452 lines
21 KiB
Python
452 lines
21 KiB
Python
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"""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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_DenseToDenseSetOperationOutput = collections.namedtuple(
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"DenseToDenseSetOperation",
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["result_indices", "result_values", "result_shape"])
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def dense_to_dense_set_operation(set1, set2, set_operation, validate_indices=True, name=None):
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r"""Applies set operation along last dimension of 2 `Tensor` inputs.
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See SetOperationOp::SetOperationFromContext for values of `set_operation`.
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Output `result` is a `SparseTensor` represented by `result_indices`,
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`result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this
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has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth`
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dimension contains the result of `set_operation` applied to the corresponding
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`[0...n-1]` dimension of `set`.
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Args:
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set1: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `string`.
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`Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.
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Dimension `n` contains values in a set, duplicates are allowed but ignored.
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set2: A `Tensor`. Must have the same type as `set1`.
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`Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set1`.
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Dimension `n` contains values in a set, duplicates are allowed but ignored.
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set_operation: A `string`.
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validate_indices: An optional `bool`. Defaults to `True`.
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name: A name for the operation (optional).
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Returns:
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A tuple of `Tensor` objects (result_indices, result_values, result_shape).
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result_indices: A `Tensor` of type `int64`.
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result_values: A `Tensor`. Has the same type as `set1`.
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result_shape: A `Tensor` of type `int64`.
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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, "DenseToDenseSetOperation", name, set1, set2, "set_operation",
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set_operation, "validate_indices", validate_indices)
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_result = _DenseToDenseSetOperationOutput._make(_result)
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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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return dense_to_dense_set_operation_eager_fallback(
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set1, set2, set_operation=set_operation,
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validate_indices=validate_indices, 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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# Add nodes to the TensorFlow graph.
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set_operation = _execute.make_str(set_operation, "set_operation")
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if validate_indices is None:
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validate_indices = True
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validate_indices = _execute.make_bool(validate_indices, "validate_indices")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"DenseToDenseSetOperation", set1=set1, set2=set2,
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set_operation=set_operation,
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validate_indices=validate_indices,
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name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("set_operation", _op.get_attr("set_operation"),
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"validate_indices", _op._get_attr_bool("validate_indices"), "T",
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_op._get_attr_type("T"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"DenseToDenseSetOperation", _inputs_flat, _attrs, _result)
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_result = _DenseToDenseSetOperationOutput._make(_result)
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return _result
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DenseToDenseSetOperation = tf_export("raw_ops.DenseToDenseSetOperation")(_ops.to_raw_op(dense_to_dense_set_operation))
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def dense_to_dense_set_operation_eager_fallback(set1, set2, set_operation, validate_indices, name, ctx):
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set_operation = _execute.make_str(set_operation, "set_operation")
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if validate_indices is None:
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validate_indices = True
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validate_indices = _execute.make_bool(validate_indices, "validate_indices")
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_attr_T, _inputs_T = _execute.args_to_matching_eager([set1, set2], ctx, [_dtypes.int8, _dtypes.int16, _dtypes.int32, _dtypes.int64, _dtypes.uint8, _dtypes.uint16, _dtypes.string, ])
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(set1, set2) = _inputs_T
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_inputs_flat = [set1, set2]
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_attrs = ("set_operation", set_operation, "validate_indices",
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validate_indices, "T", _attr_T)
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_result = _execute.execute(b"DenseToDenseSetOperation", 3,
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inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
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name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"DenseToDenseSetOperation", _inputs_flat, _attrs, _result)
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_result = _DenseToDenseSetOperationOutput._make(_result)
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return _result
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_DenseToSparseSetOperationOutput = collections.namedtuple(
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"DenseToSparseSetOperation",
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["result_indices", "result_values", "result_shape"])
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def dense_to_sparse_set_operation(set1, set2_indices, set2_values, set2_shape, set_operation, validate_indices=True, name=None):
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r"""Applies set operation along last dimension of `Tensor` and `SparseTensor`.
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See SetOperationOp::SetOperationFromContext for values of `set_operation`.
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Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`,
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and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same
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as `set1`. Dimension `n` contains values in a set, duplicates are allowed but
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ignored.
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If `validate_indices` is `True`, this op validates the order and range of `set2`
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indices.
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Output `result` is a `SparseTensor` represented by `result_indices`,
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`result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this
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has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth`
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dimension contains the result of `set_operation` applied to the corresponding
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`[0...n-1]` dimension of `set`.
