
 
Pix2Struct
	
Overview
	
The Pix2Struct model was proposed in Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova.
The abstract from the paper is the following:
Visually-situated language is ubiquitous — sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to this diversity, previous work has typically relied on domain-specific recipes with limited sharing of the underlying data, model architectures, and objectives. We present Pix2Struct, a pretrained image-to-text model for purely visual language understanding, which can be finetuned on tasks containing visually-situated language. Pix2Struct is pretrained by learning to parse masked screenshots of web pages into simplified HTML. The web, with its richness of visual elements cleanly reflected in the HTML structure, provides a large source of pretraining data well suited to the diversity of downstream tasks. Intuitively, this objective subsumes common pretraining signals such as OCR, language modeling, image captioning. In addition to the novel pretraining strategy, we introduce a variable-resolution input representation and a more flexible integration of language and vision inputs, where language prompts such as questions are rendered directly on top of the input image. For the first time, we show that a single pretrained model can achieve state-of-the-art results in six out of nine tasks across four domains: documents, illustrations, user interfaces, and natural images.
Tips:
Pix2Struct has been fine tuned on a variety of tasks and datasets, ranging from image captioning, visual question answering (VQA) over different inputs (books, charts, science diagrams), captioning UI components etc. The full list can be found in Table 1 of the paper.
We therefore advise you to use these models for the tasks they have been fine tuned on. For instance, if you want to use Pix2Struct for UI captioning, you should use the model fine tuned on the UI dataset. If you want to use Pix2Struct for image captioning, you should use the model fine tuned on the natural images captioning dataset and so on.
If you want to use the model to perform conditional text captioning, make sure to use the processor with add_special_tokens=False.
This model was contributed by ybelkada.
The original code can be found here.
Resources
	
Fine-tuning Notebook
All models
Pix2StructConfig
	
class transformers.Pix2StructConfig
<
source
>
(
text_config = None
vision_config = None
initializer_factor = 1.0
initializer_range = 0.02
is_vqa = False
tie_word_embeddings = False
is_encoder_decoder = True
**kwargs
)
Parameters 
text_config (dict, optional) —
Dictionary of configuration options used to initialize Pix2StructTextConfig.
vision_config (dict, optional) —
Dictionary of configuration options used to initialize Pix2StructVisionConfig.
initializer_factor (float, optional, defaults to 1.0) —
Factor to multiply the initialization range with.
initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
is_vqa (bool, optional, defaults to False) —
Whether the model has been fine-tuned for VQA or not.
kwargs (optional) —
Dictionary of keyword arguments.
Pix2StructConfig is the configuration class to store the configuration of a
Pix2StructForConditionalGeneration. It is used to instantiate a Pix2Struct model according to the specified
arguments, defining the text model and vision model configs. Instantiating a configuration with the defaults will
yield a similar configuration to that of the Pix2Struct-base
google/pix2struct-base architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
	Copied
 from transformers import Pix2StructConfig, Pix2StructForConditionalGeneration
 # Initializing a Pix2StructConfig with google/pix2struct-base style configuration
 configuration = Pix2StructConfig()
 # Initializing a Pix2StructForConditionalGeneration (with random weights) from the google/pix2struct-base style configuration
 model = Pix2StructForConditionalGeneration(configuration)
 # Accessing the model configuration
 configuration = model.config
 # We can also initialize a Pix2StructConfig from a Pix2StructTextConfig and a Pix2StructVisionConfig
 # Initializing a Pix2Struct text and Pix2Struct vision configuration
 config_text = Pix2StructTextConfig()
 config_vision = Pix2StructVisionConfig()
 config = Pix2StructConfig.from_text_vision_configs(config_text, config_vision)
from_text_vision_configs
<
source
>
(
text_config: Pix2StructTextConfig
vision_config: Pix2StructVisionConfig
**kwargs
)
→
Pix2StructConfig
Returns
Pix2StructConfig
An instance of a configuration object
Instantiate a Pix2StructConfig (or a derived class) from pix2struct text model configuration and pix2struct
vision model configuration.
Pix2StructTextConfig
	
