2022-05-26 10:46:41,629 INFO [train.py:906] (1/4) Training started
2022-05-26 10:46:41,629 INFO [train.py:916] (1/4) Device: cuda:1
2022-05-26 10:46:41,631 INFO [train.py:934] (1/4) {'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, 'best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 50, 'reset_interval': 200, 'valid_interval': 3000, 'feature_dim': 80, 'subsampling_factor': 4, 'encoder_dim': 512, 'nhead': 8, 'dim_feedforward': 2048, 'num_encoder_layers': 12, 'decoder_dim': 512, 'joiner_dim': 512, 'model_warm_step': 3000, 'env_info': {'k2-version': '1.15.1', 'k2-build-type': 'Debug', 'k2-with-cuda': True, 'k2-git-sha1': 'ecfe7bd6d9189964bf3ff043038918d889a43185', 'k2-git-date': 'Tue May 10 10:57:55 2022', 'lhotse-version': '1.1.0', 'torch-version': '1.10.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'streaming-conformer', 'icefall-git-sha1': '364bccb-clean', 'icefall-git-date': 'Thu May 26 10:29:08 2022', 'icefall-path': '/ceph-kw/kangwei/code/icefall_reworked2', 'k2-path': '/ceph-kw/kangwei/code/k2/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-hw/kangwei/dev_tools/anaconda3/envs/rnnt2/lib/python3.8/site-packages/lhotse-1.1.0-py3.8.egg/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-3-0307202051-57dc848959-8tmmp', 'IP address': '10.177.24.138'}, 'world_size': 4, 'master_port': 13498, 'tensorboard': True, 'num_epochs': 50, 'start_epoch': 1, 'start_batch': 0, 'exp_dir': PosixPath('streaming_pruned_transducer_stateless4/exp'), 'bpe_model': 'data/lang_bpe_500/bpe.model', 'initial_lr': 0.003, 'lr_batches': 5000, 'lr_epochs': 6, 'context_size': 2, 'prune_range': 5, 'lm_scale': 0.25, 'am_scale': 0.0, 'simple_loss_scale': 0.5, 'seed': 42, 'print_diagnostics': False, 'save_every_n': 8000, 'keep_last_k': 20, 'average_period': 100, 'use_fp16': False, 'dynamic_chunk_training': True, 'causal_convolution': True, 'short_chunk_size': 32, 'num_left_chunks': 4, 'delay_penalty': 0.0, 'return_sym_delay': False, 'full_libri': True, 'manifest_dir': PosixPath('data/fbank'), 'max_duration': 300, 'bucketing_sampler': True, 'num_buckets': 30, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2, 'enable_spec_aug': True, 'spec_aug_time_warp_factor': 80, 'enable_musan': True, 'blank_id': 0, 'vocab_size': 500}
2022-05-26 10:46:41,632 INFO [train.py:936] (1/4) About to create model
2022-05-26 10:46:42,058 INFO [train.py:940] (1/4) Number of model parameters: 78648040
2022-05-26 10:46:47,305 INFO [train.py:955] (1/4) Using DDP
2022-05-26 10:46:47,469 INFO [asr_datamodule.py:391] (1/4) About to get train-clean-100 cuts
2022-05-26 10:46:54,011 INFO [asr_datamodule.py:398] (1/4) About to get train-clean-360 cuts
2022-05-26 10:47:21,156 INFO [asr_datamodule.py:405] (1/4) About to get train-other-500 cuts
2022-05-26 10:48:07,792 INFO [asr_datamodule.py:209] (1/4) Enable MUSAN
2022-05-26 10:48:07,792 INFO [asr_datamodule.py:210] (1/4) About to get Musan cuts
2022-05-26 10:48:09,225 INFO [asr_datamodule.py:238] (1/4) Enable SpecAugment
2022-05-26 10:48:09,225 INFO [asr_datamodule.py:239] (1/4) Time warp factor: 80
2022-05-26 10:48:09,225 INFO [asr_datamodule.py:251] (1/4) Num frame mask: 10
2022-05-26 10:48:09,225 INFO [asr_datamodule.py:264] (1/4) About to create train dataset
2022-05-26 10:48:09,225 INFO [asr_datamodule.py:292] (1/4) Using BucketingSampler.
2022-05-26 10:48:14,258 INFO [asr_datamodule.py:308] (1/4) About to create train dataloader
2022-05-26 10:48:14,259 INFO [asr_datamodule.py:412] (1/4) About to get dev-clean cuts
2022-05-26 10:48:14,545 INFO [asr_datamodule.py:417] (1/4) About to get dev-other cuts
2022-05-26 10:48:14,676 INFO [asr_datamodule.py:339] (1/4) About to create dev dataset
2022-05-26 10:48:14,687 INFO [asr_datamodule.py:358] (1/4) About to create dev dataloader
2022-05-26 10:48:14,687 INFO [train.py:1082] (1/4) Sanity check -- see if any of the batches in epoch 1 would cause OOM.
2022-05-26 10:48:23,783 INFO [distributed.py:874] (1/4) Reducer buckets have been rebuilt in this iteration.
2022-05-26 10:48:40,747 INFO [train.py:842] (1/4) Epoch 1, batch 0, loss[loss=0.8059, simple_loss=1.612, pruned_loss=6.58, over 7292.00 frames.], tot_loss[loss=0.8059, simple_loss=1.612, pruned_loss=6.58, over 7292.00 frames.], batch size: 17, lr: 3.00e-03
2022-05-26 10:49:19,696 INFO [train.py:842] (1/4) Epoch 1, batch 50, loss[loss=0.523, simple_loss=1.046, pruned_loss=7.055, over 7166.00 frames.], tot_loss[loss=0.5657, simple_loss=1.131, pruned_loss=7.106, over 323908.63 frames.], batch size: 19, lr: 3.00e-03
2022-05-26 10:49:59,122 INFO [train.py:842] (1/4) Epoch 1, batch 100, loss[loss=0.3984, simple_loss=0.7968, pruned_loss=6.787, over 7006.00 frames.], tot_loss[loss=0.507, simple_loss=1.014, pruned_loss=7.011, over 566581.55 frames.], batch size: 16, lr: 3.00e-03
2022-05-26 10:50:37,771 INFO [train.py:842] (1/4) Epoch 1, batch 150, loss[loss=0.3678, simple_loss=0.7356, pruned_loss=6.634, over 7007.00 frames.], tot_loss[loss=0.4751, simple_loss=0.9503, pruned_loss=6.938, over 758005.23 frames.], batch size: 16, lr: 3.00e-03
2022-05-26 10:51:16,807 INFO [train.py:842] (1/4) Epoch 1, batch 200, loss[loss=0.4361, simple_loss=0.8722, pruned_loss=6.759, over 7278.00 frames.], tot_loss[loss=0.453, simple_loss=0.9061, pruned_loss=6.886, over 908965.97 frames.], batch size: 25, lr: 3.00e-03
2022-05-26 10:51:55,437 INFO [train.py:842] (1/4) Epoch 1, batch 250, loss[loss=0.4235, simple_loss=0.8469, pruned_loss=6.753, over 7324.00 frames.], tot_loss[loss=0.4405, simple_loss=0.8809, pruned_loss=6.836, over 1017925.80 frames.], batch size: 21, lr: 3.00e-03
2022-05-26 10:52:34,300 INFO [train.py:842] (1/4) Epoch 1, batch 300, loss[loss=0.4346, simple_loss=0.8692, pruned_loss=6.773, over 7297.00 frames.], tot_loss[loss=0.4294, simple_loss=0.8589, pruned_loss=6.793, over 1110304.74 frames.], batch size: 25, lr: 3.00e-03
2022-05-26 10:53:13,228 INFO [train.py:842] (1/4) Epoch 1, batch 350, loss[loss=0.3843, simple_loss=0.7685, pruned_loss=6.679, over 7251.00 frames.], tot_loss[loss=0.42, simple_loss=0.84, pruned_loss=6.762, over 1179590.71 frames.], batch size: 19, lr: 3.00e-03
2022-05-26 10:53:52,170 INFO [train.py:842] (1/4) Epoch 1, batch 400, loss[loss=0.3904, simple_loss=0.7809, pruned_loss=6.702, over 7416.00 frames.], tot_loss[loss=0.4133, simple_loss=0.8267, pruned_loss=6.746, over 1231452.25 frames.], batch size: 21, lr: 3.00e-03
2022-05-26 10:54:30,770 INFO [train.py:842] (1/4) Epoch 1, batch 450, loss[loss=0.4045, simple_loss=0.8091, pruned_loss=6.651, over 7416.00 frames.], tot_loss[loss=0.4083, simple_loss=0.8166, pruned_loss=6.73, over 1267586.08 frames.], batch size: 21, lr: 2.99e-03
2022-05-26 10:55:09,752 INFO [train.py:842] (1/4) Epoch 1, batch 500, loss[loss=0.3788, simple_loss=0.7576, pruned_loss=6.677, over 7188.00 frames.], tot_loss[loss=0.4017, simple_loss=0.8034, pruned_loss=6.709, over 1303269.35 frames.], batch size: 22, lr: 2.99e-03
2022-05-26 10:55:48,096 INFO [train.py:842] (1/4) Epoch 1, batch 550, loss[loss=0.3563, simple_loss=0.7126, pruned_loss=6.638, over 7321.00 frames.], tot_loss[loss=0.3953, simple_loss=0.7906, pruned_loss=6.703, over 1329299.67 frames.], batch size: 22, lr: 2.99e-03
2022-05-26 10:56:27,042 INFO [train.py:842] (1/4) Epoch 1, batch 600, loss[loss=0.3291, simple_loss=0.6583, pruned_loss=6.7, over 7116.00 frames.], tot_loss[loss=0.384, simple_loss=0.7681, pruned_loss=6.695, over 1350680.55 frames.], batch size: 21, lr: 2.99e-03
2022-05-26 10:57:05,643 INFO [train.py:842] (1/4) Epoch 1, batch 650, loss[loss=0.297, simple_loss=0.5939, pruned_loss=6.631, over 7012.00 frames.], tot_loss[loss=0.3722, simple_loss=0.7444, pruned_loss=6.699, over 1368916.71 frames.], batch size: 16, lr: 2.99e-03
2022-05-26 10:57:44,577 INFO [train.py:842] (1/4) Epoch 1, batch 700, loss[loss=0.3296, simple_loss=0.6593, pruned_loss=6.814, over 7204.00 frames.], tot_loss[loss=0.3592, simple_loss=0.7185, pruned_loss=6.695, over 1380846.17 frames.], batch size: 23, lr: 2.99e-03
2022-05-26 10:58:23,533 INFO [train.py:842] (1/4) Epoch 1, batch 750, loss[loss=0.2658, simple_loss=0.5316, pruned_loss=6.614, over 7287.00 frames.], tot_loss[loss=0.3468, simple_loss=0.6937, pruned_loss=6.696, over 1392212.43 frames.], batch size: 17, lr: 2.98e-03
2022-05-26 10:59:02,500 INFO [train.py:842] (1/4) Epoch 1, batch 800, loss[loss=0.3085, simple_loss=0.617, pruned_loss=6.701, over 7113.00 frames.], tot_loss[loss=0.3371, simple_loss=0.6742, pruned_loss=6.708, over 1397018.43 frames.], batch size: 21, lr: 2.98e-03
2022-05-26 10:59:41,349 INFO [train.py:842] (1/4) Epoch 1, batch 850, loss[loss=0.29, simple_loss=0.5799, pruned_loss=6.801, over 7226.00 frames.], tot_loss[loss=0.3256, simple_loss=0.6513, pruned_loss=6.714, over 1403220.04 frames.], batch size: 21, lr: 2.98e-03
2022-05-26 11:00:20,379 INFO [train.py:842] (1/4) Epoch 1, batch 900, loss[loss=0.3128, simple_loss=0.6256, pruned_loss=6.736, over 7329.00 frames.], tot_loss[loss=0.3159, simple_loss=0.6318, pruned_loss=6.714, over 1408635.96 frames.], batch size: 21, lr: 2.98e-03
2022-05-26 11:00:58,859 INFO [train.py:842] (1/4) Epoch 1, batch 950, loss[loss=0.2375, simple_loss=0.475, pruned_loss=6.581, over 6994.00 frames.], tot_loss[loss=0.3082, simple_loss=0.6163, pruned_loss=6.718, over 1405206.69 frames.], batch size: 16, lr: 2.97e-03
2022-05-26 11:01:37,558 INFO [train.py:842] (1/4) Epoch 1, batch 1000, loss[loss=0.234, simple_loss=0.468, pruned_loss=6.531, over 7002.00 frames.], tot_loss[loss=0.301, simple_loss=0.602, pruned_loss=6.723, over 1405422.09 frames.], batch size: 16, lr: 2.97e-03
2022-05-26 11:02:16,202 INFO [train.py:842] (1/4) Epoch 1, batch 1050, loss[loss=0.2522, simple_loss=0.5043, pruned_loss=6.658, over 6997.00 frames.], tot_loss[loss=0.2947, simple_loss=0.5895, pruned_loss=6.726, over 1407597.68 frames.], batch size: 16, lr: 2.97e-03
2022-05-26 11:02:54,897 INFO [train.py:842] (1/4) Epoch 1, batch 1100, loss[loss=0.2646, simple_loss=0.5291, pruned_loss=6.675, over 7210.00 frames.], tot_loss[loss=0.2896, simple_loss=0.5791, pruned_loss=6.73, over 1411861.03 frames.], batch size: 22, lr: 2.96e-03
2022-05-26 11:03:33,473 INFO [train.py:842] (1/4) Epoch 1, batch 1150, loss[loss=0.2957, simple_loss=0.5913, pruned_loss=6.895, over 6833.00 frames.], tot_loss[loss=0.2835, simple_loss=0.5671, pruned_loss=6.735, over 1412289.12 frames.], batch size: 31, lr: 2.96e-03
2022-05-26 11:04:12,438 INFO [train.py:842] (1/4) Epoch 1, batch 1200, loss[loss=0.2642, simple_loss=0.5283, pruned_loss=6.778, over 7164.00 frames.], tot_loss[loss=0.2782, simple_loss=0.5564, pruned_loss=6.741, over 1420161.23 frames.], batch size: 26, lr: 2.96e-03
2022-05-26 11:04:50,808 INFO [train.py:842] (1/4) Epoch 1, batch 1250, loss[loss=0.2536, simple_loss=0.5071, pruned_loss=6.837, over 7360.00 frames.], tot_loss[loss=0.2729, simple_loss=0.5459, pruned_loss=6.743, over 1413832.67 frames.], batch size: 23, lr: 2.95e-03
2022-05-26 11:05:29,858 INFO [train.py:842] (1/4) Epoch 1, batch 1300, loss[loss=0.2435, simple_loss=0.487, pruned_loss=6.86, over 7322.00 frames.], tot_loss[loss=0.2686, simple_loss=0.5371, pruned_loss=6.748, over 1421530.29 frames.], batch size: 24, lr: 2.95e-03
2022-05-26 11:06:08,504 INFO [train.py:842] (1/4) Epoch 1, batch 1350, loss[loss=0.236, simple_loss=0.472, pruned_loss=6.843, over 7157.00 frames.], tot_loss[loss=0.2648, simple_loss=0.5296, pruned_loss=6.753, over 1422555.29 frames.], batch size: 20, lr: 2.95e-03
2022-05-26 11:06:47,144 INFO [train.py:842] (1/4) Epoch 1, batch 1400, loss[loss=0.2652, simple_loss=0.5305, pruned_loss=6.846, over 7315.00 frames.], tot_loss[loss=0.2621, simple_loss=0.5242, pruned_loss=6.76, over 1419023.58 frames.], batch size: 24, lr: 2.94e-03
2022-05-26 11:07:25,777 INFO [train.py:842] (1/4) Epoch 1, batch 1450, loss[loss=0.2493, simple_loss=0.4987, pruned_loss=6.852, over 7135.00 frames.], tot_loss[loss=0.2592, simple_loss=0.5184, pruned_loss=6.767, over 1419352.03 frames.], batch size: 17, lr: 2.94e-03
2022-05-26 11:08:04,599 INFO [train.py:842] (1/4) Epoch 1, batch 1500, loss[loss=0.2283, simple_loss=0.4567, pruned_loss=6.761, over 7280.00 frames.], tot_loss[loss=0.2556, simple_loss=0.5112, pruned_loss=6.772, over 1422236.46 frames.], batch size: 24, lr: 2.94e-03
