53 lines
2.1 KiB
Python
53 lines
2.1 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Dynamic shape for structured Tensors."""
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops.ragged import dynamic_ragged_shape
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from tensorflow.python.ops.structured.structured_tensor import _find_shape_dtype
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# pylint:disable=protected-access
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def _dynamic_ragged_shape_init(fields, shape, nrows, row_partitions):
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"""Produce a DynamicRaggedShape for StructuredTensor."""
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assert isinstance(fields, dict), fields
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assert isinstance(shape, tensor_shape.TensorShape), shape
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assert nrows is None or isinstance(nrows, ops.Tensor), nrows
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assert isinstance(row_partitions, tuple), row_partitions
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rank = shape.rank
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if rank is None:
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raise TypeError("StructuredTensor's shape must have known rank.")
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# TODO(martinz): figure out whether to validate.
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dtype = _find_shape_dtype(fields, nrows, row_partitions)
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if rank == 0:
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return dynamic_ragged_shape.DynamicRaggedShape._from_inner_shape(
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array_ops.zeros((0,), dtype=dtype))
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if rank == 1:
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alt_value = shape[0]
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if isinstance(alt_value, tensor_shape.Dimension):
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alt_value = alt_value.value
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if alt_value is not None:
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nrows = alt_value
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return dynamic_ragged_shape.DynamicRaggedShape._from_inner_shape(
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[nrows], dtype=dtype)
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return dynamic_ragged_shape.DynamicRaggedShape.from_row_partitions(
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row_partitions, dtype=dtype)
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