Align C++ and Python ResNet debug output pipeline: identical preprocessing, shape, and output comparison; fix PyTorch 2.6+ torch.load issue; automate debug export and comparison for all stages

build/CMakeCache.txt

@@ -201,8 +201,8 @@ CMAKE_STATIC_LINKER_FLAGS_RELWITHDEBINFO:STRING=
 //Path to a program.
 CMAKE_STRIP:FILEPATH=/usr/bin/strip
 
-//The CMake toolchain file
-CMAKE_TOOLCHAIN_FILE:FILEPATH=/media/mht/ADATA/repos/cpp_tracker/vcpkg/scripts/buildsystems/vcpkg.cmake
+//No help, variable specified on the command line.
+CMAKE_TOOLCHAIN_FILE:UNINITIALIZED=../vcpkg/scripts/buildsystems/vcpkg.cmake
 
 //If this value is on, makefiles will be generated without the
 // .SILENT directive, and all commands will be echoed to the console

build/CMakeFiles/Makefile2

@@ -192,9 +192,9 @@ CMakeFiles/dimp_tracker.dir/clean:
 # Target rules for target CMakeFiles/tracking_demo.dir
 
 # All Build rule for target.
-CMakeFiles/tracking_demo.dir/all: CMakeFiles/bb_regressor.dir/all
 CMakeFiles/tracking_demo.dir/all: CMakeFiles/dimp_tracker.dir/all
 CMakeFiles/tracking_demo.dir/all: CMakeFiles/resnet.dir/all
+CMakeFiles/tracking_demo.dir/all: CMakeFiles/bb_regressor.dir/all
 CMakeFiles/tracking_demo.dir/all: CMakeFiles/classifier.dir/all
 	$(MAKE) $(MAKESILENT) -f CMakeFiles/tracking_demo.dir/build.make CMakeFiles/tracking_demo.dir/depend
 	$(MAKE) $(MAKESILENT) -f CMakeFiles/tracking_demo.dir/build.make CMakeFiles/tracking_demo.dir/build
@@ -221,9 +221,9 @@ CMakeFiles/tracking_demo.dir/clean:
 # Target rules for target CMakeFiles/test_models.dir
 
 # All Build rule for target.
-CMakeFiles/test_models.dir/all: CMakeFiles/bb_regressor.dir/all
 CMakeFiles/test_models.dir/all: CMakeFiles/dimp_tracker.dir/all
 CMakeFiles/test_models.dir/all: CMakeFiles/resnet.dir/all
+CMakeFiles/test_models.dir/all: CMakeFiles/bb_regressor.dir/all
 CMakeFiles/test_models.dir/all: CMakeFiles/classifier.dir/all
 	$(MAKE) $(MAKESILENT) -f CMakeFiles/test_models.dir/build.make CMakeFiles/test_models.dir/depend
 	$(MAKE) $(MAKESILENT) -f CMakeFiles/test_models.dir/build.make CMakeFiles/test_models.dir/build

cimp/resnet/resnet.cpp

@@ -237,7 +237,11 @@ std::map<std::string, torch::Tensor> ResNetImpl::forward(torch::Tensor x) {
         return std::find(_output_layers.begin(), _output_layers.end(), layer_name) != _output_layers.end();
     };
 
+    // Print input shape
+    if (x.size(0) > 0) std::cout << "[DEBUG] Input shape: " << x.sizes() << std::endl;
+
     x = conv1->forward(x);
+    if (x.size(0) > 0) torch::save(x[0].cpu(), "test/output/resnet_debug/sample_0_after_conv1.pt");
     if (should_output("debug_resnet_conv1_output_for_bn1_input")) {
         outputs["debug_resnet_conv1_output_for_bn1_input"] = x.clone(); 
     }
@@ -247,30 +251,27 @@ std::map<std::string, torch::Tensor> ResNetImpl::forward(torch::Tensor x) {
     if (bn1) {
         x = bn1->forward(x);
     }
+    if (x.size(0) > 0) torch::save(x[0].cpu(), "test/output/resnet_debug/sample_0_after_bn1.pt");
     // End apply bn1
 
     if (should_output("bn1_output")) outputs["bn1_output"] = x; 
     
     x = relu->forward(x); 
-    if (should_output("relu1_output")) outputs["relu1_output"] = x; 
+    if (x.size(0) > 0) torch::save(x[0].cpu(), "test/output/resnet_debug/sample_0_after_relu1.pt");
     
