| import math |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
|
|
| class BasicBlock(nn.Module): |
| def __init__(self, in_planes, out_planes, stride, dropRate=0.0): |
| super(BasicBlock, self).__init__() |
| self.bn1 = nn.BatchNorm2d(in_planes) |
| self.relu1 = nn.ReLU(inplace=True) |
| self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, |
| padding=1, bias=False) |
| self.bn2 = nn.BatchNorm2d(out_planes) |
| self.relu2 = nn.ReLU(inplace=True) |
| self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, |
| padding=1, bias=False) |
| self.droprate = dropRate |
| self.equalInOut = (in_planes == out_planes) |
| self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, |
| padding=0, bias=False) or None |
| def forward(self, x): |
| if not self.equalInOut: |
| x = self.relu1(self.bn1(x)) |
| else: |
| out = self.relu1(self.bn1(x)) |
| out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x))) |
| if self.droprate > 0: |
| out = F.dropout(out, p=self.droprate, training=self.training) |
| out = self.conv2(out) |
| return torch.add(x if self.equalInOut else self.convShortcut(x), out) |
|
|
| class NetworkBlock(nn.Module): |
| def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0): |
| super(NetworkBlock, self).__init__() |
| self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate) |
| def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate): |
| layers = [] |
| for i in range(int(nb_layers)): |
| layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate)) |
| return nn.Sequential(*layers) |
| def forward(self, x): |
| return self.layer(x) |
|
|
| class WideResNet(nn.Module): |
| def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0): |
| super(WideResNet, self).__init__() |
| nChannels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor] |
| assert((depth - 4) % 6 == 0) |
| n = (depth - 4) / 6 |
| block = BasicBlock |
| |
| self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1, |
| padding=1, bias=False) |
| |
| self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate) |
| |
| self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate) |
| |
| self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate) |
| |
| self.bn1 = nn.BatchNorm2d(nChannels[3]) |
| self.relu = nn.ReLU(inplace=True) |
| self.fc = nn.Linear(nChannels[3], num_classes) |
| self.nChannels = nChannels[3] |
|
|
| for m in self.modules(): |
| if isinstance(m, nn.Conv2d): |
| nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') |
| elif isinstance(m, nn.BatchNorm2d): |
| m.weight.data.fill_(1) |
| m.bias.data.zero_() |
| elif isinstance(m, nn.Linear): |
| m.bias.data.zero_() |
| def forward(self, x, mode='fc'): |
| if mode == 'c': |
| return self.fc(x) |
| else: |
| out = self.conv1(x) |
| out = self.block1(out) |
| out = self.block2(out) |
| out = self.block3(out) |
| out = self.relu(self.bn1(out)) |
| out = F.avg_pool2d(out, 8) |
| out = out.view(-1, self.nChannels) |
| return self.fc(out), out |
|
|
