RuntimeError: All input tensors must be on the same device.

Hello, I liked your work and I wanted to test the evaluation mechanism and the associated code. I executed the following command as mentioned in the README.md

python main.py --num-gpus 2 --config-file training_configs/faster_rcnn_R_50_FPN.yaml --resume --eval-only

But after the inference on 2476 images. The following error occurs:

[10/10 10:34:33 d2.evaluation.evaluator]: Start inference on 2476 images
[10/10 10:34:47 d2.evaluation.evaluator]: Inference done 11/2476. 0.1699 s / img. ETA=0:08:04
[10/10 10:34:53 d2.evaluation.evaluator]: Inference done 49/2476. 0.1263 s / img. ETA=0:05:44
[10/10 10:34:58 d2.evaluation.evaluator]: Inference done 84/2476. 0.1290 s / img. ETA=0:05:42
[10/10 10:35:03 d2.evaluation.evaluator]: Inference done 122/2476. 0.1262 s / img. ETA=0:05:30
[10/10 10:35:08 d2.evaluation.evaluator]: Inference done 160/2476. 0.1232 s / img. ETA=0:05:20
[10/10 10:35:13 d2.evaluation.evaluator]: Inference done 197/2476. 0.1227 s / img. ETA=0:05:13
[10/10 10:35:18 d2.evaluation.evaluator]: Inference done 234/2476. 0.1228 s / img. ETA=0:05:08
[10/10 10:35:23 d2.evaluation.evaluator]: Inference done 273/2476. 0.1213 s / img. ETA=0:05:00
[10/10 10:35:28 d2.evaluation.evaluator]: Inference done 311/2476. 0.1207 s / img. ETA=0:04:53
[10/10 10:35:33 d2.evaluation.evaluator]: Inference done 349/2476. 0.1206 s / img. ETA=0:04:47
[10/10 10:35:38 d2.evaluation.evaluator]: Inference done 386/2476. 0.1208 s / img. ETA=0:04:42
[10/10 10:35:43 d2.evaluation.evaluator]: Inference done 423/2476. 0.1213 s / img. ETA=0:04:38
[10/10 10:35:48 d2.evaluation.evaluator]: Inference done 460/2476. 0.1214 s / img. ETA=0:04:33
[10/10 10:35:53 d2.evaluation.evaluator]: Inference done 498/2476. 0.1211 s / img. ETA=0:04:27
[10/10 10:35:58 d2.evaluation.evaluator]: Inference done 535/2476. 0.1215 s / img. ETA=0:04:23
[10/10 10:36:03 d2.evaluation.evaluator]: Inference done 573/2476. 0.1215 s / img. ETA=0:04:18
[10/10 10:36:08 d2.evaluation.evaluator]: Inference done 611/2476. 0.1212 s / img. ETA=0:04:12
[10/10 10:36:13 d2.evaluation.evaluator]: Inference done 649/2476. 0.1211 s / img. ETA=0:04:07
[10/10 10:36:18 d2.evaluation.evaluator]: Inference done 687/2476. 0.1212 s / img. ETA=0:04:01
[10/10 10:36:24 d2.evaluation.evaluator]: Inference done 724/2476. 0.1211 s / img. ETA=0:03:56
[10/10 10:36:29 d2.evaluation.evaluator]: Inference done 762/2476. 0.1210 s / img. ETA=0:03:51
[10/10 10:36:34 d2.evaluation.evaluator]: Inference done 800/2476. 0.1212 s / img. ETA=0:03:46
[10/10 10:36:39 d2.evaluation.evaluator]: Inference done 839/2476. 0.1210 s / img. ETA=0:03:40
[10/10 10:36:44 d2.evaluation.evaluator]: Inference done 877/2476. 0.1211 s / img. ETA=0:03:35
[10/10 10:36:49 d2.evaluation.evaluator]: Inference done 915/2476. 0.1212 s / img. ETA=0:03:30
[10/10 10:36:54 d2.evaluation.evaluator]: Inference done 952/2476. 0.1213 s / img. ETA=0:03:25
[10/10 10:36:59 d2.evaluation.evaluator]: Inference done 990/2476. 0.1211 s / img. ETA=0:03:20
[10/10 10:37:04 d2.evaluation.evaluator]: Inference done 1028/2476. 0.1211 s / img. ETA=0:03:15
[10/10 10:37:09 d2.evaluation.evaluator]: Inference done 1067/2476. 0.1210 s / img. ETA=0:03:09
[10/10 10:37:14 d2.evaluation.evaluator]: Inference done 1105/2476. 0.1210 s / img. ETA=0:03:04
[10/10 10:37:20 d2.evaluation.evaluator]: Inference done 1142/2476. 0.1212 s / img. ETA=0:02:59
[10/10 10:37:25 d2.evaluation.evaluator]: Inference done 1179/2476. 0.1213 s / img. ETA=0:02:54
[10/10 10:37:30 d2.evaluation.evaluator]: Inference done 1217/2476. 0.1212 s / img. ETA=0:02:49
[10/10 10:37:35 d2.evaluation.evaluator]: Inference done 1255/2476. 0.1213 s / img. ETA=0:02:44
[10/10 10:37:40 d2.evaluation.evaluator]: Inference done 1293/2476. 0.1212 s / img. ETA=0:02:39
[10/10 10:37:45 d2.evaluation.evaluator]: Inference done 1330/2476. 0.1213 s / img. ETA=0:02:34
[10/10 10:37:50 d2.evaluation.evaluator]: Inference done 1368/2476. 0.1212 s / img. ETA=0:02:29
[10/10 10:37:55 d2.evaluation.evaluator]: Inference done 1406/2476. 0.1211 s / img. ETA=0:02:23
[10/10 10:38:00 d2.evaluation.evaluator]: Inference done 1444/2476. 0.1211 s / img. ETA=0:02:18
[10/10 10:38:05 d2.evaluation.evaluator]: Inference done 1482/2476. 0.1212 s / img. ETA=0:02:13
[10/10 10:38:10 d2.evaluation.evaluator]: Inference done 1520/2476. 0.1211 s / img. ETA=0:02:08
[10/10 10:38:15 d2.evaluation.evaluator]: Inference done 1557/2476. 0.1212 s / img. ETA=0:02:03
[10/10 10:38:20 d2.evaluation.evaluator]: Inference done 1594/2476. 0.1212 s / img. ETA=0:01:58
[10/10 10:38:25 d2.evaluation.evaluator]: Inference done 1633/2476. 0.1211 s / img. ETA=0:01:53
[10/10 10:38:30 d2.evaluation.evaluator]: Inference done 1672/2476. 0.1210 s / img. ETA=0:01:48
[10/10 10:38:35 d2.evaluation.evaluator]: Inference done 1709/2476. 0.1211 s / img. ETA=0:01:43
[10/10 10:38:41 d2.evaluation.evaluator]: Inference done 1747/2476. 0.1211 s / img. ETA=0:01:38
[10/10 10:38:46 d2.evaluation.evaluator]: Inference done 1785/2476. 0.1211 s / img. ETA=0:01:32
[10/10 10:38:51 d2.evaluation.evaluator]: Inference done 1822/2476. 0.1211 s / img. ETA=0:01:27
[10/10 10:38:56 d2.evaluation.evaluator]: Inference done 1860/2476. 0.1211 s / img. ETA=0:01:22
[10/10 10:39:01 d2.evaluation.evaluator]: Inference done 1898/2476. 0.1211 s / img. ETA=0:01:17
[10/10 10:39:06 d2.evaluation.evaluator]: Inference done 1935/2476. 0.1211 s / img. ETA=0:01:12
[10/10 10:39:11 d2.evaluation.evaluator]: Inference done 1973/2476. 0.1211 s / img. ETA=0:01:07
[10/10 10:39:16 d2.evaluation.evaluator]: Inference done 2011/2476. 0.1211 s / img. ETA=0:01:02
[10/10 10:39:21 d2.evaluation.evaluator]: Inference done 2048/2476. 0.1212 s / img. ETA=0:00:57
[10/10 10:39:26 d2.evaluation.evaluator]: Inference done 2086/2476. 0.1212 s / img. ETA=0:00:52
[10/10 10:39:31 d2.evaluation.evaluator]: Inference done 2124/2476. 0.1212 s / img. ETA=0:00:47
[10/10 10:39:36 d2.evaluation.evaluator]: Inference done 2162/2476. 0.1212 s / img. ETA=0:00:42
[10/10 10:39:41 d2.evaluation.evaluator]: Inference done 2201/2476. 0.1212 s / img. ETA=0:00:36
[10/10 10:39:47 d2.evaluation.evaluator]: Inference done 2238/2476. 0.1212 s / img. ETA=0:00:31
[10/10 10:39:52 d2.evaluation.evaluator]: Inference done 2276/2476. 0.1213 s / img. ETA=0:00:26
[10/10 10:39:57 d2.evaluation.evaluator]: Inference done 2313/2476. 0.1213 s / img. ETA=0:00:21
[10/10 10:40:02 d2.evaluation.evaluator]: Inference done 2351/2476. 0.1213 s / img. ETA=0:00:16
[10/10 10:40:07 d2.evaluation.evaluator]: Inference done 2389/2476. 0.1211 s / img. ETA=0:00:11
[10/10 10:40:12 d2.evaluation.evaluator]: Inference done 2426/2476. 0.1212 s / img. ETA=0:00:06
[10/10 10:40:17 d2.evaluation.evaluator]: Inference done 2464/2476. 0.1212 s / img. ETA=0:00:01
[10/10 10:40:19 d2.evaluation.evaluator]: Total inference time: 0:05:33.130279 (0.134816 s / img per device, on 2 devices)
[10/10 10:40:19 d2.evaluation.evaluator]: Total inference pure compute time: 0:04:59 (0.121209 s / img per device, on 2 devices)
/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torch/storage.py:34: FutureWarning: pickle support for Storage will be removed in 1.5. Use `torch.save` instead
  warnings.warn("pickle support for Storage will be removed in 1.5. Use `torch.save` instead", FutureWarning)
/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torch/storage.py:34: FutureWarning: pickle support for Storage will be removed in 1.5. Use `torch.save` instead
  warnings.warn("pickle support for Storage will be removed in 1.5. Use `torch.save` instead", FutureWarning)
[10/10 10:40:29 detectron2]: Image level evaluation complete for custom_voc_2007_test
[10/10 10:40:29 detectron2]: Results for custom_voc_2007_test
Traceback (most recent call last):
  File "main.py", line 198, in <module>
    args=(args,),
  File "/home/dksingh/objdet/detectron2/detectron2/engine/launch.py", line 59, in launch
    daemon=False,
  File "/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 200, in spawn
    return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
  File "/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 158, in start_processes
    while not context.join():
  File "/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 119, in join
    raise Exception(msg)
Exception:

-- Process 0 terminated with the following error:
Traceback (most recent call last):
  File "/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 20, in _wrap
    fn(i, *args)
  File "/home/dksingh/objdet/detectron2/detectron2/engine/launch.py", line 94, in _distributed_worker
    main_func(*args)
  File "/home/dksingh/paper_impl/Elephant-of-object-detection/main.py", line 177, in main
    return do_test(cfg, model)
  File "/home/dksingh/paper_impl/Elephant-of-object-detection/main.py", line 63, in do_test
    evaluator._coco_api.cats)
  File "/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py", line 42, in only_mAP_analysis
    scores = torch.cat(scores)
RuntimeError: All input tensors must be on the same device. Received cuda:0 and cuda:1

Has there been any update to this code or do you have any inputs on how to fix this? Thank you

I later executed the code on a single GPU as following:

python main.py --num-gpus 1 --config-file training_configs/faster_rcnn_R_50_FPN.yaml --resume --eval-only

It throws the following error:


[10/10 10:56:44 d2.evaluation.evaluator]: Start inference on 4952 images
[10/10 10:56:45 d2.evaluation.evaluator]: Inference done 11/4952. 0.0779 s / img. ETA=0:06:34
[10/10 10:56:50 d2.evaluation.evaluator]: Inference done 72/4952. 0.0798 s / img. ETA=0:06:40
[10/10 10:56:55 d2.evaluation.evaluator]: Inference done 136/4952. 0.0782 s / img. ETA=0:06:28
[10/10 10:57:00 d2.evaluation.evaluator]: Inference done 200/4952. 0.0774 s / img. ETA=0:06:19
[10/10 10:57:05 d2.evaluation.evaluator]: Inference done 264/4952. 0.0771 s / img. ETA=0:06:12
[10/10 10:57:10 d2.evaluation.evaluator]: Inference done 328/4952. 0.0769 s / img. ETA=0:06:06
[10/10 10:57:16 d2.evaluation.evaluator]: Inference done 392/4952. 0.0769 s / img. ETA=0:06:01
[10/10 10:57:21 d2.evaluation.evaluator]: Inference done 456/4952. 0.0767 s / img. ETA=0:05:55
[10/10 10:57:26 d2.evaluation.evaluator]: Inference done 519/4952. 0.0768 s / img. ETA=0:05:50
[10/10 10:57:31 d2.evaluation.evaluator]: Inference done 583/4952. 0.0768 s / img. ETA=0:05:45
[10/10 10:57:36 d2.evaluation.evaluator]: Inference done 647/4952. 0.0767 s / img. ETA=0:05:40
[10/10 10:57:41 d2.evaluation.evaluator]: Inference done 710/4952. 0.0768 s / img. ETA=0:05:35
[10/10 10:57:46 d2.evaluation.evaluator]: Inference done 774/4952. 0.0767 s / img. ETA=0:05:30
[10/10 10:57:51 d2.evaluation.evaluator]: Inference done 838/4952. 0.0767 s / img. ETA=0:05:25
[10/10 10:57:56 d2.evaluation.evaluator]: Inference done 902/4952. 0.0767 s / img. ETA=0:05:20
[10/10 10:58:01 d2.evaluation.evaluator]: Inference done 966/4952. 0.0767 s / img. ETA=0:05:15
[10/10 10:58:06 d2.evaluation.evaluator]: Inference done 1029/4952. 0.0767 s / img. ETA=0:05:10
[10/10 10:58:11 d2.evaluation.evaluator]: Inference done 1093/4952. 0.0767 s / img. ETA=0:05:05
[10/10 10:58:16 d2.evaluation.evaluator]: Inference done 1156/4952. 0.0768 s / img. ETA=0:05:00
[10/10 10:58:21 d2.evaluation.evaluator]: Inference done 1220/4952. 0.0768 s / img. ETA=0:04:55
[10/10 10:58:26 d2.evaluation.evaluator]: Inference done 1283/4952. 0.0768 s / img. ETA=0:04:50
[10/10 10:58:31 d2.evaluation.evaluator]: Inference done 1347/4952. 0.0768 s / img. ETA=0:04:45
[10/10 10:58:36 d2.evaluation.evaluator]: Inference done 1411/4952. 0.0768 s / img. ETA=0:04:40
[10/10 10:58:41 d2.evaluation.evaluator]: Inference done 1474/4952. 0.0768 s / img. ETA=0:04:35
[10/10 10:58:46 d2.evaluation.evaluator]: Inference done 1537/4952. 0.0769 s / img. ETA=0:04:30
[10/10 10:58:51 d2.evaluation.evaluator]: Inference done 1600/4952. 0.0769 s / img. ETA=0:04:25
[10/10 10:58:56 d2.evaluation.evaluator]: Inference done 1663/4952. 0.0769 s / img. ETA=0:04:20
[10/10 10:59:01 d2.evaluation.evaluator]: Inference done 1727/4952. 0.0769 s / img. ETA=0:04:15
[10/10 10:59:06 d2.evaluation.evaluator]: Inference done 1791/4952. 0.0769 s / img. ETA=0:04:10
[10/10 10:59:12 d2.evaluation.evaluator]: Inference done 1855/4952. 0.0769 s / img. ETA=0:04:05
[10/10 10:59:17 d2.evaluation.evaluator]: Inference done 1919/4952. 0.0769 s / img. ETA=0:04:00
[10/10 10:59:22 d2.evaluation.evaluator]: Inference done 1983/4952. 0.0769 s / img. ETA=0:03:55
[10/10 10:59:27 d2.evaluation.evaluator]: Inference done 2047/4952. 0.0769 s / img. ETA=0:03:50
[10/10 10:59:32 d2.evaluation.evaluator]: Inference done 2110/4952. 0.0769 s / img. ETA=0:03:45
[10/10 10:59:37 d2.evaluation.evaluator]: Inference done 2174/4952. 0.0769 s / img. ETA=0:03:40
[10/10 10:59:42 d2.evaluation.evaluator]: Inference done 2238/4952. 0.0769 s / img. ETA=0:03:35
[10/10 10:59:47 d2.evaluation.evaluator]: Inference done 2301/4952. 0.0769 s / img. ETA=0:03:30
[10/10 10:59:52 d2.evaluation.evaluator]: Inference done 2364/4952. 0.0769 s / img. ETA=0:03:25
[10/10 10:59:57 d2.evaluation.evaluator]: Inference done 2427/4952. 0.0769 s / img. ETA=0:03:20
[10/10 11:00:02 d2.evaluation.evaluator]: Inference done 2490/4952. 0.0769 s / img. ETA=0:03:15
[10/10 11:00:07 d2.evaluation.evaluator]: Inference done 2553/4952. 0.0770 s / img. ETA=0:03:10
[10/10 11:00:12 d2.evaluation.evaluator]: Inference done 2616/4952. 0.0770 s / img. ETA=0:03:05
[10/10 11:00:17 d2.evaluation.evaluator]: Inference done 2678/4952. 0.0770 s / img. ETA=0:03:00
[10/10 11:00:22 d2.evaluation.evaluator]: Inference done 2741/4952. 0.0770 s / img. ETA=0:02:55
[10/10 11:00:27 d2.evaluation.evaluator]: Inference done 2804/4952. 0.0770 s / img. ETA=0:02:50
[10/10 11:00:32 d2.evaluation.evaluator]: Inference done 2867/4952. 0.0770 s / img. ETA=0:02:45
[10/10 11:00:37 d2.evaluation.evaluator]: Inference done 2926/4952. 0.0771 s / img. ETA=0:02:41
[10/10 11:00:42 d2.evaluation.evaluator]: Inference done 2989/4952. 0.0772 s / img. ETA=0:02:36
[10/10 11:00:47 d2.evaluation.evaluator]: Inference done 3053/4952. 0.0772 s / img. ETA=0:02:31
[10/10 11:00:52 d2.evaluation.evaluator]: Inference done 3116/4952. 0.0772 s / img. ETA=0:02:26
[10/10 11:00:57 d2.evaluation.evaluator]: Inference done 3179/4952. 0.0772 s / img. ETA=0:02:21
[10/10 11:01:02 d2.evaluation.evaluator]: Inference done 3242/4952. 0.0772 s / img. ETA=0:02:16
[10/10 11:01:08 d2.evaluation.evaluator]: Inference done 3305/4952. 0.0772 s / img. ETA=0:02:11
[10/10 11:01:13 d2.evaluation.evaluator]: Inference done 3368/4952. 0.0772 s / img. ETA=0:02:06
[10/10 11:01:18 d2.evaluation.evaluator]: Inference done 3431/4952. 0.0772 s / img. ETA=0:02:01
[10/10 11:01:23 d2.evaluation.evaluator]: Inference done 3494/4952. 0.0772 s / img. ETA=0:01:56
[10/10 11:01:28 d2.evaluation.evaluator]: Inference done 3557/4952. 0.0772 s / img. ETA=0:01:51
[10/10 11:01:33 d2.evaluation.evaluator]: Inference done 3619/4952. 0.0773 s / img. ETA=0:01:46
[10/10 11:01:38 d2.evaluation.evaluator]: Inference done 3682/4952. 0.0773 s / img. ETA=0:01:41
[10/10 11:01:43 d2.evaluation.evaluator]: Inference done 3745/4952. 0.0773 s / img. ETA=0:01:36
[10/10 11:01:48 d2.evaluation.evaluator]: Inference done 3808/4952. 0.0773 s / img. ETA=0:01:31
[10/10 11:01:53 d2.evaluation.evaluator]: Inference done 3872/4952. 0.0773 s / img. ETA=0:01:26
[10/10 11:01:58 d2.evaluation.evaluator]: Inference done 3935/4952. 0.0773 s / img. ETA=0:01:21
[10/10 11:02:03 d2.evaluation.evaluator]: Inference done 3998/4952. 0.0773 s / img. ETA=0:01:16
[10/10 11:02:08 d2.evaluation.evaluator]: Inference done 4061/4952. 0.0773 s / img. ETA=0:01:10
[10/10 11:02:13 d2.evaluation.evaluator]: Inference done 4125/4952. 0.0773 s / img. ETA=0:01:05
[10/10 11:02:18 d2.evaluation.evaluator]: Inference done 4188/4952. 0.0773 s / img. ETA=0:01:00
[10/10 11:02:23 d2.evaluation.evaluator]: Inference done 4251/4952. 0.0773 s / img. ETA=0:00:55
[10/10 11:02:28 d2.evaluation.evaluator]: Inference done 4313/4952. 0.0773 s / img. ETA=0:00:50
[10/10 11:02:33 d2.evaluation.evaluator]: Inference done 4375/4952. 0.0773 s / img. ETA=0:00:46
[10/10 11:02:38 d2.evaluation.evaluator]: Inference done 4439/4952. 0.0773 s / img. ETA=0:00:40
[10/10 11:02:43 d2.evaluation.evaluator]: Inference done 4501/4952. 0.0774 s / img. ETA=0:00:35
[10/10 11:02:48 d2.evaluation.evaluator]: Inference done 4564/4952. 0.0774 s / img. ETA=0:00:30
[10/10 11:02:54 d2.evaluation.evaluator]: Inference done 4627/4952. 0.0774 s / img. ETA=0:00:25
[10/10 11:02:59 d2.evaluation.evaluator]: Inference done 4690/4952. 0.0774 s / img. ETA=0:00:20
[10/10 11:03:04 d2.evaluation.evaluator]: Inference done 4753/4952. 0.0774 s / img. ETA=0:00:15
[10/10 11:03:09 d2.evaluation.evaluator]: Inference done 4816/4952. 0.0774 s / img. ETA=0:00:10
[10/10 11:03:14 d2.evaluation.evaluator]: Inference done 4879/4952. 0.0774 s / img. ETA=0:00:05
[10/10 11:03:19 d2.evaluation.evaluator]: Inference done 4942/4952. 0.0774 s / img. ETA=0:00:00
[10/10 11:03:19 d2.evaluation.evaluator]: Total inference time: 0:06:34.603606 (0.079766 s / img per device, on 1 devices)
[10/10 11:03:19 d2.evaluation.evaluator]: Total inference pure compute time: 0:06:22 (0.077365 s / img per device, on 1 devices)
[10/10 11:03:20 detectron2]: Image level evaluation complete for custom_voc_2007_test
[10/10 11:03:20 detectron2]: Results for custom_voc_2007_test
Traceback (most recent call last):
  File "main.py", line 198, in <module>
    args=(args,),
  File "/home/dksingh/objdet/detectron2/detectron2/engine/launch.py", line 62, in launch
    main_func(*args)
  File "main.py", line 177, in main
    return do_test(cfg, model)
  File "main.py", line 63, in do_test
    evaluator._coco_api.cats)
  File "/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py", line 55, in only_mAP_analysis
    PR_plotter(Precision, Recall, categories[cls_no+1]['name'], ap)
  File "/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py", line 17, in PR_plotter
    plt.savefig(f"PR/{cls_name}_Precision_recall.pdf", bbox_inches="tight")
  File "/home/dksingh/.local/lib/python3.7/site-packages/matplotlib/pyplot.py", line 722, in savefig
    res = fig.savefig(*args, **kwargs)
  File "/home/dksingh/.local/lib/python3.7/site-packages/matplotlib/figure.py", line 2180, in savefig
    self.canvas.print_figure(fname, **kwargs)
  File "/home/dksingh/.local/lib/python3.7/site-packages/matplotlib/backend_bases.py", line 2082, in print_figure
    **kwargs)
  File "/home/dksingh/.local/lib/python3.7/site-packages/matplotlib/backends/backend_pdf.py", line 2496, in print_pdf
    file = PdfFile(filename, metadata=metadata)
  File "/home/dksingh/.local/lib/python3.7/site-packages/matplotlib/backends/backend_pdf.py", line 432, in __init__
    fh, opened = cbook.to_filehandle(filename, "wb", return_opened=True)
  File "/home/dksingh/.local/lib/python3.7/site-packages/matplotlib/cbook/__init__.py", line 432, in to_filehandle
    fh = open(fname, flag, encoding=encoding)
FileNotFoundError: [Errno 2] No such file or directory: 'PR/aeroplane_Precision_recall.pdf'

Are there any missing files in the repo?

