Vision Transformer with support for patch or hybrid CNN input stage

Vision Transformer with support for patch or hybrid CNN input stage

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

Load your own video classification dataset.

Image to Patch Embedding

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]

Apply for video.

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

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

Vision Transformer with support for patch or hybrid CNN input stage

Vision Transformer with support for patch or hybrid CNN input stage

Crop the given PIL Image to random size and aspect ratio with random interpolation. A crop of random size (default: of 0.08 to 1.0) of the origina

Load your own video classification dataset.

Load your own video classification dataset. Parameters ---------- root : str, required. Path to the root folder storing the datase

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,

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]

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]

Randomly change the brightness, contrast and saturation and hue of the clip Args: brightness (float): How much to jitter brightness. brightnes

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

Fast Collate w/ Mixup/Cutmix that applies different params to each element or whole batch A Mixup impl that's performed while collating the batc

Rescales the input PIL.Image to the given 'size'. 'size' will be the size of the smaller edge. For example, if height > width, then image wil

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

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

Horizontally flip the list of given images randomly with a probability 0.5

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

Rotate entire clip randomly by a random angle within given bounds Args: degrees (sequence or int): Range of degrees to select from If

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

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

Converts numpy array to tensor