mirror of https://github.com/JosephKJ/OWOD.git
Joseph
parent
9ecd3801f0
commit
92312cc043
configs/OWOD
detectron2
modeling/roi_heads
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@ -4,16 +4,13 @@ MODEL:
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# WEIGHTS: "/home/fk1/workspace/OWOD/output/expr_training_with_unk_with_clustering_Z_DIMENSION_256/model_final.pth"
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# WEIGHTS: "/home/fk1/workspace/OWOD/output/t1/model_final.pth"
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DATASETS:
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TRAIN: ('voc_2007_trainval', 'voc_2012_trainval', 't2_test_unk')
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TEST: ('voc_2007_test_unk', )
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# TEST: ('t2_all_test_unk', )
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# TEST: ('voc_2007_test','t2_test_unk', 't3_test_unk', 't4_test_unk')
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TRAIN: ('t1_voc_coco_2007_train', ) # t1_voc_coco_2007_train, t1_voc_coco_2007_ft
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TEST: ('voc_coco_2007_test', ) # t1_voc_coco_2007_test, t1_voc_coco_2007_val
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SOLVER:
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STEPS: (12000, 16000)
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MAX_ITER: 18000
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MAX_ITER: 100
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WARMUP_ITERS: 100
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OUTPUT_DIR: "./output/expr_training_with_unk_with_clustering_cdist_10"
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OUTPUT_DIR: "./output/t1_train"
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OWOD:
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PREV_INTRODUCED_CLS: 0
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CUR_INTRODUCED_CLS: 20
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
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File diff suppressed because it is too large
Load Diff
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@ -0,0 +1,300 @@
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2011_001862
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2008_008725
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2008_002776
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||||
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|
||||
000000397184
|
||||
000000308849
|
||||
000000350842
|
||||
000000489218
|
||||
000000517084
|
||||
000000275657
|
||||
000000246197
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,400 @@
|
|||
2010_000889
|
||||
007089
|
||||
004746
|
||||
2009_004532
|
||||
2011_000780
|
||||
009287
|
||||
2008_006382
|
||||
2010_001934
|
||||
2008_004036
|
||||
2010_004173
|
||||
2008_000176
|
||||
002045
|
||||
2011_001862
|
||||
2011_000477
|
||||
2011_002697
|
||||
006233
|
||||
2008_008725
|
||||
2010_001036
|
||||
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|
||||
2009_002676
|
||||
2008_002776
|
||||
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|
||||
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|
||||
2009_001326
|
||||
2011_000725
|
||||
2011_000485
|
||||
2008_006543
|
||||
2009_003522
|
||||
2008_008511
|
||||
009603
|
||||
2008_008206
|
||||
006719
|
||||
2009_002054
|
||||
2009_001493
|
||||
2011_002674
|
||||
002891
|
||||
007524
|
||||
2009_002317
|
||||
2009_003087
|
||||
2008_001199
|
||||
2011_001854
|
||||
001858
|
||||
2010_005922
|
||||
2008_006282
|
||||
002058
|
||||
004867
|
||||
003611
|
||||
003301
|
||||
2008_001649
|
||||
2010_003779
|
||||
2010_002873
|
||||
2010_003467
|
||||
2009_001812
|
||||
009283
|
||||
009773
|
||||
2008_004431
|
||||
002055
|
||||
2011_000971
|
||||
2009_003613
|
||||
2010_005731
|
||||
2010_001315
|
||||
2008_004515
|
||||
2010_005424
|
||||
2008_006682
|
||||
2008_000817
|
||||
000294
|
||||
2008_006433
|
||||
2010_000602
|
||||
2008_008043
|
||||
2009_001848
|
||||
2009_004986
|
||||
2008_004106
|
||||
2011_001611
|
||||
006124
|
||||
2008_007124
|
||||
2009_002792
|
||||
2008_006285
|
||||
007868
|
||||
2008_001070
|
||||
2008_008671
|
||||
2008_000626
|
||||
2008_000078
|
||||
009734
|
||||
007809
|
||||
2008_000815
|
||||
2010_000254
|
||||
2010_003630
|
||||
006896
|
||||
2011_003059
|
||||
004033
|
||||
002757
|
||||
007040
|
||||
009166
|
||||
001110
|
||||
2008_003881
|
||||
2008_000619
|
||||
2010_002747
|
||||
2009_003507
|
||||
009155
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
000000024061
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
000000368321
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -199,7 +199,7 @@ def print_instances_class_histogram(dataset_dicts, class_names):
|
|||
|
||||
|
||||
def get_detection_dataset_dicts(
|
||||
dataset_names, filter_empty=True, min_keypoints=0, proposal_files=None
|
||||
dataset_names, filter_empty=True, min_keypoints=0, proposal_files=None, cfg=None
|
||||
):
|
||||
"""
|
||||
Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation.
