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37ad22e | 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 | import torch
import torch.nn as nn
import torchvision.transforms.functional as TF
from torch.optim.lr_scheduler import ReduceLROnPlateau
import torch.nn.functional as F
class DoubleConv(nn.Module):
def __init__(self, in_channels, out_channels, dropout_prob=0.0):
super(DoubleConv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Dropout2d(p=dropout_prob) # Add dropout layer
)
def forward(self, x):
return self.conv(x)
class UNET(nn.Module):
def __init__(
self, in_channels=3, out_channels=1, features=[64, 128, 256, 512], dropout_prob=0.0,
):
super(UNET, self).__init__()
self.ups = nn.ModuleList()
self.downs = nn.ModuleList()
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
# Down part of UNET
for feature in features:
self.downs.append(DoubleConv(in_channels, feature, dropout_prob=dropout_prob))
in_channels = feature
# Up part of UNET
for feature in reversed(features):
self.ups.append(
nn.ConvTranspose2d(
feature * 2, feature, kernel_size=2, stride=2,
)
)
self.ups.append(DoubleConv(feature * 2, feature, dropout_prob=dropout_prob))
self.bottleneck = DoubleConv(features[-1], features[-1] * 2, dropout_prob=dropout_prob)
self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)
# Gradient clipping
self.gradient_clip = nn.utils.clip_grad_norm_
def forward(self, x):
skip_connections = []
for down in self.downs:
x = down(x)
skip_connections.append(x)
x = self.pool(x)
x = self.bottleneck(x)
skip_connections = skip_connections[::-1]
for idx in range(0, len(self.ups), 2):
x = self.ups[idx](x)
skip_connection = skip_connections[idx // 2]
# Adjust padding to ensure skip connection compatibility
diffY = skip_connection.size()[2] - x.size()[2]
diffX = skip_connection.size()[3] - x.size()[3]
x = F.pad(x, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
concat_skip = torch.cat((skip_connection, x), dim=1)
x = self.ups[idx + 1](concat_skip)
return self.final_conv(x)
def test():
x = torch.randn((3, 1, 161, 161))
model = UNET(in_channels=1, out_channels=1)
preds = model(x)
assert preds.shape == x.shape
if __name__ == '__main__':
test()
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