kopia lustrzana https://github.com/lzzcd001/MeshDiffusion
remove unnecessary dependency on Tensorflow
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# coding=utf-8
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# Copyright 2020 The Google Research Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# pylint: skip-file
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"""Return training and evaluation/test datasets from config files."""
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import jax
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import tensorflow as tf
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import tensorflow_datasets as tfds
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def get_data_scaler(config):
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"""Data normalizer. Assume data are always in [0, 1]."""
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if config.data.centered:
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# Rescale to [-1, 1]
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return lambda x: x * 2. - 1.
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else:
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return lambda x: x
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def get_data_inverse_scaler(config):
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"""Inverse data normalizer."""
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if config.data.centered:
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# Rescale [-1, 1] to [0, 1]
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return lambda x: (x + 1.) / 2.
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else:
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return lambda x: x
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def crop_resize(image, resolution):
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"""Crop and resize an image to the given resolution."""
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crop = tf.minimum(tf.shape(image)[0], tf.shape(image)[1])
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h, w = tf.shape(image)[0], tf.shape(image)[1]
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image = image[(h - crop) // 2:(h + crop) // 2,
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(w - crop) // 2:(w + crop) // 2]
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image = tf.image.resize(
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image,
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size=(resolution, resolution),
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antialias=True,
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method=tf.image.ResizeMethod.BICUBIC)
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return tf.cast(image, tf.uint8)
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def resize_small(image, resolution):
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"""Shrink an image to the given resolution."""
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h, w = image.shape[0], image.shape[1]
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ratio = resolution / min(h, w)
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h = tf.round(h * ratio, tf.int32)
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w = tf.round(w * ratio, tf.int32)
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return tf.image.resize(image, [h, w], antialias=True)
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def central_crop(image, size):
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"""Crop the center of an image to the given size."""
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top = (image.shape[0] - size) // 2
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left = (image.shape[1] - size) // 2
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return tf.image.crop_to_bounding_box(image, top, left, size, size)
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def get_dataset(config, uniform_dequantization=False, evaluation=False):
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"""Create data loaders for training and evaluation.
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Args:
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config: A ml_collection.ConfigDict parsed from config files.
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uniform_dequantization: If `True`, add uniform dequantization to images.
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evaluation: If `True`, fix number of epochs to 1.
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Returns:
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train_ds, eval_ds, dataset_builder.
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"""
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# Compute batch size for this worker.
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batch_size = config.training.batch_size if not evaluation else config.eval.batch_size
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if batch_size % jax.device_count() != 0:
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raise ValueError(f'Batch sizes ({batch_size} must be divided by'
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f'the number of devices ({jax.device_count()})')
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# Reduce this when image resolution is too large and data pointer is stored
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shuffle_buffer_size = 10000
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prefetch_size = tf.data.experimental.AUTOTUNE
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num_epochs = None if not evaluation else 1
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# Create dataset builders for each dataset.
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if config.data.dataset == 'CIFAR10':
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dataset_builder = tfds.builder('cifar10')
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train_split_name = 'train'
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eval_split_name = 'test'
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def resize_op(img):
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img = tf.image.convert_image_dtype(img, tf.float32)
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return tf.image.resize(img, [config.data.image_size, config.data.image_size], antialias=True)
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elif config.data.dataset == 'SVHN':
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dataset_builder = tfds.builder('svhn_cropped')
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train_split_name = 'train'
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eval_split_name = 'test'
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def resize_op(img):
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img = tf.image.convert_image_dtype(img, tf.float32)
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return tf.image.resize(img, [config.data.image_size, config.data.image_size], antialias=True)
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elif config.data.dataset == 'CELEBA':
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dataset_builder = tfds.builder('celeb_a')
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train_split_name = 'train'
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eval_split_name = 'validation'
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def resize_op(img):
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img = tf.image.convert_image_dtype(img, tf.float32)
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img = central_crop(img, 140)
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img = resize_small(img, config.data.image_size)
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return img
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elif config.data.dataset == 'LSUN':
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dataset_builder = tfds.builder(f'lsun/{config.data.category}')
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train_split_name = 'train'
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eval_split_name = 'validation'
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if config.data.image_size == 128:
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def resize_op(img):
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img = tf.image.convert_image_dtype(img, tf.float32)
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img = resize_small(img, config.data.image_size)
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img = central_crop(img, config.data.image_size)
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return img
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else:
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def resize_op(img):
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img = crop_resize(img, config.data.image_size)
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img = tf.image.convert_image_dtype(img, tf.float32)
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return img
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elif config.data.dataset in ['FFHQ', 'CelebAHQ']:
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dataset_builder = tf.data.TFRecordDataset(config.data.tfrecords_path)
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train_split_name = eval_split_name = 'train'
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else:
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raise NotImplementedError(
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f'Dataset {config.data.dataset} not yet supported.')
