Given scale, ratio, height and width, return sampled coordinates of the videos.

Convert the image to gray scale. Args: image (tensor): image to convert to gray scale. Dimension is `channel` x `height`

Perform horizontal flip on the given images and corresponding boxes. Args: prob (float): probility to flip the images. images

One-stop checkpoint loading and applying function. Args: model: Model to load weights into checkpoint_path: Checkpoint p

Perform random spatial crop on the given images and corresponding boxes. Args: images (tensor): images to perform random crop. The di

Reset the metric.

Reset the metric.

Scale the short side of the image to size. Args: size (int): size to scale the image. image (array): image to perform short s

Perform uniform spatial sampling on the images and corresponding boxes. Args: images (tensor): images to perform uniform crop. The di

Write row to internal buffers.

Simple block wrapping a mixer class with LayerNorm/RMSNorm and residual connection" This Block has a slightly different structure co

Blend two images with a given weight alpha. Args: images1 (tensor): the first images to be blended, the dimension is `num

Blend two images with a given weight alpha. Args: images1 (tensor): the first images to be blended, the dimension is `num

Blend two images with a given weight alpha. Args: images1 (tensor): the first images to be blended, the dimension is `num

Blend two images with a given weight alpha. Args: images1 (tensor): the first images to be blended, the dimension is `num

Blend two images with a given weight alpha. Args: images1 (tensor): the first images to be blended, the dimension is `num

Peform crop on the bounding boxes given the offsets. Args: boxes (ndarray or None): bounding boxes to peform crop. The dimension

Peform crop on the bounding boxes given the offsets. Args: boxes (ndarray or None): bounding boxes to peform crop. The dimension

Decode the video and perform temporal sampling. Args: container (container): pyav container. sampling_rate (int): frame sampl

embed_dim: output dimension for each position grid_size: int of the grid length returns: pos_embed: [grid_size, embed_dim] (w/o c

grid_size: int of the grid height and width returns: pos_embed: [grid_size*grid_size, embed_dim] (w/o cls_token) or [

grid_size: int of the grid height and width grid_depth: int of the grid depth returns: pos_embed: [grid_depth*grid_size*grid_size

When multigrid training uses a fewer number of frames, we randomly increase the sampling rate so that some clips cover the original span.

Sinusoid position encoding table

Sample a clip of size clip_size from a video of size video_size and return the indices of the first and last frame of the clip. If clip_idx i

Given the path to the video, return the pyav video container. Args: path_to_vid (str): path to the video. multi_thread_decode

Get the grayscale for the input images. The channels of images should be in order BGR. Args: images (tensor): the input images fo

Load a state dict into the model, handling 2D to 3D weight inflation if necessary. Args: model (nn.Module): The model to load the st

Load a state dict into the model, handling 2D to 3D weight inflation if necessary. Args: model (nn.Module): The model to load the st

Load a state dict into the model, handling 2D to 3D weight inflation if necessary. Args: model (nn.Module): The model to load the st

Load a state dict into the model, handling 2D to 3D weight inflation if necessary. Args: model (nn.Module): The model to load the st

Load a state dict into the model, handling 2D to 3D weight inflation if necessary. Args: model (nn.Module): The model to load the st

Load a state dict into the model, handling 2D to 3D weight inflation if necessary. Args: model (nn.Module): The model to load the st

Perform spatial sampling on the given video frames. If spatial_idx is -1, perform random scale, random crop, and random flip on the given

Normalize a given tensor by subtracting the mean and dividing the std. Args: tensor (tensor): tensor to normalize. mean (tens

Simple block wrapping a mixer class with LayerNorm/RMSNorm and residual connection. This Block has a slightly different structure co

Simple block wrapping a mixer class with LayerNorm/RMSNorm and residual connection. This Block has a slightly different structure co

Simple block wrapping a mixer class with LayerNorm/RMSNorm and residual connection" This Block has a slightly different structure co

Simple block wrapping a mixer class with LayerNorm/RMSNorm and residual connection" This Block has a slightly different structure co

Simple block wrapping a mixer class with LayerNorm/RMSNorm and residual connection. This Block has a slightly different structure co

Simple block wrapping a mixer class with LayerNorm/RMSNorm and residual connection. This Block has a slightly different structure co

Write header to internal buffers.