2022-05-26 11:08:43,071 INFO [train.py:842] (1/4) Epoch 1, batch 1550, loss[loss=0.2433, simple_loss=0.4867, pruned_loss=6.776, over 7112.00 frames.], tot_loss[loss=0.2519, simple_loss=0.5037, pruned_loss=6.773, over 1422638.41 frames.], batch size: 21, lr: 2.93e-03
2022-05-26 11:09:22,180 INFO [train.py:842] (1/4) Epoch 1, batch 1600, loss[loss=0.2514, simple_loss=0.5029, pruned_loss=6.845, over 7332.00 frames.], tot_loss[loss=0.2488, simple_loss=0.4975, pruned_loss=6.777, over 1420671.56 frames.], batch size: 20, lr: 2.93e-03
2022-05-26 11:10:01,430 INFO [train.py:842] (1/4) Epoch 1, batch 1650, loss[loss=0.2317, simple_loss=0.4634, pruned_loss=6.803, over 7175.00 frames.], tot_loss[loss=0.2463, simple_loss=0.4926, pruned_loss=6.784, over 1422343.05 frames.], batch size: 18, lr: 2.92e-03
2022-05-26 11:10:40,835 INFO [train.py:842] (1/4) Epoch 1, batch 1700, loss[loss=0.2552, simple_loss=0.5104, pruned_loss=6.916, over 6360.00 frames.], tot_loss[loss=0.2434, simple_loss=0.4868, pruned_loss=6.785, over 1417526.63 frames.], batch size: 37, lr: 2.92e-03
2022-05-26 11:11:19,945 INFO [train.py:842] (1/4) Epoch 1, batch 1750, loss[loss=0.2431, simple_loss=0.4862, pruned_loss=6.72, over 6432.00 frames.], tot_loss[loss=0.2409, simple_loss=0.4819, pruned_loss=6.787, over 1417706.29 frames.], batch size: 37, lr: 2.91e-03
2022-05-26 11:12:00,002 INFO [train.py:842] (1/4) Epoch 1, batch 1800, loss[loss=0.2443, simple_loss=0.4887, pruned_loss=6.805, over 7108.00 frames.], tot_loss[loss=0.2399, simple_loss=0.4798, pruned_loss=6.793, over 1418356.20 frames.], batch size: 28, lr: 2.91e-03
2022-05-26 11:12:39,028 INFO [train.py:842] (1/4) Epoch 1, batch 1850, loss[loss=0.2776, simple_loss=0.5552, pruned_loss=6.957, over 5024.00 frames.], tot_loss[loss=0.237, simple_loss=0.4739, pruned_loss=6.792, over 1419264.46 frames.], batch size: 54, lr: 2.91e-03
2022-05-26 11:13:18,090 INFO [train.py:842] (1/4) Epoch 1, batch 1900, loss[loss=0.2293, simple_loss=0.4586, pruned_loss=6.825, over 7254.00 frames.], tot_loss[loss=0.2349, simple_loss=0.4697, pruned_loss=6.793, over 1419881.25 frames.], batch size: 19, lr: 2.90e-03
2022-05-26 11:13:56,909 INFO [train.py:842] (1/4) Epoch 1, batch 1950, loss[loss=0.2379, simple_loss=0.4757, pruned_loss=6.852, over 7319.00 frames.], tot_loss[loss=0.2338, simple_loss=0.4676, pruned_loss=6.792, over 1422210.76 frames.], batch size: 21, lr: 2.90e-03
2022-05-26 11:14:35,959 INFO [train.py:842] (1/4) Epoch 1, batch 2000, loss[loss=0.1948, simple_loss=0.3896, pruned_loss=6.697, over 6810.00 frames.], tot_loss[loss=0.2313, simple_loss=0.4626, pruned_loss=6.79, over 1422970.91 frames.], batch size: 15, lr: 2.89e-03
2022-05-26 11:15:15,096 INFO [train.py:842] (1/4) Epoch 1, batch 2050, loss[loss=0.2063, simple_loss=0.4125, pruned_loss=6.768, over 7221.00 frames.], tot_loss[loss=0.2297, simple_loss=0.4594, pruned_loss=6.786, over 1421062.21 frames.], batch size: 26, lr: 2.89e-03
2022-05-26 11:15:53,870 INFO [train.py:842] (1/4) Epoch 1, batch 2100, loss[loss=0.2091, simple_loss=0.4183, pruned_loss=6.748, over 7174.00 frames.], tot_loss[loss=0.2292, simple_loss=0.4584, pruned_loss=6.792, over 1417662.66 frames.], batch size: 18, lr: 2.88e-03
2022-05-26 11:16:32,690 INFO [train.py:842] (1/4) Epoch 1, batch 2150, loss[loss=0.2247, simple_loss=0.4495, pruned_loss=6.814, over 7335.00 frames.], tot_loss[loss=0.2282, simple_loss=0.4563, pruned_loss=6.793, over 1421273.39 frames.], batch size: 22, lr: 2.88e-03
2022-05-26 11:17:11,548 INFO [train.py:842] (1/4) Epoch 1, batch 2200, loss[loss=0.2216, simple_loss=0.4432, pruned_loss=6.732, over 7320.00 frames.], tot_loss[loss=0.2273, simple_loss=0.4545, pruned_loss=6.792, over 1420503.07 frames.], batch size: 25, lr: 2.87e-03
2022-05-26 11:17:50,079 INFO [train.py:842] (1/4) Epoch 1, batch 2250, loss[loss=0.2413, simple_loss=0.4826, pruned_loss=6.912, over 7218.00 frames.], tot_loss[loss=0.2271, simple_loss=0.4542, pruned_loss=6.796, over 1418980.77 frames.], batch size: 21, lr: 2.86e-03
2022-05-26 11:18:28,796 INFO [train.py:842] (1/4) Epoch 1, batch 2300, loss[loss=0.2754, simple_loss=0.5507, pruned_loss=6.829, over 7263.00 frames.], tot_loss[loss=0.226, simple_loss=0.452, pruned_loss=6.799, over 1413878.49 frames.], batch size: 19, lr: 2.86e-03
2022-05-26 11:19:07,777 INFO [train.py:842] (1/4) Epoch 1, batch 2350, loss[loss=0.2564, simple_loss=0.5128, pruned_loss=6.9, over 4812.00 frames.], tot_loss[loss=0.2248, simple_loss=0.4496, pruned_loss=6.801, over 1414042.69 frames.], batch size: 52, lr: 2.85e-03
2022-05-26 11:19:47,051 INFO [train.py:842] (1/4) Epoch 1, batch 2400, loss[loss=0.1949, simple_loss=0.3898, pruned_loss=6.767, over 7424.00 frames.], tot_loss[loss=0.224, simple_loss=0.448, pruned_loss=6.805, over 1412471.71 frames.], batch size: 20, lr: 2.85e-03
2022-05-26 11:20:25,506 INFO [train.py:842] (1/4) Epoch 1, batch 2450, loss[loss=0.2321, simple_loss=0.4642, pruned_loss=6.695, over 5042.00 frames.], tot_loss[loss=0.2236, simple_loss=0.4471, pruned_loss=6.804, over 1412295.57 frames.], batch size: 52, lr: 2.84e-03
2022-05-26 11:21:04,502 INFO [train.py:842] (1/4) Epoch 1, batch 2500, loss[loss=0.2435, simple_loss=0.4871, pruned_loss=7.054, over 7332.00 frames.], tot_loss[loss=0.222, simple_loss=0.4439, pruned_loss=6.805, over 1418133.50 frames.], batch size: 20, lr: 2.84e-03
2022-05-26 11:21:42,882 INFO [train.py:842] (1/4) Epoch 1, batch 2550, loss[loss=0.2034, simple_loss=0.4069, pruned_loss=6.778, over 7405.00 frames.], tot_loss[loss=0.2218, simple_loss=0.4436, pruned_loss=6.805, over 1418545.14 frames.], batch size: 18, lr: 2.83e-03
2022-05-26 11:22:21,932 INFO [train.py:842] (1/4) Epoch 1, batch 2600, loss[loss=0.2422, simple_loss=0.4845, pruned_loss=6.996, over 7229.00 frames.], tot_loss[loss=0.2201, simple_loss=0.4402, pruned_loss=6.801, over 1420560.66 frames.], batch size: 20, lr: 2.83e-03
2022-05-26 11:23:00,467 INFO [train.py:842] (1/4) Epoch 1, batch 2650, loss[loss=0.2061, simple_loss=0.4123, pruned_loss=6.783, over 7230.00 frames.], tot_loss[loss=0.2193, simple_loss=0.4385, pruned_loss=6.793, over 1421719.49 frames.], batch size: 20, lr: 2.82e-03
2022-05-26 11:23:39,482 INFO [train.py:842] (1/4) Epoch 1, batch 2700, loss[loss=0.207, simple_loss=0.4141, pruned_loss=6.731, over 7144.00 frames.], tot_loss[loss=0.2187, simple_loss=0.4373, pruned_loss=6.791, over 1421274.86 frames.], batch size: 20, lr: 2.81e-03
2022-05-26 11:24:17,970 INFO [train.py:842] (1/4) Epoch 1, batch 2750, loss[loss=0.2081, simple_loss=0.4162, pruned_loss=6.892, over 7334.00 frames.], tot_loss[loss=0.2188, simple_loss=0.4377, pruned_loss=6.795, over 1422020.60 frames.], batch size: 20, lr: 2.81e-03
2022-05-26 11:24:56,716 INFO [train.py:842] (1/4) Epoch 1, batch 2800, loss[loss=0.2456, simple_loss=0.4913, pruned_loss=6.874, over 7149.00 frames.], tot_loss[loss=0.2176, simple_loss=0.4352, pruned_loss=6.795, over 1420883.43 frames.], batch size: 20, lr: 2.80e-03
2022-05-26 11:25:35,330 INFO [train.py:842] (1/4) Epoch 1, batch 2850, loss[loss=0.2231, simple_loss=0.4462, pruned_loss=6.806, over 7367.00 frames.], tot_loss[loss=0.2177, simple_loss=0.4354, pruned_loss=6.797, over 1423831.81 frames.], batch size: 19, lr: 2.80e-03
2022-05-26 11:26:13,826 INFO [train.py:842] (1/4) Epoch 1, batch 2900, loss[loss=0.2175, simple_loss=0.4351, pruned_loss=6.927, over 7334.00 frames.], tot_loss[loss=0.2178, simple_loss=0.4356, pruned_loss=6.801, over 1419328.26 frames.], batch size: 20, lr: 2.79e-03
2022-05-26 11:26:52,586 INFO [train.py:842] (1/4) Epoch 1, batch 2950, loss[loss=0.2019, simple_loss=0.4038, pruned_loss=6.808, over 7122.00 frames.], tot_loss[loss=0.2161, simple_loss=0.4322, pruned_loss=6.8, over 1415571.13 frames.], batch size: 26, lr: 2.78e-03
2022-05-26 11:27:31,355 INFO [train.py:842] (1/4) Epoch 1, batch 3000, loss[loss=0.3972, simple_loss=0.4393, pruned_loss=1.775, over 7276.00 frames.], tot_loss[loss=0.2508, simple_loss=0.4323, pruned_loss=6.774, over 1419641.94 frames.], batch size: 17, lr: 2.78e-03
2022-05-26 11:27:31,356 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 11:27:40,551 INFO [train.py:871] (1/4) Epoch 1, validation: loss=2.017, simple_loss=0.4861, pruned_loss=1.774, over 868885.00 frames.
2022-05-26 11:28:19,125 INFO [train.py:842] (1/4) Epoch 1, batch 3050, loss[loss=0.3254, simple_loss=0.4416, pruned_loss=1.046, over 6583.00 frames.], tot_loss[loss=0.2772, simple_loss=0.4417, pruned_loss=5.565, over 1420020.78 frames.], batch size: 38, lr: 2.77e-03
2022-05-26 11:28:58,787 INFO [train.py:842] (1/4) Epoch 1, batch 3100, loss[loss=0.2825, simple_loss=0.4204, pruned_loss=0.7232, over 7420.00 frames.], tot_loss[loss=0.2813, simple_loss=0.4366, pruned_loss=4.507, over 1425587.45 frames.], batch size: 21, lr: 2.77e-03
2022-05-26 11:29:37,528 INFO [train.py:842] (1/4) Epoch 1, batch 3150, loss[loss=0.2748, simple_loss=0.4446, pruned_loss=0.5245, over 7420.00 frames.], tot_loss[loss=0.2795, simple_loss=0.4341, pruned_loss=3.639, over 1426940.62 frames.], batch size: 21, lr: 2.76e-03
2022-05-26 11:30:16,516 INFO [train.py:842] (1/4) Epoch 1, batch 3200, loss[loss=0.2639, simple_loss=0.4434, pruned_loss=0.4222, over 7297.00 frames.], tot_loss[loss=0.275, simple_loss=0.4331, pruned_loss=2.936, over 1424044.17 frames.], batch size: 24, lr: 2.75e-03
2022-05-26 11:30:54,985 INFO [train.py:842] (1/4) Epoch 1, batch 3250, loss[loss=0.2787, simple_loss=0.4795, pruned_loss=0.3898, over 7154.00 frames.], tot_loss[loss=0.2685, simple_loss=0.4304, pruned_loss=2.364, over 1423849.45 frames.], batch size: 20, lr: 2.75e-03
2022-05-26 11:31:34,101 INFO [train.py:842] (1/4) Epoch 1, batch 3300, loss[loss=0.2423, simple_loss=0.4268, pruned_loss=0.2885, over 7379.00 frames.], tot_loss[loss=0.2639, simple_loss=0.4301, pruned_loss=1.919, over 1418380.03 frames.], batch size: 23, lr: 2.74e-03
2022-05-26 11:32:12,632 INFO [train.py:842] (1/4) Epoch 1, batch 3350, loss[loss=0.2519, simple_loss=0.4421, pruned_loss=0.3082, over 7266.00 frames.], tot_loss[loss=0.2575, simple_loss=0.4264, pruned_loss=1.552, over 1423169.19 frames.], batch size: 24, lr: 2.73e-03
2022-05-26 11:32:51,530 INFO [train.py:842] (1/4) Epoch 1, batch 3400, loss[loss=0.2084, simple_loss=0.3737, pruned_loss=0.215, over 7257.00 frames.], tot_loss[loss=0.253, simple_loss=0.4247, pruned_loss=1.27, over 1423731.57 frames.], batch size: 19, lr: 2.73e-03
2022-05-26 11:33:30,157 INFO [train.py:842] (1/4) Epoch 1, batch 3450, loss[loss=0.2632, simple_loss=0.4656, pruned_loss=0.3046, over 7250.00 frames.], tot_loss[loss=0.2489, simple_loss=0.4225, pruned_loss=1.048, over 1423498.30 frames.], batch size: 25, lr: 2.72e-03
2022-05-26 11:34:09,050 INFO [train.py:842] (1/4) Epoch 1, batch 3500, loss[loss=0.2853, simple_loss=0.4904, pruned_loss=0.4012, over 7201.00 frames.], tot_loss[loss=0.2479, simple_loss=0.4248, pruned_loss=0.879, over 1421272.82 frames.], batch size: 26, lr: 2.72e-03
2022-05-26 11:34:47,685 INFO [train.py:842] (1/4) Epoch 1, batch 3550, loss[loss=0.2472, simple_loss=0.4419, pruned_loss=0.2629, over 7224.00 frames.], tot_loss[loss=0.2437, simple_loss=0.421, pruned_loss=0.7392, over 1423026.96 frames.], batch size: 21, lr: 2.71e-03
2022-05-26 11:35:26,431 INFO [train.py:842] (1/4) Epoch 1, batch 3600, loss[loss=0.1822, simple_loss=0.3307, pruned_loss=0.169, over 7413.00 frames.], tot_loss[loss=0.2397, simple_loss=0.4173, pruned_loss=0.6281, over 1421688.75 frames.], batch size: 17, lr: 2.70e-03
2022-05-26 11:36:05,098 INFO [train.py:842] (1/4) Epoch 1, batch 3650, loss[loss=0.2313, simple_loss=0.414, pruned_loss=0.2426, over 7209.00 frames.], tot_loss[loss=0.2374, simple_loss=0.4157, pruned_loss=0.5421, over 1422519.92 frames.], batch size: 21, lr: 2.70e-03
2022-05-26 11:36:43,946 INFO [train.py:842] (1/4) Epoch 1, batch 3700, loss[loss=0.2393, simple_loss=0.4283, pruned_loss=0.2513, over 6784.00 frames.], tot_loss[loss=0.2353, simple_loss=0.4141, pruned_loss=0.4739, over 1426483.23 frames.], batch size: 31, lr: 2.69e-03
2022-05-26 11:37:22,477 INFO [train.py:842] (1/4) Epoch 1, batch 3750, loss[loss=0.1977, simple_loss=0.3574, pruned_loss=0.19, over 7280.00 frames.], tot_loss[loss=0.2343, simple_loss=0.414, pruned_loss=0.4234, over 1418570.45 frames.], batch size: 18, lr: 2.68e-03
