 with Donut is by checking the tutorial
notebooks, which show how to use the model
at inference time as well as fine-tuning on custom data.
Donut is always used within the VisionEncoderDecoder framework.
Inference
	
Donut’s VisionEncoderDecoder model accepts images as input and makes use of
generate() to autoregressively generate text given the input image.
The DonutImageProcessor class is responsible for preprocessing the input image and
[XLMRobertaTokenizer/XLMRobertaTokenizerFast] decodes the generated target tokens to the target string. The
DonutProcessor wraps DonutImageProcessor and [XLMRobertaTokenizer/XLMRobertaTokenizerFast]
into a single instance to both extract the input features and decode the predicted token ids.
Step-by-step Document Image Classification
	Copied
 import re
 from transformers import DonutProcessor, VisionEncoderDecoderModel
 from datasets import load_dataset
 import torch
 processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-rvlcdip")
 model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-rvlcdip")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model.to(device)
 # load document image
 dataset = load_dataset("hf-internal-testing/example-documents", split="test")
 image = dataset[1]["image"]
 # prepare decoder inputs
 task_prompt = "<s_rvlcdip>"
 decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
 pixel_values = processor(image, return_tensors="pt").pixel_values
 outputs = model.generate(
...     pixel_values.to(device),
...     decoder_input_ids=decoder_input_ids.to(device),
...     max_length=model.decoder.config.max_position_embeddings,
...     early_stopping=True,
...     pad_token_id=processor.tokenizer.pad_token_id,
...     eos_token_id=processor.tokenizer.eos_token_id,
...     use_cache=True,
...     num_beams=1,
...     bad_words_ids=[[processor.tokenizer.unk_token_id]],
...     return_dict_in_generate=True,
... )
 sequence = processor.batch_decode(outputs.sequences)[0]
 sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
 sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
 print(processor.token2json(sequence))
{'class': 'advertisement'}
Step-by-step Document Parsing
	Copied
 import re
 from transformers import DonutProcessor, VisionEncoderDecoderModel
 from datasets import load_dataset
 import torch
 processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
 model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model.to(device)
 # load document image
 dataset = load_dataset("hf-internal-testing/example-documents", split="test")
 image = dataset[2]["image"]
 # prepare decoder inputs
 task_prompt = "<s_cord-v2>"
 decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
 pixel_values = processor(image, return_tensors="pt").pixel_values
 outputs = model.generate(
...     pixel_values.to(device),
...     decoder_input_ids=decoder_input_ids.to(device),
...     max_length=model.decoder.config.max_position_embeddings,
...     early_stopping=True,
...     pad_token_id=processor.tokenizer.pad_token_id,
...     eos_token_id=processor.tokenizer.eos_token_id,
...     use_cache=True,
...     num_beams=1,
...     bad_words_ids=[[processor.tokenizer.unk_token_id]],
...     return_dict_in_generate=True,
... )
 sequence = processor.batch_decode(outputs.sequences)[0]
 sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
 sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
 print(processor.token2json(sequence))
{'menu': {'nm': 'CINNAMON SUGAR', 'unitprice': '17,000', 'cnt': '1 x', 'price': '17,000'}, 'sub_total': {'subtotal_price': '17,000'}, 'total': {'total_price': '17,000', 'cashprice': '20,000', 'changeprice': '3,000'}}
Step-by-step Document Visual Question Answering (DocVQA)
	Copied
 import re
 from transformers import DonutProcessor, VisionEncoderDecoderModel
 from datasets import load_dataset
 import torch
 processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
 model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model.to(device)
 # load document image from the DocVQA dataset
 dataset = load_dataset("hf-internal-testing/example-documents", split="test")
 image = dataset[0]["image"]
 # prepare decoder inputs
 task_prompt = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
 question = "When is the coffee break?"
 prompt = task_prompt.replace("{user_input}", question)
 decoder_input_ids = processor.tokenizer(prompt, add_special_tokens=False, return_tensors="pt").input_ids
 pixel_values = processor(image, return_tensors="pt").pixel_values
 outputs = model.generate(
...     pixel_values.to(device),
...     decoder_input_ids=decoder_input_ids.to(device),
...     max_length=model.decoder.config.max_position_embeddings,
...     early_stopping=True,
...     pad_token_id=processor.tokenizer.pad_token_id,
...     eos_token_id=processor.tokenizer.eos_token_id,
...     use_cache=True,
...     num_beams=1,
...     bad_words_ids=[[processor.tokenizer.unk_token_id]],
...     return_dict_in_generate=True,
... )
 sequence = processor.batch_decode(outputs.sequences)[0]
 sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
 sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token
 print(processor.token2json(sequence))
{'question': 'When is the coffee break?', 'answer': '11-14 to 11:39 a.m.'}
See the model hub to look for Donut checkpoints.
Training
	
