AllentDan
commented
2 years ago Hi, @gzxy-0102 please update mmdeploy to the latest as we just fixed a bug of converting yolox to tensorrt.
Closed gzxy-0102 closed 2 years ago
AllentDan
commented
2 years ago Hi, @gzxy-0102 please update mmdeploy to the latest as we just fixed a bug of converting yolox to tensorrt.
gzxy-0102
commented
2 years ago Hi, @gzxy-0102 please update mmdeploy to the latest as we just fixed a bug of converting yolox to tensorrt.
I am using the latest version
AllentDan
commented
2 years ago Just to make sure, https://github.com/open-mmlab/mmdeploy/pull/758 was merged 20 hours ago. And it fixed the bug.
gzxy-0102
commented
2 years ago ennnn it's git master branch? I am using 0.6.0 release
AllentDan
commented
2 years ago Yes, just fixed hours ago. The bug is only triggered for some versions of TensorRT and 8.4.1.5 is in the list.
gzxy-0102
commented
2 years ago ok got it But why doesn't the onnx model I converted continue to execute after the Detector is instantiated?
AllentDan
commented
2 years ago Where did you import the Detector from? There is no such a class named Detector. If you want to inference the backend engines, you may try:
from mmdeploy.apis.utils import build_task_processor
task_processor = build_task_processor(model_cfg, deploy_cfg, device)
model = task_processor.init_backend_model(backend_files)
input_shape = get_input_shape(deploy_cfg)
model_inputs, _ = task_processor.create_input(img, input_shape)
with torch.no_grad():
result = task_processor.run_inference(model, model_inputs)ennn
I am import Detector from mmdeploy_python
gzxy-0102
commented
2 years ago I know it from document
AllentDan
commented
2 years ago I see. It is from the SDK of MMDeploy. Would you please show the contents of work_dir passed to Detector. And what is your operating system by the way?
gzxy-0102
commented
2 years ago 2022-07-21 11:00:41 - tortoise.db_client:197 - DEBUG - SELECT `updated_at`,`cfg`,`state`,`dataset_root`,`epoch`,`best_checkpoint`,`total_epoch`,`huey_id`,`cfg_file`,`id`,`work_dir`,`best_onnx`,`accuracy`,`dataset_type`,`eta`,`created_at` FROM `mmdet_train` WHERE `id`='bcccd9e0-41a1-408d-9dfa-f4e634e9608c' LIMIT 2: None
[2022-07-21 11:00:41.480] [mmdeploy] [info] [model.cpp:38] DirectoryModel successfully load sdk model /static/work_dirs/bcccd9e0-41a1-408d-9dfa-f4e634e9608c
[2022-07-21 11:00:41.500] [mmdeploy] [info] [common.h:29] config: {
"context": {
"device": "<any>",
"stream": "<any>"
},
"input": [
"image"
],
"name": "mmdetection",
"output": [
"det"
],
"params": {
"model": "<any>"
},
"type": "Inference"
}
[2022-07-21 11:00:41.501] [mmdeploy] [info] [common.h:29] config: {
"context": {
"device": "<any>",
"model": "<any>",
"stream": "<any>"
},
"input": [
"img"
],
"module": "Transform",
"name": "Preprocess",
"output": [
"prep_output"
],
"transforms": [
{
"type": "LoadImageFromFile"
},
{
"keep_ratio": true,
"size": [
640,
640
],
"type": "Resize"
},
{
"pad_to_square": true,
"pad_val": {
"img": [
114.0,
114.0,
114.0
]
},
"type": "Pad"
},
{
"type": "DefaultFormatBundle"
},
{
"keys": [
"img"
],
"meta_keys": [
"img_shape",
"ori_shape",
"flip",
"pad_shape",
"flip_direction",
"filename",
"scale_factor",
"valid_ratio",
"img_norm_cfg",
"ori_filename"
],
"type": "Collect"
}
],
"type": "Task"
}
[2022-07-21 11:00:41.504] [mmdeploy] [info] [common.h:29] config: {
"context": {
"device": "<any>",
"model": "<any>",
"stream": "<any>"
},
"input": [
"img"
],
"module": "Transform",
"name": "Preprocess",
"output": [
"prep_output"
],
"transforms": [
{
"type": "LoadImageFromFile"
},
{
"keep_ratio": true,
"size": [
640,
640
],
"type": "Resize"
},
{
"pad_to_square": true,
"pad_val": {
"img": [
114.0,
114.0,
114.0
]
},
"type": "Pad"
},
{
"type": "DefaultFormatBundle"
},
{
"keys": [
"img"
],
"meta_keys": [
"img_shape",
"ori_shape",
"flip",
"pad_shape",
"flip_direction",
"filename",
"scale_factor",
"valid_ratio",
"img_norm_cfg",
"ori_filename"
],
"type": "Collect"
}
],
"type": "Task"
}
[2022-07-21 11:00:41.506] [mmdeploy] [info] [common.h:29] config: {
"context": {
"device": "<any>",
"model": "<any>",
"stream": "<any>"
},
"input": [
"prep_output"
],
"input_map": {
"img": "input"
},
"module": "Net",
"name": "yolox",
"output": [
"infer_output"
],
"type": "Task"
}
[2022-07-21 11:00:41.507] [mmdeploy] [info] [common.h:29] config: {
"context": {
"device": "<any>",
"model": "<any>",
"stream": "<any>"
},
"input": [
"prep_output"
],
"input_map": {
"img": "input"
},
"module": "Net",
"name": "yolox",
"output": [
"infer_output"
],
"type": "Task"
}
My operating system is windows 11
AllentDan
commented
2 years ago Please use --dump-info when running tools/deploy.py. And you are using ONNXRuntime backend now? As the bug fixed already, you may try TensorRT.
