Closed Xia-sj closed 1 year ago
hanrui1sensetime
commented
2 years ago Hi, @Xia-sj , would you please provide the link of the model config and the checkpoint of the model you mentioned, i.e. mmdeploy/detectors_cascade_rcnn_r50_1x_coco.py and mmdeploy/detectors_htc_r50_1x_coco-329b1453.pth?
Underwater-Lab-SHU
commented
2 years ago @hanrui1sensetime Tnanks for your reply. The corresponding link is provided in:
https://pan.baidu.com/s/1_q_5UkkWSfrKuCEv9gGu0w 提取码：on80
Note that the layer ConvAWS is not supported.
hanrui1sensetime
commented
2 years ago @hanrui1sensetime Tnanks for your reply. The corresponding link is provided in:
https://pan.baidu.com/s/1_q_5UkkWSfrKuCEv9gGu0w 提取码：on80
Note that the layer ConvAWS is not supported.
The download on Baidu NetDisk is too slow, please provide other link better. My email is hanrui1@sensetime.com, you can attach them to email and send to me. The big attached files are saved on OneDrive, which is better than baidu cloud disk I think.
Underwater-Lab-SHU
commented
2 years ago Thanks for your reply. The corresponding model and config are provided in the following. Note that the layer ConvAWS is not supported in MMdeploy, maybe.
元宝 @.***
------------------ 原始邮件 ------------------ 发件人: "open-mmlab/mmdeploy" @.>; 发送时间: 2022年8月22日(星期一) 下午5:34 @.>; @.**@.>; 主题: Re: [open-mmlab/mmdeploy] RuntimeError: Failed to parse onnx, In node 28 (parseGraph): INVALID_NODE: Invalid Node - Conv_28; Conv_28: two inputs (data and weights) are allowed only in explicit-quantization mode. (Issue #926)
@hanrui1sensetime Tnanks for your reply. The corresponding link is provided in:
https://pan.baidu.com/s/1_q_5UkkWSfrKuCEv9gGu0w 提取码：on80
Note that the layer ConvAWS is not supported.
The download on Baidu NetDisk is too slow, please provide other link better. My email is @.***, you can attach them to email and send to me. The big attached files are saved on OneDrive, which is better than baidu cloud disk I think.
— Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: @.***>
从QQ邮箱发来的超大附件
Model-Config.zip (874.48M, 2022年09月21日 17:49 到期)进入下载页面：http://mail.qq.com/cgi-bin/ftnExs_download?k=753766393e7a3ea8392204034431544e52010401570700531d00505a071c520752014b580755024c56035058040252575255565f6221662c5f5303554f72090f565e0117185816610d&t=exs_ftn_download&code=07f9b1fa
hanrui1sensetime
commented
2 years ago Thanks for your reply. The corresponding model and config are provided in the following. Note that the layer ConvAWS is not supported in MMdeploy, maybe. 元宝 @. … ------------------ 原始邮件 ------------------ 发件人: "open-mmlab/mmdeploy" @.>; 发送时间: 2022年8月22日(星期一) 下午5:34 @.>; @*.**@*.>; 主题: Re: [open-mmlab/mmdeploy] RuntimeError: Failed to parse onnx, In node 28 (parseGraph): INVALID_NODE: Invalid Node - Conv_28; Conv_28: two inputs (data and weights) are allowed only in explicit-quantization mode. (Issue #926) @hanrui1sensetime Tnanks for your reply. The corresponding link is provided in: https://pan.baidu.com/s/1_q_5UkkWSfrKuCEv9gGu0w 提取码：on80 Note that the layer ConvAWS is not supported. The download on Baidu NetDisk is too slow, please provide other link better. My email is @., you can attach them to email and send to me. The big attached files are saved on OneDrive, which is better than baidu cloud disk I think. — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you commented.Message ID: @.***> 从QQ邮箱发来的超大附件 Model-Config.zip (874.48M, 2022年09月21日 17:49 到期)进入下载页面：http://mail.qq.com/cgi-bin/ftnExs_download?k=753766393e7a3ea8392204034431544e52010401570700531d00505a071c520752014b580755024c56035058040252575255565f6221662c5f5303554f72090f565e0117185816610d&t=exs_ftn_download&code=07f9b1fa
Thank you, I will check it.
