Deep High Resolution Net（HRNet）代码分析及网络架构

技术2022-07-11 88

2020/07/01

Hey，HRNet之前已经在论文层面做过介绍了，今天我从网络结构的角度和代码层面再给给大家分析一下。

1、网络架构图：

2、代码分析

2.1 ResNet模块

虽然很熟悉了，但是还是介绍一下resnet网络的基本模块。如下的左图对应于resnet-18/34使用的基本块，右图是50/101/152所使用的，由于他们都比较深，所以有图相比于左图使用了1x1卷积来降维。

(a) conv3x3: 没啥好解释的，将原有的pytorch函数固定卷积和尺寸为3重新封装了一次；(b) BasicBlock: 搭建上图左边的模块。(1) 每个卷积块后面连接BN层进行归一化；(2) 残差连接前的3x3卷积之后只接入BN，不使用ReLU，避免加和之后的特征皆为正，保持特征的多样；(3) 跳层连接：两种情况，当模块输入和残差支路（3x3->3x3）的通道数一致时，直接相加；当两者通道不一致时（一般发生在分辨率降低之后，同分辨率一般通道数一致），需要对模块输入特征使用1x1卷积进行升/降维（步长为2，上面说了分辨率会降低），之后同样接BN，不用ReLU。(c) Bottleneck: 搭建上图右边的模块。(1) 使用1x1卷积先降维，再使用3x3卷积进行特征提取，最后再使用1x1卷积把维度升回去；(2) 每个卷积块后面连接BN层进行归一化；(2) 残差连接前的1x1卷积之后只接入BN，不使用ReLU，避免加和之后的特征皆为正，保持特征的多样性。(3) 跳层连接：两种情况，当模块输入和残差支路（1x1->3x3->1x1）的通道数一致时，直接相加；当两者通道不一致时（一般发生在分辨率降低之后，同分辨率一般通道数一致），需要对模块输入特征使用1x1卷积进行升/降维（步长为2，上面说了分辨率会降低），之后同样接BN，不用ReLU。

· 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 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

2.2 HighResolutionModule (高分辨率模块)

当仅包含一个分支时，生成该分支，没有融合模块，直接返回；当包含不仅一个分支时，先将对应分支的输入特征输入到对应分支，得到对应分支的输出特征；紧接着执行融合模块。

(a) _check_branches: 判断num_branches (int) 和 num_blocks, num_inchannels, num_channels (list) 三者的长度是否一致，否则报错；(b) _make_one_branch: 搭建一个分支，单个分支内部分辨率相等，一个分支由num_blocks[branch_index]个block组成，block可以是两种ResNet模块中的一种；(1) 首先判断是否降维或者输入输出的通道(num_inchannels[branch_index]和 num_channels[branch_index] * block.expansion(通道扩张率))是否一致，不一致使用1z1卷积进行维度升/降，后接BN，不使用ReLU；(2) 顺序搭建num_blocks[branch_index]个block，第一个block需要考虑是否降维的情况，所以单独拿出来，后面1 到 num_blocks[branch_index]个block完全一致，使用循环搭建就行。此时注意在执行完第一个block后将num_inchannels[branch_index重新赋值为 num_channels[branch_index] * block.expansion。(c) _make_branches: 循环调用_make_one_branch函数创建多个分支；(d) _make_fuse_layers:(1) 如果分支数等于1，返回None，说明此事不需要使用融合模块；(2) 双层循环：for i in range(num_branches if self.multi_scale_output else 1):的作用是，如果需要产生多分辨率的结果，就双层循环num_branches 次，如果只需要产生最高分辨率的表示，就将i确定为0。

o (2.1) 如果j > i，此时的目标是将所有分支上采样到和i分支相同的分辨率并融合，也就是说j所代表的分支分辨率比i分支低，2**(j-i)表示j分支上采样这么多倍才能和i分支分辨率相同。先使用1x1卷积将j分支的通道数变得和i分支一致，进而跟着BN，然后依据上采样因子将j分支分辨率上采样到和i分支分辨率相同，此处使用最近邻插值；

