diff --git a/build/CMakeCache.txt b/build/CMakeCache.txt index aed7087..406c555 100644 --- a/build/CMakeCache.txt +++ b/build/CMakeCache.txt @@ -201,8 +201,8 @@ CMAKE_STATIC_LINKER_FLAGS_RELWITHDEBINFO:STRING= //Path to a program. CMAKE_STRIP:FILEPATH=/usr/bin/strip -//No help, variable specified on the command line. -CMAKE_TOOLCHAIN_FILE:UNINITIALIZED=../vcpkg/scripts/buildsystems/vcpkg.cmake +//The CMake toolchain file +CMAKE_TOOLCHAIN_FILE:FILEPATH=/media/mht/ADATA/repos/cpp_tracker/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 @@ -465,15 +465,6 @@ _VCPKG_INSTALLED_DIR:PATH=/media/mht/ADATA/repos/cpp_tracker/build/vcpkg_install //Path to a library. c10_LIBRARY:FILEPATH=/home/mht/libtorch_1.8.0_cu111/libtorch/lib/libc10.so -//Value Computed by CMake -cpp_tracker_BINARY_DIR:STATIC=/media/mht/ADATA/repos/cpp_tracker/build - -//Value Computed by CMake -cpp_tracker_IS_TOP_LEVEL:STATIC=ON - -//Value Computed by CMake -cpp_tracker_SOURCE_DIR:STATIC=/media/mht/ADATA/repos/cpp_tracker - ######################## # INTERNAL cache entries diff --git a/build/CMakeFiles/3.22.1/CMakeDetermineCompilerABI_CUDA.bin b/build/CMakeFiles/3.22.1/CMakeDetermineCompilerABI_CUDA.bin index 65a0b41..9ab9f9c 100644 Binary files a/build/CMakeFiles/3.22.1/CMakeDetermineCompilerABI_CUDA.bin and b/build/CMakeFiles/3.22.1/CMakeDetermineCompilerABI_CUDA.bin differ diff --git a/build/CMakeFiles/CMakeOutput.log b/build/CMakeFiles/CMakeOutput.log index c1fd888..893470c 100644 --- a/build/CMakeFiles/CMakeOutput.log +++ b/build/CMakeFiles/CMakeOutput.log @@ -35,8 +35,8 @@ The output was: #$ _TARGET_DIR_=targets/x86_64-linux #$ TOP=/usr/local/cuda-11.8/bin/.. #$ NVVMIR_LIBRARY_DIR=/usr/local/cuda-11.8/bin/../nvvm/libdevice -#$ LD_LIBRARY_PATH=/usr/local/cuda-11.8/bin/../lib:/usr/local/cuda-11.8/lib64:/tmp/.mount_CursorgZNQU4/usr/lib/:/tmp/.mount_CursorgZNQU4/usr/lib32/:/tmp/.mount_CursorgZNQU4/usr/lib64/:/tmp/.mount_CursorgZNQU4/lib/:/tmp/.mount_CursorgZNQU4/lib/i386-linux-gnu/:/tmp/.mount_CursorgZNQU4/lib/x86_64-linux-gnu/:/tmp/.mount_CursorgZNQU4/lib/aarch64-linux-gnu/:/tmp/.mount_CursorgZNQU4/lib32/:/tmp/.mount_CursorgZNQU4/lib64/: -#$ PATH=/usr/local/cuda-11.8/bin/../nvvm/bin:/usr/local/cuda-11.8/bin:/usr/local/cuda-11.8/bin:/home/mht/anaconda3/bin:/home/mht/anaconda3/condabin:/tmp/.mount_CursorgZNQU4/usr/bin:/tmp/.mount_CursorgZNQU4/usr/sbin:/tmp/.mount_CursorgZNQU4/usr/games:/tmp/.mount_CursorgZNQU4/bin:/tmp/.mount_CursorgZNQU4/sbin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/snap/bin:/home/mht/.local/share/JetBrains/Toolbox/scripts:/home/mht/.local/share/JetBrains/Toolbox/scripts:/home/mht/.local/share/JetBrains/Toolbox/scripts +#$ LD_LIBRARY_PATH=/usr/local/cuda-11.8/bin/../lib:/usr/local/cuda-11.8/lib64: +#$ PATH=/usr/local/cuda-11.8/bin/../nvvm/bin:/usr/local/cuda-11.8/bin:/usr/local/cuda-11.8/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/snap/bin:/home/mht/.local/share/JetBrains/Toolbox/scripts #$ INCLUDES="-I/usr/local/cuda-11.8/bin/../targets/x86_64-linux/include" #$ LIBRARIES= "-L/usr/local/cuda-11.8/bin/../targets/x86_64-linux/lib/stubs" "-L/usr/local/cuda-11.8/bin/../targets/x86_64-linux/lib" #$ CUDAFE_FLAGS= @@ -60,7 +60,7 @@ Compilation of the CUDA compiler identification source "CMakeCUDACompilerId.cu" The CUDA compiler identification is NVIDIA, found in "/media/mht/ADATA/repos/cpp_tracker/build/CMakeFiles/3.22.1/CompilerIdCUDA/a.out" Parsed CUDA nvcc implicit link information from above output: - found 'PATH=' string: [/usr/local/cuda-11.8/bin/../nvvm/bin:/usr/local/cuda-11.8/bin:/usr/local/cuda-11.8/bin:/home/mht/anaconda3/bin:/home/mht/anaconda3/condabin:/tmp/.mount_CursorgZNQU4/usr/bin:/tmp/.mount_CursorgZNQU4/usr/sbin:/tmp/.mount_CursorgZNQU4/usr/games:/tmp/.mount_CursorgZNQU4/bin:/tmp/.mount_CursorgZNQU4/sbin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/snap/bin:/home/mht/.local/share/JetBrains/Toolbox/scripts:/home/mht/.local/share/JetBrains/Toolbox/scripts:/home/mht/.local/share/JetBrains/Toolbox/scripts] + found 'PATH=' string: [/usr/local/cuda-11.8/bin/../nvvm/bin:/usr/local/cuda-11.8/bin:/usr/local/cuda-11.8/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/snap/bin:/home/mht/.local/share/JetBrains/Toolbox/scripts] found 'LIBRARIES=' string: ["-L/usr/local/cuda-11.8/bin/../targets/x86_64-linux/lib/stubs" "-L/usr/local/cuda-11.8/bin/../targets/x86_64-linux/lib"] considering line: [#$ rm tmp/a_dlink.reg.c] considering line: [gcc -D__CUDA_ARCH__=520 -D__CUDA_ARCH_LIST__=520 -E -x c++ -DCUDA_DOUBLE_MATH_FUNCTIONS -D__CUDACC__ -D__NVCC__ "-I/usr/local/cuda-11.8/bin/../targets/x86_64-linux/include" -D__CUDACC_VER_MAJOR__=11 -D__CUDACC_VER_MINOR__=8 -D__CUDACC_VER_BUILD__=89 -D__CUDA_API_VER_MAJOR__=11 -D__CUDA_API_VER_MINOR__=8 -D__NVCC_DIAG_PRAGMA_SUPPORT__=1 -include "cuda_runtime.h" -m64 "CMakeCUDACompilerId.cu" -o "tmp/CMakeCUDACompilerId.cpp1.ii" ] @@ -108,7 +108,7 @@ Parsed CUDA nvcc implicit link information from above output: Parsed CUDA nvcc include information from above output: - found 'PATH=' string: [/usr/local/cuda-11.8/bin/../nvvm/bin:/usr/local/cuda-11.8/bin:/usr/local/cuda-11.8/bin:/home/mht/anaconda3/bin:/home/mht/anaconda3/condabin:/tmp/.mount_CursorgZNQU4/usr/bin:/tmp/.mount_CursorgZNQU4/usr/sbin:/tmp/.mount_CursorgZNQU4/usr/games:/tmp/.mount_CursorgZNQU4/bin:/tmp/.mount_CursorgZNQU4/sbin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/snap/bin:/home/mht/.local/share/JetBrains/Toolbox/scripts:/home/mht/.local/share/JetBrains/Toolbox/scripts:/home/mht/.local/share/JetBrains/Toolbox/scripts] + found 'PATH=' string: [/usr/local/cuda-11.8/bin/../nvvm/bin:/usr/local/cuda-11.8/bin:/usr/local/cuda-11.8/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/snap/bin:/home/mht/.local/share/JetBrains/Toolbox/scripts] found 