#
BLIP-2

##
개요

BLIP-2 모델은 Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi의 BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models 논문에서 제안되었습니다. BLIP-2는 동결된 사전 학습 이미지 인코더와 대규모 언어 모델(LLM)을 연결하는 12층의 경량 Transformer 인코더를 학습시켜, 여러 비전-언어 작업에서 SOTA(현재 최고의 성능)을 달성했습니다. 특히, BLIP-2는 800억 개의 파라미터를 가진 Flamingo 모델보다 제로샷 VQAv2에서 8.7% 더 높은 성능을 기록했으며, 학습 가능한 파라미터 수는 Flamingo보다 54배 적습니다.

논문의 초록은 다음과 같습니다:

비전-언어 사전 학습의 비용은 대규모 모델의 엔드-투-엔드 학습으로 인해 점점 더 부담스러워지고 있습니다. 본 논문은 사전 학습된 이미지 인코더와 대규모 언어 모델을 활용하여 비전-언어 사전 학습을 부트스트래핑하는 일반적이고 효율적인 사전 학습 전략인 BLIP-2를 제안합니다. BLIP-2는 경량화된 Querying Transformer를 통해 모달리티 간의 차이를 연결하며, 두 단계로 사전 학습됩니다. 첫 번째 단계는 동결된 이미지 인코더로부터 비전-언어 표현 학습을 부트스트래핑하고, 두 번째 단계는 동결된 언어 모델로부터 비전-언어 생성 학습을 부트스트래핑합니다. BLIP-2는 기존 방법들에 비해 훨씬 적은 학습 가능한 파라미터로 다양한 비전-언어 작업에서 최첨단 성능을 달성합니다. 예를 들어, 우리 모델은 제로샷 VQAv2에서 Flamingo80B보다 8.7% 높은 성능을 기록하며, 학습 가능한 파라미터 수는 54배 적습니다. 우리는 또한 자연어 명령을 따를 수 있는 제로샷 이미지-텍스트 생성의 새로운 기능을 입증했습니다.

BLIP-2 구조. 원본 논문 에서 발췌.

이 모델은 nielsr가 기여했습니다. 원본 코드는 여기에서 확인할 수 있습니다.

##
사용 팁

- BLIP-2는 이미지와 조건에 따라 텍스트 프롬프트를 입력받아 조건부 텍스트를 생성합니다. 추론 시 `generate` 메소드를 사용하는 것이 권장됩니다.
- Blip2Processor를 사용하여 모델에 이미지를 준비하고, 예측된 토큰 ID를 텍스트로 디코딩할 수 있습니다.

##
자료

BLIP-2를 시작하는 데 도움이 되는 공식 Hugging Face 및 커뮤니티(🌎 표시) 자료 목록입니다.

- 이미지 캡셔닝, 시각 질문 응답(VQA), 채팅과 같은 대화형 작업을 위한 BLIP-2 데모 노트북은 여기에서 찾을 수 있습니다.

리소스를 제출하여 여기에 포함하고 싶다면 언제든지 풀 리퀘스트를 열어주세요! 리소스는 기존 리소스를 복제하지 않고 새로운 내용이어야 합니다.

##
Blip2Config

###
class transformers.Blip2Config

< source >

( vision_config = None qformer_config = None text_config = None num_query_tokens = 32 image_text_hidden_size = 256 image_token_index = None **kwargs )

Parameters

- vision_config (`dict`, optional) — Dictionary of configuration options used to initialize Blip2VisionConfig.
- qformer_config (`dict`, optional) — Dictionary of configuration options used to initialize Blip2QFormerConfig.
- text_config (`dict`, optional) — Dictionary of configuration options used to initialize any PretrainedConfig.
- num_query_tokens (`int`, optional, defaults to 32) — The number of query tokens passed through the Transformer.
- image_text_hidden_size (`int`, optional, defaults to 256) — Dimensionality of the hidden state of the image-text fusion layer.
- image_token_index (`int`, optional) — Token index of special image token.
- kwargs (optional) — Dictionary of keyword arguments.

Blip2Config is the configuration class to store the configuration of a Blip2ForConditionalGeneration. It is used to instantiate a BLIP-2 model according to the specified arguments, defining the vision model, Q-Former model and language model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the BLIP-2 Salesforce/blip2-opt-2.7b architecture.

