This module builds a dynamic U-Net from any backbone pretrained on ImageNet, automatically inferring the intermediate sizes.
This is the original U-Net. The difference here is that the left part is a pretrained model.
This U-Net will sit on top of an
encoder (that can be a pretrained model) and with a final output of
n_classes. During the initialization, it uses
Hooks to determine the intermediate features sizes by passing a dummy input through the model and create the upward path automatically.
blur is used to avoid checkerboard artifacts at each layer,
blur_final is specific to the last layer.
self_attention determines if we use a self attention layer at the third block before the end. If
y_range is passed, the last activations go through a sigmoid rescaled to that range.
last_cross determines if we use a cross-connection with the direct input of the model, and in this case
bottle flags if we use a bottleneck or not for that skip connection.
This block receives the output of the last block to be upsampled (size
up_in_c) and the activations features from an intermediate layer of the
x_in_c, this is the lateral connection). The
hook is set to this intermediate layer to store the output needed for this block.
final_div determines if we devide the number of features by 2 with the upsampling,
blur is to avoid checkerboard artifacts. If
leaky is set, use a leaky ReLU with a slope equals to that parameter instead of a ReLU, and
self_attention determines if we use a self-attention layer.
kwargs are passed to