Transformers documentation
PP-DocLayoutV2
This model was released on 2025-10-16 and added to Hugging Face Transformers on 2026-02-27.
PP-DocLayoutV2
Overview
PP-DocLayoutV2 is a dedicated lightweight model for layout analysis, focusing specifically on element detection, classification, and reading order prediction.
Model Architecture
PP-DocLayoutV2 is composed of two sequentially connected networks. The first is an RT-DETR-based detection model that performs layout element detection and classification. The detected bounding boxes and class labels are then passed to a subsequent pointer network, which is responsible for ordering these layout elements.
Usage
Single input inference
The example below demonstrates how to generate text with PP-DocLayoutV2 using Pipeline or the AutoModel.
import requests
from PIL import Image
from transformers import pipeline
image = Image.open(requests.get("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_demo.jpg", stream=True).raw)
layout_detector = pipeline("object-detection", model="PaddlePaddle/PP-DocLayoutV2_safetensors")
result = layout_detector(image)
print(result)Batched inference
Here is how you can do it with PP-DocLayoutV2 using Pipeline or the AutoModel:
import requests
from PIL import Image
from transformers import pipeline
image = Image.open(requests.get("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_demo.jpg", stream=True).raw)
layout_detector = pipeline("object-detection", model="PaddlePaddle/PP-DocLayoutV2_safetensors")
result = layout_detector([image, image])
print(result[0])
print(result[1])PPDocLayoutV2Config
class transformers.PPDocLayoutV2Config
< source >( initializer_range = 0.01 initializer_bias_prior_prob = None layer_norm_eps = 1e-05 batch_norm_eps = 1e-05 backbone_config = None freeze_backbone_batch_norms = True encoder_hidden_dim = 256 encoder_in_channels = [512, 1024, 2048] feat_strides = [8, 16, 32] encoder_layers = 1 encoder_ffn_dim = 1024 encoder_attention_heads = 8 dropout = 0.0 activation_dropout = 0.0 encode_proj_layers = [2] positional_encoding_temperature = 10000 encoder_activation_function = 'gelu' activation_function = 'silu' eval_size = None normalize_before = False hidden_expansion = 1.0 d_model = 256 num_queries = 300 decoder_in_channels = [256, 256, 256] decoder_ffn_dim = 1024 num_feature_levels = 3 decoder_n_points = 4 decoder_layers = 6 decoder_attention_heads = 8 decoder_activation_function = 'relu' attention_dropout = 0.0 num_denoising = 100 label_noise_ratio = 0.5 box_noise_scale = 1.0 learn_initial_query = False anchor_image_size = None disable_custom_kernels = True is_encoder_decoder = True class_thresholds = None class_order = None reading_order_config = None **kwargs )
Parameters
- initializer_range (
float, optional, defaults to 0.01) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - initializer_bias_prior_prob (
float, optional) — The prior probability used by the bias initializer to initialize biases forenc_score_headandclass_embed. IfNone,prior_probcomputed asprior_prob = 1 / (num_labels + 1)while initializing model weights. - layer_norm_eps (
float, optional, defaults to 1e-05) — The epsilon used by the layer normalization layers. - batch_norm_eps (
float, optional, defaults to 1e-05) — The epsilon used by the batch normalization layers. - backbone_config (
Union[dict, "PreTrainedConfig"], optional, defaults toRTDetrResNetConfig()) — The configuration of the backbone model. - freeze_backbone_batch_norms (
bool, optional, defaults toTrue) — Whether to freeze the batch normalization layers in the backbone. - encoder_hidden_dim (
int, optional, defaults to 256) — Dimension of the layers in hybrid encoder. - encoder_in_channels (
list, optional, defaults to[512, 1024, 2048]) — Multi level features input for encoder. - feat_strides (
list[int], optional, defaults to[8, 16, 32]) — Strides used in each feature map. - encoder_layers (
int, optional, defaults to 1) — Total of layers to be used by the encoder. - encoder_ffn_dim (
int, optional, defaults to 1024) — Dimension of the “intermediate” (often named feed-forward) layer in decoder. - encoder_attention_heads (
int, optional, defaults to 8) — Number of attention heads for each attention layer in the Transformer encoder. - dropout (
float, optional, defaults to 0.0) — The ratio for all dropout layers. - activation_dropout (
float, optional, defaults to 0.0) — The dropout ratio for activations inside the fully connected layer. - encode_proj_layers (
list[int], optional, defaults to[2]) — Indexes of the projected layers to be used in the encoder. - positional_encoding_temperature (
int, optional, defaults to 10000) — The temperature parameter used to create the positional encodings. - encoder_activation_function (
str, optional, defaults to"gelu") — The non-linear activation function (function or string) in the encoder and pooler. If string,"gelu","relu","silu"and"gelu_new"are supported. - activation_function (
