Thanks so much for the cool project. I tried to reproduce the results reported in your paper and failed to reproduce it though I did not change anything. Here is the result of icarl training on cifar100 base 0:
It would be great if you could run the same experiment from your side and compare with what I have here. Thanks and really appreciate your help.
2023-05-22 17:20:04,654 [trainer.py] => prefix: reproduce
2023-05-22 17:20:04,654 [trainer.py] => dataset: cifar100
2023-05-22 17:20:04,654 [trainer.py] => memory_size: 2000
2023-05-22 17:20:04,654 [trainer.py] => memory_per_class: 20
2023-05-22 17:20:04,654 [trainer.py] => fixed_memory: False
2023-05-22 17:20:04,654 [trainer.py] => shuffle: True
2023-05-22 17:20:04,654 [trainer.py] => init_cls: 10
2023-05-22 17:20:04,654 [trainer.py] => increment: 10
2023-05-22 17:20:04,654 [trainer.py] => model_name: icarl
2023-05-22 17:20:04,654 [trainer.py] => convnet_type: resnet32
2023-05-22 17:20:04,654 [trainer.py] => device: [device(type='cuda', index=0), device(type='cuda', index=1), device(type='cuda', index=2), device(type='cuda', index=3)]
2023-05-22 17:20:04,654 [trainer.py] => seed: 1993
2023-05-22 17:20:06,016 [data_manager.py] => [68, 56, 78, 8, 23, 84, 90, 65, 74, 76, 40, 89, 3, 92, 55, 9, 26, 80, 43, 38, 58, 70, 77, 1, 85, 19, 17, 50, 28, 53, 13, 81, 45, 82, 6, 59, 83, 16, 15, 44, 91, 41, 72, 60, 79, 52, 20, 10, 31, 54, 37, 95, 14, 71, 96, 98, 97, 2, 64, 66, 42, 22, 35, 86, 24, 34, 87, 21, 99, 0, 88, 27, 18, 94, 11, 12, 47, 25, 30, 46, 62, 69, 36, 61, 7, 63, 75, 5, 32, 4, 51, 48, 73, 93, 39, 67, 29, 49, 57, 33]
2023-05-22 17:20:06,132 [trainer.py] => All params: 464154
2023-05-22 17:20:06,132 [trainer.py] => Trainable params: 464154
2023-05-22 17:20:06,132 [icarl.py] => Learning on 0-10
2023-05-22 18:17:16,663 [icarl.py] => Task 0, Epoch 200/200 => Loss 0.038, Train_accy 99.44
2023-05-22 18:17:16,663 [base.py] => Reducing exemplars...(200 per classes)
2023-05-22 18:17:16,663 [base.py] => Constructing exemplars...(200 per classes)
2023-05-22 18:17:29,541 [icarl.py] => Exemplar size: 2000
2023-05-22 18:17:29,541 [trainer.py] => CNN: {'total': 91.2, '00-09': 91.2, 'old': 0, 'new': 91.2}
2023-05-22 18:17:29,541 [trainer.py] => NME: {'total': 91.6, '00-09': 91.6, 'old': 0, 'new': 91.6}
2023-05-22 18:17:29,541 [trainer.py] => CNN top1 curve: [91.2]
2023-05-22 18:17:29,541 [trainer.py] => CNN top5 curve: [99.3]
2023-05-22 18:17:29,541 [trainer.py] => NME top1 curve: [91.6]
2023-05-22 18:17:29,541 [trainer.py] => NME top5 curve: [99.8]
2023-05-22 18:17:29,541 [trainer.py] => All params: 464804
2023-05-22 18:17:29,541 [trainer.py] => Trainable params: 464804
2023-05-22 18:17:29,545 [icarl.py] => Learning on 10-20
2023-05-22 19:24:28,935 [icarl.py] => Task 1, Epoch 170/170 => Loss 1.043, Train_accy 99.64
2023-05-22 19:24:28,935 [base.py] => Reducing exemplars...(100 per classes)
2023-05-22 19:24:33,625 [base.py] => Constructing exemplars...(100 per classes)
2023-05-22 19:24:45,992 [icarl.py] => Exemplar size: 2000
2023-05-22 19:24:45,992 [trainer.py] => CNN: {'total': 79.6, '00-09': 79.6, '10-19': 79.6, 'old': 79.6, 'new': 79.6}
2023-05-22 19:24:45,992 [trainer.py] => NME: {'total': 79.1, '00-09': 81.8, '10-19': 76.4, 'old': 81.8, 'new': 76.4}
2023-05-22 19:24:45,992 [trainer.py] => CNN top1 curve: [91.2, 79.6]
2023-05-22 19:24:45,992 [trainer.py] => CNN top5 curve: [99.3, 96.5]
2023-05-22 19:24:45,992 [trainer.py] => NME top1 curve: [91.6, 79.1]
2023-05-22 19:24:45,992 [trainer.py] => NME top5 curve: [99.8, 96.3]
2023-05-22 19:24:45,992 [trainer.py] => All params: 465454
2023-05-22 19:24:45,992 [trainer.py] => Trainable params: 465454
2023-05-22 19:24:45,999 [icarl.py] => Learning on 20-30
2023-05-22 20:31:37,699 [icarl.py] => Task 2, Epoch 170/170 => Loss 1.192, Train_accy 99.81
2023-05-22 20:31:37,700 [base.py] => Reducing exemplars...(66 per classes)
