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- import math
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- import numpy as np
-
- __all__ = ['wrn']
-
- 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(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, in_channel=3):
- super(WideResNet, self).__init__()
- nChannels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor]
- assert (depth - 4) % 6 == 0, 'depth should be 6n+4'
- n = (depth - 4) // 6
- block = BasicBlock
- # 1st conv before any network block
- self.conv1 = nn.Conv2d(in_channel, nChannels[0], kernel_size=3, stride=1,
- padding=1, bias=False)
- # 1st block
- self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate)
- # 2nd block
- self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate)
- # 3rd block
- self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate)
- # global average pooling and classifier
- self.bn1 = nn.BatchNorm2d(nChannels[3])
- self.relu = nn.ReLU(inplace=True)
- self.fc = nn.Linear(nChannels[3], num_classes)
- self.nChannels = nChannels[3]
- self.final_act = nn.Softmax(dim=1)
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- n = m.kernel_size[0] * m.kernel_size[1] * m.in_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):
- n = 1. / np.sqrt(m.weight.data.size(1))
- m.weight.data.uniform_(-n, n)
- m.bias.data.zero_()
-
- def forward(self, x):
- 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)
- rep = out.view(-1, self.nChannels)
- out = self.fc(rep)
- return out, rep
-
- def wrn(**kwargs):
- """
- Constructs a Wide Residual Networks.
- """
- model = WideResNet(**kwargs)
- return model
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