str, optional, defaults to"silu") — The non-linear activation function (function or string) in the general layer. If string,"gelu","relu","silu"and"gelu_new"are supported. - eval_size (
tuple[int, int], optional) — Height and width used to computes the effective height and width of the position embeddings after taking into account the stride. - normalize_before (
bool, optional, defaults toFalse) — Determine whether to apply layer normalization in the transformer encoder layer before self-attention and feed-forward modules. - hidden_expansion (
float, optional, defaults to 1.0) — Expansion ratio to enlarge the dimension size of RepVGGBlock and CSPRepLayer. - d_model (
int, optional, defaults to 256) — Dimension of the layers exclude hybrid encoder. - num_queries (
int, optional, defaults to 300) — Number of object queries. - decoder_in_channels (
list, optional, defaults to[256, 256, 256]) — Multi level features dimension for decoder - decoder_ffn_dim (
int, optional, defaults to 1024) — Dimension of the “intermediate” (often named feed-forward) layer in decoder. - num_feature_levels (
int, optional, defaults to 3) — The number of input feature levels. - decoder_n_points (
int, optional, defaults to 4) — The number of sampled keys in each feature level for each attention head in the decoder. - decoder_layers (
int, optional, defaults to 6) — Number of decoder layers. - decoder_attention_heads (
int, optional, defaults to 8) — Number of attention heads for each attention layer in the Transformer decoder. - decoder_activation_function (
str, optional, defaults to"relu") — The non-linear activation function (function or string) in the decoder. If string,"gelu","relu","silu"and"gelu_new"are supported. - attention_dropout (
float, optional, defaults to 0.0) — The dropout ratio for the attention probabilities. - num_denoising (
int, optional, defaults to 100) — The total number of denoising tasks or queries to be used for contrastive denoising. - label_noise_ratio (
float, optional, defaults to 0.5) — The fraction of denoising labels to which random noise should be added. - box_noise_scale (
float, optional, defaults to 1.0) — Scale or magnitude of noise to be added to the bounding boxes. - learn_initial_query (
bool, optional, defaults toFalse) — Indicates whether the initial query embeddings for the decoder should be learned during training - anchor_image_size (
tuple[int, int], optional) — Height and width of the input image used during evaluation to generate the bounding box anchors. If None, automatic generate anchor is applied. - disable_custom_kernels (
bool, optional, defaults toTrue) — Whether to disable custom kernels. - is_encoder_decoder (
bool, optional, defaults toTrue) — Whether the architecture has an encoder decoder structure. - class_thresholds (
list[float], optional) — The thresholds for each label. - class_order (
list[int], optional) — The priority for each label. - reading_order_config (
dict, optional) — The configuration of aPPDocLayoutV2ReadingOrder.
This is the configuration class to store the configuration of a PP-DocLayoutV2. It is used to instantiate a
PP-DocLayoutV2 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 PP-DocLayoutV2
PaddlePaddle/PP-DocLayoutV2_safetensors architecture.
Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.
Examples:
>>> from transformers import PPDocLayoutV2Config, PPDocLayoutV2ForObjectDetection
>>> # Initializing a PP-DocLayoutV2 configuration
>>> configuration = PPDocLayoutV2Config()
>>> # Initializing a model (with random weights) from the configuration
>>> model = PPDocLayoutV2ForObjectDetection(configuration)
>>> # Accessing the model configuration
>>> configuration = model.configPPDocLayoutV2ForObjectDetection
class transformers.PPDocLayoutV2ForObjectDetection
< source >( config: PPDocLayoutV2Config )
Parameters
- config (PPDocLayoutV2Config) — 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.
PP-DocLayoutV2 Model (consisting of a backbone and encoder-decoder) outputting bounding boxes, logits and order_logits to be further decoded into scores, classes and their reading order.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( pixel_values: FloatTensor pixel_mask: torch.LongTensor | None = None encoder_outputs: torch.FloatTensor | None = None inputs_embeds: torch.FloatTensor | None = None decoder_inputs_embeds: torch.FloatTensor | None = None labels: list[dict] | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → PPDocLayoutV2ForObjectDetectionOutput or tuple(torch.FloatTensor)
Parameters
- pixel_values (
torch.FloatTensorof shape(batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. Pixel values can be obtained usingimage_processor_class. Seeimage_processor_class.__call__for details (processor_classusesimage_processor_classfor processing images). - pixel_mask (
torch.LongTensorof shape(batch_size, height, width), optional) — Mask to avoid performing attention on padding pixel values. Mask values selected in[0, 1]:- 1 for pixels that are real (i.e. not masked),
- 0 for pixels that are padding (i.e. masked).