gzxy-0102
commented
2 years ago Using the latest version but still getting an error
2022-07-21 14:53:55,486 - mmdeploy - INFO - 当前任务ID：bcccd9e0-41a1-408d-9dfa-f4e634e9608c
Registry：{'input_size': (640, 640), 'random_size_range': (15, 25), 'random_size_interval': 10, 'backbone': {'type': 'CSPDarknet', 'deepen_factor': 1.0, 'widen_factor': 1.0}, 'neck': {'type': 'YOLOXPAFPN', 'in_channels': [256, 512, 1024], 'out_channels': 256, 'num_csp_blocks': 3}, 'bbox_head': {'type': 'YOLOXHead', 'num_classes': 5, 'in_channels': 256, 'feat_channels': 256}, 'train_cfg': None, 'test_cfg': {'score_thr': 0.01, 'nms': {'type': 'nms', 'iou_threshold': 0.65}}}
Registry：{'deepen_factor': 1.0, 'widen_factor': 1.0}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{'in_channels': [256, 512, 1024], 'out_channels': 256, 'num_csp_blocks': 3}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{'num_classes': 5, 'in_channels': 256, 'feat_channels': 256, 'train_cfg': None, 'test_cfg': {'score_thr': 0.01, 'nms': {'type': 'nms', 'iou_threshold': 0.65}}}
Registry：{'use_sigmoid': True, 'reduction': 'sum', 'loss_weight': 1.0}
Registry：{'mode': 'square', 'eps': 1e-16, 'reduction': 'sum', 'loss_weight': 5.0}
Registry：{'use_sigmoid': True, 'reduction': 'sum', 'loss_weight': 1.0}
Registry：{'reduction': 'sum', 'loss_weight': 1.0}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
Registry：{}
load checkpoint from local path: D:\static\work_dirs\bcccd9e0-41a1-408d-9dfa-f4e634e9608c\best_bbox_mAP_epoch_149.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_c
onv_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_bia
s, 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_con
v_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_c
onv_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_co
nv2_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, em
a_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, e
ma_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_stag
e2_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_
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n_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_trac
ked, 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_bac
kbone_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_tr
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Registry：{}
Registry：{'img_scale': (640, 640), 'flip': False, 'transforms': [{'type': 'Resize', 'keep_ratio': True}, {'type': 'RandomFlip'}, {'type': 'Pad', 'pad_to_square': True, 'pad_val': {'img': (114.0, 114.0, 114.0)}}, {'type': 'DefaultFormatBundle'}, {'type': 'Collect', 'keys': ['img']}]}
Registry：{'keep_ratio': True}
Registry：{}
Registry：{'pad_to_square': True, 'pad_val': {'img': (114.0, 114.0, 114.0)}}
Registry：{}
Registry：{'keys': ['img']}
2022-07-21 14:54:02,764 - mmdeploy - WARNING - DeprecationWarning: get_onnx_config will be deprecated in the future.
2022-07-21 14:54:02,764 - mmdeploy - INFO - Export PyTorch model to ONNX: /static/work_dirs/bcccd9e0-41a1-408d-9dfa-f4e634e9608c\end2end.onnx.
2022-07-21 14:54:03,983 - mmdeploy - WARNING - Can not find torch._C._jit_pass_onnx_deduplicate_initializers, function rewrite will not be applied
d:\mmdeploy\mmdeploy\core\optimizers\function_marker.py:158: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
ys_shape = tuple(int(s) for s in ys.shape)
D:\Anaconda3\envs\aoc\lib\site-packages\torch\functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\TensorShape.cpp:2228.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
d:\mmdeploy\mmdeploy\codebase\mmdet\core\post_processing\bbox_nms.py:259: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
dets, labels = TRTBatchedNMSop.apply(boxes, scores, int(scores.shape[-1]),
d:\mmdeploy\mmdeploy\mmcv\ops\nms.py:178: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
out_boxes = min(num_boxes, after_topk)
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
2022-07-21 14:54:20,884 - mmdeploy - INFO - Execute onnx optimize passes.
2022-07-21 14:54:20,884 - mmdeploy - WARNING - Can not optimize model, please build torchscipt extension.
