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141 lines
5.8 KiB
141 lines
5.8 KiB
# 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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# TODO: define the extention functions
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__all__ = ['diag_embed']
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import numpy as np
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from ...fluid.data_feeder import check_dtype
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from ...fluid.layer_helper import LayerHelper
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from ...fluid.framework import Variable, in_dygraph_mode
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from ...fluid.layers.tensor import assign
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from ...fluid import core, dygraph_utils
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from ...fluid.layers.layer_function_generator import templatedoc
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def diag_embed(input, offset=0, dim1=-2, dim2=-1):
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"""
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This OP creates a tensor whose diagonals of certain 2D planes (specified by dim1 and dim2)
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are filled by ``input``. By default, a 2D plane formed by the last two dimensions
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of the returned tensor will be selected.
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The argument ``offset`` determines which diagonal is generated:
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- If offset = 0, it is the main diagonal.
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- If offset > 0, it is above the main diagonal.
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- If offset < 0, it is below the main diagonal.
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Args:
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input(Tensor|numpy.ndarray): The input tensor. Must be at least 1-dimensional. The input data type should be float32, float64, int32, int64.
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offset(int, optional): Which diagonal to consider. Default: 0 (main diagonal).
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dim1(int, optional): The first dimension with respect to which to take diagonal. Default: -2.
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dim2(int, optional): The second dimension with respect to which to take diagonal. Default: -1.
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Returns:
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Tensor, the output data type is the same as input data type.
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Examples:
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.. code-block:: python
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import paddle.nn.functional as F
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import numpy as np
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diag_embed = np.random.randn(2, 3).astype('float32')
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# [[ 0.7545889 , -0.25074545, 0.5929117 ],
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# [-0.6097662 , -0.01753256, 0.619769 ]]
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data1 = F.diag_embed(diag_embed)
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data1.numpy()
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# [[[ 0.7545889 , 0. , 0. ],
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# [ 0. , -0.25074545, 0. ],
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# [ 0. , 0. , 0.5929117 ]],
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# [[-0.6097662 , 0. , 0. ],
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# [ 0. , -0.01753256, 0. ],
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# [ 0. , 0. , 0.619769 ]]]
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data2 = F.diag_embed(diag_embed, offset=-1, dim1=0, dim2=2)
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data2.numpy()
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# [[[ 0. , 0. , 0. , 0. ],
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# [ 0.7545889 , 0. , 0. , 0. ],
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# [ 0. , -0.25074545, 0. , 0. ],
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# [ 0. , 0. , 0.5929117 , 0. ]],
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#
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# [[ 0. , 0. , 0. , 0. ],
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# [-0.6097662 , 0. , 0. , 0. ],
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# [ 0. , -0.01753256, 0. , 0. ],
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# [ 0. , 0. , 0.619769 , 0. ]]]
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data3 = F.diag_embed(diag_embed, offset=1, dim1=0, dim2=2)
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data3.numpy()
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# [[[ 0. , 0.7545889 , 0. , 0. ],
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# [ 0. , -0.6097662 , 0. , 0. ]],
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#
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# [[ 0. , 0. , -0.25074545, 0. ],
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# [ 0. , 0. , -0.01753256, 0. ]],
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#
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# [[ 0. , 0. , 0. , 0.5929117 ],
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# [ 0. , 0. , 0. , 0.619769 ]],
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#
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# [[ 0. , 0. , 0. , 0. ],
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# [ 0. , 0. , 0. , 0. ]]]
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"""
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inputs = {'Input': [input]}
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attrs = {'offset': offset, 'dim1': dim1, 'dim2': dim2}
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if not isinstance(input, Variable):
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input = assign(input)
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def __check_input(input, offset, dim1, dim2):
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check_dtype(input.dtype, 'Input',
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['int32', 'int64', 'float16', 'float32', 'float64'],
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'diag_embed')
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input_shape = list(input.shape)
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assert len(input_shape) >= 1, \
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"Input must be at least 1-dimensional, " \
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"But received Input's dimensional: %s.\n" % \
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len(input_shape)
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assert np.abs(dim1) <= len(input_shape), \
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"Dim1 is out of range (expected to be in range of [%d, %d], but got %d).\n" \
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% (-(len(input_shape) + 1), len(input_shape), dim1)
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assert np.abs(dim2) <= len(input_shape), \
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"Dim2 is out of range (expected to be in range of [%d, %d], but got %d).\n" \
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% (-(len(input_shape) + 1), len(input_shape), dim2)
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dim1_ = dim1 if dim1 >= 0 else len(input_shape) + dim1 + 1
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dim2_ = dim2 if dim2 >= 0 else len(input_shape) + dim2 + 1
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assert dim1_ != dim2_, \
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"dim1 and dim2 cannot be the same dimension." \
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"But received dim1 = %d, dim2 = %d\n"%(dim1, dim2)
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if not in_dygraph_mode():
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__check_input(input, offset, dim1, dim2)
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helper = LayerHelper("diag_embed", **locals())
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out = helper.create_variable_for_type_inference(dtype=input.dtype)
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helper.append_op(
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type='diag_embed',
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inputs={'Input': [input]},
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attrs={'offset': offset,
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'dim1': dim1,
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'dim2': dim2},
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outputs={'Out': [out]})
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out.stop_gradient = True
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return out
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