add reshape in paddle.complex (#24176)
* add reshape in paddle.complex, test=develop * fix typos in paddle.complex.kron's comment, fix unittest, test=developrevert-24314-dev/fix_err_msg
Feiyu Chan
5 years ago
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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from paddle.common_ops_import import *
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from ..helper import is_complex, is_real, complex_variable_exists
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from ...fluid.framework import ComplexVariable
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from ...fluid import layers
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__all__ = ['reshape', ]
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def reshape(x, shape, inplace=False, name=None):
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"""
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To change the shape of ``x`` without changing its data.
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There are some tricks when specifying the target shape.
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1. -1 means the value of this dimension is inferred from the total element
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number of x and remaining dimensions. Thus one and only one dimension can
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be set -1.
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2. 0 means the actual dimension value is going to be copied from the
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corresponding dimension of x. The index of 0s in shape can not exceed
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the dimension of x.
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Here are some examples to explain it.
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1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
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is [6, 8], the reshape operator will transform x into a 2-D tensor with
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shape [6, 8] and leaving x's data unchanged.
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2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
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specified is [2, 3, -1, 2], the reshape operator will transform x into a
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4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this
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case, one dimension of the target shape is set to -1, the value of this
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dimension is inferred from the total element number of x and remaining
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dimensions.
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3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
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is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor
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with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case,
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besides -1, 0 means the actual dimension value is going to be copied from
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the corresponding dimension of x.
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Args:
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x(ComplexVariable): the input. A ``Tensor`` or ``LoDTensor`` , data
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type: ``complex64`` or ``complex128``.
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shape(list|tuple|Variable): target shape. At most one dimension of
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the target shape can be -1. If ``shape`` is a list or tuple, the
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elements of it should be integers or Tensors with shape [1] and
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data type ``int32``. If ``shape`` is an Variable, it should be
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an 1-D Tensor of data type ``int32``.
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inplace(bool, optional): If ``inplace`` is True, the output of
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``reshape`` is the same ComplexVariable as the input. Otherwise,
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the input and output of ``reshape`` are different
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ComplexVariables. Defaults to False. Note that if ``x``is more
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than one OPs' input, ``inplace`` must be False.
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name(str, optional): The default value is None. Normally there is no
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need for user to set this property. For more information, please
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refer to :ref:`api_guide_Name` .
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Returns:
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Variable: A ``Tensor`` or ``LoDTensor``. The data type is same as ``x``. It is a new ComplexVariable if ``inplace`` is ``False``, otherwise it is ``x``.
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Raises:
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ValueError: If more than one elements of ``shape`` is -1.
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ValueError: If the element of ``shape`` is 0, the corresponding dimension should be less than or equal to the dimension of ``x``.
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ValueError: If the elements in ``shape`` is negative except -1.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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import paddle.complex as cpx
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import paddle.fluid.dygraph as dg
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import numpy as np
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x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
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place = fluid.CPUPlace()
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with dg.guard(place):
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x_var = dg.to_variable(x_np)
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y_var = cpx.reshape(x_var, (2, -1))
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y_np = y_var.numpy()
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print(y_np.shape)
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# (2, 12)
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"""
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complex_variable_exists([x], "reshape")
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if inplace:
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x.real = fluid.layers.reshape(x.real, shape, inplace=inplace, name=name)
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x.imag = fluid.layers.reshape(x.imag, shape, inplace=inplace, name=name)
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return x
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out_real = fluid.layers.reshape(x.real, shape, inplace=inplace, name=name)
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out_imag = fluid.layers.reshape(x.imag, shape, inplace=inplace, name=name)
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return ComplexVariable(out_real, out_imag)
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@ -0,0 +1,51 @@
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# Copyright (c) 2020 PaddlePaddle 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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import paddle.fluid as fluid
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import paddle.complex as cpx
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import paddle.fluid.dygraph as dg
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import numpy as np
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import unittest
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class TestComplexReshape(unittest.TestCase):
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def test_case1(self):
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x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
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shape = (2, -1)
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place = fluid.CPUPlace()
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with dg.guard(place):
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x_var = dg.to_variable(x_np)
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y_var = cpx.reshape(x_var, shape)
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y_np = y_var.numpy()
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np.testing.assert_allclose(np.reshape(x_np, shape), y_np)
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def test_case2(self):
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x_np = np.random.randn(2, 3, 4) + 1j * np.random.randn(2, 3, 4)
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shape = (0, -1)
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shape_ = (2, 12)
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place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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with dg.guard(place):
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x_var = dg.to_variable(x_np)
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y_var = cpx.reshape(x_var, shape, inplace=True)
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y_np = y_var.numpy()
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np.testing.assert_allclose(np.reshape(x_np, shape_), y_np)
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if __name__ == "__main__":
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unittest.main()
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