class transformers.Pix2StructTextConfig
<
source
>
(
vocab_size = 50244
hidden_size = 768
d_kv = 64
d_ff = 2048
num_layers = 12
num_heads = 12
relative_attention_num_buckets = 32
relative_attention_max_distance = 128
dropout_rate = 0.1
layer_norm_epsilon = 1e-06
initializer_factor = 1.0
dense_act_fn = 'gelu_new'
decoder_start_token_id = 0
use_cache = False
pad_token_id = 0
eos_token_id = 1
tie_word_embeddings = False
is_decoder = True
**kwargs
)
Parameters 
vocab_size (int, optional, defaults to 50244) —
Vocabulary size of the Pix2Struct text model. Defines the number of different tokens that can be
represented by the inputs_ids passed when calling Pix2StructTextModel.
hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
d_kv (int, optional, defaults to 64) —
Dimensionality of the key, query, value projections in each attention head.
d_ff (int, optional, defaults to 2048) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
num_layers (int, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder.
num_heads (int, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder.
relative_attention_num_buckets (int, optional, defaults to 32) —
The number of buckets to use for each attention layer.
relative_attention_max_distance (int, optional, defaults to 128) —
The maximum distance of the longer sequences for the bucket separation.
dropout_rate (float, optional, defaults to 0.1) —
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
layer_norm_epsilon (float, optional, defaults to 1e-6) —
The epsilon used by the layer normalization layers.
initializer_factor (float, optional, defaults to 1.0) —
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
dense_act_fn (Union[Callable, str], optional, defaults to "gelu_new") —
The non-linear activation function (function or string).
decoder_start_token_id (int, optional, defaults to 0) —
The id of the decoder_start_token_id token.
use_cache (bool, optional, defaults to False) —
Whether or not the model should return the last key/values attentions (not used by all models).
pad_token_id (int, optional, defaults to 0) —
The id of the padding token.
eos_token_id (int, optional, defaults to 1) —
The id of the end-of-sequence token.
This is the configuration class to store the configuration of a Pix2StructTextModel. It is used to instantiate
a Pix2Struct text 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 Pix2Struct text decoder used by
the google/pix2struct-base architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
	Copied
 from transformers import Pix2StructTextConfig, Pix2StructTextModel
 # Initializing a Pix2StructTextConfig with google/pix2struct-base style configuration
 configuration = Pix2StructTextConfig()
 # Initializing a Pix2StructTextModel (with random weights) from the google/pix2struct-base style configuration
 model = Pix2StructTextModel(configuration)
 # Accessing the model configuration
 configuration = model.config
Pix2StructVisionConfig
	
