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| import torch | |
| from torch import nn | |
| import numpy as np | |
| __all__ = ['UNet', 'NestedUNet3', 'NestedUNet4', 'NestedUNet5'] | |
| class VGGBlock(nn.Module): | |
| def __init__(self, in_channels, middle_channels, out_channels, ks=7): | |
| super().__init__() | |
| padding = int((ks - 1) / 2) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv1 = nn.Conv1d(in_channels, middle_channels, kernel_size=ks, padding=padding) | |
| self.bn1 = nn.BatchNorm1d(middle_channels) | |
| self.conv2 = nn.Conv1d(middle_channels, out_channels, kernel_size=ks, padding=padding) | |
| self.bn2 = nn.BatchNorm1d(out_channels) | |
| def forward(self, x): | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| out = self.relu(out) | |
| return out | |
| class MultiHeadAttentation(nn.Module): | |
| def __init__(self, d, n_heads=2): | |
| super(MultiHeadAttentation, self).__init__() | |
| self.d = d | |
| self.n_heads = n_heads | |
| self.q_mappings = nn.ModuleList([nn.Linear(d, d) for _ in range(self.n_heads)]) | |
| self.k_mappings = nn.ModuleList([nn.Linear(d, d) for _ in range(self.n_heads)]) | |
| self.v_mappings = nn.ModuleList([nn.Linear(d, d) for _ in range(self.n_heads)]) | |
| self.o_mappings = nn.Linear(d * n_heads, d) | |
| self.softmax = nn.Softmax(dim=-1) | |
| def forward(self, sequences): | |
| # Sequences has shape (N, seq_length, token_dim) | |
| # We go into shape (N, seq_length, n_heads, token_dim / n_heads) | |
| # And come back to (N, seq_length, item_dim) (through concatenation) | |
| result_head = [] | |
| # multi-head | |
| for head in range(self.n_heads): | |
| q_mapping = self.q_mappings[head] | |
| k_mapping = self.k_mappings[head] | |
| v_mapping = self.v_mappings[head] | |
| q, k, v = q_mapping(sequences), k_mapping(sequences), v_mapping(sequences) | |
| k = torch.transpose(k, 1, 2) | |
| a = torch.matmul(q, k) | |
| attention = self.softmax(a / (self.d ** 0.5)) | |
| #print("attention: ", attention.shape) | |
| o = torch.matmul(attention, v) | |
| result_head.append(o) | |
| #print("result_head: ", len(result_head)) | |
| o = torch.cat(result_head, axis=-1) | |
| #print("output1: ", o.shape) | |
| o = self.o_mappings(o) | |
| #print("output2: ", o.shape) | |
| return o | |
| class AttentionBlock(nn.Module): | |
| def __init__(self, hidden_d, n_heads, mlp_ratio=4): | |
| super(AttentionBlock, self).__init__() | |
| self.norm1 = nn.LayerNorm(hidden_d) | |
| self.mha = MultiHeadAttentation(hidden_d, n_heads) | |
| self.norm2 = nn.LayerNorm(hidden_d) | |
| self.mlp = nn.Sequential( | |
| nn.Linear(hidden_d, mlp_ratio * hidden_d), | |
| nn.GELU(), | |
| nn.Linear(mlp_ratio * hidden_d, hidden_d) | |
| ) | |
| def forward(self, x): | |
| out = x + self.mha(self.norm1(x)) | |
| out = out + self.mlp(self.norm2(out)) | |
| return out | |
| class UNetpp3_Transformer(nn.Module): | |
| def __init__(self, num_classes, input_channels=30, n_patches=7, n_blocks=2, n_heads=2): | |
| super(UNetpp3_Transformer, self).__init__() | |
| #device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| device = torch.device("cpu") | |
| nb_filter = [32, 64, 128, 256] | |
| self.pool = nn.MaxPool1d(2) | |
| # self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) | |
| self.up = nn.Upsample(scale_factor=2, mode='linear', align_corners=False) | |
| self.positional_embeddings = self.get_positional_embeddings(30, 1024).to(device) | |
| self.blocks0_0 = nn.ModuleList([AttentionBlock(1024, n_heads) for _ in range(n_blocks)]) | |
| self.blocks1_0 = nn.ModuleList([AttentionBlock(512, n_heads) for _ in range(n_blocks)]) | |
| self.blocks2_0 = nn.ModuleList([AttentionBlock(256, n_heads) for _ in range(n_blocks)]) | |
