def __init__(self, input_dataset, num_workers):
self._input_dataset = input_dataset
def recalculate_output_shapes(output_shapes):
"""Recalculates the output_shapes after dividing it by num_workers."""
if len(output_shapes) < 1:
raise ValueError(
"Input shape should have at least one dimension.")
if (tensor_shape.dimension_value(output_shapes[0])
and tensor_shape.dimension_value(output_shapes[0]) %
num_workers != 0):
raise errors.InvalidArgumentError(
None, None,
"First dim of input shape: %d is not divisible by num_workers: %d"
% (output_shapes[0], num_workers))
output_dims = [d for d in output_shapes.dims]
output_dims[0] = output_dims[0] // num_workers
return tensor_shape.TensorShape(output_dims)
input_types = dataset_ops.get_legacy_output_types(self._input_dataset)
input_shapes = dataset_ops.get_legacy_output_shapes(
self._input_dataset)
input_classes = dataset_ops.get_legacy_output_classes(
self._input_dataset)
output_shapes = nest.map_structure(recalculate_output_shapes,
input_shapes)
self._structure = structure.convert_legacy_structure(
input_types, output_shapes, input_classes)
variant_tensor = ged_ops.experimental_rebatch_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
num_workers=num_workers,
**dataset_ops.flat_structure(self))
super(_RebatchDataset, self).__init__(input_dataset, variant_tensor)
def __init__(self, input_dataset, num_workers):
self._input_dataset = input_dataset
def recalculate_output_shapes(output_shapes):
"""Recalculates the output_shapes after dividing it by num_workers."""
if len(output_shapes) < 1:
raise ValueError(
"Input shape should have at least one dimension. "
"Perhaps your input dataset is not batched?")
output_dims = [d for d in output_shapes.dims]
output_dims[0] = (output_dims[0] + num_workers - 1) // num_workers
return tensor_shape.TensorShape(output_dims)
input_types = dataset_ops.get_legacy_output_types(self._input_dataset)
input_shapes = dataset_ops.get_legacy_output_shapes(self._input_dataset)
input_classes = dataset_ops.get_legacy_output_classes(self._input_dataset)
output_shapes = nest.map_structure(recalculate_output_shapes, input_shapes)
self._structure = structure.convert_legacy_structure(
input_types, output_shapes, input_classes)
variant_tensor = ged_ops.experimental_rebatch_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
num_workers=num_workers,
**self._flat_structure)
super(_RebatchDataset, self).__init__(input_dataset, variant_tensor)
def __init__(self, input_dataset, num_workers):
self._input_dataset = input_dataset
def recalculate_output_shapes(output_shapes):
"""Recalculates the output_shapes after dividing it by num_workers."""
if len(output_shapes) < 1:
raise ValueError("Input shape should have at least one dimension.")
if (tensor_shape.dimension_value(output_shapes[0]) and
tensor_shape.dimension_value(output_shapes[0]) % num_workers != 0):
raise errors.InvalidArgumentError(
None, None,
"First dim of input shape: %d is not divisible by num_workers: %d" %
(output_shapes[0], num_workers))
output_dims = [d for d in output_shapes.dims]
output_dims[0] = output_dims[0] // num_workers
return tensor_shape.TensorShape(output_dims)
input_types = dataset_ops.get_legacy_output_types(self._input_dataset)
input_shapes = dataset_ops.get_legacy_output_shapes(self._input_dataset)
input_classes = dataset_ops.get_legacy_output_classes(self._input_dataset)
output_shapes = nest.map_structure(recalculate_output_shapes, input_shapes)
self._structure = structure.convert_legacy_structure(
input_types, output_shapes, input_classes)
variant_tensor = ged_ops.experimental_rebatch_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
num_workers=num_workers,
**dataset_ops.flat_structure(self))
super(_RebatchDataset, self).__init__(input_dataset, variant_tensor)
def __init__(self, input_dataset, num_workers, use_fallback=True):
self._input_dataset = input_dataset
def recalculate_output_shapes(output_shapes):
"""Recalculates the output_shapes after dividing it by num_workers."""
if len(output_shapes) < 1:
raise ValueError(
"Input shape should have at least one dimension. "
"Perhaps your input dataset is not batched?")
output_dims = [d.value for d in output_shapes.dims]
if output_dims[0] is not None and output_dims[0] % num_workers == 0:
output_dims[0] = output_dims[0] // num_workers
else:
# Set the batch dimension to unknown. If the global batch size does not
# divide num_workers evenly, the minibatches may have different sizes.
output_dims[0] = None
return tensor_shape.TensorShape(output_dims)
input_types = dataset_ops.get_legacy_output_types(self._input_dataset)
input_shapes = dataset_ops.get_legacy_output_shapes(
self._input_dataset)
input_classes = dataset_ops.get_legacy_output_classes(
self._input_dataset)
output_shapes = nest.map_structure(recalculate_output_shapes,
input_shapes)
self._element_spec = structure.convert_legacy_structure(
input_types, output_shapes, input_classes)
if compat.forward_compatible(2019, 8, 13) or not use_fallback:
variant_tensor = ged_ops.rebatch_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
num_workers=num_workers,
use_fallback=use_fallback,
**self._flat_structure)
elif compat.forward_compatible(2019, 8, 3):
variant_tensor = ged_ops.rebatch_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
num_workers=num_workers,
**self._flat_structure)
else:
variant_tensor = ged_ops.experimental_rebatch_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
num_workers=num_workers,
**self._flat_structure)
super(_RebatchDataset, self).__init__(input_dataset, variant_tensor)
def __init__(self, input_dataset, num_workers):
self._input_dataset = input_dataset
output_shapes = input_dataset.output_shapes
if len(output_shapes) < 1:
raise ValueError("Input shape should have at least one dimension.")
if not output_shapes.dims[0].value:
raise ValueError("Cannot rebatch unknown batch size datasets.")
if output_shapes.dims[0].value % num_workers != 0:
raise ValueError(
"First dim of input shape: %d is not divisible by num_workers: %d" %
(output_shapes[0], num_workers))
output_dims = [d for d in output_shapes.dims]
output_dims[0] = output_dims[0] // num_workers
output_shapes = tensor_shape.TensorShapeV1(output_dims)
self._structure = structure.convert_legacy_structure(
self._input_dataset.output_types, output_shapes,
self._input_dataset.output_classes)
variant_tensor = ged_ops.experimental_rebatch_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
num_workers=num_workers,
**dataset_ops.flat_structure(self))
super(_RebatchDataset, self).__init__(input_dataset, variant_tensor)
def __init__(self, input_dataset, num_workers):
self._input_dataset = input_dataset
output_shapes = input_dataset.output_shapes
if len(output_shapes) < 1:
raise ValueError("Input shape should have at least one dimension.")
if not output_shapes.dims[0].value:
raise ValueError("Cannot rebatch unknown batch size datasets.")
if output_shapes.dims[0].value % num_workers != 0:
raise ValueError(
"First dim of input shape: %d is not divisible by num_workers: %d" %
(output_shapes[0], num_workers))
output_dims = [d for d in output_shapes.dims]
output_dims[0] = output_dims[0] // num_workers
output_shapes = tensor_shape.TensorShapeV1(output_dims)
self._structure = structure.convert_legacy_structure(
self._input_dataset.output_types, output_shapes,
self._input_dataset.output_classes)
variant_tensor = ged_ops.experimental_rebatch_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
num_workers=num_workers,
**dataset_ops.flat_structure(self))
super(_RebatchDataset, self).__init__(input_dataset, variant_tensor)