2023-05-22 20:31:47,398 [base.py] => Constructing exemplars...(66 per classes)
2023-05-22 20:31:59,640 [icarl.py] => Exemplar size: 1980
2023-05-22 20:31:59,640 [trainer.py] => CNN: {'total': 73.17, '00-09': 71.6, '10-19': 58.9, '20-29': 89.0, 'old': 65.25, 'new': 89.0}
2023-05-22 20:31:59,640 [trainer.py] => NME: {'total': 75.4, '00-09': 76.7, '10-19': 65.1, '20-29': 84.4, 'old': 70.9, 'new': 84.4}
2023-05-22 20:31:59,640 [trainer.py] => CNN top1 curve: [91.2, 79.6, 73.17]
2023-05-22 20:31:59,641 [trainer.py] => CNN top5 curve: [99.3, 96.5, 94.27]
2023-05-22 20:31:59,641 [trainer.py] => NME top1 curve: [91.6, 79.1, 75.4]
2023-05-22 20:31:59,641 [trainer.py] => NME top5 curve: [99.8, 96.3, 94.27]
2023-05-22 20:31:59,641 [trainer.py] => All params: 466104
2023-05-22 20:31:59,641 [trainer.py] => Trainable params: 466104
2023-05-22 20:31:59,648 [icarl.py] => Learning on 30-40
2023-05-22 21:35:22,460 [icarl.py] => Task 3, Epoch 170/170 => Loss 1.494, Train_accy 99.89
2023-05-22 21:35:22,461 [base.py] => Reducing exemplars...(50 per classes)
2023-05-22 21:35:36,634 [base.py] => Constructing exemplars...(50 per classes)
2023-05-22 21:35:48,986 [icarl.py] => Exemplar size: 2000
2023-05-22 21:35:48,987 [trainer.py] => CNN: {'total': 64.18, '00-09': 62.3, '10-19': 46.0, '20-29': 65.7, '30-39': 82.7, 'old': 58.0, 'new': 82.7}
2023-05-22 21:35:48,987 [trainer.py] => NME: {'total': 69.0, '00-09': 71.2, '10-19': 55.4, '20-29': 71.8, '30-39': 77.6, 'old': 66.13, 'new': 77.6}
2023-05-22 21:35:48,987 [trainer.py] => CNN top1 curve: [91.2, 79.6, 73.17, 64.18]
2023-05-22 21:35:48,987 [trainer.py] => CNN top5 curve: [99.3, 96.5, 94.27, 91.05]
2023-05-22 21:35:48,987 [trainer.py] => NME top1 curve: [91.6, 79.1, 75.4, 69.0]
2023-05-22 21:35:48,987 [trainer.py] => NME top5 curve: [99.8, 96.3, 94.27, 91.32]
2023-05-22 21:35:48,987 [trainer.py] => All params: 466754
2023-05-22 21:35:48,987 [trainer.py] => Trainable params: 466754
2023-05-22 21:35:48,991 [icarl.py] => Learning on 40-50
2023-05-22 23:03:43,196 [trainer.py] => config: ./exps/icarl.json
2023-05-22 23:03:43,196 [trainer.py] => prefix: reproduce
2023-05-22 23:03:43,196 [trainer.py] => dataset: cifar100
2023-05-22 23:03:43,196 [trainer.py] => memory_size: 2000
2023-05-22 23:03:43,196 [trainer.py] => memory_per_class: 20
2023-05-22 23:03:43,196 [trainer.py] => fixed_memory: False
2023-05-22 23:03:43,196 [trainer.py] => shuffle: True
2023-05-22 23:03:43,196 [trainer.py] => init_cls: 10
2023-05-22 23:03:43,196 [trainer.py] => increment: 10
2023-05-22 23:03:43,196 [trainer.py] => model_name: icarl
2023-05-22 23:03:43,196 [trainer.py] => convnet_type: resnet32
2023-05-22 23:03:43,197 [trainer.py] => device: [device(type='cuda', index=0), device(type='cuda', index=1), device(type='cuda', index=2), device(type='cuda', index=3)]
2023-05-22 23:03:43,197 [trainer.py] => seed: 1993
2023-05-22 23:03:44,415 [data_manager.py] => [68, 56, 78, 8, 23, 84, 90, 65, 74, 76, 40, 89, 3, 92, 55, 9, 26, 80, 43, 38, 58, 70, 77, 1, 85, 19, 17, 50, 28, 53, 13, 81, 45, 82, 6, 59, 83, 16, 15, 44, 91, 41, 72, 60, 79, 52, 20, 10, 31, 54, 37, 95, 14, 71, 96, 98, 97, 2, 64, 66, 42, 22, 35, 86, 24, 34, 87, 21, 99, 0, 88, 27, 18, 94, 11, 12, 47, 25, 30, 46, 62, 69, 36, 61, 7, 63, 75, 5, 32, 4, 51, 48, 73, 93, 39, 67, 29, 49, 57, 33]
2023-05-22 23:03:44,523 [trainer.py] => All params: 464154
2023-05-22 23:03:44,524 [trainer.py] => Trainable params: 464154
2023-05-22 23:03:44,524 [icarl.py] => Learning on 0-10
2023-05-22 23:05:02,666 [trainer.py] => config: ./exps/icarl.json
2023-05-22 23:05:02,666 [trainer.py] => prefix: reproduce
2023-05-22 23:05:02,666 [trainer.py] => dataset: cifar100
2023-05-22 23:05:02,666 [trainer.py] => memory_size: 2000
2023-05-22 23:05:02,666 [trainer.py] => memory_per_class: 20
2023-05-22 23:05:02,666 [trainer.py] => fixed_memory: False
