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VideoMAE

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VideoMAE

Overview

The VideoMAE model was proposed in VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Zhan Tong, Yibing Song, Jue Wang, Limin Wang. VideoMAE extends masked auto encoders (MAE) to video, claiming state-of-the-art performance on several video classification benchmarks.

The abstract from the paper is the following:

Pre-training video transformers on extra large-scale datasets is generally required to achieve premier performance on relatively small datasets. In this paper, we show that video masked autoencoders (VideoMAE) are data-efficient learners for self-supervised video pre-training (SSVP). We are inspired by the recent ImageMAE and propose customized video tube masking and reconstruction. These simple designs turn out to be effective for overcoming information leakage caused by the temporal correlation during video reconstruction. We obtain three important findings on SSVP: (1) An extremely high proportion of masking ratio (i.e., 90% to 95%) still yields favorable performance of VideoMAE. The temporally redundant video content enables higher masking ratio than that of images. (2) VideoMAE achieves impressive results on very small datasets (i.e., around 3k-4k videos) without using any extra data. This is partially ascribed to the challenging task of video reconstruction to enforce high-level structure learning. (3) VideoMAE shows that data quality is more important than data quantity for SSVP. Domain shift between pre-training and target datasets are important issues in SSVP. Notably, our VideoMAE with the vanilla ViT backbone can achieve 83.9% on Kinects-400, 75.3% on Something-Something V2, 90.8% on UCF101, and 61.1% on HMDB51 without using any extra data.

Tips:

drawing VideoMAE pre-training. Taken from the original paper.

This model was contributed by nielsr. The original code can be found here.

VideoMAEConfig

class transformers.VideoMAEConfig

< >

( image_size = 224 patch_size = 16 num_channels = 3 num_frames = 16 tubelet_size = 2 hidden_size = 768 num_hidden_layers = 12 num_attention_heads = 12 intermediate_size = 3072 hidden_act = 'gelu' hidden_dropout_prob = 0.0 attention_probs_dropout_prob = 0.0 initializer_range = 0.02 layer_norm_eps = 1e-12 qkv_bias = True use_mean_pooling = True decoder_num_attention_heads = 6 decoder_hidden_size = 384 decoder_num_hidden_layers = 4 decoder_intermediate_size = 1536 norm_pix_loss = True **kwargs )

Parameters

  • image_size (int, optional, defaults to 224) — The size (resolution) of each image.
  • patch_size (int, optional, defaults to 16) — The size (resolution) of each patch.
  • num_channels (int, optional, defaults to 3) — The number of input channels.
  • num_frames (int, optional, defaults to 16) — The number of frames in each video.
  • tubelet_size (int, optional, defaults to 2) — The number of tubelets.
  • hidden_size (int, optional, defaults to 768) — Dimensionality of the encoder layers and the pooler layer.
  • 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.
  • intermediate_size (int, optional, defaults to 3072) — Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
  • hidden_act (str or function, optional, defaults to "gelu") — The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu", "selu" and "gelu_new" are supported.
  • hidden_dropout_prob (float, optional, defaults to 0.0) — The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
  • attention_probs_dropout_prob (float, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
  • 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.
  • qkv_bias (bool, optional, defaults to True) — Whether to add a bias to the queries, keys and values.
  • use_mean_pooling (bool, optional, defaults to True) — Whether to mean pool the final hidden states instead of using the final hidden state of the [CLS] token.
  • decoder_num_attention_heads (int, optional, defaults to 6) — Number of attention heads for each attention layer in the decoder.
  • decoder_hidden_size (int, optional, defaults to 384) — Dimensionality of the decoder.
  • decoder_num_hidden_layers (int, optional, defaults to 4) — Number of hidden layers in the decoder.
  • decoder_intermediate_size (int, optional, defaults to 1536) — Dimensionality of the “intermediate” (i.e., feed-forward) layer in the decoder.
  • norm_pix_loss (bool, optional, defaults to True) — Whether to normalize the target patch pixels.