- encoder_outputs (
torch.FloatTensor, optional) — Tuple consists of (last_hidden_state, optional:hidden_states, optional:attentions)last_hidden_stateof shape(batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. - inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you can choose to directly pass a flattened representation of an image. - decoder_inputs_embeds (
torch.FloatTensorof shape(batch_size, num_queries, hidden_size), optional) — Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an embedded representation. - labels (
list[Dict]of len(batch_size,), optional) — Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the following 2 keys: ‘class_labels’ and ‘boxes’ (the class labels and bounding boxes of an image in the batch respectively). The class labels themselves should be atorch.LongTensorof len(number of bounding boxes in the image,)and the boxes atorch.FloatTensorof shape(number of bounding boxes in the image, 4).
Returns
PPDocLayoutV2ForObjectDetectionOutput or tuple(torch.FloatTensor)
A PPDocLayoutV2ForObjectDetectionOutput 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 (None) and inputs.
The PPDocLayoutV2ForObjectDetection forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
logits (
torch.FloatTensorof shape(batch_size, num_queries, num_classes + 1)) — Classification logits (including no-object) for all queries.pred_boxes (
torch.FloatTensorof shape(batch_size, num_queries, 4)) — Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding possible padding). You can use post_process_object_detection() to retrieve the unnormalized (absolute) bounding boxes.order_logits (
tupleoftorch.FloatTensorof shape(batch_size, num_queries, num_queries)) — Order logits for all queries. The first dimension of each tensor is the batch size. The second dimension is the number of queries.last_hidden_state (
torch.FloatTensorof shape(batch_size, num_queries, hidden_size)) — Sequence of hidden-states at the output of the last layer of the decoder of the model.intermediate_hidden_states (
torch.FloatTensorof shape(batch_size, config.decoder_layers, num_queries, hidden_size)) — Stacked intermediate hidden states (output of each layer of the decoder).intermediate_logits (
torch.FloatTensorof shape(batch_size, config.decoder_layers, num_queries, config.num_labels)) — Stacked intermediate logits (logits of each layer of the decoder).intermediate_reference_points (
torch.FloatTensorof shape(batch_size, config.decoder_layers, num_queries, 4)) — Stacked intermediate reference points (reference points of each layer of the decoder).intermediate_predicted_corners (
torch.FloatTensorof shape(batch_size, config.decoder_layers, num_queries, 4)) — Stacked intermediate predicted corners (predicted corners of each layer of the decoder).initial_reference_points (
torch.FloatTensorof shape(batch_size, config.decoder_layers, num_queries, 4)) — Stacked initial reference points (initial reference points of each layer of the decoder).decoder_hidden_states (
tuple[torch.FloatTensor], optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional, defaults toNone) — 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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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.
