Hugging Face's logo

Transformers documentation

Vision Encoder Decoder Models

You are viewing main version, which requires installation from source. If you'd like regular pip install, checkout the latest stable version (v4.53.3).
Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

This model was released on 2021-09-21 and added to Hugging Face Transformers on 2021-10-13.

Vision Encoder Decoder Models

PyTorch FlashAttention SDPA

Overview

The VisionEncoderDecoderModel can be used to initialize an image-to-text model with any pretrained Transformer-based vision model as the encoder (e.g. ViT, BEiT, DeiT, Swin) and any pretrained language model as the decoder (e.g. RoBERTa, GPT2, BERT, DistilBERT).

The effectiveness of initializing image-to-text-sequence models with pretrained checkpoints has been shown in (for example) TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.

After such a VisionEncoderDecoderModel has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples below for more information).

An example application is image captioning, in which the encoder is used to encode the image, after which an autoregressive language model generates the caption. Another example is optical character recognition. Refer to TrOCR, which is an instance of VisionEncoderDecoderModel.

Randomly initializing VisionEncoderDecoderModel from model configurations.

VisionEncoderDecoderModel can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default ViTModel configuration for the encoder and the default BertForCausalLM configuration for the decoder.

>>> from transformers import BertConfig, ViTConfig, VisionEncoderDecoderConfig, VisionEncoderDecoderModel

>>> config_encoder = ViTConfig()
>>> config_decoder = BertConfig()

>>> config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
>>> model = VisionEncoderDecoderModel(config=config)

Initialising VisionEncoderDecoderModel from a pretrained encoder and a pretrained decoder.

VisionEncoderDecoderModel can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. Note that any pretrained Transformer-based vision model, e.g. Swin, can serve as the encoder and both pretrained auto-encoding models, e.g. BERT, pretrained causal language models, e.g. GPT2, as well as the pretrained decoder part of sequence-to-sequence models, e.g. decoder of BART, can be used as the decoder. Depending on which architecture you choose as the decoder, the cross-attention layers might be randomly initialized. Initializing VisionEncoderDecoderModel from a pretrained encoder and decoder checkpoint requires the model to be fine-tuned on a downstream task, as has been shown in the Warm-starting-encoder-decoder blog post. To do so, the VisionEncoderDecoderModel class provides a VisionEncoderDecoderModel.from_encoder_decoder_pretrained() method.

>>> from transformers import VisionEncoderDecoderModel

>>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
...     "microsoft/swin-base-patch4-window7-224-in22k", "google-bert/bert-base-uncased"
... )

Loading an existing VisionEncoderDecoderModel checkpoint and perform inference.

To load fine-tuned checkpoints of the VisionEncoderDecoderModel class, VisionEncoderDecoderModel provides the from_pretrained(...) method just like any other model architecture in Transformers.

To perform inference, one uses the generate method, which allows to autoregressively generate text. This method supports various forms of decoding, such as greedy, beam search and multinomial sampling.

>>> import requests
>>> from PIL import Image

>>> from transformers import GPT2TokenizerFast, ViTImageProcessor, VisionEncoderDecoderModel

>>> # load a fine-tuned image captioning model and corresponding tokenizer and image processor
>>> model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
>>> tokenizer = GPT2TokenizerFast.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
>>> image_processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")

>>> # let's perform inference on an image
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> pixel_values = image_processor(image, return_tensors="pt").pixel_values

>>> # autoregressively generate caption (uses greedy decoding by default)
>>> generated_ids = model.generate(pixel_values)
>>> generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> print(generated_text)
a cat laying on a blanket next to a cat laying on a bed

Training

Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model on a dataset of (image, text) pairs. As you can see, only 2 inputs are required for the model in order to compute a loss: pixel_values (which are the images) and labels (which are the input_ids of the encoded target sequence).

