189 lines
8.5 KiB
Python
189 lines
8.5 KiB
Python
"""Python wrappers around TensorFlow ops.
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This file is MACHINE GENERATED! Do not edit.
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"""
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import collections
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from tensorflow.python import pywrap_tfe as pywrap_tfe
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from tensorflow.python.eager import context as _context
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from tensorflow.python.eager import core as _core
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from tensorflow.python.eager import execute as _execute
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from tensorflow.python.framework import dtypes as _dtypes
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from tensorflow.python.framework import op_def_registry as _op_def_registry
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from tensorflow.python.framework import ops as _ops
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from tensorflow.python.framework import op_def_library as _op_def_library
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from tensorflow.python.util.deprecation import deprecated_endpoints
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from tensorflow.python.util import dispatch as _dispatch
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from tensorflow.python.util.tf_export import tf_export
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from typing import TypeVar
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@_dispatch.add_fallback_dispatch_list
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@_dispatch.add_type_based_api_dispatcher
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@tf_export('io.encode_proto')
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def encode_proto(sizes, values, field_names, message_type, descriptor_source="local://", name=None):
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r"""The op serializes protobuf messages provided in the input tensors.
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The types of the tensors in `values` must match the schema for the fields
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specified in `field_names`. All the tensors in `values` must have a common
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shape prefix, *batch_shape*.
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The `sizes` tensor specifies repeat counts for each field. The repeat count
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(last dimension) of a each tensor in `values` must be greater than or equal
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to corresponding repeat count in `sizes`.
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A `message_type` name must be provided to give context for the field names.
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The actual message descriptor can be looked up either in the linked-in
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descriptor pool or a filename provided by the caller using the
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`descriptor_source` attribute.
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For the most part, the mapping between Proto field types and TensorFlow dtypes
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is straightforward. However, there are a few special cases:
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- A proto field that contains a submessage or group can only be converted
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to `DT_STRING` (the serialized submessage). This is to reduce the complexity
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of the API. The resulting string can be used as input to another instance of
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the decode_proto op.
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- TensorFlow lacks support for unsigned integers. The ops represent uint64
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types as a `DT_INT64` with the same twos-complement bit pattern (the obvious
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way). Unsigned int32 values can be represented exactly by specifying type
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`DT_INT64`, or using twos-complement if the caller specifies `DT_INT32` in
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the `output_types` attribute.
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The `descriptor_source` attribute selects the source of protocol
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descriptors to consult when looking up `message_type`. This may be:
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- An empty string or "local://", in which case protocol descriptors are
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created for C++ (not Python) proto definitions linked to the binary.
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- A file, in which case protocol descriptors are created from the file,
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which is expected to contain a `FileDescriptorSet` serialized as a string.
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NOTE: You can build a `descriptor_source` file using the `--descriptor_set_out`
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and `--include_imports` options to the protocol compiler `protoc`.
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- A "bytes://<bytes>", in which protocol descriptors are created from `<bytes>`,
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which is expected to be a `FileDescriptorSet` serialized as a string.
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Args:
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sizes: A `Tensor` of type `int32`.
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Tensor of int32 with shape `[batch_shape, len(field_names)]`.
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values: A list of `Tensor` objects.
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List of tensors containing values for the corresponding field.
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field_names: A list of `strings`.
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List of strings containing proto field names.
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message_type: A `string`. Name of the proto message type to decode.
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descriptor_source: An optional `string`. Defaults to `"local://"`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor` of type `string`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "EncodeProto", name, sizes, values, "field_names", field_names,
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"message_type", message_type, "descriptor_source", descriptor_source)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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_result = _dispatcher_for_encode_proto(
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(sizes, values, field_names, message_type, descriptor_source,
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name,), None)
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if _result is not NotImplemented:
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return _result
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return encode_proto_eager_fallback(
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sizes, values, field_names=field_names, message_type=message_type,
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descriptor_source=descriptor_source, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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except (TypeError, ValueError):
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_result = _dispatch.dispatch(
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encode_proto, (), dict(sizes=sizes, values=values,
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field_names=field_names,
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message_type=message_type,
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descriptor_source=descriptor_source,
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name=name)
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)
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if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
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return _result
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raise
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else:
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_result = _dispatcher_for_encode_proto(
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(sizes, values, field_names, message_type, descriptor_source, name,),
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None)
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if _result is not NotImplemented:
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return _result
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# Add nodes to the TensorFlow graph.
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if not isinstance(field_names, (list, tuple)):
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raise TypeError(
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"Expected list for 'field_names' argument to "
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"'encode_proto' Op, not %r." % field_names)
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field_names = [_execute.make_str(_s, "field_names") for _s in field_names]
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message_type = _execute.make_str(message_type, "message_type")
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if descriptor_source is None:
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descriptor_source = "local://"
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descriptor_source = _execute.make_str(descriptor_source, "descriptor_source")
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try:
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"EncodeProto", sizes=sizes, values=values, field_names=field_names,
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message_type=message_type,
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descriptor_source=descriptor_source, name=name)
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except (TypeError, ValueError):
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_result = _dispatch.dispatch(
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encode_proto, (), dict(sizes=sizes, values=values,
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field_names=field_names,
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message_type=message_type,
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descriptor_source=descriptor_source,
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name=name)
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)
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if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
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return _result
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raise
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("field_names", _op.get_attr("field_names"), "message_type",
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_op.get_attr("message_type"), "descriptor_source",
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_op.get_attr("descriptor_source"), "Tinput_types",
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_op.get_attr("Tinput_types"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"EncodeProto", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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EncodeProto = tf_export("raw_ops.EncodeProto")(_ops.to_raw_op(encode_proto))
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_dispatcher_for_encode_proto = encode_proto._tf_type_based_dispatcher.Dispatch
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def encode_proto_eager_fallback(sizes, values, field_names, message_type, descriptor_source, name, ctx):
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if not isinstance(field_names, (list, tuple)):
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raise TypeError(
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"Expected list for 'field_names' argument to "
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"'encode_proto' Op, not %r." % field_names)
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field_names = [_execute.make_str(_s, "field_names") for _s in field_names]
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message_type = _execute.make_str(message_type, "message_type")
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if descriptor_source is None:
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descriptor_source = "local://"
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descriptor_source = _execute.make_str(descriptor_source, "descriptor_source")
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_attr_Tinput_types, values = _execute.convert_to_mixed_eager_tensors(values, ctx)
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sizes = _ops.convert_to_tensor(sizes, _dtypes.int32)
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_inputs_flat = [sizes] + list(values)
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_attrs = ("field_names", field_names, "message_type", message_type,
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"descriptor_source", descriptor_source, "Tinput_types", _attr_Tinput_types)
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_result = _execute.execute(b"EncodeProto", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"EncodeProto", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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