Open yczhan opened 2 years ago
Hi authors, I noticed that the NUM_TRAIN_TASKS under UCF setting is only 2500, is it enough to train the model? cuz I tried to reproduce your method, but the output seems strange (val_epoch "min_top1_err": 33.023999 , "top1_err": 66.112001) Besides, I also noticed that you implement supervised loss (USE_CLASSIFICATION=True) and set different weights for ucf and ssv2 (0.15 and 0.6, respectively), the data augmentations are also complex (including random erasing). I wonder how much of an impact these extra settings will have on the final result?
Hi, thanks for your attention. Since the ImageNet pre-trained ResNet-50 is relatively friendly for the UCF101 dataset, i.e. the UCF101 dataset is more appearance-based, 2500 iters are enough. For your reproduced results, could it be that the ImageNet pre-trained model was not loaded? Because the results are too low and heavily overfitted. Regarding supervised loss and data augmentation, we performed HyRSM on top of reproducing ITANet, and we found that we could not reproduce ITANet without supervised loss and data augmentation, and the results were about 2% worse for your reference.
I downloaded the pretrained Resnet-50 and loaded it before training. I tried 1-shot UCF and I set the query_num = 1 due to limited CUDA memory, the results: (val_epoch "min_top1_err": 33.02 , "top1_err": 66.11). I found that when I set the query_num = 3, the results becomes (val epoch "min_top1_err": 26.56, "top1_err": 49.84). Does the number of query make such a big difference? Besides, I read the log and found that the top1_err is best at first, then becomes worse during training. This is shown in the results above (26.56% at first and 49.84% at final). Also, the NUM_TRAIN_TASKS is only 2500 and the lr is still warming up at the end of training. Are these phenomena normal? or maybe you could share your training logs with me?
Hi, for reproduce our results, you may need to set the same settings as ours. The log for 5-way 1-shot UCF101 (evaluation per 300 iters) is here:
[09/09 23:34:41][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "111 days, 8:29:35", "iter": "50/250000", "loss": 7.711459, "lr": 0.000013, "time_diff": 3.848467, "top1_err": 28.571426, "top5_err": 0.000000} [09/09 23:37:05][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "35 days, 8:52:15", "iter": "100/250000", "loss": 5.949836, "lr": 0.000016, "time_diff": 1.222423, "top1_err": 22.857141, "top5_err": 0.000000} [09/09 23:39:36][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "252 days, 6:03:22", "iter": "150/250000", "loss": 4.992104, "lr": 0.000019, "time_diff": 8.718364, "top1_err": 24.285713, "top5_err": 0.000000} [09/09 23:41:52][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "35 days, 13:41:24", "iter": "200/250000", "loss": 3.955957, "lr": 0.000021, "time_diff": 1.229412, "top1_err": 17.142857, "top5_err": 0.000000} [09/09 23:44:14][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "216 days, 7:33:26", "iter": "250/250000", "loss": 3.184105, "lr": 0.000024, "time_diff": 7.476590, "top1_err": 14.285713, "top5_err": 0.000000}
[09/09 23:46:36][INFO] utils.bucket: 60: putting 'output/test/checkpoints/checkpoint_epoch_00001.pyth' to 'model/few-shot/Few_shot_Relation-Attention_final/UCF/v1/checkpoint_epoch_00001.pyth' [09/09 23:47:09][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:14:40", "gpu_mem": "27.09 GB", "iter": "50/2500", "time_diff": 0.359567, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:47:28][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:04:22", "gpu_mem": "27.09 GB", "iter": "100/2500", "time_diff": 0.109315, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:47:46][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:04:28", "gpu_mem": "27.09 GB", "iter": "150/2500", "time_diff": 0.114141, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:48:05][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:04:16", "gpu_mem": "27.09 GB", "iter": "200/2500", "time_diff": 0.111340, "top1_err": 0.000000, "top5_err": 0.000000} [09/09 23:48:21][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:04:08", "gpu_mem": "27.09 GB", "iter": "250/2500", "time_diff": 0.110474, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:48:35][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:04:02", "gpu_mem": "27.09 GB", "iter": "300/2500", "time_diff": 0.110003, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:48:49][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:04:09", "gpu_mem": "27.09 GB", "iter": "350/2500", "time_diff": 0.116002, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:49:03][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:31:46", "gpu_mem": "27.09 GB", "iter": "400/2500", "time_diff": 0.908024, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:49:17][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:03:47", "gpu_mem": "27.09 GB", "iter": "450/2500", "time_diff": 0.110890, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:49:30][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:17:46", "gpu_mem": "27.09 GB", "iter": "500/2500", "time_diff": 0.533087, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:49:43][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:03:38", "gpu_mem": "27.09 