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Args:
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set1: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `string`.
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`Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.
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Dimension `n` contains values in a set, duplicates are allowed but ignored.
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set2_indices: A `Tensor` of type `int64`.
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2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
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order.
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set2_values: A `Tensor`. Must have the same type as `set1`.
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1D `Tensor`, values of a `SparseTensor`. Must be in row-major
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order.
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set2_shape: A `Tensor` of type `int64`.
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1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must
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be the same as the 1st `n-1` dimensions of `set1`, `result_shape[n]` is the
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max set size across `n-1` dimensions.
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set_operation: A `string`.
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validate_indices: An optional `bool`. Defaults to `True`.
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name: A name for the operation (optional).
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Returns:
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A tuple of `Tensor` objects (result_indices, result_values, result_shape).
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result_indices: A `Tensor` of type `int64`.
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result_values: A `Tensor`. Has the same type as `set1`.
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result_shape: A `Tensor` of type `int64`.
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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, "DenseToSparseSetOperation", name, set1, set2_indices,
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set2_values, set2_shape, "set_operation", set_operation,
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"validate_indices", validate_indices)
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_result = _DenseToSparseSetOperationOutput._make(_result)
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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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return dense_to_sparse_set_operation_eager_fallback(
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set1, set2_indices, set2_values, set2_shape,
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set_operation=set_operation, validate_indices=validate_indices,
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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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# Add nodes to the TensorFlow graph.
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set_operation = _execute.make_str(set_operation, "set_operation")
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if validate_indices is None:
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validate_indices = True
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validate_indices = _execute.make_bool(validate_indices, "validate_indices")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"DenseToSparseSetOperation", set1=set1, set2_indices=set2_indices,
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set2_values=set2_values,
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set2_shape=set2_shape,
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set_operation=set_operation,
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validate_indices=validate_indices,
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name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("set_operation", _op.get_attr("set_operation"),
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"validate_indices", _op._get_attr_bool("validate_indices"), "T",
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_op._get_attr_type("T"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"DenseToSparseSetOperation", _inputs_flat, _attrs, _result)
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_result = _DenseToSparseSetOperationOutput._make(_result)
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return _result
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DenseToSparseSetOperation = tf_export("raw_ops.DenseToSparseSetOperation")(_ops.to_raw_op(dense_to_sparse_set_operation))
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def dense_to_sparse_set_operation_eager_fallback(set1, set2_indices, set2_values, set2_shape, set_operation, validate_indices, name, ctx):
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set_operation = _execute.make_str(set_operation, "set_operation")
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if validate_indices is None:
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validate_indices = True
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validate_indices = _execute.make_bool(validate_indices, "validate_indices")
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_attr_T, _inputs_T = _execute.args_to_matching_eager([set1, set2_values], ctx, [_dtypes.int8, _dtypes.int16, _dtypes.int32, _dtypes.int64, _dtypes.uint8, _dtypes.uint16, _dtypes.string, ])
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(set1, set2_values) = _inputs_T
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set2_indices = _ops.convert_to_tensor(set2_indices, _dtypes.int64)
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set2_shape = _ops.convert_to_tensor(set2_shape, _dtypes.int64)
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_inputs_flat = [set1, set2_indices, set2_values, set2_shape]
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_attrs = ("set_operation", set_operation, "validate_indices",
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validate_indices, "T", _attr_T)
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_result = _execute.execute(b"DenseToSparseSetOperation", 3,
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inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
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name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"DenseToSparseSetOperation", _inputs_flat, _attrs, _result)
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_result = _DenseToSparseSetOperationOutput._make(_result)
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return _result
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def set_size(set_indices, set_values, set_shape, validate_indices=True, name=None):
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r"""Number of unique elements along last dimension of input `set`.
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Input `set` is a `SparseTensor` represented by `set_indices`, `set_values`,
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and `set_shape`. The last dimension contains values in a set, duplicates are
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allowed but ignored.
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If `validate_indices` is `True`, this op validates the order and range of `set`
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indices.
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Args:
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set_indices: A `Tensor` of type `int64`.
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2D `Tensor`, indices of a `SparseTensor`.
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set_values: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `string`.
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1D `Tensor`, values of a `SparseTensor`.
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set_shape: A `Tensor` of type `int64`.