'LIBRARIES=' string: ["-L/usr/local/cuda-11.8/bin/../targets/x86_64-linux/lib/stubs" "-L/usr/local/cuda-11.8/bin/../targets/x86_64-linux/lib"] considering line: [#$ rm tmp/a_dlink.reg.c] considering line: [gcc -D__CUDA_ARCH__=520 -D__CUDA_ARCH_LIST__=520 -E -x c++ -DCUDA_DOUBLE_MATH_FUNCTIONS -D__CUDACC__ -D__NVCC__ "-I/usr/local/cuda-11.8/bin/../targets/x86_64-linux/include" -D__CUDACC_VER_MAJOR__=11 -D__CUDACC_VER_MINOR__=8 -D__CUDACC_VER_BUILD__=89 -D__CUDA_API_VER_MAJOR__=11 -D__CUDA_API_VER_MINOR__=8 -D__NVCC_DIAG_PRAGMA_SUPPORT__=1 -include "cuda_runtime.h" -m64 "CMakeCUDACompilerId.cu" -o "tmp/CMakeCUDACompilerId.cpp1.ii" ] @@ -159,10 +159,10 @@ Parsed CUDA nvcc include information from above output: Detecting CXX compiler ABI info compiled with the following output: Change Dir: /media/mht/ADATA/repos/cpp_tracker/build/CMakeFiles/CMakeTmp -Run Build Command(s):/usr/bin/gmake -f Makefile cmTC_34c90/fast && /usr/bin/gmake -f CMakeFiles/cmTC_34c90.dir/build.make CMakeFiles/cmTC_34c90.dir/build +Run Build Command(s):/usr/bin/gmake -f Makefile cmTC_8534d/fast && /usr/bin/gmake -f CMakeFiles/cmTC_8534d.dir/build.make CMakeFiles/cmTC_8534d.dir/build gmake[1]: Entering directory '/media/mht/ADATA/repos/cpp_tracker/build/CMakeFiles/CMakeTmp' -Building CXX object CMakeFiles/cmTC_34c90.dir/CMakeCXXCompilerABI.cpp.o -/usr/bin/c++ -v -o CMakeFiles/cmTC_34c90.dir/CMakeCXXCompilerABI.cpp.o -c /usr/share/cmake-3.22/Modules/CMakeCXXCompilerABI.cpp +Building CXX object CMakeFiles/cmTC_8534d.dir/CMakeCXXCompilerABI.cpp.o +/usr/bin/c++ -v -o CMakeFiles/cmTC_8534d.dir/CMakeCXXCompilerABI.cpp.o -c /usr/share/cmake-3.22/Modules/CMakeCXXCompilerABI.cpp Using built-in specs. 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b/build/CMakeFiles/bb_regressor.dir/cimp/bb_regressor/bb_regressor.cpp.o index 2cc8498..3fe0b60 100644 Binary files a/build/CMakeFiles/bb_regressor.dir/cimp/bb_regressor/bb_regressor.cpp.o and b/build/CMakeFiles/bb_regressor.dir/cimp/bb_regressor/bb_regressor.cpp.o differ diff --git a/build/CMakeFiles/classifier.dir/cimp/classifier/classifier.cpp.o b/build/CMakeFiles/classifier.dir/cimp/classifier/classifier.cpp.o index a36add7..60c5a56 100644 Binary files a/build/CMakeFiles/classifier.dir/cimp/classifier/classifier.cpp.o and b/build/CMakeFiles/classifier.dir/cimp/classifier/classifier.cpp.o differ diff --git a/build/install_manifest.txt b/build/install_manifest.txt deleted file mode 100644 index 4853997..0000000 --- a/build/install_manifest.txt +++ /dev/null @@ -1,2 +0,0 @@ -/media/mht/ADATA/repos/cpp_tracker/bin/tracking_demo -/media/mht/ADATA/repos/cpp_tracker/bin/test_models \ No newline at end of file diff --git a/build/libbb_regressor.a b/build/libbb_regressor.a index 51d14e8..f2adc9d 100644 Binary files a/build/libbb_regressor.a and b/build/libbb_regressor.a differ diff --git a/build/libclassifier.a b/build/libclassifier.a index 45618c2..d1d16c7 100644 Binary files a/build/libclassifier.a and b/build/libclassifier.a differ diff --git a/build/tracking_demo b/build/tracking_demo index cba7457..2ed6e80 100644 Binary files a/build/tracking_demo and b/build/tracking_demo differ diff --git a/cimp/bb_regressor/bb_regressor.cpp b/cimp/bb_regressor/bb_regressor.cpp index 5298fa6..b48c49c 100644 --- a/cimp/bb_regressor/bb_regressor.cpp +++ b/cimp/bb_regressor/bb_regressor.cpp @@ -507,49 +507,32 @@ void BBRegressor::to(torch::Device device) { } // Get IoU features from backbone features -std::vector BBRegressor::get_iou_feat(std::vector feat2) { - // Convert to double precision for better numerical stability - auto feat2_double0 = feat2[0].to(torch::kFloat64); - auto feat2_double1 = feat2[1].to(torch::kFloat64); +std::vector BBRegressor::get_iou_feat(std::vector feat2_input) { + torch::Tensor feat3_t_original = feat2_input[0]; + torch::Tensor feat4_t_original = feat2_input[1]; // Reshape exactly as in Python implementation - // In Python: feat2 = [f.reshape(-1, *f.shape[-3:]) if f.dim()==5 else f for f in feat2] - if (feat2_double0.dim() == 5) { - auto shape = feat2_double0.sizes(); - feat2_double0 = feat2_double0.reshape({-1, shape[2], shape[3], shape[4]}).contiguous(); + if (feat3_t_original.dim() == 5) { + auto shape = feat3_t_original.sizes(); + feat3_t_original = feat3_t_original.reshape({-1, shape[2], shape[3], shape[4]}); } - - if (feat2_double1.dim() == 5) { - auto shape = feat2_double1.sizes(); - feat2_double1 = feat2_double1.reshape({-1, shape[2], shape[3], shape[4]}).contiguous(); + if (feat4_t_original.dim() == 5) { + auto shape = feat4_t_original.sizes(); + feat4_t_original = feat4_t_original.reshape({-1, shape[2], shape[3], shape[4]}); } - // Convert back to float32 for convolution operations - feat2[0] = feat2_double0.to(torch::kFloat32).contiguous(); - feat2[1] = feat2_double1.to(torch::kFloat32).contiguous(); - - // Apply convolutions exactly as in Python - torch::Tensor feat3_t = feat2[0]; - torch::Tensor feat4_t = feat2[1]; + // Ensure inputs to conv are contiguous and kFloat32 (ResNet output should be float32) + torch::Tensor feat3_t = feat3_t_original.contiguous().to(torch::kFloat32); + torch::Tensor feat4_t = feat4_t_original.contiguous().to(torch::kFloat32); - // Ensure we're in evaluation mode torch::NoGradGuard no_grad; - // Apply convolutions just like Python version torch::Tensor c3_t_1 = conv3_1t->forward(feat3_t); - c3_t_1 = c3_t_1.contiguous(); - torch::Tensor c3_t = conv3_2t->forward(c3_t_1); - c3_t = c3_t.contiguous(); - torch::Tensor c4_t_1 = conv4_1t->forward(feat4_t); - c4_t_1 = c4_t_1.contiguous(); - torch::Tensor c4_t = conv4_2t->forward(c4_t_1); - c4_t = c4_t.contiguous(); - // Return