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Conventions

Please check the following conventions if you would like to modify MMDetection as your own project.

Loss

In MMDetection, a dict containing losses and metrics will be returned by model(**data).

For example, in bbox head,

class BBoxHead(nn.Module):
    ...
    def loss(self, ...):
        losses = dict()
        # classification loss
        losses['loss_cls'] = self.loss_cls(...)
        # classification accuracy
        losses['acc'] = accuracy(...)
        # bbox regression loss
        losses['loss_bbox'] = self.loss_bbox(...)
        return losses

bbox_head.loss() will be called during model forward. The returned dict contains 'loss_bbox', 'loss_cls', 'acc' . Only 'loss_bbox', 'loss_cls' will be used during back propagation, 'acc' will only be used as a metric to monitor training process.

By default, only values whose keys contain 'loss' will be back propagated. This behavior could be changed by modifying BaseDetector.train_step().

Empty Proposals

In MMDetection, We have added special handling and unit test for empty proposals of two-stage. We need to deal with the empty proposals of the entire batch and single image at the same time. For example, in CascadeRoIHead,

# simple_test method
...
# There is no proposal in the whole batch
if rois.shape[0] == 0:
    bbox_results = [[
        np.zeros((0, 5), dtype=np.float32)
        for _ in range(self.bbox_head[-1].num_classes)
    ]] * num_imgs
    if self.with_mask:
        mask_classes = self.mask_head[-1].num_classes
        segm_results = [[[] for _ in range(mask_classes)]
                        for _ in range(num_imgs)]
        results = list(zip(bbox_results, segm_results))
    else:
        results = bbox_results
    return results
...

# There is no proposal in the single image
for i in range(self.num_stages):
    ...
    if i < self.num_stages - 1:
          for j in range(num_imgs):
                   # Handle empty proposal
                   if rois[j].shape[0] > 0:
                       bbox_label = cls_score[j][:, :-1].argmax(dim=1)
                       refine_roi = self.bbox_head[i].regress_by_class(
                            rois[j], bbox_label, bbox_pred[j], img_metas[j])
                       refine_roi_list.append(refine_roi)

If you have customized RoIHead, you can refer to the above method to deal with empty proposals.

Coco Panoptic Dataset

In MMDetection, we have supported COCO Panoptic dataset. We clarify a few conventions about the implementation of CocoPanopticDataset here.

  1. For mmdet<=2.16.0, the range of foreground and background labels in semantic segmentation are different from the default setting of MMDetection. The label 0 stands for VOID label and the category labels start from 1. Since mmdet=2.17.0, the category labels of semantic segmentation start from 0 and label 255 stands for VOID for consistency with labels of bounding boxes. To achieve that, the Pad pipeline supports setting the padding value for seg.

  2. In the evaluation, the panoptic result is a map with the same shape as the original image. Each value in the result map has the format of instance_id * INSTANCE_OFFSET + category_id.

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