We refer to the tutorial notebooks.
DonutSwinConfig
	
class transformers.DonutSwinConfig
<
source
>
(
image_size = 224
patch_size = 4
num_channels = 3
embed_dim = 96
depths = [2, 2, 6, 2]
num_heads = [3, 6, 12, 24]
window_size = 7
mlp_ratio = 4.0
qkv_bias = True
hidden_dropout_prob = 0.0
attention_probs_dropout_prob = 0.0
drop_path_rate = 0.1
hidden_act = 'gelu'
use_absolute_embeddings = False
initializer_range = 0.02
layer_norm_eps = 1e-05
**kwargs
)
Parameters 
image_size (int, optional, defaults to 224) —
The size (resolution) of each image.
patch_size (int, optional, defaults to 4) —
The size (resolution) of each patch.
num_channels (int, optional, defaults to 3) —
The number of input channels.
embed_dim (int, optional, defaults to 96) —
Dimensionality of patch embedding.
depths (list(int), optional, defaults to [2, 2, 6, 2]) —
Depth of each layer in the Transformer encoder.
num_heads (list(int), optional, defaults to [3, 6, 12, 24]) —
Number of attention heads in each layer of the Transformer encoder.
window_size (int, optional, defaults to 7) —
Size of windows.
mlp_ratio (float, optional, defaults to 4.0) —
Ratio of MLP hidden dimensionality to embedding dimensionality.
qkv_bias (bool, optional, defaults to True) —
Whether or not a learnable bias should be added to the queries, keys and values.
hidden_dropout_prob (float, optional, defaults to 0.0) —
The dropout probability for all fully connected layers in the embeddings and encoder.
attention_probs_dropout_prob (float, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities.
drop_path_rate (float, optional, defaults to 0.1) —
Stochastic depth rate.
hidden_act (str or function, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder. If string, "gelu", "relu",
"selu" and "gelu_new" are supported.
use_absolute_embeddings (bool, optional, defaults to False) —
Whether or not to add absolute position embeddings to the patch embeddings.
initializer_range (float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (float, optional, defaults to 1e-12) —
The epsilon used by the layer normalization layers.
This is the configuration class to store the configuration of a DonutSwinModel. It is used to instantiate a
Donut 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 Donut
naver-clova-ix/donut-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 DonutSwinConfig, DonutSwinModel
 # Initializing a Donut naver-clova-ix/donut-base style configuration
 configuration = DonutSwinConfig()
 # Randomly initializing a model from the naver-clova-ix/donut-base style configuration
 model = DonutSwinModel(configuration)
 # Accessing the model configuration
 configuration = model.config
DonutImageProcessor
	
