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Could not find any implementation for node MaxPool on Jetson NX

See original GitHub issue

Checklist

  • I have searched related issues but cannot get the expected help.
  • 2. I have read the FAQ documentation but cannot get the expected help.
  • 3. The bug has not been fixed in the latest version.

Describe the bug

Jetson Xavier NX Jetpack: 4.6.1 CUDA: 10.2 TensorRT: 8.2.1.8

Converting mmdet model yolox raise exception: “Could not find any implementation for node MaxPool_102.”

Reproduction

python ./tools/deploy.py configs/mmdet/detection/base_tensorrt_static-640x640.py yolox_s_8x8_300e_coco.py yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth test.jpg --work-dir ./work-dir/ --device cuda:0 --dump-info

mmdeploy config as follow:

_base_ = ['../_base_/base_static.py', '../../_base_/backends/tensorrt.py']

onnx_config = dict(input_shape=(640, 640))

backend_config = dict(
    common_config=dict(max_workspace_size=1 << 30),
    model_inputs=[
        dict(
            input_shapes=dict(
                input=dict(
                    min_shape=[1, 3, 640, 640],
                    opt_shape=[1, 3, 640, 640],
                    max_shape=[1, 3, 640, 640])))
    ])

mmdet model is the official yolox model.

Environment

2022-09-16 07:00:29,768 - mmdeploy - INFO -

2022-09-16 07:00:29,768 - mmdeploy - INFO - **********Environmental information**********
2022-09-16 07:00:30,823 - mmdeploy - INFO - sys.platform: linux
2022-09-16 07:00:30,824 - mmdeploy - INFO - Python: 3.6.15 | packaged by conda-forge | (default, Dec  3 2021, 19:12:04) [GCC 9.4.0]
2022-09-16 07:00:30,825 - mmdeploy - INFO - CUDA available: True
2022-09-16 07:00:30,825 - mmdeploy - INFO - GPU 0: Xavier
2022-09-16 07:00:30,825 - mmdeploy - INFO - CUDA_HOME: /usr/local/cuda-10.2
2022-09-16 07:00:30,826 - mmdeploy - INFO - NVCC: Cuda compilation tools, release 10.2, V10.2.300
2022-09-16 07:00:30,826 - mmdeploy - INFO - GCC: gcc (Ubuntu/Linaro 7.5.0-3ubuntu1~18.04) 7.5.0
2022-09-16 07:00:30,826 - mmdeploy - INFO - PyTorch: 1.10.0
2022-09-16 07:00:30,827 - mmdeploy - INFO - PyTorch compiling details: PyTorch built with:
  - GCC 7.5
  - C++ Version: 201402
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: NO AVX
  - CUDA Runtime 10.2
  - NVCC architecture flags: -gencode;arch=compute_53,code=sm_53;-gencode;arch=compute_62,code=sm_62;-gencode;arch=compute_72,code=sm_72
  - CuDNN 8.2.1
    - Built with CuDNN 8.0
  - Build settings: BLAS_INFO=open, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=8.0.0, CXX_COMPILER=/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -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-variable -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 -DMISSING_ARM_VST1 -DMISSING_ARM_VLD1 -Wno-stringop-overflow, FORCE_FALLBACK_CUDA_MPI=1, LAPACK_INFO=open, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EIGEN_FOR_BLAS=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=OFF, USE_MKLDNN=OFF, USE_MPI=ON, USE_NCCL=0, USE_NNPACK=ON, USE_OPENMP=ON,

2022-09-16 07:00:30,827 - mmdeploy - INFO - TorchVision: 0.11.1
2022-09-16 07:00:30,828 - mmdeploy - INFO - OpenCV: 4.6.0
2022-09-16 07:00:30,828 - mmdeploy - INFO - MMCV: 1.6.1
2022-09-16 07:00:30,828 - mmdeploy - INFO - MMCV Compiler: GCC 7.5
2022-09-16 07:00:30,829 - mmdeploy - INFO - MMCV CUDA Compiler: 10.2
2022-09-16 07:00:30,829 - mmdeploy - INFO - MMDeploy: 0.8.0+a1a19f0
2022-09-16 07:00:30,829 - mmdeploy - INFO -