This is the configuration class to store the configuration of a VideoMAEModel. It is used to instantiate a VideoMAE 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 VideoMAE MCG-NJU/videomae-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:

>>> from transformers import VideoMAEConfig, VideoMAEModel

>>> # Initializing a VideoMAE videomae-base style configuration
>>> configuration = VideoMAEConfig()

>>> # Randomly initializing a model from the configuration
>>> model = VideoMAEModel(configuration)

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

VideoMAEFeatureExtractor

class transformers.VideoMAEFeatureExtractor

< >

( do_resize = True size = 224 resample = <Resampling.BILINEAR: 2> do_center_crop = True do_normalize = True image_mean = None image_std = None **kwargs )

Parameters

  • do_resize (bool, optional, defaults to True) — Whether to resize the shorter edge of the input to a certain size.
  • size (int, optional, defaults to 224) — Resize the shorter edge of the input to the given size. Only has an effect if do_resize is set to True.
  • resample (int, optional, defaults to PIL.Image.BILINEAR) — An optional resampling filter. This can be one of PIL.Image.NEAREST, PIL.Image.BOX, PIL.Image.BILINEAR, PIL.Image.HAMMING, PIL.Image.BICUBIC or PIL.Image.LANCZOS. Only has an effect if do_resize is set to True.
  • do_center_crop (bool, optional, defaults to True) — Whether to center crop the input to a certain size.
  • do_normalize (bool, optional, defaults to True) — Whether or not to normalize the input with mean and standard deviation.
  • image_mean (List[int], defaults to [0.485, 0.456, 0.406]) — The sequence of means for each channel, to be used when normalizing images.
  • image_std (List[int], defaults to [0.229, 0.224, 0.225]) — The sequence of standard deviations for each channel, to be used when normalizing images.

Constructs a VideoMAE feature extractor. This feature extractor can be used to prepare videos for the model.

This feature extractor inherits from FeatureExtractionMixin which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

__call__

< >

( videos: typing.Union[PIL.Image.Image, numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]] return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None **kwargs ) → BatchFeature

Parameters

  • videos (List[PIL.Image.Image], List[np.ndarray], List[torch.Tensor], List[List[PIL.Image.Image]], List[List[np.ndarrray]], — List[List[torch.Tensor]]): The video or batch of videos to be prepared. Each video should be a list of frames, which can be either PIL images or NumPy arrays. In case of NumPy arrays/PyTorch tensors, each frame should be of shape (H, W, C), where H and W are frame height and width, and C is a number of channels.
  • return_tensors (str or TensorType, optional, defaults to 'np') — If set, will return tensors of a particular framework. Acceptable values are:

    • 'tf': Return TensorFlow tf.constant objects.
    • 'pt': Return PyTorch torch.Tensor objects.
    • 'np': Return NumPy np.ndarray objects.
    • 'jax': Return JAX jnp.ndarray objects.

Returns

BatchFeature

A BatchFeature with the following fields:

  • pixel_values — Pixel values to be fed to a model, of shape (batch_size, num_channels, num_frames, height, width).

Main method to prepare for the model one or several video(s).

NumPy arrays are converted to PIL images when resizing, so the most efficient is to pass PIL images.

VideoMAEModel

class transformers.VideoMAEModel

< >

( config )

Parameters

  • config (VideoMAEConfig) — 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 VideoMAE 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

< >

( pixel_values bool_masked_pos = None head_mask = None output_attentions = None output_hidden_states = None return_dict = None ) → transformers.modeling_outputs.BaseModelOutput or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.FloatTensor of shape (batch_size, num_frames, num_channels, height, width)) — Pixel values. Pixel values can be obtained using VideoMAEFeatureExtractor. See VideoMAEFeatureExtractor.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.

Returns

transformers.modeling_outputs.BaseModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.BaseModelOutput 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 (VideoMAEConfig) 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.

  • 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 VideoMAEModel 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.