GB", "iter": "550/2500", "time_diff": 0.112168, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:49:54][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:11:41", "gpu_mem": "27.09 GB", "iter": "600/2500", "time_diff": 0.369428, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:50:06][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:14:04", "gpu_mem": "27.09 GB", "iter": "650/2500", "time_diff": 0.456663, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:50:19][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:03:27", "gpu_mem": "27.09 GB", "iter": "700/2500", "time_diff": 0.115359, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:50:31][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:15:54", "gpu_mem": "27.09 GB", "iter": "750/2500", "time_diff": 0.545533, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:50:43][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:03:09", "gpu_mem": "27.09 GB", "iter": "800/2500", "time_diff": 0.111549, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:50:54][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:03:05", "gpu_mem": "27.09 GB", "iter": "850/2500", "time_diff": 0.112293, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:51:06][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:04:35", "gpu_mem": "27.09 GB", "iter": "900/2500", "time_diff": 0.172106, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:51:17][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:05:14", "gpu_mem": "27.09 GB", "iter": "950/2500", "time_diff": 0.203196, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:51:29][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:02:48", "gpu_mem": "27.09 GB", "iter": "1000/2500", "time_diff": 0.112029, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:51:41][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:02:46", "gpu_mem": "27.09 GB", "iter": "1050/2500", "time_diff": 0.114718, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:51:53][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:02:36", "gpu_mem": "27.09 GB", "iter": "1100/2500", "time_diff": 0.112047, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:52:05][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:02:29", "gpu_mem": "27.09 GB", "iter": "1150/2500", "time_diff": 0.111061, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:52:17][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:10:33", "gpu_mem": "27.09 GB", "iter": "1200/2500", "time_diff": 0.487579, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:52:28][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:02:18", "gpu_mem": "27.09 GB", "iter": "1250/2500", "time_diff": 0.111185, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:52:40][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:02:15", "gpu_mem": "27.09 GB", "iter": "1300/2500", "time_diff": 0.112725, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:52:52][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:02:17", "gpu_mem": "27.09 GB", "iter": "1350/2500", "time_diff": 0.119642, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:53:03][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:02:07", "gpu_mem": "27.09 GB", "iter": "1400/2500", "time_diff": 0.116044, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:53:15][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:01:55", "gpu_mem": "27.09 GB", "iter": "1450/2500", "time_diff": 0.109696, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:53:26][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:01:51", "gpu_mem": "27.09 GB", "iter": "1500/2500", "time_diff": 0.111350, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:53:38][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:08:43", "gpu_mem": "27.09 GB", "iter": "1550/2500", "time_diff": 0.550837, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:53:49][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:01:42", "gpu_mem": "27.09 GB", "iter": "1600/2500", "time_diff": 0.114408, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:54:01][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:01:37", "gpu_mem": "27.09 GB", "iter": "1650/2500", "time_diff": 0.114540, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:54:13][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:06:17", "gpu_mem": "27.09 GB", "iter": "1700/2500", "time_diff": 0.471832, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:54:24][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:01:26", "gpu_mem": "27.09 GB", "iter": "1750/2500", "time_diff": 0.115058, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:54:35][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:01:20", "gpu_mem": "27.09 GB", "iter": "1800/2500", "time_diff": 0.114749, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:54:47][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:01:12", "gpu_mem": "27.09 GB", "iter": "1850/2500", "time_diff": 0.112099, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:55:00][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:05:23", "gpu_mem": "27.09 GB", "iter": "1900/2500", "time_diff": 0.538911, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:55:12][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:01:01", "gpu_mem": "27.09 