diff --git a/.idea/vcs.xml b/.idea/vcs.xml
index 82d5217..92f542b 100644
--- a/.idea/vcs.xml
+++ b/.idea/vcs.xml
@@ -1,6 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
     <mapping directory="$PROJECT_DIR$/vcpkg" vcs="Git" />
   </component>
 </project>
\ No newline at end of file
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8135ff4..8d74671 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -12,19 +12,13 @@ find_package(Torch REQUIRED)
 
 message(STATUS "Found LibTorch: ${TORCH_LIBRARIES}")
 
-# Determine whether to use CPU-only or CUDA implementation
-option(CPU_ONLY "Build without CUDA support" TRUE)
-
-if(CPU_ONLY)
-    message(STATUS "Building in CPU-only mode")
-    add_definitions(-DCPU_ONLY)
-else()
-    message(STATUS "Building with CUDA support")
-endif()
+# Always use CUDA implementation (no CPU fallback)
+message(STATUS "Building with CUDA support")
 
 # Define source files for the libraries
 set(BB_REGRESSOR_SOURCES
     cimp/bb_regressor/bb_regressor.cpp
+    cimp/bb_regressor/prroi_pooling/prroi_pooling_gpu.cpp
 )
 
 set(CLASSIFIER_SOURCES
@@ -53,13 +47,23 @@ add_executable(tracking_demo cimp/demo.cpp)
 # Link the demo with the libraries
 target_link_libraries(tracking_demo PRIVATE bb_regressor classifier ${TORCH_LIBRARIES})
 
+# Create the test models executable
+add_executable(test_models test/test_models.cpp)
+
+# Link the test with the libraries
+target_link_libraries(test_models PRIVATE bb_regressor classifier ${TORCH_LIBRARIES})
+
+# Create the test sample generator executable (without dependencies on our libraries)
+add_executable(generate_test_samples test/generate_test_samples.cpp)
+
+# Link the test sample generator only with LibTorch
+target_link_libraries(generate_test_samples PRIVATE ${TORCH_LIBRARIES})
+
 # Copy the executable to the binary directory
 install(TARGETS tracking_demo DESTINATION bin)
+install(TARGETS test_models DESTINATION bin)
+install(TARGETS generate_test_samples DESTINATION bin)
 
 # Print some info during the build
 message(STATUS "LibTorch found at: ${TORCH_INCLUDE_DIRS}")
-if(CPU_ONLY)
-    message(STATUS "Using CPU-only build")
-else()
-    message(STATUS "Using CUDA-enabled build")
-endif() 
\ No newline at end of file
+message(STATUS "Using CUDA-enabled build") 
\ No newline at end of file
diff --git a/bb_regressor_stats.txt b/bb_regressor_stats.txt
index e14e5c5..f473a72 100644
--- a/bb_regressor_stats.txt
+++ b/bb_regressor_stats.txt
@@ -1,45 +1,45 @@
 Output 0:
   Shape: [1, 256, 18, 18]
-  Mean: 0.261343
-  Std: 0.373308
+  Mean: 0.260712
+  Std: 0.375281
   Min: 0
-  Max: 2.83079
-  Sum: 21676.9
-  Sample values: [0.273305, 0.153942, 0.0177385]
+  Max: 2.83518
+  Sum: 21624.5
+  Sample values: [0.270668, 0.159451, 0]
 
 Output 1:
   Shape: [1, 256, 9, 9]
-  Mean: 0.334883
-  Std: 0.854804
+  Mean: 0.338533
+  Std: 0.864997
   Min: 0
-  Max: 7.18794
-  Sum: 6944.14
-  Sample values: [6.22439, 0, 0]
+  Max: 7.52249
+  Sum: 7019.83
+  Sample values: [6.37661, 0, 0]
 
 Output 2:
-  Shape: [1, 256, 1, 1]
-  Mean: 0.412101
-  Std: 0.568328
+  Shape: [1, 256]
+  Mean: 0.0984123
+  Std: 0.273788
   Min: 0
-  Max: 2.80441
-  Sum: 105.498
+  Max: 1.96461
+  Sum: 25.1935
   Sample values: [0]
 
 Output 3:
-  Shape: [1, 256, 1, 1]
-  Mean: 0.413812
-  Std: 0.62911
+  Shape: [1, 256]
+  Mean: 0.651337
+  Std: 1.65163
   Min: 0
-  Max: 3.27012
-  Sum: 105.936
+  Max: 10.0534
+  Sum: 166.742
   Sample values: [0]
 
 Output 4:
   Shape: [1, 5]
-  Mean: 0.441597
-  Std: 0.430799
-  Min: 0.0278997
-  Max: 0.899234
-  Sum: 2.20798
-  Sample values: [0.0278997]
+  Mean: -2.65813
+  Std: 8.42937e-08
+  Min: -2.65813
+  Max: -2.65813
+  Sum: -13.2907
+  Sample values: [-2.65813]
 
diff --git a/bin/tracking_demo b/bin/tracking_demo
index 2fea828..2910218 100644
Binary files a/bin/tracking_demo and b/bin/tracking_demo differ
diff --git a/build.sh b/build.sh
index faa0ce5..a2c5463 100644
--- a/build.sh
+++ b/build.sh
@@ -28,7 +28,9 @@ if [ $CUDA_AVAILABLE -eq 1 ]; then
     CUDA_VERSION=$(nvcc --version | grep "release" | awk '{print $6}' | cut -c2- | cut -d'.' -f1-2)
     echo "Detected CUDA version: $CUDA_VERSION"
 else
-    echo "CUDA not found, building in CPU-only mode"
+    echo "CUDA not found. The project requires CUDA to build."
+    echo "Please install CUDA and try again."
+    exit 1
 fi
 
 # Download and extract LibTorch with appropriate CUDA support if not already present
@@ -36,18 +38,13 @@ LIBTORCH_DIR="$HOME/libtorch"
 if [ ! -d "$LIBTORCH_DIR" ]; then
     echo "Downloading LibTorch..."
     # Use a compatible version based on detected CUDA
-    if [ $CUDA_AVAILABLE -eq 1 ]; then
-        echo "Downloading CUDA-enabled LibTorch"
-        if [[ "$CUDA_VERSION" == "11.5" || "$CUDA_VERSION" == "11.6" || "$CUDA_VERSION" == "11.7" ]]; then
-            LIBTORCH_URL="https://download.pytorch.org/libtorch/cu116/libtorch-cxx11-abi-shared-with-deps-1.13.0%2Bcu116.zip"
-        elif [[ "$CUDA_VERSION" == "11.3" || "$CUDA_VERSION" == "11.4" ]]; then
-            LIBTORCH_URL="https://download.pytorch.org/libtorch/cu113/libtorch-cxx11-abi-shared-with-deps-1.12.1%2Bcu113.zip"
-        else
-            LIBTORCH_URL="https://download.pytorch.org/libtorch/cu118/libtorch-cxx11-abi-shared-with-deps-2.0.0%2Bcu118.zip"
-        fi
+    echo "Downloading CUDA-enabled LibTorch"
+    if [[ "$CUDA_VERSION" == "11.5" || "$CUDA_VERSION" == "11.6" || "$CUDA_VERSION" == "11.7" ]]; then
+        LIBTORCH_URL="https://download.pytorch.org/libtorch/cu116/libtorch-cxx11-abi-shared-with-deps-1.13.0%2Bcu116.zip"
+    elif [[ "$CUDA_VERSION" == "11.3" || "$CUDA_VERSION" == "11.4" ]]; then
+        LIBTORCH_URL="https://download.pytorch.org/libtorch/cu113/libtorch-cxx11-abi-shared-with-deps-1.12.1%2Bcu113.zip"
     else
-        echo "Downloading CPU-only LibTorch"
-        LIBTORCH_URL="https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.0.0%2Bcpu.zip"
+        LIBTORCH_URL="https://download.pytorch.org/libtorch/cu118/libtorch-cxx11-abi-shared-with-deps-2.0.0%2Bcu118.zip"
     fi
     
     wget $LIBTORCH_URL -O libtorch.zip
@@ -67,13 +64,9 @@ cd build
 # Create local bin directory
 mkdir -p ../bin
 
-# Configure with CMake
+# Configure with CMake - always use CUDA, never use CPU_ONLY
 echo "Configuring with CMake..."
-if [ $CUDA_AVAILABLE -eq 1 ]; then
-    cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=.. -DCPU_ONLY=OFF
-else
-    cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=.. -DCPU_ONLY=ON
-fi
+cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=.. -DCPU_ONLY=OFF
 
 # Build the project
 echo "Building the project..."
diff --git a/build/CMakeCache.txt b/build/CMakeCache.txt
index 12291d9..cccec27 100644
--- a/build/CMakeCache.txt
+++ b/build/CMakeCache.txt
@@ -207,8 +207,8 @@ CMAKE_STRIP:FILEPATH=/usr/bin/strip
 // Studio IDE projects all commands are done without /nologo.
 CMAKE_VERBOSE_MAKEFILE:BOOL=FALSE
 
-//Build without CUDA support
-CPU_ONLY:BOOL=OFF
+//No help, variable specified on the command line.
+CPU_ONLY:UNINITIALIZED=OFF
 
 //Compile device code in 64 bit mode
 CUDA_64_BIT_DEVICE_CODE:BOOL=ON
@@ -373,9 +373,6 @@ TORCH_LIBRARY:FILEPATH=/home/mht/libtorch/lib/libtorch.so
 //The directory containing a CMake configuration file for Torch.
 Torch_DIR:PATH=/home/mht/libtorch/share/cmake/Torch
 
-//Use ROI Align from torchvision
-USE_ROI_ALIGN:BOOL=OFF
-
 //Path to a library.
 c10_LIBRARY:FILEPATH=/home/mht/libtorch/lib/libc10.so
 
@@ -388,15 +385,6 @@ cpp_tracker_IS_TOP_LEVEL:STATIC=ON
 //Value Computed by CMake
 cpp_tracker_SOURCE_DIR:STATIC=/media/mht/ADATA/repos/cpp_tracker
 
-//Value Computed by CMake
-cpp_tracker_tests_BINARY_DIR:STATIC=/media/mht/ADATA/repos/cpp_tracker/build/test
-
-//Value Computed by CMake
-cpp_tracker_tests_IS_TOP_LEVEL:STATIC=OFF
-
-//Value Computed by CMake
-cpp_tracker_tests_SOURCE_DIR:STATIC=/media/mht/ADATA/repos/cpp_tracker/test
-
 //Path to a library.
 kineto_LIBRARY:FILEPATH=/home/mht/libtorch/lib/libkineto.a
 
diff --git a/build/CMakeFiles/3.22.1/CMakeDetermineCompilerABI_CUDA.bin b/build/CMakeFiles/3.22.1/CMakeDetermineCompilerABI_CUDA.bin
index 67b043e..2872fba 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 ba095b4..5b15ffb 100644
--- a/build/CMakeFiles/CMakeOutput.log
+++ b/build/CMakeFiles/CMakeOutput.log
@@ -12,13 +12,150 @@ Compilation of the CXX compiler identification source "CMakeCXXCompilerId.cpp" p
 
 The CXX compiler identification is GNU, found in "/media/mht/ADATA/repos/cpp_tracker/build/CMakeFiles/3.22.1/CompilerIdCXX/a.out"
 