Examples:

>>> from decord import VideoReader, cpu
>>> import numpy as np

>>> from transformers import VideoMAEFeatureExtractor, VideoMAEModel
>>> from huggingface_hub import hf_hub_download


>>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
...     converted_len = int(clip_len * frame_sample_rate)
...     end_idx = np.random.randint(converted_len, seg_len)
...     start_idx = end_idx - converted_len
...     indices = np.linspace(start_idx, end_idx, num=clip_len)
...     indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
...     return indices


>>> # video clip consists of 300 frames (10 seconds at 30 FPS)
>>> file_path = hf_hub_download(
...     repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
... )
>>> videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0))

>>> # sample 16 frames
>>> videoreader.seek(0)
>>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader))
>>> video = videoreader.get_batch(indices).asnumpy()

>>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base")
>>> model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base")

>>> # prepare video for the model
>>> inputs = feature_extractor(list(video), return_tensors="pt")

>>> # forward pass
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 1568, 768]

VideoMAEForPreTraining

class transformers.VideoMAEForPreTraining

< >

( config )

Parameters

  • config (VideoMAEConfig) — 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 VideoMAE Model transformer with the decoder on top for self-supervised pre-training. 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

< >

( pixel_values bool_masked_pos head_mask = None output_attentions = None output_hidden_states = None return_dict = None ) → transformers.models.videomae.modeling_videomae.VideoMAEForPreTrainingOutput or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.FloatTensor of shape (batch_size, num_frames, num_channels, height, width)) — Pixel values. Pixel values can be obtained using VideoMAEFeatureExtractor. See VideoMAEFeatureExtractor.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.

Returns

transformers.models.videomae.modeling_videomae.VideoMAEForPreTrainingOutput or tuple(torch.FloatTensor)

A transformers.models.videomae.modeling_videomae.VideoMAEForPreTrainingOutput 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 (VideoMAEConfig) and inputs.

  • loss (torch.FloatTensor of shape (1,)) — Pixel reconstruction loss.
  • logits (torch.FloatTensor of shape (batch_size, patch_size ** 2 * num_channels)) — Pixel reconstruction logits.
  • 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 layer) 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 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 VideoMAEForPreTraining 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.

Examples:

>>> from transformers import VideoMAEFeatureExtractor, VideoMAEForPreTraining
>>> import numpy as np
>>> import torch

>>> num_frames = 16
>>> video = list(np.random.randn(16, 3, 224, 224))

>>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base")
>>> model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base")

>>> pixel_values = feature_extractor(video, return_tensors="pt").pixel_values

>>> num_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2
>>> seq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame
>>> bool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool()

>>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
>>> loss = outputs.loss

VideoMAEForVideoClassification

class transformers.VideoMAEForVideoClassification

< >

( config )

Parameters

  • config (VideoMAEConfig) — 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.

VideoMAE Model transformer with a video classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet. 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

< >

( pixel_values: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None labels: 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.ImageClassifierOutput or tuple(torch.FloatTensor)

Parameters

  • pixel_values (torch.FloatTensor of shape (batch_size, num_frames, num_channels, height, width)) — Pixel values. Pixel values can be obtained using VideoMAEFeatureExtractor. See VideoMAEFeatureExtractor.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.
  • labels (torch.LongTensor of shape (batch_size,), optional) — Labels for computing the image classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If config.num_labels > 1 a classification loss is computed (Cross-Entropy).

Returns

transformers.modeling_outputs.ImageClassifierOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.ImageClassifierOutput 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 (VideoMAEConfig) and inputs.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.

  • logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (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 stage) of shape (batch_size, sequence_length, hidden_size). Hidden-states (also called feature maps) of the model at the output of each stage.

  • 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, patch_size, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The VideoMAEForVideoClassification 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.

Examples:

>>> from decord import VideoReader, cpu
>>> import torch
>>> import numpy as np

>>> from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification
>>> from huggingface_hub import hf_hub_download

>>> np.random.seed(0)


>>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
...     converted_len = int(clip_len * frame_sample_rate)
...     end_idx = np.random.randint(converted_len, seg_len)
...     start_idx = end_idx - converted_len
...     indices = np.linspace(start_idx, end_idx, num=clip_len)
...     indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
...     return indices


>>> # video clip consists of 300 frames (10 seconds at 30 FPS)
>>> file_path = hf_hub_download(
...     repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
... )
>>> videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0))

>>> # sample 16 frames
>>> videoreader.seek(0)
>>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader))
>>> video = videoreader.get_batch(indices).asnumpy()

>>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics")
>>> model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics")

>>> inputs = feature_extractor(list(video), return_tensors="pt")

>>> with torch.no_grad():
...     outputs = model(**inputs)
...     logits = outputs.logits

>>> # model predicts one of the 400 Kinetics-400 classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
eating spaghetti
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