Delete convert_pt.py
Browse files- convert_pt.py +0 -59
convert_pt.py
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from safetensors.torch import save_file
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import torch
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from torchvision.models import resnet34
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model_path = "RESNET_34_cancer_350px_lr_1e-2_decay_5_jitter_val6slides_harder_tcga_none_0403_0204_0.9826153355179645_16.t7"
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orig_model = torch.load(model_path, map_location="cpu")
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state_dict = orig_model["model"].module.state_dict()
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keys_missing = [
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"bn1.num_batches_tracked",
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"layer1.0.bn1.num_batches_tracked",
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"layer1.0.bn2.num_batches_tracked",
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"layer1.1.bn1.num_batches_tracked",
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"layer1.1.bn2.num_batches_tracked",
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"layer1.2.bn1.num_batches_tracked",
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"layer1.2.bn2.num_batches_tracked",
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"layer2.0.bn1.num_batches_tracked",
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"layer2.0.bn2.num_batches_tracked",
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"layer2.0.downsample.1.num_batches_tracked",
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"layer2.1.bn1.num_batches_tracked",
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"layer2.1.bn2.num_batches_tracked",
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"layer2.2.bn1.num_batches_tracked",
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"layer2.2.bn2.num_batches_tracked",
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"layer2.3.bn1.num_batches_tracked",
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"layer2.3.bn2.num_batches_tracked",
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"layer3.0.bn1.num_batches_tracked",
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"layer3.0.bn2.num_batches_tracked",
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"layer3.0.downsample.1.num_batches_tracked",
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"layer3.1.bn1.num_batches_tracked",
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"layer3.1.bn2.num_batches_tracked",
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"layer3.2.bn1.num_batches_tracked",
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"layer3.2.bn2.num_batches_tracked",
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"layer3.3.bn1.num_batches_tracked",
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"layer3.3.bn2.num_batches_tracked",
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"layer3.4.bn1.num_batches_tracked",
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"layer3.4.bn2.num_batches_tracked",
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"layer3.5.bn1.num_batches_tracked",
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"layer3.5.bn2.num_batches_tracked",
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"layer4.0.bn1.num_batches_tracked",
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"layer4.0.bn2.num_batches_tracked",
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"layer4.0.downsample.1.num_batches_tracked",
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"layer4.1.bn1.num_batches_tracked",
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"layer4.1.bn2.num_batches_tracked",
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"layer4.2.bn1.num_batches_tracked",
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"layer4.2.bn2.num_batches_tracked",
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]
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assert not any(
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key in state_dict.keys() for key in keys_missing
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), "key present that should be missing"
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for key in keys_missing:
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state_dict[key] = torch.as_tensor(0)
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torch.save(state_dict, "pytorch_model.pt")
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save_file(state_dict, "model.safetensors")
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model = resnet34(weights=None)
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model.fc = torch.nn.Linear(model.fc.in_features, out_features=5, bias=True)
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model.load_state_dict(state_dict)
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model_jit = torch.jit.script(model, example_inputs=[(torch.ones(1, 3, 224, 224),)])
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torch.jit.save(model_jit, "torchscript_model.bin")
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