clinicadl.networks.nn.UNet

class clinicadl.networks.nn.UNet(spatial_dims: int, in_channels: int, out_channels: int, channels: Sequence[int] = (64, 128, 256, 512, 1024), act: ActFunction | tuple[ActFunction, dict[str, Any]] = ActFunction.RELU, output_act: ActFunction | tuple[ActFunction, dict[str, Any]] | None = None, dropout: float | None = None)

UNet, based on U-Net: Convolutional Networks for Biomedical Image Segmentation.

The user can customize the number of encoding blocks, the number of channels in each block, as well as other parameters like the activation function.

Works with 2D or 3D images (with additional batch and channel dimensions).

Warning

UNet works only with images whose dimensions are high enough powers of 2. More precisely, if n is the number of max pooling operation in your UNet (which is equal to len(channels)-1), the image must have \(2^{k}\) pixels in each dimension, with \(k \geq n\) (e.g. shape (\(2^{n}\), \(2^{n+3}\), \(2^{n+1}\)) for a 3D image).

Note

The implementation proposed here is not exactly the one described in the original paper. Padding is added to convolutions so that the feature maps keep a constant size, batch normalization is used, and “up-conv” layers are here made with a torch.nn.Upsample layer followed by a 3x3 convolution.

Parameters:

• spatial_dims (int) – Number of spatial dimensions of the input image.

• in_channels (int) – Number of channels in the input image.

• out_channels (int) – Number of output channels.

• channels (Sequence[int], default=(64, 128, 256, 512, 1024)) –

  Number of channels in each UNet block. Thus, this parameter also controls the number of UNet blocks (equal to the length of the sequence). The length channels should be no less than 2.

  Default to (64, 128, 256, 512, 1024), as in the original paper.

• act (ActivationParameters, default="relu") –

  The activation function used, and optionally its arguments. Must be passed as activation_name or (activation_name, arguments), where arguments is a dictionary.

  activation_name can be any value in {"celu", "elu", "gelu", "leakyrelu", "logsoftmax", "mish", "prelu", "relu", "relu6", "selu", "sigmoid", "softmax", "tanh"}. Please refer to PyTorch activation functions to know the arguments for each of them.

  Default is relu, as in the original paper.

• output_act (Optional[ActivationParameters], default=None) – A potential activation layer applied to the output of the network. Must be passed in the same way as act. If None, no last activation will be applied.

• dropout (Optional[float], default=None) – Dropout ratio. If None, no dropout.

See also

torch.nn.Module

To see all the methods of this neural network.

Examples

# a UNet with 1 downsampling (instead of 4 in the original paper)
>>> UNet(
        spatial_dims=2,
        in_channels=1,
        out_channels=2,
        channels=(4, 8),
        act="elu",
        output_act=("softmax", {"dim": 1}),
        dropout=0.1,
    )
UNet(
    (doubleconv): ConvBlock(
        (0): Convolution(
            (conv): Conv2d(1, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (adn): ADN(
                (N): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (D): Dropout(p=0.1, inplace=False)
                (A): ELU(alpha=1.0)
            )
        )
        (1): Convolution(
            (conv): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (adn): ADN(
                (N): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                (D): Dropout(p=0.1, inplace=False)
                (A): ELU(alpha=1.0)
            )
        )
    )
    (down1): DownBlock(
        (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
        (doubleconv): ConvBlock(
            (0): Convolution(
                (conv): Conv2d(4, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                (adn): ADN(
                    (N): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                    (D): Dropout(p=0.1, inplace=False)
                    (A): ELU(alpha=1.0)
                )
            )
            (1): Convolution(
                (conv): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                (adn): ADN(
                    (N): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                    (D): Dropout(p=0.1, inplace=False)
                    (A): ELU(alpha=1.0)
                )
            )
        )
    )
    (up1): UpBlock(
        (upsample): UpSample(
            (0): Upsample(scale_factor=2.0, mode='nearest')
            (1): Convolution(
                (conv): Conv2d(8, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                (adn): ADN(
                    (N): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                    (D): Dropout(p=0.1, inplace=False)
                    (A): ELU(alpha=1.0)
                )
            )
        )
        (doubleconv): ConvBlock(
            (0): Convolution(
                (conv): Conv2d(8, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                (adn): ADN(
                    (N): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                    (D): Dropout(p=0.1, inplace=False)
                    (A): ELU(alpha=1.0)
                )
            )
            (1): Convolution(
                (conv): Conv2d(4, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
                (adn): ADN(
                    (N): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
                    (D): Dropout(p=0.1, inplace=False)
                    (A): ELU(alpha=1.0)
                )
            )
        )
    )
    (reduce_channels): Convolution(
        (conv): Conv2d(4, 2, kernel_size=(1, 1), stride=(1, 1))
    )
    (output_act): Softmax(dim=1)
)

forward(x: Tensor) → Tensor

Define 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.