2023-05-22 23:05:02,666 [trainer.py] => shuffle: True
2023-05-22 23:05:02,666 [trainer.py] => init_cls: 10
2023-05-22 23:05:02,666 [trainer.py] => increment: 10
2023-05-22 23:05:02,666 [trainer.py] => model_name: icarl
2023-05-22 23:05:02,666 [trainer.py] => convnet_type: resnet32
2023-05-22 23:05:02,666 [trainer.py] => device: [device(type='cuda', index=0), device(type='cuda', index=1), device(type='cuda', index=2), device(type='cuda', index=3)]
2023-05-22 23:05:02,667 [trainer.py] => seed: 1993
2023-05-22 23:05:03,899 [data_manager.py] => [68, 56, 78, 8, 23, 84, 90, 65, 74, 76, 40, 89, 3, 92, 55, 9, 26, 80, 43, 38, 58, 70, 77, 1, 85, 19, 17, 50, 28, 53, 13, 81, 45, 82, 6, 59, 83, 16, 15, 44, 91, 41, 72, 60, 79, 52, 20, 10, 31, 54, 37, 95, 14, 71, 96, 98, 97, 2, 64, 66, 42, 22, 35, 86, 24, 34, 87, 21, 99, 0, 88, 27, 18, 94, 11, 12, 47, 25, 30, 46, 62, 69, 36, 61, 7, 63, 75, 5, 32, 4, 51, 48, 73, 93, 39, 67, 29, 49, 57, 33]
2023-05-22 23:05:04,014 [trainer.py] => All params: 464154
2023-05-22 23:05:04,014 [trainer.py] => Trainable params: 464154
2023-05-22 23:05:04,014 [icarl.py] => Learning on 0-10
2023-05-22 23:10:46,408 [trainer.py] => config: ./exps/icarl.json
2023-05-22 23:10:46,408 [trainer.py] => prefix: reproduce
2023-05-22 23:10:46,408 [trainer.py] => dataset: cifar100
2023-05-22 23:10:46,408 [trainer.py] => memory_size: 2000
2023-05-22 23:10:46,408 [trainer.py] => memory_per_class: 20
2023-05-22 23:10:46,408 [trainer.py] => fixed_memory: False
2023-05-22 23:10:46,408 [trainer.py] => shuffle: True
2023-05-22 23:10:46,408 [trainer.py] => init_cls: 10
2023-05-22 23:10:46,408 [trainer.py] => increment: 10
2023-05-22 23:10:46,408 [trainer.py] => model_name: icarl
2023-05-22 23:10:46,409 [trainer.py] => convnet_type: resnet32
2023-05-22 23:10:46,409 [trainer.py] => device: [device(type='cuda', index=0), device(type='cuda', index=1), device(type='cuda', index=2), device(type='cuda', index=3)]
2023-05-22 23:10:46,409 [trainer.py] => seed: 1993
2023-05-22 23:10:47,635 [data_manager.py] => [68, 56, 78, 8, 23, 84, 90, 65, 74, 76, 40, 89, 3, 92, 55, 9, 26, 80, 43, 38, 58, 70, 77, 1, 85, 19, 17, 50, 28, 53, 13, 81, 45, 82, 6, 59, 83, 16, 15, 44, 91, 41, 72, 60, 79, 52, 20, 10, 31, 54, 37, 95, 14, 71, 96, 98, 97, 2, 64, 66, 42, 22, 35, 86, 24, 34, 87, 21, 99, 0, 88, 27, 18, 94, 11, 12, 47, 25, 30, 46, 62, 69, 36, 61, 7, 63, 75, 5, 32, 4, 51, 48, 73, 93, 39, 67, 29, 49, 57, 33]
2023-05-22 23:10:47,750 [trainer.py] => All params: 464154
2023-05-22 23:10:47,751 [trainer.py] => Trainable params: 464154
2023-05-22 23:10:47,751 [icarl.py] => Learning on 0-10
2023-05-22 23:12:14,130 [trainer.py] => config: ./exps/icarl.json
2023-05-22 23:12:14,130 [trainer.py] => prefix: reproduce
2023-05-22 23:12:14,130 [trainer.py] => dataset: cifar100
2023-05-22 23:12:14,130 [trainer.py] => memory_size: 2000
2023-05-22 23:12:14,130 [trainer.py] => memory_per_class: 20
2023-05-22 23:12:14,130 [trainer.py] => fixed_memory: False
2023-05-22 23:12:14,130 [trainer.py] => shuffle: True
2023-05-22 23:12:14,130 [trainer.py] => init_cls: 10
2023-05-22 23:12:14,130 [trainer.py] => increment: 10
2023-05-22 23:12:14,130 [trainer.py] => model_name: icarl
2023-05-22 23:12:14,131 [trainer.py] => convnet_type: resnet32
2023-05-22 23:12:14,131 [trainer.py] => device: [device(type='cuda', index=0), device(type='cuda', index=1), device(type='cuda', index=2), device(type='cuda', index=3)]
2023-05-22 23:12:14,131 [trainer.py] => seed: 1993
2023-05-22 23:12:15,363 [data_manager.py] => [68, 56, 78, 8, 23, 84, 90, 65, 74, 76, 40, 89, 3, 92, 55, 9, 26, 80, 43, 38, 58, 70, 77, 1, 85, 19, 17, 50, 28, 53, 13, 81, 45, 82, 6, 59, 83, 16, 15, 44, 91, 41, 72, 60, 79, 52, 20, 10, 31, 54, 37, 95, 14, 71, 96, 98, 97, 2, 64, 66, 42, 22, 35, 86, 24, 34, 87, 21, 99, 0, 88, 27, 18, 94, 11, 12, 47, 25, 30, 46, 62, 69, 36, 61, 7, 63, 75, 5, 32, 4, 51, 48, 73, 93, 39, 67, 29, 49, 57, 33]
