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TypeError: can't convert cuda:0 device type tensor to numpy. #506

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TypeError: can't convert cuda:0 device type tensor to numpy.#506
#511
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RunningLeon
@421psh

Description

@421psh
421psh
opened on May 20, 2022

Describe the bug

This bug was already mentioned here #339, but it is still occurring.

Reproduction

  1. What command or script did you run?
python mmdeploy/tools/test.py ./mmdeploy/configs/mmdet/instance-seg/instance-seg_tensorrt-fp16_dynamic-320x320-1344x1344.py ./experiments/experiment_13/cascade_mask_rcnn_r50_fpn_20e_coco.py --model ./experiments/experiment_13/tensorrt/end2end.engine --out ./experiments/experiment_13/tensorrt/evaluation_log.pkl --metrics bbox segm --speed-test  --warmup 10 --log2file ./experiments/experiment_13/tensorrt/log.pkl --device cuda:0
  1. Did you make any modifications on the code or config? Did you understand what you have modified? - No modifications from my side.

Environment

2022-05-20 19:09:04,047 - mmdeploy - INFO - **********Environmental information**********
2022-05-20 19:09:04,209 - mmdeploy - INFO - sys.platform: linux
2022-05-20 19:09:04,209 - mmdeploy - INFO - Python: 3.8.5 (default, May 27 2021, 13:30:53) [GCC 9.3.0]
2022-05-20 19:09:04,209 - mmdeploy - INFO - CUDA available: True
2022-05-20 19:09:04,209 - mmdeploy - INFO - GPU 0: NVIDIA GeForce RTX 3070 Ti
2022-05-20 19:09:04,209 - mmdeploy - INFO - CUDA_HOME: /usr/local/cuda
2022-05-20 19:09:04,209 - mmdeploy - INFO - NVCC: Cuda compilation tools, release 11.3, V11.3.109
2022-05-20 19:09:04,209 - mmdeploy - INFO - GCC: x86_64-linux-gnu-gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
2022-05-20 19:09:04,209 - mmdeploy - INFO - PyTorch: 1.11.0+cu113
2022-05-20 19:09:04,209 - mmdeploy - INFO - PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.3
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86
  - CuDNN 8.2
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

2022-05-20 19:09:04,210 - mmdeploy - INFO - TorchVision: 0.12.0+cu113
2022-05-20 19:09:04,210 - mmdeploy - INFO - OpenCV: 4.5.5
2022-05-20 19:09:04,210 - mmdeploy - INFO - MMCV: 1.5.1
2022-05-20 19:09:04,210 - mmdeploy - INFO - MMCV Compiler: GCC 7.3
2022-05-20 19:09:04,210 - mmdeploy - INFO - MMCV CUDA Compiler: 11.3
2022-05-20 19:09:04,210 - mmdeploy - INFO - MMDeploy: 0.4.0+8cd17f4
2022-05-20 19:09:04,210 - mmdeploy - INFO - 

2022-05-20 19:09:04,210 - mmdeploy - INFO - **********Backend information**********
[2022-05-20 19:09:04.262] [mmdeploy] [info] [model.cpp:95] Register 'DirectoryModel'
2022-05-20 19:09:04,267 - mmdeploy - INFO - onnxruntime: 1.8.1	ops_is_avaliable : True
2022-05-20 19:09:04,268 - mmdeploy - INFO - tensorrt: 7.2.3.4	ops_is_avaliable : True
2022-05-20 19:09:04,269 - mmdeploy - INFO - ncnn: None	ops_is_avaliable : False
2022-05-20 19:09:04,270 - mmdeploy - INFO - pplnn_is_avaliable: False
2022-05-20 19:09:04,271 - mmdeploy - INFO - openvino_is_avaliable: False
2022-05-20 19:09:04,271 - mmdeploy - INFO - 

