sphinx and + + Shibuya theme. +

+
+
+
+
+
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zpXY|Kq?w6(Nf6*Y2(~nEKzK!hR2nd2L>)B4U9(bd&yZY9>Jd=1n8x9YcS6KV?@Aim2sim\.[a-zA-Z0-9_.]+)``" +) + +COMMON_TYPES_PATTERNS = { + k: re.compile(rf"``{k}``")for k in COMMON_TYPES_LINKS} + +COMMON_TYPES_REPLACEMENTS = { + k: rf"`{k} <{v}>`_" for k, v in COMMON_TYPES_LINKS.items()} + +CODE_LETTER_PATTERN = re.compile(r"``(?P\w+)``(?P[a-zA-Z])") +CODE_LETTER_REPL = r"``\g``\ \g" + +LINK_PATTERN = re.compile(r"``(?P[\w\.]+)``_") +LINK_REPL = r"`\g`_" + +import inspect + + +def process_docstring_text(text): + """Process a docstring and convert it to Sphinx RST.""" + # Replace Note: and Warning: by RST equivalents. + rst_lines = [] + admonition_lines = None + + for line in text.splitlines(): + if admonition_lines is None: + # We are not in an admonition right now. Check if this line will start + # one. + if (line.strip().startswith('Warning:') or + line.strip().startswith('Note:')): + label_position = line.index(':') + admonition_type = line[:label_position].strip().lower() + admonition_content = line[label_position + 1:].strip() + leading_whitespace = ' ' * (len(line) - len(line.lstrip())) + extra_indentation = ' ' + + admonition_lines = [ + f"{leading_whitespace}.. {admonition_type}::", + leading_whitespace + extra_indentation + admonition_content, + ] + else: + rst_lines.append(line) + else: + # Check if this is the end of the admonition. + if line.strip() == '': + rst_lines.extend(admonition_lines) + admonition_lines = None + else: + admonition_lines.append(extra_indentation + line) + + # If we reached the end and are still in an admonition, add it. + if admonition_lines is not None: + rst_lines.extend(admonition_lines) + + # Replace markdown literal markers (`) by ReST literal markers (``). + text = '\n'.join(rst_lines) + text = text.replace('`', '``') + text = text.replace(':math:``', ':math:`') + + # Correct inline code followed by word characters. + text = CODE_LETTER_PATTERN.sub(CODE_LETTER_REPL, text) + + # Add links to common types. + for k in COMMON_TYPES_LINKS: + text = COMMON_TYPES_PATTERNS[k].sub( + COMMON_TYPES_REPLACEMENTS[k], + text, + ) + + + # Add links to im2sim objects. + for match in IM2SIM_OBJECTS_PATTERN.finditer(text): + object_name = match.group('name') + url = get_doc_url(object_name) + + pattern = rf"``{object_name}``" + repl = rf"`{object_name} <{url}>`_" + text = text.replace(pattern, repl) + + # Correct double quotes. + text = LINK_PATTERN.sub(LINK_REPL, text) + + return text + + +def process_docstring( + app, what, name, obj, options, lines +): # pylint: disable=missing-param-doc,unused-argument + """Process autodoc docstrings.""" + text = process_docstring_text('\n'.join(lines)) + lines[:] = text.splitlines() + + if what != 'class': + return + + presets = getattr(obj, '_presets', None) + if not presets: + return + + lines.append('') + lines.append('.. rubric:: Preset Library') + lines.append('') + + for preset_name, fn in presets.items(): + doc = inspect.getdoc(fn) or 'No description provided.' + + lines.append(f'**{preset_name}**') + lines.append('') + + # Process preset documentation using exactly the same rules + # as the class documentation. + preset_text = process_docstring_text(doc) + + lines.extend(preset_text.splitlines()) + lines.append('') + + +def get_doc_url(name): + """Get doc URL for the given im2sim name.""" + url = 'https://mrphys.github.io/im2sim/api_docs/' + url += name.replace('.', '/') + return url + + +def setup(app): + app.connect('autodoc-process-docstring', process_docstring) diff --git a/tools/docs/conf_helper.py b/tools/docs/conf_helper.py new file mode 100644 index 0000000..a226687 --- /dev/null +++ b/tools/docs/conf_helper.py @@ -0,0 +1,3 @@ +class AutosummaryFilenameMap(dict): + def get(self, k, default=None): + return k.split('.')