results - return {c3_t, c4_t}; + return {c3_t.contiguous(), c4_t.contiguous()}; // Ensure output is contiguous and float32 } // Get modulation vectors for the target diff --git a/cimp/classifier/classifier.h b/cimp/classifier/classifier.h index e83a2f5..32cda88 100644 --- a/cimp/classifier/classifier.h +++ b/cimp/classifier/classifier.h @@ -69,6 +69,9 @@ private: torch::Tensor weight; InstanceL2Norm norm; + // Constructor to initialize norm with specific scale + FeatureExtractor() : norm(true, 1e-5f, 0.011048543456039804f) {} + torch::Tensor forward(torch::Tensor x); torch::Tensor extract_feat(torch::Tensor x); void load_weights(const std::string& weights_dir, torch::Device device); diff --git a/cimp/resnet/resnet.cpp b/cimp/resnet/resnet.cpp index 418528e..6304d7f 100644 --- a/cimp/resnet/resnet.cpp +++ b/cimp/resnet/resnet.cpp @@ -5,6 +5,7 @@ #include // ensure this is included #include // Added for std::ifstream #include // Added for std::vector +#include // Added for std::fixed and std::setprecision namespace cimp { namespace resnet { @@ -66,34 +67,37 @@ BottleneckImpl::BottleneckImpl(const std::string& base_weights_dir, // conv1 conv1 = torch::nn::Conv2d(torch::nn::Conv2dOptions(inplanes, planes, 1).bias(false)); - bn1 = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(planes)); + bn1 = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(planes).eps(static_cast(1e-5)).momentum(0.1).affine(true).track_running_stats(true)); conv1->weight = load_named_tensor(base_weights_dir, block_param_prefix + "conv1.weight", device); bn1->weight = load_named_tensor(base_weights_dir, block_param_prefix + "bn1.weight", device); bn1->bias = load_named_tensor(base_weights_dir, block_param_prefix + "bn1.bias", device); bn1->named_buffers()["running_mean"] = load_named_tensor(base_weights_dir, block_param_prefix + "bn1.running_mean", device); bn1->named_buffers()["running_var"] = load_named_tensor(base_weights_dir, block_param_prefix + "bn1.running_var", device); + bn1->named_buffers()["num_batches_tracked"] = load_named_tensor(base_weights_dir, block_param_prefix + "bn1.num_batches_tracked", device); register_module("conv1", conv1); register_module("bn1", bn1); // conv2 conv2 = torch::nn::Conv2d(torch::nn::Conv2dOptions(planes, planes, 3).stride(stride_member).padding(1).bias(false)); - bn2 = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(planes)); + bn2 = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(planes).eps(static_cast(1e-5)).momentum(0.1).affine(true).track_running_stats(true)); conv2->weight = load_named_tensor(base_weights_dir, block_param_prefix + "conv2.weight", device); bn2->weight = load_named_tensor(base_weights_dir, block_param_prefix + "bn2.weight", device); bn2->bias = load_named_tensor(base_weights_dir, block_param_prefix + "bn2.bias", device); bn2->named_buffers()["running_mean"] = load_named_tensor(base_weights_dir, block_param_prefix + "bn2.running_mean", device); bn2->named_buffers()["running_var"] = load_named_tensor(base_weights_dir, block_param_prefix + "bn2.running_var", device); + bn2->named_buffers()["num_batches_tracked"] = load_named_tensor(base_weights_dir, block_param_prefix + "bn2.num_batches_tracked", device); register_module("conv2", conv2); register_module("bn2", bn2); // conv3 conv3 = torch::nn::Conv2d(torch::nn::Conv2dOptions(planes, planes * expansion_factor, 1).bias(false)); - bn3 = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(planes * expansion_factor)); + bn3 = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(planes * expansion_factor).eps(static_cast(1e-5)).momentum(0.1).affine(true).track_running_stats(true)); conv3->weight = load_named_tensor(base_weights_dir, block_param_prefix + "conv3.weight", device); bn3->weight = load_named_tensor(base_weights_dir, block_param_prefix + "bn3.weight", device); bn3->bias = load_named_tensor(base_weights_dir, block_param_prefix + "bn3.bias", device); bn3->named_buffers()["running_mean"] = load_named_tensor(base_weights_dir, block_param_prefix + "bn3.running_mean", device); bn3->named_buffers()["running_var"] = load_named_tensor(base_weights_dir, block_param_prefix + "bn3.running_var", device); + bn3->named_buffers()["num_batches_tracked"] = load_named_tensor(base_weights_dir, block_param_prefix + "bn3.num_batches_tracked", device); register_module("conv3", conv3); register_module("bn3", bn3); @@ -144,15 +148,21 @@ ResNetImpl::ResNetImpl(const std::string& base_weights_dir_path, : _output_layers(output_layers_param), _base_weights_dir(base_weights_dir_path) { conv1 = torch::nn::Conv2d(torch::nn::Conv2dOptions(3, 64, 7).stride(2).padding(3).bias(false)); - bn1 = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(64)); + bn1 = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(64).eps(static_cast(1e-5)).momentum(0.1).affine(true).track_running_stats(true)); this->conv1->weight = load_named_tensor(this->_base_weights_dir, "conv1.weight", device); this->bn1->weight = load_named_tensor(this->_base_weights_dir, "bn1.weight", device); this->bn1->bias = load_named_tensor(this->_base_weights_dir, "bn1.bias", device); + this->bn1->named_buffers()["running_mean"] = load_named_tensor(this->_base_weights_dir, "bn1.running_mean", device); this->bn1->named_buffers()["running_var"] = load_named_tensor(this->_base_weights_dir, "bn1.running_var", device); + + this->bn1->named_buffers()["num_batches_tracked"] = load_named_tensor(this->_base_weights_dir, "bn1.num_batches_tracked", device); register_module("conv1", conv1); register_module("bn1", bn1); + std::cout << "CPP ResNetImpl::bn1 running_mean sum: " << std::fixed << std::setprecision(10) << this->bn1->running_mean.sum().item() << std::endl; + std::cout << "CPP ResNetImpl::bn1 running_var sum: " << std::fixed << std::setprecision(10) << this->bn1->running_var.sum().item() << std::endl; + relu = torch::nn::ReLU(torch::nn::ReLUOptions().inplace(true)); maxpool = torch::nn::MaxPool2d(torch::nn::MaxPool2dOptions(3).stride(2).padding(1)); register_module("relu", relu); @@ -178,7 +188,7 @@ torch::nn::Sequential ResNetImpl::_make_layer(int64_t planes_for_block, int64_t if (stride_for_first_block != 1 || this->inplanes != planes_for_block * ResNetImpl::expansion) { torch::nn::Sequential ds_seq; auto conv_down = torch::nn::Conv2d(torch::nn::Conv2dOptions(this->inplanes, planes_for_block * ResNetImpl::expansion, 1).stride(stride_for_first_block).bias(false)); - auto bn_down = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(planes_for_block * ResNetImpl::expansion)); + auto bn_down = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(planes_for_block * ResNetImpl::expansion).eps(static_cast(1e-5)).momentum(0.1).affine(true).track_running_stats(true)); std::string ds_block_prefix = layer_param_prefix + "0.downsample."; @@ -187,6 +197,7 @@ torch::nn::Sequential ResNetImpl::_make_layer(int64_t planes_for_block, int64_t bn_down->bias = load_named_tensor(this->_base_weights_dir, ds_block_prefix + "1.bias", device); bn_down->named_buffers()["running_mean"] = load_named_tensor(this->_base_weights_dir, ds_block_prefix + "1.running_mean", device); bn_down->named_buffers()["running_var"] = load_named_tensor(this->_base_weights_dir, ds_block_prefix + "1.running_var", device); + bn_down->named_buffers()["num_batches_tracked"] = load_named_tensor(this->_base_weights_dir, ds_block_prefix + "1.num_batches_tracked", device); ds_seq->push_back(conv_down); ds_seq->push_back(bn_down); @@ -212,66 +223,78 @@ torch::nn::Sequential ResNetImpl::_make_layer(int64_t planes_for_block, int64_t std::map ResNetImpl::forward(torch::Tensor x) { std::map outputs; + auto should_output = [&](const std::string& layer_name) { return std::find(_output_layers.begin(), _output_layers.end(), layer_name) != _output_layers.end(); }; - // Original GPU path for conv1 x = conv1->forward(x); if (should_output("conv1_output")) outputs["conv1_output"] = x; - x = bn1->forward(x.clone()); + x = bn1->forward(x); if (should_output("bn1_output")) outputs["bn1_output"] = x; - x = relu->forward(x.clone()); + x = relu->forward(x); if (should_output("relu1_output")) outputs["relu1_output"] = x; - torch::Tensor x_pre_layer1 = maxpool->forward(x.clone()); + torch::Tensor x_pre_layer1 = maxpool->forward(x); if (should_output("maxpool_output")) outputs["maxpool_output"] = x_pre_layer1; - // Pass x_pre_layer1 to layer1 - torch::Tensor x_after_layer1 = layer1->forward(x_pre_layer1.clone()); // Use .clone() if layer1 might modify input inplace, good practice + // 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 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(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: " + << (base_module_ptr ? typeid(*base_module_ptr).name() : "null") << std::endl; + } + } catch (const std::exception& e) { + std::cerr << "EXCEPTION while getting layer1_0_block_output: " << e.what() << std::endl; + } + } + } + + torch::Tensor x_after_layer1 = layer1->forward(x_pre_layer1); if (should_output("layer1")) outputs["layer1"] = x_after_layer1; if (should_output("layer1_0_shortcut_output")) { if (layer1 && !layer1->is_empty()) { try { - // Get the first module (Bottleneck) from layer1 Sequential container std::shared_ptr first_block_module_ptr = layer1->ptr(0); - // Attempt to dynamically cast to Bottleneck type auto bottleneck_module_holder = std::dynamic_pointer_cast(first_block_module_ptr); - if (bottleneck_module_holder) { // Check if cast was successful - // Accessing projection_shortcut directly from BottleneckImpl + if (bottleneck_module_holder) { if (bottleneck_module_holder->projection_shortcut) { - torch::Tensor shortcut_out = bottleneck_module_holder->projection_shortcut->forward(x_pre_layer1.clone()); + torch::Tensor shortcut_out = bottleneck_module_holder->projection_shortcut->forward(x_pre_layer1); outputs["layer1_0_shortcut_output"] = shortcut_out; - } else { - // std::cout << "DEBUG: layer1.0 projection_shortcut is null." << std::endl; } - } else { - // std::cerr << "ERROR: Failed to cast first block of layer1 to BottleneckImpl." << std::endl; } } catch (const std::exception& e) { // std::cerr << "ERROR: Exception while getting layer1_0_shortcut_output: " << e.what() << std::endl; } - } else { - // std::cout << "DEBUG: layer1 is null or empty, cannot get shortcut output." << std::endl; } } - torch::Tensor x_current = x_after_layer1; // Continue with the output of layer1 + torch::Tensor x_current = x_after_layer1; - x_current = layer2->forward(x_current.clone()); + x_current = layer2->forward(x_current); if (should_output("layer2")) outputs["layer2"] = x_current; - x_current = layer3->forward(x_current.clone()); + x_current = layer3->forward(x_current); if (should_output("layer3")) outputs["layer3"] = x_current; - x_current = layer4->forward(x_current.clone()); + x_current = layer4->forward(x_current); if (should_output("layer4")) outputs["layer4"] = x_current; - if (should_output("features")) outputs["features"] = x_current; // 'features' is typically layer4 output + if (should_output("features")) outputs["features"] = x_current; return outputs; } diff --git a/ltr/models/bbreg/__pycache__/atom_iou_net.cpython-311.pyc b/ltr/models/bbreg/__pycache__/atom_iou_net.cpython-311.pyc index 8c4dac2..2222866 100644 Binary files a/ltr/models/bbreg/__pycache__/atom_iou_net.cpython-311.pyc and b/ltr/models/bbreg/__pycache__/atom_iou_net.cpython-311.pyc