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January 10, 2019 23:30
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import torch.nn as nn | |
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) | |
def conv1x1(in_planes, out_planes, stride=1): | |
"""1x1 convolution""" | |
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, 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): | |
identity = 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: | |
identity = self.downsample(x) | |
out += identity | |
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 = conv1x1(inplanes, planes) | |
self.bn1 = nn.BatchNorm2d(planes) | |
self.conv2 = conv3x3(planes, planes, stride) | |
self.bn2 = nn.BatchNorm2d(planes) | |
self.conv3 = conv1x1(planes, planes * self.expansion) | |
self.bn3 = nn.BatchNorm2d(planes * self.expansion) | |
self.relu = nn.ReLU(inplace=True) | |
self.downsample = downsample | |
self.stride = stride | |
def forward(self, x): | |
identity = 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: | |
identity = self.downsample(x) | |
out += identity | |
out = self.relu(out) | |
return out | |
class ResNet(nn.Module): | |
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False): | |
super(ResNet, self).__init__() | |
self.inplanes = 64 | |
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, | |
bias=False) | |
self.bn1 = nn.BatchNorm2d(64) | |
self.relu = nn.ReLU(inplace=True) | |
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) | |
self.layer1 = self._make_layer(block, 64, layers[0]) | |
self.layer2 = self._make_layer(block, 128, layers[1], stride=2) | |
self.layer3 = self._make_layer(block, 256, layers[2], stride=2) | |
self.layer4 = self._make_layer(block, 512, layers[3], stride=2) | |
self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) | |
self.fc = nn.Linear(512 * block.expansion, num_classes) | |
for m in self.modules(): | |
if isinstance(m, nn.Conv2d): | |
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') | |
elif isinstance(m, nn.BatchNorm2d): | |
nn.init.constant_(m.weight, 1) | |
nn.init.constant_(m.bias, 0) | |
# Zero-initialize the last BN in each residual branch, | |
# so that the residual branch starts with zeros, and each residual block behaves like an identity. | |
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 | |
if zero_init_residual: | |
for m in self.modules(): | |
if isinstance(m, Bottleneck): | |
nn.init.constant_(m.bn3.weight, 0) | |
elif isinstance(m, BasicBlock): | |
nn.init.constant_(m.bn2.weight, 0) | |
def _make_layer(self, block, planes, blocks, stride=1): | |
downsample = None | |
if stride != 1 or self.inplanes != planes * block.expansion: | |
downsample = nn.Sequential( | |
conv1x1(self.inplanes, planes * block.expansion, stride), | |
nn.BatchNorm2d(planes * block.expansion), | |
) | |
layers = [] | |
layers.append(block(self.inplanes, planes, stride, downsample)) | |
self.inplanes = planes * block.expansion | |
for _ 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) | |
x = self.maxpool(x) | |
x = self.layer1(x) | |
x = self.layer2(x) | |
x = self.layer3(x) | |
x = self.layer4(x) | |
x = self.avgpool(x) | |
x = x.view(x.size(0), -1) | |
x = self.fc(x) | |
return x |
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