492 lines
24 KiB
Python
492 lines
24 KiB
Python
|
"""Python wrappers around TensorFlow ops.
|
||
|
|
|
||
|
|
This file is MACHINE GENERATED! Do not edit.
|
||
|
|
"""
|
||
|
|
|
||
|
|
import collections
|
||
|
|
|
||
|
|
from tensorflow.python import pywrap_tfe as pywrap_tfe
|
||
|
|
from tensorflow.python.eager import context as _context
|
||
|
|
from tensorflow.python.eager import core as _core
|
||
|
|
from tensorflow.python.eager import execute as _execute
|
||
|
|
from tensorflow.python.framework import dtypes as _dtypes
|
||
|
|
from tensorflow.security.fuzzing.py import annotation_types as _atypes
|
||
|
|
|
||
|
|
from tensorflow.python.framework import op_def_registry as _op_def_registry
|
||
|
|
from tensorflow.python.framework import ops as _ops
|
||
|
|
from tensorflow.python.framework import op_def_library as _op_def_library
|
||
|
|
from tensorflow.python.util.deprecation import deprecated_endpoints
|
||
|
|
from tensorflow.python.util import dispatch as _dispatch
|
||
|
|
from tensorflow.python.util.tf_export import tf_export
|
||
|
|
|
||
|
|
from typing import TypeVar, List, Any
|
||
|
|
from typing_extensions import Annotated
|
||
|
|
_CTCBeamSearchDecoderOutput = collections.namedtuple(
|
||
|
|
"CTCBeamSearchDecoder",
|
||
|
|
["decoded_indices", "decoded_values", "decoded_shape", "log_probability"])
|
||
|
|
|
||
|
|
|
||
|
|
TV_CTCBeamSearchDecoder_T = TypeVar("TV_CTCBeamSearchDecoder_T", _atypes.Float32, _atypes.Float64)
|
||
|
|
|
||
|
|
def ctc_beam_search_decoder(inputs: Annotated[Any, TV_CTCBeamSearchDecoder_T], sequence_length: Annotated[Any, _atypes.Int32], beam_width: int, top_paths: int, merge_repeated:bool=True, name=None):
|
||
|
|
r"""Performs beam search decoding on the logits given in input.
|
||
|
|
|
||
|
|
A note about the attribute merge_repeated: For the beam search decoder,
|
||
|
|
this means that if consecutive entries in a beam are the same, only
|
||
|
|
the first of these is emitted. That is, when the top path is "A B B B B",
|
||
|
|
"A B" is returned if merge_repeated = True but "A B B B B" is
|
||
|
|
returned if merge_repeated = False.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
inputs: A `Tensor`. Must be one of the following types: `float32`, `float64`.
|
||
|
|
3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
|
||
|
|
sequence_length: A `Tensor` of type `int32`.
|
||
|
|
A vector containing sequence lengths, size `(batch)`.
|
||
|
|
beam_width: An `int` that is `>= 1`.
|
||
|
|
A scalar >= 0 (beam search beam width).
|
||
|
|
top_paths: An `int` that is `>= 1`.
|
||
|
|
A scalar >= 0, <= beam_width (controls output size).
|
||
|
|
merge_repeated: An optional `bool`. Defaults to `True`.
|
||
|
|
If true, merge repeated classes in output.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tuple of `Tensor` objects (decoded_indices, decoded_values, decoded_shape, log_probability).
|
||
|
|
|
||
|
|
decoded_indices: A list of `top_paths` `Tensor` objects with type `int64`.
|
||
|
|
decoded_values: A list of `top_paths` `Tensor` objects with type `int64`.
|
||
|
|
decoded_shape: A list of `top_paths` `Tensor` objects with type `int64`.
