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| import torch | |
| import torch.nn as nn | |
| import math | |
| from collections import OrderedDict | |
| ################## AlexNet ################## | |
| def bn_relu(inplanes): | |
| return nn.Sequential(nn.BatchNorm2d(inplanes), nn.ReLU(inplace=True)) | |
| def bn_relu_pool(inplanes, kernel_size=3, stride=2): | |
| return nn.Sequential(nn.BatchNorm2d(inplanes), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=kernel_size, stride=stride)) | |
| class AlexNet(nn.Module): | |
| def __init__(self, num_classes=1): | |
| super(AlexNet, self).__init__() | |
| self.conv1 = nn.Conv2d(3, 96, kernel_size=11, stride=4, bias=False) | |
| self.relu_pool1 = bn_relu_pool(inplanes=96) | |
| self.conv2 = nn.Conv2d(96, 192, kernel_size=5, padding=2, groups=2, bias=False) | |
| self.relu_pool2 = bn_relu_pool(inplanes=192) | |
| self.conv3 = nn.Conv2d(192, 384, kernel_size=3, padding=1, groups=2, bias=False) | |
| self.relu3 = bn_relu(inplanes=384) | |
| self.conv4 = nn.Conv2d(384, 384, kernel_size=3, padding=1, groups=2, bias=False) | |
| self.relu4 = bn_relu(inplanes=384) | |
| self.conv5 = nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2, bias=False) | |
| self.relu_pool5 = bn_relu_pool(inplanes=256) | |
| # classifier | |
| self.conv6 = nn.Conv2d(256, 256, kernel_size=5, groups=2, bias=False) | |
| self.relu6 = bn_relu(inplanes=256) | |
| self.conv7 = nn.Conv2d(256, num_classes, kernel_size=1, bias=False) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.relu_pool1(x) | |
| x = self.conv2(x) | |
| x = self.relu_pool2(x) | |
| x = self.conv3(x) | |
| x = self.relu3(x) | |
| x = self.conv4(x) | |
| x = self.relu4(x) | |
| x = self.conv5(x) | |
| x = self.relu_pool5(x) | |
| x = self.conv6(x) | |
| x = self.relu6(x) | |
| x = self.conv7(x) | |
| x = x.view(x.size(0), -1) | |
| return x | |
| ################## ResNet ################## | |
| 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) | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None): | |
| super(BasicBlock, self).__init__() | |
| m = OrderedDict() | |
| m['conv1'] = conv3x3(inplanes, planes, stride) | |
| m['bn1'] = nn.BatchNorm2d(planes) | |
| m['relu1'] = nn.ReLU(inplace=True) | |
| m['conv2'] = conv3x3(planes, planes) | |
| m['bn2'] = nn.BatchNorm2d(planes) | |
| self.group1 = nn.Sequential(m) | |
| self.relu = nn.Sequential(nn.ReLU(inplace=True)) | |
| self.downsample = downsample | |
| def forward(self, x): | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| else: | |
| residual = x | |
| out = self.group1(x) + residual | |
| out = self.relu(out) | |
| return out | |
| class ResNet(nn.Module): | |
| def __init__(self, block, layers, num_classes=1000): | |
| self.inplanes = 64 | |
| super(ResNet, self).__init__() | |
| m = OrderedDict() | |
| m['conv1'] = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) | |
| m['bn1'] = nn.BatchNorm2d(64) | |
| m['relu1'] = nn.ReLU(inplace=True) | |
| m['maxpool'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
| self.group1= nn.Sequential(m) | |
| 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) | |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2) | |
| self.avgpool = nn.Sequential(nn.AvgPool2d(7)) | |
| self.group2 = nn.Sequential( | |
| OrderedDict([ | |
| ('fullyconnected', nn.Linear(512 * block.expansion, num_classes)) | |
| ]) | |
| ) | |
| 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)) | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| elif isinstance(m, nn.Linear): | |
| torch.nn.init.xavier_uniform_(m.weight.data) | |
| torch.nn.init.constant_(m.bias.data, 0) | |
| 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.group1(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.avgpool(x) | |
| x = x.view(x.size(0), -1) | |
| x = self.group2(x) | |
| return x | |