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models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
cfg = {
'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}
class VGG(nn.Module):
def __init__(self, vgg_name, bn=False, base=0):
super(VGG, self).__init__()
self.bn = bn
base = base if base != 0 else 64
self.base = base
self.features = self._make_layers(cfg[vgg_name])
self.classifier = nn.Linear(8 * base, 10)
def forward(self, x):
out = self.features(x)
out = out.view(out.size(0), -1)
out = self.classifier(out)
return out
def _make_layers(self, cfg):
layers = []
in_channels = 3
for x in cfg:
if x == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
out_channels = x * self.base // 64
layers += [nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)]
if self.bn:
layers += [nn.BatchNorm2d(x)]
layers += [nn.ReLU()]
in_channels = out_channels
layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
return nn.Sequential(*layers)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1, bn=False):
super(BasicBlock, self).__init__()
self.bn = bn
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=not bn)
if bn:
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=not bn)
if bn:
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
if self.bn:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*planes)
)
else:
self.shortcut = nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
def forward(self, x):
if self.bn:
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
else:
out = F.relu(self.conv1(x))
out = self.conv2(out)
out += self.shortcut(x)
out = F.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, 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, self.expansion*planes, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion*planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10, bn=False):
super(ResNet, self).__init__()
self.bn = bn
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=not bn)
if bn:
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.linear = nn.Linear(512*block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride, self.bn))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
if self.bn:
out = self.bn1(self.conv1(x))
else:
out = self.conv1(x)
out = F.relu(out)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def ResNet18(bn=True):
return ResNet(BasicBlock, [2,2,2,2], bn=bn)
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])