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

Example:

    >>> from transformers import (
    ...     Blip2VisionConfig,
    ...     Blip2QFormerConfig,
    ...     OPTConfig,
    ...     Blip2Config,
    ...     Blip2ForConditionalGeneration,
    ... )

    >>> # Initializing a Blip2Config with Salesforce/blip2-opt-2.7b style configuration
    >>> configuration = Blip2Config()

    >>> # Initializing a Blip2ForConditionalGeneration (with random weights) from the Salesforce/blip2-opt-2.7b style configuration
    >>> model = Blip2ForConditionalGeneration(configuration)

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

    >>> # We can also initialize a Blip2Config from a Blip2VisionConfig, Blip2QFormerConfig and any PretrainedConfig

    >>> # Initializing BLIP-2 vision, BLIP-2 Q-Former and language model configurations
    >>> vision_config = Blip2VisionConfig()
    >>> qformer_config = Blip2QFormerConfig()
    >>> text_config = OPTConfig()

    >>> config = Blip2Config.from_text_vision_configs(vision_config, qformer_config, text_config)

####
from_vision_qformer_text_configs

< source >

( vision_config: Blip2VisionConfig qformer_config: Blip2QFormerConfig text_config: typing.Optional[transformers.configuration_utils.PretrainedConfig] = None **kwargs ) →
Blip2Config

Parameters

- vision_config (`dict`) — Dictionary of configuration options used to initialize Blip2VisionConfig.
- qformer_config (`dict`) — Dictionary of configuration options used to initialize Blip2QFormerConfig.
- text_config (`dict`, optional) — Dictionary of configuration options used to initialize any PretrainedConfig.

Returns

Blip2Config

An instance of a configuration object

Instantiate a Blip2Config (or a derived class) from a BLIP-2 vision model, Q-Former and language model configurations.

##
Blip2VisionConfig

###
class transformers.Blip2VisionConfig

< source >

( hidden_size = 1408 intermediate_size = 6144 num_hidden_layers = 39 num_attention_heads = 16 image_size = 224 patch_size = 14 hidden_act = 'gelu' layer_norm_eps = 1e-06 attention_dropout = 0.0 initializer_range = 1e-10 qkv_bias = True **kwargs )

Parameters

- hidden_size (`int`, optional, defaults to 1408) — Dimensionality of the encoder layers and the pooler layer.
- intermediate_size (`int`, optional, defaults to 6144) — Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
- num_hidden_layers (`int`, optional, defaults to 39) — Number of hidden layers in the Transformer encoder.
- num_attention_heads (`int`, optional, defaults to 16) — 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 14) — 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.
- qkv_bias (`bool`, optional, defaults to `True`) — Whether to add a bias to the queries and values in the self-attention layers.

This is the configuration class to store the configuration of a Blip2VisionModel. It is used to instantiate a BLIP-2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration defaults will yield a similar configuration to that of the BLIP-2 Salesforce/blip2-opt-2.7b architecture.

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

Example:

    >>> from transformers import Blip2VisionConfig, Blip2VisionModel

    >>> # Initializing a Blip2VisionConfig with Salesforce/blip2-opt-2.7b style configuration
    >>> configuration = Blip2VisionConfig()

    >>> # Initializing a Blip2VisionModel (with random weights) from the Salesforce/blip2-opt-2.7b style configuration
    >>> model = Blip2VisionModel(configuration)

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

##
Blip2QFormerConfig

###
class transformers.Blip2QFormerConfig

< source >

( vocab_size = 30522 hidden_size = 768 num_hidden_layers = 12 num_attention_heads = 12 intermediate_size = 3072 hidden_act = 'gelu' hidden_dropout_prob = 0.1 attention_probs_dropout_prob = 0.1 max_position_embeddings = 512 initializer_range = 0.02 layer_norm_eps = 1e-12 pad_token_id = 0 position_embedding_type = 'absolute' cross_attention_frequency = 2 encoder_hidden_size = 1408 use_qformer_text_input = False **kwargs )

