Dump the detection results to a COCO style json file. There are 3 types of results: proposals, bbox predictions, mask predictions, an

Dump the detection results to a COCO style json file. There are 3 types of results: proposals, bbox predictions, mask predictions, an

Main entry for the master device in each forward pass. The messages were first collected from each devices (including the master devi

Saturation distortion. Args: img (np.ndarray): The input image. Returns: np.ndarray: Image after saturation c

Inference by sliding-window with overlap. If h_crop > h_img or w_crop > w_img, the small patch will be used to decode without padding

Calculate evaluation metrics Args: total_area_intersect (np.ndarray): The intersection of prediction and ground tr

Check if detected bboxes are true positive or false positive. Args: det_bboxes (ndarray): Detected bboxes of this image, of shape (m, 6).

Mask head forward function used in both training and testing. Args: x (tuple[Tensor]): Tuple of multi-level img features.

Unpack ``gt_instances``, ``gt_instances_ignore`` and ``img_metas`` based on ``batch_data_samples`` Args: batch_data_samples (List[:ob

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Copy a model state_dict to cpu. Args: state_dict (OrderedDict): Model weights on GPU. Returns: OrderedDict: Model weights on

Inference with full image. Args: inputs (Tensor): The tensor should have a shape NxCxHxW, which contains all imag

Convert bounding box coordinates from (x1, y1, x2, y2) format to (x, y, width, height, normalized) format. x, y, width and height are normali

Call function to apply transforms sequentially. Args: data (dict): A result dict contains the data to transform. Returns

Convert gt from rbb to hbb, and calculate IoU between hbboxes. Args: bboxes1 (:obj:`RotatedBoxes` or Tensor): bboxes have shape (

Get the idx-th image and data information of dataset after ``self.pipeline``, and ``full_init`` will be called if the dataset has not

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par