I touched PR/aeroplane_Precision_recall.pdf just to check what happens with the following code:

python main.py --num-gpus 1 --config-file training_configs/faster_rcnn_R_50_FPN.yaml --resume --eval-only

Log:

[10/10 11:15:08 d2.data.common]: Serializing 4952 elements to byte tensors and concatenating them all ...
[10/10 11:15:08 d2.data.common]: Serialized dataset takes 1.87 MiB
[10/10 11:15:08 d2.data.dataset_mapper]: Augmentations used in training: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[10/10 11:15:08 d2.evaluation.evaluator]: Start inference on 4952 images
[10/10 11:15:09 d2.evaluation.evaluator]: Inference done 11/4952. 0.0750 s / img. ETA=0:06:19
[10/10 11:15:14 d2.evaluation.evaluator]: Inference done 73/4952. 0.0785 s / img. ETA=0:06:34
[10/10 11:15:19 d2.evaluation.evaluator]: Inference done 137/4952. 0.0773 s / img. ETA=0:06:23
[10/10 11:15:24 d2.evaluation.evaluator]: Inference done 202/4952. 0.0767 s / img. ETA=0:06:15
[10/10 11:15:29 d2.evaluation.evaluator]: Inference done 266/4952. 0.0766 s / img. ETA=0:06:10
[10/10 11:15:34 d2.evaluation.evaluator]: Inference done 330/4952. 0.0765 s / img. ETA=0:06:04
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[10/10 11:21:17 d2.evaluation.evaluator]: Inference done 4627/4952. 0.0774 s / img. ETA=0:00:25
[10/10 11:21:22 d2.evaluation.evaluator]: Inference done 4689/4952. 0.0774 s / img. ETA=0:00:20
[10/10 11:21:27 d2.evaluation.evaluator]: Inference done 4753/4952. 0.0774 s / img. ETA=0:00:15
[10/10 11:21:33 d2.evaluation.evaluator]: Inference done 4816/4952. 0.0774 s / img. ETA=0:00:10
[10/10 11:21:38 d2.evaluation.evaluator]: Inference done 4879/4952. 0.0774 s / img. ETA=0:00:05
[10/10 11:21:43 d2.evaluation.evaluator]: Inference done 4942/4952. 0.0774 s / img. ETA=0:00:00
[10/10 11:21:43 d2.evaluation.evaluator]: Total inference time: 0:06:34.775224 (0.079801 s / img per device, on 1 devices)
[10/10 11:21:43 d2.evaluation.evaluator]: Total inference pure compute time: 0:06:22 (0.077401 s / img per device, on 1 devices)
[10/10 11:21:44 detectron2]: Image level evaluation complete for custom_voc_2007_test
[10/10 11:21:44 detectron2]: Results for custom_voc_2007_test
[10/10 11:21:44 detectron2]: AP for 0: 2.896947080444079e-06
[10/10 11:21:44 detectron2]: AP for 1: 0.0
[10/10 11:21:44 detectron2]: AP for 2: 7.036307215457782e-05
[10/10 11:21:44 detectron2]: AP for 3: 0.0
[10/10 11:21:45 detectron2]: AP for 4: 0.0
[10/10 11:21:45 detectron2]: AP for 5: 0.0
[10/10 11:21:45 detectron2]: AP for 6: 0.0
[10/10 11:21:45 detectron2]: AP for 7: 0.0
[10/10 11:21:45 detectron2]: AP for 8: 0.0
[10/10 11:21:46 detectron2]: AP for 9: 0.00013395248970482498
[10/10 11:21:46 detectron2]: AP for 10: 0.0
[10/10 11:21:46 detectron2]: AP for 11: 0.0
[10/10 11:21:46 detectron2]: AP for 12: 0.0
[10/10 11:21:47 detectron2]: AP for 13: 6.641517302341526e-06
[10/10 11:21:47 detectron2]: AP for 15: 0.0
[10/10 11:21:47 detectron2]: AP for 16: 0.0
[10/10 11:21:47 detectron2]: AP for 17: 0.0
[10/10 11:21:47 detectron2]: AP for 18: 0.0
[10/10 11:21:47 detectron2]: AP for 19: 0.0
[10/10 11:21:47 detectron2]: mAP: 1.125547532865312e-05
WARNING [10/10 11:21:48 d2.data.datasets.coco]:
Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.

[10/10 11:21:48 d2.data.datasets.coco]: Loaded 4952 images in COCO format from protocol/custom_protocols/WR1_Mixed_Unknowns.json
[10/10 11:21:48 d2.data.build]: Distribution of instances among all 21 categories:
|  category  | #instances   |  category   | #instances   |  category   | #instances   |
|:----------:|:-------------|:-----------:|:-------------|:-----------:|:-------------|
|  unknown   | 15235        |  aeroplane  | 0            |   bicycle   | 0            |
|    bird    | 0            |    boat     | 0            |   bottle    | 0            |
|    bus     | 0            |     car     | 0            |     cat     | 0            |
|   chair    | 0            |     cow     | 0            | diningtable | 0            |
|    dog     | 0            |    horse    | 0            |  motorbike  | 0            |
|   person   | 0            | pottedplant | 0            |    sheep    | 0            |
|    sofa    | 0            |    train    | 0            |  tvmonitor  | 0            |
|            |              |             |              |             |              |
|   total    | 15235        |             |              |             |              |
[10/10 11:21:48 d2.data.common]: Serializing 4952 elements to byte tensors and concatenating them all ...
[10/10 11:21:48 d2.data.common]: Serialized dataset takes 8.39 MiB
[10/10 11:21:48 d2.data.dataset_mapper]: Augmentations used in training: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[10/10 11:21:48 d2.evaluation.evaluator]: Start inference on 4952 images
[10/10 11:21:50 d2.evaluation.evaluator]: Inference done 11/4952. 0.0778 s / img. ETA=0:06:34
[10/10 11:21:55 d2.evaluation.evaluator]: Inference done 74/4952. 0.0781 s / img. ETA=0:06:32
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[10/10 11:26:27 d2.evaluation.evaluator]: Inference done 3402/4952. 0.0795 s / img. ETA=0:02:06
[10/10 11:26:32 d2.evaluation.evaluator]: Inference done 3465/4952. 0.0795 s / img. ETA=0:02:01
[10/10 11:26:37 d2.evaluation.evaluator]: Inference done 3527/4952. 0.0795 s / img. ETA=0:01:56
[10/10 11:26:42 d2.evaluation.evaluator]: Inference done 3588/4952. 0.0795 s / img. ETA=0:01:51
[10/10 11:26:47 d2.evaluation.evaluator]: Inference done 3649/4952. 0.0795 s / img. ETA=0:01:46
[10/10 11:26:52 d2.evaluation.evaluator]: Inference done 3711/4952. 0.0795 s / img. ETA=0:01:41
[10/10 11:26:57 d2.evaluation.evaluator]: Inference done 3772/4952. 0.0795 s / img. ETA=0:01:36
[10/10 11:27:02 d2.evaluation.evaluator]: Inference done 3833/4952. 0.0795 s / img. ETA=0:01:31
[10/10 11:27:07 d2.evaluation.evaluator]: Inference done 3894/4952. 0.0795 s / img. ETA=0:01:26
[10/10 11:27:12 d2.evaluation.evaluator]: Inference done 3956/4952. 0.0795 s / img. ETA=0:01:21
[10/10 11:27:17 d2.evaluation.evaluator]: Inference done 4017/4952. 0.0795 s / img. ETA=0:01:16
[10/10 11:27:22 d2.evaluation.evaluator]: Inference done 4079/4952. 0.0795 s / img. ETA=0:01:11
[10/10 11:27:27 d2.evaluation.evaluator]: Inference done 4141/4952. 0.0795 s / img. ETA=0:01:06
[10/10 11:27:33 d2.evaluation.evaluator]: Inference done 4202/4952. 0.0795 s / img. ETA=0:01:01
[10/10 11:27:38 d2.evaluation.evaluator]: Inference done 4263/4952. 0.0795 s / img. ETA=0:00:56
[10/10 11:27:43 d2.evaluation.evaluator]: Inference done 4325/4952. 0.0795 s / img. ETA=0:00:51
[10/10 11:27:48 d2.evaluation.evaluator]: Inference done 4387/4952. 0.0795 s / img. ETA=0:00:46
[10/10 11:27:53 d2.evaluation.evaluator]: Inference done 4449/4952. 0.0795 s / img. ETA=0:00:41
[10/10 11:27:58 d2.evaluation.evaluator]: Inference done 4511/4952. 0.0795 s / img. ETA=0:00:36
[10/10 11:28:03 d2.evaluation.evaluator]: Inference done 4572/4952. 0.0795 s / img. ETA=0:00:31
[10/10 11:28:08 d2.evaluation.evaluator]: Inference done 4634/4952. 0.0795 s / img. ETA=0:00:26
[10/10 11:28:13 d2.evaluation.evaluator]: Inference done 4696/4952. 0.0795 s / img. ETA=0:00:20
[10/10 11:28:18 d2.evaluation.evaluator]: Inference done 4758/4952. 0.0795 s / img. ETA=0:00:15
[10/10 11:28:23 d2.evaluation.evaluator]: Inference done 4820/4952. 0.0795 s / img. ETA=0:00:10
[10/10 11:28:28 d2.evaluation.evaluator]: Inference done 4881/4952. 0.0795 s / img. ETA=0:00:05
[10/10 11:28:33 d2.evaluation.evaluator]: Inference done 4942/4952. 0.0795 s / img. ETA=0:00:00
[10/10 11:28:34 d2.evaluation.evaluator]: Total inference time: 0:06:44.715277 (0.081810 s / img per device, on 1 devices)
[10/10 11:28:34 d2.evaluation.evaluator]: Total inference pure compute time: 0:06:33 (0.079462 s / img per device, on 1 devices)
[10/10 11:28:34 detectron2]: Image level evaluation complete for WR1_Mixed_Unknowns
[10/10 11:28:34 detectron2]: Results for WR1_Mixed_Unknowns
[10/10 11:28:34 detectron2]: AP for 0: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:34 detectron2]: AP for 1: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:35 detectron2]: AP for 2: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:35 detectron2]: AP for 3: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:35 detectron2]: AP for 4: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:35 detectron2]: AP for 5: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:35 detectron2]: AP for 6: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:36 detectron2]: AP for 7: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:36 detectron2]: AP for 8: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:36 detectron2]: AP for 9: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:36 detectron2]: AP for 10: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:36 detectron2]: AP for 11: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:37 detectron2]: AP for 12: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:37 detectron2]: AP for 13: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:37 detectron2]: AP for 15: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:37 detectron2]: AP for 16: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:37 detectron2]: AP for 17: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:38 detectron2]: AP for 18: 0.0
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.subplots()
[10/10 11:28:38 detectron2]: AP for 19: 0.0
[10/10 11:28:38 detectron2]: mAP: 0.0
[10/10 11:28:38 detectron2]: Combined results for datasets custom_voc_2007_test, WR1_Mixed_Unknowns
[10/10 11:28:38 detectron2]: AP for 0: 1.778590444700967e-06
[10/10 11:28:38 detectron2]: AP for 1: 0.0
[10/10 11:28:38 detectron2]: AP for 2: 4.170141983195208e-05
[10/10 11:28:38 detectron2]: AP for 3: 0.0
[10/10 11:28:38 detectron2]: AP for 4: 0.0
[10/10 11:28:38 detectron2]: AP for 5: 0.0
[10/10 11:28:38 detectron2]: AP for 6: 0.0
[10/10 11:28:38 detectron2]: AP for 7: 0.0
[10/10 11:28:38 detectron2]: AP for 8: 0.0
[10/10 11:28:38 detectron2]: AP for 9: 5.68300238228403e-05
[10/10 11:28:38 detectron2]: AP for 10: 0.0
[10/10 11:28:38 detectron2]: AP for 11: 0.0
[10/10 11:28:38 detectron2]: AP for 12: 0.0
[10/10 11:28:38 detectron2]: AP for 13: 3.277775022070273e-06
[10/10 11:28:38 detectron2]: AP for 15: 0.0
[10/10 11:28:38 detectron2]: AP for 16: 0.0
[10/10 11:28:38 detectron2]: AP for 17: 0.0
[10/10 11:28:38 detectron2]: AP for 18: 0.0
[10/10 11:28:38 detectron2]: AP for 19: 0.0
[10/10 11:28:38 detectron2]: mAP: 5.451990091387415e-06
/home/dksingh/paper_impl/Elephant-of-object-detection/WIC.py:63: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  eval_info['predictions'][k] = np.array([torch.tensor(_).type(torch.FloatTensor).numpy() for _ in eval_info['predictions'][k]])
[10/10 11:28:40 detectron2]: AP for 0.0: 2.896947080444079e-06
[10/10 11:28:40 detectron2]: AP for 1.0: 0.0
[10/10 11:28:40 detectron2]: AP for 2.0: 7.036307215457782e-05
[10/10 11:28:40 detectron2]: AP for 3.0: 0.0
[10/10 11:28:40 detectron2]: AP for 4.0: 0.0
[10/10 11:28:40 detectron2]: AP for 5.0: 0.0
[10/10 11:28:40 detectron2]: AP for 6.0: 0.0
[10/10 11:28:40 detectron2]: AP for 7.0: 0.0
[10/10 11:28:40 detectron2]: AP for 8.0: 0.0
[10/10 11:28:40 detectron2]: AP for 9.0: 0.00013395248970482498
[10/10 11:28:40 detectron2]: AP for 10.0: 0.0
[10/10 11:28:40 detectron2]: AP for 11.0: 0.0
[10/10 11:28:40 detectron2]: AP for 12.0: 0.0
[10/10 11:28:41 detectron2]: AP for 13.0: 6.641517302341526e-06
[10/10 11:28:41 detectron2]: AP for 15.0: 0.0
[10/10 11:28:41 detectron2]: AP for 16.0: 0.0
[10/10 11:28:41 detectron2]: AP for 17.0: 0.0
[10/10 11:28:41 detectron2]: AP for 18.0: 0.0
[10/10 11:28:41 detectron2]: AP for 19.0: 0.0
[10/10 11:28:41 detectron2]: mAP: 1.125547532865312e-05
[10/10 11:28:41 detectron2]: AP for 0.0: 2.735836005740566e-06
[10/10 11:28:41 detectron2]: AP for 1.0: 0.0
[10/10 11:28:41 detectron2]: AP for 2.0: 6.679580110358074e-05
[10/10 11:28:41 detectron2]: AP for 3.0: 0.0
[10/10 11:28:41 detectron2]: AP for 4.0: 0.0
[10/10 11:28:41 detectron2]: AP for 5.0: 0.0
[10/10 11:28:41 detectron2]: AP for 6.0: 0.0
[10/10 11:28:41 detectron2]: AP for 7.0: 0.0
[10/10 11:28:41 detectron2]: AP for 8.0: 0.0
[10/10 11:28:41 detectron2]: AP for 9.0: 0.00012175324809504673
[10/10 11:28:41 detectron2]: AP for 10.0: 0.0
[10/10 11:28:41 detectron2]: AP for 11.0: 0.0
[10/10 11:28:41 detectron2]: AP for 12.0: 0.0
[10/10 11:28:41 detectron2]: AP for 13.0: 6.086168014007853e-06
[10/10 11:28:41 detectron2]: AP for 15.0: 0.0
[10/10 11:28:41 detectron2]: AP for 16.0: 0.0
[10/10 11:28:41 detectron2]: AP for 17.0: 0.0
[10/10 11:28:41 detectron2]: AP for 18.0: 0.0
[10/10 11:28:41 detectron2]: AP for 19.0: 0.0
[10/10 11:28:41 detectron2]: mAP: 1.0387950169388205e-05
[10/10 11:28:42 detectron2]: AP for 0.0: 2.585729816928506e-06
[10/10 11:28:42 detectron2]: AP for 1.0: 0.0
[10/10 11:28:42 detectron2]: AP for 2.0: 6.30000649834983e-05
[10/10 11:28:42 detectron2]: AP for 3.0: 0.0
[10/10 11:28:42 detectron2]: AP for 4.0: 0.0
[10/10 11:28:42 detectron2]: AP for 5.0: 0.0
[10/10 11:28:42 detectron2]: AP for 6.0: 0.0
[10/10 11:28:42 detectron2]: AP for 7.0: 0.0
[10/10 11:28:42 detectron2]: AP for 8.0: 0.0
[10/10 11:28:42 detectron2]: AP for 9.0: 0.00010935335740214214
[10/10 11:28:42 detectron2]: AP for 10.0: 0.0
[10/10 11:28:42 detectron2]: AP for 11.0: 0.0
[10/10 11:28:42 detectron2]: AP for 12.0: 0.0
[10/10 11:28:42 detectron2]: AP for 13.0: 5.587185569311259e-06
[10/10 11:28:42 detectron2]: AP for 15.0: 0.0
[10/10 11:28:42 detectron2]: AP for 16.0: 0.0
[10/10 11:28:42 detectron2]: AP for 17.0: 0.0
[10/10 11:28:42 detectron2]: AP for 18.0: 0.0
[10/10 11:28:42 detectron2]: AP for 19.0: 0.0
[10/10 11:28:42 detectron2]: mAP: 9.501385648036376e-06
[10/10 11:28:42 detectron2]: AP for 0.0: 2.4474111341987737e-06
[10/10 11:28:42 detectron2]: AP for 1.0: 0.0
[10/10 11:28:42 detectron2]: AP for 2.0: 5.934013825026341e-05
[10/10 11:28:42 detectron2]: AP for 3.0: 0.0
[10/10 11:28:42 detectron2]: AP for 4.0: 0.0
[10/10 11:28:42 detectron2]: AP for 5.0: 0.0
[10/10 11:28:42 detectron2]: AP for 6.0: 0.0
[10/10 11:28:43 detectron2]: AP for 7.0: 0.0
[10/10 11:28:43 detectron2]: AP for 8.0: 0.0
[10/10 11:28:43 detectron2]: AP for 9.0: 9.990010585170239e-05
[10/10 11:28:43 detectron2]: AP for 10.0: 0.0
[10/10 11:28:43 detectron2]: AP for 11.0: 0.0
[10/10 11:28:43 detectron2]: AP for 12.0: 0.0
[10/10 11:28:43 detectron2]: AP for 13.0: 5.13871964358259e-06
[10/10 11:28:43 detectron2]: AP for 15.0: 0.0
[10/10 11:28:43 detectron2]: AP for 16.0: 0.0
[10/10 11:28:43 detectron2]: AP for 17.0: 0.0
[10/10 11:28:43 detectron2]: AP for 18.0: 0.0
[10/10 11:28:43 detectron2]: AP for 19.0: 0.0
[10/10 11:28:43 detectron2]: mAP: 8.780335519986693e-06
[10/10 11:28:43 detectron2]: AP for 0.0: 2.3299867280002218e-06
[10/10 11:28:43 detectron2]: AP for 1.0: 0.0
[10/10 11:28:43 detectron2]: AP for 2.0: 5.4797521443106234e-05
[10/10 11:28:43 detectron2]: AP for 3.0: 0.0
[10/10 11:28:43 detectron2]: AP for 4.0: 0.0
[10/10 11:28:43 detectron2]: AP for 5.0: 0.0
[10/10 11:28:43 detectron2]: AP for 6.0: 0.0
[10/10 11:28:43 detectron2]: AP for 7.0: 0.0
[10/10 11:28:44 detectron2]: AP for 8.0: 0.0
[10/10 11:28:44 detectron2]: AP for 9.0: 8.895213977666572e-05
[10/10 11:28:44 detectron2]: AP for 10.0: 0.0
[10/10 11:28:44 detectron2]: AP for 11.0: 0.0
[10/10 11:28:44 detectron2]: AP for 12.0: 0.0
[10/10 11:28:44 detectron2]: AP for 13.0: 4.765626727021299e-06
[10/10 11:28:44 detectron2]: AP for 15.0: 0.0
[10/10 11:28:44 detectron2]: AP for 16.0: 0.0
[10/10 11:28:44 detectron2]: AP for 17.0: 0.0
[10/10 11:28:44 detectron2]: AP for 18.0: 0.0
[10/10 11:28:44 detectron2]: AP for 19.0: 0.0
[10/10 11:28:44 detectron2]: mAP: 7.93922481534537e-06
[10/10 11:28:44 detectron2]: AP for 0.0: 2.2166461803863058e-06
[10/10 11:28:44 detectron2]: AP for 1.0: 0.0
[10/10 11:28:44 detectron2]: AP for 2.0: 5.150656579644419e-05
[10/10 11:28:44 detectron2]: AP for 3.0: 0.0
[10/10 11:28:44 detectron2]: AP for 4.0: 0.0
[10/10 11:28:44 detectron2]: AP for 5.0: 0.0
[10/10 11:28:44 detectron2]: AP for 6.0: 0.0
[10/10 11:28:44 detectron2]: AP for 7.0: 0.0
[10/10 11:28:44 detectron2]: AP for 8.0: 0.0
[10/10 11:28:44 detectron2]: AP for 9.0: 8.148409688146785e-05
[10/10 11:28:45 detectron2]: AP for 10.0: 0.0
[10/10 11:28:45 detectron2]: AP for 11.0: 0.0
[10/10 11:28:45 detectron2]: AP for 12.0: 0.0
[10/10 11:28:45 detectron2]: AP for 13.0: 4.396841177367605e-06
[10/10 11:28:45 detectron2]: AP for 15.0: 0.0
[10/10 11:28:45 detectron2]: AP for 16.0: 0.0
[10/10 11:28:45 detectron2]: AP for 17.0: 0.0
[10/10 11:28:45 detectron2]: AP for 18.0: 0.0
[10/10 11:28:45 detectron2]: AP for 19.0: 0.0
[10/10 11:28:45 detectron2]: mAP: 7.347587597905658e-06
[10/10 11:28:45 detectron2]: AP for 0.0: 2.1033060875197407e-06
[10/10 11:28:45 detectron2]: AP for 1.0: 0.0
[10/10 11:28:45 detectron2]: AP for 2.0: 4.9380279961042106e-05
[10/10 11:28:45 detectron2]: AP for 3.0: 0.0
[10/10 11:28:45 detectron2]: AP for 4.0: 0.0
[10/10 11:28:45 detectron2]: AP for 5.0: 0.0
[10/10 11:28:45 detectron2]: AP for 6.0: 0.0
[10/10 11:28:45 detectron2]: AP for 7.0: 0.0
[10/10 11:28:45 detectron2]: AP for 8.0: 0.0
[10/10 11:28:45 detectron2]: AP for 9.0: 7.429236575262621e-05
[10/10 11:28:46 detectron2]: AP for 10.0: 0.0
[10/10 11:28:46 detectron2]: AP for 11.0: 0.0
[10/10 11:28:46 detectron2]: AP for 12.0: 0.0
[10/10 11:28:46 detectron2]: AP for 13.0: 4.0986965359479655e-06
[10/10 11:28:46 detectron2]: AP for 15.0: 0.0
[10/10 11:28:46 detectron2]: AP for 16.0: 0.0
[10/10 11:28:46 detectron2]: AP for 17.0: 0.0
[10/10 11:28:46 detectron2]: AP for 18.0: 0.0
[10/10 11:28:46 detectron2]: AP for 19.0: 0.0
[10/10 11:28:46 detectron2]: mAP: 6.835507974756183e-06
[10/10 11:28:46 detectron2]: AP for 0.0: 2.0148736439296044e-06
[10/10 11:28:46 detectron2]: AP for 1.0: 0.0
[10/10 11:28:47 detectron2]: AP for 2.0: 4.75465931231156e-05
[10/10 11:28:47 detectron2]: AP for 3.0: 0.0
[10/10 11:28:47 detectron2]: AP for 4.0: 0.0
[10/10 11:28:47 detectron2]: AP for 5.0: 0.0
[10/10 11:28:47 detectron2]: AP for 6.0: 0.0
[10/10 11:28:47 detectron2]: AP for 7.0: 0.0
[10/10 11:28:47 detectron2]: AP for 8.0: 0.0
[10/10 11:28:47 detectron2]: AP for 9.0: 6.867975025670603e-05
[10/10 11:28:47 detectron2]: AP for 10.0: 0.0
[10/10 11:28:47 detectron2]: AP for 11.0: 0.0
[10/10 11:28:47 detectron2]: AP for 12.0: 0.0
[10/10 11:28:47 detectron2]: AP for 13.0: 3.874572485074168e-06
[10/10 11:28:47 detectron2]: AP for 15.0: 0.0
[10/10 11:28:47 detectron2]: AP for 16.0: 0.0
[10/10 11:28:47 detectron2]: AP for 17.0: 0.0
[10/10 11:28:47 detectron2]: AP for 18.0: 0.0
[10/10 11:28:47 detectron2]: AP for 19.0: 0.0
[10/10 11:28:47 detectron2]: mAP: 6.427146672649542e-06
[10/10 11:28:48 detectron2]: AP for 0.0: 1.926529876072891e-06
[10/10 11:28:48 detectron2]: AP for 1.0: 0.0
[10/10 11:28:48 detectron2]: AP for 2.0: 4.56370908068493e-05
[10/10 11:28:48 detectron2]: AP for 3.0: 0.0
[10/10 11:28:48 detectron2]: AP for 4.0: 0.0
[10/10 11:28:48 detectron2]: AP for 5.0: 0.0
[10/10 11:28:48 detectron2]: AP for 6.0: 0.0
[10/10 11:28:48 detectron2]: AP for 7.0: 0.0
[10/10 11:28:48 detectron2]: AP for 8.0: 0.0
[10/10 11:28:48 detectron2]: AP for 9.0: 6.498148286482319e-05
[10/10 11:28:48 detectron2]: AP for 10.0: 0.0
[10/10 11:28:48 detectron2]: AP for 11.0: 0.0
[10/10 11:28:48 detectron2]: AP for 12.0: 0.0
[10/10 11:28:48 detectron2]: AP for 13.0: 3.6445273963181535e-06
[10/10 11:28:48 detectron2]: AP for 15.0: 0.0
[10/10 11:28:48 detectron2]: AP for 16.0: 0.0
[10/10 11:28:48 detectron2]: AP for 17.0: 0.0
[10/10 11:28:48 detectron2]: AP for 18.0: 0.0
[10/10 11:28:48 detectron2]: AP for 19.0: 0.0
[10/10 11:28:48 detectron2]: mAP: 6.115243650128832e-06
[10/10 11:28:49 detectron2]: AP for 0.0: 1.848534793680301e-06
[10/10 11:28:49 detectron2]: AP for 1.0: 0.0
[10/10 11:28:49 detectron2]: AP for 2.0: 4.347637150203809e-05
[10/10 11:28:49 detectron2]: AP for 3.0: 0.0
[10/10 11:28:49 detectron2]: AP for 4.0: 0.0
[10/10 11:28:49 detectron2]: AP for 5.0: 0.0
[10/10 11:28:49 detectron2]: AP for 6.0: 0.0
[10/10 11:28:49 detectron2]: AP for 7.0: 0.0
[10/10 11:28:49 detectron2]: AP for 8.0: 0.0
[10/10 11:28:49 detectron2]: AP for 9.0: 6.008532363921404e-05
[10/10 11:28:49 detectron2]: AP for 10.0: 0.0
[10/10 11:28:49 detectron2]: AP for 11.0: 0.0
[10/10 11:28:49 detectron2]: AP for 12.0: 0.0
[10/10 11:28:50 detectron2]: AP for 13.0: 3.4480974591133418e-06
[10/10 11:28:50 detectron2]: AP for 15.0: 0.0
[10/10 11:28:50 detectron2]: AP for 16.0: 0.0
[10/10 11:28:50 detectron2]: AP for 17.0: 0.0
[10/10 11:28:50 detectron2]: AP for 18.0: 0.0
[10/10 11:28:50 detectron2]: AP for 19.0: 0.0
[10/10 11:28:50 detectron2]: mAP: 5.729385975428158e-06
[10/10 11:28:50 detectron2]: AP for 0.0: 1.778590444700967e-06
[10/10 11:28:50 detectron2]: AP for 1.0: 0.0
[10/10 11:28:50 detectron2]: AP for 2.0: 4.170141983195208e-05
[10/10 11:28:50 detectron2]: AP for 3.0: 0.0
[10/10 11:28:50 detectron2]: AP for 4.0: 0.0
[10/10 11:28:50 detectron2]: AP for 5.0: 0.0
[10/10 11:28:50 detectron2]: AP for 6.0: 0.0
[10/10 11:28:50 detectron2]: AP for 7.0: 0.0
[10/10 11:28:50 detectron2]: AP for 8.0: 0.0
[10/10 11:28:50 detectron2]: AP for 9.0: 5.68300238228403e-05
[10/10 11:28:51 detectron2]: AP for 10.0: 0.0
[10/10 11:28:51 detectron2]: AP for 11.0: 0.0
[10/10 11:28:51 detectron2]: AP for 12.0: 0.0
[10/10 11:28:51 detectron2]: AP for 13.0: 3.277775022070273e-06
[10/10 11:28:51 detectron2]: AP for 15.0: 0.0
[10/10 11:28:51 detectron2]: AP for 16.0: 0.0
[10/10 11:28:51 detectron2]: AP for 17.0: 0.0
[10/10 11:28:51 detectron2]: AP for 18.0: 0.0
[10/10 11:28:51 detectron2]: AP for 19.0: 0.0
[10/10 11:28:51 detectron2]: mAP: 5.451990091387415e-06