Underwater-Lab-SHU
commented
2 years ago @hanrui1sensetime Handsome boy, do you solve this problem?
hanrui1sensetime
commented
2 years ago @hanrui1sensetime Handsome boy, do you solve this problem?
Hi, bro. The model config you provided in qq mailbox may have something wrong, missing test_cfg, please check the folder you sent me.
hanrui1sensetime
commented
2 years ago Yes, the bug is because mmdeploy not support DetectorRS with plugin layer in bottleneck.
hanrui1sensetime
commented
2 years ago We will support this model in the future, but may be not now, if you have interest to support this model, you can put the PR on our repo.
Underwater-Lab-SHU
commented
2 years ago @hanrui1sensetime I think your statement may be incorrect, because I remove the ConvAWS layer of DetectorRS and it can be compiled. Maybe because this mmdeploy doesn't support convAWS instead of DetectorRS with plugin layer.
hanrui1sensetime
commented
2 years ago @hanrui1sensetime I think your statement may be incorrect, because I remove the ConvAWS layer of DetectorRS and it can be compiled. Maybe because this mmdeploy doesn't support convAWS instead of DetectorRS with plugin layer.
Yeah, you are right, we will consider add ConvAWS support later.
Charlyo
commented
1 year ago Any news regarding the ConvAWS layer?? This is still happening for mmdeploy 1.1.0. I installed everything according to the mmdeploy installation docs.
ENV:
home/cgarriga/projects/machinelearning-logo-library/venv/bin/python3.8 /home/cgarriga/projects/mmdeploy/tools/check_env.py 05/25 16:16:24 - mmengine - INFO -
05/25 16:16:24 - mmengine - INFO - **Environmental information** 05/25 16:16:25 - mmengine - INFO - sys.platform: linux 05/25 16:16:25 - mmengine - INFO - Python: 3.8.16 (default, Dec 7 2022, 01:12:06) [GCC 11.3.0] 05/25 16:16:25 - mmengine - INFO - CUDA available: True 05/25 16:16:25 - mmengine - INFO - numpy_random_seed: 2147483648 05/25 16:16:25 - mmengine - INFO - GPU 0: NVIDIA GeForce GTX 1650 Ti 05/25 16:16:25 - mmengine - INFO - CUDA_HOME: /usr/local/cuda 05/25 16:16:25 - mmengine - INFO - NVCC: Cuda compilation tools, release 11.7, V11.7.64 05/25 16:16:25 - mmengine - INFO - GCC: x86_64-linux-gnu-gcc (Ubuntu 11.3.0-1ubuntu1~22.04.1) 11.3.0 05/25 16:16:25 - mmengine - INFO - PyTorch: 2.0.0+cu118 05/25 16:16:25 - mmengine - INFO - PyTorch compiling details: PyTorch built with:
05/25 16:16:25 - mmengine - INFO - TorchVision: 0.15.1+cu118 05/25 16:16:25 - mmengine - INFO - OpenCV: 4.5.4 05/25 16:16:25 - mmengine - INFO - MMEngine: 0.7.3 05/25 16:16:25 - mmengine - INFO - MMCV: 2.0.0 05/25 16:16:25 - mmengine - INFO - MMCV Compiler: GCC 9.3 05/25 16:16:25 - mmengine - INFO - MMCV CUDA Compiler: 11.8 05/25 16:16:25 - mmengine - INFO - MMDeploy: 1.1.0+faf05fe 05/25 16:16:25 - mmengine - INFO -
05/25 16:16:25 - mmengine - INFO - **Backend information** 05/25 16:16:25 - mmengine - INFO - tensorrt: 8.2.3.0 05/25 16:16:25 - mmengine - INFO - tensorrt custom ops: Available 05/25 16:16:25 - mmengine - INFO - ONNXRuntime: 1.14.1 05/25 16:16:25 - mmengine - INFO - ONNXRuntime-gpu: 1.8.1 05/25 16:16:25 - mmengine - INFO - ONNXRuntime custom ops: Available 05/25 16:16:25 - mmengine - INFO - pplnn: None 05/25 16:16:25 - mmengine - INFO - ncnn: None 05/25 16:16:25 - mmengine - INFO - snpe: None 05/25 16:16:25 - mmengine - INFO - openvino: None 05/25 16:16:25 - mmengine - INFO - torchscript: 2.0.0+cu118 05/25 16:16:25 - mmengine - INFO - torchscript custom ops: Available 05/25 16:16:25 - mmengine - INFO - rknn-toolkit: None 05/25 16:16:25 - mmengine - INFO - rknn-toolkit2: None 05/25 16:16:25 - mmengine - INFO - ascend: None 05/25 16:16:25 - mmengine - INFO - coreml: None 05/25 16:16:25 - mmengine - INFO - tvm: None 05/25 16:16:25 - mmengine - INFO - vacc: None 05/25 16:16:25 - mmengine - INFO -
