-
Notifications
You must be signed in to change notification settings - Fork 25.2k
/
configuration_upernet.py
138 lines (119 loc) 路 6.56 KB
/
configuration_upernet.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
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. 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.
""" UperNet model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
logger = logging.get_logger(__name__)
class UperNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`UperNetForSemanticSegmentation`]. It is used to
instantiate an UperNet model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the UperNet
[openmmlab/upernet-convnext-tiny](https://huggingface.co/openmmlab/upernet-convnext-tiny) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
backbone_config (`PretrainedConfig` or `dict`, *optional*, defaults to `ResNetConfig()`):
The configuration of the backbone model.
backbone (`str`, *optional*):
Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
use_pretrained_backbone (`bool`, *optional*, `False`):
Whether to use pretrained weights for the backbone.
use_timm_backbone (`bool`, *optional*, `False`):
Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
library.
backbone_kwargs (`dict`, *optional*):
Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
hidden_size (`int`, *optional*, defaults to 512):
The number of hidden units in the convolutional layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
pool_scales (`Tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`):
Pooling scales used in Pooling Pyramid Module applied on the last feature map.
use_auxiliary_head (`bool`, *optional*, defaults to `True`):
Whether to use an auxiliary head during training.
auxiliary_loss_weight (`float`, *optional*, defaults to 0.4):
Weight of the cross-entropy loss of the auxiliary head.
auxiliary_channels (`int`, *optional*, defaults to 256):
Number of channels to use in the auxiliary head.
auxiliary_num_convs (`int`, *optional*, defaults to 1):
Number of convolutional layers to use in the auxiliary head.
auxiliary_concat_input (`bool`, *optional*, defaults to `False`):
Whether to concatenate the output of the auxiliary head with the input before the classification layer.
loss_ignore_index (`int`, *optional*, defaults to 255):
The index that is ignored by the loss function.
Examples:
```python
>>> from transformers import UperNetConfig, UperNetForSemanticSegmentation
>>> # Initializing a configuration
>>> configuration = UperNetConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = UperNetForSemanticSegmentation(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "upernet"
def __init__(
self,
backbone_config=None,
backbone=None,
use_pretrained_backbone=False,
use_timm_backbone=False,
backbone_kwargs=None,
hidden_size=512,
initializer_range=0.02,
pool_scales=[1, 2, 3, 6],
use_auxiliary_head=True,
auxiliary_loss_weight=0.4,
auxiliary_in_channels=384,
auxiliary_channels=256,
auxiliary_num_convs=1,
auxiliary_concat_input=False,
loss_ignore_index=255,
**kwargs,
):
super().__init__(**kwargs)
if use_pretrained_backbone:
raise ValueError("Pretrained backbones are not supported yet.")
if backbone_config is not None and backbone is not None:
raise ValueError("You can't specify both `backbone` and `backbone_config`.")
if backbone_config is None and backbone is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"])
elif isinstance(backbone_config, dict):
backbone_model_type = backbone_config.get("model_type")
config_class = CONFIG_MAPPING[backbone_model_type]
backbone_config = config_class.from_dict(backbone_config)
if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
self.backbone_config = backbone_config
self.backbone = backbone
self.use_pretrained_backbone = use_pretrained_backbone
self.use_timm_backbone = use_timm_backbone
self.backbone_kwargs = backbone_kwargs
self.hidden_size = hidden_size
self.initializer_range = initializer_range
self.pool_scales = pool_scales
self.use_auxiliary_head = use_auxiliary_head
self.auxiliary_loss_weight = auxiliary_loss_weight
self.auxiliary_in_channels = auxiliary_in_channels
self.auxiliary_channels = auxiliary_channels
self.auxiliary_num_convs = auxiliary_num_convs
self.auxiliary_concat_input = auxiliary_concat_input
self.loss_ignore_index = loss_ignore_index