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| import math | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.utils.model_zoo as model_zoo | |
| def conv1x1(in_planes, out_planes, stride=1): | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) | |
| 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__() | |
| self.conv1 = conv1x1(inplanes, planes) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3(planes, planes, stride) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| 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) | |
| 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, block, layers): | |
| self.inplanes = 32 | |
| super(ResNet, self).__init__() | |
| self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, | |
| bias=False) | |
| self.bn1 = nn.BatchNorm2d(32) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.layer1 = self._make_layer(block, 32, layers[0], stride=2) | |
| self.layer2 = self._make_layer(block, 64, layers[1], stride=1) | |
| self.layer3 = self._make_layer(block, 128, layers[2], stride=2) | |
| self.layer4 = self._make_layer(block, 256, layers[3], stride=1) | |
| self.layer5 = self._make_layer(block, 512, layers[4], stride=1) | |
| 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_() | |
| 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.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.layer5(x) | |
| return x | |
| def resnet45(): | |
| return ResNet(BasicBlock, [3, 4, 6, 6, 3]) | |