"""
Model definition adapted from: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import math
from typing import Optional, List, Union, Type
import torch.nn as nn
from torchvision.models.resnet import Bottleneck, BasicBlock
model_urls = {
'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',
}
[docs]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)
[docs]class ResNet(nn.Module):
def __init__(self, block: Type[Union[BasicBlock, Bottleneck]], layers: List[int],
replace_stride_with_dilation: Optional[List[bool]] = None, **kwargs):
super(ResNet, self).__init__()
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError(
f"replace_stride_with_dilation should be None or a 3-tuple, got {replace_stride_with_dilation}")
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(self.inplanes)
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, dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2])
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)
def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int, stride: int = 1,
dilate: bool = False):
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
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),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(inplanes=self.inplanes, planes=planes, stride=stride, downsample=downsample,
dilation=previous_dilation))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, dilation=self.dilation, norm_layer=nn.BatchNorm2d))
return nn.Sequential(*layers)
[docs] 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)
return x
[docs]class ResNet18(ResNet):
def __init__(self, **kwargs):
super(ResNet18, self).__init__(BasicBlock, [2, 2, 2, 2], **kwargs)
[docs]class ResNet34(ResNet):
def __init__(self, **kwargs):
super(ResNet34, self).__init__(BasicBlock, [3, 4, 6, 3], **kwargs)
[docs]class ResNet50(ResNet):
def __init__(self, **kwargs):
super(ResNet50, self).__init__(Bottleneck, [3, 4, 6, 3], **kwargs)
[docs]class ResNet101(ResNet):
def __init__(self, **kwargs):
super(ResNet101, self).__init__(Bottleneck, [3, 4, 23, 3], **kwargs)
[docs]class ResNet152(ResNet):
def __init__(self, **kwargs):
super(ResNet152, self).__init__(Bottleneck, [3, 8, 36, 3], **kwargs)