This repository has been archived by the owner on Feb 22, 2020. It is now read-only.
/
base.py
75 lines (51 loc) · 2.21 KB
/
base.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
# Tencent is pleased to support the open source community by making GNES available.
#
# Copyright (C) 2019 THL A29 Limited, a Tencent company. 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.
# pylint: disable=low-comment-ratio
from typing import List, Any
import numpy as np
from ..base import TrainableBase, CompositionalTrainableBase
class BaseEncoder(TrainableBase):
def encode(self, data: Any, *args, **kwargs) -> Any:
pass
def _copy_from(self, x: 'BaseEncoder') -> None:
pass
class BaseImageEncoder(BaseEncoder):
def encode(self, img: List['np.ndarray'], *args, **kwargs) -> np.ndarray:
pass
class BaseTextEncoder(BaseEncoder):
def encode(self, text: List[str], *args, **kwargs) -> np.ndarray:
pass
class BaseNumericEncoder(BaseEncoder):
def encode(self, text: np.ndarray, *args, **kwargs) -> np.ndarray:
pass
class BaseBinaryEncoder(BaseEncoder):
def encode(self, data: np.ndarray, *args, **kwargs) -> bytes:
if data.dtype != np.uint8:
raise ValueError('data must be np.uint8 but received %s' % data.dtype)
return data.tobytes()
class PipelineEncoder(CompositionalTrainableBase):
def encode(self, data: Any, *args, **kwargs) -> Any:
if not self.component:
raise NotImplementedError
for be in self.component:
data = be.encode(data, *args, **kwargs)
return data
def train(self, data, *args, **kwargs):
if not self.component:
raise NotImplementedError
for idx, be in enumerate(self.component):
be.train(data, *args, **kwargs)
if idx + 1 < len(self.component):
data = be.encode(data, *args, **kwargs)