05/25 16:16:25 - mmengine - INFO - **Codebase information** 05/25 16:16:25 - mmengine - INFO - mmdet: 3.0.0 05/25 16:16:25 - mmengine - INFO - mmseg: None 05/25 16:16:25 - mmengine - INFO - mmpretrain: None 05/25 16:16:25 - mmengine - INFO - mmocr: None 05/25 16:16:25 - mmengine - INFO - mmagic: None 05/25 16:16:25 - mmengine - INFO - mmdet3d: None 05/25 16:16:25 - mmengine - INFO - mmpose: None 05/25 16:16:25 - mmengine - INFO - mmrotate: None 05/25 16:16:25 - mmengine - INFO - mmaction: None 05/25 16:16:25 - mmengine - INFO - mmrazor: None
Process finished with exit code 0
Charlyo
commented
1 year ago @hanrui1sensetime @RunningLeon Also tried with the current mmdeploy docker image ubuntu20.04-cuda11.3-mmdeploy1.1.0. Same results apply:
05/26 08:37:11 - 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.
05/26 08:37:11 - 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.
05/26 08:37:13 - mmengine - INFO - Start pipeline mmdeploy.apis.pytorch2onnx.torch2onnx in subprocess
05/26 08:37:13 - 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.
05/26 08:37:13 - 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.
Loads checkpoint by local backend from path: robust_logo_r50_rfp_1x_nc_e18.pth
05/26 08:37:19 - mmengine - WARNING - DeprecationWarning: get_onnx_config will be deprecated in the future.
05/26 08:37:19 - mmengine - INFO - Export PyTorch model to ONNX: work_dir/end2end.onnx.
05/26 08:37:19 - mmengine - WARNING - Can not find torch._C._jit_pass_onnx_autograd_function_process, function rewrite will not be applied
05/26 08:37:19 - mmengine - WARNING - Can not find torch._C._jit_pass_onnx_deduplicate_initializers, function rewrite will not be applied
05/26 08:37:19 - mmengine - WARNING - Can not find mmdet.models.utils.transformer.PatchMerging.forward, function rewrite will not be applied
/root/workspace/mmdeploy/mmdeploy/codebase/mmdet/models/detectors/two_stage.py:73: 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]
/root/workspace/mmdeploy/mmdeploy/codebase/mmdet/models/detectors/two_stage.py:73: 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]
/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)
/usr/local/lib/python3.8/dist-packages/mmcv/ops/deform_conv.py:215: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
/usr/local/lib/python3.8/dist-packages/mmcv/ops/deform_conv.py:216: 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 not all(map(lambda s: s > 0, output_size)):
/usr/local/lib/python3.8/dist-packages/mmcv/ops/deform_conv.py:112: 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!
int(i)
/usr/local/lib/python3.8/dist-packages/mmcv/ops/deform_conv.py:118: 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!
cur_im2col_step = min(ctx.im2col_step, input.size(0))
/root/workspace/mmdeploy/mmdeploy/pytorch/functions/mod.py:20: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
return input - (input // other) * other
/usr/local/lib/python3.8/dist-packages/mmcv/ops/deform_conv.py:119: 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 (input.size(0) % cur_im2col_step
/usr/local/lib/python3.8/dist-packages/mmdet/models/dense_heads/anchor_head.py:115: UserWarning: DeprecationWarning: anchor_generator is deprecated, please use "prior_generator" instead
warnings.warn('DeprecationWarning: anchor_generator is deprecated, '
/usr/local/lib/python3.8/dist-packages/mmdet/models/task_modules/prior_generators/anchor_generator.py:356: UserWarning: ``grid_anchors`` would be deprecated soon. Please use ``grid_priors``
warnings.warn('``grid_anchors`` would be deprecated soon. '
/usr/local/lib/python3.8/dist-packages/mmdet/models/task_modules/prior_generators/anchor_generator.py:392: UserWarning: ``single_level_grid_anchors`` would be deprecated soon. Please use ``single_level_grid_priors``
warnings.warn(
/root/workspace/mmdeploy/mmdeploy/codebase/mmdet/models/dense_heads/rpn_head.py:89: 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 cls_score.size()[-2:] == bbox_pred.size()[-2:]
/root/workspace/mmdeploy/mmdeploy/pytorch/functions/topk.py:58: 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 k > size:
05/26 08:37:21 - mmengine - WARNING - Maximum K of TopK in TensorRT is 3840, but given 5000. Note that k will be set to 3840.