+Checking whether the CUDA compiler is NVIDIA using "" matched "nvcc: NVIDIA \(R\) Cuda compiler driver":
+nvcc: NVIDIA (R) Cuda compiler driver
+Copyright (c) 2005-2021 NVIDIA Corporation
+Built on Thu_Nov_18_09:45:30_PST_2021
+Cuda compilation tools, release 11.5, V11.5.119
+Build cuda_11.5.r11.5/compiler.30672275_0
+Compiling the CUDA compiler identification source file "CMakeCUDACompilerId.cu" succeeded.
+Compiler: /usr/bin/nvcc 
+Build flags: 
+Id flags: --keep;--keep-dir;tmp -v
+
+The output was:
+0
+#$ _NVVM_BRANCH_=nvvm
+#$ _SPACE_= 
+#$ _CUDART_=cudart
+#$ _HERE_=/usr/lib/nvidia-cuda-toolkit/bin
+#$ _THERE_=/usr/lib/nvidia-cuda-toolkit/bin
+#$ _TARGET_SIZE_=
+#$ _TARGET_DIR_=
+#$ _TARGET_SIZE_=64
+#$ NVVMIR_LIBRARY_DIR=/usr/lib/nvidia-cuda-toolkit/libdevice
+#$ PATH=/usr/lib/nvidia-cuda-toolkit/bin:/home/mht/anaconda3/bin:/home/mht/anaconda3/condabin:/tmp/.mount_cursorF6bS95/usr/bin:/tmp/.mount_cursorF6bS95/usr/sbin:/tmp/.mount_cursorF6bS95/usr/games:/tmp/.mount_cursorF6bS95/bin:/tmp/.mount_cursorF6bS95/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
+#$ LIBRARIES=  -L/usr/lib/x86_64-linux-gnu/stubs -L/usr/lib/x86_64-linux-gnu
+#$ rm tmp/a_dlink.reg.c
+#$ gcc -D__CUDA_ARCH__=520 -D__CUDA_ARCH_LIST__=520 -E -x c++  -DCUDA_DOUBLE_MATH_FUNCTIONS -D__CUDACC__ -D__NVCC__   -D__CUDACC_VER_MAJOR__=11 -D__CUDACC_VER_MINOR__=5 -D__CUDACC_VER_BUILD__=119 -D__CUDA_API_VER_MAJOR__=11 -D__CUDA_API_VER_MINOR__=5 -D__NVCC_DIAG_PRAGMA_SUPPORT__=1 -include "cuda_runtime.h" -m64 "CMakeCUDACompilerId.cu" -o "tmp/CMakeCUDACompilerId.cpp1.ii" 
+#$ cicc --c++17 --gnu_version=110400 --display_error_number --orig_src_file_name "CMakeCUDACompilerId.cu" --orig_src_path_name "/media/mht/ADATA/repos/cpp_tracker/build/CMakeFiles/3.22.1/CompilerIdCUDA/CMakeCUDACompilerId.cu" --allow_managed  -arch compute_52 -m64 --no-version-ident -ftz=0 -prec_div=1 -prec_sqrt=1 -fmad=1 --include_file_name "CMakeCUDACompilerId.fatbin.c" -tused --gen_module_id_file --module_id_file_name "tmp/CMakeCUDACompilerId.module_id" --gen_c_file_name "tmp/CMakeCUDACompilerId.cudafe1.c" --stub_file_name "tmp/CMakeCUDACompilerId.cudafe1.stub.c" --gen_device_file_name "tmp/CMakeCUDACompilerId.cudafe1.gpu"  "tmp/CMakeCUDACompilerId.cpp1.ii" -o "tmp/CMakeCUDACompilerId.ptx"
+#$ ptxas -arch=sm_52 -m64 "tmp/CMakeCUDACompilerId.ptx"  -o "tmp/CMakeCUDACompilerId.sm_52.cubin" 
+#$ fatbinary --create="tmp/CMakeCUDACompilerId.fatbin" -64 --cicc-cmdline="-ftz=0 -prec_div=1 -prec_sqrt=1 -fmad=1 " "--image3=kind=elf,sm=52,file=tmp/CMakeCUDACompilerId.sm_52.cubin" "--image3=kind=ptx,sm=52,file=tmp/CMakeCUDACompilerId.ptx" --embedded-fatbin="tmp/CMakeCUDACompilerId.fatbin.c" 
+#$ gcc -D__CUDA_ARCH_LIST__=520 -E -x c++ -D__CUDACC__ -D__NVCC__   -D__CUDACC_VER_MAJOR__=11 -D__CUDACC_VER_MINOR__=5 -D__CUDACC_VER_BUILD__=119 -D__CUDA_API_VER_MAJOR__=11 -D__CUDA_API_VER_MINOR__=5 -D__NVCC_DIAG_PRAGMA_SUPPORT__=1 -include "cuda_runtime.h" -m64 "CMakeCUDACompilerId.cu" -o "tmp/CMakeCUDACompilerId.cpp4.ii" 
+#$ cudafe++ --c++17 --gnu_version=110400 --display_error_number --orig_src_file_name "CMakeCUDACompilerId.cu" --orig_src_path_name "/media/mht/ADATA/repos/cpp_tracker/build/CMakeFiles/3.22.1/CompilerIdCUDA/CMakeCUDACompilerId.cu" --allow_managed --m64 --parse_templates --gen_c_file_name "tmp/CMakeCUDACompilerId.cudafe1.cpp" --stub_file_name "CMakeCUDACompilerId.cudafe1.stub.c" --module_id_file_name "tmp/CMakeCUDACompilerId.module_id" "tmp/CMakeCUDACompilerId.cpp4.ii" 
+#$ gcc -D__CUDA_ARCH__=520 -D__CUDA_ARCH_LIST__=520 -c -x c++  -DCUDA_DOUBLE_MATH_FUNCTIONS -m64 "tmp/CMakeCUDACompilerId.cudafe1.cpp" -o "tmp/CMakeCUDACompilerId.o" 
+#$ nvlink -m64 --arch=sm_52 --register-link-binaries="tmp/a_dlink.reg.c"    -L/usr/lib/x86_64-linux-gnu/stubs -L/usr/lib/x86_64-linux-gnu -cpu-arch=X86_64 "tmp/CMakeCUDACompilerId.o"  -lcudadevrt  -o "tmp/a_dlink.sm_52.cubin"
+#$ fatbinary --create="tmp/a_dlink.fatbin" -64 --cicc-cmdline="-ftz=0 -prec_div=1 -prec_sqrt=1 -fmad=1 " -link "--image3=kind=elf,sm=52,file=tmp/a_dlink.sm_52.cubin" --embedded-fatbin="tmp/a_dlink.fatbin.c" 
+#$ gcc -D__CUDA_ARCH_LIST__=520 -c -x c++ -DFATBINFILE="\"tmp/a_dlink.fatbin.c\"" -DREGISTERLINKBINARYFILE="\"tmp/a_dlink.reg.c\"" -I. -D__NV_EXTRA_INITIALIZATION= -D__NV_EXTRA_FINALIZATION= -D__CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__   -D__CUDACC_VER_MAJOR__=11 -D__CUDACC_VER_MINOR__=5 -D__CUDACC_VER_BUILD__=119 -D__CUDA_API_VER_MAJOR__=11 -D__CUDA_API_VER_MINOR__=5 -D__NVCC_DIAG_PRAGMA_SUPPORT__=1 -m64 "/usr/lib/nvidia-cuda-toolkit/bin/crt/link.stub" -o "tmp/a_dlink.o" 
+#$ g++ -D__CUDA_ARCH_LIST__=520 -m64 -Wl,--start-group "tmp/a_dlink.o" "tmp/CMakeCUDACompilerId.o"   -L/usr/lib/x86_64-linux-gnu/stubs -L/usr/lib/x86_64-linux-gnu  -lcudadevrt  -lcudart_static  -lrt -lpthread  -ldl  -Wl,--end-group -o "a.out" 
+
+
+Compilation of the CUDA compiler identification source "CMakeCUDACompilerId.cu" produced "a.out"
+
+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/lib/nvidia-cuda-toolkit/bin:/home/mht/anaconda3/bin:/home/mht/anaconda3/condabin:/tmp/.mount_cursorF6bS95/usr/bin:/tmp/.mount_cursorF6bS95/usr/sbin:/tmp/.mount_cursorF6bS95/usr/games:/tmp/.mount_cursorF6bS95/bin:/tmp/.mount_cursorF6bS95/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 'LIBRARIES=' string: [-L/usr/lib/x86_64-linux-gnu/stubs -L/usr/lib/x86_64-linux-gnu]
+  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__   -D__CUDACC_VER_MAJOR__=11 -D__CUDACC_VER_MINOR__=5 -D__CUDACC_VER_BUILD__=119 -D__CUDA_API_VER_MAJOR__=11 -D__CUDA_API_VER_MINOR__=5 -D__NVCC_DIAG_PRAGMA_SUPPORT__=1 -include "cuda_runtime.h" -m64 "CMakeCUDACompilerId.cu" -o "tmp/CMakeCUDACompilerId.cpp1.ii" ]
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+  considering line: [ptxas -arch=sm_52 -m64 "tmp/CMakeCUDACompilerId.ptx"  -o "tmp/CMakeCUDACompilerId.sm_52.cubin" ]
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+  considering line: [cudafe++ --c++17 --gnu_version=110400 --display_error_number --orig_src_file_name "CMakeCUDACompilerId.cu" --orig_src_path_name "/media/mht/ADATA/repos/cpp_tracker/build/CMakeFiles/3.22.1/CompilerIdCUDA/CMakeCUDACompilerId.cu" --allow_managed --m64 --parse_templates --gen_c_file_name "tmp/CMakeCUDACompilerId.cudafe1.cpp" --stub_file_name "CMakeCUDACompilerId.cudafe1.stub.c" --module_id_file_name "tmp/CMakeCUDACompilerId.module_id" "tmp/CMakeCUDACompilerId.cpp4.ii" ]
+  considering line: [gcc -D__CUDA_ARCH__=520 -D__CUDA_ARCH_LIST__=520 -c -x c++  -DCUDA_DOUBLE_MATH_FUNCTIONS -m64 "tmp/CMakeCUDACompilerId.cudafe1.cpp" -o "tmp/CMakeCUDACompilerId.o" ]
+  considering line: [nvlink -m64 --arch=sm_52 --register-link-binaries="tmp/a_dlink.reg.c"    -L/usr/lib/x86_64-linux-gnu/stubs -L/usr/lib/x86_64-linux-gnu -cpu-arch=X86_64 "tmp/CMakeCUDACompilerId.o"  -lcudadevrt  -o "tmp/a_dlink.sm_52.cubin"]
+    ignoring nvlink line
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+    #$ gcc -D__CUDA_ARCH__=520 -D__CUDA_ARCH_LIST__=520 -c -x c++  -DCUDA_DOUBLE_MATH_FUNCTIONS -m64 "tmp/CMakeCUDACompilerId.cudafe1.cpp" -o "tmp/CMakeCUDACompilerId.o" 
+    #$ nvlink -m64 --arch=sm_52 --register-link-binaries="tmp/a_dlink.reg.c"    -L/usr/lib/x86_64-linux-gnu/stubs -L/usr/lib/x86_64-linux-gnu -cpu-arch=X86_64 "tmp/CMakeCUDACompilerId.o"  -lcudadevrt  -o "tmp/a_dlink.sm_52.cubin"
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-/home/mht/libtorch/include/ATen/ops/fake_quantize_per_tensor_affine_cachemask_backward_ops.h:
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-/home/mht/libtorch/include/ATen/ops/miopen_batch_norm_backward.h:
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-/home/mht/libtorch/include/ATen/ops/as_strided_scatter_ops.h:
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-/home/mht/libtorch/include/ATen/ops/miopen_batch_norm_backward_ops.h:
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-/home/mht/libtorch/include/ATen/ops/_upsample_nearest_exact3d_backward_native.h:
-
-/home/mht/libtorch/include/ATen/ops/miopen_convolution_add_relu.h:
-
-/home/mht/libtorch/include/ATen/ops/miopen_rnn_backward.h:
+# Empty compiler generated dependencies file for tracking_demo.
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index 3a86673..17875e3 100644
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index 2352c74..2b26e57 100644
--- a/build/Makefile
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 .PHONY : tracking_demo/fast
 
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+
+# Build rule for target.
+test_models: cmake_check_build_system
+	$(MAKE) $(MAKESILENT) -f CMakeFiles/Makefile2 test_models
+.PHONY : test_models
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+
+#=============================================================================
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+# fast build rule for target.
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+.PHONY : cimp/bb_regressor/prroi_pooling/prroi_pooling_gpu.o
+
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+
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+.PHONY : cimp/bb_regressor/prroi_pooling/prroi_pooling_gpu.i
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+
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+
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+
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+
+# target to build an object file
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@@ -286,16 +384,27 @@ help:
 	@echo "... rebuild_cache"
 	@echo "... bb_regressor"
 	@echo "... classifier"
+	@echo "... generate_test_samples"
+	@echo "... test_models"
 	@echo "... tracking_demo"
 	@echo "... cimp/bb_regressor/bb_regressor.o"
 	@echo "... cimp/bb_regressor/bb_regressor.i"
 	@echo "... cimp/bb_regressor/bb_regressor.s"
+	@echo "... cimp/bb_regressor/prroi_pooling/prroi_pooling_gpu.o"
+	@echo "... cimp/bb_regressor/prroi_pooling/prroi_pooling_gpu.i"
+	@echo "... cimp/bb_regressor/prroi_pooling/prroi_pooling_gpu.s"
 	@echo "... cimp/classifier/classifier.o"
 	@echo "... cimp/classifier/classifier.i"
 	@echo "... cimp/classifier/classifier.s"
 	@echo "... cimp/demo.o"
 	@echo "... cimp/demo.i"
 	@echo "... cimp/demo.s"
+	@echo "... test/generate_test_samples.o"
+	@echo "... test/generate_test_samples.i"
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+	@echo "... test/test_models.o"
+	@echo "... test/test_models.i"
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index 72effbb..29039dd 100644
--- a/build/cmake_install.cmake
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@@ -62,6 +62,46 @@ if("x${CMAKE_INSTALL_COMPONENT}x" STREQUAL "xUnspecifiedx" OR NOT CMAKE_INSTALL_
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+    file(RPATH_CHANGE
+         FILE "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/bin/test_models"
+         OLD_RPATH "/home/mht/libtorch/lib:"
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+      execute_process(COMMAND "/usr/bin/strip" "$ENV{DESTDIR}${CMAKE_INSTALL_PREFIX}/bin/test_models")
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+    file(RPATH_CHANGE
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diff --git a/cimp/bb_regressor/bb_regressor.cpp b/cimp/bb_regressor/bb_regressor.cpp
index a280c79..900f413 100644
--- a/cimp/bb_regressor/bb_regressor.cpp
+++ b/cimp/bb_regressor/bb_regressor.cpp
@@ -30,42 +30,37 @@ torch::Tensor PrRoIPool2D::forward(torch::Tensor feat, torch::Tensor rois) {
     int channels = feat.size(1);
     int num_rois = rois.size(0);
     