More details: https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/experimental/onnx_optimizer.md
2022-07-21 14:54:25,433 - mmdeploy - INFO - Start pipeline mmdeploy.backend.tensorrt.onnx2tensorrt.onnx2tensorrt in subprocess
2022-07-21 14:54:25,609 - mmdeploy - WARNING - Could not load the library of tensorrt plugins. Because the file does not exist:
[07/21/2022-14:54:25] [TRT] [I] [MemUsageChange] Init CUDA: CPU +221, GPU +0, now: CPU 13566, GPU 990 (MiB)
[07/21/2022-14:54:27] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +4, GPU +2, now: CPU 13757, GPU 992 (MiB)
[07/21/2022-14:54:27] [TRT] [W] onnx2trt_utils.cpp:369: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[07/21/2022-14:54:27] [TRT] [W] onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
[07/21/2022-14:54:28] [TRT] [W] onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
[07/21/2022-14:54:28] [TRT] [W] onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
[07/21/2022-14:54:28] [TRT] [I] No importer registered for op: TRTBatchedNMS. Attempting to import as plugin.
[07/21/2022-14:54:28] [TRT] [I] Searching for plugin: TRTBatchedNMS, plugin_version: 1, plugin_namespace:
Process Process-3:
Traceback (most recent call last):
File "D:\Anaconda3\envs\aoc\lib\multiprocessing\process.py", line 315, in _bootstrap
self.run()
File "D:\Anaconda3\envs\aoc\lib\multiprocessing\process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "d:\mmdeploy\mmdeploy\apis\core\pipeline_manager.py", line 107, in __call__
ret = func(*args, **kwargs)
File "d:\mmdeploy\mmdeploy\backend\tensorrt\onnx2tensorrt.py", line 79, in onnx2tensorrt
from_onnx(
File "d:\mmdeploy\mmdeploy\backend\tensorrt\utils.py", line 113, in from_onnx
raise RuntimeError(f'Failed to parse onnx, {error_msgs}')
RuntimeError: Failed to parse onnx, In node 716 (importFallbackPluginImporter): UNSUPPORTED_NODE: Assertion failed: creator && "Plugin not found, are the plugin name, version, and namespace correct?"Please use
--dump-infowhen runningtools/deploy.py. And you are using ONNXRuntime backend now? As the bug fixed already, you may try TensorRT.
AllentDan
commented
2 years ago You need build MMDeploy from source. There is also another quick methods if you want to use prebuilt MMDeploy package.
Change the function mmdet.core.anchor.MlvlPointGenerator.single_level_grid_priors of MMDet. Change the line from:
stride_w = shift_xx.new_full((shift_xx.shape[0], ),
stride_w).to(dtype)
stride_h = shift_xx.new_full((shift_yy.shape[0], ),
stride_h).to(dtype)to
stride_w = shift_xx.new_full((feat_h*feat_w, ),
stride_w).to(dtype)
stride_h = shift_xx.new_full((feat_h*feat_w, ),
stride_h).to(dtype)I just compiled MMDeploy from the source code of the master branch
AllentDan
commented
2 years ago As the warning of your log says Could not load the library of tensorrt plugins. Because the file does not exist:. Make sure there are libs inside build/bin
gzxy-0102
commented
2 years ago I will check the compile options and log files Confirm correct installation
AllentDan
commented
2 years ago Cool
gzxy-0102
commented
2 years ago when i recompile mmdeploy i got an error
it says can't find loader.cpp.in but loader.cpp.in file exists in cmake directory
(aoc) PS D:\mmdeploy\build> cmake .. -G "Visual Studio 16 2019" -A x64 -T v142 -DTENSORRT_DIR="D:\TensorRT\8.4.1.5" -DCUDNN_DIR="D:\CUDAToolkit\lib\x64" -DMMDEPLOY_BUILD_SDK="ON" -DMMDEPLOY_TARGET_DEVICES="cpu,cuda" -DMMDEPLOY_TARGET_BACKENDS="ort,trt" -DMMDEPLOY_CODEBASES="all" -DMMDEPLOY_BUILD_SDK_PYTHON_API="ON"
-- CMAKE_INSTALL_PREFIX: D:/mmdeploy/build/install
-- The C compiler identification is MSVC 19.29.30145.0
-- The CXX compiler identification is MSVC 19.29.30145.0
-- Check for working C compiler: D:/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.29.30133/bin/Hostx64/x64/cl.exe
-- Check for working C compiler: D:/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.29.30133/bin/Hostx64/x64/cl.exe -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: D:/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.29.30133/bin/Hostx64/x64/cl.exe
-- Check for working CXX compiler: D:/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.29.30133/bin/Hostx64/x64/cl.exe -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- OpenCV ARCH: x64
-- OpenCV RUNTIME: vc15
-- OpenCV STATIC: OFF
-- Found OpenCV: D:/opencv/build (found version "4.6.0")
-- Found OpenCV 4.6.0 in D:/opencv/build/x64/vc15/lib
-- You might need to add D:\opencv\build\x64\vc15\bin to your PATH to be able to run your applications.