|
||
|
|
log_probability: A `Tensor`. Has the same type as `inputs`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "CTCBeamSearchDecoder", name, inputs, sequence_length,
|
||
|
|
"beam_width", beam_width, "top_paths", top_paths, "merge_repeated",
|
||
|
|
merge_repeated)
|
||
|
|
_result = _CTCBeamSearchDecoderOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return ctc_beam_search_decoder_eager_fallback(
|
||
|
|
inputs, sequence_length, beam_width=beam_width, top_paths=top_paths,
|
||
|
|
merge_repeated=merge_repeated, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
beam_width = _execute.make_int(beam_width, "beam_width")
|
||
|
|
top_paths = _execute.make_int(top_paths, "top_paths")
|
||
|
|
if merge_repeated is None:
|
||
|
|
merge_repeated = True
|
||
|
|
merge_repeated = _execute.make_bool(merge_repeated, "merge_repeated")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"CTCBeamSearchDecoder", inputs=inputs,
|
||
|
|
sequence_length=sequence_length,
|
||
|
|
beam_width=beam_width, top_paths=top_paths,
|
||
|
|
merge_repeated=merge_repeated, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("beam_width", _op._get_attr_int("beam_width"), "top_paths",
|
||
|
|
_op._get_attr_int("top_paths"), "merge_repeated",
|
||
|
|
_op._get_attr_bool("merge_repeated"), "T",
|
||
|
|
_op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"CTCBeamSearchDecoder", _inputs_flat, _attrs, _result)
|
||
|
|
_result = [_result[:top_paths]] + _result[top_paths:]
|
||
|
|
_result = _result[:1] + [_result[1:1 + top_paths]] + _result[1 + top_paths:]
|
||
|
|
_result = _result[:2] + [_result[2:2 + top_paths]] + _result[2 + top_paths:]
|
||
|
|
_result = _CTCBeamSearchDecoderOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
CTCBeamSearchDecoder = tf_export("raw_ops.CTCBeamSearchDecoder")(_ops.to_raw_op(ctc_beam_search_decoder))
|
||
|
|
|
||
|
|
|
||
|
|
def ctc_beam_search_decoder_eager_fallback(inputs: Annotated[Any, TV_CTCBeamSearchDecoder_T], sequence_length: Annotated[Any, _atypes.Int32], beam_width: int, top_paths: int, merge_repeated: bool, name, ctx):
|
||
|
|
beam_width = _execute.make_int(beam_width, "beam_width")
|
||
|
|
top_paths = _execute.make_int(top_paths, "top_paths")
|
||
|
|
if merge_repeated is None:
|
||
|
|
merge_repeated = True
|
||
|
|
merge_repeated = _execute.make_bool(merge_repeated, "merge_repeated")
|
||
|
|
_attr_T, (inputs,) = _execute.args_to_matching_eager([inputs], ctx, [_dtypes.float32, _dtypes.float64, ], _dtypes.float32)
|
||
|
|
sequence_length = _ops.convert_to_tensor(sequence_length, _dtypes.int32)
|
||
|
|
_inputs_flat = [inputs, sequence_length]
|
||
|
|
_attrs = ("beam_width", beam_width, "top_paths", top_paths,
|
||
|
|
"merge_repeated", merge_repeated, "T", _attr_T)
|
||
|
|
_result = _execute.execute(b"CTCBeamSearchDecoder", top_paths + top_paths +
|
||
|
|
top_paths + 1, inputs=_inputs_flat, attrs=_attrs,
|
||
|
|
ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"CTCBeamSearchDecoder", _inputs_flat, _attrs, _result)
|
||
|
|
_result = [_result[:top_paths]] + _result[top_paths:]
|
||
|
|
_result = _result[:1] + [_result[1:1 + top_paths]] + _result[1 + top_paths:]
|
||
|
|
_result = _result[:2] + [_result[2:2 + top_paths]] + _result[2 + top_paths:]
|
||
|
|
_result = _CTCBeamSearchDecoderOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
_CTCGreedyDecoderOutput = collections.namedtuple(
|
||
|
|
"CTCGreedyDecoder",
|
||
|
|
["decoded_indices", "decoded_values", "decoded_shape", "log_probability"])
|
||
|
|
|
||
|
|
|
||
|
|
TV_CTCGreedyDecoder_T = TypeVar("TV_CTCGreedyDecoder_T", _atypes.Float32, _atypes.Float64)
|
||
|
|
|
||
|
|
def ctc_greedy_decoder(inputs: Annotated[Any, TV_CTCGreedyDecoder_T], sequence_length: Annotated[Any, _atypes.Int32], merge_repeated:bool=False, blank_index:int=-1, name=None):
|
||
|
|
r"""Performs greedy decoding on the logits given in inputs.
