File size: 5,386 Bytes
984cdba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
# -------------------------------------------------
# Basic blocks
# -------------------------------------------------
class DoubleConv(nn.Module):
"""(Conv => BN => ReLU) * 2"""
def __init__(self, in_ch, out_ch):
super().__init__()
self.net = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.net(x)
class Down(nn.Module):
"""Downscaling with maxpool then double conv"""
def __init__(self, in_ch, out_ch):
super().__init__()
self.net = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_ch, out_ch)
)
def forward(self, x):
return self.net(x)
class Up(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_ch, out_ch):
super().__init__()
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)
self.conv = DoubleConv(in_ch, out_ch)
def forward(self, x, skip):
x = self.up(x)
if skip is not None:
# pad if needed (for odd input sizes)
diffY = skip.size(2) - x.size(2)
diffX = skip.size(3) - x.size(3)
x = F.pad(x, [
diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2
])
x = torch.cat([skip, x], dim=1)
return self.conv(x)
class OutConv(nn.Module):
def __init__(self, in_ch, num_classes):
super().__init__()
self.conv = nn.Conv2d(in_ch, num_classes, 1)
def forward(self, x):
return self.conv(x)
class ASPP(nn.Module):
def __init__(self, in_channels, out_channels):
super(ASPP, self).__init__()
self.atrous_block1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0, dilation=1)
self.atrous_block6 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=6, dilation=6)
self.atrous_block12 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=12, dilation=12)
self.atrous_block18 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=18, dilation=18)
self.lower_features = nn.Sequential(
nn.AvgPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0, dilation=1),
nn.Sequential(
nn.Upsample(scale_factor=2, mode="bilinear", align_corners=False),
nn.Conv2d(out_channels, out_channels, kernel_size=1, bias=False)
)
)
self.conv_1x1_output = nn.Conv2d(out_channels * 5, out_channels, kernel_size=1)
def forward(self, x):
x1 = self.atrous_block1(x)
x2 = self.atrous_block6(x)
x3 = self.atrous_block12(x)
x4 = self.atrous_block18(x)
x5 = self.lower_features(x)
x = torch.cat((x1, x2, x3, x4, x5), dim=1)
x = self.conv_1x1_output(x)
return x
class ResNetEncoder(nn.Module):
def __init__(self, in_channels=3, pretrained=True):
super().__init__()
resnet = models.resnet34(weights="IMAGENET1K_V1" if pretrained else None)
if in_channels != 3:
resnet.conv1 = nn.Conv2d(
in_channels, 64, kernel_size=7, stride=2, padding=3, bias=False
)
self.initial = nn.Sequential(
resnet.conv1,
resnet.bn1,
resnet.relu
)
self.maxpool = resnet.maxpool # 64x64
self.layer1 = resnet.layer1 # 64x64
self.layer2 = resnet.layer2 # 32x32
self.layer3 = resnet.layer3 # 16x16
self.layer4 = resnet.layer4 # 8x8
def forward(self, x):
x1 = self.initial(x)
x2 = self.layer1(self.maxpool(x1))
x3 = self.layer2(x2)
x4 = self.layer3(x3)
x5 = self.layer4(x4)
return x1, x2, x3, x4, x5
# -------------------------------------------------
# Standard U-Net
# -------------------------------------------------
class model(nn.Module):
def __init__(self, in_channels=3, num_classes=1, freeze_encoder=False):
super().__init__()
self.encoder = ResNetEncoder(in_channels, pretrained=True)
if freeze_encoder:
for p in self.encoder.parameters():
p.requires_grad = False
self.up1 = Up(512 + 256, 256)
self.up2 = Up(256 + 128, 128)
self.up3 = Up(128 + 64, 64)
self.up4 = Up(64 + 64, 64)
self.up5 = Up(64, 64)
self.aspp1 = ASPP(64, 64)
self.aspp2 = ASPP(64, 64)
self.aspp3 = ASPP(128, 128)
self.aspp4 = ASPP(256, 256)
self.outc = OutConv(64, num_classes)
def forward(self, x):
x1, x2, x3, x4, x5 = self.encoder(x)
s4 = self.aspp4(x4)
s3 = self.aspp3(x3)
s2 = self.aspp2(x2)
s1 = self.aspp1(x1)
x = self.up1(x5, s4)
x = self.up2(x, s3)
x = self.up3(x, s2)
x = self.up4(x, s1)
x = self.up5(x, None)
return self.outc(x) |