Project Code

class PoseEstimationWithMobileNet(nn.Module):
    def __init__(self, num_refinement_stages=1, num_channels=128, num_heatmaps=19, num_pafs=38):
        super().__init__()
        self.model = nn.Sequential(
            conv(     3,  32, stride=2, bias=False),
            conv_dw( 32,  64),
            conv_dw( 64, 128, stride=2),
            conv_dw(128, 128),
            conv_dw(128, 256, stride=2),
            conv_dw(256, 256),
            conv_dw(256, 512),  # conv4_2
            conv_dw(512, 512, dilation=2, padding=2),
            conv_dw(512, 512),
            conv_dw(512, 512),
            conv_dw(512, 512),
            conv_dw(512, 512)   # conv5_5
        )
        self.cpm = Cpm(512, num_channels)

        self.initial_stage = InitialStage(num_channels, num_heatmaps, num_pafs)
        self.refinement_stages = nn.ModuleList()
        for idx in range(num_refinement_stages):
            self.refinement_stages.append(RefinementStage(num_channels + num_heatmaps + num_pafs, num_channels,
                                                          num_heatmaps, num_pafs))

    def forward(self, x):
        backbone_features = self.model(x)
        backbone_features = self.cpm(backbone_features)

        stages_output = self.initial_stage(backbone_features)
        for refinement_stage in self.refinement_stages:
            stages_output.extend(
                refinement_stage(torch.cat([backbone_features, stages_output[-2], stages_output[-1]], dim=1)))

        return stages_output

The PoseEstimationWithMobileNet class is the main model for lightweight human pose estimation. It combines a MobileNet-based backbone for feature extraction with additional stages for keypoint detection and refinement. The model outputs heatmaps (for keypoint locations) and part affinity fields (PAFs, for connections between keypoints).

Class Definition

class PoseEstimationWithMobileNet(nn.Module):
    def __init__(self, num_refinement_stages=1, num_channels=128, num_heatmaps=19, num_pafs=38):
        super().__init__()

Purpose: This class defines the full pose estimation model.
Parameters:
- num_refinement_stages: Number of refinement stages to iteratively improve predictions.
- num_channels: Number of channels in the intermediate feature maps.
- 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

Backbone (self.model)

self.model = nn.Sequential(
    conv(     3,  32, stride=2, bias=False),
    conv_dw( 32,  64),
    conv_dw( 64, 128, stride=2),
    conv_dw(128, 128),
    conv_dw(128, 256, stride=2),
    conv_dw(256, 256),
    conv_dw(256, 512),  # conv4_2
    conv_dw(512, 512, dilation=2, padding=2),
    conv_dw(512, 512),
    conv_dw(512, 512),
    conv_dw(512, 512),
    conv_dw(512, 512)   # conv5_5
)

A MobileNet-inspired backbone consisting of standard and depthwise separable convolutions (conv and conv_dw).
Purpose: Extracts hierarchical features from the input image.
Outputs a feature map with 512 channels.

Convolutional Pose Machine (self.cpm)
```
self.cpm = Cpm(512, num_channels)
```
- A Cpm module that processes the backbone's output and reduces it to num_channels.
- Purpose: Prepares the feature map for the pose estimation stages.
Initial Stage (self.initial_stage)
```
self.initial_stage = InitialStage(num_channels, num_heatmaps, num_pafs)
```
- An InitialStage module that generates the first set of heatmaps and PAFs.
- Purpose: Provides the initial predictions for keypoints and their connections.
Refinement Stages (self.refinement_stages)
```
self.refinement_stages = nn.ModuleList()
for idx in range(num_refinement_stages):
    self.refinement_stages.append(RefinementStage(num_channels + num_heatmaps + num_pafs, num_channels,
                                                  num_heatmaps, num_pafs))
```
- A list of RefinementStage modules.
- Each stage takes the concatenated features (backbone features + previous heatmaps + previous PAFs) and refines the predictions.
- Purpose: Iteratively improves the accuracy of heatmaps and PAFs.

Forward Method

def forward(self, x):
    backbone_features = self.model(x)
    backbone_features = self.cpm(backbone_features)

    stages_output = self.initial_stage(backbone_features)
    for refinement_stage in self.refinement_stages:
        stages_output.extend(
            refinement_stage(torch.cat([backbone_features, stages_output[-2], stages_output[-1]], dim=1))
        )

    return stages_output

Step-by-Step Explanation:
1. Backbone Features:
```
backbone_features = self.model(x)
backbone_features = self.cpm(backbone_features)
```
  - The input image (x) is passed through the backbone and the CPM module to extract feature maps.
2. Initial Predictions:
```
stages_output = self.initial_stage(backbone_features)
```
  - The backbone features are passed through the InitialStage to generate the first set of heatmaps and PAFs.
3. Refinement Stages:
```
for refinement_stage in self.refinement_stages:
    stages_output.extend(
        refinement_stage(torch.cat([backbone_features, stages_output[-2], stages_output[-1]], dim=1))
    )
```
  - For each refinement stage:
    - The backbone features are concatenated with the previous stage's heatmaps and PAFs.
    - The concatenated features are passed through the refinement stage to generate refined heatmaps and PAFs.
    - The new heatmaps and PAFs are added to stages_output.
4. Output:
```
return stages_output
```
  - Returns a list of all heatmaps and PAFs from the initial and refinement stages.