(2.2) 如果j = i，也就是说自身与自身之间不需要融合，nothing to do；(2.3) 如果j < i，转换角色，此时最终目标是将所有分支采样到和i分支相同的分辨率并融合，注意，此时j所代表的分支分辨率比i分支高，正好和(2.1)相反。此时再次内嵌了一个循环，这层循环的作用是当i-j > 1时，也就是说两个分支的分辨率差了不止二倍，此时还是两倍两倍往上采样，例如i-j = 2时，j分支的分辨率比i分支大4倍，就需要上采样两次，循环次数就是2； (2.3.1) 当k == i - j - 1时，举个例子，i = 2,j = 1, 此时仅循环一次，并采用当前模块，此时直接将j分支使用3x3的步长为2的卷积下采样(不使用bias)，后接BN，不使用ReLU；

(2.3.2) 当k != i - j - 1时，举个例子，i = 3,j = 1, 此时循环两次，先采用当前模块，将j分支使用3x3的步长为2的卷积下采样(不使用bias)两倍，后接BN和ReLU，紧跟着再使用(2.3.1)中的模块，这是为了保证最后一次二倍下采样的卷积操作不使用ReLU，猜测也是为了保证融合后特征的多样性；

(e) forward: 前向传播函数，利用以上函数的功能搭建一个HighResolutionModule；(1) 当仅包含一个分支时，生成该分支，没有融合模块，直接返回；(2) 当包含不仅一个分支时，先将对应分支的输入特征输入到对应分支，得到对应分支的输出特征；紧接着执行融合模块； (2.1) 循环将对应分支的输入特征输入到对应分支模型中，得到对应分支的输出特征；(2.2) 融合模块：对着这张图看，很容易看懂。每次多尺度之间的加法运算都是从最上面的尺度开始往下加，所以y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])；加到他自己的时候，不需要经过融合函数的处理，直接加，所以if i == j: y = y + x[j]；遇到不是最上面的尺度那个特征图或者它本身相同分辨率的那个特征图时，需要经过融合函数处理再加，所以y = y + self.fuse_layers[i][j](x[j])。最后将ReLU激活后的融合(加法)特征append到x_fuse，x_fuse的长度等于1（单尺度输出）或者num_branches（多尺度输出）。

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__()

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

self.branches = self._make_branches(

num_branches, blocks, num_blocks, num_channels)

self.fuse_layers = self._make_fuse_layers()

self.relu = nn.ReLU(True)

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)

def _make_one_branch(self, branch_index, block, num_blocks, num_channels,

stride=1):

# ---------------------------(1) begin---------------------------- # downsample = None

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

)

# ---------------------------(1) end---------------------------- #

# ---------------------------(2) begin---------------------------- # layers = []

layers.append(

block(

self.num_inchannels[branch_index],

num_channels[branch_index],

stride,

downsample

)

# ---------------------------(2) middle---------------------------- # self.num_inchannels[branch_index] = num_channels[branch_index] * block.expansion

for i in range(1, num_blocks[branch_index]):

layers.append(

block(

self.num_inchannels[branch_index],

num_channels[branch_index]

)

# ---------------------------(2) end---------------------------- # return nn.Sequential(*layers)

def _make_branches(self, num_branches, block, num_blocks, num_channels):

branches = []

for i in range(num_branches):

branches.append(

self._make_one_branch(i, block, num_blocks, num_channels)

)

return nn.ModuleList(branches)

def _make_fuse_layers(self):

# ---------------------------(1) begin---------------------------- # if self.num_branches == 1:

return None

# ---------------------------(1) end---------------------------- #

num_branches = self.num_branches

num_inchannels = self.num_inchannels

# ---------------------------(2) begin---------------------------- # fuse_layers = []

for i in range(num_branches if self.multi_scale_output else 1):

fuse_layer = []

for j in range(num_branches):

# ---------------------------(2.1) begin---------------------------- # if j > i:

fuse_layer.append(

nn.Sequential(

nn.Conv2d(

num_inchannels[j],

num_inchannels[i],

1, 1, 0, bias=False

nn.BatchNorm2d(num_inchannels[i]),

nn.Upsample(scale_factor=2**(j-i), mode='nearest')

