Perform a spatial short scale jittering on the given images and corresponding boxes. Args: images (tensor): images to perform sca

Perform a spatial short scale jittering on the given images and corresponding boxes. Args: images (list): list of images to perfo

Perform random sized cropping on the given image. Random crop with size 8% - 100% image area and aspect ratio in [3/4, 4/3]. Args:

Performs Inception-style cropping (used for training).

Perform random sized cropping on the given list of images. Random crop with size 8% - 100% image area and aspect ratio in [3/4, 4/3].

Reset the deque.

Reset the Meter.

Reset the Meter.

Reset the epoch timer.

Reset the deque.

Reset the Meter.

Reset the Meter.

Reset the epoch timer.

How many results are ready to be returned.

This function is to load images with support of retrying for failed load. Args: image_paths (list): paths of images needed to be loa

Revert scaled input boxes to match the original image size. Args: size (int): size of the cropped image. boxes (array): shape

Revert normalization for a given tensor by multiplying by the std and adding the mean. Args: tensor (tensor): tensor to revert normal

Convert clip frames from RGB mode to BRG mode. Args: x (Tensor): A tensor of the clip's RGB frames with shape: (channel,

Runs a function from a child process. Args: local_rank (int): rank of the current process on the current machine. num_proc (i

Run prediction asynchronously.

Run visualization asynchronously.

Returns: (torch.utils.data.Sampler): video sampler for the dataset.

Save a checkpoint. Args: model (model): model to save the weight to the checkpoint. optimizer_list (optim): optimizer to save

Save a checkpoint. Args: model (model): model to save the weight to the checkpoint. optimizer (optim): optimizer to save the

Save a checkpoint. Args: model (model): model to save the weight to the checkpoint. optimizer (optim): optimizer to save the

Save a checkpoint. Args: model (model): model to save the weight to the checkpoint. optimizer_list (optim): optimizer to save

Save a checkpoint. Args: model (model): model to save the weight to the checkpoint. optimizer (optim): optimizer to save the

Save a checkpoint. Args: model (model): model to save the weight to the checkpoint. optimizer (optim): optimizer to save the

Save a checkpoint. Args: model (model): model to save the weight to the checkpoint. optimizer (optim): optimizer to save the

Sets the optimizer lr to the specified value. Args: optimizer (optim): the optimizer using to optimize the current network. n

Sets the optimizer lr to the specified value. Args: optimizer (optim): the optimizer using to optimize the current network. n

Sets the optimizer lr to the specified value. Args: optimizer (optim): the optimizer using to optimize the current network. n

Sets up the logging for multiple processes. Only enable the logging for the master process, and suppress logging for the non-master processes

" Shuffles the data. Args: loader (loader): data loader to perform shuffle. cur_epoch (int): number of the current epoch.

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

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

Perform left, center, or right crop of the given list of images. Args: size (int): size to crop. image (list): ilist of image

Start threads to read and write frames.

Helper function to synchronize (barrier) among all processes when using distributed training

Perform multi-view testing on the pretrained video model. Args: cfg (CfgNode): configs. Details can be found in slowfast/

Returns the tokenized representation of given input string(s) Parameters ---------- texts : Union[str, List[str]] An input s

Computes the top-k accuracy for each k. Args: preds (array): array of predictions. Dimension is N. labels (array): array of l

Computes the top-k error for each k. Args: preds (array): array of predictions. Dimension is N. labels (array): array of labe

Train a video model for many epochs on train set and evaluate it on val set. Args: cfg (CfgNode): configs. Details can be found in

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

Update predictions and labels. Args: preds (tensor): model output predictions. labels (tensor): labels.

Collect the predictions from the current batch and perform on-the-flight summation as ensemble. Args: preds (tens

Update the current stats. Args: top1_err (float): top1 error rate. top5_err (float): top5 error rate.

Update the current stats. Args: top1_err (float): top1 error rate. top5_err (float): top5 error rate.

Update the current stats. Args: preds (tensor): prediction embedding. ori_boxes (tensor): original boxes (x1,

Collect the predictions from the current batch and perform on-the-flight summation as ensemble. Args: preds (tens

Update the current stats. Args: top1_err (float): top1 error rate. top5_err (float): top5 error rate.

Update the current stats. Args: top1_err (float): top1 error rate. top5_err (float): top5 error rate.

Perform layer weights and activations visualization on the model. Args: cfg (CfgNode): configs. Details can be found in s