Comments (8)
G-U-N
commented on July 28, 2024
Hi, thanks for your interest. Could you please attach the detailed training/error log here?
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tnalyahya
commented on July 28, 2024
Hi, thanks for the quick reply!
Here is a test of memory size 180, it has the same error. I printed the vectors, S, mu_p from the _construct_exemplar method in the base model to trace the error.
The dataset has 18 classes each has 100 samples. I may consider more samples so I want to know how to find the required memory size.
2023-05-09 18:07:52,260 [trainer.py] => config: ./exps/bic.json
2023-05-09 18:07:52,260 [trainer.py] => prefix: reproduce
2023-05-09 18:07:52,260 [trainer.py] => dataset: Mydataset
2023-05-09 18:07:52,260 [trainer.py] => memory_size: 180
2023-05-09 18:07:52,260 [trainer.py] => memory_per_class: 20
2023-05-09 18:07:52,260 [trainer.py] => fixed_memory: False
2023-05-09 18:07:52,260 [trainer.py] => shuffle: True
2023-05-09 18:07:52,260 [trainer.py] => init_cls: 2
2023-05-09 18:07:52,260 [trainer.py] => increment: 2
2023-05-09 18:07:52,260 [trainer.py] => model_name: bic
2023-05-09 18:07:52,260 [trainer.py] => convnet_type: resnet32
2023-05-09 18:07:52,260 [trainer.py] => device: [device(type='cpu')]
2023-05-09 18:07:52,260 [trainer.py] => seed: 1993
05-09 18:07:52,394 [trainer.py] => All params: 464154
2023-05-09 18:07:52,395 [trainer.py] => Trainable params: 464154
2023-05-09 18:07:52,395 [bic.py] => Learning on 0-2
2023-05-09 18:07:52,396 [bic.py] => Parameters of bias layer:
2023-05-09 18:07:52,396 [bic.py] => 0 => 1.000, 0.000
2023-05-09 18:08:08,732 [bic.py] => training => Task 0, Epoch 1/10 => Loss 6.796, Train_accy 79.120, Test_accy 90.320
2023-05-09 18:08:26,310 [bic.py] => training => Task 0, Epoch 2/10 => Loss 0.841, Train_accy 79.120, Test_accy 90.320
2023-05-09 18:08:42,263 [bic.py] => training => Task 0, Epoch 3/10 => Loss 0.078, Train_accy 79.120, Test_accy 90.320
2023-05-09 18:09:02,221 [bic.py] => training => Task 0, Epoch 4/10 => Loss 0.000, Train_accy 80.220, Test_accy 90.320
2023-05-09 18:09:17,682 [bic.py] => training => Task 0, Epoch 5/10 => Loss 0.000, Train_accy 19.780, Test_accy 9.680
2023-05-09 18:09:37,056 [bic.py] => training => Task 0, Epoch 6/10 => Loss 0.000, Train_accy 19.780, Test_accy 9.680
2023-05-09 18:09:53,528 [bic.py] => training => Task 0, Epoch 7/10 => Loss 0.000, Train_accy 24.180, Test_accy 19.350
2023-05-09 18:10:12,367 [bic.py] => training => Task 0, Epoch 8/10 => Loss 0.000, Train_accy 100.000, Test_accy 100.000
2023-05-09 18:10:29,918 [bic.py] => training => Task 0, Epoch 9/10 => Loss 0.000, Train_accy 100.000, Test_accy 100.000
2023-05-09 18:10:47,447 [bic.py] => training => Task 0, Epoch 10/10 => Loss 0.000, Train_accy 100.000, Test_accy 100.0002023-05-09 18:10:47,448 [base.py] => Reducing exemplars...(90 per classes)
2023-05-09 18:10:47,448 [base.py] => Constructing exemplars...(90 per classes)
vectors shape L213: (19, 64)
vectors shape L214: (19, 64)
S shape L221: ()
mu_p shape L224: (19, 64)
S shape L221: (64,)
mu_p shape L224: (18, 64)
S shape L221: (64,)
mu_p shape L224: (17, 64)
S shape L221: (64,)
mu_p shape L224: (16, 64)
S shape L221: (64,)
mu_p shape L224: (15, 64)
S shape L221: (64,)
mu_p shape L224: (14, 64)
S shape L221: (64,)
mu_p shape L224: (13, 64)
S shape L221: (64,)
mu_p shape L224: (12, 64)
S shape L221: (64,)
mu_p shape L224: (11, 64)
S shape L221: (64,)
mu_p shape L224: (10, 64)
S shape L221: (64,)