2022-05-20 19:09:04,271 - mmdeploy - INFO - **********Codebase information**********
2022-05-20 19:09:04,273 - mmdeploy - INFO - mmdet:	2.24.1
2022-05-20 19:09:04,273 - mmdeploy - INFO - mmseg:	None
2022-05-20 19:09:04,273 - mmdeploy - INFO - mmcls:	None
2022-05-20 19:09:04,273 - mmdeploy - INFO - mmocr:	None
2022-05-20 19:09:04,273 - mmdeploy - INFO - mmedit:	None
2022-05-20 19:09:04,273 - mmdeploy - INFO - mmdet3d:	None
2022-05-20 19:09:04,273 - mmdeploy - INFO - mmpose:	None

Docker container

# NVIDIA DRIVER = 465.19.01, CUDA = 1.3.1.005, TensorRT = 7.2.3.4
FROM nvcr.io/nvidia/tensorrt:21.06-py3

# Install dependencies
ENV TZ=Europe/Moscow
RUN ln -snf /usr/share/zoneinfo/$TZ /etc/localtime && echo $TZ > /etc/timezone
RUN apt-get update && apt-get install -y build-essential
RUN apt-get update --fix-missing
RUN apt-get install -y libgtk-3-dev libavcodec-dev libavformat-dev libswscale-dev libv4l-dev \
    libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev \
    gfortran openexr libatlas-base-dev python3-dev python3-numpy \
    libtbb2 libtbb-dev libdc1394-22-dev libopenexr-dev \
    libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev  \
    libsm6 libxext6 libxrender-dev libgl1-mesa-glx git wget libssl-dev libopencv-dev libspdlog-dev python3-tk

# Workspace directory
WORKDIR /workspace

# Enviroment variables
ENV TENSORRT_DIR=/workspace/tensorrt
ENV LD_LIBRARY_PATH=$TENSORRT_DIR/lib:$LD_LIBRARY_PATH
ENV CUDNN_DIR=/usr/local/cuda
ENV CUDA_HOME=/usr/local/cuda
ENV LD_LIBRARY_PATH=$CUDNN_DIR/lib64:$LD_LIBRARY_PATH
ENV BACKUP_LD_LIBRARY_PATH=$LD_LIBRARY_PATH

# Upgrade pip and install cmake
RUN pip install pip -U
RUN pip install cmake

# OpenCV
RUN mkdir opencv_build
WORKDIR /workspace/opencv_build
RUN git clone https://github.com/opencv/opencv.git --branch 4.5.5
RUN git clone https://github.com/opencv/opencv_contrib.git --branch 4.5.5
WORKDIR /workspace/opencv_build/opencv
RUN mkdir build
WORKDIR /workspace/opencv_build/opencv/build
RUN cmake -D CMAKE_BUILD_TYPE=RELEASE \
    -D CMAKE_INSTALL_PREFIX=/usr/local \
    -D INSTALL_C_EXAMPLES=ON \
    -D INSTALL_PYTHON_EXAMPLES=ON \
    -D OPENCV_GENERATE_PKGCONFIG=ON \
    -D OPENCV_EXTRA_MODULES_PATH=/workspace/opencv_build/opencv_contrib/modules \
    -D BUILD_EXAMPLES=ON ..
RUN make -j$(nproc) && make install
WORKDIR /workspace

# PPL CV
RUN git clone https://github.com/openppl-public/ppl.cv.git --branch v0.6.2
WORKDIR /workspace/ppl.cv
RUN ./build.sh cuda
ENV PPLCV_DIR=/workspace/ppl.cv
WORKDIR /workspace

# ONNXRUNTIME
ARG ONNXRUNTIME_VERSION=1.8.1
RUN wget https://github.com/microsoft/onnxruntime/releases/download/v${ONNXRUNTIME_VERSION}/onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}.tgz \
    && tar -zxvf onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}.tgz &&\
    pip install onnxruntime-gpu==${ONNXRUNTIME_VERSION}
ENV ONNXRUNTIME_DIR=/workspace/onnxruntime-linux-x64-${ONNXRUNTIME_VERSION}
WORKDIR /workspace