-- Build spdlog: 1.10.0
-- Looking for pthread.h
-- Looking for pthread.h - not found
-- Found Threads: TRUE
-- Build type: Release
-- build codebase: mmcls
-- build codebase: mmdet
-- build codebase: mmseg
-- build codebase: mmocr
-- build codebase: mmedit
-- build codebase: mmpose
-- build codebase: mmrotate
-- pybind11 v2.9.0 dev1
-- Found PythonInterp: D:/Anaconda3/envs/aoc/python.exe (found version "3.9")
-- Found PythonLibs: D:/Anaconda3/envs/aoc/libs/python39.lib
-- Performing Test HAS_MSVC_GL_LTCG
-- Performing Test HAS_MSVC_GL_LTCG - Success
CMake Error: File /loader.cpp.in does not exist.
CMake Error at cmake/MMDeploy.cmake:155 (configure_file):
configure_file Problem configuring file
Call Stack (most recent call first):
csrc/mmdeploy/apis/python/CMakeLists.txt:25 (mmdeploy_load_dynamic)
-- Configuring incomplete, errors occurred!
See also "D:/mmdeploy/build/CMakeFiles/CMakeOutput.log".
See also "D:/mmdeploy/build/CMakeFiles/CMakeError.log".@irexyc Hi, could you kindly give some help?
irexyc
commented
2 years ago The problem is due to these line: https://github.com/open-mmlab/mmdeploy/blob/master/cmake/MMDeploy.cmake#L153-L157
What is your cmake version? It seems like your cmake doesn't detect the right ${CMAKE_CURRENT_FUNCTION_LIST_DIR}
gzxy-0102
commented
2 years ago The problem is due to these line: https://github.com/open-mmlab/mmdeploy/blob/master/cmake/MMDeploy.cmake#L153-L157
What is your cmake version? It seems like your cmake doesn't detect the right ${CMAKE_CURRENT_FUNCTION_LIST_DIR}
oh~ my bad I looked at the cmake file. my cmake version does not match. thank you
irexyc
commented
2 years ago And the var in list should seperate by ;
-DMMDEPLOY_TARGET_DEVICES="cpu;cuda"
-DMMDEPLOY_TARGET_BACKENDS="ort;trt"@AllentDan Hi. I was rebuild, it still gives the same error
2022-07-22 14:11:15,848 - mmdeploy - INFO - 当前任务ID：f65b4ca8-11ae-4714-bf1b-5cf3c35ec715
load checkpoint from local path: D:\static\work_dirs\f65b4ca8-11ae-4714-bf1b-5cf3c35ec715\best_bbox_mAP_epoch_42.pth
The model and loaded state dict do not match exactly
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convs_0_1_bn_weight, ema_bbox_head_multi_level_cls_convs_0_1_bn_bias, ema_bbox_head_multi_level_cls_convs_0_1_bn_running_mean, ema_bbox_head_multi_level_cls_convs_0_1_bn_running_var, ema_bbox_head_multi_level_cls_convs_0_1_bn_num_batches_tracked, ema_bbox_head_multi_level_cls_convs_1_0_conv_weight, ema_bbox_head_multi_level_cls_convs_1_0_bn_weight, ema_bbox_head_multi_level_cls_convs_1_0_bn_bias, ema_bbox_head_multi_le
vel_cls_convs_1_0_bn_running_mean, ema_bbox_head_multi_level_cls_convs_1_0_bn_running_var, ema_bbox_head_multi_level_cls_convs_1_0_bn_num_batches_tracked, ema_bbox_head_multi_level_cls_convs_1_1_conv_weight, ema_bbox_head_multi_level_cls_convs_1_1_bn_weight, ema_bbox_head_multi_level_cls_convs_1_1_bn_bias, ema_bbox_head_multi_level_cls_convs_1_1_bn_running_mean, ema_bbox_head_multi_level_cls_convs_1_1_bn_running_var, e
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_bn_num_batches_tracked, ema_bbox_head_multi_level_reg_convs_1_1_conv_weight, ema_bbox_head_multi_level_reg_convs_1_1_bn_weight, ema_bbox_head_multi_level_reg_convs_1_1_bn_bias, ema_bbox_head_multi_level_reg_convs_1_1_bn_running_mean, ema_bbox_head_multi_level_reg_convs_1_1_bn_running_var, ema_bbox_head_multi_level_reg_convs_1_1_bn_num_batches_tracked, ema_bbox_head_multi_level_reg_convs_2_0_conv_weight, ema_bbox_head_
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ox_head_multi_level_reg_convs_2_1_bn_running_mean, ema_bbox_head_multi_level_reg_convs_2_1_bn_running_var, ema_bbox_head_multi_level_reg_convs_2_1_bn_num_batches_tracked, ema_bbox_head_multi_level_conv_cls_0_weight, ema_bbox_head_multi_level_conv_cls_0_bias, ema_bbox_head_multi_level_conv_cls_1_weight, ema_bbox_head_multi_level_conv_cls_1_bias, ema_bbox_head_multi_level_conv_cls_2_weight, ema_bbox_head_multi_level_conv
_cls_2_bias, ema_bbox_head_multi_level_conv_reg_0_weight, ema_bbox_head_multi_level_conv_reg_0_bias, ema_bbox_head_multi_level_conv_reg_1_weight, ema_bbox_head_multi_level_conv_reg_1_bias, ema_bbox_head_multi_level_conv_reg_2_weight, ema_bbox_head_multi_level_conv_reg_2_bias, ema_bbox_head_multi_level_conv_obj_0_weight, ema_bbox_head_multi_level_conv_obj_0_bias, ema_bbox_head_multi_level_conv_obj_1_weight, ema_bbox_head_multi_level_conv_obj_1_bias, ema_bbox_head_multi_level_conv_obj_2_weight, ema_bbox_head_multi_level_conv_obj_2_bias
2022-07-22 14:11:22,772 - mmdeploy - WARNING - DeprecationWarning: get_onnx_config will be deprecated in the future.
