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

Draw a list of text labels for some bounding box on the image. Args: text_ls (list of strings): a list of text labels.

Draw text at the specified position. Args: text (str): the text to draw on image. position (list of 2 ints):

Stochastic Depth per sample.

Evaluates detections and returns a dictionary of metrics.

Filter to keep only boxes and masks with score exceeding a given threshold. This op keeps the collection of boxes and masks whose corresponding s

Filter to keep only boxes with score exceeding a given threshold. This op keeps the collection of boxes whose corresponding scores are greate

Calculate and log the final AVA metrics.

Get the visualized clips if any.

Obtain all activations from layers that we register hooks for. Args: input (tensors, list of tensors): the model input.

Get a copy of the default config.

Get the last checkpoint from the checkpointing folder. Args: path_to_job (string): the path to the folder of the current job.

Get the last checkpoint from the checkpointing folder. Args: path_to_job (string): the path to the folder of the current job.

Given the configs, retrieve the specified lr policy function. Args: lr_policy (string): the learning rate policy to use for the job.

Compute mAP for multi-label case. Args: preds (numpy tensor): num_examples x num_classes. labels (numpy tensor): num_examples

Compute statistics for the current model given the config. Args: model (model): model to perform analysis. cfg (CfgNode): con

Get the function to convert Caffe2 layer names to PyTorch layer names. Returns: (func): function to convert parameter name from Caffe

Given the video index, return the list of sampled frame indexes. Args: index (int): the video index. temporal

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

Determines if the given directory contains a checkpoint. Args: path_to_job (string): the path to the folder of the current job.

Determines if the given directory contains a checkpoint. Args: path_to_job (string): the path to the folder of the current job.

Compute pairwise intersection areas between boxes. Args: boxes1: a numpy array with shape [N, 4] holding N boxes boxes2: a numpy arra

Compute pairwise intersection areas between masks. Args: masks1: a numpy array with shape [N, height, width] holding N masks. Masks

Get the time for the last epoch.

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

Loads label map proto. Args: path: path to StringIntLabelMap proto text file. Returns: a StringIntLabelMapProto

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

Returns a unique identifier for a video id & timestamp.

Prepare output as a list of tensors. Each tensor corresponding to a unique pathway. Args: frames (tensor): frames of images sampl

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

Visualize weights/ activations tensors to Tensorboard. Args: weight_activation_dict (dict[str, tensor]): a dictionary of

Replace all `keyframe_sec` in `dictionary` with `keyframe_idx` and merge all [`box_coordinate`, `box_labels`] pairs into [`al

Normalize and prepare inputs as a list of tensors. Each tensor correspond to a unique pathway. Args: frames (list of array): list

RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719 Create a RandAugment transform :param config_st

Reads a CSV file of excluded timestamps. Args: exclusions_file: A file object containing a csv of video-id,timestamp. Returns: A s

Read label map and class ids.

AVA evaluation main logic.

Sort boxes and associated fields according to a scalar field. A common use case is reordering the boxes according to descending scores. Args

Given the predictions, labels, and a list of top-k values, compute the number of correct predictions for each top-k value. Args:

Before every epoch, check if long cycle shape should change. If it should, update cfg accordingly. Args: cfg

Check if checkpoint_wrapper is imported.

Write prediction results into official formats.

Modify video data to have dtype of uint8 and values range in [0, 255]. Args: frames (array-like): 4D array of shape (T, H

Calculate the number of text labels to plot at the bottom of the box without going out of frames. Args: box_coord

Calculate the number of text labels to plot on top of the box without going out of frames. Args: box_coordinate (

Calculate localization map for all inputs with Grad-CAM. Args: inputs (list of tensor(s)): the input clips. l

Labels true/false positives of detections of an image across all classes. Args: detected_boxes: A float numpy array of shape [N, 4]

Labels boxes detected with the same class from the same image as tp/fp. Args: detected_boxes: A numpy array of shape [N, 4] represe

Constructs the imdb.

Construct the video loader.

Construct the video loader.

Construct the video loader.

Construct the video loader.

Builds a single pathway ResNet model. Args: cfg (CfgNode): model building configs, details are in the co

Builds a SlowFast model. Args: cfg (CfgNode): model building configs, details are in the comments of the

Builds a X3D model. Args: cfg (CfgNode): model building configs, details are in the comments of the conf

Builds a single pathway ResNet model. Args: cfg (CfgNode): model building configs, details are in the co

Builds a single pathway R(2+1)D model. Args: cfg (CfgNode): model building configs, details are in the c

Builds a MViT model. Args: cfg (CfgNode): model building configs, details are in the comments of the con

Builds a SlowFast model. The first pathway is the Slow pathway and the second pathway is the Fast pathway. Args:

Builds a single pathway ResNet model. Args: cfg (CfgNode): model building configs, details are in the co

Builds a single pathway X3D model. Args: cfg (CfgNode): model building configs, details are in the comme

Create text labels. Args: classes (list[int]): a list of class ids for each example. scores (list[float] or None): list of sc

Calculate the aggregated mean and stds. Args: means (tensor): mean values. stds (tensor): standard deviations

Return a process group based on gloo backend, containing all the ranks The result is cached. Returns: (group): pytorch dist group

Returns numpy arrays belonging to class with index `class_index`. Args: detected_boxes: A numpy array containing detected boxes.

Return a dummy input for model analysis with batch size 1. The input is used for analyzing the model (counting flops and activations etc.

Computes overlaps and scores between detected and groudntruth boxes. Args: detected_boxes: A numpy array of shape [N, 4] representi

Compute a thresholds array for all classes based on `self.thes` and `self.lower_thres`. Args: common_class_names (Optiona

This function performs preprocessing for the input images and corresponding boxes for one clip. Args: imgs (tens

This function performs preprocessing for the input images and corresponding boxes for one clip with opencv as backend. Args:

Check whether data fullfills the format of N*[ymin, xmin, ymax, xmin]. Args: data: a numpy array of shape [N, 4] representing box c