未验证 提交 52a34649 编写于 作者: J JYChen 提交者: GitHub

remove paddle.fluid.layers.reduce_prod (#47525)

上级 30c7758f
...@@ -19,6 +19,7 @@ from . import nn ...@@ -19,6 +19,7 @@ from . import nn
import math import math
import numpy as np import numpy as np
import warnings import warnings
import paddle
from ..data_feeder import ( from ..data_feeder import (
convert_dtype, convert_dtype,
...@@ -660,7 +661,7 @@ class MultivariateNormalDiag(Distribution): ...@@ -660,7 +661,7 @@ class MultivariateNormalDiag(Distribution):
one_diag = tensor.diag( one_diag = tensor.diag(
tensor.ones(shape=[batch_shape[0]], dtype=self.loc.dtype) tensor.ones(shape=[batch_shape[0]], dtype=self.loc.dtype)
) )
det_diag = nn.reduce_prod(value + one_all - one_diag) det_diag = paddle.prod(value + one_all - one_diag)
return det_diag return det_diag
......
...@@ -86,7 +86,6 @@ __all__ = [ ...@@ -86,7 +86,6 @@ __all__ = [
'reduce_mean', 'reduce_mean',
'reduce_max', 'reduce_max',
'reduce_min', 'reduce_min',
'reduce_prod',
'reduce_all', 'reduce_all',
'reduce_any', 'reduce_any',
'dropout', 'dropout',
...@@ -5323,97 +5322,6 @@ def reduce_min(input, dim=None, keep_dim=False, name=None): ...@@ -5323,97 +5322,6 @@ def reduce_min(input, dim=None, keep_dim=False, name=None):
return out return out
def reduce_prod(input, dim=None, keep_dim=False, name=None):
"""
Computes the product of tensor elements over the given dimension.
Args:
input (Variable): The input variable which is a Tensor, the data type is float32,
float64, int32, int64.
dim (int|list|tuple, optional): The dimensions along which the product is performed. If
:attr:`None`, multiply all elements of :attr:`input` and return a
Tensor variable with a single element, otherwise must be in the
range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
the dimension to reduce is :math:`rank + dim[i]`.
keep_dim (bool, optional): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true, default
value is False.
name(str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name`
Returns:
Variable: Tensor, result of product on the specified dim of input tensor,
it's data type is the same as input's Tensor.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
# x is a Tensor variable with following elements:
# [[0.2, 0.3, 0.5, 0.9]
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the corresponding output tensor.
x = fluid.data(name='x', shape=[2, 4], dtype='float32')
fluid.layers.reduce_prod(x) # [0.0002268]
fluid.layers.reduce_prod(x, dim=0) # [0.02, 0.06, 0.3, 0.63]
fluid.layers.reduce_prod(x, dim=-1) # [0.027, 0.0084]
fluid.layers.reduce_prod(x, dim=1,
keep_dim=True) # [[0.027], [0.0084]]
# y is a Tensor variable with shape [2, 2, 2] and elements as below:
# [[[1.0, 2.0], [3.0, 4.0]],
# [[5.0, 6.0], [7.0, 8.0]]]
# Each example is followed by the corresponding output tensor.
y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
fluid.layers.reduce_prod(y, dim=[1, 2]) # [24.0, 1680.0]
fluid.layers.reduce_prod(y, dim=[0, 1]) # [105.0, 384.0]
"""
if dim is not None and not isinstance(dim, list):
if isinstance(dim, tuple):
dim = list(dim)
elif isinstance(dim, int):
dim = [dim]
else:
raise TypeError(
"The type of axis must be int, list or tuple, but received {}".format(
type(dim)
)
)
if in_dygraph_mode():
return _C_ops.prod(
input,
dim if dim is not None and dim != [] else [0],
keep_dim,
True
if dim is None or dim == [] or len(dim) == len(input.shape)
else False,
)
helper = LayerHelper('reduce_prod', **locals())
check_variable_and_dtype(
input, 'input', ['float32', 'float64', 'int32', 'int64'], 'reduce_prod'
)
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.append_op(
type='reduce_prod',
inputs={'X': input},
outputs={'Out': out},
attrs={
'dim': dim if dim is not None and dim != [] else [0],
'keep_dim': keep_dim,
'reduce_all': True
if dim is None or dim == [] or len(dim) == len(input.shape)
else False,
},
)
return out
def reduce_all(input, dim=None, keep_dim=False, name=None): def reduce_all(input, dim=None, keep_dim=False, name=None):
""" """
......
...@@ -131,11 +131,6 @@ class TestMin(TestMean): ...@@ -131,11 +131,6 @@ class TestMin(TestMean):
self.op = paddle.fluid.layers.reduce_min self.op = paddle.fluid.layers.reduce_min
class TestProd(TestMean):
def set_test_op(self):
self.op = paddle.fluid.layers.reduce_prod
class TestSum(TestMean): class TestSum(TestMean):
def set_test_op(self): def set_test_op(self):
self.op = paddle.fluid.layers.reduce_sum self.op = paddle.fluid.layers.reduce_sum
......
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