class transformers.Pix2StructVisionConfig
<
source
>
(
hidden_size = 768
patch_embed_hidden_size = 768
d_ff = 2048
d_kv = 64
num_hidden_layers = 12
num_attention_heads = 12
dense_act_fn = 'gelu_new'
layer_norm_eps = 1e-06
dropout_rate = 0.0
attention_dropout = 0.0
initializer_range = 1e-10
initializer_factor = 1.0
seq_len = 4096
relative_attention_num_buckets = 32
relative_attention_max_distance = 128
**kwargs
)
Parameters 
hidden_size (int, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer.
patch_embed_hidden_size (int, optional, defaults to 768) —
Dimensionality of the input patch_embedding layer in the Transformer encoder.
d_ff (int, optional, defaults to 2048) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
d_kv (int, optional, defaults to 64) —
Dimensionality of the key, query, value projections per attention head.
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.
dense_act_fn (str or function, optional, defaults to "gelu_new") —
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-6) —
The epsilon used by the layer normalization layers.
dropout_rate (float, optional, defaults to 0.0) —
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (float, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities.
initializer_range (float, optional, defaults to 1e-10) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
initializer_factor (`float“, optional, defaults to 1) —
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
seq_len (int, optional, defaults to 4096) —
Maximum sequence length (here number of patches) supported by the model.
relative_attention_num_buckets (int, optional, defaults to 32) —
The number of buckets to use for each attention layer.
relative_attention_max_distance (int, optional, defaults to 128) —
The maximum distance (in tokens) to use for each attention layer.
This is the configuration class to store the configuration of a Pix2StructVisionModel. It is used to
instantiate a Pix2Struct vision model according to the specified arguments, defining the model architecture.
Instantiating a configuration defaults will yield a similar configuration to that of the Pix2Struct-base
google/pix2struct-base architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Example:
	Copied
 from transformers import Pix2StructVisionConfig, Pix2StructVisionModel
 # Initializing a Pix2StructVisionConfig with google/pix2struct-base style configuration
 configuration = Pix2StructVisionConfig()
 # Initializing a Pix2StructVisionModel (with random weights) from the google/pix2struct-base style configuration
 model = Pix2StructVisionModel(configuration)
 # Accessing the model configuration
 configuration = model.config
Pix2StructProcessor
	
class transformers.Pix2StructProcessor
<
source
>
(
image_processor
tokenizer
)
Parameters 
image_processor (Pix2StructImageProcessor) —
An instance of Pix2StructImageProcessor. The image processor is a required input.
tokenizer (Union[T5TokenizerFast, T5Tokenizer]) —
An instance of [‘T5TokenizerFast`] or [‘T5Tokenizer`]. The tokenizer is a required input.
Constructs a PIX2STRUCT processor which wraps a BERT tokenizer and PIX2STRUCT image processor into a single
processor.
Pix2StructProcessor offers all the functionalities of Pix2StructImageProcessor and T5TokenizerFast. See
the docstring of __call__() and decode() for more information.
batch_decode
<
source
>
(
*args
**kwargs
)
This method forwards all its arguments to Pix2StructTokenizerFast’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 Pix2StructTokenizerFast’s decode(). Please
refer to the docstring of this method for more information.
Pix2StructImageProcessor
	
class transformers.Pix2StructImageProcessor
<
source
>
(
do_convert_rgb: bool = True
do_normalize: bool = True
patch_size: typing.Dict[str, int] = None
max_patches: int = 2048
is_vqa: bool = False
**kwargs
)
Parameters 
do_convert_rgb (bool, optional, defaults to True) —
Whether to convert the image to RGB.
do_normalize (bool, optional, defaults to True) —
Whether to normalize the image. Can be overridden by the do_normalize parameter in the preprocess
method. According to Pix2Struct paper and code, the image is normalized with its own mean and standard
deviation.
patch_size (Dict[str, int], optional, defaults to {"height" -- 16, "width": 16}):
The patch size to use for the image. According to Pix2Struct paper and code, the patch size is 16x16.
max_patches (int, optional, defaults to 2048) —
The maximum number of patches to extract from the image as per the Pix2Struct
paper.
is_vqa (bool, optional, defaults to False) —
Whether or not the image processor is for the VQA task. If True and header_text is passed in, text is
rendered onto the input images.
Constructs a Pix2Struct image processor.
preprocess
<
source
>
(
images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]
header_text: typing.Optional[str] = None
do_convert_rgb: bool = None
do_normalize: typing.Optional[bool] = None
max_patches: typing.Optional[int] = None
patch_size: typing.Union[typing.Dict[str, int], NoneType] = None
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
data_format: ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>
**kwargs
)
Parameters 
images (ImageInput) —
Image to preprocess.
header_text (Union[List[str], str], optional) —
Text to render as a header. Only has an effect if image_processor.is_vqa is True.
do_convert_rgb (bool, optional, defaults to self.do_convert_rgb) —
Whether to convert the image to RGB.
do_normalize (bool, optional, defaults to self.do_normalize) —
Whether to normalize the image.
max_patches (int, optional, defaults to self.max_patches) —
Maximum number of patches to extract.
patch_size (dict, optional, defaults to self.patch_size) —
Dictionary containing the patch height and width.
return_tensors (str or TensorType, optional) —
The type of tensors to return. Can be one of:
Unset: Return a list of np.ndarray.
TensorType.TENSORFLOW or 'tf': Return a batch of type tf.Tensor.
TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.
TensorType.NUMPY or 'np': Return a batch of type np.ndarray.
TensorType.JAX or 'jax': Return a batch of type jax.numpy.ndarray.
Preprocess an image or batch of images. The processor first computes the maximum possible number of
aspect-ratio preserving patches of size patch_size that can be extracted from the image. It then pads the
image with zeros to make the image respect the constraint of max_patches. Before extracting the patches the
images are standardized following the tensorflow implementation of per_image_standardization
(https://www.tensorflow.org/api_docs/python/tf/image/per_image_standardization).
Pix2StructTextModel
	