[-1] diff --git a/tools/docs/create_documents.py b/tools/docs/create_documents.py new file mode 100644 index 0000000..b4b1063 --- /dev/null +++ b/tools/docs/create_documents.py @@ -0,0 +1,146 @@ +# Copyright 2026 University College London. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""RST document generator.""" + +import dataclasses +import inspect +import os +import string +import sys +import typing + +DOCS_PATH = os.path.dirname(os.path.realpath(__file__)) +ROOT_PATH = os.path.join(DOCS_PATH, '..', '..') +TEMPLATES_PATH = os.path.join(DOCS_PATH, 'templates') +API_DOCS_PATH = os.path.join(DOCS_PATH, 'api_docs') + +sys.path.insert(0, ROOT_PATH) + +from im2sim.src.utils import api_util + +# Create API docs directory. +os.makedirs(os.path.join(API_DOCS_PATH, 'im2sim'), exist_ok=True) + +# Read the index template. +with open(os.path.join(TEMPLATES_PATH, 'index.rst'), 'r') as f: + INDEX_TEMPLATE = string.Template(f.read()) + +im2sim_DOC_TEMPLATE = string.Template( +"""im2sim +===== + +.. automodule:: im2sim + +Modules +------- + +.. autosummary:: + :nosignatures: + + ${namespaces} + + +Classes +------- + +.. autosummary:: + :toctree: im2sim + :template: ops/class.rst + :nosignatures: + + + +Functions +--------- + +.. autosummary:: + :toctree: im2sim + :template: ops/function.rst + :nosignatures: +""") + +MODULE_DOC_TEMPLATE = string.Template( +"""im2sim.${module} +======${underline} + +.. automodule:: im2sim.${module} + +Classes +------- + +.. autosummary:: + :toctree: ${module} + :template: ${module}/class.rst + :nosignatures: + + ${classes} + +Functions +--------- + +.. autosummary:: + :toctree: ${module} + :template: ${module}/function.rst + :nosignatures: + + ${functions} +""") + + +@dataclasses.dataclass +class Module: + """A module.""" + classes: typing.List[str] = dataclasses.field(default_factory=list) + functions: typing.List[str] = dataclasses.field(default_factory=list) + +modules = {namespace: Module() for namespace in api_util.get_submodule_names()} + + +for name, symbol in api_util.get_api_symbols().items(): + name = api_util.get_canonical_name_for_symbol(symbol) + namespace, name = name.split('.', maxsplit=1) + + if inspect.isclass(symbol): + modules[namespace].classes.append(name) + elif inspect.isfunction(symbol): + modules[namespace].functions.append(name) + +# Write namespace templates. +for name, module in modules.items(): + classes = '\n '.join(sorted(set(module.classes))) + functions = '\n '.join(sorted(set(module.functions))) + + filename = os.path.join(API_DOCS_PATH, f'im2sim/{name}.rst') + with open(filename, 'w') as f: + f.write(MODULE_DOC_TEMPLATE.substitute( + module=name, + underline='=' * len(name), + classes=classes, + functions=functions)) + +# Write top-level API doc im2sim.rst. +filename = os.path.join(API_DOCS_PATH, 'im2sim.rst') +with open(filename, 'w') as f: + namespaces = api_util.get_submodule_names() + f.write(im2sim_DOC_TEMPLATE.substitute( + namespaces='\n '.join(sorted(namespaces)))) + +# Write index.rst. +filename = os.path.join(DOCS_PATH, 'index.rst') +with open(filename, 'w') as f: + namespaces = api_util.get_submodule_names() + namespaces = ['api_docs/im2sim/' + namespace for namespace in namespaces] + f.write(INDEX_TEMPLATE.substitute( + namespaces='\n '.join(sorted(namespaces)))) diff --git a/tools/docs/create_templates.py b/tools/docs/create_templates.py new file mode 100644 index 0000000..0ee484d --- /dev/null +++ b/tools/docs/create_templates.py @@ -0,0 +1,70 @@ +# Copyright 2026 University College London. