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# 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 | |
) | |