-
Notifications
You must be signed in to change notification settings - Fork 34
/
_maximum.py
65 lines (55 loc) · 2.11 KB
/
_maximum.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
# Copyright 2019 The FastEstimator 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 typing import TypeVar
import numpy as np
import tensorflow as tf
import torch
Tensor = TypeVar('Tensor', tf.Tensor, torch.Tensor, np.ndarray)
def maximum(tensor1: Tensor, tensor2: Tensor) -> Tensor:
"""Get the maximum of the given `tensors`.
This method can be used with Numpy data:
```python
n1 = np.array([[2, 7, 6]])
n2 = np.array([[2, 7, 5]])
res = fe.backend.maximum(n1, n2) # [[2, 7, 6]]
```
This method can be used with TensorFlow tensors:
```python
t1 = tf.constant([[2, 7, 6]])
t2 = tf.constant([[2, 7, 5]])
res = fe.backend.maximum(t1, t2) # [[2, 7, 6]]
```
This method can be used with PyTorch tensors:
```python
p1 = torch.tensor([[2, 7, 6]])
p2 = torch.tensor([[2, 7, 5]])
res = fe.backend.maximum(p1, p2) # [[2, 7, 6]]
```
Args:
tensor1: First tensor.
tensor2: Second tensor.
Returns:
The maximum of two `tensors`.
Raises:
ValueError: If `tensor` is an unacceptable data type.
"""
if tf.is_tensor(tensor1) and tf.is_tensor(tensor2):
return tf.maximum(tensor1, tensor2)
elif isinstance(tensor1, torch.Tensor) and isinstance(tensor2, torch.Tensor):
return torch.max(tensor1, tensor2)
elif isinstance(tensor1, np.ndarray) and isinstance(tensor2, np.ndarray):
return np.maximum(tensor1, tensor2)
else:
raise ValueError("Unrecognized tensor type {}".format(type(tensor1)))