About converting .pt to .onnx

[leeyunhome]

❔Question

Hello,

Following the guide, I tried to convert the .pt file to .onnx with the following command,
but I got the following error. Can I see what's wrong?
And can I ignore the warning in the middle?

---

python3 models/export.py --weights weights/last.pt --img 640 --batch 1
Namespace(batch_size=1, img_size=[640, 640], weights='weights/last.pt')
/home/hongildong/.local/lib/python3.6/site-packages/torch/serialization.py:658: SourceChangeWarning: source code of class 'models.yolo.Model' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
/home/hongildong/.local/lib/python3.6/site-packages/torch/serialization.py:658: SourceChangeWarning: source code of class 'torch.nn.modules.container.Sequential' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
/home/hongildong/.local/lib/python3.6/site-packages/torch/serialization.py:658: SourceChangeWarning: source code of class 'models.common.Focus' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
/home/hongildong/.local/lib/python3.6/site-packages/torch/serialization.py:658: SourceChangeWarning: source code of class 'models.common.Conv' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
/home/hongildong/.local/lib/python3.6/site-packages/torch/serialization.py:658: SourceChangeWarning: source code of class 'torch.nn.modules.conv.Conv2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
/home/hongildong/.local/lib/python3.6/site-packages/torch/serialization.py:658: SourceChangeWarning: source code of class 'torch.nn.modules.batchnorm.BatchNorm2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
/home/hongildong/.local/lib/python3.6/site-packages/torch/serialization.py:658: SourceChangeWarning: source code of class 'torch.nn.modules.activation.LeakyReLU' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
/home/hongildong/.local/lib/python3.6/site-packages/torch/serialization.py:658: SourceChangeWarning: source code of class 'models.common.BottleneckCSP' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
/home/hongildong/.local/lib/python3.6/site-packages/torch/serialization.py:658: SourceChangeWarning: source code of class 'torch.nn.modules.container.ModuleList' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
/home/hongildong/.local/lib/python3.6/site-packages/torch/serialization.py:658: SourceChangeWarning: source code of class 'torch.nn.modules.pooling.MaxPool2d' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
/home/hongildong/.local/lib/python3.6/site-packages/torch/serialization.py:658: SourceChangeWarning: source code of class 'torch.nn.modules.upsampling.Upsample' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
/home/hongildong/.local/lib/python3.6/site-packages/torch/serialization.py:658: SourceChangeWarning: source code of class 'models.yolo.Detect' has changed. you can retrieve the original source code by accessing the object's source attribute or set torch.nn.Module.dump_patches = True and use the patch tool to revert the changes.
warnings.warn(msg, SourceChangeWarning)
Fusing layers...
Model Summary: 231 layers, 7521612 parameters, 0 gradients
Traceback (most recent call last):
File "models/export.py", line 51, in
y = model(img) # dry run
File "/home/hongildong/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "./models/yolo.py", line 121, in forward
return self.forward_once(x, profile) # single-scale inference, train
File "./models/yolo.py", line 137, in forward_once
x = m(x) # run
File "/home/hongildong/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "./models/yolo.py", line 48, in forward
x[i] = self.mi # conv
File "/home/hongildong/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 779, in getattr
type(self).name, name))
torch.nn.modules.module.ModuleAttributeError: 'Detect' object has no attribute 'm'
Thank you.

Additional context

[glenn-jocher]

@leeyunhome your error is not reproducible. Code exports to ONNX correctly. I've provided you our default response below.

Hello, thank you for your interest in our work! This issue seems to lack the minimum requirements for a proper response, or is insufficiently detailed for us to help you. Please note that most technical problems are due to:

• Your modified or out-of-date code. If your issue is not reproducible in a new git clone version of this repo we can not debug it. Before going further run this code and verify your issue persists:

$ git clone https://github.com/ultralytics/yolov5 yolov5_new  # clone latest
$ cd yolov5_new
$ python detect.py  # verify detection

# CODE TO REPRODUCE YOUR ISSUE HERE

• Your custom data. If your issue is not reproducible in one of our 3 common datasets (COCO, COCO128, or VOC) we can not debug it. Visit our Custom Training Tutorial for guidelines on training your custom data. Examine train_batch0.jpg and test_batch0.jpg for a sanity check of your labels and images.

