GeneralizedRCNN：features = OrderedDict([(“0“, features)])

代码：

features = self.backbone(images.tensors)
#        #这一步应该是修正一下，统一一下格式
        #因为当backbone使用了fpn的时候，会有多个feature_map，被存放到了OrderedDict中
        #而没有使用的时候，只会返回一个feature_map
        #因此当只有1个的时候，也将其存在OrderedDict中，统一格式
        if isinstance(features, torch.Tensor):
            features = OrderedDict([("0", features)])
        #rpn
        proposals, proposal_losses = self.rpn(images, features, targets)
        #roi_heads:roi_pooling + 分类
        detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)
        #后处理，进行NMS 同时将 box 通过 original_images_size映射回原图
        detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes)  # type: ignore[operator]

        losses = {}
        losses.update(detector_losses)
        losses.update(proposal_losses)
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——————————————————————————————————————————————

    @torch.jit.unused
    def eager_outputs(self, losses, detections):
        # type: (Dict[str, Tensor], List[Dict[str, Tensor]]) -> Union[Dict[str, Tensor], List[Dict[str, Tensor]]]
        if self.training:
            return losses

        return detections
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        if torch.jit.is_scripting():
            if not self._has_warned:
                warnings.warn("RCNN always returns a (Losses, Detections) tuple in scripting")
                self._has_warned = True
            return losses, detections
        else:
        ##训练模式下，只返回losses；推断模式下 只返回detections
            return self.eager_outputs(losses, detections)
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