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| # coding=utf-8 | |
| # Copyright 2024 Zhenwei Shao and MILVLG team. | |
| # Licensed under the Apache License, Version 2.0. | |
| # Adopted from https://github.com/huggingface/transformers/tree/main/src/transformers/models/siglip. | |
| # Below is the original copyright: | |
| # Copyright 2024 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. | |
| """ Siglip model configuration""" | |
| import os | |
| from typing import Union | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class SiglipVisionConfig(PretrainedConfig): | |
| model_type = "siglip_vision_model" | |
| def __init__( | |
| self, | |
| hidden_size=768, | |
| intermediate_size=3072, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| num_channels=3, | |
| image_size=224, | |
| patch_size=16, | |
| hidden_act="gelu_pytorch_tanh", | |
| layer_norm_eps=1e-6, | |
| attention_dropout=0.0, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.num_channels = num_channels | |
| self.patch_size = patch_size | |
| self.image_size = image_size | |
| self.attention_dropout = attention_dropout | |
| self.layer_norm_eps = layer_norm_eps | |
| self.hidden_act = hidden_act | |
| def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": | |
| cls._set_token_in_kwargs(kwargs) | |
| config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) | |
| # get the vision config dict if we are loading from SiglipConfig | |
| if config_dict.get("model_type") == "siglip": | |
| config_dict = config_dict["vision_config"] | |
| if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: | |
| logger.warning( | |
| f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " | |
| f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." | |
| ) | |
| return cls.from_dict(config_dict, **kwargs) | |