datasets.py

# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# pylint: skip-file
"""Return training and evaluation/test datasets from config files."""
from torch.utils.data import Dataset, DataLoader
import numpy as np


def get_data_scaler(config):
  """Data normalizer. Assume data are always in [0, 1]."""
  if config.data.centered:
    # Rescale to [-1, 1]
    return lambda x: x * 2. - 1.
  else:
    return lambda x: x


def get_data_inverse_scaler(config):
  """Inverse data normalizer."""
  if config.data.centered:
    # Rescale [-1, 1] to [0, 1]
    return lambda x: (x + 1.) / 2.
  else:
    return lambda x: x


def crop_resize(image, resolution):
  """Crop and resize an image to the given resolution."""
  crop = tf.minimum(tf.shape(image)[0], tf.shape(image)[1])
  h, w = tf.shape(image)[0], tf.shape(image)[1]
  image = image[(h - crop) // 2:(h + crop) // 2,
          (w - crop) // 2:(w + crop) // 2]
  image = tf.image.resize(
    image,
    size=(resolution, resolution),
    antialias=True,
    method=tf.image.ResizeMethod.BICUBIC)
  return tf.cast(image, tf.uint8)


def resize_small(image, resolution):
  """Shrink an image to the given resolution."""
  h, w = image.shape[0], image.shape[1]
  ratio = resolution / min(h, w)
  h = tf.round(h * ratio, tf.int32)
  w = tf.round(w * ratio, tf.int32)
  return tf.image.resize(image, [h, w], antialias=True)


def central_crop(image, size):
  """Crop the center of an image to the given size."""
  top = (image.shape[0] - size) // 2
  left = (image.shape[1] - size) // 2
  return tf.image.crop_to_bounding_box(image, top, left, size, size)


def get_dataset(config, uniform_dequantization=False, evaluation=False):
  """Create data loaders for training and evaluation.

  Args:
    config: A ml_collection.ConfigDict parsed from config files.
    uniform_dequantization: If `True`, add uniform dequantization to images.
    evaluation: If `True`, fix number of epochs to 1.

  Returns:
    train_ds, eval_ds, dataset_builder.
  """
  # Compute batch size for this worker.
  batch_size = config.training.batch_size if not evaluation else config.eval.batch_size
  if batch_size % jax.device_count() != 0:
    raise ValueError(f'Batch sizes ({batch_size} must be divided by'
                     f'the number of devices ({jax.device_count()})')

  # Reduce this when image resolution is too large and data pointer is stored
  shuffle_buffer_size = 10000
  prefetch_size = tf.data.experimental.AUTOTUNE
  num_epochs = None if not evaluation else 1

  # Create dataset builders for each dataset.
  if config.data.dataset == 'CIFAR10':
    dataset_builder = tfds.builder('cifar10')
    train_split_name = 'train'
    eval_split_name = 'test'

    def resize_op(img):
      img = tf.image.convert_image_dtype(img, tf.float32)
      # Added to train grayscale models
      # img = tf.image.rgb_to_grayscale(img)
      return tf.image.resize(img, [config.data.image_size, config.data.image_size], antialias=True)


  elif config.data.dataset == 'SVHN':
    dataset_builder = tfds.builder('svhn_cropped')
    train_split_name = 'train'
    eval_split_name = 'test'

    def resize_op(img):
      img = tf.image.convert_image_dtype(img, tf.float32)
      return tf.image.resize(img, [config.data.image_size, config.data.image_size], antialias=True)

  elif config.data.dataset == 'CELEBA':
    dataset_builder = tfds.builder('celeb_a')
    train_split_name = 'train'
    eval_split_name = 'validation'

    def resize_op(img):
      img = tf.image.convert_image_dtype(img, tf.float32)
      img = central_crop(img, 140)
      img = resize_small(img, config.data.image_size)
      return img

  elif config.data.dataset == 'LSUN':
    dataset_builder = tfds.builder(f'lsun/{config.data.category}')
    train_split_name = 'train'
    eval_split_name = 'validation'

    if config.data.image_size == 128:
      def resize_op(img):
        img = tf.image.convert_image_dtype(img, tf.float32)
        img = resize_small(img, config.data.image_size)
        img = central_crop(img, config.data.image_size)
        return img

    else:
      def resize_op(img):
        img = crop_resize(img, config.data.image_size)
        img = tf.image.convert_image_dtype(img, tf.float32)
        return img

  elif config.data.dataset in ['FFHQ', 'CelebAHQ']:
    dataset_builder = tf.data.TFRecordDataset(config.data.tfrecords_path)
    train_split_name = eval_split_name = 'train'

  else:
    raise NotImplementedError(
      f'Dataset {config.data.dataset} not yet supported.')

