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Model conversion error

See original GitHub issue

root@84a3a2a7c8f3:/app# python3 prepare_models.py 
mxnet version: 1.6.0
onnx version: 1.7.0
[12:45:13] INFO - Preparing 'arcface_r100_v1' model...
Downloading model: arcface_r100_v1...
Model file is not found. Downloading.
Downloading /models/mxnet/arcface_r100_v1.zip from http://insightface.ai/files/models/arcface_r100_v1.zip...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 237710/237710 [03:47<00:00, 1046.77KB/s]
[12:49:02] INFO - Creating intermediate copy of source model...
[12:49:03] INFO - Applying RetinaFace specific fixes to input MXNet model before conversion...
[12:49:03] INFO - Exporting to ONNX...
[12:49:03] INFO - Converting json and weight file to sym and params
[12:49:03] src/nnvm/legacy_json_util.cc:209: Loading symbol saved by previous version v1.0.0. Attempting to upgrade...
[12:49:03] src/nnvm/legacy_json_util.cc:217: Symbol successfully upgraded!
[12:49:11] INFO - Input shape of the model [(1, 3, 112, 112)] 
[12:49:11] INFO - Exported ONNX file /models/onnx/arcface_r100_v1/arcface_r100_v1.onnx saved to disk
[12:49:11] INFO - Applying ArcFace specific fixes to output ONNX
[12:49:14] INFO - Removing initializer from inputs in ONNX model...
[12:49:14] INFO - Removing intermediate *.symbol and *.params
[12:49:14] INFO - Building TRT engine for arcface_r100_v1...
Traceback (most recent call last):
  File "prepare_models.py", line 45, in <module>
    prepare_models()
  File "prepare_models.py", line 40, in prepare_models
    prepare_backend(model_name=model, backend_name=backend_name, im_size=max_size, force_fp16=force_fp16,
  File "/app/modules/model_zoo/getter.py", line 127, in prepare_backend
    convert_onnx(temp_onnx_model,
TypeError: 'NoneType' object is not callable

Issue Analytics

State:
Created 2 years ago
Comments:6 (4 by maintainers)

Top GitHub Comments

1reaction

SthPhoenixcommented, Apr 28, 2021

Workspace could be increased. Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. might be fixed by converting mxnet weights to fp32 during ONNX conversion, but I believe it wold have same results as TRT automatic cast down.

No implementation obeys reformatting-free rules, at least 2 reformatting nodes are needed, now picking the fastest path instead. This warning is specific to fp16 inference, and I have to admit I have no idea what does it mean yet. I believe if you see some quality drop during detection it is mostly caused by fp16 inference, though I haven’t noticed any significant changes, except for bboxes and landmarks sometimes being different for about 1-2 pixels compared to fp32 Inference. If you have any examples to share, that would be great for debugging.

0reactions

SthPhoenixcommented, Jun 1, 2021

Closing for now as resolved, since above warnings doesn’t affect usability.

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