2022-05-26 10:46:41,638 INFO [train.py:906] (2/4) Training started
2022-05-26 10:46:41,638 INFO [train.py:916] (2/4) Device: cuda:2
2022-05-26 10:46:41,640 INFO [train.py:934] (2/4) {'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 10:46:41,640 INFO [train.py:936] (2/4) About to create model
2022-05-26 10:46:42,066 INFO [train.py:940] (2/4) Number of model parameters: 78648040
2022-05-26 10:46:47,122 INFO [train.py:955] (2/4) Using DDP
2022-05-26 10:46:47,469 INFO [asr_datamodule.py:391] (2/4) About to get train-clean-100 cuts
2022-05-26 10:46:54,115 INFO [asr_datamodule.py:398] (2/4) About to get train-clean-360 cuts
2022-05-26 10:47:21,042 INFO [asr_datamodule.py:405] (2/4) About to get train-other-500 cuts
2022-05-26 10:48:06,960 INFO [asr_datamodule.py:209] (2/4) Enable MUSAN
2022-05-26 10:48:06,960 INFO [asr_datamodule.py:210] (2/4) About to get Musan cuts
2022-05-26 10:48:08,394 INFO [asr_datamodule.py:238] (2/4) Enable SpecAugment
2022-05-26 10:48:08,394 INFO [asr_datamodule.py:239] (2/4) Time warp factor: 80
2022-05-26 10:48:08,394 INFO [asr_datamodule.py:251] (2/4) Num frame mask: 10
2022-05-26 10:48:08,394 INFO [asr_datamodule.py:264] (2/4) About to create train dataset
2022-05-26 10:48:08,394 INFO [asr_datamodule.py:292] (2/4) Using BucketingSampler.
2022-05-26 10:48:13,474 INFO [asr_datamodule.py:308] (2/4) About to create train dataloader
2022-05-26 10:48:13,475 INFO [asr_datamodule.py:412] (2/4) About to get dev-clean cuts
2022-05-26 10:48:13,757 INFO [asr_datamodule.py:417] (2/4) About to get dev-other cuts
2022-05-26 10:48:13,887 INFO [asr_datamodule.py:339] (2/4) About to create dev dataset
2022-05-26 10:48:13,897 INFO [asr_datamodule.py:358] (2/4) About to create dev dataloader
2022-05-26 10:48:13,898 INFO [train.py:1082] (2/4) Sanity check -- see if any of the batches in epoch 1 would cause OOM.
2022-05-26 10:48:23,784 INFO [distributed.py:874] (2/4) Reducer buckets have been rebuilt in this iteration.
2022-05-26 10:48:40,736 INFO [train.py:842] (2/4) Epoch 1, batch 0, loss[loss=0.8248, simple_loss=1.65, pruned_loss=6.655, over 7296.00 frames.], tot_loss[loss=0.8248, simple_loss=1.65, pruned_loss=6.655, over 7296.00 frames.], batch size: 17, lr: 3.00e-03
2022-05-26 10:49:19,665 INFO [train.py:842] (2/4) Epoch 1, batch 50, loss[loss=0.5201, simple_loss=1.04, pruned_loss=6.985, over 7158.00 frames.], tot_loss[loss=0.5651, simple_loss=1.13, pruned_loss=7.1, over 323665.70 frames.], batch size: 19, lr: 3.00e-03
2022-05-26 10:49:59,137 INFO [train.py:842] (2/4) Epoch 1, batch 100, loss[loss=0.42, simple_loss=0.8399, pruned_loss=6.779, over 6991.00 frames.], tot_loss[loss=0.5057, simple_loss=1.011, pruned_loss=7.008, over 566611.93 frames.], batch size: 16, lr: 3.00e-03
2022-05-26 10:50:37,765 INFO [train.py:842] (2/4) Epoch 1, batch 150, loss[loss=0.3748, simple_loss=0.7496, pruned_loss=6.754, over 6984.00 frames.], tot_loss[loss=0.4738, simple_loss=0.9476, pruned_loss=6.939, over 757952.40 frames.], batch size: 16, lr: 3.00e-03
