-
Notifications
You must be signed in to change notification settings - Fork 5.5k
/
math.py
245 lines (203 loc) · 8.94 KB
/
math.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
# Copyright (c) 2022 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 paddle import _C_ops
from paddle.fluid.data_feeder import check_variable_and_dtype
from paddle.fluid.layer_helper import LayerHelper
from paddle.framework import in_dynamic_mode
__all__ = []
def segment_sum(data, segment_ids, name=None):
r"""
Segment Sum Operator.
This operator sums the elements of input `data` which with
the same index in `segment_ids`.
It computes a tensor such that $out_i = \\sum_{j} data_{j}$
where sum is over j such that `segment_ids[j] == i`.
Args:
data (Tensor): A tensor, available data type float32, float64, int32, int64, float16.
segment_ids (Tensor): A 1-D tensor, which have the same size
with the first dimension of input data.
Available data type is int32, int64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
- output (Tensor), the reduced result.
Examples:
.. code-block:: python
import paddle
data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
out = paddle.geometric.segment_sum(data, segment_ids)
#Outputs: [[4., 4., 4.], [4., 5., 6.]]
"""
if in_dynamic_mode():
return _C_ops.segment_pool(data, segment_ids, "SUM")
else:
check_variable_and_dtype(
data,
"X",
("float32", "float64", "int32", "int64", "float16", "uint16"),
"segment_pool",
)
check_variable_and_dtype(
segment_ids, "SegmentIds", ("int32", "int64"), "segment_pool"
)
helper = LayerHelper("segment_sum", **locals())
out = helper.create_variable_for_type_inference(dtype=data.dtype)
summed_ids = helper.create_variable_for_type_inference(dtype=data.dtype)
helper.append_op(
type="segment_pool",
inputs={"X": data, "SegmentIds": segment_ids},
outputs={"Out": out, "SummedIds": summed_ids},
attrs={"pooltype": "SUM"},
)
return out
def segment_mean(data, segment_ids, name=None):
r"""
Segment mean Operator.
This operator calculate the mean value of input `data` which
with the same index in `segment_ids`.
It computes a tensor such that $out_i = \\frac{1}{n_i} \\sum_{j} data[j]$
where sum is over j such that 'segment_ids[j] == i' and $n_i$ is the number
of all index 'segment_ids[j] == i'.
Args:
data (tensor): a tensor, available data type float32, float64, int32, int64, float16.
segment_ids (tensor): a 1-d tensor, which have the same size
with the first dimension of input data.
available data type is int32, int64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
- output (Tensor), the reduced result.
Examples:
.. code-block:: python
import paddle
data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
out = paddle.geometric.segment_mean(data, segment_ids)
#Outputs: [[2., 2., 2.], [4., 5., 6.]]
"""
if in_dynamic_mode():
return _C_ops.segment_pool(data, segment_ids, "MEAN")
else:
check_variable_and_dtype(
data,
"X",
("float32", "float64", "int32", "int64", "float16", "uint16"),
"segment_pool",
)
check_variable_and_dtype(
segment_ids, "SegmentIds", ("int32", "int64"), "segment_pool"
)
helper = LayerHelper("segment_mean", **locals())
out = helper.create_variable_for_type_inference(dtype=data.dtype)
summed_ids = helper.create_variable_for_type_inference(dtype=data.dtype)
helper.append_op(
type="segment_pool",
inputs={"X": data, "SegmentIds": segment_ids},
outputs={"Out": out, "SummedIds": summed_ids},
attrs={"pooltype": "MEAN"},
)
return out
def segment_min(data, segment_ids, name=None):
r"""
Segment min operator.
This operator calculate the minimum elements of input `data` which with
the same index in `segment_ids`.
It computes a tensor such that $out_i = \\min_{j} data_{j}$
where min is over j such that `segment_ids[j] == i`.
Args:
data (tensor): a tensor, available data type float32, float64, int32, int64, float16.
segment_ids (tensor): a 1-d tensor, which have the same size
with the first dimension of input data.
available data type is int32, int64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
- output (Tensor), the reduced result.
Examples:
.. code-block:: python
import paddle
data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
out = paddle.geometric.segment_min(data, segment_ids)
#Outputs: [[1., 2., 1.], [4., 5., 6.]]
"""
if in_dynamic_mode():
return _C_ops.segment_pool(data, segment_ids, "MIN")
else:
check_variable_and_dtype(
data,
"X",
("float32", "float64", "int32", "int64", "float16", "uint16"),
"segment_pool",
)
check_variable_and_dtype(
segment_ids, "SegmentIds", ("int32", "int64"), "segment_pool"
)
helper = LayerHelper("segment_min", **locals())
out = helper.create_variable_for_type_inference(dtype=data.dtype)
summed_ids = helper.create_variable_for_type_inference(dtype=data.dtype)
helper.append_op(
type="segment_pool",
inputs={"X": data, "SegmentIds": segment_ids},
outputs={"Out": out, "SummedIds": summed_ids},
attrs={"pooltype": "MIN"},
)
return out
def segment_max(data, segment_ids, name=None):
r"""
Segment max operator.
This operator calculate the maximum elements of input `data` which with
the same index in `segment_ids`.
It computes a tensor such that $out_i = \\max_{j} data_{j}$
where max is over j such that `segment_ids[j] == i`.
Args:
data (tensor): a tensor, available data type float32, float64, int32, int64, float16.
segment_ids (tensor): a 1-d tensor, which have the same size
with the first dimension of input data.
available data type is int32, int64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
- output (Tensor), the reduced result.
Examples:
.. code-block:: python
import paddle
data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
out = paddle.geometric.segment_max(data, segment_ids)
#Outputs: [[3., 2., 3.], [4., 5., 6.]]
"""
if in_dynamic_mode():
return _C_ops.segment_pool(data, segment_ids, "MAX")
else:
check_variable_and_dtype(
data,
"X",
("float32", "float64", "int32", "int64", "float16", "uint16"),
"segment_pool",
)
check_variable_and_dtype(
segment_ids, "SegmentIds", ("int32", "int64"), "segment_pool"
)
helper = LayerHelper("segment_max", **locals())
out = helper.create_variable_for_type_inference(dtype=data.dtype)
summed_ids = helper.create_variable_for_type_inference(dtype=data.dtype)
helper.append_op(
type="segment_pool",
inputs={"X": data, "SegmentIds": segment_ids},
outputs={"Out": out, "SummedIds": summed_ids},
attrs={"pooltype": "MAX"},
)
return out