|
||||
|
|
@ -236,6 +236,17 @@ def get_detection_dataset_dicts(
|
|||
if min_keypoints > 0 and has_instances:
|
||||
dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints)
|
||||
|
||||
d_name = dataset_names[0]
|
||||
if 'voc_coco' in d_name:
|
||||
if 'train' in d_name:
|
||||
dataset_dicts = remove_prev_class_and_unk_instances(cfg, dataset_dicts)
|
||||
elif 'test' in d_name:
|
||||
dataset_dicts = label_known_class_and_unknown(cfg, dataset_dicts)
|
||||
elif 'val' in d_name:
|
||||
dataset_dicts = label_known_class_and_unknown(cfg, dataset_dicts)
|
||||
elif 'ft' in d_name:
|
||||
dataset_dicts = remove_unknown_instances(cfg, dataset_dicts)
|
||||
|
||||
if has_instances:
|
||||
try:
|
||||
class_names = MetadataCatalog.get(dataset_names[0]).thing_classes
|
||||
|
|
@ -247,6 +258,66 @@ def get_detection_dataset_dicts(
|
|||
assert len(dataset_dicts), "No valid data found in {}.".format(",".join(dataset_names))
|
||||
return dataset_dicts
|
||||
|
||||
def remove_prev_class_and_unk_instances(cfg, dataset_dicts):
|
||||
# For training data.
|
||||
prev_intro_cls = cfg.OWOD.PREV_INTRODUCED_CLS
|
||||
curr_intro_cls = cfg.OWOD.CUR_INTRODUCED_CLS
|
||||
valid_classes = range(prev_intro_cls, curr_intro_cls)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.info("Valid classes: " + str(valid_classes))
|
||||
logger.info("Removing earlier seen class objects and the unknown objects...")
|
||||
|
||||
for entry in copy.copy(dataset_dicts):
|
||||
annos = entry["annotations"]
|
||||
for annotation in copy.copy(annos):
|
||||
if annotation["category_id"] not in valid_classes:
|
||||
annos.remove(annotation)
|
||||
if len(annos) == 0:
|
||||
dataset_dicts.remove(entry)
|
||||
|
||||
return dataset_dicts
|
||||
|
||||
def remove_unknown_instances(cfg, dataset_dicts):
|
||||
# For finetune data.
|
||||
prev_intro_cls = cfg.OWOD.PREV_INTRODUCED_CLS
|
||||
curr_intro_cls = cfg.OWOD.CUR_INTRODUCED_CLS
|
||||
valid_classes = range(0, prev_intro_cls+curr_intro_cls)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.info("Valid classes: " + str(valid_classes))
|
||||
logger.info("Removing the unknown objects...")
|
||||
|
||||
for entry in copy.copy(dataset_dicts):
|
||||
annos = entry["annotations"]
|
||||
for annotation in copy.copy(annos):
|
||||
if annotation["category_id"] not in valid_classes:
|
||||
annos.remove(annotation)
|
||||
if len(annos) == 0:
|
||||
dataset_dicts.remove(entry)
|
||||
|
||||
return dataset_dicts
|
||||
|
||||
def label_known_class_and_unknown(cfg, dataset_dicts):
|
||||
# For test and validation data.
|
||||
# Label known instances the corresponding label and unknown instances as unknown.
|
||||
prev_intro_cls = cfg.OWOD.PREV_INTRODUCED_CLS
|
||||
curr_intro_cls = cfg.OWOD.CUR_INTRODUCED_CLS
|
||||
total_num_class = cfg.MODEL.ROI_HEADS.NUM_CLASSES
|
||||
|
||||
known_classes = range(0, prev_intro_cls+curr_intro_cls)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.info("Known classes: " + str(known_classes))
|
||||
logger.info("Labelling known instances the corresponding label, and unknown instances as unknown...")
