Project Code

class InitialStage(nn.Module):
    def __init__(self, num_channels, num_heatmaps, num_pafs):
        super().__init__()
        self.trunk = nn.Sequential(
            conv(num_channels, num_channels, bn=False),
            conv(num_channels, num_channels, bn=False),
            conv(num_channels, num_channels, bn=False)
        )
        self.heatmaps = nn.Sequential(
            conv(num_channels, 512, kernel_size=1, padding=0, bn=False),
            conv(512, num_heatmaps, kernel_size=1, padding=0, bn=False, relu=False)
        )
        self.pafs = nn.Sequential(
            conv(num_channels, 512, kernel_size=1, padding=0, bn=False),
            conv(512, num_pafs, kernel_size=1, padding=0, bn=False, relu=False)
        )

    def forward(self, x):
        trunk_features = self.trunk(x)
        heatmaps = self.heatmaps(trunk_features)
        pafs = self.pafs(trunk_features)
        return [heatmaps, pafs]

The InitialStage class is a PyTorch module that represents the first stage of the pose estimation pipeline. It processes feature maps from the backbone network (or the CPM module) and generates two outputs: heatmaps and part affinity fields (PAFs). These outputs are essential for detecting keypoints and their connections in human pose estimation.

Class Definition

class InitialStage(nn.Module):
    def __init__(self, num_channels, num_heatmaps, num_pafs):
        super().__init__()

• Purpose: The InitialStage processes input feature maps and produces:
  • Heatmaps: Represent the likelihood of keypoints (e.g., joints) at each spatial location.
  • PAFs: Represent the direction and strength of connections between keypoints (e.g., limbs).
• Parameters:
  • num_channels: Number of input channels in the feature map.
  • num_heatmaps: Number of heatmaps to output (one for each keypoint type).
  • num_pafs: Number of PAFs to output (two for each connection type: x and y directions).

Components

1. self.trunk

   self.trunk = nn.Sequential(
       conv(num_channels, num_channels, bn=False),
       conv(num_channels, num_channels, bn=False),
       conv(num_channels, num_channels, bn=False)
   )
   

   • A sequence of three convolutional layers (conv), each with:
     • No batch normalization (bn=False).
     • ReLU activation (default in conv).
   • Purpose: Extracts and refines features from the input feature map.

2. self.heatmaps

   self.heatmaps = nn.Sequential(
       conv(num_channels, 512, kernel_size=1, padding=0, bn=False),
       conv(512, num_heatmaps, kernel_size=1, padding=0, bn=False, relu=False)
   )
   

   • A sequence of two convolutional layers:
     • The first layer reduces the feature map to 512 channels using a 1x1 convolution.
     • The second layer outputs the heatmaps with num_heatmaps channels, without ReLU activation (relu=False).
   • Purpose: Generates heatmaps that indicate the likelihood of keypoints.

3. self.pafs

   self.pafs = nn.Sequential(
       conv(num_channels, 512, kernel_size=1, padding=0, bn=False),
       conv(512, num_pafs, kernel_size=1, padding=0, bn=False, relu=False)
   )
   

   • Similar to self.heatmaps, but outputs num_pafs channels for the PAFs.
   • Purpose: Generates PAFs that represent the connections between keypoints.

Forward Method

def forward(self, x):
    trunk_features = self.trunk(x)
    heatmaps = self.heatmaps(trunk_features)
    pafs = self.pafs(trunk_features)
    return [heatmaps, pafs]

• Step-by-Step Explanation:

  1. Trunk Features:

     trunk_features = self.trunk(x)
     

     • The input feature map (x) is processed by the self.trunk to extract refined features.

  2. Heatmaps:

     heatmaps = self.heatmaps(trunk_features)
     

     • The refined features are passed through self.heatmaps to generate the heatmaps.

  3. PAFs:

     pafs = self.pafs(trunk_features)
     

     • The same refined features are passed through self.pafs to generate the PAFs.

  4. Output:

     return [heatmaps, pafs]
     

     • Returns a list containing the heatmaps and PAFs.

Purpose in the Model