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Questions - onnx

Convert Darknet Weights and cfg file to ONNX or Pytorch weight files

I have some pretrained darknet weights and config files used with a Yolov3 model. I recently switched to Ultralytics to use Yolo v5. I don't really want to retrain, and a lot of the models relied on t...
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Viraj Vaitha

pytorch

object-detection

yolo

onnx

darknet

Votes: 0

Answers: 0

onnx.load() | ALBert throws DecodeError: Error parsing message

Goal: re-develop this BERT Notebook to use textattack/albert-base-v2-MRPC. Kernel: conda_pytorch_p36. PyTorch 1.8.1+cpu. I convert a PyTorch / HuggingFace Transformers model to ONNX and store it. Deco...
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DanielBell99

python

deep-learning

onnx

quantization

onnxruntime

Votes: 0

Answers: 1

Latest Answer

The problem was with updating the config variables for my new model. Changes: configs.output_dir = "albert-base-v2-MRPC" configs.model_name_or_path = "albert-base-v2-MRPC" I then ...
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DanielBell99

Converting PyTorch to ONNX model increases file size for ALBert

Goal: Use this Notebook to perform quantisation on albert-base-v2 model. Kernel: conda_pytorch_p36. Outputs in Sections 1.2 & 2.2 show that: converting vanilla BERT from PyTorch to ONNX stays th...
test-img

DanielBell99

python

pytorch

onnx

quantization

onnxruntime

Votes: 0

Answers: 1

Latest Answer

Explanation ALBert model has shared weights among layers. torch.onnx.export outputs the weights to different tensors, which causes the model size to grow larger. A number of Git Issues have been marke...
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DanielBell99

OpenCV getMemoryShapes error when executing a forward pass of the object detection model

I am using this pre-trained TensorFlow model. I convert it to an onnx file, then load it into OpenCV. I am pretty sure it has something to do with the channels not lining up properly. Here is the shap...
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Isaac Padberg

python

tensorflow

opencv

onnx

Votes: 0

Answers: 1

Latest Answer

You should check input shapes. I think your onnx model input shape is (None, 224, 224, 3), and you are putting (None, 3, 224, 224) (blob) into the model. Try this instead: img = cv2.imread(~) img = cv...
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imkyjkk

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