class transformers.Pix2StructTextModel
<
source
>
(
config
)
Parameters 
config (Union[Pix2StructConfig, Pix2StructTextConfig]) —
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 standalone text decoder of Pix2Struct
The Pix2Struct model was proposed in Pix2Struct: Screenshot Parsing as Pretraining for Visual Language
Understanding by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu,
Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova. It’s an encoder decoder
transformer pre-trained in a image-to-text setting.
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 = None
attention_mask = None
encoder_hidden_states = None
encoder_attention_mask = None
inputs_embeds = None
head_mask = None
cross_attn_head_mask = None
past_key_values = None
use_cache = None
output_attentions = None
output_hidden_states = None
labels = None
return_dict = None
**kwargs
)
→
transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Pix2StructText is a model with relative position
embeddings so you should be able to pad the inputs on both the right and the left.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for detail.
What are input IDs?
To know more on how to prepare input_ids for pretraining take a look a Pix2StructText
Training.
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?
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?
Pix2StructText 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 Pix2StructText
Training.
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.
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
decoder_head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
cross_attn_head_mask (torch.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
[0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
encoder_outputs (tuple(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) 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)) 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 layers. 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).
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
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. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be
input (see past_key_values). 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.
If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value
of inputs_embeds.
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.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions 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 (Pix2StructConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
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).
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 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).
Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if config.is_decoder = True.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
past_key_values input) to speed up sequential decoding.
The Pix2StructTextModel 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.
Example:
	Copied
 from transformers import AutoProcessor, Pix2StructTextModel
 processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
 model = Pix2StructTextModel.from_pretrained("google/pix2struct-textcaps-base")
 inputs = processor(text="Hello, my dog is cute", return_tensors="pt")
 outputs = model(**inputs)
 loss = outputs.loss
Pix2StructVisionModel
	
class transformers.Pix2StructVisionModel
<
source
>
(
config: Pix2StructConfig
)
Parameters 
config (Pix2StructConfig) — 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 Pix2StructVision Model transformer outputting raw hidden-states without any specific head on top.
This model is 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
>
(
flattened_patches: typing.Optional[torch.Tensor] = None
attention_mask: typing.Optional[torch.Tensor] = None
head_mask: 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.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters 
flattened_patches (torch.FloatTensor of shape (batch_size, sequence_length, num_channels x patch_height x patch_width)) —
Flattened and padded pixel values. These values can be obtained using AutoImageProcessor. See
Pix2StructVisionImageProcessor.__call__ for details. Check the original
paper (figure 5) for more details.
attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding pixel values. Mask values selected in [0, 1]:
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.
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.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 (Pix2StructConfig) 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 Pix2StructVisionModel 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.
Example:
	Copied
 import requests
 from PIL import Image
 from transformers import AutoProcessor, Pix2StructVisionModel
 image_processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
 model = Pix2StructVisionModel.from_pretrained("google/pix2struct-textcaps-base")
 url = "https://www.ilankelman.org/stopsigns/australia.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 inputs = image_processor(images=image, return_tensors="pt")
 with torch.no_grad():
...     outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
 list(last_hidden_states.shape)
[1, 2048, 768]
Pix2StructForConditionalGeneration
	