-    // Ensure both tensors are on CUDA initially (as they come from GPU operations)
+    // Ensure both tensors are on CUDA
     if (!feat.is_cuda() || !rois.is_cuda()) {
-        // This case should ideally not happen if inputs are from CUDA model parts
-        // but if it does, move them to CUDA first for consistency, then to CPU for the C function
-        std::cout << "Warning: PrRoIPool2D received non-CUDA tensor(s). Moving to CUDA then CPU." << std::endl;
-        feat = feat.to(torch::kCUDA);
-        rois = rois.to(torch::kCUDA);
+        throw std::runtime_error("PrRoIPool2D requires CUDA tensors - CPU mode is not supported");
     }
     
     // Print ROI values for debugging
-    std::cout << "    ROI values (on device " << rois.device() << "): " << std::endl;
-    auto rois_cpu_for_print = rois.to(torch::kCPU).contiguous(); // Temp CPU copy for printing
+    std::cout << "    ROI values: " << std::endl;
     for (int i = 0; i < std::min(num_rois, 3); i++) {
         std::cout << "      ROI " << i << ": [";
-        for (int j = 0; j < rois_cpu_for_print.size(1); j++) {
-            std::cout << rois_cpu_for_print[i][j].item<float>();
-            if (j < rois_cpu_for_print.size(1) - 1) std::cout << ", ";
+        for (int j = 0; j < rois.size(1); j++) {
+            std::cout << rois[i][j].item<float>();
+            if (j < rois.size(1) - 1) std::cout << ", ";
         }
         std::cout << "]" << std::endl;
     }
     
-    // Create output tensor on the same original device as feat (CUDA)
+    // Create output tensor on the same device
     auto output = torch::zeros({num_rois, channels, pooled_height_, pooled_width_}, 
                               feat.options());
                               
-    // REVERTED: Copy tensors to CPU for the C implementation, as prroi_pooling_forward_cuda expects CPU pointers
+    // Copy tensors to CPU for the C implementation
     auto feat_cpu = feat.to(torch::kCPU).contiguous();
-    auto rois_cpu = rois.to(torch::kCPU).contiguous(); // Already on CPU for printing, ensure contiguous
-    auto output_cpu = output.to(torch::kCPU).contiguous(); // Create CPU version for the C function to fill
+    auto rois_cpu = rois.to(torch::kCPU).contiguous();
+    auto output_cpu = output.to(torch::kCPU).contiguous();
     
-    // Call the C wrapper function (which is a CPU implementation)
-    std::cout << "    Calling prroi_pooling_forward_cuda (CPU implementation)..." << std::endl;
+    // Call the C wrapper function
+    std::cout << "    Calling prroi_pooling_forward_cuda..." << std::endl;
     prroi_pooling_forward_cuda(
         feat_cpu.data_ptr<float>(),
-        rois_cpu.data_ptr<float>(), // Pass the CPU tensor data
-        output_cpu.data_ptr<float>(), // Pass CPU output tensor data
+        static_cast<float*>(rois_cpu.data_ptr()),
+        static_cast<float*>(output_cpu.data_ptr()),
         channels,
         feat.size(2),
         feat.size(3),
@@ -76,7 +71,7 @@ torch::Tensor PrRoIPool2D::forward(torch::Tensor feat, torch::Tensor rois) {
     );
     std::cout << "    prroi_pooling_forward_cuda completed" << std::endl;
     
-    // Copy result back to original device (GPU)
+    // Copy result back to GPU
     output.copy_(output_cpu);
     
     return output;
@@ -92,9 +87,6 @@ LinearBlock::LinearBlock(int in_planes, int out_planes, int input_sz, bool bias,
     if (use_bn) {
         // Important: use BatchNorm2d to match Python implementation
         bn = register_module("bn", torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(out_planes)));
-        // Initialize BatchNorm weights and biases like Python
-        bn->weight.data().uniform_();
-        bn->bias.data().zero_();
     }
     
     use_relu = relu;
@@ -104,53 +96,46 @@ LinearBlock::LinearBlock(int in_planes, int out_planes, int input_sz, bool bias,
 }
 
 torch::Tensor LinearBlock::forward(torch::Tensor x) {
-    // Store original dtype for later (though we will stick to it)
-    // auto original_dtype = x.dtype();
+    // Store original dtype for later
+    auto original_dtype = x.dtype();
     
-    // REMOVED: Conversions to double precision
-    // auto x_double = x.to(torch::kFloat64);
+    // Use double precision for higher accuracy
+    auto x_double = x.to(torch::kFloat64);
     
     // Reshape exactly as in Python: x.reshape(x.shape[0], -1)
-    // x_double = x_double.reshape({x_double.size(0), -1}).contiguous();
-    x = x.reshape({x.size(0), -1}).contiguous(); // Operate on original tensor x
+    x_double = x_double.reshape({x_double.size(0), -1}).contiguous();
     
-    // REMOVED: Conversion back to original precision for the linear operation
-    // auto x_float = x_double.to(original_dtype);
-    // x_float = linear->forward(x_float);
-    x = linear->forward(x); // Operate on original tensor x
+    // Convert back to original precision for the linear operation
+    auto x_float = x_double.to(original_dtype);
+    x_float = linear->forward(x_float);
     
-    // REMOVED: Back to double precision for further operations
-    // x_double = x_float.to(torch::kFloat64);
+    // Back to double precision for further operations
+    x_double = x_float.to(torch::kFloat64);
     
     if (use_bn) {
         // This is crucial: reshape to 4D tensor for BatchNorm2d exactly as in Python
         // In Python: x = self.bn(x.reshape(x.shape[0], x.shape[1], 1, 1))
-        // x_double = x_double.reshape({x_double.size(0), x_double.size(1), 1, 1}).contiguous();
-        x = x.reshape({x.size(0), x.size(1), 1, 1}).contiguous(); // Operate on original tensor x
+        x_double = x_double.reshape({x_double.size(0), x_double.size(1), 1, 1}).contiguous();
         
-        // Apply batch norm (convert to float32 for the operation - NOT NEEDED if x is already float32)
-        // x_float = x_double.to(original_dtype);
-        // x_float = bn->forward(x_float);
-        // x_double = x_float.to(torch::kFloat64);
-        x = bn->forward(x); // Operate on original tensor x
+        // Apply batch norm (convert to float32 for the operation)
+        x_float = x_double.to(original_dtype);
+        x_float = bn->forward(x_float);
+        x_double = x_float.to(torch::kFloat64);
     }
     
     // Apply ReLU if needed
     if (use_relu) {
-        // Apply ReLU in float32 precision - NOT NEEDED if x is already float32
-        // x_float = x_double.to(original_dtype);
-        // x_float = relu_->forward(x_float);
-        // x_double = x_float.to(torch::kFloat64);
-        x = relu_->forward(x); // Operate on original tensor x
+        // Apply ReLU in float32 precision
+        x_float = x_double.to(original_dtype);
+        x_float = relu_->forward(x_float);
+        x_double = x_float.to(torch::kFloat64);
     }
     
     // Final reshape to 2D tensor, exactly matching Python's behavior
-    // x_double = x_double.reshape({x_double.size(0), -1}).contiguous();
-    x = x.reshape({x.size(0), -1}).contiguous(); // Operate on original tensor x
+    x_double = x_double.reshape({x_double.size(0), -1}).contiguous();
     
     // Return tensor in original precision
-    // return x_double.to(original_dtype);
-    return x; // Return modified x directly
+    return x_double.to(original_dtype);
 }
 
 // Create convolutional block
@@ -167,11 +152,7 @@ torch::nn::Sequential BBRegressor::create_conv_block(int in_planes, int out_plan
                           .stride(stride).padding(padding).dilation(dilation).bias(true)));
     
     // Add batch normalization layer
-    auto bn_layer = torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(out_planes));
-    // Initialize BatchNorm weights and biases like Python
-    bn_layer->weight.data().uniform_();
-    bn_layer->bias.data().zero_();
-    seq->push_back(bn_layer);
+    seq->push_back(torch::nn::BatchNorm2d(torch::nn::BatchNorm2dOptions(out_planes)));
     
     // Add ReLU activation
     seq->push_back(torch::nn::ReLU(torch::nn::ReLUOptions().inplace(true)));
@@ -553,366 +534,260 @@ void BBRegressor::to(torch::Device device) {
 }
 
 // Get IoU features from backbone features
-std::vector<torch::Tensor> BBRegressor::get_iou_feat(std::vector<torch::Tensor> feat_in) {
-    torch::NoGradGuard no_grad;
-
-    if (feat_in.size() != 2) {
-        throw std::runtime_error("get_iou_feat expects 2 input features (layer2, layer3).");
+std::vector<torch::Tensor> BBRegressor::get_iou_feat(std::vector<torch::Tensor> 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);
+    
+    // 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();
     }
-
-    // feat_in[0] is backbone layer2 (e.g., [B, 512, H1, W1])
-    // feat_in[1] is backbone layer3 (e.g., [B, 1024, H2, W2])
-    auto feat3_t_in = feat_in[0].to(device);
-    auto feat4_t_in = feat_in[1].to(device);
-
-    // Process through conv layers
-    // conv3_1t should take 512 -> 256 channels
-    // conv3_2t should take 256 -> 256 channels (pred_input_dim[0])
-    auto c3_t = conv3_2t->forward(conv3_1t->forward(feat3_t_in));
-
-    // conv4_1t should take 1024 -> 256 channels
-    // conv4_2t should take 256 -> 256 channels (pred_input_dim[1])
-    auto c4_t = conv4_2t->forward(conv4_1t->forward(feat4_t_in));
     
-    return {c3_t.contiguous(), c4_t.contiguous()};
+    if (feat2_double1.dim() == 5) {
+        auto shape = feat2_double1.sizes();
+        feat2_double1 = feat2_double1.reshape({-1, shape[2], shape[3], shape[4]}).contiguous();
+    }
+    
+    // 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 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};
 }
 
 // Get modulation vectors for the target
-std::vector<torch::Tensor> BBRegressor::get_modulation(std::vector<torch::Tensor> feat_in, torch::Tensor bb_in) {
-    torch::NoGradGuard no_grad;
-
-    auto feat3_r_in = feat_in[0].to(device); // Backbone layer2 features, e.g., [1, 512, H1, W1]
-    auto feat4_r_in = feat_in[1].to(device); // Backbone layer3 features, e.g., [1, 1024, H2, W2]
-    auto bb = bb_in.to(device);         // Target bounding box, e.g., [1, 1, 4] (x,y,w,h)
-
-    // Ensure bb is [batch_size, 1, 4] then reshape to [batch_size, 4] for PrRoIPooling
-    // (as PrRoIPooling expects [batch_idx, x1, y1, x2, y2])
-    if (bb.dim() == 3 && bb.size(1) == 1) {
-        bb = bb.squeeze(1); // Now [batch_size, 4]
-    } else if (bb.dim() != 2 || bb.size(1) != 4) {
-        throw std::runtime_error("get_modulation: bb must be [batch, 1, 4] or [batch, 4]");
+std::vector<torch::Tensor> BBRegressor::get_modulation(std::vector<torch::Tensor> feat, torch::Tensor bb) {
+    // feat should contain two tensors: feat3_r and feat4_r (backbone features)
+    // bb is the initial bounding box [batch_size, 1, 4] (x,y,w,h) or [batch_size, 4]
+    // Ensure inputs are on the correct device
+    torch::NoGradGuard no_grad; // Ensure no gradients are computed
+
+    auto feat3_r = feat[0].to(device);
+    auto feat4_r = feat[1].to(device);
+    auto current_bb = bb.to(device);
+
+    // Reshape bb if it's [batch, 1, 4] to [batch, 4]
+    if (current_bb.dim() == 3 && current_bb.size(1) == 1) {
+        current_bb = current_bb.squeeze(1);
+    }
+    if (current_bb.dim() != 2 || current_bb.size(1) != 4) {
+        throw std::runtime_error("BBRegressor::get_modulation: bb must be [batch, 4] or [batch, 1, 4]");
     }
 
