Please check the following conventions if you would like to modify MMDetection as your own project.
About the order of image shape¶
In OpenMMLab 2.0, to be consistent with the input argument of OpenCV, the argument about image shape in the data transformation pipeline is always in the
(width, height) order. On the contrary, for computation convenience, the order of the field going through the data pipeline and the model is
(height, width). Specifically, in the results processed by each data transform pipeline, the fields and their value meaning is as below:
img_shape: (height, width)
ori_shape: (height, width)
pad_shape: (height, width)
batch_input_shape: (height, width)
As an example, the initialization arguments of
Mosaic are as below:
@TRANSFORMS.register_module() class Mosaic(BaseTransform): def __init__(self, img_scale: Tuple[int, int] = (640, 640), center_ratio_range: Tuple[float, float] = (0.5, 1.5), bbox_clip_border: bool = True, pad_val: float = 114.0, prob: float = 1.0) -> None: ... # img_scale order should be (width, height) self.img_scale = img_scale def transform(self, results: dict) -> dict: ... results['img'] = mosaic_img # (height, width) results['img_shape'] = mosaic_img.shape[:2]
In MMDetection, a
dict containing losses and metrics will be returned by
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_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
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: 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: 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
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
VOIDlabel and the category labels start from
1. Since mmdet=2.17.0, the category labels of semantic segmentation start from
VOIDfor consistency with labels of bounding boxes. To achieve that, the
Padpipeline supports setting the padding value for
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.