/root/workspace/mmdeploy/mmdeploy/codebase/mmdet/models/task_modules/coders/delta_xywh_bbox_coder.py:38: 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 pred_bboxes.size(0) == bboxes.size(0)
/root/workspace/mmdeploy/mmdeploy/codebase/mmdet/models/task_modules/coders/delta_xywh_bbox_coder.py:40: 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 pred_bboxes.size(1) == bboxes.size(1)
/root/workspace/mmdeploy/mmdeploy/mmcv/ops/nms.py:451: 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!
int(scores.shape[-1]),
/root/workspace/mmdeploy/mmdeploy/mmcv/ops/nms.py:148: 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)
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d 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::GatherTopk 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::GatherTopk type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
/usr/local/lib/python3.8/dist-packages/torch/onnx/symbolic_opset9.py:2815: UserWarning: Exporting aten::index operator of advanced indexing in opset 11 is achieved by combination of multiple ONNX operators, including Reshape, Transpose, Concat, and Gather. If indices include negative values, the exported graph will produce incorrect results.
warnings.warn("Exporting aten::index operator of advanced indexing in opset " +
WARNING: The shape inference of mmdeploy::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::MMCVMultiLevelRoiAlign 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::MMCVMultiLevelRoiAlign 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::MMCVMultiLevelRoiAlign 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: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::MMCVMultiLevelRoiAlign 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::MMCVMultiLevelRoiAlign 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::MMCVMultiLevelRoiAlign 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: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
Warning: Constant folding - Only steps=1 can be constant folded for opset >= 10 onnx::Slice op. Constant folding not applied.
WARNING: The shape inference of mmdeploy::MMCVDeformConv2d 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::MMCVDeformConv2d 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::GatherTopk 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::MMCVMultiLevelRoiAlign 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::MMCVMultiLevelRoiAlign 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::MMCVMultiLevelRoiAlign 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.
05/26 08:38:52 - mmengine - INFO - Execute onnx optimize passes.
05/26 08:38:56 - mmengine - INFO - Finish pipeline mmdeploy.apis.pytorch2onnx.torch2onnx
05/26 08:38:58 - mmengine - INFO - Start pipeline mmdeploy.apis.utils.utils.to_backend in subprocess
05/26 08:38:58 - mmengine - INFO - Successfully loaded tensorrt plugins from /root/workspace/mmdeploy/mmdeploy/lib/libmmdeploy_tensorrt_ops.so
[05/26/2023-08:38:58] [TRT] [I] [MemUsageChange] Init CUDA: CPU +323, GPU +0, now: CPU 407, GPU 191 (MiB)
[05/26/2023-08:38:59] [TRT] [I] [MemUsageSnapshot] Begin constructing builder kernel library: CPU 407 MiB, GPU 191 MiB
[05/26/2023-08:38:59] [TRT] [I] [MemUsageSnapshot] End constructing builder kernel library: CPU 542 MiB, GPU 225 MiB
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:604] Reading dangerously large protocol message. If the message turns out to be larger than 2147483647 bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:81] The total number of bytes read was 689495957
[05/26/2023-08:38:59] [TRT] [I] ----------------------------------------------------------------
[05/26/2023-08:38:59] [TRT] [I] Input filename: work_dir/end2end.onnx
[05/26/2023-08:38:59] [TRT] [I] ONNX IR version: 0.0.7
[05/26/2023-08:38:59] [TRT] [I] Opset version: 11
[05/26/2023-08:38:59] [TRT] [I] Producer name: pytorch
[05/26/2023-08:38:59] [TRT] [I] Producer version: 1.10
[05/26/2023-08:38:59] [TRT] [I] Domain:
[05/26/2023-08:38:59] [TRT] [I] Model version: 0
[05/26/2023-08:38:59] [TRT] [I] Doc string:
[05/26/2023-08:38:59] [TRT] [I] ----------------------------------------------------------------
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:604] Reading dangerously large protocol message. If the message turns out to be larger than 2147483647 bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:81] The total number of bytes read was 689495957
[05/26/2023-08:39:00] [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.
