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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import torch.nn as nn
from scripts.maskscratches import Downsample
from scripts.maskscratches import DataParallelWithCallback
class UNet(nn.Module):
def __init__(
self,
in_channels=3,
out_channels=3,
depth=5,
conv_num=2,
wf=6,
padding=True,
batch_norm=True,
up_mode="upsample",
with_tanh=False,
sync_bn=True,
antialiasing=True,
):
"""
Implementation of
U-Net: Convolutional Networks for Biomedical Image Segmentation
(Ronneberger et al., 2015)
https://arxiv.org/abs/1505.04597
Using the default arguments will yield the exact version used
in the original paper
Args:
in_channels (int): number of input channels
out_channels (int): number of output channels
depth (int): depth of the network
wf (int): number of filters in the first layer is 2**wf
padding (bool): if True, apply padding such that the input shape
is the same as the output.
This may introduce artifacts
batch_norm (bool): Use BatchNorm after layers with an
activation function
up_mode (str): one of 'upconv' or 'upsample'.
'upconv' will use transposed convolutions for
learned upsampling.
'upsample' will use bilinear upsampling.
"""
super().__init__()
assert up_mode in ("upconv", "upsample")
self.padding = padding
self.depth = depth - 1
prev_channels = in_channels
self.first = nn.Sequential(
*[nn.ReflectionPad2d(3), nn.Conv2d(in_channels, 2 ** wf, kernel_size=7), nn.LeakyReLU(0.2, True)]
)
prev_channels = 2 ** wf
self.down_path = nn.ModuleList()
self.down_sample = nn.ModuleList()
for i in range(depth):
if antialiasing and depth > 0:
self.down_sample.append(
nn.Sequential(
*[
nn.ReflectionPad2d(1),
nn.Conv2d(prev_channels, prev_channels, kernel_size=3, stride=1, padding=0),
nn.BatchNorm2d(prev_channels),
nn.LeakyReLU(0.2, True),
Downsample(channels=prev_channels, stride=2),
]
)
)
else:
self.down_sample.append(
nn.Sequential(
*[
nn.ReflectionPad2d(1),
nn.Conv2d(prev_channels, prev_channels, kernel_size=4, stride=2, padding=0),
nn.BatchNorm2d(prev_channels),
nn.LeakyReLU(0.2, True),
]
)
)
self.down_path.append(
UNetConvBlock(conv_num, prev_channels, 2 ** (wf + i + 1), padding, batch_norm)
)
prev_channels = 2 ** (wf + i + 1)
self.up_path = nn.ModuleList()
for i in reversed(range(depth)):
self.up_path.append(
UNetUpBlock(conv_num, prev_channels, 2 ** (wf + i), up_mode, padding, batch_norm)
)
prev_channels = 2 ** (wf + i)
if with_tanh:
self.last = nn.Sequential(
*[nn.ReflectionPad2d(1), nn.Conv2d(prev_channels, out_channels, kernel_size=3), nn.Tanh()]
)
else:
self.last = nn.Sequential(
*[nn.ReflectionPad2d(1), nn.Conv2d(prev_channels, out_channels, kernel_size=3)]
)
if sync_bn:
self = DataParallelWithCallback(self)
def forward(self, x):
x = self.first(x)
blocks = []
for i, down_block in enumerate(self.down_path):
blocks.append(x)
x = self.down_sample[i](x)
x = down_block(x)
for i, up in enumerate(self.up_path):
x = up(x, blocks[-i - 1])
return self.last(x)
class UNetConvBlock(nn.Module):
def __init__(self, conv_num, in_size, out_size, padding, batch_norm):
super(UNetConvBlock, self).__init__()
block = []
for _ in range(conv_num):
block.append(nn.ReflectionPad2d(padding=int(padding)))
block.append(nn.Conv2d(in_size, out_size, kernel_size=3, padding=0))
if batch_norm:
block.append(nn.BatchNorm2d(out_size))
block.append(nn.LeakyReLU(0.2, True))
in_size = out_size
self.block = nn.Sequential(*block)
def forward(self, x):
out = self.block(x)
return out
class UNetUpBlock(nn.Module):
def __init__(self, conv_num, in_size, out_size, up_mode, padding, batch_norm):
super(UNetUpBlock, self).__init__()
if up_mode == "upconv":
self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=2, stride=2)
elif up_mode == "upsample":
self.up = nn.Sequential(
nn.Upsample(mode="bilinear", scale_factor=2, align_corners=False),
nn.ReflectionPad2d(1),
nn.Conv2d(in_size, out_size, kernel_size=3, padding=0),
)
self.conv_block = UNetConvBlock(conv_num, in_size, out_size, padding, batch_norm)
def center_crop(self, layer, target_size):
_, _, layer_height, layer_width = layer.size()
diff_y = (layer_height - target_size[0]) // 2
diff_x = (layer_width - target_size[1]) // 2
return layer[:, :, diff_y: (diff_y + target_size[0]), diff_x: (diff_x + target_size[1])]
def forward(self, x, bridge):
up = self.up(x)
crop1 = self.center_crop(bridge, up.shape[2:])
out = torch.cat([up, crop1], 1)
out = self.conv_block(out)
return out
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