loss_functions.py

import tensorflow as tf
import numpy as np


def flow_loss(gen_flows, gt_flows):
    print(gen_flows['flow'])
    return tf.reduce_mean(tf.abs(gen_flows['flow'] - gt_flows['flow']))


def intensity_loss(gen_frames, gt_frames, l_num):
    """
    Calculates the sum of lp losses between the predicted and ground truth frames.

    @param gen_frames: The predicted frames at each scale.
    @param gt_frames: The ground truth frames at each scale
    @param l_num: 1 or 2 for l1 and l2 loss, respectively).

    @return: The lp loss.
    """
    return tf.reduce_mean(tf.abs((gen_frames - gt_frames) ** l_num))


def gradient_loss(gen_frames, gt_frames, alpha):
    """
    Calculates the sum of GDL losses between the predicted and ground truth frames.

    @param gen_frames: The predicted frames at each scale.
    @param gt_frames: The ground truth frames at each scale
    @param alpha: The power to which each gradient term is raised.

    @return: The GDL loss.
    """
    # calculate the loss for each scale
    # create filters [-1, 1] and [[1],[-1]] for diffing to the left and down respectively.

    channels = gen_frames.get_shape().as_list()[-1]
    pos = tf.constant(np.identity(channels), dtype=tf.float32)     # 3 x 3
    neg = -1 * pos
    filter_x = tf.expand_dims(tf.stack([neg, pos]), 0)  # [-1, 1]
    filter_y = tf.stack([tf.expand_dims(pos, 0), tf.expand_dims(neg, 0)])  # [[1],[-1]]
    strides = [1, 1, 1, 1]  # stride of (1, 1)
    padding = 'SAME'

    gen_dx = tf.abs(tf.nn.conv2d(gen_frames, filter_x, strides, padding=padding))
    gen_dy = tf.abs(tf.nn.conv2d(gen_frames, filter_y, strides, padding=padding))
    gt_dx = tf.abs(tf.nn.conv2d(gt_frames, filter_x, strides, padding=padding))
    gt_dy = tf.abs(tf.nn.conv2d(gt_frames, filter_y, strides, padding=padding))

    grad_diff_x = tf.abs(gt_dx - gen_dx)
    grad_diff_y = tf.abs(gt_dy - gen_dy)

    # condense into one tensor and avg
    return tf.reduce_mean(grad_diff_x ** alpha + grad_diff_y ** alpha)