|
||
|
|
|
||
|
|
A note about the attribute merge_repeated: if enabled, when
|
||
|
|
consecutive logits' maximum indices are the same, only the first of
|
||
|
|
these is emitted. Labeling the blank '*', the sequence "A B B * B B"
|
||
|
|
becomes "A B B" if merge_repeated = True and "A B B B B" if
|
||
|
|
merge_repeated = False.
|
||
|
|
|
||
|
|
Regardless of the value of merge_repeated, if the maximum index of a given
|
||
|
|
time and batch corresponds to the blank, index `(num_classes - 1)`, no new
|
||
|
|
element is emitted.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
inputs: A `Tensor`. Must be one of the following types: `float32`, `float64`.
|
||
|
|
3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
|
||
|
|
sequence_length: A `Tensor` of type `int32`.
|
||
|
|
A vector containing sequence lengths, size `(batch_size)`.
|
||
|
|
merge_repeated: An optional `bool`. Defaults to `False`.
|
||
|
|
If True, merge repeated classes in output.
|
||
|
|
blank_index: An optional `int`. Defaults to `-1`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tuple of `Tensor` objects (decoded_indices, decoded_values, decoded_shape, log_probability).
|
||
|
|
|
||
|
|
decoded_indices: A `Tensor` of type `int64`.
|
||
|
|
decoded_values: A `Tensor` of type `int64`.
|
||
|
|
decoded_shape: A `Tensor` of type `int64`.
|
||
|
|
log_probability: A `Tensor`. Has the same type as `inputs`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "CTCGreedyDecoder", name, inputs, sequence_length,
|
||
|
|
"merge_repeated", merge_repeated, "blank_index", blank_index)
|
||
|
|
_result = _CTCGreedyDecoderOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return ctc_greedy_decoder_eager_fallback(
|
||
|
|
inputs, sequence_length, merge_repeated=merge_repeated,
|
||
|
|
blank_index=blank_index, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
if merge_repeated is None:
|
||
|
|
merge_repeated = False
|
||
|
|
merge_repeated = _execute.make_bool(merge_repeated, "merge_repeated")
|
||
|
|
if blank_index is None:
|
||
|
|
blank_index = -1
|
||
|
|
blank_index = _execute.make_int(blank_index, "blank_index")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"CTCGreedyDecoder", inputs=inputs, sequence_length=sequence_length,
|
||
|
|
merge_repeated=merge_repeated,
|
||
|
|
blank_index=blank_index, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("merge_repeated", _op._get_attr_bool("merge_repeated"),
|
||
|
|
"blank_index", _op._get_attr_int("blank_index"), "T",
|
||
|
|
_op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"CTCGreedyDecoder", _inputs_flat, _attrs, _result)
|
||
|
|
_result = _CTCGreedyDecoderOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
CTCGreedyDecoder = tf_export("raw_ops.CTCGreedyDecoder")(_ops.to_raw_op(ctc_greedy_decoder))
|
||
|
|
|
||
|
|
|
||
|
|
def ctc_greedy_decoder_eager_fallback(inputs: Annotated[Any, TV_CTCGreedyDecoder_T], sequence_length: Annotated[Any, _atypes.Int32], merge_repeated: bool, blank_index: int, name, ctx):
|
||
|
|
if merge_repeated is None:
|
||
|
|
merge_repeated = False
|
||
|
|