| self.blocks3_0 = nn.ModuleList([AttentionBlock(128, n_heads) for _ in range(n_blocks)]) | |
| self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0]) | |
| self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1]) | |
| self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2]) | |
| self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3]) | |
| self.conv0_1 = VGGBlock(nb_filter[0] + nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_1 = VGGBlock(nb_filter[1] + nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv2_1 = VGGBlock(nb_filter[2] + nb_filter[3], nb_filter[2], nb_filter[2]) | |
| self.conv0_2 = VGGBlock(nb_filter[0] * 2 + nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_2 = VGGBlock(nb_filter[1] * 2 + nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv0_3 = VGGBlock(nb_filter[0] * 3 + nb_filter[1], nb_filter[0], nb_filter[0]) | |
| # if self.deep_supervision: | |
| self.final1 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final2 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final3 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| def forward(self, input): | |
| new_input = input + self.positional_embeddings | |
| for block in self.blocks0_0: | |
| x0_0 = block(new_input) | |
| x0_0 = self.conv0_0(x0_0) | |
| for block in self.blocks1_0: | |
| x1_0 = block(self.pool(x0_0)) | |
| x1_0 = self.conv1_0(x1_0) | |
| x0_1 = self.conv0_1(torch.cat([x0_0, self.up(x1_0)], 1)) | |
| #print("input:", input.shape) | |
| #print("x0_0: ", x0_0.shape) | |
| #print("pool: ", self.pool(x0_0).shape) | |
| #print("x1_0: ", x1_0.shape) | |
| #print("up: ", self.up(x1_0).shape) | |
| #print("cat: ", torch.cat([x0_0, self.up(x1_0)], 1).shape) | |
| #print("x0_1: ", x0_1.shape) | |
| for block in self.blocks2_0: | |
| x2_0 = block(self.pool(x1_0)) | |
| x2_0 = self.conv2_0(x2_0) | |
| x1_1 = self.conv1_1(torch.cat([x1_0, self.up(x2_0)], 1)) | |
| x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.up(x1_1)], 1)) | |
| for block in self.blocks3_0: | |
| x3_0 = block(self.pool(x2_0)) | |
| x3_0 = self.conv3_0(x3_0) | |
| x2_1 = self.conv2_1(torch.cat([x2_0, self.up(x3_0)], 1)) | |
| x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.up(x2_1)], 1)) | |
| x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.up(x1_2)], 1)) | |
| #if self.deep_supervision: | |
| output1 = self.final1(x0_1) | |
| output2 = self.final2(x0_2) | |
| output3 = self.final3(x0_3) | |
| return output1, output2, output3 | |
| def get_positional_embeddings(self, channel, time_len): | |
| result = torch.ones(channel, time_len) | |
| for i in range(channel): | |
| for j in range(time_len): | |
| result[i][j] = np.sin(j / (10000 ** (i / channel))) if i % 2 == 0 else np.cos(j / (10000 ** ((i - 1) / channel))) | |
| return result | |
| class UNet(nn.Module): | |
| def __init__(self, num_classes, input_channels=3, **kwargs): | |
| super().__init__() | |
| nb_filter = [32, 64, 128, 256, 512] | |
| self.pool = nn.MaxPool1d(2) | |
| #self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) | |
| self.up = nn.Upsample(scale_factor=2, mode='linear', align_corners=False) | |
| # input_channel => 32; 32 => 64; 64=>128; 128=>256 | |
| self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0]) | |
| self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1]) | |
| self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2]) | |
| self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3]) | |
| self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4]) | |
| self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3]) | |
| self.conv2_2 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2]) | |