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# Customize preprocess functions for each dataset.
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if config.data.dataset in ['FFHQ', 'CelebAHQ']:
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def preprocess_fn(d):
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sample = tf.io.parse_single_example(d, features={
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'shape': tf.io.FixedLenFeature([3], tf.int64),
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'data': tf.io.FixedLenFeature([], tf.string)})
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data = tf.io.decode_raw(sample['data'], tf.uint8)
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data = tf.reshape(data, sample['shape'])
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data = tf.transpose(data, (1, 2, 0))
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img = tf.image.convert_image_dtype(data, tf.float32)
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if config.data.random_flip and not evaluation:
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img = tf.image.random_flip_left_right(img)
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if uniform_dequantization:
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img = (tf.random.uniform(img.shape, dtype=tf.float32) + img * 255.) / 256.
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return dict(image=img, label=None)
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else:
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def preprocess_fn(d):
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"""Basic preprocessing function scales data to [0, 1) and randomly flips."""
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img = resize_op(d['image'])
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if config.data.random_flip and not evaluation:
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img = tf.image.random_flip_left_right(img)
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if uniform_dequantization:
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img = (tf.random.uniform(img.shape, dtype=tf.float32) + img * 255.) / 256.
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return dict(image=img, label=d.get('label', None))
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def create_dataset(dataset_builder, split):
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dataset_options = tf.data.Options()
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dataset_options.experimental_optimization.map_parallelization = True
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dataset_options.experimental_threading.private_threadpool_size = 48
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dataset_options.experimental_threading.max_intra_op_parallelism = 1
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read_config = tfds.ReadConfig(options=dataset_options)
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if isinstance(dataset_builder, tfds.core.DatasetBuilder):
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dataset_builder.download_and_prepare()
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ds = dataset_builder.as_dataset(
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split=split, shuffle_files=True, read_config=read_config)
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else:
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ds = dataset_builder.with_options(dataset_options)
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ds = ds.repeat(count=num_epochs)
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ds = ds.shuffle(shuffle_buffer_size)
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ds = ds.map(preprocess_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
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ds = ds.batch(batch_size, drop_remainder=True)
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return ds.prefetch(prefetch_size)
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train_ds = create_dataset(dataset_builder, train_split_name)
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eval_ds = create_dataset(dataset_builder, eval_split_name)
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return train_ds, eval_ds, dataset_builder
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@ -1,12 +1,11 @@
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import torch
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import tensorflow as tf
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import os
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import logging
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def restore_checkpoint(ckpt_dir, state, device, strict=False):
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if not tf.io.gfile.exists(ckpt_dir):
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tf.io.gfile.makedirs(os.path.dirname(ckpt_dir))
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if not os.path.exists(ckpt_dir):
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os.makedirs(os.path.dirname(ckpt_dir))
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logging.warning(f"No checkpoint found at {ckpt_dir}. "
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f"Returned the same state as input")
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if strict:
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