|
|
|
|
|
''' |
|
@File : resnet.py |
|
@Time : 2022/04/23 14:08:10 |
|
@Author : BQH |
|
@Version : 1.0 |
|
@Contact : raogx.vip@hotmail.com |
|
@License : (C)Copyright 2017-2018, Liugroup-NLPR-CASIA |
|
@Desc : Backbone |
|
''' |
|
|
|
|
|
import torch |
|
import torch.nn as nn |
|
from addict import Dict |
|
import torch.utils.model_zoo as model_zoo |
|
|
|
BN_MOMENTUM = 0.1 |
|
|
|
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', } |
|
|
|
|
|
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) |
|
|
|
|
|
class InvertedResidual(nn.Module): |
|
def __init__(self, in_channels, hidden_dim, out_channels=3): |
|
super(InvertedResidual, self).__init__() |
|
|
|
self.conv = nn.Sequential( |
|
nn.Conv2d(in_channels, hidden_dim, kernel_size=1, stride=1, padding=0, bias=True), |
|
nn.BatchNorm2d(hidden_dim, momentum=BN_MOMENTUM), |
|
nn.ReLU6(inplace=True), |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
nn.Conv2d(hidden_dim, out_channels, kernel_size=1, stride=1, padding=0, bias=False), |
|
nn.BatchNorm2d(out_channels, momentum=BN_MOMENTUM), |
|
nn.ReLU(inplace=True) |
|
) |
|
|
|
def forward(self, x): |
|
return self.conv(x) |
|
|
|
|
|
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, momentum=BN_MOMENTUM) |
|
self.relu = nn.ReLU(inplace=True) |
|
self.conv2 = conv3x3(planes, planes) |
|
self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) |
|
self.downsample = downsample |
|
self.stride = stride |
|
|
|
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) |
|
|
|
if self.downsample is not None: |
|
residual = self.downsample(x) |
|
|
|
out += residual |
|
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 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
|
self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) |
|
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) |
|
self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) |
|
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) |
|
self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM) |
|
self.relu = nn.ReLU(inplace=True) |
|
self.downsample = downsample |
|
self.stride = stride |
|
|
|
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): |
|
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, momentum=BN_MOMENTUM) |
|
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) |
|
|
|
def _make_layer(self, block, planes, blocks, stride=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), |
|
nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM)) |
|
|
|
layers = [] |
|
layers.append(block(self.inplanes, planes, stride, downsample)) |
|
self.inplanes = planes * block.expansion |
|
for i in range(1, blocks): |
|
layers.append(block(self.inplanes, planes)) |
|
return nn.Sequential(*layers) |
|
|
|
def forward(self, input_x): |
|
out = {} |
|
x = self.conv1(input_x) |
|
x = self.bn1(x) |
|
x = self.relu(x) |
|
feature1 = self.maxpool(x) |
|
|
|
feature2 = self.layer1(feature1) |
|
out['res2'] = feature2 |
|
|
|
feature3 = self.layer2(feature2) |
|
out['res3'] = feature3 |
|
|
|
feature4 = self.layer3(feature3) |
|
out['res4'] = feature4 |
|
|
|
feature5 = self.layer4(feature4) |
|
out['res5'] = feature5 |
|
|
|
return out |
|
|
|
def init_weights(self, num_layers=50): |
|
|
|
|
|
|
|
pertained_model = r'/home/code/pytorch_model/resnet50-19c8e357.pth' |
|
pretrained_state_dict = torch.load(pertained_model) |
|
|
|
self.load_state_dict(pretrained_state_dict, strict=False) |
|
|
|
|
|
resnet_spec = {'resnet18': (BasicBlock, [2, 2, 2, 2]), |
|
'resnet34': (BasicBlock, [3, 4, 6, 3]), |
|
'resnet50': (Bottleneck, [3, 4, 6, 3]), |
|
'resnet101': (Bottleneck, [3, 4, 23, 3]), |
|
'resnet152': (Bottleneck, [3, 8, 36, 3])} |
