Residual U-Net

class biapy.models.resunet.ResUNet(image_shape=(256, 256, 1), activation='ELU', feature_maps=[32, 64, 128, 256], drop_values=[0.1, 0.1, 0.1, 0.1], batch_norm=False, k_size=3, upsample_layer='convtranspose', z_down=[2, 2, 2, 2], n_classes=1, output_channels='BC', upsampling_factor=(), upsampling_position='pre')[source]

Bases: Module

Create 2D/3D Residual U-Net.

Reference: Road Extraction by Deep Residual U-Net.

Parameters:

  • image_shape (3D/4D tuple) – Dimensions of the input image. E.g. (y, x, channels) or (z, y, x, channels).

  • activation (str, optional) – Activation layer.

  • feature_maps (array of ints, optional) – Feature maps to use on each level.

  • drop_values (float, optional) – Dropout value to be fixed.

  • batch_norm (bool, optional) – Make batch normalization.

  • k_size (int, optional) – Kernel size.

  • upsample_layer (str, optional) – Type of layer to use to make upsampling. Two options: “convtranspose” or “upsampling”.

  • z_down (List of ints, optional) – Downsampling used in z dimension. Set it to 1 if the dataset is not isotropic.

  • n_classes (int, optional) – Number of classes.

  • output_channels (str, optional) – Channels to operate with. Possible values: BC, BCD, BP, BCDv2, BDv2, Dv2 and BCM.

  • upsampling_factor (tuple of ints, optional) – Factor of upsampling for super resolution workflow for each dimension.

  • upsampling_position (str, optional) – Whether the upsampling is going to be made previously (pre option) to the model or after the model (post option).

Returns:

model – Residual U-Net model.

Return type:

Torch model

Calling this function with its default parameters returns the following network:

../../_images/unet.png

Image created with PlotNeuralNet.

forward(x) → Tensor | List[Tensor][source]

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.