• Your environment. If your issue is not reproducible in one of the verified environments below we can not debug it. If you are running YOLOv5 locally, verify your environment meets all of the requirements.txt dependencies specified below. If in doubt, download Python 3.8.0 from https://www.python.org/, create a new venv, and then install requirements.

If none of these apply to you, we suggest you close this issue and raise a new one using the Bug Report template, providing screenshots and minimum viable code to reproduce your issue. Thank you!

Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.6. To install run:

$ pip install -r requirements.txt

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab Notebook with free GPU:
• Kaggle Notebook with free GPU: https://www.kaggle.com/models/ultralytics/yolov5
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Docker Image https://hub.docker.com/r/ultralytics/yolov5. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are passing. These tests evaluate proper operation of basic YOLOv5 functionality, including training (train.py), testing (test.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu.

[leeyunhome]

@leeyunhome your error is not reproducible. Code exports to ONNX correctly. I've provided you our default response below.

Hello, thank you for your interest in our work! This issue seems to lack the minimum requirements for a proper response, or is insufficiently detailed for us to help you. Please note that most technical problems are due to:

• Your modified or out-of-date code. If your issue is not reproducible in a new git clone version of this repo we can not debug it. Before going further run this code and verify your issue persists:

$ git clone https://github.com/ultralytics/yolov5 yolov5_new  # clone latest
$ cd yolov5_new
$ python detect.py  # verify detection

# CODE TO REPRODUCE YOUR ISSUE HERE

• Your custom data. If your issue is not reproducible in one of our 3 common datasets (COCO, COCO128, or VOC) we can not debug it. Visit our Custom Training Tutorial for guidelines on training your custom data. Examine train_batch0.jpg and test_batch0.jpg for a sanity check of your labels and images.
• Your environment. If your issue is not reproducible in one of the verified environments below we can not debug it. If you are running YOLOv5 locally, verify your environment meets all of the requirements.txt dependencies specified below. If in doubt, download Python 3.8.0 from https://www.python.org/, create a new venv, and then install requirements.

If none of these apply to you, we suggest you close this issue and raise a new one using the Bug Report template, providing screenshots and minimum viable code to reproduce your issue. Thank you!

Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.6. To install run:

$ pip install -r requirements.txt

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab Notebook with free GPU:
• Kaggle Notebook with free GPU: https://www.kaggle.com/models/ultralytics/yolov5
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Docker Image https://hub.docker.com/r/ultralytics/yolov5. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are passing. These tests evaluate proper operation of basic YOLOv5 functionality, including training (train.py), testing (test.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu.

Hello,

I'm following below.

$ git clone https://github.com/ultralytics/yolov5 yolov5_new # clone latest
$ cd yolov5_new
$ python detect.py # verify detection

CODE TO REPRODUCE YOUR ISSUE HERE

=================================================================
hodu@hodu-desktop:~/coding/GitHub/yolov5_new$ python3 detect.py
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images', update=False, view_img=False, weights='yolov5s.pt')
Using torch 1.7.0 CUDA:0 (Xavier, 7763MB)

Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5s.pt to yolov5s.pt...
100%|███████████████████████████████████████████████████████████████████████████████| 14.5M/14.5M [00:07<00:00, 2.09MB/s]

Fusing layers...
Model Summary: 232 layers, 7459581 parameters, 0 gradients
image 1/2 /home/hodu/coding/GitHub/yolov5_new/data/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.336s)
image 2/2 /home/hodu/coding/GitHub/yolov5_new/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.116s)
Results saved to runs/detect/exp
Done. (46.625s)
hodu@hodu-desktop:~/coding/GitHub/yolov5_new$

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

[erolgerceker]

i got this error

from scipy.linalg import _fblas: ImportError: DLL load failed: The specified module could not be found

[glenn-jocher]

@erolgerceker you may want to raise this issue directly on the scipy repo.

[leeyunhome]

thank you.

[pderrenger]

@leeyunhome you're welcome! If you have any more questions, feel free to ask.