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fcrn.py
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fcrn.py
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import torch
import torch.nn as nn
import torch.nn.functional
import math
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1,
bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class UpProject(nn.Module):
def __init__(self, in_channels, out_channels, batch_size):
super(UpProject, self).__init__()
self.batch_size = batch_size
self.conv1_1 = nn.Conv2d(in_channels, out_channels, 3)
self.conv1_2 = nn.Conv2d(in_channels, out_channels, (2, 3))
self.conv1_3 = nn.Conv2d(in_channels, out_channels, (3, 2))
self.conv1_4 = nn.Conv2d(in_channels, out_channels, 2)
self.conv2_1 = nn.Conv2d(in_channels, out_channels, 3)
self.conv2_2 = nn.Conv2d(in_channels, out_channels, (2, 3))
self.conv2_3 = nn.Conv2d(in_channels, out_channels, (3, 2))
self.conv2_4 = nn.Conv2d(in_channels, out_channels, 2)
self.bn1_1 = nn.BatchNorm2d(out_channels)
self.bn1_2 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
self.conv3 = nn.Conv2d(out_channels, out_channels, 3, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
def forward(self, x):
out1_1 = self.conv1_1(nn.functional.pad(x, (1, 1, 1, 1)))
#out1_2 = self.conv1_2(nn.functional.pad(x, (1, 1, 0, 1)))#right interleaving padding
out1_2 = self.conv1_2(nn.functional.pad(x, (1, 1, 1, 0)))#author's interleaving pading in github
#out1_3 = self.conv1_3(nn.functional.pad(x, (0, 1, 1, 1)))#right interleaving padding
out1_3 = self.conv1_3(nn.functional.pad(x, (1, 0, 1, 1)))#author's interleaving pading in github
#out1_4 = self.conv1_4(nn.functional.pad(x, (0, 1, 0, 1)))#right interleaving padding
out1_4 = self.conv1_4(nn.functional.pad(x, (1, 0, 1, 0)))#author's interleaving pading in github
out2_1 = self.conv2_1(nn.functional.pad(x, (1, 1, 1, 1)))
#out2_2 = self.conv2_2(nn.functional.pad(x, (1, 1, 0, 1)))#right interleaving padding
out2_2 = self.conv2_2(nn.functional.pad(x, (1, 1, 1, 0)))#author's interleaving pading in github
#out2_3 = self.conv2_3(nn.functional.pad(x, (0, 1, 1, 1)))#right interleaving padding
out2_3 = self.conv2_3(nn.functional.pad(x, (1, 0, 1, 1)))#author's interleaving pading in github
#out2_4 = self.conv2_4(nn.functional.pad(x, (0, 1, 0, 1)))#right interleaving padding
out2_4 = self.conv2_4(nn.functional.pad(x, (1, 0, 1, 0)))#author's interleaving pading in github
height = out1_1.size()[2]
width = out1_1.size()[3]
out1_1_2 = torch.stack((out1_1, out1_2), dim=-3).permute(0, 1, 3, 4, 2).contiguous().view(
self.batch_size, -1, height, width * 2)
out1_3_4 = torch.stack((out1_3, out1_4), dim=-3).permute(0, 1, 3, 4, 2).contiguous().view(
self.batch_size, -1, height, width * 2)
out1_1234 = torch.stack((out1_1_2, out1_3_4), dim=-3).permute(0, 1, 3, 2, 4).contiguous().view(
self.batch_size, -1, height * 2, width * 2)
out2_1_2 = torch.stack((out2_1, out2_2), dim=-3).permute(0, 1, 3, 4, 2).contiguous().view(
self.batch_size, -1, height, width * 2)
out2_3_4 = torch.stack((out2_3, out2_4), dim=-3).permute(0, 1, 3, 4, 2).contiguous().view(
self.batch_size, -1, height, width * 2)
out2_1234 = torch.stack((out2_1_2, out2_3_4), dim=-3).permute(0, 1, 3, 2, 4).contiguous().view(
self.batch_size, -1, height * 2, width * 2)
out1 = self.bn1_1(out1_1234)
out1 = self.relu(out1)
out1 = self.conv3(out1)
out1 = self.bn2(out1)
out2 = self.bn1_2(out2_1234)
out = out1 + out2
out = self.relu(out)
return out
class FCRN(nn.Module):
def __init__(self, batch_size):
super(FCRN, self).__init__()
self.inplanes = 64
self.batch_size = batch_size
# ResNet with out avrgpool & fc
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(Bottleneck, 64, 3)
self.layer2 = self._make_layer(Bottleneck, 128, 4, stride=2)
self.layer3 = self._make_layer(Bottleneck, 256, 6, stride=2)
self.layer4 = self._make_layer(Bottleneck, 512, 3, stride=2)
# Up-Conv layers
self.conv2 = nn.Conv2d(2048, 1024, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d(1024)
self.up1 = self._make_upproj_layer(UpProject, 1024, 512, self.batch_size)
self.up2 = self._make_upproj_layer(UpProject, 512, 256, self.batch_size)
self.up3 = self._make_upproj_layer(UpProject, 256, 128, self.batch_size)
self.up4 = self._make_upproj_layer(UpProject, 128, 64, self.batch_size)
self.drop = nn.Dropout2d()
self.conv3 = nn.Conv2d(64, 1, 3, padding=1)
self.upsample = nn.Upsample((228, 304), mode='bilinear')
# initialize
if True:
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1,
stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def _make_upproj_layer(self, block, in_channels, out_channels, batch_size):
return block(in_channels, out_channels, batch_size)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.up1(x)
x = self.up2(x)
x = self.up3(x)
x = self.up4(x)
x = self.drop(x)
x = self.conv3(x)
x = self.relu(x)
x = self.upsample(x)
return x