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