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ResNetModels.py
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ResNetModels.py
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import torch
import torch.nn as nn
__all__ = ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, outplanes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(outplanes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(outplanes, outplanes * BasicBlock.expansion, kernel_size=3, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(outplanes * BasicBlock.expansion)
self.shortcut = nn.Sequential()
if stride != 1 or inplanes != BasicBlock.expansion * outplanes:
self.shortcut = nn.Sequential(
nn.Conv2d(inplanes, outplanes * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(outplanes * BasicBlock.expansion)
)
def forward(self,x):
s = self.shortcut(x)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
return self.relu(s + x)
class BottleNeck(nn.Module):
expansion = 4
def __init__(self, inplanes, outplanes, stride=1):
super(BottleNeck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, outplanes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(outplanes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(outplanes, outplanes, stride=stride, kernel_size=3, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(outplanes)
self.conv3 = nn.Conv2d(outplanes, outplanes * BottleNeck.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(outplanes * BottleNeck.expansion)
self.shortcut = nn.Sequential()
if stride != 1 or inplanes != outplanes * BottleNeck.expansion:
self.shortcut = nn.Sequential(
nn.Conv2d(inplanes, outplanes * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),
nn.BatchNorm2d(outplanes * BottleNeck.expansion)
)
def forward(self,x):
s = self.shortcut(x)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.conv3(x)
x = self.bn3(x)
return self.relu(s + x)
class ResNet(nn.Module):
def __init__(self, block, num_block, num_classes=200):
super(ResNet, self).__init__()
self.inplanes = 64
self.conv1 = nn.Sequential(
nn.Conv2d(3,64,kernel_size=7, stride=2, padding=3,bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True)
)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.conv2_x = self._make_layer(block, 64, num_block[0], 1)
self.conv3_x = self._make_layer(block, 128, num_block[1], 2)
self.conv4_x = self._make_layer(block, 256, num_block[2], 2)
self.conv5_x = self._make_layer(block, 512, num_block[3], 2)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
def forward(self, x):
output = self.conv1(x)
output = self.maxpool(output)
output = self.conv2_x(output)
output = self.conv3_x(output)
output = self.conv4_x(output)
output = self.conv5_x(output)
output = self.avg_pool(output)
output = output.view(output.size(0), -1)
output = self.fc(output)
return output
def _make_layer(self, block, outplanes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.inplanes, outplanes, stride))
self.inplanes = outplanes * block.expansion
return nn.Sequential(*layers)
def resnet18():
return ResNet(BasicBlock, [2, 2, 2, 2])
def resnet34():
return ResNet(BasicBlock, [3, 4, 6, 3])
def resnet50():
return ResNet(BottleNeck, [3, 4, 6, 3])
def resnet101():
return ResNet(BottleNeck, [3, 4, 23, 3])
def resnet152():
return ResNet(BottleNeck, [3, 8, 36, 3])