def _create_iterator(self, dataset):
# pylint: disable=protected-access
dataset = dataset._apply_options()
# Store dataset reference to ensure that dataset is alive when this iterator
# is being used. For example, `tf.data.Dataset.from_generator` registers
# a few py_funcs that are needed in `self._next_internal`. If the dataset
# is deleted, this iterator crashes on `self.__next__(...)` call.
self._dataset = dataset
ds_variant = dataset._variant_tensor
self._element_spec = dataset.element_spec
self._flat_output_types = structure.get_flat_tensor_types(
self._element_spec)
self._flat_output_shapes = structure.get_flat_tensor_shapes(
self._element_spec)
with ops.colocate_with(ds_variant):
self._iterator_resource, self._deleter = (
gen_dataset_ops.anonymous_iterator_v2(
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes))
gen_dataset_ops.make_iterator(ds_variant, self._iterator_resource)
# Delete the resource when this object is deleted
self._resource_deleter = IteratorResourceDeleter(
handle=self._iterator_resource,
device=self._device,
deleter=self._deleter)
def _create_iterator(self, dataset):
# pylint: disable=protected-access
dataset = dataset._apply_options()
ds_variant = dataset._variant_tensor
self._structure = dataset._element_structure
self._flat_output_types = self._structure._flat_types
self._flat_output_shapes = self._structure._flat_shapes
with ops.colocate_with(ds_variant):
self._iterator_resource, self._deleter = (
gen_dataset_ops.anonymous_iterator_v2(
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes))
gen_dataset_ops.make_iterator(ds_variant, self._iterator_resource)
# Delete the resource when this object is deleted
self._resource_deleter = IteratorResourceDeleter(
handle=self._iterator_resource,
device=self._device,
deleter=self._deleter)
def __init__(self, dataset):
"""Creates a new iterator over the given dataset.
For example:
```python
dataset = tf.data.Dataset.range(4)
for x in Iterator(dataset):
print(x)
```
Tensors produced will be placed on the device on which this iterator object
was created.
Args:
dataset: A `tf.data.Dataset` object.
Raises:
RuntimeError: When invoked without eager execution enabled.
"""
self._device = context.context().device_name
with ops.device("/cpu:0"):
# pylint: disable=protected-access
dataset = dataset._apply_options()
ds_variant = dataset._variant_tensor
self._structure = dataset._element_structure
self._flat_output_types = self._structure._flat_types
self._flat_output_shapes = self._structure._flat_shapes
with ops.colocate_with(ds_variant):
self._iterator_resource, self._deleter = (
gen_dataset_ops.anonymous_iterator_v2(
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes))
gen_dataset_ops.make_iterator(ds_variant,
self._iterator_resource)
# Delete the resource when this object is deleted
self._resource_deleter = IteratorResourceDeleter(
handle=self._iterator_resource,
device=self._device,
deleter=self._deleter)
def __init__(self, dataset):
"""Creates a new iterator over the given dataset.
For example:
```python
dataset = tf.data.Dataset.range(4)
for x in Iterator(dataset):
print(x)
```
Tensors produced will be placed on the device on which this iterator object
was created.
Args:
dataset: A `tf.data.Dataset` object.
Raises:
RuntimeError: When invoked without eager execution enabled.
"""
self._device = context.context().device_name
with ops.device("/cpu:0"):
# pylint: disable=protected-access
dataset = dataset._apply_options()
ds_variant = dataset._variant_tensor
self._structure = dataset._element_structure
self._flat_output_types = self._structure._flat_types
self._flat_output_shapes = self._structure._flat_shapes
with ops.colocate_with(ds_variant):
self._iterator_resource, self._deleter = (
gen_dataset_ops.anonymous_iterator_v2(
output_types=self._flat_output_types,
output_shapes=self._flat_output_shapes))
gen_dataset_ops.make_iterator(ds_variant, self._iterator_resource)
# Delete the resource when this object is deleted
self._resource_deleter = IteratorResourceDeleter(
handle=self._iterator_resource,
device=self._device,
deleter=self._deleter)
def create_iter():
return gen_dataset_ops.anonymous_iterator_v2(
output_types=[dtypes.float32], output_shapes=[[]])