+    // Pass through early conv layers (reference branch)
     // Python: c3_r = self.conv3_1r(feat3_r)
-    auto c3_r = conv3_1r->forward(feat3_r_in).contiguous(); // Output: [B, 128, H1, W1]
-
-    // Python: roi1 from bb (batch_idx, x1,y1,x2,y2)
-    auto batch_size = bb.size(0);
-    auto roi1 = torch::zeros({batch_size, 5}, bb.options());
-    for (int64_t i = 0; i < batch_size; ++i) {
-        roi1.index_put_({i, 0}, static_cast<float>(i));
+    auto c3_r = conv3_1r->forward(feat3_r);
+
+    // Prepare ROIs: convert bb from [x,y,w,h] to [batch_idx, x1,y1,x2,y2]
+    int batch_size = current_bb.size(0);
+    auto batch_idx = torch::arange(0, batch_size, current_bb.options().dtype(torch::kFloat)).unsqueeze(1);
+    
+    auto rois = torch::zeros({batch_size, 5}, current_bb.options());
+    rois.index_put_({torch::indexing::Slice(), 0}, batch_idx.squeeze(1)); // batch index
+    rois.index_put_({torch::indexing::Slice(), 1}, current_bb.index({torch::indexing::Slice(), 0})); // x1
+    rois.index_put_({torch::indexing::Slice(), 2}, current_bb.index({torch::indexing::Slice(), 1})); // y1
+    rois.index_put_({torch::indexing::Slice(), 3}, current_bb.index({torch::indexing::Slice(), 0}) + current_bb.index({torch::indexing::Slice(), 2})); // x2 = x1 + w
+    rois.index_put_({torch::indexing::Slice(), 4}, current_bb.index({torch::indexing::Slice(), 1}) + current_bb.index({torch::indexing::Slice(), 3})); // y2 = y1 + h
+    
+    rois = rois.to(device); // Ensure ROIs are on the correct device
+
+    std::cout << "  BBRegressor::get_modulation: Converted ROIs (first item): [";
+    if (batch_size > 0) {
+        for (int j = 0; j < rois.size(1); j++) {
+            std::cout << rois[0][j].item<float>();
+            if (j < rois.size(1) - 1) std::cout << ", ";
+        }
     }
-    roi1.index_put_({torch::indexing::Slice(), 1}, bb.index({torch::indexing::Slice(), 0})); // x1
-    roi1.index_put_({torch::indexing::Slice(), 2}, bb.index({torch::indexing::Slice(), 1})); // y1
-    roi1.index_put_({torch::indexing::Slice(), 3}, bb.index({torch::indexing::Slice(), 0}) + bb.index({torch::indexing::Slice(), 2})); // x2
-    roi1.index_put_({torch::indexing::Slice(), 4}, bb.index({torch::indexing::Slice(), 1}) + bb.index({torch::indexing::Slice(), 3})); // y2
+    std::cout << "]" << std::endl;
+    std::cout << "  BBRegressor::get_modulation: c3_r shape: " << c3_r.sizes() << ", device: " << c3_r.device() << std::endl;
+
 
     // Python: roi3r = self.prroi_pool3r(c3_r, roi1)
-    // prroi_pool3r is (3,3, 1/8)
-    auto roi3r = prroi_pool3r->forward(c3_r, roi1).contiguous(); // Output: [B, 128, 3, 3]
+    auto roi3r = prroi_pool3r->forward(c3_r, rois);
+    std::cout << "  BBRegressor::get_modulation: roi3r shape: " << roi3r.sizes() << std::endl;
 
     // Python: c4_r = self.conv4_1r(feat4_r)
-    auto c4_r = conv4_1r->forward(feat4_r_in).contiguous(); // Output: [B, 256, H2, W2]
+    auto c4_r = conv4_1r->forward(feat4_r);
+    std::cout << "  BBRegressor::get_modulation: c4_r shape: " << c4_r.sizes() << ", device: " << c4_r.device() << std::endl;
+
     // Python: roi4r = self.prroi_pool4r(c4_r, roi1)
-    // prroi_pool4r is (1,1, 1/16)
-    auto roi4r = prroi_pool4r->forward(c4_r, roi1).contiguous(); // Output: [B, 256, 1, 1]
+    auto roi4r = prroi_pool4r->forward(c4_r, rois);
+    std::cout << "  BBRegressor::get_modulation: roi4r shape: " << roi4r.sizes() << std::endl;
+
 
     // Python: fc3_r = self.fc3_1r(roi3r)
-    // fc3_1r is conv(128, 256, kernel_size=3, stride=1, padding=0)
-    auto fc3_r = fc3_1r->forward(roi3r).contiguous(); // Output: [B, 256, 1, 1] (due to 3x3 kernel, padding 0 on 3x3 input)
+    // fc3_1r is a conv block: conv(128, 256, kernel_size=3, stride=1, padding=0)
+    // Input roi3r is (batch, 128, 3, 3) -> Output fc3_r is (batch, 256, 1, 1)
+    auto fc3_r = fc3_1r->forward(roi3r);
+    std::cout << "  BBRegressor::get_modulation: fc3_r shape: " << fc3_r.sizes() << std::endl;
 
     // Python: fc34_r = torch.cat((fc3_r, roi4r), dim=1)
-    auto fc34_r = torch::cat({fc3_r, roi4r}, 1).contiguous(); // Output: [B, 256+256=512, 1, 1]
+    // fc3_r is (batch, 256, 1, 1), roi4r is (batch, 256, 1, 1)
+    // Result fc34_r is (batch, 512, 1, 1)
+    auto fc34_r = torch::cat({fc3_r, roi4r}, 1);
+    std::cout << "  BBRegressor::get_modulation: fc34_r shape: " << fc34_r.sizes() << std::endl;
 
     // Python: fc34_3_r = self.fc34_3r(fc34_r)
     // fc34_3r is conv(512, 256, kernel_size=1, stride=1, padding=0)
-    auto fc34_3_r_out = fc34_3r->forward(fc34_r).contiguous(); // Output: [B, 256, 1, 1]
+    // Output fc34_3_r is (batch, 256, 1, 1)
+    auto mod_vec1 = fc34_3r->forward(fc34_r);
+    std::cout << "  BBRegressor::get_modulation: mod_vec1 (fc34_3_r) shape: " << mod_vec1.sizes() << std::endl;
 
     // Python: fc34_4_r = self.fc34_4r(fc34_r)
     // fc34_4r is conv(512, 256, kernel_size=1, stride=1, padding=0)
-    auto fc34_4_r_out = fc34_4r->forward(fc34_r).contiguous(); // Output: [B, 256, 1, 1]
+    // Output fc34_4_r is (batch, 256, 1, 1)
+    auto mod_vec2 = fc34_4r->forward(fc34_r);
+    std::cout << "  BBRegressor::get_modulation: mod_vec2 (fc34_4_r) shape: " << mod_vec2.sizes() << std::endl;
     
-    std::cout << "  get_modulation output shapes: " << std::endl;
-    std::cout << "    fc34_3_r_out: " << fc34_3_r_out.sizes() << std::endl;
-    std::cout << "    fc34_4_r_out: " << fc34_4_r_out.sizes() << std::endl;
-
-    return {fc34_3_r_out, fc34_4_r_out};
+    return {mod_vec1, mod_vec2};
 }
 
 // Predict IoU for proposals
 torch::Tensor BBRegressor::predict_iou(std::vector<torch::Tensor> modulation, 
                                      std::vector<torch::Tensor> feat, 
                                      torch::Tensor proposals) {
-    // Debug dimensions
-    std::cout << "Input dimensions:" << std::endl;
-    std::cout << "  modulation[0]: [" << modulation[0].size(0) << ", " << modulation[0].size(1) << "]" << std::endl;
-    std::cout << "  modulation[1]: [" << modulation[1].size(0) << ", " << modulation[1].size(1) << "]" << std::endl;
-    std::cout << "  feat[0]: [" << feat[0].size(0) << ", " << feat[0].size(1) << ", " 
-              << feat[0].size(2) << ", " << feat[0].size(3) << "]" << std::endl;
-    std::cout << "  feat[1]: [" << feat[1].size(0) << ", " << feat[1].size(1) << ", " 
-              << feat[1].size(2) << ", " << feat[1].size(3) << "]" << std::endl;
-    std::cout << "  proposals: [" << proposals.size(0) << ", " << proposals.size(1) << ", " << proposals.size(2) << "]" << std::endl;
-    
-    // Convert proposals from [batch, num_proposals, 4] to [num_proposals, 5] format
-    // with batch index as the first element
-    auto batch_size = proposals.size(0);
-    auto num_proposals = proposals.size(1);
-    
-    // Reshape proposals to [num_proposals, 4]
-    auto proposals_view = proposals.reshape({-1, 4});
-    
-    // Create batch indices tensor [0, 0, 0, ...] for all proposals
-    auto batch_indices = torch::zeros({num_proposals, 1}, proposals.options());
-    
-    // Convert proposals from [x, y, w, h] to [batch_idx, x1, y1, x2, y2] format
-    auto roi = torch::zeros({num_proposals, 5}, proposals.options());
-    roi.index_put_({torch::indexing::Slice(), 0}, batch_indices.squeeze());
-    roi.index_put_({torch::indexing::Slice(), 1}, proposals_view.index({torch::indexing::Slice(), 0}));
-    roi.index_put_({torch::indexing::Slice(), 2}, proposals_view.index({torch::indexing::Slice(), 1}));
-    
-    // Calculate x2, y2 from width and height
-    auto x2 = proposals_view.index({torch::indexing::Slice(), 0}) + proposals_view.index({torch::indexing::Slice(), 2});
-    auto y2 = proposals_view.index({torch::indexing::Slice(), 1}) + proposals_view.index({torch::indexing::Slice(), 3});
-    roi.index_put_({torch::indexing::Slice(), 3}, x2);
-    roi.index_put_({torch::indexing::Slice(), 4}, y2);
-    
-    // Make sure ROI is on the same device as features
-    torch::Device feat_device = feat[0].device();
+    // Ensure all inputs are on the correct device
+    auto target_device = device; // Assuming 'device' is a member of BBRegressor
+    for (auto& t : feat) { t = t.to(target_device); }
+    for (auto& m : modulation) { m = m.to(target_device); }
+    proposals = proposals.to(target_device);
+
+    // Get batch size and number of proposals
+    int batch_size = proposals.size(0);
+    int num_proposals = proposals.size(1);
+
+    // Reshape proposals to [batch_size * num_proposals, 4]
+    // and add batch index for PrRoIPooling
+    auto proposals_view = proposals.reshape({batch_size * num_proposals, 4});
+    auto roi_batch_index = torch::arange(0, batch_size, proposals.options().dtype(torch::kInt)).unsqueeze(1);
+    roi_batch_index = roi_batch_index.repeat_interleave(num_proposals, 0);
+    auto roi = torch::cat(std::vector<torch::Tensor>{roi_batch_index.to(proposals_view.options()), proposals_view}, 1);
+    
+    // Ensure ROI is on the correct device, matching features
+    auto feat_device = feat[0].device();
     roi = roi.to(feat_device);
     
     // Apply ROI pooling to get features for each proposal
-    // CORRECTED: Use prroi_pool3t and prroi_pool4t
-    auto pooled_feat1 = prroi_pool3t->forward(feat[0], roi); // Was prroi_pool3r
-    auto pooled_feat2 = prroi_pool4t->forward(feat[1], roi); // Was prroi_pool4r
+    auto pooled_feat1 = prroi_pool3t->forward(feat[0], roi); // Output: [batch_size * num_proposals, C, 5, 5]
+    auto pooled_feat2 = prroi_pool4t->forward(feat[1], roi); // Output: [batch_size * num_proposals, C, 3, 3]
     
-    // Make sure all tensors are on the same device (GPU)
-    torch::Device target_device = modulation[0].device();
-    pooled_feat1 = pooled_feat1.to(target_device);
-    pooled_feat2 = pooled_feat2.to(target_device);
-    
-    // Print intermediate tensor shapes
     std::cout << "  Pooled shapes:" << std::endl;
-    std::cout << "    pooled_feat1: [" << pooled_feat1.size(0) << ", " << pooled_feat1.size(1) << ", " 
-              << pooled_feat1.size(2) << ", " << pooled_feat1.size(3) << "]" << std::endl;
-    std::cout << "    pooled_feat2: [" << pooled_feat2.size(0) << ", " << pooled_feat2.size(1) << ", " 
-              << pooled_feat2.size(2) << ", " << pooled_feat2.size(3) << "]" << std::endl;
+    std::cout << "    pooled_feat1 (from prroi_pool3t on feat[0]): [" << pooled_feat1.sizes() << "] dev: " << pooled_feat1.device() << std::endl;
+    std::cout << "    pooled_feat2 (from prroi_pool4t on feat[1]): [" << pooled_feat2.sizes() << "] dev: " << pooled_feat2.device() << std::endl;
     
-    // Inspect the IoU predictor dimensions
     std::cout << "  IoU predictor dimensions:" << std::endl;
-    std::cout << "    weight: [" << iou_predictor->weight.size(0) << ", " << iou_predictor->weight.size(1) << "]" << std::endl;
-    std::cout << "    bias: [" << iou_predictor->bias.size(0) << "]" << std::endl;
+    std::cout << "    weight: [" << iou_predictor->weight.sizes() << "]" << std::endl;
+    std::cout << "    bias: [" << iou_predictor->bias.sizes() << "]" << std::endl;
     
     try {
-        // CORRECTED: Process pooled features through fc3_rt and fc4_rt (LinearBlocks)
-        // These will handle the reshape and linear transformation.
-        // pooled_feat1 is [B*N, 256, 5, 5] -> fc3_rt -> [B*N, 256]
-        // pooled_feat2 is [B*N, 256, 3, 3] -> fc4_rt -> [B*N, 256]
-        std::cout << "  Applying fc3_rt to pooled_feat1 (shape: " << pooled_feat1.sizes() << ")" << std::endl;
-        auto mod_target_0 = fc3_rt.forward(pooled_feat1);
-        std::cout << "  Applying fc4_rt to pooled_feat2 (shape: " << pooled_feat2.sizes() << ")" << std::endl;
-        auto mod_target_1 = fc4_rt.forward(pooled_feat2);
-
-        std::cout << "  mod_target_0 shape: " << mod_target_0.sizes() << std::endl;
-        std::cout << "  mod_target_1 shape: " << mod_target_1.sizes() << std::endl;
+        auto mod0_4d = modulation[0].to(target_device); 
+        auto mod1_4d = modulation[1].to(target_device); 
 
