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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 | |
| ) | |