Adds detections for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image.

Adds detections for a single image to be used for evaluation. Args: image_key: A unique string/integer identifier for the image.

Adds groundtruth for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image.

Adds groundtruth for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image.

Adds groundtruth for a single image to be used for evaluation. Args: image_key: A unique string/integer identifier for the image.

Draw predicted label on video and add it to Tensorboard. Args: vid (array-like): shape (C, T, H, W). Each ima

Recursively find all SubBN modules and aggregate sub-BN stats. Args: module (nn.Module) Returns: count (int): number of S

All gathers the provided tensors from all processes across machines. Args: tensors (list): tensors to perform all gather across all p

Run all_gather on arbitrary picklable data (not necessarily tensors). Args: data: any picklable object group: a torch proces

All reduce the provided tensors from all processes across machines. Args: tensors (list): tensors to perform all reduce across all pr

Computes area of masks. Args: box_mask_list: np_box_mask_list.BoxMaskList holding N boxes and masks Returns: a numpy array with

Construct binary label vector given a list of label indices. Args: labels (list): The input label list. num_classes (int): Nu

Benchmark the speed of data loading in PySlowFast. Args: cfg (CfgNode): configs. Details can be found in slowfast/config

Change coordinate frame of the boxlist to be relative to window's frame. Given a window of the form [ymin, xmin, ymax, xmax], changes boundin

Determine whether the loss is NaN (not a number). Args: loss (loss): loss to check whether is NaN.

Clean up open video files and windows.

Clears the state to prepare for a fresh evaluation.

Clip an array of boxes to an image with the given height and width. Args: boxes (ndarray): bounding boxes to perform clipping.

Clip the boxes with the height and width of the image size. Args: boxes (ndarray): bounding boxes to peform crop. The dimension is

Clip bounding boxes to a window. This op clips input bounding boxes (represented by bounding box corners) to a window, optionally filtering o

Perform color jitter on the given image. Args: image (array): image to perform color jitter. img_brightness (float): jitter r

Perform color jitter on the list of images. Args: images (list): list of images to perform color jitter. img_brightness (floa

Perform color nomration on the given images. Args: images (tensor): images to perform color normalization. Dimension is `

Perform color normalization on the image with the given mean and stddev. Args: image (array): image to perform color normalization.

Compute and update the batch norm stats to make it more precise. During training both bn stats and the weight are changing after every iterat

Compute Average Precision according to the definition in VOCdevkit. Precision is modified to ensure that it does not decrease as recall decre

Compute CorLoc according to the definition in the following paper. https://www.robots.ox.ac.uk/~vgg/rg/papers/deselaers-eccv10.pdf Returns n

Compute precision and recall. Args: scores: A float numpy array representing detection score labels: A boolean numpy array representi

Concatenate list of box_mask_lists. This op concatenates a list of input box_mask_lists into a larger box_mask_list. It also handles con

Constructs the data loader for the given dataset. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/d

Construct a stochastic gradient descent or ADAM optimizer with momentum. Details can be found in: Herbert Robbins, and Sutton Monro. "A s

Convert the caffe2_layer_name to pytorch format by apply the list of regular expressions. Args: caffe2_layer_name

Aggregate annotations from all frames of a video to form video-level labels. Args: labels (list): The input label list. Returns:

Compute the system memory (RAM) usage for the current device (GB). Returns: usage (float): used memory (GB). total (float): t

Reads a label map and returns a category index. Args: label_map_path: Path to `StringIntLabelMap` proto text file. Returns: A ca

Creates a category index with a single `object` class.

Create sampler for the given dataset. Args: dataset (torch.utils.data.Dataset): the given dataset. shuffle (bool): set to ``T

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

Run inference on an input video or stream from webcam. Args: cfg (CfgNode): configs. Details can be found in slowfast/con

Collate function for detection task. Concatanate bboxes, labels and metadata from different samples in the first dimension instead of sta

Add the visualized task to the write queue for display/write to outputfile. Args: task (TaskInfo object): task object tha

Scale clip frames from [0, 255] to [0, 1]. Args: x (Tensor): A tensor of the clip's RGB frames with shape: (channel, time

Compute evaluation result. Returns: A dictionary of metrics with the following fields - 1. summary_metrics:

Compute evaluation result. Returns: A named tuple with the following fields - average_precision: float numpy array of a

Run AVA evaluation given annotation/prediction files.

Calculate and log the final ensembled metrics. ks (tuple): list of top-k values for topk_accuracies. For example, ks = (1