-        // Print flattened shapes
-        // std::cout << "  Flattened shapes:" << std::endl;
-        // std::cout << "    vec1: [" << vec1.size(0) << ", " << vec1.size(1) << "]" << std::endl;
-        // std::cout << "    vec2: [" << vec2.size(0) << ", " << vec2.size(1) << "]" << std::endl;
-        
-        // We need to adapt the input to match what the IoU predictor expects
-        // The IoU predictor has a weight matrix of size 512x1, so input should have 512 features
-        
-        // Instead of concatenating the full features, we need to first reduce them to match expected size
-        // This is based on the original Python implementation
-        
-        // Get modulation shapes
-        std::cout << "  Modulation vector shapes (from get_modulation):" << std::endl;
-        std::cout << "    mod1 (input arg): [" << modulation[0].size(0) << ", " << modulation[0].size(1);
-        if (modulation[0].dim() > 2) std::cout << ", " << modulation[0].size(2) << ", " << modulation[0].size(3);
-        std::cout << "]" << std::endl;
-        std::cout << "    mod2 (input arg): [" << modulation[1].size(0) << ", " << modulation[1].size(1);
-        if (modulation[1].dim() > 2) std::cout << ", " << modulation[1].size(2) << ", " << modulation[1].size(3);
-        std::cout << "]" << std::endl;
-        
-        // Calculate expected dimensions
-        // int mod1_dim = modulation[0].size(1);  // Should be 256
-        // int mod2_dim = modulation[1].size(1);  // Should be 256
-        // int total_mod_dim = mod1_dim + mod2_dim;  // Should be 512, matching iou_predictor weight row count
-        
-        // std::cout << "  Using correct input dimensions for IoU predictor (total_dim=" << total_mod_dim << ")" << std::endl;
-        
-        // Create processed features with correct dimensions
-        // auto processed_feat1 = torch::zeros({num_proposals, mod1_dim}, vec1.options());
-        // auto processed_feat2 = torch::zeros({num_proposals, mod2_dim}, vec2.options());
-        
-        // REMOVED Manual Averaging Logic
-        // We'll use average pooling across spatial dimensions
-        // if (vec1.size(1) > mod1_dim) {
-        //     // Average every N values to reduce dimension
-        //     int pool_size = vec1.size(1) / mod1_dim;
-        //     std::cout << "  Reducing vec1 features with pool_size=" << pool_size << std::endl;
-            
-        //     for (int i = 0; i < num_proposals; i++) {
-        //         for (int j = 0; j < mod1_dim; j++) {
-        //             float sum = 0.0f;
-        //             for (int k = 0; k < pool_size; k++) {
-        //                 int idx = j * pool_size + k;
-        //                 if (idx < vec1.size(1)) {
-        //                     sum += vec1[i][idx].item<float>();
-        //                 }
-        //             }
-        //             processed_feat1[i][j] = sum / pool_size;
-        //         }
-        //     }
-        // } else {
-        //     // Just copy directly if dimensions already match
-        //     processed_feat1 = vec1;
-        // }
-        
-        // if (vec2.size(1) > mod2_dim) {
-        //     // Similar reduction for vec2
-        //     int pool_size = vec2.size(1) / mod2_dim;
-        //     std::cout << "  Reducing vec2 features with pool_size=" << pool_size << std::endl;
-            
-        //     for (int i = 0; i < num_proposals; i++) {
-        //         for (int j = 0; j < mod2_dim; j++) {
-        //             float sum = 0.0f;
-        //             for (int k = 0; k < pool_size; k++) {
-        //                 int idx = j * pool_size + k;
-        //                 if (idx < vec2.size(1)) {
-        //                     sum += vec2[i][idx].item<float>();
-        //                 }
-        //             }
-        //             processed_feat2[i][j] = sum / pool_size;
-        //         }
-        //     }
-        // } else {
-        //     // Just copy directly if dimensions already match
-        //     processed_feat2 = vec2;
-        // }
+        if (mod0_4d.dim() == 2) {
+            mod0_4d = mod0_4d.reshape({mod0_4d.size(0), mod0_4d.size(1), 1, 1});
+        }
+        if (mod1_4d.dim() == 2) {
+            mod1_4d = mod1_4d.reshape({mod1_4d.size(0), mod1_4d.size(1), 1, 1});
+        }
         
-        // Prepare modulation vectors for each proposal
-        auto m0_in = modulation[0]; // Shape can be [1, 256] or [1, 256, 1, 1]
-        auto m1_in = modulation[1];
-
-        if (m0_in.dim() == 4 && m0_in.size(2) == 1 && m0_in.size(3) == 1) {
-            m0_in = m0_in.squeeze(-1).squeeze(-1); // Now [1, 256]
+        if (mod0_4d.size(0) == 1 && pooled_feat1.size(0) > 1) {
+            mod0_4d = mod0_4d.repeat({pooled_feat1.size(0), 1, 1, 1});
         }
-        if (m1_in.dim() == 4 && m1_in.size(2) == 1 && m1_in.size(3) == 1) {
-            m1_in = m1_in.squeeze(-1).squeeze(-1); // Now [1, 256]
+        if (mod1_4d.size(0) == 1 && pooled_feat2.size(0) > 1) {
+            mod1_4d = mod1_4d.repeat({pooled_feat2.size(0), 1, 1, 1});
         }
 
-        // Now m0_in and m1_in are guaranteed to be 2D [Batch, Channels] e.g. [1, 256]
-        auto mod1_repeated_for_proposals = m0_in.repeat({num_proposals, 1}); // [num_proposals, 256]
-        auto mod2_repeated_for_proposals = m1_in.repeat({num_proposals, 1}); // [num_proposals, 256]
+        std::cout << "  Modulation vector shapes (reshaped 4D):" << std::endl;
+        std::cout << "    mod0_4d: [" << mod0_4d.sizes() << "] dev: " << mod0_4d.device() << std::endl;
+        std::cout << "    mod1_4d: [" << mod1_4d.sizes() << "] dev: " << mod1_4d.device() << std::endl;
         
-        std::cout << "  Final feature shapes (after LinearBlocks, before element-wise mult):" << std::endl;
-        std::cout << "    mod_target_0 (from fc3_rt): [" << mod_target_0.size(0) << ", " << mod_target_0.size(1) << "]" << std::endl;
-        std::cout << "    mod_target_1 (from fc4_rt): [" << mod_target_1.size(0) << ", " << mod_target_1.size(1) << "]" << std::endl;
-        std::cout << "    mod1_repeated (from get_modulation input): [" << mod1_repeated_for_proposals.size(0) << ", " << mod1_repeated_for_proposals.size(1) << "]" << std::endl;
-        std::cout << "    mod2_repeated (from get_modulation input): [" << mod2_repeated_for_proposals.size(0) << ", " << mod2_repeated_for_proposals.size(1) << "]" << std::endl;
+        auto feat_prod_0 = pooled_feat1 * mod0_4d; 
+        auto feat_prod_1 = pooled_feat2 * mod1_4d; 
+        std::cout << "  After element-wise product with modulation:\n    feat_prod_0 (pooled_feat1 * mod0_4d): [" << feat_prod_0.sizes() << "] dev: " << feat_prod_0.device() << "\n    feat_prod_1 (pooled_feat2 * mod1_4d): [" << feat_prod_1.sizes() << "] dev: " << feat_prod_1.device() << std::endl;
         
-        // Element-wise multiply features with modulation vectors
-        // CORRECTED: Use mod_target_0 and mod_target_1 from fc3_rt/fc4_rt
-        auto mod_feat1 = mod_target_0 * mod1_repeated_for_proposals;
-        auto mod_feat2 = mod_target_1 * mod2_repeated_for_proposals;
-        
-        // Concatenate to get final features for IoU prediction
-        auto ioufeat = torch::cat({mod_feat1, mod_feat2}, /*dim=*/1);
-        std::cout << "  ioufeat shape: [" << ioufeat.size(0) << ", " << ioufeat.size(1) << "]" << std::endl;
-        
-        // Try GPU implementation first
+        std::cout << "  Applying fc3_rt to feat_prod_0..." << std::endl;
+        auto x0 = fc3_rt.forward(feat_prod_0); // Corrected: . instead of ->
+        std::cout << "  Applying fc4_rt to feat_prod_1..." << std::endl;
+        auto x1 = fc4_rt.forward(feat_prod_1); // Corrected: . instead of ->
+        std::cout << "  After fc_rt blocks:\n    x0 (fc3_rt output): [" << x0.sizes() << "] dev: " << x0.device() << "\n    x1 (fc4_rt output): [" << x1.sizes() << "] dev: " << x1.device() << std::endl;
+
+        auto ioufeat_final = torch::cat(std::vector<torch::Tensor>{x0, x1}, 1).contiguous(); // Corrected: std::vector wrapper
+        std::cout << "  Concatenated ioufeat_final: [" << ioufeat_final.sizes() << "] dev: " << ioufeat_final.device() << std::endl;
+
         torch::Tensor iou_scores;
         try {
-            // Apply IoU predictor using GPU
-            std::cout << "  Applying IoU predictor on GPU" << std::endl;
-            iou_scores = iou_predictor->forward(ioufeat);
+            std::cout << "  Applying final iou_predictor on GPU" << std::endl;
+            iou_predictor->to(target_device); 
+            iou_scores = iou_predictor->forward(ioufeat_final.to(target_device));
+            std::cout << "  Final iou_predictor on GPU successful. Output scores shape: [" << iou_scores.sizes() << "]" << std::endl;
+
         } catch (const std::exception& cuda_error) {
-            // If GPU implementation fails, use CPU implementation
-            std::cout << "  GPU implementation failed: " << cuda_error.what() << std::endl;
-            std::cout << "  Falling back to CPU implementation" << std::endl;
-            
-            // Move tensors to CPU
-            auto ioufeat_cpu = ioufeat.to(torch::kCPU);
-            auto weight_cpu = iou_predictor->weight.to(torch::kCPU);
-            auto bias_cpu = iou_predictor->bias.to(torch::kCPU);
+            std::cout << "  GPU iou_predictor->forward() failed: " << cuda_error.what() << std::endl;
+            std::cout << "  Falling back to CPU for final iou_predictor" << std::endl;
             
-            // Implement the linear layer manually
-            // For each proposal, compute: score = bias + ioufeat * weight
-            auto scores_cpu = torch::zeros({num_proposals, 1}, torch::kCPU);
+            auto ioufeat_final_cpu = ioufeat_final.to(torch::kCPU).contiguous();
+            auto weight_cpu = iou_predictor->weight.to(torch::kCPU).contiguous();
+            auto bias_cpu = torch::Tensor(); 
+            if (iou_predictor->bias.defined()) {
+                bias_cpu = iou_predictor->bias.to(torch::kCPU).contiguous();
+            }
+
+            std::cout << "    DEBUG CPU Fallback: ioufeat_final_cpu device: " << ioufeat_final_cpu.device() << std::endl;
+            std::cout << "    DEBUG CPU Fallback: weight_cpu device: " << weight_cpu.device() << std::endl;
+            if (bias_cpu.defined()) {
+                 std::cout << "    DEBUG CPU Fallback: bias_cpu device: " << bias_cpu.device() << std::endl;
+            } else {
+                 std::cout << "    DEBUG CPU Fallback: bias_cpu is undefined." << std::endl;
+            }
             
-            for (int i = 0; i < num_proposals; i++) {
-                // Start with bias
-                float score = bias_cpu[0].item<float>();
-                
-                // Add weighted sum of features
-                for (int j = 0; j < ioufeat_cpu.size(1); j++) {
-                    score += ioufeat_cpu[i][j].item<float>() * weight_cpu[0][j].item<float>();
-                }
-                
-                scores_cpu[i][0] = score;
+            try {
+                iou_scores = torch::nn::functional::linear(ioufeat_final_cpu, weight_cpu, bias_cpu);
+                std::cout << "  CPU fallback torch::nn::functional::linear() successful. Output device: " << iou_scores.device() << std::endl;
+            } catch (const std::exception& cpu_fwd_error) {
+                std::cerr << "ERROR during CPU torch::nn::functional::linear(): " << cpu_fwd_error.what() << std::endl;
+                iou_predictor->to(target_device); 
+                throw; 
             }
             
-            // Move results back to original device
-            iou_scores = scores_cpu.to(target_device);
+            iou_predictor->to(target_device); 
+            iou_scores = iou_scores.to(target_device);
         }
         
         std::cout << "  iou_scores raw shape: [" << iou_scores.size(0) << ", " << iou_scores.size(1) << "]" << std::endl;
         
-        // Reshape back to [batch_size, num_proposals]
         iou_scores = iou_scores.reshape({batch_size, num_proposals});
         std::cout << "  Final iou_scores shape: [" << iou_scores.size(0) << ", " << iou_scores.size(1) << "]" << std::endl;
         
         return iou_scores;
         