| self.conv1_3 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv0_4 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.final = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| def forward(self, input): | |
| x0_0 = self.conv0_0(input) | |
| x1_0 = self.conv1_0(self.pool(x0_0)) | |
| x2_0 = self.conv2_0(self.pool(x1_0)) | |
| x3_0 = self.conv3_0(self.pool(x2_0)) | |
| x4_0 = self.conv4_0(self.pool(x3_0)) | |
| x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1)) | |
| x2_2 = self.conv2_2(torch.cat([x2_0, self.up(x3_1)], 1)) | |
| x1_3 = self.conv1_3(torch.cat([x1_0, self.up(x2_2)], 1)) | |
| x0_4 = self.conv0_4(torch.cat([x0_0, self.up(x1_3)], 1)) | |
| output = self.final(x0_4) | |
| return output | |
| class NestedUNet3(nn.Module): | |
| def __init__(self, num_classes, input_channels=30, deep_supervision=False, **kwargs): | |
| super().__init__() | |
| nb_filter = [32, 64, 128, 256] | |
| self.deep_supervision = deep_supervision | |
| self.pool = nn.MaxPool1d(2) | |
| # self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) | |
| self.up = nn.Upsample(scale_factor=2, mode='linear', align_corners=False) | |
| self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0]) | |
| self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1]) | |
| self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2]) | |
| self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3]) | |
| self.conv0_1 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_1 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv2_1 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2]) | |
| self.conv0_2 = VGGBlock(nb_filter[0]*2+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_2 = VGGBlock(nb_filter[1]*2+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv0_3 = VGGBlock(nb_filter[0]*3+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| #if self.deep_supervision: | |
| self.final1 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final2 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final3 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| #else: | |
| #self.final = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| def forward(self, input): | |
| x0_0 = self.conv0_0(input) | |
| x1_0 = self.conv1_0(self.pool(x0_0)) | |
| x0_1 = self.conv0_1(torch.cat([x0_0, self.up(x1_0)], 1)) | |
| #print("input:", input.shape) | |
| #print("x0_0: ", x0_0.shape) | |
| #print("pool: ", self.pool(x0_0).shape) | |
| #print("x1_0: ", x1_0.shape) | |
| #print("up: ", self.up(x1_0).shape) | |
| #print("cat: ", torch.cat([x0_0, self.up(x1_0)], 1).shape) | |
| #print("x0_1: ", x0_1.shape) | |
| x2_0 = self.conv2_0(self.pool(x1_0)) | |
| x1_1 = self.conv1_1(torch.cat([x1_0, self.up(x2_0)], 1)) | |
| x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.up(x1_1)], 1)) | |
| x3_0 = self.conv3_0(self.pool(x2_0)) | |
| x2_1 = self.conv2_1(torch.cat([x2_0, self.up(x3_0)], 1)) | |
| x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.up(x2_1)], 1)) | |
| x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.up(x1_2)], 1)) | |
| #if self.deep_supervision: | |
| output1 = self.final1(x0_1) | |
| output2 = self.final2(x0_2) | |
| output3 = self.final3(x0_3) | |
| return output1, output2, output3 | |
| """ | |
| else: | |
| output = self.final(x0_4) | |
| return output | |
| """ | |
| class NestedUNet4(nn.Module): | |
| def __init__(self, num_classes, input_channels=30, deep_supervision=False, **kwargs): | |
| super().__init__() | |
| nb_filter = [32, 64, 128, 256, 512] | |
| self.deep_supervision = deep_supervision | |
| self.pool = nn.MaxPool1d(2) | |
| # self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) | |
| self.up = nn.Upsample(scale_factor=2, mode='linear', align_corners=False) | |
| self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0]) | |
