U-Net #359
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U-Net #359
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I have implemented a generic, customizable U-Net that comes with options for:
in_channelsis the number of channels in the input image.out_channelsis the number of channels in the output image.bilinear = False: Transposed convolution with a 2x2 kernel applied with stride 2. This is followed by a ReLU.bilinear = True: Factor 2 bilinear upsampling followed by convolution with a 1x1 kernel applied with stride 1.pad = True: The input size is retained in the output by zero-padding convolutions and, if necessary, the results of the upsampling operations.pad = False: The output is smaller than the input as in the original implementation. In this case, every 3x3 convolution layer reduces the height and width by 2 pixels each. Consequently, the right side of the U-Net has a smaller spatial size than the left size. Therefore, before concatenating, the central slice of the left tensor is cropped along the spatial dimensions to match those of the right tensor.normalization = None: Applies no normalization.normalization = "bn": Applies batch normalization.normalization = "ln": Applies layer normalization. A permutation of dimensions is performed before the layer to ensure normalization is applied over the channel dimension. Afterward, the dimensions are permuted back to their original order.