| from typing import Sequence, Union |
|
|
| import torch |
| import torch.nn as nn |
|
|
| from monai.networks.layers.factories import Conv |
| from monai.networks.nets.basic_unet import Down, TwoConv, UpCat |
| from monai.utils import ensure_tuple_rep |
|
|
| __all__ = ["BasicUnetPlusPlus", "BasicunetPlusPlus", "basicunetplusplus", "BasicUNetPlusPlus"] |
|
|
|
|
| class BasicUNetPlusPlus(nn.Module): |
| def __init__( |
| self, |
| spatial_dims: int = 3, |
| in_channels: int = 1, |
| out_channels: int = 2, |
| features: Sequence[int] = (32, 32, 64, 128, 256, 32), |
| deep_supervision: bool = False, |
| act: Union[str, tuple] = ("LeakyReLU", {"negative_slope": 0.1, "inplace": True}), |
| norm: Union[str, tuple] = ("instance", {"affine": True}), |
| bias: bool = True, |
| dropout: Union[float, tuple] = 0.0, |
| upsample: str = "deconv", |
| ): |
| """ |
| A UNet++ implementation with 1D/2D/3D supports. |
| |
| Based on: |
| |
| Zhou et al. "UNet++: A Nested U-Net Architecture for Medical Image |
| Segmentation". 4th Deep Learning in Medical Image Analysis (DLMIA) |
| Workshop, DOI: https://doi.org/10.48550/arXiv.1807.10165 |
| |
| |
| Args: |
| spatial_dims: number of spatial dimensions. Defaults to 3 for spatial 3D inputs. |
| in_channels: number of input channels. Defaults to 1. |
| out_channels: number of output channels. Defaults to 2. |
| features: six integers as numbers of features. |
| Defaults to ``(32, 32, 64, 128, 256, 32)``, |
| |
| - the first five values correspond to the five-level encoder feature sizes. |
| - the last value corresponds to the feature size after the last upsampling. |
| |
| deep_supervision: whether to prune the network at inference time. Defaults to False. If true, returns a list, |
| whose elements correspond to outputs at different nodes. |
| act: activation type and arguments. Defaults to LeakyReLU. |
| norm: feature normalization type and arguments. Defaults to instance norm. |
| bias: whether to have a bias term in convolution blocks. Defaults to True. |
| According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_, |
| if a conv layer is directly followed by a batch norm layer, bias should be False. |
| dropout: dropout ratio. Defaults to no dropout. |
| upsample: upsampling mode, available options are |
| ``"deconv"``, ``"pixelshuffle"``, ``"nontrainable"``. |
| |
| Examples:: |
| |
| # for spatial 2D |
| >>> net = BasicUNetPlusPlus(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128)) |
| |
| # for spatial 2D, with deep supervision enabled |
| >>> net = BasicUNetPlusPlus(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128), deep_supervision=True) |
| |
| # for spatial 2D, with group norm |
| >>> net = BasicUNetPlusPlus(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128), norm=("group", {"num_groups": 4})) |
| |
| # for spatial 3D |
| >>> net = BasicUNetPlusPlus(spatial_dims=3, features=(32, 32, 64, 128, 256, 32)) |
| |
| See Also |
| - :py:class:`monai.networks.nets.BasicUNet` |
| - :py:class:`monai.networks.nets.DynUNet` |
| - :py:class:`monai.networks.nets.UNet` |
| |
| """ |
| super().__init__() |
|
|
| self.deep_supervision = deep_supervision |
|
|
| fea = ensure_tuple_rep(features, 6) |
| print(f"BasicUNetPlusPlus features: {fea}.") |
|
|
| self.conv_0_0 = TwoConv(spatial_dims, in_channels, fea[0], act, norm, bias, dropout) |
| self.conv_1_0 = Down(spatial_dims, fea[0], fea[1], act, norm, bias, dropout) |
