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import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
urls_dic = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
layers_dic = {
'resnet18' : [2, 2, 2, 2],
'resnet34' : [3, 4, 6, 3],
'resnet50' : [3, 4, 6, 3],
'resnet101' : [3, 4, 23, 3],
'resnet152' : [3, 8, 36, 3]
}
def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion: int = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1, batch_norm_fn=nn.BatchNorm2d):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = batch_norm_fn(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = batch_norm_fn(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, dilation=1, batch_norm_fn=nn.BatchNorm2d):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = batch_norm_fn(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=dilation, bias=False, dilation=dilation)
self.bn2 = batch_norm_fn(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = batch_norm_fn(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
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, block, layers, strides=(2, 2, 2, 2), dilations=(1, 1, 1, 1), batch_norm_fn=nn.BatchNorm2d):
self.batch_norm_fn = batch_norm_fn
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=strides[0], padding=3,
bias=False)
self.bn1 = self.batch_norm_fn(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], stride=1, dilation=dilations[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1])
self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2])
self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3])
self.inplanes = 1024
#self.avgpool = nn.AvgPool2d(7, stride=1)
#self.fc = nn.Linear(512 * block.expansion, 1000)
def _make_layer(self, block, planes, blocks, stride=1, dilation=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),
self.batch_norm_fn(planes * block.expansion),
)
layers = [block(self.inplanes, planes, stride, downsample, dilation=1, batch_norm_fn=self.batch_norm_fn)]
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, dilation=dilation, batch_norm_fn=self.batch_norm_fn))
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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