GB", "iter": "1950/2500", "time_diff": 0.112354, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:55:24][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:00:58", "gpu_mem": "27.09 GB", "iter": "2000/2500", "time_diff": 0.116810, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:55:36][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:00:52", "gpu_mem": "27.09 GB", "iter": "2050/2500", "time_diff": 0.117685, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:55:48][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:00:49", "gpu_mem": "27.09 GB", "iter": "2100/2500", "time_diff": 0.124157, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:56:00][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:00:41", "gpu_mem": "27.09 GB", "iter": "2150/2500", "time_diff": 0.119030, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:56:12][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:00:34", "gpu_mem": "27.09 GB", "iter": "2200/2500", "time_diff": 0.114651, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:56:24][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:02:35", "gpu_mem": "27.09 GB", "iter": "2250/2500", "time_diff": 0.622380, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:56:36][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:00:22", "gpu_mem": "27.09 GB", "iter": "2300/2500", "time_diff": 0.111999, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:56:48][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:00:16", "gpu_mem": "27.09 GB", "iter": "2350/2500", "time_diff": 0.111899, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:57:02][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:00:11", "gpu_mem": "27.09 GB", "iter": "2400/2500", "time_diff": 0.112014, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:57:15][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:00:05", "gpu_mem": "27.09 GB", "iter": "2450/2500", "time_diff": 0.116999, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:57:29][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:00:00", "gpu_mem": "27.09 GB", "iter": "2500/2500", "time_diff": 0.108913, "top1_err": 19.999998, "top5_err": 0.000000} [09/09 23:57:29][INFO] utils.logging: 90: {"RAM": "0.01/585.94 GB", "_type": "val_epoch", "epoch": "1/10", "gpu_mem": "27.09 GB", "min_top1_err": 19.007999, "min_top5_err": 0.000000, "time_diff": 0.567919, "top1_err": 19.007999, "top5_err": 0.000000} [09/09 23:57:30][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "19347 days, 7:38:49", "iter": "300/250000", "loss": 2.774728, "lr": 0.000027, "time_diff": 668.723579, "top1_err": 14.285713, "top5_err": 0.000000} [09/09 23:59:34][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "107 days, 5:54:31", "iter": "350/250000", "loss": 2.504072, "lr": 0.000030, "time_diff": 3.706947, "top1_err": 12.857142, "top5_err": 0.000000} [09/10 00:01:52][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "34 days, 22:21:54", "iter": "400/250000", "loss": 1.967621, "lr": 0.000033, "time_diff": 1.207439, "top1_err": 8.571428, "top5_err": 0.000000} [09/10 00:04:09][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "202 days, 1:07:04", "iter": "450/250000", "loss": 1.837473, "lr": 0.000036, "time_diff": 6.983987, "top1_err": 8.571428, "top5_err": 0.000000} [09/10 00:06:21][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "35 days, 10:08:12", "iter": "500/250000", "loss": 1.635736, "lr": 0.000039, "time_diff": 1.224442, "top1_err": 10.000000, "top5_err": 0.000000} [09/10 00:08:35][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "200 days, 6:11:49", "iter": "550/250000", "loss": 1.572966, "lr": 0.000041, "time_diff": 6.922447, "top1_err": 5.714285, "top5_err": 0.000000}
[09/10 00:10:58][INFO] utils.bucket: 60: putting 'output/test/checkpoints/checkpoint_epoch_00001.pyth' to 'model/few-shot/Few_shot_Relation-Attention_final/UCF/v1/checkpoint_epoch_00001.pyth' [09/10 00:11:26][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:05:03", "gpu_mem": "27.09 GB", "iter": "50/2500", "time_diff": 0.124000, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 00:11:38][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:04:27", "gpu_mem": "27.09 GB", "iter": "100/2500", "time_diff": 0.111529, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 00:11:50][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:16:34", "gpu_mem": "27.09 GB", "iter": "150/2500", "time_diff": 0.423382, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 00:12:04][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:04:15", "gpu_mem": "27.09 GB", "iter": "200/2500", "time_diff": 0.111018, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 00:12:17][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:04:19", "gpu_mem": "27.09 GB", "iter": "250/2500", "time_diff": 0.115280, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 00:12:30][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:04:08", "gpu_mem": "27.09 GB", "iter": "300/2500", "time_diff": 0.112777, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 00:12:44][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:04:01", "gpu_mem": "27.09 GB", "iter": "350/2500", "time_diff": 