Vision Encoder Decoder Models
Last updated
Last updated
The can be used to initialize an image-to-text model with any pretrained Transformer-based vision model as the encoder (e.g. , , , ) and any pretrained language model as the decoder (e.g. , , , ).
The effectiveness of initializing image-to-text-sequence models with pretrained checkpoints has been shown in (for example) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
After such a 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 , which is an instance of .
can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default configuration for the encoder and the default BertForCausalLM configuration for the decoder.
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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.
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TFVisionEncoderDecoderModel.from_pretrained() currently doesn’t support initializing the model from a PyTorch checkpoint. Passing from_pt=True to this method will throw an exception. If there are only PyTorch checkpoints for a particular vision encoder-decoder model, a workaround is:
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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).
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( **kwargs )
Parameters
kwargs (optional) — Dictionary of keyword arguments. Notably:
Examples:
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from_encoder_decoder_configs
Returns
An instance of a configuration object
( config: typing.Optional[transformers.configuration_utils.PretrainedConfig] = Noneencoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = Nonedecoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None )
Parameters
After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information).
forward
Parameters
decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
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) — This tuple must consist of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) is a tensor 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)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) — Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_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) — If set to True, the model will return a ~utils.Seq2SeqLMOutput instead of a plain tuple.
kwargs (optional) — Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:
Without a prefix which will be input as **encoder_kwargs for the encoder forward function.
With a decoder_ prefix which will be input as **decoder_kwargs for the decoder forward function.
Returns
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 (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — 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)) and 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
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.
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:
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from_encoder_decoder_pretrained
( encoder_pretrained_model_name_or_path: str = Nonedecoder_pretrained_model_name_or_path: 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 boincai.com. An example is google/vit-base-patch16-224-in21k.
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 boincai.com. Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased.
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 remaning 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:
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( *args**kwargs )
Parameters
After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information).
call
Parameters
decoder_input_ids (np.ndarray or tf.Tensor of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values).
decoder_attention_mask (np.ndarray or tf.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.
encoder_outputs (tuple(tuple(tf.Tensor), optional) — This tuple must consist of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) is a tensor 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(tf.Tensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) — Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).
decoder_inputs_embeds (np.ndarray or tf.Tensor 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 (np.ndarray or tf.Tensor 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) — If set to True, the model will return a ~utils.Seq2SeqLMOutput instead of a plain tuple.
training (bool, optional, defaults to False) — Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
kwargs (optional) — Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:
Without a prefix which will be input as **encoder_kwargs for the encoder forward function.
With a decoder_ prefix which will be input as **decoder_kwargs for the decoder forward function.
Returns
loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) — Language modeling loss.
logits (tf.Tensor 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 (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) — List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + 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(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (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(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (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 (tf.Tensor 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(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of tf.Tensor (one for the output of the embeddings + 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(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of tf.Tensor (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.
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:
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from_encoder_decoder_pretrained
( encoder_pretrained_model_name_or_path: str = Nonedecoder_pretrained_model_name_or_path: str = None*model_args**kwargs )
Parameters
encoder_pretrained_model_name_or_path (str, optional) — Information necessary to initiate the encoder. Can be either:
A string, the model id of a pretrained model hosted inside a model repo on boincai.com. An example is google/vit-base-patch16-224-in21k.
A path or url to a pytorch index checkpoint file (e.g, ./pt_model/). In this case, encoder_from_pt should be set to True.
decoder_pretrained_model_name_or_path (str, optional, defaults to None) — Information necessary to initiate the decoder. Can be either:
A string, the model id of a pretrained model hosted inside a model repo on boincai.com. Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased.
A path or url to a pytorch checkpoint file (e.g, ./pt_model/). In this case, decoder_from_pt should be set to True.
model_args (remaining positional arguments, optional) — All remaning 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.
Example:
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( config: VisionEncoderDecoderConfiginput_shape: typing.Optional[typing.Tuple] = Noneseed: int = 0dtype: dtype = <class 'jax.numpy.float32'>_do_init: bool = True**kwargs )
Parameters
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) — The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.
After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information).
__call__
Parameters
decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length), optional) — Indices of decoder input sequence tokens in the vocabulary.
decoder_attention_mask (jnp.ndarray 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.
decoder_position_ids (jnp.ndarray of shape (batch_size, sequence_length), optional) — Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.decoder.max_position_embeddings - 1].
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) — If set to True, the model will return a ~utils.FlaxSeq2SeqLMOutput instead of a plain tuple.
Returns
logits (jnp.ndarray 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 (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(jnp.ndarray) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
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(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + 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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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 (jnp.ndarray 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(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (one for the output of the embeddings + 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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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.
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:
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from_encoder_decoder_pretrained
( encoder_pretrained_model_name_or_path: typing.Union[str, os.PathLike, NoneType] = Nonedecoder_pretrained_model_name_or_path: typing.Union[str, os.PathLike, NoneType] = None*model_args**kwargs )
Parameters
encoder_pretrained_model_name_or_path (Union[str, os.PathLike], optional) — Information necessary to initiate the encoder. Can be either:
A string, the model id of a pretrained model hosted inside a model repo on boincai.com. An example is google/vit-base-patch16-224-in21k.
decoder_pretrained_model_name_or_path (Union[str, os.PathLike], optional, defaults to None) — Information necessary to initiate the decoder. Can be either:
A string, the model id of a pretrained model hosted inside a model repo on boincai.com. Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased.
model_args (remaining positional arguments, optional) — All remaning 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.