[05/26/2023-08:39:00] [TRT] [E] Conv_23: two inputs (data and weights) are allowed only in explicit-quantization mode.
[05/26/2023-08:39:00] [TRT] [E] ModelImporter.cpp:773: While parsing node number 23 [Conv -> "1228"]:
[05/26/2023-08:39:00] [TRT] [E] ModelImporter.cpp:774: --- Begin node ---
[05/26/2023-08:39:00] [TRT] [E] ModelImporter.cpp:775: input: "input"
input: "1227"
output: "1228"
name: "Conv_23"
op_type: "Conv"
attribute {
name: "dilations"
ints: 1
ints: 1
type: INTS
}
attribute {
name: "group"
i: 1
type: INT
}
attribute {
name: "kernel_shape"
ints: 7
ints: 7
type: INTS
}
attribute {
name: "pads"
ints: 3
ints: 3
ints: 3
ints: 3
type: INTS
}
attribute {
name: "strides"
ints: 2
ints: 2
type: INTS
}
[05/26/2023-08:39:00] [TRT] [E] ModelImporter.cpp:776: --- End node ---
[05/26/2023-08:39:00] [TRT] [E] ModelImporter.cpp:779: ERROR: ModelImporter.cpp:179 In function parseGraph:
[6] Invalid Node - Conv_23
Conv_23: two inputs (data and weights) are allowed only in explicit-quantization mode.
Process Process-3:
Traceback (most recent call last):
File "/usr/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap
self.run()
File "/usr/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/utils/utils.py", line 98, in to_backend
return backend_mgr.to_backend(
File "/root/workspace/mmdeploy/mmdeploy/backend/tensorrt/backend_manager.py", line 127, in to_backend
onnx2tensorrt(
File "/root/workspace/mmdeploy/mmdeploy/backend/tensorrt/onnx2tensorrt.py", line 79, in onnx2tensorrt
from_onnx(
File "/root/workspace/mmdeploy/mmdeploy/backend/tensorrt/utils.py", line 185, in from_onnx
raise RuntimeError(f'Failed to parse onnx, {error_msgs}')
RuntimeError: Failed to parse onnx, In node 23 (parseGraph): INVALID_NODE: Invalid Node - Conv_23
Conv_23: two inputs (data and weights) are allowed only in explicit-quantization mode.
05/26 08:39:00 - mmengine - ERROR - /root/workspace/mmdeploy/mmdeploy/apis/core/pipeline_manager.py - pop_mp_output - 80 - `mmdeploy.apis.utils.utils.to_backend` with Call id: 1 failed. exit.
RunningLeon
commented
1 year ago @Charlyo hi, have posted this issue in the TensorRT forum or tried with the latest TensorRT? It seems to be a restriction from TensorRT when using int8.
Charlyo
commented
1 year ago @RunningLeon I was trying to follow the guidelines that you guys posted in the documentation, where TensorRT 8.2.3 is installed. I will try with the new versions. Also, it would be really nice if you can update the documentation to include / test the last versions of TensorRT / torch / cuda. Version 8.2.3 is already 1+ years old.
Thank you.
RunningLeon
commented
1 year ago Thanks for your feedback. We'll consider your suggestion.
github-actions[bot]
commented
1 year ago This issue is marked as stale because it has been marked as invalid or awaiting response for 7 days without any further response. It will be closed in 5 days if the stale label is not removed or if there is no further response.
github-actions[bot]
commented
1 year ago This issue is closed because it has been stale for 5 days. Please open a new issue if you have similar issues or you have any new updates now.
Charlyo
commented
1 year ago @RunningLeon Please, let me know if you update the doc with updated tensorrt and dependencies.
