from abc import ABCMeta, abstractmethod import torch.nn as nn class BaseDenseHead(nn.Module, metaclass=ABCMeta): """Base class for DenseHeads.""" def __init__(self): super(BaseDenseHead, self).__init__() @abstractmethod def loss(self, **kwargs): """Compute losses of the head.""" pass @abstractmethod def get_bboxes(self, **kwargs): """Transform network output for a batch into bbox predictions.""" pass def forward_train(self, x, img_metas, gt_bboxes, gt_labels=None, gt_bboxes_ignore=None, proposal_cfg=None, **kwargs): """ Args: x (list[Tensor]): Features from FPN. img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. gt_bboxes (Tensor): Ground truth bboxes of the image, shape (num_gts, 4). gt_labels (Tensor): Ground truth labels of each box, shape (num_gts,). gt_bboxes_ignore (Tensor): Ground truth bboxes to be ignored, shape (num_ignored_gts, 4). proposal_cfg (mmcv.Config): Test / postprocessing configuration, if None, test_cfg would be used Returns: tuple: losses: (dict[str, Tensor]): A dictionary of loss components. proposal_list (list[Tensor]): Proposals of each image. """ outs = self(x) if gt_labels is None: loss_inputs = outs + (gt_bboxes, img_metas) else: loss_inputs = outs + (gt_bboxes, gt_labels, img_metas) losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) if proposal_cfg is None: return losses else: proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg) return losses, proposal_list