import torch from torch import nn class ConvBlock(nn.Module): def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'): super(ConvBlock, self).__init__() ops = [] for i in range(n_stages): if i==0: input_channel = n_filters_in else: input_channel = n_filters_out ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1)) if normalization == 'batchnorm': ops.append(nn.BatchNorm3d(n_filters_out)) elif normalization == 'groupnorm': ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out)) elif normalization == 'instancenorm': ops.append(nn.InstanceNorm3d(n_filters_out)) elif normalization != 'none': assert False ops.append(nn.ReLU(inplace=True)) self.conv = nn.Sequential(*ops) def forward(self, x): x = self.conv(x) return x class ResidualConvBlock(nn.Module): def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'): super(ResidualConvBlock, self).__init__() ops = [] for i in range(n_stages): if i == 0: input_channel = n_filters_in else: input_channel = n_filters_out ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1)) if normalization == 'batchnorm': ops.append(nn.BatchNorm3d(n_filters_out)) elif normalization == 'groupnorm': ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out)) elif normalization == 'instancenorm': ops.append(nn.InstanceNorm3d(n_filters_out)) elif normalization != 'none': assert False if i != n_stages-1: ops.append(nn.ReLU(inplace=True)) self.conv = nn.Sequential(*ops) self.relu = nn.ReLU(inplace=True) def forward(self, x): x = (self.conv(x) + x) x = self.relu(x) return x class DownsamplingConvBlock(nn.Module): def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'): super(DownsamplingConvBlock, self).__init__() ops = [] if normalization != 'none': ops.append(nn.Conv3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride)) if normalization == 'batchnorm': ops.append(nn.BatchNorm3d(n_filters_out)) elif normalization == 'groupnorm': ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out)) elif normalization == 'instancenorm': ops.append(nn.InstanceNorm3d(n_filters_out)) else: assert False else: ops.append(nn.Conv3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride)) ops.append(nn.ReLU(inplace=True)) self.conv = nn.Sequential(*ops) def forward(self, x): x = self.conv(x) return x class Upsampling_function(nn.Module): def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none', mode_upsampling = 1): super(Upsampling_function, self).__init__() ops = [] if mode_upsampling == 0: ops.append(nn.ConvTranspose3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride)) if mode_upsampling == 1: ops.append(nn.Upsample(scale_factor=stride, mode="trilinear", align_corners=True)) ops.append(nn.Conv3d(n_filters_in, n_filters_out, kernel_size=3, padding=1)) elif mode_upsampling == 2: ops.append(nn.Upsample(scale_factor=stride, mode="nearest")) ops.append(nn.Conv3d(n_filters_in, n_filters_out, kernel_size=3, padding=1)) if normalization == 'batchnorm': ops.append(nn.BatchNorm3d(n_filters_out)) elif normalization == 'groupnorm': ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out)) elif normalization == 'instancenorm': ops.append(nn.InstanceNorm3d(n_filters_out)) elif normalization != 'none': assert False ops.append(nn.ReLU(inplace=True)) self.conv = nn.Sequential(*ops) def forward(self, x): x = self.conv(x) return x class Encoder(nn.Module): def __init__(self, n_channels=3, n_classes=2, n_filters=16, normalization='none', has_dropout=False, has_residual=False): super(Encoder, self).__init__() self.has_dropout = has_dropout convBlock = ConvBlock if not has_residual else ResidualConvBlock self.block_one = convBlock(1, n_channels, n_filters, normalization=normalization) self.block_one_dw = DownsamplingConvBlock(n_filters, 2 * n_filters, normalization=normalization) self.block_two = convBlock(2, n_filters * 2, n_filters * 2, normalization=normalization) self.block_two_dw = DownsamplingConvBlock(n_filters * 2, n_filters * 4, normalization=normalization) self.block_three = convBlock(3, n_filters * 4, n_filters * 4, normalization=normalization) self.block_three_dw = DownsamplingConvBlock(n_filters * 4, n_filters * 8, normalization=normalization) self.block_four = convBlock(3, n_filters * 8, n_filters * 8, normalization=normalization) self.block_four_dw = DownsamplingConvBlock(n_filters * 8, n_filters * 