| self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1]) | |
| self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2]) | |
| self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3]) | |
| self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4]) | |
| self.conv0_1 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_1 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv2_1 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2]) | |
| self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3]) | |
| self.conv0_2 = VGGBlock(nb_filter[0]*2+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_2 = VGGBlock(nb_filter[1]*2+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv2_2 = VGGBlock(nb_filter[2]*2+nb_filter[3], nb_filter[2], nb_filter[2]) | |
| self.conv0_3 = VGGBlock(nb_filter[0]*3+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_3 = VGGBlock(nb_filter[1]*3+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv0_4 = VGGBlock(nb_filter[0]*4+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| #if self.deep_supervision: | |
| self.final1 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final2 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final3 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final4 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| #else: | |
| #self.final = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| def forward(self, input): | |
| x0_0 = self.conv0_0(input) | |
| x1_0 = self.conv1_0(self.pool(x0_0)) | |
| x0_1 = self.conv0_1(torch.cat([x0_0, self.up(x1_0)], 1)) | |
| #print("input:", input.shape) | |
| #print("x0_0: ", x0_0.shape) | |
| #print("pool: ", self.pool(x0_0).shape) | |
| #print("x1_0: ", x1_0.shape) | |
| #print("up: ", self.up(x1_0).shape) | |
| #print("cat: ", torch.cat([x0_0, self.up(x1_0)], 1).shape) | |
| #print("x0_1: ", x0_1.shape) | |
| x2_0 = self.conv2_0(self.pool(x1_0)) | |
| x1_1 = self.conv1_1(torch.cat([x1_0, self.up(x2_0)], 1)) | |
| x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.up(x1_1)], 1)) | |
| x3_0 = self.conv3_0(self.pool(x2_0)) | |
| x2_1 = self.conv2_1(torch.cat([x2_0, self.up(x3_0)], 1)) | |
| x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.up(x2_1)], 1)) | |
| x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.up(x1_2)], 1)) | |
| x4_0 = self.conv4_0(self.pool(x3_0)) | |
| x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1)) | |
| x2_2 = self.conv2_2(torch.cat([x2_0, x2_1, self.up(x3_1)], 1)) | |
| x1_3 = self.conv1_3(torch.cat([x1_0, x1_1, x1_2, self.up(x2_2)], 1)) | |
| x0_4 = self.conv0_4(torch.cat([x0_0, x0_1, x0_2, x0_3, self.up(x1_3)], 1)) | |
| #if self.deep_supervision: | |
| output1 = self.final1(x0_1) | |
| output2 = self.final2(x0_2) | |
| output3 = self.final3(x0_3) | |
| output4 = self.final4(x0_4) | |
| return output1, output2, output3, output4 | |
| """ | |
| else: | |
| output = self.final(x0_4) | |
| return output | |
| """ | |
| class NestedUNet5(nn.Module): | |
| def __init__(self, num_classes, input_channels=30, deep_supervision=False, **kwargs): | |
| super().__init__() | |
| nb_filter = [32, 64, 128, 256, 512, 1024] | |
| self.deep_supervision = deep_supervision | |
| self.pool = nn.MaxPool1d(2) | |
| # self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) | |
| self.up = nn.Upsample(scale_factor=2, mode='linear', align_corners=False) | |
| self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0]) | |
| self.conv1_0 = VGGBlock(nb_filter[0], nb_filter[1], nb_filter[1]) | |
| self.conv2_0 = VGGBlock(nb_filter[1], nb_filter[2], nb_filter[2]) | |
| self.conv3_0 = VGGBlock(nb_filter[2], nb_filter[3], nb_filter[3]) | |
| self.conv4_0 = VGGBlock(nb_filter[3], nb_filter[4], nb_filter[4]) | |
| self.conv5_0 = VGGBlock(nb_filter[4], nb_filter[5], nb_filter[5]) | |