2022-07-22 14:11:22,772 - mmdeploy - INFO - Export PyTorch model to ONNX: /static/work_dirs/f65b4ca8-11ae-4714-bf1b-5cf3c35ec715\end2end.onnx.
2022-07-22 14:11:22,871 - mmdeploy - WARNING - Can not find torch._C._jit_pass_onnx_deduplicate_initializers, function rewrite will not be applied
d:\mmdeploy\mmdeploy\core\optimizers\function_marker.py:158: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
ys_shape = tuple(int(s) for s in ys.shape)
D:\Anaconda3\envs\aoc\lib\site-packages\torch\functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\TensorShape.cpp:2228.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
d:\mmdeploy\mmdeploy\codebase\mmdet\core\post_processing\bbox_nms.py:259: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
dets, labels = TRTBatchedNMSop.apply(boxes, scores, int(scores.shape[-1]),
d:\mmdeploy\mmdeploy\mmcv\ops\nms.py:178: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
out_boxes = min(num_boxes, after_topk)
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
WARNING: The shape inference of mmdeploy::TRTBatchedNMS type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.
2022-07-22 14:11:38,751 - mmdeploy - INFO - Execute onnx optimize passes.
2022-07-22 14:11:38,751 - mmdeploy - WARNING - Can not optimize model, please build torchscipt extension.
More details: https://github.com/open-mmlab/mmdeploy/blob/master/docs/en/experimental/onnx_optimizer.md
2022-07-22 14:11:43,154 - mmdeploy - INFO - Start pipeline mmdeploy.backend.tensorrt.onnx2tensorrt.onnx2tensorrt in subprocess
2022-07-22 14:11:43,316 - mmdeploy - WARNING - Could not load the library of tensorrt plugins. Because the file does not exist:
[07/22/2022-14:11:43] [TRT] [I] [MemUsageChange] Init CUDA: CPU +221, GPU +0, now: CPU 11069, GPU 990 (MiB)
[07/22/2022-14:11:44] [TRT] [I] [MemUsageChange] Init builder kernel library: CPU +6, GPU +2, now: CPU 11256, GPU 992 (MiB)
[07/22/2022-14:11:44] [TRT] [W] onnx2trt_utils.cpp:369: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[07/22/2022-14:11:45] [TRT] [W] onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
[07/22/2022-14:11:45] [TRT] [W] onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
[07/22/2022-14:11:45] [TRT] [W] onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped
[07/22/2022-14:11:45] [TRT] [I] No importer registered for op: TRTBatchedNMS. Attempting to import as plugin.
[07/22/2022-14:11:45] [TRT] [I] Searching for plugin: TRTBatchedNMS, plugin_version: 1, plugin_namespace:
Process Process-3:
Traceback (most recent call last):
File "D:\Anaconda3\envs\aoc\lib\multiprocessing\process.py", line 315, in _bootstrap
self.run()
File "D:\Anaconda3\envs\aoc\lib\multiprocessing\process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "d:\mmdeploy\mmdeploy\apis\core\pipeline_manager.py", line 107, in __call__
ret = func(*args, **kwargs)
File "d:\mmdeploy\mmdeploy\backend\tensorrt\onnx2tensorrt.py", line 79, in onnx2tensorrt
from_onnx(
File "d:\mmdeploy\mmdeploy\backend\tensorrt\utils.py", line 113, in from_onnx
raise RuntimeError(f'Failed to parse onnx, {error_msgs}')
RuntimeError: Failed to parse onnx, In node 716 (importFallbackPluginImporter): UNSUPPORTED_NODE: Assertion failed: creator && "Plugin not found, are the plugin name, version, and namespace correct?"
