unet

UNet

Bases: Module

A standard UNet implementation in PyTorch.

This class is intentionally not @traceable (models and layers are handled by a different process).

Parameters:

Name	Type	Description	Default
input_size	Tuple[int, int, int]	The size of the input tensor (channels, height, width).	(1, 128, 128)
output_channel	int	The number of output channels.	1

Raises:

Type	Description
ValueError	Length of input_size is not 3.
ValueError	input_size[1] or input_size[2] is not a multiple of 16.

Source code in fastestimator/fastestimator/architecture/pytorch/unet.py

class UNet(nn.Module):
    """A standard UNet implementation in PyTorch.

    This class is intentionally not @traceable (models and layers are handled by a different process).

    Args:
        input_size: The size of the input tensor (channels, height, width).
        output_channel: The number of output channels.

    Raises:
        ValueError: Length of `input_size` is not 3.
        ValueError: `input_size`[1] or `input_size`[2] is not a multiple of 16.
    """
    def __init__(self, input_size: Tuple[int, int, int] = (1, 128, 128), output_channel: int = 1) -> None:
        UNet._check_input_size(input_size)
        super().__init__()
        self.input_size = input_size
        self.enc1 = UNetEncoderBlock(in_channels=input_size[0], out_channels=64)
        self.enc2 = UNetEncoderBlock(in_channels=64, out_channels=128)
        self.enc3 = UNetEncoderBlock(in_channels=128, out_channels=256)
        self.enc4 = UNetEncoderBlock(in_channels=256, out_channels=512)
        self.bottle_neck = UNetDecoderBlock(in_channels=512, mid_channels=1024, out_channels=512)
        self.dec4 = UNetDecoderBlock(in_channels=1024, mid_channels=512, out_channels=256)
        self.dec3 = UNetDecoderBlock(in_channels=512, mid_channels=256, out_channels=128)
        self.dec2 = UNetDecoderBlock(in_channels=256, mid_channels=128, out_channels=64)
        self.dec1 = nn.Sequential(nn.Conv2d(128, 64, 3, padding=1),
                                  nn.ReLU(inplace=True),
                                  nn.Conv2d(64, 64, 3, padding=1),
                                  nn.ReLU(inplace=True),
                                  nn.Conv2d(64, output_channel, 1),
                                  nn.Sigmoid())

        for layer in self.dec1:
            if isinstance(layer, nn.Conv2d):
                he_normal(layer.weight.data)
                layer.bias.data.zero_()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x1, x_e1 = self.enc1(x)
        x2, x_e2 = self.enc2(x_e1)
        x3, x_e3 = self.enc3(x_e2)
        x4, x_e4 = self.enc4(x_e3)

        x_bottle_neck = self.bottle_neck(x_e4)
        x_d4 = self.dec4(torch.cat((x_bottle_neck, x4), 1))
        x_d3 = self.dec3(torch.cat((x_d4, x3), 1))
        x_d2 = self.dec2(torch.cat((x_d3, x2), 1))
        x_out = self.dec1(torch.cat((x_d2, x1), 1))
        return x_out

    @staticmethod
    def _check_input_size(input_size):
        if len(input_size) != 3:
            raise ValueError("Length of `input_size` is not 3 (channel, height, width)")

        _, height, width = input_size

        if height < 16 or not (height / 16.0).is_integer() or width < 16 or not (width / 16.0).is_integer():
            raise ValueError("Both height and width of input_size need to be multiples of 16 (16, 32, 48...)")

UNetDecoderBlock

Bases: Module

A UNet decoder block.

This class is intentionally not @traceable (models and layers are handled by a different process).

Parameters:

Name	Type	Description	Default
in_channels	int	How many channels enter the decoder.	required
mid_channels	int	How many channels are used for the decoder's intermediate layer.	required
out_channels	int	How many channels leave the decoder.	required

Source code in fastestimator/fastestimator/architecture/pytorch/unet.py

class UNetDecoderBlock(nn.Module):
    """A UNet decoder block.

    This class is intentionally not @traceable (models and layers are handled by a different process).

    Args:
        in_channels: How many channels enter the decoder.
        mid_channels: How many channels are used for the decoder's intermediate layer.
        out_channels: How many channels leave the decoder.
    """
    def __init__(self, in_channels: int, mid_channels: int, out_channels: int) -> None:
        super().__init__()
        self.layers = nn.Sequential(nn.Conv2d(in_channels, mid_channels, 3, padding=1),
                                    nn.ReLU(inplace=True),
                                    nn.Conv2d(mid_channels, mid_channels, 3, padding=1),
                                    nn.ReLU(inplace=True),
                                    nn.Upsample(scale_factor=2),
                                    nn.Conv2d(mid_channels, out_channels, 3, padding=1),
                                    nn.ReLU(inplace=True))

        for layer in self.layers:
            if isinstance(layer, nn.Conv2d):
                he_normal(layer.weight.data)
                layer.bias.data.zero_()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.layers(x)

UNetEncoderBlock

Bases: Module

A UNet encoder block.

This class is intentionally not @traceable (models and layers are handled by a different process).

Parameters:

Name	Type	Description	Default
in_channels	int	How many channels enter the encoder.	required
out_channels	int	How many channels leave the encoder.	required

Source code in fastestimator/fastestimator/architecture/pytorch/unet.py

class UNetEncoderBlock(nn.Module):
    """A UNet encoder block.

    This class is intentionally not @traceable (models and layers are handled by a different process).

    Args:
        in_channels: How many channels enter the encoder.
        out_channels: How many channels leave the encoder.
    """
    def __init__(self, in_channels: int, out_channels: int) -> None:
        super().__init__()
        self.layers = nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
                                    nn.ReLU(inplace=True),
                                    nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
                                    nn.ReLU(inplace=True))

        for layer in self.layers:
            if isinstance(layer, nn.Conv2d):
                he_normal(layer.weight.data)
                layer.bias.data.zero_()

    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        out = self.layers(x)
        return out, F.max_pool2d(out, 2)