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""" CLIP model configuration"""
import copy
from transformers.configuration_utils import PretrainedConfig
from transformers.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 [`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 [`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 49408):
Vocabulary size of the CLIP text model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`CLIPModel`].
hidden_size (`int`, *optional*, defaults to 512):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 2048):
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 `"quick_gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_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.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float``, *optional*, defaults to 1):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
Example:
```python
>>> 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 [`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 [`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 `"quick_gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*,
defaults to 1e-5): The epsilon used by the layer normalization layers.
dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy 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.
initializer_factor (`float``, *optional*, defaults to 1):
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
Example:
```python
>>> 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"""
[`CLIPConfig`] is the configuration class to store the configuration of a [`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 [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
text_config_dict (`dict`, *optional*):
Dictionary of configuration options used to initialize [`CLIPTextConfig`].
vision_config_dict (`dict`, *optional*):
Dictionary of configuration options used to initialize [`CLIPVisionConfig`].
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 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 [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model
configuration.
Returns:
[`CLIPConfig`]: An instance of a configuration object
"""
return cls(text_config_dict=text_config.to_dict(), vision_config_dict=vision_config.to_dict(), **kwargs)
[docs] def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
Returns:
`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