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| import torch | |
| import torch.nn as nn | |
| import math | |
| '''https://github.com/blandocs/Tag2Pix/blob/master/model/pretrained.py''' | |
| # Pretrained version | |
| class Selayer(nn.Module): | |
| def __init__(self, inplanes): | |
| super(Selayer, self).__init__() | |
| self.global_avgpool = nn.AdaptiveAvgPool2d(1) | |
| self.conv1 = nn.Conv2d(inplanes, inplanes // 16, kernel_size=1, stride=1) | |
| self.conv2 = nn.Conv2d(inplanes // 16, inplanes, kernel_size=1, stride=1) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.sigmoid = nn.Sigmoid() | |
| def forward(self, x): | |
| out = self.global_avgpool(x) | |
| out = self.conv1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.sigmoid(out) | |
| return x * out | |
| class BottleneckX_Origin(nn.Module): | |
| expansion = 4 | |
| def __init__(self, inplanes, planes, cardinality, stride=1, downsample=None): | |
| super(BottleneckX_Origin, self).__init__() | |
| self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False) | |
| self.bn1 = nn.BatchNorm2d(planes * 2) | |
| self.conv2 = nn.Conv2d(planes * 2, planes * 2, kernel_size=3, stride=stride, | |
| padding=1, groups=cardinality, bias=False) | |
| self.bn2 = nn.BatchNorm2d(planes * 2) | |
| self.conv3 = nn.Conv2d(planes * 2, planes * 4, kernel_size=1, bias=False) | |
| self.bn3 = nn.BatchNorm2d(planes * 4) | |
| self.selayer = Selayer(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) | |
| out = self.selayer(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class SEResNeXt_Origin(nn.Module): | |
| def __init__(self, block, layers, input_channels=3, cardinality=32, num_classes=1000): | |
| super(SEResNeXt_Origin, self).__init__() | |
| self.cardinality = cardinality | |
| self.inplanes = 64 | |
| self.input_channels = input_channels | |
| self.conv1 = nn.Conv2d(input_channels, 64, kernel_size=7, stride=2, padding=3, | |
| bias=False) | |
| self.bn1 = nn.BatchNorm2d(64) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.layer1 = self._make_layer(block, 64, layers[0]) | |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2) | |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2) | |
| 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)) | |
| if m.bias is not None: | |
| m.bias.data.zero_() | |
| 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, self.cardinality, stride, downsample)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes, self.cardinality)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x1 = self.relu(x) | |
| x2 = self.layer1(x1) | |
| x3 = self.layer2(x2) | |
| x4 = self.layer3(x3) | |
| return x1, x2, x3, x4 | |