| self.conv0_1 = VGGBlock(nb_filter[0]+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_1 = VGGBlock(nb_filter[1]+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv2_1 = VGGBlock(nb_filter[2]+nb_filter[3], nb_filter[2], nb_filter[2]) | |
| self.conv3_1 = VGGBlock(nb_filter[3]+nb_filter[4], nb_filter[3], nb_filter[3]) | |
| self.conv4_1 = VGGBlock(nb_filter[4]+nb_filter[5], nb_filter[4], nb_filter[4]) | |
| self.conv0_2 = VGGBlock(nb_filter[0]*2+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_2 = VGGBlock(nb_filter[1]*2+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv2_2 = VGGBlock(nb_filter[2]*2+nb_filter[3], nb_filter[2], nb_filter[2]) | |
| self.conv3_2 = VGGBlock(nb_filter[3]*2+nb_filter[4], nb_filter[3], nb_filter[3]) | |
| self.conv0_3 = VGGBlock(nb_filter[0]*3+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_3 = VGGBlock(nb_filter[1]*3+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv2_3 = VGGBlock(nb_filter[2]*3+nb_filter[3], nb_filter[2], nb_filter[2]) | |
| self.conv0_4 = VGGBlock(nb_filter[0]*4+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| self.conv1_4 = VGGBlock(nb_filter[1]*4+nb_filter[2], nb_filter[1], nb_filter[1]) | |
| self.conv0_5 = VGGBlock(nb_filter[0]*5+nb_filter[1], nb_filter[0], nb_filter[0]) | |
| #if self.deep_supervision: | |
| self.final1 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final2 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final3 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final4 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| self.final5 = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| #else: | |
| #self.final = nn.Conv1d(nb_filter[0], num_classes, kernel_size=1) | |
| def forward(self, input): | |
| x0_0 = self.conv0_0(input) | |
| x1_0 = self.conv1_0(self.pool(x0_0)) | |
| x0_1 = self.conv0_1(torch.cat([x0_0, self.up(x1_0)], 1)) | |
| #print("input:", input.shape) | |
| #print("x0_0: ", x0_0.shape) | |
| #print("pool: ", self.pool(x0_0).shape) | |
| #print("x1_0: ", x1_0.shape) | |
| #print("up: ", self.up(x1_0).shape) | |
| #print("cat: ", torch.cat([x0_0, self.up(x1_0)], 1).shape) | |
| #print("x0_1: ", x0_1.shape) | |
| x2_0 = self.conv2_0(self.pool(x1_0)) | |
| x1_1 = self.conv1_1(torch.cat([x1_0, self.up(x2_0)], 1)) | |
| x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.up(x1_1)], 1)) | |
| x3_0 = self.conv3_0(self.pool(x2_0)) | |
| x2_1 = self.conv2_1(torch.cat([x2_0, self.up(x3_0)], 1)) | |
| x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.up(x2_1)], 1)) | |
| x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.up(x1_2)], 1)) | |
| x4_0 = self.conv4_0(self.pool(x3_0)) | |
| x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1)) | |
| x2_2 = self.conv2_2(torch.cat([x2_0, x2_1, self.up(x3_1)], 1)) | |
| x1_3 = self.conv1_3(torch.cat([x1_0, x1_1, x1_2, self.up(x2_2)], 1)) | |
| x0_4 = self.conv0_4(torch.cat([x0_0, x0_1, x0_2, x0_3, self.up(x1_3)], 1)) | |
| x5_0 = self.conv5_0(self.pool(x4_0)) | |
| x4_1 = self.conv4_1(torch.cat([x4_0, self.up(x5_0)], 1)) | |
| x3_2 = self.conv3_2(torch.cat([x3_0, x3_1, self.up(x4_1)], 1)) | |
| x2_3 = self.conv2_3(torch.cat([x2_0, x2_1, x2_2, self.up(x3_2)], 1)) | |
| x1_4 = self.conv1_4(torch.cat([x1_0, x1_1, x1_2, x1_3, self.up(x2_3)], 1)) | |
| x0_5 = self.conv0_5(torch.cat([x0_0, x0_1, x0_2, x0_3, x0_4, self.up(x1_4)], 1)) | |
| #if self.deep_supervision: | |
| output1 = self.final1(x0_1) | |
| output2 = self.final2(x0_2) | |
| output3 = self.final3(x0_3) | |
| output4 = self.final4(x0_4) | |
| output5 = self.final5(x0_5) | |
| return output1, output2, output3, output4, output5 | |
| """ | |
| else: | |
| output = self.final(x0_4) | |
| return output | |
| """ | |