0.112360, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 00:12:58][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:03:58", "gpu_mem": "27.09 GB", "iter": "400/2500", "time_diff": 0.113510, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 00:13:12][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "1/10", "eta": "0:12:33", "gpu_mem": "27.09 GB", 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[09/10 00:21:47][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "19213 days, 0:59:15", "iter": "600/250000", "loss": 1.536787, "lr": 0.000044, "time_diff": 664.162101, "top1_err": 5.714285, "top5_err": 0.000000} [09/10 00:23:58][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "36 days, 6:37:37", "iter": "650/250000", "loss": 1.300092, "lr": 0.000047, "time_diff": 1.254029, "top1_err": 5.714285, "top5_err": 0.000000} [09/10 00:26:20][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "1/10", "eta": "35 days, 1:12:37", "iter": "700/250000", "loss": 1.180781, "lr": 0.000050, "time_diff": 1.211682, "top1_err": 5.714285, "top5_err": 0.000000} [09/10 00:28:43][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "2/10", "eta": "140 days, 6:22:41", "iter": "750/250000", "loss": 1.003870, "lr": 0.000050, "time_diff": 5.388001, "top1_err": 2.857143, "top5_err": 0.000000} [09/10 00:30:59][INFO] utils.logging: 90: {"_type": 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[09/10 00:35:47][INFO] utils.bucket: 60: putting 'output/test/checkpoints/checkpoint_epoch_00002.pyth' to 'model/few-shot/Few_shot_Relation-Attention_final/UCF/v1/checkpoint_epoch_00002.pyth' [09/10 00:36:15][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "2/10", "eta": "0:11:30", "gpu_mem": "27.09 GB", "iter": "50/2500", "time_diff": 0.281743, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 00:36:30][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "2/10", "eta": "0:04:26", "gpu_mem": "27.09 GB", "iter": "100/2500", "time_diff": 0.110972, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 00:36:45][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "2/10", "eta": "0:04:17", "gpu_mem": "27.09 GB", "iter": "150/2500", "time_diff": 0.109466, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 00:36:59][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "2/10", "eta": "0:04:12", "gpu_mem": "27.09 GB", "iter": "200/2500", "time_diff": 0.109875, "top1_err": 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[09/10 01:01:25][INFO] utils.bucket: 60: putting 'output/test/checkpoints/checkpoint_epoch_00002.pyth' to 'model/few-shot/Few_shot_Relation-Attention_final/UCF/v1/checkpoint_epoch_00002.pyth' [09/10 01:01:57][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "2/10", "eta": "0:33:10", "gpu_mem": "27.09 GB", "iter": "50/2500", "time_diff": 0.812588, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 01:02:12][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "2/10", "eta": "0:04:24", "gpu_mem": "27.09 GB", "iter": "100/2500", "time_diff": 0.110273, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 01:02:29][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "2/10", "eta": "0:14:42", "gpu_mem": "27.09 GB", "iter": "150/2500", "time_diff": 0.375395, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 01:02:46][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "2/10", "eta": "0:04:23", "gpu_mem": "27.09 GB", "iter": "200/2500", "time_diff": 0.114374, "top1_err": 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[09/10 01:29:13][INFO] utils.bucket: 60: putting 'output/test/checkpoints/checkpoint_epoch_00003.pyth' to 'model/few-shot/Few_shot_Relation-Attention_final/UCF/v1/checkpoint_epoch_00003.pyth' [09/10 01:29:43][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "3/10", "eta": "0:04:56", "gpu_mem": "27.09 GB", "iter": "50/2500", "time_diff": 0.121073, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 01:29:58][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "3/10", "eta": "0:04:26", "gpu_mem": "27.09 GB", "iter": "100/2500", "time_diff": 0.111027, "top1_err": 9.999999, "top5_err": 0.000000} [09/10 01:30:12][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "3/10", "eta": "0:04:23", "gpu_mem": "27.09 GB", "iter": "150/2500", "time_diff": 0.112056, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 01:30:25][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "3/10", "eta": "0:04:34", "gpu_mem": "27.09 GB", "iter": "200/2500", "time_diff": 0.119195, "top1_err": 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[09/10 01:40:46][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "3/10", "eta": "16376 days, 18:01:21", "iter": "1500/250000", "loss": 0.321338, "lr": 0.000025, "time_diff": 708.006646, "top1_err": 0.000000, "top5_err": 0.000000} [09/10 01:42:50][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "3/10", "eta": "55 days, 7:36:12", "iter": "1550/250000", "loss": 0.295718, "lr": 0.000025, "time_diff": 2.391540, "top1_err": 0.000000, "top5_err": 0.000000} [09/10 01:45:07][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "3/10", "eta": "28 days, 7:20:18", "iter": "1600/250000", "loss": 0.290204, "lr": 0.000025, "time_diff": 1.223788, "top1_err": 0.000000, "top5_err": 0.000000} [09/10 01:47:24][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "3/10", "eta": "91 days, 4:59:27", "iter": "1650/250000", "loss": 0.288918, "lr": 0.000025, "time_diff": 3.943437, "top1_err": 0.000000, "top5_err": 0.000000} [09/10 01:49:38][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "3/10", "eta": "28 days, 10:32:40", "iter": "1700/250000", "loss": 0.229426, "lr": 0.000025, "time_diff": 1.229626, "top1_err": 0.000000, "top5_err": 0.000000} [09/10 01:51:53][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "3/10", "eta": "161 days, 0:22:26", "iter": "1750/250000", "loss": 0.248469, "lr": 0.000025, "time_diff": 6.961965, "top1_err": 0.000000, "top5_err": 0.000000}