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1D `Tensor`, shape of a `SparseTensor`.
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validate_indices: An optional `bool`. Defaults to `True`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor` of type `int32`.
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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, "SetSize", name, set_indices, set_values, set_shape,
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"validate_indices", validate_indices)
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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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return set_size_eager_fallback(
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set_indices, set_values, set_shape,
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validate_indices=validate_indices, 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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# Add nodes to the TensorFlow graph.
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if validate_indices is None:
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validate_indices = True
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validate_indices = _execute.make_bool(validate_indices, "validate_indices")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"SetSize", set_indices=set_indices, set_values=set_values,
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set_shape=set_shape, validate_indices=validate_indices,
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name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("validate_indices", _op._get_attr_bool("validate_indices"), "T",
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_op._get_attr_type("T"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"SetSize", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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SetSize = tf_export("raw_ops.SetSize")(_ops.to_raw_op(set_size))
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def set_size_eager_fallback(set_indices, set_values, set_shape, validate_indices, name, ctx):
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if validate_indices is None:
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validate_indices = True
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validate_indices = _execute.make_bool(validate_indices, "validate_indices")
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_attr_T, (set_values,) = _execute.args_to_matching_eager([set_values], ctx, [_dtypes.int8, _dtypes.int16, _dtypes.int32, _dtypes.int64, _dtypes.uint8, _dtypes.uint16, _dtypes.string, ])
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set_indices = _ops.convert_to_tensor(set_indices, _dtypes.int64)
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set_shape = _ops.convert_to_tensor(set_shape, _dtypes.int64)
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_inputs_flat = [set_indices, set_values, set_shape]
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|
_attrs = ("validate_indices", validate_indices, "T", _attr_T)
|
||
|
|
_result = _execute.execute(b"SetSize", 1, inputs=_inputs_flat, attrs=_attrs,
|
||
|
|
ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"SetSize", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
_SparseToSparseSetOperationOutput = collections.namedtuple(
|
||
|
|
"SparseToSparseSetOperation",
|
||
|
|
["result_indices", "result_values", "result_shape"])
|
||
|
|
|
||
|
|
|
||
|
|
def sparse_to_sparse_set_operation(set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, set_operation, validate_indices=True, name=None):
|
||
|
|
r"""Applies set operation along last dimension of 2 `SparseTensor` inputs.
|
||
|
|
|
||
|
|
See SetOperationOp::SetOperationFromContext for values of `set_operation`.
|
||
|
|
|
||
|
|
If `validate_indices` is `True`, `SparseToSparseSetOperation` validates the
|
||
|
|
order and range of `set1` and `set2` indices.
|
||
|
|
|
||
|
|
Input `set1` is a `SparseTensor` represented by `set1_indices`, `set1_values`,
|
||
|
|
and `set1_shape`. For `set1` ranked `n`, 1st `n-1` dimensions must be the same
|
||
|
|
as `set2`. Dimension `n` contains values in a set, duplicates are allowed but
|
||
|
|
ignored.
|
||
|
|
|
||
|
|
Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`,
|
||
|
|
and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same
|
||
|
|
as `set1`. Dimension `n` contains values in a set, duplicates are allowed but
|
||
|
|
ignored.
|
||
|
|
|
||
|
|
If `validate_indices` is `True`, this op validates the order and range of `set1`
|
||
|
|
and `set2` indices.
|
||
|
|
|
||
|
|
Output `result` is a `SparseTensor` represented by `result_indices`,
|
||
|
|
`result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this
|
||
|
|
has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth`
|
||
|
|
dimension contains the result of `set_operation` applied to the corresponding
|
||
|
|
`[0...n-1]` dimension of `set`.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
set1_indices: A `Tensor` of type `int64`.
|
||
|
|
2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
|
||
|
|
order.
|
||
|
|
set1_values: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `string`.
|
||
|
|
1D `Tensor`, values of a `SparseTensor`. Must be in row-major
|
||
|
|
order.
|
||
|
|
set1_shape: A `Tensor` of type `int64`.
|
||
|
|
1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must
|
||
|
|
be the same as `set2_shape[0...n-1]`, `set1_shape[n]` is the
|
||
|
|
max set size across `0...n-1` dimensions.
|
||
|
|
set2_indices: A `Tensor` of type `int64`.
|
||
|
|
2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
|
||
|
|
order.