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Autodoc template generator.""" + +import os +import string +import sys + +DOCS_PATH = os.path.dirname(os.path.realpath(__file__)) +ROOT_PATH = os.path.join(DOCS_PATH, '..', '..') + +sys.path.insert(0, ROOT_PATH) + +from im2sim.src.utils import api_util + + +CLASS_TEMPLATE = string.Template( +"""${module}.{{ objname | escape | underline }}${underline} + +.. currentmodule:: {{ module }} + +.. auto{{ objtype }}:: {{ objname }} + :members: + :show-inheritance: +""") + +FUNCTION_TEMPLATE = string.Template( +"""${module}.{{ objname | escape | underline }}${underline} + +.. currentmodule:: {{ module }} + +.. auto{{ objtype }}:: {{ objname }} +""") + +NAMESPACES = api_util.get_submodule_names() + +TEMPLATE_PATH = os.path.join( + os.path.dirname(os.path.realpath(__file__)), '_templates') + +for namespace in NAMESPACES: + # Create directory for this namespace. + os.makedirs(os.path.join(TEMPLATE_PATH, namespace), exist_ok=True) + + # Special treatment for namespace `ops`, which maps to the `im2sim` parent + # module. + module = f'im2sim.{namespace}' + + # Substitute the templates for this module. + class_template = CLASS_TEMPLATE.substitute( + module=module, underline='=' * (len(module) + 1)) + function_template = FUNCTION_TEMPLATE.substitute( + module=module, underline='=' * (len(module) + 1)) + + # Write template files. + with open(os.path.join(TEMPLATE_PATH, namespace, 'class.rst'), 'w') as f: + f.write(class_template) + with open(os.path.join(TEMPLATE_PATH, namespace, 'function.rst'), 'w') as f: + f.write(function_template) diff --git a/tools/docs/guide.rst b/tools/docs/guide.rst new file mode 100644 index 0000000..ac46b84 --- /dev/null +++ b/tools/docs/guide.rst @@ -0,0 +1,2 @@ +IM2SIM guide +==================== diff --git a/tools/docs/guide/faq.rst b/tools/docs/guide/faq.rst new file mode 100644 index 0000000..a699674 --- /dev/null +++ b/tools/docs/guide/faq.rst @@ -0,0 +1,15 @@ +Frequently Asked Questions +========================== + +**When trying to install TensorFlow MRI, I get an error about OpenEXR which +includes: +``OpenEXR.cpp:36:10: fatal error: ImathBox.h: No such file or directory``. What +do I do?** + +OpenEXR is needed by TensorFlow Graphics, which is a dependency of TensorFlow +MRI. This issue can be fixed by installing the OpenEXR library. On +Debian/Ubuntu: + +.. code-block:: console + + $ apt install libopenexr-dev diff --git a/tools/docs/guide/install.rst b/tools/docs/guide/install.rst new file mode 100644 index 0000000..404c4a7 --- /dev/null +++ b/tools/docs/guide/install.rst @@ -0,0 +1,89 @@ +Install TensorFlow MRI +====================== + +Requirements +------------ + +TensorFlow MRI should work in most Linux systems that meet the +`requirements for TensorFlow `_. + +.. warning:: + + TensorFlow MRI is not yet available for Windows or macOS. + `Help us support them! `_. + + +TensorFlow compatibility +~~~~~~~~~~~~~~~~~~~~~~~~ + +Each TensorFlow MRI release is compiled against a specific version of +TensorFlow. To ensure compatibility, it is recommended to install matching +versions of TensorFlow and TensorFlow MRI according to the +:ref:`TensorFlow compatibility table`. + +.. warning:: + + Each TensorFlow MRI version aims to target and support the latest TensorFlow + version only. A new version of TensorFlow MRI will be released shortly after + each TensorFlow release. TensorFlow MRI versions that target older versions + of TensorFlow will not generally receive any updates. + + +Set up your system +------------------ + +You will need a working TensorFlow installation. Follow