merge_repeated = _execute.make_bool(merge_repeated, "merge_repeated")
|
||
|
|
if blank_index is None:
|
||
|
|
blank_index = -1
|
||
|
|
blank_index = _execute.make_int(blank_index, "blank_index")
|
||
|
|
_attr_T, (inputs,) = _execute.args_to_matching_eager([inputs], ctx, [_dtypes.float32, _dtypes.float64, ], _dtypes.float32)
|
||
|
|
sequence_length = _ops.convert_to_tensor(sequence_length, _dtypes.int32)
|
||
|
|
_inputs_flat = [inputs, sequence_length]
|
||
|
|
_attrs = ("merge_repeated", merge_repeated, "blank_index", blank_index, "T",
|
||
|
|
_attr_T)
|
||
|
|
_result = _execute.execute(b"CTCGreedyDecoder", 4, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"CTCGreedyDecoder", _inputs_flat, _attrs, _result)
|
||
|
|
_result = _CTCGreedyDecoderOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
_CTCLossOutput = collections.namedtuple(
|
||
|
|
"CTCLoss",
|
||
|
|
["loss", "gradient"])
|
||
|
|
|
||
|
|
|
||
|
|
TV_CTCLoss_T = TypeVar("TV_CTCLoss_T", _atypes.Float32, _atypes.Float64)
|
||
|
|
|
||
|
|
def ctc_loss(inputs: Annotated[Any, TV_CTCLoss_T], labels_indices: Annotated[Any, _atypes.Int64], labels_values: Annotated[Any, _atypes.Int32], sequence_length: Annotated[Any, _atypes.Int32], preprocess_collapse_repeated:bool=False, ctc_merge_repeated:bool=True, ignore_longer_outputs_than_inputs:bool=False, name=None):
|
||
|
|
r"""Calculates the CTC Loss (log probability) for each batch entry. Also calculates
|
||
|
|
|
||
|
|
the gradient. This class performs the softmax operation for you, so inputs
|
||
|
|
should be e.g. linear projections of outputs by an LSTM.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
inputs: A `Tensor`. Must be one of the following types: `float32`, `float64`.
|
||
|
|
3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
|
||
|
|
labels_indices: A `Tensor` of type `int64`.
|
||
|
|
The indices of a `SparseTensor<int32, 2>`.
|
||
|
|
`labels_indices(i, :) == [b, t]` means `labels_values(i)` stores the id for
|
||
|
|
`(batch b, time t)`.
|
||
|
|
labels_values: A `Tensor` of type `int32`.
|
||
|
|
The values (labels) associated with the given batch and time.
|
||
|
|
sequence_length: A `Tensor` of type `int32`.
|
||
|
|
A vector containing sequence lengths (batch).
|
||
|
|
preprocess_collapse_repeated: An optional `bool`. Defaults to `False`.
|
||
|
|
Scalar, if true then repeated labels are
|
||
|
|
collapsed prior to the CTC calculation.
|
||
|
|
ctc_merge_repeated: An optional `bool`. Defaults to `True`.
|
||
|
|
Scalar. If set to false, *during* CTC calculation
|
||
|
|
repeated non-blank labels will not be merged and are interpreted as
|
||
|
|
individual labels. This is a simplified version of CTC.
|
||
|
|
ignore_longer_outputs_than_inputs: An optional `bool`. Defaults to `False`.
|
||
|
|
Scalar. If set to true, during CTC
|
||
|
|
calculation, items that have longer output sequences than input sequences
|
||
|
|
are skipped: they don't contribute to the loss term and have zero-gradient.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tuple of `Tensor` objects (loss, gradient).
|
||
|
|
|
||
|
|
loss: A `Tensor`. Has the same type as `inputs`.
