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PE Video (Perception Encoder Video)

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This model was released on {release_date} and added to Hugging Face Transformers on 2025-12-16.

PE Video (Perception Encoder Video)

Overview

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Usage

Basic usage

TODO

PeVideoVideoProcessor

class transformers.PeVideoVideoProcessor

< >

( **kwargs: typing_extensions.Unpack[transformers.processing_utils.VideosKwargs] )

__call__

< >

( videos **kwargs )

PeVideoProcessor

class transformers.PeVideoProcessor

< >

( *args **kwargs )

__call__

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None text: typing.Union[str, list[str], list[list[str]], NoneType] = None videos: typing.Union[list['PIL.Image.Image'], numpy.ndarray, ForwardRef('torch.Tensor'), list[numpy.ndarray], list['torch.Tensor'], list[list['PIL.Image.Image']], list[list[numpy.ndarray]], list[list['torch.Tensor']], transformers.video_utils.URL, list[transformers.video_utils.URL], list[list[transformers.video_utils.URL]], transformers.video_utils.Path, list[transformers.video_utils.Path], list[list[transformers.video_utils.Path]], NoneType] = None audio: typing.Union[numpy.ndarray, ForwardRef('torch.Tensor'), collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence['torch.Tensor'], NoneType] = None **kwargs: typing_extensions.Unpack[transformers.processing_utils.ProcessingKwargs] ) → BatchFeature

Parameters

  • images (PIL.Image.Image, np.ndarray, torch.Tensor, list[PIL.Image.Image], list[np.ndarray], list[torch.Tensor]) — The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. Both channels-first and channels-last formats are supported.
  • text (TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput], optional) — The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set is_split_into_words=True (to lift the ambiguity with a batch of sequences).
  • videos (np.ndarray, torch.Tensor, List[np.ndarray], List[torch.Tensor]) — The video or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
  • audio (np.ndarray, torch.Tensor, list[np.ndarray], list[torch.Tensor]) — The audio or batch of audio to be prepared. Each audio can be a NumPy array or PyTorch tensor.
  • return_tensors (str or TensorType, optional) — If set, will return tensors of a particular framework. Acceptable values are:

    • 'pt': Return PyTorch torch.Tensor objects.
    • 'np': Return NumPy np.ndarray objects.

Returns

BatchFeature

A BatchFeature object with processed inputs in a dict format.

Main method to prepare for model inputs. This method forwards the each modality argument to its own processor along with kwargs. Please refer to the docstring of the each processor attributes for more information.

PeVideoEncoderConfig

class transformers.PeVideoEncoderConfig

< >

( vision_config: typing.Union[dict, transformers.configuration_utils.PreTrainedConfig, NoneType] = None hidden_size: typing.Optional[int] = 1792 intermediate_size: typing.Optional[int] = 4800 num_hidden_layers: typing.Optional[int] = 6 num_attention_heads: typing.Optional[int] = 14 num_key_value_heads: typing.Optional[int] = None head_dim: typing.Optional[int] = 128 hidden_act: typing.Optional[str] = 'silu' max_position_embeddings: typing.Optional[int] = 10000 initializer_range: typing.Optional[float] = 0.02 rms_norm_eps: typing.Optional[float] = 1e-05 rope_parameters: typing.Union[transformers.modeling_rope_utils.RopeParameters, dict, NoneType] = {'rope_theta': 20000} attention_bias: typing.Optional[bool] = False attention_dropout: typing.Optional[float] = 0.0 **kwargs )

Parameters

  • vision_config (Union[PreTrainedConfig, dict], optional) — Configuration for the vision backbone used to extract frame embeddings. If a dictionary is provided, it is used to instantiate a TimmWrapperConfig with the PE default arguments.
  • hidden_size (int, optional, defaults to 1792) — Dimension of the hidden representations.
  • intermediate_size (int, optional, defaults to 4800) — Dimension of the feedforward layers in the Transformer blocks.
  • num_hidden_layers (int, optional, defaults to 6) — Number of Transformer encoder blocks.
  • num_attention_heads (int, optional, defaults to 14) — Number of attention heads used in each attention layer.
  • num_key_value_heads (int, optional) — Number of key and value heads for grouped-query attention. If unset, this defaults to num_attention_heads.
  • head_dim (int, optional, defaults to 128) — Dimension of each attention head for query, key, and value projections.
  • hidden_act (str, optional, defaults to "silu") — The non-linear activation function (function or string) in the Transformer blocks.
  • max_position_embeddings (int, optional, defaults to 10000) — Maximum sequence length supported by the rotary position embeddings.
  • initializer_range (float, optional, defaults to 0.02) — Standard deviation of the truncated normal initializer for weight matrices.
  • rms_norm_eps (float, optional, defaults to 1e-05) — Epsilon used by the RMS normalization layers.
  • rope_parameters (Union[RopeParameters, dict], optional, defaults to {'rope_theta' -- 20000}): Parameters for the rotary position embeddings, such as the base rope_theta.
  • attention_bias (bool, optional, defaults to False) — Whether to use bias terms in the query, key, value, and output projections.
  • attention_dropout (float, optional, defaults to 0.0) — Dropout ratio applied to attention probabilities.

This is the configuration class to store the configuration of a PeVideoEncoder. It is used to instantiate a PeVideoEncoder model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of pe-av-large. e.g. facebook/pe-av-large

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

>>> from transformers import PeAudioEncoder, PeAudioEncoderConfig

>>> # Initializing a PeAudioEncoder style configuration
>>> configuration = PeAudioEncoderConfig()

>>> # Initializing a model from the pe-av-large style configuration
>>> model = PeAudioEncoder(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

PeVideoConfig

class transformers.PeVideoConfig

< >

( text_config = None video_config = None **kwargs )

Parameters

  • text_config (dict or PreTrainedConfig, optional) — Configuration for the text model component.
  • video_config (dict or PreTrainedConfig, optional) — Configuration for the video encoder component.

This is the configuration class to store the configuration of a PeVideoModel. It is used to instantiate a PeVideoModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of pe-av-large. e.g. facebook/pe-av-large

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

>>> from transformers import PeVideoModel, PeVideoConfig

>>> # Initializing a PeVideoModel style configuration
>>> configuration = PeVideoConfig()

>>> # Initializing a model from the pe-av-large style configuration
>>> model = PeVideoModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

PeVideoModel

class transformers.PeVideoModel

< >

( config: PeVideoConfig )

forward

< >

( input_ids: Tensor pixel_values_videos: Tensor attention_mask: typing.Optional[torch.Tensor] = None padding_mask_videos: typing.Optional[torch.Tensor] = None return_loss: typing.Optional[bool] = None **kwargs )

PeVideoEncoder

class transformers.PeVideoEncoder

< >

( config: PeVideoEncoderConfig )

Parameters

  • config (PeVideoEncoderConfig) — 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 PeVideo Encoder model.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< >

( pixel_values_videos: Tensor padding_mask_videos: typing.Optional[torch.Tensor] = None **kwargs )

Update on GitHub

←XLSR-Wav2Vec2 SAM2 Video→