Spaces:
Runtime error
Runtime error
File size: 6,234 Bytes
1ce5e18 |
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 |
# 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.
""" BiT model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
logger = logging.get_logger(__name__)
BIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class BitConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BitModel`]. It is used to instantiate an BiT
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 BiT
[google/bit-50](https://huggingface.co/google/bit-50) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embedding_size (`int`, *optional*, defaults to 64):
Dimensionality (hidden size) for the embedding layer.
hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`):
Dimensionality (hidden size) at each stage.
depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`):
Depth (number of layers) for each stage.
layer_type (`str`, *optional*, defaults to `"preactivation"`):
The layer to use, it can be either `"preactivation"` or `"bottleneck"`.
hidden_act (`str`, *optional*, defaults to `"relu"`):
The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"`
are supported.
global_padding (`str`, *optional*):
Padding strategy to use for the convolutional layers. Can be either `"valid"`, `"same"`, or `None`.
num_groups (`int`, *optional*, defaults to 32):
Number of groups used for the `BitGroupNormActivation` layers.
drop_path_rate (`float`, *optional*, defaults to 0.0):
The drop path rate for the stochastic depth.
embedding_dynamic_padding (`bool`, *optional*, defaults to `False`):
Whether or not to make use of dynamic padding for the embedding layer.
output_stride (`int`, *optional*, defaults to 32):
The output stride of the model.
width_factor (`int`, *optional*, defaults to 1):
The width factor for the model.
out_features (`List[str]`, *optional*):
If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
corresponding stages. If unset and `out_indices` is unset, will default to the last stage.
out_indices (`List[int]`, *optional*):
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
If unset and `out_features` is unset, will default to the last stage.
Example:
```python
>>> from transformers import BitConfig, BitModel
>>> # Initializing a BiT bit-50 style configuration
>>> configuration = BitConfig()
>>> # Initializing a model (with random weights) from the bit-50 style configuration
>>> model = BitModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "bit"
layer_types = ["preactivation", "bottleneck"]
supported_padding = ["SAME", "VALID"]
def __init__(
self,
num_channels=3,
embedding_size=64,
hidden_sizes=[256, 512, 1024, 2048],
depths=[3, 4, 6, 3],
layer_type="preactivation",
hidden_act="relu",
global_padding=None,
num_groups=32,
drop_path_rate=0.0,
embedding_dynamic_padding=False,
output_stride=32,
width_factor=1,
out_features=None,
out_indices=None,
**kwargs,
):
super().__init__(**kwargs)
if layer_type not in self.layer_types:
raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types)}")
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
global_padding = global_padding.upper()
else:
raise ValueError(f"Padding strategy {global_padding} not supported")
self.num_channels = num_channels
self.embedding_size = embedding_size
self.hidden_sizes = hidden_sizes
self.depths = depths
self.layer_type = layer_type
self.hidden_act = hidden_act
self.global_padding = global_padding
self.num_groups = num_groups
self.drop_path_rate = drop_path_rate
self.embedding_dynamic_padding = embedding_dynamic_padding
self.output_stride = output_stride
self.width_factor = width_factor
self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
)
|