segfault while yolox engine making on TensorRT 8
victor-yudin opened this issue · 3 comments
victor-yudin commented
Describe the bug
I try to convert the yolox_l based model to trt engine, but get the error:
load checkpoint from local path: epoch_60.pth
The model and loaded state dict do not match exactly
unexpected key in source state_dict: ema_backbone_stem_conv_conv_weight, ema_backbone_stem_conv_bn_weight, ema_backbone_stem_conv_bn_bias, ema_backbone_stem_conv_bn_running_mean, ema_backbone_stem_conv_bn_running_var, ema_backbone_stem_conv_bn_num_batches_tracked, ema_backbone_stage1_0_conv_weight, ema_backbone_stage1_0_bn_weight, ema_backbone_stage1_0_bn_bias, ema_backbone_stage1_0_bn_running_mean, ema_backbone_stage1_0_bn_running_var, ema_backbone_stage1_0_bn_num_batches_tracked, ema_backbone_stage1_1_main_conv_conv_weight, ema_backbone_stage1_1_main_conv_bn_weight, ema_backbone_stage1_1_main_conv_bn_bias, ema_backbone_stage1_1_main_conv_bn_running_mean, ema_backbone_stage1_1_main_conv_bn_running_var, ema_backbone_stage1_1_main_conv_bn_num_batches_tracked, ema_backbone_stage1_1_short_conv_conv_weight, ema_backbone_stage1_1_short_conv_bn_weight, ema_backbone_stage1_1_short_conv_bn_bias, ema_backbone_stage1_1_short_conv_bn_running_mean, ema_backbone_stage1_1_short_conv_bn_running_var, ema_backbone_stage1_1_short_conv_bn_num_batches_tracked, ema_backbone_stage1_1_final_conv_conv_weight, ema_backbone_stage1_1_final_conv_bn_weight, ema_backbone_stage1_1_final_conv_bn_bias, ema_backbone_stage1_1_final_conv_bn_running_mean, ema_backbone_stage1_1_final_conv_bn_running_var, ema_backbone_stage1_1_final_conv_bn_num_batches_tracked, ema_backbone_stage1_1_blocks_0_conv1_conv_weight, ema_backbone_stage1_1_blocks_0_conv1_bn_weight, ema_backbone_stage1_1_blocks_0_conv1_bn_bias, ema_backbone_stage1_1_blocks_0_conv1_bn_running_mean, ema_backbone_stage1_1_blocks_0_conv1_bn_running_var, ema_backbone_stage1_1_blocks_0_conv1_bn_num_batches_tracked, ema_backbone_stage1_1_blocks_0_conv2_conv_weight, ema_backbone_stage1_1_blocks_0_conv2_bn_weight, ema_backbone_stage1_1_blocks_0_conv2_bn_bias, ema_backbone_stage1_1_blocks_0_conv2_bn_running_mean, ema_backbone_stage1_1_blocks_0_conv2_bn_running_var, ema_backbone_stage1_1_blocks_0_conv2_bn_num_batches_tracked, ema_backbone_stage1_1_blocks_1_conv1_conv_weight, ema_backbone_stage1_1_blocks_1_conv1_bn_weight, ema_backbone_stage1_1_blocks_1_conv1_bn_bias, ema_backbone_stage1_1_blocks_1_conv1_bn_running_mean, ema_backbone_stage1_1_blocks_1_conv1_bn_running_var, ema_backbone_stage1_1_blocks_1_conv1_bn_num_batches_tracked, ema_backbone_stage1_1_blocks_1_conv2_conv_weight, ema_backbone_stage1_1_blocks_1_conv2_bn_weight, ema_backbone_stage1_1_blocks_1_conv2_bn_bias, ema_backbone_stage1_1_blocks_1_conv2_bn_running_mean, ema_backbone_stage1_1_blocks_1_conv2_bn_running_var, ema_backbone_stage1_1_blocks_1_conv2_bn_num_batches_tracked, ema_backbone_stage1_1_blocks_2_conv1_conv_weight, ema_backbone_stage1_1_blocks_2_conv1_bn_weight, ema_backbone_stage1_1_blocks_2_conv1_bn_bias, ema_backbone_stage1_1_blocks_2_conv1_bn_running_mean, ema_backbone_stage1_1_blocks_2_conv1_bn_running_var, ema_backbone_stage1_1_blocks_2_conv1_bn_num_batches_tracked, ema_backbone_stage1_1_blocks_2_conv2_conv_weight, ema_backbone_stage1_1_blocks_2_conv2_bn_weight, ema_backbone_stage1_1_blocks_2_conv2_bn_bias, ema_backbone_stage1_1_blocks_2_conv2_bn_running_mean, ema_backbone_stage1_1_blocks_2_conv2_bn_running_var, ema_backbone_stage1_1_blocks_2_conv2_bn_num_batches_tracked, ema_backbone_stage2_0_conv_weight, ema_backbone_stage2_0_bn_weight, ema_backbone_stage2_0_bn_bias, ema_backbone_stage2_0_bn_running_mean, ema_backbone_stage2_0_bn_running_var, ema_backbone_stage2_0_bn_num_batches_tracked, ema_backbone_stage2_1_main_conv_conv_weight, ema_backbone_stage2_1_main_conv_bn_weight, ema_backbone_stage2_1_main_conv_bn_bias, ema_backbone_stage2_1_main_conv_bn_running_mean, ema_backbone_stage2_1_main_conv_bn_running_var, ema_backbone_stage2_1_main_conv_bn_num_batches_tracked, ema_backbone_stage2_1_short_conv_conv_weight, ema_backbone_stage2_1_short_conv_bn_weight, ema_backbone_stage2_1_short_conv_bn_bias, ema_backbone_stage2_1_short_conv_bn_running_mean, ema_backbone_stage2_1_short_conv_bn_running_var, ema_backbone_stage2_1_short_conv_bn_num_batches_tracked, ema_backbone_stage2_1_final_conv_conv_weight, ema_backbone_stage2_1_final_conv_bn_weight, ema_backbone_stage2_1_final_conv_bn_bias, ema_backbone_stage2_1_final_conv_bn_running_mean, ema_backbone_stage2_1_final_conv_bn_running_var, ema_backbone_stage2_1_final_conv_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_0_conv1_conv_weight, ema_backbone_stage2_1_blocks_0_conv1_bn_weight, ema_backbone_stage2_1_blocks_0_conv1_bn_bias, ema_backbone_stage2_1_blocks_0_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_0_conv1_bn_running_var, ema_backbone_stage2_1_blocks_0_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_0_conv2_conv_weight, ema_backbone_stage2_1_blocks_0_conv2_bn_weight, ema_backbone_stage2_1_blocks_0_conv2_bn_bias, ema_backbone_stage2_1_blocks_0_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_0_conv2_bn_running_var, ema_backbone_stage2_1_blocks_0_conv2_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_1_conv1_conv_weight, ema_backbone_stage2_1_blocks_1_conv1_bn_weight, ema_backbone_stage2_1_blocks_1_conv1_bn_bias, ema_backbone_stage2_1_blocks_1_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_1_conv1_bn_running_var, ema_backbone_stage2_1_blocks_1_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_1_conv2_conv_weight, ema_backbone_stage2_1_blocks_1_conv2_bn_weight, ema_backbone_stage2_1_blocks_1_conv2_bn_bias, ema_backbone_stage2_1_blocks_1_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_1_conv2_bn_running_var, ema_backbone_stage2_1_blocks_1_conv2_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_2_conv1_conv_weight, ema_backbone_stage2_1_blocks_2_conv1_bn_weight, ema_backbone_stage2_1_blocks_2_conv1_bn_bias, ema_backbone_stage2_1_blocks_2_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_2_conv1_bn_running_var, ema_backbone_stage2_1_blocks_2_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_2_conv2_conv_weight, ema_backbone_stage2_1_blocks_2_conv2_bn_weight, ema_backbone_stage2_1_blocks_2_conv2_bn_bias, ema_backbone_stage2_1_blocks_2_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_2_conv2_bn_running_var, ema_backbone_stage2_1_blocks_2_conv2_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_3_conv1_conv_weight, ema_backbone_stage2_1_blocks_3_conv1_bn_weight, ema_backbone_stage2_1_blocks_3_conv1_bn_bias, ema_backbone_stage2_1_blocks_3_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_3_conv1_bn_running_var, ema_backbone_stage2_1_blocks_3_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_3_conv2_conv_weight, ema_backbone_stage2_1_blocks_3_conv2_bn_weight, ema_backbone_stage2_1_blocks_3_conv2_bn_bias, ema_backbone_stage2_1_blocks_3_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_3_conv2_bn_running_var, ema_backbone_stage2_1_blocks_3_conv2_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_4_conv1_conv_weight, ema_backbone_stage2_1_blocks_4_conv1_bn_weight, ema_backbone_stage2_1_blocks_4_conv1_bn_bias, ema_backbone_stage2_1_blocks_4_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_4_conv1_bn_running_var, ema_backbone_stage2_1_blocks_4_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_4_conv2_conv_weight, ema_backbone_stage2_1_blocks_4_conv2_bn_weight, ema_backbone_stage2_1_blocks_4_conv2_bn_bias, ema_backbone_stage2_1_blocks_4_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