2022-05-26 11:38:01,299 INFO [train.py:842] (1/4) Epoch 1, batch 3800, loss[loss=0.2394, simple_loss=0.4255, pruned_loss=0.2671, over 7123.00 frames.], tot_loss[loss=0.2341, simple_loss=0.4147, pruned_loss=0.3836, over 1418687.69 frames.], batch size: 17, lr: 2.68e-03
2022-05-26 11:38:40,185 INFO [train.py:842] (1/4) Epoch 1, batch 3850, loss[loss=0.1943, simple_loss=0.352, pruned_loss=0.183, over 7132.00 frames.], tot_loss[loss=0.2319, simple_loss=0.4122, pruned_loss=0.348, over 1424189.84 frames.], batch size: 17, lr: 2.67e-03
2022-05-26 11:39:18,926 INFO [train.py:842] (1/4) Epoch 1, batch 3900, loss[loss=0.1888, simple_loss=0.3431, pruned_loss=0.1722, over 7244.00 frames.], tot_loss[loss=0.2311, simple_loss=0.4118, pruned_loss=0.3223, over 1420803.47 frames.], batch size: 16, lr: 2.66e-03
2022-05-26 11:39:57,450 INFO [train.py:842] (1/4) Epoch 1, batch 3950, loss[loss=0.258, simple_loss=0.4572, pruned_loss=0.2942, over 6859.00 frames.], tot_loss[loss=0.2295, simple_loss=0.4099, pruned_loss=0.2998, over 1418872.72 frames.], batch size: 31, lr: 2.66e-03
2022-05-26 11:40:36,048 INFO [train.py:842] (1/4) Epoch 1, batch 4000, loss[loss=0.239, simple_loss=0.431, pruned_loss=0.2356, over 7173.00 frames.], tot_loss[loss=0.2295, simple_loss=0.4107, pruned_loss=0.2842, over 1419547.67 frames.], batch size: 26, lr: 2.65e-03
2022-05-26 11:41:14,462 INFO [train.py:842] (1/4) Epoch 1, batch 4050, loss[loss=0.2667, simple_loss=0.4735, pruned_loss=0.2993, over 5218.00 frames.], tot_loss[loss=0.229, simple_loss=0.4106, pruned_loss=0.2703, over 1421906.37 frames.], batch size: 52, lr: 2.64e-03
2022-05-26 11:41:53,416 INFO [train.py:842] (1/4) Epoch 1, batch 4100, loss[loss=0.2306, simple_loss=0.4148, pruned_loss=0.2314, over 6454.00 frames.], tot_loss[loss=0.2275, simple_loss=0.4087, pruned_loss=0.2575, over 1420958.97 frames.], batch size: 37, lr: 2.64e-03
2022-05-26 11:42:32,059 INFO [train.py:842] (1/4) Epoch 1, batch 4150, loss[loss=0.2332, simple_loss=0.4238, pruned_loss=0.2129, over 7442.00 frames.], tot_loss[loss=0.2275, simple_loss=0.4092, pruned_loss=0.2491, over 1425369.84 frames.], batch size: 20, lr: 2.63e-03
2022-05-26 11:43:10,840 INFO [train.py:842] (1/4) Epoch 1, batch 4200, loss[loss=0.2173, simple_loss=0.396, pruned_loss=0.1935, over 7322.00 frames.], tot_loss[loss=0.2271, simple_loss=0.4088, pruned_loss=0.2426, over 1428950.14 frames.], batch size: 21, lr: 2.63e-03
2022-05-26 11:43:49,381 INFO [train.py:842] (1/4) Epoch 1, batch 4250, loss[loss=0.2397, simple_loss=0.4315, pruned_loss=0.2389, over 7147.00 frames.], tot_loss[loss=0.2258, simple_loss=0.4069, pruned_loss=0.2353, over 1428090.00 frames.], batch size: 20, lr: 2.62e-03
2022-05-26 11:44:28,199 INFO [train.py:842] (1/4) Epoch 1, batch 4300, loss[loss=0.2156, simple_loss=0.395, pruned_loss=0.1807, over 7197.00 frames.], tot_loss[loss=0.2237, simple_loss=0.4036, pruned_loss=0.2286, over 1424320.31 frames.], batch size: 22, lr: 2.61e-03
2022-05-26 11:45:06,727 INFO [train.py:842] (1/4) Epoch 1, batch 4350, loss[loss=0.198, simple_loss=0.3653, pruned_loss=0.1532, over 7150.00 frames.], tot_loss[loss=0.2234, simple_loss=0.4031, pruned_loss=0.2253, over 1426560.76 frames.], batch size: 19, lr: 2.61e-03
2022-05-26 11:45:45,329 INFO [train.py:842] (1/4) Epoch 1, batch 4400, loss[loss=0.2302, simple_loss=0.4168, pruned_loss=0.2181, over 7218.00 frames.], tot_loss[loss=0.224, simple_loss=0.4045, pruned_loss=0.2228, over 1426884.25 frames.], batch size: 21, lr: 2.60e-03
2022-05-26 11:46:23,908 INFO [train.py:842] (1/4) Epoch 1, batch 4450, loss[loss=0.2458, simple_loss=0.4419, pruned_loss=0.2487, over 7151.00 frames.], tot_loss[loss=0.2233, simple_loss=0.4036, pruned_loss=0.2191, over 1429402.74 frames.], batch size: 19, lr: 2.59e-03
2022-05-26 11:47:02,774 INFO [train.py:842] (1/4) Epoch 1, batch 4500, loss[loss=0.2374, simple_loss=0.4293, pruned_loss=0.2281, over 7257.00 frames.], tot_loss[loss=0.2232, simple_loss=0.4039, pruned_loss=0.2162, over 1431435.54 frames.], batch size: 19, lr: 2.59e-03
2022-05-26 11:47:41,422 INFO [train.py:842] (1/4) Epoch 1, batch 4550, loss[loss=0.1962, simple_loss=0.3588, pruned_loss=0.1677, over 7066.00 frames.], tot_loss[loss=0.2229, simple_loss=0.4034, pruned_loss=0.2146, over 1429175.99 frames.], batch size: 18, lr: 2.58e-03
2022-05-26 11:48:20,132 INFO [train.py:842] (1/4) Epoch 1, batch 4600, loss[loss=0.2066, simple_loss=0.3742, pruned_loss=0.1947, over 7264.00 frames.], tot_loss[loss=0.2221, simple_loss=0.4022, pruned_loss=0.2119, over 1427950.37 frames.], batch size: 19, lr: 2.57e-03
2022-05-26 11:48:58,610 INFO [train.py:842] (1/4) Epoch 1, batch 4650, loss[loss=0.2223, simple_loss=0.4047, pruned_loss=0.1992, over 7083.00 frames.], tot_loss[loss=0.2216, simple_loss=0.4017, pruned_loss=0.2094, over 1429740.34 frames.], batch size: 28, lr: 2.57e-03
2022-05-26 11:49:37,493 INFO [train.py:842] (1/4) Epoch 1, batch 4700, loss[loss=0.1833, simple_loss=0.3348, pruned_loss=0.159, over 7285.00 frames.], tot_loss[loss=0.2214, simple_loss=0.4014, pruned_loss=0.2084, over 1428616.08 frames.], batch size: 17, lr: 2.56e-03
2022-05-26 11:50:15,979 INFO [train.py:842] (1/4) Epoch 1, batch 4750, loss[loss=0.2903, simple_loss=0.514, pruned_loss=0.3335, over 5212.00 frames.], tot_loss[loss=0.2216, simple_loss=0.4019, pruned_loss=0.2077, over 1427539.68 frames.], batch size: 52, lr: 2.55e-03
2022-05-26 11:50:54,737 INFO [train.py:842] (1/4) Epoch 1, batch 4800, loss[loss=0.1883, simple_loss=0.349, pruned_loss=0.1378, over 7424.00 frames.], tot_loss[loss=0.2217, simple_loss=0.4023, pruned_loss=0.2064, over 1429162.54 frames.], batch size: 20, lr: 2.55e-03
2022-05-26 11:51:33,204 INFO [train.py:842] (1/4) Epoch 1, batch 4850, loss[loss=0.1976, simple_loss=0.361, pruned_loss=0.1709, over 7255.00 frames.], tot_loss[loss=0.2212, simple_loss=0.4015, pruned_loss=0.2049, over 1426408.98 frames.], batch size: 19, lr: 2.54e-03
2022-05-26 11:52:11,928 INFO [train.py:842] (1/4) Epoch 1, batch 4900, loss[loss=0.2121, simple_loss=0.3855, pruned_loss=0.1933, over 7336.00 frames.], tot_loss[loss=0.22, simple_loss=0.3997, pruned_loss=0.2018, over 1427960.99 frames.], batch size: 20, lr: 2.54e-03
2022-05-26 11:52:50,284 INFO [train.py:842] (1/4) Epoch 1, batch 4950, loss[loss=0.214, simple_loss=0.3872, pruned_loss=0.2038, over 7352.00 frames.], tot_loss[loss=0.2207, simple_loss=0.401, pruned_loss=0.2027, over 1423244.48 frames.], batch size: 19, lr: 2.53e-03
2022-05-26 11:53:29,137 INFO [train.py:842] (1/4) Epoch 1, batch 5000, loss[loss=0.2139, simple_loss=0.394, pruned_loss=0.1688, over 7328.00 frames.], tot_loss[loss=0.2211, simple_loss=0.4017, pruned_loss=0.2025, over 1423398.00 frames.], batch size: 22, lr: 2.52e-03
2022-05-26 11:54:07,527 INFO [train.py:842] (1/4) Epoch 1, batch 5050, loss[loss=0.251, simple_loss=0.4485, pruned_loss=0.2674, over 7322.00 frames.], tot_loss[loss=0.2201, simple_loss=0.4001, pruned_loss=0.2008, over 1422890.10 frames.], batch size: 21, lr: 2.52e-03
2022-05-26 11:54:46,110 INFO [train.py:842] (1/4) Epoch 1, batch 5100, loss[loss=0.2043, simple_loss=0.3742, pruned_loss=0.1716, over 7206.00 frames.], tot_loss[loss=0.2195, simple_loss=0.3991, pruned_loss=0.2, over 1421216.94 frames.], batch size: 22, lr: 2.51e-03
2022-05-26 11:55:24,656 INFO [train.py:842] (1/4) Epoch 1, batch 5150, loss[loss=0.237, simple_loss=0.4248, pruned_loss=0.2457, over 7412.00 frames.], tot_loss[loss=0.2188, simple_loss=0.3979, pruned_loss=0.1988, over 1422559.24 frames.], batch size: 20, lr: 2.50e-03
2022-05-26 11:56:03,342 INFO [train.py:842] (1/4) Epoch 1, batch 5200, loss[loss=0.2666, simple_loss=0.479, pruned_loss=0.2707, over 7322.00 frames.], tot_loss[loss=0.2198, simple_loss=0.3996, pruned_loss=0.1999, over 1421565.64 frames.], batch size: 25, lr: 2.50e-03
2022-05-26 11:56:41,772 INFO [train.py:842] (1/4) Epoch 1, batch 5250, loss[loss=0.2489, simple_loss=0.4457, pruned_loss=0.2608, over 5234.00 frames.], tot_loss[loss=0.2186, simple_loss=0.3977, pruned_loss=0.1976, over 1419946.58 frames.], batch size: 53, lr: 2.49e-03
2022-05-26 11:57:20,428 INFO [train.py:842] (1/4) Epoch 1, batch 5300, loss[loss=0.231, simple_loss=0.4173, pruned_loss=0.224, over 7278.00 frames.], tot_loss[loss=0.2187, simple_loss=0.3978, pruned_loss=0.1975, over 1418014.94 frames.], batch size: 17, lr: 2.49e-03
2022-05-26 11:57:58,735 INFO [train.py:842] (1/4) Epoch 1, batch 5350, loss[loss=0.2047, simple_loss=0.3768, pruned_loss=0.1624, over 7368.00 frames.], tot_loss[loss=0.2169, simple_loss=0.3949, pruned_loss=0.1947, over 1414674.51 frames.], batch size: 23, lr: 2.48e-03
2022-05-26 11:58:37,515 INFO [train.py:842] (1/4) Epoch 1, batch 5400, loss[loss=0.2178, simple_loss=0.398, pruned_loss=0.1881, over 7070.00 frames.], tot_loss[loss=0.2158, simple_loss=0.3931, pruned_loss=0.1923, over 1420185.22 frames.], batch size: 28, lr: 2.47e-03
2022-05-26 11:59:16,038 INFO [train.py:842] (1/4) Epoch 1, batch 5450, loss[loss=0.2142, simple_loss=0.3935, pruned_loss=0.1746, over 7146.00 frames.], tot_loss[loss=0.2146, simple_loss=0.3911, pruned_loss=0.1904, over 1421950.90 frames.], batch size: 20, lr: 2.47e-03
2022-05-26 11:59:54,859 INFO [train.py:842] (1/4) Epoch 1, batch 5500, loss[loss=0.2326, simple_loss=0.4191, pruned_loss=0.2308, over 5078.00 frames.], tot_loss[loss=0.2138, simple_loss=0.3898, pruned_loss=0.1894, over 1420245.18 frames.], batch size: 52, lr: 2.46e-03
2022-05-26 12:00:33,672 INFO [train.py:842] (1/4) Epoch 1, batch 5550, loss[loss=0.1902, simple_loss=0.3479, pruned_loss=0.1626, over 6816.00 frames.], tot_loss[loss=0.2126, simple_loss=0.3878, pruned_loss=0.1874, over 1422057.46 frames.], batch size: 15, lr: 2.45e-03
2022-05-26 12:01:12,585 INFO [train.py:842] (1/4) Epoch 1, batch 5600, loss[loss=0.216, simple_loss=0.3968, pruned_loss=0.1766, over 6403.00 frames.], tot_loss[loss=0.2142, simple_loss=0.3906, pruned_loss=0.189, over 1423567.89 frames.], batch size: 38, lr: 2.45e-03
2022-05-26 12:01:51,147 INFO [train.py:842] (1/4) Epoch 1, batch 5650, loss[loss=0.1919, simple_loss=0.3517, pruned_loss=0.1604, over 7293.00 frames.], tot_loss[loss=0.2141, simple_loss=0.3906, pruned_loss=0.188, over 1421518.72 frames.], batch size: 17, lr: 2.44e-03
2022-05-26 12:02:29,830 INFO [train.py:842] (1/4) Epoch 1, batch 5700, loss[loss=0.1837, simple_loss=0.3391, pruned_loss=0.1412, over 7429.00 frames.], tot_loss[loss=0.2134, simple_loss=0.3895, pruned_loss=0.1863, over 1421195.16 frames.], batch size: 20, lr: 2.44e-03
2022-05-26 12:03:08,174 INFO [train.py:842] (1/4) Epoch 1, batch 5750, loss[loss=0.2028, simple_loss=0.3667, pruned_loss=0.1951, over 7280.00 frames.], tot_loss[loss=0.2135, simple_loss=0.3899, pruned_loss=0.1856, over 1422740.84 frames.], batch size: 18, lr: 2.43e-03
2022-05-26 12:03:47,070 INFO [train.py:842] (1/4) Epoch 1, batch 5800, loss[loss=0.196, simple_loss=0.3628, pruned_loss=0.1462, over 7199.00 frames.], tot_loss[loss=0.2138, simple_loss=0.3905, pruned_loss=0.1857, over 1428723.19 frames.], batch size: 22, lr: 2.42e-03
2022-05-26 12:04:25,399 INFO [train.py:842] (1/4) Epoch 1, batch 5850, loss[loss=0.2147, simple_loss=0.3904, pruned_loss=0.1953, over 7422.00 frames.], tot_loss[loss=0.2136, simple_loss=0.3902, pruned_loss=0.1852, over 1426379.26 frames.], batch size: 20, lr: 2.42e-03
2022-05-26 12:05:04,403 INFO [train.py:842] (1/4) Epoch 1, batch 5900, loss[loss=0.2231, simple_loss=0.4064, pruned_loss=0.1992, over 7323.00 frames.], tot_loss[loss=0.2129, simple_loss=0.3889, pruned_loss=0.1849, over 1430341.98 frames.], batch size: 21, lr: 2.41e-03
2022-05-26 12:05:43,144 INFO [train.py:842] (1/4) Epoch 1, batch 5950, loss[loss=0.2078, simple_loss=0.378, pruned_loss=0.1878, over 7156.00 frames.], tot_loss[loss=0.2135, simple_loss=0.3898, pruned_loss=0.1857, over 1430729.37 frames.], batch size: 19, lr: 2.41e-03
2022-05-26 12:06:21,966 INFO [train.py:842] (1/4) Epoch 1, batch 6000, loss[loss=0.3729, simple_loss=0.3756, pruned_loss=0.1851, over 7145.00 frames.], tot_loss[loss=0.2147, simple_loss=0.3893, pruned_loss=0.1848, over 1427590.86 frames.], batch size: 26, lr: 2.40e-03
2022-05-26 12:06:21,966 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 12:06:31,825 INFO [train.py:871] (1/4) Epoch 1, validation: loss=0.2892, simple_loss=0.3436, pruned_loss=0.1174, over 868885.00 frames.
