# Differentiable Augmentation for Data-Efficient GAN Training

# Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han

# https://arxiv.org/pdf/2006.10738

# https://github.com/mit-han-lab/data-efficient-gans/blob/master/DiffAugment_tf.py

import tensorflow as tf

def diff_augment(x, policy: str = None, channels_first=False):

    if policy:

        if channels_first:

            x = tf.transpose(x, [0, 2, 3, 1])

        for p in policy.split(','):

            for f in AUGMENT_FNS[p]:

                x = f(x)

        if channels_first:

            x = tf.transpose(x, [0, 3, 1, 2])

    return x

def rand_brightness(x):

    magnitude = tf.random.uniform([tf.shape(x)[0], 1, 1, 1]) - 0.5

    x = x + magnitude

    return x

def rand_saturation(x):

    magnitude = tf.random.uniform([tf.shape(x)[0], 1, 1, 1]) * 2

    x_mean = tf.reduce_mean(x, axis=3, keepdims=True)

    x = (x - x_mean) * magnitude + x_mean

    return x

def rand_contrast(x):

    magnitude = tf.random.uniform([tf.shape(x)[0], 1, 1, 1]) + 0.5

    x_mean = tf.reduce_mean(x, axis=[1, 2, 3], keepdims=True)

    x = (x - x_mean) * magnitude + x_mean

    return x

def rand_translation(x, ratio=0.125):

    batch_size = tf.shape(x)[0]

    image_size = tf.shape(x)[1:3]

    shift = tf.cast(tf.cast(image_size, tf.float32) * ratio + 0.5, tf.int32)

    translation_x = tf.random.uniform([batch_size, 1], -shift[0], shift[0] + 1, dtype=tf.int32)

    translation_y = tf.random.uniform([batch_size, 1], -shift[1], shift[1] + 1, dtype=tf.int32)

    grid_x = tf.clip_by_value(tf.expand_dims(tf.range(image_size[0], dtype=tf.int32), 0) + translation_x + 1, 0,

                              image_size[0] + 1)

    grid_y = tf.clip_by_value(tf.expand_dims(tf.range(image_size[1], dtype=tf.int32), 0) + translation_y + 1, 0,

                              image_size[1] + 1)

    x = tf.gather_nd(tf.pad(x, [[0, 0], [1, 1], [0, 0], [0, 0]]), tf.expand_dims(grid_x, -1), batch_dims=1)

    x = tf.transpose(tf.gather_nd(tf.pad(tf.transpose(x, [0, 2, 1, 3]), [[0, 0], [1, 1], [0, 0], [0, 0]]),

                                  tf.expand_dims(grid_y, -1), batch_dims=1), [0, 2, 1, 3])

    return x

def rand_cutout(x, ratio=0.5):

    batch_size = tf.shape(x)[0]

    image_size = tf.shape(x)[1:3]

    cutout_size = tf.cast(tf.cast(image_size, tf.float32) * ratio + 0.5, tf.int32)

    offset_x = tf.random.uniform([tf.shape(x)[0], 1, 1], maxval=image_size[0] + (1 - cutout_size[0] % 2),

                                 dtype=tf.int32)

    offset_y = tf.random.uniform([tf.shape(x)[0], 1, 1], maxval=image_size[1] + (1 - cutout_size[1] % 2),

                                 dtype=tf.int32)

    grid_batch, grid_x, grid_y = tf.meshgrid(tf.range(batch_size, dtype=tf.int32),

                                             tf.range(cutout_size[0], dtype=tf.int32),

                                             tf.range(cutout_size[1], dtype=tf.int32), indexing='ij')

    cutout_grid = tf.stack(

        [grid_batch, grid_x + offset_x - cutout_size[0] // 2, grid_y + offset_y - cutout_size[1] // 2], axis=-1)

    mask_shape = tf.stack([batch_size, image_size[0], image_size[1]])

    cutout_grid = tf.maximum(cutout_grid, 0)

    cutout_grid = tf.minimum(cutout_grid, tf.reshape(mask_shape - 1, [1, 1, 1, 3]))

    mask = tf.maximum(

        1 - tf.scatter_nd(cutout_grid, tf.ones([batch_size, cutout_size[0], cutout_size[1]], dtype=tf.float32),

                          mask_shape), 0)

    x = x * tf.expand_dims(mask, axis=3)

    return x

AUGMENT_FNS = {

    'color': [rand_brightness, rand_saturation, rand_contrast],

    'translation': [rand_translation],

    'cutout': [rand_cutout],

}