the `TensorFlow +installation instructions `_ if you do not +have one already. + + +Use a GPU +~~~~~~~~~ + +If you need GPU support, we suggest that you use one of the +`TensorFlow Docker images `_. +These come with a GPU-enabled TensorFlow installation and are the easiest way +to run TensorFlow and TensorFlow MRI on your system. + +.. code-block:: console + + $ docker pull tensorflow/tensorflow:latest-gpu + +Alternatively, make sure you follow +`these instructions `_ when setting up +your system. + + +Download from PyPI +------------------ + +TensorFlow MRI is available on the Python package index (PyPI) and can be +installed using the ``pip`` package manager: + +.. code-block:: console + + $ pip install tensorflow-mri + + +Run in Google Colab +------------------- + +To get started without installing anything on your system, you can use +`Google Colab `_. +Simply create a new notebook and use ``pip`` to install TensorFlow MRI. + +.. code:: python + + !pip install tensorflow-mri + + +The Colab environment is already configured to run TensorFlow and has GPU +support. + + +TensorFlow compatibility table +------------------------------ + +.. include:: ../../../README.rst + :start-after: start-compatibility-table + :end-before: end-compatibility-table diff --git a/tools/docs/index.rst b/tools/docs/index.rst new file mode 100644 index 0000000..a8aa51c --- /dev/null +++ b/tools/docs/index.rst @@ -0,0 +1,29 @@ +IM2SIM |release| +======================== + +.. image:: https://img.shields.io/badge/-View%20on%20GitHub-128091?logo=github&labelColor=grey + :target: https://github.com/mrphys/im2sim + :alt: View on GitHub + +.. include:: ../../README.rst + :start-after: start-intro + :end-before: end-intro + + +.. toctree:: + :caption: API Documentation + :hidden: + + API documentation + api_docs/im2sim + api_docs/im2sim/configs + api_docs/im2sim/data + api_docs/im2sim/layers + api_docs/im2sim/losses + api_docs/im2sim/models + api_docs/im2sim/ops + api_docs/im2sim/plot + + +.. meta:: + :google-site-verification: 8PySedj6KJ0kc5qC1CbO6_9blFB9Nho3SgXvbRzyVOU diff --git a/tools/docs/make.bat b/tools/docs/make.bat new file mode 100644 index 0000000..2119f51 --- /dev/null +++ b/tools/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/tools/docs/requirements.txt b/tools/docs/requirements.txt new file mode 100644 index 0000000..a95ae18 --- /dev/null +++ b/tools/docs/requirements.txt @@ -0,0 +1 @@ +furo diff --git a/tools/docs/robots.txt b/tools/docs/robots.txt new file mode 100644 index 0000000..6d0bb8a --- /dev/null +++ b/tools/docs/robots.txt @@ -0,0 +1,3 @@ +User-agent: * + +Sitemap: https://mrphys.github.io/im2sim/sitemap.xml diff --git a/tools/docs/templates/index.rst b/tools/docs/templates/index.rst new file mode 100644 index 0000000..aef3f8f --- /dev/null +++ b/tools/docs/templates/index.rst @@ -0,0 +1,23 @@ +IM2SIM |release| +======================== + +.. image:: https://img.shields.io/badge/-View%20on%20GitHub-128091?logo=github&labelColor=grey + :target: https://github.com/mrphys/im2sim + :alt: View on GitHub + +.. include:: ../../README.rst + :start-after: start-intro + :end-before: end-intro + + +.. toctree:: + :caption: API Documentation + :hidden: + + API documentation + api_docs/im2sim + ${namespaces} + + +.. meta:: + :google-site-verification: 8PySedj6KJ0kc5qC1CbO6_9blFB9Nho3SgXvbRzyVOU diff --git a/tools/docs/test_docs.py b/tools/docs/test_docs.py new file mode 100644 index 0000000..139e6b2 --- /dev/null +++ b/tools/docs/test_docs.py @@ -0,0 +1,13 @@ +# import doctest +# import pathlib +# import sys +# wdir = pathlib.Path().absolute() +# sys.path.insert(0, str(wdir)) + +# from