easydel.modules.siglip.__init__

class easydel.modules.siglip.__init__.SiglipConfig(text_config=None, vision_config=None, **kwargs)

Bases: EasyDeLBaseConfig

[SiglipConfig] is the configuration class to store the configuration of a [SiglipModel]. It is used to instantiate a Siglip 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 Siglip [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.

Configuration objects inherit from [EasyDeLBaseConfig] and can be used to control the model outputs. Read the documentation from [EasyDeLBaseConfig] for more information.

Parameters

• text_config (dict, optional) – Dictionary of configuration options used to initialize [SiglipTextConfig].

• vision_config (dict, optional) – Dictionary of configuration options used to initialize [SiglipVisionConfig].

• kwargs (optional) – Dictionary of keyword arguments.

classmethod from_text_vision_configs(text_config: SiglipTextConfig, vision_config: SiglipVisionConfig, **kwargs)

Instantiate a [SiglipConfig] (or a derived class) from siglip text model configuration and siglip vision model configuration.

Returns

An instance of a configuration object

Return type

[SiglipConfig]

get_partition_rules(*args, **kwargs)

Get the partition rules for the model. :returns: The partition rules. :rtype: tp.Tuple[tp.Tuple[str, PartitionSpec]]

model_type: str = 'siglip'

sub_configs: Dict[str, 'PretrainedConfig'] = {'text_config': <class 'easydel.modules.siglip.configuration_siglip.SiglipTextConfig'>, 'vision_config': <class 'easydel.modules.siglip.configuration_siglip.SiglipVisionConfig'>}

class easydel.modules.siglip.__init__.SiglipForImageClassification(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule

class easydel.modules.siglip.__init__.SiglipModel(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule

get_image_features(pixel_values: Optional[Union[Array, ndarray, bool, number]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, interpolate_pos_encoding: bool = False) → Union[Array, ndarray, bool, number]

get_text_features(input_ids: Optional[Union[Array, ndarray, bool, number]] = None, attention_mask: Optional[Union[Array, ndarray, bool, number]] = None, position_ids: Optional[Union[Array, ndarray, bool, number]] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) → Union[Array, ndarray, bool, number]

class easydel.modules.siglip.__init__.SiglipTextConfig(vocab_size=32000, hidden_size=768, intermediate_size=3072, num_hidden_layers=12, num_attention_heads=12, max_position_embeddings=64, hidden_act='gelu_pytorch_tanh', layer_norm_eps=1e-06, attention_dropout=0.0, pad_token_id=1, bos_token_id=49406, eos_token_id=49407, projection_size=None, **kwargs)

Bases: EasyDeLBaseConfig

Configuration objects inherit from [EasyDeLBaseConfig] and can be used to control the model outputs. Read the documentation from [EasyDeLBaseConfig] for more information.

Parameters

• vocab_size (int, optional, defaults to 32000) – Vocabulary size of the Siglip text model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling [SiglipModel].

• 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.

• max_position_embeddings (int, optional, defaults to 64) – 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_pytorch_tanh”) – 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-06) – The epsilon used by the layer normalization layers.

• attention_dropout (float, optional, defaults to 0.0) – The dropout ratio for the attention probabilities.

• pad_token_id (int, optional, defaults to 1) – The id of the padding token in the vocabulary.

• bos_token_id (int, optional, defaults to 49406) – The id of the beginning-of-sequence token in the vocabulary.

• eos_token_id (int, optional, defaults to 49407) – The id of the end-of-sequence token in the vocabulary.

• projection_size (int, optional, defaults to hidden_size) – The size of the projection head.

Example:

```python >>> from transformers import SiglipTextConfig, SiglipTextModel

>>> # Initializing a SiglipTextConfig with google/siglip-base-patch16-224 style configuration
>>> configuration = SiglipTextConfig()

>>> # Initializing a SiglipTextModel (with random weights) from the google/siglip-base-patch16-224 style configuration
>>> model = SiglipTextModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

base_config_key: str = 'text_config'

get_partition_rules(*args, **kwargs)

Get the partition rules for the model. :returns: The partition rules. :rtype: tp.Tuple[tp.Tuple[str, PartitionSpec]]

model_type: str = 'siglip_text_model'

class easydel.modules.siglip.__init__.SiglipTextModel(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule

class easydel.modules.siglip.__init__.SiglipVisionConfig(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-06, attention_dropout=0.0, **kwargs)

Bases: EasyDeLBaseConfig

Configuration objects inherit from [EasyDeLBaseConfig] and can be used to control the model outputs. Read the documentation from [EasyDeLBaseConfig] for more information.

Parameters

• 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.

• num_channels (int, optional, defaults to 3) – Number of channels in the input images.

• image_size (int, optional, defaults to 224) – The size (resolution) of each image.

• patch_size (int, optional, defaults to 16) – The size (resolution) of each patch.

• hidden_act (str or function, optional, defaults to “gelu_pytorch_tanh”) – 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-06) – The epsilon used by the layer normalization layers.

• attention_dropout (float, optional, defaults to 0.0) – The dropout ratio for the attention probabilities.

```

base_config_key: str = 'vision_config'

get_partition_rules(*args, **kwargs)

Get the partition rules for the model. :returns: The partition rules. :rtype: tp.Tuple[tp.Tuple[str, PartitionSpec]]

model_type: str = 'siglip_vision_model'

class easydel.modules.siglip.__init__.SiglipVisionModel(*args: Any, **kwargs: Any)

Bases: Module