mu_p shape L224: (9, 64)
S shape L221: (64,)
mu_p shape L224: (8, 64)
S shape L221: (64,)
mu_p shape L224: (7, 64)
S shape L221: (64,)
mu_p shape L224: (6, 64)
S shape L221: (64,)
mu_p shape L224: (5, 64)
S shape L221: (64,)
mu_p shape L224: (4, 64)
S shape L221: (64,)
mu_p shape L224: (3, 64)
S shape L221: (64,)
mu_p shape L224: (2, 64)
S shape L221: (64,)
mu_p shape L224: (1, 64)
S shape L221: (64,)
mu_p shape L224: (0, 64)
Traceback (most recent call last):
File "main.py", line 31, in
main()
File "main.py", line 12, in main
train(args)
File "/home/iot/PyCIL/trainer.py", line 18, in train
_train(args)
File "/home/iot/PyCIL/trainer.py", line 65, in _train
model.incremental_train(data_manager)
File "/home/iot/PyCIL/models/bic.py", line 89, in incremental_train
self.build_rehearsal_memory(data_manager, self.samples_per_class)
File "/home/iot/PyCIL/models/base.py", line 57, in build_rehearsal_memory
self._construct_exemplar(data_manager, per_class)
File "/home/iot/PyCIL/models/base.py", line 229, in _construct_exemplar
i = np.argmin(np.sqrt(np.sum((class_mean - mu_p) ** 2, axis=1)))
File "<array_function internals>", line 180, in argmin
File "/home/iot/anaconda3/envs/torchenv/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 1312, in argmin
return _wrapfunc(a, 'argmin', axis=axis, out=out, **kwds)
File "/home/iot/anaconda3/envs/torchenv/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 57, in _wrapfunc
return bound(*args, **kwds)
ValueError: attempt to get argmin of an empty sequence
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G-U-N
commented on July 28, 2024
As you claimed that each class possesses 100 samples, the shape of mu_p should be (100, 64) at the very beginning. But your log reals that only 19 samples for that class are loaded.
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tnalyahya
commented on July 28, 2024
I found that strange, as it reads from the loader, but it has never been 100! even when I tested using only 6 classes showing no errors.
2023-05-08 18:43:36,651 [trainer.py] => config: ./exps/bic.json
2023-05-08 18:43:36,651 [trainer.py] => prefix: reproduce
2023-05-08 18:43:36,651 [trainer.py] => dataset: Mydataset
2023-05-08 18:43:36,651 [trainer.py] => memory_size: 120
2023-05-08 18:43:36,651 [trainer.py] => memory_per_class: 20
2023-05-08 18:43:36,652 [trainer.py] => fixed_memory: False
2023-05-08 18:43:36,652 [trainer.py] => shuffle: True
2023-05-08 18:43:36,652 [trainer.py] => init_cls: 2
2023-05-08 18:43:36,652 [trainer.py] => increment: 2
2023-05-08 18:43:36,652 [trainer.py] => model_name: bic
2023-05-08 18:43:36,652 [trainer.py] => convnet_type: resnet32
2023-05-08 18:43:36,652 [trainer.py] => device: [device(type='cpu')]
2023-05-08 18:43:36,652 [trainer.py] => seed: 1993
2023-05-08 18:43:36,730 [data_manager.py] => [0, 2, 3, 4, 5, 1]
2023-05-08 18:43:36,749 [trainer.py] => All params: 464154
2023-05-08 18:43:36,749 [trainer.py] => Trainable params: 464154
2023-05-08 18:43:36,750 [bic.py] => Learning on 0-2
2023-05-08 18:43:36,750 [bic.py] => Parameters of bias layer:
2023-05-08 18:43:36,750 [bic.py] => 0 => 1.000, 0.000
2023-05-08 18:44:05,156 [bic.py] => training => Task 0, Epoch 1/10 => Loss 1.092, Train_accy 49.320, Test_accy 44.230
2023-05-08 18:44:41,376 [bic.py] => training => Task 0, Epoch 2/10 => Loss 0.272, Train_accy 50.680, Test_accy 48.080
2023-05-08 18:45:13,448 [bic.py] => training => Task 0, Epoch 3/10 => Loss 0.460, Train_accy 49.320, Test_accy 44.230