|
||
|
|
set2_values: A `Tensor`. Must have the same type as `set1_values`.
|
||
|
|
1D `Tensor`, values of a `SparseTensor`. Must be in row-major
|
||
|
|
order.
|
||
|
|
set2_shape: A `Tensor` of type `int64`.
|
||
|
|
1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must
|
||
|
|
be the same as `set1_shape[0...n-1]`, `set2_shape[n]` is the
|
||
|
|
max set size across `0...n-1` dimensions.
|
||
|
|
set_operation: A `string`.
|
||
|
|
validate_indices: An optional `bool`. Defaults to `True`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tuple of `Tensor` objects (result_indices, result_values, result_shape).
|
||
|
|
|
||
|
|
result_indices: A `Tensor` of type `int64`.
|
||
|
|
result_values: A `Tensor`. Has the same type as `set1_values`.
|
||
|
|
result_shape: A `Tensor` of type `int64`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "SparseToSparseSetOperation", name, set1_indices, set1_values,
|
||
|
|
set1_shape, set2_indices, set2_values, set2_shape, "set_operation",
|
||
|
|
set_operation, "validate_indices", validate_indices)
|
||
|
|
_result = _SparseToSparseSetOperationOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return sparse_to_sparse_set_operation_eager_fallback(
|
||
|
|
set1_indices, set1_values, set1_shape, set2_indices, set2_values,
|
||
|
|
set2_shape, set_operation=set_operation,
|
||
|
|
validate_indices=validate_indices, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
set_operation = _execute.make_str(set_operation, "set_operation")
|
||
|
|
if validate_indices is None:
|
||
|
|
validate_indices = True
|
||
|
|
validate_indices = _execute.make_bool(validate_indices, "validate_indices")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"SparseToSparseSetOperation", set1_indices=set1_indices,
|
||
|
|
set1_values=set1_values,
|
||
|
|
set1_shape=set1_shape,
|
||
|
|
set2_indices=set2_indices,
|
||
|
|
set2_values=set2_values,
|
||
|
|
set2_shape=set2_shape,
|
||
|
|
set_operation=set_operation,
|
||
|
|
validate_indices=validate_indices,
|
||
|
|
name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("set_operation", _op.get_attr("set_operation"),
|
||
|
|
"validate_indices", _op._get_attr_bool("validate_indices"), "T",
|
||
|
|
_op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"SparseToSparseSetOperation", _inputs_flat, _attrs, _result)
|
||
|
|
_result = _SparseToSparseSetOperationOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
SparseToSparseSetOperation = tf_export("raw_ops.SparseToSparseSetOperation")(_ops.to_raw_op(sparse_to_sparse_set_operation))
|
||
|
|
|
||
|
|
|
||
|
|
def sparse_to_sparse_set_operation_eager_fallback(set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, set_operation, validate_indices, name, ctx):
|
||
|
|
set_operation = _execute.make_str(set_operation, "set_operation")
|
||
|
|
if validate_indices is None:
|
||
|
|
validate_indices = True
|
||
|
|
validate_indices = _execute.make_bool(validate_indices, "validate_indices")
|
||
|
|
_attr_T, _inputs_T = _execute.args_to_matching_eager([set1_values, set2_values], ctx, [_dtypes.int8, _dtypes.int16, _dtypes.int32, _dtypes.int64, _dtypes.uint8, _dtypes.uint16, _dtypes.string, ])
|
||
|
|
(set1_values, set2_values) = _inputs_T
|
||
|
|
set1_indices = _ops.convert_to_tensor(set1_indices, _dtypes.int64)
|
||
|
|
set1_shape = _ops.convert_to_tensor(set1_shape, _dtypes.int64)
|
||
|
|
set2_indices = _ops.convert_to_tensor(set2_indices, _dtypes.int64)
|
||
|
|
set2_shape = _ops.convert_to_tensor(set2_shape, _dtypes.int64)
|
||
|
|
_inputs_flat = [set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape]
|
||
|
|
_attrs = ("set_operation", set_operation, "validate_indices",
|
||
|
|
validate_indices, "T", _attr_T)
|
||
|
|
_result = _execute.execute(b"SparseToSparseSetOperation", 3,
|
||
|
|
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
|
||
|
|
name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"SparseToSparseSetOperation", _inputs_flat, _attrs, _result)
|
||
|
|
_result = _SparseToSparseSetOperationOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
|