2022-07-22 14:11:45,731 - mmdeploy - ERROR - `mmdeploy.backend.tensorrt.onnx2tensorrt.onnx2tensorrt` with Call id: 1 failed. exit.
[2022-07-22 14:11:45,731] ERROR:huey.consumer:Worker-1:Process Worker-1 died!
Traceback (most recent call last):
File "D:\Anaconda3\envs\aoc\lib\site-packages\huey\consumer.py", line 356, in _run
process.loop()
File "D:\Anaconda3\envs\aoc\lib\site-packages\huey\consumer.py", line 117, in loop
self.huey.execute(task, now)
File "D:\Anaconda3\envs\aoc\lib\site-packages\huey\api.py", line 362, in execute
return self._execute(task, timestamp)
File "D:\Anaconda3\envs\aoc\lib\site-packages\huey\api.py", line 379, in _execute
task_value = task.execute()
File "D:\Anaconda3\envs\aoc\lib\site-packages\huey\api.py", line 772, in execute
return func(*args, **kwargs)
File "D:\workspace\python\ai-online-core\publish.py", line 155, in deploy
onnx2tensorrt(
File "d:\mmdeploy\mmdeploy\apis\core\pipeline_manager.py", line 356, in _wrap
return self.call_function(func_name_, *args, **kwargs)
File "d:\mmdeploy\mmdeploy\apis\core\pipeline_manager.py", line 324, in call_function
return self.get_result_sync(call_id)
File "d:\mmdeploy\mmdeploy\apis\core\pipeline_manager.py", line 305, in get_result_sync
ret = self.get_caller(func_name).pop_mp_output(call_id)
File "d:\mmdeploy\mmdeploy\apis\core\pipeline_manager.py", line 82, in pop_mp_output
exit(1)
File "D:\Anaconda3\envs\aoc\lib\_sitebuiltins.py", line 26, in __call__
raise SystemExit(code)
SystemExit: 1there is the new env check info
2022-07-22 14:12:16,238 - mmdeploy - INFO -
2022-07-22 14:12:16,238 - mmdeploy - INFO - **********Environmental information**********
2022-07-22 14:12:19,981 - mmdeploy - INFO - sys.platform: win32
2022-07-22 14:12:19,981 - mmdeploy - INFO - Python: 3.9.0 (default, Nov 15 2020, 08:30:55) [MSC v.1916 64 bit (AMD64)]
2022-07-22 14:12:19,981 - mmdeploy - INFO - CUDA available: True
2022-07-22 14:12:19,981 - mmdeploy - INFO - GPU 0: NVIDIA GeForce GTX 1060 6GB
2022-07-22 14:12:19,981 - mmdeploy - INFO - CUDA_HOME: D:\CUDAToolkit
2022-07-22 14:12:19,981 - mmdeploy - INFO - NVCC: Cuda compilation tools, release 11.3, V11.3.109
2022-07-22 14:12:19,982 - mmdeploy - INFO - MSVC: 用于 x64 的 Microsoft (R) C/C++ 优化编译器 19.29.30145 版
2022-07-22 14:12:19,982 - mmdeploy - INFO - GCC: n/a
2022-07-22 14:12:19,982 - mmdeploy - INFO - PyTorch: 1.11.0+cu113
2022-07-22 14:12:19,982 - mmdeploy - INFO - PyTorch compiling details: PyTorch built with:
- C++ Version: 199711
- MSVC 192829337
- Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)
- OpenMP 2019
- LAPACK is enabled (usually provided by MKL)
- CPU capability usage: AVX2
- CUDA Runtime 11.3
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-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;-gencode;arch=compute_37,code=compute_37
- CuDNN 8.2
- Magma 2.5.4
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=C:/actions-runner/_work/pytorch/pytorch/builder/windows/tmp_bin/sccache-cl.exe, CXX_FLAGS=/DWIN32 /D_WINDOWS /GR /EHsc /w /bigobj -DUSE_PTHREADPOOL -openmp:experimental -IC:/actions-runner/_work/pytorch/pytorch/builder/windows/mkl/include -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DUSE_FBGEMM -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=OFF, USE_OPENMP=ON, USE_ROCM=OFF,
2022-07-22 14:12:19,983 - mmdeploy - INFO - TorchVision: 0.12.0+cu113
2022-07-22 14:12:19,983 - mmdeploy - INFO - OpenCV: 4.6.0
2022-07-22 14:12:19,983 - mmdeploy - INFO - MMCV: 1.6.0
2022-07-22 14:12:19,983 - mmdeploy - INFO - MMCV Compiler: MSVC 192930140
2022-07-22 14:12:19,984 - mmdeploy - INFO - MMCV CUDA Compiler: 11.3