Parameters

- vocab_size (`int`, optional, defaults to 30522) — Vocabulary size of the Q-Former model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling the model.
- hidden_size (`int`, optional, defaults to 768) — Dimensionality of the encoder layers and the pooler layer.
- 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.
- intermediate_size (`int`, optional, defaults to 3072) — Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder.
- hidden_act (`str` or `Callable`, optional, defaults to `"gelu"`) — The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported.
- hidden_dropout_prob (`float`, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
- attention_probs_dropout_prob (`float`, optional, defaults to 0.1) — The dropout ratio for the attention probabilities.
- max_position_embeddings (`int`, optional, defaults to 512) — 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).
- initializer_range (`float`, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- layer_norm_eps (`float`, optional, defaults to 1e-12) — The epsilon used by the layer normalization layers.
- pad_token_id (`int`, optional, defaults to 0) — Index to be used for padding token.
- position_embedding_type (`str`, optional, defaults to `"absolute"`) — Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to Self-Attention with Relative Position Representations (Shaw et al.). For more information on `"relative_key_query"`, please refer to Method 4 in Improve Transformer Models with Better Relative Position Embeddings (Huang et al.).
- cross_attention_frequency (`int`, optional, defaults to 2) — The frequency of adding cross-attention to the Transformer layers.
- encoder_hidden_size (`int`, optional, defaults to 1408) — The hidden size of the hidden states for cross-attention.
- use_qformer_text_input (`bool`, optional, defaults to `False`) — Whether to use BERT-style embeddings.

This is the configuration class to store the configuration of a Blip2QFormerModel. It is used to instantiate a BLIP-2 Querying Transformer (Q-Former) 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 BLIP-2 Salesforce/blip2-opt-2.7b architecture. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Note that Blip2QFormerModel is very similar to BertLMHeadModel with interleaved cross-attention.

Examples:

    >>> from transformers import Blip2QFormerConfig, Blip2QFormerModel

    >>> # Initializing a BLIP-2 Salesforce/blip2-opt-2.7b style configuration
    >>> configuration = Blip2QFormerConfig()

    >>> # Initializing a model (with random weights) from the Salesforce/blip2-opt-2.7b style configuration
    >>> model = Blip2QFormerModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config

##
Blip2Processor

###
class transformers.Blip2Processor

< source >

( image_processor tokenizer num_query_tokens = None **kwargs )

Parameters

- image_processor (`BlipImageProcessor`) — An instance of BlipImageProcessor. The image processor is a required input.
- tokenizer (`AutoTokenizer`) — An instance of [‘PreTrainedTokenizer`]. The tokenizer is a required input.
- num_query_tokens (`int`, optional) — Number of tokens used by the Qformer as queries, should be same as in model’s config.

Constructs a BLIP-2 processor which wraps a BLIP image processor and an OPT/T5 tokenizer into a single processor.

BlipProcessor offers all the functionalities of BlipImageProcessor and AutoTokenizer. See the docstring of `__call__()` and decode() for more information.

####
batch_decode

< source >

( *args **kwargs )

This method forwards all its arguments to PreTrainedTokenizer’s batch_decode(). Please refer to the docstring of this method for more information.

####
decode

< source >

( *args **kwargs )

This method forwards all its arguments to PreTrainedTokenizer’s decode(). Please refer to the docstring of this method for more information.

##
Blip2VisionModel

###
class transformers.Blip2VisionModel

< source >

( config: Blip2VisionConfig )

Parameters

- config (Blip2VisionConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The bare Blip 2 Model outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( pixel_values: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None interpolate_pos_encoding: bool = False ) →
transformers.modeling_outputs.BaseModelOutputWithPooling or `tuple(torch.FloatTensor)`

Parameters

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, optional) — The tensors corresponding to the input images. Pixel values can be obtained using `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- interpolate_pos_encoding (`bool`, defaults to `False`) — Whether to interpolate the pre-trained position encodings.

Returns

transformers.modeling_outputs.BaseModelOutputWithPooling or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.BaseModelOutputWithPooling or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (Blip2Config) and inputs.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.

- pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) — Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The Blip2VisionModel forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

##
Blip2QFormerModel

###
class transformers.Blip2QFormerModel

< source >

( config: Blip2QFormerConfig )

Parameters

- config (Blip2QFormerConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

BLIP-2 Querying Transformer (Q-Former).

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( query_embeds: FloatTensor query_length: typing.Optional[int] = None attention_mask: typing.Optional[torch.FloatTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None encoder_hidden_states: typing.Optional[torch.FloatTensor] = None encoder_attention_mask: typing.Optional[torch.FloatTensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or `tuple(torch.FloatTensor)`

Parameters

- query_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Hidden states to be used in the attention computation. If cross-attention, will be used for the query (i.e., key and value will use the encoder_hidden_states).
- query_length (`int`, optional) — Length of the query, usually based on the number of query tokens. If no value is provided, query_length will be inferred by the query_embeds.
- attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  - 1 indicates the head is not masked,
  - 0 indicates the head is masked.
- encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder.
- encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.
- past_key_values (`Tuple[Tuple[torch.FloatTensor]]`, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Two formats are allowed:

  - a Cache instance, see our kv cache guide;
  - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format.

