Project Code

class Cpm(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.align = conv(in_channels, out_channels, kernel_size=1, padding=0, bn=False)
        self.trunk = nn.Sequential(
            conv_dw_no_bn(out_channels, out_channels),
            conv_dw_no_bn(out_channels, out_channels),
            conv_dw_no_bn(out_channels, out_channels)
        )
        self.conv = conv(out_channels, out_channels, bn=False)

    def forward(self, x):
        x = self.align(x)
        x = self.conv(x + self.trunk(x))
        return x

The Cpm class (short for Convolutional Pose Machine) is a PyTorch module designed to process feature maps and refine them for pose estimation tasks. Here's a detailed breakdown of its components and functionality:

Class Definition

class Cpm(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()

• Purpose: The Cpm class is a building block in the pose estimation model. It processes input feature maps and outputs refined feature maps.
• Parameters:
  • in_channels: Number of input channels in the feature map.
  • out_channels: Number of output channels in the feature map.

Components

1. self.align

   self.align = conv(in_channels, out_channels, kernel_size=1, padding=0, bn=False)
   

   • A 1x1 convolution layer without batch normalization (bn=False).
   • Purpose: Aligns the number of input channels (in_channels) to the desired number of output channels (out_channels).

2. self.trunk

   self.trunk = nn.Sequential(
       conv_dw_no_bn(out_channels, out_channels),
       conv_dw_no_bn(out_channels, out_channels),
       conv_dw_no_bn(out_channels, out_channels)
   )
   

   • A sequence of three depthwise separable convolution layers (conv_dw_no_bn), each with:
     • No batch normalization.
     • ELU activation instead of ReLU.
   • Purpose: Extracts and refines features from the aligned input.

3. self.conv

   self.conv = conv(out_channels, out_channels, bn=False)
   

   • A standard convolutional layer without batch normalization (bn=False).
   • Purpose: Further processes the combined features from the trunk and aligned input.

Forward Method

def forward(self, x):
    x = self.align(x)
    x = self.conv(x + self.trunk(x))
    return x

• Step-by-Step Explanation:

  1. Align Input:

     x = self.align(x)
     

     • The input feature map is passed through the self.align layer to adjust the number of channels.

  2. Trunk Features:

     self.trunk(x)
     

     • The aligned feature map is processed by the self.trunk (three depthwise separable convolution layers) to extract refined features.

  3. Feature Combination:

     x + self.trunk(x)
     

     • The aligned feature map (x) is added element-wise to the trunk's output. This acts as a residual connection, helping preserve the original input features.

  4. Final Convolution:

     self.conv(...)
     

     • The combined features are passed through the self.conv layer for further refinement.

  5. Output:

     return x
     

     • The final refined feature map is returned.

Purpose in the Model