2023-05-22 23:12:15,473 [trainer.py] => All params: 464154
2023-05-22 23:12:15,474 [trainer.py] => Trainable params: 464154
2023-05-22 23:12:15,474 [icarl.py] => Learning on 0-10
2023-05-22 23:13:36,958 [trainer.py] => config: ./exps/icarl.json
2023-05-22 23:13:36,958 [trainer.py] => prefix: reproduce
2023-05-22 23:13:36,958 [trainer.py] => dataset: cifar100
2023-05-22 23:13:36,958 [trainer.py] => memory_size: 2000
2023-05-22 23:13:36,958 [trainer.py] => memory_per_class: 20
2023-05-22 23:13:36,958 [trainer.py] => fixed_memory: False
2023-05-22 23:13:36,958 [trainer.py] => shuffle: True
2023-05-22 23:13:36,958 [trainer.py] => init_cls: 10
2023-05-22 23:13:36,958 [trainer.py] => increment: 10
2023-05-22 23:13:36,958 [trainer.py] => model_name: icarl
2023-05-22 23:13:36,958 [trainer.py] => convnet_type: resnet32
2023-05-22 23:13:36,958 [trainer.py] => device: [device(type='cuda', index=0), device(type='cuda', index=1), device(type='cuda', index=2), device(type='cuda', index=3)]
2023-05-22 23:13:36,958 [trainer.py] => seed: 1993
2023-05-22 23:13:38,185 [data_manager.py] => [68, 56, 78, 8, 23, 84, 90, 65, 74, 76, 40, 89, 3, 92, 55, 9, 26, 80, 43, 38, 58, 70, 77, 1, 85, 19, 17, 50, 28, 53, 13, 81, 45, 82, 6, 59, 83, 16, 15, 44, 91, 41, 72, 60, 79, 52, 20, 10, 31, 54, 37, 95, 14, 71, 96, 98, 97, 2, 64, 66, 42, 22, 35, 86, 24, 34, 87, 21, 99, 0, 88, 27, 18, 94, 11, 12, 47, 25, 30, 46, 62, 69, 36, 61, 7, 63, 75, 5, 32, 4, 51, 48, 73, 93, 39, 67, 29, 49, 57, 33]
2023-05-22 23:13:38,297 [trainer.py] => All params: 464154
2023-05-22 23:13:38,298 [trainer.py] => Trainable params: 464154
2023-05-22 23:13:38,298 [icarl.py] => Learning on 0-10
2023-05-22 23:13:51,633 [trainer.py] => config: ./exps/icarl.json
2023-05-22 23:13:51,633 [trainer.py] => prefix: reproduce
2023-05-22 23:13:51,633 [trainer.py] => dataset: cifar100
2023-05-22 23:13:51,633 [trainer.py] => memory_size: 2000
2023-05-22 23:13:51,633 [trainer.py] => memory_per_class: 20
2023-05-22 23:13:51,633 [trainer.py] => fixed_memory: False
2023-05-22 23:13:51,633 [trainer.py] => shuffle: True
2023-05-22 23:13:51,633 [trainer.py] => init_cls: 10
2023-05-22 23:13:51,633 [trainer.py] => increment: 10
2023-05-22 23:13:51,633 [trainer.py] => model_name: icarl
2023-05-22 23:13:51,633 [trainer.py] => convnet_type: resnet32
2023-05-22 23:13:51,633 [trainer.py] => device: [device(type='cuda', index=0), device(type='cuda', index=1), device(type='cuda', index=2), device(type='cuda', index=3)]
2023-05-22 23:13:51,633 [trainer.py] => seed: 1993
2023-05-22 23:13:52,851 [data_manager.py] => [68, 56, 78, 8, 23, 84, 90, 65, 74, 76, 40, 89, 3, 92, 55, 9, 26, 80, 43, 38, 58, 70, 77, 1, 85, 19, 17, 50, 28, 53, 13, 81, 45, 82, 6, 59, 83, 16, 15, 44, 91, 41, 72, 60, 79, 52, 20, 10, 31, 54, 37, 95, 14, 71, 96, 98, 97, 2, 64, 66, 42, 22, 35, 86, 24, 34, 87, 21, 99, 0, 88, 27, 18, 94, 11, 12, 47, 25, 30, 46, 62, 69, 36, 61, 7, 63, 75, 5, 32, 4, 51, 48, 73, 93, 39, 67, 29, 49, 57, 33]
2023-05-22 23:13:52,960 [trainer.py] => All params: 464154
2023-05-22 23:13:52,961 [trainer.py] => Trainable params: 464154
2023-05-22 23:13:52,961 [icarl.py] => Learning on 0-10
2023-05-22 23:17:00,922 [trainer.py] => config: ./exps/icarl.json
2023-05-22 23:17:00,922 [trainer.py] => prefix: reproduce
2023-05-22 23:17:00,922 [trainer.py] => dataset: cifar100
2023-05-22 23:17:00,922 [trainer.py] => memory_size: 2000
2023-05-22 23:17:00,922 [trainer.py] => memory_per_class: 20
2023-05-22 23:17:00,922 [trainer.py] => fixed_memory: False
2023-05-22 23:17:00,922 [trainer.py] => shuffle: True
2023-05-22 23:17:00,922 [trainer.py] => init_cls: 10
2023-05-22 23:17:00,922 [trainer.py] => increment: 10
2023-05-22 23:17:00,922 [trainer.py] => model_name: icarl
2023-05-22 23:17:00,922 [trainer.py] => convnet_type: resnet32
