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import torch | |
from torch import nn | |
class BasicBlock(nn.Module): | |
expansion = 1 | |
def __init__(self, inplanes, planes, stride=1, downsample=None): | |
super(BasicBlock, self).__init__() | |
self.conv1 = self._conv3x3(inplanes, planes) | |
self.bn1 = nn.BatchNorm2d(planes) | |
self.conv2 = self._conv3x3(planes, planes) | |
self.bn2 = nn.BatchNorm2d(planes) | |
self.relu = nn.ReLU(inplace=True) | |
self.downsample = downsample | |
self.stride = stride | |
def _conv3x3(self, in_planes, out_planes, stride=1): | |
"3x3 convolution with padding" | |
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, | |
padding=1, bias=False) | |
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) | |
if self.downsample is not None: | |
residual = self.downsample(x) | |
out += residual | |
out = self.relu(out) | |
return out | |
class ResNet(nn.Module): | |
def __init__(self, input_channel, output_channel, block, layers): | |
super(ResNet, self).__init__() | |
self.output_channel_block = [int(output_channel / 4), int(output_channel / 2), output_channel, output_channel] | |
self.inplanes = int(output_channel / 8) | |
self.conv0_1 = nn.Conv2d(input_channel, int(output_channel / 16), | |
kernel_size=3, stride=1, padding=1, bias=False) | |
self.bn0_1 = nn.BatchNorm2d(int(output_channel / 16)) | |
self.conv0_2 = nn.Conv2d(int(output_channel / 16), self.inplanes, | |
kernel_size=3, stride=1, padding=1, bias=False) | |
self.bn0_2 = nn.BatchNorm2d(self.inplanes) | |
self.relu = nn.ReLU(inplace=True) | |
self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) | |
self.layer1 = self._make_layer(block, self.output_channel_block[0], layers[0]) | |
self.conv1 = nn.Conv2d(self.output_channel_block[0], self.output_channel_block[ | |
0], kernel_size=3, stride=1, padding=1, bias=False) | |
self.bn1 = nn.BatchNorm2d(self.output_channel_block[0]) | |
self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) | |
self.layer2 = self._make_layer(block, self.output_channel_block[1], layers[1], stride=1) | |
self.conv2 = nn.Conv2d(self.output_channel_block[1], self.output_channel_block[ | |
1], kernel_size=3, stride=1, padding=1, bias=False) | |
self.bn2 = nn.BatchNorm2d(self.output_channel_block[1]) | |
self.maxpool3 = nn.MaxPool2d(kernel_size=2, stride=(2, 1), padding=(0, 1)) | |
self.layer3 = self._make_layer(block, self.output_channel_block[2], layers[2], stride=1) | |
self.conv3 = nn.Conv2d(self.output_channel_block[2], self.output_channel_block[ | |
2], kernel_size=3, stride=1, padding=1, bias=False) | |
self.bn3 = nn.BatchNorm2d(self.output_channel_block[2]) | |
self.layer4 = self._make_layer(block, self.output_channel_block[3], layers[3], stride=1) | |
self.conv4_1 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[ | |
3], kernel_size=2, stride=(2, 1), padding=(0, 1), bias=False) | |
self.bn4_1 = nn.BatchNorm2d(self.output_channel_block[3]) | |
self.conv4_2 = nn.Conv2d(self.output_channel_block[3], self.output_channel_block[ | |
3], kernel_size=2, stride=1, padding=0, bias=False) | |
self.bn4_2 = nn.BatchNorm2d(self.output_channel_block[3]) | |
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 forward(self, x): | |
x = self.conv0_1(x) | |
x = self.bn0_1(x) | |
x = self.relu(x) | |
x = self.conv0_2(x) | |
x = self.bn0_2(x) | |
x = self.relu(x) | |
x = self.maxpool1(x) | |
x = self.layer1(x) | |
x = self.conv1(x) | |
x = self.bn1(x) | |
x = self.relu(x) | |
x = self.maxpool2(x) | |
x = self.layer2(x) | |
x = self.conv2(x) | |
x = self.bn2(x) | |
x = self.relu(x) | |
x = self.maxpool3(x) | |
x = self.layer3(x) | |
x = self.conv3(x) | |
x = self.bn3(x) | |
x = self.relu(x) | |
x = self.layer4(x) | |
x = self.conv4_1(x) | |
x = self.bn4_1(x) | |
x = self.relu(x) | |
x = self.conv4_2(x) | |
x = self.bn4_2(x) | |
conv = self.relu(x) | |
conv = conv.transpose(-1, -2) | |
conv = conv.flatten(2) | |
conv = conv.permute(-1, 0, 1) | |
return conv | |
def Resnet50(ss, hidden): | |
return ResNet(3, hidden, BasicBlock, [1, 2, 5, 3]) | |