|
||
|
|
gradient: A `Tensor`. Has the same type as `inputs`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "CTCLoss", name, inputs, labels_indices, labels_values,
|
||
|
|
sequence_length, "preprocess_collapse_repeated",
|
||
|
|
preprocess_collapse_repeated, "ctc_merge_repeated",
|
||
|
|
ctc_merge_repeated, "ignore_longer_outputs_than_inputs",
|
||
|
|
ignore_longer_outputs_than_inputs)
|
||
|
|
_result = _CTCLossOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return ctc_loss_eager_fallback(
|
||
|
|
inputs, labels_indices, labels_values, sequence_length,
|
||
|
|
preprocess_collapse_repeated=preprocess_collapse_repeated,
|
||
|
|
ctc_merge_repeated=ctc_merge_repeated,
|
||
|
|
ignore_longer_outputs_than_inputs=ignore_longer_outputs_than_inputs,
|
||
|
|
name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
if preprocess_collapse_repeated is None:
|
||
|
|
preprocess_collapse_repeated = False
|
||
|
|
preprocess_collapse_repeated = _execute.make_bool(preprocess_collapse_repeated, "preprocess_collapse_repeated")
|
||
|
|
if ctc_merge_repeated is None:
|
||
|
|
ctc_merge_repeated = True
|
||
|
|
ctc_merge_repeated = _execute.make_bool(ctc_merge_repeated, "ctc_merge_repeated")
|
||
|
|
if ignore_longer_outputs_than_inputs is None:
|
||
|
|
ignore_longer_outputs_than_inputs = False
|
||
|
|
ignore_longer_outputs_than_inputs = _execute.make_bool(ignore_longer_outputs_than_inputs, "ignore_longer_outputs_than_inputs")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"CTCLoss", inputs=inputs, labels_indices=labels_indices,
|
||
|
|
labels_values=labels_values,
|
||
|
|
sequence_length=sequence_length,
|
||
|
|
preprocess_collapse_repeated=preprocess_collapse_repeated,
|
||
|
|
ctc_merge_repeated=ctc_merge_repeated,
|
||
|
|
ignore_longer_outputs_than_inputs=ignore_longer_outputs_than_inputs,
|
||
|
|
name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("preprocess_collapse_repeated",
|
||
|
|
_op._get_attr_bool("preprocess_collapse_repeated"),
|
||
|
|
"ctc_merge_repeated", _op._get_attr_bool("ctc_merge_repeated"),
|
||
|
|
"ignore_longer_outputs_than_inputs",
|
||
|
|
_op._get_attr_bool("ignore_longer_outputs_than_inputs"), "T",
|
||
|
|
_op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"CTCLoss", _inputs_flat, _attrs, _result)
|
||
|
|
_result = _CTCLossOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
CTCLoss = tf_export("raw_ops.CTCLoss")(_ops.to_raw_op(ctc_loss))
|
||
|
|
|
||
|
|
|
||
|
|
def ctc_loss_eager_fallback(inputs: Annotated[Any, TV_CTCLoss_T], labels_indices: Annotated[Any, _atypes.Int64], labels_values: Annotated[Any, _atypes.Int32], sequence_length: Annotated[Any, _atypes.Int32], preprocess_collapse_repeated: bool, ctc_merge_repeated: bool, ignore_longer_outputs_than_inputs: bool, name, ctx):
|
||
|
|
if preprocess_collapse_repeated is None:
|
||
|
|
preprocess_collapse_repeated = False
|
||
|
|
preprocess_collapse_repeated = _execute.make_bool(preprocess_collapse_repeated, "preprocess_collapse_repeated")
|
||
|
|
if ctc_merge_repeated is None:
|
||
|
|
ctc_merge_repeated = True
|
||
|
|
ctc_merge_repeated = _execute.make_bool(ctc_merge_repeated, "ctc_merge_repeated")
|
||
|
|
if ignore_longer_outputs_than_inputs is None:
|
||
|
|
ignore_longer_outputs_than_inputs = False
|
||
|
|
