The convert_to_onnx.py script is a utility for converting a PyTorch model into the ONNX (Open Neural Network Exchange) format. ONNX is a widely used format for exporting models to be used in other frameworks or deployment environments, such as TensorRT, OpenVINO, or ONNX Runtime.

Key Components

1. convert_to_onnx Function

def convert_to_onnx(net, output_name):
    input = torch.randn(1, 3, 256, 456)
    input_names = ['data']
    output_names = ['stage_0_output_1_heatmaps', 'stage_0_output_0_pafs',
                    'stage_1_output_1_heatmaps', 'stage_1_output_0_pafs']

    torch.onnx.export(net, input, output_name, verbose=True, input_names=input_names, output_names=output_names)

• Purpose: Converts the PyTorch model (net) into an ONNX model and saves it to a file.
• Parameters:
  • net: The PyTorch model to be converted.
  • output_name: The name of the output ONNX file.
• Steps:

  1. Define a Dummy Input:

     input = torch.randn(1, 3, 256, 456)
     

     • A dummy input tensor of shape [1, 3, 256, 456] is created to simulate a single RGB image with a height of 256 and a width of 456.
     • This is required by torch.onnx.export to trace the model's computation graph.

  2. Specify Input and Output Names:

     input_names = ['data']
     output_names = ['stage_0_output_1_heatmaps', 'stage_0_output_0_pafs',
                     'stage_1_output_1_heatmaps', 'stage_1_output_0_pafs']
     

     • input_names: The name of the input tensor (e.g., 'data').
     • output_names: The names of the output tensors, corresponding to the heatmaps and PAFs generated by the model at different stages.

  3. Export the Model:

     torch.onnx.export(net, input, output_name, verbose=True, input_names=input_names, output_names=output_names)
     

     • Converts the PyTorch model into ONNX format using torch.onnx.export.
     • The computation graph is traced using the dummy input.
     • The ONNX model is saved to the file specified by output_name.
     • The verbose=True flag prints detailed information about the exported model.

2. Main Script

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--checkpoint-path', type=str, required=True, help='path to the checkpoint')
    parser.add_argument('--output-name', type=str, default='human-pose-estimation.onnx',
                        help='name of output model in ONNX format')
    args = parser.parse_args()

    net = PoseEstimationWithMobileNet()
    checkpoint = torch.load(args.checkpoint_path)
    load_state(net, checkpoint)

    convert_to_onnx(net, args.output_name)

• Purpose: Handles command-line arguments, loads the model and checkpoint, and calls the convert_to_onnx function.
• Steps:

  1. Parse Command-Line Arguments:

     parser.add_argument('--checkpoint-path', type=str, required=True, help='path to the checkpoint')
     parser.add_argument('--output-name', type=str, default='human-pose-estimation.onnx',
                         help='name of output model in ONNX format')
     

     • -checkpoint-path: Path to the PyTorch checkpoint file containing the pre-trained model weights.
     • -output-name: Name of the output ONNX file (default: 'human-pose-estimation.onnx').

  2. Load the Model:

     net = PoseEstimationWithMobileNet()
     checkpoint = torch.load(args.checkpoint_path)
     load_state(net, checkpoint)
     

     • Initializes the PoseEstimationWithMobileNet model.
     • Loads the checkpoint file using torch.load.
     • Loads the checkpoint weights into the model using the load_state function.

  3. Convert to ONNX:

     convert_to_onnx(net, args.output_name)
     

     • Calls the convert_to_onnx function to export the model to ONNX format.

How It Works

1. The script is executed from the command line with the required arguments:

   python convert_to_onnx.py --checkpoint-path path/to/checkpoint.pth --output-name model.onnx
   

2. The script:

   • Loads the pre-trained PyTorch model from the checkpoint.
   • Converts the model to ONNX format using a dummy input.
   • Saves the ONNX model to the specified file.

Use Case

This script is useful for: