EVOLUTION-MANAGER

Edit File: gen_ctc_ops.py

"""Python wrappers around TensorFlow ops. This file is MACHINE GENERATED! Do not edit. Original C++ source file: ctc_ops.cc """ 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.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 _CTCBeamSearchDecoderOutput = collections.namedtuple( "CTCBeamSearchDecoder", ["decoded_indices", "decoded_values", "decoded_shape", "log_probability"]) def ctc_beam_search_decoder(inputs, sequence_length, beam_width, top_paths, merge_repeated=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, sequence_length, beam_width, top_paths, merge_repeated, 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"]) def ctc_greedy_decoder(inputs, sequence_length, merge_repeated=False, 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. 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) _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, 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") _, _, _op, _outputs = _op_def_library._apply_op_helper( "CTCGreedyDecoder", inputs=inputs, sequence_length=sequence_length, merge_repeated=merge_repeated, name=name) _result = _outputs[:] if _execute.must_record_gradient(): _attrs = ("merge_repeated", _op._get_attr_bool("merge_repeated"), "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, sequence_length, merge_repeated, name, ctx): if merge_repeated is None: merge_repeated = False 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 = ("merge_repeated", merge_repeated, "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"]) def ctc_loss(inputs, labels_indices, labels_values, sequence_length, preprocess_collapse_repeated=False, ctc_merge_repeated=True, ignore_longer_outputs_than_inputs=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, labels_indices, labels_values, sequence_length, preprocess_collapse_repeated, ctc_merge_repeated, ignore_longer_outputs_than_inputs, 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, labels_indices, labels_values, sequence_length, preprocess_collapse_repeated=False, ctc_merge_repeated=True, ignore_longer_outputs_than_inputs=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, labels_indices, labels_values, sequence_length, preprocess_collapse_repeated, ctc_merge_repeated, ignore_longer_outputs_than_inputs, 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