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

Returns a unique identifier for a video id & timestamp.

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

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

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

Decode the video with PyAV decoder. Args: container (container): PyAV container. start_pts (int): the starting Presentation T

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

Loads boxes and class labels from a CSV file in the AVA format. CSV file format described at https://research.google.com/ava/download.html. Ar

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:

Write prediction results into official formats.

Args: num_splits (int): number of splits. args (list): other arguments.

The `__init__` method of any subclass should also contain these arguments. X3DHead takes a 5-dim feature tensor (BxCxTxHx

Args: dim_in (int): the channel dimensions of the input. ratio (float): the channel reduction ratio for squeeze.

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

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

Construct the video loader.

Construct the video loader.

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

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

Apply dropconnect to x

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. dete

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] representing detec

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

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

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 VOC dev

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

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

Add input to tensorboard SummaryWriter as a video. Args: vid_tensor (tensor): shape of (B, T, C, H, W). Values should lie

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

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

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

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

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

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

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

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

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

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 which

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

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

Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for Ef

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

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