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data_provider.py
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data_provider.py
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import os
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.python.data.experimental import AUTOTUNE
def random_flip(lr_img, hr_img):
""" Randomly flips LR and HR images for data augmentation."""
rn = tf.random.uniform(shape=(), maxval=1)
return tf.cond(rn < 0.5,
lambda: (lr_img, hr_img),
lambda: (tf.image.flip_left_right(lr_img),
tf.image.flip_left_right(hr_img)))
def random_rotate(lr_img, hr_img):
""" Randomly rotates LR and HR images for data augmentation."""
rn = tf.random.uniform(shape=(), maxval=4, dtype=tf.int32)
return tf.image.rot90(lr_img, rn), tf.image.rot90(hr_img, rn)
def get_div2k_data(HParams,
mode='train',
shuffle=True,
repeat_count=None):
""" Downloads and loads DIV2K dataset.
Args:
HParams : For getting values for different parameters.
mode : Either 'train' or 'valid'.
shuffle : Whether to shuffle the images in the dataset.
repeat_count : Repetition of data during training.
Returns:
A tf.data.Dataset with pairs of LR image and HR image tensors.
Raises:
TypeError : If the data directory(data_dir) is not specified.
"""
bs = HParams.batch_size
split = 'train' if mode == 'train' else 'validation'
def scale(image, *args):
hr_size = HParams.hr_dimension
scale = HParams.scale
hr_image = image
hr_image = tf.image.resize(hr_image, [hr_size, hr_size])
lr_image = tf.image.resize(hr_image, [hr_size//scale, hr_size//scale], method='bicubic')
hr_image = tf.clip_by_value(hr_image, 0, 255)
lr_image = tf.clip_by_value(lr_image, 0, 255)
return lr_image, hr_image
dataset = (tfds.load('div2k/bicubic_x4',
split=split,
data_dir=HParams.data_dir,
as_supervised=True)
.map(scale, num_parallel_calls=4)
.cache())
if shuffle:
dataset = dataset.shuffle(
buffer_size=10000, reshuffle_each_iteration=True)
dataset = dataset.batch(HParams.batch_size)
dataset = dataset.repeat(repeat_count)
dataset = dataset.prefetch(buffer_size=AUTOTUNE)
return dataset