2022-05-26 10:51:16,823 INFO [train.py:842] (2/4) Epoch 1, batch 200, loss[loss=0.4422, simple_loss=0.8844, pruned_loss=6.828, over 7280.00 frames.], tot_loss[loss=0.4518, simple_loss=0.9035, pruned_loss=6.888, over 907906.25 frames.], batch size: 25, lr: 3.00e-03
2022-05-26 10:51:55,426 INFO [train.py:842] (2/4) Epoch 1, batch 250, loss[loss=0.4296, simple_loss=0.8591, pruned_loss=6.756, over 7329.00 frames.], tot_loss[loss=0.4388, simple_loss=0.8776, pruned_loss=6.835, over 1017359.64 frames.], batch size: 21, lr: 3.00e-03
2022-05-26 10:52:34,296 INFO [train.py:842] (2/4) Epoch 1, batch 300, loss[loss=0.4437, simple_loss=0.8875, pruned_loss=6.784, over 7297.00 frames.], tot_loss[loss=0.4283, simple_loss=0.8566, pruned_loss=6.796, over 1109521.62 frames.], batch size: 25, lr: 3.00e-03
2022-05-26 10:53:13,228 INFO [train.py:842] (2/4) Epoch 1, batch 350, loss[loss=0.3849, simple_loss=0.7699, pruned_loss=6.683, over 7259.00 frames.], tot_loss[loss=0.4193, simple_loss=0.8386, pruned_loss=6.763, over 1179464.43 frames.], batch size: 19, lr: 3.00e-03
2022-05-26 10:53:52,160 INFO [train.py:842] (2/4) Epoch 1, batch 400, loss[loss=0.4318, simple_loss=0.8636, pruned_loss=6.788, over 7419.00 frames.], tot_loss[loss=0.4136, simple_loss=0.8272, pruned_loss=6.749, over 1231809.80 frames.], batch size: 21, lr: 3.00e-03
2022-05-26 10:54:30,788 INFO [train.py:842] (2/4) Epoch 1, batch 450, loss[loss=0.4083, simple_loss=0.8165, pruned_loss=6.766, over 7403.00 frames.], tot_loss[loss=0.4076, simple_loss=0.8151, pruned_loss=6.73, over 1267309.64 frames.], batch size: 21, lr: 2.99e-03
2022-05-26 10:55:09,750 INFO [train.py:842] (2/4) Epoch 1, batch 500, loss[loss=0.3701, simple_loss=0.7402, pruned_loss=6.599, over 7205.00 frames.], tot_loss[loss=0.4011, simple_loss=0.8022, pruned_loss=6.711, over 1303382.45 frames.], batch size: 22, lr: 2.99e-03
2022-05-26 10:55:48,092 INFO [train.py:842] (2/4) Epoch 1, batch 550, loss[loss=0.3862, simple_loss=0.7724, pruned_loss=6.745, over 7339.00 frames.], tot_loss[loss=0.3943, simple_loss=0.7886, pruned_loss=6.702, over 1329380.89 frames.], batch size: 22, lr: 2.99e-03
2022-05-26 10:56:27,037 INFO [train.py:842] (2/4) Epoch 1, batch 600, loss[loss=0.3397, simple_loss=0.6794, pruned_loss=6.669, over 7111.00 frames.], tot_loss[loss=0.3836, simple_loss=0.7673, pruned_loss=6.694, over 1350918.06 frames.], batch size: 21, lr: 2.99e-03
2022-05-26 10:57:05,638 INFO [train.py:842] (2/4) Epoch 1, batch 650, loss[loss=0.2636, simple_loss=0.5272, pruned_loss=6.613, over 7003.00 frames.], tot_loss[loss=0.3718, simple_loss=0.7437, pruned_loss=6.7, over 1369206.61 frames.], batch size: 16, lr: 2.99e-03
2022-05-26 10:57:44,556 INFO [train.py:842] (2/4) Epoch 1, batch 700, loss[loss=0.3144, simple_loss=0.6287, pruned_loss=6.722, over 7213.00 frames.], tot_loss[loss=0.3588, simple_loss=0.7177, pruned_loss=6.695, over 1380923.28 frames.], batch size: 23, lr: 2.99e-03
2022-05-26 10:58:23,539 INFO [train.py:842] (2/4) Epoch 1, batch 750, loss[loss=0.2523, simple_loss=0.5045, pruned_loss=6.543, over 7282.00 frames.], tot_loss[loss=0.3463, simple_loss=0.6926, pruned_loss=6.694, over 1392198.14 frames.], batch size: 17, lr: 2.98e-03