2022-07-22 14:12:19,984 - mmdeploy - INFO - MMDeploy: 0.6.0+1d6437c
2022-07-22 14:12:19,984 - mmdeploy - INFO -
2022-07-22 14:12:19,984 - mmdeploy - INFO - **********Backend information**********
2022-07-22 14:12:20,589 - mmdeploy - INFO - onnxruntime: 1.11.1 ops_is_avaliable : False
2022-07-22 14:12:20,627 - mmdeploy - INFO - tensorrt: 8.4.1.5 ops_is_avaliable : False
2022-07-22 14:12:20,696 - mmdeploy - INFO - ncnn: None ops_is_avaliable : False
2022-07-22 14:12:20,697 - mmdeploy - INFO - pplnn_is_avaliable: False
2022-07-22 14:12:20,699 - mmdeploy - INFO - openvino_is_avaliable: False
2022-07-22 14:12:20,699 - mmdeploy - INFO -
2022-07-22 14:12:20,699 - mmdeploy - INFO - **********Codebase information**********
2022-07-22 14:12:20,702 - mmdeploy - INFO - mmdet: 2.25.0
2022-07-22 14:12:20,702 - mmdeploy - INFO - mmseg: None
2022-07-22 14:12:20,702 - mmdeploy - INFO - mmcls: None
2022-07-22 14:12:20,702 - mmdeploy - INFO - mmocr: None
2022-07-22 14:12:20,702 - mmdeploy - INFO - mmedit: None
2022-07-22 14:12:20,703 - mmdeploy - INFO - mmdet3d: None
2022-07-22 14:12:20,703 - mmdeploy - INFO - mmpose: None
2022-07-22 14:12:20,703 - mmdeploy - INFO - mmrotate: Noneis it the reason why pplcv is not installed? I just saw that pplcv needs to be installed
AllentDan
commented
2 years ago Please check if file exists: build/bin/*/mmdeploy_tensorrt_ops.dll or mmdeploy/lib/mmdeploy_tensorrt_ops.dll
gzxy-0102
commented
2 years ago no mmdeploy_tensorrt_ops.dll in the directory my build command is
cmake .. -G "Visual Studio 16 2019" -A x64 -T v142 -DTENSORRT_DIR="D:\TensorRT\8.4.1.5" -DCUDNN_DIR="D:\CUDAToolkit\lib\x64" -DMMDEPLOY_BUILD_SDK="ON" -DMMDEPLOY_TARGET_DEVICES="cpu,cuda" -DMMDEPLOY_TARGET_BACKENDS="ort,trt" -DMMDEPLOY_CODEBASES="all" -DMMDEPLOY_BUILD_SDK_PYTHON_API="ON"Please use cmake --build . --config Release to install the xxx_ops.dll to {ROOT}/mmdeploy/lib/ dir.
And you should seperate var by comma like "cpu;cuda"
gzxy-0102
commented
2 years ago the CUDNN_DIR option can i use cudatoolkit install directory，the cudatoolkit has cudnn in the directory
gzxy-0102
commented
2 years ago it not works @irexyc the CUDA_PATH is my cuda and cudnn install directory
(aoc) PS D:\mmdeploy\build> cmake .. -G "Visual Studio 16 2019" -A x64 -T v142 -DTENSORRT_DIR="D:\TensorRT\8.4.1.5" -DCUDNN_DIR="$env:CUDA_PATH" -DMMDEPLOY_BUILD_SDK="ON" -DMMDEPLOY_TARGET_DEVICES="cuda" -DMMDEPLOY_TARGET_BACKENDS="trt" -DMMDEPLOY_CODEBASES="all" -DMMDEPLOY_BUILD_SDK_PYTHON_API="ON" -DMMDEPLOY_BUILD_SDK="ON"
-- CMAKE_INSTALL_PREFIX: D:/mmdeploy/build/install
-- The C compiler identification is MSVC 19.29.30145.0
-- The CXX compiler identification is MSVC 19.29.30145.0
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: D:/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.29.30133/bin/Hostx64/x64/cl.exe - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: D:/Microsoft Visual Studio/2019/Community/VC/Tools/MSVC/14.29.30133/bin/Hostx64/x64/cl.exe - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found CUDA: D:/CUDAToolkit (found version "11.3")
CMake Error at D:/CMake/share/cmake-3.23/Modules/CMakeDetermineCompilerId.cmake:491 (message):
No CUDA toolset found.
Call Stack (most recent call first):
D:/CMake/share/cmake-3.23/Modules/CMakeDetermineCompilerId.cmake:6 (CMAKE_DETERMINE_COMPILER_ID_BUILD)
D:/CMake/share/cmake-3.23/Modules/CMakeDetermineCompilerId.cmake:59 (__determine_compiler_id_test)
D:/CMake/share/cmake-3.23/Modules/CMakeDetermineCUDACompiler.cmake:339 (CMAKE_DETERMINE_COMPILER_ID)
cmake/cuda.cmake:35 (enable_language)
CMakeLists.txt:73 (include)
-- Configuring incomplete, errors occurred!
