remove complex module direction (#29419)
chentianyu03
5 years ago
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GitHub
parent
6296f4ed09
commit
acce962133
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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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from . import tensor
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from .tensor_op_patch import monkey_patch_math_complex
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from .tensor import *
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__all__ = tensor.__all__ + []
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monkey_patch_math_complex()
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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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from ...fluid import framework
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def is_complex(x):
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"""
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Return true if the input(x) is a ComplexVariable.
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"""
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return isinstance(x, framework.ComplexVariable)
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def is_real(x):
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"""
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Return true if the input(x) is a real number Variable.
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"""
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return isinstance(x, framework.Variable)
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def complex_variable_exists(inputs, layer_name):
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for inp in inputs:
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if is_complex(inp):
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return
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err_msg = "At least one inputs of layer complex." if len(inputs) > 1 \
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else "The input of layer complex."
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raise ValueError(err_msg + layer_name +
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"() must be ComplexVariable, please "
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"use the layer for real numher instead.")
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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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from . import linalg
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from . import math
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from . import manipulation
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from .linalg import *
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from .math import *
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from .manipulation import *
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__all__ = math.__all__
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__all__ += linalg.__all__
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__all__ += manipulation.__all__
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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 ..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__ = ['matmul', ]
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def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
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"""
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Applies matrix multiplication to two complex number tensors. See the
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detailed description in :ref:`api_fluid_layers_matmul`.
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Args:
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x (ComplexVariable|Variable): The first input, can be a ComplexVariable
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with data type complex64 or complex128, or a Variable with data type
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float32 or float64.
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y (ComplexVariable|Variable): The second input, can be a ComplexVariable
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with data type complex64 or complex128, or a Variable with data type
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float32 or float64.
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transpose_x (bool): Whether to transpose :math:`x` before multiplication.
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transpose_y (bool): Whether to transpose :math:`y` before multiplication.
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alpha (float): The scale of output. Default 1.0.
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name(str|None): A name for this layer(optional). If set None, the layer
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will be named automatically.
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Returns:
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ComplexVariable: The product result, with the same data type as inputs.
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Examples:
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.. code-block:: python
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import numpy as np
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import paddle
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import paddle.fluid.dygraph as dg
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with dg.guard():
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x = np.array([[1.0 + 1j, 2.0 + 1j], [3.0+1j, 4.0+1j]])
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y = np.array([1.0 + 1j, 1.0 + 1j])
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x_var = dg.to_variable(x)
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y_var = dg.to_variable(y)
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result = paddle.complex.matmul(x_var, y_var)
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print(result.numpy())
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# [1.+5.j 5.+9.j]
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"""
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# x = a + bi, y = c + di
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# P1 = ac; P2 = (a + b)(c + d); P3 = bd; then mm(x, y) = (P1-P3) + (P2-P1-P3)j
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complex_variable_exists([x, y], "matmul")
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a, b = (x.real, x.imag) if is_complex(x) else (x, None)
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c, d = (y.real, y.imag) if is_complex(y) else (y, None)
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P1 = layers.matmul(a, c, transpose_x, transpose_y, alpha, name)
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if is_real(b) and is_real(d):
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P2 = layers.matmul(a + b, c + d, transpose_x, transpose_y, alpha, name)
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P3 = layers.matmul(b, d, transpose_x, transpose_y, alpha, name)
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real = P1 - P3
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imag = P2 - P1 - P3
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elif is_real(b):
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real = P1
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imag = layers.matmul(b, c, transpose_x, transpose_y, alpha, name)
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else:
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real = P1
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imag = layers.matmul(a, d, transpose_x, transpose_y, alpha, name)
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return ComplexVariable(real, imag)
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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__ = [
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'reshape',
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'transpose',
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]
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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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ComplexVariable: 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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def transpose(x, perm, name=None):
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"""
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Permute the data dimensions for complex number :attr:`input` according to `perm`.
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See :ref:`api_fluid_layers_transpose` for the real number API.
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Args:
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x (ComplexVariable): The input n-D ComplexVariable with data type
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complex64 or complex128.
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perm (list): Permute the input according to the value of perm.
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name (str): The name of this layer. It is optional.
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Returns:
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ComplexVariable: A transposed n-D ComplexVariable, with the same data type as :attr:`input`.
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Examples:
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.. code-block:: python
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import paddle
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x = paddle.to_tensor([[1.0 + 1.0j, 2.0 + 1.0j], [3.0+1.0j, 4.0+1.0j], [5.0+1.0j, 6.0+1.0j]])
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x_transposed = paddle.complex.transpose(x, [1, 0])
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print(x_transposed.numpy())
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#[[1.+1.j 3.+1.j 5.+1.j]
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# [2.+1.j 4.+1.j 6.+1.j]]
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"""
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complex_variable_exists([x], "transpose")
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real = layers.transpose(x.real, perm, name)
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imag = layers.transpose(x.imag, perm, name)
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return ComplexVariable(real, imag)
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File diff suppressed because it is too large
Load Diff
@ -1,53 +0,0 @@
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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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from __future__ import division
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from ...fluid import framework
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from . import tensor
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def monkey_patch_math_complex():
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# complexVariable do not support scaler type now, so here not contains
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# reverse methods, such as "__radd__", "__rsub__", "__rmul__", "__rdiv__",
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# "__rtruediv__", "__rmatmul__".
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complex_methods = [
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('__add__', _binary_creator_('__add__', "elementwise_add", False)),
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('__sub__', _binary_creator_('__sub__', "elementwise_sub", False)),
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('__mul__', _binary_creator_('__mul__', "elementwise_mul", False)),
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('__div__', _binary_creator_('__div__', "elementwise_div", False)),
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('__truediv__', _binary_creator_('__truediv__', "elementwise_div",
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False)),
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('__matmul__', _binary_creator_('__matmul__', "matmul", False)),
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]
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for method in complex_methods:
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method_name = method[0]
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method_impl = method[1]
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if method_impl:
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setattr(framework.ComplexVariable, method_name, method_impl)
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for method in tensor.__all__:
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method_impl = getattr(tensor, method)
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if method_impl:
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setattr(framework.ComplexVariable, method, method_impl)
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# for binary operator such as elementwise
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def _binary_creator_(method_name, op_type, reverse=False):
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def __impl__(self, other_var):
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math_op = getattr(tensor, op_type)
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return math_op(self, other_var)
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__impl__.__name__ = method_name
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return __impl__
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