Source code for transformers.models.clip.configuration_clip

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""" CLIP model configuration """

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/config.json",
    # See all CLIP models at https://huggingface.co/models?filter=clip
}


[docs]class CLIPTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.CLIPModel`. It is used to instantiate an CLIP 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 CLIP `openai/clip-vit-base-patch32 <https://huggingface.co/openai/clip-vit-base-patch32>`__ architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: vocab_size (:obj:`int`, `optional`, defaults to 49408): Vocabulary size of the CLIP text model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.CLIPModel`. hidden_size (:obj:`int`, `optional`, defaults to 512): Dimensionality of the encoder layers and the pooler layer. intermediate_size (:obj:`int`, `optional`, defaults to 2048): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (:obj:`int`, `optional`, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (:obj:`int`, `optional`, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. max_position_embeddings (:obj:`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 (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"quick_gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` :obj:`"quick_gelu"` are supported. layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-5): The epsilon used by the layer normalization layers. attention_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for the attention probabilities. dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (:obj:`float`, `optional`, defaults to 1): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). Example:: >>> from transformers import CLIPTextModel, CLIPTextConfig >>> # Initializing a CLIPTextModel with openai/clip-vit-base-patch32 style configuration >>> configuration = CLIPTextConfig() >>> # Initializing a CLIPTextConfig from the openai/clip-vit-base-patch32 style configuration >>> model = CLIPTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "clip_text_model" def __init__( self, vocab_size=49408, hidden_size=512, intermediate_size=2048, num_hidden_layers=12, num_attention_heads=8, max_position_embeddings=77, hidden_act="quick_gelu", layer_norm_eps=0.00001, dropout=0.0, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.dropout = dropout 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.initializer_factor = initializer_factor self.attention_dropout = attention_dropout
[docs]class CLIPVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.CLIPModel`. It is used to instantiate an CLIP 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 CLIP `openai/clip-vit-base-patch32 <https://huggingface.co/openai/clip-vit-base-patch32>`__ architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: hidden_size (:obj:`int`, `optional`, defaults to 768): Dimensionality of the encoder layers and the pooler layer. intermediate_size (:obj:`int`, `optional`, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (:obj:`int`, `optional`, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (:obj:`int`, `optional`, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. image_size (:obj:`int`, `optional`, defaults to 224): The size (resolution) of each image. patch_size (:obj:`int`, `optional`, defaults to 32): The size (resolution) of each patch. hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"quick_gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` :obj:`"quick_gelu"` are supported. layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-5): The epsilon used by the layer normalization layers. dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (:obj:`float`, `optional`, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (:obj:`float`, `optional`, defaults to 1): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). Example:: >>> from transformers import CLIPVisionModel, CLIPVisionConfig >>> # Initializing a CLIPVisionModel with openai/clip-vit-base-patch32 style configuration >>> configuration = CLIPVisionConfig() >>> # Initializing a CLIPVisionModel model from the openai/clip-vit-base-patch32 style configuration >>> model = CLIPVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "clip_vision_model" def __init__( self, hidden_size=768, intermediate_size=3072, num_hidden_layers=12, num_attention_heads=12, image_size=224, patch_size=32, hidden_act="quick_gelu", layer_norm_eps=0.00001, dropout=0.0, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, **kwargs ): super().__init__(**kwargs) self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.dropout = dropout 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.initializer_factor = initializer_factor self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act
[docs]class CLIPConfig(PretrainedConfig): r""" :class:`~transformers.CLIPConfig` is the configuration class to store the configuration of a :class:`~transformers.CLIPModel`. It is used to instantiate CLIP model according to the specified arguments, defining the text model and vision model configs. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: text_config_dict (:obj:`dict`, `optional`): Dictionary of configuration options used to initialize :class:`~transformers.CLIPTextConfig`. vision_config_dict (:obj:`dict`, `optional`): Dictionary of configuration options used to initialize :class:`~transformers.CLIPVisionConfig`. projection_dim (:obj:`int`, `optional`, defaults to 512): Dimentionality of text and vision projection layers. logit_scale_init_value (:obj:`float`, `optional`, defaults to 2.6592): The inital value of the `logit_scale` paramter. Default is used as per the original CLIP implementation. kwargs (`optional`): Dictionary of keyword arguments. """ model_type = "clip" is_composition = True def __init__( self, text_config_dict=None, vision_config_dict=None, projection_dim=512, logit_scale_init_value=2.6592, **kwargs ): super().__init__(text_config_dict=text_config_dict, vision_config_dict=vision_config_dict, **kwargs) if text_config_dict is None: text_config_dict = {} logger.info("text_config_dict is None. Initializing the CLIPTextConfig with default values.") if vision_config_dict is None: vision_config_dict = {} logger.info("vision_config_dict is None. initializing the CLIPVisionConfig with default values.") self.text_config = CLIPTextConfig(**text_config_dict) self.vision_config = CLIPVisionConfig(**vision_config_dict) self.projection_dim = projection_dim self.logit_scale_init_value = logit_scale_init_value self.initializer_factor = 1.0
[docs] @classmethod def from_text_vision_configs(cls, text_config: CLIPTextConfig, vision_config: CLIPVisionConfig, **kwargs): r""" Instantiate a :class:`~transformers.CLIPConfig` (or a derived class) from clip text model configuration and clip vision model configuration. Returns: :class:`CLIPConfig`: An instance of a configuration object """ return cls(text_config_dict=text_config.to_dict(), vision_config_dict=vision_config.to_dict(), **kwargs)
def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default :meth:`~transformers.PretrainedConfig.to_dict`. Returns: :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = copy.deepcopy(self.__dict__) output["text_config"] = self.text_config.to_dict() output["vision_config"] = self.vision_config.to_dict() output["model_type"] = self.__class__.model_type return output