init_reference_points (
torch.FloatTensorof shape(batch_size, num_queries, 4)) — Initial reference points sent through the Transformer decoder.enc_topk_logits (
torch.FloatTensorof shape(batch_size, sequence_length, config.num_labels), optional, returned whenconfig.with_box_refine=Trueandconfig.two_stage=True) — Logits of predicted bounding boxes coordinates in the encoder.enc_topk_bboxes (
torch.FloatTensorof shape(batch_size, sequence_length, 4), optional, returned whenconfig.with_box_refine=Trueandconfig.two_stage=True) — Logits of predicted bounding boxes coordinates in the encoder.enc_outputs_class (
torch.FloatTensorof shape(batch_size, sequence_length, config.num_labels), optional, returned whenconfig.with_box_refine=Trueandconfig.two_stage=True) — Predicted bounding boxes scores where the topconfig.two_stage_num_proposalsscoring bounding boxes are picked as region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and background).enc_outputs_coord_logits (
torch.FloatTensorof shape(batch_size, sequence_length, 4), optional, returned whenconfig.with_box_refine=Trueandconfig.two_stage=True) — Logits of predicted bounding boxes coordinates in the first stage.denoising_meta_values (
dict, optional, defaults toNone) — Extra dictionary for the denoising related values
Examples:
>>> from transformers import AutoModelForObjectDetection, AutoImageProcessor
>>> from PIL import Image
>>> import requests
>>> import torch
>>> url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_demo.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> model_path = "PaddlePaddle/PP-DocLayoutV2_safetensors"
>>> image_processor = AutoImageProcessor.from_pretrained(model_path)
>>> model = AutoModelForObjectDetection.from_pretrained(model_path)
>>> # prepare image for the model
>>> inputs = image_processor(images=[image], return_tensors="pt")
>>> # forward pass
>>> outputs = model(**inputs)
>>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
>>> results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]))
>>> # print outputs
>>> for result in results:
... for idx, (score, label_id, box) in enumerate(zip(result["scores"], result["labels"], result["boxes"])):
... score, label = score.item(), label_id.item()
... box = [round(i, 2) for i in box.tolist()]
... print(f"Order {idx + 1}: {model.config.id2label[label]}: {score:.2f} {box}")
Order 1: text: 0.99 [335.39, 184.26, 896.49, 654.48]
Order 2: paragraph_title: 0.97 [337.14, 683.49, 869.42, 798.27]
Order 3: text: 0.99 [335.71, 843.04, 891.17, 1454.15]
Order 4: text: 0.99 [920.42, 185.53, 1476.39, 464.25]
Order 5: text: 0.98 [920.62, 483.75, 1480.52, 765.34]
Order 6: text: 0.98 [920.58, 846.75, 1481.94, 1220.53]
Order 7: text: 0.97 [921.12, 1239.27, 1468.87, 1377.33]
Order 8: footnote: 0.82 [334.58, 1614.67, 1483.84, 1731.61]
Order 9: text: 0.51 [334.58, 1614.67, 1483.84, 1731.61]
Order 10: footnote: 0.83 [334.7, 1757.26, 1471.07, 1845.33]
Order 11: text: 0.87 [336.65, 1910.28, 661.33, 1939.92]
Order 12: footnote: 0.95 [336.16, 2114.52, 1450.28, 2171.74]
Order 13: number: 0.87 [106.04, 2257.37, 136.05, 2281.98]
Order 14: footer: 0.93 [338.6, 2255.94, 985.67, 2283.57]PPDocLayoutV2Model
class transformers.PPDocLayoutV2Model
< source >( config: PPDocLayoutV2Config )
Parameters
- config (PPDocLayoutV2Config) — 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.
PP-DocLayoutV2 Model (consisting of a backbone and encoder-decoder) outputting raw hidden states without any head on top.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward
< source >( pixel_values: FloatTensor pixel_mask: torch.LongTensor | None = None encoder_outputs: torch.FloatTensor | None = None inputs_embeds: torch.FloatTensor | None = None labels: list[dict] | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → PPDocLayoutV2ModelOutput or tuple(torch.FloatTensor)
Parameters
- pixel_values (
torch.FloatTensorof shape(batch_size, num_channels, image_size, image_size)) — The tensors corresponding to the input images. Pixel values can be obtained usingimage_processor_class. Seeimage_processor_class.__call__for details (processor_classusesimage_processor_classfor processing images). - pixel_mask (
torch.LongTensorof shape(batch_size, height, width), optional) — Mask to avoid performing attention on padding pixel values. Mask values selected in[0, 1]:- 1 for pixels that are real (i.e. not masked),
- 0 for pixels that are padding (i.e. masked).
- encoder_outputs (
torch.FloatTensor, optional) — Tuple consists of (last_hidden_state, optional:hidden_states, optional:attentions)last_hidden_stateof shape(batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. - inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you can choose to directly pass a flattened representation of an image. - labels (
list[Dict]of len(batch_size,), optional) — Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the following 2 keys: ‘class_labels’ and ‘boxes’ (the class labels and bounding boxes of an image in the batch respectively). The class labels themselves should be atorch.LongTensorof len(number of bounding boxes in the image,)and the boxes atorch.FloatTensorof shape(number of bounding boxes in the image, 4).
Returns
PPDocLayoutV2ModelOutput or tuple(torch.FloatTensor)
A PPDocLayoutV2ModelOutput 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 (None) and inputs.
