def _eval_epoch(self, subset: Text, batch_size: int):
"""Evaluates an epoch."""
num_samples = 0.
summed_scalars = None
params = helpers.get_first(self._byol_state.online_params)
state = helpers.get_first(self._byol_state.online_state)
split = dataset.Split.from_string(subset)
dataset_iterator = dataset.load(
split,
preprocess_mode=dataset.PreprocessMode.EVAL,
transpose=self._should_transpose_images(),
batch_dims=[batch_size])
for inputs in dataset_iterator:
num_samples += inputs['labels'].shape[0]
scalars = self.eval_batch_jit(params, state, inputs)
# Accumulate the sum of scalars for each step.
scalars = jax.tree_map(lambda x: jnp.sum(x, axis=0), scalars)
if summed_scalars is None:
summed_scalars = scalars
else:
summed_scalars = jax.tree_multimap(jnp.add, summed_scalars,
scalars)
mean_scalars = jax.tree_map(lambda x: x / num_samples, summed_scalars)
return mean_scalars
def _build_train_input(self) -> Generator[dataset.Batch, None, None]:
"""Loads the (infinitely looping) dataset iterator."""
num_devices = jax.device_count()
global_batch_size = self._batch_size
per_device_batch_size, ragged = divmod(global_batch_size, num_devices)
if ragged:
raise ValueError(
f'Global batch size {global_batch_size} must be divisible by '
f'num devices {num_devices}')
return dataset.load(
dataset.Split.TRAIN_AND_VALID,
preprocess_mode=dataset.PreprocessMode.PRETRAIN,
transpose=self._should_transpose_images(),
batch_dims=[jax.local_device_count(), per_device_batch_size])
def _build_train_input(self):
"""See base class."""
num_devices = jax.device_count()
global_batch_size = self._batch_size
per_device_batch_size, ragged = divmod(global_batch_size, num_devices)
if ragged:
raise ValueError(
f'Global batch size {global_batch_size} must be divisible by '
f'num devices {num_devices}')
return dataset.load(
dataset.Split.TRAIN_AND_VALID,
preprocess_mode=dataset.PreprocessMode.LINEAR_TRAIN,
transpose=self._should_transpose_images(),
batch_dims=[jax.local_device_count(), per_device_batch_size])