Could you explain a little about how to infer these numbers, and most of them are nearly 0. There are new files created under PR/ and all of them have 0.0% across the Precision vs Recall graph.

Hi, It looks like you aren't using a trained model, please refer to the updated README. Also, the logging messages have been updated to better explain the values. Thanks!

Hello Akshay, I trained the model as mentioned in the updated README.

The evaluation does not happen for multi GPU yet again.
I performed the evaluation on 1 GPU and I notice that, the first test evaluation happens for custom_voc_2007_test. Then evaluation happens for WR1_Mixed_Unknowns. In the logs, I notice that there are multiple sequences of evaluation after evaluating WR1_Mixed_Unknowns. Could you explain how to understand the 2nd evaluation values?

Log:

<starting log cropped>
[32m[11/05 16:03:58 fvcore.common.checkpoint]: [0mLoading checkpoint from ./output/model_final.pth
[5m[31mWARNING[0m [32m[11/05 16:03:58 d2.data.datasets.coco]: [0m
Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.

[32m[11/05 16:03:58 d2.data.datasets.coco]: [0mLoaded 4952 images in COCO format from protocol/custom_protocols/custom_voc_2007_test.json
[32m[11/05 16:03:59 d2.data.build]: [0mDistribution of instances among all 21 categories:
[36m|  category  | #instances   |  category   | #instances   |  category   | #instances   |
|:----------:|:-------------|:-----------:|:-------------|:-----------:|:-------------|
|  unknown   | 0            |  aeroplane  | 311          |   bicycle   | 389          |
|    bird    | 576          |    boat     | 393          |   bottle    | 657          |
|    bus     | 254          |     car     | 1541         |     cat     | 370          |
|   chair    | 1374         |     cow     | 329          | diningtable | 299          |
|    dog     | 530          |    horse    | 395          |  motorbike  | 369          |
|   person   | 5227         | pottedplant | 592          |    sheep    | 311          |
|    sofa    | 396          |    train    | 302          |  tvmonitor  | 361          |
|            |              |             |              |             |              |
|   total    | 14976        |             |              |             |              |[0m
[32m[11/05 16:03:59 d2.data.common]: [0mSerializing 4952 elements to byte tensors and concatenating them all ...
[32m[11/05 16:03:59 d2.data.common]: [0mSerialized dataset takes 1.87 MiB
[32m[11/05 16:03:59 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[11/05 16:03:59 d2.evaluation.evaluator]: [0mStart inference on 4952 images
[32m[11/05 16:04:00 d2.evaluation.evaluator]: [0mInference done 11/4952. 0.0777 s / img. ETA=0:06:33
[32m[11/05 16:04:05 d2.evaluation.evaluator]: [0mInference done 75/4952. 0.0772 s / img. ETA=0:06:26
[32m[11/05 16:04:10 d2.evaluation.evaluator]: [0mInference done 138/4952. 0.0775 s / img. ETA=0:06:23
[32m[11/05 16:04:15 d2.evaluation.evaluator]: [0mInference done 202/4952. 0.0773 s / img. ETA=0:06:17
[32m[11/05 16:04:21 d2.evaluation.evaluator]: [0mInference done 265/4952. 0.0774 s / img. ETA=0:06:12
[32m[11/05 16:04:26 d2.evaluation.evaluator]: [0mInference done 328/4952. 0.0774 s / img. ETA=0:06:07
[32m[11/05 16:04:31 d2.evaluation.evaluator]: [0mInference done 390/4952. 0.0776 s / img. ETA=0:06:03
[32m[11/05 16:04:36 d2.evaluation.evaluator]: [0mInference done 453/4952. 0.0776 s / img. ETA=0:05:58
[32m[11/05 16:04:41 d2.evaluation.evaluator]: [0mInference done 516/4952. 0.0776 s / img. ETA=0:05:53
[32m[11/05 16:04:46 d2.evaluation.evaluator]: [0mInference done 579/4952. 0.0776 s / img. ETA=0:05:48
[32m[11/05 16:04:51 d2.evaluation.evaluator]: [0mInference done 642/4952. 0.0776 s / img. ETA=0:05:43
[32m[11/05 16:04:56 d2.evaluation.evaluator]: [0mInference done 705/4952. 0.0776 s / img. ETA=0:05:38
[32m[11/05 16:05:01 d2.evaluation.evaluator]: [0mInference done 767/4952. 0.0777 s / img. ETA=0:05:34
[32m[11/05 16:05:06 d2.evaluation.evaluator]: [0mInference done 830/4952. 0.0777 s / img. ETA=0:05:29
[32m[11/05 16:05:11 d2.evaluation.evaluator]: [0mInference done 892/4952. 0.0778 s / img. ETA=0:05:24
[32m[11/05 16:05:16 d2.evaluation.evaluator]: [0mInference done 955/4952. 0.0778 s / img. ETA=0:05:19
[32m[11/05 16:05:21 d2.evaluation.evaluator]: [0mInference done 1018/4952. 0.0779 s / img. ETA=0:05:14
[32m[11/05 16:05:26 d2.evaluation.evaluator]: [0mInference done 1080/4952. 0.0779 s / img. ETA=0:05:09
[32m[11/05 16:05:31 d2.evaluation.evaluator]: [0mInference done 1143/4952. 0.0779 s / img. ETA=0:05:04
[32m[11/05 16:05:36 d2.evaluation.evaluator]: [0mInference done 1205/4952. 0.0780 s / img. ETA=0:05:00
[32m[11/05 16:05:41 d2.evaluation.evaluator]: [0mInference done 1268/4952. 0.0780 s / img. ETA=0:04:55
[32m[11/05 16:05:46 d2.evaluation.evaluator]: [0mInference done 1331/4952. 0.0780 s / img. ETA=0:04:50
[32m[11/05 16:05:51 d2.evaluation.evaluator]: [0mInference done 1394/4952. 0.0780 s / img. ETA=0:04:45
[32m[11/05 16:05:56 d2.evaluation.evaluator]: [0mInference done 1456/4952. 0.0780 s / img. ETA=0:04:40
[32m[11/05 16:06:01 d2.evaluation.evaluator]: [0mInference done 1519/4952. 0.0780 s / img. ETA=0:04:35
[32m[11/05 16:06:06 d2.evaluation.evaluator]: [0mInference done 1582/4952. 0.0781 s / img. ETA=0:04:30
[32m[11/05 16:06:11 d2.evaluation.evaluator]: [0mInference done 1645/4952. 0.0781 s / img. ETA=0:04:25
[32m[11/05 16:06:16 d2.evaluation.evaluator]: [0mInference done 1708/4952. 0.0781 s / img. ETA=0:04:20
[32m[11/05 16:06:21 d2.evaluation.evaluator]: [0mInference done 1771/4952. 0.0781 s / img. ETA=0:04:15
[32m[11/05 16:06:26 d2.evaluation.evaluator]: [0mInference done 1834/4952. 0.0781 s / img. ETA=0:04:09
[32m[11/05 16:06:32 d2.evaluation.evaluator]: [0mInference done 1897/4952. 0.0781 s / img. ETA=0:04:04
[32m[11/05 16:06:37 d2.evaluation.evaluator]: [0mInference done 1960/4952. 0.0781 s / img. ETA=0:03:59
[32m[11/05 16:06:42 d2.evaluation.evaluator]: [0mInference done 2024/4952. 0.0780 s / img. ETA=0:03:54
[32m[11/05 16:06:47 d2.evaluation.evaluator]: [0mInference done 2086/4952. 0.0781 s / img. ETA=0:03:49
[32m[11/05 16:06:52 d2.evaluation.evaluator]: [0mInference done 2149/4952. 0.0781 s / img. ETA=0:03:44
[32m[11/05 16:06:57 d2.evaluation.evaluator]: [0mInference done 2212/4952. 0.0781 s / img. ETA=0:03:39
[32m[11/05 16:07:02 d2.evaluation.evaluator]: [0mInference done 2275/4952. 0.0781 s / img. ETA=0:03:34
[32m[11/05 16:07:07 d2.evaluation.evaluator]: [0mInference done 2338/4952. 0.0781 s / img. ETA=0:03:29
[32m[11/05 16:07:12 d2.evaluation.evaluator]: [0mInference done 2401/4952. 0.0781 s / img. ETA=0:03:24
[32m[11/05 16:07:17 d2.evaluation.evaluator]: [0mInference done 2464/4952. 0.0781 s / img. ETA=0:03:19
[32m[11/05 16:07:22 d2.evaluation.evaluator]: [0mInference done 2526/4952. 0.0781 s / img. ETA=0:03:14
[32m[11/05 16:07:27 d2.evaluation.evaluator]: [0mInference done 2588/4952. 0.0781 s / img. ETA=0:03:09
[32m[11/05 16:07:32 d2.evaluation.evaluator]: [0mInference done 2651/4952. 0.0781 s / img. ETA=0:03:04
[32m[11/05 16:07:37 d2.evaluation.evaluator]: [0mInference done 2713/4952. 0.0782 s / img. ETA=0:02:59
[32m[11/05 16:07:42 d2.evaluation.evaluator]: [0mInference done 2775/4952. 0.0782 s / img. ETA=0:02:54
[32m[11/05 16:07:47 d2.evaluation.evaluator]: [0mInference done 2838/4952. 0.0782 s / img. ETA=0:02:49
[32m[11/05 16:07:52 d2.evaluation.evaluator]: [0mInference done 2902/4952. 0.0782 s / img. ETA=0:02:44
[32m[11/05 16:07:57 d2.evaluation.evaluator]: [0mInference done 2964/4952. 0.0782 s / img. ETA=0:02:39
[32m[11/05 16:08:02 d2.evaluation.evaluator]: [0mInference done 3025/4952. 0.0782 s / img. ETA=0:02:34
[32m[11/05 16:08:08 d2.evaluation.evaluator]: [0mInference done 3088/4952. 0.0782 s / img. ETA=0:02:29
[32m[11/05 16:08:13 d2.evaluation.evaluator]: [0mInference done 3150/4952. 0.0783 s / img. ETA=0:02:24
[32m[11/05 16:08:18 d2.evaluation.evaluator]: [0mInference done 3212/4952. 0.0783 s / img. ETA=0:02:19
[32m[11/05 16:08:23 d2.evaluation.evaluator]: [0mInference done 3274/4952. 0.0783 s / img. ETA=0:02:14
[32m[11/05 16:08:28 d2.evaluation.evaluator]: [0mInference done 3336/4952. 0.0783 s / img. ETA=0:02:09
[32m[11/05 16:08:33 d2.evaluation.evaluator]: [0mInference done 3398/4952. 0.0783 s / img. ETA=0:02:04
[32m[11/05 16:08:38 d2.evaluation.evaluator]: [0mInference done 3461/4952. 0.0783 s / img. ETA=0:01:59
[32m[11/05 16:08:43 d2.evaluation.evaluator]: [0mInference done 3522/4952. 0.0783 s / img. ETA=0:01:55
[32m[11/05 16:08:48 d2.evaluation.evaluator]: [0mInference done 3584/4952. 0.0784 s / img. ETA=0:01:50
[32m[11/05 16:08:53 d2.evaluation.evaluator]: [0mInference done 3646/4952. 0.0784 s / img. ETA=0:01:45
[32m[11/05 16:08:58 d2.evaluation.evaluator]: [0mInference done 3708/4952. 0.0784 s / img. ETA=0:01:40
[32m[11/05 16:09:03 d2.evaluation.evaluator]: [0mInference done 3770/4952. 0.0784 s / img. ETA=0:01:35
[32m[11/05 16:09:08 d2.evaluation.evaluator]: [0mInference done 3833/4952. 0.0784 s / img. ETA=0:01:30
[32m[11/05 16:09:13 d2.evaluation.evaluator]: [0mInference done 3895/4952. 0.0784 s / img. ETA=0:01:25
[32m[11/05 16:09:18 d2.evaluation.evaluator]: [0mInference done 3958/4952. 0.0784 s / img. ETA=0:01:20
[32m[11/05 16:09:23 d2.evaluation.evaluator]: [0mInference done 4019/4952. 0.0784 s / img. ETA=0:01:15
[32m[11/05 16:09:28 d2.evaluation.evaluator]: [0mInference done 4081/4952. 0.0784 s / img. ETA=0:01:10
[32m[11/05 16:09:33 d2.evaluation.evaluator]: [0mInference done 4143/4952. 0.0784 s / img. ETA=0:01:05
[32m[11/05 16:09:38 d2.evaluation.evaluator]: [0mInference done 4205/4952. 0.0784 s / img. ETA=0:01:00
[32m[11/05 16:09:43 d2.evaluation.evaluator]: [0mInference done 4266/4952. 0.0785 s / img. ETA=0:00:55
[32m[11/05 16:09:48 d2.evaluation.evaluator]: [0mInference done 4328/4952. 0.0785 s / img. ETA=0:00:50
[32m[11/05 16:09:53 d2.evaluation.evaluator]: [0mInference done 4389/4952. 0.0785 s / img. ETA=0:00:45
[32m[11/05 16:09:58 d2.evaluation.evaluator]: [0mInference done 4452/4952. 0.0785 s / img. ETA=0:00:40
[32m[11/05 16:10:03 d2.evaluation.evaluator]: [0mInference done 4514/4952. 0.0785 s / img. ETA=0:00:35
[32m[11/05 16:10:08 d2.evaluation.evaluator]: [0mInference done 4576/4952. 0.0785 s / img. ETA=0:00:30
[32m[11/05 16:10:13 d2.evaluation.evaluator]: [0mInference done 4638/4952. 0.0785 s / img. ETA=0:00:25
[32m[11/05 16:10:18 d2.evaluation.evaluator]: [0mInference done 4699/4952. 0.0786 s / img. ETA=0:00:20
[32m[11/05 16:10:23 d2.evaluation.evaluator]: [0mInference done 4761/4952. 0.0786 s / img. ETA=0:00:15
[32m[11/05 16:10:29 d2.evaluation.evaluator]: [0mInference done 4823/4952. 0.0786 s / img. ETA=0:00:10
[32m[11/05 16:10:34 d2.evaluation.evaluator]: [0mInference done 4886/4952. 0.0786 s / img. ETA=0:00:05
[32m[11/05 16:10:39 d2.evaluation.evaluator]: [0mInference done 4948/4952. 0.0786 s / img. ETA=0:00:00
[32m[11/05 16:10:39 d2.evaluation.evaluator]: [0mTotal inference time: 0:06:39.141057 (0.080683 s / img per device, on 1 devices)
[32m[11/05 16:10:39 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:06:28 (0.078568 s / img per device, on 1 devices)
[32m[11/05 16:10:39 detectron2]: [0mImage level evaluation complete for custom_voc_2007_test
[32m[11/05 16:10:39 detectron2]: [0mResults for custom_voc_2007_test
[32m[11/05 16:10:39 detectron2]: [0mAP for 0: 0.808440625667572
[32m[11/05 16:10:39 detectron2]: [0mAP for 1: 0.7770674228668213
[32m[11/05 16:10:40 detectron2]: [0mAP for 2: 0.7009527683258057
[32m[11/05 16:10:40 detectron2]: [0mAP for 3: 0.5980304479598999
[32m[11/05 16:10:40 detectron2]: [0mAP for 4: 0.5711202621459961
[32m[11/05 16:10:40 detectron2]: [0mAP for 5: 0.7750421762466431
[32m[11/05 16:10:40 detectron2]: [0mAP for 6: 0.7938251495361328
[32m[11/05 16:10:41 detectron2]: [0mAP for 7: 0.88051837682724
[32m[11/05 16:10:41 detectron2]: [0mAP for 8: 0.5696967840194702
[32m[11/05 16:10:41 detectron2]: [0mAP for 9: 0.7881757616996765
[32m[11/05 16:10:41 detectron2]: [0mAP for 10: 0.6557427048683167
[32m[11/05 16:10:41 detectron2]: [0mAP for 11: 0.8663510680198669
[32m[11/05 16:10:41 detectron2]: [0mAP for 12: 0.8503947854042053
[32m[11/05 16:10:41 detectron2]: [0mAP for 13: 0.8271569609642029
[32m[11/05 16:10:42 detectron2]: [0mAP for 14: 0.788679838180542
[32m[11/05 16:10:42 detectron2]: [0mAP for 15: 0.4945039451122284
[32m[11/05 16:10:42 detectron2]: [0mAP for 16: 0.6859263181686401
[32m[11/05 16:10:42 detectron2]: [0mAP for 17: 0.6699324250221252
[32m[11/05 16:10:42 detectron2]: [0mAP for 18: 0.8395748734474182
[32m[11/05 16:10:43 detectron2]: [0mAP for 19: 0.7555124163627625
[32m[11/05 16:10:43 detectron2]: [0mmAP: 0.7348322868347168
[5m[31mWARNING[0m [32m[11/05 16:10:43 d2.data.datasets.coco]: [0m
Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.