|
||||
|
||||
for entry in dataset_dicts:
|
||||
annos = entry["annotations"]
|
||||
for annotation in annos:
|
||||
if annotation["category_id"] not in known_classes:
|
||||
annotation["category_id"] = total_num_class
|
||||
|
||||
return dataset_dicts
|
||||
|
||||
def build_batch_data_loader(
|
||||
dataset, sampler, total_batch_size, *, aspect_ratio_grouping=False, num_workers=0
|
||||
|
|
@ -326,6 +397,7 @@ def build_detection_train_loader(cfg, mapper=None):
|
|||
if cfg.MODEL.KEYPOINT_ON
|
||||
else 0,
|
||||
proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
|
||||
cfg=cfg,
|
||||
)
|
||||
dataset = DatasetFromList(dataset_dicts, copy=False)
|
||||
|
||||
|
|
@ -380,6 +452,7 @@ def build_detection_test_loader(cfg, dataset_name, mapper=None):
|
|||
]
|
||||
if cfg.MODEL.LOAD_PROPOSALS
|
||||
else None,
|
||||
cfg=cfg
|
||||
)
|
||||
|
||||
dataset = DatasetFromList(dataset_dicts)
|
||||
|
|
|
|||
|
|
@ -214,7 +214,16 @@ def register_all_pascal_voc(root):
|
|||
("voc_2012_trainval", "VOC2012", "trainval"),
|
||||
("voc_2012_train", "VOC2012", "train"),
|
||||
("voc_2012_val", "VOC2012", "val"),
|
||||
("voc_2007_test_unk", "VOC2007", "test_unk"),
|
||||
("t1_voc_coco_2007_train", "VOC2007", "t1_train"),
|
||||
("voc_coco_2007_test", "VOC2007", "all_task_test"),
|
||||
("voc_coco_2007_val", "VOC2007", "all_task_val"),
|
||||
("t1_voc_coco_2007_ft", "VOC2007", "t1_ft"),
|
||||
("t2_voc_coco_2007_train", "VOC2007", "t2_train"),
|
||||
("t2_voc_coco_2007_ft", "VOC2007", "t2_ft"),
|
||||
("t3_voc_coco_2007_train", "VOC2007", "t3_train"),
|
||||
("t3_voc_coco_2007_ft", "VOC2007", "t3_ft"),
|
||||
("t4_voc_coco_2007_train", "VOC2007", "t4_train"),
|
||||
("t4_voc_coco_2007_ft", "VOC2007", "t4_ft"),
|
||||
]
|
||||
for name, dirname, split in SPLITS:
|
||||
year = 2007 if "2007" in name else 2012
|
||||
|
|
@ -222,23 +231,23 @@ def register_all_pascal_voc(root):
|
|||
MetadataCatalog.get(name).evaluator_type = "pascal_voc"
|
||||
|
||||
|
||||
def register_all_voc_style_coco(root):
|
||||
SPLITS = [
|
||||
("t2_train", "coco17_voc_style"),
|
||||
("t2_test", "coco17_voc_style"),
|
||||
("t2_test_unk", "coco17_voc_style"),
|
||||
("t3_train", "coco17_voc_style"),
|
||||
("t3_test", "coco17_voc_style"),
|
||||
("t3_test_unk", "coco17_voc_style"),
|
||||
("t4_train", "coco17_voc_style"),
|
||||
("t4_test", "coco17_voc_style"),
|
||||
("t4_test_unk", "coco17_voc_style"),
|
||||
("t2_all_test_unk", "coco17_voc_style")
|
||||
]
|
||||
for name, dirname in SPLITS:
|
||||
year = 2007
|
||||
register_voc_style_coco(name, os.path.join(root, dirname), name, year)
|
||||
MetadataCatalog.get(name).evaluator_type = "pascal_voc"
|
||||
# def register_all_voc_style_coco(root):
|
||||
# SPLITS = [
|
||||
# ("t2_train", "coco17_voc_style"),
|
||||
# ("t2_test", "coco17_voc_style"),
|
||||
# ("t2_test_unk", "coco17_voc_style"),
|
||||
# ("t3_train", "coco17_voc_style"),
|
||||
# ("t3_test", "coco17_voc_style"),
|
||||
# ("t3_test_unk", "coco17_voc_style"),
|
||||
