def main()

in demo.py [0:0]

• 36 lines of code
• 1 McCabe index (conditional complexity)

def main():
    """Main"""

    LOGGER.info('Starting demo...')

    # ------------------- Data loader -------------------

    data_transform = transforms.Compose([
        trsf.ImageTrsf(),
        trsf.Joints3DTrsf(),
        trsf.ToTensor()])

    # let's load data from validation set as example
    data = Mocap(
        config.dataset.val,
        SetType.VAL,
        transform=data_transform)
    data_loader = DataLoader(
        data,
        batch_size=config.data_loader.batch_size,
        shuffle=config.data_loader.shuffle)

    # ------------------- Evaluation -------------------

    eval_body = evaluate.EvalBody()
    eval_upper = evaluate.EvalUpperBody()
    eval_lower = evaluate.EvalUpperBody()

    # ------------------- Read dataset frames -------------------

    for it, (img, p2d, p3d, action) in enumerate(data_loader):

        LOGGER.info('Iteration: {}'.format(it))
        LOGGER.info('Images: {}'.format(img.shape))
        LOGGER.info('p2ds: {}'.format(p2d.shape))
        LOGGER.info('p3ds: {}'.format(p3d.shape))
        LOGGER.info('Actions: {}'.format(action))

        # -----------------------------------------------------------
        # ------------------- Run your model here -------------------
        # -----------------------------------------------------------

        # TODO: replace p3d_hat with model preditions
        p3d_hat = torch.ones_like(p3d)

        # Evaluate results using different evaluation metrices
        y_output = p3d_hat.data.cpu().numpy()
        y_target = p3d.data.cpu().numpy()

        eval_body.eval(y_output, y_target, action)
        eval_upper.eval(y_output, y_target, action)
        eval_lower.eval(y_output, y_target, action)

        # TODO: remove break
        break

    # ------------------- Save results -------------------

    LOGGER.info('Saving evaluation results...')
    res = {'FullBody': eval_body.get_results(),
           'UpperBody': eval_upper.get_results(),
           'LowerBody': eval_lower.get_results()}

    io.write_json(config.eval.output_file, res)

    LOGGER.info('Done.')