2023-05-22 23:17:00,922 [trainer.py] => device: [device(type='cuda', index=1)]
2023-05-22 23:17:00,922 [trainer.py] => seed: 1993
2023-05-22 23:17:02,143 [data_manager.py] => [68, 56, 78, 8, 23, 84, 90, 65, 74, 76, 40, 89, 3, 92, 55, 9, 26, 80, 43, 38, 58, 70, 77, 1, 85, 19, 17, 50, 28, 53, 13, 81, 45, 82, 6, 59, 83, 16, 15, 44, 91, 41, 72, 60, 79, 52, 20, 10, 31, 54, 37, 95, 14, 71, 96, 98, 97, 2, 64, 66, 42, 22, 35, 86, 24, 34, 87, 21, 99, 0, 88, 27, 18, 94, 11, 12, 47, 25, 30, 46, 62, 69, 36, 61, 7, 63, 75, 5, 32, 4, 51, 48, 73, 93, 39, 67, 29, 49, 57, 33]
2023-05-22 23:17:02,258 [trainer.py] => All params: 464154
2023-05-22 23:17:02,259 [trainer.py] => Trainable params: 464154
2023-05-22 23:17:02,259 [icarl.py] => Learning on 0-10
2023-05-22 23:17:17,629 [trainer.py] => config: ./exps/icarl.json
2023-05-22 23:17:17,629 [trainer.py] => prefix: reproduce
2023-05-22 23:17:17,629 [trainer.py] => dataset: cifar100
2023-05-22 23:17:17,629 [trainer.py] => memory_size: 2000
2023-05-22 23:17:17,629 [trainer.py] => memory_per_class: 20
2023-05-22 23:17:17,629 [trainer.py] => fixed_memory: False
2023-05-22 23:17:17,629 [trainer.py] => shuffle: True
2023-05-22 23:17:17,629 [trainer.py] => init_cls: 10
2023-05-22 23:17:17,630 [trainer.py] => increment: 10
2023-05-22 23:17:17,630 [trainer.py] => model_name: icarl
2023-05-22 23:17:17,630 [trainer.py] => convnet_type: resnet32
2023-05-22 23:17:17,630 [trainer.py] => device: [device(type='cuda', index=1)]
2023-05-22 23:17:17,630 [trainer.py] => seed: 1993
2023-05-22 23:17:18,849 [data_manager.py] => [68, 56, 78, 8, 23, 84, 90, 65, 74, 76, 40, 89, 3, 92, 55, 9, 26, 80, 43, 38, 58, 70, 77, 1, 85, 19, 17, 50, 28, 53, 13, 81, 45, 82, 6, 59, 83, 16, 15, 44, 91, 41, 72, 60, 79, 52, 20, 10, 31, 54, 37, 95, 14, 71, 96, 98, 97, 2, 64, 66, 42, 22, 35, 86, 24, 34, 87, 21, 99, 0, 88, 27, 18, 94, 11, 12, 47, 25, 30, 46, 62, 69, 36, 61, 7, 63, 75, 5, 32, 4, 51, 48, 73, 93, 39, 67, 29, 49, 57, 33]
2023-05-22 23:17:18,961 [trainer.py] => All params: 464154
2023-05-22 23:17:18,962 [trainer.py] => Trainable params: 464154
2023-05-22 23:17:18,962 [icarl.py] => Learning on 0-10
2023-05-22 23:22:28,264 [icarl.py] => Task 0, Epoch 200/200 => Loss 0.031, Train_accy 99.38
2023-05-22 23:22:28,264 [base.py] => Reducing exemplars...(200 per classes)
2023-05-22 23:22:28,264 [base.py] => Constructing exemplars...(200 per classes)
2023-05-22 23:22:36,462 [icarl.py] => Exemplar size: 2000
2023-05-22 23:22:36,463 [trainer.py] => CNN: {'total': 89.8, '00-09': 89.8, 'old': 0, 'new': 89.8}
2023-05-22 23:22:36,463 [trainer.py] => NME: {'total': 89.8, '00-09': 89.8, 'old': 0, 'new': 89.8}
2023-05-22 23:22:36,463 [trainer.py] => CNN top1 curve: [89.8]
2023-05-22 23:22:36,463 [trainer.py] => CNN top5 curve: [99.5]
2023-05-22 23:22:36,463 [trainer.py] => NME top1 curve: [89.8]
2023-05-22 23:22:36,463 [trainer.py] => NME top5 curve: [99.4]
2023-05-22 23:22:36,463 [trainer.py] => All params: 464804
2023-05-22 23:22:36,464 [trainer.py] => Trainable params: 464804
2023-05-22 23:22:36,464 [icarl.py] => Learning on 10-20
2023-05-22 23:29:08,574 [icarl.py] => Task 1, Epoch 170/170 => Loss 1.007, Train_accy 99.89
2023-05-22 23:29:08,574 [base.py] => Reducing exemplars...(100 per classes)
2023-05-22 23:29:11,659 [base.py] => Constructing exemplars...(100 per classes)
2023-05-22 23:29:19,328 [icarl.py] => Exemplar size: 2000
2023-05-22 23:29:19,328 [trainer.py] => CNN: {'total': 77.95, '00-09': 78.3, '10-19': 77.6, 'old': 78.3, 'new': 77.6}
2023-05-22 23:29:19,328 [trainer.py] => NME: {'total': 79.1, '00-09': 81.8, '10-19': 76.4, 'old': 81.8, 'new': 76.4}
2023-05-22 23:29:19,328 [trainer.py] => CNN top1 curve: [89.8, 77.95]
2023-05-22 23:29:19,328 [trainer.py] => CNN top5 curve: [99.5, 96.25]
2023-05-22 23:29:19,328 [trainer.py] => NME top1 curve: [89.8, 79.1]
