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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 | |