人体姿态估计HRNet网络模型搭建代码详解

HRNet-v1模型详解

源码参考：https://github.com/HRNet/HRNet-Human-Pose-Estimation
内容参考：点击跳转

仅作为个人的学习笔记，欢迎交流学习。

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整体结构如下图
整体代码详解：

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import logging

import torch
import torch.nn as nn


BN_MOMENTUM = 0.1
logger = logging.getLogger(__name__)

# 定义3x3卷积操作
def conv3x3(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,padding=1, bias=False)

# 3x3的残差块
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

# 高分辨率模块
class HighResolutionModule(nn.Module):
    def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
                 num_channels, fuse_method, multi_scale_output=True):
        super(HighResolutionModule, self).__init__()

        """
        :param num_branches: 当前 stage 分支平行子网络的数目
        :param blocks: BasicBlock或者BasicBlock
        :param num_blocks: BasicBlock或者BasicBlock的数目

        :param num_inchannels: 输入通道数
                    当stage = 2时： num_inchannels = [32, 64]
                    当stage = 3时： num_inchannels = [32, 64, 128]
                    当stage = 4时： num_inchannels = [32, 64, 128, 256]

        :param num_channels: 输出通道数目
                    当stage = 2时： num_inchannels = [32, 64]
                    当stage = 3时： num_inchannels = [32, 64, 128]
                    当stage = 4时： num_inchannels = [32, 64, 128, 256]

        :param fuse_method: 默认SUM
        :param multi_scale_output:
                    当stage = 2时： multi_scale_output=Ture
                    当stage = 3时： multi_scale_output=Ture
                    当stage = 4时： multi_scale_output=False

        """
        self._check_branches(
            num_branches, blocks, num_blocks, num_inchannels, num_channels)

        self.num_inchannels = num_inchannels
        self.fuse_method = fuse_method
        self.num_branches = num_branches
        self.multi_scale_output = multi_scale_output

        # 为每个分支构建分支网络
        # 当stage=2,3,4时,num_branches分别为：2,3,4,表示每个stage平行网络的数目
        # 当stage=2,3,4时,num_blocks分别为：[4,4], [4,4,4], [4,4,4,4],
        self.branches = self._make_branches(
            num_branches, blocks, num_blocks, num_channels)

        # 创建一个多尺度融合层,当stage=2,3,4时
        # len(self.fuse_layers)分别为2,3,4. 其与num_branches在每个stage的数目是一致的
        self.fuse_layers = self._make_fuse_layers()
        self.relu = nn.ReLU(True)

    # 检查num_branches  num_blocks  num_inchannels  num_channels 长度是否一致
    def _check_branches(self, num_branches, blocks, num_blocks,
                        num_inchannels, num_channels):
        if num_branches != len(num_blocks):
            error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(
                num_branches, len(num_blocks))
            logger.error(error_msg)
            raise ValueError(error_msg)

        if num_branches != len(num_channels):
            error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(
                num_branches, len(num_channels))
            logger.error(error_msg)
            raise ValueError(error_msg)

        if num_branches != len(num_inchannels):
            error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(
                num_branches, len(num_inchannels))
            logger.error(error_msg)
            raise ValueError(error_msg)

    # 搭建分支,单个分支内部分辨率相等
    # for i in range(num_branches):   2 3 4
    #   self._make_one_branch(i, block, num_blocks, num_channels)
    def _make_one_branch(self, branch_index, block, num_blocks, num_channels,
                         stride=1):
        downsample = None

        # 如果stride不为1, 或者输入通道数目与输出通道数目不一致
        # 则通过卷积,对其通道数进行改变
        if stride != 1 or \
           self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(
                    self.num_inchannels[branch_index],
                    num_channels[branch_index] * block.expansion,
                    kernel_size=1, stride=stride, bias=False
                ),
                nn.BatchNorm2d(
                    num_channels[branch_index] * block.expansion,
                    momentum=BN_MOMENTUM
                ),
            )

        layers = []
        # 为当前分支branch_index创建一个block,该处进行下采样
        layers.append(
            block(
                self.num_inchannels[branch_index],
                num_channels[branch_index],
                stride,
                downsample
            )
        )

        # 把输出通道数,赋值给输入通道数,为下一stage作准备
        self.num_inchannels[branch_index] = \
            num_channels[branch_index] * block.expansion

        # 为[1, num_blocks[branch_index]]分支创建block
        for i in range(1, num_blocks[branch_index]):
            layers.append(
                block(
                    self.num_inchannels[branch_index],
                    num_channels[branch_index]
                )
            )

        return nn.Sequential(*layers)

    # 循环调用 make_one_branch创建多个分支
    def _make_branches(self, num_branches, block, num_blocks, num_channels):
        branches = []

        # 循环为每个分支构建网络
        # 当stage=2,3,4时,num_branches分别为：2,3,4,表示每个stage平行网络的数目
        # stage=2时, self._make_one_branch(0, BASICBLOCK, [4,4], [32,64]) ,s