mirror of
https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 12:10:45 +02:00
119 lines
No EOL
3.6 KiB
Python
119 lines
No EOL
3.6 KiB
Python
"""
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wide resnet for cifar in pytorch
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Reference:
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[1] S. Zagoruyko and N. Komodakis. Wide residual networks. In BMVC, 2016.
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"""
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import torch
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import torch.nn as nn
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import math
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#from models.resnet_cifar import BasicBlock
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def conv3x3(in_planes, out_planes, stride=1):
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" 3x3 convolution with padding "
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
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class BasicBlock(nn.Module):
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expansion=1
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(BasicBlock, self).__init__()
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self.conv1 = conv3x3(inplanes, planes, stride)
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self.bn1 = nn.BatchNorm2d(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(planes, planes)
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self.bn2 = nn.BatchNorm2d(planes)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class Wide_ResNet_Cifar(nn.Module):
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def __init__(self, block, layers, wfactor, num_classes=10):
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super(Wide_ResNet_Cifar, self).__init__()
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self.depth=layers[0]*6+2
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self.widen_factor=wfactor
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self.inplanes = 16
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self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(16)
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self.relu = nn.ReLU(inplace=True)
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self.layer1 = self._make_layer(block, 16*wfactor, layers[0])
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self.layer2 = self._make_layer(block, 32*wfactor, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 64*wfactor, layers[2], stride=2)
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self.avgpool = nn.AvgPool2d(8, stride=1)
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self.fc = nn.Linear(64*block.expansion*wfactor, num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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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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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(planes * block.expansion)
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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x = self.fc(x)
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return x
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def __str__(self):
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""" Get name of model
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"""
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return "Wide_ResNet_cifar%d_%d"%(self.depth,self.widen_factor)
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def wide_resnet_cifar(depth, width, **kwargs):
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assert (depth - 2) % 6 == 0
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n = int((depth - 2) / 6)
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return Wide_ResNet_Cifar(BasicBlock, [n, n, n], width, **kwargs)
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if __name__=='__main__':
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net = wide_resnet_cifar(20, 10)
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y = net(torch.randn(1, 3, 32, 32))
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print(isinstance(net, Wide_ResNet_Cifar))
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print(y.size()) |