[32m[11/05 16:10:43 d2.data.datasets.coco]: [0mLoaded 4952 images in COCO format from protocol/custom_protocols/WR1_Mixed_Unknowns.json
[32m[11/05 16:10:43 d2.data.build]: [0mDistribution of instances among all 21 categories:
[36m|  category  | #instances   |  category   | #instances   |  category   | #instances   |
|:----------:|:-------------|:-----------:|:-------------|:-----------:|:-------------|
|  unknown   | 15235        |  aeroplane  | 0            |   bicycle   | 0            |
|    bird    | 0            |    boat     | 0            |   bottle    | 0            |
|    bus     | 0            |     car     | 0            |     cat     | 0            |
|   chair    | 0            |     cow     | 0            | diningtable | 0            |
|    dog     | 0            |    horse    | 0            |  motorbike  | 0            |
|   person   | 0            | pottedplant | 0            |    sheep    | 0            |
|    sofa    | 0            |    train    | 0            |  tvmonitor  | 0            |
|            |              |             |              |             |              |
|   total    | 15235        |             |              |             |              |[0m
[32m[11/05 16:10:43 d2.data.common]: [0mSerializing 4952 elements to byte tensors and concatenating them all ...
[32m[11/05 16:10:43 d2.data.common]: [0mSerialized dataset takes 8.39 MiB
[32m[11/05 16:10:43 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[11/05 16:10:43 d2.evaluation.evaluator]: [0mStart inference on 4952 images
[32m[11/05 16:10:45 d2.evaluation.evaluator]: [0mInference done 11/4952. 0.0794 s / img. ETA=0:06:40
[32m[11/05 16:10:50 d2.evaluation.evaluator]: [0mInference done 74/4952. 0.0786 s / img. ETA=0:06:33
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[32m[11/05 16:12:51 d2.evaluation.evaluator]: [0mInference done 1552/4952. 0.0798 s / img. ETA=0:04:38
[32m[11/05 16:12:56 d2.evaluation.evaluator]: [0mInference done 1613/4952. 0.0798 s / img. ETA=0:04:33
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[32m[11/05 16:13:56 d2.evaluation.evaluator]: [0mInference done 2350/4952. 0.0799 s / img. ETA=0:03:33
[32m[11/05 16:14:01 d2.evaluation.evaluator]: [0mInference done 2411/4952. 0.0800 s / img. ETA=0:03:28
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[32m[11/05 16:14:27 d2.evaluation.evaluator]: [0mInference done 2718/4952. 0.0800 s / img. ETA=0:03:03
[32m[11/05 16:14:32 d2.evaluation.evaluator]: [0mInference done 2780/4952. 0.0800 s / img. ETA=0:02:58
[32m[11/05 16:14:37 d2.evaluation.evaluator]: [0mInference done 2842/4952. 0.0800 s / img. ETA=0:02:53
[32m[11/05 16:14:42 d2.evaluation.evaluator]: [0mInference done 2904/4952. 0.0800 s / img. ETA=0:02:47
[32m[11/05 16:14:47 d2.evaluation.evaluator]: [0mInference done 2966/4952. 0.0800 s / img. ETA=0:02:42
[32m[11/05 16:14:52 d2.evaluation.evaluator]: [0mInference done 3028/4952. 0.0800 s / img. ETA=0:02:37
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[32m[11/05 16:15:02 d2.evaluation.evaluator]: [0mInference done 3152/4952. 0.0800 s / img. ETA=0:02:27
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[32m[11/05 16:15:32 d2.evaluation.evaluator]: [0mInference done 3521/4952. 0.0799 s / img. ETA=0:01:57
[32m[11/05 16:15:37 d2.evaluation.evaluator]: [0mInference done 3582/4952. 0.0800 s / img. ETA=0:01:52
[32m[11/05 16:15:42 d2.evaluation.evaluator]: [0mInference done 3643/4952. 0.0800 s / img. ETA=0:01:47
[32m[11/05 16:15:47 d2.evaluation.evaluator]: [0mInference done 3705/4952. 0.0800 s / img. ETA=0:01:42
[32m[11/05 16:15:52 d2.evaluation.evaluator]: [0mInference done 3767/4952. 0.0800 s / img. ETA=0:01:37
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[32m[11/05 16:16:03 d2.evaluation.evaluator]: [0mInference done 3889/4952. 0.0800 s / img. ETA=0:01:27
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[32m[11/05 16:16:13 d2.evaluation.evaluator]: [0mInference done 4012/4952. 0.0800 s / img. ETA=0:01:17
[32m[11/05 16:16:18 d2.evaluation.evaluator]: [0mInference done 4073/4952. 0.0800 s / img. ETA=0:01:12
[32m[11/05 16:16:23 d2.evaluation.evaluator]: [0mInference done 4134/4952. 0.0800 s / img. ETA=0:01:07
[32m[11/05 16:16:28 d2.evaluation.evaluator]: [0mInference done 4196/4952. 0.0800 s / img. ETA=0:01:01
[32m[11/05 16:16:33 d2.evaluation.evaluator]: [0mInference done 4256/4952. 0.0800 s / img. ETA=0:00:57
[32m[11/05 16:16:38 d2.evaluation.evaluator]: [0mInference done 4318/4952. 0.0800 s / img. ETA=0:00:51
[32m[11/05 16:16:43 d2.evaluation.evaluator]: [0mInference done 4380/4952. 0.0800 s / img. ETA=0:00:46
[32m[11/05 16:16:48 d2.evaluation.evaluator]: [0mInference done 4442/4952. 0.0800 s / img. ETA=0:00:41
[32m[11/05 16:16:53 d2.evaluation.evaluator]: [0mInference done 4503/4952. 0.0800 s / img. ETA=0:00:36
[32m[11/05 16:16:58 d2.evaluation.evaluator]: [0mInference done 4564/4952. 0.0800 s / img. ETA=0:00:31
[32m[11/05 16:17:03 d2.evaluation.evaluator]: [0mInference done 4625/4952. 0.0800 s / img. ETA=0:00:26
[32m[11/05 16:17:08 d2.evaluation.evaluator]: [0mInference done 4687/4952. 0.0800 s / img. ETA=0:00:21
[32m[11/05 16:17:13 d2.evaluation.evaluator]: [0mInference done 4749/4952. 0.0800 s / img. ETA=0:00:16
[32m[11/05 16:17:18 d2.evaluation.evaluator]: [0mInference done 4810/4952. 0.0800 s / img. ETA=0:00:11
[32m[11/05 16:17:23 d2.evaluation.evaluator]: [0mInference done 4871/4952. 0.0800 s / img. ETA=0:00:06
[32m[11/05 16:17:28 d2.evaluation.evaluator]: [0mInference done 4933/4952. 0.0800 s / img. ETA=0:00:01
[32m[11/05 16:17:30 d2.evaluation.evaluator]: [0mTotal inference time: 0:06:45.566370 (0.081982 s / img per device, on 1 devices)
[32m[11/05 16:17:30 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:06:35 (0.079976 s / img per device, on 1 devices)
[32m[11/05 16:17:30 detectron2]: [0mImage level evaluation complete for WR1_Mixed_Unknowns
[32m[11/05 16:17:30 detectron2]: [0mResults for WR1_Mixed_Unknowns
[32m[11/05 16:17:30 detectron2]: [0mAP for 0: 0.0
[32m[11/05 16:17:30 detectron2]: [0mAP for 1: 0.0
[32m[11/05 16:17:30 detectron2]: [0mAP for 2: 0.0
[32m[11/05 16:17:31 detectron2]: [0mAP for 3: 0.0
[32m[11/05 16:17:31 detectron2]: [0mAP for 4: 0.0
[32m[11/05 16:17:31 detectron2]: [0mAP for 5: 0.0
[32m[11/05 16:17:31 detectron2]: [0mAP for 6: 0.0
[32m[11/05 16:17:31 detectron2]: [0mAP for 7: 0.0
[32m[11/05 16:17:31 detectron2]: [0mAP for 8: 0.0
[32m[11/05 16:17:32 detectron2]: [0mAP for 9: 0.0
[32m[11/05 16:17:32 detectron2]: [0mAP for 10: 0.0
[32m[11/05 16:17:32 detectron2]: [0mAP for 11: 0.0
[32m[11/05 16:17:32 detectron2]: [0mAP for 12: 0.0
[32m[11/05 16:17:32 detectron2]: [0mAP for 13: 0.0
[32m[11/05 16:17:32 detectron2]: [0mAP for 14: 0.0
[32m[11/05 16:17:32 detectron2]: [0mAP for 15: 0.0
[32m[11/05 16:17:33 detectron2]: [0mAP for 16: 0.0
[32m[11/05 16:17:33 detectron2]: [0mAP for 17: 0.0
[32m[11/05 16:17:33 detectron2]: [0mAP for 18: 0.0
[32m[11/05 16:17:33 detectron2]: [0mAP for 19: 0.0
[32m[11/05 16:17:33 detectron2]: [0mmAP: 0.0
[32m[11/05 16:17:33 detectron2]: [0mCombined results for datasets custom_voc_2007_test, WR1_Mixed_Unknowns
[32m[11/05 16:17:33 detectron2]: [0mAP for 0: 0.7963087558746338
[32m[11/05 16:17:33 detectron2]: [0mAP for 1: 0.7675489783287048
[32m[11/05 16:17:33 detectron2]: [0mAP for 2: 0.6266054511070251
[32m[11/05 16:17:33 detectron2]: [0mAP for 3: 0.5853570699691772
[32m[11/05 16:17:33 detectron2]: [0mAP for 4: 0.5181585550308228
[32m[11/05 16:17:33 detectron2]: [0mAP for 5: 0.735167920589447
[32m[11/05 16:17:33 detectron2]: [0mAP for 6: 0.7736961245536804
[32m[11/05 16:17:33 detectron2]: [0mAP for 7: 0.8574226498603821
[32m[11/05 16:17:33 detectron2]: [0mAP for 8: 0.5211853384971619
[32m[11/05 16:17:33 detectron2]: [0mAP for 9: 0.6248680353164673
[32m[11/05 16:17:33 detectron2]: [0mAP for 10: 0.4676196277141571
[32m[11/05 16:17:33 detectron2]: [0mAP for 11: 0.7827640175819397
[32m[11/05 16:17:33 detectron2]: [0mAP for 12: 0.7323558330535889
[32m[11/05 16:17:33 detectron2]: [0mAP for 13: 0.8122451901435852
[32m[11/05 16:17:33 detectron2]: [0mAP for 14: 0.7807108163833618
[32m[11/05 16:17:33 detectron2]: [0mAP for 15: 0.42341139912605286
[32m[11/05 16:17:33 detectron2]: [0mAP for 16: 0.5794138312339783
[32m[11/05 16:17:33 detectron2]: [0mAP for 17: 0.5766554474830627
[32m[11/05 16:17:33 detectron2]: [0mAP for 18: 0.8170406818389893
[32m[11/05 16:17:33 detectron2]: [0mAP for 19: 0.6788268685340881
[32m[11/05 16:17:33 detectron2]: [0mmAP: 0.6728681325912476
[32m[11/05 16:17:35 detectron2]: [0mAP for 0.0: 0.808440625667572
[32m[11/05 16:17:35 detectron2]: [0mAP for 1.0: 0.7770674228668213
[32m[11/05 16:17:35 detectron2]: [0mAP for 2.0: 0.7009527683258057
[32m[11/05 16:17:35 detectron2]: [0mAP for 3.0: 0.5980304479598999
[32m[11/05 16:17:35 detectron2]: [0mAP for 4.0: 0.5711202621459961
[32m[11/05 16:17:35 detectron2]: [0mAP for 5.0: 0.7750421762466431
[32m[11/05 16:17:35 detectron2]: [0mAP for 6.0: 0.7938251495361328
[32m[11/05 16:17:35 detectron2]: [0mAP for 7.0: 0.88051837682724
[32m[11/05 16:17:35 detectron2]: [0mAP for 8.0: 0.5696967840194702
[32m[11/05 16:17:35 detectron2]: [0mAP for 9.0: 0.7881757616996765
[32m[11/05 16:17:35 detectron2]: [0mAP for 10.0: 0.6557427048683167
[32m[11/05 16:17:35 detectron2]: [0mAP for 11.0: 0.8663510680198669
[32m[11/05 16:17:35 detectron2]: [0mAP for 12.0: 0.8503947854042053
[32m[11/05 16:17:35 detectron2]: [0mAP for 13.0: 0.8271569609642029
[32m[11/05 16:17:35 detectron2]: [0mAP for 14.0: 0.788679838180542
[32m[11/05 16:17:35 detectron2]: [0mAP for 15.0: 0.4945039451122284
[32m[11/05 16:17:35 detectron2]: [0mAP for 16.0: 0.6859263181686401
[32m[11/05 16:17:35 detectron2]: [0mAP for 17.0: 0.6699324250221252
[32m[11/05 16:17:35 detectron2]: [0mAP for 18.0: 0.8395748734474182
[32m[11/05 16:17:35 detectron2]: [0mAP for 19.0: 0.7555124163627625
[32m[11/05 16:17:35 detectron2]: [0mmAP: 0.7348322868347168
[32m[11/05 16:17:35 detectron2]: [0mAP for 0.0: 0.8074251413345337
[32m[11/05 16:17:35 detectron2]: [0mAP for 1.0: 0.7757116556167603
[32m[11/05 16:17:35 detectron2]: [0mAP for 2.0: 0.6927441358566284
[32m[11/05 16:17:35 detectron2]: [0mAP for 3.0: 0.5954269170761108
[32m[11/05 16:17:35 detectron2]: [0mAP for 4.0: 0.564703106880188
[32m[11/05 16:17:35 detectron2]: [0mAP for 5.0: 0.7729313373565674
[32m[11/05 16:17:35 detectron2]: [0mAP for 6.0: 0.793378472328186
[32m[11/05 16:17:35 detectron2]: [0mAP for 7.0: 0.8772933483123779
[32m[11/05 16:17:35 detectron2]: [0mAP for 8.0: 0.5648582577705383
[32m[11/05 16:17:35 detectron2]: [0mAP for 9.0: 0.7626721858978271
[32m[11/05 16:17:35 detectron2]: [0mAP for 10.0: 0.6286855936050415
[32m[11/05 16:17:35 detectron2]: [0mAP for 11.0: 0.8550533056259155
[32m[11/05 16:17:35 detectron2]: [0mAP for 12.0: 0.8281086087226868
[32m[11/05 16:17:35 detectron2]: [0mAP for 13.0: 0.8261915445327759
[32m[11/05 16:17:35 detectron2]: [0mAP for 14.0: 0.7882627844810486
[32m[11/05 16:17:35 detectron2]: [0mAP for 15.0: 0.4836746156215668
[32m[11/05 16:17:35 detectron2]: [0mAP for 16.0: 0.6525588631629944
[32m[11/05 16:17:35 detectron2]: [0mAP for 17.0: 0.6623187065124512
[32m[11/05 16:17:35 detectron2]: [0mAP for 18.0: 0.8355180621147156
[32m[11/05 16:17:35 detectron2]: [0mAP for 19.0: 0.7453069686889648
[32m[11/05 16:17:35 detectron2]: [0mmAP: 0.7256411910057068
[32m[11/05 16:17:35 detectron2]: [0mAP for 0.0: 0.8071157336235046
[32m[11/05 16:17:35 detectron2]: [0mAP for 1.0: 0.7746164202690125
[32m[11/05 16:17:35 detectron2]: [0mAP for 2.0: 0.682853102684021
[32m[11/05 16:17:35 detectron2]: [0mAP for 3.0: 0.5943691730499268
[32m[11/05 16:17:35 detectron2]: [0mAP for 4.0: 0.5593255162239075
[32m[11/05 16:17:35 detectron2]: [0mAP for 5.0: 0.7691256999969482
[32m[11/05 16:17:35 detectron2]: [0mAP for 6.0: 0.7894589900970459
[32m[11/05 16:17:35 detectron2]: [0mAP for 7.0: 0.8758093118667603
[32m[11/05 16:17:35 detectron2]: [0mAP for 8.0: 0.5579070448875427
[32m[11/05 16:17:35 detectron2]: [0mAP for 9.0: 0.7415065765380859
[32m[11/05 16:17:35 detectron2]: [0mAP for 10.0: 0.6064231395721436
[32m[11/05 16:17:35 detectron2]: [0mAP for 11.0: 0.8470325469970703
[32m[11/05 16:17:35 detectron2]: [0mAP for 12.0: 0.8122345209121704
[32m[11/05 16:17:35 detectron2]: [0mAP for 13.0: 0.8237715363502502
[32m[11/05 16:17:35 detectron2]: [0mAP for 14.0: 0.7872760891914368
[32m[11/05 16:17:35 detectron2]: [0mAP for 15.0: 0.4740290641784668
[32m[11/05 16:17:35 detectron2]: [0mAP for 16.0: 0.6393458843231201
[32m[11/05 16:17:35 detectron2]: [0mAP for 17.0: 0.6541965007781982
[32m[11/05 16:17:35 detectron2]: [0mAP for 18.0: 0.8327656388282776
[32m[11/05 16:17:35 detectron2]: [0mAP for 19.0: 0.7375192046165466
[32m[11/05 16:17:35 detectron2]: [0mmAP: 0.7183341383934021
[32m[11/05 16:17:35 detectron2]: [0mAP for 0.0: 0.8049968481063843
[32m[11/05 16:17:35 detectron2]: [0mAP for 1.0: 0.7736841440200806
[32m[11/05 16:17:35 detectron2]: [0mAP for 2.0: 0.6740680932998657
[32m[11/05 16:17:35 detectron2]: [0mAP for 3.0: 0.5930513739585876
[32m[11/05 16:17:35 detectron2]: [0mAP for 4.0: 0.5483116507530212
[32m[11/05 16:17:35 detectron2]: [0mAP for 5.0: 0.7650444507598877
[32m[11/05 16:17:35 detectron2]: [0mAP for 6.0: 0.7877101898193359
[32m[11/05 16:17:35 detectron2]: [0mAP for 7.0: 0.8685283660888672
[32m[11/05 16:17:35 detectron2]: [0mAP for 8.0: 0.552049458026886
[32m[11/05 16:17:35 detectron2]: [0mAP for 9.0: 0.7125534415245056
[32m[11/05 16:17:35 detectron2]: [0mAP for 10.0: 0.5847764611244202
[32m[11/05 16:17:35 detectron2]: [0mAP for 11.0: 0.8309327960014343
[32m[11/05 16:17:35 detectron2]: [0mAP for 12.0: 0.7989715337753296
[32m[11/05 16:17:35 detectron2]: [0mAP for 13.0: 0.822482705116272
[32m[11/05 16:17:35 detectron2]: [0mAP for 14.0: 0.7867444753646851
[32m[11/05 16:17:35 detectron2]: [0mAP for 15.0: 0.467607706785202
[32m[11/05 16:17:35 detectron2]: [0mAP for 16.0: 0.624614953994751
[32m[11/05 16:17:35 detectron2]: [0mAP for 17.0: 0.64666748046875
[32m[11/05 16:17:35 detectron2]: [0mAP for 18.0: 0.83025723695755
[32m[11/05 16:17:35 detectron2]: [0mAP for 19.0: 0.7287084460258484
[32m[11/05 16:17:35 detectron2]: [0mmAP: 0.7100880742073059
[32m[11/05 16:17:35 detectron2]: [0mAP for 0.0: 0.8049968481063843
[32m[11/05 16:17:35 detectron2]: [0mAP for 1.0: 0.77252596616745
[32m[11/05 16:17:35 detectron2]: [0mAP for 2.0: 0.6679394245147705
[32m[11/05 16:17:35 detectron2]: [0mAP for 3.0: 0.5917534828186035
[32m[11/05 16:17:35 detectron2]: [0mAP for 4.0: 0.5431528687477112
[32m[11/05 16:17:35 detectron2]: [0mAP for 5.0: 0.763018786907196
[32m[11/05 16:17:35 detectron2]: [0mAP for 6.0: 0.7866762280464172
[32m[11/05 16:17:35 detectron2]: [0mAP for 7.0: 0.8674986362457275
[32m[11/05 16:17:35 detectron2]: [0mAP for 8.0: 0.5460099577903748
[32m[11/05 16:17:35 detectron2]: [0mAP for 9.0: 0.6895670294761658
[32m[11/05 16:17:35 detectron2]: [0mAP for 10.0: 0.5694316029548645
[32m[11/05 16:17:35 detectron2]: [0mAP for 11.0: 0.8236904144287109
[32m[11/05 16:17:35 detectron2]: [0mAP for 12.0: 0.7914615273475647
[32m[11/05 16:17:35 detectron2]: [0mAP for 13.0: 0.8206184506416321
[32m[11/05 16:17:36 detectron2]: [0mAP for 14.0: 0.7857439517974854
[32m[11/05 16:17:36 detectron2]: [0mAP for 15.0: 0.4596113860607147
[32m[11/05 16:17:36 detectron2]: [0mAP for 16.0: 0.6189888119697571
[32m[11/05 16:17:36 detectron2]: [0mAP for 17.0: 0.6328640580177307
[32m[11/05 16:17:36 detectron2]: [0mAP for 18.0: 0.827954888343811
[32m[11/05 16:17:36 detectron2]: [0mAP for 19.0: 0.7236618995666504
[32m[11/05 16:17:36 detectron2]: [0mmAP: 0.7043583989143372
[32m[11/05 16:17:36 detectron2]: [0mAP for 0.0: 0.8033401370048523
[32m[11/05 16:17:36 detectron2]: [0mAP for 1.0: 0.7720251679420471
[32m[11/05 16:17:36 detectron2]: [0mAP for 2.0: 0.662475049495697
[32m[11/05 16:17:36 detectron2]: [0mAP for 3.0: 0.5906260013580322
[32m[11/05 16:17:36 detectron2]: [0mAP for 4.0: 0.5390602350234985
[32m[11/05 16:17:36 detectron2]: [0mAP for 5.0: 0.7610435485839844
[32m[11/05 16:17:36 detectron2]: [0mAP for 6.0: 0.7842805981636047
[32m[11/05 16:17:36 detectron2]: [0mAP for 7.0: 0.8644418716430664
[32m[11/05 16:17:36 detectron2]: [0mAP for 8.0: 0.5414519309997559
[32m[11/05 16:17:36 detectron2]: [0mAP for 9.0: 0.683279275894165
[32m[11/05 16:17:36 detectron2]: [0mAP for 10.0: 0.5558233261108398
[32m[11/05 16:17:36 detectron2]: [0mAP for 11.0: 0.8159647583961487
[32m[11/05 16:17:36 detectron2]: [0mAP for 12.0: 0.7788593769073486
[32m[11/05 16:17:36 detectron2]: [0mAP for 13.0: 0.819983720779419
[32m[11/05 16:17:36 detectron2]: [0mAP for 14.0: 0.7851929068565369
[32m[11/05 16:17:36 detectron2]: [0mAP for 15.0: 0.4529585838317871
[32m[11/05 16:17:36 detectron2]: [0mAP for 16.0: 0.613614022731781
[32m[11/05 16:17:36 detectron2]: [0mAP for 17.0: 0.6216874122619629
[32m[11/05 16:17:36 detectron2]: [0mAP for 18.0: 0.8259549736976624
[32m[11/05 16:17:36 detectron2]: [0mAP for 19.0: 0.7115657329559326
[32m[11/05 16:17:36 detectron2]: [0mmAP: 0.6991814374923706
[32m[11/05 16:17:36 detectron2]: [0mAP for 0.0: 0.7981526851654053
[32m[11/05 16:17:36 detectron2]: [0mAP for 1.0: 0.7715327739715576
[32m[11/05 16:17:36 detectron2]: [0mAP for 2.0: 0.6526334285736084
[32m[11/05 16:17:36 detectron2]: [0mAP for 3.0: 0.5900876522064209
[32m[11/05 16:17:36 detectron2]: [0mAP for 4.0: 0.5349059700965881
[32m[11/05 16:17:36 detectron2]: [0mAP for 5.0: 0.756045937538147
[32m[11/05 16:17:36 detectron2]: [0mAP for 6.0: 0.782234251499176
[32m[11/05 16:17:36 detectron2]: [0mAP for 7.0: 0.8632158637046814
[32m[11/05 16:17:36 detectron2]: [0mAP for 8.0: 0.5364787578582764
[32m[11/05 16:17:36 detectron2]: [0mAP for 9.0: 0.6754810810089111
[32m[11/05 16:17:36 detectron2]: [0mAP for 10.0: 0.536702573299408
[32m[11/05 16:17:36 detectron2]: [0mAP for 11.0: 0.8106412887573242
[32m[11/05 16:17:36 detectron2]: [0mAP for 12.0: 0.7694507837295532
[32m[11/05 16:17:36 detectron2]: [0mAP for 13.0: 0.8182892203330994
[32m[11/05 16:17:36 detectron2]: [0mAP for 14.0: 0.7837110757827759
[32m[11/05 16:17:36 detectron2]: [0mAP for 15.0: 0.4455527365207672
[32m[11/05 16:17:36 detectron2]: [0mAP for 16.0: 0.610645592212677
[32m[11/05 16:17:36 detectron2]: [0mAP for 17.0: 0.6072170734405518
[32m[11/05 16:17:36 detectron2]: [0mAP for 18.0: 0.8240447640419006
[32m[11/05 16:17:36 detectron2]: [0mAP for 19.0: 0.7019145488739014
[32m[11/05 16:17:36 detectron2]: [0mmAP: 0.6934468746185303
[32m[11/05 16:17:36 detectron2]: [0mAP for 0.0: 0.7981526851654053
[32m[11/05 16:17:36 detectron2]: [0mAP for 1.0: 0.7703367471694946
[32m[11/05 16:17:36 detectron2]: [0mAP for 2.0: 0.6466332077980042
[32m[11/05 16:17:36 detectron2]: [0mAP for 3.0: 0.5887355804443359
[32m[11/05 16:17:36 detectron2]: [0mAP for 4.0: 0.5296403169631958
[32m[11/05 16:17:36 detectron2]: [0mAP for 5.0: 0.752450704574585
[32m[11/05 16:17:36 detectron2]: [0mAP for 6.0: 0.7786900997161865
[32m[11/05 16:17:36 detectron2]: [0mAP for 7.0: 0.8613957762718201
[32m[11/05 16:17:36 detectron2]: [0mAP for 8.0: 0.5334128737449646
[32m[11/05 16:17:36 detectron2]: [0mAP for 9.0: 0.667311429977417
[32m[11/05 16:17:36 detectron2]: [0mAP for 10.0: 0.522779107093811
[32m[11/05 16:17:36 detectron2]: [0mAP for 11.0: 0.8044999837875366
[32m[11/05 16:17:36 detectron2]: [0mAP for 12.0: 0.7649497389793396
[32m[11/05 16:17:36 detectron2]: [0mAP for 13.0: 0.8165357112884521
[32m[11/05 16:17:36 detectron2]: [0mAP for 14.0: 0.7826014161109924
[32m[11/05 16:17:36 detectron2]: [0mAP for 15.0: 0.43944063782691956
[32m[11/05 16:17:36 detectron2]: [0mAP for 16.0: 0.6019951701164246
[32m[11/05 16:17:36 detectron2]: [0mAP for 17.0: 0.6002336740493774
[32m[11/05 16:17:36 detectron2]: [0mAP for 18.0: 0.8227123022079468
[32m[11/05 16:17:36 detectron2]: [0mAP for 19.0: 0.6941025257110596
[32m[11/05 16:17:36 detectron2]: [0mmAP: 0.6888304948806763
[32m[11/05 16:17:36 detectron2]: [0mAP for 0.0: 0.7972252368927002
[32m[11/05 16:17:36 detectron2]: [0mAP for 1.0: 0.7689640522003174
[32m[11/05 16:17:36 detectron2]: [0mAP for 2.0: 0.6400614380836487
[32m[11/05 16:17:36 detectron2]: [0mAP for 3.0: 0.5880363583564758
[32m[11/05 16:17:36 detectron2]: [0mAP for 4.0: 0.5246222019195557
[32m[11/05 16:17:36 detectron2]: [0mAP for 5.0: 0.7388509511947632
[32m[11/05 16:17:36 detectron2]: [0mAP for 6.0: 0.7777529358863831
[32m[11/05 16:17:36 detectron2]: [0mAP for 7.0: 0.859672486782074
[32m[11/05 16:17:36 detectron2]: [0mAP for 8.0: 0.5305948257446289
[32m[11/05 16:17:36 detectron2]: [0mAP for 9.0: 0.6403424739837646
[32m[11/05 16:17:36 detectron2]: [0mAP for 10.0: 0.49243098497390747
[32m[11/05 16:17:36 detectron2]: [0mAP for 11.0: 0.793095052242279
[32m[11/05 16:17:36 detectron2]: [0mAP for 12.0: 0.7520943284034729
[32m[11/05 16:17:36 detectron2]: [0mAP for 13.0: 0.814483642578125
[32m[11/05 16:17:36 detectron2]: [0mAP for 14.0: 0.7821661233901978
[32m[11/05 16:17:36 detectron2]: [0mAP for 15.0: 0.43383291363716125
[32m[11/05 16:17:36 detectron2]: [0mAP for 16.0: 0.5963069200515747
[32m[11/05 16:17:36 detectron2]: [0mAP for 17.0: 0.5941689014434814
[32m[11/05 16:17:36 detectron2]: [0mAP for 18.0: 0.8211178183555603
[32m[11/05 16:17:36 detectron2]: [0mAP for 19.0: 0.6900832653045654
[32m[11/05 16:17:36 detectron2]: [0mmAP: 0.6817951798439026
[32m[11/05 16:17:37 detectron2]: [0mAP for 0.0: 0.7972252368927002
[32m[11/05 16:17:37 detectron2]: [0mAP for 1.0: 0.7681931853294373
[32m[11/05 16:17:37 detectron2]: [0mAP for 2.0: 0.631178617477417
[32m[11/05 16:17:37 detectron2]: [0mAP for 3.0: 0.5863597393035889
[32m[11/05 16:17:37 detectron2]: [0mAP for 4.0: 0.5224187970161438
[32m[11/05 16:17:37 detectron2]: [0mAP for 5.0: 0.7380679845809937
[32m[11/05 16:17:37 detectron2]: [0mAP for 6.0: 0.7751660943031311
[32m[11/05 16:17:37 detectron2]: [0mAP for 7.0: 0.8585228323936462
[32m[11/05 16:17:37 detectron2]: [0mAP for 8.0: 0.5277168154716492
[32m[11/05 16:17:37 detectron2]: [0mAP for 9.0: 0.6364344954490662
[32m[11/05 16:17:37 detectron2]: [0mAP for 10.0: 0.47778645157814026
[32m[11/05 16:17:37 detectron2]: [0mAP for 11.0: 0.788770318031311
[32m[11/05 16:17:37 detectron2]: [0mAP for 12.0: 0.7422476410865784
[32m[11/05 16:17:37 detectron2]: [0mAP for 13.0: 0.8132762312889099
[32m[11/05 16:17:37 detectron2]: [0mAP for 14.0: 0.7815334796905518
[32m[11/05 16:17:37 detectron2]: [0mAP for 15.0: 0.4281567335128784
[32m[11/05 16:17:37 detectron2]: [0mAP for 16.0: 0.5900235772132874
[32m[11/05 16:17:37 detectron2]: [0mAP for 17.0: 0.5849940776824951
[32m[11/05 16:17:37 detectron2]: [0mAP for 18.0: 0.8193250894546509
[32m[11/05 16:17:37 detectron2]: [0mAP for 19.0: 0.6870383024215698
[32m[11/05 16:17:37 detectron2]: [0mmAP: 0.6777218580245972
[32m[11/05 16:17:37 detectron2]: [0mAP for 0.0: 0.7963087558746338
[32m[11/05 16:17:37 detectron2]: [0mAP for 1.0: 0.7675489783287048
[32m[11/05 16:17:37 detectron2]: [0mAP for 2.0: 0.6266054511070251
[32m[11/05 16:17:37 detectron2]: [0mAP for 3.0: 0.5853570699691772
[32m[11/05 16:17:37 detectron2]: [0mAP for 4.0: 0.5181585550308228
[32m[11/05 16:17:37 detectron2]: [0mAP for 5.0: 0.735167920589447
[32m[11/05 16:17:37 detectron2]: [0mAP for 6.0: 0.7736961245536804
[32m[11/05 16:17:37 detectron2]: [0mAP for 7.0: 0.8574226498603821
[32m[11/05 16:17:37 detectron2]: [0mAP for 8.0: 0.5211853384971619
[32m[11/05 16:17:37 detectron2]: [0mAP for 9.0: 0.6248680353164673
[32m[11/05 16:17:37 detectron2]: [0mAP for 10.0: 0.4676196277141571
[32m[11/05 16:17:37 detectron2]: [0mAP for 11.0: 0.7827640175819397
[32m[11/05 16:17:37 detectron2]: [0mAP for 12.0: 0.7323558330535889
[32m[11/05 16:17:37 detectron2]: [0mAP for 13.0: 0.8122451901435852
[32m[11/05 16:17:37 detectron2]: [0mAP for 14.0: 0.7807108163833618
[32m[11/05 16:17:37 detectron2]: [0mAP for 15.0: 0.42341139912605286
[32m[11/05 16:17:37 detectron2]: [0mAP for 16.0: 0.5794138312339783
[32m[11/05 16:17:37 detectron2]: [0mAP for 17.0: 0.5766554474830627
[32m[11/05 16:17:37 detectron2]: [0mAP for 18.0: 0.8170406818389893
[32m[11/05 16:17:37 detectron2]: [0mAP for 19.0: 0.6788268685340881
[32m[11/05 16:17:37 detectron2]: [0mmAP: 0.6728681325912476

