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import torch
from torch import nn
__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 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
"""
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