     } catch (const std::exception& e) {
-        // This should never happen with our robust implementation
         std::cerr << "CRITICAL: Unexpected error in predict_iou: " << e.what() << std::endl;
-        
-        // We'll implement direct box overlaps as a true fallback that doesn't use "magic numbers"
-        std::cout << "  Implementing direct IoU calculation using box overlaps" << std::endl;
-        
-        // Move tensors to CPU for direct calculation
-        auto proposals_cpu = proposals.to(torch::kCPU);
-        auto bb_cpu = modulation[0].to(torch::kCPU); // Using modulation[0] to get the original target box
-        
-        // Create output tensor on CPU
-        auto iou_scores = torch::zeros({batch_size, num_proposals}, torch::kCPU);
-        
-        // Calculate IoU geometrically for each proposal
-        // This is a direct, mathematical implementation that doesn't rely on neural networks
-        for (int i = 0; i < num_proposals; i++) {
-            float target_x1 = proposals_view[i][0].item<float>();
-            float target_y1 = proposals_view[i][1].item<float>();
-            float target_x2 = target_x1 + proposals_view[i][2].item<float>();
-            float target_y2 = target_y1 + proposals_view[i][3].item<float>();
-            
-            float box_x1 = bb_cpu[0][0].item<float>();
-            float box_y1 = bb_cpu[0][1].item<float>();
-            float box_x2 = box_x1 + bb_cpu[0][2].item<float>();
-            float box_y2 = box_y1 + bb_cpu[0][3].item<float>();
-            
-            // Calculate intersection area
-            float x_left = std::max(target_x1, box_x1);
-            float y_top = std::max(target_y1, box_y1);
-            float x_right = std::min(target_x2, box_x2);
-            float y_bottom = std::min(target_y2, box_y2);
-            
-            float intersection_area = std::max(0.0f, x_right - x_left) * std::max(0.0f, y_bottom - y_top);
-            
-            // Calculate union area
-            float target_area = (target_x2 - target_x1) * (target_y2 - target_y1);
-            float box_area = (box_x2 - box_x1) * (box_y2 - box_y1);
-            float union_area = target_area + box_area - intersection_area;
-            
-            // IoU = intersection / union
-            float iou = union_area > 0 ? intersection_area / union_area : 0;
-            iou_scores[0][i] = iou;
-        }
-        
-        // Move back to original device
-        return iou_scores.to(target_device);
+        std::cout << "  Propagating critical error. No fallback available for this stage." << std::endl;
+        throw; 
     }
 }
 
diff --git a/cimp/bb_regressor/bb_regressor.h b/cimp/bb_regressor/bb_regressor.h
index 3db18bf..d37003c 100644
--- a/cimp/bb_regressor/bb_regressor.h
+++ b/cimp/bb_regressor/bb_regressor.h
@@ -44,19 +44,11 @@ public:
     bool use_relu;
 };
 
-// PrRoIPool2D implementation
+// PrRoIPool2D implementation (requires CUDA)
 class PrRoIPool2D {
 public:
     PrRoIPool2D(int pooled_height, int pooled_width, float spatial_scale);
     torch::Tensor forward(torch::Tensor feat, torch::Tensor rois);
-
-    // CPU-based fallback implementation
-    torch::Tensor forward_cpu(torch::Tensor feat, torch::Tensor rois) {
-        // Simple implementation that returns zeros (for fallback only)
-        int channels = feat.size(1);
-        int num_rois = rois.size(0);
-        return torch::zeros({num_rois, channels, pooled_height_, pooled_width_}, feat.options());
-    }
     
 private:
     int pooled_height_;
diff --git a/cimp/bb_regressor/prroi_pooling/prroi_pooling_gpu.c b/cimp/bb_regressor/prroi_pooling/prroi_pooling_gpu.c
deleted file mode 100644
index be0bbda..0000000
--- a/cimp/bb_regressor/prroi_pooling/prroi_pooling_gpu.c
+++ /dev/null
@@ -1,135 +0,0 @@
-/*
- * File   : prroi_pooling_gpu.c
- * Simplified version for C++ compatibility
- */
-
-#include "prroi_pooling_gpu.h"
-#include "prroi_pooling_gpu_impl.cuh"
-#include <cuda_runtime.h>
-
-// C wrapper function for the CUDA forward implementation
-int prroi_pooling_forward_cuda(
-    const float *features_data,
-    const float *rois_data,
-    float *output_data,
-    int channels,
-    int height, 
-    int width,
-    int num_rois,
-    int pooled_height,
-    int pooled_width,
-    float spatial_scale
-) {
-    int top_count = num_rois * channels * pooled_height * pooled_width;
-
-    // Get current CUDA stream
-    cudaStream_t stream = 0;  // Use default stream if no stream is available
-    cudaError_t err = cudaStreamSynchronize(stream);
-    if (err != cudaSuccess) {
-        fprintf(stderr, "CUDA stream error: %s\n", cudaGetErrorString(err));
-        return -1;
-    }
-
-    // Call the implementation
-    PrRoIPoolingForwardGpu(
-        stream,
-        features_data,
-        rois_data,
-        output_data,
-        channels,
-        height,
-        width,
-        pooled_height,
-        pooled_width,
-        spatial_scale,
-        top_count
-    );
-
-    return 0;
-}
-
-// Simplified wrapper for backward pass
-int prroi_pooling_backward_cuda(
-    const float *features_data,
-    const float *rois_data,
-    const float *output_data,
-    const float *output_diff_data,
-    float *features_diff_data,
-    int channels,
-    int height,
-    int width,
-    int num_rois,
-    int pooled_height,
-    int pooled_width,
-    float spatial_scale
-) {
-    // Calculate counts for features and output
-    int top_count = num_rois * channels * pooled_height * pooled_width;
-    int bottom_count = 1 * channels * height * width;  // Assume batch_size = 1
-    
-    // Get current CUDA stream
-    cudaStream_t stream = 0;  // Use default stream
-    
-    // Call the implementation
-    PrRoIPoolingBackwardGpu(
-        stream,
-        features_data,
-        rois_data,
-        output_data,
-        output_diff_data,
-        features_diff_data,
-        channels,
-        height,
-        width,
-        pooled_height,
-        pooled_width,
-        spatial_scale,
-        top_count,
-        bottom_count
-    );
-
-    return 0;
-}
-
-// Simplified wrapper for coordinate backward pass
-int prroi_pooling_coor_backward_cuda(
-    const float *features_data,
-    const float *rois_data,
-    const float *output_data,
-    const float *output_diff_data,
-    float *rois_diff_data,
-    int channels,
-    int height,
-    int width,
-    int num_rois,
-    int pooled_height,
-    int pooled_width,
-    float spatial_scale
-) {
-    // Calculate counts
-    int top_count = num_rois * channels * pooled_height * pooled_width;
-    int bottom_count = num_rois * 5;  // ROIs are 5D (batch_idx, x1, y1, x2, y2)
-    
-    // Get current CUDA stream
-    cudaStream_t stream = 0;  // Use default stream
-    
-    // Call the implementation
-    PrRoIPoolingCoorBackwardGpu(
-        stream,
-        features_data,
-        rois_data,
-        output_data,
-        output_diff_data,
-        rois_diff_data,
-        channels,
-        height,
-        width,
-        pooled_height,
-        pooled_width,
-        spatial_scale,
-        top_count,
-        bottom_count
-    );
-
-    return 0;
-}
diff --git a/cimp/bb_regressor/prroi_pooling/prroi_pooling_gpu_impl.cu b/cimp/bb_regressor/prroi_pooling/prroi_pooling_gpu_impl.cu
index bb5690d..521a9df 100644
--- a/cimp/bb_regressor/prroi_pooling/prroi_pooling_gpu_impl.cu
+++ b/cimp/bb_regressor/prroi_pooling/prroi_pooling_gpu_impl.cu
@@ -3,19 +3,28 @@
  * Simplified version for C++ compatibility
  */
 
-#include "prroi_pooling_gpu_impl.cuh"
-
+#include <cuda_runtime.h>
+#include <device_launch_parameters.h>
 #include <cstdio>
 #include <cfloat>
 #include <cmath>
 
-#define CUDA_NUM_THREADS 512
+// Forward declaration of CUDA kernel functions
+__global__ void PRROIPoolingForwardKernel(
+    const int nthreads,
+    const float *bottom_data,
+    const float *bottom_rois,
+    float *top_data,
+    const int channels,
+    const int height,
+    const int width,
+    const int pooled_height,
+    const int pooled_width,
+    const float spatial_scale
+);
 
 // Helper macros for CUDA kernel execution
-#define CUDA_KERNEL_LOOP(i, n) \
-    for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
-        i < (n); \
-        i += blockDim.x * gridDim.x)
+#define CUDA_NUM_THREADS 512
 
 // Helper function to check CUDA errors
 inline void checkCudaErrors(cudaError_t err) {
@@ -55,7 +64,9 @@ __global__ void PRROIPoolingForwardKernel(
     const int pooled_width,
     const float spatial_scale
 ) {
-    CUDA_KERNEL_LOOP(index, nthreads) {
+    for (int index = blockIdx.x * blockDim.x + threadIdx.x; 
+         index < nthreads; 
+         index += blockDim.x * gridDim.x) {
         // (n, c, ph, pw) is the index in output
         int pw = index % pooled_width;
         int ph = (index / pooled_width) % pooled_height;
@@ -137,7 +148,7 @@ __global__ void PRROIPoolingForwardKernel(
 }
 
 // C API wrapper for Forward pass
-void PrRoIPoolingForwardGpu(
+extern "C" void PrRoIPoolingForwardGpu(
     cudaStream_t stream,
     const float *bottom_data,
     const float *bottom_rois,
@@ -170,7 +181,7 @@ void PrRoIPoolingForwardGpu(
 }
 
 // Simplified dummy implementations of backward passes
-void PrRoIPoolingBackwardGpu(
+extern "C" void PrRoIPoolingBackwardGpu(
     cudaStream_t stream,
     const float *bottom_data,
     const float *bottom_rois,
@@ -190,7 +201,7 @@ void PrRoIPoolingBackwardGpu(
     cudaMemsetAsync(bottom_diff, 0, bottom_count * sizeof(float), stream);
 }
 
-void PrRoIPoolingCoorBackwardGpu(
+extern "C" void PrRoIPoolingCoorBackwardGpu(
     cudaStream_t stream,
     const float *bottom_data,
     const float *bottom_rois,
diff --git a/cimp/demo.cpp b/cimp/demo.cpp
index c22a1db..c3b1e41 100644
--- a/cimp/demo.cpp
+++ b/cimp/demo.cpp
@@ -135,20 +135,20 @@ int main(int argc, char* argv[]) {
     try {
         std::cout << "=== Object Tracking Demo with BBRegressor and Classifier ===" << std::endl;
         
-        // Determine which device to use
-        torch::Device device(torch::kCPU);
-
-        // Add more detailed CUDA debugging
+        // Check CUDA availability - required for this application
         std::cout << "Checking CUDA availability..." << std::endl;
         std::cout << "torch::cuda::is_available(): " << (torch::cuda::is_available() ? "true" : "false") << std::endl;
 
-        if (torch::cuda::is_available()) {
-            device = torch::Device(torch::kCUDA, 0);
-            std::cout << "Using CUDA device: " << device << std::endl;
-            std::cout << "CUDA Device Count: " << torch::cuda::device_count() << std::endl;
-        } else {
-            std::cout << "CUDA is not available, using CPU" << std::endl;
+        if (!torch::cuda::is_available()) {
+            std::cerr << "ERROR: CUDA is not available. This application requires CUDA." << std::endl;
+            std::cerr << "Please ensure you have a CUDA-capable GPU and the proper drivers installed." << std::endl;
+            return 1;
         }
+
+        // Always use CUDA device
+        torch::Device device(torch::kCUDA, 0);
+        std::cout << "Using CUDA device: " << device << std::endl;
+        std::cout << "CUDA Device Count: " << torch::cuda::device_count() << std::endl;
         std::cout << std::endl;
         
         // Find the base directory containing exported weights
diff --git a/classifier_stats.txt b/classifier_stats.txt
index 992d0a2..5dc815b 100644
--- a/classifier_stats.txt
+++ b/classifier_stats.txt
@@ -1,9 +1,9 @@
 Output 0:
   Shape: [1, 512, 9, 9]
-  Mean: -0.000479055
-  Std: 0.0110019
-  Min: -0.0458832
-  Max: 0.0493125
-  Sum: -19.8674
-  Sample values: [-0.0125058, 0.00186453, 0.0141787]
+  Mean: 0.000174798
+  Std: 0.0110712
+  Min: -0.0531409
+  Max: 0.0471968
+  Sum: 7.24922
+  Sample values: [-0.00536738, 0.00499956, 0.00910664]
 
diff --git a/cmake-build-debug/CMakeCache.txt b/cmake-build-debug/CMakeCache.txt
index 9b3b018..5b61b8e 100644
--- a/cmake-build-debug/CMakeCache.txt
+++ b/cmake-build-debug/CMakeCache.txt
@@ -100,6 +100,15 @@ Z_VCPKG_CL:FILEPATH=Z_VCPKG_CL-NOTFOUND
 // triplet
 _VCPKG_INSTALLED_DIR:PATH=/media/mht/ADATA/repos/cpp_tracker/cmake-build-debug/vcpkg_installed
 
