查看: 3101|回复: 0

[其他资源] 骨架图算法Graph Embedded Pose Clustering

字体大小: 正常放大

杨利霞

5273 主题	82 听众	17万积分

TA的每日心情

	开心 2021-8-11 17:59

签到天数: 17 天

[LV.4]偶尔看看III

网络挑战赛参赛者

自我介绍: 本人女，毕业于内蒙古科技大学，担任文职专业，毕业专业英语。

群组: 2018美赛大象算法课程

群组: 2018美赛护航培训课程

群组: 2019年数学中国站长建

群组: 2019年数据分析师课程

群组: 2018年大象老师国赛优

电梯直达

1^#

发表于 2022-9-5 15:49 |只看该作者 |倒序浏览

|招呼Ta 关注Ta

骨架图算法Graph Embedded Pose Clustering

骨架图算法

Graph Embedded Pose Clustering for Anomaly Detection
paper code
https://arxiv.org/abs/1912.11850 https://github.com/amirmk89/gepc
我们提出了一种用于人类行为异常检测的新方法。我们的方法直接适用于可以从输入视频序列计算的人体姿势图。这使得分析独立于扰动参数，如视点或照明。我们将这些图映射到一个潜在空间并将它们聚类。然后，每个操作都由其对每个聚类的软赋值来表示。这为数据提供了一种“词袋”表示，其中每个动作都由其与一组基本动作词的相似性来表示。然后，我们使用基于狄利克雷过程的混合物，这对于处理比例数据（例如我们的软赋值向量）很有用，以确定一个动作是否正常。

首先，我们对输入数据使用人体姿态检测器。这抽象了问题，并防止下一步处理诸如视点或照明变化等有害参数。人的行为被表示为时空图，我们将其嵌入（第3.1、3.2小节）并聚类（第3.3小节）到一些潜在空间中。现在，每个动作都表示为一组基本动作的软分配向量。这抽象了动作的基本类型（即细粒度或粗粒度），从而进入学习其分布的最后阶段。我们用于学习软分配向量分布的工具是Dirichlet过程混合（第3.4小节），我们将模型拟合到数据中。然后使用该模型确定动作是否正常。

图的每个节点对应于一个关键点、一个身体关节，每个边表示两个节点之间的某种关系。存在许多"关键点关系"，如解剖学上定义的物理关系（例如，左手腕和肘部连接）和由运动定义的动作关系，这些运动往往在特定动作的上下文中高度相关（例如，跑步时左右膝盖倾向于朝相反方向移动）。图的方向来自于这样一个事实，即一些关系是在优化过程中学习的，并且不是对称的。这种表示的一个好处是紧凑，这对于高效的视频分析非常重要。
为了在时间上扩展，将从视频序列中提取的姿势关键点表示为姿势图的时间序列。时间姿势图是人体关节位置的时间序列。时域邻接可以类似地通过连接连续帧中的关节来定义，允许我们利用姿势图序列的空间和时间维度执行图卷积运算

我们提出了一种基于深度时态图自动编码器的结构，用于嵌入时态姿态图。基于图2所示ST-GCN的基本块设计，我们将基本GCN算子替换为新的空间注意力图卷积，如下所示。

3.2. Spatial Attention Graph Convolution
我们提出了一个新的图算子，如图3所示，它使用三种类型的邻接矩阵：静态、全局学习和推断（基于注意力）。每个邻接类型使用单独的权重应用其自己的GCN。

GCN的输出按通道维度堆叠。采用1×1卷积作为加权叠加输出的可学习缩减度量，并提供所需的输出信道数。

三个邻接矩阵捕捉了模型的不同方面：
（i）使用身体部位连通性作为优先于节点关系，使用静态邻接矩阵表示。
（ii）由全局邻接矩阵捕获的数据集级关键点关系，以及
（iii）由推断邻接矩阵获取的样本特定关系。最后，可学习约简度量对不同的输出进行加权

后续段落介绍了静态、全局学习和推断的邻接矩阵的设置方法，即图3中的A,B和C，在此略过。
3.3. Deep Embedded Clustering
为了构建我们的底层动作词典，我们采用训练集样本，并将它们联合嵌入和聚类到一些潜在空间中。然后，每个样本由其分配给每个底层聚类的概率表示。选择目标是为了提供不同的潜在集群，这些集群上存在动作。

我们采用了深嵌入聚类的概念[32]，用我们的ST-GCAE架构对时间图进行聚类。所提出的聚类模型由编码器、解码器和软聚类层三部分组成。

具体地说，我们的ST-GCAE模型保持了图的结构，但使用了较大的时间步长和不断增加的通道数来将输入序列压缩为潜在向量。解码器使用时间上采样层和额外的图卷积块，用于逐渐恢复原始信道计数和时间维度。