_4_conv2_bn_running_var, ema_backbone_stage2_1_blocks_4_conv2_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_5_conv1_conv_weight, ema_backbone_stage2_1_blocks_5_conv1_bn_weight, ema_backbone_stage2_1_blocks_5_conv1_bn_bias, ema_backbone_stage2_1_blocks_5_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_5_conv1_bn_running_var, ema_backbone_stage2_1_blocks_5_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_5_conv2_conv_weight, ema_backbone_stage2_1_blocks_5_conv2_bn_weight, ema_backbone_stage2_1_blocks_5_conv2_bn_bias, ema_backbone_stage2_1_blocks_5_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_5_conv2_bn_running_var, ema_backbone_stage2_1_blocks_5_conv2_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_6_conv1_conv_weight, ema_backbone_stage2_1_blocks_6_conv1_bn_weight, ema_backbone_stage2_1_blocks_6_conv1_bn_bias, ema_backbone_stage2_1_blocks_6_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_6_conv1_bn_running_var, ema_backbone_stage2_1_blocks_6_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_6_conv2_conv_weight, ema_backbone_stage2_1_blocks_6_conv2_bn_weight, ema_backbone_stage2_1_blocks_6_conv2_bn_bias, ema_backbone_stage2_1_blocks_6_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_6_conv2_bn_running_var, ema_backbone_stage2_1_blocks_6_conv2_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_7_conv1_conv_weight, ema_backbone_stage2_1_blocks_7_conv1_bn_weight, ema_backbone_stage2_1_blocks_7_conv1_bn_bias, ema_backbone_stage2_1_blocks_7_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_7_conv1_bn_running_var, ema_backbone_stage2_1_blocks_7_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_7_conv2_conv_weight, ema_backbone_stage2_1_blocks_7_conv2_bn_weight, ema_backbone_stage2_1_blocks_7_conv2_bn_bias, ema_backbone_stage2_1_blocks_7_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_7_conv2_bn_running_var, ema_backbone_stage2_1_blocks_7_conv2_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_8_conv1_conv_weight, ema_backbone_stage2_1_blocks_8_conv1_bn_weight, ema_backbone_stage2_1_blocks_8_conv1_bn_bias, ema_backbone_stage2_1_blocks_8_conv1_bn_running_mean, ema_backbone_stage2_1_blocks_8_conv1_bn_running_var, ema_backbone_stage2_1_blocks_8_conv1_bn_num_batches_tracked, ema_backbone_stage2_1_blocks_8_conv2_conv_weight, ema_backbone_stage2_1_blocks_8_conv2_bn_weight, ema_backbone_stage2_1_blocks_8_conv2_bn_bias, ema_backbone_stage2_1_blocks_8_conv2_bn_running_mean, ema_backbone_stage2_1_blocks_8_conv2_bn_running_var, ema_backbone_stage2_1_blocks_8_conv2_bn_num_batches_tracked, ema_backbone_stage3_0_conv_weight, ema_backbone_stage3_0_bn_weight, ema_backbone_stage3_0_bn_bias, ema_backbone_stage3_0_bn_running_mean, ema_backbone_stage3_0_bn_running_var, ema_backbone_stage3_0_bn_num_batches_tracked, ema_backbone_stage3_1_main_conv_conv_weight, ema_backbone_stage3_1_main_conv_bn_weight, ema_backbone_stage3_1_main_conv_bn_bias, ema_backbone_stage3_1_main_conv_bn_running_mean, ema_backbone_stage3_1_main_conv_bn_running_var, ema_backbone_stage3_1_main_conv_bn_num_batches_tracked, ema_backbone_stage3_1_short_conv_conv_weight, ema_backbone_stage3_1_short_conv_bn_weight, ema_backbone_stage3_1_short_conv_bn_bias, ema_backbone_stage3_1_short_conv_bn_running_mean, ema_backbone_stage3_1_short_conv_bn_running_var, ema_backbone_stage3_1_short_conv_bn_num_batches_tracked, ema_backbone_stage3_1_final_conv_conv_weight, ema_backbone_stage3_1_final_conv_bn_weight, ema_backbone_stage3_1_final_conv_bn_bias, ema_backbone_stage3_1_final_conv_bn_running_mean, ema_backbone_stage3_1_final_conv_bn_running_var, ema_backbone_stage3_1_final_conv_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_0_conv1_conv_weight, ema_backbone_stage3_1_blocks_0_conv1_bn_weight, ema_backbone_stage3_1_blocks_0_conv1_bn_bias, ema_backbone