228 lines
6.1 KiB
Python
228 lines
6.1 KiB
Python
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# Copyright 2015 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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"""Inplace operations.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_array_ops
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from tensorflow.python.ops import math_ops
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def _inplace_helper(x, i, v, op):
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"""Applies an inplace op on (x, i, v).
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op is one of gen_array_ops.alias_inplace_update,
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gen_array_ops.alias_inplace_add, or gen_array_ops.alias_inplace_sub.
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If i is None, x and v must be the same shape. Computes
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x op v;
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If i is a scalar, x has a rank 1 higher than v's. Computes
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x[i, :] op v;
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Otherwise, x and v must have the same rank. Computes
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x[i, :] op v;
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Args:
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x: A Tensor.
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i: None, a scalar or a vector.
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v: A Tensor.
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op: alias_inplace_update, alias_inplace_add, or alias_inplace_sub.
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Returns:
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Returns x.
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"""
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x = ops.convert_to_tensor(x)
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v = ops.convert_to_tensor(v, x.dtype)
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if i is None:
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# Full tensor.
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return array_ops.reshape(
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op(array_ops.reshape(x, [1, -1]), [0], array_ops.reshape(v, [1, -1])),
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array_ops.shape(x))
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i = math_ops.to_int32(i)
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if i.get_shape().ndims == 0:
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# Single 0-dim update.
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return op(x, array_ops.reshape(i, [1]), array_ops.expand_dims(v, 0))
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return op(x, i, v)
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def alias_inplace_update(x, i, v):
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"""Applies an inplace update on input x at index i with value v. Aliases x.
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If i is None, x and v must be the same shape. Computes
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x = v;
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If i is a scalar, x has a rank 1 higher than v's. Computes
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x[i, :] = v;
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Otherwise, x and v must have the same rank. Computes
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x[i, :] = v;
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Args:
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x: A Tensor.
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i: None, a scalar or a vector.
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v: A Tensor.
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Returns:
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Returns x.
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"""
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return _inplace_helper(x, i, v, gen_array_ops.inplace_update)
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def alias_inplace_add(x, i, v):
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"""Applies an inplace add on input x at index i with value v. Aliases x.
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If i is None, x and v must be the same shape. Computes
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x += v;
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If i is a scalar, x has a rank 1 higher than v's. Computes
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x[i, :] += v;
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Otherwise, x and v must have the same rank. Computes
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x[i, :] += v;
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Args:
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x: A Tensor.
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i: None, a scalar or a vector.
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v: A Tensor.
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Returns:
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Returns x.
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"""
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return _inplace_helper(x, i, v, gen_array_ops.inplace_add)
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def alias_inplace_sub(x, i, v):
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"""Applies an inplace sub on input x at index i with value v. Aliases x.
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If i is None, x and v must be the same shape. Computes
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x -= v;
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If i is a scalar, x has a rank 1 higher than v's. Computes
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x[i, :] -= v;
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Otherwise, x and v must have the same rank. Computes
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x[i, :] -= v;
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Args:
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x: A Tensor.
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i: None, a scalar or a vector.
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v: A Tensor.
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Returns:
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Returns x.
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"""
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return _inplace_helper(x, i, v, gen_array_ops.inplace_sub)
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def empty_like(x, init=None):
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"""Returns a non-initialized tensor with the same shape and dtype as x.
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Args:
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x: A Tensor.
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init: Initialize the returned tensor with the default value of
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x.dtype(), if True. Otherwise, do not initialize. Defaults to
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None.
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Returns:
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A tensor y, whose dtype and shape are the same as those of x.
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y is guaranteed not to be an alias of x. Upon return, y may contain
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arbitrary data.
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"""
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x = ops.convert_to_tensor(x)
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return gen_array_ops.empty(array_ops.shape(x), x.dtype, init=init)
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def inplace_update(x, i, v):
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"""Applies an inplace update on input x at index i with value v.
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Note that this function is not actually inplace - it allocates
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a copy of x. The utility is not avoiding memory copies but rather
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specifying a sparse update.
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If i is None, x and v must be the same shape. Computes
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y = x; y = v;
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If i is a scalar, x has a rank 1 higher than v's. Computes
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y = x; y[i, :] = v;
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Otherwise, x and v must have the same rank. Computes
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y = x; y[i, :] = v;
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Args:
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x: A Tensor.
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i: None, a scalar or a vector.
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v: A Tensor.
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Returns:
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Returns y, which is guaranteed not to be an alias of x.
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"""
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return alias_inplace_update(gen_array_ops.deep_copy(x), i, v)
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def inplace_add(x, i, v):
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"""Applies an inplace add on input x at index i with value v.
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Note that this function is not actually inplace - it allocates
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a copy of x. The utility is not avoiding memory copies but rather
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specifying a sparse update.
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If i is None, x and v must be the same shape. Computes
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y = x; y += v;
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If i is a scalar, x has a rank 1 higher than v's. Computes
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y = x; y[i, :] += v;
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Otherwise, x and v must have the same rank. Computes
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y = x; y[i, :] += v;
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Args:
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x: A Tensor.
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i: None, a scalar or a vector.
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v: A Tensor.
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Returns:
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Returns y, which is guaranteed not to be an alias of x.
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"""
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return alias_inplace_add(gen_array_ops.deep_copy(x), i, v)
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def inplace_sub(x, i, v):
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"""Applies an inplace sub on input x at index i with value v.
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Note that this function is not actually inplace - it allocates
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a copy of x. The utility is not avoiding memory copies but rather
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specifying a sparse update.
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If i is None, x and v must be the same shape. Computes
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y = x; y -= v;
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If i is a scalar, x has a rank 1 higher than v's. Computes
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y = x; y[i, :] -= v;
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Otherwise, x and v must have the same rank. Computes
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y = x; y[i, :] -= v;
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Args:
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x: A Tensor.
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i: None, a scalar or a vector.
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v: A Tensor.
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Returns:
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Returns y, which is guaranteed not to be an alias of x.
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"""
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return alias_inplace_sub(gen_array_ops.deep_copy(x), i, v)
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empty = gen_array_ops.empty
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