>>> from transformers import ViTImageProcessor, BertTokenizer, VisionEncoderDecoderModel
>>> from datasets import load_dataset

>>> image_processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
>>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
...     "google/vit-base-patch16-224-in21k", "google-bert/bert-base-uncased"
... )

>>> model.config.decoder_start_token_id = tokenizer.cls_token_id
>>> model.config.pad_token_id = tokenizer.pad_token_id

>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> pixel_values = image_processor(image, return_tensors="pt").pixel_values

>>> labels = tokenizer(
...     "an image of two cats chilling on a couch",
...     return_tensors="pt",
... ).input_ids

>>> # the forward function automatically creates the correct decoder_input_ids
>>> loss = model(pixel_values=pixel_values, labels=labels).loss

This model was contributed by nielsr.

VisionEncoderDecoderConfig

class transformers.VisionEncoderDecoderConfig

< >

( **kwargs )

Parameters

  • kwargs (optional) — Dictionary of keyword arguments. Notably:

    • encoder (PretrainedConfig, optional) — An instance of a configuration object that defines the encoder config.
    • decoder (PretrainedConfig, optional) — An instance of a configuration object that defines the decoder config.

VisionEncoderDecoderConfig is the configuration class to store the configuration of a VisionEncoderDecoderModel. It is used to instantiate a Vision-Encoder-Text-Decoder model according to the specified arguments, defining the encoder and decoder configs.

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

Examples:

>>> from transformers import BertConfig, ViTConfig, VisionEncoderDecoderConfig, VisionEncoderDecoderModel

>>> # Initializing a ViT & BERT style configuration
>>> config_encoder = ViTConfig()
>>> config_decoder = BertConfig()

>>> config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)

>>> # Initializing a ViTBert model (with random weights) from a ViT & google-bert/bert-base-uncased style configurations
>>> model = VisionEncoderDecoderModel(config=config)

>>> # Accessing the model configuration
>>> config_encoder = model.config.encoder
>>> config_decoder = model.config.decoder
>>> # set decoder config to causal lm
>>> config_decoder.is_decoder = True
>>> config_decoder.add_cross_attention = True

>>> # Saving the model, including its configuration
>>> model.save_pretrained("my-model")

>>> # loading model and config from pretrained folder
>>> encoder_decoder_config = VisionEncoderDecoderConfig.from_pretrained("my-model")
>>> model = VisionEncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config)

from_encoder_decoder_configs

< >

( encoder_config: PretrainedConfig decoder_config: PretrainedConfig **kwargs ) → VisionEncoderDecoderConfig

Returns

VisionEncoderDecoderConfig

An instance of a configuration object

Instantiate a VisionEncoderDecoderConfig (or a derived class) from a pre-trained encoder model configuration and decoder model configuration.

VisionEncoderDecoderModel

class transformers.VisionEncoderDecoderModel

< >

( config: typing.Optional[transformers.configuration_utils.PretrainedConfig] = None encoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None decoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None )

Parameters

  • config (PretrainedConfig, optional) — 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.
  • encoder (PreTrainedModel, optional) — The encoder model to use.
  • decoder (PreTrainedModel, optional) — The decoder model to use.

The bare Vision Encoder Decoder 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

< >

( pixel_values: typing.Optional[torch.FloatTensor] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.BoolTensor] = None encoder_outputs: typing.Optional[tuple[torch.FloatTensor]] = None past_key_values: typing.Optional[tuple[tuple[torch.FloatTensor]]] = None decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = 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 cache_position: typing.Optional[torch.LongTensor] = None **kwargs ) → transformers.modeling_outputs.Seq2SeqLMOutput 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).
  • 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 PreTrainedTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are input IDs?

    If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values).

    For training, decoder_input_ids are automatically created by the model by shifting the labels to the right, replacing -100 by the pad_token_id and prepending them with the decoder_start_token_id.

  • decoder_attention_mask (torch.BoolTensor 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.
  • encoder_outputs (tuple[torch.FloatTensor], optional) — Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
  • 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.

    Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) — Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
  • labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss for the decoder. Indices should be in [-100, 0, ..., config.vocab_size] (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]
  • 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.
  • cache_position (torch.LongTensor of shape (sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.

Returns

transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Seq2SeqLMOutput 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 (VisionEncoderDecoderConfig) and inputs.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.

  • logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

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

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

  • decoder_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 decoder at the output of each layer plus the initial embedding outputs.

  • decoder_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 of the decoder, 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 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.

  • encoder_last_hidden_state (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 of the model.

  • encoder_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 encoder at the output of each layer plus the initial embedding outputs.

  • encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The VisionEncoderDecoderModel 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 transformers import AutoProcessor, VisionEncoderDecoderModel
>>> import requests
>>> from PIL import Image
>>> import torch

>>> processor = AutoProcessor.from_pretrained("microsoft/trocr-base-handwritten")
>>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")

>>> # load image from the IAM dataset
>>> url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

>>> # training
>>> model.config.decoder_start_token_id = processor.tokenizer.eos_token_id
>>> model.config.pad_token_id = processor.tokenizer.pad_token_id
>>> model.config.vocab_size = model.config.decoder.vocab_size

>>> pixel_values = processor(image, return_tensors="pt").pixel_values
>>> text = "hello world"
>>> labels = processor.tokenizer(text, return_tensors="pt").input_ids
>>> outputs = model(pixel_values=pixel_values, labels=labels)
>>> loss = outputs.loss

>>> # inference (generation)
>>> generated_ids = model.generate(pixel_values)
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

from_encoder_decoder_pretrained

< >

( encoder_pretrained_model_name_or_path: typing.Optional[str] = None decoder_pretrained_model_name_or_path: typing.Optional[str] = None *model_args **kwargs )

Parameters

  • encoder_pretrained_model_name_or_path (str, optional) — Information necessary to initiate the image encoder. Can be either:

    • A string, the model id of a pretrained model hosted inside a model repo on huggingface.co. An example is google/vit-base-patch16-224-in21k.
    • A path to a directory containing model weights saved using save_pretrained(), e.g., ./my_model_directory/.
    • A path or url to a tensorflow index checkpoint file (e.g, ./tf_model/model.ckpt.index). In this case, from_tf should be set to True and a configuration object should be provided as config argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
  • decoder_pretrained_model_name_or_path (str, optional, defaults to None) — Information necessary to initiate the text decoder. Can be either:

    • A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
    • A path to a directory containing model weights saved using save_pretrained(), e.g., ./my_model_directory/.
    • A path or url to a tensorflow index checkpoint file (e.g, ./tf_model/model.ckpt.index). In this case, from_tf should be set to True and a configuration object should be provided as config argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
  • model_args (remaining positional arguments, optional) — All remaining positional arguments will be passed to the underlying model’s __init__ method.
  • kwargs (remaining dictionary of keyword arguments, optional) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., output_attentions=True).

    • To update the encoder configuration, use the prefix encoder_ for each configuration parameter.
    • To update the decoder configuration, use the prefix decoder_ for each configuration parameter.
    • To update the parent model configuration, do not use a prefix for each configuration parameter.

    Behaves differently depending on whether a config is provided or automatically loaded.

Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model checkpoints.

The model is set in evaluation mode by default using model.eval() (Dropout modules are deactivated). To train the model, you need to first set it back in training mode with model.train().

Example:

>>> from transformers import VisionEncoderDecoderModel

>>> # initialize a vit-bert from a pretrained ViT and a pretrained BERT model. Note that the cross-attention layers will be randomly initialized
>>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
...     "google/vit-base-patch16-224-in21k", "google-bert/bert-base-uncased"
... )
>>> # saving model after fine-tuning
>>> model.save_pretrained("./vit-bert")
>>> # load fine-tuned model
>>> model = VisionEncoderDecoderModel.from_pretrained("./vit-bert")
< > Update on GitHub

←VipLlava Vision Text Dual Encoder→