ignore_longer_outputs_than_inputs = _execute.make_bool(ignore_longer_outputs_than_inputs, "ignore_longer_outputs_than_inputs")
|
||
|
|
_attr_T, (inputs,) = _execute.args_to_matching_eager([inputs], ctx, [_dtypes.float32, _dtypes.float64, ], _dtypes.float32)
|
||
|
|
labels_indices = _ops.convert_to_tensor(labels_indices, _dtypes.int64)
|
||
|
|
labels_values = _ops.convert_to_tensor(labels_values, _dtypes.int32)
|
||
|
|
sequence_length = _ops.convert_to_tensor(sequence_length, _dtypes.int32)
|
||
|
|
_inputs_flat = [inputs, labels_indices, labels_values, sequence_length]
|
||
|
|
_attrs = ("preprocess_collapse_repeated", preprocess_collapse_repeated,
|
||
|
|
"ctc_merge_repeated", ctc_merge_repeated,
|
||
|
|
"ignore_longer_outputs_than_inputs", ignore_longer_outputs_than_inputs, "T",
|
||
|
|
_attr_T)
|
||
|
|
_result = _execute.execute(b"CTCLoss", 2, inputs=_inputs_flat, attrs=_attrs,
|
||
|
|
ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"CTCLoss", _inputs_flat, _attrs, _result)
|
||
|
|
_result = _CTCLossOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
_CTCLossV2Output = collections.namedtuple(
|
||
|
|
"CTCLossV2",
|
||
|
|
["loss", "gradient"])
|
||
|
|
|
||
|
|
|
||
|
|
def ctc_loss_v2(inputs: Annotated[Any, _atypes.Float32], labels_indices: Annotated[Any, _atypes.Int64], labels_values: Annotated[Any, _atypes.Int32], sequence_length: Annotated[Any, _atypes.Int32], preprocess_collapse_repeated:bool=False, ctc_merge_repeated:bool=True, ignore_longer_outputs_than_inputs:bool=False, name=None):
|
||
|
|
r"""Calculates the CTC Loss (log probability) for each batch entry. Also calculates
|
||
|
|
|
||
|
|
the gradient. This class performs the softmax operation for you, so inputs
|
||
|
|
should be e.g. linear projections of outputs by an LSTM.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
inputs: A `Tensor` of type `float32`.
|
||
|
|
3-D, shape: `(max_time x batch_size x num_classes)`, the logits. Default blank
|
||
|
|
label is 0 rather num_classes - 1.
|
||
|
|
labels_indices: A `Tensor` of type `int64`.
|
||
|
|
The indices of a `SparseTensor<int32, 2>`.
|
||
|
|
`labels_indices(i, :) == [b, t]` means `labels_values(i)` stores the id for
|
||
|
|
`(batch b, time t)`.
|
||
|
|
labels_values: A `Tensor` of type `int32`.
|
||
|
|
The values (labels) associated with the given batch and time.
|
||
|
|
sequence_length: A `Tensor` of type `int32`.
|
||
|
|
A vector containing sequence lengths (batch).
|
||
|
|
preprocess_collapse_repeated: An optional `bool`. Defaults to `False`.
|
||
|
|
Scalar, if true then repeated labels are
|
||
|
|
collapsed prior to the CTC calculation.
|
||
|
|
ctc_merge_repeated: An optional `bool`. Defaults to `True`.
|
||
|
|
Scalar. If set to false, *during* CTC calculation
|
||
|
|
repeated non-blank labels will not be merged and are interpreted as
|
||
|
|
individual labels. This is a simplified version of CTC.
|
||
|
|
ignore_longer_outputs_than_inputs: An optional `bool`. Defaults to `False`.
|
||
|
|
Scalar. If set to true, during CTC
|
||
|
|
calculation, items that have longer output sequences than input sequences
|
||
|
|
are skipped: they don't contribute to the loss term and have zero-gradient.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tuple of `Tensor` objects (loss, gradient).
|
||
|
|
|
||
|
|
loss: A `Tensor` of type `float32`.