2022-05-26 12:07:10,563 INFO [train.py:842] (1/4) Epoch 1, batch 6050, loss[loss=0.4296, simple_loss=0.3991, pruned_loss=0.23, over 6760.00 frames.], tot_loss[loss=0.2569, simple_loss=0.3917, pruned_loss=0.1896, over 1423677.29 frames.], batch size: 15, lr: 2.39e-03
2022-05-26 12:07:49,762 INFO [train.py:842] (1/4) Epoch 1, batch 6100, loss[loss=0.3002, simple_loss=0.3283, pruned_loss=0.136, over 6789.00 frames.], tot_loss[loss=0.2844, simple_loss=0.391, pruned_loss=0.1887, over 1426401.18 frames.], batch size: 15, lr: 2.39e-03
2022-05-26 12:08:28,488 INFO [train.py:842] (1/4) Epoch 1, batch 6150, loss[loss=0.3193, simple_loss=0.3626, pruned_loss=0.138, over 7099.00 frames.], tot_loss[loss=0.3047, simple_loss=0.3909, pruned_loss=0.187, over 1427085.17 frames.], batch size: 21, lr: 2.38e-03
2022-05-26 12:09:07,162 INFO [train.py:842] (1/4) Epoch 1, batch 6200, loss[loss=0.4045, simple_loss=0.4205, pruned_loss=0.1943, over 7322.00 frames.], tot_loss[loss=0.3224, simple_loss=0.3922, pruned_loss=0.1867, over 1428287.18 frames.], batch size: 22, lr: 2.38e-03
2022-05-26 12:09:45,805 INFO [train.py:842] (1/4) Epoch 1, batch 6250, loss[loss=0.3981, simple_loss=0.4143, pruned_loss=0.191, over 7368.00 frames.], tot_loss[loss=0.3347, simple_loss=0.3926, pruned_loss=0.1854, over 1429298.30 frames.], batch size: 23, lr: 2.37e-03
2022-05-26 12:10:25,217 INFO [train.py:842] (1/4) Epoch 1, batch 6300, loss[loss=0.3963, simple_loss=0.4054, pruned_loss=0.1935, over 7274.00 frames.], tot_loss[loss=0.3418, simple_loss=0.3912, pruned_loss=0.1829, over 1426234.31 frames.], batch size: 18, lr: 2.37e-03
2022-05-26 12:11:03,817 INFO [train.py:842] (1/4) Epoch 1, batch 6350, loss[loss=0.3621, simple_loss=0.3805, pruned_loss=0.1719, over 7152.00 frames.], tot_loss[loss=0.3485, simple_loss=0.3909, pruned_loss=0.1816, over 1425921.91 frames.], batch size: 20, lr: 2.36e-03
2022-05-26 12:11:42,706 INFO [train.py:842] (1/4) Epoch 1, batch 6400, loss[loss=0.3219, simple_loss=0.3492, pruned_loss=0.1473, over 7362.00 frames.], tot_loss[loss=0.3546, simple_loss=0.391, pruned_loss=0.1812, over 1425971.50 frames.], batch size: 19, lr: 2.35e-03
2022-05-26 12:12:21,102 INFO [train.py:842] (1/4) Epoch 1, batch 6450, loss[loss=0.3537, simple_loss=0.3967, pruned_loss=0.1554, over 7111.00 frames.], tot_loss[loss=0.3583, simple_loss=0.3917, pruned_loss=0.1797, over 1426060.68 frames.], batch size: 21, lr: 2.35e-03
2022-05-26 12:12:59,891 INFO [train.py:842] (1/4) Epoch 1, batch 6500, loss[loss=0.3398, simple_loss=0.3645, pruned_loss=0.1575, over 7117.00 frames.], tot_loss[loss=0.3598, simple_loss=0.39, pruned_loss=0.1783, over 1421918.10 frames.], batch size: 17, lr: 2.34e-03
2022-05-26 12:13:38,147 INFO [train.py:842] (1/4) Epoch 1, batch 6550, loss[loss=0.2745, simple_loss=0.3297, pruned_loss=0.1097, over 7319.00 frames.], tot_loss[loss=0.3602, simple_loss=0.3891, pruned_loss=0.1762, over 1417127.41 frames.], batch size: 21, lr: 2.34e-03
2022-05-26 12:14:17,094 INFO [train.py:842] (1/4) Epoch 1, batch 6600, loss[loss=0.3698, simple_loss=0.3976, pruned_loss=0.1711, over 7157.00 frames.], tot_loss[loss=0.3634, simple_loss=0.39, pruned_loss=0.1766, over 1423065.74 frames.], batch size: 26, lr: 2.33e-03
2022-05-26 12:14:55,787 INFO [train.py:842] (1/4) Epoch 1, batch 6650, loss[loss=0.3082, simple_loss=0.3552, pruned_loss=0.1306, over 7060.00 frames.], tot_loss[loss=0.3651, simple_loss=0.3902, pruned_loss=0.1763, over 1421150.23 frames.], batch size: 18, lr: 2.33e-03
2022-05-26 12:15:34,740 INFO [train.py:842] (1/4) Epoch 1, batch 6700, loss[loss=0.4244, simple_loss=0.4237, pruned_loss=0.2126, over 4750.00 frames.], tot_loss[loss=0.3653, simple_loss=0.3904, pruned_loss=0.1751, over 1422720.32 frames.], batch size: 52, lr: 2.32e-03
2022-05-26 12:16:13,267 INFO [train.py:842] (1/4) Epoch 1, batch 6750, loss[loss=0.3211, simple_loss=0.3542, pruned_loss=0.144, over 7299.00 frames.], tot_loss[loss=0.3635, simple_loss=0.3892, pruned_loss=0.1727, over 1426085.69 frames.], batch size: 24, lr: 2.31e-03
2022-05-26 12:16:52,116 INFO [train.py:842] (1/4) Epoch 1, batch 6800, loss[loss=0.4018, simple_loss=0.3954, pruned_loss=0.2041, over 7433.00 frames.], tot_loss[loss=0.3632, simple_loss=0.3891, pruned_loss=0.1717, over 1427997.46 frames.], batch size: 20, lr: 2.31e-03
2022-05-26 12:17:30,586 INFO [train.py:842] (1/4) Epoch 1, batch 6850, loss[loss=0.4096, simple_loss=0.4336, pruned_loss=0.1928, over 7188.00 frames.], tot_loss[loss=0.3616, simple_loss=0.388, pruned_loss=0.17, over 1426856.34 frames.], batch size: 23, lr: 2.30e-03
2022-05-26 12:18:19,079 INFO [train.py:842] (1/4) Epoch 1, batch 6900, loss[loss=0.3582, simple_loss=0.3897, pruned_loss=0.1633, over 7412.00 frames.], tot_loss[loss=0.3607, simple_loss=0.3867, pruned_loss=0.1691, over 1427563.62 frames.], batch size: 21, lr: 2.30e-03
2022-05-26 12:18:57,541 INFO [train.py:842] (1/4) Epoch 1, batch 6950, loss[loss=0.35, simple_loss=0.3754, pruned_loss=0.1623, over 7267.00 frames.], tot_loss[loss=0.3636, simple_loss=0.3886, pruned_loss=0.1707, over 1423878.47 frames.], batch size: 18, lr: 2.29e-03
2022-05-26 12:19:36,274 INFO [train.py:842] (1/4) Epoch 1, batch 7000, loss[loss=0.391, simple_loss=0.401, pruned_loss=0.1905, over 7172.00 frames.], tot_loss[loss=0.3635, simple_loss=0.3887, pruned_loss=0.1702, over 1421318.26 frames.], batch size: 18, lr: 2.29e-03
2022-05-26 12:20:14,728 INFO [train.py:842] (1/4) Epoch 1, batch 7050, loss[loss=0.3323, simple_loss=0.3654, pruned_loss=0.1496, over 7156.00 frames.], tot_loss[loss=0.362, simple_loss=0.3875, pruned_loss=0.1691, over 1422258.03 frames.], batch size: 19, lr: 2.28e-03
2022-05-26 12:20:53,594 INFO [train.py:842] (1/4) Epoch 1, batch 7100, loss[loss=0.3286, simple_loss=0.3849, pruned_loss=0.1362, over 7341.00 frames.], tot_loss[loss=0.3622, simple_loss=0.3877, pruned_loss=0.1691, over 1424588.77 frames.], batch size: 22, lr: 2.28e-03
2022-05-26 12:21:32,668 INFO [train.py:842] (1/4) Epoch 1, batch 7150, loss[loss=0.3922, simple_loss=0.401, pruned_loss=0.1917, over 7211.00 frames.], tot_loss[loss=0.3625, simple_loss=0.3877, pruned_loss=0.1691, over 1419487.30 frames.], batch size: 22, lr: 2.27e-03
2022-05-26 12:22:11,506 INFO [train.py:842] (1/4) Epoch 1, batch 7200, loss[loss=0.3208, simple_loss=0.375, pruned_loss=0.1333, over 7333.00 frames.], tot_loss[loss=0.3633, simple_loss=0.3888, pruned_loss=0.1693, over 1422580.43 frames.], batch size: 22, lr: 2.27e-03
2022-05-26 12:22:50,067 INFO [train.py:842] (1/4) Epoch 1, batch 7250, loss[loss=0.3018, simple_loss=0.3458, pruned_loss=0.1289, over 7059.00 frames.], tot_loss[loss=0.3621, simple_loss=0.3881, pruned_loss=0.1683, over 1417432.42 frames.], batch size: 18, lr: 2.26e-03
2022-05-26 12:23:28,698 INFO [train.py:842] (1/4) Epoch 1, batch 7300, loss[loss=0.3468, simple_loss=0.3796, pruned_loss=0.157, over 7068.00 frames.], tot_loss[loss=0.3622, simple_loss=0.3886, pruned_loss=0.1682, over 1417419.27 frames.], batch size: 28, lr: 2.26e-03
2022-05-26 12:24:07,152 INFO [train.py:842] (1/4) Epoch 1, batch 7350, loss[loss=0.305, simple_loss=0.3364, pruned_loss=0.1368, over 6769.00 frames.], tot_loss[loss=0.3617, simple_loss=0.3882, pruned_loss=0.1678, over 1416018.03 frames.], batch size: 15, lr: 2.25e-03
2022-05-26 12:24:45,818 INFO [train.py:842] (1/4) Epoch 1, batch 7400, loss[loss=0.277, simple_loss=0.317, pruned_loss=0.1185, over 7400.00 frames.], tot_loss[loss=0.3614, simple_loss=0.3885, pruned_loss=0.1673, over 1417066.63 frames.], batch size: 18, lr: 2.24e-03
2022-05-26 12:25:24,577 INFO [train.py:842] (1/4) Epoch 1, batch 7450, loss[loss=0.3491, simple_loss=0.3751, pruned_loss=0.1616, over 7399.00 frames.], tot_loss[loss=0.3625, simple_loss=0.3898, pruned_loss=0.1677, over 1425195.15 frames.], batch size: 18, lr: 2.24e-03
2022-05-26 12:26:03,342 INFO [train.py:842] (1/4) Epoch 1, batch 7500, loss[loss=0.3852, simple_loss=0.3966, pruned_loss=0.1869, over 7436.00 frames.], tot_loss[loss=0.3639, simple_loss=0.3907, pruned_loss=0.1686, over 1422563.17 frames.], batch size: 20, lr: 2.23e-03
2022-05-26 12:26:41,950 INFO [train.py:842] (1/4) Epoch 1, batch 7550, loss[loss=0.3757, simple_loss=0.4015, pruned_loss=0.1749, over 7345.00 frames.], tot_loss[loss=0.3611, simple_loss=0.3883, pruned_loss=0.167, over 1420535.30 frames.], batch size: 20, lr: 2.23e-03
2022-05-26 12:27:21,047 INFO [train.py:842] (1/4) Epoch 1, batch 7600, loss[loss=0.3255, simple_loss=0.3762, pruned_loss=0.1374, over 7406.00 frames.], tot_loss[loss=0.3567, simple_loss=0.3852, pruned_loss=0.1641, over 1423874.91 frames.], batch size: 21, lr: 2.22e-03
2022-05-26 12:28:28,324 INFO [train.py:842] (1/4) Epoch 1, batch 7650, loss[loss=0.4382, simple_loss=0.4306, pruned_loss=0.2229, over 7316.00 frames.], tot_loss[loss=0.3562, simple_loss=0.3855, pruned_loss=0.1635, over 1427094.70 frames.], batch size: 20, lr: 2.22e-03
2022-05-26 12:29:07,140 INFO [train.py:842] (1/4) Epoch 1, batch 7700, loss[loss=0.3755, simple_loss=0.3879, pruned_loss=0.1815, over 7231.00 frames.], tot_loss[loss=0.3569, simple_loss=0.3859, pruned_loss=0.164, over 1424854.83 frames.], batch size: 20, lr: 2.21e-03
2022-05-26 12:29:46,009 INFO [train.py:842] (1/4) Epoch 1, batch 7750, loss[loss=0.3279, simple_loss=0.3621, pruned_loss=0.1469, over 7365.00 frames.], tot_loss[loss=0.3542, simple_loss=0.3844, pruned_loss=0.162, over 1425678.85 frames.], batch size: 19, lr: 2.21e-03
2022-05-26 12:30:24,818 INFO [train.py:842] (1/4) Epoch 1, batch 7800, loss[loss=0.3405, simple_loss=0.3816, pruned_loss=0.1497, over 7037.00 frames.], tot_loss[loss=0.3555, simple_loss=0.3852, pruned_loss=0.1629, over 1427944.51 frames.], batch size: 28, lr: 2.20e-03
2022-05-26 12:31:03,151 INFO [train.py:842] (1/4) Epoch 1, batch 7850, loss[loss=0.3543, simple_loss=0.4014, pruned_loss=0.1536, over 7295.00 frames.], tot_loss[loss=0.3546, simple_loss=0.3855, pruned_loss=0.1619, over 1430762.35 frames.], batch size: 24, lr: 2.20e-03
2022-05-26 12:31:41,895 INFO [train.py:842] (1/4) Epoch 1, batch 7900, loss[loss=0.4133, simple_loss=0.4079, pruned_loss=0.2093, over 7434.00 frames.], tot_loss[loss=0.3554, simple_loss=0.3857, pruned_loss=0.1625, over 1428886.48 frames.], batch size: 20, lr: 2.19e-03
2022-05-26 12:32:20,431 INFO [train.py:842] (1/4) Epoch 1, batch 7950, loss[loss=0.4053, simple_loss=0.4359, pruned_loss=0.1873, over 6216.00 frames.], tot_loss[loss=0.3529, simple_loss=0.384, pruned_loss=0.1609, over 1423671.96 frames.], batch size: 37, lr: 2.19e-03
2022-05-26 12:33:01,914 INFO [train.py:842] (1/4) Epoch 1, batch 8000, loss[loss=0.3249, simple_loss=0.3668, pruned_loss=0.1415, over 7142.00 frames.], tot_loss[loss=0.3518, simple_loss=0.3838, pruned_loss=0.1599, over 1425947.39 frames.], batch size: 17, lr: 2.18e-03
2022-05-26 12:33:40,596 INFO [train.py:842] (1/4) Epoch 1, batch 8050, loss[loss=0.2829, simple_loss=0.3256, pruned_loss=0.1201, over 7135.00 frames.], tot_loss[loss=0.3503, simple_loss=0.3825, pruned_loss=0.1591, over 1430049.30 frames.], batch size: 17, lr: 2.18e-03
2022-05-26 12:34:19,324 INFO [train.py:842] (1/4) Epoch 1, batch 8100, loss[loss=0.3091, simple_loss=0.348, pruned_loss=0.1351, over 7260.00 frames.], tot_loss[loss=0.352, simple_loss=0.3841, pruned_loss=0.16, over 1428386.41 frames.], batch size: 19, lr: 2.17e-03
2022-05-26 12:34:57,736 INFO [train.py:842] (1/4) Epoch 1, batch 8150, loss[loss=0.4417, simple_loss=0.4564, pruned_loss=0.2135, over 7196.00 frames.], tot_loss[loss=0.3561, simple_loss=0.3869, pruned_loss=0.1627, over 1424557.11 frames.], batch size: 22, lr: 2.17e-03
2022-05-26 12:35:36,459 INFO [train.py:842] (1/4) Epoch 1, batch 8200, loss[loss=0.313, simple_loss=0.3557, pruned_loss=0.1352, over 7147.00 frames.], tot_loss[loss=0.3527, simple_loss=0.3847, pruned_loss=0.1603, over 1422070.71 frames.], batch size: 18, lr: 2.16e-03
2022-05-26 12:36:15,274 INFO [train.py:842] (1/4) Epoch 1, batch 8250, loss[loss=0.3907, simple_loss=0.4023, pruned_loss=0.1896, over 7263.00 frames.], tot_loss[loss=0.3535, simple_loss=0.3849, pruned_loss=0.1611, over 1422932.22 frames.], batch size: 19, lr: 2.16e-03
2022-05-26 12:36:53,996 INFO [train.py:842] (1/4) Epoch 1, batch 8300, loss[loss=0.3663, simple_loss=0.399, pruned_loss=0.1668, over 6824.00 frames.], tot_loss[loss=0.3513, simple_loss=0.3834, pruned_loss=0.1596, over 1422250.63 frames.], batch size: 31, lr: 2.15e-03
2022-05-26 12:37:32,676 INFO [train.py:842] (1/4) Epoch 1, batch 8350, loss[loss=0.3033, simple_loss=0.34, pruned_loss=0.1333, over 7267.00 frames.], tot_loss[loss=0.3505, simple_loss=0.3824, pruned_loss=0.1593, over 1424951.58 frames.], batch size: 18, lr: 2.15e-03
2022-05-26 12:38:11,588 INFO [train.py:842] (1/4) Epoch 1, batch 8400, loss[loss=0.3756, simple_loss=0.4005, pruned_loss=0.1754, over 7288.00 frames.], tot_loss[loss=0.3509, simple_loss=0.3834, pruned_loss=0.1591, over 1423973.02 frames.], batch size: 25, lr: 2.15e-03
2022-05-26 12:38:49,943 INFO [train.py:842] (1/4) Epoch 1, batch 8450, loss[loss=0.4182, simple_loss=0.4309, pruned_loss=0.2027, over 7119.00 frames.], tot_loss[loss=0.3497, simple_loss=0.3829, pruned_loss=0.1583, over 1423485.17 frames.], batch size: 21, lr: 2.14e-03
2022-05-26 12:39:28,664 INFO [train.py:842] (1/4) Epoch 1, batch 8500, loss[loss=0.3703, simple_loss=0.4037, pruned_loss=0.1685, over 7142.00 frames.], tot_loss[loss=0.349, simple_loss=0.3822, pruned_loss=0.1578, over 1422555.83 frames.], batch size: 20, lr: 2.14e-03
2022-05-26 12:40:07,539 INFO [train.py:842] (1/4) Epoch 1, batch 8550, loss[loss=0.3721, simple_loss=0.3948, pruned_loss=0.1747, over 7155.00 frames.], tot_loss[loss=0.346, simple_loss=0.3801, pruned_loss=0.1559, over 1423985.34 frames.], batch size: 18, lr: 2.13e-03
2022-05-26 12:40:46,260 INFO [train.py:842] (1/4) Epoch 1, batch 8600, loss[loss=0.3253, simple_loss=0.3534, pruned_loss=0.1486, over 7067.00 frames.], tot_loss[loss=0.3472, simple_loss=0.3807, pruned_loss=0.1568, over 1420978.34 frames.], batch size: 18, lr: 2.13e-03
2022-05-26 12:41:24,622 INFO [train.py:842] (1/4) Epoch 1, batch 8650, loss[loss=0.3599, simple_loss=0.3943, pruned_loss=0.1627, over 7312.00 frames.], tot_loss[loss=0.3477, simple_loss=0.3813, pruned_loss=0.157, over 1412808.78 frames.], batch size: 21, lr: 2.12e-03
2022-05-26 12:42:03,388 INFO [train.py:842] (1/4) Epoch 1, batch 8700, loss[loss=0.3314, simple_loss=0.3601, pruned_loss=0.1514, over 7129.00 frames.], tot_loss[loss=0.3496, simple_loss=0.3829, pruned_loss=0.1582, over 1409323.58 frames.], batch size: 17, lr: 2.12e-03
2022-05-26 12:42:41,799 INFO [train.py:842] (1/4) Epoch 1, batch 8750, loss[loss=0.3876, simple_loss=0.4203, pruned_loss=0.1774, over 6771.00 frames.], tot_loss[loss=0.3519, simple_loss=0.385, pruned_loss=0.1594, over 1411615.09 frames.], batch size: 31, lr: 2.11e-03
2022-05-26 12:43:20,394 INFO [train.py:842] (1/4) Epoch 1, batch 8800, loss[loss=0.3623, simple_loss=0.3981, pruned_loss=0.1632, over 6795.00 frames.], tot_loss[loss=0.3503, simple_loss=0.3841, pruned_loss=0.1582, over 1415058.41 frames.], batch size: 31, lr: 2.11e-03
2022-05-26 12:43:58,637 INFO [train.py:842] (1/4) Epoch 1, batch 8850, loss[loss=0.4971, simple_loss=0.4853, pruned_loss=0.2545, over 5162.00 frames.], tot_loss[loss=0.3516, simple_loss=0.3853, pruned_loss=0.1589, over 1412211.62 frames.], batch size: 52, lr: 2.10e-03
2022-05-26 12:44:37,294 INFO [train.py:842] (1/4) Epoch 1, batch 8900, loss[loss=0.2517, simple_loss=0.307, pruned_loss=0.09815, over 6986.00 frames.], tot_loss[loss=0.3502, simple_loss=0.3842, pruned_loss=0.1581, over 1403741.50 frames.], batch size: 16, lr: 2.10e-03
2022-05-26 12:45:15,583 INFO [train.py:842] (1/4) Epoch 1, batch 8950, loss[loss=0.3552, simple_loss=0.3805, pruned_loss=0.165, over 7323.00 frames.], tot_loss[loss=0.3484, simple_loss=0.3832, pruned_loss=0.1568, over 1406106.45 frames.], batch size: 21, lr: 2.10e-03
2022-05-26 12:45:54,265 INFO [train.py:842] (1/4) Epoch 1, batch 9000, loss[loss=0.3828, simple_loss=0.403, pruned_loss=0.1813, over 5245.00 frames.], tot_loss[loss=0.3497, simple_loss=0.3849, pruned_loss=0.1573, over 1399386.58 frames.], batch size: 52, lr: 2.09e-03
2022-05-26 12:45:54,266 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 12:46:03,568 INFO [train.py:871] (1/4) Epoch 1, validation: loss=0.2508, simple_loss=0.3369, pruned_loss=0.08236, over 868885.00 frames.