)

# ---------------------------(2.1) end---------------------------- #

# ---------------------------(2.2) begin---------------------------- # elif j == i:

fuse_layer.append(None)

# ---------------------------(2.2) end---------------------------- #

# ---------------------------(2.3) begin---------------------------- # else:

conv3x3s = []

for k in range(i-j):

# ---------------------------(2.3.1) begin---------------------------- # if k == i - j - 1:

num_outchannels_conv3x3 = num_inchannels[i]

conv3x3s.append(

nn.Sequential(

nn.Conv2d(

num_inchannels[j],

num_outchannels_conv3x3,

3, 2, 1, bias=False

nn.BatchNorm2d(num_outchannels_conv3x3)

)

# ---------------------------(2.3.1) end---------------------------- #

# ---------------------------(2.3.1) begin---------------------------- # else:

num_outchannels_conv3x3 = num_inchannels[j]

conv3x3s.append(

nn.Sequential(

nn.Conv2d(

num_inchannels[j],

num_outchannels_conv3x3,

3, 2, 1, bias=False

nn.BatchNorm2d(num_outchannels_conv3x3),

nn.ReLU(True)

)

# ---------------------------(2.3.1) end---------------------------- # # ---------------------------(2.3) end---------------------------- # fuse_layer.append(nn.Sequential(*conv3x3s))

fuse_layers.append(nn.ModuleList(fuse_layer))

# ---------------------------(2) end---------------------------- #

return nn.ModuleList(fuse_layers)

def get_num_inchannels(self):

return self.num_inchannels

def forward(self, x):

# ---------------------------(1) begin---------------------------- # if self.num_branches == 1:

return [self.branches[0](x[0])]

# ---------------------------(1) end---------------------------- #

# ---------------------------(2) begin---------------------------- # # ---------------------------(2.1) begin---------------------------- # for i in range(self.num_branches):

x[i] = self.branches[i](x[i])

# ---------------------------(2.1) end---------------------------- #

# ---------------------------(2.2) begin---------------------------- # x_fuse = []

for i in range(len(self.fuse_layers)):

y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])

for j in range(1, self.num_branches):

if i == j:

y = y + x[j]

else:

y = y + self.fuse_layers[i][j](x[j])

x_fuse.append(self.relu(y))

# ---------------------------(2.2) end---------------------------- # # ---------------------------(2) end---------------------------- #

return x_fuse

2.3 PoseHighResolutionNet (模型整体结构)

该模块主要包含5个阶段，第一阶段由stem模块构成，第2、3、4阶段由transition模块，HighResolutionModule模块构成，最后一个阶段就是final_layer，其中第一阶段包含两次下采样，剩余阶段的最终结果没有下采样，所以产生的热图为原始输入图像分辨率的1/4。第2、3、4阶段的主要超参数有：

POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_MODULES = 1 # 当前阶段HighResolutionModule模块的数目 POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BRANCHES = 2 # 当前阶段分支数目 POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BLOCKS = [4, 4] # 当前阶段每个分支上使用BLOCK块结构的数目 POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_CHANNELS = [32, 64] # 当前阶段每个分支的通道数（*=expansion） POSE_HIGH_RESOLUTION_NET.STAGE2.BLOCK = 'BASIC' # 使用4.1定义的块结构的哪一种 POSE_HIGH_RESOLUTION_NET.STAGE2.FUSE_METHOD = 'SUM' # 融合方式，求和还是串联

POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_MODULES = 1

POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BRANCHES = 3

POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BLOCKS = [4, 4, 4]

POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_CHANNELS = [32, 64, 128]

POSE_HIGH_RESOLUTION_NET.STAGE3.BLOCK = 'BASIC'

POSE_HIGH_RESOLUTION_NET.STAGE3.FUSE_METHOD = 'SUM'

POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_MODULES = 1

POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BRANCHES = 4

POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4]

POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_CHANNELS = [32, 64, 128, 256]

POSE_HIGH_RESOLUTION_NET.STAGE4.BLOCK = 'BASIC'

POSE_HIGH_RESOLUTION_NET.STAGE4.FUSE_METHOD = 'SUM'

(a) __init__ & forward: 这两个模块一般都是对应的，所以放在一块；

(1) stem:

卷积（步长2）-> BN -> 卷积（步长2）-> BN -> ReLU -> Bottleneck x 4 (H/4, W/4, 256)；

(2)stage2: transition + HighResolutionModule BRANCHES = 2, BASIC : [32, 64], [4, 4]；(3)stage3: transition + HighResolutionModule BRANCHES = 3, BASIC : [32, 64, 128], [4, 4, 4]；(4)stage4: transition + HighResolutionModule BRANCHES = 4, BASIC : [32, 64, 128, 256], [4, 4, 4, 4]；(5)final_layer: 卷积，no BN, no ReLU；(b) _make_layer: 其在stem模块中调用，使用BasicBlock或者Bottleneck作为基本构造块，循环搭建，第一个块结构单独搭建，不包含在循环内部，主要是因为如果需要进行通道维度变换(64->256)，只需要使用第一个块结构来执行，剩余1到blocks的块循环搭建，这些块结构输入输出通道都一致(=256)；(c) _make_transition_layer: 从前一个stage的输出得到当前stage的不同分支，也就是不同分辨率的特征图。举例1：从stage1到stage2，上一阶段输出特征图为单个分辨率(H/4, W/4, 256)，stage2需要两个分辨率的分支，_make_transition_layer就需要产生(H/4, W/4)和(H/8, W/8)两个特征图；(1) 以当前stage要产生的最终分支数为最外层循环，如果stage1存在(H/4, W/4)的特征图，就判断该特征图的通道和所需要的通道数是否一致，一致的话，直接拿过来用，不一致的话使用3x3卷积做维度变换（不解：为什么使用了3x3卷积，但是没有进行padding？），后接BN和ReLU。此时stage1不存在(H/8, W/8)的特征图，就需要降采样来产生3x3卷积（步长2），后接BN和ReLU，每次降采样都将前一阶段最后一个分支（分辨率最小）的特征图作为输入；(d) _make_stage: 依据参数文件定义的每个stage的NUM_MODULES（都设定为1）,调用HighResolutionModule模块进行多分支构建和分支之间的融合；此处代码中备注multi_scale_output is only used last module，只有当multi_scale_output = True并且是该阶段的最后一个模块时，才会设定reset_multi_scale_output = True,依据__init__函数中的设置，也就是说只有stage2、3中的那个HighResolutionModule模块是多尺度输出，stage4那个是单尺度输出；x init_weights: 暂时还没看

· blocks_dict = {

· 'BASIC': BasicBlock,

· 'BOTTLENECK': Bottleneck

· }

· class PoseHighResolutionNet(nn.Module):

· def __init__(self, cfg, **kwargs):

· self.inplanes = 64

· extra = cfg.MODEL.EXTRA

· super(PoseHighResolutionNet, self).__init__()

· # ---------------------------(1) begin---------------------------- # # stem net self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,

· bias=False)

· self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)

· self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1,

· bias=False)

· self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)

· self.relu = nn.ReLU(inplace=True)

· self.layer1 = self._make_layer(Bottleneck, 64, 4)

· # ---------------------------(1) end---------------------------- #

· # ---------------------------(2) begin---------------------------- # self.stage2_cfg = cfg['MODEL']['EXTRA']['STAGE2']

· num_channels = self.stage2_cfg['NUM_CHANNELS']

· block = blocks_dict[self.stage2_cfg['BLOCK']]

· num_channels = [

· num_channels[i] * block.expansion for i in range(len(num_channels))

· ]

· self.transition1 = self._make_transition_layer([256], num_channels)

· self.stage2, pre_stage_channels = self._make_stage(

· self.stage2_cfg, num_channels)