2022-05-26 10:59:02,466 INFO [train.py:842] (2/4) Epoch 1, batch 800, loss[loss=0.3401, simple_loss=0.6802, pruned_loss=6.745, over 7124.00 frames.], tot_loss[loss=0.3357, simple_loss=0.6713, pruned_loss=6.704, over 1397496.29 frames.], batch size: 21, lr: 2.98e-03
2022-05-26 10:59:41,342 INFO [train.py:842] (2/4) Epoch 1, batch 850, loss[loss=0.2799, simple_loss=0.5597, pruned_loss=6.837, over 7224.00 frames.], tot_loss[loss=0.3249, simple_loss=0.6499, pruned_loss=6.71, over 1402422.36 frames.], batch size: 21, lr: 2.98e-03
2022-05-26 11:00:20,374 INFO [train.py:842] (2/4) Epoch 1, batch 900, loss[loss=0.3084, simple_loss=0.6167, pruned_loss=6.747, over 7326.00 frames.], tot_loss[loss=0.315, simple_loss=0.6299, pruned_loss=6.711, over 1406765.04 frames.], batch size: 21, lr: 2.98e-03
2022-05-26 11:00:58,853 INFO [train.py:842] (2/4) Epoch 1, batch 950, loss[loss=0.2329, simple_loss=0.4657, pruned_loss=6.582, over 7006.00 frames.], tot_loss[loss=0.3069, simple_loss=0.6138, pruned_loss=6.715, over 1404077.12 frames.], batch size: 16, lr: 2.97e-03
2022-05-26 11:01:37,553 INFO [train.py:842] (2/4) Epoch 1, batch 1000, loss[loss=0.2388, simple_loss=0.4776, pruned_loss=6.622, over 6989.00 frames.], tot_loss[loss=0.2999, simple_loss=0.5998, pruned_loss=6.72, over 1404548.90 frames.], batch size: 16, lr: 2.97e-03
2022-05-26 11:02:16,197 INFO [train.py:842] (2/4) Epoch 1, batch 1050, loss[loss=0.2183, simple_loss=0.4366, pruned_loss=6.549, over 7013.00 frames.], tot_loss[loss=0.2946, simple_loss=0.5892, pruned_loss=6.727, over 1406660.06 frames.], batch size: 16, lr: 2.97e-03
2022-05-26 11:02:54,892 INFO [train.py:842] (2/4) Epoch 1, batch 1100, loss[loss=0.2668, simple_loss=0.5337, pruned_loss=6.783, over 7201.00 frames.], tot_loss[loss=0.2894, simple_loss=0.5789, pruned_loss=6.729, over 1411370.98 frames.], batch size: 22, lr: 2.96e-03
2022-05-26 11:03:33,473 INFO [train.py:842] (2/4) Epoch 1, batch 1150, loss[loss=0.3004, simple_loss=0.6007, pruned_loss=6.949, over 6700.00 frames.], tot_loss[loss=0.2838, simple_loss=0.5676, pruned_loss=6.734, over 1411338.54 frames.], batch size: 31, lr: 2.96e-03
2022-05-26 11:04:12,438 INFO [train.py:842] (2/4) Epoch 1, batch 1200, loss[loss=0.2608, simple_loss=0.5216, pruned_loss=6.762, over 7150.00 frames.], tot_loss[loss=0.2772, simple_loss=0.5545, pruned_loss=6.737, over 1419044.69 frames.], batch size: 26, lr: 2.96e-03
2022-05-26 11:04:50,803 INFO [train.py:842] (2/4) Epoch 1, batch 1250, loss[loss=0.2722, simple_loss=0.5445, pruned_loss=6.888, over 7382.00 frames.], tot_loss[loss=0.2728, simple_loss=0.5456, pruned_loss=6.739, over 1412427.30 frames.], batch size: 23, lr: 2.95e-03
2022-05-26 11:05:29,853 INFO [train.py:842] (2/4) Epoch 1, batch 1300, loss[loss=0.2779, simple_loss=0.5557, pruned_loss=6.897, over 7305.00 frames.], tot_loss[loss=0.2687, simple_loss=0.5375, pruned_loss=6.746, over 1420872.13 frames.], batch size: 24, lr: 2.95e-03
2022-05-26 11:06:08,501 INFO [train.py:842] (2/4) Epoch 1, batch 1350, loss[loss=0.2744, simple_loss=0.5487, pruned_loss=6.858, over 7144.00 frames.], tot_loss[loss=0.265, simple_loss=0.5301, pruned_loss=6.753, over 1422167.50 frames.], batch size: 20, lr: 2.95e-03
2022-05-26 11:06:47,143 INFO [train.py:842] (2/4) Epoch 1, batch 1400, loss[loss=0.2678, simple_loss=0.5357, pruned_loss=6.963, over 7294.00 frames.], tot_loss[loss=0.2625, simple_loss=0.5251, pruned_loss=6.759, over 1418838.73 frames.], batch size: 24, lr: 2.94e-03
2022-05-26 11:07:25,757 INFO [train.py:842] (2/4) Epoch 1, batch 1450, loss[loss=0.2156, simple_loss=0.4311, pruned_loss=6.581, over 7137.00 frames.], tot_loss[loss=0.2585, simple_loss=0.5169, pruned_loss=6.76, over 1419909.96 frames.], batch size: 17, lr: 2.94e-03
2022-05-26 11:08:04,594 INFO [train.py:842] (2/4) Epoch 1, batch 1500, loss[loss=0.258, simple_loss=0.516, pruned_loss=6.904, over 7314.00 frames.], tot_loss[loss=0.2548, simple_loss=0.5096, pruned_loss=6.769, over 1422812.09 frames.], batch size: 24, lr: 2.94e-03
2022-05-26 11:08:43,069 INFO [train.py:842] (2/4) Epoch 1, batch 1550, loss[loss=0.2549, simple_loss=0.5099, pruned_loss=6.893, over 7121.00 frames.], tot_loss[loss=0.2513, simple_loss=0.5027, pruned_loss=6.774, over 1422967.57 frames.], batch size: 21, lr: 2.93e-03
2022-05-26 11:09:22,185 INFO [train.py:842] (2/4) Epoch 1, batch 1600, loss[loss=0.2558, simple_loss=0.5115, pruned_loss=6.877, over 7329.00 frames.], tot_loss[loss=0.249, simple_loss=0.4981, pruned_loss=6.778, over 1419802.00 frames.], batch size: 20, lr: 2.93e-03
2022-05-26 11:10:01,420 INFO [train.py:842] (2/4) Epoch 1, batch 1650, loss[loss=0.2365, simple_loss=0.4731, pruned_loss=6.784, over 7161.00 frames.], tot_loss[loss=0.2459, simple_loss=0.4918, pruned_loss=6.781, over 1421656.08 frames.], batch size: 18, lr: 2.92e-03
2022-05-26 11:10:40,812 INFO [train.py:842] (2/4) Epoch 1, batch 1700, loss[loss=0.2473, simple_loss=0.4947, pruned_loss=6.874, over 6407.00 frames.], tot_loss[loss=0.2438, simple_loss=0.4876, pruned_loss=6.785, over 1416713.61 frames.], batch size: 38, lr: 2.92e-03
2022-05-26 11:11:19,945 INFO [train.py:842] (2/4) Epoch 1, batch 1750, loss[loss=0.24, simple_loss=0.48, pruned_loss=6.758, over 6418.00 frames.], tot_loss[loss=0.2407, simple_loss=0.4813, pruned_loss=6.784, over 1416764.15 frames.], batch size: 37, lr: 2.91e-03
2022-05-26 11:11:59,997 INFO [train.py:842] (2/4) Epoch 1, batch 1800, loss[loss=0.2453, simple_loss=0.4906, pruned_loss=6.752, over 7094.00 frames.], tot_loss[loss=0.2404, simple_loss=0.4808, pruned_loss=6.795, over 1418116.81 frames.], batch size: 28, lr: 2.91e-03
2022-05-26 11:12:39,024 INFO [train.py:842] (2/4) Epoch 1, batch 1850, loss[loss=0.2649, simple_loss=0.5298, pruned_loss=6.855, over 5086.00 frames.], tot_loss[loss=0.2371, simple_loss=0.4741, pruned_loss=6.792, over 1419976.86 frames.], batch size: 53, lr: 2.91e-03
2022-05-26 11:13:18,082 INFO [train.py:842] (2/4) Epoch 1, batch 1900, loss[loss=0.2344, simple_loss=0.4689, pruned_loss=6.867, over 7259.00 frames.], tot_loss[loss=0.2351, simple_loss=0.4702, pruned_loss=6.795, over 1420727.07 frames.], batch size: 19, lr: 2.90e-03