2022-05-26 12:46:41,376 INFO [train.py:842] (1/4) Epoch 1, batch 9050, loss[loss=0.3844, simple_loss=0.3996, pruned_loss=0.1846, over 4936.00 frames.], tot_loss[loss=0.354, simple_loss=0.3881, pruned_loss=0.16, over 1387460.73 frames.], batch size: 52, lr: 2.09e-03
2022-05-26 12:47:18,745 INFO [train.py:842] (1/4) Epoch 1, batch 9100, loss[loss=0.3956, simple_loss=0.4109, pruned_loss=0.1901, over 4693.00 frames.], tot_loss[loss=0.3584, simple_loss=0.3909, pruned_loss=0.1629, over 1344143.97 frames.], batch size: 52, lr: 2.08e-03
2022-05-26 12:47:56,213 INFO [train.py:842] (1/4) Epoch 1, batch 9150, loss[loss=0.3974, simple_loss=0.4153, pruned_loss=0.1898, over 4816.00 frames.], tot_loss[loss=0.3631, simple_loss=0.394, pruned_loss=0.1661, over 1286201.71 frames.], batch size: 52, lr: 2.08e-03
2022-05-26 12:48:47,794 INFO [train.py:842] (1/4) Epoch 2, batch 0, loss[loss=0.3512, simple_loss=0.3815, pruned_loss=0.1604, over 7194.00 frames.], tot_loss[loss=0.3512, simple_loss=0.3815, pruned_loss=0.1604, over 7194.00 frames.], batch size: 26, lr: 2.06e-03
2022-05-26 12:49:27,346 INFO [train.py:842] (1/4) Epoch 2, batch 50, loss[loss=0.337, simple_loss=0.3799, pruned_loss=0.147, over 7239.00 frames.], tot_loss[loss=0.3393, simple_loss=0.3781, pruned_loss=0.1503, over 312457.70 frames.], batch size: 20, lr: 2.06e-03
2022-05-26 12:50:06,221 INFO [train.py:842] (1/4) Epoch 2, batch 100, loss[loss=0.3489, simple_loss=0.3752, pruned_loss=0.1613, over 7440.00 frames.], tot_loss[loss=0.3521, simple_loss=0.3856, pruned_loss=0.1593, over 560857.63 frames.], batch size: 20, lr: 2.05e-03
2022-05-26 12:50:45,175 INFO [train.py:842] (1/4) Epoch 2, batch 150, loss[loss=0.2641, simple_loss=0.3346, pruned_loss=0.09681, over 7323.00 frames.], tot_loss[loss=0.3455, simple_loss=0.3815, pruned_loss=0.1548, over 751250.89 frames.], batch size: 20, lr: 2.05e-03
2022-05-26 12:51:23,764 INFO [train.py:842] (1/4) Epoch 2, batch 200, loss[loss=0.3312, simple_loss=0.3687, pruned_loss=0.1468, over 7166.00 frames.], tot_loss[loss=0.3412, simple_loss=0.3781, pruned_loss=0.1521, over 901061.57 frames.], batch size: 19, lr: 2.04e-03
2022-05-26 12:52:03,056 INFO [train.py:842] (1/4) Epoch 2, batch 250, loss[loss=0.3623, simple_loss=0.3917, pruned_loss=0.1664, over 7379.00 frames.], tot_loss[loss=0.3425, simple_loss=0.3792, pruned_loss=0.1529, over 1015262.24 frames.], batch size: 23, lr: 2.04e-03
2022-05-26 12:52:42,060 INFO [train.py:842] (1/4) Epoch 2, batch 300, loss[loss=0.281, simple_loss=0.3299, pruned_loss=0.1161, over 7265.00 frames.], tot_loss[loss=0.3402, simple_loss=0.3778, pruned_loss=0.1513, over 1104877.00 frames.], batch size: 19, lr: 2.03e-03
2022-05-26 12:53:21,160 INFO [train.py:842] (1/4) Epoch 2, batch 350, loss[loss=0.37, simple_loss=0.4108, pruned_loss=0.1646, over 7217.00 frames.], tot_loss[loss=0.3371, simple_loss=0.3758, pruned_loss=0.1492, over 1173794.80 frames.], batch size: 21, lr: 2.03e-03
2022-05-26 12:53:59,751 INFO [train.py:842] (1/4) Epoch 2, batch 400, loss[loss=0.3389, simple_loss=0.3764, pruned_loss=0.1507, over 7141.00 frames.], tot_loss[loss=0.3359, simple_loss=0.3747, pruned_loss=0.1486, over 1230767.28 frames.], batch size: 20, lr: 2.03e-03
2022-05-26 12:54:38,384 INFO [train.py:842] (1/4) Epoch 2, batch 450, loss[loss=0.2757, simple_loss=0.3313, pruned_loss=0.1101, over 7149.00 frames.], tot_loss[loss=0.3365, simple_loss=0.3757, pruned_loss=0.1487, over 1276106.99 frames.], batch size: 19, lr: 2.02e-03
2022-05-26 12:55:16,803 INFO [train.py:842] (1/4) Epoch 2, batch 500, loss[loss=0.3306, simple_loss=0.3584, pruned_loss=0.1514, over 7154.00 frames.], tot_loss[loss=0.337, simple_loss=0.3759, pruned_loss=0.149, over 1307949.85 frames.], batch size: 18, lr: 2.02e-03
2022-05-26 12:55:56,052 INFO [train.py:842] (1/4) Epoch 2, batch 550, loss[loss=0.2912, simple_loss=0.3439, pruned_loss=0.1192, over 7354.00 frames.], tot_loss[loss=0.3366, simple_loss=0.3754, pruned_loss=0.1489, over 1332890.86 frames.], batch size: 19, lr: 2.01e-03
2022-05-26 12:56:34,282 INFO [train.py:842] (1/4) Epoch 2, batch 600, loss[loss=0.3454, simple_loss=0.3845, pruned_loss=0.1531, over 7371.00 frames.], tot_loss[loss=0.3391, simple_loss=0.3777, pruned_loss=0.1502, over 1353855.73 frames.], batch size: 23, lr: 2.01e-03
2022-05-26 12:57:13,125 INFO [train.py:842] (1/4) Epoch 2, batch 650, loss[loss=0.3071, simple_loss=0.3499, pruned_loss=0.1322, over 7282.00 frames.], tot_loss[loss=0.336, simple_loss=0.3756, pruned_loss=0.1482, over 1367603.58 frames.], batch size: 18, lr: 2.01e-03
2022-05-26 12:57:51,836 INFO [train.py:842] (1/4) Epoch 2, batch 700, loss[loss=0.4692, simple_loss=0.4644, pruned_loss=0.237, over 5024.00 frames.], tot_loss[loss=0.3328, simple_loss=0.3736, pruned_loss=0.146, over 1379671.48 frames.], batch size: 53, lr: 2.00e-03
2022-05-26 12:58:30,909 INFO [train.py:842] (1/4) Epoch 2, batch 750, loss[loss=0.3736, simple_loss=0.3968, pruned_loss=0.1752, over 7249.00 frames.], tot_loss[loss=0.3354, simple_loss=0.3753, pruned_loss=0.1477, over 1391080.18 frames.], batch size: 19, lr: 2.00e-03
2022-05-26 12:59:09,567 INFO [train.py:842] (1/4) Epoch 2, batch 800, loss[loss=0.3045, simple_loss=0.3534, pruned_loss=0.1277, over 7067.00 frames.], tot_loss[loss=0.3343, simple_loss=0.3745, pruned_loss=0.147, over 1400775.99 frames.], batch size: 18, lr: 1.99e-03
2022-05-26 12:59:48,504 INFO [train.py:842] (1/4) Epoch 2, batch 850, loss[loss=0.3935, simple_loss=0.4166, pruned_loss=0.1853, over 7339.00 frames.], tot_loss[loss=0.3312, simple_loss=0.3722, pruned_loss=0.1451, over 1408503.68 frames.], batch size: 20, lr: 1.99e-03
2022-05-26 13:00:27,174 INFO [train.py:842] (1/4) Epoch 2, batch 900, loss[loss=0.285, simple_loss=0.3408, pruned_loss=0.1146, over 7445.00 frames.], tot_loss[loss=0.3319, simple_loss=0.3729, pruned_loss=0.1455, over 1413182.49 frames.], batch size: 20, lr: 1.99e-03
2022-05-26 13:01:06,408 INFO [train.py:842] (1/4) Epoch 2, batch 950, loss[loss=0.2997, simple_loss=0.3422, pruned_loss=0.1286, over 7261.00 frames.], tot_loss[loss=0.3314, simple_loss=0.3729, pruned_loss=0.145, over 1415120.82 frames.], batch size: 19, lr: 1.98e-03
2022-05-26 13:01:45,062 INFO [train.py:842] (1/4) Epoch 2, batch 1000, loss[loss=0.4083, simple_loss=0.4234, pruned_loss=0.1966, over 6832.00 frames.], tot_loss[loss=0.3296, simple_loss=0.3717, pruned_loss=0.1437, over 1416869.14 frames.], batch size: 31, lr: 1.98e-03
2022-05-26 13:02:24,227 INFO [train.py:842] (1/4) Epoch 2, batch 1050, loss[loss=0.3419, simple_loss=0.3896, pruned_loss=0.1471, over 7442.00 frames.], tot_loss[loss=0.3309, simple_loss=0.3723, pruned_loss=0.1448, over 1418270.17 frames.], batch size: 20, lr: 1.97e-03
2022-05-26 13:03:02,460 INFO [train.py:842] (1/4) Epoch 2, batch 1100, loss[loss=0.3073, simple_loss=0.3557, pruned_loss=0.1295, over 7171.00 frames.], tot_loss[loss=0.3346, simple_loss=0.3748, pruned_loss=0.1472, over 1420016.04 frames.], batch size: 18, lr: 1.97e-03
2022-05-26 13:03:41,564 INFO [train.py:842] (1/4) Epoch 2, batch 1150, loss[loss=0.3252, simple_loss=0.3706, pruned_loss=0.1399, over 7235.00 frames.], tot_loss[loss=0.3329, simple_loss=0.3737, pruned_loss=0.1461, over 1423385.29 frames.], batch size: 20, lr: 1.97e-03
2022-05-26 13:04:19,994 INFO [train.py:842] (1/4) Epoch 2, batch 1200, loss[loss=0.3701, simple_loss=0.401, pruned_loss=0.1696, over 7081.00 frames.], tot_loss[loss=0.3326, simple_loss=0.3736, pruned_loss=0.1458, over 1422500.34 frames.], batch size: 28, lr: 1.96e-03
2022-05-26 13:04:58,729 INFO [train.py:842] (1/4) Epoch 2, batch 1250, loss[loss=0.2492, simple_loss=0.3165, pruned_loss=0.09095, over 7272.00 frames.], tot_loss[loss=0.3345, simple_loss=0.3755, pruned_loss=0.1467, over 1422555.59 frames.], batch size: 18, lr: 1.96e-03
2022-05-26 13:05:37,181 INFO [train.py:842] (1/4) Epoch 2, batch 1300, loss[loss=0.2998, simple_loss=0.3596, pruned_loss=0.12, over 7224.00 frames.], tot_loss[loss=0.3331, simple_loss=0.3743, pruned_loss=0.1459, over 1416769.82 frames.], batch size: 21, lr: 1.95e-03
2022-05-26 13:06:15,968 INFO [train.py:842] (1/4) Epoch 2, batch 1350, loss[loss=0.3314, simple_loss=0.3551, pruned_loss=0.1538, over 7283.00 frames.], tot_loss[loss=0.3323, simple_loss=0.3737, pruned_loss=0.1454, over 1420359.49 frames.], batch size: 17, lr: 1.95e-03
2022-05-26 13:06:54,264 INFO [train.py:842] (1/4) Epoch 2, batch 1400, loss[loss=0.3616, simple_loss=0.3945, pruned_loss=0.1643, over 7214.00 frames.], tot_loss[loss=0.333, simple_loss=0.3744, pruned_loss=0.1458, over 1418929.46 frames.], batch size: 21, lr: 1.95e-03
2022-05-26 13:07:33,455 INFO [train.py:842] (1/4) Epoch 2, batch 1450, loss[loss=0.3445, simple_loss=0.3915, pruned_loss=0.1488, over 7181.00 frames.], tot_loss[loss=0.3326, simple_loss=0.3741, pruned_loss=0.1456, over 1422632.00 frames.], batch size: 26, lr: 1.94e-03
2022-05-26 13:08:12,022 INFO [train.py:842] (1/4) Epoch 2, batch 1500, loss[loss=0.3445, simple_loss=0.3835, pruned_loss=0.1527, over 6385.00 frames.], tot_loss[loss=0.3329, simple_loss=0.3742, pruned_loss=0.1458, over 1422762.33 frames.], batch size: 37, lr: 1.94e-03
2022-05-26 13:08:50,746 INFO [train.py:842] (1/4) Epoch 2, batch 1550, loss[loss=0.2819, simple_loss=0.3493, pruned_loss=0.1073, over 7435.00 frames.], tot_loss[loss=0.3309, simple_loss=0.3731, pruned_loss=0.1443, over 1425485.27 frames.], batch size: 20, lr: 1.94e-03
2022-05-26 13:09:29,427 INFO [train.py:842] (1/4) Epoch 2, batch 1600, loss[loss=0.2934, simple_loss=0.3351, pruned_loss=0.1258, over 7179.00 frames.], tot_loss[loss=0.3279, simple_loss=0.3709, pruned_loss=0.1425, over 1424658.15 frames.], batch size: 18, lr: 1.93e-03
2022-05-26 13:10:08,289 INFO [train.py:842] (1/4) Epoch 2, batch 1650, loss[loss=0.354, simple_loss=0.3959, pruned_loss=0.156, over 7425.00 frames.], tot_loss[loss=0.3286, simple_loss=0.3714, pruned_loss=0.1429, over 1426054.27 frames.], batch size: 20, lr: 1.93e-03
2022-05-26 13:10:46,854 INFO [train.py:842] (1/4) Epoch 2, batch 1700, loss[loss=0.4089, simple_loss=0.424, pruned_loss=0.1969, over 7402.00 frames.], tot_loss[loss=0.3292, simple_loss=0.3717, pruned_loss=0.1433, over 1424029.02 frames.], batch size: 21, lr: 1.92e-03
2022-05-26 13:11:25,779 INFO [train.py:842] (1/4) Epoch 2, batch 1750, loss[loss=0.2838, simple_loss=0.3332, pruned_loss=0.1172, over 7276.00 frames.], tot_loss[loss=0.33, simple_loss=0.3729, pruned_loss=0.1435, over 1423178.80 frames.], batch size: 18, lr: 1.92e-03
2022-05-26 13:12:04,206 INFO [train.py:842] (1/4) Epoch 2, batch 1800, loss[loss=0.2887, simple_loss=0.3333, pruned_loss=0.1221, over 7365.00 frames.], tot_loss[loss=0.3303, simple_loss=0.3727, pruned_loss=0.1439, over 1424544.45 frames.], batch size: 19, lr: 1.92e-03
2022-05-26 13:12:43,085 INFO [train.py:842] (1/4) Epoch 2, batch 1850, loss[loss=0.2993, simple_loss=0.3602, pruned_loss=0.1192, over 7342.00 frames.], tot_loss[loss=0.3261, simple_loss=0.3694, pruned_loss=0.1415, over 1425236.14 frames.], batch size: 20, lr: 1.91e-03
2022-05-26 13:13:21,386 INFO [train.py:842] (1/4) Epoch 2, batch 1900, loss[loss=0.2865, simple_loss=0.3238, pruned_loss=0.1246, over 7011.00 frames.], tot_loss[loss=0.3243, simple_loss=0.3686, pruned_loss=0.14, over 1428801.81 frames.], batch size: 16, lr: 1.91e-03
2022-05-26 13:14:00,102 INFO [train.py:842] (1/4) Epoch 2, batch 1950, loss[loss=0.3969, simple_loss=0.3833, pruned_loss=0.2053, over 7271.00 frames.], tot_loss[loss=0.3248, simple_loss=0.3693, pruned_loss=0.1402, over 1428408.12 frames.], batch size: 18, lr: 1.91e-03
2022-05-26 13:14:38,216 INFO [train.py:842] (1/4) Epoch 2, batch 2000, loss[loss=0.3845, simple_loss=0.4096, pruned_loss=0.1797, over 7116.00 frames.], tot_loss[loss=0.327, simple_loss=0.3711, pruned_loss=0.1415, over 1423430.06 frames.], batch size: 21, lr: 1.90e-03
2022-05-26 13:15:17,090 INFO [train.py:842] (1/4) Epoch 2, batch 2050, loss[loss=0.3137, simple_loss=0.3671, pruned_loss=0.1302, over 7116.00 frames.], tot_loss[loss=0.3275, simple_loss=0.3713, pruned_loss=0.1419, over 1424728.97 frames.], batch size: 28, lr: 1.90e-03
2022-05-26 13:15:55,559 INFO [train.py:842] (1/4) Epoch 2, batch 2100, loss[loss=0.3218, simple_loss=0.35, pruned_loss=0.1467, over 7399.00 frames.], tot_loss[loss=0.3282, simple_loss=0.3713, pruned_loss=0.1425, over 1425593.23 frames.], batch size: 18, lr: 1.90e-03