Hello Deepak, I am unable to replicate the issue you are facing about the multi-gpu evaluation which I have verified both by observing the gpu usage in nvidia-smi and from the logs

[11/22 01:07:31 d2.evaluation.evaluator]: Total inference time: 0:01:12.698808 (0.118402 s / img per device, on 8 devices)
[11/22 01:07:31 d2.evaluation.evaluator]: Total inference pure compute time: 0:01:10 (0.114994 s / img per device, on 8 devices)

If you are still facing this issue, please make sure you are on the most recent commit and share the exact command used along with a screen shot of nvidia-smi.

Regarding the meaning of the log outputs, please have a look at the WIC curve in Figure 5 of the paper. Each average precision (AP) value corresponds to a specific object's AP on a specific wilderness level on the x-axis as in Figure 5. These values are later used in the formula mentioned in the paper to calculate the wilderness impact. Also please make sure you are on the latest commit for improved log output, your previous log output corresponds to a stale commit. I hope this helps.

Hello Akshay, Thank you for the prompt response. I noticed that my previous issue submission was on a stale commit. I updated my cloned repo to the latest commit and tried to reproduce the result on Faster-RCNN. [Table. 1 of your paper] Table 1. shows that for Faster RCNN, we should have 81.86% mAP on the PASCAL test, and 77.09% for WR1. I tried the following command for training:

python main.py --num-gpus 4 --config-file training_configs/faster_rcnn_R_50_FPN.yaml

During test it throws the error. Notice that its on 4 GPUS. Error log:

==========================================
SLURM_JOB_ID = 265254
SLURM_NODELIST = gnode02
SLURM_JOB_GPUS = 0,1,2,3
==========================================
Command Line Args: Namespace(config_file='training_configs/faster_rcnn_R_50_FPN.yaml', dist_url='tcp://127.0.0.1:50712', eval_only=False, machine_rank=0, num_gpus=4, num_machines=1, opts=[], resume=False)
[32m[11/26 15:46:20 detectron2]: [0mRank of current process: 0. World size: 4
[32m[11/26 15:46:22 detectron2]: [0mEnvironment info:
----------------------  -------------------------------------------------------------------------------
sys.platform            linux
Python                  3.7.3 | packaged by conda-forge | (default, Jul  1 2019, 21:52:21) [GCC 7.3.0]
numpy                   1.16.4
detectron2              0.3 @/home/dksingh/inseg/detectron2/detectron2
Compiler                GCC 5.5
CUDA compiler           CUDA 10.2
detectron2 arch flags   6.1
DETECTRON2_ENV_MODULE   <not set>
PyTorch                 1.6.0 @/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torch
PyTorch debug build     False
GPU available           True
GPU 0,1,2,3             GeForce GTX 1080 Ti (arch=6.1)
CUDA_HOME               /usr/local/cuda-10.2
Pillow                  7.1.2
torchvision             0.7.0 @/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torchvision
torchvision arch flags  3.5, 5.0, 6.0, 7.0, 7.5
fvcore                  0.1.2.post20201103
cv2                     4.1.0
----------------------  -------------------------------------------------------------------------------
PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.2
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
  - CuDNN 7.6.5
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF, 

[32m[11/26 15:46:22 detectron2]: [0mCommand line arguments: Namespace(config_file='training_configs/faster_rcnn_R_50_FPN.yaml', dist_url='tcp://127.0.0.1:50712', eval_only=False, machine_rank=0, num_gpus=4, num_machines=1, opts=[], resume=False)
[32m[11/26 15:46:22 detectron2]: [0mContents of args.config_file=training_configs/faster_rcnn_R_50_FPN.yaml:
# Configuration for training with 4 gpus
_BASE_: "~/detectron2/detectron2/configs/Base-RCNN-FPN.yaml"
MODEL:
  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
  MASK_ON: False
  RESNETS:
    DEPTH: 50
  ROI_HEADS:
    NUM_CLASSES: 20
DATASETS:
  TRAIN: ('custom_voc_2007_train','custom_voc_2007_val','custom_voc_2012_train','custom_voc_2012_val',)
  TEST: ('custom_voc_2007_test','WR1_Mixed_Unknowns')
#  TEST: ('custom_voc_2007_test','Mixed_Unknowns')

SOLVER:
  BASE_LR: 0.01
  STEPS: (24000, 32000)
  MAX_ITER: 36000
  WARMUP_ITERS: 100
OUTPUT_DIR: /ssd_scratch/cvit/dksingh/overlooked_elephant/fasterrcnn/fasterrcnn_36k_iters/
[32m[11/26 15:46:22 detectron2]: [0mRunning with full config:
CUDNN_BENCHMARK: False
DATALOADER:
  ASPECT_RATIO_GROUPING: True
  FILTER_EMPTY_ANNOTATIONS: True
  NUM_WORKERS: 4
  REPEAT_THRESHOLD: 0.0
  SAMPLER_TRAIN: TrainingSampler
DATASETS:
  PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000
  PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000
  PROPOSAL_FILES_TEST: ()
  PROPOSAL_FILES_TRAIN: ()
  TEST: ('custom_voc_2007_test', 'WR1_Mixed_Unknowns')
  TRAIN: ('custom_voc_2007_train', 'custom_voc_2007_val', 'custom_voc_2012_train', 'custom_voc_2012_val')
GLOBAL:
  HACK: 1.0
INPUT:
  CROP:
    ENABLED: False
    SIZE: [0.9, 0.9]
    TYPE: relative_range
  FORMAT: BGR
  MASK_FORMAT: polygon
  MAX_SIZE_TEST: 1333
  MAX_SIZE_TRAIN: 1333
  MIN_SIZE_TEST: 800
  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
  MIN_SIZE_TRAIN_SAMPLING: choice
  RANDOM_FLIP: horizontal
MODEL:
  ANCHOR_GENERATOR:
    ANGLES: [[-90, 0, 90]]
    ASPECT_RATIOS: [[0.5, 1.0, 2.0]]
    NAME: DefaultAnchorGenerator
    OFFSET: 0.0
    SIZES: [[32], [64], [128], [256], [512]]
  BACKBONE:
    FREEZE_AT: 2
    NAME: build_resnet_fpn_backbone
  DEVICE: cuda
  FPN:
    FUSE_TYPE: sum
    IN_FEATURES: ['res2', 'res3', 'res4', 'res5']
    NORM: 
    OUT_CHANNELS: 256
  KEYPOINT_ON: False
  LOAD_PROPOSALS: False
  MASK_ON: False
  META_ARCHITECTURE: GeneralizedRCNN
  PANOPTIC_FPN:
    COMBINE:
      ENABLED: True
      INSTANCES_CONFIDENCE_THRESH: 0.5
      OVERLAP_THRESH: 0.5
      STUFF_AREA_LIMIT: 4096
    INSTANCE_LOSS_WEIGHT: 1.0
  PIXEL_MEAN: [103.53, 116.28, 123.675]
  PIXEL_STD: [1.0, 1.0, 1.0]
  PROPOSAL_GENERATOR:
    MIN_SIZE: 0
    NAME: RPN
  RESNETS:
    DEFORM_MODULATED: False
    DEFORM_NUM_GROUPS: 1
    DEFORM_ON_PER_STAGE: [False, False, False, False]
    DEPTH: 50
    NORM: FrozenBN
    NUM_GROUPS: 1
    OUT_FEATURES: ['res2', 'res3', 'res4', 'res5']
    RES2_OUT_CHANNELS: 256
    RES5_DILATION: 1
    STEM_OUT_CHANNELS: 64
    STRIDE_IN_1X1: True
    WIDTH_PER_GROUP: 64
  RETINANET:
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)
    FOCAL_LOSS_ALPHA: 0.25
    FOCAL_LOSS_GAMMA: 2.0
    IN_FEATURES: ['p3', 'p4', 'p5', 'p6', 'p7']
    IOU_LABELS: [0, -1, 1]
    IOU_THRESHOLDS: [0.4, 0.5]
    NMS_THRESH_TEST: 0.5
    NORM: 
    NUM_CLASSES: 80
    NUM_CONVS: 4
    PRIOR_PROB: 0.01
    SCORE_THRESH_TEST: 0.05
    SMOOTH_L1_LOSS_BETA: 0.1
    TOPK_CANDIDATES_TEST: 1000
  ROI_BOX_CASCADE_HEAD:
    BBOX_REG_WEIGHTS: ((10.0, 10.0, 5.0, 5.0), (20.0, 20.0, 10.0, 10.0), (30.0, 30.0, 15.0, 15.0))
    IOUS: (0.5, 0.6, 0.7)
  ROI_BOX_HEAD:
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_LOSS_WEIGHT: 1.0
    BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
    CLS_AGNOSTIC_BBOX_REG: False
    CONV_DIM: 256
    FC_DIM: 1024
    NAME: FastRCNNConvFCHead
    NORM: 
    NUM_CONV: 0
    NUM_FC: 2
    POOLER_RESOLUTION: 7
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
    SMOOTH_L1_BETA: 0.0
    TRAIN_ON_PRED_BOXES: False
  ROI_HEADS:
    BATCH_SIZE_PER_IMAGE: 512
    IN_FEATURES: ['p2', 'p3', 'p4', 'p5']
    IOU_LABELS: [0, 1]
    IOU_THRESHOLDS: [0.5]
    NAME: StandardROIHeads
    NMS_THRESH_TEST: 0.5
    NUM_CLASSES: 20
    POSITIVE_FRACTION: 0.25
    PROPOSAL_APPEND_GT: True
    SCORE_THRESH_TEST: 0.05
  ROI_KEYPOINT_HEAD:
    CONV_DIMS: (512, 512, 512, 512, 512, 512, 512, 512)
    LOSS_WEIGHT: 1.0
    MIN_KEYPOINTS_PER_IMAGE: 1
    NAME: KRCNNConvDeconvUpsampleHead
    NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: True
    NUM_KEYPOINTS: 17
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
  ROI_MASK_HEAD:
    CLS_AGNOSTIC_MASK: False
    CONV_DIM: 256
    NAME: MaskRCNNConvUpsampleHead
    NORM: 
    NUM_CONV: 4
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
  RPN:
    BATCH_SIZE_PER_IMAGE: 256
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_LOSS_WEIGHT: 1.0
    BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)
    BOUNDARY_THRESH: -1
    HEAD_NAME: StandardRPNHead
    IN_FEATURES: ['p2', 'p3', 'p4', 'p5', 'p6']
    IOU_LABELS: [0, -1, 1]
    IOU_THRESHOLDS: [0.3, 0.7]
    LOSS_WEIGHT: 1.0
    NMS_THRESH: 0.7
    POSITIVE_FRACTION: 0.5
    POST_NMS_TOPK_TEST: 1000
    POST_NMS_TOPK_TRAIN: 1000
    PRE_NMS_TOPK_TEST: 1000
    PRE_NMS_TOPK_TRAIN: 2000
    SMOOTH_L1_BETA: 0.0
  SEM_SEG_HEAD:
    COMMON_STRIDE: 4
    CONVS_DIM: 128
    IGNORE_VALUE: 255
    IN_FEATURES: ['p2', 'p3', 'p4', 'p5']
    LOSS_WEIGHT: 1.0
    NAME: SemSegFPNHead
    NORM: GN
    NUM_CLASSES: 54
  WEIGHTS: detectron2://ImageNetPretrained/MSRA/R-50.pkl
OUTPUT_DIR: /ssd_scratch/cvit/dksingh/overlooked_elephant/fasterrcnn/fasterrcnn_36k_iters/
SEED: -1
SOLVER:
  AMP:
    ENABLED: False
  BASE_LR: 0.01
  BIAS_LR_FACTOR: 1.0
  CHECKPOINT_PERIOD: 5000
  CLIP_GRADIENTS:
    CLIP_TYPE: value
    CLIP_VALUE: 1.0
    ENABLED: False
    NORM_TYPE: 2.0
  GAMMA: 0.1
  IMS_PER_BATCH: 16
  LR_SCHEDULER_NAME: WarmupMultiStepLR
  MAX_ITER: 36000
  MOMENTUM: 0.9
  NESTEROV: False
  REFERENCE_WORLD_SIZE: 0
  STEPS: (24000, 32000)
  WARMUP_FACTOR: 0.001
  WARMUP_ITERS: 100
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.0001
  WEIGHT_DECAY_BIAS: 0.0001
  WEIGHT_DECAY_NORM: 0.0
TEST:
  AUG:
    ENABLED: False
    FLIP: True
    MAX_SIZE: 4000
    MIN_SIZES: (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
  DETECTIONS_PER_IMAGE: 100
  EVAL_PERIOD: 0
  EXPECTED_RESULTS: []
  KEYPOINT_OKS_SIGMAS: []
  PRECISE_BN:
    ENABLED: False
    NUM_ITER: 200
VERSION: 2
VIS_PERIOD: 0
[32m[11/26 15:46:22 detectron2]: [0mFull config saved to /ssd_scratch/cvit/dksingh/overlooked_elephant/fasterrcnn/fasterrcnn_36k_iters/config.yaml
[32m[11/26 15:46:22 d2.utils.env]: [0mUsing a generated random seed 22657084
[32m[11/26 15:46:23 detectron2]: [0mModel:
GeneralizedRCNN(
  (backbone): FPN(
    (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (top_block): LastLevelMaxPool()
    (bottom_up): ResNet(
      (stem): BasicStem(
        (conv1): Conv2d(
          3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
        )
      )
      (res2): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv1): Conv2d(
            64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
      )
      (res3): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv1): Conv2d(
            256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (3): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
      )
      (res4): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
          (conv1): Conv2d(
            512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (3): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (4): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (5): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
      )
      (res5): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
          (conv1): Conv2d(
            1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
      )
    )
  )
  (proposal_generator): RPN(
    (rpn_head): StandardRPNHead(
      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
      (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
    )
    (anchor_generator): DefaultAnchorGenerator(
      (cell_anchors): BufferList()
    )
  )
  (roi_heads): StandardROIHeads(
    (box_pooler): ROIPooler(
      (level_poolers): ModuleList(
        (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)
        (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)
        (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
        (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
      )
    )
    (box_head): FastRCNNConvFCHead(
      (flatten): Flatten()
      (fc1): Linear(in_features=12544, out_features=1024, bias=True)
      (fc_relu1): ReLU()
      (fc2): Linear(in_features=1024, out_features=1024, bias=True)
      (fc_relu2): ReLU()
    )
    (box_predictor): FastRCNNOutputLayers(
      (cls_score): Linear(in_features=1024, out_features=21, bias=True)
      (bbox_pred): Linear(in_features=1024, out_features=80, bias=True)
    )
  )
)
[32m[11/26 15:46:23 fvcore.common.checkpoint]: [0mLoading checkpoint from detectron2://ImageNetPretrained/MSRA/R-50.pkl
[32m[11/26 15:46:24 fvcore.common.file_io]: [0mURL https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl cached in /home/dksingh/.torch/fvcore_cache/detectron2/ImageNetPretrained/MSRA/R-50.pkl
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mRemapping C2 weights ......
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res2.0.conv1.norm.bias            loaded from res2_0_branch2a_bn_beta           of shape (64,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res2.0.conv1.norm.running_mean    loaded from res2_0_branch2a_bn_running_mean   of shape (64,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res2.0.conv1.norm.running_var     loaded from res2_0_branch2a_bn_running_var    of shape (64,)
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[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.0.shortcut.weight            loaded from res5_0_branch1_w                  of shape (2048, 1024, 1, 1)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv1.norm.bias            loaded from res5_1_branch2a_bn_beta           of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv1.norm.running_mean    loaded from res5_1_branch2a_bn_running_mean   of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv1.norm.running_var     loaded from res5_1_branch2a_bn_running_var    of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv1.norm.weight          loaded from res5_1_branch2a_bn_gamma          of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv1.weight               loaded from res5_1_branch2a_w                 of shape (512, 2048, 1, 1)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv2.norm.bias            loaded from res5_1_branch2b_bn_beta           of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv2.norm.running_mean    loaded from res5_1_branch2b_bn_running_mean   of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv2.norm.running_var     loaded from res5_1_branch2b_bn_running_var    of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv2.norm.weight          loaded from res5_1_branch2b_bn_gamma          of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv2.weight               loaded from res5_1_branch2b_w                 of shape (512, 512, 3, 3)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv3.norm.bias            loaded from res5_1_branch2c_bn_beta           of shape (2048,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv3.norm.running_mean    loaded from res5_1_branch2c_bn_running_mean   of shape (2048,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv3.norm.running_var     loaded from res5_1_branch2c_bn_running_var    of shape (2048,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv3.norm.weight          loaded from res5_1_branch2c_bn_gamma          of shape (2048,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.1.conv3.weight               loaded from res5_1_branch2c_w                 of shape (2048, 512, 1, 1)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv1.norm.bias            loaded from res5_2_branch2a_bn_beta           of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv1.norm.running_mean    loaded from res5_2_branch2a_bn_running_mean   of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv1.norm.running_var     loaded from res5_2_branch2a_bn_running_var    of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv1.norm.weight          loaded from res5_2_branch2a_bn_gamma          of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv1.weight               loaded from res5_2_branch2a_w                 of shape (512, 2048, 1, 1)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv2.norm.bias            loaded from res5_2_branch2b_bn_beta           of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv2.norm.running_mean    loaded from res5_2_branch2b_bn_running_mean   of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv2.norm.running_var     loaded from res5_2_branch2b_bn_running_var    of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv2.norm.weight          loaded from res5_2_branch2b_bn_gamma          of shape (512,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv2.weight               loaded from res5_2_branch2b_w                 of shape (512, 512, 3, 3)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv3.norm.bias            loaded from res5_2_branch2c_bn_beta           of shape (2048,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv3.norm.running_mean    loaded from res5_2_branch2c_bn_running_mean   of shape (2048,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv3.norm.running_var     loaded from res5_2_branch2c_bn_running_var    of shape (2048,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv3.norm.weight          loaded from res5_2_branch2c_bn_gamma          of shape (2048,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.res5.2.conv3.weight               loaded from res5_2_branch2c_w                 of shape (2048, 512, 1, 1)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.stem.conv1.norm.bias              loaded from res_conv1_bn_beta                 of shape (64,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.stem.conv1.norm.running_mean      loaded from res_conv1_bn_running_mean         of shape (64,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.stem.conv1.norm.running_var       loaded from res_conv1_bn_running_var          of shape (64,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.stem.conv1.norm.weight            loaded from res_conv1_bn_gamma                of shape (64,)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mbackbone.bottom_up.stem.conv1.weight                 loaded from conv1_w                           of shape (64, 3, 7, 7)
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mSome model parameters or buffers are not found in the checkpoint:
  [34mbackbone.fpn_lateral2.{bias, weight}[0m
  [34mbackbone.fpn_lateral3.{bias, weight}[0m
  [34mbackbone.fpn_lateral4.{bias, weight}[0m
  [34mbackbone.fpn_lateral5.{bias, weight}[0m
  [34mbackbone.fpn_output2.{bias, weight}[0m
  [34mbackbone.fpn_output3.{bias, weight}[0m
  [34mbackbone.fpn_output4.{bias, weight}[0m
  [34mbackbone.fpn_output5.{bias, weight}[0m
  [34mpixel_mean[0m
  [34mpixel_std[0m
  [34mproposal_generator.anchor_generator.cell_anchors.{0, 1, 2, 3, 4}[0m
  [34mproposal_generator.rpn_head.anchor_deltas.{bias, weight}[0m
  [34mproposal_generator.rpn_head.conv.{bias, weight}[0m
  [34mproposal_generator.rpn_head.objectness_logits.{bias, weight}[0m
  [34mroi_heads.box_head.fc1.{bias, weight}[0m
  [34mroi_heads.box_head.fc2.{bias, weight}[0m
  [34mroi_heads.box_predictor.bbox_pred.{bias, weight}[0m
  [34mroi_heads.box_predictor.cls_score.{bias, weight}[0m
[32m[11/26 15:46:24 d2.checkpoint.c2_model_loading]: [0mThe checkpoint state_dict contains keys that are not used by the model:
  [35mfc1000_b[0m
  [35mfc1000_w[0m
  [35mconv1_b[0m
[32m[11/26 15:46:25 d2.data.datasets.coco]: [0mLoaded 2501 images in COCO format from protocol/custom_protocols/custom_voc_2007_train.json
[32m[11/26 15:46:26 d2.data.datasets.coco]: [0mLoaded 2510 images in COCO format from protocol/custom_protocols/custom_voc_2007_val.json
[32m[11/26 15:46:26 d2.data.datasets.coco]: [0mLoaded 5717 images in COCO format from protocol/custom_protocols/custom_voc_2012_train.json
[32m[11/26 15:46:26 d2.data.datasets.coco]: [0mLoaded 5823 images in COCO format from protocol/custom_protocols/custom_voc_2012_val.json
[32m[11/26 15:46:26 d2.data.build]: [0mRemoved 0 images with no usable annotations. 16551 images left.
[32m[11/26 15:46:26 d2.data.build]: [0mDistribution of instances among all 20 categories:
[36m|  category   | #instances   |  category   | #instances   |  category  | #instances   |
|:-----------:|:-------------|:-----------:|:-------------|:----------:|:-------------|
|  aeroplane  | 1285         |   bicycle   | 1208         |    bird    | 1820         |
|    boat     | 1397         |   bottle    | 2116         |    bus     | 909          |
|     car     | 4008         |     cat     | 1616         |   chair    | 4338         |
|     cow     | 1058         | diningtable | 1057         |    dog     | 2079         |
|    horse    | 1156         |  motorbike  | 1141         |   person   | 15576        |
| pottedplant | 1724         |    sheep    | 1347         |    sofa    | 1211         |
|    train    | 984          |  tvmonitor  | 1193         |            |              |
|    total    | 47223        |             |              |            |              |[0m
[32m[11/26 15:46:26 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()]
[32m[11/26 15:46:26 d2.data.build]: [0mUsing training sampler TrainingSampler
[32m[11/26 15:46:26 d2.data.common]: [0mSerializing 16551 elements to byte tensors and concatenating them all ...
[32m[11/26 15:46:27 d2.data.common]: [0mSerialized dataset takes 6.17 MiB
[32m[11/26 15:46:27 detectron2]: [0mStarting training from iteration 0
[32m[11/26 15:46:45 d2.utils.events]: [0m iter: 20  total_loss: 0.7846  loss_box_reg: 0.02849  loss_cls: 0.2678  loss_rpn_cls: 0.5485  loss_rpn_loc: 0.04709  lr: 0.0019081  max_mem: 5120M
[32m[11/26 22:08:10 d2.utils.events]: [0m eta: 0:00:11  iter: 35980  total_loss: 0.1699  loss_box_reg: 0.1006  loss_cls: 0.05196  loss_rpn_cls: 0.002539  loss_rpn_loc: 0.01681  lr: 0.0001  max_mem: 5120M
[32m[11/26 22:08:22 fvcore.common.checkpoint]: [0mSaving checkpoint to /ssd_scratch/cvit/dksingh/overlooked_elephant/fasterrcnn/fasterrcnn_36k_iters/model_final.pth
[32m[11/26 22:08:24 fvcore.common.checkpoint]: [0mSaving checkpoint to /ssd_scratch/cvit/dksingh/overlooked_elephant/fasterrcnn/fasterrcnn_36k_iters/model_final.pth
/home/dksingh/inseg/detectron2/detectron2/modeling/roi_heads/fast_rcnn.py:217: UserWarning: This overload of nonzero is deprecated:
    nonzero()
Consider using one of the following signatures instead:
    nonzero(*, bool as_tuple) (Triggered internally at  /opt/conda/conda-bld/pytorch_1595629427478/work/torch/csrc/utils/python_arg_parser.cpp:766.)
  num_fg = fg_inds.nonzero().numel()
/home/dksingh/inseg/detectron2/detectron2/modeling/roi_heads/fast_rcnn.py:217: UserWarning: This overload of nonzero is deprecated:
    nonzero()
Consider using one of the following signatures instead:
    nonzero(*, bool as_tuple) (Triggered internally at  /opt/conda/conda-bld/pytorch_1595629427478/work/torch/csrc/utils/python_arg_parser.cpp:766.)
  num_fg = fg_inds.nonzero().numel()
/home/dksingh/inseg/detectron2/detectron2/modeling/roi_heads/fast_rcnn.py:217: UserWarning: This overload of nonzero is deprecated:
    nonzero()
Consider using one of the following signatures instead:
    nonzero(*, bool as_tuple) (Triggered internally at  /opt/conda/conda-bld/pytorch_1595629427478/work/torch/csrc/utils/python_arg_parser.cpp:766.)
  num_fg = fg_inds.nonzero().numel()
[5m[31mWARNING[0m [32m[11/26 22:08:26 d2.data.datasets.coco]: [0m
Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.

