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

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

Load frame paths and annotations from files Args: cfg (CfgNode): config

Padding all the tensors from different GPUs to the largest ones. Args: tensor (tensor): tensor to pad. group (group): pytorch

Register hooks to layers in `self.layers_names`.

Register hooks to layers in `self.target_layers`.

Register hook to a layer, given layer_name, to obtain activations. Args: layer_name (str): name of the layer.

Register forward and backward hook to a layer, given layer_name, to obtain gradients and activations. Args: layer

Removes entries with invalid boxes. A box is invalid if either its xmax is smaller than its xmin, or its ymax is smaller than its ymi

Round number of layers based on depth multiplier.

Round width of filters based on width multiplier Args: width (int): the channel dimensions of the input. mult

Seriialize the tensor to ByteTensor. Note that only `gloo` and `nccl` backend is supported. Args: data (data): data to be ser

Suppresses printing from the current process.

Update grouth truth statitistics. 1. Difficult boxes are ignored when counting the number of ground truth instances as done in Pascal

Checks if a label map is valid. Args: label_map: StringIntLabelMap to validate. Raises: ValueError: if label map is invalid.

Add the corresponding action predictions.

Add correspondding bounding boxes.

Visualize and add tensors of n-dimentionals to a Tensorboard SummaryWriter. Tensors will be visualized as a 2D grid image. Args:

Join a list of label list. Args: labels (list): The input label list. Returns: labels (list): The joint list of all lists

Synchronize running_mean, and running_var. Call this before eval.

Computes area of boxes. Args: boxlist: BoxList holding N boxes Returns: a numpy array with shape [N*1] representing box areas

Returns the names of available CLIP models

Perform color brightness on the given image. Args: var (float): variance. image (array): image to perform color brightness.

Perfrom brightness jittering on the input images. The channels of images should be in order BGR. Args: var (float): jitter ratio

Perform color brightness on the given list of images. Args: var (float): variance. images (list): list of images to perform c

Build a dataset, defined by `dataset_name`. Args: dataset_name (str): the name of the dataset to be constructed. cfg (CfgNode

Build training model and its associated tools, including optimizer, dataloaders and meters. Args: cfg (CfgNode): configs. Details

Convert BN parameters to Sub-BN parameters if model contains Sub-BNs. Args: key (OrderedDict): source dict of parameters. mdo

Convert BN parameters to Sub-BN parameters if model contains Sub-BNs. Args: key (OrderedDict): source dict of parameters. mdo

Convert BN parameters to Sub-BN parameters if model contains Sub-BNs. Args: key (OrderedDict): source dict of parameters. mdo

Convert BN parameters to Sub-BN parameters if model contains Sub-BNs. Args: key (OrderedDict): source dict of parameters. mdo

Update the stats in bn layers by calculate the precise stats. Args: loader (loader): data loader to provide training data. mo

Evaluates detections as being tp, fp or ignored from a single image. The evaluation is done in two stages: 1. All detections are mat

Concatenate list of BoxLists. This op concatenates a list of input BoxLists into a larger BoxList. It also handles concatenation of BoxList

Perform color contrast on the given image. Args: var (float): variance. image (array): image to perform color contrast. R

Perfrom contrast jittering on the input images. The channels of images should be in order BGR. Args: var (float): jitter ratio fo

Perform color contrast on the given list of images. Args: var (float): variance. images (list): list of images to perform col

Loads label map proto and returns categories list compatible with eval. This function loads a label map and returns a list of dicts, each of whic

Convert applicable model parameters to fp16

Creates dictionary of COCO compatible categories keyed by category id. Args: categories: a list of dicts, each of which has the following k

Crop the boxes given the offsets. Args: boxes (array): boxes to crop. x_offset (int): offset on x. y_offset (int): of

Draw prediction for the given task. Args: task (TaskInfo object): task object that contain the necessary information for

Start to record time.

Stop to record time.

Evaluate the model on the val set. Args: val_loader (loader): data loader to provide validation data. model (model): model to

Calculate and log the final AVA metrics.

Horizontally flip the boxes. Args: boxes (array): box to flip. im_width (int): width of the image. Returns: boxes

Perform forwarding, gathering the stats across different process/ GPU group.

Return a task object in the correct order based on task id if result(s) is available. Otherwise, raise queue.Empty exception.

Convert our data format into the data format used in official AVA evaluation.

Get the groundtruth annotations corresponding the "subset" of AVA val set. We define the subset to be the frames such that (second % 4 == 0).

Get the groundtruth annotations corresponding the "subset" of AVA val set. We define the subset to be the frames such that (second % 4 == 0).

Get the best checkpoint from the checkpointing folder.

Get the best checkpoint from the checkpointing folder.

Get corner coordinates of boxes. Returns: a list of 4 1-d numpy arrays [y_min, x_min, y_max, x_max]

Given a schedule and epoch index, return the long cycle base shape. Args: schedule (configs): configs that contains training and mult

Generates the box coordinates for cutmix. Args: img_shape (tuple): Image shape as tuple lam (float): Cutmix lambda value

Parse numpy-like fancy indexing from a string. Args: string (str): string represent the indices to take a subset of from

Get input clip from the video/folder of images for a given keyframe index. Args: keyframe_idx (int): index of the

Retrieve the logger with the specified name or, if name is None, return a logger which is the root logger of the hierarchy. Args:

Based on multigrid hyperparameters, define the schedule of a long cycle. Args: cfg (configs): configs that contains train

Return a video writer object. Args: path (str): path to the output video file. fps (int or float): frames per