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