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resnet.py
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import torch
from torch import nn
import torch.nn.functional as F
from pairwise import Pairwise
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
stride=stride, bias=False, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion,
kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
self.relu = nn.ReLU()
self.downsample = downsample
def forward(self, x):
shortcut = 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)
out = self.relu(out)
if self.downsample is not None:
shortcut = self.downsample(x)
out += shortcut
out = self.relu(out)
return out
class DeconvBottleneck(nn.Module):
def __init__(self, in_channels, out_channels, expansion=2, stride=1, upsample=None):
super(DeconvBottleneck, self).__init__()
self.expansion = expansion
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
if stride == 1:
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
stride=stride, bias=False, padding=1)
else:
self.conv2 = nn.ConvTranspose2d(out_channels, out_channels,
kernel_size=3,
stride=stride, bias=False,
padding=1,
output_padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion,
kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
self.relu = nn.ReLU()
self.upsample = upsample
def forward(self, x):
shortcut = 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)
out = self.relu(out)
if self.upsample is not None:
shortcut = self.upsample(x)
out += shortcut
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, downblock, upblock, num_layers, n_classes):
super(ResNet, self).__init__()
self.in_channels = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.dlayer1 = self._make_downlayer(downblock, 64, num_layers[0])
self.dlayer2 = self._make_downlayer(downblock, 128, num_layers[1],
stride=2)
self.dlayer3 = self._make_downlayer(downblock, 256, num_layers[2],
stride=2)
self.dlayer4 = self._make_downlayer(downblock, 512, num_layers[3],
stride=2)
self.uplayer1 = self._make_up_block(upblock, 512, 1, stride=2)
self.uplayer2 = self._make_up_block(upblock, 256, num_layers[2], stride=2)
self.uplayer3 = self._make_up_block(upblock, 128, num_layers[1], stride=2)
self.uplayer4 = self._make_up_block(upblock, 64, 2, stride=2)
upsample = nn.Sequential(
nn.ConvTranspose2d(self.in_channels, # 256
64,
kernel_size=1, stride=2,
bias=False, output_padding=1),
nn.BatchNorm2d(64),
)
self.uplayer_top = DeconvBottleneck(self.in_channels, 64, 1, 2, upsample)
self.conv1_1 = nn.ConvTranspose2d(64, n_classes, kernel_size=1, stride=1,
bias=False)
def _make_downlayer(self, block, init_channels, num_layer, stride=1):
downsample = None
if stride != 1 or self.in_channels != init_channels * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.in_channels, init_channels*block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(init_channels*block.expansion),
)
layers = []
layers.append(block(self.in_channels, init_channels, stride, downsample))
self.in_channels = init_channels * block.expansion
for i in range(1, num_layer):
layers.append(block(self.in_channels, init_channels))
return nn.Sequential(*layers)
def _make_up_block(self, block, init_channels, num_layer, stride=1):
upsample = None
# expansion = block.expansion
if stride != 1 or self.in_channels != init_channels * 2:
upsample = nn.Sequential(
nn.ConvTranspose2d(self.in_channels, init_channels*2,
kernel_size=1, stride=stride,
bias=False, output_padding=1),
nn.BatchNorm2d(init_channels*2),
)
layers = []
for i in range(1, num_layer):
layers.append(block(self.in_channels, init_channels, 4))
layers.append(block(self.in_channels, init_channels, 2, stride, upsample))
self.in_channels = init_channels * 2
return nn.Sequential(*layers)
def forward(self, x):
img = x
x_size = x.size()
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.dlayer1(x)
x = self.dlayer2(x)
x = self.dlayer3(x)
x = self.dlayer4(x)
x = self.uplayer1(x)
x = self.uplayer2(x)
x = self.uplayer3(x)
x = self.uplayer4(x)
x = self.uplayer_top(x)
x = self.conv1_1(x, output_size=img.size())
return x
def ResNet50(**kwargs):
return ResNet(Bottleneck, DeconvBottleneck, [3, 4, 6, 3], 22, **kwargs)
def ResNet101(**kwargs):
return ResNet(Bottleneck, [3, 4, 23, 2], 22, **kwargs)