class transformers.DonutImageProcessor
<
source
>
(
do_resize: bool = True
size: typing.Dict[str, int] = None
resample: Resampling = <Resampling.BILINEAR: 2>
do_thumbnail: bool = True
do_align_long_axis: bool = False
do_pad: bool = True
do_rescale: bool = True
rescale_factor: typing.Union[int, float] = 0.00392156862745098
do_normalize: bool = True
image_mean: typing.Union[float, typing.List[float], NoneType] = None
image_std: typing.Union[float, typing.List[float], NoneType] = None
**kwargs
)
Parameters 
do_resize (bool, optional, defaults to True) —
Whether to resize the image’s (height, width) dimensions to the specified size. Can be overridden by
do_resize in the preprocess method.
size (Dict[str, int] optional, defaults to {"shortest_edge" -- 224}):
Size of the image after resizing. The shortest edge of the image is resized to size[“shortest_edge”], with
the longest edge resized to keep the input aspect ratio. Can be overridden by size in the preprocess
method.
resample (PILImageResampling, optional, defaults to PILImageResampling.BILINEAR) —
Resampling filter to use if resizing the image. Can be overridden by resample in the preprocess method.
do_thumbnail (bool, optional, defaults to True) —
Whether to resize the image using thumbnail method.
do_align_long_axis (bool, optional, defaults to False) —
Whether to align the long axis of the image with the long axis of size by rotating by 90 degrees.
do_pad (bool, optional, defaults to True) —
Whether to pad the image. If random_padding is set to True in preprocess, each image is padded with a
random amont of padding on each size, up to the largest image size in the batch. Otherwise, all images are
padded to the largest image size in the batch.
do_rescale (bool, optional, defaults to True) —
Whether to rescale the image by the specified scale rescale_factor. Can be overridden by do_rescale in
the preprocess method.
rescale_factor (int or float, optional, defaults to 1/255) —
Scale factor to use if rescaling the image. Can be overridden by rescale_factor in the preprocess
method.
do_normalize —
Whether to normalize the image. Can be overridden by do_normalize in the preprocess method.
image_mean (float or List[float], optional, defaults to IMAGENET_STANDARD_MEAN) —
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the image_mean parameter in the preprocess method.
image_std (float or List[float], optional, defaults to IMAGENET_STANDARD_STD) —
Image standard deviation.
Constructs a Donut 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')]]
do_resize: bool = None
size: typing.Dict[str, int] = None
resample: Resampling = None
do_thumbnail: bool = None
do_align_long_axis: bool = None
do_pad: bool = None
random_padding: bool = False
do_rescale: bool = None
rescale_factor: float = None
do_normalize: bool = None
image_mean: typing.Union[float, typing.List[float], NoneType] = None
image_std: typing.Union[float, typing.List[float], NoneType] = None
return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None
data_format: typing.Optional[transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'>
**kwargs
)
Parameters 
images (ImageInput) —
Image to preprocess.
do_resize (bool, optional, defaults to self.do_resize) —
Whether to resize the image.
size (Dict[str, int], optional, defaults to self.size) —
Size of the image after resizing. Shortest edge of the image is resized to min(size[“height”],
size[“width”]) with the longest edge resized to keep the input aspect ratio.
resample (int, optional, defaults to self.resample) —
Resampling filter to use if resizing the image. This can be one of the enum PILImageResampling. Only
has an effect if do_resize is set to True.
do_thumbnail (bool, optional, defaults to self.do_thumbnail) —
Whether to resize the image using thumbnail method.
do_align_long_axis (bool, optional, defaults to self.do_align_long_axis) —
Whether to align the long axis of the image with the long axis of size by rotating by 90 degrees.
do_pad (bool, optional, defaults to self.do_pad) —
Whether to pad the image. If random_padding is set to True, each image is padded with a random
amont of padding on each size, up to the largest image size in the batch. Otherwise, all images are
padded to the largest image size in the batch.
random_padding (bool, optional, defaults to self.random_padding) —
Whether to use random padding when padding the image. If True, each image in the batch with be padded
with a random amount of padding on each side up to the size of the largest image in the batch.
do_rescale (bool, optional, defaults to self.do_rescale) —
Whether to rescale the image pixel values.
rescale_factor (float, optional, defaults to self.rescale_factor) —
Rescale factor to rescale the image by if do_rescale is set to True.
do_normalize (bool, optional, defaults to self.do_normalize) —
Whether to normalize the image.
image_mean (float or List[float], optional, defaults to self.image_mean) —
Image mean to use for normalization.
image_std (float or List[float], optional, defaults to self.image_std) —
Image standard deviation to use for normalization.
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.
data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) —
The channel dimension format for the output image. Can be one of:
ChannelDimension.FIRST: image in (num_channels, height, width) format.
ChannelDimension.LAST: image in (height, width, num_channels) format.
Unset: defaults to the channel dimension format of the input image.
Preprocess an image or batch of images.
DonutFeatureExtractor
	
class transformers.DonutFeatureExtractor
<
source
>
(
*args
**kwargs
)
__call__
<
source
>
(
images
**kwargs
)
Preprocess an image or a batch of images.
DonutProcessor
	