RunningLeon
commented
1 year ago hi, sorry for the trouble. Currently, we have no plan to upgrade TensorRT to the latest version. BTW, what's your result of testing on the latest TensorRT?
python mmdeploy/tools/deploy.py \ mmdeploy/configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py \ mmdeploy/detectors_cascade_rcnn_r50_1x_coco.py \ mmdeploy/detectors_htc_r50_1x_coco-329b1453.pth \ mmdetection2.25.1/demo/guang_testB_22_000001.jpg \ --work-dir mmdeploy/mmdeploy_model/detector_test \ --device cuda:0 \ --dump-info --quant
When i used 'deploy.py' to convert DetectorRS, i encountered the issue as title. Is this beacuse mmdeploy doesn't support DetectorRS?
And my environment is:
2022-08-17 11:30:47,696 - mmdeploy - INFO - Environmental information fatal: ambiguous argument 'HEAD': unknown revision or path not in the working tree. Use '--' to separate paths from revisions, like this: 'git [...] -- [...]' 2022-08-17 11:30:47,780 - mmdeploy - INFO - sys.platform: linux 2022-08-17 11:30:47,780 - mmdeploy - INFO - Python: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:04:18) [GCC 10.3.0] 2022-08-17 11:30:47,780 - mmdeploy - INFO - CUDA available: True 2022-08-17 11:30:47,780 - mmdeploy - INFO - GPU 0,1: GeForce RTX 3080 2022-08-17 11:30:47,780 - mmdeploy - INFO - CUDA_HOME: /usr/local/cuda 2022-08-17 11:30:47,780 - mmdeploy - INFO - NVCC: Cuda compilation tools, release 11.0, V11.0.194 2022-08-17 11:30:47,780 - mmdeploy - INFO - GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 2022-08-17 11:30:47,781 - mmdeploy - INFO - PyTorch: 1.9.0+cu111 2022-08-17 11:30:47,781 - 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 v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) OpenMP 201511 (a.k.a. OpenMP 4.5) NNPACK is enabled CPU capability usage: AVX2 CUDA Runtime 11.1 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 8.0.5 Magma 2.5.2 Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, 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 -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=1.9.0, 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, 2022-08-17 11:30:47,781 - mmdeploy - INFO - TorchVision: 0.10.0+cu111 2022-08-17 11:30:47,781 - mmdeploy - INFO - OpenCV: 4.6.0 2022-08-17 11:30:47,781 - mmdeploy - INFO - MMCV: 1.6.0 2022-08-17 11:30:47,781 - mmdeploy - INFO - MMCV Compiler: GCC 7.3 2022-08-17 11:30:47,781 - mmdeploy - INFO - MMCV CUDA Compiler: 11.1 2022-08-17 11:30:47,781 - mmdeploy - INFO - MMDeploy: 0.7.0+HEAD 2022-08-17 11:30:47,781 - mmdeploy - INFO -
2022-08-17 11:30:47,781 - mmdeploy - INFO - Backend information 2022-08-17 11:30:48,039 - mmdeploy - INFO - onnxruntime: 1.8.1 ops_is_avaliable : True 2022-08-17 11:30:48,054 - mmdeploy - INFO - tensorrt: 8.2.3.0 ops_is_avaliable : True 2022-08-17 11:30:48,069 - mmdeploy - INFO - ncnn: None ops_is_avaliable : False 2022-08-17 11:30:48,073 - mmdeploy - INFO - pplnn_is_avaliable: False 2022-08-17 11:30:48,073 - mmdeploy - INFO - openvino_is_avaliable: False 2022-08-17 11:30:48,082 - mmdeploy - INFO - snpe_is_available: False 2022-08-17 11:30:48,082 - mmdeploy - INFO -
2022-08-17 11:30:48,082 - mmdeploy - INFO - Codebase information 2022-08-17 11:30:48,083 - mmdeploy - INFO - mmdet: 2.25.1 2022-08-17 11:30:48,083 - mmdeploy - INFO - mmseg: None 2022-08-17 11:30:48,083 - mmdeploy - INFO - mmcls: None 2022-08-17 11:30:48,083 - mmdeploy - INFO - mmocr: None 2022-08-17 11:30:48,083 - mmdeploy - INFO - mmedit: None 2022-08-17 11:30:48,083 - mmdeploy - INFO - mmdet3d: None 2022-08-17 11:30:48,083 - mmdeploy - INFO - mmpose: None 2022-08-17 11:30:48,083 - mmdeploy - INFO - mmrotate: None