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example_encoding.py
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example_encoding.py
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# coding=utf-8
# Copyright 2018 The TF-Agents Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities for easily encoding nests of numpy arrays into example protos."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from tf_agents.utils import nest_utils
def get_example_encoder(spec):
"""Get example encoder function for the given spec.
Given a spec, returns an example encoder function. The example encoder
function takes a nest of np.array feature values as input and returns a
TF Example proto.
Example:
spec = {
'lidar': array_spec.ArraySpec((900,), np.float32),
'joint_positions': {
'arm': array_spec.ArraySpec((7,), np.float32),
'hand': array_spec.BoundedArraySpec((3, 3), np.int32, -1, 1)
},
}
example_encoder = get_example_encoder(spec)
serialized = example_encoder({
'lidar': np.zeros((900,), np.float32),
'joint_positions': {
'arm': np.array([0.0, 1.57, 0.707, 0.2, 0.0, -1.57, 0.0],
np.float32),
'hand': np.ones((3, 3), np.int32)
},
})
The returned example encoder function requires that the feature nest passed
has the shape and exact dtype specified in the spec. For example, it is
an error to pass an array with np.float64 dtype where np.float32 is expected.
Args:
spec: list/tuple/nest of ArraySpecs describing a single example.
Returns:
Function
```python
encoder(features_nest of np.arrays) -> tf.train.Example
```
"""
# pylint: disable=g-complex-comprehension
feature_encoders = [(path, _get_feature_encoder(spec.shape, spec.dtype))
for (path,
spec) in nest_utils.flatten_with_joined_paths(spec)]
# pylint: enable=g-complex-comprehension
def _example_encoder(features_nest):
flat_features = tf.nest.flatten(features_nest)
feature_dict = {
path: feature_encoder(feature)
for feature, (path,
feature_encoder) in zip(flat_features, feature_encoders)
}
return tf.train.Example(features=tf.train.Features(feature=feature_dict))
return _example_encoder
def get_example_serializer(spec):
"""Returns string serializer of example protos."""
encoder = get_example_encoder(spec)
return lambda features_nest: encoder(features_nest).SerializeToString()
def get_example_decoder(example_spec, batched=False):
"""Get an example decoder function for a nested spec.
Given a spec, returns an example decoder function. The decoder function parses
string serialized example protos into tensors according to the given spec.
Args:
example_spec: list/tuple/nest of ArraySpecs describing a single example.
batched: Boolean indicating if the decoder will receive batches of
serialized data.
Returns:
Function
```python
decoder(serialized_proto: tf.tensor[string]) -> example_spec nest of tensors
```
"""
features_dict = {}
parsers = []
for (path, spec) in nest_utils.flatten_with_joined_paths(example_spec):
feature, parser = _get_feature_parser(spec.shape, spec.dtype)
features_dict[path] = feature
parsers.append((path, parser))
def _example_decoder(serialized):
"""Parses string serialized example protos into tensors."""
if batched:
raw_features = tf.io.parse_example(
serialized=serialized, features=features_dict)
decoded_features = []
dtypes = [s.dtype for s in tf.nest.flatten(example_spec)]
for (path, parser), dtype in zip(parsers, dtypes):
decoded_features.append(
tf.map_fn(parser, raw_features[path], dtype=dtype))
return tf.nest.pack_sequence_as(example_spec, decoded_features)
else:
raw_features = tf.io.parse_single_example(
serialized=serialized, features=features_dict)
return tf.nest.pack_sequence_as(
example_spec,
[parser(raw_features[path]) for path, parser in parsers])
return _example_decoder
def _validate_shape(shape):
"""Check that shape is a valid array shape."""
if not isinstance(shape, collections.Iterable):
raise TypeError('shape must be a tuple or other iterable object, not %s' %
type(shape).__name__)
validated_shape = []
for i, dim in enumerate(shape):
if not dim or dim != int(dim) or int(dim) <= 0:
raise ValueError(
'Dimension %d is invalid in %s, expected positive int' % (i, shape))
validated_shape.append(int(dim))
return tuple(validated_shape)
def _validate_dtype(dtype):
"""Check that dtype is supported by tf.decode_raw."""
dtype = tf.as_dtype(dtype)
supported_dtypes = (tf.half, tf.float32, tf.float64, tf.uint8, tf.int8,
tf.uint16, tf.int16, tf.int32, tf.int64)
if dtype not in supported_dtypes:
raise ValueError('%s is not supported, dtype must be one of %s' %
(dtype.name, ', '.join(d.name for d in supported_dtypes)))
return dtype
def _check_shape_and_dtype(value, shape, dtype):
"""Check that `value` has expected shape and dtype."""
value_dtype = tf.as_dtype(value.dtype.newbyteorder('N'))
if shape != value.shape or dtype != value_dtype:
raise ValueError('Expected shape %s of %s, got: shape %s of %s' %
(shape, dtype.name, value.shape, value_dtype.name))
def _get_feature_encoder(shape, dtype):
"""Get feature encoder function for shape and dtype."""
shape = _validate_shape(shape)
dtype = _validate_dtype(dtype)
if dtype == tf.float32: # Serialize float32 to FloatList.
def _encode_to_float_list(value):
value = np.asarray(value)
_check_shape_and_dtype(value, shape, dtype)
return tf.train.Feature(
float_list=tf.train.FloatList(
value=value.flatten(order='C').tolist()))
return _encode_to_float_list
elif dtype == tf.int64: # Serialize int64 to Int64List.
def _encode_to_int64_list(value):
value = np.asarray(value)
_check_shape_and_dtype(value, shape, dtype)
return tf.train.Feature(
int64_list=tf.train.Int64List(
value=value.flatten(order='C').tolist()))
return _encode_to_int64_list
else: # Serialize anything else to BytesList in little endian order.
le_dtype = dtype.as_numpy_dtype(0).newbyteorder('L')
def _encode_to_bytes_list(value):
value = np.asarray(value)
_check_shape_and_dtype(value, shape, dtype)
bytes_list_value = np.require(
value, dtype=le_dtype, requirements='C').tostring()
return tf.train.Feature(
bytes_list=tf.train.BytesList(value=[bytes_list_value]))
return _encode_to_bytes_list
def _get_feature_parser(shape, dtype):
"""Get tf.train.Features entry and decoder function for parsing feature."""
shape = _validate_shape(shape)
dtype = _validate_dtype(dtype)
if dtype == tf.float32:
return (tf.io.FixedLenFeature(shape=shape, dtype=tf.float32), lambda x: x)
elif dtype == tf.int64:
return (tf.io.FixedLenFeature(shape=shape, dtype=tf.int64), lambda x: x)
def decode(x):
return tf.reshape(tf.io.decode_raw(x, dtype), shape)
return (tf.io.FixedLenFeature(shape=[], dtype=tf.string), decode)
# pylint: enable=g-long-lambda