# Copyright (c) MONAI Consortium # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations from collections.abc import Sequence 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: str | tuple = ("LeakyReLU", {"negative_slope": 0.1, "inplace": True}), norm: str | tuple = ("instance", {"affine": True}), bias: bool = True, dropout: 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 `_, 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