U-Net implementation for PyTorch based on https://arxiv.org/abs/1505.04597
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The U-Net is an encoder-decoder neural network used for semantic segmentation. The implementation in this repository is a modified version of the U-Net proposed in this paper.
You can alter the U-Net's depth. The original U-Net uses a depth of 5, as depicted in the diagram above. The word "depth" specifically refers to the number of different spatially-sized convolutional outputs. With this U-Net implementation, you can easily vary the depth.
You can merge decoder and encoder pathways in two ways. In the original U-Net, the decoder and encoder activations are merged by concatenating channels. I've implemented a ResNet-style merging of the decoder and encoder activations by adding these activations. This was easy to code up, but it may not make sense theoretically and has not been tested.
I had some trouble getting the pixel-wise loss working correctly for a semantic segmentation task. Here's how I got it working in the end.
from model import UNet
model = UNet()
set up dataloaders, etc.
output = model(some_input_data)
permute is like np.transpose: (N, C, H, W) => (H, W, N, C)
contiguous is required because of this issue: https://github.com/pytorch/pytorch/issues/764
view: reshapes the output tensor so that we have (H * W * N, num_class)
NOTE: num_class == C (number of output channels)
output = output.permute(2, 3, 0, 1).contiguous().view(-1, num_classes)