tensorflow_mri.python.ops import array_ops +# from tensorflow_mri.python.ops import wavelet_ops + +# kwargs = dict(raise_on_error=True) + +# doctest.testmod(array_ops, **kwargs) +# doctest.testmod(wavelet_ops, **kwargs) diff --git a/tools/docs/tutorials.rst b/tools/docs/tutorials.rst new file mode 100644 index 0000000..9c52220 --- /dev/null +++ b/tools/docs/tutorials.rst @@ -0,0 +1,10 @@ +TensorFlow MRI tutorials +======================== + +All TensorFlow MRI tutorials are written as Jupyter notebooks. + +In addition to viewing them on this website, you can run them directly in +Google Colab, a hosted notebook environment with no setup and free access to +GPUs. Click on the **Run in Colab** button to begin. + +Alternatively, you can also download the notebooks to run on your machine. From b2a4f9991dfd92da7700286345c14c544f7994cc Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 10 Aug 2026 12:21:04 +0100 Subject: [PATCH 44/49] removed some git actions --- .github/workflows/api.yaml | 28 ----------------------- .github/workflows/docs.yaml | 43 ------------------------------------ .github/workflows/tests.yaml | 30 ------------------------- 3 files changed, 101 deletions(-) delete mode 100644 .github/workflows/api.yaml delete mode 100644 .github/workflows/docs.yaml delete mode 100644 .github/workflows/tests.yaml diff --git a/.github/workflows/api.yaml b/.github/workflows/api.yaml deleted file mode 100644 index 9d9619f..0000000 --- a/.github/workflows/api.yaml +++ /dev/null @@ -1,28 +0,0 @@ -name: Build API - -on: - push: - pull_request: - paths: - - "tools/**" - - "src/**" - -permissions: - contents: write - -jobs: - docs: - runs-on: ubuntu-latest - - steps: - - name: Checkout repository - uses: actions/checkout@v5 - - - name: Setup Python - uses: actions/setup-python@v6 - with: - python-version: "3.10" - - - name: Install dependencies - run: | - pip install -e \ No newline at end of file diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml deleted file mode 100644 index b1e8f65..0000000 --- a/.github/workflows/docs.yaml +++ /dev/null @@ -1,43 +0,0 @@ -name: Documentation - -on: - push: - pull_request: - paths: - - "tools/**" - - "src/**" - -permissions: - contents: write - -jobs: - docs: - runs-on: ubuntu-latest - - steps: - - name: Checkout repository - uses: actions/checkout@v5 - - - name: Setup Python - uses: actions/setup-python@v6 - with: - python-version: "3.10" - - - name: Install dependencies - run: | - pip install -e ".[docs]" - - - name: Build docs - run: | - rm -rf tools/docs/_build - rm -rf tools/docs/_templates - rm -rf tools/docs/api_docs - $(PYTHON) tools/docs/create_templates.py - $(PYTHON) tools/docs/create_documents.py - $(MAKE) -C tools/docs dirhtml PY_VERSION=$(PY_VERSION) - - - name: Deploy - uses: peaceiris/actions-gh-pages@v4 - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: docs/build/html \ No newline at end of file diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml deleted file mode 100644 index 28e1dd8..0000000 --- a/.github/workflows/tests.yaml +++ /dev/null @@ -1,30 +0,0 @@ -name: Run Tests - -on: - push: - pull_request: - -jobs: - test: - runs-on: ubuntu-latest - - steps: - - name: Checkout code - uses: actions/checkout@v5 - - - name: Set up Python - uses: actions/setup-python@v6 - with: - python-version: '3.10' - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r requirements.txt - pip install -e ".