_stage3_1_blocks_0_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_0_conv1_bn_running_var, ema_backbone_stage3_1_blocks_0_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_0_conv2_conv_weight, ema_backbone_stage3_1_blocks_0_conv2_bn_weight, ema_backbone_stage3_1_blocks_0_conv2_bn_bias, ema_backbone_stage3_1_blocks_0_conv2_bn_running_mean, ema_backbone_stage3_1_blocks_0_conv2_bn_running_var, ema_backbone_stage3_1_blocks_0_conv2_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_1_conv1_conv_weight, ema_backbone_stage3_1_blocks_1_conv1_bn_weight, ema_backbone_stage3_1_blocks_1_conv1_bn_bias, ema_backbone_stage3_1_blocks_1_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_1_conv1_bn_running_var, ema_backbone_stage3_1_blocks_1_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_1_conv2_conv_weight, ema_backbone_stage3_1_blocks_1_conv2_bn_weight, ema_backbone_stage3_1_blocks_1_conv2_bn_bias, ema_backbone_stage3_1_blocks_1_conv2_bn_running_mean, ema_backbone_stage3_1_blocks_1_conv2_bn_running_var, ema_backbone_stage3_1_blocks_1_conv2_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_2_conv1_conv_weight, ema_backbone_stage3_1_blocks_2_conv1_bn_weight, ema_backbone_stage3_1_blocks_2_conv1_bn_bias, ema_backbone_stage3_1_blocks_2_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_2_conv1_bn_running_var, ema_backbone_stage3_1_blocks_2_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_2_conv2_conv_weight, ema_backbone_stage3_1_blocks_2_conv2_bn_weight, ema_backbone_stage3_1_blocks_2_conv2_bn_bias, ema_backbone_stage3_1_blocks_2_conv2_bn_running_mean, ema_backbone_stage3_1_blocks_2_conv2_bn_running_var, ema_backbone_stage3_1_blocks_2_conv2_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_3_conv1_conv_weight, ema_backbone_stage3_1_blocks_3_conv1_bn_weight, ema_backbone_stage3_1_blocks_3_conv1_bn_bias, ema_backbone_stage3_1_blocks_3_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_3_conv1_bn_running_var, ema_backbone_stage3_1_blocks_3_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_3_conv2_conv_weight, ema_backbone_stage3_1_blocks_3_conv2_bn_weight, ema_backbone_stage3_1_blocks_3_conv2_bn_bias, ema_backbone_stage3_1_blocks_3_conv2_bn_running_mean, ema_backbone_stage3_1_blocks_3_conv2_bn_running_var, ema_backbone_stage3_1_blocks_3_conv2_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_4_conv1_conv_weight, ema_backbone_stage3_1_blocks_4_conv1_bn_weight, ema_backbone_stage3_1_blocks_4_conv1_bn_bias, ema_backbone_stage3_1_blocks_4_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_4_conv1_bn_running_var, ema_backbone_stage3_1_blocks_4_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_4_conv2_conv_weight, ema_backbone_stage3_1_blocks_4_conv2_bn_weight, ema_backbone_stage3_1_blocks_4_conv2_bn_bias, ema_backbone_stage3_1_blocks_4_conv2_bn_running_mean, ema_backbone_stage3_1_blocks_4_conv2_bn_running_var, ema_backbone_stage3_1_blocks_4_conv2_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_5_conv1_conv_weight, ema_backbone_stage3_1_blocks_5_conv1_bn_weight, ema_backbone_stage3_1_blocks_5_conv1_bn_bias, ema_backbone_stage3_1_blocks_5_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_5_conv1_bn_running_var, ema_backbone_stage3_1_blocks_5_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_5_conv2_conv_weight, ema_backbone_stage3_1_blocks_5_conv2_bn_weight, ema_backbone_stage3_1_blocks_5_conv2_bn_bias, ema_backbone_stage3_1_blocks_5_conv2_bn_running_mean, ema_backbone_stage3_1_blocks_5_conv2_bn_running_var, ema_backbone_stage3_1_blocks_5_conv2_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_6_conv1_conv_weight, ema_backbone_stage3_1_blocks_6_conv1_bn_weight, ema_backbone_stage3_1_blocks_6_conv1_bn_bias, ema_backbone_stage3_1_blocks_6_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_6_conv1_bn_running