Window unpartition into original sequences and removing padding. Args: x (tensor): input tokens with [B * num_windows, window_size, w

Args: min_sizes: list of short-edge size to resize the image to max_size: maximum height or width of resized images

Args: backbone: either a backbone module or a mmdet config dict that defines a backbone. The backbone takes a 4D

NOTE: this interface is experimental. Args: num_keypoints (int): number of keypoints to predict loss_weight

NOTE: this interface is experimental. Args: loss_weight (float): multiplier of the loss vis_period (int): vi

Args: backbone: a backbone module, must follow detectron2's backbone interface proposal_generator: a module that gene

Args: center_sampling_radius: radius of the "center" of a groundtruth box, within which all anchor points are lab

NOTE: this interface is experimental. Args: input_shape: shapes (channels and stride) of the input features

NOTE: this interface is experimental. Args: backbone: a backbone module, must follow detectron2's backbone interface

NOTE: this interface is experimental. Args: in_features (list[str]): list of names of input features to use

Args: size (int): the total number of data of the underlying dataset to sample from shuffle (bool): whether to shuffl

Args: image (ndarray): (H,W) or (H,W,C) ndarray of type uint8 in range [0, 255], or floating point in range [0, 1

Args: model, data_loader, optimizer, gather_metric_period, zero_grad_before_forward, async_write_metrics: same as

The `__init__` method of any subclass should also contain these arguments. Args: in_channels (int): out_chan

Deformable convolution from :paper:`deformconv`. Arguments are similar to :class:`Conv2D`. Extra arguments: Args:

Add from_instances methods to the scripted Instances class.

Applies the exif orientation correctly. This code exists per the bug: https://github.com/python-pillow/Pillow/issues/3973 with the

ONNX exported network doesn't have concept of device, assign necessary device option for each op in order to make it runable on GPU runtime.

For each untracked instance, assign a new id Args: instances: D2 Instances, for predictions of the current frame

Configurations for some libraries.

Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension.

Convert different format of masks or polygons to a tuple of masks and polygons. Returns: list[GenericMask]:

Creates gradient clipping closure to clip by value or by norm, according to the provided config.

Create an instance of predictor outputs by copying the outputs from the base predictor and initializing confidence Args:

Create an instance of predictor outputs by copying the outputs from the base predictor and initializing confidence Args:

For all instances in previous and current frames, create pairs. For each pair, store index of the instance in current frame predcitio

Args: boxes (list[Tensor]): per-image predicted boxes, each of shape Ri x 4 image_sizes (list[tuple]): list of image

Decode boxes and classification predictions of one featuer level, by the following steps: 1. filter the predictions based on

Accumulate instances data for one image, given that the data is not empty Args: box_xywh_gt (tensor): GT bounding box

Accumulate instances data for one image, given that the data is not empty Args: box_xywh_gt (tensor): GT bounding box

Args: masks: N, 1, H, W boxes: N, 4 img_h, img_w (int): skip_empty (bool): only paste masks within the region tha

Evaluate the box proposals in predictions. Fill self._results with the metrics for "box_proposals" task.

Evaluate the box proposals in predictions. Fill self._results with the metrics for "box_proposals" task.

Evaluate predictions. Fill self._results with the metrics of the tasks.

Evaluate predictions. Fill self._results with the metrics of the tasks. Args: predictions (list[dict]): list of outputs

Evaluate predictions on densepose. Return results with the metrics of the tasks.

Evaluate detection proposal recall metrics. This function is a much faster alternative to the official COCO API recall evaluation code. Howev

Evaluate detection proposal recall metrics. This function is a much faster alternative to the official LVIS API recall evaluation code. Howev

Evaluate the coco results using COCOEval API.

Evaluate the coco results using COCOEval API.

Args: iou_type (str): max_dets_per_image (None or int): limit on maximum detections per image in evaluating AP This l