2022-05-26 13:16:34,411 INFO [train.py:842] (1/4) Epoch 2, batch 2150, loss[loss=0.3655, simple_loss=0.4017, pruned_loss=0.1646, over 7407.00 frames.], tot_loss[loss=0.3283, simple_loss=0.3714, pruned_loss=0.1425, over 1424073.31 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 13:17:12,978 INFO [train.py:842] (1/4) Epoch 2, batch 2200, loss[loss=0.3913, simple_loss=0.4278, pruned_loss=0.1774, over 7116.00 frames.], tot_loss[loss=0.3256, simple_loss=0.3694, pruned_loss=0.1409, over 1423270.60 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 13:17:52,136 INFO [train.py:842] (1/4) Epoch 2, batch 2250, loss[loss=0.2871, simple_loss=0.3478, pruned_loss=0.1132, over 7215.00 frames.], tot_loss[loss=0.3237, simple_loss=0.368, pruned_loss=0.1398, over 1424622.57 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 13:18:30,738 INFO [train.py:842] (1/4) Epoch 2, batch 2300, loss[loss=0.3867, simple_loss=0.4211, pruned_loss=0.1761, over 7220.00 frames.], tot_loss[loss=0.3232, simple_loss=0.3681, pruned_loss=0.1392, over 1425004.73 frames.], batch size: 22, lr: 1.88e-03
2022-05-26 13:19:09,890 INFO [train.py:842] (1/4) Epoch 2, batch 2350, loss[loss=0.3779, simple_loss=0.4102, pruned_loss=0.1728, over 7242.00 frames.], tot_loss[loss=0.3252, simple_loss=0.3695, pruned_loss=0.1404, over 1424214.70 frames.], batch size: 20, lr: 1.88e-03
2022-05-26 13:19:48,360 INFO [train.py:842] (1/4) Epoch 2, batch 2400, loss[loss=0.3608, simple_loss=0.4143, pruned_loss=0.1537, over 7318.00 frames.], tot_loss[loss=0.3244, simple_loss=0.3685, pruned_loss=0.1401, over 1423777.74 frames.], batch size: 21, lr: 1.87e-03
2022-05-26 13:20:27,497 INFO [train.py:842] (1/4) Epoch 2, batch 2450, loss[loss=0.3526, simple_loss=0.3888, pruned_loss=0.1582, over 7324.00 frames.], tot_loss[loss=0.3227, simple_loss=0.3679, pruned_loss=0.1388, over 1426409.27 frames.], batch size: 21, lr: 1.87e-03
2022-05-26 13:21:06,122 INFO [train.py:842] (1/4) Epoch 2, batch 2500, loss[loss=0.3727, simple_loss=0.4133, pruned_loss=0.166, over 7142.00 frames.], tot_loss[loss=0.3227, simple_loss=0.3684, pruned_loss=0.1385, over 1426587.40 frames.], batch size: 26, lr: 1.87e-03
2022-05-26 13:21:44,928 INFO [train.py:842] (1/4) Epoch 2, batch 2550, loss[loss=0.2425, simple_loss=0.2951, pruned_loss=0.09496, over 6982.00 frames.], tot_loss[loss=0.3213, simple_loss=0.3673, pruned_loss=0.1376, over 1426158.51 frames.], batch size: 16, lr: 1.86e-03
2022-05-26 13:22:23,551 INFO [train.py:842] (1/4) Epoch 2, batch 2600, loss[loss=0.3531, simple_loss=0.3982, pruned_loss=0.154, over 7147.00 frames.], tot_loss[loss=0.3189, simple_loss=0.3654, pruned_loss=0.1362, over 1428080.53 frames.], batch size: 26, lr: 1.86e-03
2022-05-26 13:23:02,369 INFO [train.py:842] (1/4) Epoch 2, batch 2650, loss[loss=0.4074, simple_loss=0.4257, pruned_loss=0.1945, over 6309.00 frames.], tot_loss[loss=0.3191, simple_loss=0.3655, pruned_loss=0.1363, over 1426330.87 frames.], batch size: 38, lr: 1.86e-03
2022-05-26 13:23:41,044 INFO [train.py:842] (1/4) Epoch 2, batch 2700, loss[loss=0.3261, simple_loss=0.3703, pruned_loss=0.141, over 6704.00 frames.], tot_loss[loss=0.3171, simple_loss=0.3642, pruned_loss=0.135, over 1426283.71 frames.], batch size: 31, lr: 1.85e-03
2022-05-26 13:24:20,362 INFO [train.py:842] (1/4) Epoch 2, batch 2750, loss[loss=0.3497, simple_loss=0.3768, pruned_loss=0.1613, over 7307.00 frames.], tot_loss[loss=0.3168, simple_loss=0.3634, pruned_loss=0.135, over 1423289.44 frames.], batch size: 24, lr: 1.85e-03
2022-05-26 13:24:58,826 INFO [train.py:842] (1/4) Epoch 2, batch 2800, loss[loss=0.2519, simple_loss=0.3296, pruned_loss=0.08711, over 7191.00 frames.], tot_loss[loss=0.3169, simple_loss=0.3633, pruned_loss=0.1353, over 1425636.50 frames.], batch size: 23, lr: 1.85e-03
2022-05-26 13:25:37,871 INFO [train.py:842] (1/4) Epoch 2, batch 2850, loss[loss=0.303, simple_loss=0.3538, pruned_loss=0.1261, over 7283.00 frames.], tot_loss[loss=0.3177, simple_loss=0.3637, pruned_loss=0.1358, over 1425314.41 frames.], batch size: 24, lr: 1.84e-03
2022-05-26 13:26:16,449 INFO [train.py:842] (1/4) Epoch 2, batch 2900, loss[loss=0.3886, simple_loss=0.419, pruned_loss=0.1791, over 7239.00 frames.], tot_loss[loss=0.3189, simple_loss=0.3648, pruned_loss=0.1365, over 1420496.11 frames.], batch size: 20, lr: 1.84e-03
2022-05-26 13:26:55,513 INFO [train.py:842] (1/4) Epoch 2, batch 2950, loss[loss=0.24, simple_loss=0.3157, pruned_loss=0.08214, over 7232.00 frames.], tot_loss[loss=0.3194, simple_loss=0.365, pruned_loss=0.1369, over 1422258.47 frames.], batch size: 20, lr: 1.84e-03
2022-05-26 13:27:34,171 INFO [train.py:842] (1/4) Epoch 2, batch 3000, loss[loss=0.2454, simple_loss=0.3037, pruned_loss=0.09353, over 7272.00 frames.], tot_loss[loss=0.3193, simple_loss=0.3654, pruned_loss=0.1366, over 1426040.14 frames.], batch size: 17, lr: 1.84e-03
2022-05-26 13:27:34,171 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 13:27:43,281 INFO [train.py:871] (1/4) Epoch 2, validation: loss=0.238, simple_loss=0.3286, pruned_loss=0.07369, over 868885.00 frames.
2022-05-26 13:28:22,617 INFO [train.py:842] (1/4) Epoch 2, batch 3050, loss[loss=0.2363, simple_loss=0.3095, pruned_loss=0.08154, over 7282.00 frames.], tot_loss[loss=0.3186, simple_loss=0.365, pruned_loss=0.136, over 1422425.08 frames.], batch size: 18, lr: 1.83e-03
2022-05-26 13:29:00,952 INFO [train.py:842] (1/4) Epoch 2, batch 3100, loss[loss=0.51, simple_loss=0.4866, pruned_loss=0.2666, over 4991.00 frames.], tot_loss[loss=0.3189, simple_loss=0.3652, pruned_loss=0.1363, over 1421773.17 frames.], batch size: 52, lr: 1.83e-03
2022-05-26 13:29:39,781 INFO [train.py:842] (1/4) Epoch 2, batch 3150, loss[loss=0.2974, simple_loss=0.3358, pruned_loss=0.1295, over 7198.00 frames.], tot_loss[loss=0.317, simple_loss=0.3643, pruned_loss=0.1349, over 1424244.07 frames.], batch size: 16, lr: 1.83e-03
2022-05-26 13:30:18,098 INFO [train.py:842] (1/4) Epoch 2, batch 3200, loss[loss=0.3998, simple_loss=0.4145, pruned_loss=0.1925, over 5077.00 frames.], tot_loss[loss=0.3204, simple_loss=0.3668, pruned_loss=0.137, over 1413875.93 frames.], batch size: 52, lr: 1.82e-03
2022-05-26 13:30:56,871 INFO [train.py:842] (1/4) Epoch 2, batch 3250, loss[loss=0.2977, simple_loss=0.3694, pruned_loss=0.1131, over 7202.00 frames.], tot_loss[loss=0.3196, simple_loss=0.3667, pruned_loss=0.1362, over 1415946.87 frames.], batch size: 23, lr: 1.82e-03
2022-05-26 13:31:35,490 INFO [train.py:842] (1/4) Epoch 2, batch 3300, loss[loss=0.2911, simple_loss=0.3584, pruned_loss=0.1119, over 7212.00 frames.], tot_loss[loss=0.3182, simple_loss=0.3655, pruned_loss=0.1355, over 1420173.97 frames.], batch size: 22, lr: 1.82e-03
2022-05-26 13:32:14,256 INFO [train.py:842] (1/4) Epoch 2, batch 3350, loss[loss=0.3717, simple_loss=0.4049, pruned_loss=0.1692, over 7190.00 frames.], tot_loss[loss=0.3183, simple_loss=0.366, pruned_loss=0.1353, over 1423303.23 frames.], batch size: 26, lr: 1.81e-03
2022-05-26 13:32:52,904 INFO [train.py:842] (1/4) Epoch 2, batch 3400, loss[loss=0.2386, simple_loss=0.2893, pruned_loss=0.09395, over 7149.00 frames.], tot_loss[loss=0.3177, simple_loss=0.365, pruned_loss=0.1352, over 1424889.50 frames.], batch size: 17, lr: 1.81e-03
2022-05-26 13:33:31,551 INFO [train.py:842] (1/4) Epoch 2, batch 3450, loss[loss=0.3279, simple_loss=0.3812, pruned_loss=0.1373, over 7277.00 frames.], tot_loss[loss=0.319, simple_loss=0.3656, pruned_loss=0.1362, over 1427001.61 frames.], batch size: 24, lr: 1.81e-03
2022-05-26 13:34:10,040 INFO [train.py:842] (1/4) Epoch 2, batch 3500, loss[loss=0.312, simple_loss=0.3675, pruned_loss=0.1282, over 6446.00 frames.], tot_loss[loss=0.3197, simple_loss=0.3661, pruned_loss=0.1366, over 1423880.87 frames.], batch size: 37, lr: 1.80e-03
2022-05-26 13:34:48,900 INFO [train.py:842] (1/4) Epoch 2, batch 3550, loss[loss=0.3519, simple_loss=0.3928, pruned_loss=0.1555, over 7297.00 frames.], tot_loss[loss=0.3201, simple_loss=0.367, pruned_loss=0.1366, over 1424116.89 frames.], batch size: 25, lr: 1.80e-03
2022-05-26 13:35:27,123 INFO [train.py:842] (1/4) Epoch 2, batch 3600, loss[loss=0.372, simple_loss=0.4118, pruned_loss=0.1661, over 7229.00 frames.], tot_loss[loss=0.3194, simple_loss=0.3672, pruned_loss=0.1359, over 1424877.61 frames.], batch size: 20, lr: 1.80e-03
2022-05-26 13:36:06,061 INFO [train.py:842] (1/4) Epoch 2, batch 3650, loss[loss=0.2847, simple_loss=0.3318, pruned_loss=0.1188, over 6786.00 frames.], tot_loss[loss=0.318, simple_loss=0.3661, pruned_loss=0.1349, over 1427115.47 frames.], batch size: 15, lr: 1.79e-03
2022-05-26 13:36:44,549 INFO [train.py:842] (1/4) Epoch 2, batch 3700, loss[loss=0.2984, simple_loss=0.3498, pruned_loss=0.1234, over 7153.00 frames.], tot_loss[loss=0.3148, simple_loss=0.3643, pruned_loss=0.1326, over 1428663.75 frames.], batch size: 19, lr: 1.79e-03
2022-05-26 13:37:23,454 INFO [train.py:842] (1/4) Epoch 2, batch 3750, loss[loss=0.3267, simple_loss=0.3783, pruned_loss=0.1375, over 7288.00 frames.], tot_loss[loss=0.3147, simple_loss=0.3645, pruned_loss=0.1325, over 1429381.81 frames.], batch size: 24, lr: 1.79e-03
2022-05-26 13:38:02,063 INFO [train.py:842] (1/4) Epoch 2, batch 3800, loss[loss=0.2134, simple_loss=0.288, pruned_loss=0.06945, over 7006.00 frames.], tot_loss[loss=0.3139, simple_loss=0.3637, pruned_loss=0.1321, over 1430339.70 frames.], batch size: 16, lr: 1.79e-03
2022-05-26 13:38:40,881 INFO [train.py:842] (1/4) Epoch 2, batch 3850, loss[loss=0.3, simple_loss=0.3591, pruned_loss=0.1205, over 7215.00 frames.], tot_loss[loss=0.3141, simple_loss=0.3637, pruned_loss=0.1322, over 1431449.95 frames.], batch size: 22, lr: 1.78e-03
2022-05-26 13:39:19,561 INFO [train.py:842] (1/4) Epoch 2, batch 3900, loss[loss=0.3325, simple_loss=0.383, pruned_loss=0.141, over 6483.00 frames.], tot_loss[loss=0.3118, simple_loss=0.3618, pruned_loss=0.1309, over 1433510.22 frames.], batch size: 39, lr: 1.78e-03
2022-05-26 13:39:58,485 INFO [train.py:842] (1/4) Epoch 2, batch 3950, loss[loss=0.3142, simple_loss=0.3618, pruned_loss=0.1334, over 7321.00 frames.], tot_loss[loss=0.3124, simple_loss=0.3618, pruned_loss=0.1314, over 1431203.48 frames.], batch size: 21, lr: 1.78e-03
2022-05-26 13:40:36,949 INFO [train.py:842] (1/4) Epoch 2, batch 4000, loss[loss=0.4084, simple_loss=0.4255, pruned_loss=0.1957, over 5132.00 frames.], tot_loss[loss=0.3127, simple_loss=0.3625, pruned_loss=0.1314, over 1431838.16 frames.], batch size: 52, lr: 1.77e-03
2022-05-26 13:41:15,592 INFO [train.py:842] (1/4) Epoch 2, batch 4050, loss[loss=0.3453, simple_loss=0.3903, pruned_loss=0.1502, over 6854.00 frames.], tot_loss[loss=0.3144, simple_loss=0.3635, pruned_loss=0.1326, over 1427521.45 frames.], batch size: 31, lr: 1.77e-03
2022-05-26 13:41:54,117 INFO [train.py:842] (1/4) Epoch 2, batch 4100, loss[loss=0.3388, simple_loss=0.38, pruned_loss=0.1487, over 7116.00 frames.], tot_loss[loss=0.316, simple_loss=0.3643, pruned_loss=0.1338, over 1429439.32 frames.], batch size: 28, lr: 1.77e-03
2022-05-26 13:42:32,960 INFO [train.py:842] (1/4) Epoch 2, batch 4150, loss[loss=0.3327, simple_loss=0.3737, pruned_loss=0.1459, over 7214.00 frames.], tot_loss[loss=0.3151, simple_loss=0.3635, pruned_loss=0.1333, over 1425798.07 frames.], batch size: 26, lr: 1.76e-03
2022-05-26 13:43:11,631 INFO [train.py:842] (1/4) Epoch 2, batch 4200, loss[loss=0.2494, simple_loss=0.3029, pruned_loss=0.09792, over 6994.00 frames.], tot_loss[loss=0.3127, simple_loss=0.3622, pruned_loss=0.1316, over 1424456.48 frames.], batch size: 16, lr: 1.76e-03
2022-05-26 13:43:50,298 INFO [train.py:842] (1/4) Epoch 2, batch 4250, loss[loss=0.3694, simple_loss=0.4042, pruned_loss=0.1672, over 7212.00 frames.], tot_loss[loss=0.3138, simple_loss=0.3631, pruned_loss=0.1322, over 1423279.55 frames.], batch size: 22, lr: 1.76e-03
2022-05-26 13:44:28,859 INFO [train.py:842] (1/4) Epoch 2, batch 4300, loss[loss=0.2946, simple_loss=0.351, pruned_loss=0.1191, over 7343.00 frames.], tot_loss[loss=0.3136, simple_loss=0.3625, pruned_loss=0.1324, over 1425516.91 frames.], batch size: 22, lr: 1.76e-03
2022-05-26 13:45:07,468 INFO [train.py:842] (1/4) Epoch 2, batch 4350, loss[loss=0.2815, simple_loss=0.3483, pruned_loss=0.1073, over 7156.00 frames.], tot_loss[loss=0.3128, simple_loss=0.362, pruned_loss=0.1318, over 1422042.46 frames.], batch size: 19, lr: 1.75e-03
2022-05-26 13:45:45,797 INFO [train.py:842] (1/4) Epoch 2, batch 4400, loss[loss=0.3486, simple_loss=0.4088, pruned_loss=0.1442, over 7288.00 frames.], tot_loss[loss=0.3132, simple_loss=0.3625, pruned_loss=0.1319, over 1423078.40 frames.], batch size: 24, lr: 1.75e-03
2022-05-26 13:46:25,237 INFO [train.py:842] (1/4) Epoch 2, batch 4450, loss[loss=0.2843, simple_loss=0.3372, pruned_loss=0.1156, over 7409.00 frames.], tot_loss[loss=0.3096, simple_loss=0.3596, pruned_loss=0.1298, over 1423818.66 frames.], batch size: 18, lr: 1.75e-03
2022-05-26 13:47:03,781 INFO [train.py:842] (1/4) Epoch 2, batch 4500, loss[loss=0.3199, simple_loss=0.3733, pruned_loss=0.1332, over 7330.00 frames.], tot_loss[loss=0.3088, simple_loss=0.3589, pruned_loss=0.1293, over 1425560.44 frames.], batch size: 20, lr: 1.74e-03