2022-05-26 11:13:56,904 INFO [train.py:842] (2/4) Epoch 1, batch 1950, loss[loss=0.1967, simple_loss=0.3934, pruned_loss=6.724, over 7323.00 frames.], tot_loss[loss=0.2337, simple_loss=0.4674, pruned_loss=6.79, over 1423718.24 frames.], batch size: 21, lr: 2.90e-03
2022-05-26 11:14:35,959 INFO [train.py:842] (2/4) Epoch 1, batch 2000, loss[loss=0.191, simple_loss=0.382, pruned_loss=6.708, over 6797.00 frames.], tot_loss[loss=0.2322, simple_loss=0.4643, pruned_loss=6.79, over 1424582.70 frames.], batch size: 15, lr: 2.89e-03
2022-05-26 11:15:15,093 INFO [train.py:842] (2/4) Epoch 1, batch 2050, loss[loss=0.2507, simple_loss=0.5015, pruned_loss=6.918, over 7178.00 frames.], tot_loss[loss=0.2301, simple_loss=0.4602, pruned_loss=6.785, over 1422997.05 frames.], batch size: 26, lr: 2.89e-03
2022-05-26 11:15:53,865 INFO [train.py:842] (2/4) Epoch 1, batch 2100, loss[loss=0.1961, simple_loss=0.3922, pruned_loss=6.636, over 7177.00 frames.], tot_loss[loss=0.2301, simple_loss=0.4601, pruned_loss=6.79, over 1420028.81 frames.], batch size: 18, lr: 2.88e-03
2022-05-26 11:16:32,684 INFO [train.py:842] (2/4) Epoch 1, batch 2150, loss[loss=0.2443, simple_loss=0.4886, pruned_loss=6.93, over 7340.00 frames.], tot_loss[loss=0.2295, simple_loss=0.459, pruned_loss=6.794, over 1424040.53 frames.], batch size: 22, lr: 2.88e-03
2022-05-26 11:17:11,542 INFO [train.py:842] (2/4) Epoch 1, batch 2200, loss[loss=0.2319, simple_loss=0.4639, pruned_loss=6.914, over 7279.00 frames.], tot_loss[loss=0.2284, simple_loss=0.4568, pruned_loss=6.789, over 1422581.30 frames.], batch size: 25, lr: 2.87e-03
2022-05-26 11:17:50,098 INFO [train.py:842] (2/4) Epoch 1, batch 2250, loss[loss=0.229, simple_loss=0.4581, pruned_loss=6.81, over 7227.00 frames.], tot_loss[loss=0.2272, simple_loss=0.4544, pruned_loss=6.79, over 1420939.34 frames.], batch size: 21, lr: 2.86e-03
2022-05-26 11:18:28,790 INFO [train.py:842] (2/4) Epoch 1, batch 2300, loss[loss=0.2035, simple_loss=0.407, pruned_loss=6.702, over 7259.00 frames.], tot_loss[loss=0.2254, simple_loss=0.4508, pruned_loss=6.792, over 1415830.36 frames.], batch size: 19, lr: 2.86e-03
2022-05-26 11:19:07,807 INFO [train.py:842] (2/4) Epoch 1, batch 2350, loss[loss=0.2182, simple_loss=0.4363, pruned_loss=6.762, over 4835.00 frames.], tot_loss[loss=0.2254, simple_loss=0.4509, pruned_loss=6.797, over 1415632.41 frames.], batch size: 53, lr: 2.85e-03
2022-05-26 11:19:47,045 INFO [train.py:842] (2/4) Epoch 1, batch 2400, loss[loss=0.2052, simple_loss=0.4104, pruned_loss=6.807, over 7434.00 frames.], tot_loss[loss=0.225, simple_loss=0.45, pruned_loss=6.801, over 1412969.60 frames.], batch size: 20, lr: 2.85e-03
2022-05-26 11:20:25,501 INFO [train.py:842] (2/4) Epoch 1, batch 2450, loss[loss=0.232, simple_loss=0.464, pruned_loss=6.853, over 5060.00 frames.], tot_loss[loss=0.2232, simple_loss=0.4463, pruned_loss=6.797, over 1413769.64 frames.], batch size: 53, lr: 2.84e-03
