def __init__(self, selector_input, data_inputs):
self._selector_input = selector_input
self._data_inputs = list(data_inputs)
first_output_types = dataset_ops.get_legacy_output_types(
data_inputs[0])
first_output_classes = dataset_ops.get_legacy_output_classes(
data_inputs[0])
for data_input in data_inputs[1:]:
if (dataset_ops.get_legacy_output_types(data_input) !=
first_output_types
or dataset_ops.get_legacy_output_classes(data_input) !=
first_output_classes):
raise TypeError(
"All datasets must have the same type and class.")
output_shapes = dataset_ops.get_legacy_output_shapes(
self._data_inputs[0])
for data_input in self._data_inputs[1:]:
output_shapes = nest.pack_sequence_as(output_shapes, [
ts1.most_specific_compatible_shape(ts2) for (ts1, ts2) in zip(
nest.flatten(output_shapes),
nest.flatten(
dataset_ops.get_legacy_output_shapes(data_input)))
])
self._element_spec = structure.convert_legacy_structure(
first_output_types, output_shapes, first_output_classes)
# pylint: disable=protected-access
variant_tensor = gen_experimental_dataset_ops.directed_interleave_dataset(
self._selector_input._variant_tensor,
[data_input._variant_tensor for data_input in self._data_inputs],
**self._flat_structure)
super(_DirectedInterleaveDataset, self).__init__(variant_tensor)
def _as_variant_tensor(self):
# pylint: disable=protected-access
return (
gen_experimental_dataset_ops.directed_interleave_dataset(
self._selector_input._variant_tensor,
[data_input._variant_tensor for data_input in self._data_inputs],
**self._flat_structure))
def __init__(self,
selector_input,
data_inputs,
stop_on_empty_dataset=False):
self._selector_input = selector_input
self._data_inputs = list(data_inputs)
self._stop_on_empty_dataset = stop_on_empty_dataset
first_output_types = dataset_ops.get_legacy_output_types(
data_inputs[0])
first_output_classes = dataset_ops.get_legacy_output_classes(
data_inputs[0])
for i, data_input in enumerate(data_inputs[1:]):
if (dataset_ops.get_legacy_output_types(data_input) !=
first_output_types
or dataset_ops.get_legacy_output_classes(data_input) !=
first_output_classes):
raise TypeError(
"All datasets must have the same type and class.\n"
"dataset 0 vs dataset %s types: %s ; %s\n"
"classes: %s ; %s" %
(i + 1, first_output_types,
dataset_ops.get_legacy_output_types(data_input),
first_output_classes,
dataset_ops.get_legacy_output_classes(data_input)))
output_shapes = dataset_ops.get_legacy_output_shapes(
self._data_inputs[0])
for data_input in self._data_inputs[1:]:
output_shapes = nest.pack_sequence_as(output_shapes, [
ts1.most_specific_compatible_shape(ts2) for (ts1, ts2) in zip(
nest.flatten(output_shapes),
nest.flatten(
dataset_ops.get_legacy_output_shapes(data_input)))
])
self._element_spec = structure.convert_legacy_structure(
first_output_types, output_shapes, first_output_classes)
compat_kwargs = {}
if compat.forward_compatible(2021, 5,
14) or self._stop_on_empty_dataset:
compat_kwargs[
"stop_on_empty_dataset"] = self._stop_on_empty_dataset
# pylint: disable=protected-access
variant_tensor = (
gen_experimental_dataset_ops.directed_interleave_dataset(
self._selector_input._variant_tensor, [
data_input._variant_tensor
for data_input in self._data_inputs
], **compat_kwargs, **self._flat_structure))
super(_DirectedInterleaveDataset, self).__init__(variant_tensor)
def _as_variant_tensor(self):
# pylint: disable=protected-access
if compat.forward_compatible(2019, 8, 3):
return (
gen_experimental_dataset_ops.directed_interleave_dataset(
self._selector_input._variant_tensor,
[data_input._variant_tensor for data_input in self._data_inputs],
**self._flat_structure))
else:
return (
gen_experimental_dataset_ops.experimental_directed_interleave_dataset(
self._selector_input._variant_tensor,
[data_input._variant_tensor for data_input in self._data_inputs],
**self._flat_structure))
def __init__(self, selector_input, data_inputs, stop_on_empty_dataset=False):
self._selector_input = selector_input
self._data_inputs = list(data_inputs)
self._stop_on_empty_dataset = stop_on_empty_dataset
first_output_types = dataset_ops.get_legacy_output_types(data_inputs[0])
first_output_classes = dataset_ops.get_legacy_output_classes(data_inputs[0])
for i, data_input in enumerate(data_inputs[1:]):
if (dataset_ops.get_legacy_output_types(data_input) != first_output_types
or dataset_ops.get_legacy_output_classes(data_input) !=
first_output_classes):
raise TypeError("All datasets must have the same type and class.\n"
"dataset 0 vs dataset %s types: %s ; %s\n"
"classes: %s ; %s" %
(i + 1, first_output_types,
dataset_ops.get_legacy_output_types(data_input),
first_output_classes,
dataset_ops.get_legacy_output_classes(data_input)))
spec = self._data_inputs[0].element_spec
for data_input in self._data_inputs[1:]:
spec = nest.pack_sequence_as(spec, [
x.most_specific_compatible_type(y) for (x, y) in zip(
nest.flatten(spec),
nest.flatten(data_input.element_spec))
])
self._element_spec = spec
# pylint: disable=protected-access
variant_tensor = (
gen_experimental_dataset_ops.directed_interleave_dataset(
self._selector_input._variant_tensor,
[data_input._variant_tensor for data_input in self._data_inputs],
stop_on_empty_dataset=self._stop_on_empty_dataset,
**self._flat_structure))
super(_DirectedInterleaveDataset, self).__init__(variant_tensor)