[32m[11/26 22:08:26 d2.data.datasets.coco]: [0mLoaded 4952 images in COCO format from protocol/custom_protocols/custom_voc_2007_test.json
[32m[11/26 22:08:26 d2.data.build]: [0mDistribution of instances among all 21 categories:
[36m|  category  | #instances   |  category   | #instances   |  category   | #instances   |
|:----------:|:-------------|:-----------:|:-------------|:-----------:|:-------------|
|  unknown   | 0            |  aeroplane  | 311          |   bicycle   | 389          |
|    bird    | 576          |    boat     | 393          |   bottle    | 657          |
|    bus     | 254          |     car     | 1541         |     cat     | 370          |
|   chair    | 1374         |     cow     | 329          | diningtable | 299          |
|    dog     | 530          |    horse    | 395          |  motorbike  | 369          |
|   person   | 5227         | pottedplant | 592          |    sheep    | 311          |
|    sofa    | 396          |    train    | 302          |  tvmonitor  | 361          |
|            |              |             |              |             |              |
|   total    | 14976        |             |              |             |              |[0m
[32m[11/26 22:08:26 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[11/26 22:08:26 d2.data.common]: [0mSerializing 4952 elements to byte tensors and concatenating them all ...
[32m[11/26 22:08:26 d2.data.common]: [0mSerialized dataset takes 1.87 MiB
[32m[11/26 22:08:26 d2.evaluation.evaluator]: [0mStart inference on 1238 images
/home/dksingh/inseg/detectron2/detectron2/modeling/roi_heads/fast_rcnn.py:217: UserWarning: This overload of nonzero is deprecated:
    nonzero()
Consider using one of the following signatures instead:
    nonzero(*, bool as_tuple) (Triggered internally at  /opt/conda/conda-bld/pytorch_1595629427478/work/torch/csrc/utils/python_arg_parser.cpp:766.)
  num_fg = fg_inds.nonzero().numel()
[32m[11/26 22:08:33 d2.evaluation.evaluator]: [0mInference done 11/1238. 0.0654 s / img. ETA=0:01:22
[32m[11/26 22:08:38 d2.evaluation.evaluator]: [0mInference done 86/1238. 0.0650 s / img. ETA=0:01:17
[32m[11/26 22:08:43 d2.evaluation.evaluator]: [0mInference done 162/1238. 0.0644 s / img. ETA=0:01:11
[32m[11/26 22:08:48 d2.evaluation.evaluator]: [0mInference done 238/1238. 0.0642 s / img. ETA=0:01:06
[32m[11/26 22:08:53 d2.evaluation.evaluator]: [0mInference done 315/1238. 0.0640 s / img. ETA=0:01:01
[32m[11/26 22:08:58 d2.evaluation.evaluator]: [0mInference done 390/1238. 0.0641 s / img. ETA=0:00:56
[32m[11/26 22:09:03 d2.evaluation.evaluator]: [0mInference done 466/1238. 0.0641 s / img. ETA=0:00:51
[32m[11/26 22:09:08 d2.evaluation.evaluator]: [0mInference done 543/1238. 0.0640 s / img. ETA=0:00:46
[32m[11/26 22:09:13 d2.evaluation.evaluator]: [0mInference done 619/1238. 0.0640 s / img. ETA=0:00:40
[32m[11/26 22:09:18 d2.evaluation.evaluator]: [0mInference done 695/1238. 0.0640 s / img. ETA=0:00:35
[32m[11/26 22:09:23 d2.evaluation.evaluator]: [0mInference done 771/1238. 0.0640 s / img. ETA=0:00:30
[32m[11/26 22:09:28 d2.evaluation.evaluator]: [0mInference done 847/1238. 0.0640 s / img. ETA=0:00:25
[32m[11/26 22:09:33 d2.evaluation.evaluator]: [0mInference done 923/1238. 0.0640 s / img. ETA=0:00:20
[32m[11/26 22:09:38 d2.evaluation.evaluator]: [0mInference done 998/1238. 0.0641 s / img. ETA=0:00:15
[32m[11/26 22:09:43 d2.evaluation.evaluator]: [0mInference done 1074/1238. 0.0641 s / img. ETA=0:00:10
[32m[11/26 22:09:48 d2.evaluation.evaluator]: [0mInference done 1150/1238. 0.0641 s / img. ETA=0:00:05
[32m[11/26 22:09:53 d2.evaluation.evaluator]: [0mInference done 1226/1238. 0.0641 s / img. ETA=0:00:00
[32m[11/26 22:09:54 d2.evaluation.evaluator]: [0mTotal inference time: 0:01:22.036135 (0.066534 s / img per device, on 4 devices)
[32m[11/26 22:09:54 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:01:19 (0.064101 s / img per device, on 4 devices)
[32m[11/26 22:10:05 detectron2]: [0mImage level evaluation complete for custom_voc_2007_test
[32m[11/26 22:10:05 detectron2]: [0mResults for custom_voc_2007_test
Traceback (most recent call last):
  File "main.py", line 199, in <module>
    args=(args,),
  File "/home/dksingh/inseg/detectron2/detectron2/engine/launch.py", line 59, in launch
    daemon=False,
  File "/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 200, in spawn
    return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
  File "/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 158, in start_processes
    while not context.join():
  File "/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 119, in join
    raise Exception(msg)
Exception: 

-- Process 0 terminated with the following error:
Traceback (most recent call last):
  File "/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 20, in _wrap
    fn(i, *args)
  File "/home/dksingh/inseg/detectron2/detectron2/engine/launch.py", line 94, in _distributed_worker
    main_func(*args)
  File "/home/dksingh/paper_impl2/Elephant-of-object-detection/main.py", line 187, in main
    return do_test(cfg, model)
  File "/home/dksingh/paper_impl2/Elephant-of-object-detection/main.py", line 63, in do_test
    evaluator._coco_api.cats)
  File "/home/dksingh/paper_impl2/Elephant-of-object-detection/WIC.py", line 45, in only_mAP_analysis
    scores = torch.cat(scores)
RuntimeError: All input tensors must be on the same device. Received cuda:0 and cuda:3

So I performed the evaluation on a single GPU using the trained model, using the following command:

python main.py --num-gpus 1 --config-file training_configs/faster_rcnn_R_50_FPN.yaml --resume --eval-only

I think it produces 73.87 mAP on PASCAL test, and 67.32 mAP on WR1. Test log:

Command Line Args: Namespace(config_file='training_configs/faster_rcnn_R_50_FPN.yaml', dist_url='tcp://127.0.0.1:50712', eval_only=True, machine_rank=0, num_gpus=1, num_machines=1, opts=[], resume=True)
[32m[11/26 22:12:53 detectron2]: [0mRank of current process: 0. World size: 1
[32m[11/26 22:12:58 detectron2]: [0mEnvironment info:
----------------------  -------------------------------------------------------------------------------
sys.platform            linux
Python                  3.7.3 | packaged by conda-forge | (default, Jul  1 2019, 21:52:21) [GCC 7.3.0]
numpy                   1.16.4
detectron2              0.3 @/home/dksingh/inseg/detectron2/detectron2
Compiler                GCC 5.5
CUDA compiler           CUDA 10.2
detectron2 arch flags   6.1
DETECTRON2_ENV_MODULE   <not set>
PyTorch                 1.6.0 @/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torch
PyTorch debug build     False
GPU available           True
GPU 0,1,2,3             GeForce GTX 1080 Ti (arch=6.1)
CUDA_HOME               /usr/local/cuda
Pillow                  7.1.2
torchvision             0.7.0 @/home/dksingh/anaconda3/envs/dev/lib/python3.7/site-packages/torchvision
torchvision arch flags  3.5, 5.0, 6.0, 7.0, 7.5
fvcore                  0.1.2.post20201103
cv2                     4.1.0
----------------------  -------------------------------------------------------------------------------
PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2019.0.4 Product Build 20190411 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 10.2
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
  - CuDNN 7.6.5
  - Magma 2.5.2
  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF, 

[32m[11/26 22:12:58 detectron2]: [0mCommand line arguments: Namespace(config_file='training_configs/faster_rcnn_R_50_FPN.yaml', dist_url='tcp://127.0.0.1:50712', eval_only=True, machine_rank=0, num_gpus=1, num_machines=1, opts=[], resume=True)
[32m[11/26 22:12:58 detectron2]: [0mContents of args.config_file=training_configs/faster_rcnn_R_50_FPN.yaml:
# Configuration for training with 4 gpus
_BASE_: "~/detectron2/detectron2/configs/Base-RCNN-FPN.yaml"
MODEL:
  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
  MASK_ON: False
  RESNETS:
    DEPTH: 50
  ROI_HEADS:
    NUM_CLASSES: 20
DATASETS:
  TRAIN: ('custom_voc_2007_train','custom_voc_2007_val','custom_voc_2012_train','custom_voc_2012_val',)
  TEST: ('custom_voc_2007_test','WR1_Mixed_Unknowns')
#  TEST: ('custom_voc_2007_test','Mixed_Unknowns')

SOLVER:
  BASE_LR: 0.01
  STEPS: (24000, 32000)
  MAX_ITER: 36000
  WARMUP_ITERS: 100
OUTPUT_DIR: /ssd_scratch/cvit/dksingh/overlooked_elephant/fasterrcnn/fasterrcnn_36k_iters/
[32m[11/26 22:12:58 detectron2]: [0mRunning with full config:
CUDNN_BENCHMARK: False
DATALOADER:
  ASPECT_RATIO_GROUPING: True
  FILTER_EMPTY_ANNOTATIONS: True
  NUM_WORKERS: 4
  REPEAT_THRESHOLD: 0.0
  SAMPLER_TRAIN: TrainingSampler
DATASETS:
  PRECOMPUTED_PROPOSAL_TOPK_TEST: 1000
  PRECOMPUTED_PROPOSAL_TOPK_TRAIN: 2000
  PROPOSAL_FILES_TEST: ()
  PROPOSAL_FILES_TRAIN: ()
  TEST: ('custom_voc_2007_test', 'WR1_Mixed_Unknowns')
  TRAIN: ('custom_voc_2007_train', 'custom_voc_2007_val', 'custom_voc_2012_train', 'custom_voc_2012_val')
GLOBAL:
  HACK: 1.0
INPUT:
  CROP:
    ENABLED: False
    SIZE: [0.9, 0.9]
    TYPE: relative_range
  FORMAT: BGR
  MASK_FORMAT: polygon
  MAX_SIZE_TEST: 1333
  MAX_SIZE_TRAIN: 1333
  MIN_SIZE_TEST: 800
  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
  MIN_SIZE_TRAIN_SAMPLING: choice
  RANDOM_FLIP: horizontal
MODEL:
  ANCHOR_GENERATOR:
    ANGLES: [[-90, 0, 90]]
    ASPECT_RATIOS: [[0.5, 1.0, 2.0]]
    NAME: DefaultAnchorGenerator
    OFFSET: 0.0
    SIZES: [[32], [64], [128], [256], [512]]
  BACKBONE:
    FREEZE_AT: 2
    NAME: build_resnet_fpn_backbone
  DEVICE: cuda
  FPN:
    FUSE_TYPE: sum
    IN_FEATURES: ['res2', 'res3', 'res4', 'res5']
    NORM: 
    OUT_CHANNELS: 256
  KEYPOINT_ON: False
  LOAD_PROPOSALS: False
  MASK_ON: False
  META_ARCHITECTURE: GeneralizedRCNN
  PANOPTIC_FPN:
    COMBINE:
      ENABLED: True
      INSTANCES_CONFIDENCE_THRESH: 0.5
      OVERLAP_THRESH: 0.5
      STUFF_AREA_LIMIT: 4096
    INSTANCE_LOSS_WEIGHT: 1.0
  PIXEL_MEAN: [103.53, 116.28, 123.675]
  PIXEL_STD: [1.0, 1.0, 1.0]
  PROPOSAL_GENERATOR:
    MIN_SIZE: 0
    NAME: RPN
  RESNETS:
    DEFORM_MODULATED: False
    DEFORM_NUM_GROUPS: 1
    DEFORM_ON_PER_STAGE: [False, False, False, False]
    DEPTH: 50
    NORM: FrozenBN
    NUM_GROUPS: 1
    OUT_FEATURES: ['res2', 'res3', 'res4', 'res5']
    RES2_OUT_CHANNELS: 256
    RES5_DILATION: 1
    STEM_OUT_CHANNELS: 64
    STRIDE_IN_1X1: True
    WIDTH_PER_GROUP: 64
  RETINANET:
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)
    FOCAL_LOSS_ALPHA: 0.25
    FOCAL_LOSS_GAMMA: 2.0
    IN_FEATURES: ['p3', 'p4', 'p5', 'p6', 'p7']
    IOU_LABELS: [0, -1, 1]
    IOU_THRESHOLDS: [0.4, 0.5]
    NMS_THRESH_TEST: 0.5
    NORM: 
    NUM_CLASSES: 80
    NUM_CONVS: 4
    PRIOR_PROB: 0.01
    SCORE_THRESH_TEST: 0.05
    SMOOTH_L1_LOSS_BETA: 0.1
    TOPK_CANDIDATES_TEST: 1000
  ROI_BOX_CASCADE_HEAD:
    BBOX_REG_WEIGHTS: ((10.0, 10.0, 5.0, 5.0), (20.0, 20.0, 10.0, 10.0), (30.0, 30.0, 15.0, 15.0))
    IOUS: (0.5, 0.6, 0.7)
  ROI_BOX_HEAD:
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_LOSS_WEIGHT: 1.0
    BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)
    CLS_AGNOSTIC_BBOX_REG: False
    CONV_DIM: 256
    FC_DIM: 1024
    NAME: FastRCNNConvFCHead
    NORM: 
    NUM_CONV: 0
    NUM_FC: 2
    POOLER_RESOLUTION: 7
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
    SMOOTH_L1_BETA: 0.0
    TRAIN_ON_PRED_BOXES: False
  ROI_HEADS:
    BATCH_SIZE_PER_IMAGE: 512
    IN_FEATURES: ['p2', 'p3', 'p4', 'p5']
    IOU_LABELS: [0, 1]
    IOU_THRESHOLDS: [0.5]
    NAME: StandardROIHeads
    NMS_THRESH_TEST: 0.5
    NUM_CLASSES: 20
    POSITIVE_FRACTION: 0.25
    PROPOSAL_APPEND_GT: True
    SCORE_THRESH_TEST: 0.05
  ROI_KEYPOINT_HEAD:
    CONV_DIMS: (512, 512, 512, 512, 512, 512, 512, 512)
    LOSS_WEIGHT: 1.0
    MIN_KEYPOINTS_PER_IMAGE: 1
    NAME: KRCNNConvDeconvUpsampleHead
    NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: True
    NUM_KEYPOINTS: 17
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
  ROI_MASK_HEAD:
    CLS_AGNOSTIC_MASK: False
    CONV_DIM: 256
    NAME: MaskRCNNConvUpsampleHead
    NORM: 
    NUM_CONV: 4
    POOLER_RESOLUTION: 14
    POOLER_SAMPLING_RATIO: 0
    POOLER_TYPE: ROIAlignV2
  RPN:
    BATCH_SIZE_PER_IMAGE: 256
    BBOX_REG_LOSS_TYPE: smooth_l1
    BBOX_REG_LOSS_WEIGHT: 1.0
    BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)
    BOUNDARY_THRESH: -1
    HEAD_NAME: StandardRPNHead
    IN_FEATURES: ['p2', 'p3', 'p4', 'p5', 'p6']
    IOU_LABELS: [0, -1, 1]
    IOU_THRESHOLDS: [0.3, 0.7]
    LOSS_WEIGHT: 1.0
    NMS_THRESH: 0.7
    POSITIVE_FRACTION: 0.5
    POST_NMS_TOPK_TEST: 1000
    POST_NMS_TOPK_TRAIN: 1000
    PRE_NMS_TOPK_TEST: 1000
    PRE_NMS_TOPK_TRAIN: 2000
    SMOOTH_L1_BETA: 0.0
  SEM_SEG_HEAD:
    COMMON_STRIDE: 4
    CONVS_DIM: 128
    IGNORE_VALUE: 255
    IN_FEATURES: ['p2', 'p3', 'p4', 'p5']
    LOSS_WEIGHT: 1.0
    NAME: SemSegFPNHead
    NORM: GN
    NUM_CLASSES: 54
  WEIGHTS: detectron2://ImageNetPretrained/MSRA/R-50.pkl
OUTPUT_DIR: /ssd_scratch/cvit/dksingh/overlooked_elephant/fasterrcnn/fasterrcnn_36k_iters/
SEED: -1
SOLVER:
  AMP:
    ENABLED: False
  BASE_LR: 0.01
  BIAS_LR_FACTOR: 1.0
  CHECKPOINT_PERIOD: 5000
  CLIP_GRADIENTS:
    CLIP_TYPE: value
    CLIP_VALUE: 1.0
    ENABLED: False
    NORM_TYPE: 2.0
  GAMMA: 0.1
  IMS_PER_BATCH: 16
  LR_SCHEDULER_NAME: WarmupMultiStepLR
  MAX_ITER: 36000
  MOMENTUM: 0.9
  NESTEROV: False
  REFERENCE_WORLD_SIZE: 0
  STEPS: (24000, 32000)
  WARMUP_FACTOR: 0.001
  WARMUP_ITERS: 100
  WARMUP_METHOD: linear
  WEIGHT_DECAY: 0.0001
  WEIGHT_DECAY_BIAS: 0.0001
  WEIGHT_DECAY_NORM: 0.0
TEST:
  AUG:
    ENABLED: False
    FLIP: True
    MAX_SIZE: 4000
    MIN_SIZES: (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
  DETECTIONS_PER_IMAGE: 100
  EVAL_PERIOD: 0
  EXPECTED_RESULTS: []
  KEYPOINT_OKS_SIGMAS: []
  PRECISE_BN:
    ENABLED: False
    NUM_ITER: 200
VERSION: 2
VIS_PERIOD: 0
[32m[11/26 22:12:58 detectron2]: [0mFull config saved to /ssd_scratch/cvit/dksingh/overlooked_elephant/fasterrcnn/fasterrcnn_36k_iters/config.yaml
[32m[11/26 22:12:58 d2.utils.env]: [0mUsing a generated random seed 58088618
[32m[11/26 22:13:07 detectron2]: [0mModel:
GeneralizedRCNN(
  (backbone): FPN(
    (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))
    (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (top_block): LastLevelMaxPool()
    (bottom_up): ResNet(
      (stem): BasicStem(
        (conv1): Conv2d(
          3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
        )
      )
      (res2): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv1): Conv2d(
            64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv2): Conv2d(
            64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)
          )
          (conv3): Conv2d(
            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
        )
      )
      (res3): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv1): Conv2d(
            256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
        (3): BottleneckBlock(
          (conv1): Conv2d(
            512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv2): Conv2d(
            128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)
          )
          (conv3): Conv2d(
            128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
        )
      )
      (res4): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
          (conv1): Conv2d(
            512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (3): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (4): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
        (5): BottleneckBlock(
          (conv1): Conv2d(
            1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv2): Conv2d(
            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)
          )
          (conv3): Conv2d(
            256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)
          )
        )
      )
      (res5): Sequential(
        (0): BottleneckBlock(
          (shortcut): Conv2d(
            1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
          (conv1): Conv2d(
            1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
        (1): BottleneckBlock(
          (conv1): Conv2d(
            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
        (2): BottleneckBlock(
          (conv1): Conv2d(
            2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv2): Conv2d(
            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)
          )
          (conv3): Conv2d(
            512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False
            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)
          )
        )
      )
    )
  )
  (proposal_generator): RPN(
    (rpn_head): StandardRPNHead(
      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))
      (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))
    )
    (anchor_generator): DefaultAnchorGenerator(
      (cell_anchors): BufferList()
    )
  )
  (roi_heads): StandardROIHeads(
    (box_pooler): ROIPooler(
      (level_poolers): ModuleList(
        (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)
        (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)
        (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)
        (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)
      )
    )
    (box_head): FastRCNNConvFCHead(
      (flatten): Flatten()
      (fc1): Linear(in_features=12544, out_features=1024, bias=True)
      (fc_relu1): ReLU()
      (fc2): Linear(in_features=1024, out_features=1024, bias=True)
      (fc_relu2): ReLU()
    )
    (box_predictor): FastRCNNOutputLayers(
      (cls_score): Linear(in_features=1024, out_features=21, bias=True)
      (bbox_pred): Linear(in_features=1024, out_features=80, bias=True)
    )
  )
)
[32m[11/26 22:13:07 fvcore.common.checkpoint]: [0mLoading checkpoint from /ssd_scratch/cvit/dksingh/overlooked_elephant/fasterrcnn/fasterrcnn_36k_iters/model_final.pth
[5m[31mWARNING[0m [32m[11/26 22:13:08 d2.data.datasets.coco]: [0m
Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.

