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Some error in export to onnx #49
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👋 Hello @Halafz7, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution. If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you. If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available. For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at [email protected]. RequirementsPython>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started: $ git clone https://github.com/ultralytics/yolov5
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StatusIf this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit. |
Hi @Halafz7, |
Sure, i ran it on Colab and saved the weights on my Gdrive ! python export.py --data coco128.yaml --weights /content/gdrive/MyDrive/models/CheckpointsYOLO/YOLOv5/yolov5medium/weights/last.pt --nodetect |
As the error states, you are trying to load weights from 80 classes into a model with only one class. However, your export arguments seem correct. I think that attempt_load could not found your model.yaml. Can you replace last.pt with best.pt in your arguments? |
Won't the classes switch automatically during training? I did replaced the weight with best.pt but still got the same error |
So I tried to train yolov5m-quant.yaml with coco128.yaml. When it's done, i want to export it but error come like this
Traceback (most recent call last):
File "/content/quantized-yolov5/models/experimental.py", line 97, in attempt_load
ema = ckpt['ema' if ckpt.get('ema') else 'model'].float()
KeyError: 'model'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/content/quantized-yolov5/export.py", line 418, in
main(opt)
File "/content/quantized-yolov5/export.py", line 406, in main
run(**vars(opt))
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/content/quantized-yolov5/export.py", line 317, in run
model = attempt_load(weights, map_location=device, inplace=True, fuse=True) # load FP32 model
File "/content/quantized-yolov5/models/experimental.py", line 105, in attempt_load
ema.load_state_dict(ckpt)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 2152, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for Model:
Missing key(s) in state_dict: "model.13.m.0.quant_identity.act_quant.fused_activation_quant_proxy.tensor_quant.scaling_impl.value", "model.13.m.1.quant_identity.act_quant.fused_activation_quant_proxy.tensor_quant.scaling_impl.value", "model.17.m.0.quant_identity.act_quant.fused_activation_quant_proxy.tensor_quant.scaling_impl.value", "model.17.m.1.quant_identity.act_quant.fused_activation_quant_proxy.tensor_quant.scaling_impl.value", "model.20.m.0.quant_identity.act_quant.fused_activation_quant_proxy.tensor_quant.scaling_impl.value", "model.20.m.1.quant_identity.act_quant.fused_activation_quant_proxy.tensor_quant.scaling_impl.value", "model.23.m.0.quant_identity.act_quant.fused_activation_quant_proxy.tensor_quant.scaling_impl.value", "model.23.m.1.quant_identity.act_quant.fused_activation_quant_proxy.tensor_quant.scaling_impl.value".
size mismatch for model.24.m.0.weight: copying a param with shape torch.Size([255, 192, 1, 1]) from checkpoint, the shape in current model is torch.Size([18, 192, 1, 1]).
size mismatch for model.24.m.1.weight: copying a param with shape torch.Size([255, 384, 1, 1]) from checkpoint, the shape in current model is torch.Size([18, 384, 1, 1]).
size mismatch for model.24.m.2.weight: copying a param with shape torch.Size([255, 768, 1, 1]) from checkpoint, the shape in current model is torch.Size([18, 768, 1, 1]).
Do you have some advice or suggestion about this error? Thankyou
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