The PPDocLayoutV2Model forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
last_hidden_state (
torch.FloatTensorof shape(batch_size, num_queries, hidden_size)) — Sequence of hidden-states at the output of the last layer of the decoder of the model.intermediate_hidden_states (
torch.FloatTensorof shape(batch_size, config.decoder_layers, num_queries, hidden_size)) — Stacked intermediate hidden states (output of each layer of the decoder).intermediate_logits (
torch.FloatTensorof shape(batch_size, config.decoder_layers, sequence_length, config.num_labels)) — Stacked intermediate logits (logits of each layer of the decoder).intermediate_reference_points (
torch.FloatTensorof shape(batch_size, config.decoder_layers, num_queries, 4)) — Stacked intermediate reference points (reference points of each layer of the decoder).intermediate_predicted_corners (
torch.FloatTensorof shape(batch_size, config.decoder_layers, num_queries, 4)) — Stacked intermediate predicted corners (predicted corners of each layer of the decoder).initial_reference_points (
torch.FloatTensorof shape(batch_size, num_queries, 4)) — Initial reference points used for the first decoder layer.decoder_hidden_states (
tuple[torch.FloatTensor], optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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.FloatTensorof shape(batch_size, sequence_length, hidden_size), optional, defaults toNone) — 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 whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.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 whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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.
init_reference_points (
torch.FloatTensorof shape(batch_size, num_queries, 4)) — Initial reference points sent through the Transformer decoder.enc_topk_logits (
torch.FloatTensorof shape(batch_size, sequence_length, config.num_labels)) — Predicted bounding boxes scores where the topconfig.two_stage_num_proposalsscoring bounding boxes are picked as region proposals in the encoder stage. Output of bounding box binary classification (i.e. foreground and background).enc_topk_bboxes (
torch.FloatTensorof shape(batch_size, sequence_length, 4)) — Logits of predicted bounding boxes coordinates in the encoder stage.enc_outputs_class (
torch.FloatTensorof shape(batch_size, sequence_length, config.num_labels), optional, returned whenconfig.with_box_refine=Trueandconfig.two_stage=True) — Predicted bounding boxes scores where the topconfig.two_stage_num_proposalsscoring bounding boxes are picked as region proposals in the first stage. Output of bounding box binary classification (i.e. foreground and background).enc_outputs_coord_logits (
torch.FloatTensorof shape(batch_size, sequence_length, 4), optional, returned whenconfig.with_box_refine=Trueandconfig.two_stage=True) — Logits of predicted bounding boxes coordinates in the first stage.denoising_meta_values (
dict, optional, defaults toNone) — Extra dictionary for the denoising related values.
Examples:
>>> from transformers import AutoImageProcessor, PPDocLayoutV2Model
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("PekingU/PPDocLayoutV2_r50vd")
>>> model = PPDocLayoutV2Model.from_pretrained("PekingU/PPDocLayoutV2_r50vd")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 300, 256]PPDocLayoutV2ReadingOrder
class transformers.PPDocLayoutV2ReadingOrder
< source >( config )
Parameters
- config (PPDocLayoutV2ReadingOrder) — 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.
PP-DocLayoutV2 ReadingOrder Model. This model consists of an encoder and a GlobalPointer head. It takes layout features as input and outputs logits representing the relative ordering relationships between elements, which are used to determine the final reading sequence.
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 >( boxes labels = None mask = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] )
Parameters
- boxes (
torch.Tensorof shape(batch_size, sequence_length, 4)) — Bounding box coordinates of the detected layout elements in [0, 1000] scale. Format is[x_min, y_min, x_max, y_max]. The tensor usually contains sorted valid boxes followed by zero-padding. - labels (
torch.Tensorof shape(batch_size, sequence_length), optional) — The remapped class indices for each layout element. These are not necessarily the raw detection class IDs, but indices mapped viaconfig.class_order(e.g., mapping text/title/figure to specific reading-order category IDs). - mask (
torch.Tensorof shape(batch_size, sequence_length), optional) — Boolean or Binary mask indicating valid detected elements after threshold filtering.- True: Valid layout element.
- False: Padding/Empty element.
Used to determine the sequence length (
num_pred) for the pointer mechanism.
The PPDocLayoutV2ReadingOrder forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
PPDocLayoutV2ImageProcessorFast
class transformers.PPDocLayoutV2ImageProcessorFast
< source >( **kwargs: typing_extensions.Unpack[transformers.processing_utils.ImagesKwargs] )
Constructs a PPDocLayoutV2ImageProcessorFast image processor.
post_process_object_detection
< source >( outputs threshold: float = 0.5 target_sizes: transformers.utils.generic.TensorType | list[tuple] | None = None ) → list[Dict]
Converts the raw output of DetrForObjectDetection into final bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch.
PPDocLayoutV2 is identical to PPDocLayoutV3, except that it does not return polygon_points.