[32m[11/26 22:13:08 d2.data.datasets.coco]: [0mLoaded 4952 images in COCO format from protocol/custom_protocols/custom_voc_2007_test.json
[32m[11/26 22:13:08 d2.data.build]: [0mDistribution of instances among all 21 categories:
[36m|  category  | #instances   |  category   | #instances   |  category   | #instances   |
|:----------:|:-------------|:-----------:|:-------------|:-----------:|:-------------|
|  unknown   | 0            |  aeroplane  | 311          |   bicycle   | 389          |
|    bird    | 576          |    boat     | 393          |   bottle    | 657          |
|    bus     | 254          |     car     | 1541         |     cat     | 370          |
|   chair    | 1374         |     cow     | 329          | diningtable | 299          |
|    dog     | 530          |    horse    | 395          |  motorbike  | 369          |
|   person   | 5227         | pottedplant | 592          |    sheep    | 311          |
|    sofa    | 396          |    train    | 302          |  tvmonitor  | 361          |
|            |              |             |              |             |              |
|   total    | 14976        |             |              |             |              |[0m
[32m[11/26 22:13:08 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[11/26 22:13:08 d2.data.common]: [0mSerializing 4952 elements to byte tensors and concatenating them all ...
[32m[11/26 22:13:08 d2.data.common]: [0mSerialized dataset takes 1.87 MiB
[32m[11/26 22:13:08 d2.evaluation.evaluator]: [0mStart inference on 4952 images
[32m[11/26 22:13:10 d2.evaluation.evaluator]: [0mInference done 11/4952. 0.0765 s / img. ETA=0:06:25
[32m[11/26 22:13:15 d2.evaluation.evaluator]: [0mInference done 76/4952. 0.0761 s / img. ETA=0:06:20
[32m[11/26 22:13:20 d2.evaluation.evaluator]: [0mInference done 141/4952. 0.0760 s / img. ETA=0:06:14
[32m[11/26 22:13:25 d2.evaluation.evaluator]: [0mInference done 206/4952. 0.0760 s / img. ETA=0:06:09
[32m[11/26 22:13:30 d2.evaluation.evaluator]: [0mInference done 271/4952. 0.0759 s / img. ETA=0:06:04
[32m[11/26 22:13:35 d2.evaluation.evaluator]: [0mInference done 336/4952. 0.0759 s / img. ETA=0:05:59
[32m[11/26 22:13:40 d2.evaluation.evaluator]: [0mInference done 400/4952. 0.0761 s / img. ETA=0:05:55
[32m[11/26 22:13:45 d2.evaluation.evaluator]: [0mInference done 464/4952. 0.0761 s / img. ETA=0:05:50
[32m[11/26 22:13:51 d2.evaluation.evaluator]: [0mInference done 529/4952. 0.0761 s / img. ETA=0:05:45
[32m[11/26 22:13:56 d2.evaluation.evaluator]: [0mInference done 593/4952. 0.0761 s / img. ETA=0:05:40
[32m[11/26 22:14:01 d2.evaluation.evaluator]: [0mInference done 658/4952. 0.0761 s / img. ETA=0:05:34
[32m[11/26 22:14:06 d2.evaluation.evaluator]: [0mInference done 722/4952. 0.0762 s / img. ETA=0:05:30
[32m[11/26 22:14:11 d2.evaluation.evaluator]: [0mInference done 786/4952. 0.0762 s / img. ETA=0:05:25
[32m[11/26 22:14:16 d2.evaluation.evaluator]: [0mInference done 850/4952. 0.0762 s / img. ETA=0:05:20
[32m[11/26 22:14:21 d2.evaluation.evaluator]: [0mInference done 914/4952. 0.0763 s / img. ETA=0:05:15
[32m[11/26 22:14:26 d2.evaluation.evaluator]: [0mInference done 978/4952. 0.0763 s / img. ETA=0:05:10
[32m[11/26 22:14:31 d2.evaluation.evaluator]: [0mInference done 1042/4952. 0.0764 s / img. ETA=0:05:06
[32m[11/26 22:14:36 d2.evaluation.evaluator]: [0mInference done 1106/4952. 0.0764 s / img. ETA=0:05:01
[32m[11/26 22:14:41 d2.evaluation.evaluator]: [0mInference done 1170/4952. 0.0764 s / img. ETA=0:04:56
[32m[11/26 22:14:46 d2.evaluation.evaluator]: [0mInference done 1234/4952. 0.0765 s / img. ETA=0:04:51
[32m[11/26 22:14:51 d2.evaluation.evaluator]: [0mInference done 1298/4952. 0.0765 s / img. ETA=0:04:46
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[32m[11/26 22:15:01 d2.evaluation.evaluator]: [0mInference done 1426/4952. 0.0765 s / img. ETA=0:04:36
[32m[11/26 22:15:06 d2.evaluation.evaluator]: [0mInference done 1490/4952. 0.0765 s / img. ETA=0:04:31
[32m[11/26 22:15:11 d2.evaluation.evaluator]: [0mInference done 1554/4952. 0.0766 s / img. ETA=0:04:26
[32m[11/26 22:15:16 d2.evaluation.evaluator]: [0mInference done 1617/4952. 0.0766 s / img. ETA=0:04:21
[32m[11/26 22:15:21 d2.evaluation.evaluator]: [0mInference done 1682/4952. 0.0766 s / img. ETA=0:04:16
[32m[11/26 22:15:26 d2.evaluation.evaluator]: [0mInference done 1746/4952. 0.0766 s / img. ETA=0:04:11
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[32m[11/26 22:15:36 d2.evaluation.evaluator]: [0mInference done 1875/4952. 0.0766 s / img. ETA=0:04:01
[32m[11/26 22:15:42 d2.evaluation.evaluator]: [0mInference done 1939/4952. 0.0766 s / img. ETA=0:03:56
[32m[11/26 22:15:47 d2.evaluation.evaluator]: [0mInference done 2003/4952. 0.0766 s / img. ETA=0:03:51
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[32m[11/26 22:15:57 d2.evaluation.evaluator]: [0mInference done 2130/4952. 0.0766 s / img. ETA=0:03:41
[32m[11/26 22:16:02 d2.evaluation.evaluator]: [0mInference done 2194/4952. 0.0767 s / img. ETA=0:03:36
[32m[11/26 22:16:07 d2.evaluation.evaluator]: [0mInference done 2258/4952. 0.0767 s / img. ETA=0:03:31
[32m[11/26 22:16:12 d2.evaluation.evaluator]: [0mInference done 2322/4952. 0.0766 s / img. ETA=0:03:26
[32m[11/26 22:16:17 d2.evaluation.evaluator]: [0mInference done 2386/4952. 0.0767 s / img. ETA=0:03:21
[32m[11/26 22:16:22 d2.evaluation.evaluator]: [0mInference done 2450/4952. 0.0767 s / img. ETA=0:03:16
[32m[11/26 22:16:27 d2.evaluation.evaluator]: [0mInference done 2514/4952. 0.0767 s / img. ETA=0:03:11
[32m[11/26 22:16:32 d2.evaluation.evaluator]: [0mInference done 2578/4952. 0.0767 s / img. ETA=0:03:06
[32m[11/26 22:16:37 d2.evaluation.evaluator]: [0mInference done 2642/4952. 0.0767 s / img. ETA=0:03:01
[32m[11/26 22:16:42 d2.evaluation.evaluator]: [0mInference done 2705/4952. 0.0767 s / img. ETA=0:02:56
[32m[11/26 22:16:47 d2.evaluation.evaluator]: [0mInference done 2768/4952. 0.0768 s / img. ETA=0:02:51
[32m[11/26 22:16:52 d2.evaluation.evaluator]: [0mInference done 2832/4952. 0.0768 s / img. ETA=0:02:46
[32m[11/26 22:16:57 d2.evaluation.evaluator]: [0mInference done 2897/4952. 0.0767 s / img. ETA=0:02:41
[32m[11/26 22:17:02 d2.evaluation.evaluator]: [0mInference done 2961/4952. 0.0767 s / img. ETA=0:02:36
[32m[11/26 22:17:07 d2.evaluation.evaluator]: [0mInference done 3025/4952. 0.0768 s / img. ETA=0:02:31
[32m[11/26 22:17:12 d2.evaluation.evaluator]: [0mInference done 3089/4952. 0.0768 s / img. ETA=0:02:26
[32m[11/26 22:17:17 d2.evaluation.evaluator]: [0mInference done 3153/4952. 0.0768 s / img. ETA=0:02:21
[32m[11/26 22:17:22 d2.evaluation.evaluator]: [0mInference done 3217/4952. 0.0768 s / img. ETA=0:02:16
[32m[11/26 22:17:27 d2.evaluation.evaluator]: [0mInference done 3281/4952. 0.0768 s / img. ETA=0:02:11
[32m[11/26 22:17:32 d2.evaluation.evaluator]: [0mInference done 3345/4952. 0.0768 s / img. ETA=0:02:06
[32m[11/26 22:17:38 d2.evaluation.evaluator]: [0mInference done 3409/4952. 0.0768 s / img. ETA=0:02:01
[32m[11/26 22:17:43 d2.evaluation.evaluator]: [0mInference done 3472/4952. 0.0768 s / img. ETA=0:01:56
[32m[11/26 22:17:48 d2.evaluation.evaluator]: [0mInference done 3535/4952. 0.0768 s / img. ETA=0:01:51
[32m[11/26 22:17:53 d2.evaluation.evaluator]: [0mInference done 3598/4952. 0.0768 s / img. ETA=0:01:46
[32m[11/26 22:17:58 d2.evaluation.evaluator]: [0mInference done 3662/4952. 0.0768 s / img. ETA=0:01:41
[32m[11/26 22:18:03 d2.evaluation.evaluator]: [0mInference done 3726/4952. 0.0768 s / img. ETA=0:01:36
[32m[11/26 22:18:08 d2.evaluation.evaluator]: [0mInference done 3790/4952. 0.0768 s / img. ETA=0:01:31
[32m[11/26 22:18:13 d2.evaluation.evaluator]: [0mInference done 3854/4952. 0.0768 s / img. ETA=0:01:26
[32m[11/26 22:18:18 d2.evaluation.evaluator]: [0mInference done 3918/4952. 0.0768 s / img. ETA=0:01:21
[32m[11/26 22:18:23 d2.evaluation.evaluator]: [0mInference done 3982/4952. 0.0768 s / img. ETA=0:01:16
[32m[11/26 22:18:28 d2.evaluation.evaluator]: [0mInference done 4045/4952. 0.0769 s / img. ETA=0:01:11
[32m[11/26 22:18:33 d2.evaluation.evaluator]: [0mInference done 4109/4952. 0.0768 s / img. ETA=0:01:06
[32m[11/26 22:18:38 d2.evaluation.evaluator]: [0mInference done 4174/4952. 0.0768 s / img. ETA=0:01:01
[32m[11/26 22:18:43 d2.evaluation.evaluator]: [0mInference done 4237/4952. 0.0768 s / img. ETA=0:00:56
[32m[11/26 22:18:48 d2.evaluation.evaluator]: [0mInference done 4300/4952. 0.0769 s / img. ETA=0:00:51
[32m[11/26 22:18:53 d2.evaluation.evaluator]: [0mInference done 4363/4952. 0.0769 s / img. ETA=0:00:46
[32m[11/26 22:18:58 d2.evaluation.evaluator]: [0mInference done 4427/4952. 0.0769 s / img. ETA=0:00:41
[32m[11/26 22:19:03 d2.evaluation.evaluator]: [0mInference done 4491/4952. 0.0769 s / img. ETA=0:00:36
[32m[11/26 22:19:08 d2.evaluation.evaluator]: [0mInference done 4555/4952. 0.0769 s / img. ETA=0:00:31
[32m[11/26 22:19:13 d2.evaluation.evaluator]: [0mInference done 4619/4952. 0.0769 s / img. ETA=0:00:26
[32m[11/26 22:19:18 d2.evaluation.evaluator]: [0mInference done 4683/4952. 0.0769 s / img. ETA=0:00:21
[32m[11/26 22:19:23 d2.evaluation.evaluator]: [0mInference done 4747/4952. 0.0769 s / img. ETA=0:00:16
[32m[11/26 22:19:28 d2.evaluation.evaluator]: [0mInference done 4811/4952. 0.0769 s / img. ETA=0:00:11
[32m[11/26 22:19:33 d2.evaluation.evaluator]: [0mInference done 4875/4952. 0.0769 s / img. ETA=0:00:06
[32m[11/26 22:19:38 d2.evaluation.evaluator]: [0mInference done 4939/4952. 0.0769 s / img. ETA=0:00:01
[32m[11/26 22:19:39 d2.evaluation.evaluator]: [0mTotal inference time: 0:06:29.762767 (0.078788 s / img per device, on 1 devices)
[32m[11/26 22:19:39 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:06:20 (0.076857 s / img per device, on 1 devices)
[32m[11/26 22:19:39 detectron2]: [0mImage level evaluation complete for custom_voc_2007_test
[32m[11/26 22:19:39 detectron2]: [0mResults for custom_voc_2007_test
[32m[11/26 22:19:40 detectron2]: [0mAP for class no. 0: 0.8057809472084045
[32m[11/26 22:19:40 detectron2]: [0mAP for class no. 1: 0.7805687189102173
[32m[11/26 22:19:40 detectron2]: [0mAP for class no. 2: 0.6984018683433533
[32m[11/26 22:19:40 detectron2]: [0mAP for class no. 3: 0.6062182784080505
[32m[11/26 22:19:40 detectron2]: [0mAP for class no. 4: 0.5762754082679749
[32m[11/26 22:19:41 detectron2]: [0mAP for class no. 5: 0.7758022546768188
[32m[11/26 22:19:41 detectron2]: [0mAP for class no. 6: 0.7926622629165649
[32m[11/26 22:19:41 detectron2]: [0mAP for class no. 7: 0.8785473108291626
[32m[11/26 22:19:41 detectron2]: [0mAP for class no. 8: 0.5703489184379578
[32m[11/26 22:19:41 detectron2]: [0mAP for class no. 9: 0.7864148616790771
[32m[11/26 22:19:41 detectron2]: [0mAP for class no. 10: 0.6691756844520569
[32m[11/26 22:19:41 detectron2]: [0mAP for class no. 11: 0.8530338406562805
[32m[11/26 22:19:42 detectron2]: [0mAP for class no. 12: 0.8538536429405212
[32m[11/26 22:19:42 detectron2]: [0mAP for class no. 13: 0.8232013583183289
[32m[11/26 22:19:42 detectron2]: [0mAP for class no. 14: 0.786263108253479
[32m[11/26 22:19:42 detectron2]: [0mAP for class no. 15: 0.4942459166049957
[32m[11/26 22:19:42 detectron2]: [0mAP for class no. 16: 0.7428907155990601
[32m[11/26 22:19:42 detectron2]: [0mAP for class no. 17: 0.6694597601890564
[32m[11/26 22:19:42 detectron2]: [0mAP for class no. 18: 0.8503661751747131
[32m[11/26 22:19:43 detectron2]: [0mAP for class no. 19: 0.761128842830658
[32m[11/26 22:19:43 detectron2]: [0mmAP: 0.7387319207191467
[5m[31mWARNING[0m [32m[11/26 22:19:43 d2.data.datasets.coco]: [0m
Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.