2022-09-16 07:00:30,829 - mmdeploy - INFO - **********Backend information**********
2022-09-16 07:00:33,716 - mmdeploy - INFO - onnxruntime: 1.10.0 ops_is_avaliable : False
2022-09-16 07:00:33,883 - mmdeploy - INFO - tensorrt: 8.2.1.8   ops_is_avaliable : True
2022-09-16 07:00:33,986 - mmdeploy - INFO - ncnn: None  ops_is_avaliable : False
2022-09-16 07:00:33,994 - mmdeploy - INFO - pplnn_is_avaliable: False
2022-09-16 07:00:34,002 - mmdeploy - INFO - openvino_is_avaliable: False
2022-09-16 07:00:34,119 - mmdeploy - INFO - snpe_is_available: False
2022-09-16 07:00:34,131 - mmdeploy - INFO - ascend_is_available: False
2022-09-16 07:00:34,138 - mmdeploy - INFO - coreml_is_available: False
2022-09-16 07:00:34,139 - mmdeploy - INFO -

2022-09-16 07:00:34,139 - mmdeploy - INFO - **********Codebase information**********
2022-09-16 07:00:34,149 - mmdeploy - INFO - mmdet:      2.25.1
2022-09-16 07:00:34,149 - mmdeploy - INFO - mmseg:      None
2022-09-16 07:00:34,150 - mmdeploy - INFO - mmcls:      None
2022-09-16 07:00:34,150 - mmdeploy - INFO - mmocr:      None
2022-09-16 07:00:34,150 - mmdeploy - INFO - mmedit:     None
2022-09-16 07:00:34,151 - mmdeploy - INFO - mmdet3d:    None
2022-09-16 07:00:34,151 - mmdeploy - INFO - mmpose:     0.28.1
2022-09-16 07:00:34,151 - mmdeploy - INFO - mmrotate:   None



