
 
Video Vision Transformer (ViViT)
	
Overview
	
The Vivit model was proposed in ViViT: A Video Vision Transformer by Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid.
The paper proposes one of the first successful pure-transformer based set of models for video understanding.
The abstract from the paper is the following:
We present pure-transformer based models for video classification, drawing upon the recent success of such models in image classification. Our model extracts spatio-temporal tokens from the input video, which are then encoded by a series of transformer layers. In order to handle the long sequences of tokens encountered in video, we propose several, efficient variants of our model which factorise the spatial- and temporal-dimensions of the input. Although transformer-based models are known to only be effective when large training datasets are available, we show how we can effectively regularise the model during training and leverage pretrained image models to be able to train on comparatively small datasets. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple video classification benchmarks including Kinetics 400 and 600, Epic Kitchens, Something-Something v2 and Moments in Time, outperforming prior methods based on deep 3D convolutional networks.
This model was contributed by jegormeister. The original code (written in JAX) can be found here.
VivitConfig
	
class transformers.VivitConfig
<
source
>
(
image_size = 224
num_frames = 32
tubelet_size = [2, 16, 16]
num_channels = 3
hidden_size = 768
num_hidden_layers = 12
num_attention_heads = 12
intermediate_size = 3072
hidden_act = 'gelu_fast'
hidden_dropout_prob = 0.0
attention_probs_dropout_prob = 0.0
initializer_range = 0.02
layer_norm_eps = 1e-06
qkv_bias = True
**kwargs
)
Parameters 
image_size (int, optional, defaults to 224) —
The size (resolution) of each image.
num_frames (int, optional, defaults to 32) —
The number of frames in each video.
tubelet_size (List[int], optional, defaults to [2, 16, 16]) —
The size (resolution) of each tubelet.
num_channels (int, optional, defaults to 3) —
The number of input channels.
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_fast") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "selu", "gelu_fast" and "gelu_new" are supported.
hidden_dropout_prob (float, optional, defaults to 0.0) —
The dropout probability 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-06) —
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.
This is the configuration class to store the configuration of a VivitModel. It is used to instantiate a ViViT
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 ViViT
google/vivit-b-16x2-kinetics400 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 VivitConfig, VivitModel
 # Initializing a ViViT google/vivit-b-16x2-kinetics400 style configuration
 configuration = VivitConfig()
 # Initializing a model (with random weights) from the google/vivit-b-16x2-kinetics400 style configuration
 model = VivitModel(configuration)
 # Accessing the model configuration
 configuration = model.config
VivitImageProcessor
	
class transformers.VivitImageProcessor
<
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
offset: bool = True
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" -- 256}):
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.
offset (bool, optional, defaults to True) —
Whether to scale the image in both negative and positive directions. Can be overriden by the offset 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 Vivit 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
offset: bool = 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 
videos (ImageInput) —
Video frames 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 [-1 - 1] if offset is True, [0, 1] otherwise.
rescale_factor (float, optional, defaults to self.rescale_factor) —
Rescale factor to rescale the image by if do_rescale is set to True.
offset (bool, optional, defaults to self.offset) —
Whether to scale the image in both negative and positive directions.
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.
VivitModel
	
class transformers.VivitModel
<
source
>
(
config
add_pooling_layer = True
)
Parameters 
config (VivitConfig) — 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 ViViT Transformer model 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 = 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 VivitImageProcessor. See
VivitImageProcessor.preprocess() 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 (VivitConfig) 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 VivitModel 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 VivitImageProcessor, VivitModel
 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 32 frames
 indices = sample_frame_indices(clip_len=32, frame_sample_rate=1, seg_len=len(videoreader))
 video = videoreader.get_batch(indices).asnumpy()
 image_processor = VivitImageProcessor.from_pretrained("google/vivit-b-16x2-kinetics400")
 model = VivitModel.from_pretrained("google/vivit-b-16x2-kinetics400")
 # 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, 3137, 768]
VivitForVideoClassification
	
class transformers.VivitForVideoClassification
<
source
>
(
config
)
Parameters 
config (VivitConfig) — 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.
ViViT Transformer model with a video classification head on top (a linear layer on top of the final hidden state of the
[CLS] token) e.g. for Kinetics-400.
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 = None
head_mask = None
labels = None
output_attentions = None
output_hidden_states = None
return_dict = 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 VivitImageProcessor. See
VivitImageProcessor.preprocess() 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 (VivitConfig) 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 VivitForVideoClassification 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 VivitImageProcessor, VivitModel
 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 32 frames
 indices = sample_frame_indices(clip_len=32, frame_sample_rate=1, seg_len=len(videoreader))
 video = videoreader.get_batch(indices).asnumpy()
 image_processor = VivitImageProcessor.from_pretrained("google/vivit-b-16x2-kinetics400")
 model = VivitForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400")
 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
←ViTMSN
YOLOS→
Video Vision Transformer (ViViT)
Overview
VivitConfig
VivitImageProcessor
VivitModel
VivitForVideoClassification