2022-05-26 11:21:04,495 INFO [train.py:842] (2/4) Epoch 1, batch 2500, loss[loss=0.1956, simple_loss=0.3911, pruned_loss=6.772, over 7334.00 frames.], tot_loss[loss=0.2215, simple_loss=0.4429, pruned_loss=6.797, over 1418694.44 frames.], batch size: 20, lr: 2.84e-03
2022-05-26 11:21:42,877 INFO [train.py:842] (2/4) Epoch 1, batch 2550, loss[loss=0.1761, simple_loss=0.3522, pruned_loss=6.71, over 7412.00 frames.], tot_loss[loss=0.2218, simple_loss=0.4437, pruned_loss=6.801, over 1419095.51 frames.], batch size: 18, lr: 2.83e-03
2022-05-26 11:22:21,926 INFO [train.py:842] (2/4) Epoch 1, batch 2600, loss[loss=0.2275, simple_loss=0.4549, pruned_loss=6.861, over 7239.00 frames.], tot_loss[loss=0.2198, simple_loss=0.4395, pruned_loss=6.794, over 1421810.49 frames.], batch size: 20, lr: 2.83e-03
2022-05-26 11:23:00,462 INFO [train.py:842] (2/4) Epoch 1, batch 2650, loss[loss=0.2001, simple_loss=0.4003, pruned_loss=6.794, over 7240.00 frames.], tot_loss[loss=0.2185, simple_loss=0.4371, pruned_loss=6.788, over 1423171.18 frames.], batch size: 20, lr: 2.82e-03
2022-05-26 11:23:39,473 INFO [train.py:842] (2/4) Epoch 1, batch 2700, loss[loss=0.2247, simple_loss=0.4494, pruned_loss=6.851, over 7140.00 frames.], tot_loss[loss=0.217, simple_loss=0.4341, pruned_loss=6.782, over 1422542.92 frames.], batch size: 20, lr: 2.81e-03
2022-05-26 11:24:17,967 INFO [train.py:842] (2/4) Epoch 1, batch 2750, loss[loss=0.2106, simple_loss=0.4212, pruned_loss=6.831, over 7314.00 frames.], tot_loss[loss=0.2167, simple_loss=0.4334, pruned_loss=6.785, over 1423912.32 frames.], batch size: 20, lr: 2.81e-03
2022-05-26 11:24:56,712 INFO [train.py:842] (2/4) Epoch 1, batch 2800, loss[loss=0.2194, simple_loss=0.4389, pruned_loss=6.848, over 7135.00 frames.], tot_loss[loss=0.2164, simple_loss=0.4328, pruned_loss=6.786, over 1423509.39 frames.], batch size: 20, lr: 2.80e-03
2022-05-26 11:25:35,290 INFO [train.py:842] (2/4) Epoch 1, batch 2850, loss[loss=0.2198, simple_loss=0.4396, pruned_loss=6.814, over 7352.00 frames.], tot_loss[loss=0.2173, simple_loss=0.4346, pruned_loss=6.791, over 1426374.86 frames.], batch size: 19, lr: 2.80e-03
2022-05-26 11:26:13,795 INFO [train.py:842] (2/4) Epoch 1, batch 2900, loss[loss=0.2042, simple_loss=0.4084, pruned_loss=6.827, over 7322.00 frames.], tot_loss[loss=0.217, simple_loss=0.4341, pruned_loss=6.795, over 1422056.82 frames.], batch size: 20, lr: 2.79e-03
2022-05-26 11:26:52,582 INFO [train.py:842] (2/4) Epoch 1, batch 2950, loss[loss=0.226, simple_loss=0.4519, pruned_loss=6.99, over 7191.00 frames.], tot_loss[loss=0.2162, simple_loss=0.4323, pruned_loss=6.798, over 1417863.13 frames.], batch size: 26, lr: 2.78e-03
2022-05-26 11:27:31,327 INFO [train.py:842] (2/4) Epoch 1, batch 3000, loss[loss=0.3873, simple_loss=0.4302, pruned_loss=1.722, over 7292.00 frames.], tot_loss[loss=0.2498, simple_loss=0.431, pruned_loss=6.77, over 1421667.91 frames.], batch size: 17, lr: 2.78e-03
2022-05-26 11:27:31,327 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 11:27:40,551 INFO [train.py:871] (2/4) Epoch 1, validation: loss=2.017, simple_loss=0.4861, pruned_loss=1.774, over 868885.00 frames.