2023-05-08 18:45:49,218 [bic.py] => training => Task 0, Epoch 4/10 => Loss 0.720, Train_accy 98.650, Test_accy 96.150
2023-05-08 18:46:22,451 [bic.py] => training => Task 0, Epoch 5/10 => Loss 1.455, Train_accy 49.320, Test_accy 44.230
2023-05-08 18:46:55,399 [bic.py] => training => Task 0, Epoch 6/10 => Loss 3.391, Train_accy 50.680, Test_accy 48.080
2023-05-08 18:47:27,371 [bic.py] => training => Task 0, Epoch 7/10 => Loss 0.641, Train_accy 50.680, Test_accy 48.080
2023-05-08 18:47:58,513 [bic.py] => training => Task 0, Epoch 8/10 => Loss 0.352, Train_accy 50.680, Test_accy 48.080
2023-05-08 18:48:28,024 [bic.py] => training => Task 0, Epoch 9/10 => Loss 0.277, Train_accy 50.680, Test_accy 48.080
2023-05-08 18:48:59,313 [bic.py] => training => Task 0, Epoch 10/10 => Loss 0.183, Train_accy 50.680, Test_accy 48.080
2023-05-08 18:48:59,316 [base.py] => Reducing exemplars...(60 per classes)
2023-05-08 18:48:59,316 [base.py] => Constructing exemplars...(60 per classes)
vectors shape L213: (75, 64)
vectors shape L214: (75, 64)
S shape L221: ()
mu_p shape L224: (75, 64)
S shape L221: (64,)
mu_p shape L224: (74, 64)
S shape L221: (64,)
mu_p shape L224: (73, 64)
S shape L221: (64,)
mu_p shape L224: (72, 64)
S shape L221: (64,)
mu_p shape L224: (71, 64)
S shape L221: (64,)
mu_p shape L224: (70, 64)
S shape L221: (64,)
mu_p shape L224: (69, 64)
S shape L221: (64,)
mu_p shape L224: (68, 64)
S shape L221: (64,)
mu_p shape L224: (67, 64)
S shape L221: (64,)
mu_p shape L224: (66, 64)
S shape L221: (64,)
mu_p shape L224: (65, 64)
S shape L221: (64,)
mu_p shape L224: (64, 64)
S shape L221: (64,)
mu_p shape L224: (63, 64)
S shape L221: (64,)
mu_p shape L224: (62, 64)
S shape L221: (64,)
mu_p shape L224: (61, 64)
S shape L221: (64,)
mu_p shape L224: (60, 64)
S shape L221: (64,)
mu_p shape L224: (59, 64)
S shape L221: (64,)
mu_p shape L224: (58, 64)
S shape L221: (64,)
mu_p shape L224: (57, 64)
S shape L221: (64,)
mu_p shape L224: (56, 64)
S shape L221: (64,)
mu_p shape L224: (55, 64)
S shape L221: (64,)
mu_p shape L224: (54, 64)
S shape L221: (64,)
mu_p shape L224: (53, 64)
S shape L221: (64,)
mu_p shape L224: (52, 64)
S shape L221: (64,)
mu_p shape L224: (51, 64)
S shape L221: (64,)
mu_p shape L224: (50, 64)
S shape L221: (64,)
mu_p shape L224: (49, 64)
S shape L221: (64,)
mu_p shape L224: (48, 64)
S shape L221: (64,)
mu_p shape L224: (47, 64)
S shape L221: (64,)
mu_p shape L224: (46, 64)
S shape L221: (64,)
mu_p shape L224: (45, 64)
S shape L221: (64,)
mu_p shape L224: (44, 64)
S shape L221: (64,)
mu_p shape L224: (43, 64)
S shape L221: (64,)
mu_p shape L224: (42, 64)
S shape L221: (64,)
mu_p shape L224: (41, 64)
S shape L221: (64,)
mu_p shape L224: (40, 64)
S shape L221: (64,)
mu_p shape L224: (39, 64)
S shape L221: (64,)
mu_p shape L224: (38, 64)
S shape L221: (64,)
mu_p shape L224: (37, 64)
S shape L221: (64,)
mu_p shape L224: (36, 64)
S shape L221: (64,)
mu_p shape L224: (35, 64)
S shape L221: (64,)
mu_p shape L224: (34, 64)
S shape L221: (64,)
mu_p shape L224: (33, 64)
S shape L221: (64,)
mu_p shape L224: (32, 64)
S shape L221: (64,)
mu_p shape L224: (31, 64)
S shape L221: (64,)
mu_p shape L224: (30, 64)
S shape L221: (64,)
mu_p shape L224: (29, 64)
S shape L221: (64,)
mu_p shape L224: (28, 64)
S shape L221: (64,)
mu_p shape L224: (27, 64)
S shape L221: (64,)
mu_p shape L224: (26, 64)
S shape L221: (64,)
mu_p shape L224: (25, 64)
S shape L221: (64,)
mu_p shape L224: (24, 64)
S shape L221: (64,)
mu_p shape L224: (23, 64)