  # Customize preprocess functions for each dataset.
  if config.data.dataset in ['FFHQ', 'CelebAHQ']:
    def preprocess_fn(d):
      sample = tf.io.parse_single_example(d, features={
        'shape': tf.io.FixedLenFeature([3], tf.int64),
        'data': tf.io.FixedLenFeature([], tf.string)})
      data = tf.io.decode_raw(sample['data'], tf.uint8)
      data = tf.reshape(data, sample['shape'])
      data = tf.transpose(data, (1, 2, 0))
      img = tf.image.convert_image_dtype(data, tf.float32)
      if config.data.random_flip and not evaluation:
        img = tf.image.random_flip_left_right(img)
      if uniform_dequantization:
        img = (tf.random.uniform(img.shape, dtype=tf.float32) + img * 255.) / 256.
      return dict(image=img, label=None)

  else:
    def preprocess_fn(d):
      """Basic preprocessing function scales data to [0, 1) and randomly flips."""
      img = resize_op(d['image'])
      if config.data.random_flip and not evaluation:
        img = tf.image.random_flip_left_right(img)
      if uniform_dequantization:
        img = (tf.random.uniform(img.shape, dtype=tf.float32) + img * 255.) / 256.

      return dict(image=img, label=d.get('label', None))

  def create_dataset(dataset_builder, split):
    dataset_options = tf.data.Options()
    dataset_options.experimental_optimization.map_parallelization = True
    dataset_options.experimental_threading.private_threadpool_size = 48
    dataset_options.experimental_threading.max_intra_op_parallelism = 1
    read_config = tfds.ReadConfig(options=dataset_options)
    if isinstance(dataset_builder, tfds.core.DatasetBuilder):
      dataset_builder.download_and_prepare()
      ds = dataset_builder.as_dataset(
        split=split, shuffle_files=True, read_config=read_config)
    else:
      ds = dataset_builder.with_options(dataset_options)
    ds = ds.repeat(count=num_epochs)
    ds = ds.shuffle(shuffle_buffer_size)
    ds = ds.map(preprocess_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
    ds = ds.batch(batch_size, drop_remainder=True)
    return ds.prefetch(prefetch_size)

  train_ds = create_dataset(dataset_builder, train_split_name)
  eval_ds = create_dataset(dataset_builder, eval_split_name)
  return train_ds, eval_ds, dataset_builder


from pathlib import Path

class fastmri_knee(Dataset):
  """ Simple pytorch dataset for fastmri knee singlecoil dataset """
  def __init__(self, root, is_complex=False):
    self.root = root
    self.data_list = list(root.glob('*/*.npy'))
    self.is_complex = is_complex

  def __len__(self):
    return len(self.data_list)

  def __getitem__(self, idx):
    fname = self.data_list[idx]
    if not self.is_complex:
      data = np.load(fname)
    else:
      data = np.load(fname).astype(np.complex64)
    data = np.expand_dims(data, axis=0)
    return data


class AAPM(Dataset):
  def __init__(self, root, sort):
    self.root = root
    self.data_list = list(root.glob('full_dose/*.npy'))
    self.sort = sort
    if sort:
      self.data_list = sorted(self.data_list)

  def __len__(self):
    return len(self.data_list)

  def __getitem__(self, idx):
    fname = self.data_list[idx]
    data = np.load(fname)
    data = np.expand_dims(data, axis=0)
    return data


class Object5(Dataset):
  def __init__(self, root, slice, fast=False):
    """
    slice - range of the 2000 _volumes_ that you want,
    but the dataset will return images, so will be 256 times longer

    fast - set to true to get a tiny version of the dataset
    """
    if fast:
      self.NUM_SLICES = 10
    else:
      self.NUM_SLICES = 256


    self.root = root
    self.data_list = list(root.glob('*.npz'))

    if len(self.data_list) == 0:
      raise ValueError(f"No npz files found in {root}")

    self.data_list = sorted(self.data_list)[slice]

  def __len__(self):
    return len(self.data_list) * self.NUM_SLICES

  def __getitem__(self, idx):
    vol_index = idx // self.NUM_SLICES
    slice_index = idx % self.NUM_SLICES
    fname = self.data_list[vol_index]
    data = np.load(fname)['x'][slice_index]
    data = np.expand_dims(data, axis=0)
    return data

