update classifier to load model from hf hub #7
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pavaris-pm
commented
Oct 29, 2023
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add webapp optimization into #1 , since we have made a redundant model initialiation
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| def classify(image: Image.Image)->Tuple[torch.Tensor, | ||
| torch.Tensor, | ||
| str | ||
| ]: | ||
| # init this by ourselves | ||
| weight_file = None#'/workspaces/dog-breed-classification-webapp/src/production/models/model_weights/best_model_convnext.pth' | ||
| _ , idx2label = get_total_class() | ||
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| # init the model | ||
| classifier, extractor = init_model(weight_file) | ||
| classifier, extractor = init_model(engine='convnext_v2') | ||
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we will see that it will re-init the model everytime we upload new image, which is time-consuming since the ConvNext itself is very large. Consider optimize the speed by downloading everything first before making an inference at the end would much help
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update the source code to load the model directly from hugginface. In order to prevent the case that the weight cannot be download directly from the .pth file