2022-05-26 13:47:42,580 INFO [train.py:842] (1/4) Epoch 2, batch 4550, loss[loss=0.3073, simple_loss=0.3522, pruned_loss=0.1313, over 7267.00 frames.], tot_loss[loss=0.3083, simple_loss=0.3586, pruned_loss=0.129, over 1425960.53 frames.], batch size: 18, lr: 1.74e-03
2022-05-26 13:48:20,814 INFO [train.py:842] (1/4) Epoch 2, batch 4600, loss[loss=0.2621, simple_loss=0.3402, pruned_loss=0.09203, over 7212.00 frames.], tot_loss[loss=0.3075, simple_loss=0.3583, pruned_loss=0.1284, over 1421177.68 frames.], batch size: 22, lr: 1.74e-03
2022-05-26 13:48:59,686 INFO [train.py:842] (1/4) Epoch 2, batch 4650, loss[loss=0.2771, simple_loss=0.3469, pruned_loss=0.1037, over 7303.00 frames.], tot_loss[loss=0.3061, simple_loss=0.3577, pruned_loss=0.1273, over 1424565.12 frames.], batch size: 25, lr: 1.74e-03
2022-05-26 13:49:38,508 INFO [train.py:842] (1/4) Epoch 2, batch 4700, loss[loss=0.3154, simple_loss=0.3736, pruned_loss=0.1285, over 7317.00 frames.], tot_loss[loss=0.3071, simple_loss=0.3584, pruned_loss=0.1279, over 1425531.30 frames.], batch size: 21, lr: 1.73e-03
2022-05-26 13:50:16,920 INFO [train.py:842] (1/4) Epoch 2, batch 4750, loss[loss=0.3809, simple_loss=0.4178, pruned_loss=0.172, over 7404.00 frames.], tot_loss[loss=0.3105, simple_loss=0.3604, pruned_loss=0.1303, over 1418397.88 frames.], batch size: 21, lr: 1.73e-03
2022-05-26 13:50:55,343 INFO [train.py:842] (1/4) Epoch 2, batch 4800, loss[loss=0.2977, simple_loss=0.3627, pruned_loss=0.1164, over 7273.00 frames.], tot_loss[loss=0.3121, simple_loss=0.3614, pruned_loss=0.1313, over 1416319.58 frames.], batch size: 24, lr: 1.73e-03
2022-05-26 13:51:34,140 INFO [train.py:842] (1/4) Epoch 2, batch 4850, loss[loss=0.3551, simple_loss=0.3845, pruned_loss=0.1628, over 7159.00 frames.], tot_loss[loss=0.3116, simple_loss=0.361, pruned_loss=0.1311, over 1416238.64 frames.], batch size: 18, lr: 1.73e-03
2022-05-26 13:52:12,643 INFO [train.py:842] (1/4) Epoch 2, batch 4900, loss[loss=0.2327, simple_loss=0.2935, pruned_loss=0.08595, over 7292.00 frames.], tot_loss[loss=0.3076, simple_loss=0.3584, pruned_loss=0.1284, over 1419836.00 frames.], batch size: 17, lr: 1.72e-03
2022-05-26 13:52:51,833 INFO [train.py:842] (1/4) Epoch 2, batch 4950, loss[loss=0.3294, simple_loss=0.3855, pruned_loss=0.1367, over 7238.00 frames.], tot_loss[loss=0.3086, simple_loss=0.3592, pruned_loss=0.129, over 1422628.70 frames.], batch size: 20, lr: 1.72e-03
2022-05-26 13:53:30,421 INFO [train.py:842] (1/4) Epoch 2, batch 5000, loss[loss=0.2706, simple_loss=0.3239, pruned_loss=0.1087, over 7268.00 frames.], tot_loss[loss=0.3092, simple_loss=0.3602, pruned_loss=0.1291, over 1424933.16 frames.], batch size: 17, lr: 1.72e-03
2022-05-26 13:54:08,984 INFO [train.py:842] (1/4) Epoch 2, batch 5050, loss[loss=0.297, simple_loss=0.366, pruned_loss=0.1141, over 7422.00 frames.], tot_loss[loss=0.3128, simple_loss=0.3629, pruned_loss=0.1313, over 1418193.00 frames.], batch size: 21, lr: 1.71e-03
2022-05-26 13:54:47,658 INFO [train.py:842] (1/4) Epoch 2, batch 5100, loss[loss=0.2792, simple_loss=0.3394, pruned_loss=0.1095, over 7160.00 frames.], tot_loss[loss=0.3109, simple_loss=0.3611, pruned_loss=0.1304, over 1420987.11 frames.], batch size: 19, lr: 1.71e-03
2022-05-26 13:55:26,646 INFO [train.py:842] (1/4) Epoch 2, batch 5150, loss[loss=0.3302, simple_loss=0.3771, pruned_loss=0.1417, over 7221.00 frames.], tot_loss[loss=0.3113, simple_loss=0.3616, pruned_loss=0.1305, over 1421963.02 frames.], batch size: 21, lr: 1.71e-03
2022-05-26 13:56:05,021 INFO [train.py:842] (1/4) Epoch 2, batch 5200, loss[loss=0.3225, simple_loss=0.3754, pruned_loss=0.1348, over 7296.00 frames.], tot_loss[loss=0.3093, simple_loss=0.3606, pruned_loss=0.129, over 1423068.99 frames.], batch size: 25, lr: 1.71e-03
2022-05-26 13:56:43,800 INFO [train.py:842] (1/4) Epoch 2, batch 5250, loss[loss=0.3485, simple_loss=0.3964, pruned_loss=0.1503, over 6828.00 frames.], tot_loss[loss=0.3094, simple_loss=0.3602, pruned_loss=0.1293, over 1425247.73 frames.], batch size: 31, lr: 1.70e-03
2022-05-26 13:57:22,566 INFO [train.py:842] (1/4) Epoch 2, batch 5300, loss[loss=0.3299, simple_loss=0.3887, pruned_loss=0.1356, over 7387.00 frames.], tot_loss[loss=0.3105, simple_loss=0.361, pruned_loss=0.13, over 1422362.95 frames.], batch size: 23, lr: 1.70e-03
2022-05-26 13:58:01,624 INFO [train.py:842] (1/4) Epoch 2, batch 5350, loss[loss=0.2375, simple_loss=0.3119, pruned_loss=0.08156, over 7358.00 frames.], tot_loss[loss=0.309, simple_loss=0.3599, pruned_loss=0.129, over 1419303.25 frames.], batch size: 19, lr: 1.70e-03
2022-05-26 13:58:40,182 INFO [train.py:842] (1/4) Epoch 2, batch 5400, loss[loss=0.2707, simple_loss=0.3467, pruned_loss=0.09738, over 6506.00 frames.], tot_loss[loss=0.3084, simple_loss=0.3589, pruned_loss=0.129, over 1420439.86 frames.], batch size: 38, lr: 1.70e-03
2022-05-26 13:59:19,543 INFO [train.py:842] (1/4) Epoch 2, batch 5450, loss[loss=0.2428, simple_loss=0.3033, pruned_loss=0.09118, over 7320.00 frames.], tot_loss[loss=0.3079, simple_loss=0.3579, pruned_loss=0.1289, over 1422173.27 frames.], batch size: 16, lr: 1.69e-03
2022-05-26 13:59:58,104 INFO [train.py:842] (1/4) Epoch 2, batch 5500, loss[loss=0.2142, simple_loss=0.2837, pruned_loss=0.07233, over 7133.00 frames.], tot_loss[loss=0.3043, simple_loss=0.3557, pruned_loss=0.1264, over 1423436.71 frames.], batch size: 17, lr: 1.69e-03
2022-05-26 14:00:37,017 INFO [train.py:842] (1/4) Epoch 2, batch 5550, loss[loss=0.2022, simple_loss=0.2661, pruned_loss=0.06912, over 7002.00 frames.], tot_loss[loss=0.3038, simple_loss=0.3551, pruned_loss=0.1263, over 1424203.09 frames.], batch size: 16, lr: 1.69e-03
2022-05-26 14:01:15,384 INFO [train.py:842] (1/4) Epoch 2, batch 5600, loss[loss=0.3302, simple_loss=0.3906, pruned_loss=0.1349, over 7291.00 frames.], tot_loss[loss=0.3048, simple_loss=0.356, pruned_loss=0.1268, over 1424858.62 frames.], batch size: 24, lr: 1.69e-03
2022-05-26 14:01:54,149 INFO [train.py:842] (1/4) Epoch 2, batch 5650, loss[loss=0.335, simple_loss=0.3946, pruned_loss=0.1376, over 7175.00 frames.], tot_loss[loss=0.3057, simple_loss=0.3569, pruned_loss=0.1273, over 1425511.99 frames.], batch size: 23, lr: 1.68e-03
2022-05-26 14:02:32,781 INFO [train.py:842] (1/4) Epoch 2, batch 5700, loss[loss=0.2363, simple_loss=0.3014, pruned_loss=0.08559, over 7276.00 frames.], tot_loss[loss=0.3056, simple_loss=0.3564, pruned_loss=0.1274, over 1423980.66 frames.], batch size: 18, lr: 1.68e-03
2022-05-26 14:03:11,578 INFO [train.py:842] (1/4) Epoch 2, batch 5750, loss[loss=0.3088, simple_loss=0.3764, pruned_loss=0.1206, over 7321.00 frames.], tot_loss[loss=0.305, simple_loss=0.356, pruned_loss=0.127, over 1421969.94 frames.], batch size: 21, lr: 1.68e-03
2022-05-26 14:03:50,315 INFO [train.py:842] (1/4) Epoch 2, batch 5800, loss[loss=0.3018, simple_loss=0.3649, pruned_loss=0.1194, over 7177.00 frames.], tot_loss[loss=0.3055, simple_loss=0.3564, pruned_loss=0.1273, over 1426553.54 frames.], batch size: 26, lr: 1.68e-03
2022-05-26 14:04:29,352 INFO [train.py:842] (1/4) Epoch 2, batch 5850, loss[loss=0.31, simple_loss=0.3686, pruned_loss=0.1256, over 7406.00 frames.], tot_loss[loss=0.3057, simple_loss=0.3567, pruned_loss=0.1273, over 1421142.48 frames.], batch size: 21, lr: 1.67e-03
2022-05-26 14:05:07,952 INFO [train.py:842] (1/4) Epoch 2, batch 5900, loss[loss=0.2575, simple_loss=0.3122, pruned_loss=0.1014, over 7261.00 frames.], tot_loss[loss=0.3041, simple_loss=0.356, pruned_loss=0.1261, over 1423487.43 frames.], batch size: 17, lr: 1.67e-03
2022-05-26 14:05:46,683 INFO [train.py:842] (1/4) Epoch 2, batch 5950, loss[loss=0.3278, simple_loss=0.3781, pruned_loss=0.1388, over 7217.00 frames.], tot_loss[loss=0.3056, simple_loss=0.3574, pruned_loss=0.1269, over 1422676.12 frames.], batch size: 22, lr: 1.67e-03
2022-05-26 14:06:25,163 INFO [train.py:842] (1/4) Epoch 2, batch 6000, loss[loss=0.3033, simple_loss=0.3668, pruned_loss=0.1199, over 7416.00 frames.], tot_loss[loss=0.3043, simple_loss=0.3564, pruned_loss=0.1261, over 1419730.99 frames.], batch size: 21, lr: 1.67e-03
2022-05-26 14:06:25,165 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 14:06:34,412 INFO [train.py:871] (1/4) Epoch 2, validation: loss=0.2262, simple_loss=0.3196, pruned_loss=0.0664, over 868885.00 frames.
2022-05-26 14:07:13,297 INFO [train.py:842] (1/4) Epoch 2, batch 6050, loss[loss=0.336, simple_loss=0.3813, pruned_loss=0.1454, over 7212.00 frames.], tot_loss[loss=0.301, simple_loss=0.3542, pruned_loss=0.1239, over 1424218.81 frames.], batch size: 23, lr: 1.66e-03
2022-05-26 14:07:51,787 INFO [train.py:842] (1/4) Epoch 2, batch 6100, loss[loss=0.2841, simple_loss=0.343, pruned_loss=0.1126, over 7372.00 frames.], tot_loss[loss=0.3027, simple_loss=0.3556, pruned_loss=0.1249, over 1425806.81 frames.], batch size: 23, lr: 1.66e-03
2022-05-26 14:08:30,704 INFO [train.py:842] (1/4) Epoch 2, batch 6150, loss[loss=0.3122, simple_loss=0.365, pruned_loss=0.1297, over 7009.00 frames.], tot_loss[loss=0.3019, simple_loss=0.3548, pruned_loss=0.1245, over 1426870.30 frames.], batch size: 28, lr: 1.66e-03
2022-05-26 14:09:09,182 INFO [train.py:842] (1/4) Epoch 2, batch 6200, loss[loss=0.3469, simple_loss=0.3799, pruned_loss=0.1569, over 6716.00 frames.], tot_loss[loss=0.3026, simple_loss=0.3556, pruned_loss=0.1248, over 1424467.16 frames.], batch size: 31, lr: 1.66e-03
2022-05-26 14:09:47,963 INFO [train.py:842] (1/4) Epoch 2, batch 6250, loss[loss=0.308, simple_loss=0.367, pruned_loss=0.1245, over 7100.00 frames.], tot_loss[loss=0.3042, simple_loss=0.3562, pruned_loss=0.1261, over 1427813.57 frames.], batch size: 21, lr: 1.65e-03
2022-05-26 14:10:27,106 INFO [train.py:842] (1/4) Epoch 2, batch 6300, loss[loss=0.3174, simple_loss=0.3632, pruned_loss=0.1358, over 7208.00 frames.], tot_loss[loss=0.3048, simple_loss=0.357, pruned_loss=0.1263, over 1431609.00 frames.], batch size: 26, lr: 1.65e-03
2022-05-26 14:11:05,638 INFO [train.py:842] (1/4) Epoch 2, batch 6350, loss[loss=0.2848, simple_loss=0.351, pruned_loss=0.1093, over 6453.00 frames.], tot_loss[loss=0.3055, simple_loss=0.3579, pruned_loss=0.1266, over 1430192.59 frames.], batch size: 38, lr: 1.65e-03
2022-05-26 14:11:44,204 INFO [train.py:842] (1/4) Epoch 2, batch 6400, loss[loss=0.3122, simple_loss=0.3738, pruned_loss=0.1253, over 6839.00 frames.], tot_loss[loss=0.3058, simple_loss=0.3578, pruned_loss=0.1269, over 1425581.06 frames.], batch size: 31, lr: 1.65e-03
2022-05-26 14:12:23,048 INFO [train.py:842] (1/4) Epoch 2, batch 6450, loss[loss=0.2648, simple_loss=0.3186, pruned_loss=0.1055, over 7404.00 frames.], tot_loss[loss=0.3051, simple_loss=0.3569, pruned_loss=0.1267, over 1425040.87 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 14:13:01,662 INFO [train.py:842] (1/4) Epoch 2, batch 6500, loss[loss=0.2863, simple_loss=0.341, pruned_loss=0.1158, over 7214.00 frames.], tot_loss[loss=0.304, simple_loss=0.3555, pruned_loss=0.1263, over 1423429.10 frames.], batch size: 22, lr: 1.64e-03
2022-05-26 14:13:40,422 INFO [train.py:842] (1/4) Epoch 2, batch 6550, loss[loss=0.2844, simple_loss=0.3336, pruned_loss=0.1176, over 7071.00 frames.], tot_loss[loss=0.3039, simple_loss=0.3559, pruned_loss=0.126, over 1420340.13 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 14:14:18,982 INFO [train.py:842] (1/4) Epoch 2, batch 6600, loss[loss=0.3049, simple_loss=0.3529, pruned_loss=0.1285, over 7265.00 frames.], tot_loss[loss=0.303, simple_loss=0.355, pruned_loss=0.1255, over 1420075.71 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 14:14:57,587 INFO [train.py:842] (1/4) Epoch 2, batch 6650, loss[loss=0.3556, simple_loss=0.4023, pruned_loss=0.1544, over 7200.00 frames.], tot_loss[loss=0.3041, simple_loss=0.3556, pruned_loss=0.1263, over 1413321.53 frames.], batch size: 23, lr: 1.63e-03
2022-05-26 14:15:36,109 INFO [train.py:842] (1/4) Epoch 2, batch 6700, loss[loss=0.2162, simple_loss=0.2868, pruned_loss=0.0728, over 7280.00 frames.], tot_loss[loss=0.3014, simple_loss=0.3537, pruned_loss=0.1245, over 1418939.83 frames.], batch size: 17, lr: 1.63e-03
2022-05-26 14:16:14,711 INFO [train.py:842] (1/4) Epoch 2, batch 6750, loss[loss=0.3114, simple_loss=0.3725, pruned_loss=0.1251, over 7227.00 frames.], tot_loss[loss=0.3016, simple_loss=0.3545, pruned_loss=0.1244, over 1421636.09 frames.], batch size: 20, lr: 1.63e-03
2022-05-26 14:16:53,065 INFO [train.py:842] (1/4) Epoch 2, batch 6800, loss[loss=0.3057, simple_loss=0.3711, pruned_loss=0.1202, over 7109.00 frames.], tot_loss[loss=0.3006, simple_loss=0.3543, pruned_loss=0.1235, over 1423999.24 frames.], batch size: 21, lr: 1.63e-03
2022-05-26 14:17:34,599 INFO [train.py:842] (1/4) Epoch 2, batch 6850, loss[loss=0.3012, simple_loss=0.3679, pruned_loss=0.1172, over 7324.00 frames.], tot_loss[loss=0.3014, simple_loss=0.355, pruned_loss=0.1239, over 1421362.63 frames.], batch size: 20, lr: 1.63e-03
