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Data.py
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import tensorflow as tf
import multiprocessing
def make_anime_dataset(img_paths, batch_size, resize=64, drop_remainder=True, shuffle=True, repeat=1):
@tf.function
def _map_fn(img):
img = tf.image.resize(img, [resize, resize])
img = tf.clip_by_value(img, 0, 255)
img = img / 127.5 - 1
return img
dataset = disk_image_batch_dataset(img_paths, batch_size, drop_remainder=drop_remainder,
map_fn=_map_fn, shuffle=shuffle, repeat=repeat)
img_shape = (resize, resize, 3)
len_dataset = len(img_paths) // batch_size
return dataset, img_shape, len_dataset
def batch_dataset(dataset,
batch_size,
drop_remainder=True,
n_prefetch_batch=1,
filter_fn=None,
map_fn=None,
n_map_threads=None,
filter_after_map=False,
shuffle=True,
shuffle_buffer_size=None,
repeat=None):
# set defaults
if n_map_threads is None:
n_map_threads = multiprocessing.cpu_count()
if shuffle and shuffle_buffer_size is None:
shuffle_buffer_size = max(batch_size * 128, 2048) # set the minimum buffer size as 2048
# [*] it is efficient to conduct `shuffle` before `map`/`filter` because `map`/`filter` is sometimes costly
if shuffle:
dataset = dataset.shuffle(shuffle_buffer_size)
if not filter_after_map:
if filter_fn:
dataset = dataset.filter(filter_fn)
if map_fn:
dataset = dataset.map(map_fn, num_parallel_calls=n_map_threads)
else: # [*] this is slower
if map_fn:
dataset = dataset.map(map_fn, num_parallel_calls=n_map_threads)
if filter_fn:
dataset = dataset.filter(filter_fn)
dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)
dataset = dataset.repeat(repeat).prefetch(n_prefetch_batch)
return dataset
def memory_data_batch_dataset(memory_data,
batch_size,
drop_remainder=True,
n_prefetch_batch=1,
filter_fn=None,
map_fn=None,
n_map_threads=None,
filter_after_map=False,
shuffle=True,
shuffle_buffer_size=None,
repeat=None):
"""Batch dataset of memory data.
Parameters
----------
memory_data : nested structure of tensors/ndarrays/lists
"""
dataset = tf.data.Dataset.from_tensor_slices(memory_data)
dataset = batch_dataset(dataset, batch_size,
drop_remainder=drop_remainder,
n_prefetch_batch=n_prefetch_batch,
filter_fn=filter_fn,
map_fn=map_fn,
n_map_threads=n_map_threads,
filter_after_map=filter_after_map,
shuffle=shuffle,
shuffle_buffer_size=shuffle_buffer_size,
repeat=repeat)
return dataset
def disk_image_batch_dataset(img_paths,
batch_size,
labels=None,
drop_remainder=True,
n_prefetch_batch=1,
filter_fn=None,
map_fn=None,
n_map_threads=None,
filter_after_map=False,
shuffle=True,
shuffle_buffer_size=None,
repeat=None):
"""Batch dataset of disk image for PNG and JPEG.
Parameters
----------
img_paths : 1d-tensor/ndarray/list of str
labels : nested structure of tensors/ndarrays/lists
"""
if labels is None:
memory_data = img_paths
else:
memory_data = (img_paths, labels)
def parse_fn(path, *label):
img = tf.io.read_file(path)
img = tf.image.decode_png(img, 3) # fix channels to 3
return (img,) + label
if map_fn: # fuse `map_fn` and `parse_fn`
def map_fn_(*args):
return map_fn(*parse_fn(*args))
else:
map_fn_ = parse_fn
dataset = memory_data_batch_dataset(memory_data,
batch_size,
drop_remainder=drop_remainder,
n_prefetch_batch=n_prefetch_batch,
filter_fn=filter_fn,
map_fn=map_fn_,
n_map_threads=n_map_threads,
filter_after_map=filter_after_map,
shuffle=shuffle,
shuffle_buffer_size=shuffle_buffer_size,
repeat=repeat)
return dataset