modeling_cuda/backbone/resnet.py

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@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
'''

# here put the import lib
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),

            # dw
            # nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=1, padding=1, bias=False),
            # nn.BatchNorm2d(hidden_dim, momentum=BN_MOMENTUM),
            # nn.ReLU(inplace=True),

            # pw-linear
            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):
        # url = model_urls['resnet{}'.format(num_layers)]
        # pretrained_state_dict = model_zoo.load_url(url, model_dir='/home/code/pytorch_model/')
        # print('=> loading pretrained model {}'.format(url))
        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])}