2023-05-22 23:29:19,328 [trainer.py] => NME top5 curve: [99.4, 96.0]
2023-05-22 23:29:19,328 [trainer.py] => All params: 465454
2023-05-22 23:29:19,329 [trainer.py] => Trainable params: 465454
2023-05-22 23:29:19,329 [icarl.py] => Learning on 20-30
2023-05-22 23:35:51,984 [icarl.py] => Task 2, Epoch 170/170 => Loss 1.163, Train_accy 99.91
2023-05-22 23:35:51,984 [base.py] => Reducing exemplars...(66 per classes)
2023-05-22 23:35:58,094 [base.py] => Constructing exemplars...(66 per classes)
2023-05-22 23:36:06,453 [icarl.py] => Exemplar size: 1980
2023-05-22 23:36:06,453 [trainer.py] => CNN: {'total': 72.6, '00-09': 69.9, '10-19': 60.4, '20-29': 87.5, 'old': 65.15, 'new': 87.5}
2023-05-22 23:36:06,453 [trainer.py] => NME: {'total': 74.27, '00-09': 76.2, '10-19': 65.1, '20-29': 81.5, 'old': 70.65, 'new': 81.5}
2023-05-22 23:36:06,453 [trainer.py] => CNN top1 curve: [89.8, 77.95, 72.6]
2023-05-22 23:36:06,454 [trainer.py] => CNN top5 curve: [99.5, 96.25, 93.77]
2023-05-22 23:36:06,454 [trainer.py] => NME top1 curve: [89.8, 79.1, 74.27]
2023-05-22 23:36:06,454 [trainer.py] => NME top5 curve: [99.4, 96.0, 94.37]
2023-05-22 23:36:06,454 [trainer.py] => All params: 466104
2023-05-22 23:36:06,454 [trainer.py] => Trainable params: 466104
2023-05-22 23:36:06,455 [icarl.py] => Learning on 30-40
2023-05-22 23:42:45,877 [icarl.py] => Task 3, Epoch 170/170 => Loss 1.461, Train_accy 99.90
2023-05-22 23:42:45,877 [base.py] => Reducing exemplars...(50 per classes)
2023-05-22 23:42:53,898 [base.py] => Constructing exemplars...(50 per classes)
2023-05-22 23:43:01,767 [icarl.py] => Exemplar size: 2000
2023-05-22 23:43:01,767 [trainer.py] => CNN: {'total': 63.72, '00-09': 59.4, '10-19': 46.3, '20-29': 67.2, '30-39': 82.0, 'old': 57.63, 'new': 82.0}
2023-05-22 23:43:01,767 [trainer.py] => NME: {'total': 68.75, '00-09': 70.2, '10-19': 58.3, '20-29': 72.9, '30-39': 73.6, 'old': 67.13, 'new': 73.6}
2023-05-22 23:43:01,767 [trainer.py] => CNN top1 curve: [89.8, 77.95, 72.6, 63.72]
2023-05-22 23:43:01,767 [trainer.py] => CNN top5 curve: [99.5, 96.25, 93.77, 90.4]
2023-05-22 23:43:01,767 [trainer.py] => NME top1 curve: [89.8, 79.1, 74.27, 68.75]
2023-05-22 23:43:01,767 [trainer.py] => NME top5 curve: [99.4, 96.0, 94.37, 91.35]
2023-05-22 23:43:01,767 [trainer.py] => All params: 466754
2023-05-22 23:43:01,767 [trainer.py] => Trainable params: 466754
2023-05-22 23:43:01,768 [icarl.py] => Learning on 40-50
2023-05-22 23:49:38,147 [icarl.py] => Task 4, Epoch 170/170 => Loss 1.550, Train_accy 99.97
2023-05-22 23:49:38,148 [base.py] => Reducing exemplars...(40 per classes)
2023-05-22 23:49:48,599 [base.py] => Constructing exemplars...(40 per classes)
2023-05-22 23:49:56,533 [icarl.py] => Exemplar size: 2000
2023-05-22 23:49:56,534 [trainer.py] => CNN: {'total': 58.54, '00-09': 51.8, '10-19': 38.1, '20-29': 54.8, '30-39': 60.0, '40-49': 88.0, 'old': 51.18, 'new': 88.0}
2023-05-22 23:49:56,534 [trainer.py] => NME: {'total': 64.44, '00-09': 66.1, '10-19': 48.4, '20-29': 66.4, '30-39': 63.6, '40-49': 77.7, 'old': 61.12, 'new': 77.7}
2023-05-22 23:49:56,534 [trainer.py] => CNN top1 curve: [89.8, 77.95, 72.6, 63.72, 58.54]
2023-05-22 23:49:56,535 [trainer.py] => CNN top5 curve: [99.5, 96.25, 93.77, 90.4, 87.38]
2023-05-22 23:49:56,535 [trainer.py] => NME top1 curve: [89.8, 79.1, 74.27, 68.75, 64.44]
2023-05-22 23:49:56,535 [trainer.py] => NME top5 curve: [99.4, 96.0, 94.37, 91.35, 89.32]
2023-05-22 23:49:56,536 [trainer.py] => All params: 467404
2023-05-22 23:49:56,536 [trainer.py] => Trainable params: 467404
2023-05-22 23:49:56,537 [icarl.py] => Learning on 50-60
2023-05-22 23:56:42,554 [icarl.py] => Task 5, Epoch 170/170 => Loss 1.676, Train_accy 99.96
2023-05-22 23:56:42,555 [base.py] => Reducing exemplars...(33 per classes)
2023-05-22 23:56:55,700 [base.py] => Constructing exemplars...(33 per classes)