Example:
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can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. Note that any pretrained Transformer-based vision model, e.g. , 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 from a pretrained encoder and decoder checkpoint requires the model to be fine-tuned on a downstream task, as has been shown in . To do so, the VisionEncoderDecoderModel class provides a method.
To load fine-tuned checkpoints of the VisionEncoderDecoderModel class, provides the from_pretrained(...) method just like any other model architecture in Transformers.
This model was contributed by . This model’s TensorFlow and Flax versions were contributed by .
encoder (, optional) — An instance of a configuration object that defines the encoder config.
decoder (, optional) — An instance of a configuration object that defines the decoder config.
is the configuration class to store the configuration of a . 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 and can be used to control the model outputs. Read the documentation from for more information.
( encoder_config: PretrainedConfigdecoder_config: PretrainedConfig**kwargs ) →
Instantiate a (or a derived class) from a pre-trained encoder model configuration and decoder model configuration.
config () — 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 method to load the model weights.
This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via function and the decoder is loaded via function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream generative task, like image captioning.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks was shown in by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
Additionally, in it is shown how leveraging large pretrained vision models for optical character recognition (OCR) yields a significant performance improvement.
This model inherits from . 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 subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
is a generic model class that will be instantiated as a transformer architecture with one of the base vision model classes of the library as encoder and another one as decoder when created with the :meth~transformers.AutoModel.from_pretrained class method for the encoder and :meth~transformers.AutoModelForCausalLM.from_pretrained class method for the decoder.
( pixel_values: typing.Optional[torch.FloatTensor] = Nonedecoder_input_ids: typing.Optional[torch.LongTensor] = Nonedecoder_attention_mask: typing.Optional[torch.BoolTensor] = Noneencoder_outputs: typing.Optional[typing.Tuple[torch.FloatTensor]] = Nonepast_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = Nonedecoder_inputs_embeds: typing.Optional[torch.FloatTensor] = Nonelabels: typing.Optional[torch.LongTensor] = Noneuse_cache: typing.Optional[bool] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None**kwargs ) → or tuple(torch.FloatTensor)
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using an image processor (e.g. if you use ViT as the encoder, you should use ). See for details.
Indices can be obtained using . See and for details.
or tuple(torch.FloatTensor)
A 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 () and inputs.
The forward method, overrides the __call__ special method.
A path to a directory containing model weights saved using , e.g., ./my_model_directory/.
A path to a directory containing model weights saved using , e.g., ./my_model_directory/.
config () — 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 method to load the model weights.
This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via function and the decoder is loaded via function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream generative task, like image captioning.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks was shown in by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
Additionally, in it is shown how leveraging large pretrained vision models for optical character recognition (OCR) yields a significant performance improvement.
This model inherits from . 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 subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
is a generic model class that will be instantiated as a transformer architecture with one of the base vision model classes of the library as encoder and another one of the base model classes as decoder when created with the class method for the encoder and class method for the decoder.
( pixel_values: np.ndarray | tf.Tensor | None = Nonedecoder_input_ids: np.ndarray | tf.Tensor | None = Nonedecoder_attention_mask: np.ndarray | tf.Tensor | None = Noneencoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = Nonepast_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = Nonedecoder_inputs_embeds: np.ndarray | tf.Tensor | None = Nonelabels: np.ndarray | tf.Tensor | None = Noneuse_cache: Optional[bool] = Noneoutput_attentions: Optional[bool] = Noneoutput_hidden_states: Optional[bool] = Nonereturn_dict: Optional[bool] = Nonetraining: bool = False**kwargs ) → or tuple(tf.Tensor)
pixel_values (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using the vision’s model’s image processor. For example, using . See for details.
Indices can be obtained using . See and for details.
Provide for sequence to sequence training to the decoder. Indices can be obtained using . See and for details.
or tuple(tf.Tensor)
A or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration () and inputs.
The forward method, overrides the __call__ special method.
A path to a directory containing model weights saved using , e.g., ./my_model_directory/.
A path to a directory containing model weights saved using , e.g., ./my_model_directory/.
config () — 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 method to load the model weights.
If you wish to change the dtype of the model parameters, see and .
This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via function and the decoder is loaded via function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream generative task, like image captioning.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks was shown in by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
Additionally, in it is shown how leveraging large pretrained vision models for optical character recognition (OCR) yields a significant performance improvement.
This model inherits from . 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 Flax Linen subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
is a generic model class that will be instantiated as a transformer architecture with the module (flax.nn.Module) of one of the base vision model classes of the library as encoder module and another one as decoder module when created with the :meth~transformers.FlaxAutoModel.from_pretrained class method for the encoder and :meth~transformers.FlaxAutoModelForCausalLM.from_pretrained class method for the decoder.
( pixel_values: Arraydecoder_input_ids: typing.Optional[jax.Array] = Nonedecoder_attention_mask: typing.Optional[jax.Array] = Nonedecoder_position_ids: typing.Optional[jax.Array] = Noneoutput_attentions: typing.Optional[bool] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = Nonetrain: bool = Falseparams: dict = Nonedropout_rng: PRNGKey = None ) → or tuple(torch.FloatTensor)
pixel_values (jnp.ndarray of shape (batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using the vision model’s image processor. For example, using . See for details.
Indices can be obtained using . See and for details.
or tuple(torch.FloatTensor)
A 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 () and inputs.
The forward method, overrides the __call__ special method.
A path to a directory containing model weights saved using , e.g., ./my_model_directory/.
A path to a directory containing model weights saved using , e.g., ./my_model_directory/.