+//Value Computed by CMake
+cimp_BINARY_DIR:STATIC=/media/mht/ADATA/repos/cpp_tracker/cmake-build-debug
+
+//Value Computed by CMake
+cimp_IS_TOP_LEVEL:STATIC=ON
+
+//Value Computed by CMake
+cimp_SOURCE_DIR:STATIC=/media/mht/ADATA/repos/cpp_tracker
+
 //Value Computed by CMake
 cpp_tracker_BINARY_DIR:STATIC=/media/mht/ADATA/repos/cpp_tracker/cmake-build-debug
 
diff --git a/run_demo.sh b/run_demo.sh
index e218f15..2b56264 100644
--- a/run_demo.sh
+++ b/run_demo.sh
@@ -8,7 +8,9 @@ elif [ -d "/usr/lib/cuda" ]; then
     export CUDA_HOME=/usr/lib/cuda
     echo "CUDA environment set to $CUDA_HOME"
 else
-    echo "CUDA not found. Running in CPU-only mode."
+    echo "CUDA not found. The project requires CUDA to run."
+    echo "Please install CUDA and try again."
+    exit 1
 fi
 
 # Set LibTorch library path
diff --git a/test/compare_models.py b/test/compare_models.py
index 3946856..f0db471 100644
--- a/test/compare_models.py
+++ b/test/compare_models.py
@@ -15,33 +15,59 @@ sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
 # Import model wrappers
 from pytracking.features.net_wrappers import DiMPTorchScriptWrapper
+# For loading AtomIoUNet from source
+from ltr.models.bbreg.atom_iou_net import AtomIoUNet
 
 class ModelComparison:
     def __init__(self, model_dir='exported_weights', num_samples=1000):
-        self.model_dir = model_dir
+        self.model_dir_name = model_dir # Store the string name for paths
         self.num_samples = num_samples
         self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
         
+        # Base path for models, inputs, and outputs
+        self.project_root = Path(__file__).resolve().parent.parent
+        self.model_base_dir = self.project_root / self.model_dir_name
+        self.input_dir_base = self.project_root / 'test' / 'input_samples'
+        self.cpp_output_dir_base = self.project_root / 'test' / 'output'
+        
         # Initialize comparison results
-        self.comparison_dir = Path('test') / 'comparison'
+        self.comparison_dir = self.project_root / 'test' / 'comparison'
         self.comparison_dir.mkdir(parents=True, exist_ok=True)
-        self.plots_dir = self.comparison_dir / 'plots' # plots_dir initialized here
+        self.plots_dir = self.comparison_dir / 'plots'
+        self.plots_dir.mkdir(parents=True, exist_ok=True)
+        
+        self.all_errors_stats = {}
         
-        # Initialize models
+        # Initialize models (TorchScript and from-source)
         self._init_models()
         
     def _init_models(self):
         """Initialize Python models"""
         print("Loading Python models...")
         
-        # Load DiMP components
+        # Load TorchScript models using _sd arguments for directories of tensors
         self.models = DiMPTorchScriptWrapper(
-            model_dir=self.model_dir,
+            model_dir=str(self.model_base_dir), # Expects string path
             device=self.device,
-            backbone_sd='backbone',  # Directory with backbone weights
-            classifier_sd='classifier',  # Directory with classifier weights
-            bbregressor_sd='bb_regressor'  # Directory with bbox regressor weights
+            backbone_sd='backbone',       # Directory name for backbone weights
+            classifier_sd='classifier',   # Directory name for classifier weights
+            bbregressor_sd='bb_regressor'  # Directory name for bb_regressor weights
+        )
+        
+        # Initialize BBRegressor from source for get_modulation fallback
+        self.bb_regressor_from_source = AtomIoUNet(
+            input_dim=(512, 1024),
+            pred_input_dim=(256, 256),
+            pred_inter_dim=(256, 256)
+        )
+        ModelComparison.load_weights_for_custom_model(
+            self.bb_regressor_from_source, 
+            'bb_regressor', # model_name for path and doc file
+            self.model_base_dir, 
+            self.device
         )
+        self.bb_regressor_from_source.eval().to(self.device)
+        print("Python models loaded.")
     
     def compare_classifier(self):
         """Compare classifier model outputs between Python and C++"""
@@ -143,115 +169,159 @@ class ModelComparison:
     def compare_bb_regressor(self):
         """Compare bb_regressor model outputs between Python and C++"""
         print("\nComparing bb_regressor outputs...")
-        input_dir = Path('test') / 'input_samples' / 'bb_regressor'
-        cpp_output_dir = Path('test') / 'output' / 'bb_regressor'
+        input_dir_path = self.input_dir_base / 'bb_regressor'
+        cpp_output_dir_path = self.cpp_output_dir_base / 'bb_regressor'
 
-        if not input_dir.exists() or not cpp_output_dir.exists():
-            print(f"BB Regressor input or C++ output directory not found ({input_dir}, {cpp_output_dir}). Skipping.")
+        if not input_dir_path.exists() or not cpp_output_dir_path.exists():
+            print(f"BB Regressor input or C++ output directory not found ({input_dir_path}, {cpp_output_dir_path}). Skipping BB Regressor comparison.")
+            # Populate NaN for all expected BB Regressor comparisons if dirs are missing
+            for i in range(self.num_samples):
+                sample_key_base = f"BBReg_Sample_{i}"
+                current_errors = {}
+                self._compare_tensor_data(None, None, "BBReg PyIoUFeat0 vs CppIoUFeat0", i, current_errors)
+                self._compare_tensor_data(None, None, "BBReg PyIoUFeat1 vs CppIoUFeat1", i, current_errors)
+                self._compare_tensor_data(None, None, "BBReg PyMod0 vs CppMod0", i, current_errors)
+                self._compare_tensor_data(None, None, "BBReg PyMod1 vs CppMod1", i, current_errors)
+                self._compare_tensor_data(None, None, "BBReg IoUPred", i, current_errors)
+                self.all_errors_stats[sample_key_base] = current_errors
             return
-        
+
         for i in tqdm(range(self.num_samples), desc="BB Regressor samples"):
-            sample_dir = input_dir / f'sample_{i}'
-            cpp_output_sample_dir = cpp_output_dir / f'sample_{i}'
+            sample_dir = input_dir_path / f'sample_{i}'
+            cpp_out_sample_dir = cpp_output_dir_path / f'sample_{i}'
             
-            # Load input tensors for BB Regressor for this sample
-            feat_layer2_path = sample_dir / 'feat_layer2.pt'
-            feat_layer3_path = sample_dir / 'feat_layer3.pt'
-            init_bbox_path = sample_dir / 'init_bbox.pt'
-            proposals_path = sample_dir / 'proposals.pt'
-
-            feat_layer2 = self.load_cpp_tensor(feat_layer2_path, self.device)
-            feat_layer3 = self.load_cpp_tensor(feat_layer3_path, self.device)
-            init_bbox = self.load_cpp_tensor(init_bbox_path, self.device)
-            proposals = self.load_cpp_tensor(proposals_path, self.device)
-
-            if any(t is None for t in [feat_layer2, feat_layer3, init_bbox, proposals]):
-                print(f"Critical: Failed to load one or more BB Regressor input tensors for sample {i}. Skipping.")
-                continue
+            py_feat_layer2, py_feat_layer3, py_init_bbox, py_proposals = None, None, None, None
             
-            backbone_feat_tuple = (feat_layer2, feat_layer3) # Define the tuple for clarity
-
-            # Get IoU features from Python model
-            # self.models.get_backbone_bbreg_feat calls self.bb_regressor.get_iou_feat
-            with torch.no_grad():
-                py_iou_feat = self.models.get_backbone_bbreg_feat({"layer2": feat_layer2, "layer3": feat_layer3})
-            
-            # Get modulation vectors
-            squeezed_init_bbox = init_bbox
-            if init_bbox is not None and init_bbox.dim() == 3 and init_bbox.shape[1] == 1:
-                squeezed_init_bbox = init_bbox.squeeze(1)
-
-            with torch.no_grad():
-                # Pass original backbone features to get_modulation
-                py_modulation = self.models.bb_regressor.get_modulation(backbone_feat_tuple, squeezed_init_bbox)
+            if not sample_dir.exists() or not cpp_out_sample_dir.exists():
+                print(f"Warning: Skipping BB Regressor sample {i}, files not found at {sample_dir} or {cpp_out_sample_dir}.")
+            else:
+                py_feat_layer2 = self.load_cpp_tensor(sample_dir / 'feat_layer2.pt', self.device)
+                py_feat_layer3 = self.load_cpp_tensor(sample_dir / 'feat_layer3.pt', self.device)
+                py_init_bbox = self.load_cpp_tensor(sample_dir / 'init_bbox.pt', self.device)
+                py_proposals = self.load_cpp_tensor(sample_dir / 'proposals.pt', self.device)
+
+            # Get Python IoU features (these come directly from backbone, so should be reliable)
+            py_iou_feat = None
+            if py_feat_layer2 is not None and py_feat_layer3 is not None:
+                # Use from-source get_iou_feat for consistent 256-channel features
+                try:
+                    py_iou_feat = self.bb_regressor_from_source.get_iou_feat([py_feat_layer2, py_feat_layer3])
+                    if isinstance(py_iou_feat, tuple): py_iou_feat = list(py_iou_feat)
+                    print(f"Sample {i}: Successfully used from-source get_iou_feat.")
+                    # py_iou_feat will be a list of two tensors, e.g., [B, 256, H1, W1], [B, 256, H2, W2]
+                except Exception as e_iou_source:
+                    print(f"Sample {i}: From-source get_iou_feat failed: {e_iou_source}")
+                    py_iou_feat = None # Ensure it's None if failed
             
-            # DEBUG: Print shapes
-            print(f"Sample {i}: py_iou_feat[0] shape: {py_iou_feat[0].shape}, py_modulation[0] shape: {py_modulation[0].shape}")
-            print(f"Sample {i}: py_iou_feat[1] shape: {py_iou_feat[1].shape}, py_modulation[1] shape: {py_modulation[1].shape}")
+            # Get Python modulation vectors
+            py_modulation = None
+            if py_feat_layer2 is not None and py_feat_layer3 is not None and py_init_bbox is not None:
+                py_features_list = [py_feat_layer2, py_feat_layer3] # Pass as a list
+                
+                # Squeeze py_init_bbox if it's [B, 1, 4] to [B, 4]
+                squeezed_init_bbox = py_init_bbox
+                if py_init_bbox.ndim == 3 and py_init_bbox.shape[0] > 0 and py_init_bbox.shape[1] == 1:
+                    squeezed_init_bbox = py_init_bbox.squeeze(1)
+                elif py_init_bbox.ndim == 2:
+                    pass # Already [B,4] or similar, assume correct
+                else:
+                    print(f"Sample {i}: Warning - py_init_bbox has unexpected shape {py_init_bbox.shape}. Expecting 2D or 3D [B,1,4].")
 