2023-05-22 23:57:03,836 [icarl.py] => Exemplar size: 1980
2023-05-22 23:57:03,837 [trainer.py] => CNN: {'total': 54.17, '00-09': 46.4, '10-19': 34.5, '20-29': 50.0, '30-39': 47.5, '40-49': 64.3, '50-59': 82.3, 'old': 48.54, 'new': 82.3}
2023-05-22 23:57:03,837 [trainer.py] => NME: {'total': 60.77, '00-09': 59.6, '10-19': 44.4, '20-29': 61.7, '30-39': 56.3, '40-49': 70.0, '50-59': 72.6, 'old': 58.4, 'new': 72.6}
2023-05-22 23:57:03,837 [trainer.py] => CNN top1 curve: [89.8, 77.95, 72.6, 63.72, 58.54, 54.17]
2023-05-22 23:57:03,837 [trainer.py] => CNN top5 curve: [99.5, 96.25, 93.77, 90.4, 87.38, 83.02]
2023-05-22 23:57:03,837 [trainer.py] => NME top1 curve: [89.8, 79.1, 74.27, 68.75, 64.44, 60.77]
2023-05-22 23:57:03,837 [trainer.py] => NME top5 curve: [99.4, 96.0, 94.37, 91.35, 89.32, 85.45]
2023-05-22 23:57:03,837 [trainer.py] => All params: 468054
2023-05-22 23:57:03,837 [trainer.py] => Trainable params: 468054
2023-05-22 23:57:03,838 [icarl.py] => Learning on 60-70
2023-05-23 00:03:44,924 [icarl.py] => Task 6, Epoch 170/170 => Loss 1.724, Train_accy 99.87
2023-05-23 00:03:44,924 [base.py] => Reducing exemplars...(28 per classes)
2023-05-23 00:04:00,661 [base.py] => Constructing exemplars...(28 per classes)
2023-05-23 00:04:08,875 [icarl.py] => Exemplar size: 1960
2023-05-23 00:04:08,875 [trainer.py] => CNN: {'total': 52.29, '00-09': 45.2, '10-19': 31.0, '20-29': 48.7, '30-39': 42.1, '40-49': 54.7, '50-59': 55.3, '60-69': 89.0, 'old': 46.17, 'new': 89.0}
2023-05-23 00:04:08,875 [trainer.py] => NME: {'total': 58.09, '00-09': 56.7, '10-19': 42.5, '20-29': 59.5, '30-39': 49.7, '40-49': 64.5, '50-59': 58.6, '60-69': 75.1, 'old': 55.25, 'new': 75.1}
2023-05-23 00:04:08,875 [trainer.py] => CNN top1 curve: [89.8, 77.95, 72.6, 63.72, 58.54, 54.17, 52.29]
2023-05-23 00:04:08,876 [trainer.py] => CNN top5 curve: [99.5, 96.25, 93.77, 90.4, 87.38, 83.02, 80.8]
2023-05-23 00:04:08,876 [trainer.py] => NME top1 curve: [89.8, 79.1, 74.27, 68.75, 64.44, 60.77, 58.09]
2023-05-23 00:04:08,876 [trainer.py] => NME top5 curve: [99.4, 96.0, 94.37, 91.35, 89.32, 85.45, 83.87]
2023-05-23 00:04:08,876 [trainer.py] => All params: 468704
2023-05-23 00:04:08,876 [trainer.py] => Trainable params: 468704
2023-05-23 00:04:08,877 [icarl.py] => Learning on 70-80
2023-05-23 00:10:58,431 [icarl.py] => Task 7, Epoch 170/170 => Loss 1.712, Train_accy 99.93
2023-05-23 00:10:58,432 [base.py] => Reducing exemplars...(25 per classes)
2023-05-23 00:11:16,920 [base.py] => Constructing exemplars...(25 per classes)
2023-05-23 00:11:25,218 [icarl.py] => Exemplar size: 2000
2023-05-23 00:11:25,218 [trainer.py] => CNN: {'total': 47.32, '00-09': 40.2, '10-19': 30.1, '20-29': 48.8, '30-39': 36.7, '40-49': 43.6, '50-59': 34.8, '60-69': 60.2, '70-79': 84.2, 'old': 42.06, 'new': 84.2}
2023-05-23 00:11:25,219 [trainer.py] => NME: {'total': 54.55, '00-09': 53.9, '10-19': 40.6, '20-29': 57.7, '30-39': 45.8, '40-49': 57.1, '50-59': 45.1, '60-69': 63.8, '70-79': 72.4, 'old': 52.0, 'new': 72.4}
2023-05-23 00:11:25,219 [trainer.py] => CNN top1 curve: [89.8, 77.95, 72.6, 63.72, 58.54, 54.17, 52.29, 47.32]
2023-05-23 00:11:25,219 [trainer.py] => CNN top5 curve: [99.5, 96.25, 93.77, 90.4, 87.38, 83.02, 80.8, 77.85]
2023-05-23 00:11:25,219 [trainer.py] => NME top1 curve: [89.8, 79.1, 74.27, 68.75, 64.44, 60.77, 58.09, 54.55]
2023-05-23 00:11:25,219 [trainer.py] => NME top5 curve: [99.4, 96.0, 94.37, 91.35, 89.32, 85.45, 83.87, 82.19]
2023-05-23 00:11:25,219 [trainer.py] => All params: 469354
2023-05-23 00:11:25,219 [trainer.py] => Trainable params: 469354
2023-05-23 00:11:25,219 [icarl.py] => Learning on 80-90
2023-05-23 00:18:11,022 [icarl.py] => Task 8, Epoch 170/170 => Loss 1.937, Train_accy 99.87
2023-05-23 00:18:11,023 [base.py] => Reducing exemplars...(22 per classes)
2023-05-23 00:18:32,082 [base.py] => Constructing exemplars...(22 per classes)
