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Paddle/python/paddle/tensor/logic.py

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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..fluid.layer_helper import LayerHelper
from ..fluid.data_feeder import check_type
from ..fluid.layers.layer_function_generator import templatedoc
# TODO: define logic functions of a tensor
__all__ = [
'equal',
# 'greater_equal',
# 'greater_than',
# 'is_empty',
# 'isfinite',
# 'less_equal',
# 'less_than',
# 'logical_and',
# 'logical_not',
# 'logical_or',
# 'logical_xor',
# 'not_equal',
# 'reduce_all',
# 'reduce_any',
'allclose',
'elementwise_equal',
# 'isnan'
]
def equal(x, y, axis=-1, name=None):
"""
This OP returns the truth value of :math:`x == y`. True if two inputs have the same elements, False otherwise.
**NOTICE**: The output of this OP has no gradient, and this OP supports broadcasting by :attr:`axis`.
Args:
x(Variable): Tensor, data type is float32, float64, int32, int64.
y(Variable): Tensor, data type is float32, float64, int32, int64.
axis(int32, optional): If X.dimension != Y.dimension, Y.dimension
must be a subsequence of x.dimension. And axis is the start
dimension index for broadcasting Y onto X. For more detail,
please refer to OP:`elementwise_add`.
name(str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name`.Default: None.
Returns:
Variable: output Tensor, data type is bool, value is [False] or [True].
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
import numpy as np
label = fluid.layers.assign(np.array([3, 4], dtype="int32"))
label_1 = fluid.layers.assign(np.array([1, 2], dtype="int32"))
limit = fluid.layers.assign(np.array([3, 4], dtype="int32"))
out1 = paddle.equal(x=label, y=limit) #out1=[True]
out2 = paddle.equal(x=label_1, y=limit) #out2=[False]
.. code-block:: python
import paddle.fluid as fluid
import paddle
import numpy as np
def gen_data():
return {
"x": np.ones((2, 3, 4, 5)).astype('float32'),
"y": np.zeros((3, 4)).astype('float32')
}
x = fluid.data(name="x", shape=[2,3,4,5], dtype='float32')
y = fluid.data(name="y", shape=[3,4], dtype='float32')
out = paddle.equal(x, y, axis=1)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
res = exe.run(feed=gen_data(),
fetch_list=[out])
print(res[0]) #[False]
"""
helper = LayerHelper("equal_reduce", **locals())
out = helper.create_variable_for_type_inference(dtype='bool')
attrs = {}
attrs['axis'] = axis
helper.append_op(
type='equal_reduce',
inputs={'X': [x],
'Y': [y]},
attrs=attrs,
outputs={'Out': [out]})
return out
@templatedoc()
def allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False, name=None):
"""
${comment}
Args:
input(inputtype):{input_comment}.
other(othertype):{other_comment}.
rtol(rtoltype,optional):{rtol_comment}.
atol(atoltype,optional):{atol_comment}.
equal_nan(equalnantype,optional):{equal_nan_comment}.
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:
${out_comment}.
Return Type:
${out_type}
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
use_cuda = fluid.core.is_compiled_with_cuda()
a = fluid.data(name="a", shape=[2], dtype='float32')
b = fluid.data(name="b", shape=[2], dtype='float32')
result = paddle.allclose(a, b, rtol=1e-05, atol=1e-08,
equal_nan=False, name="ignore_nan")
result_nan = paddle.allclose(a, b, rtol=1e-05, atol=1e-08,
equal_nan=True, name="equal_nan")
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
x = np.array([10000., 1e-07]).astype("float32")
y = np.array([10000.1, 1e-08]).astype("float32")
result_v, result_nan_v = exe.run(
feed={'a': x, 'b': y},
fetch_list=[result, result_nan])
print(result_v, result_nan_v)
# Output: (array([False]), array([False]))
x = np.array([10000., 1e-08]).astype("float32")
y = np.array([10000.1, 1e-09]).astype("float32")
result_v, result_nan_v = exe.run(
feed={'a': x, 'b': y},
fetch_list=[result, result_nan])
print(result_v, result_nan_v)
# Output: (array([ True]), array([ True]))
x = np.array([1.0, float('nan')]).astype("float32")
y = np.array([1.0, float('nan')]).astype("float32")
result_v, result_nan_v = exe.run(
feed={'a': x, 'b': y},
fetch_list=[result, result_nan])
print(result_v, result_nan_v)
# Output: (array([False]), array([ True]))
"""
check_type(rtol, 'rtol', float, 'allclose')
check_type(atol, 'atol', float, 'allclose')
check_type(equal_nan, 'equal_nan', bool, 'allclose')
helper = LayerHelper("allclose", **locals())
out = helper.create_variable_for_type_inference(dtype='bool')
inputs = {'Input': input, 'Other': other}
outputs = {'Out': out}
attrs = {'rtol': rtol, 'atol': atol, 'equal_nan': equal_nan}
helper.append_op(
type='allclose', inputs=inputs, outputs=outputs, attrs=attrs)
return out
def elementwise_equal(x, y, name=None):
"""
This layer returns the truth value of :math:`x == y` elementwise.
Args:
x(Variable): Tensor, data type is float32, float64, int32, int64.
y(Variable): Tensor, data type is float32, float64, int32, int64.
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: output Tensor, it's shape is the same as the input's Tensor,
and the data type is bool. The result of this op is stop_gradient.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
label = fluid.layers.assign(np.array([3, 3], dtype="int32"))
limit = fluid.layers.assign(np.array([3, 2], dtype="int32"))
out1 = paddle.elementwise_equal(x=label, y=limit) #out1=[True, False]
"""
helper = LayerHelper("elementwise_equal", **locals())
out = helper.create_variable_for_type_inference(dtype='bool')
out.stop_gradient = True
helper.append_op(
type='equal',
inputs={'X': [x],
'Y': [y]},
outputs={'Out': [out]},
attrs={'force_cpu': False})
return out