class fastmri_knee_infer(Dataset):
  """ Simple pytorch dataset for fastmri knee singlecoil dataset """
  def __init__(self, root, sort=True, is_complex=False):
    self.root = root
    self.data_list = list(root.glob('*/*.npy'))
    self.is_complex = is_complex
    if sort:
      self.data_list = sorted(self.data_list)

  def __len__(self):
    return len(self.data_list)

  def __getitem__(self, idx):
    fname = self.data_list[idx]
    if not self.is_complex:
      data = np.load(fname)
    else:
      data = np.load(fname).astype(np.complex64)
    data = np.expand_dims(data, axis=0)
    return data, str(fname)


class fastmri_knee_magpha(Dataset):
  """ Simple pytorch dataset for fastmri knee singlecoil dataset """
  def __init__(self, root):
    self.root = root
    self.data_list = list(root.glob('*/*.npy'))

  def __len__(self):
    return len(self.data_list)

  def __getitem__(self, idx):
    fname = self.data_list[idx]
    data = np.load(fname).astype(np.float32)
    return data


class fastmri_knee_magpha_infer(Dataset):
  """ Simple pytorch dataset for fastmri knee singlecoil dataset """
  def __init__(self, root, sort=True):
    self.root = root
    self.data_list = list(root.glob('*/*.npy'))
    if sort:
      self.data_list = sorted(self.data_list)

  def __len__(self):
    return len(self.data_list)

  def __getitem__(self, idx):
    fname = self.data_list[idx]
    data = np.load(fname).astype(np.float32)
    return data, str(fname)


def create_dataloader(configs, evaluation=False, sort=True):
  shuffle = True if not evaluation else False
  if configs.data.dataset == 'Object5':
    train_dataset = Object5(Path(configs.data.root), slice(None,1800))  
    val_dataset = Object5(Path(configs.data.root), slice(1800,None)) 
  elif configs.data.dataset == 'Object5Fast':
    train_dataset = Object5(Path(configs.data.root), slice(None,1), fast=True)
    val_dataset = Object5(Path(configs.data.root), slice(1,2), fast=True)
  elif configs.data.dataset == 'AAPM':
    train_dataset = AAPM(Path(configs.data.root) / f'train', sort=False)
    val_dataset = AAPM(Path(configs.data.root) / f'test', sort=True)
  elif configs.data.is_multi:
    train_dataset = fastmri_knee(Path(configs.data.root) / f'knee_multicoil_{configs.data.image_size}_train')
    val_dataset = fastmri_knee_infer(Path(configs.data.root) / f'knee_{configs.data.image_size}_val', sort=sort)
  elif configs.data.is_complex:
    if configs.data.magpha:
      train_dataset = fastmri_knee_magpha(Path(configs.data.root) / f'knee_complex_magpha_{configs.data.image_size}_train')
      val_dataset = fastmri_knee_magpha_infer(Path(configs.data.root) / f'knee_complex_magpha_{configs.data.image_size}_val')
    else:
      train_dataset = fastmri_knee(Path(configs.data.root) / f'knee_complex_{configs.data.image_size}_train', is_complex=True)
      val_dataset = fastmri_knee_infer(Path(configs.data.root) / f'knee_complex_{configs.data.image_size}_val', is_complex=True)
  elif configs.data.dataset == 'fastmri_knee':
    train_dataset = fastmri_knee(Path(configs.data.root) / f'knee_{configs.data.image_size}_train')
    val_dataset = fastmri_knee_infer(Path(configs.data.root) / f'knee_{configs.data.image_size}_val', sort=sort)
  else:
    raise ValueError(f'Dataset {configs.data.dataset} not recognized.')

  train_loader = DataLoader(
    dataset=train_dataset,
    batch_size=configs.training.batch_size,
    shuffle=shuffle,
    drop_last=True
  )
  val_loader = DataLoader(
    dataset=val_dataset,
    batch_size=configs.training.batch_size,
    # shuffle=False,
    shuffle=True,
    drop_last=True
  )
  return train_loader, val_loader



def create_dataloader_regression(configs, evaluation=False):
  shuffle = True if not evaluation else False
  train_dataset = fastmri_knee(Path(configs.root) / f'knee_{configs.image_size}_train')
  val_dataset = fastmri_knee_infer(Path(configs.root) / f'knee_{configs.image_size}_val')

  train_loader = DataLoader(
    dataset=train_dataset,
    batch_size=configs.batch_size,
    shuffle=shuffle,
    drop_last=True
  )
  val_loader = DataLoader(
    dataset=val_dataset,
    batch_size=configs.batch_size,
    shuffle=False,
    drop_last=True
  )
  return train_loader, val_loader