[dev]" - - - name: Lint - run: ruff check . - - - name: Run tests - run: pytest \ No newline at end of file From ff9dad5b1e5ccbc37091335b3bee31fb1919e986 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 10 Aug 2026 12:23:38 +0100 Subject: [PATCH 45/49] fixed build_package.yml --- .github/workflows/build_package.yml | 39 +++++++++++++++-------------- 1 file changed, 20 insertions(+), 19 deletions(-) diff --git a/.github/workflows/build_package.yml b/.github/workflows/build_package.yml index 0cbc0c5..a9657cc 100644 --- a/.github/workflows/build_package.yml +++ b/.github/workflows/build_package.yml @@ -5,33 +5,34 @@ on: pull_request: jobs: - test: - runs-on: ubuntu-latest + # test: + # runs-on: ubuntu-latest - steps: - - name: Checkout code - uses: actions/checkout@v5 + # steps: + # - name: Checkout code + # uses: actions/checkout@v5 - - name: Set up Python - uses: actions/setup-python@v6 - with: - python-version: '3.10' + # - name: Set up Python + # uses: actions/setup-python@v6 + # with: + # python-version: '3.10' - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r requirements.txt - pip install -e ".[dev]" + # - name: Install dependencies + # run: | + # python -m pip install --upgrade pip + # pip install -r requirements.txt + # pip install -e ".[dev]" - - name: Lint - run: ruff check . + # - name: Lint + # run: ruff check . - - name: Run tests - run: pytest + # - name: Run tests + # run: pytest build_docs: runs-on: ubuntu-latest - name: Build API + + - name: Build API run: | python tools/build/create_api.py From c3a05475c942f9297ccfc896f8120bc69b534d37 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 10 Aug 2026 12:26:47 +0100 Subject: [PATCH 46/49] fixed build_package.yml2 --- .github/workflows/build_package.yml | 89 +++++++++++++---------------- 1 file changed, 40 insertions(+), 49 deletions(-) diff --git a/.github/workflows/build_package.yml b/.github/workflows/build_package.yml index a9657cc..d245b7a 100644 --- a/.github/workflows/build_package.yml +++ b/.github/workflows/build_package.yml @@ -1,67 +1,58 @@ -name: Run Tests +name: Build Package on: push: pull_request: jobs: - # test: - # runs-on: ubuntu-latest - - # steps: - # - name: Checkout code - # uses: actions/checkout@v5 - - # - name: Set up Python - # uses: actions/setup-python@v6 - # with: - # python-version: '3.10' - - # - name: Install dependencies - # run: | - # python -m pip install --upgrade pip - # pip install -r requirements.txt - # pip install -e ".[dev]" - - # - name: Lint - # run: ruff check . - - # - name: Run tests - # run: pytest - build_docs: runs-on: ubuntu-latest - - name: Build API - run: | - python tools/build/create_api.py + steps: + - name: Checkout code + uses: actions/checkout@v5 - - name: Build Docs - run: | - cd tools/docs - make clean - python create_templates.py - python create_documents.py - make dirhtml - - - name: Upload Pages artifact + - name: Set up Python + uses: actions/setup-python@v6 + with: + python-version: '3.10' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + pip install -e ".[dev]" + + - name: Build API + run: | + python tools/build/create_api.py + + - name: Build Docs + run: | + cd tools/docs + make clean + python create_templates.py + python create_documents.py + make dirhtml + + - name: Upload Pages artifact uses: actions/upload-pages-artifact@v3 with: path: tools/docs/_build/dirhtml deploy_docs: - needs: build_docs - runs-on: ubuntu-latest + needs: build_docs + runs-on: ubuntu-latest - permissions: - pages: write - id-token: write + permissions: + pages: write + id-token: write - environment: - name: github-pages - url: ${{ steps.deployment.outputs.page_url }} + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} - steps: - - name: Deploy to GitHub Pages - id: deployment - uses: actions/deploy-pages@v4 \ No newline at end of file + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 \ No newline at end of file From 