| self.conv_2_0 = Down(spatial_dims, fea[1], fea[2], act, norm, bias, dropout) |
| self.conv_3_0 = Down(spatial_dims, fea[2], fea[3], act, norm, bias, dropout) |
| self.conv_4_0 = Down(spatial_dims, fea[3], fea[4], act, norm, bias, dropout) |
|
|
| self.upcat_0_1 = UpCat(spatial_dims, fea[1], fea[0], fea[0], act, norm, bias, dropout, upsample, halves=False) |
| self.upcat_1_1 = UpCat(spatial_dims, fea[2], fea[1], fea[1], act, norm, bias, dropout, upsample) |
| self.upcat_2_1 = UpCat(spatial_dims, fea[3], fea[2], fea[2], act, norm, bias, dropout, upsample) |
| self.upcat_3_1 = UpCat(spatial_dims, fea[4], fea[3], fea[3], act, norm, bias, dropout, upsample) |
|
|
| self.upcat_0_2 = UpCat( |
| spatial_dims, fea[1], fea[0] * 2, fea[0], act, norm, bias, dropout, upsample, halves=False |
| ) |
| self.upcat_1_2 = UpCat(spatial_dims, fea[2], fea[1] * 2, fea[1], act, norm, bias, dropout, upsample) |
| self.upcat_2_2 = UpCat(spatial_dims, fea[3], fea[2] * 2, fea[2], act, norm, bias, dropout, upsample) |
|
|
| self.upcat_0_3 = UpCat( |
| spatial_dims, fea[1], fea[0] * 3, fea[0], act, norm, bias, dropout, upsample, halves=False |
| ) |
| self.upcat_1_3 = UpCat(spatial_dims, fea[2], fea[1] * 3, fea[1], act, norm, bias, dropout, upsample) |
|
|
| self.upcat_0_4 = UpCat( |
| spatial_dims, fea[1], fea[0] * 4, fea[5], act, norm, bias, dropout, upsample, halves=False |
| ) |
|
|
| self.final_conv_0_1 = Conv["conv", spatial_dims](fea[0], out_channels, kernel_size=1) |
| self.final_conv_0_2 = Conv["conv", spatial_dims](fea[0], out_channels, kernel_size=1) |
| self.final_conv_0_3 = Conv["conv", spatial_dims](fea[0], out_channels, kernel_size=1) |
| self.final_conv_0_4 = Conv["conv", spatial_dims](fea[5], out_channels, kernel_size=1) |
|
|
|
|
| def forward(self, x: torch.Tensor): |
| """ |
| Args: |
| x: input should have spatially N dimensions |
| ``(Batch, in_channels, dim_0[, dim_1, ..., dim_N-1])``, N is defined by `dimensions`. |
| It is recommended to have ``dim_n % 16 == 0`` to ensure all maxpooling inputs have |
| even edge lengths. |
| |
| Returns: |
| A torch Tensor of "raw" predictions in shape |
| ``(Batch, out_channels, dim_0[, dim_1, ..., dim_N-1])``. |
| """ |
| x_0_0 = self.conv_0_0(x) |
| x_1_0 = self.conv_1_0(x_0_0) |
| x_0_1 = self.upcat_0_1(x_1_0, x_0_0) |
|
|
| x_2_0 = self.conv_2_0(x_1_0) |
| x_1_1 = self.upcat_1_1(x_2_0, x_1_0) |
| x_0_2 = self.upcat_0_2(x_1_1, torch.cat([x_0_0, x_0_1], dim=1)) |
|
|
| x_3_0 = self.conv_3_0(x_2_0) |
| x_2_1 = self.upcat_2_1(x_3_0, x_2_0) |
| x_1_2 = self.upcat_1_2(x_2_1, torch.cat([x_1_0, x_1_1], dim=1)) |
| x_0_3 = self.upcat_0_3(x_1_2, torch.cat([x_0_0, x_0_1, x_0_2], dim=1)) |
|
|
| x_4_0 = self.conv_4_0(x_3_0) |
| x_3_1 = self.upcat_3_1(x_4_0, x_3_0) |
| x_2_2 = self.upcat_2_2(x_3_1, torch.cat([x_2_0, x_2_1], dim=1)) |
| x_1_3 = self.upcat_1_3(x_2_2, torch.cat([x_1_0, x_1_1, x_1_2], dim=1)) |
| x_0_4 = self.upcat_0_4(x_1_3, torch.cat([x_0_0, x_0_1, x_0_2, x_0_3], dim=1)) |
|
|
| output_0_1 = self.final_conv_0_1(x_0_1) |
| output_0_2 = self.final_conv_0_2(x_0_2) |
| output_0_3 = self.final_conv_0_3(x_0_3) |
| output_0_4 = self.final_conv_0_4(x_0_4) |
|
|
| if self.deep_supervision: |
| output = [output_0_1, output_0_2, output_0_3, output_0_4] |
| else: |
| output = output_0_4 |
|
|
| return output |
|
|
|
|
|
|
| BasicUnetPlusPlus = BasicunetPlusPlus = basicunetplusplus = BasicUNetPlusPlus |
