Reproducibility about pycil HOT 11 CLOSED

{
    "prefix": "reproduce",
    "dataset": "cifar100",
    "memory_size": 2000,
    "memory_per_class": 20,
    "fixed_memory": false,
    "shuffle": true,
    "init_cls": 10,
    "increment": 10,
    "model_name": "icarl",
    "convnet_type": "resnet32",
    "device": ["1"],
    "seed": [1993]
}

2023-05-23 01:18:52,588 [foster.py] => Exemplar size: 2000
2023-05-23 01:18:52,589 [trainer.py] => CNN: {'total': 54.55, '00-09': 48.8, '10-19': 33.5, '20-29': 51.3, '30-39': 46.8, '40-49': 59.0, '50-59': 52.7, '60-69': 66.9, '70-79': 60.5, '80-89': 64.0, '90-99': 62.0, 'old': 53.72, 'new': 62.0}
2023-05-23 01:18:52,589 [trainer.py] => NME: {'total': 48.13, '00-09': 46.9, '10-19': 32.9, '20-29': 49.5, '30-39': 42.9, '40-49': 52.0, '50-59': 42.4, '60-69': 50.2, '70-79': 43.1, '80-89': 45.6, '90-99': 75.8, 'old': 45.06, 'new': 75.8}
2023-05-23 01:18:52,589 [trainer.py] => CNN top1 curve: [90.8, 81.2, 76.27, 67.72, 63.86, 60.65, 59.81, 56.8, 55.58, 54.55]
2023-05-23 01:18:52,589 [trainer.py] => CNN top5 curve: [99.2, 95.75, 93.83, 90.25, 87.82, 85.35, 84.29, 83.19, 82.04, 81.68]
2023-05-23 01:18:52,589 [trainer.py] => NME top1 curve: [90.2, 82.35, 77.07, 68.8, 64.28, 59.82, 57.11, 51.94, 51.09, 48.13]
2023-05-23 01:18:52,589 [trainer.py] => NME top5 curve: [99.2, 96.85, 95.03, 91.02, 88.38, 85.8, 83.57, 81.66, 79.91, 76.7]

{
    "prefix": "cil",
    "dataset": "cifar100",
    "memory_size": 2000,
    "memory_per_class": 20,
    "fixed_memory": false,
    "shuffle": true,
    "init_cls": 10,
    "increment": 10,
    "model_name": "foster",
    "convnet_type": "resnet32",
    "device": ["3"],
    "seed": [1993],
    "beta1":0.96,
    "beta2":0.97,
    "oofc":"ft",
    "is_teacher_wa":false,
    "is_student_wa":false,
    "lambda_okd":1,
    "wa_value":1,
    "init_epochs": 200,
    "init_lr" : 0.1,
    "init_weight_decay" : 5e-4,
    "boosting_epochs" : 170,
    "compression_epochs" : 130,
    "lr" : 0.1,
    "batch_size" : 128,
    "weight_decay" : 5e-4,
    "num_workers" : 8,
    "T" : 2
}

2023-05-23 01:51:11,906 [bic.py] => Exemplar size: 2000
2023-05-23 01:51:11,906 [trainer.py] => CNN: {'total': 39.06, '00-09': 53.9, '10-19': 36.5, '20-29': 47.1, '30-39': 39.9, '40-49': 51.6, '50-59': 42.1, '60-69': 0.0, '70-79': 11.5, '80-89': 46.4, '90-99': 61.6, 'old': 36.56, 'new': 61.6}
2023-05-23 01:51:11,906 [trainer.py] => NME: {'total': 46.11, '00-09': 49.6, '10-19': 32.3, '20-29': 50.6, '30-39': 38.5, '40-49': 50.4, '50-59': 38.4, '60-69': 46.0, '70-79': 45.5, '80-89': 52.6, '90-99': 57.2, 'old': 44.88, 'new': 57.2}
2023-05-23 01:51:11,906 [trainer.py] => CNN top1 curve: [86.5, 71.3, 68.4, 62.35, 61.42, 58.17, 48.27, 42.31, 40.74, 39.06]
2023-05-23 01:51:11,906 [trainer.py] => CNN top5 curve: [99.0, 94.25, 91.77, 88.98, 87.98, 86.15, 72.44, 70.03, 68.52, 66.57]
2023-05-23 01:51:11,906 [trainer.py] => NME top1 curve: [86.3, 71.6, 68.73, 62.7, 61.72, 57.62, 55.69, 50.46, 48.24, 46.11]
2023-05-23 01:51:11,906 [trainer.py] => NME top5 curve: [98.7, 94.0, 91.43, 88.88, 87.56, 84.62, 82.54, 79.0, 77.08, 75.35]

{
    "prefix": "reproduce",
    "dataset": "cifar100",
    "memory_size": 2000,
    "memory_per_class": 20,
    "fixed_memory": false,
    "shuffle": true,
    "init_cls": 10,
    "increment": 10,
    "model_name": "bic",
    "convnet_type": "resnet32",
    "device": ["0","1","2","3"],
    "seed": [1993]
}