
 
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:
One can use VideoMAEImageProcessor to prepare videos for the model. It will resize + normalize all frames of a video for you.
VideoMAEForPreTraining includes the decoder on top for self-supervised pre-training.
VideoMAE pre-training. Taken from the original paper. 
This model was contributed by nielsr.
The original code can be found here.
Resources
	
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with VideoMAE. If
you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll
review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
Video classification
A notebook that shows how
to fine-tune a VideoMAE model on a custom dataset.
Video classification task guide
A 🤗 Space showing how to perform inference with a video classification model.
VideoMAEConfig
	
class transformers.VideoMAEConfig
<
source
>
(
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:
	Copied
 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
<
source
>
(
*args
**kwargs
)
__call__
<
source
>
(
images
**kwargs
)
Preprocess an image or a batch of images.
VideoMAEImageProcessor
	
class transformers.VideoMAEImageProcessor
<
source
>
(
do_resize: bool = True
size: typing.Dict[str, int] = None
resample: Resampling = <Resampling.BILINEAR: 2>
do_center_crop: bool = True
crop_size: typing.Dict[str, int] = None
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 the
do_resize parameter in the preprocess method.
size (Dict[str, int] optional, defaults to {"shortest_edge" -- 224}):
Size of the output image after resizing. The shortest edge of the image will be resized to
size["shortest_edge"] while maintaining the aspect ratio of the original image. Can be overriden 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 the resample parameter in the
preprocess method.
do_center_crop (bool, optional, defaults to True) —
Whether to center crop the image to the specified crop_size. Can be overridden by the do_center_crop
parameter in the preprocess method.
crop_size (Dict[str, int], optional, defaults to {"height" -- 224, "width": 224}):
Size of the image after applying the center crop. Can be overridden by the crop_size parameter in the
preprocess method.
do_rescale (bool, optional, defaults to True) —
Whether to rescale the image by the specified scale rescale_factor. Can be overridden by the do_rescale
parameter in the preprocess method.
rescale_factor (int or float, optional, defaults to 1/255) —
Defines the scale factor to use if rescaling the image. Can be overridden by the rescale_factor parameter
in the preprocess method.
do_normalize (bool, optional, defaults to True) —
Whether to normalize the image. Can be overridden by the do_normalize parameter 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) —
Standard deviation 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_std parameter in the preprocess method.
Constructs a VideoMAE image processor.
preprocess
<
source
>
(
videos: 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_center_crop: bool = None
crop_size: typing.Dict[str, int] = None
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: 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 applying resize.
resample (PILImageResampling, 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_center_crop (bool, optional, defaults to self.do_centre_crop) —
Whether to centre crop the image.
crop_size (Dict[str, int], optional, defaults to self.crop_size) —
Size of the image after applying the centre crop.
do_rescale (bool, optional, defaults to self.do_rescale) —
Whether to rescale the image values between [0 - 1].
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.
image_std (float or List[float], optional, defaults to self.image_std) —
Image standard deviation.
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: Use the inferred channel dimension format of the input image.
Preprocess an image or batch of images.
VideoMAEModel
	
class transformers.VideoMAEModel
<
source
>
(
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
<
source
>
(
pixel_values: FloatTensor
bool_masked_pos: typing.Optional[torch.BoolTensor] = 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.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 AutoImageProcessor. See
VideoMAEImageProcessor.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, sequence_length), optional) —
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0). Each video in the
batch must have the same number of masked patches. If None, then all patches are considered. Sequence
length is (num_frames // tubelet_size) * (image_size // patch_size) ** 2.
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:
	Copied
 import av
 import numpy as np
 from transformers import AutoImageProcessor, VideoMAEModel
 from huggingface_hub import hf_hub_download
 np.random.seed(0)
 def read_video_pyav(container, indices):
...     '''
...     Decode the video with PyAV decoder.
...     Args:
...         container (`av.container.input.InputContainer`): PyAV container.
...         indices (`List[int]`): List of frame indices to decode.
...     Returns:
...         result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
...     '''
...     frames = []
...     container.seek(0)
...     start_index = indices[0]
...     end_index = indices[-1]
...     for i, frame in enumerate(container.decode(video=0)):
...         if i > end_index:
...             break
...         if i >= start_index and i in indices:
...             frames.append(frame)
...     return np.stack([x.to_ndarray(format="rgb24") for x in frames])
 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"
... )
 container = av.open(file_path)
 # sample 16 frames
 indices = sample_frame_indices(clip_len=16, frame_sample_rate=1, seg_len=container.streams.video[0].frames)
 video = read_video_pyav(container, indices)
 image_processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base")
 model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base")
 # prepare video for the model
 inputs = image_processor(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
<
source
>
(
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
<
source
>
(
pixel_values: FloatTensor
bool_masked_pos: BoolTensor
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.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 AutoImageProcessor. See
VideoMAEImageProcessor.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, sequence_length)) —
Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0). Each video in the
batch must have the same number of masked patches. Sequence length is (num_frames // tubelet_size) * (image_size // patch_size) ** 2.
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:
	Copied
 from transformers import AutoImageProcessor, VideoMAEForPreTraining
 import numpy as np
 import torch
 num_frames = 16
 video = list(np.random.randint(0, 256, (num_frames, 3, 224, 224)))
 image_processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base")
 model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base")
 pixel_values = image_processor(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
<
source
>
(
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 average pooled hidden
states of all tokens) 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
<
source
>
(
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 AutoImageProcessor. See
VideoMAEImageProcessor.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:
	Copied
 import av
 import torch
 import numpy as np
 from transformers import AutoImageProcessor, VideoMAEForVideoClassification
 from huggingface_hub import hf_hub_download
 np.random.seed(0)
 def read_video_pyav(container, indices):
...     '''
...     Decode the video with PyAV decoder.
...     Args:
...         container (`av.container.input.InputContainer`): PyAV container.
...         indices (`List[int]`): List of frame indices to decode.
...     Returns:
...         result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
...     '''
...     frames = []
...     container.seek(0)
...     start_index = indices[0]
...     end_index = indices[-1]
...     for i, frame in enumerate(container.decode(video=0)):
...         if i > end_index:
...             break
...         if i >= start_index and i in indices:
...             frames.append(frame)
...     return np.stack([x.to_ndarray(format="rgb24") for x in frames])
 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"
... )
 container = av.open(file_path)
 # sample 16 frames
 indices = sample_frame_indices(clip_len=16, frame_sample_rate=1, seg_len=container.streams.video[0].frames)
 video = read_video_pyav(container, indices)
 image_processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics")
 model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics")
 inputs = image_processor(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
←VAN
Vision Transformer (ViT)→
VideoMAE
Overview
Resources
VideoMAEConfig
VideoMAEFeatureExtractor
VideoMAEImageProcessor
VideoMAEModel
VideoMAEForPreTraining
VideoMAEForVideoClassification