S shape L221: (64,)
mu_p shape L224: (22, 64)
S shape L221: (64,)
mu_p shape L224: (21, 64)
S shape L221: (64,)
mu_p shape L224: (20, 64)
S shape L221: (64,)
mu_p shape L224: (19, 64)
S shape L221: (64,)
mu_p shape L224: (18, 64)
S shape L221: (64,)
mu_p shape L224: (17, 64)
S shape L221: (64,)
mu_p shape L224: (16, 64)
vectors shape L213: (73, 64)
vectors shape L214: (73, 64)
S shape L221: ()
mu_p shape L224: (73, 64)
S shape L221: (64,)
mu_p shape L224: (72, 64)
S shape L221: (64,)
mu_p shape L224: (71, 64)
S shape L221: (64,)
mu_p shape L224: (70, 64)
S shape L221: (64,)
mu_p shape L224: (69, 64)
S shape L221: (64,)
mu_p shape L224: (68, 64)
S shape L221: (64,)
mu_p shape L224: (67, 64)
S shape L221: (64,)
mu_p shape L224: (66, 64)
S shape L221: (64,)
mu_p shape L224: (65, 64)
S shape L221: (64,)
mu_p shape L224: (64, 64)
S shape L221: (64,)
mu_p shape L224: (63, 64)
S shape L221: (64,)
mu_p shape L224: (62, 64)
S shape L221: (64,)
mu_p shape L224: (61, 64)
S shape L221: (64,)
mu_p shape L224: (60, 64)
S shape L221: (64,)
mu_p shape L224: (59, 64)
S shape L221: (64,)
mu_p shape L224: (58, 64)
S shape L221: (64,)
mu_p shape L224: (57, 64)
S shape L221: (64,)
mu_p shape L224: (56, 64)
S shape L221: (64,)
mu_p shape L224: (55, 64)
S shape L221: (64,)
mu_p shape L224: (54, 64)
S shape L221: (64,)
mu_p shape L224: (53, 64)
S shape L221: (64,)
mu_p shape L224: (52, 64)
S shape L221: (64,)
mu_p shape L224: (51, 64)
S shape L221: (64,)
mu_p shape L224: (50, 64)
S shape L221: (64,)
mu_p shape L224: (49, 64)
S shape L221: (64,)
mu_p shape L224: (48, 64)
S shape L221: (64,)
mu_p shape L224: (47, 64)
S shape L221: (64,)
mu_p shape L224: (46, 64)
S shape L221: (64,)
mu_p shape L224: (45, 64)
S shape L221: (64,)
mu_p shape L224: (44, 64)
S shape L221: (64,)
mu_p shape L224: (43, 64)
S shape L221: (64,)
mu_p shape L224: (42, 64)
S shape L221: (64,)
mu_p shape L224: (41, 64)
S shape L221: (64,)
mu_p shape L224: (40, 64)
S shape L221: (64,)
mu_p shape L224: (39, 64)
S shape L221: (64,)
mu_p shape L224: (38, 64)
S shape L221: (64,)
mu_p shape L224: (37, 64)
S shape L221: (64,)
mu_p shape L224: (36, 64)
S shape L221: (64,)
mu_p shape L224: (35, 64)
S shape L221: (64,)
mu_p shape L224: (34, 64)
S shape L221: (64,)
mu_p shape L224: (33, 64)
S shape L221: (64,)
mu_p shape L224: (32, 64)
S shape L221: (64,)
mu_p shape L224: (31, 64)
S shape L221: (64,)
mu_p shape L224: (30, 64)
S shape L221: (64,)
mu_p shape L224: (29, 64)
S shape L221: (64,)
mu_p shape L224: (28, 64)
S shape L221: (64,)
mu_p shape L224: (27, 64)
S shape L221: (64,)
mu_p shape L224: (26, 64)
S shape L221: (64,)
mu_p shape L224: (25, 64)
S shape L221: (64,)
mu_p shape L224: (24, 64)
S shape L221: (64,)
mu_p shape L224: (23, 64)
S shape L221: (64,)
mu_p shape L224: (22, 64)
S shape L221: (64,)
mu_p shape L224: (21, 64)
S shape L221: (64,)
mu_p shape L224: (20, 64)
S shape L221: (64,)
mu_p shape L224: (19, 64)
S shape L221: (64,)
mu_p shape L224: (18, 64)
S shape L221: (64,)
mu_p shape L224: (17, 64)
S shape L221: (64,)
mu_p shape L224: (16, 64)
S shape L221: (64,)
mu_p shape L224: (15, 64)
S shape L221: (64,)
mu_p shape L224: (14, 64)
2023-05-08 18:49:01,517 [bic.py] => Parameters of bias layer:
2023-05-08 18:49:01,517 [bic.py] => 0 => 1.000, 0.000
2023-05-08 18:49:05,115 [bic.py] => Exemplar size: 120
2023-05-08 18:49:05,115 [trainer.py] => CNN: {'total': 48.08, '00-09': 48.08, 'old': 0, 'new': 48.08}
2023-05-08 18:49:05,115 [trainer.py] => NME: {'total': 80.77, '00-09': 80.77, 'old': 0, 'new': 80.77}