class transformers.DonutProcessor
<
source
>
(
image_processor = None
tokenizer = None
**kwargs
)
Parameters 
image_processor (DonutImageProcessor) —
An instance of DonutImageProcessor. The image processor is a required input.
tokenizer ([XLMRobertaTokenizer/XLMRobertaTokenizerFast]) —
An instance of [XLMRobertaTokenizer/XLMRobertaTokenizerFast]. The tokenizer is a required input.
Constructs a Donut processor which wraps a Donut image processor and an XLMRoBERTa tokenizer into a single
processor.
DonutProcessor offers all the functionalities of DonutImageProcessor and
[XLMRobertaTokenizer/XLMRobertaTokenizerFast]. See the call() and
decode() for more information.
__call__
<
source
>
(
*args
**kwargs
)
When used in normal mode, this method forwards all its arguments to AutoImageProcessor’s
__call__() and returns its output. If used in the context
as_target_processor() this method forwards all its arguments to DonutTokenizer’s
~DonutTokenizer.__call__. Please refer to the doctsring of the above two methods for more information.
from_pretrained
<
source
>
(
pretrained_model_name_or_path: typing.Union[str, os.PathLike]
cache_dir: typing.Union[str, os.PathLike, NoneType] = None
force_download: bool = False
local_files_only: bool = False
token: typing.Union[bool, str, NoneType] = None
revision: str = 'main'
**kwargs
)
Parameters 
pretrained_model_name_or_path (str or os.PathLike) —
This can be either:
a string, the model id of a pretrained feature_extractor hosted inside a model repo on
huggingface.co. 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 to a directory containing a feature extractor file saved using the
save_pretrained() method, e.g., ./my_model_directory/.
a path or url to a saved feature extractor JSON file, e.g.,
./my_model_directory/preprocessor_config.json.
**kwargs —
Additional keyword arguments passed along to both
from_pretrained() and
~tokenization_utils_base.PreTrainedTokenizer.from_pretrained.
Instantiate a processor associated with a pretrained model.
This class method is simply calling the feature extractor
from_pretrained(), image processor
ImageProcessingMixin and the tokenizer
~tokenization_utils_base.PreTrainedTokenizer.from_pretrained methods. Please refer to the docstrings of the
methods above for more information.
save_pretrained
<
source
>
(
save_directory
push_to_hub: bool = False
**kwargs
)
Parameters 
save_directory (str or os.PathLike) —
Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will
be created if it does not exist).
push_to_hub (bool, optional, defaults to False) —
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with repo_id (will default to the name of save_directory in your
namespace).
kwargs (Dict[str, Any], optional) —
Additional key word arguments passed along to the push_to_hub() method.
Saves the attributes of this processor (feature extractor, tokenizer…) in the specified directory so that it
can be reloaded using the from_pretrained() method.
This class method is simply calling save_pretrained() and
save_pretrained(). Please refer to the docstrings of the
methods above for more information.
batch_decode
<
source
>
(
*args
**kwargs
)
This method forwards all its arguments to DonutTokenizer’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 DonutTokenizer’s decode(). Please refer to the
docstring of this method for more information.
DonutSwinModel
	
class transformers.DonutSwinModel
<
source
>
(
config
add_pooling_layer = True
use_mask_token = False
)
Parameters 
config (DonutSwinConfig) — 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 Donut Swin Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch torch.nn.Module sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
forward
<
source
>
(
pixel_values: typing.Optional[torch.FloatTensor] = None
bool_masked_pos: typing.Optional[torch.BoolTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.models.donut.modeling_donut_swin.DonutSwinModelOutput or tuple(torch.FloatTensor)
Parameters 
pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using AutoImageProcessor. See
DonutImageProcessor.call() for details.
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.
bool_masked_pos (torch.BoolTensor of shape (batch_size, num_patches)) —
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
Returns
transformers.models.donut.modeling_donut_swin.DonutSwinModelOutput or tuple(torch.FloatTensor)
A transformers.models.donut.modeling_donut_swin.DonutSwinModelOutput 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 (DonutSwinConfig) 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), optional, returned when add_pooling_layer=True is passed) — Average pooling of the last layer hidden-state.
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 + one for the output of each stage) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the 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 stage) 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.
reshaped_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 + one for the output of each stage) of
shape (batch_size, hidden_size, height, width).
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
The DonutSwinModel 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 AutoImageProcessor, DonutSwinModel
 import torch
 from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 image_processor = AutoImageProcessor.from_pretrained("https://huggingface.co/naver-clova-ix/donut-base")
 model = DonutSwinModel.from_pretrained("https://huggingface.co/naver-clova-ix/donut-base")
 inputs = image_processor(image, return_tensors="pt")
 with torch.no_grad():
...     outputs = model(**inputs)
 last_hidden_states = outputs.last_hidden_state
 list(last_hidden_states.shape)
[1, 49, 768]
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Overview
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DonutSwinConfig
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