· # ---------------------------(2) end---------------------------- #

· # ---------------------------(3) begin---------------------------- # self.stage3_cfg = cfg['MODEL']['EXTRA']['STAGE3']

· num_channels = self.stage3_cfg['NUM_CHANNELS']

· block = blocks_dict[self.stage3_cfg['BLOCK']]

· num_channels = [

· num_channels[i] * block.expansion for i in range(len(num_channels))

· ]

· self.transition2 = self._make_transition_layer(

· pre_stage_channels, num_channels)

· self.stage3, pre_stage_channels = self._make_stage(

· self.stage3_cfg, num_channels)

· # ---------------------------(3) end---------------------------- #

· # ---------------------------(4) begin---------------------------- # self.stage4_cfg = cfg['MODEL']['EXTRA']['STAGE4']

· num_channels = self.stage4_cfg['NUM_CHANNELS']

· block = blocks_dict[self.stage4_cfg['BLOCK']]

· num_channels = [

· num_channels[i] * block.expansion for i in range(len(num_channels))

· ]

· self.transition3 = self._make_transition_layer(

· pre_stage_channels, num_channels)

· self.stage4, pre_stage_channels = self._make_stage(

· self.stage4_cfg, num_channels, multi_scale_output=False)

· # ---------------------------(4) end---------------------------- #

· # ---------------------------(5) begin---------------------------- # self.final_layer = nn.Conv2d(

· in_channels=pre_stage_channels[0],

· out_channels=cfg.MODEL.NUM_JOINTS,

· kernel_size=extra.FINAL_CONV_KERNEL,

· stride=1,

· padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0

· )

· # ---------------------------(5) end---------------------------- #

· self.pretrained_layers = cfg['MODEL']['EXTRA']['PRETRAINED_LAYERS']

· def _make_transition_layer(

· self, num_channels_pre_layer, num_channels_cur_layer):

· num_branches_cur = len(num_channels_cur_layer)

· num_branches_pre = len(num_channels_pre_layer)

· # ---------------------------(1) begin---------------------------- # transition_layers = []

· for i in range(num_branches_cur):

· if i < num_branches_pre:

· if num_channels_cur_layer[i] != num_channels_pre_layer[i]:

· transition_layers.append(

· nn.Sequential(

· nn.Conv2d(

· num_channels_pre_layer[i],

· num_channels_cur_layer[i],

· 3, 1, 1, bias=False

· ),

· nn.BatchNorm2d(num_channels_cur_layer[i]),

· nn.ReLU(inplace=True)

· )

· else:

· transition_layers.append(None)

· else:

· conv3x3s = []

· for j in range(i+1-num_branches_pre):

· inchannels = num_channels_pre_layer[-1]

· outchannels = num_channels_cur_layer[i] \

· if j == i-num_branches_pre else inchannels

· conv3x3s.append(

· nn.Sequential(

· nn.Conv2d(

· inchannels, outchannels, 3, 2, 1, bias=False

· ),

· nn.BatchNorm2d(outchannels),

· nn.ReLU(inplace=True)

· )

· transition_layers.append(nn.Sequential(*conv3x3s))

· # ---------------------------(1) end---------------------------- #

· return nn.ModuleList(transition_layers)

· 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 _make_stage(self, layer_config, num_inchannels,

· multi_scale_output=True):

· num_modules = layer_config['NUM_MODULES']

· num_branches = layer_config['NUM_BRANCHES']

· num_blocks = layer_config['NUM_BLOCKS']

· num_channels = layer_config['NUM_CHANNELS']

· block = blocks_dict[layer_config['BLOCK']]

· fuse_method = layer_config['FUSE_METHOD']

· modules = []

· for i in range(num_modules):

· # multi_scale_output is only used last module if not multi_scale_output and i == num_modules - 1:

· reset_multi_scale_output = False

· else:

· reset_multi_scale_output = True

· modules.append(

· HighResolutionModule(

· num_branches,

· block,

· num_blocks,

· num_inchannels,

· num_channels,

· fuse_method,

· reset_multi_scale_output

· )

· num_inchannels = modules[-1].get_num_inchannels()