2022-05-26 11:28:19,122 INFO [train.py:842] (2/4) Epoch 1, batch 3050, loss[loss=0.3568, simple_loss=0.4963, pruned_loss=1.087, over 6130.00 frames.], tot_loss[loss=0.2765, simple_loss=0.4409, pruned_loss=5.566, over 1420587.10 frames.], batch size: 37, lr: 2.77e-03
2022-05-26 11:28:58,769 INFO [train.py:842] (2/4) Epoch 1, batch 3100, loss[loss=0.2964, simple_loss=0.4422, pruned_loss=0.7533, over 7413.00 frames.], tot_loss[loss=0.2812, simple_loss=0.4367, pruned_loss=4.509, over 1426104.06 frames.], batch size: 21, lr: 2.77e-03
2022-05-26 11:29:37,544 INFO [train.py:842] (2/4) Epoch 1, batch 3150, loss[loss=0.2484, simple_loss=0.4037, pruned_loss=0.465, over 7411.00 frames.], tot_loss[loss=0.2788, simple_loss=0.4334, pruned_loss=3.639, over 1427799.32 frames.], batch size: 21, lr: 2.76e-03
2022-05-26 11:30:16,510 INFO [train.py:842] (2/4) Epoch 1, batch 3200, loss[loss=0.2486, simple_loss=0.4195, pruned_loss=0.3889, over 7301.00 frames.], tot_loss[loss=0.2738, simple_loss=0.4313, pruned_loss=2.937, over 1423403.49 frames.], batch size: 24, lr: 2.75e-03
2022-05-26 11:30:54,979 INFO [train.py:842] (2/4) Epoch 1, batch 3250, loss[loss=0.2567, simple_loss=0.4454, pruned_loss=0.3397, over 7148.00 frames.], tot_loss[loss=0.2679, simple_loss=0.4295, pruned_loss=2.365, over 1423510.86 frames.], batch size: 20, lr: 2.75e-03
2022-05-26 11:31:34,094 INFO [train.py:842] (2/4) Epoch 1, batch 3300, loss[loss=0.2626, simple_loss=0.4595, pruned_loss=0.328, over 7372.00 frames.], tot_loss[loss=0.2629, simple_loss=0.4285, pruned_loss=1.918, over 1419459.19 frames.], batch size: 23, lr: 2.74e-03
2022-05-26 11:32:12,602 INFO [train.py:842] (2/4) Epoch 1, batch 3350, loss[loss=0.2698, simple_loss=0.4717, pruned_loss=0.3394, over 7294.00 frames.], tot_loss[loss=0.2567, simple_loss=0.4251, pruned_loss=1.553, over 1423574.37 frames.], batch size: 24, lr: 2.73e-03
2022-05-26 11:32:51,525 INFO [train.py:842] (2/4) Epoch 1, batch 3400, loss[loss=0.2087, simple_loss=0.3699, pruned_loss=0.2374, over 7260.00 frames.], tot_loss[loss=0.2526, simple_loss=0.424, pruned_loss=1.27, over 1424044.75 frames.], batch size: 19, lr: 2.73e-03
2022-05-26 11:33:30,151 INFO [train.py:842] (2/4) Epoch 1, batch 3450, loss[loss=0.2457, simple_loss=0.4339, pruned_loss=0.2874, over 7289.00 frames.], tot_loss[loss=0.2485, simple_loss=0.4218, pruned_loss=1.048, over 1423531.84 frames.], batch size: 25, lr: 2.72e-03
2022-05-26 11:34:09,044 INFO [train.py:842] (2/4) Epoch 1, batch 3500, loss[loss=0.2901, simple_loss=0.4976, pruned_loss=0.4131, over 7125.00 frames.], tot_loss[loss=0.2468, simple_loss=0.4229, pruned_loss=0.8772, over 1422349.50 frames.], batch size: 26, lr: 2.72e-03
2022-05-26 11:34:47,674 INFO [train.py:842] (2/4) Epoch 1, batch 3550, loss[loss=0.2431, simple_loss=0.4347, pruned_loss=0.2579, over 7224.00 frames.], tot_loss[loss=0.2435, simple_loss=0.4206, pruned_loss=0.7393, over 1422910.57 frames.], batch size: 21, lr: 2.71e-03
2022-05-26 11:35:26,427 INFO [train.py:842] (2/4) Epoch 1, batch 3600, loss[loss=0.193, simple_loss=0.351, pruned_loss=0.1747, over 7008.00 frames.], tot_loss[loss=0.2407, simple_loss=0.4188, pruned_loss=0.6305, over 1422346.04 frames.], batch size: 16, lr: 2.70e-03
2022-05-26 11:36:05,092 INFO [train.py:842] (2/4) Epoch 1, batch 3650, loss[loss=0.234, simple_loss=0.4186, pruned_loss=0.247, over 7216.00 frames.], tot_loss[loss=0.2378, simple_loss=0.4165, pruned_loss=0.5429, over 1422813.07 frames.], batch size: 21, lr: 2.70e-03