ST-GCAE的嵌入是数据聚类的起点。在我们的聚类优化阶段，对基于重构的初始嵌入进行微调，以达到最终的聚类优化嵌入。

符号表示
x i x_ix
i

输入示例
z i z_iz
i

编码器的潜在嵌入
y i y_iy
i

使用聚类层计算的软聚类分配
Θ ΘΘ 聚类层的参数
p i k p_{ik}p
ik

probability for the i-th sample to be assigned to the k-th cluster

我们采用[32]提出的聚类目标和优化算法。聚类目标是最小化当前模型概率聚类预测P和目标分布Q之间的KL散度：

目标分布旨在通过标准化和将每个值推到更接近0或1的值来加强当前的群集分配。反复应用将P转换为Q的函数将最终导致硬分配向量。使用以下等式计算目标分布的每个成员：

聚类层由为编码训练集计算的K均值质心初始化。优化以期望最大化（EM）的方式进行。
在期望步骤期间，整个模型是固定的，并且目标分布Q被更新。在最大化阶段，优化模型以最小化聚类损失Lcluster。

3.4. Normality Scoring
该模型支持两种类型的多模分布。一个是集群分配级别；另一个是在软分配向量级别。例如，一个动作可能被分配给多个集群（集群级分配），导致多模式软分配向量。
软分配向量本身（捕获动作）也可以通过多模态分布建模。

Dirichlet过程混合模型（DPMM）是评估比例数据分布的一种有效方法。它满足我们所需的设置：（i）估计（拟合）阶段，在此阶段，一组分布参数为评估，和（ii）推理阶段，为每个嵌入样本使用拟合模型。彻底的Blei和Jordan[4]给出了该模型的概述。

Dirichlet过程混合模型（DPMM）是评估比例数据分布的有效方法。它符合我们要求的设置：
（i）估计（拟合）阶段，在此期间评估一组分布参数，以及
（ii）推理阶段，使用拟合模型为每个嵌入样本提供分数。Blei和Jordan[4]对模型进行了全面概述。

DPMM是单峰Dirichlet分布的常见混合扩展，并使用Dirichllet过程，这DirichletDistribution的无限维扩展。该模型是多模态的，能够将每个模式捕获为混合成分。拟合模型具有多个模式，每个模式表示对应于一个正常行为的一组比例。在测试时，使用拟合模型通过其对数概率对每个样本进行评分。[4,8]中提供了关于DPMM使用的进一步解释和讨论。

3.5. Training
该模型的训练阶段包括两个阶段，一个是自动编码器的预训练阶段，其中网络的聚类分支保持不变，另一个是微调阶段，其中嵌入和聚类都得到优化。具体而言：

Pre-Training: 该模型通过最小化重建损失（表示为Lrec）来学习编码和重建序列，Lrec是原始瞬时位姿图和ST-GCAE重建的位姿图之间的L2损失

Fine-Tuning:
该模型优化了由重建损失和聚类损失组成的组合损失函数。
进行优化，使得聚类层优化为w.r.t.Lcluster，解码器优化为w.r.t.Lrec，编码器优化为w.r.t.两者。
集群层的初始化是通过Kmeans完成的。如[9]所示，当编码器针对这两种损失进行优化时，解码器保持不变，并充当正则化器，以保持编码器的嵌入质量。
本阶段的综合损失为：

实现细节

def calc_reg_loss(model, reg_type='l2', avg=True):
reg_loss = None
parameters = list(param for name, param in model.named_parameters() if 'bias' not in name)
num_params = len(parameters)
if reg_type.lower() == 'l2':
      for param in parameters:
         if reg_loss is None:
            reg_loss = 0.5 * torch.sum(param ** 2)
         else:
            reg_loss = reg_loss + 0.5 * param.norm(2) ** 2

      if avg:
         reg_loss /= num_params
      return reg_loss
else:
      return torch.tensor(0.0, device=model.device)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