# ("t4_train", "coco17_voc_style"),
|
||||
# ("t4_test", "coco17_voc_style"),
|
||||
# ("t4_test_unk", "coco17_voc_style"),
|
||||
# ("t2_all_test_unk", "coco17_voc_style")
|
||||
# ]
|
||||
# for name, dirname in SPLITS:
|
||||
# year = 2007
|
||||
# register_voc_style_coco(name, os.path.join(root, dirname), name, year)
|
||||
# MetadataCatalog.get(name).evaluator_type = "pascal_voc"
|
||||
|
||||
|
||||
def register_all_ade20k(root):
|
||||
|
|
@ -270,5 +279,5 @@ if __name__.endswith(".builtin"):
|
|||
# register_all_pascal_voc(_root)
|
||||
# register_all_pascal_voc('/home/joseph/workspace/OWOD/datasets')
|
||||
register_all_pascal_voc('/home/fk1/workspace/OWOD/datasets')
|
||||
register_all_voc_style_coco('/home/fk1/workspace/OWOD/datasets')
|
||||
# register_all_voc_style_coco('/home/fk1/workspace/OWOD/datasets')
|
||||
register_all_ade20k(_root)
|
||||
|
|
|
|||
|
|
@ -19,12 +19,53 @@ __all__ = ["load_voc_instances", "register_pascal_voc"]
|
|||
# "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
|
||||
# "pottedplant", "sheep", "sofa", "train", "tvmonitor"
|
||||
# )
|
||||
CLASS_NAMES = (
|
||||
# CLASS_NAMES = (
|
||||
# "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
|
||||
# "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
|
||||
# "pottedplant", "sheep", "sofa", "train", "tvmonitor", "unknown"
|
||||
# )
|
||||
# fmt: on
|
||||
|
||||
VOC_CLASS_NAMES_COCOFIED = [
|
||||
"airplane", "dining table", "motorcycle",
|
||||
"potted plant", "couch", "tv"
|
||||
]
|
||||
|
||||
BASE_VOC_CLASS_NAMES = [
|
||||
"aeroplane", "diningtable", "motorbike",
|
||||
"pottedplant", "sofa", "tvmonitor"
|
||||
]
|
||||
|
||||
VOC_CLASS_NAMES = [
|
||||
"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
|
||||
"chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
|
||||
"pottedplant", "sheep", "sofa", "train", "tvmonitor", "unknown"
|
||||
)
|
||||
# fmt: on
|
||||
"pottedplant", "sheep", "sofa", "train", "tvmonitor"
|
||||
]
|
||||
|
||||
T2_CLASS_NAMES = [
|
||||
"truck", "traffic light", "fire hydrant", "stop sign", "parking meter",
|
||||
"bench", "elephant", "bear", "zebra", "giraffe",
|
||||
"backpack", "umbrella", "handbag", "tie", "suitcase",
|
||||
"microwave", "oven", "toaster", "sink", "refrigerator"
|
||||
]
|
||||
|
||||
T3_CLASS_NAMES = [
|
||||
"frisbee", "skis", "snowboard", "sports ball", "kite",
|
||||
"baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket",
|
||||
"banana", "apple", "sandwich", "orange", "broccoli",
|
||||
"carrot", "hot dog", "pizza", "donut", "cake"
|
||||
]
|
||||
|
||||
T4_CLASS_NAMES = [
|
||||
"bed", "toilet", "laptop", "mouse",
|
||||
"remote", "keyboard", "cell phone", "book", "clock",
|
||||
"vase", "scissors", "teddy bear", "hair drier", "toothbrush",
|
||||
"wine glass", "cup", "fork", "knife", "spoon", "bowl"
|
||||
]
|
||||
|
||||
UNK_CLASS = ["unknown"]
|
||||
|
||||
VOC_COCO_CLASS_NAMES = tuple(itertools.chain(VOC_CLASS_NAMES, T2_CLASS_NAMES, T3_CLASS_NAMES, T4_CLASS_NAMES, UNK_CLASS))
|
||||
|
||||