  The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned.

  If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`.

- use_cache (`bool`, optional) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (Blip2Config) and inputs.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.

- pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) — Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

- cross_attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

- past_key_values (`Cache`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — It is a Cache instance. For more details, see our kv cache guide.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

The Blip2QFormerModel forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

##
Blip2Model

###
class transformers.Blip2Model

< source >

( config: Blip2Config )

Parameters

- config (Blip2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

BLIP-2 Model for generating text and image features. The model consists of a vision encoder, Querying Transformer (Q-Former) and a language model.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( pixel_values: FloatTensor input_ids: FloatTensor attention_mask: typing.Optional[torch.LongTensor] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.LongTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None labels: typing.Optional[torch.LongTensor] = None return_dict: typing.Optional[bool] = None interpolate_pos_encoding: bool = False **kwargs: typing_extensions.Unpack[transformers.models.blip_2.modeling_blip_2.KwargsForCausalLM] ) →
`transformers.models.blip_2.modeling_blip_2.Blip2ForConditionalGenerationModelOutput` or `tuple(torch.FloatTensor)`

Parameters

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) — The tensors corresponding to the input images. Pixel values can be obtained using `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- input_ids (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be provided to serve as text prompt, which the language model can continue.

  Indices can be obtained using Blip2Processor. See `Blip2Processor.__call__()` for details.

  What are input IDs?

- attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional) — Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are decoder input IDs?

- decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional) — Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.

  Only relevant in case an encoder-decoder language model (like T5) is used.

- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- interpolate_pos_encoding (`bool`, defaults to `False`) — Whether to interpolate the pre-trained position encodings.

Returns

`transformers.models.blip_2.modeling_blip_2.Blip2ForConditionalGenerationModelOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.blip_2.modeling_blip_2.Blip2ForConditionalGenerationModelOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (Blip2Config) and inputs.

- loss (`torch.FloatTensor`, optional, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`) — Language modeling loss from the language model.
- logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head of the language model.
- vision_outputs (`BaseModelOutputWithPooling`) — Outputs of the vision encoder.
- qformer_outputs (`BaseModelOutputWithPoolingAndCrossAttentions`) — Outputs of the Q-Former (Querying Transformer).
- language_model_outputs (`CausalLMOutputWithPast` or `Seq2SeqLMOutput`) — Outputs of the language model.

The Blip2Model forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

    >>> from PIL import Image
    >>> import requests
    >>> from transformers import Blip2Processor, Blip2Model
    >>> import torch

    >>> device = "cuda" if torch.cuda.is_available() else "cpu"

    >>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
    >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
    >>> model.to(device)
    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)

    >>> prompt = "Question: how many cats are there? Answer:"
    >>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device, torch.float16)

    >>> outputs = model(**inputs)

####
get_text_features

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None decoder_input_ids: typing.Optional[torch.Tensor] = None decoder_attention_mask: typing.Optional[torch.Tensor] = None labels: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None **kwargs: typing_extensions.Unpack[transformers.models.blip_2.modeling_blip_2.KwargsForCausalLM] ) →
text_outputs (`CausalLMOutputWithPast`, or `tuple(torch.FloatTensor)` if `return_dict=False`)

Parameters

- input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- decoder_input_ids (`torch.Tensor` of shape `(batch_size, target_sequence_length)`, optional) — Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are decoder input IDs?

  T5 uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`).

  To know more on how to prepare `decoder_input_ids` for pretraining take a look at T5 Training.

- decoder_attention_mask (`torch.Tensor` of shape `(batch_size, target_sequence_length)`, optional) — Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.
- labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

text_outputs (`CausalLMOutputWithPast`, or `tuple(torch.FloatTensor)` if `return_dict=False`)

The language model outputs. If `return_dict=True`, the output is a `CausalLMOutputWithPast` that contains the language model logits, the past key values and the hidden states if `output_hidden_states=True`.