PatchModel(
  (patch_fe): Identity()
  (gcae): GCAE(
(data_bn): BatchNorm1d(54, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act): ReLU(inplace=True)
(st_gcn_enc): ModuleList(
   (0): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(3, 8, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(3, 8, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(3, 8, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(3, 8, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(3, 8, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(3, 8, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (down): Sequential(
            (0): Conv2d(3, 32, kernel_size=(1, 1), stride=(1, 1))
            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
         (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(3, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(96, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(32, 32, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0))
      (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0.3, inplace=True)
      )
   )
   (1): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(32, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(96, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(32, 32, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0))
      (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0.3, inplace=True)
      )
   )
   (2): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(32, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(96, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(32, 32, kernel_size=(9, 1), stride=(2, 1), padding=(4, 0))
      (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0.3, inplace=True)
      )
      (residual): Sequential(
      (0): Conv2d(32, 32, kernel_size=(1, 1), stride=(2, 1))
      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
   )
   (3): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(32, 12, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(32, 12, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(32, 12, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(32, 12, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(32, 12, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(32, 12, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (down): Sequential(
            (0): Conv2d(32, 48, kernel_size=(1, 1), stride=(1, 1))
            (1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
         (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(32, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(144, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(48, 48, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0))
      (3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0.3, inplace=True)
      )
      (residual): Sequential(
      (0): Conv2d(32, 48, kernel_size=(1, 1), stride=(1, 1))
      (1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
   )
   (4): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(48, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(144, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(48, 48, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0))
      (3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0.3, inplace=True)
      )
   )
   (5): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(48, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(144, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(48, 48, kernel_size=(9, 1), stride=(3, 1), padding=(4, 0))
      (3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0.3, inplace=True)
      )
      (residual): Sequential(
      (0): Conv2d(48, 48, kernel_size=(1, 1), stride=(3, 1))
      (1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
   )
   (6): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(48, 16, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(48, 16, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(48, 16, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(48, 16, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(48, 16, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(48, 16, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (down): Sequential(
            (0): Conv2d(48, 64, kernel_size=(1, 1), stride=(1, 1))
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
         (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(48, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(64, 64, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0))
      (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0.3, inplace=True)
      )
      (residual): Sequential(
      (0): Conv2d(48, 64, kernel_size=(1, 1), stride=(1, 1))
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
   )
   (7): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(64, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(64, 64, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0))
      (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0.3, inplace=True)
      )
   )
   (8): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(64, 8, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (down): Sequential(
            (0): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
            (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
         (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(64, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(96, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(32, 32, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0))
      (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0.3, inplace=True)
      )
      (residual): Sequential(
      (0): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
   )
)
(dec_final_gcn): ConvBlock(
   (act): ReLU(inplace=True)
   (gcn): PyGeoConv(
      (g_conv): ConvTemporalGraphical(
      (conv): Conv2d(48, 9, kernel_size=(1, 1), stride=(1, 1))
      )
   )
   (tcn): Sequential(
      (0): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(3, 3, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0))
      (3): Identity()
      (4): Dropout(p=0.3, inplace=True)
   )
)
(st_gcn_dec): ModuleList(
   (0): Upsample(scale_factor=(3.0, 1.0), mode=bilinear)
   (1): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (down): Sequential(
            (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1))
            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
         (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(32, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(64, 64, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0))
      (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0, inplace=True)
      )
      (residual): Sequential(
      (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1))
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
   )
   (2): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(64, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(64, 64, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0))
      (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0, inplace=True)
      )
   )
   (3): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(64, 12, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(64, 12, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(64, 12, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(64, 12, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(64, 12, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(64, 12, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (down): Sequential(
            (0): Conv2d(64, 48, kernel_size=(1, 1), stride=(1, 1))
            (1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         )
         (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(64, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(144, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(48, 48, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0))
      (3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0, inplace=True)
      )
      (residual): Sequential(
      (0): Conv2d(64, 48, kernel_size=(1, 1), stride=(1, 1))
      (1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
   )
   (4): Upsample(scale_factor=(2.0, 1.0), mode=bilinear)
   (5): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(48, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(144, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(48, 48, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0))
      (3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0, inplace=True)
      )
   )
   (6): ConvBlock(
      (act): ReLU(inplace=True)
      (gcn): PyGeoConv(
      (g_conv): SAGC(
         (conv_a): ModuleList(
            (0): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
         )
         (conv_b): ModuleList(
            (0): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (1): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
            (2): Conv2d(48, 12, kernel_size=(1, 1), stride=(1, 1))
         )
         (gconv): ModuleList(
            (0): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (1): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
            (2): GraphConvBR(
            (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (act): ReLU(inplace=True)
            )
         )
         (bn): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
         (soft): Softmax(dim=-2)
         (relu): CELU(alpha=0.01)
         (expanding_conv): Conv2d(48, 432, kernel_size=(1, 1), stride=(1, 1), bias=False)
         (reduction_conv): Conv2d(144, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      )
      (tcn): Sequential(
      (0): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (1): ReLU(inplace=True)
      (2): Conv2d(48, 48, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0))
      (3): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): Dropout(p=0, inplace=True)
      )
   )
)
  )
)
————————————————
版权声明：本文为CSDN博主「FakeOccupational」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/ResumeProject/article/details/126678496

zan