2023-05-23 00:18:40,388 [icarl.py] => Exemplar size: 1980
2023-05-23 00:18:40,388 [trainer.py] => CNN: {'total': 44.53, '00-09': 39.7, '10-19': 26.6, '20-29': 40.9, '30-39': 33.7, '40-49': 40.1, '50-59': 33.4, '60-69': 44.7, '70-79': 56.2, '80-89': 85.5, 'old': 39.41, 'new': 85.5}
2023-05-23 00:18:40,388 [trainer.py] => NME: {'total': 51.34, '00-09': 51.1, '10-19': 33.7, '20-29': 53.4, '30-39': 43.4, '40-49': 53.7, '50-59': 43.7, '60-69': 56.1, '70-79': 60.8, '80-89': 66.2, 'old': 49.49, 'new': 66.2}
2023-05-23 00:18:40,388 [trainer.py] => CNN top1 curve: [89.8, 77.95, 72.6, 63.72, 58.54, 54.17, 52.29, 47.32, 44.53]
2023-05-23 00:18:40,389 [trainer.py] => CNN top5 curve: [99.5, 96.25, 93.77, 90.4, 87.38, 83.02, 80.8, 77.85, 75.43]
2023-05-23 00:18:40,389 [trainer.py] => NME top1 curve: [89.8, 79.1, 74.27, 68.75, 64.44, 60.77, 58.09, 54.55, 51.34]
2023-05-23 00:18:40,389 [trainer.py] => NME top5 curve: [99.4, 96.0, 94.37, 91.35, 89.32, 85.45, 83.87, 82.19, 79.02]
2023-05-23 00:18:40,389 [trainer.py] => All params: 470004
2023-05-23 00:18:40,389 [trainer.py] => Trainable params: 470004
2023-05-23 00:18:40,390 [icarl.py] => Learning on 90-100
2023-05-23 00:25:32,625 [icarl.py] => Task 9, Epoch 170/170 => Loss 2.071, Train_accy 99.90
2023-05-23 00:25:32,625 [base.py] => Reducing exemplars...(20 per classes)
2023-05-23 00:25:57,312 [base.py] => Constructing exemplars...(20 per classes)
2023-05-23 00:26:06,251 [icarl.py] => Exemplar size: 2000
2023-05-23 00:26:06,251 [trainer.py] => CNN: {'total': 42.06, '00-09': 35.4, '10-19': 23.3, '20-29': 34.9, '30-39': 29.6, '40-49': 38.7, '50-59': 29.2, '60-69': 43.1, '70-79': 44.5, '80-89': 60.9, '90-99': 81.0, 'old': 37.73, 'new': 81.0}
2023-05-23 00:26:06,251 [trainer.py] => NME: {'total': 49.4, '00-09': 47.4, '10-19': 32.0, '20-29': 48.5, '30-39': 40.2, '40-49': 51.3, '50-59': 42.1, '60-69': 55.2, '70-79': 53.2, '80-89': 59.3, '90-99': 64.8, 'old': 47.69, 'new': 64.8}
2023-05-23 00:26:06,251 [trainer.py] => CNN top1 curve: [89.8, 77.95, 72.6, 63.72, 58.54, 54.17, 52.29, 47.32, 44.53, 42.06]
2023-05-23 00:26:06,251 [trainer.py] => CNN top5 curve: [99.5, 96.25, 93.77, 90.4, 87.38, 83.02, 80.8, 77.85, 75.43, 72.11]
2023-05-23 00:26:06,251 [trainer.py] => NME top1 curve: [89.8, 79.1, 74.27, 68.75, 64.44, 60.77, 58.09, 54.55, 51.34, 49.4]
2023-05-23 00:26:06,251 [trainer.py] => NME top5 curve: [99.4, 96.0, 94.37, 91.35, 89.32, 85.45, 83.87, 82.19, 79.02, 77.53]
2023-07-04 17:19:05,818 [trainer.py] => config: ./exps/icarl.json
2023-07-04 17:19:05,818 [trainer.py] => prefix: reproduce
2023-07-04 17:19:05,818 [trainer.py] => dataset: cifar100
2023-07-04 17:19:05,818 [trainer.py] => memory_size: 2000
2023-07-04 17:19:05,818 [trainer.py] => memory_per_class: 20
2023-07-04 17:19:05,818 [trainer.py] => fixed_memory: False
2023-07-04 17:19:05,818 [trainer.py] => shuffle: True
2023-07-04 17:19:05,818 [trainer.py] => init_cls: 10
2023-07-04 17:19:05,818 [trainer.py] => increment: 10
2023-07-04 17:19:05,818 [trainer.py] => model_name: icarl
2023-07-04 17:19:05,818 [trainer.py] => convnet_type: resnet32
2023-07-04 17:19:05,818 [trainer.py] => device: [device(type='cuda', index=1)]
2023-07-04 17:19:05,819 [trainer.py] => seed: 1993
2023-07-04 17:19:07,166 [data_manager.py] => [68, 56, 78, 8, 23, 84, 90, 65, 74, 76, 40, 89, 3, 92, 55, 9, 26, 80, 43, 38, 58, 70, 77, 1, 85, 19, 17, 50, 28, 53, 13, 81, 45, 82, 6, 59, 83, 16, 15, 44, 91, 41, 72, 60, 79, 52, 20, 10, 31, 54, 37, 95, 14, 71, 96, 98, 97, 2, 64, 66, 42, 22, 35, 86, 24, 34, 87, 21, 99, 0, 88, 27, 18, 94, 11, 12, 47, 25, 30, 46, 62, 69, 36, 61, 7, 63, 75, 5, 32, 4, 51, 48, 73, 93, 39, 67, 29, 49, 57, 33]
2023-07-04 17:19:07,273 [trainer.py] => All params: 464154
2023-07-04 17:19:07,273 [trainer.py] => Trainable params: 464154
2023-07-04 17:19:07,274 [icarl.py] => Learning on 0-10```