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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. | |
| """ Blip model configuration""" | |
| import os | |
| from typing import Union | |
| from ...configuration_utils import PretrainedConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json", | |
| "Salesforce/blip-vqa-capfit-large": ( | |
| "https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json" | |
| ), | |
| "Salesforce/blip-image-captioning-base": ( | |
| "https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json" | |
| ), | |
| "Salesforce/blip-image-captioning-large": ( | |
| "https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json" | |
| ), | |
| "Salesforce/blip-itm-base-coco": "https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json", | |
| "Salesforce/blip-itm-large-coco": "https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json", | |
| "Salesforce/blip-itm-base-flikr": "https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json", | |
| "Salesforce/blip-itm-large-flikr": ( | |
| "https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json" | |
| ), | |
| } | |
| class BlipTextConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`BlipTextModel`]. It is used to instantiate a BLIP | |
| text 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 `BlipText` used by the [base | |
| architectures](https://huggingface.co/Salesforce/blip-vqa-base). | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
| documentation from [`PretrainedConfig`] for more information. | |
| Args: | |
| vocab_size (`int`, *optional*, defaults to 30522): | |
| Vocabulary size of the `Blip` text model. Defines the number of different tokens that can be represented by | |
| the `inputs_ids` passed when calling [`BlipModel`]. | |
| hidden_size (`int`, *optional*, defaults to 768): | |
| Dimensionality of the encoder layers and the pooler layer. | |
| encoder_hidden_size (`int`, *optional*, defaults to 768): | |
| Dimensionality of the encoder layers from the vision model. | |
| intermediate_size (`int`, *optional*, defaults to 3072): | |
| Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. | |
| num_hidden_layers (`int`, *optional*, defaults to 12): | |
| Number of hidden layers in the Transformer encoder. | |
| num_attention_heads (`int`, *optional*, defaults to 8): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| max_position_embeddings (`int`, *optional*, defaults to 77): | |
| The maximum sequence length that this model might ever be used with. Typically set this to something large | |
| just in case (e.g., 512 or 1024 or 2048). | |
| hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): | |
| The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, | |
| `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported. | |
| layer_norm_eps (`float`, *optional*, defaults to 1e-12): | |
| The epsilon used by the layer normalization layers. | |
| hidden_dropout_prob (`float`, *optional*, defaults to 0.0): | |
| The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
| attention_dropout (`float`, *optional*, defaults to 0.0): | |
| The dropout ratio for the attention probabilities. | |
| initializer_range (`float`, *optional*, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| bos_token_id (`int`, *optional*, defaults to 30522): | |
| The id of the `beginning-of-sequence` token. | |
| eos_token_id (`int`, *optional*, defaults to 2): | |
| The id of the `end-of-sequence` token. | |
| pad_token_id (`int`, *optional*, defaults to 0): | |
| The id of the `padding` token. | |
| sep_token_id (`int`, *optional*, defaults to 102): | |
| The id of the `separator` token. | |
| is_decoder (`bool`, *optional*, defaults to `False`): | |
| Whether the model is used as a decoder. | |
| use_cache (`bool`, *optional*, defaults to `True`): | |
| Whether or not the model should return the last key/values attentions (not used by all models). | |
| Example: | |
| ```python | |
| >>> from transformers import BlipTextConfig, BlipTextModel | |
| >>> # Initializing a BlipTextConfig with Salesforce/blip-vqa-base style configuration | |
| >>> configuration = BlipTextConfig() | |
| >>> # Initializing a BlipTextModel (with random weights) from the Salesforce/blip-vqa-base style configuration | |
| >>> model = BlipTextModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "blip_text_model" | |
| def __init__( | |
| self, | |
| vocab_size=30524, | |
| hidden_size=768, | |
| encoder_hidden_size=768, | |
| intermediate_size=3072, | |
| projection_dim=768, | |
| num_hidden_layers=12, | |
| num_attention_heads=8, | |
| max_position_embeddings=512, | |
| hidden_act="gelu", | |
| layer_norm_eps=1e-12, | |
| hidden_dropout_prob=0.0, | |
| attention_probs_dropout_prob=0.0, | |
| initializer_range=0.02, | |
| bos_token_id=30522, | |
| eos_token_id=2, | |
| pad_token_id=0, | |
| sep_token_id=102, | |
| is_decoder=True, | |
| use_cache=True, | |
| **kwargs, | |
| ): | |
| super().__init__( | |
| pad_token_id=pad_token_id, | |
| bos_token_id=bos_token_id, | |
| eos_token_id=eos_token_id, | |
| sep_token_id=sep_token_id, | |
| **kwargs, | |
| ) | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.encoder_hidden_size = encoder_hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.projection_dim = projection_dim | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.max_position_embeddings = max_position_embeddings | |
| self.layer_norm_eps = layer_norm_eps | |
| self.hidden_act = hidden_act | |
| self.initializer_range = initializer_range | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.is_decoder = is_decoder | |
| self.use_cache = use_cache | |
| 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 text config dict if we are loading from BlipConfig | |
| if config_dict.get("model_type") == "blip": | |
| config_dict = config_dict["text_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) | |
| class BlipVisionConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`BlipVisionModel`]. It is used to instantiate a | |
| BLIP vision model according to the specified arguments, defining the model architecture. Instantiating a | |
