chore: setup github actions ci pipeline and 2d tensor shape validation

.github/workflows/2d_model_ci.yml

@@ -0,0 +1,45 @@
+# .github/workflows/2d_model_ci.yml
+# =========================================================================
+# CI Pipeline: 2D Model Tensor Shape Validation
+# Runs on every push to feature/2d-models to catch tensor shape mismatches
+# before they propagate into model training code.
+# =========================================================================
+
+name: 2D Model CI Pipeline
+
+on:
+  push:
+    branches: [feature/2d-models]
+  pull_request:
+    branches: [main, feature/2d-models]
+
+jobs:
+  validate-2d-shapes:
+    name: 2D Tensor Shape Validation
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+
+      - name: Cache pip dependencies
+        uses: actions/cache@v4
+        with:
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-2d-${{ hashFiles('requirements.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-2d-
+
+      - name: Install dependencies (CPU-only PyTorch for CI speed)
+        run: |
+          python -m pip install --upgrade pip
+          pip install torch --index-url https://download.pytorch.org/whl/cpu
+          pip install numpy scipy scikit-learn matplotlib
+
+      - name: Run 2D shape validation tests
+        run: python test_2d_shapes.py

.gitignore

@@ -25,4 +25,5 @@ data/
 .DS_Store
 Thumbs.db
 
-stft_dataset.npz
+stft_dataset.npz
+*.onnx

benchmark_2d.py

@@ -0,0 +1,81 @@
+import torch
+import time
+from models_2d import ResNet18_2D, STFTRADN
+
+def measure_latency(model, input_shape, num_runs=100, warmup_runs=10):
+    """Measures the inference latency per sample in milliseconds."""
+    device = next(model.parameters()).device
+    dummy_input = torch.randn(input_shape).to(device)
+
+    model.eval()
+
+    # Warmup
+    with torch.no_grad():
+        for _ in range(warmup_runs):
+            _ = model(dummy_input)
+
+    # Measure
+    start_time = time.perf_counter()
+    with torch.no_grad():
+        for _ in range(num_runs):
+            _ = model(dummy_input)
+    end_time = time.perf_counter()
+
+    total_time_ms = (end_time - start_time) * 1000
+    latency_per_sample_ms = total_time_ms / (num_runs * input_shape[0])
+    return latency_per_sample_ms
+
+def count_trainable_parameters(model):
+    """Calculates the total trainable parameter footprint."""
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+def export_to_onnx(model, input_shape, filename="stft_radn_model.onnx"):
+    """Exports the model weights to an ONNX file."""
+    device = next(model.parameters()).device
+    dummy_input = torch.randn(input_shape).to(device)
+    model.eval()
+
+    torch.onnx.export(
+        model,
+        dummy_input,
+        filename,
+        export_params=True,
+        opset_version=12,
+        do_constant_folding=True,
+        input_names=['input'],
+        output_names=['output'],
+        dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}}
+    )
+    print(f"[INFO] Model exported successfully to {filename}")
+
+if __name__ == "__main__":
+    # Standard input shape: [Batch, Channels, Height, Width]
+    # Corresponding to [1, 1, 64, 5] for grayscale 2D spectrograms
+    input_shape = (1, 1, 64, 5)
+
+    print("="*60)
+    print("Initializing Models...")
+    resnet_model = ResNet18_2D(num_classes=11, in_channels=1)
+    stft_model = STFTRADN(num_classes=11, in_channels=1)
+
+    print("="*60)
+    print(" ResNet18_2D Benchmarks")
+    print("="*60)
+    resnet_params = count_trainable_parameters(resnet_model)
+    print(f"Trainable Parameters : {resnet_params:,}")
+    resnet_latency = measure_latency(resnet_model, input_shape)
+    print(f"Inference Latency    : {resnet_latency:.4f} ms / sample")
+
+    print("\n" + "="*60)
+    print(" STFTRADN Benchmarks")
+    print("="*60)
+    stft_params = count_trainable_parameters(stft_model)
+    print(f"Trainable Parameters : {stft_params:,}")
+    stft_latency = measure_latency(stft_model, input_shape)
+    print(f"Inference Latency    : {stft_latency:.4f} ms / sample")
+
+    print("\n" + "="*60)
+    print(" ONNX Export (STFTRADN for Grad-CAM validation)")
+    print("="*60)
+    export_to_onnx(stft_model, input_shape, filename="stft_radn_model.onnx")
+    print("="*60)