| 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]) | |