def load_voc_instances(dirname: str, split: str, class_names: Union[List[str], Tuple[str, ...]]):
|
||||
"""
|
||||
|
|
@ -42,11 +83,6 @@ def load_voc_instances(dirname: str, split: str, class_names: Union[List[str], T
|
|||
annotation_dirname = PathManager.get_local_path(os.path.join(dirname, "Annotations/"))
|
||||
dicts = []
|
||||
for fileid in fileids:
|
||||
has_unk = False
|
||||
if 'unk' in fileid:
|
||||
has_unk = True
|
||||
fileid = fileid.replace('_unk','')
|
||||
|
||||
anno_file = os.path.join(annotation_dirname, fileid + ".xml")
|
||||
jpeg_file = os.path.join(dirname, "JPEGImages", fileid + ".jpg")
|
||||
|
||||
|
|
@ -63,11 +99,8 @@ def load_voc_instances(dirname: str, split: str, class_names: Union[List[str], T
|
|||
|
||||
for obj in tree.findall("object"):
|
||||
cls = obj.find("name").text
|
||||
if has_unk:
|
||||
if cls not in class_names:
|
||||
cls = 'unknown'
|
||||
# else:
|
||||
# continue
|
||||
if cls in VOC_CLASS_NAMES_COCOFIED:
|
||||
cls = BASE_VOC_CLASS_NAMES[VOC_CLASS_NAMES_COCOFIED.index(cls)]
|
||||
# We include "difficult" samples in training.
|
||||
# Based on limited experiments, they don't hurt accuracy.
|
||||
# difficult = int(obj.find("difficult").text)
|
||||
|
|
@ -89,7 +122,11 @@ def load_voc_instances(dirname: str, split: str, class_names: Union[List[str], T
|
|||
return dicts
|
||||
|
||||
|
||||
def register_pascal_voc(name, dirname, split, year, class_names=CLASS_NAMES):
|
||||
def register_pascal_voc(name, dirname, split, year):
|
||||
if "voc_coco" in name:
|
||||
class_names = VOC_COCO_CLASS_NAMES
|
||||
else:
|
||||
class_names = tuple(VOC_CLASS_NAMES)
|
||||
DatasetCatalog.register(name, lambda: load_voc_instances(dirname, split, class_names))
|
||||
MetadataCatalog.get(name).set(
|
||||
thing_classes=list(class_names), dirname=dirname, year=year, split=split
|
||||
|
|
|
|||
|
|
@ -0,0 +1,96 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
||||
|
||||
import numpy as np
|
||||
import os
|
||||
import xml.etree.ElementTree as ET
|
||||
from typing import List, Tuple, Union
|
||||
from fvcore.common.file_io import PathManager
|
||||
|
||||
from detectron2.data import DatasetCatalog, MetadataCatalog
|
||||
from detectron2.structures import BoxMode
|
||||
|
||||
__all__ = ["load_voc_instances", "register_pascal_voc"]
|
||||
|
||||
|
||||
# fmt: off
|
||||
# CLASS_NAMES = (
|
||||
# "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
|
||||
# "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
|
||||
# "pottedplant", "sheep", "sofa", "train", "tvmonitor"
|
||||
# )
|
||||
CLASS_NAMES = (
|
||||
"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
|
||||
"chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
|
||||
"pottedplant", "sheep", "sofa", "train", "tvmonitor", "unknown"
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
def load_voc_instances(dirname: str, split: str, class_names: Union[List[str], Tuple[str, ...]]):
|
||||
"""
|
||||
Load Pascal VOC detection annotations to Detectron2 format.
