File size: 3,986 Bytes
29b2705 |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
class DoubleConv(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv = nn.Sequential(
nn.Conv1d(in_channels, mid_channels, kernel_size=3, padding=1),
nn.GroupNorm(num_groups=4, num_channels=mid_channels),
nn.ReLU(inplace=True),
nn.Conv1d(mid_channels, out_channels, kernel_size=3, padding=1),
nn.GroupNorm(num_groups=4, num_channels=out_channels),
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.double_conv(x)
class DoubleConvX(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
if not mid_channels:
mid_channels = out_channels
self.double_conv = nn.Sequential(
nn.Conv1d(in_channels, mid_channels, kernel_size=15, padding=7),
nn.GroupNorm(num_groups=8, num_channels=mid_channels),
nn.ReLU(inplace=True),
nn.Conv1d(mid_channels, out_channels, kernel_size=15, padding=7),
nn.GroupNorm(num_groups=8, num_channels=out_channels),
nn.ReLU(inplace=True),
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
"""Downscaling with maxpool then double conv"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool1d(2), DoubleConv(in_channels, out_channels)
)
def forward(self, x):
return self.maxpool_conv(x)
class Up(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.up = nn.Upsample(scale_factor=2, mode="linear", align_corners=True)
self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
def forward(self, x1, x2):
x1 = self.up(x1)
# input is CHW
diffX = x2.size()[2] - x1.size()[2]
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2])
# if you have padding issues, see
# https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
# https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
x = torch.cat([x2, x1], dim=1)
return self.conv(x)
class OutConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutConv, self).__init__()
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(x)
class UNet1d(nn.Module):
def __init__(self, n_channels, n_classes, nfilter=24):
super(UNet1d, self).__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.inc = DoubleConv(n_channels, nfilter)
self.down1 = Down(nfilter, nfilter * 2)
self.down2 = Down(nfilter * 2, nfilter * 4)
self.down3 = Down(nfilter * 4, nfilter * 8)
self.down4 = Down(nfilter * 8, nfilter * 8)
self.up1 = Up(nfilter * 16, nfilter * 4)
self.up2 = Up(nfilter * 8, nfilter * 2)
self.up3 = Up(nfilter * 4, nfilter * 1)
self.up4 = Up(nfilter * 2, nfilter)
self.outc = OutConv(nfilter, n_classes)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
logits = self.outc(x)
return logits
if __name__ == "__main__":
model = UNet1d(1, 1)
print(model)
|