class transformers.Pix2StructForConditionalGeneration
<
source
>
(
config: Pix2StructConfig
)
Parameters 
config (Union[Pix2StructConfig, Pix2StructTextConfig]) —
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.
A conditional generation model with a language modeling head. Can be used for sequence generation tasks.
The Pix2Struct model was proposed in Pix2Struct: Screenshot Parsing as Pretraining for Visual Language
Understanding by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu,
Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova. It’s an encoder decoder
transformer pre-trained in a image-to-text setting.
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
>
(
flattened_patches: typing.Optional[torch.FloatTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
decoder_input_ids: typing.Optional[torch.LongTensor] = None
decoder_attention_mask: typing.Optional[torch.BoolTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
decoder_head_mask: typing.Optional[torch.FloatTensor] = None
cross_attn_head_mask: typing.Optional[torch.Tensor] = None
encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
labels: typing.Optional[torch.LongTensor] = None
decoder_inputs_embeds: typing.Optional[torch.Tensor] = 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.Seq2SeqModelOutput or tuple(torch.FloatTensor)
Parameters 
flattened_patches (torch.FloatTensor of shape (batch_size, seq_length, hidden_size)) —
Flattened pixel patches. the hidden_size is obtained by the following formula: hidden_size =
num_channels  patch_size  patch_size
The process of flattening the pixel patches is done by Pix2StructProcessor.
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?
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?
Pix2StructText 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 Pix2StructText
Training.
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.
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
decoder_head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
cross_attn_head_mask (torch.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
[0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
encoder_outputs (tuple(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) 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)) 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 layers. 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. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be
input (see past_key_values). 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.
If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value
of inputs_embeds.
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for computing the masked language modeling loss for the decoder.
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.Seq2SeqModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.Seq2SeqModelOutput 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 (Pix2StructConfig) 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 decoder of the model.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
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 optional 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 optional 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 Pix2StructForConditionalGeneration 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.
Example:
Inference:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, Pix2StructForConditionalGeneration
 processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base")
 model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-textcaps-base")
 url = "https://www.ilankelman.org/stopsigns/australia.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 inputs = processor(images=image, return_tensors="pt")
 # autoregressive generation
 generated_ids = model.generate(**inputs, max_new_tokens=50)
 generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(generated_text)
A stop sign is on a street corner.
 # conditional generation
 text = "A picture of"
 inputs = processor(text=text, images=image, return_tensors="pt", add_special_tokens=False)
 generated_ids = model.generate(**inputs, max_new_tokens=50)
 generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(generated_text)
A picture of a stop sign with a red stop sign
Training:
	Copied
 from PIL import Image
 import requests
 from transformers import AutoProcessor, Pix2StructForConditionalGeneration
 processor = AutoProcessor.from_pretrained("google/pix2struct-base")
 model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-base")
 url = "https://www.ilankelman.org/stopsigns/australia.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 text = "A stop sign is on the street corner."
 inputs = processor(images=image, return_tensors="pt")
 labels = processor(text=text, return_tensors="pt").input_ids
 # forward pass
 outputs = model(**inputs, labels=labels)
 loss = outputs.loss
 print(f"{loss.item():.5f}")
5.94282
←Perceiver
Segment Anything→
Pix2Struct
Overview
Resources
Pix2StructConfig
Pix2StructTextConfig
Pix2StructVisionConfig
Pix2StructProcessor
Pix2StructImageProcessor
Pix2StructTextModel
Pix2StructVisionModel
Pix2StructForConditionalGeneration
