76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
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import numpy as np
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.nn.init as init
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class wide_basic(nn.Module):
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def __init__(self, in_planes, planes, dropout_rate, stride=1):
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super(wide_basic, self).__init__()
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self.bn1 = nn.BatchNorm2d(in_planes)
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self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1)
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self.dropout = nn.Dropout(p=dropout_rate)
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self.shortcut = nn.Sequential()
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if stride != 1 or in_planes != planes:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride),
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)
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def forward(self, x):
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out = self.conv1(F.relu(self.bn1(x)))
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out = self.dropout(out)
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out = self.conv2(F.relu(self.bn2(out)))
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out += self.shortcut(x)
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return out
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class WideResNet(nn.Module):
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def __init__(self, depth, widen_factor, dropout_rate, n_classes):
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super(WideResNet, self).__init__()
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self.in_planes = 16
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assert (depth - 4) % 6 == 0, "Wide-ResNet depth should be 6n+4"
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n = (depth - 4) // 6
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k = widen_factor
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stages = [16, 16 * k, 32 * k, 64 * k]
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self.conv1 = nn.Conv2d(3, stages[0], kernel_size=3, stride=1, padding=1)
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self.layer1 = self._wide_layer(wide_basic, stages[1], n, dropout_rate, stride=1)
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self.layer2 = self._wide_layer(wide_basic, stages[2], n, dropout_rate, stride=2)
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self.layer3 = self._wide_layer(wide_basic, stages[3], n, dropout_rate, stride=2)
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self.bn1 = nn.BatchNorm2d(stages[3], momentum=0.9)
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self.linear = nn.Linear(stages[3], n_classes)
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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.Linear):
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nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
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nn.init.constant_(m.bias, 0)
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def _wide_layer(self, block, planes, n_blocks, dropout_rate, stride):
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strides = [stride] + [1] * (int(n_blocks) - 1)
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layers = []
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for stride in strides:
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layers.append(block(self.in_planes, planes, dropout_rate, stride))
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self.in_planes = planes
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return nn.Sequential(*layers)
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def forward(self, x):
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out = self.conv1(x)
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out = self.layer1(out)
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out = self.layer2(out)
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out = self.layer3(out)
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out = F.relu(self.bn1(out))
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out = F.avg_pool2d(out, 8)
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out = out.view(out.size(0), -1)
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out = self.linear(out)
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return out
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