[32m[11/26 22:19:43 d2.data.datasets.coco]: [0mLoaded 4952 images in COCO format from protocol/custom_protocols/WR1_Mixed_Unknowns.json
[32m[11/26 22:19:43 d2.data.build]: [0mDistribution of instances among all 21 categories:
[36m|  category  | #instances   |  category   | #instances   |  category   | #instances   |
|:----------:|:-------------|:-----------:|:-------------|:-----------:|:-------------|
|  unknown   | 15235        |  aeroplane  | 0            |   bicycle   | 0            |
|    bird    | 0            |    boat     | 0            |   bottle    | 0            |
|    bus     | 0            |     car     | 0            |     cat     | 0            |
|   chair    | 0            |     cow     | 0            | diningtable | 0            |
|    dog     | 0            |    horse    | 0            |  motorbike  | 0            |
|   person   | 0            | pottedplant | 0            |    sheep    | 0            |
|    sofa    | 0            |    train    | 0            |  tvmonitor  | 0            |
|            |              |             |              |             |              |
|   total    | 15235        |             |              |             |              |[0m
[32m[11/26 22:19:43 d2.data.dataset_mapper]: [0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')]
[32m[11/26 22:19:43 d2.data.common]: [0mSerializing 4952 elements to byte tensors and concatenating them all ...
[32m[11/26 22:19:43 d2.data.common]: [0mSerialized dataset takes 8.39 MiB
[32m[11/26 22:19:43 d2.evaluation.evaluator]: [0mStart inference on 4952 images
[32m[11/26 22:19:45 d2.evaluation.evaluator]: [0mInference done 11/4952. 0.0770 s / img. ETA=0:06:28
[32m[11/26 22:19:50 d2.evaluation.evaluator]: [0mInference done 74/4952. 0.0777 s / img. ETA=0:06:27
[32m[11/26 22:19:55 d2.evaluation.evaluator]: [0mInference done 138/4952. 0.0773 s / img. ETA=0:06:20
[32m[11/26 22:20:00 d2.evaluation.evaluator]: [0mInference done 202/4952. 0.0774 s / img. ETA=0:06:15
[32m[11/26 22:20:05 d2.evaluation.evaluator]: [0mInference done 265/4952. 0.0776 s / img. ETA=0:06:11
[32m[11/26 22:20:10 d2.evaluation.evaluator]: [0mInference done 328/4952. 0.0776 s / img. ETA=0:06:07
[32m[11/26 22:20:15 d2.evaluation.evaluator]: [0mInference done 391/4952. 0.0778 s / img. ETA=0:06:02
[32m[11/26 22:20:20 d2.evaluation.evaluator]: [0mInference done 455/4952. 0.0777 s / img. ETA=0:05:57
[32m[11/26 22:20:25 d2.evaluation.evaluator]: [0mInference done 518/4952. 0.0778 s / img. ETA=0:05:52
[32m[11/26 22:20:30 d2.evaluation.evaluator]: [0mInference done 582/4952. 0.0776 s / img. ETA=0:05:47
[32m[11/26 22:20:35 d2.evaluation.evaluator]: [0mInference done 645/4952. 0.0777 s / img. ETA=0:05:42
[32m[11/26 22:20:40 d2.evaluation.evaluator]: [0mInference done 710/4952. 0.0776 s / img. ETA=0:05:36
[32m[11/26 22:20:45 d2.evaluation.evaluator]: [0mInference done 774/4952. 0.0776 s / img. ETA=0:05:31
[32m[11/26 22:20:50 d2.evaluation.evaluator]: [0mInference done 838/4952. 0.0775 s / img. ETA=0:05:26
[32m[11/26 22:20:55 d2.evaluation.evaluator]: [0mInference done 902/4952. 0.0775 s / img. ETA=0:05:21
[32m[11/26 22:21:00 d2.evaluation.evaluator]: [0mInference done 966/4952. 0.0775 s / img. ETA=0:05:15
[32m[11/26 22:21:05 d2.evaluation.evaluator]: [0mInference done 1030/4952. 0.0774 s / img. ETA=0:05:10
[32m[11/26 22:21:11 d2.evaluation.evaluator]: [0mInference done 1094/4952. 0.0774 s / img. ETA=0:05:05
[32m[11/26 22:21:16 d2.evaluation.evaluator]: [0mInference done 1157/4952. 0.0774 s / img. ETA=0:05:00
[32m[11/26 22:21:21 d2.evaluation.evaluator]: [0mInference done 1220/4952. 0.0774 s / img. ETA=0:04:55
[32m[11/26 22:21:26 d2.evaluation.evaluator]: [0mInference done 1283/4952. 0.0775 s / img. ETA=0:04:50
[32m[11/26 22:21:31 d2.evaluation.evaluator]: [0mInference done 1346/4952. 0.0775 s / img. ETA=0:04:45
[32m[11/26 22:21:36 d2.evaluation.evaluator]: [0mInference done 1409/4952. 0.0775 s / img. ETA=0:04:41
[32m[11/26 22:21:41 d2.evaluation.evaluator]: [0mInference done 1473/4952. 0.0775 s / img. ETA=0:04:35
[32m[11/26 22:21:46 d2.evaluation.evaluator]: [0mInference done 1537/4952. 0.0775 s / img. ETA=0:04:30
[32m[11/26 22:21:51 d2.evaluation.evaluator]: [0mInference done 1600/4952. 0.0775 s / img. ETA=0:04:25
[32m[11/26 22:21:56 d2.evaluation.evaluator]: [0mInference done 1662/4952. 0.0776 s / img. ETA=0:04:21
[32m[11/26 22:22:01 d2.evaluation.evaluator]: [0mInference done 1726/4952. 0.0775 s / img. ETA=0:04:16
[32m[11/26 22:22:06 d2.evaluation.evaluator]: [0mInference done 1791/4952. 0.0775 s / img. ETA=0:04:10
[32m[11/26 22:22:11 d2.evaluation.evaluator]: [0mInference done 1855/4952. 0.0775 s / img. ETA=0:04:05
[32m[11/26 22:22:16 d2.evaluation.evaluator]: [0mInference done 1919/4952. 0.0775 s / img. ETA=0:04:00
[32m[11/26 22:22:21 d2.evaluation.evaluator]: [0mInference done 1981/4952. 0.0775 s / img. ETA=0:03:55
[32m[11/26 22:22:26 d2.evaluation.evaluator]: [0mInference done 2044/4952. 0.0775 s / img. ETA=0:03:50
[32m[11/26 22:22:31 d2.evaluation.evaluator]: [0mInference done 2107/4952. 0.0776 s / img. ETA=0:03:45
[32m[11/26 22:22:36 d2.evaluation.evaluator]: [0mInference done 2171/4952. 0.0775 s / img. ETA=0:03:40
[32m[11/26 22:22:41 d2.evaluation.evaluator]: [0mInference done 2234/4952. 0.0775 s / img. ETA=0:03:35
[32m[11/26 22:22:46 d2.evaluation.evaluator]: [0mInference done 2297/4952. 0.0776 s / img. ETA=0:03:30
[32m[11/26 22:22:51 d2.evaluation.evaluator]: [0mInference done 2360/4952. 0.0776 s / img. ETA=0:03:25
[32m[11/26 22:22:56 d2.evaluation.evaluator]: [0mInference done 2424/4952. 0.0776 s / img. ETA=0:03:20
[32m[11/26 22:23:01 d2.evaluation.evaluator]: [0mInference done 2487/4952. 0.0776 s / img. ETA=0:03:15
[32m[11/26 22:23:06 d2.evaluation.evaluator]: [0mInference done 2551/4952. 0.0776 s / img. ETA=0:03:10
[32m[11/26 22:23:11 d2.evaluation.evaluator]: [0mInference done 2614/4952. 0.0776 s / img. ETA=0:03:05
[32m[11/26 22:23:16 d2.evaluation.evaluator]: [0mInference done 2678/4952. 0.0776 s / img. ETA=0:03:00
[32m[11/26 22:23:22 d2.evaluation.evaluator]: [0mInference done 2742/4952. 0.0776 s / img. ETA=0:02:55
[32m[11/26 22:23:27 d2.evaluation.evaluator]: [0mInference done 2807/4952. 0.0775 s / img. ETA=0:02:50
[32m[11/26 22:23:32 d2.evaluation.evaluator]: [0mInference done 2871/4952. 0.0775 s / img. ETA=0:02:45
[32m[11/26 22:23:37 d2.evaluation.evaluator]: [0mInference done 2934/4952. 0.0775 s / img. ETA=0:02:40
[32m[11/26 22:23:42 d2.evaluation.evaluator]: [0mInference done 2998/4952. 0.0775 s / img. ETA=0:02:34
[32m[11/26 22:23:47 d2.evaluation.evaluator]: [0mInference done 3061/4952. 0.0775 s / img. ETA=0:02:30
[32m[11/26 22:23:52 d2.evaluation.evaluator]: [0mInference done 3125/4952. 0.0775 s / img. ETA=0:02:24
[32m[11/26 22:23:57 d2.evaluation.evaluator]: [0mInference done 3189/4952. 0.0775 s / img. ETA=0:02:19
[32m[11/26 22:24:02 d2.evaluation.evaluator]: [0mInference done 3252/4952. 0.0775 s / img. ETA=0:02:14
[32m[11/26 22:24:07 d2.evaluation.evaluator]: [0mInference done 3315/4952. 0.0775 s / img. ETA=0:02:09
[32m[11/26 22:24:12 d2.evaluation.evaluator]: [0mInference done 3378/4952. 0.0775 s / img. ETA=0:02:04
[32m[11/26 22:24:17 d2.evaluation.evaluator]: [0mInference done 3443/4952. 0.0775 s / img. ETA=0:01:59
[32m[11/26 22:24:22 d2.evaluation.evaluator]: [0mInference done 3506/4952. 0.0775 s / img. ETA=0:01:54
[32m[11/26 22:24:27 d2.evaluation.evaluator]: [0mInference done 3569/4952. 0.0775 s / img. ETA=0:01:49
[32m[11/26 22:24:32 d2.evaluation.evaluator]: [0mInference done 3632/4952. 0.0775 s / img. ETA=0:01:44
[32m[11/26 22:24:37 d2.evaluation.evaluator]: [0mInference done 3696/4952. 0.0775 s / img. ETA=0:01:39
[32m[11/26 22:24:42 d2.evaluation.evaluator]: [0mInference done 3760/4952. 0.0775 s / img. ETA=0:01:34
[32m[11/26 22:24:47 d2.evaluation.evaluator]: [0mInference done 3822/4952. 0.0775 s / img. ETA=0:01:29
[32m[11/26 22:24:52 d2.evaluation.evaluator]: [0mInference done 3886/4952. 0.0775 s / img. ETA=0:01:24
[32m[11/26 22:24:57 d2.evaluation.evaluator]: [0mInference done 3949/4952. 0.0775 s / img. ETA=0:01:19
[32m[11/26 22:25:02 d2.evaluation.evaluator]: [0mInference done 4013/4952. 0.0775 s / img. ETA=0:01:14
[32m[11/26 22:25:07 d2.evaluation.evaluator]: [0mInference done 4076/4952. 0.0775 s / img. ETA=0:01:09
[32m[11/26 22:25:12 d2.evaluation.evaluator]: [0mInference done 4139/4952. 0.0775 s / img. ETA=0:01:04
[32m[11/26 22:25:17 d2.evaluation.evaluator]: [0mInference done 4202/4952. 0.0776 s / img. ETA=0:00:59
[32m[11/26 22:25:22 d2.evaluation.evaluator]: [0mInference done 4265/4952. 0.0776 s / img. ETA=0:00:54
[32m[11/26 22:25:28 d2.evaluation.evaluator]: [0mInference done 4329/4952. 0.0776 s / img. ETA=0:00:49
[32m[11/26 22:25:33 d2.evaluation.evaluator]: [0mInference done 4393/4952. 0.0776 s / img. ETA=0:00:44
[32m[11/26 22:25:38 d2.evaluation.evaluator]: [0mInference done 4457/4952. 0.0776 s / img. ETA=0:00:39
[32m[11/26 22:25:43 d2.evaluation.evaluator]: [0mInference done 4521/4952. 0.0776 s / img. ETA=0:00:34
[32m[11/26 22:25:48 d2.evaluation.evaluator]: [0mInference done 4584/4952. 0.0776 s / img. ETA=0:00:29
[32m[11/26 22:25:53 d2.evaluation.evaluator]: [0mInference done 4648/4952. 0.0776 s / img. ETA=0:00:24
[32m[11/26 22:25:58 d2.evaluation.evaluator]: [0mInference done 4712/4952. 0.0775 s / img. ETA=0:00:19
[32m[11/26 22:26:03 d2.evaluation.evaluator]: [0mInference done 4775/4952. 0.0776 s / img. ETA=0:00:14
[32m[11/26 22:26:08 d2.evaluation.evaluator]: [0mInference done 4838/4952. 0.0776 s / img. ETA=0:00:09
[32m[11/26 22:26:13 d2.evaluation.evaluator]: [0mInference done 4901/4952. 0.0776 s / img. ETA=0:00:04
[32m[11/26 22:26:17 d2.evaluation.evaluator]: [0mTotal inference time: 0:06:32.709267 (0.079383 s / img per device, on 1 devices)
[32m[11/26 22:26:17 d2.evaluation.evaluator]: [0mTotal inference pure compute time: 0:06:23 (0.077551 s / img per device, on 1 devices)
[32m[11/26 22:26:17 detectron2]: [0mImage level evaluation complete for WR1_Mixed_Unknowns
[32m[11/26 22:26:17 detectron2]: [0mResults for WR1_Mixed_Unknowns
[32m[11/26 22:26:17 detectron2]: [0mAP for class no. 0: 0.0
[32m[11/26 22:26:17 detectron2]: [0mAP for class no. 1: 0.0
[32m[11/26 22:26:18 detectron2]: [0mAP for class no. 2: 0.0
[32m[11/26 22:26:18 detectron2]: [0mAP for class no. 3: 0.0
[32m[11/26 22:26:18 detectron2]: [0mAP for class no. 4: 0.0
[32m[11/26 22:26:18 detectron2]: [0mAP for class no. 5: 0.0
[32m[11/26 22:26:18 detectron2]: [0mAP for class no. 6: 0.0
[32m[11/26 22:26:18 detectron2]: [0mAP for class no. 7: 0.0
[32m[11/26 22:26:19 detectron2]: [0mAP for class no. 8: 0.0
[32m[11/26 22:26:19 detectron2]: [0mAP for class no. 9: 0.0
[32m[11/26 22:26:19 detectron2]: [0mAP for class no. 10: 0.0
[32m[11/26 22:26:19 detectron2]: [0mAP for class no. 11: 0.0
[32m[11/26 22:26:19 detectron2]: [0mAP for class no. 12: 0.0
[32m[11/26 22:26:19 detectron2]: [0mAP for class no. 13: 0.0
[32m[11/26 22:26:19 detectron2]: [0mAP for class no. 14: 0.0
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 15: 0.0
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 16: 0.0
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 17: 0.0
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 18: 0.0
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 19: 0.0
[32m[11/26 22:26:20 detectron2]: [0mmAP: 0.0
[32m[11/26 22:26:20 detectron2]: [0mCombined results for datasets custom_voc_2007_test, WR1_Mixed_Unknowns
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 0: 0.7918533086776733
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 1: 0.7716333866119385
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 2: 0.6370219588279724
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 3: 0.5901530385017395
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 4: 0.5282232165336609
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 5: 0.746399462223053
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 6: 0.7705965638160706
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 7: 0.8529040813446045
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 8: 0.5244924426078796
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 9: 0.6216275691986084
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 10: 0.4555111825466156
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 11: 0.7658306360244751
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 12: 0.7069307565689087
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 13: 0.8073904514312744
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 14: 0.7774863839149475
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 15: 0.40883171558380127
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 16: 0.6260425448417664
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 17: 0.5717162489891052
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 18: 0.8286178708076477
[32m[11/26 22:26:20 detectron2]: [0mAP for class no. 19: 0.6816982626914978
[32m[11/26 22:26:20 detectron2]: [0mmAP: 0.6732480525970459
[32m[11/26 22:26:21 detectron2]: [0m************************** Performance at Wilderness level 0.00 **************************
[32m[11/26 22:26:21 detectron2]: [0mAP for class no. 0 at wilderness 0.00: 0.8057809472084045
[32m[11/26 22:26:21 detectron2]: [0mAP for class no. 1 at wilderness 0.00: 0.7805687189102173
[32m[11/26 22:26:21 detectron2]: [0mAP for class no. 2 at wilderness 0.00: 0.6984018683433533
[32m[11/26 22:26:21 detectron2]: [0mAP for class no. 3 at wilderness 0.00: 0.6062182784080505
[32m[11/26 22:26:21 detectron2]: [0mAP for class no. 4 at wilderness 0.00: 0.5762754082679749
[32m[11/26 22:26:21 detectron2]: [0mAP for class no. 5 at wilderness 0.00: 0.7758022546768188
[32m[11/26 22:26:21 detectron2]: [0mAP for class no. 6 at wilderness 0.00: 0.7926622629165649
[32m[11/26 22:26:21 detectron2]: [0mAP for class no. 7 at wilderness 0.00: 0.8785473108291626
[32m[11/26 22:26:21 detectron2]: [0mAP for class no. 8 at wilderness 0.00: 0.5703489184379578
[32m[11/26 22:26:21 detectron2]: [0mAP for class no. 9 at wilderness 0.00: 0.7864148616790771
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 10 at wilderness 0.00: 0.6691756844520569
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 11 at wilderness 0.00: 0.8530338406562805
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 12 at wilderness 0.00: 0.8538536429405212
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 13 at wilderness 0.00: 0.8232013583183289
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 14 at wilderness 0.00: 0.786263108253479
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 15 at wilderness 0.00: 0.4942459166049957
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 16 at wilderness 0.00: 0.7428907155990601
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 17 at wilderness 0.00: 0.6694597601890564
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 18 at wilderness 0.00: 0.8503661751747131
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 19 at wilderness 0.00: 0.761128842830658
[32m[11/26 22:26:22 detectron2]: [0mmAP at wilderness 0.00: 0.7387319207191467
[32m[11/26 22:26:22 detectron2]: [0m************************** Performance at Wilderness level 0.10 **************************
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 0 at wilderness 0.10: 0.804438054561615
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 1 at wilderness 0.10: 0.7796733975410461
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 2 at wilderness 0.10: 0.6916602253913879
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 3 at wilderness 0.10: 0.6043260097503662
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 4 at wilderness 0.10: 0.5696216821670532
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 5 at wilderness 0.10: 0.7739158868789673
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 6 at wilderness 0.10: 0.7923885583877563
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 7 at wilderness 0.10: 0.8749563694000244
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 8 at wilderness 0.10: 0.566605806350708
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 9 at wilderness 0.10: 0.7556943893432617
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 10 at wilderness 0.10: 0.6324914693832397
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 11 at wilderness 0.10: 0.83938068151474
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 12 at wilderness 0.10: 0.8256282806396484
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 13 at wilderness 0.10: 0.8221734762191772
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 14 at wilderness 0.10: 0.7857226729393005
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 15 at wilderness 0.10: 0.4817371666431427
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 16 at wilderness 0.10: 0.7131674289703369
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 17 at wilderness 0.10: 0.6609698534011841
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 18 at wilderness 0.10: 0.8460453152656555
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 19 at wilderness 0.10: 0.752105176448822
[32m[11/26 22:26:22 detectron2]: [0mmAP at wilderness 0.10: 0.7286350131034851
[32m[11/26 22:26:22 detectron2]: [0m************************** Performance at Wilderness level 0.20 **************************
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 0 at wilderness 0.20: 0.8038345575332642
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 1 at wilderness 0.20: 0.7789458632469177
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 2 at wilderness 0.20: 0.6834487318992615
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 3 at wilderness 0.20: 0.6027707457542419
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 4 at wilderness 0.20: 0.5652671456336975
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 5 at wilderness 0.20: 0.7709394097328186
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 6 at wilderness 0.20: 0.7894852161407471
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 7 at wilderness 0.20: 0.8729369640350342
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 8 at wilderness 0.20: 0.5598958730697632
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 9 at wilderness 0.20: 0.726253092288971
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 10 at wilderness 0.20: 0.6147267818450928
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 11 at wilderness 0.20: 0.831853449344635
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 12 at wilderness 0.20: 0.8023135662078857
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 13 at wilderness 0.20: 0.8200926184654236
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 14 at wilderness 0.20: 0.7845827341079712
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 15 at wilderness 0.20: 0.4718787968158722
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 16 at wilderness 0.20: 0.6918453574180603
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 17 at wilderness 0.20: 0.6525976061820984
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 18 at wilderness 0.20: 0.8430576324462891
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 19 at wilderness 0.20: 0.742803156375885
[32m[11/26 22:26:22 detectron2]: [0mmAP at wilderness 0.20: 0.720476508140564
[32m[11/26 22:26:22 detectron2]: [0m************************** Performance at Wilderness level 0.30 **************************
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 0 at wilderness 0.30: 0.8021126389503479
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 1 at wilderness 0.30: 0.7785171270370483
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 2 at wilderness 0.30: 0.6781996488571167
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 3 at wilderness 0.30: 0.6018539071083069
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 4 at wilderness 0.30: 0.5566883683204651
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 5 at wilderness 0.30: 0.7685409784317017
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 6 at wilderness 0.30: 0.7867055535316467
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 7 at wilderness 0.30: 0.8666488528251648
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 8 at wilderness 0.30: 0.5532926321029663
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 9 at wilderness 0.30: 0.694472074508667
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 10 at wilderness 0.30: 0.5853824019432068
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 11 at wilderness 0.30: 0.8148987293243408
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 12 at wilderness 0.30: 0.7823802828788757
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 13 at wilderness 0.30: 0.818779706954956
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 14 at wilderness 0.30: 0.783781111240387
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 15 at wilderness 0.30: 0.4620145857334137
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 16 at wilderness 0.30: 0.6779978275299072
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 17 at wilderness 0.30: 0.6455773711204529
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 18 at wilderness 0.30: 0.8411329388618469
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 19 at wilderness 0.30: 0.7316110134124756
[32m[11/26 22:26:22 detectron2]: [0mmAP at wilderness 0.30: 0.7115293741226196
[32m[11/26 22:26:22 detectron2]: [0m************************** Performance at Wilderness level 0.40 **************************
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 0 at wilderness 0.40: 0.8021126389503479
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 1 at wilderness 0.40: 0.7769022583961487
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 2 at wilderness 0.40: 0.6708421111106873
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 3 at wilderness 0.40: 0.5991898775100708
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 4 at wilderness 0.40: 0.5525819659233093
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 5 at wilderness 0.40: 0.7674712538719177
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 6 at wilderness 0.40: 0.7853753566741943
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 7 at wilderness 0.40: 0.8651075959205627
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 8 at wilderness 0.40: 0.5485300421714783
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 9 at wilderness 0.40: 0.6755051016807556
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 10 at wilderness 0.40: 0.5651013255119324
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 11 at wilderness 0.40: 0.8073036074638367
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 12 at wilderness 0.40: 0.7736687064170837
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 13 at wilderness 0.40: 0.8166764974594116
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 14 at wilderness 0.40: 0.7825345396995544
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 15 at wilderness 0.40: 0.45249173045158386
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 16 at wilderness 0.40: 0.6693383455276489
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 17 at wilderness 0.40: 0.6324578523635864
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 18 at wilderness 0.40: 0.8387308716773987
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 19 at wilderness 0.40: 0.726614236831665
[32m[11/26 22:26:22 detectron2]: [0mmAP at wilderness 0.40: 0.7054267525672913
[32m[11/26 22:26:22 detectron2]: [0m************************** Performance at Wilderness level 0.50 **************************
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 0 at wilderness 0.50: 0.8012487888336182
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 1 at wilderness 0.50: 0.7758060097694397
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 2 at wilderness 0.50: 0.665831983089447
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 3 at wilderness 0.50: 0.5980117917060852
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 4 at wilderness 0.50: 0.5491288900375366
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 5 at wilderness 0.50: 0.7666521072387695
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 6 at wilderness 0.50: 0.7830488681793213
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 7 at wilderness 0.50: 0.8607292771339417
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 8 at wilderness 0.50: 0.5443422198295593
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 9 at wilderness 0.50: 0.6675994992256165
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 10 at wilderness 0.50: 0.5508967041969299
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 11 at wilderness 0.50: 0.8002014756202698
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 12 at wilderness 0.50: 0.7540932297706604
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 13 at wilderness 0.50: 0.8157699108123779
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 14 at wilderness 0.50: 0.7819458246231079
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 15 at wilderness 0.50: 0.4405312240123749
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 16 at wilderness 0.50: 0.6639074683189392
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 17 at wilderness 0.50: 0.6185230016708374
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 18 at wilderness 0.50: 0.836235761642456
[32m[11/26 22:26:22 detectron2]: [0mAP for class no. 19 at wilderness 0.50: 0.7149853110313416
[32m[11/26 22:26:22 detectron2]: [0mmAP at wilderness 0.50: 0.6994744539260864
[32m[11/26 22:26:22 detectron2]: [0m************************** Performance at Wilderness level 0.60 **************************
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 0 at wilderness 0.60: 0.7966757416725159
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 1 at wilderness 0.60: 0.7748817801475525
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 2 at wilderness 0.60: 0.6601483821868896
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 3 at wilderness 0.60: 0.5965762138366699
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 4 at wilderness 0.60: 0.5440265536308289
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 5 at wilderness 0.60: 0.7626188397407532
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 6 at wilderness 0.60: 0.7800941467285156
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 7 at wilderness 0.60: 0.8594475388526917
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 8 at wilderness 0.60: 0.5392826199531555
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 9 at wilderness 0.60: 0.6606490612030029
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 10 at wilderness 0.60: 0.5300838947296143
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 11 at wilderness 0.60: 0.7948688864707947
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 12 at wilderness 0.60: 0.7452663779258728
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 13 at wilderness 0.60: 0.8138160109519958
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 14 at wilderness 0.60: 0.7807357907295227
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 15 at wilderness 0.60: 0.42916011810302734
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 16 at wilderness 0.60: 0.6592159867286682
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 17 at wilderness 0.60: 0.6051133871078491
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 18 at wilderness 0.60: 0.8345475792884827
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 19 at wilderness 0.60: 0.7058286070823669
[32m[11/26 22:26:23 detectron2]: [0mmAP at wilderness 0.60: 0.6936518549919128
[32m[11/26 22:26:23 detectron2]: [0m************************** Performance at Wilderness level 0.70 **************************
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 0 at wilderness 0.70: 0.7958589196205139
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 1 at wilderness 0.70: 0.7733601927757263
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 2 at wilderness 0.70: 0.6554779410362244
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 3 at wilderness 0.70: 0.5954850316047668
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 4 at wilderness 0.70: 0.5386600494384766
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 5 at wilderness 0.70: 0.7579004764556885
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 6 at wilderness 0.70: 0.7753849625587463
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 7 at wilderness 0.70: 0.8579299449920654
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 8 at wilderness 0.70: 0.5357330441474915
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 9 at wilderness 0.70: 0.6533847451210022
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 10 at wilderness 0.70: 0.5144810080528259
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 11 at wilderness 0.70: 0.7861344814300537
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 12 at wilderness 0.70: 0.7389581203460693
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 13 at wilderness 0.70: 0.8119361996650696
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 14 at wilderness 0.70: 0.7797604203224182
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 15 at wilderness 0.70: 0.4227277636528015
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 16 at wilderness 0.70: 0.651122510433197
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 17 at wilderness 0.70: 0.5960981249809265
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 18 at wilderness 0.70: 0.8328948616981506
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 19 at wilderness 0.70: 0.6976178884506226
[32m[11/26 22:26:23 detectron2]: [0mmAP at wilderness 0.70: 0.6885453462600708
[32m[11/26 22:26:23 detectron2]: [0m************************** Performance at Wilderness level 0.80 **************************
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 0 at wilderness 0.80: 0.7941478490829468
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 1 at wilderness 0.80: 0.7725073099136353
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 2 at wilderness 0.80: 0.6468381285667419
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 3 at wilderness 0.80: 0.5937878489494324
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 4 at wilderness 0.80: 0.5349993705749512
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 5 at wilderness 0.80: 0.7492397427558899
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 6 at wilderness 0.80: 0.773811399936676
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 7 at wilderness 0.80: 0.8561802506446838
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 8 at wilderness 0.80: 0.5323456525802612
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 9 at wilderness 0.80: 0.6361750364303589
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 10 at wilderness 0.80: 0.48390886187553406
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 11 at wilderness 0.80: 0.7755957245826721
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 12 at wilderness 0.80: 0.7253042459487915
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 13 at wilderness 0.80: 0.809639573097229
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 14 at wilderness 0.80: 0.7793474197387695
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 15 at wilderness 0.80: 0.41905367374420166
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 16 at wilderness 0.80: 0.6441572904586792
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 17 at wilderness 0.80: 0.5905238389968872
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 18 at wilderness 0.80: 0.8312480449676514
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 19 at wilderness 0.80: 0.691842794418335
[32m[11/26 22:26:23 detectron2]: [0mmAP at wilderness 0.80: 0.6820327639579773
[32m[11/26 22:26:23 detectron2]: [0m************************** Performance at Wilderness level 0.90 **************************
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 0 at wilderness 0.90: 0.792736828327179
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 1 at wilderness 0.90: 0.7721492648124695
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 2 at wilderness 0.90: 0.6420716643333435
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 3 at wilderness 0.90: 0.5918503403663635
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 4 at wilderness 0.90: 0.5326953530311584
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 5 at wilderness 0.90: 0.7487021088600159
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 6 at wilderness 0.90: 0.7716047763824463
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 7 at wilderness 0.90: 0.854636549949646
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 8 at wilderness 0.90: 0.5296754240989685
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 9 at wilderness 0.90: 0.631446897983551
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 10 at wilderness 0.90: 0.4674086570739746
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 11 at wilderness 0.90: 0.7721824049949646
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 12 at wilderness 0.90: 0.7174472212791443
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 13 at wilderness 0.90: 0.8082637786865234
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 14 at wilderness 0.90: 0.7785699963569641
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 15 at wilderness 0.90: 0.41314542293548584
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 16 at wilderness 0.90: 0.6373736262321472
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 17 at wilderness 0.90: 0.5822394490242004
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 18 at wilderness 0.90: 0.8297340869903564
[32m[11/26 22:26:23 detectron2]: [0mAP for class no. 19 at wilderness 0.90: 0.6896778345108032
[32m[11/26 22:26:23 detectron2]: [0mmAP at wilderness 0.90: 0.6781805753707886
[32m[11/26 22:26:23 detectron2]: [0m************************** Performance at Wilderness level 1.00 **************************
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 0 at wilderness 1.00: 0.7918533086776733
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 1 at wilderness 1.00: 0.7716333866119385
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 2 at wilderness 1.00: 0.6370219588279724
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 3 at wilderness 1.00: 0.5901530385017395
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 4 at wilderness 1.00: 0.5282232165336609
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 5 at wilderness 1.00: 0.746399462223053
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 6 at wilderness 1.00: 0.7705965638160706
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 7 at wilderness 1.00: 0.8529040813446045
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 8 at wilderness 1.00: 0.5244924426078796
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 9 at wilderness 1.00: 0.6216275691986084
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 10 at wilderness 1.00: 0.4555111825466156
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 11 at wilderness 1.00: 0.7658306360244751
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 12 at wilderness 1.00: 0.7069307565689087
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 13 at wilderness 1.00: 0.8073904514312744
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 14 at wilderness 1.00: 0.7774851322174072
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 15 at wilderness 1.00: 0.40883171558380127
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 16 at wilderness 1.00: 0.6260425448417664
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 17 at wilderness 1.00: 0.5717162489891052
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 18 at wilderness 1.00: 0.8286178708076477
[32m[11/26 22:26:24 detectron2]: [0mAP for class no. 19 at wilderness 1.00: 0.6816982626914978
[32m[11/26 22:26:24 detectron2]: [0mmAP at wilderness 1.00: 0.6732479333877563

Could you confirm whether the discrepancy is reasonable on 4 GPUs.

@akshay-raj-dhamija , hello, can you explain how to obtain the Wilderness Impact in table 2 of the paper from the logging result?

Thanks,

Vastlab / Elephant-of-object-detection

RuntimeError: All input tensors must be on the same device. #3