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# Copyright 2023 Xiaomi Corp. (authors: Fangjun Kuang)
import torch
class UNet(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv = torch.nn.Conv2d(2, 16, kernel_size=5, stride=(2, 2), padding=0)
self.bn = torch.nn.BatchNorm2d(
16, track_running_stats=True, eps=1e-3, momentum=0.01
)
#
self.conv1 = torch.nn.Conv2d(16, 32, kernel_size=5, stride=(2, 2), padding=0)
self.bn1 = torch.nn.BatchNorm2d(
32, track_running_stats=True, eps=1e-3, momentum=0.01
)
self.conv2 = torch.nn.Conv2d(32, 64, kernel_size=5, stride=(2, 2), padding=0)
self.bn2 = torch.nn.BatchNorm2d(
64, track_running_stats=True, eps=1e-3, momentum=0.01
)
self.conv3 = torch.nn.Conv2d(64, 128, kernel_size=5, stride=(2, 2), padding=0)
self.bn3 = torch.nn.BatchNorm2d(
128, track_running_stats=True, eps=1e-3, momentum=0.01
)
self.conv4 = torch.nn.Conv2d(128, 256, kernel_size=5, stride=(2, 2), padding=0)
self.bn4 = torch.nn.BatchNorm2d(
256, track_running_stats=True, eps=1e-3, momentum=0.01
)
self.conv5 = torch.nn.Conv2d(256, 512, kernel_size=5, stride=(2, 2), padding=0)
self.up1 = torch.nn.ConvTranspose2d(512, 256, kernel_size=5, stride=2)
self.bn5 = torch.nn.BatchNorm2d(
256, track_running_stats=True, eps=1e-3, momentum=0.01
)
self.up2 = torch.nn.ConvTranspose2d(512, 128, kernel_size=5, stride=2)
self.bn6 = torch.nn.BatchNorm2d(
128, track_running_stats=True, eps=1e-3, momentum=0.01
)
self.up3 = torch.nn.ConvTranspose2d(256, 64, kernel_size=5, stride=2)
self.bn7 = torch.nn.BatchNorm2d(
64, track_running_stats=True, eps=1e-3, momentum=0.01
)
self.up4 = torch.nn.ConvTranspose2d(128, 32, kernel_size=5, stride=2)
self.bn8 = torch.nn.BatchNorm2d(
32, track_running_stats=True, eps=1e-3, momentum=0.01
)
self.up5 = torch.nn.ConvTranspose2d(64, 16, kernel_size=5, stride=2)
self.bn9 = torch.nn.BatchNorm2d(
16, track_running_stats=True, eps=1e-3, momentum=0.01
)
self.up6 = torch.nn.ConvTranspose2d(32, 1, kernel_size=5, stride=2)
self.bn10 = torch.nn.BatchNorm2d(
1, track_running_stats=True, eps=1e-3, momentum=0.01
)
# output logit is False, so we need self.up7
self.up7 = torch.nn.Conv2d(1, 2, kernel_size=4, dilation=2, padding=3)
def forward(self, x):
in_x = x
# in_x is (3, 2, 512, 1024) = (T, 2, 512, 1024)
x = torch.nn.functional.pad(x, (1, 2, 1, 2), "constant", 0)
conv1 = self.conv(x)
batch1 = self.bn(conv1)
rel1 = torch.nn.functional.leaky_relu(batch1, negative_slope=0.2)
x = torch.nn.functional.pad(rel1, (1, 2, 1, 2), "constant", 0)
conv2 = self.conv1(x) # (3, 32, 128, 256)
batch2 = self.bn1(conv2)
rel2 = torch.nn.functional.leaky_relu(
batch2, negative_slope=0.2
) # (3, 32, 128, 256)
x = torch.nn.functional.pad(rel2, (1, 2, 1, 2), "constant", 0)
conv3 = self.conv2(x) # (3, 64, 64, 128)
batch3 = self.bn2(conv3)
rel3 = torch.nn.functional.leaky_relu(
batch3, negative_slope=0.2
) # (3, 64, 64, 128)
x = torch.nn.functional.pad(rel3, (1, 2, 1, 2), "constant", 0)
conv4 = self.conv3(x) # (3, 128, 32, 64)
batch4 = self.bn3(conv4)
rel4 = torch.nn.functional.leaky_relu(
batch4, negative_slope=0.2
) # (3, 128, 32, 64)
x = torch.nn.functional.pad(rel4, (1, 2, 1, 2), "constant", 0)
conv5 = self.conv4(x) # (3, 256, 16, 32)
batch5 = self.bn4(conv5)
rel6 = torch.nn.functional.leaky_relu(
batch5, negative_slope=0.2
) # (3, 256, 16, 32)
x = torch.nn.functional.pad(rel6, (1, 2, 1, 2), "constant", 0)
conv6 = self.conv5(x) # (3, 512, 8, 16)
up1 = self.up1(conv6)
up1 = up1[:, :, 1:-2, 1:-2] # (3, 256, 16, 32)
up1 = torch.nn.functional.relu(up1)
batch7 = self.bn5(up1)
merge1 = torch.cat([conv5, batch7], axis=1) # (3, 512, 16, 32)
up2 = self.up2(merge1)
up2 = up2[:, :, 1:-2, 1:-2]
up2 = torch.nn.functional.relu(up2)
batch8 = self.bn6(up2)
merge2 = torch.cat([conv4, batch8], axis=1) # (3, 256, 32, 64)
up3 = self.up3(merge2)
up3 = up3[:, :, 1:-2, 1:-2]
up3 = torch.nn.functional.relu(up3)
batch9 = self.bn7(up3)
merge3 = torch.cat([conv3, batch9], axis=1) # (3, 128, 64, 128)
up4 = self.up4(merge3)
up4 = up4[:, :, 1:-2, 1:-2]
up4 = torch.nn.functional.relu(up4)
batch10 = self.bn8(up4)
merge4 = torch.cat([conv2, batch10], axis=1) # (3, 64, 128, 256)
up5 = self.up5(merge4)
up5 = up5[:, :, 1:-2, 1:-2]
up5 = torch.nn.functional.relu(up5)
batch11 = self.bn9(up5)
merge5 = torch.cat([conv1, batch11], axis=1) # (3, 32, 256, 512)
up6 = self.up6(merge5)
up6 = up6[:, :, 1:-2, 1:-2]
up6 = torch.nn.functional.relu(up6)
batch12 = self.bn10(up6) # (3, 1, 512, 1024) = (T, 1, 512, 1024)
up7 = self.up7(batch12)
up7 = torch.sigmoid(up7) # (3, 2, 512, 1024)
return up7 * in_x
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