· return nn.Sequential(*modules), num_inchannels

· def forward(self, x):

· # ---------------------------(1) begin---------------------------- # x = self.conv1(x)

· x = self.bn1(x)

· x = self.relu(x)

· x = self.conv2(x)

· x = self.bn2(x)

· x = self.relu(x)

· x = self.layer1(x)

· # ---------------------------(1) end---------------------------- #

· # ---------------------------(2) begin---------------------------- # x_list = []

· for i in range(self.stage2_cfg['NUM_BRANCHES']):

· if self.transition1[i] is not None:

· x_list.append(self.transition1[i](x))

· else:

· x_list.append(x)

· y_list = self.stage2(x_list)

· # ---------------------------(2) end---------------------------- #

· # ---------------------------(3) begin---------------------------- # x_list = []

· for i in range(self.stage3_cfg['NUM_BRANCHES']):

· if self.transition2[i] is not None:

· x_list.append(self.transition2[i](y_list[-1]))

· else:

· x_list.append(y_list[i])

· y_list = self.stage3(x_list)

· # ---------------------------(3) end---------------------------- #

· # ---------------------------(4) begin---------------------------- # x_list = []

· for i in range(self.stage4_cfg['NUM_BRANCHES']):

· if self.transition3[i] is not None:

· x_list.append(self.transition3[i](y_list[-1]))

· else:

· x_list.append(y_list[i])

· y_list = self.stage4(x_list)

· # ---------------------------(4) end---------------------------- #

· # ---------------------------(5) begin---------------------------- # x = self.final_layer(y_list[0])

· # ---------------------------(5) end---------------------------- #

· return x

· def init_weights(self, pretrained=''):

· logger.info('=> init weights from normal distribution')

· for m in self.modules():

· if isinstance(m, nn.Conv2d):

· # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') nn.init.normal_(m.weight, std=0.001)

· for name, _ in m.named_parameters():

· if name in ['bias']:

· nn.init.constant_(m.bias, 0)

· elif isinstance(m, nn.BatchNorm2d):

· nn.init.constant_(m.weight, 1)

· nn.init.constant_(m.bias, 0)

· elif isinstance(m, nn.ConvTranspose2d):

· nn.init.normal_(m.weight, std=0.001)

· for name, _ in m.named_parameters():

· if name in ['bias']:

· nn.init.constant_(m.bias, 0)

· if os.path.isfile(pretrained):

· pretrained_state_dict = torch.load(pretrained)

· logger.info('=> loading pretrained model {}'.format(pretrained))

· need_init_state_dict = {}

· for name, m in pretrained_state_dict.items():

· if name.split('.')[0] in self.pretrained_layers \

· or self.pretrained_layers[0] is '*':

· need_init_state_dict[name] = m

· self.load_state_dict(need_init_state_dict, strict=False)

· elif pretrained:

· logger.error('=> please download pre-trained models first!')

· raise ValueError('{} is not exist!'.format(pretrained))

· def get_pose_net(cfg, is_train, **kwargs):

· model = PoseHighResolutionNet(cfg, **kwargs)

· if is_train and cfg.MODEL.INIT_WEIGHTS:

· model.init_weights(cfg.MODEL.PRETRAINED)

· return model

今天内容有些枯燥，放了很多代码在这里，也是便于自己日后理解。最近还没法回实验室，所以在智星云租了GPU用来做实验。这家的价格还是很合理的，用起来也很方便，不用花精力在环境配置上，有同样需求的朋友可以参考，智星云官网： http://www.ai-galaxy.cn/，淘宝店：https://shop36573300.taobao.com/公众号: 智星AI，

今天是个值得纪念的日子，中国共产党成立99周年，作为一名中国共产党党员，在这样特别的日子里，祝我党生日快乐。

PEACE

参考资料：

https://arxiv.org/pdf/1902.09212.pdf

https://blog.csdn.net/yudw15/article/details/99600651

https://niecongchong.github.io/2019/05/13/HRNet阅读笔记及代码理解/

http://www.ai-galaxy.cn/

https://shop36573300.taobao.com/

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