def create_gradient_log_loss_4_scales()

in depth_upsampling/losses/gradient_loss.py [0:0]

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

def create_gradient_log_loss_4_scales(log_prediction, log_ground_truth, mask):
    log_prediction_d = log_prediction
    log_gt = log_ground_truth
    mask = mask

    log_prediction_d_scale_1 = log_prediction_d[:, :, ::2, ::2]
    log_prediction_d_scale_2 = log_prediction_d_scale_1[:, :, ::2, ::2]
    log_prediction_d_scale_3 = log_prediction_d_scale_2[:, :, ::2, ::2]

    mask_scale_1 = mask[:, :, ::2, ::2]
    mask_scale_2 = mask_scale_1[:, :, ::2, ::2]
    mask_scale_3 = mask_scale_2[:, :, ::2, ::2]

    log_gt_scale_1 = log_gt[:, :, ::2, ::2]
    log_gt_scale_2 = log_gt_scale_1[:, :, ::2, ::2]
    log_gt_scale_3 = log_gt_scale_2[:, :, ::2, ::2]

    gradient_loss_scale_0 = create_gradient_log_loss(log_prediction_d, mask, log_gt)

    gradient_loss_scale_1 = create_gradient_log_loss(
        log_prediction_d_scale_1, mask_scale_1, log_gt_scale_1
    )

    gradient_loss_scale_2 = create_gradient_log_loss(
        log_prediction_d_scale_2, mask_scale_2, log_gt_scale_2
    )

    gradient_loss_scale_3 = create_gradient_log_loss(
        log_prediction_d_scale_3, mask_scale_3, log_gt_scale_3
    )

    gradient_loss_4_scales = (
        gradient_loss_scale_0 + gradient_loss_scale_1 + gradient_loss_scale_2 + gradient_loss_scale_3
    )

    return gradient_loss_4_scales