Initialize the weights

Initialize the weights

Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel

Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel

Computes the precision@k for the specified values of k

Computes the precision@k for the specified values of k

Computes the precision@k for the specified values of k

Computes the precision@k for the specified values of k

Computes the precision@k for the specified values of k

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.

Sets the learning rate to the initial LR decayed by 10 every 30 epochs

Run all_gather on arbitrary picklable data (not necessarily tensors) Args: data: any picklable object Returns: list[data]

Run all_gather on arbitrary picklable data (not necessarily tensors) Args: data: any picklable object Returns: list[data]

Run all_gather on arbitrary picklable data (not necessarily tensors) Args: data: any picklable object Returns: list[data]

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

same output as PIL.ImageOps.autocontrast

same output as PIL.ImageEnhance.Contrast

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

Clears the state to prepare for a fresh evaluation.

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

same output as PIL.ImageEnhance.Color

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

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

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

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

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 concatena

same output as PIL.ImageEnhance.Contrast

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

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

Download an given URL Parameters ---------- url : str URL to download path : str, optional Destination path to store d

same output as PIL.ImageOps.equalize PIL's implementation is different from cv2.equalize

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 arra

The forward expects a NestedTensor, which consists of: - samples.tensor: batched images, of shape [batch_size x 3 x H x W]

This performs the loss computation. Parameters: outputs: dict of tensors, see the output specification of the model for the form

This performs the loss computation. Parameters: outputs: dict of tensors, see the output specification of the model for the form

Perform the computation Parameters: outputs: raw outputs of the model target_sizes: tensor of dimension [batch_size x