Computes pairwise intersection-over-area between box collections. Intersection-over-area (ioa) between two boxes box1 and box2 is defined as

Computes pairwise intersection-over-area between box collections. Intersection-over-area (ioa) between two masks, mask1 and mask2 is defined as

Computes pairwise intersection-over-union between box and mask collections. Args: box_mask_list1: BoxMaskList holding N boxes and masks

Computes pairwise intersection-over-union between box collections. Args: boxes1: a numpy array with shape [N, 4] holding N boxes. box

Computes pairwise intersection-over-union between box collections. Args: boxlist1: BoxList holding N boxes boxlist2: BoxList holding

Computes pairwise intersection-over-union between mask collections. Args: masks1: a numpy array with shape [N, height, width] holding N mas

Determine if a checkpoint should be saved on current epoch. Args: cfg (CfgNode): configs to save. cur_epoch (int): current nu

Determine if a checkpoint should be saved on current epoch. Args: cfg (CfgNode): configs to save. cur_epoch (int): current nu

Determine if a checkpoint should be saved on current epoch. Args: cfg (CfgNode): configs to save. cur_epoch (int): current nu

Determine if a checkpoint should be saved on current epoch. Args: cfg (CfgNode): configs to save. cur_epoch (int): current nu

Determine if the model should be evaluated at the current epoch. Args: cfg (CfgNode): configs. Details can be found in sl

Determines if the current process is the master process.

Determines if the current process is the root process.

Start to record time.

Start to record time.

Start to record time.

Start to record time.

Start to record time.

Start to record time.

Start to record time.

Stop to record time.

Stop to record time.

Stop to record time.

Stop to record time.

Stop to record time.

Stop to record time.

Stop to record time.

Get the time for the last epoch.

Run 'func' on one or more GPUs, specified in cfg Args: cfg_list (CfgNode): configs. Details can be found in slowfast/conf

Perform AlexNet-style PCA jitter on the given image. Args: image (array): list of images to perform lighting jitter. alphastd

Perform AlexNet-style PCA jitter on the given images. Args: images (tensor): images to perform lighting jitter. Dimension is

Perform AlexNet-style PCA jitter on the given list of images. Args: images (list): list of images to perform lighting jitter.

Load a CLIP model Parameters ---------- name : str A model name listed by `clip.available_models()`, or the path to a model check

Loading boxes and labels from csv files. Args: cfg (CfgNode): config. mode (str): 'train', 'val', or 'test' mode. Return

Given the arguemnts, load and initialize the configs. Args: args (argument): arguments includes `shard_id`, `num_shards`,

Load image paths and labels from a "frame list". Each line of the frame list contains: `original_vido_id video_id frame_id path labels`

Loading image paths from corresponding files. Args: cfg (CfgNode): config. is_train (bool): if it is training dataset or not

Loading checkpoint logic for testing.

Loading checkpoint logic for testing.

Loading checkpoint logic for testing.

Loading checkpoint logic for testing.

Loading checkpoint logic for training.

Loading checkpoint logic for training.

Loading checkpoint logic for training.

Loading checkpoint logic for training.

Create init function passed to pytorch data loader. Args: dataset (torch.utils.data.Dataset): the given dataset.

Log the stats of the current epoch. Args: cur_epoch (int): the number of current epoch.

Log the stats of the current epoch. Args: cur_epoch (int): the number of current epoch.

Log the stats of the current epoch. Args: cur_epoch (int): the number of current epoch.

Log the stats of the current epoch. Args: cur_iter (int): the number of current iteration.

Log the stats of the current epoch. Args: cur_epoch (int): the number of current epoch.

Log the stats. Args: cur_iter (int): the current iteration of testing.

log the stats of the current iteration. Args: cur_epoch (int): the number of current epoch. cur_iter (int): t

log the stats of the current iteration. Args: cur_epoch (int): the number of current epoch. cur_iter (int): t

Log the stats. Args: cur_epoch (int): the current epoch. cur_iter (int): the current iteration.

Log the stats. Args: cur_iter (int): the current iteration of testing.

log the stats of the current iteration. Args: cur_iter (int): the number of current iteration.

log the stats of the current iteration. Args: cur_epoch (int): the number of current epoch. cur_iter (int): t

Logs json stats. Args: stats (dict): a dictionary of statistical information to log.

Log info, includes number of parameters, gpu usage, gflops and activation count. The model info is computed when the model is in validati

Retrieve the learning rate to specified values at specified epoch with the cosine learning rate schedule. Details can be found in: Ilya L

Retrieve the learning rate to specified values at specified epoch with the steps with relative learning rate schedule. Args: cfg

Creates the checkpoint directory (if not present already). Args: path_to_job (string): the path to the folder of the current job.

Creates the checkpoint directory (if not present already). Args: path_to_job (string): the path to the folder of the current job.

Creates the checkpoint directory (if not present already). Args: path_to_job (string): the path to the folder of the current job.

Creates the checkpoint directory (if not present already). Args: path_to_job (string): the path to the folder of the current job.

Calculate the median epoch time among the recorded epochs.

Merge data from precomputed and ground-truth boxes dictionaries. Args: pred_dict (dict): a dict which maps from `frame_idx` to a list

Multi-class version of non maximum suppression. This op greedily selects a subset of detection bounding boxes, pruning away boxes that have h

Multi-class version of non maximum suppression. This op greedily selects a subset of detection bounding boxes, pruning away boxes that have h

Collate function for repeated augmentation. Each instance in the batch has more than one sample. Args: batch (tuple or list): dat

Returns: (int): the number of videos in the dataset.

Returns: (int): the number of videos in the dataset.

Returns: (int): the number of videos in the dataset.

Returns: (int): the number of videos in the dataset.

Returns: (int): the number of clips in total in the dataset.

Returns: (int): the number of videos in the dataset.

Returns: (int): the number of videos in the dataset.

Returns: (int): the number of videos in the dataset.

Parse the following arguments for a default parser for PySlowFast users. Args: shard_id (int): shard id for the current machine. Star

A function to create a colored and labeled confusion matrix matplotlib figure given true labels and preds. Args: cmtx (ndarray):

Plot the input tensor with the optional bounding box and save it to disk. Args: tensor (tensor): a tensor with shape of `NxCxHxW`.

Plot histogram of top-k value from the given array. Args: tag (str): histogram title. array (tensor): a tensor to draw top k

Process the video label for Charades dataset. Use video-level label for training mode, otherwise use clip-level label. Then convert the label

Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2. For each box in boxlist1, we want its IOA to be more than minoverlap wi

Prunes the boxes in list1 that overlap less than thresh with list2. For each mask in box_mask_list1, we want its IOA to be more than minoverlap

Prunes bounding boxes that fall outside a given window. This function prunes bounding boxes that even partially fall outside the given window

Put task into task queue for prediction and visualization. Args: task (TaskInfo object): task object that contain

Grabbing frames from VideoCapture.

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

Perform random crop on a list of images. Args: images (list): list of images to perform random crop. size (int): size to crop

Crop the given images to random size and aspect ratio. A crop of random size (default: of 0.08 to 1.0) of the original size and a random aspe

This is similar to random_resized_crop. However, it samples two different boxes (for cropping) for the first and last frame. It then linearly

Perform ResNet style random scale jittering: randomly select the scale from [1/max_size, 1/min_size]. Args: image (array): im

Perform ResNet style random scale jittering on a list of image: randomly select the scale from [1/max_size, 1/min_size]. Note that all th