_var, ema_backbone_stage3_1_blocks_6_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_6_conv2_conv_weight, ema_backbone_stage3_1_blocks_6_conv2_bn_weight, ema_backbone_stage3_1_blocks_6_conv2_bn_bias, ema_backbone_stage3_1_blocks_6_conv2_bn_running_mean, ema_backbone_stage3_1_blocks_6_conv2_bn_running_var, ema_backbone_stage3_1_blocks_6_conv2_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_7_conv1_conv_weight, ema_backbone_stage3_1_blocks_7_conv1_bn_weight, ema_backbone_stage3_1_blocks_7_conv1_bn_bias, ema_backbone_stage3_1_blocks_7_conv1_bn_running_mean, ema_backbone_stage3_1_blocks_7_conv1_bn_running_var, ema_backbone_stage3_1_blocks_7_conv1_bn_num_batches_tracked, ema_backbone_stage3_1_blocks_7_conv2_conv_weight, ema_backbone_stage3_1_blocks_7_conv2_bn_weight, 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INFO:mmdet2trt:Model warmup
INFO:mmdet2trt:Converting model
[12/20/2021-15:16:55] [TRT] [I] [MemUsageChange] Init CUDA: CPU +313, GPU +0, now: CPU 2896, GPU 2337 (MiB)
[12/20/2021-15:16:55] [TRT] [I] [MemUsageSnapshot] Begin constructing builder kernel library: CPU 2896 MiB, GPU 2329 MiB
[12/20/2021-15:16:55] [TRT] [I] [MemUsageSnapshot] End constructing builder kernel library: CPU 3031 MiB, GPU 2371 MiB
Segmentation fault (core dumped)
"Unexpected keys" warning is happend on mmdetection inference too, but detected objects are returned well (bounding boxes coordinates are right).
Also I tried to run command on TensorRT 8.2.1.8 and with cudnn 8.0.4 and on TensorRT 8.0.1.6 with cudnn 8.0.4 . Result the same - segfault error.
GPU memory usage is OK (1-2GB).
fp32 precision is also crashed on Tensorrt 8.
Running on Tensorrt 7.2.2.3, cudnn 8.0.1 works well - engine is created successfully.
To Reproduce
mmdet2trt yolox_l_8x8_300e_barcodes.py epoch_60.pth ./output_engine --fp16=1 --save-engine=1 --trt-log-level=VERBOSE
environment:
PyTorch version: 1.8.0+cu111
Is debug build: False
CUDA used to build PyTorch: 11.1
OS: Ubuntu 20.04.2 LTS (x86_64)
GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
Clang version: Could not collect
CMake version: version 3.19.1
Libc version: glibc-2.31
Python version: 3.8.10 (default, Sep 28 2021, 16:10:42) [GCC 9.3.0] (64-bit runtime)
Python platform: Linux-5.11.0-41-generic-x86_64-with-glibc2.29
Is CUDA available: True
CUDA runtime version: 11.1.105
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 2070
Nvidia driver version: 470.86
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.2.1
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn.so.8.0.4
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.0.4
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.0.4
/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.0.4
Versions of relevant libraries:
[pip3] mmcv-full==1.3.18
[pip3] mmdet==2.18.1
[pip3] mmdet2trt==0.5.0
[pip3] tensorrt==8.2.1.8
[pip3] torch==1.8.0+cu111
[pip3] torch2trt-dynamic==0.5.0
[pip3] torchaudio==0.8.0
[pip3] torchvision==0.9.0+cu111
[conda] Could not collect
Additional context
Link to model and config:
https://drive.google.com/drive/folders/1DuDR3LZJfYkanZe743dYarfQCyr7vZJa?usp=sharing
grimoire commented
Hi
Can you reproduce this error on yolox_s? MMDetection did not provide a checkpoint of yolox_l.
victor-yudin commented
Hi Can you reproduce this error on yolox_s? MMDetection did not provide a checkpoint of yolox_l.
Do you mean yolo_l config? It's inherited from yolox_s one, only sizes and ratios are changed
grimoire commented
Sorry for the late reply.
It seems that you have not set the opt_shape_param when converting the model. Since the input shape of Yolox is different from other models in MMDetection. Please try to set the opt shape and try it again. Thank you.
By the way, OpenMMLab has released MMDeploy which might have better support about some new models.