See also "D:/mmdeploy/build/CMakeFiles/CMakeOutput.log".I probably know the reason. Maybe I didn't choose vs when installing CUDA, which led to enable_ The language method cannot find the compiled file of CUDA
Because c++ doesn't know much, it's troublesome for you
irexyc
commented
2 years ago Q: the CUDNN_DIR option can i use cudatoolkit install directory，the cudatoolkit has cudnn in the directory A: The cmake script will check cudnn header and library, it will print errors if it can't search these files from the provided path.
Q: The enable language cuda error:
A: You should copy the four files from C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.3\extras\visual_studio_integration\MSBuildExtensions to C:\Software\Microsoft Visual Studio\2022\Community\Msbuild\Microsoft\VC\v160\BuildCustomizations\
The path may different on your PC. If there are multi version like v160, v150, it's better to copy to both.
Another link you can refer to
gzxy-0102
commented
2 years ago Q: the CUDNN_DIR option can i use cudatoolkit install directory，the cudatoolkit has cudnn in the directory A: The cmake script will check cudnn header and library, it will print errors if it can't search these files from the provided path.
Q: The enable language cuda error: A: You should copy the four files from
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.3\extras\visual_studio_integration\MSBuildExtensionstoC:\Software\Microsoft Visual Studio\2022\Community\Msbuild\Microsoft\VC\v160\BuildCustomizations\The path may different on your PC. If there are multi version like v160, v150, it's better to copy to both.
Another link you can refer to
I have solved this problem by reinstalling CUDA
but how to use the compiled SDK? now i add the directory of pyd file and others dll file to the environment variable to import, it always shows that the model is loaded successfully, but it does not continue to execute the detector i saw that many dll failed to load, but I put the dll that failed to load into the environment variable
try:
sys.path.append("SDK_PATH")
os.environ["PATH"] = "SDK_PATH;" + os.environ["PATH"]
from mmdeploy_sdk.mmdeploy_python import Detector
except ImportError as e:
print("SDK Import Error")
image = cv2.imread(param.img)
task = await MMDetTrain.get(id=param.task_id)
detector = Detector(task.work_dir, "cuda", 0)
bboxes, labels, _ = detector([image])[0]
print("----------------------------------------")
print(bboxes)loading mmdeploy_execution ...
loading mmdeploy_cpu_device ...
loading mmdeploy_cuda_device ...
loading mmdeploy_graph ...
loading mmdeploy_directory_model ...
[2022-07-25 14:25:44.588] [mmdeploy] [info] [model.cpp:95] Register 'DirectoryModel'
loading mmdeploy_transform ...
loading mmdeploy_cpu_transform_impl ...
failed to load library mmdeploy_cpu_transform_impl
loading mmdeploy_cuda_transform_impl ...
loading mmdeploy_transform_module ...
loading mmdeploy_trt_net ...
failed to load library mmdeploy_trt_net
loading mmdeploy_net_module ...
loading mmdeploy_mmcls ...
loading mmdeploy_mmdet ...
failed to load library mmdeploy_mmdet
loading mmdeploy_mmseg ...
failed to load library mmdeploy_mmseg
loading mmdeploy_mmocr ...
failed to load library mmdeploy_mmocr
loading mmdeploy_mmedit ...
failed to load library mmdeploy_mmedit
loading mmdeploy_mmpose ...
failed to load library mmdeploy_mmpose
loading mmdeploy_mmrotate ...
INFO: Started server process [15388]
INFO: Waiting for application startup.
INFO: Application startup complete.
[2022-07-25 14:25:55.715] [mmdeploy] [info] [model.cpp:38] DirectoryModel successfully load model D:\static\work_dirs\f65b4ca8-11ae-4714-bf1b-5cf3c35ec715
INFO: Stopping reloader process [18552]I don't konw If you set the path, but you can build the sdk with -DMMDEPLOY_SHARED_LIBS=OFF to avoid the load process.
gzxy-0102
commented
2 years ago I don't konw If you set the path, but you can build the sdk with to avoid the load process.
-DMMDEPLOY_SHARED_LIBS=OFF
i found the problem. there is an opencv_ word460.dll not found, now it's good
Describe the bug 使用代码调用模型转换，将pytorch模型转换为tensorRT模型时执行失败 Using code to call model conversion, the execution fails when converting a pytorch model to a tensorRT model
运行流程为fastapi接收到模型转换请求 下发到huey队列 huey队列代码与deploy.py代码基本一致 The running process is that fastapi receives the model conversion request and sends it to the huey queue. The huey queue code is basically the same as the deploy.py code.
当我放弃tensorRT转为onnx后 可以正常转换 但是在实例化Detector后 接口层被阻塞在那里 并没有往下执行 When I gave up tensorRT and switched to onnx, I could convert normally, but after instantiating the Detector, the interface layer was blocked there and did not go down.
也没有错误信息 除了几个config的info日志 并没有任何其他的反馈 There is no error message, except for a few config info logs and no other feedback
Reproduction
Environment
Error traceback 这是pytorch转tensorRT模型的日志 This is the log of pytorch to tensorRT model