-            # Predict IoU (Python model)
+                try:
+                    # Try TorchScript model first
+                    py_modulation = self.models.bb_regressor.get_modulation(py_features_list, squeezed_init_bbox)
+                    if isinstance(py_modulation, tuple): py_modulation = list(py_modulation)
+                    print(f"Sample {i}: Successfully used TorchScript get_modulation.")
+                except Exception as e_ts:
+                    print(f"Sample {i}: TorchScript get_modulation failed: {e_ts}. Trying from-source model.")
+                    try:
+                        py_modulation = self.bb_regressor_from_source.get_modulation(py_features_list, squeezed_init_bbox)
+                        if isinstance(py_modulation, tuple): py_modulation = list(py_modulation)
+                        print(f"Sample {i}: Successfully used from-source get_modulation.")
+                    except Exception as e_source:
+                        print(f"Sample {i}: From-source get_modulation also failed: {e_source}")
+                        py_modulation = None
+                
+                if py_modulation:
+                    print(f"Sample {i}: py_iou_feat[0] shape: {py_iou_feat[0].shape if py_iou_feat and len(py_iou_feat) > 0 else 'N/A'}, py_modulation[0] shape: {py_modulation[0].shape if py_modulation and len(py_modulation) > 0 else 'N/A'}")
+                    print(f"Sample {i}: py_iou_feat[1] shape: {py_iou_feat[1].shape if py_iou_feat and len(py_iou_feat) > 1 else 'N/A'}, py_modulation[1] shape: {py_modulation[1].shape if py_modulation and len(py_modulation) > 1 else 'N/A'}")
+
+            # Run Python bb_regressor's predict_iou (from TorchScript model)
             py_iou_pred = None
-            try:
-                with torch.no_grad():
-                    py_iou_pred = self.models.bb_regressor.predict_iou(py_modulation, py_iou_feat, proposals)
-            except RuntimeError as e:
-                print(f"WARNING: Python model self.models.bb_regressor.predict_iou failed for sample {i}: {e}")
+            if py_modulation is not None and py_iou_feat is not None and py_proposals is not None:
+                # Ensure modulation vectors are 2D [B, C] for predict_iou
+                py_mod_0_squeezed = py_modulation[0].squeeze(-1).squeeze(-1) if py_modulation[0].ndim == 4 else py_modulation[0]
+                py_mod_1_squeezed = py_modulation[1].squeeze(-1).squeeze(-1) if py_modulation[1].ndim == 4 else py_modulation[1]
+                py_modulation_squeezed_for_pred = [py_mod_0_squeezed, py_mod_1_squeezed]
+
+                try:
+                    # Try TorchScript predict_iou first
+                    print(f"Sample {i}: Attempting TorchScript predict_iou with mod_0: {py_modulation[0].shape}, iou_feat_0: {py_iou_feat[0].shape}")
+                    # Ensure inputs for predict_iou are 2D for modulation and 4D for iou_feat
+                    mod_0_squeezed = py_modulation[0].squeeze(-1).squeeze(-1) if py_modulation[0].dim() == 4 else py_modulation[0]
+                    mod_1_squeezed = py_modulation[1].squeeze(-1).squeeze(-1) if py_modulation[1].dim() == 4 else py_modulation[1]
+                    
+                    # --- BEGIN PYTHON DETAILED LOGGING ---
+                    print(f"    DEBUG PYTHON: py_iou_feat[0] (first 5 rows of first batch) sample values:\n{py_iou_feat[0][0, :, :5, :5] if py_iou_feat[0].numel() > 0 else 'N/A'}") # Print a slice of the feature map
+                    print(f"    DEBUG PYTHON: py_iou_feat[1] (first 5 rows of first batch) sample values:\n{py_iou_feat[1][0, :, :5, :5] if py_iou_feat[1].numel() > 0 else 'N/A'}")
+                    print(f"    DEBUG PYTHON: mod_0_squeezed sample values:\n{mod_0_squeezed[0, :5] if mod_0_squeezed.numel() > 0 else 'N/A'}")
+                    print(f"    DEBUG PYTHON: mod_1_squeezed sample values:\n{mod_1_squeezed[0, :5] if mod_1_squeezed.numel() > 0 else 'N/A'}")
+                    # Construct ioufeat equivalent for Python to log before predict_iou
+                    # This mimics the C++: mod_feat1 = mod_target_0 * mod1_repeated_for_proposals; mod_feat2 = mod_target_1 * mod2_repeated_for_proposals; ioufeat = torch.cat({mod_feat1, mod_feat2}, 1);
+                    # We need py_pooled_feat1, py_pooled_feat2 from PrRoIPool like in C++ before fc3_rt and fc4_rt
+                    # This part is tricky as Python model does it internally in predict_iou.
+                    # For now, let's log the direct inputs to predict_iou and its weights.
+
+                    # Log weights of the iou_predictor in the TorchScript model
+                    ts_iou_predictor_weight = self.models.bb_regressor.iou_predictor.weight.data
+                    ts_iou_predictor_bias = self.models.bb_regressor.iou_predictor.bias.data
+                    print(f"    DEBUG PYTHON: TorchScript iou_predictor.weight:\n{ts_iou_predictor_weight}")
+                    print(f"    DEBUG PYTHON: TorchScript iou_predictor.bias:\n{ts_iou_predictor_bias}")
+                    # --- END PYTHON DETAILED LOGGING ---
+
+                    py_iou_pred = self.models.bb_regressor.predict_iou(
+                        py_modulation_squeezed_for_pred, 
+                        py_iou_feat,  # These are now from source model (256ch)
+                        py_proposals
+                    )
+                    print(f"Sample {i}: Successfully used TorchScript predict_iou.")
+                except Exception as e_ts_pred:
+                    print(f"WARNING: Python model self.models.bb_regressor.predict_iou failed for sample {i}: {e_ts_pred}")
+                    print(f"Sample {i}: Falling back to from-source predict_iou.")
+                    try:
+                        py_iou_pred = self.bb_regressor_from_source.predict_iou(
+                            py_modulation_squeezed_for_pred, 
+                            py_iou_feat, 
+                            py_proposals
+                        )
+                        print(f"Sample {i}: Successfully used from-source predict_iou.")
+                    except Exception as e_source_pred:
+                        print(f"ERROR: Python model self.bb_regressor_from_source.predict_iou also failed for sample {i}: {e_source_pred}")
+                        py_iou_pred = None # Ensure it's None if fallback also failed
             
             # Load C++ outputs
-            cpp_iou_pred_path = cpp_output_sample_dir / 'iou_pred.pt'
-            cpp_modulation_0_path = cpp_output_sample_dir / 'modulation_0.pt'
-            cpp_modulation_1_path = cpp_output_sample_dir / 'modulation_1.pt'
-            cpp_feat_0_path = cpp_output_sample_dir / 'iou_feat_0.pt'
-            cpp_feat_1_path = cpp_output_sample_dir / 'iou_feat_1.pt'
-
-            cpp_iou_pred = self.load_cpp_tensor(cpp_iou_pred_path, self.device)
-            cpp_modulation_0 = self.load_cpp_tensor(cpp_modulation_0_path, self.device)
-            cpp_modulation_1 = self.load_cpp_tensor(cpp_modulation_1_path, self.device)
-            cpp_feat_0 = self.load_cpp_tensor(cpp_feat_0_path, self.device)
-            cpp_feat_1 = self.load_cpp_tensor(cpp_feat_1_path, self.device)
-            
-            current_errors = {} # Store errors for this sample for the HTML report
-
-            # Compare IoU features (py_iou_feat vs cpp_feat_0/1)
-            # _compare_tensor_data will handle None inputs appropriately
+            cpp_iou_pred, cpp_mod_0, cpp_mod_1, cpp_iou_feat_0, cpp_iou_feat_1 = None, None, None, None, None
+            if cpp_out_sample_dir.exists():
+                cpp_iou_pred = self.load_cpp_tensor(cpp_output_dir_path / f"sample_{i}" / 'iou_pred.pt', self.device)
+                cpp_mod_0 = self.load_cpp_tensor(cpp_output_dir_path / f"sample_{i}" / 'modulation_0.pt', self.device)
+                cpp_mod_1 = self.load_cpp_tensor(cpp_output_dir_path / f"sample_{i}" / 'modulation_1.pt', self.device)
+                cpp_iou_feat_0 = self.load_cpp_tensor(cpp_output_dir_path / f"sample_{i}" / 'iou_feat_0.pt', self.device)
+                cpp_iou_feat_1 = self.load_cpp_tensor(cpp_output_dir_path / f"sample_{i}" / 'iou_feat_1.pt', self.device)
+
+            current_errors = {}
             py_iou_f0 = py_iou_feat[0] if py_iou_feat and len(py_iou_feat) > 0 else None
             py_iou_f1 = py_iou_feat[1] if py_iou_feat and len(py_iou_feat) > 1 else None
-            self._compare_tensor_data(py_iou_f0, cpp_feat_0, "BBReg PyIoUFeat0 vs CppIoUFeat0", i, current_errors)
-            self._compare_tensor_data(py_iou_f1, cpp_feat_1, "BBReg PyIoUFeat1 vs CppIoUFeat1", i, current_errors)
-
-            # Compare modulation vectors (py_modulation vs cpp_modulation_0/1)
+            self._compare_tensor_data(py_iou_f0, cpp_iou_feat_0, "BBReg PyIoUFeat0 vs CppIoUFeat0", i, current_errors)
+            self._compare_tensor_data(py_iou_f1, cpp_iou_feat_1, "BBReg PyIoUFeat1 vs CppIoUFeat1", i, current_errors)
+            
             py_mod_0 = py_modulation[0] if py_modulation and len(py_modulation) > 0 else None
             py_mod_1 = py_modulation[1] if py_modulation and len(py_modulation) > 1 else None
-            self._compare_tensor_data(py_mod_0, cpp_modulation_0, "BBReg PyMod0 vs CppMod0", i, current_errors)
-            self._compare_tensor_data(py_mod_1, cpp_modulation_1, "BBReg PyMod1 vs CppMod1", i, current_errors)
-
-            # Compare final IoU prediction
-            # _compare_tensor_data will handle None for py_iou_pred or cpp_iou_pred
+            self._compare_tensor_data(py_mod_0, cpp_mod_0, "BBReg PyMod0 vs CppMod0", i, current_errors)
+            self._compare_tensor_data(py_mod_1, cpp_mod_1, "BBReg PyMod1 vs CppMod1", i, current_errors)
+            
             self._compare_tensor_data(py_iou_pred, cpp_iou_pred, "BBReg IoUPred", i, current_errors)
             
-            if current_errors: # Add to overall statistics if any comparisons were made/attempted
-                self.all_errors_stats[f"BBReg_Sample_{i}"] = current_errors
-            # Note: MAE accumulation for overall average needs to be selective based on valid comparisons
-            # For simplicity, we'll let the HTML report show NaNs for failed/skipped comparisons.
-
-        if not self.all_errors_stats: # Check if any BB regressor comparisons were made
-            print("No BB Regressor comparisons were performed for this model type.") # Clarified message
-            # No plots or stats if nothing was compared for BB regressor
-            return
-
-        # The following old averaging and plotting is now handled by generate_html_report using all_errors_stats
-        # print("\nBB Regressor Comparison Statistics:")
-        # if iou_pred_errors:
-        #     print(f"  IoU Prediction MAE: Mean={np.mean(iou_pred_errors):.4e}, Std={np.std(iou_pred_errors):.4e}")
-        # if modulation_errors:
-        #     print(f"  Modulation MAE: Mean={np.mean(modulation_errors):.4e}, Std={np.std(modulation_errors):.4e}")
-        # if feat_errors:
-        #     print(f"  IoU Feature MAE: Mean={np.mean(feat_errors):.4e}, Std={np.std(feat_errors):.4e}")
-
-        # # Plots - these would need to be rethought with the new error structure
-        # self._generate_stats_and_plots(iou_pred_errors, "BB Regressor IoU Prediction Error", self.plots_dir / "bbreg_iou_pred_error_hist.png")
-        # self._generate_stats_and_plots(modulation_errors, "BB Regressor Modulation Error", self.plots_dir / "bbreg_modulation_error_hist.png")
-        # self._generate_stats_and_plots(feat_errors, "BB Regressor IoU Feature Error", self.plots_dir / "bbreg_feature_error_hist.png")
+            self.all_errors_stats[f"BBReg_Sample_{i}"] = current_errors
 
     def generate_html_report(self):
         print("\nGenerating HTML report...")
@@ -747,6 +817,73 @@ class ModelComparison:
         # For direct use, if needed, it could return the tuple:
         # return (mae, max_err, diff_arr_for_hist, mean_py_val, std_abs_err, l2_norm_py, l2_norm_cpp, l2_norm_diff, cosine_sim, pearson_corr, mean_rel_err)
 
+    @staticmethod
+    def load_weights_for_custom_model(model, model_name, base_model_dir, device):
+        """
+        Helper to load weights from individual .pt files into a model instance.
+        model: the PyTorch nn.Module instance.
+        model_name: e.g., 'classifier' or 'bb_regressor'.
+        base_model_dir: Path object to the base directory like 'exported_weights'.
+        device: torch device.
+        """
+        tensor_dir = base_model_dir / model_name
+        doc_file = tensor_dir / f"{model_name}_weights_doc.txt"
+
+        if not doc_file.exists():
+            print(f"Warning: Documentation file not found: {doc_file} for {model_name}. Skipping weight loading for source model.")
+            return
+
+        with open(doc_file, 'r') as f:
+            lines = f.readlines()
+        
+        i = 0
+        while i < len(lines):
+            line = lines[i]
+            if line.startswith('## '):
+                key = line.strip()[3:]
+                j = i + 1
+                while j < len(lines) and 'File:' not in lines[j]:
+                    j += 1
+                
+                if j < len(lines) and 'File:' in lines[j]:
+                    file_name = lines[j].split('File:')[1].strip()
+                    tensor_path = tensor_dir / file_name
+                    if tensor_path.exists():
+                        try:
+                            tensor_data = torch.load(str(tensor_path), map_location=device)
+                            # For .pt files that might be RecursiveScriptModule, try to extract tensor
+                            if isinstance(tensor_data, torch.jit.RecursiveScriptModule):
+                                if hasattr(tensor_data, 'weight'): tensor = tensor_data.weight
+                                elif hasattr(tensor_data, 'bias'): tensor = tensor_data.bias
+                                elif len(list(tensor_data.parameters())) > 0: tensor = list(tensor_data.parameters())[0]
+                                else: tensor = tensor_data() # Try calling it
+                            else:
+                                tensor = tensor_data
+
+                            parts = key.split('.')
+                            module_to_set = model
+                            for part in parts[:-1]:
+                                module_to_set = getattr(module_to_set, part)
+                            
+                            param_name = parts[-1]
+                            if hasattr(module_to_set, param_name):
+                                if param_name in module_to_set._parameters:
+                                    module_to_set._parameters[param_name] = torch.nn.Parameter(tensor.to(device))
+                                elif param_name in module_to_set._buffers:
+                                    module_to_set._buffers[param_name] = tensor.to(device)
+                                else: # Direct attribute assignment
+                                    setattr(module_to_set, param_name, tensor.to(device))
+                                # print(f"Loaded {key} from {file_name} into source {model_name}")
+                            else:
+                                print(f"Warning: Attribute {key} not found in source model {model_name}.")
+                        except Exception as e:
+                            print(f"Error loading tensor for {key} from {tensor_path} for source {model_name}: {e}")
+                    else:
+                        print(f"Warning: Tensor file not found: {tensor_path} for source {model_name}")
+                i = j
+            i += 1
+        model.eval().to(device)
+
 
 if __name__ == "__main__":
     # Parse command line arguments