Examples:

    >>> import torch
    >>> from transformers import AutoTokenizer, Blip2Model

    >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")

    >>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/blip2-opt-2.7b")
    >>> inputs = tokenizer(["a photo of a cat"], padding=True, return_tensors="pt")
    >>> text_features = model.get_text_features(**inputs)

####
get_image_features

< source >

( pixel_values: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None interpolate_pos_encoding: bool = False ) →
vision_outputs (`BaseModelOutputWithPooling` or tuple of `torch.FloatTensor`)

Parameters

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, optional) — The tensors corresponding to the input images. Pixel values can be obtained using `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- interpolate_pos_encoding (`bool`, defaults to `False`) — Whether to interpolate the pre-trained position encodings.

Returns

vision_outputs (`BaseModelOutputWithPooling` or tuple of `torch.FloatTensor`)

The vision model outputs. If `return_dict=True`, the output is a `BaseModelOutputWithPooling` that contains the image features, the pooled image features and the hidden states if `output_hidden_states=True`.

Examples:

    >>> import torch
    >>> from PIL import Image
    >>> import requests
    >>> from transformers import AutoProcessor, Blip2Model

    >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")

    >>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)
    >>> inputs = processor(images=image, return_tensors="pt")
    >>> image_outputs = model.get_image_features(**inputs)

####
get_qformer_features

< source >

( pixel_values: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None interpolate_pos_encoding: bool = False ) →
vision_outputs (`BaseModelOutputWithPooling` or tuple of `torch.FloatTensor`)

Parameters

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, optional) — The tensors corresponding to the input images. Pixel values can be obtained using `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- interpolate_pos_encoding (`bool`, defaults to `False`) — Whether to interpolate the pre-trained position encodings.

Returns

vision_outputs (`BaseModelOutputWithPooling` or tuple of `torch.FloatTensor`)

The vision model outputs. If `return_dict=True`, the output is a `BaseModelOutputWithPooling` that contains the image features, the pooled image features and the hidden states if `output_hidden_states=True`.

Examples:

    >>> import torch
    >>> from PIL import Image
    >>> import requests
    >>> from transformers import Blip2Processor, Blip2Model

    >>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
    >>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")

    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)
    >>> inputs = processor(images=image, return_tensors="pt")
    >>> qformer_outputs = model.get_qformer_features(**inputs)

##
Blip2ForConditionalGeneration

###
class transformers.Blip2ForConditionalGeneration

< source >

( config: Blip2Config )

Parameters

- config (Blip2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

BLIP-2 Model for generating text given an image and an optional text prompt. The model consists of a vision encoder, Querying Transformer (Q-Former) and a language model.

One can optionally pass `input_ids` to the model, which serve as a text prompt, to make the language model continue the prompt. Otherwise, the language model starts generating text from the [BOS] (beginning-of-sequence) token.

Note that Flan-T5 checkpoints cannot be cast to float16. They are pre-trained using bfloat16.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( pixel_values: FloatTensor input_ids: FloatTensor attention_mask: typing.Optional[torch.LongTensor] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.LongTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None labels: typing.Optional[torch.LongTensor] = None return_dict: typing.Optional[bool] = None interpolate_pos_encoding: bool = False use_cache: typing.Optional[bool] = None **kwargs: typing_extensions.Unpack[transformers.models.blip_2.modeling_blip_2.KwargsForCausalLM] ) →
`transformers.models.blip_2.modeling_blip_2.Blip2ForConditionalGenerationModelOutput` or `tuple(torch.FloatTensor)`

Parameters

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) — The tensors corresponding to the input images. Pixel values can be obtained using `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- input_ids (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be provided to serve as text prompt, which the language model can continue.

  Indices can be obtained using Blip2Processor. See `Blip2Processor.__call__()` for details.

  What are input IDs?

- attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional) — Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are decoder input IDs?

- decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, optional) — Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default.

  Only relevant in case an encoder-decoder language model (like T5) is used.

- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
- interpolate_pos_encoding (`bool`, defaults to `False`) — Whether to interpolate the pre-trained position encodings.
- use_cache (`bool`, optional) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).

Returns

`transformers.models.blip_2.modeling_blip_2.Blip2ForConditionalGenerationModelOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.blip_2.modeling_blip_2.Blip2ForConditionalGenerationModelOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (Blip2Config) and inputs.

- loss (`torch.FloatTensor`, optional, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`) — Language modeling loss from the language model.
- logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head of the language model.
- vision_outputs (`BaseModelOutputWithPooling`) — Outputs of the vision encoder.
- qformer_outputs (`BaseModelOutputWithPoolingAndCrossAttentions`) — Outputs of the Q-Former (Querying Transformer).
- language_model_outputs (`CausalLMOutputWithPast` or `Seq2SeqLMOutput`) — Outputs of the language model.