| configuration defaults will yield a similar configuration to that of the Blip-base | |
| [Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base) architecture. | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
| documentation from [`PretrainedConfig`] for more information. | |
| Args: | |
| hidden_size (`int`, *optional*, defaults to 768): | |
| Dimensionality of the encoder layers and the pooler layer. | |
| intermediate_size (`int`, *optional*, defaults to 3072): | |
| Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. | |
| num_hidden_layers (`int`, *optional*, defaults to 12): | |
| Number of hidden layers in the Transformer encoder. | |
| num_attention_heads (`int`, *optional*, defaults to 12): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| image_size (`int`, *optional*, defaults to 224): | |
| The size (resolution) of each image. | |
| patch_size (`int`, *optional*, defaults to 32): | |
| The size (resolution) of each patch. | |
| hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): | |
| The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, | |
| `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported. | |
| layer_norm_eps (`float`, *optional*, defaults to 1e-5): | |
| The epsilon used by the layer normalization layers. | |
| attention_dropout (`float`, *optional*, defaults to 0.0): | |
| The dropout ratio for the attention probabilities. | |
| initializer_range (`float`, *optional*, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| Example: | |
| ```python | |
| >>> from transformers import BlipVisionConfig, BlipVisionModel | |
| >>> # Initializing a BlipVisionConfig with Salesforce/blip-vqa-base style configuration | |
| >>> configuration = BlipVisionConfig() | |
| >>> # Initializing a BlipVisionModel (with random weights) from the Salesforce/blip-vqa-base style configuration | |
| >>> model = BlipVisionModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "blip_vision_model" | |
| def __init__( | |
| self, | |
| hidden_size=768, | |
| intermediate_size=3072, | |
| projection_dim=512, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| image_size=384, | |
| patch_size=16, | |
| hidden_act="gelu", | |
| layer_norm_eps=1e-5, | |
| attention_dropout=0.0, | |
| initializer_range=1e-10, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.projection_dim = projection_dim | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.patch_size = patch_size | |
| self.image_size = image_size | |
| self.initializer_range = initializer_range | |
| 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 BlipConfig | |
| if config_dict.get("model_type") == "blip": | |
| 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) | |
| class BlipConfig(PretrainedConfig): | |
| r""" | |
| [`BlipConfig`] is the configuration class to store the configuration of a [`BlipModel`]. It is used to instantiate | |
| a BLIP model according to the specified arguments, defining the text model and vision model configs. Instantiating | |
| a configuration with the defaults will yield a similar configuration to that of the BLIP-base | |
| [Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base) architecture. | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
| documentation from [`PretrainedConfig`] for more information. | |
| Args: | |
| text_config (`dict`, *optional*): | |
| Dictionary of configuration options used to initialize [`BlipTextConfig`]. | |
| vision_config (`dict`, *optional*): | |
| Dictionary of configuration options used to initialize [`BlipVisionConfig`]. | |
| projection_dim (`int`, *optional*, defaults to 512): | |
| Dimentionality of text and vision projection layers. | |
| logit_scale_init_value (`float`, *optional*, defaults to 2.6592): | |
| The inital value of the *logit_scale* paramter. Default is used as per the original BLIP implementation. | |
| image_text_hidden_size (`int`, *optional*, defaults to 256): | |
| Dimentionality of the hidden state of the image-text fusion layer. | |
| kwargs (*optional*): | |
| Dictionary of keyword arguments. | |
| Example: | |
| ```python | |
| >>> from transformers import BlipConfig, BlipModel | |
| >>> # Initializing a BlipConfig with Salesforce/blip-vqa-base style configuration | |
| >>> configuration = BlipConfig() | |
| >>> # Initializing a BlipPModel (with random weights) from the Salesforce/blip-vqa-base style configuration | |
| >>> model = BlipModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| >>> # We can also initialize a BlipConfig from a BlipTextConfig and a BlipVisionConfig | |
| >>> # Initializing a BLIPText and BLIPVision configuration | |
| >>> config_text = BlipTextConfig() | |
| >>> config_vision = BlipVisionConfig() | |
| >>> config = BlipConfig.from_text_vision_configs(config_text, config_vision) | |
| ```""" | |
| model_type = "blip" | |
| def __init__( | |
| self, | |
| text_config=None, | |
| vision_config=None, | |
| projection_dim=512, | |
| logit_scale_init_value=2.6592, | |
| image_text_hidden_size=256, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| if text_config is None: | |
| text_config = {} | |
| logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values.") | |
| if vision_config is None: | |
| vision_config = {} | |
| logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.") | |
| self.text_config = BlipTextConfig(**text_config) | |
| self.vision_config = BlipVisionConfig(**vision_config) | |
| self.text_config.encoder_hidden_size = self.vision_config.hidden_size | |
| self.projection_dim = projection_dim | |
| self.logit_scale_init_value = logit_scale_init_value | |
| self.initializer_factor = 1.0 | |
| self.initializer_range = 0.02 | |
| self.image_text_hidden_size = image_text_hidden_size | |
| def from_text_vision_configs(cls, text_config: BlipTextConfig, vision_config: BlipVisionConfig, **kwargs): | |
| r""" | |
| Instantiate a [`BlipConfig`] (or a derived class) from blip text model configuration and blip vision model | |
| configuration. | |
| Returns: | |
| [`BlipConfig`]: An instance of a configuration object | |
| """ | |
| return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs) | |