| import torch |
| import torch.nn as nn |
| import math |
|
|
| '''https://github.com/blandocs/Tag2Pix/blob/master/model/pretrained.py''' |
|
|
| |
| 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_extractor(nn.Module): |
| def __init__(self, block, layers, input_channels=3, cardinality=32): |
| super(SEResNeXt_extractor, 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.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
|
|
| self.layer1 = self._make_layer(block, 64, layers[0]) |
| self.layer2 = self._make_layer(block, 128, layers[1], 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) |
| x = self.relu(x) |
| x = self.maxpool(x) |
|
|
| x = self.layer1(x) |
| x = self.layer2(x) |
| |
| return x |
|
|
| def get_seresnext_extractor(): |
| return SEResNeXt_extractor(BottleneckX_Origin, [3, 4, 6, 3], 1) |