differ diff --git a/pytracking/features/__pycache__/net_wrappers.cpython-311.pyc b/pytracking/features/__pycache__/net_wrappers.cpython-311.pyc index bf09c18..f21b10e 100644 Binary files a/pytracking/features/__pycache__/net_wrappers.cpython-311.pyc and b/pytracking/features/__pycache__/net_wrappers.cpython-311.pyc differ diff --git a/test/compare_models.py b/test/compare_models.py index 7bf1af8..cee1182 100644 --- a/test/compare_models.py +++ b/test/compare_models.py @@ -98,6 +98,34 @@ class ComparisonRunner: if not os.path.exists(self.comparison_dir): os.makedirs(self.comparison_dir) + print("PYTHON: Attempting to load 'traced_resnet50.pth'...") + try: + self.models['ResNet'] = torch.jit.load('traced_resnet50.pth', map_location=self.device) + print("PYTHON: Successfully loaded 'traced_resnet50.pth'.") + self.models['ResNet'].eval() + print("PYTHON: ResNet JIT model set to eval().") + except Exception as e: + print(f"PYTHON: CRITICAL ERROR loading 'traced_resnet50.pth': {e}") + self.models['ResNet'] = None # Ensure it's None if loading failed + + # Print sums of ResNet.bn1 running_mean and running_var from state_dict + print("PYTHON: Attempting to access ResNet state_dict (if model loaded)...") + if self.models.get('ResNet'): + try: + resnet_state_dict = self.models['ResNet'].state_dict() + print("PYTHON ResNet state_dict keys:", list(resnet_state_dict.keys())) # PRINT ALL KEYS + py_bn1_running_mean = resnet_state_dict.get('bn1.running_mean') + py_bn1_running_var = resnet_state_dict.get('bn1.running_var') + if py_bn1_running_mean is not None and py_bn1_running_var is not None: + print(f"PYTHON ResNet.bn1 running_mean sum (from state_dict): {py_bn1_running_mean.sum().item():.10f}") + print(f"PYTHON ResNet.bn1 running_var sum (from state_dict): {py_bn1_running_var.sum().item():.10f}") + else: + print("PYTHON: ResNet.bn1 running_mean or running_var is None in state_dict.") + except Exception as e: + print(f"PYTHON: Error accessing ResNet.bn1 state_dict: {e}") + + # Load other models if necessary (e.g., BBRegressor, Classifier) + def load_python_models(self): print("DEBUG: ComparisonRunner.load_python_models() ENTERED") # DEBUG PRINT """Initialize Python models""" @@ -301,6 +329,93 @@ class ComparisonRunner: print(" Skipping layer1.0 parameter comparison: ResNet model or its layer1 not found/empty.") print("--- END CURRENTLY USED layer1.0 PARAMS COMPARISON ---\n") # Corrected to \n + # --- START WEIGHT COMPARISON FOR layer1.1 and layer1.2 --- + for block_idx_in_layer1 in [1, 2]: # For layer1.1 and layer1.2 + print(f"\n--- COMPARING CURRENTLY USED layer1.{block_idx_in_layer1} PARAMS (Python vs C++) ---") + layer1_block_prefix = f"layer1.{block_idx_in_layer1}." + # Components within a standard bottleneck block (no downsample for these) + block_components = { + "conv1": ["weight"], + "bn1": ["weight", "bias", "running_mean", "running_var", "num_batches_tracked"], + "conv2": ["weight"], + "bn2": ["weight", "bias", "running_mean", "running_var", "num_batches_tracked"], + "conv3": ["weight"], + "bn3": ["weight", "bias", "running_mean", "running_var", "num_batches_tracked"], + } + + if self.models.get('ResNet') and hasattr(self.models['ResNet'], 'layer1') and len(self.models['ResNet'].layer1) > block_idx_in_layer1: + py_layer1_block_module = self.models['ResNet'].layer1[block_idx_in_layer1] + + for comp_name, param_list in block_components.items(): + py_comp_module = py_layer1_block_module + try: + # No nested modules like 'downsample' for these blocks + py_comp_module = getattr(py_comp_module, comp_name) + except AttributeError: + print(f" Python ResNet model's layer1.{block_idx_in_layer1} does not have component {comp_name}. Skipping.") + continue + + for p_name in param_list: + py_param_tensor_name = f"{layer1_block_prefix}{comp_name}.{p_name}" + # C++ saves files like layer1_0_bn1_weight.pt or layer1_1_bn1_weight.pt + cpp_param_filename = f"{layer1_block_prefix.replace('.', '_')}{comp_name.replace('.', '_')}_{p_name}.pt" + + py_param_tensor = None + if hasattr(py_comp_module, p_name): + param_tensor_val = getattr(py_comp_module, p_name) + if param_tensor_val is not None: + py_param_tensor = param_tensor_val.detach().cpu() + print(f" Python ResNet {py_param_tensor_name} shape: {py_param_tensor.shape}") + else: + print(f" Python ResNet {py_param_tensor_name} is None.") + elif p_name == "num_batches_tracked" and isinstance(py_comp_module, torch.nn.BatchNorm2d): + # PyTorch stores num_batches_tracked in _buffers, not as a direct attribute usually + if py_comp_module.num_batches_tracked is not None: + py_param_tensor = py_comp_module.num_batches_tracked.detach().cpu() + print(f" Python ResNet {py_param_tensor_name} (from buffer) shape: {py_param_tensor.shape}") + else: + print(f" Python ResNet {py_param_tensor_name} (from buffer) is None.") + else: + print(f" Python ResNet module {comp_name} does not have param/buffer {p_name}.") + + cpp_param_path = os.path.join(self.root_dir, "exported_weights/backbone_regenerated", cpp_param_filename) + cpp_param_tensor = None + if os.path.exists(cpp_param_path): + try: + cpp_param_tensor = torch.load(cpp_param_path, map_location='cpu', weights_only=False) + except Exception as e: + print(f" Error loading C++ {cpp_param_filename} from {cpp_param_path}: {e}") + else: + print(f" Warning: C++ {cpp_param_filename} file not found: {cpp_param_path}") + + print(f" Comparison for {py_param_tensor_name} vs {cpp_param_filename}:") + if py_param_tensor is not None and cpp_param_tensor is not None: + if isinstance(py_param_tensor, torch.Tensor) and isinstance(cpp_param_tensor, torch.Tensor): + # Ensure tensors are float for allclose if one is int (e.g. num_batches_tracked) + py_param_tensor_float = py_param_tensor.float() + cpp_param_tensor_float = cpp_param_tensor.float() + all_close = torch.allclose(py_param_tensor_float, cpp_param_tensor_float) + print(f" torch.allclose: {all_close}") + if not all_close: + abs_diff = torch.abs(py_param_tensor_float - cpp_param_tensor_float) + mae = torch.mean(abs_diff).item() + max_abs_err = torch.max(abs_diff).item() + print(f" MAE (Weight/Buffer): {mae:.4e}") + print(f" Max Abs Err (Weight/Buffer): {max_abs_err:.4e}") + # Also print L2 norms for context + l2_py = torch.linalg.norm(py_param_tensor_float.flatten()).item() + l2_cpp = torch.linalg.norm(cpp_param_tensor_float.flatten()).item() + print(f" L2 Norm Python: {l2_py:.4e}") + print(f" L2 Norm C++: {l2_cpp:.4e}") + else: + print(f" Skipping comparison due to type mismatch after loading for {py_param_tensor_name}.") + else: + print(f" Skipping comparison because one or both tensors could not be obtained for {py_param_tensor_name}.") + else: + print(f" Skipping layer1.{block_idx_in_layer1} parameter comparison: ResNet model or its layer1 not found/long enough.") + print(f"--- END CURRENTLY USED layer1.{block_idx_in_layer1} PARAMS COMPARISON ---\n") + # --- END WEIGHT COMPARISON FOR layer1.1 and layer1.2 --- + # --- END TEMPORARY WEIGHT COMPARISON --- # This marker is now after layer1.0 checks print("\n--- Types at END of load_python_models: ---") @@ -524,14 +639,32 @@ class ComparisonRunner: if 'Classifier' in self.models: print(f" self.models['Classifier'] type: {type(self.models['Classifier'])}") if 'BBRegressor' in self.models: print(f" self.models['BBRegressor'] type: {type(self.models['BBRegressor'])}") - print("\nComparing ResNet outputs...") py_input_common_dir = os.path.join(self.root_dir, 'test', 'input_samples', 'common') cpp_output_resnet_dir = os.path.join(self.cpp_output_dir, 'resnet') + # Ensure self.py_resnet_output_dir is defined, e.g., in __init__ or where other py output dirs are + if not hasattr(self, 'py_resnet_output_dir') or not self.py_resnet_output_dir: + self.py_resnet_output_dir = Path(self.python_output_dir) / 'resnet' + self.py_resnet_output_dir.mkdir(parents=True, exist_ok=True) - # Convert to Path objects for exists() check, though os.path.exists also works with strings + # Define Path objects for directory checks py_input_common_dir_path = Path(py_input_common_dir) cpp_output_resnet_dir_path = Path(cpp_output_resnet_dir) + comparison_configs = [ + ("ResNet Conv1 Output (Pre-BN)", "_conv1_output_py.pt", "_conv1_output.pt", self.py_resnet_output_dir, cpp_output_resnet_dir), + ("ResNet Conv1", "_conv1_output.pt", "_conv1_output.pt", self.py_resnet_output_dir, cpp_output_resnet_dir), # Assumes Py also saved conv1 output if it was meant to be same as C++ pre-bn + ("ResNet BN1", "_bn1_output.pt", "_bn1_output.pt", self.py_resnet_output_dir, cpp_output_resnet_dir), + ("ResNet ReLU1", "_relu1_output.pt", "_relu1_output.pt", self.py_resnet_output_dir, cpp_output_resnet_dir), + ("ResNet MaxPool", "_maxpool_output.pt", "_maxpool_output.pt", self.py_resnet_output_dir, cpp_output_resnet_dir), + ("ResNet Layer1.0 Block Output", "_layer1_0_block_output.pt", "_layer1_0_block_output.pt", self.py_resnet_output_dir, cpp_output_resnet_dir), + ("ResNet Layer1.0 Shortcut Output", "_layer1_0_shortcut_output.pt", "_layer1_0_shortcut_output.pt", self.py_resnet_output_dir, cpp_output_resnet_dir), + ("ResNet Layer1", "_layer1_output.pt", "_layer1_output.pt", self.py_resnet_output_dir, cpp_output_resnet_dir), + ("ResNet Layer2", "_layer2_output.pt", "_layer2_output.pt", self.py_resnet_output_dir, cpp_output_resnet_dir), + ("ResNet Layer3", "_layer3_output.pt", "_layer3_output.pt", self.py_resnet_output_dir, cpp_output_resnet_dir), + ("ResNet Layer4", "_layer4_output.pt", "_layer4_output.pt", self.py_resnet_output_dir, cpp_output_resnet_dir), + ("ResNet Features", "_features_output.pt", "_features_output.pt", self.py_resnet_output_dir, cpp_output_resnet_dir) + ] + if not py_input_common_dir_path.exists() or not cpp_output_resnet_dir_path.exists(): print(f"ResNet input ({py_input_common_dir_path}) or C++ ResNet output dir ({cpp_output_resnet_dir_path}) not found. Skipping ResNet comparison.") # Populate NaN for all expected ResNet comparisons if dirs are missing @@ -584,14 +717,45 @@ class ComparisonRunner: with torch.no_grad(): py_model_resnet = self.models.get('ResNet') if py_model_resnet: - # Original GPU path for all Python ResNet layers - current_features = preprocessed_py_image_for_conv1 # Start with preprocessed image + current_features = preprocessed_py_image_for_conv1 py_conv1_out = py_model_resnet.conv1(current_features) - py_bn1_out = py_model_resnet.bn1(py_conv1_out.clone()) - py_relu1_out = py_model_resnet.relu(py_bn1_out.clone()) - py_maxpool_out = py_model_resnet.maxpool(py_relu1_out.clone()) - x_for_py_layer1_input = py_maxpool_out # This is the input to layer1 block + # Ensure self.py_resnet_output_dir is defined and is a Path object + if not hasattr(self, 'py_resnet_output_dir') or not self.py_resnet_output_dir: + self.py_resnet_output_dir = Path(self.python_output_dir) / 'resnet' + self.py_resnet_output_dir.mkdir(parents=True, exist_ok=True) + py_conv1_out_path = self.py_resnet_output_dir / f'sample_{i}_conv1_output_py.pt' + torch.save(py_conv1_out.cpu(), str(py_conv1_out_path)) + + # --- BN1 on CPU for debugging (Python) --- + py_bn1_out = py_model_resnet.bn1(py_conv1_out) # Original line + + py_relu1_out = py_model_resnet.relu(py_bn1_out) + py_maxpool_out = py_model_resnet.maxpool(py_relu1_out) + x_for_py_layer1_input = py_maxpool_out + + # Output of the first bottleneck block in layer1 + py_layer1_0_block_out_tensor = None # Initialize to