Run per image evaluation on given images and store results (a list of dict) in self.evalImgs :return: None

Input: - memory: bs, \sum{hw}, d_model - memory_padding_mask: bs, \sum{hw} - spatial_shapes: nlevel, 2 - learnedw

_summary_ Args: keypoints (torch.Tensor): ..., 51

Merge list into cfg_dict Merge the dict parsed by MultipleKVAction into this cfg. Examples: >>> options = {'model.backbo

This function disables printing when not in master process

Initialize the detectron2 logger and set its verbosity level to "INFO". Args: output (str): a file name or a directory to save log.

Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Args: inputs: A float tensor of arbitrary shape.

Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image