2022-05-26 14:18:13,261 INFO [train.py:842] (1/4) Epoch 2, batch 6900, loss[loss=0.3357, simple_loss=0.3784, pruned_loss=0.1465, over 7440.00 frames.], tot_loss[loss=0.3007, simple_loss=0.3543, pruned_loss=0.1236, over 1420272.06 frames.], batch size: 20, lr: 1.62e-03
2022-05-26 14:18:52,145 INFO [train.py:842] (1/4) Epoch 2, batch 6950, loss[loss=0.2491, simple_loss=0.3135, pruned_loss=0.09235, over 7296.00 frames.], tot_loss[loss=0.2998, simple_loss=0.353, pruned_loss=0.1232, over 1420680.74 frames.], batch size: 18, lr: 1.62e-03
2022-05-26 14:19:30,572 INFO [train.py:842] (1/4) Epoch 2, batch 7000, loss[loss=0.2696, simple_loss=0.3502, pruned_loss=0.09451, over 7315.00 frames.], tot_loss[loss=0.2993, simple_loss=0.3528, pruned_loss=0.1229, over 1423478.93 frames.], batch size: 21, lr: 1.62e-03
2022-05-26 14:20:09,760 INFO [train.py:842] (1/4) Epoch 2, batch 7050, loss[loss=0.3565, simple_loss=0.3939, pruned_loss=0.1596, over 5203.00 frames.], tot_loss[loss=0.2982, simple_loss=0.3518, pruned_loss=0.1224, over 1426442.08 frames.], batch size: 52, lr: 1.62e-03
2022-05-26 14:20:48,379 INFO [train.py:842] (1/4) Epoch 2, batch 7100, loss[loss=0.2833, simple_loss=0.3387, pruned_loss=0.1139, over 7109.00 frames.], tot_loss[loss=0.2984, simple_loss=0.3521, pruned_loss=0.1223, over 1425269.89 frames.], batch size: 21, lr: 1.61e-03
2022-05-26 14:21:26,952 INFO [train.py:842] (1/4) Epoch 2, batch 7150, loss[loss=0.2782, simple_loss=0.3395, pruned_loss=0.1084, over 7409.00 frames.], tot_loss[loss=0.3019, simple_loss=0.355, pruned_loss=0.1245, over 1422172.35 frames.], batch size: 21, lr: 1.61e-03
2022-05-26 14:22:05,298 INFO [train.py:842] (1/4) Epoch 2, batch 7200, loss[loss=0.274, simple_loss=0.3197, pruned_loss=0.1141, over 7011.00 frames.], tot_loss[loss=0.3031, simple_loss=0.3559, pruned_loss=0.1251, over 1420222.24 frames.], batch size: 16, lr: 1.61e-03
2022-05-26 14:22:44,440 INFO [train.py:842] (1/4) Epoch 2, batch 7250, loss[loss=0.2896, simple_loss=0.3528, pruned_loss=0.1132, over 7244.00 frames.], tot_loss[loss=0.301, simple_loss=0.3544, pruned_loss=0.1238, over 1425371.76 frames.], batch size: 20, lr: 1.61e-03
2022-05-26 14:23:22,973 INFO [train.py:842] (1/4) Epoch 2, batch 7300, loss[loss=0.3968, simple_loss=0.4066, pruned_loss=0.1935, over 7224.00 frames.], tot_loss[loss=0.3019, simple_loss=0.3548, pruned_loss=0.1245, over 1428222.94 frames.], batch size: 21, lr: 1.60e-03
2022-05-26 14:24:01,879 INFO [train.py:842] (1/4) Epoch 2, batch 7350, loss[loss=0.3467, simple_loss=0.3801, pruned_loss=0.1567, over 4860.00 frames.], tot_loss[loss=0.301, simple_loss=0.3538, pruned_loss=0.1241, over 1424073.58 frames.], batch size: 52, lr: 1.60e-03
2022-05-26 14:24:40,490 INFO [train.py:842] (1/4) Epoch 2, batch 7400, loss[loss=0.2348, simple_loss=0.2928, pruned_loss=0.08843, over 7423.00 frames.], tot_loss[loss=0.2994, simple_loss=0.3528, pruned_loss=0.123, over 1424290.41 frames.], batch size: 17, lr: 1.60e-03
2022-05-26 14:25:19,327 INFO [train.py:842] (1/4) Epoch 2, batch 7450, loss[loss=0.2869, simple_loss=0.3297, pruned_loss=0.122, over 7360.00 frames.], tot_loss[loss=0.3019, simple_loss=0.3545, pruned_loss=0.1247, over 1420248.30 frames.], batch size: 19, lr: 1.60e-03
2022-05-26 14:25:57,999 INFO [train.py:842] (1/4) Epoch 2, batch 7500, loss[loss=0.271, simple_loss=0.3355, pruned_loss=0.1032, over 7204.00 frames.], tot_loss[loss=0.3034, simple_loss=0.3553, pruned_loss=0.1257, over 1421331.81 frames.], batch size: 21, lr: 1.60e-03
2022-05-26 14:26:36,921 INFO [train.py:842] (1/4) Epoch 2, batch 7550, loss[loss=0.3031, simple_loss=0.3515, pruned_loss=0.1274, over 7404.00 frames.], tot_loss[loss=0.3014, simple_loss=0.3545, pruned_loss=0.1241, over 1421833.76 frames.], batch size: 21, lr: 1.59e-03
2022-05-26 14:27:15,665 INFO [train.py:842] (1/4) Epoch 2, batch 7600, loss[loss=0.3203, simple_loss=0.3692, pruned_loss=0.1357, over 4971.00 frames.], tot_loss[loss=0.2987, simple_loss=0.352, pruned_loss=0.1227, over 1421461.68 frames.], batch size: 52, lr: 1.59e-03
2022-05-26 14:27:54,291 INFO [train.py:842] (1/4) Epoch 2, batch 7650, loss[loss=0.2609, simple_loss=0.3253, pruned_loss=0.09824, over 7409.00 frames.], tot_loss[loss=0.298, simple_loss=0.3515, pruned_loss=0.1223, over 1422514.16 frames.], batch size: 21, lr: 1.59e-03
2022-05-26 14:28:32,792 INFO [train.py:842] (1/4) Epoch 2, batch 7700, loss[loss=0.3966, simple_loss=0.4121, pruned_loss=0.1905, over 7353.00 frames.], tot_loss[loss=0.2962, simple_loss=0.3502, pruned_loss=0.1211, over 1422853.62 frames.], batch size: 22, lr: 1.59e-03
2022-05-26 14:29:11,474 INFO [train.py:842] (1/4) Epoch 2, batch 7750, loss[loss=0.3426, simple_loss=0.3945, pruned_loss=0.1454, over 6967.00 frames.], tot_loss[loss=0.2977, simple_loss=0.3519, pruned_loss=0.1218, over 1423902.57 frames.], batch size: 28, lr: 1.59e-03
2022-05-26 14:29:50,006 INFO [train.py:842] (1/4) Epoch 2, batch 7800, loss[loss=0.2409, simple_loss=0.3143, pruned_loss=0.08379, over 7154.00 frames.], tot_loss[loss=0.2981, simple_loss=0.3522, pruned_loss=0.122, over 1423680.66 frames.], batch size: 20, lr: 1.58e-03
2022-05-26 14:30:28,810 INFO [train.py:842] (1/4) Epoch 2, batch 7850, loss[loss=0.2757, simple_loss=0.3272, pruned_loss=0.112, over 7319.00 frames.], tot_loss[loss=0.2983, simple_loss=0.352, pruned_loss=0.1223, over 1424945.42 frames.], batch size: 21, lr: 1.58e-03
2022-05-26 14:31:07,203 INFO [train.py:842] (1/4) Epoch 2, batch 7900, loss[loss=0.4691, simple_loss=0.473, pruned_loss=0.2326, over 5200.00 frames.], tot_loss[loss=0.3012, simple_loss=0.3538, pruned_loss=0.1243, over 1426734.28 frames.], batch size: 53, lr: 1.58e-03
2022-05-26 14:31:46,043 INFO [train.py:842] (1/4) Epoch 2, batch 7950, loss[loss=0.2569, simple_loss=0.3104, pruned_loss=0.1017, over 7166.00 frames.], tot_loss[loss=0.2993, simple_loss=0.3529, pruned_loss=0.1229, over 1428202.45 frames.], batch size: 18, lr: 1.58e-03
2022-05-26 14:32:24,259 INFO [train.py:842] (1/4) Epoch 2, batch 8000, loss[loss=0.2479, simple_loss=0.329, pruned_loss=0.08337, over 7213.00 frames.], tot_loss[loss=0.2988, simple_loss=0.353, pruned_loss=0.1223, over 1426259.03 frames.], batch size: 21, lr: 1.57e-03
2022-05-26 14:33:02,919 INFO [train.py:842] (1/4) Epoch 2, batch 8050, loss[loss=0.3265, simple_loss=0.3816, pruned_loss=0.1357, over 6629.00 frames.], tot_loss[loss=0.3, simple_loss=0.3543, pruned_loss=0.1229, over 1424867.78 frames.], batch size: 37, lr: 1.57e-03
2022-05-26 14:33:41,415 INFO [train.py:842] (1/4) Epoch 2, batch 8100, loss[loss=0.2966, simple_loss=0.3565, pruned_loss=0.1183, over 7166.00 frames.], tot_loss[loss=0.2986, simple_loss=0.353, pruned_loss=0.1221, over 1427863.37 frames.], batch size: 26, lr: 1.57e-03
2022-05-26 14:34:20,553 INFO [train.py:842] (1/4) Epoch 2, batch 8150, loss[loss=0.3115, simple_loss=0.3593, pruned_loss=0.1318, over 7061.00 frames.], tot_loss[loss=0.2971, simple_loss=0.3517, pruned_loss=0.1213, over 1429146.93 frames.], batch size: 18, lr: 1.57e-03
2022-05-26 14:34:58,973 INFO [train.py:842] (1/4) Epoch 2, batch 8200, loss[loss=0.2758, simple_loss=0.3333, pruned_loss=0.1092, over 7279.00 frames.], tot_loss[loss=0.2993, simple_loss=0.3535, pruned_loss=0.1226, over 1424686.70 frames.], batch size: 18, lr: 1.57e-03
2022-05-26 14:35:38,140 INFO [train.py:842] (1/4) Epoch 2, batch 8250, loss[loss=0.2696, simple_loss=0.3391, pruned_loss=0.1001, over 7088.00 frames.], tot_loss[loss=0.2989, simple_loss=0.3527, pruned_loss=0.1225, over 1423788.16 frames.], batch size: 28, lr: 1.56e-03
2022-05-26 14:36:16,481 INFO [train.py:842] (1/4) Epoch 2, batch 8300, loss[loss=0.2867, simple_loss=0.3572, pruned_loss=0.1081, over 7149.00 frames.], tot_loss[loss=0.3003, simple_loss=0.3543, pruned_loss=0.1231, over 1421814.22 frames.], batch size: 20, lr: 1.56e-03
2022-05-26 14:36:55,266 INFO [train.py:842] (1/4) Epoch 2, batch 8350, loss[loss=0.3843, simple_loss=0.4033, pruned_loss=0.1827, over 4996.00 frames.], tot_loss[loss=0.2987, simple_loss=0.3532, pruned_loss=0.1221, over 1420484.41 frames.], batch size: 52, lr: 1.56e-03
2022-05-26 14:37:33,572 INFO [train.py:842] (1/4) Epoch 2, batch 8400, loss[loss=0.2578, simple_loss=0.3261, pruned_loss=0.09479, over 7125.00 frames.], tot_loss[loss=0.3003, simple_loss=0.3544, pruned_loss=0.1231, over 1419141.64 frames.], batch size: 17, lr: 1.56e-03
2022-05-26 14:38:12,065 INFO [train.py:842] (1/4) Epoch 2, batch 8450, loss[loss=0.2724, simple_loss=0.3457, pruned_loss=0.09959, over 7190.00 frames.], tot_loss[loss=0.3, simple_loss=0.3545, pruned_loss=0.1227, over 1415019.40 frames.], batch size: 22, lr: 1.56e-03
2022-05-26 14:38:50,514 INFO [train.py:842] (1/4) Epoch 2, batch 8500, loss[loss=0.2739, simple_loss=0.3128, pruned_loss=0.1175, over 7128.00 frames.], tot_loss[loss=0.2994, simple_loss=0.3535, pruned_loss=0.1226, over 1419228.82 frames.], batch size: 17, lr: 1.55e-03
2022-05-26 14:39:29,181 INFO [train.py:842] (1/4) Epoch 2, batch 8550, loss[loss=0.2577, simple_loss=0.3205, pruned_loss=0.0974, over 7371.00 frames.], tot_loss[loss=0.2979, simple_loss=0.3531, pruned_loss=0.1213, over 1424390.75 frames.], batch size: 19, lr: 1.55e-03
2022-05-26 14:40:07,850 INFO [train.py:842] (1/4) Epoch 2, batch 8600, loss[loss=0.2998, simple_loss=0.3606, pruned_loss=0.1195, over 6545.00 frames.], tot_loss[loss=0.2951, simple_loss=0.3506, pruned_loss=0.1198, over 1422670.81 frames.], batch size: 38, lr: 1.55e-03
2022-05-26 14:40:46,956 INFO [train.py:842] (1/4) Epoch 2, batch 8650, loss[loss=0.2793, simple_loss=0.3474, pruned_loss=0.1056, over 7145.00 frames.], tot_loss[loss=0.2957, simple_loss=0.351, pruned_loss=0.1202, over 1424632.41 frames.], batch size: 20, lr: 1.55e-03
2022-05-26 14:41:25,754 INFO [train.py:842] (1/4) Epoch 2, batch 8700, loss[loss=0.2669, simple_loss=0.3254, pruned_loss=0.1041, over 7076.00 frames.], tot_loss[loss=0.2915, simple_loss=0.3477, pruned_loss=0.1176, over 1423112.95 frames.], batch size: 18, lr: 1.55e-03
2022-05-26 14:42:04,182 INFO [train.py:842] (1/4) Epoch 2, batch 8750, loss[loss=0.3057, simple_loss=0.3506, pruned_loss=0.1304, over 7176.00 frames.], tot_loss[loss=0.2921, simple_loss=0.3487, pruned_loss=0.1178, over 1421954.42 frames.], batch size: 18, lr: 1.54e-03
2022-05-26 14:42:42,560 INFO [train.py:842] (1/4) Epoch 2, batch 8800, loss[loss=0.3254, simple_loss=0.3852, pruned_loss=0.1328, over 7328.00 frames.], tot_loss[loss=0.2942, simple_loss=0.3496, pruned_loss=0.1193, over 1414223.20 frames.], batch size: 22, lr: 1.54e-03
2022-05-26 14:43:21,147 INFO [train.py:842] (1/4) Epoch 2, batch 8850, loss[loss=0.2973, simple_loss=0.3556, pruned_loss=0.1195, over 7279.00 frames.], tot_loss[loss=0.2938, simple_loss=0.3491, pruned_loss=0.1192, over 1412495.88 frames.], batch size: 24, lr: 1.54e-03
2022-05-26 14:43:59,318 INFO [train.py:842] (1/4) Epoch 2, batch 8900, loss[loss=0.302, simple_loss=0.3524, pruned_loss=0.1258, over 6868.00 frames.], tot_loss[loss=0.2964, simple_loss=0.351, pruned_loss=0.1209, over 1403116.12 frames.], batch size: 31, lr: 1.54e-03
2022-05-26 14:44:37,781 INFO [train.py:842] (1/4) Epoch 2, batch 8950, loss[loss=0.3751, simple_loss=0.4076, pruned_loss=0.1713, over 7113.00 frames.], tot_loss[loss=0.2989, simple_loss=0.3529, pruned_loss=0.1225, over 1402563.69 frames.], batch size: 21, lr: 1.54e-03
2022-05-26 14:45:16,069 INFO [train.py:842] (1/4) Epoch 2, batch 9000, loss[loss=0.3321, simple_loss=0.3693, pruned_loss=0.1474, over 7262.00 frames.], tot_loss[loss=0.3009, simple_loss=0.3544, pruned_loss=0.1237, over 1397513.40 frames.], batch size: 18, lr: 1.53e-03
2022-05-26 14:45:16,071 INFO [train.py:862] (1/4) Computing validation loss
2022-05-26 14:45:25,234 INFO [train.py:871] (1/4) Epoch 2, validation: loss=0.2179, simple_loss=0.3144, pruned_loss=0.06069, over 868885.00 frames.
2022-05-26 14:46:03,599 INFO [train.py:842] (1/4) Epoch 2, batch 9050, loss[loss=0.2345, simple_loss=0.3058, pruned_loss=0.08155, over 7281.00 frames.], tot_loss[loss=0.302, simple_loss=0.3552, pruned_loss=0.1244, over 1382619.49 frames.], batch size: 18, lr: 1.53e-03
2022-05-26 14:46:40,947 INFO [train.py:842] (1/4) Epoch 2, batch 9100, loss[loss=0.3093, simple_loss=0.3523, pruned_loss=0.1332, over 5088.00 frames.], tot_loss[loss=0.3079, simple_loss=0.3594, pruned_loss=0.1282, over 1328667.83 frames.], batch size: 52, lr: 1.53e-03
2022-05-26 14:47:18,827 INFO [train.py:842] (1/4) Epoch 2, batch 9150, loss[loss=0.3264, simple_loss=0.3707, pruned_loss=0.141, over 5041.00 frames.], tot_loss[loss=0.3161, simple_loss=0.3645, pruned_loss=0.1338, over 1257980.71 frames.], batch size: 52, lr: 1.53e-03