366 lines
16 KiB
Python
366 lines
16 KiB
Python
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# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Blip model configuration"""
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import os
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from typing import Union
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class BlipTextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`BlipTextModel`]. It is used to instantiate a BLIP
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text model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the `BlipText` used by the [base
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architectures](https://huggingface.co/Salesforce/blip-vqa-base).
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 30524):
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Vocabulary size of the `Blip` text model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`BlipModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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encoder_hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers from the vision model.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer encoder.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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bos_token_id (`int`, *optional*, defaults to 30522):
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The id of the `beginning-of-sequence` token.
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eos_token_id (`int`, *optional*, defaults to 2):
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The id of the `end-of-sequence` token.
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pad_token_id (`int`, *optional*, defaults to 0):
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The id of the `padding` token.
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sep_token_id (`int`, *optional*, defaults to 102):
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The id of the `separator` token.
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is_decoder (`bool`, *optional*, defaults to `True`):
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Whether the model is used as a decoder.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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label_smoothing (float, *optional*):
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A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets
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become a mixture of the original ground truth and a uniform distribution as described in
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`Rethinking the Inception Architecture for Computer Vision <https://arxiv.org/abs/1512.00567>`__. Default: :math:`0.0`.
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Example:
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```python
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>>> from transformers import BlipTextConfig, BlipTextModel
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>>> # Initializing a BlipTextConfig with Salesforce/blip-vqa-base style configuration
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>>> configuration = BlipTextConfig()
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>>> # Initializing a BlipTextModel (with random weights) from the Salesforce/blip-vqa-base style configuration
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>>> model = BlipTextModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "blip_text_model"
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def __init__(
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self,
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vocab_size=30524,
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hidden_size=768,
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encoder_hidden_size=768,
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intermediate_size=3072,
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projection_dim=768,
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num_hidden_layers=12,
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num_attention_heads=8,
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max_position_embeddings=512,
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hidden_act="gelu",
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layer_norm_eps=1e-12,
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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initializer_range=0.02,
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bos_token_id=30522,
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eos_token_id=2,
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pad_token_id=0,
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sep_token_id=102,
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is_decoder=True,
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use_cache=True,
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label_smoothing=0.0,
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**kwargs,
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):
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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sep_token_id=sep_token_id,
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**kwargs,
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)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.encoder_hidden_size = encoder_hidden_size
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self.intermediate_size = intermediate_size
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self.projection_dim = projection_dim
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self.hidden_dropout_prob = hidden_dropout_prob
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.is_decoder = is_decoder
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self.use_cache = use_cache
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self.label_smoothing = label_smoothing
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the text config dict if we are loading from BlipConfig
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if config_dict.get("model_type") == "blip":
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config_dict = config_dict["text_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class BlipVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`BlipVisionModel`]. It is used to instantiate a
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BLIP vision model according to the specified arguments, defining the model architecture. Instantiating a
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configuration defaults will yield a similar configuration to that of the Blip-base
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[Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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image_size (`int`, *optional*, defaults to 384):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 16):
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The size (resolution) of each patch.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-5):
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The epsilon used by the layer normalization layers.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 1e-10):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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Example:
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```python
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>>> from transformers import BlipVisionConfig, BlipVisionModel
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>>> # Initializing a BlipVisionConfig with Salesforce/blip-vqa-base style configuration
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>>> configuration = BlipVisionConfig()
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>>> # Initializing a BlipVisionModel (with random weights) from the Salesforce/blip-vqa-base style configuration
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>>> model = BlipVisionModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "blip_vision_model"
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def __init__(
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self,
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hidden_size=768,
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intermediate_size=3072,
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projection_dim=512,
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num_hidden_layers=12,
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num_attention_heads=12,
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image_size=384,
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patch_size=16,
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hidden_act="gelu",
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layer_norm_eps=1e-5,
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attention_dropout=0.0,
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initializer_range=1e-10,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.projection_dim = projection_dim
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.patch_size = patch_size
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self.image_size = image_size
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self.initializer_range = initializer_range
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self.attention_dropout = attention_dropout
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self.layer_norm_eps = layer_norm_eps
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self.hidden_act = hidden_act
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the vision config dict if we are loading from BlipConfig
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if config_dict.get("model_type") == "blip":
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config_dict = config_dict["vision_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class BlipConfig(PretrainedConfig):
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r"""
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[`BlipConfig`] is the configuration class to store the configuration of a [`BlipModel`]. It is used to instantiate
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a BLIP model according to the specified arguments, defining the text model and vision model configs. Instantiating
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a configuration with the defaults will yield a similar configuration to that of the BLIP-base
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[Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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text_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`BlipTextConfig`].
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vision_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`BlipVisionConfig`].
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projection_dim (`int`, *optional*, defaults to 512):
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Dimentionality of text and vision projection layers.
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logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
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The inital value of the *logit_scale* paramter. Default is used as per the original BLIP implementation.
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image_text_hidden_size (`int`, *optional*, defaults to 256):
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Dimentionality of the hidden state of the image-text fusion layer.
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label_smoothing (float, optional, *optional*, defaults to 0.0):
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A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets
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become a mixture of the original ground truth and a uniform distribution as described in
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`Rethinking the Inception Architecture for Computer Vision <https://arxiv.org/abs/1512.00567>`__. Default: :math:`0.0`.
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kwargs (*optional*):
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Dictionary of keyword arguments.
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Example:
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```python
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>>> from transformers import BlipConfig, BlipModel
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>>> # Initializing a BlipConfig with Salesforce/blip-vqa-base style configuration
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>>> configuration = BlipConfig()
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>>> # Initializing a BlipPModel (with random weights) from the Salesforce/blip-vqa-base style configuration
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>>> model = BlipModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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>>> # We can also initialize a BlipConfig from a BlipTextConfig and a BlipVisionConfig
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>>> # Initializing a BLIPText and BLIPVision configuration
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>>> config_text = BlipTextConfig()
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>>> config_vision = BlipVisionConfig()
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>>> config = BlipConfig.from_text_vision_configs(config_text, config_vision)
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```"""
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model_type = "blip"
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def __init__(
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self,
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text_config=None,
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vision_config=None,
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projection_dim=512,
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logit_scale_init_value=2.6592,
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image_text_hidden_size=256,
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label_smoothing=0.0,
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**kwargs,
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):
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super().__init__(**kwargs)
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if text_config is None:
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text_config = {}
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logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values.")
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if vision_config is None:
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vision_config = {}
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logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.")
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||
|
|
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self.text_config = BlipTextConfig(**text_config)
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|
self.vision_config = BlipVisionConfig(**vision_config)
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self.text_config.encoder_hidden_size = self.vision_config.hidden_size
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||
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|
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||
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|
self.projection_dim = projection_dim
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||
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|
self.logit_scale_init_value = logit_scale_init_value
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|
self.initializer_factor = 1.0
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||
|
|
self.initializer_range = 0.02
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||
|
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self.image_text_hidden_size = image_text_hidden_size
|
||
|
|
self.label_smoothing = label_smoothing
|
||
|
|
|
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|
|
@classmethod
|
||
|
|
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)
|