def parse_example(self, serialized_example):
"""Deserialize a single `tf.train.Example` proto.
Usage:
```
ds = tf.data.TFRecordDataset(filepath)
ds = ds.map(file_adapter.parse_example)
```
Args:
serialized_example: `tf.Tensor`, the `tf.string` tensor containing the
serialized proto to decode.
Returns:
example: A nested `dict` of `tf.Tensor` values. The structure and tensors
shape/dtype match the `example_specs` provided at construction.
"""
example = tf.io.parse_single_example(
serialized=serialized_example,
features=self._build_feature_specs(),
)
example = {
k: _deserialize_single_field(example_data, tensor_info)
for k, (example_data, tensor_info
) in utils.zip_dict(example, self._flat_example_specs)
}
# Reconstruct all nesting
example = utils.pack_as_nest_dict(example, self._example_specs)
return example
def parse_example(self, serialized_example):
"""Deserialize a single `tf.train.Example` proto.
Usage:
```
ds = tf.data.TFRecordDataset(filepath)
ds = ds.map(file_adapter.parse_example)
```
Args:
serialized_example: `tf.Tensor`, the `tf.string` tensor containing the
serialized proto to decode.
Returns:
example: A nested `dict` of `tf.Tensor` values. The structure and tensors
shape/dtype match the `example_specs` provided at construction.
"""
nested_feature_specs = self._build_feature_specs()
# Because of RaggedTensor specs, feature_specs can be a 2-level nested dict,
# so have to wrap `tf.io.parse_single_example` between
# `flatten_nest_dict`/`pack_as_nest_dict`.
# {
# 'video/image': tf.io.FixedLenSequenceFeature(...),
# 'video/object/bbox': {
# 'ragged_flat_values': tf.io.FixedLenSequenceFeature(...),
# 'ragged_row_lengths_0', tf.io.FixedLenSequenceFeature(...),
# },
# }
flat_feature_specs = utils.flatten_nest_dict(nested_feature_specs)
example = tf.io.parse_single_example(
serialized=serialized_example,
features=flat_feature_specs,
)
example = utils.pack_as_nest_dict(example, nested_feature_specs)
example = { # pylint:disable=g-complex-comprehension
k: _deserialize_single_field(example_data, tensor_info)
for k, (
example_data,
tensor_info) in utils.zip_dict(example, self._flat_example_specs)
}
# Reconstruct all nesting
example = utils.pack_as_nest_dict(example, self._example_specs)
return example