2023-05-08 18:49:05,115 [trainer.py] => CNN top1 curve: [48.08]
2023-05-08 18:49:05,115 [trainer.py] => CNN top2 curve: [100.0]
2023-05-08 18:49:05,115 [trainer.py] => NME top1 curve: [80.77]
2023-05-08 18:49:05,115 [trainer.py] => NME top2 curve: [100.0]
2023-05-08 18:49:05,115 [trainer.py] => All params: 464286
2023-05-08 18:49:05,116 [trainer.py] => Trainable params: 464286
2023-05-08 18:49:05,116 [bic.py] => Learning on 2-4
Size train_data: 253
2023-05-08 18:49:05,117 [bic.py] => Stage1 dset: 253, Stage2 dset: 24
2023-05-08 18:49:05,117 [bic.py] => Lambda: 0.500
2023-05-08 18:49:05,118 [bic.py] => Parameters of bias layer:
2023-05-08 18:49:05,118 [bic.py] => 0 => 1.000, 0.000
2023-05-08 18:49:05,118 [bic.py] => 1 => 1.000, 0.000
2023-05-08 18:50:16,701 [bic.py] => training => Task 1, Epoch 1/10 => Loss 2.795, Train_accy 31.230, Test_accy 20.000
2023-05-08 18:51:31,241 [bic.py] => training => Task 1, Epoch 2/10 => Loss 2.289, Train_accy 51.380, Test_accy 43.160
2023-05-08 18:52:43,481 [bic.py] => training => Task 1, Epoch 3/10 => Loss 0.801, Train_accy 57.310, Test_accy 53.680
2023-05-08 18:53:52,888 [bic.py] => training => Task 1, Epoch 4/10 => Loss 0.774, Train_accy 57.710, Test_accy 51.580
2023-05-08 18:55:02,622 [bic.py] => training => Task 1, Epoch 5/10 => Loss 0.609, Train_accy 56.920, Test_accy 51.580
2023-05-08 18:56:10,273 [bic.py] => training => Task 1, Epoch 6/10 => Loss 0.501, Train_accy 91.700, Test_accy 86.320
2023-05-08 18:57:27,345 [bic.py] => training => Task 1, Epoch 7/10 => Loss 0.396, Train_accy 94.470, Test_accy 94.740
2023-05-08 18:58:36,977 [bic.py] => training => Task 1, Epoch 8/10 => Loss 0.320, Train_accy 90.510, Test_accy 84.210
2023-05-08 18:59:46,209 [bic.py] => training => Task 1, Epoch 9/10 => Loss 0.264, Train_accy 91.700, Test_accy 90.530
2023-05-08 19:00:50,349 [bic.py] => training => Task 1, Epoch 10/10 => Loss 0.217, Train_accy 94.470, Test_accy 90.530
2023-05-08 19:00:58,284 [bic.py] => bias_correction => Task 1, Epoch 1/10 => Loss 0.889, Train_accy 95.830, Test_accy 94.740
2023-05-08 19:01:08,106 [bic.py] => bias_correction => Task 1, Epoch 2/10 => Loss 0.831, Train_accy 95.830, Test_accy 94.740
2023-05-08 19:01:18,964 [bic.py] => bias_correction => Task 1, Epoch 3/10 => Loss 0.909, Train_accy 100.000, Test_accy 97.890
2023-05-08 19:01:27,776 [bic.py] => bias_correction => Task 1, Epoch 4/10 => Loss 0.803, Train_accy 100.000, Test_accy 97.890
2023-05-08 19:01:38,451 [bic.py] => bias_correction => Task 1, Epoch 5/10 => Loss 0.805, Train_accy 100.000, Test_accy 97.890
2023-05-08 19:01:47,668 [bic.py] => bias_correction => Task 1, Epoch 6/10 => Loss 0.806, Train_accy 100.000, Test_accy 97.890
2023-05-08 19:01:57,343 [bic.py] => bias_correction => Task 1, Epoch 7/10 => Loss 0.801, Train_accy 100.000, Test_accy 97.890
2023-05-08 19:02:08,864 [bic.py] => bias_correction => Task 1, Epoch 8/10 => Loss 0.885, Train_accy 100.000, Test_accy 96.840
2023-05-08 19:02:18,724 [bic.py] => bias_correction => Task 1, Epoch 9/10 => Loss 0.854, Train_accy 100.000, Test_accy 96.840
2023-05-08 19:02:28,829 [bic.py] => bias_correction => Task 1, Epoch 10/10 => Loss 0.836, Train_accy 100.000, Test_accy 96.840
2023-05-08 19:02:28,830 [base.py] => Reducing exemplars...(30 per classes)
2023-05-08 19:02:31,283 [base.py] => Constructing exemplars...(30 per classes)
vectors shape L213: (82, 64)
vectors shape L214: (82, 64)
S shape L221: ()
mu_p shape L224: (82, 64)