The Blip2ForConditionalGeneration forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

Prepare processor, model and image input

    >>> from PIL import Image
    >>> import requests
    >>> from transformers import Blip2Processor, Blip2ForConditionalGeneration
    >>> import torch

    >>> device = "cuda" if torch.cuda.is_available() else "cpu"

    >>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
    >>> model = Blip2ForConditionalGeneration.from_pretrained(
    ...     "Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.float16
    ... )  # doctest: +IGNORE_RESULT

    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)

Image captioning (without providing a text prompt):

    >>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)

    >>> generated_ids = model.generate(**inputs)
    >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
    >>> print(generated_text)
    two cats laying on a couch

Visual question answering (prompt = question):

    >>> prompt = "Question: how many cats are there? Answer:"
    >>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device="cuda", dtype=torch.float16)

    >>> generated_ids = model.generate(**inputs)
    >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
    >>> print(generated_text)
    two

Note that int8 inference is also supported through bitsandbytes. This greatly reduces the amount of memory used by the model while maintaining the same performance.

    >>> model = Blip2ForConditionalGeneration.from_pretrained(
    ...     "Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.bfloat16
    ... )  # doctest: +IGNORE_RESULT

    >>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device="cuda", dtype=torch.bfloat16)

    >>> generated_ids = model.generate(**inputs)
    >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
    >>> print(generated_text)
    two

####
generate

< source >

( pixel_values: FloatTensor input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.LongTensor] = None interpolate_pos_encoding: bool = False **generate_kwargs ) →
captions (list)

Parameters

- pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)) — Input images to be processed.
- input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), optional) — The sequence used as a prompt for the generation.
- attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices

Returns

captions (list)

A list of strings of length batch_size * num_captions.

Overrides `generate` function to be able to use the model as a conditional generator.

##
Blip2ForImageTextRetrieval

###
class transformers.Blip2ForImageTextRetrieval

< source >

( config: Blip2Config )

Parameters

- config (Blip2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

BLIP-2 Model with a vision and text projector, and a classification head on top. The model is used in the context of image-text retrieval. Given an image and a text, the model returns the probability of the text being relevant to the image.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( pixel_values: FloatTensor input_ids: LongTensor attention_mask: typing.Optional[torch.LongTensor] = None use_image_text_matching_head: typing.Optional[bool] = False output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
`transformers.models.blip_2.modeling_blip_2.Blip2ImageTextMatchingModelOutput` or `tuple(torch.FloatTensor)`

Parameters

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`) — The tensors corresponding to the input images. Pixel values can be obtained using `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be provided to serve as text prompt, which the language model can continue.

  Indices can be obtained using Blip2Processor. See `Blip2Processor.__call__()` for details.

  What are input IDs?

- attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- use_image_text_matching_head (`bool`, optional) — Whether to return the Image-Text Matching or Contrastive scores.
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

`transformers.models.blip_2.modeling_blip_2.Blip2ImageTextMatchingModelOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.blip_2.modeling_blip_2.Blip2ImageTextMatchingModelOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (Blip2Config) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `return_loss` is `True`) — Contrastive loss for image-text similarity.
- logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`) — The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text similarity scores.
- logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`) — The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image similarity scores.
- text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`) — The text embeddings obtained by applying the projection layer to the pooled output.
- image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`) — The image embeddings obtained by applying the projection layer to the pooled output.
- text_model_output (`BaseModelOutputWithPooling`) — The output of the Blip2QFormerModel.
- vision_model_output (`BaseModelOutputWithPooling`) — The output of the Blip2VisionModel.

The Blip2ForImageTextRetrieval forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

    >>> import torch
    >>> from PIL import Image
    >>> import requests
    >>> from transformers import AutoProcessor, Blip2ForImageTextRetrieval

    >>> device = "cuda" if torch.cuda.is_available() else "cpu"

    >>> model = Blip2ForImageTextRetrieval.from_pretrained("Salesforce/blip2-itm-vit-g", torch_dtype=torch.float16)
    >>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-itm-vit-g")

    >>> model.to(device)
    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)
    >>> text = "two cats laying on a pink blanket"

    >>> inputs = processor(images=image, text=text, return_tensors="pt").to(device, torch.float16)
    >>> itm_out = model(**inputs, use_image_text_matching_head=True)
    >>> logits_per_image = torch.nn.functional.softmax(itm_out.logits_per_image, dim=1)
    >>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities

    >>> print(f"{probs[0][0]:.1%} that image 0 is not '{text}'")
    26.9% that image 0 is not 'two cats laying on a pink blanket'

    >>> print(f"{probs[0][1]:.1%} that image 0 is '{text}'")
    73.0% that image 0 is 'two cats laying on a pink blanket'

    >>> texts = ["a photo of a cat", "a photo of a dog"]

    >>> inputs = processor(images=image, text=texts, return_tensors="pt").to(device, torch.float16)
    >>> itc_out = model(**inputs, use_image_text_matching_head=False)
    >>> logits_per_image = itc_out.logits_per_image  # this is the image-text similarity score
    >>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities

    >>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
    55.3% that image 0 is 'a photo of a cat'

    >>> print(f"{probs[0][1]:.1%} that image 0 is '{texts[1]}'")
    44.7% that image 0 is 'a photo of a dog'

##
Blip2TextModelWithProjection

###
class transformers.Blip2TextModelWithProjection

< source >

( config: Blip2Config )

Parameters

- config (Blip2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The Blip 2 Model with a projection layer on top (a linear layer on top of the pooled output).

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
`transformers.models.blip_2.modeling_blip_2.Blip2TextModelOutput` or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  - 1 for tokens that are not masked,
  - 0 for tokens that are masked.

  What are attention masks?

- position_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`.

  What are position IDs?

- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

`transformers.models.blip_2.modeling_blip_2.Blip2TextModelOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.blip_2.modeling_blip_2.Blip2TextModelOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (Blip2Config) and inputs.

- text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` optional returned when model is initialized with `with_projection=True`) — The text embeddings obtained by applying the projection layer to the pooler_output.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The Blip2TextModelWithProjection forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

    >>> import torch
    >>> from transformers import AutoProcessor, Blip2TextModelWithProjection

    >>> device = "cuda" if torch.cuda.is_available() else "cpu"

    >>> model = Blip2TextModelWithProjection.from_pretrained(
    ...     "Salesforce/blip2-itm-vit-g", torch_dtype=torch.float16
    ... )

    >>> model.to(device)
    >>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-itm-vit-g")

    >>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], return_tensors="pt").to(device)

    >>> outputs = model(**inputs)
    >>> text_embeds = outputs.text_embeds
    >>> print(text_embeds.shape)
    torch.Size([2, 7, 256])

##
Blip2VisionModelWithProjection

###
class transformers.Blip2VisionModelWithProjection

< source >

( config: Blip2Config )

Parameters

- config (Blip2Config) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The Blip 2 Model with a projection layer on top (a linear layer on top of the pooled output).

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( pixel_values: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
`transformers.models.blip_2.modeling_blip_2.Blip2VisionModelOutput` or `tuple(torch.FloatTensor)`

Parameters

- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, optional) — The tensors corresponding to the input images. Pixel values can be obtained using `{image_processor_class}`. See `{image_processor_class}.__call__` for details (`{processor_class}` uses `{image_processor_class}` for processing images).
- output_attentions (`bool`, optional) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
- output_hidden_states (`bool`, optional) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
- return_dict (`bool`, optional) — Whether or not to return a ModelOutput instead of a plain tuple.

Returns

`transformers.models.blip_2.modeling_blip_2.Blip2VisionModelOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.blip_2.modeling_blip_2.Blip2VisionModelOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (Blip2Config) and inputs.

- image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` optional returned when model is initialized with `with_projection=True`) — The image embeddings obtained by applying the projection layer to the pooler_output.

- last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The Blip2VisionModelWithProjection forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

    >>> import torch
    >>> from PIL import Image
    >>> import requests
    >>> from transformers import AutoProcessor, Blip2VisionModelWithProjection

    >>> device = "cuda" if torch.cuda.is_available() else "cpu"

    >>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-itm-vit-g")
    >>> model = Blip2VisionModelWithProjection.from_pretrained(
    ...     "Salesforce/blip2-itm-vit-g", torch_dtype=torch.float16
    ... )
    >>> model.to(device)
    >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    >>> image = Image.open(requests.get(url, stream=True).raw)

    >>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)

    >>> outputs = model(**inputs)
    >>> image_embeds = outputs.image_embeds
    >>> print(image_embeds.shape)
    torch.Size([1, 32, 256])

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