150 lines
4.4 KiB
Python
150 lines
4.4 KiB
Python
import torch
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import torch.nn as nn
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import numpy as np
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__all__ = ['ResNet', 'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110', 'resnet1202']
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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,
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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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# Both self.conv1 and self.downsample layers downsample the input when stride != 1
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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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def forward(self, x):
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identity = 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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identity = self.downsample(x)
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identity = torch.cat((identity, torch.zeros_like(identity)), 1)
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out += identity
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out = self.relu(out)
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return out
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class ResNet(nn.Module):
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def __init__(self, block, layers, num_filters=16, num_classes=10):
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super(ResNet, self).__init__()
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self.num_layers = sum(layers)
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self.inplanes = num_filters
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self.conv1 = conv3x3(3, num_filters)
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self.bn1 = nn.BatchNorm2d(num_filters)
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self.relu = nn.ReLU(inplace=True)
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self.layer1 = self._make_layer(block, num_filters, layers[0])
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self.layer2 = self._make_layer(block, num_filters * 2, layers[1], stride=2)
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self.layer3 = self._make_layer(block, num_filters * 4, layers[2], stride=2)
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self.layer4 = self._make_layer(block, num_filters * 8, layers[2], stride=2)
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self.fc1 = nn.Linear(num_filters * 8 * 8 * 8, 64, bias=True)
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self.fc2 = nn.Linear(64, num_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.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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# Zero-initialize the last BN in each residual branch,
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# so that the residual branch starts with zeros, and each residual block behaves like an identity.
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# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
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for m in self.modules():
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if isinstance(m, BasicBlock):
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nn.init.constant_(m.bn2.weight, 0)
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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:
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downsample = nn.Sequential(
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nn.AvgPool2d(1, stride=stride),
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nn.BatchNorm2d(self.inplanes),
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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
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for _ in range(1, blocks):
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layers.append(block(planes, 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.layer4(x)
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x = x.view(x.size(0), -1)
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x = self.relu(self.fc1(x))
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x = self.fc2(x)
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return x
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def resnet20(**kwargs):
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"""Constructs a ResNet-20 model.
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"""
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model = ResNet(BasicBlock, [3, 3, 3], **kwargs)
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return model
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def resnet32(**kwargs):
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"""Constructs a ResNet-32 model.
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"""
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model = ResNet(BasicBlock, [5, 5, 5], **kwargs)
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return model
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def resnet44(**kwargs):
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"""Constructs a ResNet-44 model.
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"""
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model = ResNet(BasicBlock, [7, 7, 7], **kwargs)
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return model
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def resnet56(**kwargs):
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"""Constructs a ResNet-56 model.
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"""
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model = ResNet(BasicBlock, [9, 9, 9], **kwargs)
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return model
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def resnet110(**kwargs):
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"""Constructs a ResNet-110 model.
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"""
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model = ResNet(BasicBlock, [18, 18, 18], **kwargs)
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return model
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def resnet1202(**kwargs):
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"""Constructs a ResNet-1202 model.
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"""
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model = ResNet(BasicBlock, [200, 200, 200], **kwargs)
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return model |