### Error traceback

```Shell
2022-09-16 05:40:46,447 - mmdeploy - INFO - Start pipeline mmdeploy.apis.pytorch2onnx.torch2onnx in subprocess
load checkpoint from local path: ../action-api/actionloop/engines/mmcfgs/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth
2022-09-16 05:40:58,194 - mmdeploy - WARNING - DeprecationWarning: get_onnx_config will be deprecated in the future.
2022-09-16 05:40:58,195 - mmdeploy - INFO - Export PyTorch model to ONNX: ./work-dir/obj-dynamic4/end2end.onnx.
2022-09-16 05:40:58,393 - mmdeploy - WARNING - Can not find torch._C._jit_pass_onnx_deduplicate_initializers, function rewrite will not be applied
/home/nvidia/mmdeploy/mmdeploy/core/optimizers/function_marker.py:158: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  ys_shape = tuple(int(s) for s in ys.shape)
/home/nvidia/mmdeploy/mmdeploy/codebase/mmdet/models/detectors/base.py:24: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).
  img_shape = [int(val) for val in img_shape]
/home/nvidia/mmdeploy/mmdeploy/codebase/mmdet/models/detectors/base.py:24: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  img_shape = [int(val) for val in img_shape]
/home/nvidia/archiconda3/envs/mmdeploy/lib/python3.6/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  /media/nvidia/NVME/pytorch/pytorch-v1.10.0/aten/src/ATen/native/TensorShape.cpp:2157.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
/home/nvidia/mmdeploy/mmdeploy/codebase/mmdet/core/post_processing/bbox_nms.py:260: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  dets, labels = TRTBatchedNMSop.apply(boxes, scores, int(scores.shape[-1]),
/home/nvidia/mmdeploy/mmdeploy/mmcv/ops/nms.py:178: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  out_boxes = min(num_boxes, after_topk)
/home/nvidia/mmdeploy/mmdeploy/mmcv/ops/nms.py:181: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  (batch_size, out_boxes)).to(scores.device))
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
2022-09-16 05:41:30,497 - mmdeploy - INFO - Execute onnx optimize passes.
2022-09-16 05:41:32,095 - mmdeploy - INFO - Finish pipeline mmdeploy.apis.pytorch2onnx.torch2onnx
2022-09-16 05:41:42,141 - mmdeploy - INFO - Start pipeline mmdeploy.backend.tensorrt.onnx2tensorrt.onnx2tensorrt in subprocess
2022-09-16 05:41:42,652 - mmdeploy - INFO - Successfully loaded tensorrt plugins from /home/nvidia/mmdeploy/mmdeploy/lib/libmmdeploy_tensorrt_ops.so
[09/16/2022-05:41:44] [TRT] [I] [MemUsageChange] Init CUDA: CPU +355, GPU +0, now: CPU 441, GPU 5334 (MiB)
[09/16/2022-05:41:45] [TRT] [I] [MemUsageSnapshot] Begin constructing builder kernel library: CPU 441 MiB, GPU 5364 MiB
[09/16/2022-05:41:45] [TRT] [I] [MemUsageSnapshot] End constructing builder kernel library: CPU 546 MiB, GPU 5471 MiB
[09/16/2022-05:41:46] [TRT] [W] onnx2trt_utils.cpp:366: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/16/2022-05:41:46] [TRT] [W] onnx2trt_utils.cpp:392: One or more weights outside the range of INT32 was clamped
[09/16/2022-05:41:46] [TRT] [I] No importer registered for op: TRTBatchedNMS. Attempting to import as plugin.
[09/16/2022-05:41:46] [TRT] [I] Searching for plugin: TRTBatchedNMS, plugin_version: 1, plugin_namespace:
[09/16/2022-05:41:46] [TRT] [I] Successfully created plugin: TRTBatchedNMS
[09/16/2022-05:41:46] [TRT] [I] ---------- Layers Running on DLA ----------
[09/16/2022-05:41:46] [TRT] [I] ---------- Layers Running on GPU ----------
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Reshape_0 + Transpose_1
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Reshape_2
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_3
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_4), Mul_5)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_6
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_7), Mul_8)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_12 || Conv_9
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_13), Mul_14)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_15
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_16), Mul_17)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_18
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_10), Mul_11)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(PWN(Sigmoid_19), Mul_20), Add_21)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_23
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_24), Mul_25)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_26
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_27), Mul_28)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_32 || Conv_29
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_33), Mul_34)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_35
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_36), Mul_37)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_38
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(PWN(Sigmoid_39), Mul_40), Add_41)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_42
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_43), Mul_44)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_45
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(PWN(Sigmoid_46), Mul_47), Add_48)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_49
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_50), Mul_51)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_52
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_30), Mul_31)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(PWN(Sigmoid_53), Mul_54), Add_55)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_57
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_58), Mul_59)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_60
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_61), Mul_62)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_66 || Conv_63
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_67), Mul_68)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_69
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_70), Mul_71)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_72
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(PWN(Sigmoid_73), Mul_74), Add_75)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_76
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_77), Mul_78)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_79
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(PWN(Sigmoid_80), Mul_81), Add_82)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_83
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_84), Mul_85)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_86
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_64), Mul_65)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(PWN(Sigmoid_87), Mul_88), Add_89)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_91
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_92), Mul_93)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_94
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_95), Mul_96)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_97
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_98), Mul_99)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] MaxPool_102
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] MaxPool_101
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] MaxPool_100
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] 622 copy
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] 623 copy
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] 624 copy
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] 625 copy
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_104
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_105), Mul_106)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_110 || Conv_107
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_111), Mul_112)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_113
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_114), Mul_115)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_116
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_108), Mul_109)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_117), Mul_118)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_120
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_121), Mul_122)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_123
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_124), Mul_125)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Resize_127
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] 660 copy
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_132 || Conv_129
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_133), Mul_134)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_135