# Install MMDeploy
RUN git clone https://github.com/open-mmlab/mmdeploy.git
WORKDIR /workspace/mmdeploy
RUN git checkout a4de9f3

## Add MMDeploy third party submodules
WORKDIR /workspace/mmdeploy/third_party
RUN git clone https://github.com/NVIDIA/cub.git
WORKDIR /workspace/mmdeploy/third_party/cub
RUN git checkout c3cceac115
WORKDIR /workspace/mmdeploy/third_party
RUN git clone https://github.com/pybind/pybind11.git
WORKDIR /workspace/mmdeploy/third_party/pybind11
RUN git checkout 70a58c5

WORKDIR /workspace/mmdeploy
RUN mkdir build
WORKDIR /workspace/mmdeploy/build
RUN cmake -DMMDEPLOY_TARGET_BACKENDS="ort;trt" ..
RUN make -j$(nproc)
WORKDIR /workspace/mmdeploy
RUN pip install -e .

RUN rm -rf build/CM* build/cmake-install.cmake build/Makefile build/csrc
WORKDIR /workspace/mmdeploy/build
RUN cmake ..  \
    -DMMDEPLOY_BUILD_SDK=ON \
    -DCMAKE_CXX_COMPILER=g++ \
    -Dpplcv_DIR=${PPLCV_DIR}/cuda-build/install/lib/cmake/ppl \
    -DTENSORRT_DIR=${TENSORRT_DIR} \
    -DONNXRUNTIME_DIR=${ONNXRUNTIME_DIR} \
    -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
    -DMMDEPLOY_TARGET_DEVICES="cuda;cpu" \
    -DMMDEPLOY_TARGET_BACKENDS="ort;trt" \
    -DMMDEPLOY_CODEBASES=all
RUN cmake --build . -- -j$(nproc)
RUN cmake --install .
ENV MMDEPLOY_DIR=/workspace/mmdeploy

# Pytorch, MMCV, MMDetection
RUN pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
RUN pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html
RUN pip install mmdet==2.24.1
RUN pip install instaboostfast
RUN pip install git+https://github.com/cocodataset/panopticapi.git
RUN pip install git+https://github.com/lvis-dataset/lvis-api.git
RUN pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
RUN pip install future==0.18.2
RUN pip install tensorboard==2.9.0

ADD https://download.pytorch.org/models/resnet50-0676ba61.pth /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth

# App directory
WORKDIR /workspace/app
COPY . /workspace/app

Error traceback

Traceback (most recent call last):
  File "mmdeploy/tools/test.py", line 137, in <module>
    main()
  File "mmdeploy/tools/test.py", line 126, in main
    outputs = task_processor.single_gpu_test(model, data_loader,
  File "/workspace/mmdeploy/mmdeploy/codebase/base/task.py", line 137, in single_gpu_test
    return self.codebase_class.single_gpu_test(model, data_loader, show,
  File "/workspace/mmdeploy/mmdeploy/codebase/mmdet/deploy/mmdetection.py", line 142, in single_gpu_test
    outputs = single_gpu_test(model, data_loader, show, out_dir, **kwargs)
  File "/usr/local/lib/python3.8/dist-packages/mmdet/apis/test.py", line 65, in single_gpu_test
    result = [(bbox_results, encode_mask_results(mask_results))
  File "/usr/local/lib/python3.8/dist-packages/mmdet/apis/test.py", line 65, in <listcomp>
    result = [(bbox_results, encode_mask_results(mask_results))
  File "/usr/local/lib/python3.8/dist-packages/mmdet/core/mask/utils.py", line 59, in encode_mask_results
    np.array(
  File "/usr/local/lib/python3.8/dist-packages/torch/_tensor.py", line 734, in __array__
    return self.numpy().astype(dtype, copy=False)
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

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