CycleGAN-Tensorflow-2/data.py at master · zacbaum/CycleGAN-Tensorflow-2 · GitHub

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import numpy as np
import tensorflow as tf
import tf2lib as tl


def make_dataset(
    img_paths,
    batch_size,
    load_size,
    crop_size,
    channels,
    training,
    drop_remainder=True,
    shuffle=True,
    repeat=1,
):
    if training:

        @tf.function
        def _map_fn(img):  # preprocessing
            img = tf.image.random_flip_left_right(img)
            img = tf.image.resize(img, [load_size, load_size])
            img = tf.image.random_crop(img, [crop_size, crop_size, tf.shape(img)[-1]])
            img = tf.clip_by_value(img, 0, 255) / 255.0  # or img = tl.minmax_norm(img)
            img = img * 2 - 1
            return img

    else:

        @tf.function
        def _map_fn(img):  # preprocessing
            img = tf.image.resize(
                img, [crop_size, crop_size]
            )  # or img = tf.image.resize(img, [load_size, load_size]); img = tl.center_crop(img, crop_size)
            img = tf.clip_by_value(img, 0, 255) / 255.0  # or img = tl.minmax_norm(img)
            img = img * 2 - 1
            return img

    return tl.disk_image_batch_dataset(
        img_paths,
        channels,
        batch_size,
        drop_remainder=drop_remainder,
        map_fn=_map_fn,
        shuffle=shuffle,
        repeat=repeat,
    )


def make_zip_dataset(
    A_img_paths,
    B_img_paths,
    batch_size,
    load_size,
    crop_size,
    channels,
    training,
    shuffle=True,
    repeat=False,
):
    # zip two datasets aligned by the longer one
    if repeat:
        A_repeat = B_repeat = None  # cycle both
    else:
        if len(A_img_paths) >= len(B_img_paths):
            A_repeat = 1
            B_repeat = None  # cycle the shorter one
        else:
            A_repeat = None  # cycle the shorter one
            B_repeat = 1

    A_dataset = make_dataset(
        A_img_paths,
        batch_size,
        load_size,
        crop_size,
        channels,
        training,
        drop_remainder=True,
        shuffle=shuffle,
        repeat=A_repeat,
    )
    B_dataset = make_dataset(
        B_img_paths,
        batch_size,
        load_size,
        crop_size,
        channels,
        training,
        drop_remainder=True,
        shuffle=shuffle,
        repeat=B_repeat,
    )

    A_B_dataset = tf.data.Dataset.zip((A_dataset, B_dataset))
    len_dataset = max(len(A_img_paths), len(B_img_paths)) // batch_size

    return A_B_dataset, len_dataset


class ItemPool:
    def __init__(self, pool_size=50):
        self.pool_size = pool_size
        self.items = []

    def __call__(self, in_items):
        # `in_items` should be a batch tensor

        if self.pool_size == 0:
            return in_items

        out_items = []
        for in_item in in_items:
            if len(self.items) < self.pool_size:
                self.items.append(in_item)
                out_items.append(in_item)
            else:
                if np.random.rand() > 0.5:
                    idx = np.random.randint(0, len(self.items))
                    out_item, self.items[idx] = self.items[idx], in_item
                    out_items.append(out_item)
                else:
                    out_items.append(in_item)
        return tf.stack(out_items, axis=0)