Some questions about depth_visual_xxx.png. about mvsnet_pytorch HOT 3 OPEN

def non_zero_mean_absolute_diff(y_true, y_pred, interval):
    """ non zero mean absolute loss for one batch """
    with tf.name_scope('MAE'):
        shape = tf.shape(y_pred)
        interval = tf.reshape(interval, [shape[0]])
        mask_true = tf.cast(tf.not_equal(y_true, 0.0), dtype='float32')
        denom = tf.reduce_sum(mask_true, axis=[1, 2, 3]) + 1e-7
        masked_abs_error = tf.abs(mask_true * (y_true - y_pred))            # 4D
        masked_mae = tf.reduce_sum(masked_abs_error, axis=[1, 2, 3])        # 1D
        masked_mae = tf.reduce_sum((masked_mae / interval) / denom)         # 1
    return masked_mae
def mvsnet_regression_loss(estimated_depth_image, depth_image, depth_interval):
    """ compute loss and accuracy """
    # non zero mean absulote loss
    masked_mae = non_zero_mean_absolute_diff(depth_image, estimated_depth_image, depth_interval)
    # less one accuracy
    less_one_accuracy = less_one_percentage(depth_image, estimated_depth_image, depth_interval)
    # less three accuracy
    less_three_accuracy = less_three_percentage(depth_image, estimated_depth_image, depth_interval)

    return masked_mae, less_one_accuracy, less_three_accuracy

# regression loss
loss0, less_one_temp, less_three_temp = mvsnet_regression_loss(
    depth_map, depth_image, depth_interval)
loss1, less_one_accuracy, less_three_accuracy = mvsnet_regression_loss(
    refined_depth_map, depth_image, depth_interval)
loss = (loss0 + loss1) / 2