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Update app.py
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app.py
CHANGED
@@ -22,7 +22,7 @@ def print_bn():
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bn_data.extend(m.running_var.data.numpy().tolist())
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bn_data.append(m.momentum)
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print(len(bn_data))
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bn_data.extend(model.resnet.embedder.embedder.convolution.weight.data.reshape(-1).numpy().tolist())
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# print(model.resnet.embedder.embedder.convolution.weight.data.numpy().tolist())
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return bn_data
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@@ -50,8 +50,8 @@ def greet(image):
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bn_data = print_bn()
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return ','.join([f'{x:.2f}' for x in bn_data])
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else:
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conv_layer = model.resnet.embedder.embedder.convolution
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conv_layer.weight.data = torch.ones_like(conv_layer.weight.data)
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print(type(image))
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image = torch.tensor(image).float()
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print(image.min(), image.max())
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bn_data.extend(m.running_var.data.numpy().tolist())
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bn_data.append(m.momentum)
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print(len(bn_data))
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+
# bn_data.extend(model.resnet.embedder.embedder.convolution.weight.data.reshape(-1).numpy().tolist())
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# print(model.resnet.embedder.embedder.convolution.weight.data.numpy().tolist())
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return bn_data
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bn_data = print_bn()
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return ','.join([f'{x:.2f}' for x in bn_data])
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else:
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# conv_layer = model.resnet.embedder.embedder.convolution
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# conv_layer.weight.data = torch.ones_like(conv_layer.weight.data)
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print(type(image))
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image = torch.tensor(image).float()
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print(image.min(), image.max())
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