077ccccc488a7f9550a36e8a36d39dbed4c94a55 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 10 Aug 2026 12:33:59 +0100 Subject: [PATCH 47/49] updated toml --- im2sim/im2sim.egg-info/PKG-INFO | 54 --------------------- im2sim/im2sim.egg-info/SOURCES.txt | 41 ---------------- im2sim/im2sim.egg-info/dependency_links.txt | 1 - im2sim/im2sim.egg-info/requires.txt | 17 ------- im2sim/im2sim.egg-info/top_level.txt | 1 - pyproject.toml | 8 +-- 6 files changed, 4 insertions(+), 118 deletions(-) delete mode 100644 im2sim/im2sim.egg-info/PKG-INFO delete mode 100644 im2sim/im2sim.egg-info/SOURCES.txt delete mode 100644 im2sim/im2sim.egg-info/dependency_links.txt delete mode 100644 im2sim/im2sim.egg-info/requires.txt delete mode 100644 im2sim/im2sim.egg-info/top_level.txt diff --git a/im2sim/im2sim.egg-info/PKG-INFO b/im2sim/im2sim.egg-info/PKG-INFO deleted file mode 100644 index ca05b52..0000000 --- a/im2sim/im2sim.egg-info/PKG-INFO +++ /dev/null @@ -1,54 +0,0 @@ -Metadata-Version: 2.4 -Name: im2sim -Version: 0.1.0 -Summary: A framework for ML-accelerated digital twins -Author-email: Anirudh Raman -License: ElasticV2 -Requires-Python: >=3.10 -Description-Content-Type: text/markdown -Requires-Dist: torch==2.3.1 -Requires-Dist: torch_geometric==2.7.0 -Requires-Dist: pyvista==0.47.0 -Requires-Dist: numpy -Requires-Dist: matplotlib -Requires-Dist: scikit-learn -Provides-Extra: dev -Requires-Dist: pytest; extra == "dev" -Requires-Dist: pytest-cov; extra == "dev" -Requires-Dist: ruff; extra == "dev" -Requires-Dist: pre-commit; extra == "dev" -Provides-Extra: docs -Requires-Dist: mkdocs; extra == "docs" -Requires-Dist: mkdocs-material; extra == "docs" -Requires-Dist: mkdocstrings[python]; extra == "docs" - -# im2sim - -`im2sim` is a library designed to simplify the development of ML-accelerated digital twins based on medical images. - -This includes two main components: -1) Deep Learning (DL) frameworks based on [Pytorch](https://pytorch.org) and [Pytorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/#) -2) Mesh processing frameworks based on [VTK](https://vtk.org) and [PyVista](https://docs.pyvista.org/index.html) - -## Features - -- Image and Mesh Data Processing -- DL models for medical imaging applications -- DL models for digital twin applications -- Hybrid DL models for simulation outputs directly from images -- Building blocks for custom DL models -- Visualisation utilities - -## Installation - -1. Install the CUDA dependencies - ```bash - pip install torch-scatter torch-cluster\ - -f https://data.pyg.org/whl/torch-2.3.1+cu121.html - ``` - -2. Install the repo: - ```bash - pip install git+https://github.com/mrphys/im2sim.git - ``` - diff --git a/im2sim/im2sim.egg-info/SOURCES.txt b/im2sim/im2sim.egg-info/SOURCES.txt deleted file mode 100644 index 4fe6de4..0000000 --- a/im2sim/im2sim.egg-info/SOURCES.txt +++ /dev/null @@ -1,41 +0,0 @@ -README.md -pyproject.toml -src/im2sim/__init__.py -src/im2sim.egg-info/PKG-INFO -src/im2sim.egg-info/SOURCES.txt -src/im2sim.egg-info/dependency_links.txt -src/im2sim.egg-info/requires.txt -src/im2sim.egg-info/top_level.txt -src/im2sim/callbacks/__init__.py -src/im2sim/data/__init__.py -src/im2sim/data/core.py -src/im2sim/data/mesh_utils.py -src/im2sim/data/ops.py -src/im2sim/data/pca.py -src/im2sim/data/scaling.py -src/im2sim/data/transforms.py -src/im2sim/layers/__init__.py -src/im2sim/layers/custom_image_layers.py -src/im2sim/layers/graph_blocks.py -src/im2sim/layers/halfunet.py -src/im2sim/layers/image_blocks.py -src/im2sim/layers/image_conv_blocks.py -src/im2sim/layers/layer_util.py -src/im2sim/layers/meshgraphnets.py -src/im2sim/layers/module_config.py -src/im2sim/layers/projections.py -src/im2sim/layers/reverse_halfunet.py -src/im2sim/losses/__init__.py -src/im2sim/losses/feature.py -src/im2sim/losses/mesh.py -src/im2sim/losses/pointcloud.py -src/im2sim/losses/seg.py -src/im2sim/losses/utils.py -src/im2sim/metrics/__init__.py -src/im2sim/models/__init__.py -src/im2sim/models/image_to_graph.py -src/im2sim/models/unet.py -src/im2sim/models/utils.py -src/im2sim/plot/__init__.py -src/im2sim/plot/pointcloud.py -src/im2sim/utils/__init__.py \ No newline at end of file diff --git a/im2sim/im2sim.egg-info/dependency_links.txt b/im2sim/im2sim.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/im2sim/im2sim.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/im2sim/im2sim.egg-info/requires.txt b/im2sim/im2sim.egg-info/requires.txt deleted file mode 100644 index 5de1cbf..0000000 --- a/im2sim/im2sim.egg-info/requires.txt +++ /dev/null @@ -1,17 +0,0 @@ -torch==2.3.1 -torch_geometric==2.7.0 -pyvista==0.47.0 -numpy -matplotlib -scikit-learn - -[dev] -pytest -pytest-cov -ruff -pre-commit - -[docs] -mkdocs -mkdocs-material -mkdocstrings[python] diff --git a/im2sim/im2sim.egg-info/top_level.txt b/im2sim/im2sim.egg-info/top_level.txt deleted file mode 100644 index d156cdc..0000000 --- a/im2sim/im2sim.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -im2sim diff --git a/pyproject.toml b/pyproject.toml index 6764b14..f99cd5d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta" name = "im2sim" version = "0.1.0" description = "A framework for ML-accelerated digital twins" -readme = "README.md" +readme = "README.rst" requires-python = ">=3.10" license = { text = "ElasticV2" } authors = [ @@ -38,7 +38,7 @@ docs = [ ] [tool.setuptools.packages.find] -where = ["src"] +where = ["im2sim"] # ------------------------- @@ -78,7 +78,7 @@ fixable = [ [tool.ruff.lint.isort] known-first-party = [ - "your_package_name", + "im2sim", ] @@ -110,7 +110,7 @@ addopts = [ [tool.coverage.run] branch = true source = [ - "your_package_name", + "im2sim", ] [tool.coverage.report] From 508f18d40cba9802c5547c06ce3d5dd8b94469f7 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 10 Aug 2026 12:39:35 +0100 Subject: [PATCH 48/49] updated toml2 --- .github/workflows/build_package.yml | 2 +- pyproject.toml | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/.github/workflows/build_package.yml b/.github/workflows/build_package.yml index d245b7a..536a757 100644 --- a/.github/workflows/build_package.yml +++ b/.github/workflows/build_package.yml @@ -21,7 +21,7 @@ jobs: run: | python -m pip install --upgrade pip pip install -r requirements.txt - pip install -e ".[dev]" + pip install -e ".[docs]" - name: Build API run: | diff --git a/pyproject.toml b/pyproject.toml index f99cd5d..2c1f585 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,9 +32,11 @@ dev = [ "pre-commit", ] docs = [ - "mkdocs", - "mkdocs-material", - "mkdocstrings[python]" + "sphinx", + "shibuya", + "myst-parser", + "sphinxcontrib-bibtex", + "sphinx-design", ] [tool.setuptools.packages.find] From 40d0d41728133766d4949bb849aa4cc44113a264 Mon Sep 17 00:00:00 2001 From: Anirudh Raman Date: Mon, 10 Aug 2026 12:46:20 +0100 Subject: [PATCH 49/49] updated toml3 --- pyproject.toml | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 2c1f585..278580e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,10 +33,13 @@ dev = [ ] docs = [ "sphinx", - "shibuya", + "sphinx-autodoc-typehints", + "sphinx-last-updated-by-git", + "sphinx-sitemap", + "sphinx_rtd_theme", + "myst-nb", "myst-parser", - "sphinxcontrib-bibtex", - "sphinx-design", + "shibuya", ] [tool.setuptools.packages.find]

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