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- '''Resnet for cifar dataset.
- Ported form
- https://github.com/facebook/fb.resnet.torch
- and
- https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
- (c) YANG, Wei
- '''
- import torch.nn as nn
- import math
-
-
- __all__ = ['resnet']
-
- def conv3x3(in_planes, out_planes, stride=1):
- "3x3 convolution with padding"
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
-
-
- class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = nn.BatchNorm2d(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = nn.BatchNorm2d(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
- class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * 4)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
- class ResNet(nn.Module):
-
- def __init__(self, depth, num_classes=1000, block_name='BasicBlock', in_channels=3):
- super(ResNet, self).__init__()
- # Model type specifies number of layers for CIFAR-10 model
- if block_name.lower() == 'basicblock':
- assert (depth - 2) % 6 == 0, 'When use basicblock, depth should be 6n+2, e.g. 20, 32, 44, 56, 110, 1202'
- n = (depth - 2) // 6
- block = BasicBlock
- elif block_name.lower() == 'bottleneck':
- assert (depth - 2) % 9 == 0, 'When use bottleneck, depth should be 9n+2, e.g. 20, 29, 47, 56, 110, 1199'
- n = (depth - 2) // 9
- block = Bottleneck
- else:
- raise ValueError('block_name shoule be Basicblock or Bottleneck')
-
-
- self.inplanes = 16
- self.conv1 = nn.Conv2d(in_channels, 16, kernel_size=3, padding=1,
- bias=False)
- self.bn1 = nn.BatchNorm2d(16)
- self.relu = nn.ReLU(inplace=True)
- self.layer1 = self._make_layer(block, 16, n)
- self.layer2 = self._make_layer(block, 32, n, stride=2)
- self.layer3 = self._make_layer(block, 64, n, stride=2)
- self.avgpool = nn.AvgPool2d(8)
- self.fc = nn.Linear(64 * block.expansion, num_classes)
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- m.weight.data.normal_(0, math.sqrt(2. / n))
- elif isinstance(m, nn.BatchNorm2d):
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
- def _make_layer(self, block, planes, blocks, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(planes * block.expansion),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x) # 32x32
-
- x = self.layer1(x) # 32x32
- x = self.layer2(x) # 16x16
- x = self.layer3(x) # 8x8
-
- x = self.avgpool(x)
- feat = x.view(x.size(0), -1)
- x = self.fc(feat)
-
- return x, feat
-
-
- def resnet(**kwargs):
- """
- Constructs a ResNet model.
- """
- return ResNet(**kwargs)
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