S shape L221: (64,)
mu_p shape L224: (81, 64)
S shape L221: (64,)
mu_p shape L224: (80, 64)
S shape L221: (64,)
mu_p shape L224: (79, 64)
S shape L221: (64,)
mu_p shape L224: (78, 64)
S shape L221: (64,)
mu_p shape L224: (77, 64)
S shape L221: (64,)
mu_p shape L224: (76, 64)
S shape L221: (64,)
mu_p shape L224: (75, 64)
S shape L221: (64,)
mu_p shape L224: (74, 64)
S shape L221: (64,)
mu_p shape L224: (73, 64)
S shape L221: (64,)
mu_p shape L224: (72, 64)
S shape L221: (64,)
mu_p shape L224: (71, 64)
S shape L221: (64,)
mu_p shape L224: (70, 64)
S shape L221: (64,)
mu_p shape L224: (69, 64)
S shape L221: (64,)
mu_p shape L224: (68, 64)
S shape L221: (64,)
mu_p shape L224: (67, 64)
S shape L221: (64,)
mu_p shape L224: (66, 64)
S shape L221: (64,)
mu_p shape L224: (65, 64)
S shape L221: (64,)
mu_p shape L224: (64, 64)
S shape L221: (64,)
mu_p shape L224: (63, 64)
S shape L221: (64,)
mu_p shape L224: (62, 64)
S shape L221: (64,)
mu_p shape L224: (61, 64)
S shape L221: (64,)
mu_p shape L224: (60, 64)
S shape L221: (64,)
mu_p shape L224: (59, 64)
S shape L221: (64,)
mu_p shape L224: (58, 64)
S shape L221: (64,)
mu_p shape L224: (57, 64)
S shape L221: (64,)
mu_p shape L224: (56, 64)
S shape L221: (64,)
mu_p shape L224: (55, 64)
S shape L221: (64,)
mu_p shape L224: (54, 64)
S shape L221: (64,)
mu_p shape L224: (53, 64)
vectors shape L213: (75, 64)
vectors shape L214: (75, 64)
S shape L221: ()
mu_p shape L224: (75, 64)
S shape L221: (64,)
mu_p shape L224: (74, 64)
S shape L221: (64,)
mu_p shape L224: (73, 64)
S shape L221: (64,)
mu_p shape L224: (72, 64)
S shape L221: (64,)
mu_p shape L224: (71, 64)
S shape L221: (64,)
mu_p shape L224: (70, 64)
S shape L221: (64,)
mu_p shape L224: (69, 64)
S shape L221: (64,)
mu_p shape L224: (68, 64)
S shape L221: (64,)
mu_p shape L224: (67, 64)
S shape L221: (64,)
mu_p shape L224: (66, 64)
S shape L221: (64,)
mu_p shape L224: (65, 64)
S shape L221: (64,)
mu_p shape L224: (64, 64)
S shape L221: (64,)
mu_p shape L224: (63, 64)
S shape L221: (64,)
mu_p shape L224: (62, 64)
S shape L221: (64,)
mu_p shape L224: (61, 64)
S shape L221: (64,)
mu_p shape L224: (60, 64)
S shape L221: (64,)
mu_p shape L224: (59, 64)
S shape L221: (64,)
mu_p shape L224: (58, 64)
S shape L221: (64,)
mu_p shape L224: (57, 64)
S shape L221: (64,)
mu_p shape L224: (56, 64)
S shape L221: (64,)
mu_p shape L224: (55, 64)
S shape L221: (64,)
mu_p shape L224: (54, 64)
S shape L221: (64,)
mu_p shape L224: (53, 64)
S shape L221: (64,)
mu_p shape L224: (52, 64)
S shape L221: (64,)
mu_p shape L224: (51, 64)
S shape L221: (64,)
mu_p shape L224: (50, 64)
S shape L221: (64,)
mu_p shape L224: (49, 64)
S shape L221: (64,)
mu_p shape L224: (48, 64)
S shape L221: (64,)
mu_p shape L224: (47, 64)
S shape L221: (64,)
mu_p shape L224: (46, 64)
2023-05-08 19:02:35,484 [bic.py] => Parameters of bias layer:
2023-05-08 19:02:35,484 [bic.py] => 0 => 1.000, 0.000
2023-05-08 19:02:35,484 [bic.py] => 1 => 1.279, -0.087
2023-05-08 19:02:46,191 [bic.py] => Exemplar size: 120
2023-05-08 19:02:46,191 [trainer.py] => CNN: {'total': 96.84, '00-09': 96.84, 'old': 94.23, 'new': 100.0}
2023-05-08 19:02:46,191 [trainer.py] => NME: {'total': 97.89, '00-09': 97.89, 'old': 96.15, 'new': 100.0}
2023-05-08 19:02:46,191 [trainer.py] => CNN top1 curve: [48.08, 96.84]
2023-05-08 19:02:46,192 [trainer.py] => CNN top2 curve: [100.0, 100.0]
2023-05-08 19:02:46,192 [trainer.py] => NME top1 curve: [80.77, 97.89]
2023-05-08 19:02:46,192 [trainer.py] => NME top2 curve: [100.0, 98.95]