avoid ref before assignment if try fails + if hasattr(py_model_resnet, 'layer1') and len(py_model_resnet.layer1) > 0: + try: + py_layer1_0_block_out_tensor = py_model_resnet.layer1[0](x_for_py_layer1_input) # REMOVED .clone() for consistency with best Layer1.0 result + # Ensure cpp_resnet_sample_dir is defined, if not, use a fallback or define it earlier + # Assuming cpp_resnet_sample_dir is defined like: cpp_resnet_sample_dir = Path(self.cpp_output_dir) / 'resnet' + # Which should be: cpp_resnet_dir = Path(self.cpp_output_dir) / 'resnet' # as per usage elsewhere + # And then: cpp_resnet_sample_dir = cpp_resnet_dir # if sample specific subdirs are not used for this + # For safety, let's use the already established cpp_output_resnet_dir path from later in the code + # cpp_output_resnet_dir = os.path.join(self.cpp_output_dir, 'resnet') + # Need to ensure cpp_output_resnet_dir is a Path object if used with / + # From later code: cpp_output_resnet_dir_path = Path(self.cpp_output_dir) / 'resnet' + + current_cpp_resnet_dir = Path(self.cpp_output_dir) / 'resnet' # Define it based on existing patterns + current_cpp_resnet_dir.mkdir(parents=True, exist_ok=True) # Ensure directory exists + + py_layer1_0_block_save_path = current_cpp_resnet_dir / f'sample_{i}_layer1_0_block_output.pt' + torch.save(py_layer1_0_block_out_tensor.cpu(), str(py_layer1_0_block_save_path)) + # print(f"DEBUG: Saved Python layer1[0] block output for sample {i} to {py_layer1_0_block_save_path}") + except Exception as e_block: + print(f"ERROR: Failed to get/save Python layer1[0] block output for sample {i}: {e_block}") # Shortcut for layer1.0 (if exists) if hasattr(py_model_resnet, 'layer1') and len(py_model_resnet.layer1) > 0 and \ @@ -646,6 +810,7 @@ class ComparisonRunner: cpp_conv1_path = os.path.join(cpp_output_resnet_dir, f'sample_{i}_conv1_output.pt') # ADDED cpp_bn1_path = os.path.join(cpp_output_resnet_dir, f'sample_{i}_bn1_output.pt') # ADDED cpp_relu1_path = os.path.join(cpp_output_resnet_dir, f'sample_{i}_relu1_output.pt') # ADDED + cpp_layer1_0_block_output_path = os.path.join(cpp_output_resnet_dir, f'sample_{i}_layer1_0_block_output.pt') # ADDED cpp_layer1_out = self.load_cpp_tensor(cpp_layer1_path, self.device) cpp_layer2_out = self.load_cpp_tensor(cpp_layer2_path, self.device) @@ -657,17 +822,33 @@ class ComparisonRunner: cpp_conv1_out = self.load_cpp_tensor(cpp_conv1_path, self.device) # ADDED cpp_bn1_out = self.load_cpp_tensor(cpp_bn1_path, self.device) # ADDED cpp_relu1_out = self.load_cpp_tensor(cpp_relu1_path, self.device) # ADDED + cpp_layer1_0_block_output_tensor = self.load_cpp_tensor(cpp_layer1_0_block_output_path, self.device) # ADDED + + # Load the Python pre-BN conv1 output that was saved earlier + py_conv1_out_pre_bn_tensor = None + # Ensure self.py_resnet_output_dir is defined (it should be if the save operation worked) + if hasattr(self, 'py_resnet_output_dir') and self.py_resnet_output_dir: + py_conv1_out_pre_bn_path = self.py_resnet_output_dir / f'sample_{i}_conv1_output_py.pt' + if py_conv1_out_pre_bn_path.exists(): + try: + py_conv1_out_pre_bn_tensor = torch.load(str(py_conv1_out_pre_bn_path), map_location=self.device) + except Exception as e_load_py_conv1: + print(f"Error loading Python conv1_output_py (pre-BN) for sample {i}: {e_load_py_conv1}") + else: + print(f"Warning: self.py_resnet_output_dir not defined, cannot load py_conv1_output_py.pt for sample {i}") - self._compare_tensor_data(py_conv1_out, cpp_conv1_out, "ResNet Conv1", i, current_errors) # REVERTED to py_conv1_out - self._compare_tensor_data(py_bn1_out, cpp_bn1_out, "ResNet BN1", i, current_errors) - self._compare_tensor_data(py_relu1_out, cpp_relu1_out, "ResNet ReLU1", i, current_errors) + # Comparisons + self._compare_tensor_data(py_conv1_out_pre_bn_tensor, cpp_conv1_out, "ResNet Conv1 Output (Pre-BN)", i, current_errors) + self._compare_tensor_data(py_conv1_out, cpp_conv1_out, "ResNet Conv1", i, current_errors) + self._compare_tensor_data(py_bn1_out, cpp_bn1_out, "ResNet BN1", i, current_errors) + self._compare_tensor_data(py_relu1_out, cpp_relu1_out, "ResNet ReLU1", i, current_errors) self._compare_tensor_data(py_maxpool_out, cpp_maxpool_out, "ResNet MaxPool", i, current_errors) self._compare_tensor_data(py_layer1_out, cpp_layer1_out, "ResNet Layer1", i, current_errors) - self._compare_tensor_data(py_layer1_0_shortcut_out, cpp_layer1_0_shortcut_out, "ResNet Layer1.0 Shortcut", i, current_errors) self._compare_tensor_data(py_layer2_out, cpp_layer2_out, "ResNet Layer2", i, current_errors) self._compare_tensor_data(py_layer3_out, cpp_layer3_out, "ResNet Layer3", i, current_errors) self._compare_tensor_data(py_layer4_out, cpp_layer4_out, "ResNet Layer4", i, current_errors) self._compare_tensor_data(py_features_out, cpp_features_out, "ResNet Features", i, current_errors) + self._compare_tensor_data(py_layer1_0_shortcut_out, cpp_layer1_0_shortcut_out, "ResNet Layer1.0 Shortcut", i, current_errors) if current_errors: self.all_comparison_stats[f"ResNet_Sample_{i}"] = current_errors diff --git a/test/output_py/bb_regressor/sample_0_debug_c3_t_prepool_py.pt b/test/output_py/bb_regressor/sample_0_debug_c3_t_prepool_py.pt new file mode 100644 index 0000000..d1fbc1e Binary files /dev/null and b/test/output_py/bb_regressor/sample_0_debug_c3_t_prepool_py.pt differ diff --git a/test/output_py/bb_regressor/sample_0_debug_c4_t_prepool_py.pt 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mkdir -p build # Build the C++ code cd build echo "Building C++ code..." +# Explicitly touch the main cpp file to try and ensure build system picks up changes +touch ../test/test_models.cpp cmake .. make -j$(nproc) diff --git a/test/test_models.cpp