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.
( 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
int
, optional, defaults to 224) —
The size (resolution) of each image. int
, optional, defaults to 32) —
The number of frames in each video. List[int]
, optional, defaults to [2, 16, 16]
) —
The size (resolution) of each tubelet. int
, optional, defaults to 3) —
The number of input channels. int
, optional, defaults to 768) —
Dimensionality of the encoder layers and the pooler layer. int
, optional, defaults to 12) —
Number of hidden layers in the Transformer encoder. int
, optional, defaults to 12) —
Number of attention heads for each attention layer in the Transformer encoder. int
, optional, defaults to 3072) —
Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder. 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. float
, optional, defaults to 0.0) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. float
, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities. float
, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. float
, optional, defaults to 1e-06) —
The epsilon used by the layer normalization layers. 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:
>>> 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
( do_resize: bool = True size: Dict = None resample: Resampling = <Resampling.BILINEAR: 2> do_center_crop: bool = True crop_size: Dict = None do_rescale: bool = True rescale_factor: Union = 0.00784313725490196 offset: bool = True do_normalize: bool = True image_mean: Union = None image_std: Union = None **kwargs )
Parameters
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. 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. PILImageResampling
, optional, defaults to Resampling.BILINEAR
) —
Resampling filter to use if resizing the image. Can be overridden by the resample
parameter in the
preprocess
method. 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. 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. 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. int
or float
, optional, defaults to 1/127.5
) —
Defines the scale factor to use if rescaling the image. Can be overridden by the rescale_factor
parameter
in the preprocess
method. 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. bool
, optional, defaults to True
) —
Whether to normalize the image. Can be overridden by the do_normalize
parameter in the preprocess
method. 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. 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.
( videos: Union do_resize: bool = None size: Dict = None resample: Resampling = None do_center_crop: bool = None crop_size: Dict = None do_rescale: bool = None rescale_factor: float = None offset: bool = None do_normalize: bool = None image_mean: Union = None image_std: Union = None return_tensors: Union = None data_format: ChannelDimension = <ChannelDimension.FIRST: 'channels_first'> input_data_format: Union = None **kwargs )
Parameters
ImageInput
) —
Video frames to preprocess. Expects a single or batch of video frames with pixel values ranging from 0
to 255. If passing in frames with pixel values between 0 and 1, set do_rescale=False
. bool
, optional, defaults to self.do_resize
) —
Whether to resize the image. Dict[str, int]
, optional, defaults to self.size
) —
Size of the image after applying resize. 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
. bool
, optional, defaults to self.do_centre_crop
) —
Whether to centre crop the image. Dict[str, int]
, optional, defaults to self.crop_size
) —
Size of the image after applying the centre crop. bool
, optional, defaults to self.do_rescale
) —
Whether to rescale the image values between [-1 - 1]
if offset
is True
, [0, 1]
otherwise. float
, optional, defaults to self.rescale_factor
) —
Rescale factor to rescale the image by if do_rescale
is set to True
. bool
, optional, defaults to self.offset
) —
Whether to scale the image in both negative and positive directions. bool
, optional, defaults to self.do_normalize
) —
Whether to normalize the image. float
or List[float]
, optional, defaults to self.image_mean
) —
Image mean. float
or List[float]
, optional, defaults to self.image_std
) —
Image standard deviation. str
or TensorType
, optional) —
The type of tensors to return. Can be one 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
.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.ChannelDimension
or str
, optional) —
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:"channels_first"
or ChannelDimension.FIRST
: image in (num_channels, height, width) format."channels_last"
or ChannelDimension.LAST
: image in (height, width, num_channels) format."none"
or ChannelDimension.NONE
: image in (height, width) format.Preprocess an image or batch of images.
( config add_pooling_layer = True )
Parameters
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.
( pixel_values: Optional = None head_mask: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters
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. 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]
:
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. Returns
transformers.modeling_outputs.BaseModelOutputWithPooling or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPooling 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.
pooler_output (torch.FloatTensor
of shape (batch_size, hidden_size)
) — Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.
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:
>>> 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):
... '''
... Sample a given number of frame indices from the video.
... Args:
... clip_len (`int`): Total number of frames to sample.
... frame_sample_rate (`int`): Sample every n-th frame.
... seg_len (`int`): Maximum allowed index of sample's last frame.
... Returns:
... indices (`List[int]`): List of sampled frame indices
... '''
... 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=container.streams.video[0].frames)
>>> video = read_video_pyav(container=container, indices=indices)
>>> 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]
( config )
Parameters
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.
( pixel_values: Optional = None head_mask: Optional = None labels: Optional = None output_attentions: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.modeling_outputs.ImageClassifierOutput or tuple(torch.FloatTensor)
Parameters
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. 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]
:
bool
, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions
under returned
tensors for more detail. bool
, optional) —
Whether or not to return the hidden states of all layers. See hidden_states
under returned tensors for
more detail. bool
, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. 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:
>>> import av
>>> import numpy as np
>>> import torch
>>> from transformers import VivitImageProcessor, VivitForVideoClassification
>>> 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):
... '''
... Sample a given number of frame indices from the video.
... Args:
... clip_len (`int`): Total number of frames to sample.
... frame_sample_rate (`int`): Sample every n-th frame.
... seg_len (`int`): Maximum allowed index of sample's last frame.
... Returns:
... indices (`List[int]`): List of sampled frame indices
... '''
... 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=4, seg_len=container.streams.video[0].frames)
>>> video = read_video_pyav(container=container, indices=indices)
>>> 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])
LABEL_116