2023-05-08 19:02:46,192 [trainer.py] => All params: 464418
2023-05-08 19:02:46,192 [trainer.py] => Trainable params: 464418
2023-05-08 19:02:46,193 [bic.py] => Learning on 4-6
Size train_data: 247
2023-05-08 19:02:46,194 [bic.py] => Stage1 dset: 247, Stage2 dset: 18
2023-05-08 19:02:46,194 [bic.py] => Lambda: 0.667
2023-05-08 19:02:46,195 [bic.py] => Parameters of bias layer:
2023-05-08 19:02:46,195 [bic.py] => 0 => 1.000, 0.000
2023-05-08 19:02:46,195 [bic.py] => 1 => 1.279, -0.087
2023-05-08 19:02:46,195 [bic.py] => 2 => 1.000, 0.000
2023-05-08 19:04:01,054 [bic.py] => training => Task 2, Epoch 1/10 => Loss 1.201, Train_accy 58.300, Test_accy 48.670
2023-05-08 19:05:17,190 [bic.py] => training => Task 2, Epoch 2/10 => Loss 1.000, Train_accy 58.700, Test_accy 49.330
2023-05-08 19:06:35,572 [bic.py] => training => Task 2, Epoch 3/10 => Loss 0.826, Train_accy 50.200, Test_accy 58.670
2023-05-08 19:07:54,688 [bic.py] => training => Task 2, Epoch 4/10 => Loss 0.736, Train_accy 80.570, Test_accy 72.670
2023-05-08 19:09:12,495 [bic.py] => training => Task 2, Epoch 5/10 => Loss 0.709, Train_accy 90.690, Test_accy 88.670
2023-05-08 19:10:29,218 [bic.py] => training => Task 2, Epoch 6/10 => Loss 0.646, Train_accy 88.660, Test_accy 84.000
2023-05-08 19:11:47,301 [bic.py] => training => Task 2, Epoch 7/10 => Loss 0.620, Train_accy 83.810, Test_accy 74.000
2023-05-08 19:12:53,948 [bic.py] => training => Task 2, Epoch 8/10 => Loss 0.573, Train_accy 93.120, Test_accy 83.330
2023-05-08 19:13:52,325 [bic.py] => training => Task 2, Epoch 9/10 => Loss 0.555, Train_accy 96.360, Test_accy 89.330
2023-05-08 19:14:56,310 [bic.py] => training => Task 2, Epoch 10/10 => Loss 0.521, Train_accy 97.980, Test_accy 94.000
2023-05-08 19:15:11,720 [bic.py] => bias_correction => Task 2, Epoch 1/10 => Loss 1.230, Train_accy 88.890, Test_accy 95.330
2023-05-08 19:15:25,464 [bic.py] => bias_correction => Task 2, Epoch 2/10 => Loss 1.230, Train_accy 88.890, Test_accy 96.000
2023-05-08 19:15:36,529 [bic.py] => bias_correction => Task 2, Epoch 3/10 => Loss 1.229, Train_accy 88.890, Test_accy 96.000
2023-05-08 19:15:50,971 [bic.py] => bias_correction => Task 2, Epoch 4/10 => Loss 1.228, Train_accy 88.890, Test_accy 96.000
2023-05-08 19:16:07,220 [bic.py] => bias_correction => Task 2, Epoch 5/10 => Loss 1.227, Train_accy 88.890, Test_accy 96.000
2023-05-08 19:16:21,684 [bic.py] => bias_correction => Task 2, Epoch 6/10 => Loss 1.226, Train_accy 88.890, Test_accy 96.000
2023-05-08 19:16:35,563 [bic.py] => bias_correction => Task 2, Epoch 7/10 => Loss 1.225, Train_accy 88.890, Test_accy 96.000
2023-05-08 19:16:51,270 [bic.py] => bias_correction => Task 2, Epoch 8/10 => Loss 1.224, Train_accy 88.890, Test_accy 96.000
2023-05-08 19:17:05,477 [bic.py] => bias_correction => Task 2, Epoch 9/10 => Loss 1.224, Train_accy 88.890, Test_accy 96.000
2023-05-08 19:17:19,908 [bic.py] => bias_correction => Task 2, Epoch 10/10 => Loss 1.223, Train_accy 88.890, Test_accy 96.000
2023-05-08 19:17:19,909 [base.py] => Reducing exemplars...(20 per classes)
2023-05-08 19:17:25,590 [base.py] => Constructing exemplars...(20 per classes)
vectors shape L213: (71, 64)
vectors shape L214: (71, 64)
S shape L221: ()
mu_p shape L224: (71, 64)
S shape L221: (64,)
mu_p shape L224: (70, 64)
S shape L221: (64,)
mu_p shape L224: (69, 64)
S shape L221: (64,)
mu_p shape L224: (68, 64)
S shape L221: (64,)
mu_p shape L224: (67, 64)
S shape L221: (64,)