|
||||
|
||||
Args:
|
||||
dirname: Contain "Annotations", "ImageSets", "JPEGImages"
|
||||
split (str): one of "train", "test", "val", "trainval"
|
||||
class_names: list or tuple of class names
|
||||
"""
|
||||
with PathManager.open(os.path.join(dirname, "ImageSets", "Main", split + ".txt")) as f:
|
||||
fileids = np.loadtxt(f, dtype=np.str)
|
||||
|
||||
# Needs to read many small annotation files. Makes sense at local
|
||||
annotation_dirname = PathManager.get_local_path(os.path.join(dirname, "Annotations/"))
|
||||
dicts = []
|
||||
for fileid in fileids:
|
||||
has_unk = False
|
||||
if 'unk' in fileid:
|
||||
has_unk = True
|
||||
fileid = fileid.replace('_unk','')
|
||||
|
||||
anno_file = os.path.join(annotation_dirname, fileid + ".xml")
|
||||
jpeg_file = os.path.join(dirname, "JPEGImages", fileid + ".jpg")
|
||||
|
||||
with PathManager.open(anno_file) as f:
|
||||
tree = ET.parse(f)
|
||||
|
||||
r = {
|
||||
"file_name": jpeg_file,
|
||||
"image_id": fileid,
|
||||
"height": int(tree.findall("./size/height")[0].text),
|
||||
"width": int(tree.findall("./size/width")[0].text),
|
||||
}
|
||||
instances = []
|
||||
|
||||
for obj in tree.findall("object"):
|
||||
cls = obj.find("name").text
|
||||
if has_unk:
|
||||
if cls not in class_names:
|
||||
cls = 'unknown'
|
||||
# else:
|
||||
# continue
|
||||
# We include "difficult" samples in training.
|
||||
# Based on limited experiments, they don't hurt accuracy.
|
||||
# difficult = int(obj.find("difficult").text)
|
||||
# if difficult == 1:
|
||||
# continue
|
||||
bbox = obj.find("bndbox")
|
||||
bbox = [float(bbox.find(x).text) for x in ["xmin", "ymin", "xmax", "ymax"]]
|
||||
# Original annotations are integers in the range [1, W or H]
|
||||
# Assuming they mean 1-based pixel indices (inclusive),
|
||||
# a box with annotation (xmin=1, xmax=W) covers the whole image.
|
||||
# In coordinate space this is represented by (xmin=0, xmax=W)
|
||||
bbox[0] -= 1.0
|
||||
bbox[1] -= 1.0
|
||||
instances.append(
|
||||
{"category_id": class_names.index(cls), "bbox": bbox, "bbox_mode": BoxMode.XYXY_ABS}
|
||||
)
|
||||
r["annotations"] = instances
|
||||
dicts.append(r)
|
||||
return dicts
|
||||
|
||||
|
||||
def register_pascal_voc(name, dirname, split, year, class_names=CLASS_NAMES):
|
||||
DatasetCatalog.register(name, lambda: load_voc_instances(dirname, split, class_names))
|
||||
MetadataCatalog.get(name).set(
|
||||
thing_classes=list(class_names), dirname=dirname, year=year, split=split
|
||||
)
|
||||
|
|
@ -627,7 +627,7 @@ class FastRCNNOutputLayers(nn.Module):
|
|||
if item == None:
|
||||
all_means[i] = torch.zeros((length))
|
||||
|
||||
distances = torch.cdist(fg_features, torch.stack(all_means).cuda(), p=2.0)
|
||||
distances = torch.cdist(fg_features, torch.stack(all_means).cuda(), p=10.0)
|
||||
labels = []
|
||||
|
||||
for index, feature in enumerate(fg_features):
|
||||
|
|
|
|||
Loading…
Reference in New Issue