| 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 |
|
|
| @staticmethod |
| def _conv3x3(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 = [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]) |
|
|
