KnowledgeTree

外观

HRnet

约 393 字大约 1 分钟

hrnet

2024-04-09

Deep High-Resolution Representation Learning for Human Pose EstimationarXiv GitHub

Illustrating the architecture of the proposed HRNet

算法流程如下：

architecture

其中Bottleneck的网络如下：

Bottleneck

stage：

stage2

stage3

stage4

其中BasicBlock的网络如下：

BasicBlock

以下是Hrnet特征间信息交互过程，为x_fuse过程，本身并不改变各个分辨率特征的大小

fuse_layers是hrnet多个不同分辨率特征信息交互的网络，本身并不改变各个分辨率特征的大小 以下为fuse_layers的构造代码：

hrnet：code 
num_branches = self.num_branches
num_inchannels = self.num_inchannels
fuse_layers = []
for i in range(num_branches if self.multi_scale_output else 1):
    fuse_layer = []
    for j in range(num_branches):
        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')
                )
            )
        elif j == i:
            fuse_layer.append(None)
        else:
            conv3x3s = []
            for k in range(i-j):
                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)
                        )
                    )
                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)
                        )
                    )
            fuse_layer.append(nn.Sequential(*conv3x3s))
    fuse_layers.append(nn.ModuleList(fuse_layer))

以下为fuse_layers的前向传播代码：

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))