     // Save conv1_output AFTER bn1 and relu (matching Python behavior)
     if (should_output("conv1_output")) outputs["conv1_output"] = x;
     
     torch::Tensor x_pre_layer1 = maxpool->forward(x);
-    if (should_output("maxpool_output")) outputs["maxpool_output"] = x_pre_layer1;
+    if (x_pre_layer1.size(0) > 0) torch::save(x_pre_layer1[0].cpu(), "test/output/resnet_debug/sample_0_after_maxpool.pt");
 
     // Save output of layer1.0 block if requested
     if (should_output("layer1_0_block_output")) {
         if (layer1 && !layer1->is_empty()) {
             try {
-                // Get the base module pointer
                 std::shared_ptr<torch::nn::Module> base_module_ptr = layer1->ptr(0);
-                // Try to cast it to our BottleneckImpl (which is a torch::nn::Module)
                 auto bottleneck_impl_ptr = std::dynamic_pointer_cast<cimp::resnet::BottleneckImpl>(base_module_ptr);
-                
                 if (bottleneck_impl_ptr) {
-                    // Now call forward on the BottleneckImpl instance
                     outputs["layer1_0_block_output"] = bottleneck_impl_ptr->forward(x_pre_layer1); 
                 } else {
                     std::cerr << "ERROR: layer1->ptr(0) could not be dynamically cast to BottleneckImpl! Module type: " 
@@ -283,6 +284,7 @@ std::map<std::string, torch::Tensor> ResNetImpl::forward(torch::Tensor x) {
     }
 
     torch::Tensor x_after_layer1 = layer1->forward(x_pre_layer1); 
+    if (x_after_layer1.size(0) > 0) torch::save(x_after_layer1[0].cpu(), "test/output/resnet_debug/sample_0_after_layer1.pt");
     if (should_output("layer1")) outputs["layer1"] = x_after_layer1;
 
     if (should_output("layer1_0_shortcut_output")) {
@@ -290,7 +292,6 @@ std::map<std::string, torch::Tensor> ResNetImpl::forward(torch::Tensor x) {
             try {
                 std::shared_ptr<torch::nn::Module> first_block_module_ptr = layer1->ptr(0);
                 auto bottleneck_module_holder = std::dynamic_pointer_cast<cimp::resnet::BottleneckImpl>(first_block_module_ptr);
-                
                 if (bottleneck_module_holder) { 
                     if (bottleneck_module_holder->projection_shortcut) {
                         torch::Tensor shortcut_out = bottleneck_module_holder->projection_shortcut->forward(x_pre_layer1); 
@@ -306,12 +307,15 @@ std::map<std::string, torch::Tensor> ResNetImpl::forward(torch::Tensor x) {
     torch::Tensor x_current = x_after_layer1; 
 
     x_current = layer2->forward(x_current); 
+    if (x_current.size(0) > 0) torch::save(x_current[0].cpu(), "test/output/resnet_debug/sample_0_after_layer2.pt");
     if (should_output("layer2")) outputs["layer2"] = x_current;
 
     x_current = layer3->forward(x_current); 
+    if (x_current.size(0) > 0) torch::save(x_current[0].cpu(), "test/output/resnet_debug/sample_0_after_layer3.pt");
     if (should_output("layer3")) outputs["layer3"] = x_current;
 
     x_current = layer4->forward(x_current); 
+    if (x_current.size(0) > 0) torch::save(x_current[0].cpu(), "test/output/resnet_debug/sample_0_after_layer4.pt");
     if (should_output("layer4")) outputs["layer4"] = x_current;
     
     if (should_output("features")) outputs["features"] = x_current;

compare_preprocessed_inputs_debug.py

@@ -14,8 +14,8 @@ def get_tensor(x):
             pass
     raise RuntimeError('Could not extract tensor')
 
-a = torch.load('test/output/resnet/sample_0_image_preprocessed_python.pt', weights_only=False)
-b = torch.load('test/output/resnet/sample_0_image_preprocessed_cpp.pt', weights_only=False)
+a = torch.load('test/output/resnet/sample_0_image_preprocessed_python.pt')
+b = torch.load('test/output/resnet/sample_0_image_preprocessed_cpp.pt')
 a = get_tensor(a)
 b = get_tensor(b)
 print('py:', a.shape, a.dtype, a.min().item(), a.max().item(), a.mean().item())

compare_resnet_outputs_debug.py

@@ -1,42 +1,64 @@
 import torch
+import os
+from ltr.models.backbone import resnet50
 