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_136), Mul_137)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_138
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_130), Mul_131)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_139), Mul_140)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_142
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_143), Mul_144)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_145
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_146), Mul_147)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Resize_148
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] 691 copy
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_153 || Conv_150
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_154), Mul_155)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_156
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_157), Mul_158)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_159
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_151), Mul_152)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_160), Mul_161)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_163
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_164), Mul_165)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_206
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_166
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_207), Mul_208)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_167), Mul_168)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] 686 copy
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_173 || Conv_170
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_221 || Conv_215
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_174), Mul_175)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_222), Mul_223)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_224
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_176
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_225), Mul_226)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_177), Mul_178)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_179
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_228
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_171), Mul_172)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_180), Mul_181)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_183
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_184), Mul_185)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_209
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_186
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_210), Mul_211)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_187), Mul_188)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] 655 copy
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_193 || Conv_190
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_236 || Conv_230
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_194), Mul_195)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_237), Mul_238)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_239
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_196
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_240), Mul_241)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_197), Mul_198)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_199
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_243
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_191), Mul_192)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_200), Mul_201)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_203
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_204), Mul_205)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_212
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_213), Mul_214)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_251 || Conv_245
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_252), Mul_253)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_254
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_255), Mul_256)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_258
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] {ForeignNode[Transpose_291 + Reshape_292...Unsqueeze_340]}
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_216), Mul_217)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_218
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_219), Mul_220)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_229
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_227
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_231), Mul_232)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_233
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_234), Mul_235)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_244
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_242
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_246), Mul_247)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_248
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_249), Mul_250)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_259
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Conv_257
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Transpose_285 + Reshape_286
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Transpose_287 + Reshape_288
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Transpose_289 + Reshape_290
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] 930 copy
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] 938 copy
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] 946 copy
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Transpose_297 + Reshape_298
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Transpose_299 + Reshape_300
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Transpose_301 + Reshape_302
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(Sigmoid_306)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] Unsqueeze_338
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] PWN(PWN(Sigmoid_304), Mul_339)
[09/16/2022-05:41:46] [TRT] [I] [GpuLayer] TRTBatchedNMS_341
[09/16/2022-05:41:48] [TRT] [I] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +226, GPU +226, now: CPU 849, GPU 5776 (MiB)
[09/16/2022-05:41:48] [TRT] [I] Local timing cache in use. Profiling results in this builder pass will not be stored.
1[09/16/2022-05:44:00] [TRT] [E] 10: [optimizer.cpp::computeCosts::2011] Error Code 10: Internal Error (Could not find any implementation for node MaxPool_102.)
Process Process-3:
Traceback (most recent call last):
  File "/home/nvidia/archiconda3/envs/mmdeploy/lib/python3.6/multiprocessing/process.py", line 258, in _bootstrap
    self.run()
  File "/home/nvidia/archiconda3/envs/mmdeploy/lib/python3.6/multiprocessing/process.py", line 93, in run
    self._target(*self._args, **self._kwargs)
  File "/home/nvidia/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 107, in __call__
    ret = func(*args, **kwargs)
  File "/home/nvidia/mmdeploy/mmdeploy/backend/tensorrt/onnx2tensorrt.py", line 88, in onnx2tensorrt
    device_id=device_id)
  File "/home/nvidia/mmdeploy/mmdeploy/backend/tensorrt/utils.py", line 215, in from_onnx
    assert engine is not None, 'Failed to create TensorRT engine'
AssertionError: Failed to create TensorRT engine
2022-09-16 05:44:01,456 - mmdeploy - ERROR - `mmdeploy.backend.tensorrt.onnx2tensorrt.onnx2tensorrt` with Call id: 1 failed. exit.

Issue Analytics

  • State:open
  • Created a year ago
  • Comments:16 (4 by maintainers)

github_iconTop GitHub Comments

1reaction
tx19990922commented, Sep 20, 2022

Can you elaborate on that, because I haven’t solved this problem yet, thanks.

generate engine file using command like this:

/usr/src/tensorrt/bin/trtexec --onnx=./end2end.onnx --plugins=../../mmdeploy/lib/libmmdeploy_tensorrt_ops.so --workspace=6000 --fp16 --saveEng
ine=end2end.engine

then you can use mmdeploy scritps to load this engine file, like test.py.

Thanks a lot , it works ,this question really bothered me for a long time.

0reactions
lijoe123commented, Sep 22, 2022

I had the same problem. But when i run this command , i had failed

image

Can you elaborate on that, because I haven’t solved this problem yet, thanks.

generate engine file using command like this:

/usr/src/tensorrt/bin/trtexec --onnx=./end2end.onnx --plugins=../../mmdeploy/lib/libmmdeploy_tensorrt_ops.so --workspace=6000 --fp16 --saveEng
ine=end2end.engine

then you can use mmdeploy scritps to load this engine file, like test.py.

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why my application of mmdeploy is wrong?

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Score/confidence of prediction drop a lot after convert to trt engine