mu_p shape L224: (66, 64)
S shape L221: (64,)
mu_p shape L224: (65, 64)
S shape L221: (64,)
mu_p shape L224: (64, 64)
S shape L221: (64,)
mu_p shape L224: (63, 64)
S shape L221: (64,)
mu_p shape L224: (62, 64)
S shape L221: (64,)
mu_p shape L224: (61, 64)
S shape L221: (64,)
mu_p shape L224: (60, 64)
S shape L221: (64,)
mu_p shape L224: (59, 64)
S shape L221: (64,)
mu_p shape L224: (58, 64)
S shape L221: (64,)
mu_p shape L224: (57, 64)
S shape L221: (64,)
mu_p shape L224: (56, 64)
S shape L221: (64,)
mu_p shape L224: (55, 64)
S shape L221: (64,)
mu_p shape L224: (54, 64)
S shape L221: (64,)
mu_p shape L224: (53, 64)
S shape L221: (64,)
mu_p shape L224: (52, 64)
vectors shape L213: (74, 64)
vectors shape L214: (74, 64)
S shape L221: ()
mu_p shape L224: (74, 64)
S shape L221: (64,)
mu_p shape L224: (73, 64)
S shape L221: (64,)
mu_p shape L224: (72, 64)
S shape L221: (64,)
mu_p shape L224: (71, 64)
S shape L221: (64,)
mu_p shape L224: (70, 64)
S shape L221: (64,)
mu_p shape L224: (69, 64)
S shape L221: (64,)
mu_p shape L224: (68, 64)
S shape L221: (64,)
mu_p shape L224: (67, 64)
S shape L221: (64,)
mu_p shape L224: (66, 64)
S shape L221: (64,)
mu_p shape L224: (65, 64)
S shape L221: (64,)
mu_p shape L224: (64, 64)
S shape L221: (64,)
mu_p shape L224: (63, 64)
S shape L221: (64,)
mu_p shape L224: (62, 64)
S shape L221: (64,)
mu_p shape L224: (61, 64)
S shape L221: (64,)
mu_p shape L224: (60, 64)
S shape L221: (64,)
mu_p shape L224: (59, 64)
S shape L221: (64,)
mu_p shape L224: (58, 64)
S shape L221: (64,)
mu_p shape L224: (57, 64)
S shape L221: (64,)
mu_p shape L224: (56, 64)
S shape L221: (64,)
mu_p shape L224: (55, 64)
2023-05-08 19:17:30,071 [bic.py] => Parameters of bias layer:
2023-05-08 19:17:30,074 [bic.py] => 0 => 1.000, 0.000
2023-05-08 19:17:30,074 [bic.py] => 1 => 1.279, -0.087
2023-05-08 19:17:30,074 [bic.py] => 2 => 0.905, -0.175
2023-05-08 19:17:45,153 [bic.py] => Exemplar size: 120
2023-05-08 19:17:45,158 [trainer.py] => CNN: {'total': 96.0, '00-09': 96.0, 'old': 95.79, 'new': 96.36}
2023-05-08 19:17:45,158 [trainer.py] => NME: {'total': 96.67, '00-09': 96.67, 'old': 97.89, 'new': 94.55}
2023-05-08 19:17:45,158 [trainer.py] => CNN top1 curve: [48.08, 96.84, 96.0]
2023-05-08 19:17:45,158 [trainer.py] => CNN top2 curve: [100.0, 100.0, 98.67]
2023-05-08 19:17:45,158 [trainer.py] => NME top1 curve: [80.77, 97.89, 96.67]
2023-05-08 19:17:45,158 [trainer.py] => NME top2 curve: [100.0, 98.95, 97.33]
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tnalyahya
commented on July 28, 2024
Any suggestions of why I'm getting the wrong value for mu_p? Do I have to change the memory size in bic.json to make it work?
Noting that the image size in 32x32
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G-U-N
commented on July 28, 2024
I think the error might occur when you load your datasets. If you are adding a new dataset following our code, add the following lines in the def download_data(self): in data.py
print(self.train_data.shape)
print(self.train_targets)to check whether your dataset is loaded correctly.
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zhoudw-zdw
commented on July 28, 2024
Try setting fixed_memory to True.
This problem sometime occurs when the minimum number of instances per class is lower than the average number of exemplar.
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tnalyahya
commented on July 28, 2024
Thanks all!
It worked :)
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