This class represents a set of bounding boxes. The bounding boxes are represented as a Nx4 Tensor. In order to uniquely determine the bou

Track a series of values and provide access to smoothed values over a window or the global series average.

Composes several transforms Args: transforms (list of ``Transform`` objects): list of transforms to compose

The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between

Convert a list of m (H x W x C) numpy.ndarrays in the range [0, 255] to a torch.FloatTensor of shape (C x m x H x W) in the range [0, 1.0]

Normalize a clip with mean and standard deviation. Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, this transform will

Resizes a list of (H x W x C) numpy.ndarray to the final size The larger the original image is, the more times it takes to interpolate Arg

Get attributes >>> d = EasyDict({'foo':3}) >>> d['foo'] 3 >>> d.foo 3 >>> d.bar Traceback (most recent call last):

Randomly selects a rectangle region in an image and erases its pixels. 'Random Erasing Data Augmentation' by Zhong et al. See https://

Extract center crop at the same location for a list of images Args: size (sequence or int): Desired output size for the crop in format (h,

Subclass torch's LayerNorm to handle fp16.

Subclass torch's LayerNorm to handle fp16.

A helper class for managing registering modules, it extends a dictionary and provides a register functions. Eg. creeting a registry:

Track a series of values and provide access to smoothed values over a window or the global series average.

Vision Transformer with support for patch or hybrid CNN input stage

wraps multiple data loader

Image to Patch Embedding

Vision Transformer with support for patch or hybrid CNN input stage

Vision Transformer with support for patch or hybrid CNN input stage

Internal implementation of Pascal object detection metrics.

Vision Transformer with support for patch or hybrid CNN input stage

Track a series of values and provide access to smoothed values over a window or the global series average.

Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]

Load your own video classification dataset.

Apply for video.

Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). Args: do_lower_case (:obj:`

From a list of samples from the dataset, returns the batched objectimages and targets. This should be passed to the DataLoader

From a list of samples from the dataset, returns the batched objectimages and targets. This should be passed to the DataLoader

Construct the embeddings from word, position and token_type embeddings.

Output type of :class:`~transformers.BertForPreTraining`. Args: loss (`optional`, returned when ``labels`` is provided, ``torch.Float

Same as torch.utils.dataset.dataset.ConcatDataset, but exposes an extra method for querying the sizes of the image

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

masked language modeling loss.

Mixup/Cutmix that applies different params to each element or whole batch Args: mixup_alpha (float): mixup alpha value, mixup is active

Image to Patch Embedding

Image to Patch Embedding

Vision Transformer with support for patch or hybrid CNN input stage

Vision Transformer with support for patch or hybrid CNN input stage

Vision Transformer with support for patch or hybrid CNN input stage

Vision Transformer with support for patch or hybrid CNN input stage