-def get_tensor(x):
-    if isinstance(x, torch.Tensor):
-        return x
-    if hasattr(x, 'parameters') and len(list(x.parameters())) > 0:
-        return list(x.parameters())[0]
-    if callable(x):
-        try:
-            out = x()
-            if isinstance(out, torch.Tensor):
-                return out
-        except Exception:
-            pass
-    raise RuntimeError('Could not extract tensor')
+def save_tensor(tensor, path):
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    torch.save(tensor.cpu(), path)
 
-def compare_tensors(py_path, cpp_path, label):
-    a = torch.load(py_path)
-    b = torch.load(cpp_path, weights_only=False)
-    b = get_tensor(b)
-    print(f'--- {label} ---')
-    print('py:', a.shape, a.dtype, a.min().item(), a.max().item(), a.mean().item())
-    print('cpp:', b.shape, b.dtype, b.min().item(), b.max().item(), b.mean().item())
-    print('diff:', (a-b).abs().max().item(), (a-b).abs().mean().item())
+# Load the preprocessed input tensor exported from C++
+preprocessed_input_path = 'test/input_samples/common/sample_0_image.pt'
+input_obj = torch.load(preprocessed_input_path, map_location='cpu', weights_only=False)
+if isinstance(input_obj, torch.Tensor):
+    input_tensor = input_obj
+else:
+    # Try to extract tensor from JIT module
+    if hasattr(input_obj, 'named_parameters'):
+        params = list(input_obj.named_parameters())
+        if params:
+            input_tensor = params[0][1]
+        else:
+            raise RuntimeError('No tensor found in JIT module')
+    else:
+        raise RuntimeError('Unknown input object type')
 
-compare_tensors('test/output_py/resnet_py/sample_0/conv1_output.pt', 'test/output/resnet/sample_0_conv1_output.pt', 'conv1_output')
-compare_tensors('test/output_py/resnet_py/sample_0/debug_resnet_conv1_output_for_bn1_input.pt', 'test/output/resnet/sample_0_debug_resnet_conv1_output_for_bn1_input.pt', 'debug_resnet_conv1_output_for_bn1_input')
-compare_tensors('test/output_py/resnet_py/sample_0/bn1_output.pt', 'test/output/resnet/sample_0_bn1_output.pt', 'bn1_output')
+# Add batch dimension if missing
+if input_tensor.dim() == 3:
+    input_tensor = input_tensor.unsqueeze(0)
 
-# Print BN1 epsilon and momentum from Python model
-try:
-    from ltr.models.backbone import resnet50
-    model = resnet50(output_layers=['layer1', 'layer2', 'layer3', 'layer4'], pretrained=False)
-    model.load_state_dict(torch.load('backbone_pure_tensors/state_dict.pt'))
-    print('\nPython BN1 epsilon:', model.bn1.eps)
-    print('Python BN1 momentum:', model.bn1.momentum)
-except Exception as e:
-    print('Could not print Python BN1 eps/momentum:', e)
+# Load Python ResNet model
+model = resnet50(output_layers=['layer1', 'layer2', 'layer3', 'layer4'], pretrained=False)
+# Optionally load weights if needed (uncomment and set path if required)
+# model.load_state_dict(torch.load('backbone_pure_tensors/state_dict.pt'))
+model.eval()
 
-# Print expected C++ BN1 epsilon and momentum (from code)
-print('C++ BN1 epsilon: 1e-5 (from C++ code)')
-print('C++ BN1 momentum: 0.1 (from C++ code)') 
+# Forward pass and save intermediate outputs
+x = input_tensor
+out_dir = 'test/output_py/resnet_debug/'
+
+# After conv1
+x1 = model.conv1(x)
+save_tensor(x1[0], os.path.join(out_dir, 'sample_0_after_conv1.pt'))
+# After bn1
+x2 = model.bn1(x1)
+save_tensor(x2[0], os.path.join(out_dir, 'sample_0_after_bn1.pt'))
+# After relu1
+x3 = model.relu(x2)
+save_tensor(x3[0], os.path.join(out_dir, 'sample_0_after_relu1.pt'))
+# After maxpool
+x4 = model.maxpool(x3)
+save_tensor(x4[0], os.path.join(out_dir, 'sample_0_after_maxpool.pt'))
+# After layer1
+x5 = model.layer1(x4)
+save_tensor(x5[0], os.path.join(out_dir, 'sample_0_after_layer1.pt'))
+# After layer2
+x6 = model.layer2(x5)
+save_tensor(x6[0], os.path.join(out_dir, 'sample_0_after_layer2.pt'))
+# After layer3
+x7 = model.layer3(x6)
+save_tensor(x7[0], os.path.join(out_dir, 'sample_0_after_layer3.pt'))
+# After layer4
+x8 = model.layer4(x7)
+save_tensor(x8[0], os.path.join(out_dir, 'sample_0_after_layer4.pt'))
+
+print('Saved all intermediate outputs for sample 0 using C++-preprocessed input.') 