[09/10 01:54:15][INFO] utils.bucket: 60: putting 'output/test/checkpoints/checkpoint_epoch_00003.pyth' to 'model/few-shot/Few_shot_Relation-Attention_final/UCF/v1/checkpoint_epoch_00003.pyth' [09/10 01:54:43][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "3/10", "eta": "0:05:42", "gpu_mem": "27.09 GB", "iter": "50/2500", "time_diff": 0.139627, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 01:54:56][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "3/10", "eta": "0:04:25", "gpu_mem": "27.09 GB", "iter": "100/2500", "time_diff": 0.110527, "top1_err": 0.000000, "top5_err": 0.000000} [09/10 01:55:08][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "3/10", "eta": "0:22:19", "gpu_mem": "27.09 GB", "iter": "150/2500", "time_diff": 0.569960, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 01:55:20][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "3/10", "eta": "0:04:25", "gpu_mem": "27.09 GB", "iter": "200/2500", "time_diff": 0.115248, "top1_err": 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[09/10 02:05:03][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "3/10", "eta": "15332 days, 3:43:42", "iter": "1800/250000", "loss": 0.262196, "lr": 0.000025, "time_diff": 662.945762, "top1_err": 0.000000, "top5_err": 0.000000} [09/10 02:07:11][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "3/10", "eta": "28 days, 4:22:29", "iter": "1850/250000", "loss": 0.253639, "lr": 0.000025, "time_diff": 1.218602, "top1_err": 0.000000, "top5_err": 0.000000} [09/10 02:09:25][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "3/10", "eta": "28 days, 7:13:27", "iter": "1900/250000", "loss": 0.250613, "lr": 0.000025, "time_diff": 1.223766, "top1_err": 0.000000, "top5_err": 0.000000} [09/10 02:11:43][INFO] utils.logging: 90: {"_type": "train_iter", "epoch": "3/10", "eta": "50 days, 19:40:53", "iter": "1950/250000", "loss": 0.206233, "lr": 0.000025, "time_diff": 2.197569, "top1_err": 0.000000, "top5_err": 0.000000} [09/10 02:13:59][INFO] utils.logging: 90: {"_type": 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[09/10 02:18:48][INFO] utils.bucket: 60: putting 'output/test/checkpoints/checkpoint_epoch_00003.pyth' to 'model/few-shot/Few_shot_Relation-Attention_final/UCF/v1/checkpoint_epoch_00003.pyth' [09/10 02:19:15][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "3/10", "eta": "0:22:33", "gpu_mem": "27.09 GB", "iter": "50/2500", "time_diff": 0.552333, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 02:19:28][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "3/10", "eta": "0:04:35", "gpu_mem": "27.09 GB", "iter": "100/2500", "time_diff": 0.114988, "top1_err": 19.999998, "top5_err": 0.000000} [09/10 02:19:40][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "3/10", "eta": "0:24:47", "gpu_mem": "27.09 GB", "iter": "150/2500", "time_diff": 0.632791, "top1_err": 0.000000, "top5_err": 0.000000} [09/10 02:19:53][INFO] utils.logging: 90: {"_type": "val_iter", "epoch": "3/10", "eta": "0:16:20", "gpu_mem": "27.09 GB", "iter": "200/2500", "time_diff": 0.426371, "top1_err": 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I downloaded the pretrained Resnet-50 and loaded it before training. I tried 1-shot UCF and I set the query_num = 1 due to limited CUDA memory, the results: (val_epoch "min_top1_err": 33.02 , "top1_err": 66.11). I found that when I set the query_num = 3, the results becomes (val epoch "min_top1_err": 26.56, "top1_err": 49.84). Does the number of query make such a big difference? Besides, I read the log and found that the top1_err is best at first, then becomes worse during training. This is shown in the results above (26.56% at first and 49.84% at final). Also, the NUM_TRAIN_TASKS is only 2500 and the lr is still warming up at the end of training. Are these phenomena normal? or maybe you could share your training logs with me?
Hi, yczhan, referring to this issue, when we rewrote the open source version, there was a small bug, that is, the learning rate was adjusted in a wrong way, which has been corrected now (here).
Hi authors, I noticed that the NUM_TRAIN_TASKS under UCF setting is only 2500, is it enough to train the model? cuz I tried to reproduce your method, but the output seems strange (val_epoch "min_top1_err": 33.023999 , "top1_err": 66.112001) Besides, I also noticed that you implement supervised loss (USE_CLASSIFICATION=True) and set different weights for ucf and ssv2 (0.15 and 0.6, respectively), the data augmentations are also complex (including random erasing). I wonder how much of an impact these extra settings will have on the final result?