16, normalization=normalization) self.block_five = convBlock(3, n_filters * 16, n_filters * 16, normalization=normalization) self.dropout = nn.Dropout3d(p=0.5, inplace=False) def forward(self, input): x1 = self.block_one(input) x1_dw = self.block_one_dw(x1) x2 = self.block_two(x1_dw) x2_dw = self.block_two_dw(x2) x3 = self.block_three(x2_dw) x3_dw = self.block_three_dw(x3) x4 = self.block_four(x3_dw) x4_dw = self.block_four_dw(x4) x5 = self.block_five(x4_dw) if self.has_dropout: x5 = self.dropout(x5) res = [x1, x2, x3, x4, x5] return res class Decoder(nn.Module): def __init__(self, n_channels=3, n_classes=2, n_filters=16, normalization='none', has_dropout=False, has_residual=False, up_type=0): super(Decoder, self).__init__() self.has_dropout = has_dropout convBlock = ConvBlock if not has_residual else ResidualConvBlock self.block_five_up = Upsampling_function(n_filters * 16, n_filters * 8, normalization=normalization, mode_upsampling=up_type) self.block_six = convBlock(3, n_filters * 8, n_filters * 8, normalization=normalization) self.block_six_up = Upsampling_function(n_filters * 8, n_filters * 4, normalization=normalization, mode_upsampling=up_type) self.block_seven = convBlock(3, n_filters * 4, n_filters * 4, normalization=normalization) self.block_seven_up = Upsampling_function(n_filters * 4, n_filters * 2, normalization=normalization, mode_upsampling=up_type) self.block_eight = convBlock(2, n_filters * 2, n_filters * 2, normalization=normalization) self.block_eight_up = Upsampling_function(n_filters * 2, n_filters, normalization=normalization, mode_upsampling=up_type) self.block_nine_1 = convBlock(1, n_filters, n_filters, normalization=normalization) self.block_nine_2 = convBlock(1, n_filters, n_filters, normalization=normalization) self.block_nine_3 = convBlock(1, n_filters, n_filters, normalization=normalization) self.block_c2f = convBlock(1, n_filters*3, n_filters, normalization=normalization) self.out_conv_1 = nn.Conv3d(n_filters, n_classes, 1, padding=0) self.out_conv_2 = nn.Conv3d(n_filters, n_classes, 1, padding=0) self.out_conv_3 = nn.Conv3d(n_filters, n_classes, 1, padding=0) self.dropout = nn.Dropout3d(p=0.5, inplace=False) def forward(self, features): x1 = features[0] x2 = features[1] x3 = features[2] x4 = features[3] x5 = features[4] x5_up = self.block_five_up(x5) x5_up = x5_up + x4 x6 = self.block_six(x5_up) x6_up = self.block_six_up(x6) x6_up = x6_up + x3 x7 = self.block_seven(x6_up) x7_up = self.block_seven_up(x7) x7_up = x7_up + x2 x8 = self.block_eight(x7_up) x8_up = self.block_eight_up(x8) x8_up = x8_up + x1 x91 = self.block_nine_1(x8_up) if self.has_dropout: x91 = self.dropout(x91) out_seg_1 = self.out_conv_1(x91) x92 = self.block_nine_2(x8_up) if self.has_dropout: x92 = self.dropout(x92) out_seg_2 = self.out_conv_2(x92) x93 = self.block_nine_3(x8_up) if self.has_dropout: x93 = self.dropout(x93) out_seg_diff = torch.cat(((x92 - x91), x91, x93), dim=1) out_seg_3 = self.block_c2f(out_seg_diff) out_seg_3 = self.out_conv_3(out_seg_3) return out_seg_1, out_seg_2, out_seg_3 class VNet(nn.Module): def __init__(self, n_channels=3, n_classes=2, n_filters=16, normalization='none', has_dropout=False, has_residual=False): super(VNet, self).__init__() self.encoder = Encoder(n_channels, n_classes, n_filters, normalization, has_dropout, has_residual) self.decoder = Decoder(n_channels, n_classes, n_filters, normalization, has_dropout, has_residual, 0) def forward(self, input): features = self.encoder(input) out_seg_1, out_seg_2, out_seg_3 = self.decoder(features) return out_seg_1, out_seg_2, out_seg_3 if __name__ == '__main__': # compute FLOPS & PARAMETERS from ptflops import get_model_complexity_info model = VNet(n_channels=3, n_classes=2, normalization='batchnorm', has_dropout=True) with torch.cuda.device(0): macs, params = get_model_complexity_info(model, (3, 80, 80, 80), as_strings=True, print_per_layer_stat=True, verbose=True) print('{:<30} {:<8}'.format('Computational complexity: ', macs)) print('{:<30} {:<8}'.format('Number of parameters: ', params)) with torch.cuda.device(0): macs, params = get_model_complexity_info(model, (3, 80, 80, 80), as_strings=True, print_per_layer_stat=True, verbose=True) print('{:<30} {:<8}'.format('Computational complexity: ', macs)) print('{:<30} {:<8}'.format('Number of parameters: ', params)) import ipdb; ipdb.set_trace()