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

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

Get a copy of the default config.

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

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

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

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

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

Get parameters for ``crop`` for a random sized crop. Args: img (PIL Image): Image to be cropped. scale (tuple): ran

Get the full path to a checkpoint file. Args: path_to_job (string): the path to the folder of the current job. epoch (int): t

Get the rank of the current process.

Retrieves the stem module by name.

Retrieves the lr step index for the given epoch. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/de

Retrieves the transformation module by name.

Returns weights from registered layers. Returns: weights (Python dictionary): a dictionary of the pair {layer

Get the size of the world.

Compute the GPU memory usage for the current device (GB).

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

Inflate 2D model weights in state_dict_2d to the 3D model weights in state_dict_3d. The details can be found in: Joao Carreira, and Andre

Update cfg based on multigrid settings. Args: cfg (configs): configs that contains training and multigrid specific

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

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

Main function to spawn the train and test process.

Main function to spawn the train and test process.

feature module name accessor

Non maximum suppression. This op greedily selects a subset of detection bounding boxes, pruning away boxes that have high IOU (intersection over

Non maximum suppression. This op greedily selects a subset of detection bounding boxes, pruning away boxes that have high IOU (intersection over

Pad the given image with the size of pad_size. Args: image (array): image to pad. pad_size (int): size to pad. order

Compute the number of parameters. Args: model (model): model to count the number of parameters.

For classification: Perform mutli-view testing that uniformly samples N clips from a video along its temporal axis. For each clip, it

Visualize video inputs with wrong predictions on Tensorboard. Args: vis_loader (loader): video visualization loader. model (m

Given all predictions and all true labels, plot histograms of top-k most frequently predicted classes for each true class. Args:

Log schedule.

Extract layer names and numpy-like fancy indexing from a string. Args: layer_ls (list of strs): list of strings containing data about

Convert the video from its original fps to the target_fps. If the video support selective decoding (contain decoding information in the video

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.

Reset the metric.

Run model visualization (weights, activations and model inputs) and visualize them on Tensorboard. Args: vis_loader (loader): vid

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

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

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

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

Scale box coordinates in x and y dimensions. Args: boxlist: BoxList holding N boxes y_scale: float x_scale: float Returns: boxli

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

Perform the video training for one epoch. Args: train_loader (loader): video training loader. model (model): the video mo

Convert the Sub-BN paprameters to normal BN parameters in a state dict. There are two copies of BN layers in a Sub-BN implementation: `bn.bn`

Given the start and end frame index, sample num_samples frames between the start and end with equal interval. Args: frames (tenso

Normalize a given tensor by subtracting the mean and dividing the std. Args: tensor (tensor): tensor to normalize. mean (tens

If video_meta is not empty, perform temporal selective decoding to sample a clip from the video with TorchVision decoder. If video_meta is em

Perform the video training for one epoch. Args: train_loader (loader): video training loader. model (model): the video mo

Update predictions and labels. Args: preds (tensor): model output predictions. labels (tensor): labels.

Transpose the dimension from `channel` x `height` x `width` to `height` x `width` x `channel`. Args: image (array): image to

Transpose the dimension from `height` x `width` x `channel` to `channel` x `height` x `width`. Args: image (array): image to

Args: img (PIL Image): Image to be cropped and resized. Returns: PIL Image: Randomly cropped and resized

Args: img (PIL.Image or numpy.ndarray): List of images to be cropped in format (h, w, c) in numpy.ndarray Returns

Args: img (PIL.Image or numpy.ndarray): List of images to be cropped in format (h, w, c) in numpy.ndarray Returns

Args: img (PIL.Image or numpy.ndarray): List of images to be cropped in format (h, w, c) in numpy.ndarray Returns

Args: img (PIL.Image or numpy.ndarray): List of images to be cropped in format (h, w, c) in numpy.ndarray Returns

Args: img (PIL.Image or numpy.ndarray): List of images to be cropped in format (h, w, c) in numpy.ndarray Returns

Args: clip (list): list of PIL.Image Returns: list PIL.Image : list of transformed PIL.Image

Args: clip (Tensor): Tensor clip of size (T, C, H, W) to be normalized. Returns: Tensor: Normalized Tenso

Given the video index, return the list of frames, label, and video index if the video can be fetched and decoded successfully, otherw

Given the video index, return the list of frames, label, and video index if the video can be fetched and decoded successfully, otherw

Given the video index, return the list of frames, label, and video index if the video frames can be fetched. Args:

Args: index (int): Index Returns: tuple: (sample, target) where target is class_index of the target cla

Given the video index, return the list of frames, label, and video index if the video can be fetched and decoded successfully, othe

Given the video index, return the list of frames, label, and video index if the video can be fetched and decoded successfully, othe