|
||
|
|
gradient: A `Tensor` of type `float32`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "CTCLossV2", name, inputs, labels_indices, labels_values,
|
||
|
|
sequence_length, "preprocess_collapse_repeated",
|
||
|
|
preprocess_collapse_repeated, "ctc_merge_repeated",
|
||
|
|
ctc_merge_repeated, "ignore_longer_outputs_than_inputs",
|
||
|
|
ignore_longer_outputs_than_inputs)
|
||
|
|
_result = _CTCLossV2Output._make(_result)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return ctc_loss_v2_eager_fallback(
|
||
|
|
inputs, labels_indices, labels_values, sequence_length,
|
||
|
|
preprocess_collapse_repeated=preprocess_collapse_repeated,
|
||
|
|
ctc_merge_repeated=ctc_merge_repeated,
|
||
|
|
ignore_longer_outputs_than_inputs=ignore_longer_outputs_than_inputs,
|
||
|
|
name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
if preprocess_collapse_repeated is None:
|
||
|
|
preprocess_collapse_repeated = False
|
||
|
|
preprocess_collapse_repeated = _execute.make_bool(preprocess_collapse_repeated, "preprocess_collapse_repeated")
|
||
|
|
if ctc_merge_repeated is None:
|
||
|
|
ctc_merge_repeated = True
|
||
|
|
ctc_merge_repeated = _execute.make_bool(ctc_merge_repeated, "ctc_merge_repeated")
|
||
|
|
if ignore_longer_outputs_than_inputs is None:
|
||
|
|
ignore_longer_outputs_than_inputs = False
|
||
|
|
ignore_longer_outputs_than_inputs = _execute.make_bool(ignore_longer_outputs_than_inputs, "ignore_longer_outputs_than_inputs")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"CTCLossV2", inputs=inputs, labels_indices=labels_indices,
|
||
|
|
labels_values=labels_values,
|
||
|
|
sequence_length=sequence_length,
|
||
|
|
preprocess_collapse_repeated=preprocess_collapse_repeated,
|
||
|
|
ctc_merge_repeated=ctc_merge_repeated,
|
||
|
|
ignore_longer_outputs_than_inputs=ignore_longer_outputs_than_inputs,
|
||
|
|
name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("preprocess_collapse_repeated",
|
||
|
|
_op._get_attr_bool("preprocess_collapse_repeated"),
|
||
|
|
"ctc_merge_repeated", _op._get_attr_bool("ctc_merge_repeated"),
|
||
|
|
"ignore_longer_outputs_than_inputs",
|
||
|
|
_op._get_attr_bool("ignore_longer_outputs_than_inputs"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"CTCLossV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result = _CTCLossV2Output._make(_result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
CTCLossV2 = tf_export("raw_ops.CTCLossV2")(_ops.to_raw_op(ctc_loss_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def ctc_loss_v2_eager_fallback(inputs: Annotated[Any, _atypes.Float32], labels_indices: Annotated[Any, _atypes.Int64], labels_values: Annotated[Any, _atypes.Int32], sequence_length: Annotated[Any, _atypes.Int32], preprocess_collapse_repeated: bool, ctc_merge_repeated: bool, ignore_longer_outputs_than_inputs: bool, name, ctx):
|
||
|
|
if preprocess_collapse_repeated is None:
|
||
|
|
preprocess_collapse_repeated = False
|
||
|
|
preprocess_collapse_repeated = _execute.make_bool(preprocess_collapse_repeated, "preprocess_collapse_repeated")
|
||
|
|
if ctc_merge_repeated is None:
|
||
|
|
ctc_merge_repeated = True
|
||
|
|
ctc_merge_repeated = _execute.make_bool(ctc_merge_repeated, "ctc_merge_repeated")
|
||
|
|
if ignore_longer_outputs_than_inputs is None:
|
||
|
|
ignore_longer_outputs_than_inputs = False
|
||
|
|
ignore_longer_outputs_than_inputs = _execute.make_bool(ignore_longer_outputs_than_inputs, "ignore_longer_outputs_than_inputs")
|
||
|
|
inputs = _ops.convert_to_tensor(inputs, _dtypes.float32)
|
||
|
|
labels_indices = _ops.convert_to_tensor(labels_indices, _dtypes.int64)
|
||
|
|
labels_values = _ops.convert_to_tensor(labels_values, _dtypes.int32)
|
||
|
|
sequence_length = _ops.convert_to_tensor(sequence_length, _dtypes.int32)
|
||
|
|
_inputs_flat = [inputs, labels_indices, labels_values, sequence_length]
|
||
|
|
_attrs = ("preprocess_collapse_repeated", preprocess_collapse_repeated,
|
||
|
|
"ctc_merge_repeated", ctc_merge_repeated,
|
||
|
|
"ignore_longer_outputs_than_inputs", ignore_longer_outputs_than_inputs)
|
||
|
|
_result = _execute.execute(b"CTCLossV2", 2, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"CTCLossV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result = _CTCLossV2Output._make(_result)
|
||
|
|
return _result
|
||
|
|
|