def eval_fn(self):
""" Create dataset for evaluation """
dataset = tf.data.TFRecordDataset(filenames=self._eval)
assert len(
self._eval
) > 0, "Evaluation data not found. Did you specify --fold flag?"
dataset = dataset.cache()
dataset = dataset.map(self.parse,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
transforms = [
CenterCrop((224, 224, 155)),
Cast(dtype=tf.float32),
NormalizeImages(),
OneHotLabels(n_classes=4),
PadXYZ()
]
dataset = dataset.map(
map_func=lambda x, y, mean, stdev: apply_transforms(
x, y, mean, stdev, transforms=transforms),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.batch(batch_size=self._batch_size,
drop_remainder=False)
dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
return dataset
def train_fn(self):
assert len(self._train) > 0, "Training data not found."
ds = tf.data.TFRecordDataset(filenames=self._train)
ds = ds.shard(hvd.size(), hvd.rank())
ds = ds.cache()
ds = ds.shuffle(buffer_size=self._batch_size * 8, seed=self._seed)
ds = ds.repeat()
ds = ds.map(self.parse, num_parallel_calls=tf.data.experimental.AUTOTUNE)
transforms = [
RandomCrop3D((128, 128, 128)),
RandomHorizontalFlip() if self.params.augment else None,
Cast(dtype=tf.float32),
NormalizeImages(),
RandomBrightnessCorrection() if self.params.augment else None,
OneHotLabels(n_classes=4),
]
ds = ds.map(map_func=lambda x, y, mean, stdev: apply_transforms(x, y, mean, stdev, transforms=transforms),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds = ds.batch(batch_size=self._batch_size,
drop_remainder=True)
ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
return ds
def synth_train_fn(self):
"""Synthetic data function for testing"""
inputs = tf.random_uniform(self._xshape, dtype=tf.int32, minval=0, maxval=255, seed=self._seed,
name='synth_inputs')
masks = tf.random_uniform(self._yshape, dtype=tf.int32, minval=0, maxval=4, seed=self._seed,
name='synth_masks')
ds = tf.data.Dataset.from_tensors((inputs, masks))
ds = ds.repeat()
transforms = [
Cast(dtype=tf.uint8),
RandomCrop3D((128, 128, 128)),
RandomHorizontalFlip() if self.params.augment else None,
Cast(dtype=tf.float32),
NormalizeImages(),
RandomBrightnessCorrection() if self.params.augment else None,
OneHotLabels(n_classes=4),
]
ds = ds.map(map_func=lambda x, y: apply_transforms(x, y, transforms),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds = ds.batch(self._batch_size)
ds = ds.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
return ds
def train_fn(self):
""" Create dataset for training """
if 'debug' in self.params.exec_mode:
return self.synth_train_fn()
assert len(self._train) > 0, "Training data not found."
dataset = tf.data.TFRecordDataset(filenames=self._train)
dataset = dataset.shard(hvd.size(), hvd.rank())
dataset = dataset.cache()
dataset = dataset.shuffle(buffer_size=self._batch_size * 8,
seed=self._seed)
dataset = dataset.repeat()
dataset = dataset.map(self.parse,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
transforms = [
RandomCrop3D(self._input_shape),
RandomHorizontalFlip() if self.params.augment else None,
Cast(dtype=tf.float32),
NormalizeImages(),
RandomBrightnessCorrection() if self.params.augment else None,
OneHotLabels(n_classes=4),
]
dataset = dataset.map(
map_func=lambda x, y, mean, stdev: apply_transforms(
x, y, mean, stdev, transforms=transforms),
num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.batch(batch_size=self._batch_size,
drop_remainder=True)
dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
if self._batch_size == 1:
options = dataset.options()
options.experimental_optimization.map_and_batch_fusion = False
dataset = dataset.with_options(options)
return dataset