compare_resnet_weights.py

@@ -0,0 +1,36 @@
+import torch
+import os
+
+PYTHON_DIR = "exported_weights/backbone_pure_tensors/"
+CPP_DIR = "exported_weights/backbone_regenerated/"
+
+def compare_tensors(a, b, label):
+    a = a.float().cpu().contiguous().view(-1)
+    b = b.float().cpu().contiguous().view(-1)
+    if a.shape != b.shape:
+        print(f"{label}: Shape mismatch: {a.shape} vs {b.shape}")
+        return
+    cos_sim = torch.nn.functional.cosine_similarity(a, b, dim=0).item()
+    mae = torch.mean(torch.abs(a - b)).item()
+    max_abs = torch.max(torch.abs(a - b)).item()
+    print(f"{label}: cos_sim={cos_sim:.8f}, MAE={mae:.8e}, max_abs={max_abs:.8e}")
+
+def main():
+    py_files = {f for f in os.listdir(PYTHON_DIR) if f.endswith('.pt')}
+    cpp_files = {f for f in os.listdir(CPP_DIR) if f.endswith('.pt')}
+    common_files = sorted(py_files & cpp_files)
+    missing_in_cpp = sorted(py_files - cpp_files)
+    missing_in_py = sorted(cpp_files - py_files)
+
+    if missing_in_cpp:
+        print("Files missing in C++ export:", missing_in_cpp)
+    if missing_in_py:
+        print("Files missing in Python export:", missing_in_py)
+
+    for fname in common_files:
+        py_tensor = torch.load(os.path.join(PYTHON_DIR, fname), map_location='cpu', weights_only=False)
+        cpp_tensor = torch.load(os.path.join(CPP_DIR, fname), map_location='cpu', weights_only=False)
+        compare_tensors(py_tensor, cpp_tensor, fname)
+
+if __name__ == "__main__":
+    main() 

ltr/models/backbone/resnet.py

@@ -2,6 +2,8 @@ import math
 import torch.nn as nn
 from collections import OrderedDict
 import torch.utils.model_zoo as model_zoo
+import torch
+import os
 
 from .base import Backbone
 
@@ -119,32 +121,47 @@ class ResNet(Backbone):
         if output_layers is None:
             output_layers = self.output_layers
 
+        # Debug directory
+        debug_dir = 'test/output_py/resnet_py/sample_0/'
+        if not os.path.exists(debug_dir):
+            os.makedirs(debug_dir)
+        sample_idx = 0  # Always save for first sample
+
+        # conv1
         x = self.conv1(x)
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'debug_resnet_conv1_output_for_bn1_input.pt'))
         x = self.bn1(x)
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'bn1_output.pt'))
         x = self.relu(x)
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'relu1_output.pt'))
 
         if self._add_output_and_check('conv1', x, outputs, output_layers):
             return outputs
 
         x = self.maxpool(x)
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'maxpool_output.pt'))
 
+        # layer1
         x = self.layer1(x)
-
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'layer1_output.pt'))
         if self._add_output_and_check('layer1', x, outputs, output_layers):
             return outputs
 
+        # layer2
         x = self.layer2(x)
-
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'layer2_output.pt'))
         if self._add_output_and_check('layer2', x, outputs, output_layers):
             return outputs
 
+        # layer3
         x = self.layer3(x)
-
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'layer3_output.pt'))
         if self._add_output_and_check('layer3', x, outputs, output_layers):
             return outputs
 
+        # layer4
         x = self.layer4(x)
-
+        torch.save(x[0].cpu(), os.path.join(debug_dir, 'layer4_output.pt'))
         if self._add_output_and_check('layer4', x, outputs, output_layers):
             return outputs
 

test/compare_models.py

@@ -26,7 +26,7 @@ from pytracking.features.net_wrappers import DiMPTorchScriptWrapper
 # For loading AtomIoUNet from source
 from ltr.models.bbreg.atom_iou_net import AtomIoUNet
 # Add import for new modular comparison
-from model_comparison.bbreg_comparison import compare_debug_tensors
+from model_comparison.bbreg_comparison import compare_debug_tensors, compare_resnet_debug_tensors
 
