========== mmdeploy tools/check_env.py ========== 03/13 11:36:51 - mmengine - INFO - 03/13 11:36:51 - mmengine - INFO - **********Environmental information********** 03/13 11:36:53 - mmengine - INFO - sys.platform: linux 03/13 11:36:53 - mmengine - INFO - Python: 3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0] 03/13 11:36:53 - mmengine - INFO - CUDA available: True 03/13 11:36:53 - mmengine - INFO - numpy_random_seed: 2147483648 03/13 11:36:53 - mmengine - INFO - GPU 0: NVIDIA GeForce RTX 3090 03/13 11:36:53 - mmengine - INFO - CUDA_HOME: /usr/local/cuda 03/13 11:36:53 - mmengine - INFO - NVCC: Cuda compilation tools, release 11.3, V11.3.109 03/13 11:36:53 - mmengine - INFO - GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 03/13 11:36:53 - mmengine - INFO - PyTorch: 1.10.0+cu113 03/13 11:36:53 - mmengine - 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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX512 - 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-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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, 03/13 11:36:53 - mmengine - INFO - TorchVision: 0.11.0+cu113 03/13 11:36:53 - mmengine - INFO - OpenCV: 4.7.0 03/13 11:36:53 - mmengine - INFO - MMEngine: 0.5.0 03/13 11:36:53 - mmengine - INFO - MMCV: 2.0.0rc3 03/13 11:36:53 - mmengine - INFO - MMCV Compiler: GCC 9.3 03/13 11:36:53 - mmengine - INFO - MMCV CUDA Compiler: 11.3 03/13 11:36:53 - mmengine - INFO - MMDeploy: 1.0.0rc3+413cc76 03/13 11:36:53 - mmengine - INFO - 03/13 11:36:53 - mmengine - INFO - **********Backend information********** 03/13 11:36:53 - mmengine - INFO - tensorrt: 8.2.5.1 03/13 11:36:53 - mmengine - INFO - tensorrt custom ops: Available 03/13 11:36:53 - mmengine - INFO - ONNXRuntime: None 03/13 11:36:53 - mmengine - INFO - ONNXRuntime-gpu: 1.8.1 03/13 11:36:53 - mmengine - INFO - ONNXRuntime custom ops: Available 03/13 11:36:53 - mmengine - INFO - pplnn: 0.8.1 03/13 11:36:53 - mmengine - INFO - ncnn: 1.0.20230116 03/13 11:36:53 - mmengine - INFO - ncnn custom ops: Available 03/13 11:36:53 - mmengine - INFO - snpe: None 03/13 11:36:53 - mmengine - INFO - openvino: 2022.2.0 03/13 11:36:53 - mmengine - INFO - torchscript: 1.10.0+cu113 03/13 11:36:53 - mmengine - INFO - torchscript custom ops: Available 03/13 11:36:53 - mmengine - INFO - rknn-toolkit: None 03/13 11:36:53 - mmengine - INFO - rknn-toolkit2: None 03/13 11:36:53 - mmengine - INFO - ascend: None 03/13 11:36:53 - mmengine - INFO - coreml: None 03/13 11:36:53 - mmengine - INFO - tvm: None 03/13 11:36:53 - mmengine - INFO - 03/13 11:36:53 - mmengine - INFO - **********Codebase information********** 03/13 11:36:53 - mmengine - INFO - mmdet: 3.0.0rc5 03/13 11:36:53 - mmengine - INFO - mmseg: 1.0.0rc3 03/13 11:36:53 - mmengine - INFO - mmcls: 1.0.0rc5 03/13 11:36:53 - mmengine - INFO - mmocr: 1.0.0rc4 03/13 11:36:53 - mmengine - INFO - mmedit: 1.0.0rc4 03/13 11:36:53 - mmengine - INFO - mmdet3d: None 03/13 11:36:53 - mmengine - INFO - mmpose: 1.0.0rc0 03/13 11:36:53 - mmengine - INFO - mmrotate: 1.0.0rc0 03/13 11:36:53 - mmengine - INFO - mmaction: 1.0.0rc1 ========== param ========== {"sdk": true, "type": "convert_model", "pth_url": "https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth", "runtime": "ort1.8.1", "codebase": "mmdet-seg", "test_data": "https://raw.githubusercontent.com/open-mmlab/mmdetection/v2.26.0/tests/data/color.jpg", "input_shape": "", "codebase_url": "https://github.com/open-mmlab/mmdetection", "mmdeploy_url": "https://github.com/open-mmlab/mmdeploy/", "quantization": false, "dynamic_input": true, "codebase_branch": "v3.0.0rc5", "mmdeploy_branch": "v1.0.0rc1", "train_config_path": "configs/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco.py"} ========== deploy config ========== onnx_config = dict( type='onnx', export_params=True, keep_initializers_as_inputs=False, opset_version=11, save_file='end2end.onnx', input_names=['input'], output_names=['dets', 'labels', 'masks'], input_shape=None, optimize=True, dynamic_axes=dict( input=dict({ 0: 'batch', 2: 'height', 3: 'width' }), dets=dict({ 0: 'batch', 1: 'num_dets' }), labels=dict({ 0: 'batch', 1: 'num_dets' }), masks=dict({ 0: 'batch', 1: 'num_dets', 2: 'height', 3: 'width' }))) codebase_config = dict( type='mmdet', task='ObjectDetection', model_type='end2end', post_processing=dict( score_threshold=0.05, confidence_threshold=0.005, iou_threshold=0.5, max_output_boxes_per_class=200, pre_top_k=5000, keep_top_k=100, background_label_id=-1, export_postprocess_mask=False)) backend_config = dict(type='onnxruntime') ========== mmdeploy tools/deploy.py ========== python3 tools/deploy.py /tmp/workdir/HtPN_instance-seg_onnxruntime_dynamic.py /root/workspace/mmdeploy/../mmdetection/configs/rtmdet/rtmdet-ins_tiny_8xb32-300e_coco.py /tmp/datadir/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth /tmp/datadir/color.jpg --work-dir /tmp/workdir --device cpu --dump-info ========== stdout ========== 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "mmdet_tasks" registry tree. As a workaround, the current "mmdet_tasks" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "mmdet_tasks" registry tree. As a workaround, the current "mmdet_tasks" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "mmdet_tasks" registry tree. As a workaround, the current "mmdet_tasks" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "mmdet_tasks" registry tree. As a workaround, the current "mmdet_tasks" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "mmdet_tasks" registry tree. As a workaround, the current "mmdet_tasks" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:28 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:31 - mmengine - INFO - Start pipeline mmdeploy.apis.pytorch2onnx.torch2onnx in subprocess 03/13 11:37:32 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:32 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "mmdet_tasks" registry tree. As a workaround, the current "mmdet_tasks" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:32 - mmengine - WARNING - Failed to search registry with scope "mmdet" in the "Codebases" registry tree. As a workaround, the current "Codebases" registry in "mmdeploy" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmdet" is a correct scope, or whether the registry is initialized. 03/13 11:37:32 - mmengine - WARNING - The "model" registry in mmdet did not set import location. Fallback to call `mmdet.utils.register_all_modules` instead. 03/13 11:37:32 - mmengine - WARNING - The "task util" registry in mmdet did not set import location. Fallback to call `mmdet.utils.register_all_modules` instead. Loads checkpoint by local backend from path: /tmp/datadir/rtmdet-ins_tiny_8xb32-300e_coco_20221130_151727-ec670f7e.pth 03/13 11:37:33 - mmengine - WARNING - The "transform" registry in mmdet did not set import location. Fallback to call `mmdet.utils.register_all_modules` instead. 03/13 11:37:33 - mmengine - WARNING - DeprecationWarning: get_onnx_config will be deprecated in the future. 03/13 11:37:33 - mmengine - INFO - Export PyTorch model to ONNX: /tmp/workdir/end2end.onnx. 03/13 11:37:33 - mmengine - WARNING - Can not find torch._C._jit_pass_onnx_autograd_function_process, function rewrite will not be applied 03/13 11:37:36 - mmengine - ERROR - /root/workspace/mmdeploy/mmdeploy/apis/core/pipeline_manager.py - pop_mp_output - 80 - `mmdeploy.apis.pytorch2onnx.torch2onnx` with Call id: 0 failed. exit. ========== stderr ========== /root/workspace/mmdeploy/mmdeploy/core/optimizers/function_marker.py:160: 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) /opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/torch/nn/functional.py:3631: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details. warnings.warn( /opt/conda/envs/torch1.10.0/lib/python3.8/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 ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] /root/workspace/mmdeploy/mmdeploy/codebase/mmdet/models/dense_heads/rtmdet_ins_head.py:143: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect. iou_threshold = torch.tensor([iou_threshold], dtype=torch.float32) /root/workspace/mmdeploy/mmdeploy/codebase/mmdet/models/dense_heads/rtmdet_ins_head.py:144: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect. score_threshold = torch.tensor([score_threshold], dtype=torch.float32) /root/workspace/mmdeploy/mmdeploy/pytorch/functions/topk.py:28: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect. k = torch.tensor(k, device=input.device, dtype=torch.long) /root/workspace/mmdeploy/mmdeploy/mmcv/ops/nms.py:44: TracerWarning: Converting a tensor to a Python float 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! score_threshold = float(score_threshold) /root/workspace/mmdeploy/mmdeploy/mmcv/ops/nms.py:45: TracerWarning: Converting a tensor to a Python float 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! iou_threshold = float(iou_threshold) /opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/mmcv/ops/nms.py:123: 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! assert boxes.size(1) == 4 /opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/mmcv/ops/nms.py:124: 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! assert boxes.size(0) == scores.size(0) /opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/mmcv/ops/nms.py:30: 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! if max_num > 0: Process Process-2: Traceback (most recent call last): File "/opt/conda/envs/torch1.10.0/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap self.run() File "/opt/conda/envs/torch1.10.0/lib/python3.8/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/root/workspace/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 107, in __call__ ret = func(*args, **kwargs) File "/root/workspace/mmdeploy/mmdeploy/apis/pytorch2onnx.py", line 98, in torch2onnx export( File "/root/workspace/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 356, in _wrap return self.call_function(func_name_, *args, **kwargs) File "/root/workspace/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 326, in call_function return self.call_function_local(func_name, *args, **kwargs) File "/root/workspace/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 275, in call_function_local return pipe_caller(*args, **kwargs) File "/root/workspace/mmdeploy/mmdeploy/apis/core/pipeline_manager.py", line 107, in __call__ ret = func(*args, **kwargs) File "/root/workspace/mmdeploy/mmdeploy/apis/onnx/export.py", line 131, in export torch.onnx.export( File "/opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/torch/onnx/__init__.py", line 316, in export return utils.export(model, args, f, export_params, verbose, training, File "/opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/torch/onnx/utils.py", line 107, in export _export(model, args, f, export_params, verbose, training, input_names, output_names, File "/opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/torch/onnx/utils.py", line 724, in _export _model_to_graph(model, args, verbose, input_names, File "/root/workspace/mmdeploy/mmdeploy/apis/onnx/optimizer.py", line 11, in model_to_graph__custom_optimizer graph, params_dict, torch_out = ctx.origin_func(*args, **kwargs) File "/opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/torch/onnx/utils.py", line 493, in _model_to_graph graph, params, torch_out, module = _create_jit_graph(model, args) File "/opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/torch/onnx/utils.py", line 437, in _create_jit_graph graph, torch_out = _trace_and_get_graph_from_model(model, args) File "/opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/torch/onnx/utils.py", line 388, in _trace_and_get_graph_from_model torch.jit._get_trace_graph(model, args, strict=False, _force_outplace=False, _return_inputs_states=True) File "/opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/torch/jit/_trace.py", line 1166, in _get_trace_graph outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs) File "/opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/torch/jit/_trace.py", line 127, in forward graph, out = torch._C._create_graph_by_tracing( File "/opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/torch/jit/_trace.py", line 118, in wrapper outs.append(self.inner(*trace_inputs)) File "/opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/opt/conda/envs/torch1.10.0/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1090, in _slow_forward result = self.forward(*input, **kwargs) File "/root/workspace/mmdeploy/mmdeploy/apis/onnx/export.py", line 123, in wrapper return forward(*arg, **kwargs) File "/root/workspace/mmdeploy/mmdeploy/codebase/mmdet/models/detectors/single_stage.py", line 89, in single_stage_detector__forward return __forward_impl(self, batch_inputs, data_samples=data_samples) File "/root/workspace/mmdeploy/mmdeploy/core/optimizers/function_marker.py", line 266, in g rets = f(*args, **kwargs) File "/root/workspace/mmdeploy/mmdeploy/codebase/mmdet/models/detectors/single_stage.py", line 24, in __forward_impl output = self.bbox_head.predict(x, data_samples, rescale=False) File "/root/workspace/mmdetection/mmdet/models/dense_heads/base_dense_head.py", line 197, in predict predictions = self.predict_by_feat( File "/root/workspace/mmdeploy/mmdeploy/codebase/mmdet/models/dense_heads/rtmdet_ins_head.py", line 102, in rtmdet_ins_head__predict_by_feat return _nms_with_mask_static(self, priors, bboxes, scores, File "/root/workspace/mmdeploy/mmdeploy/codebase/mmdet/models/dense_heads/rtmdet_ins_head.py", line 184, in _nms_with_mask_static kernels = kernels[:, topk_inds, ...] IndexError: index 24 is out of bounds for dimension 0 with size 24