bbox-pose training results

[eeric]

config: lsnet_pose_bbox_r50_fpn_1x_coco.py

2021-05-31 22:31:03,588 - mmdet - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=0.75s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type bbox
DONE (t=3.70s).
Accumulating evaluation results...
DONE (t=0.70s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.448
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.625
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.499
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.151
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.607
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.734
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.186
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.482
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.509
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.150
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.700
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.816
2021-05-31 22:31:08,760 - mmdet - INFO - Evaluating keypoints...
Loading and preparing results...
DONE (t=1.40s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type keypoints
DONE (t=5.90s).
Accumulating evaluation results...
DONE (t=0.27s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.401
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.732
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.392
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.389
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.450
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 20 ] = 0.510
Average Recall (AR) @[ IoU=0.50 | area= all | maxDets= 20 ] = 0.818
Average Recall (AR) @[ IoU=0.75 | area= all | maxDets= 20 ] = 0.532
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.469
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.567
2021-05-31 22:31:16,511 - mmdet - INFO - Saving checkpoint at 12 epochs
2021-05-31 22:31:16,811 - mmdet - INFO - Epoch(val) [12][16029] bbox_mAP: 0.4480, bbox_mAP_50: 0.6250, bbox_mAP_75: 0.4990, bbox_mAP_s: 0.1510, bbox_mAP_m: 0.6070, bbox_mAP_l: 0.7340, bbox_mAP_copypaste: 0.448 0.625 0.499 0.151 0.607 0.734, keypoints_mAP: 0.4010, keypoints_mAP_50: 0.7320, keypoints_mAP_75: 0.3920, keypoints_mAP_s: 0.3890, keypoints_mAP_m: 0.4500, keypoints_mAP_l: 0.5100, keypoints_mAP_copypaste: 0.401 0.732 0.392 0.389 0.450 0.510

[Mr2er0]

你好，你是已经跑成功了是吗。我阅读代码的时候对这个地方有点不理解。
cross_iou_loss
这里所对应的cross_iou_loss是文章的所提出的损失吗？这里面loss的输入的tensor的维度和格式大概都是什么？谢谢。