 SCRIPT_DIR_FOR_INIT = os.path.dirname(os.path.abspath(__file__))
 ROOT_DIR_FOR_INIT = os.path.dirname(SCRIPT_DIR_FOR_INIT)
@@ -764,13 +764,12 @@ class ComparisonRunner:
                 processed_input_tensor = input_tensor.to(self.device) # Ensure device
             # --- END REINSTATED INPUT LOADING AND PREPROCESSING ---
 
-            # --- Save preprocessed input for sample 0 ---
-            if sample_idx == 0:
-                preprocessed_dir = Path(self.cpp_output_dir) / 'resnet'
-                preprocessed_dir.mkdir(parents=True, exist_ok=True)
-                py_preprocessed_path = preprocessed_dir / f'sample_{sample_idx}_image_preprocessed_python.pt'
-                torch.save(processed_input_tensor.cpu(), py_preprocessed_path)
-                print(f"Saved Python preprocessed image for sample {sample_idx} to {py_preprocessed_path}")
+            # --- Save preprocessed input for every sample ---
+            preprocessed_dir = Path(self.cpp_output_dir) / 'resnet'
+            preprocessed_dir.mkdir(parents=True, exist_ok=True)
+            py_preprocessed_path = preprocessed_dir / f'sample_{sample_idx}_image_preprocessed_python.pt'
+            torch.save(processed_input_tensor.cpu(), py_preprocessed_path)
+            print(f"Saved Python preprocessed image for sample {sample_idx} to {py_preprocessed_path}")
             # --- END save preprocessed input ---
 
             # Initialize dictionaries to store Python-side outputs for the current sample
@@ -987,6 +986,16 @@ class ComparisonRunner:
             # processed_samples += 1 # This variable is no longer used as loop is range-based
         print("--- ResNet Output Comparison Complete ---")
 
+    def compare_resnet_debug_outputs(self, sample_idx=0, verbose=True):
+        """
+        Compare intermediate ResNet debug outputs between C++ and Python and store results.
+        """
+        cpp_dir = os.path.join(self.cpp_output_dir, 'resnet')
+        py_dir = os.path.join(self.python_output_dir, 'resnet_debug')
+        results = compare_resnet_debug_tensors(cpp_dir, py_dir, sample_idx=sample_idx, verbose=verbose)
+        self.resnet_debug_results = results
+        self.all_comparison_stats['ResNetDebug'] = results
+
     def generate_html_report(self):
         print("\nGenerating HTML report...")
         report_path = os.path.join(self.comparison_dir, "report.html")
@@ -1260,6 +1269,23 @@ class ComparisonRunner:
             f.write(html_content)
         print(f"HTML report generated at {report_path}")
 
+        # Add ResNet Debug Output Comparison Section
+        if 'ResNetDebug' in self.all_comparison_stats:
+            html_content += "<h2>ResNet Intermediate Debug Output Comparison</h2>"
+            html_content += "<table><tr><th>Stage</th><th>Cosine Similarity</th><th>Allclose</th><th>Max Abs Diff</th><th>CPP Shape</th><th>PY Shape</th></tr>"
+            for stage, stats in self.all_comparison_stats['ResNetDebug'].items():
+                html_content += f"<tr><td>{stage}</td><td>{stats['cosine_similarity']:.6f}</td><td>{stats['allclose']}</td><td>{stats['max_abs_diff']:.6f}</td><td>{stats['cpp_shape']}</td><td>{stats['py_shape']}</td></tr>"
+            html_content += "</table>"
+
+        # ... rest of HTML ...
+        html_content += "</body></html>"
+        # Save HTML
+        report_path = os.path.join(self.comparison_dir, "comparison_report.html")
+        with open(report_path, "w") as f:
+            f.write(html_content)
+        print(f"HTML report generated at: {report_path}")
+        return report_path
+
     def _generate_single_plot(self, error_array, title, plot_path, mean_val, std_abs_err, mae, max_err):
         if error_array is None or len(error_array) == 0 or np.all(np.isnan(error_array)):
             # print(f"Skipping plot for {title} as error_array is empty or all NaNs.")
@@ -1287,6 +1313,10 @@ class ComparisonRunner:
         self.compare_classifier()
         self.compare_bb_regressor()
         self.compare_preprocessed_inputs() # ADDED
+        # Compare ResNet debug outputs before generating HTML
+        print("\nComparing ResNet intermediate debug outputs...")
+        self.compare_resnet_debug_outputs(sample_idx=0, verbose=True)
+        # ... rest of tests ...
         self.generate_html_report()
         print("All tests completed!")