b/test/test_models.cpp index 0aae4b3..912167c 100644 --- a/test/test_models.cpp +++ b/test/test_models.cpp @@ -118,13 +118,18 @@ int main(int argc, char* argv[]) { std::string bb_reg_weights_path = "../exported_weights/bb_regressor"; std::string classifier_weights_path = "../exported_weights/classifier"; std::optional resnet_model_opt; - std::optional classifier_model_opt; - std::optional bb_regressor_model_opt; + // Use TORCH_MODULE wrapper types directly + std::optional classifier_model_opt_wrapped; + std::optional bb_regressor_model_opt_wrapped; bool models_initialized_ok = true; try { - std::vector output_layers = {"conv1_output", "bn1_output", "relu1_output", "maxpool_output", "layer1", "layer2", "layer3", "layer4", "features", "layer1_0_shortcut_output"}; - resnet_model_opt.emplace(cimp::resnet::resnet50(resnet_weights_path, output_layers, device)); + std::vector output_layers_resnet = { + "conv1_output", "bn1_output", "relu1_output", "maxpool_output", + "layer1", "layer2", "layer3", "layer4", "features", + "layer1_0_shortcut_output", "layer1_0_block_output" + }; + resnet_model_opt.emplace(cimp::resnet::resnet50(resnet_weights_path, output_layers_resnet, device)); (*resnet_model_opt)->to(device); (*resnet_model_opt)->eval(); std::cout << "ResNet-50 initialized successfully." << std::endl; @@ -136,8 +141,10 @@ int main(int argc, char* argv[]) { if (models_initialized_ok) { try { std::cout << "Attempting to initialize BBRegressor with weights from: " << bb_reg_weights_path << std::endl; - bb_regressor_model_opt.emplace(bb_reg_weights_path, device); - bb_regressor_model_opt->eval(); + // BBRegressor class is in global namespace + bb_regressor_model_opt_wrapped.emplace(BBRegressor(bb_reg_weights_path, device)); + (*bb_regressor_model_opt_wrapped).to(device); + (*bb_regressor_model_opt_wrapped).eval(); std::cout << "BBRegressor initialized successfully." << std::endl; } catch (const std::exception& e) { std::cerr << "CRITICAL: Error initializing BBRegressor: " << e.what() << std::endl; @@ -148,7 +155,10 @@ int main(int argc, char* argv[]) { if (models_initialized_ok) { try { std::cout << "Attempting to initialize Classifier with weights from: " << classifier_weights_path << std::endl; - classifier_model_opt.emplace(classifier_weights_path, device); + // Classifier class is in global namespace + classifier_model_opt_wrapped.emplace(Classifier(classifier_weights_path, device)); + (*classifier_model_opt_wrapped).to(device); + // (*classifier_model_opt_wrapped).eval(); // Classifier class does not have an eval() method std::cout << "Classifier initialized successfully." << std::endl; } catch (const std::exception& e) { std::cerr << "CRITICAL: Error initializing Classifier: " << e.what() << std::endl; @@ -244,11 +254,11 @@ int main(int argc, char* argv[]) { } // 2. Classifier Processing - if (classifier_model_opt.has_value() && resnet_outputs.count("layer3")) { + if (classifier_model_opt_wrapped.has_value() && resnet_outputs.count("layer3")) { try { std::cout << "Processing Classifier for sample " << i << std::endl; auto clf_input = resnet_outputs["layer3"].clone(); - torch::Tensor clf_features = classifier_model_opt->extract_features(clf_input); + torch::Tensor clf_features = (*classifier_model_opt_wrapped).extract_features(clf_input); save_tensor_to_file(clf_features, (clf_out_dir / (sample_suffix + "_features.pt")).string()); std::cout << "Classifier processing done for sample " << i << std::endl; } catch (const std::exception& e) { @@ -259,7 +269,7 @@ int main(int argc, char* argv[]) { } // 3. BBRegressor Processing - if (bb_regressor_model_opt.has_value() && resnet_outputs.count("layer2") && resnet_outputs.count("layer3")) { + if (bb_regressor_model_opt_wrapped.has_value() && resnet_outputs.count("layer2") && resnet_outputs.count("layer3")) { try { std::cout << "Processing BBRegressor for sample " << i << std::endl; std::vector backbone_feats_for_bb = { @@ -267,22 +277,22 @@ int main(int argc, char* argv[]) { resnet_outputs["layer3"].clone() }; - std::vector iou_feats = bb_regressor_model_opt->get_iou_feat(backbone_feats_for_bb); + std::vector iou_feats = (*bb_regressor_model_opt_wrapped).get_iou_feat(backbone_feats_for_bb); if (iou_feats.size() >= 1) save_tensor_to_file(iou_feats[0], (bb_reg_out_dir / (sample_suffix + "_iou_feat0.pt")).string()); if (iou_feats.size() >= 2) save_tensor_to_file(iou_feats[1], (bb_reg_out_dir / (sample_suffix + "_iou_feat1.pt")).string()); - std::vector mod_vectors = bb_regressor_model_opt->get_modulation(backbone_feats_for_bb, bb_tensor); + std::vector mod_vectors = (*bb_regressor_model_opt_wrapped).get_modulation(backbone_feats_for_bb, bb_tensor); if (mod_vectors.size() >= 1) save_tensor_to_file(mod_vectors[0], (bb_reg_out_dir / (sample_suffix + "_mod_vec0.pt")).string()); if (mod_vectors.size() >= 2) save_tensor_to_file(mod_vectors[1], (bb_reg_out_dir / (sample_suffix + "_mod_vec1.pt")).string()); if (!iou_feats.empty() && !mod_vectors.empty()) { - torch::Tensor iou_scores = bb_regressor_model_opt->predict_iou(mod_vectors, iou_feats, proposals_tensor); + torch::Tensor iou_scores = (*bb_regressor_model_opt_wrapped).predict_iou(mod_vectors, iou_feats, proposals_tensor); save_tensor_to_file(iou_scores, (bb_reg_out_dir / (sample_suffix + "_iou_scores.pt")).string()); } else { std::cerr << " Skipping BBRegressor predict_iou for sample " << i << " (iou_feats or mod_vectors empty)." << std::endl; } std::cout << "BBRegressor processing done for sample " << i << std::endl; - } catch (const std::exception& e) { + } catch (const std::exception& e) { std::cerr << "Error during BBRegressor processing for sample " << i << ": " << e.what() << std::endl; } } else {