Takes a list of bounding boxes and a shape (height, width) and clips the bounding boxes to the shape. Args: boxes (torch.Tensor): th

input: dir:the path to DIOR dataset output_dir:the path write the coco form json file

Create polygon mask for rasterSrc, Similar to labeltools/createNPPixArray() in spacenet utilities

Evaluate proposal recall with COCO's fast_eval_recall. Args: results (List[dict]): Results of the dataset. proposal_n

Filter annotations according to filter_cfg. Returns: List[dict]: Filtered results.

Forward features from the upstream network. Args: x (tuple[Tensor]): Features from the upstream network, each is

Get anchors according to feature map sizes. Args: featmap_sizes (list[tuple]): Multi-level feature map sizes. batch_i

Get det results and gt information of a certain class. Args: det_results (list[list]): Same as `eval_map()`. annotations (list[di

Require label mapping. The ``label_map`` is a dictionary, its keys are the old label ids and its values are the new label ids, and is

Get the layer id to set the different learning rates in ``layer_wise`` decay_type. Args: var_name (str): The key of the model.

Get the layer id to set the different learning rates. Args: var_name (str): The key of the model. num_max_layer (int): Maximum nu