2022-05-26 11:36:43,948 INFO [train.py:842] (2/4) Epoch 1, batch 3700, loss[loss=0.2487, simple_loss=0.4441, pruned_loss=0.2662, over 6811.00 frames.], tot_loss[loss=0.2354, simple_loss=0.4144, pruned_loss=0.4737, over 1427055.56 frames.], batch size: 31, lr: 2.69e-03
2022-05-26 11:37:22,472 INFO [train.py:842] (2/4) Epoch 1, batch 3750, loss[loss=0.1855, simple_loss=0.3371, pruned_loss=0.1694, over 7294.00 frames.], tot_loss[loss=0.2328, simple_loss=0.4115, pruned_loss=0.4207, over 1419475.97 frames.], batch size: 18, lr: 2.68e-03
2022-05-26 11:38:01,292 INFO [train.py:842] (2/4) Epoch 1, batch 3800, loss[loss=0.1918, simple_loss=0.3482, pruned_loss=0.1768, over 7128.00 frames.], tot_loss[loss=0.2317, simple_loss=0.411, pruned_loss=0.3791, over 1418033.93 frames.], batch size: 17, lr: 2.68e-03
2022-05-26 11:38:40,179 INFO [train.py:842] (2/4) Epoch 1, batch 3850, loss[loss=0.172, simple_loss=0.315, pruned_loss=0.1449, over 7134.00 frames.], tot_loss[loss=0.2297, simple_loss=0.4088, pruned_loss=0.3436, over 1423884.39 frames.], batch size: 17, lr: 2.67e-03
2022-05-26 11:39:18,907 INFO [train.py:842] (2/4) Epoch 1, batch 3900, loss[loss=0.1986, simple_loss=0.3575, pruned_loss=0.198, over 7218.00 frames.], tot_loss[loss=0.2296, simple_loss=0.4096, pruned_loss=0.3188, over 1421326.64 frames.], batch size: 16, lr: 2.66e-03
2022-05-26 11:39:57,435 INFO [train.py:842] (2/4) Epoch 1, batch 3950, loss[loss=0.3016, simple_loss=0.5338, pruned_loss=0.3474, over 6783.00 frames.], tot_loss[loss=0.2288, simple_loss=0.409, pruned_loss=0.2978, over 1420693.21 frames.], batch size: 31, lr: 2.66e-03
2022-05-26 11:40:36,019 INFO [train.py:842] (2/4) Epoch 1, batch 4000, loss[loss=0.223, simple_loss=0.4038, pruned_loss=0.211, over 7154.00 frames.], tot_loss[loss=0.2289, simple_loss=0.4098, pruned_loss=0.2831, over 1420513.51 frames.], batch size: 26, lr: 2.65e-03
2022-05-26 11:41:14,456 INFO [train.py:842] (2/4) Epoch 1, batch 4050, loss[loss=0.2468, simple_loss=0.4418, pruned_loss=0.2596, over 5187.00 frames.], tot_loss[loss=0.2277, simple_loss=0.4084, pruned_loss=0.2685, over 1422527.06 frames.], batch size: 54, lr: 2.64e-03
2022-05-26 11:41:53,377 INFO [train.py:842] (2/4) Epoch 1, batch 4100, loss[loss=0.2243, simple_loss=0.4046, pruned_loss=0.2204, over 6199.00 frames.], tot_loss[loss=0.2269, simple_loss=0.4075, pruned_loss=0.2571, over 1420728.81 frames.], batch size: 37, lr: 2.64e-03
2022-05-26 11:42:32,078 INFO [train.py:842] (2/4) Epoch 1, batch 4150, loss[loss=0.2117, simple_loss=0.3847, pruned_loss=0.1931, over 7433.00 frames.], tot_loss[loss=0.2262, simple_loss=0.4069, pruned_loss=0.2479, over 1424754.76 frames.], batch size: 20, lr: 2.63e-03
2022-05-26 11:43:10,840 INFO [train.py:842] (2/4) Epoch 1, batch 4200, loss[loss=0.2257, simple_loss=0.4091, pruned_loss=0.212, over 7319.00 frames.], tot_loss[loss=0.2261, simple_loss=0.4071, pruned_loss=0.2411, over 1428402.17 frames.], batch size: 21, lr: 2.63e-03
2022-05-26 11:43:49,375 INFO [train.py:842] (2/4) Epoch 1, batch 4250, loss[loss=0.2364, simple_loss=0.424, pruned_loss=0.2438, over 7143.00 frames.], tot_loss[loss=0.2254, simple_loss=0.4062, pruned_loss=0.235, over 1427554.97 frames.], batch size: 20, lr: 2.62e-03
2022-05-26 11:44:28,192 INFO [train.py:842] (2/4) Epoch 1, batch 4300, loss[loss=0.2225, simple_loss=0.4056, pruned_loss=0.1969, over 7200.00 frames.], tot_loss[loss=0.2244, simple_loss=0.4048, pruned_loss=0.2298, over 1425465.73 frames.], batch size: 22, lr: 2.61e-03
2022-05-26 11:45:06,707 INFO [train.py:842] (2/4) Epoch 1, batch 4350, loss[loss=0.2026, simple_loss=0.3705, pruned_loss=0.1735, over 7156.00 frames.], tot_loss[loss=0.2237, simple_loss=0.4038, pruned_loss=0.2258, over 1427193.85 frames.], batch size: 19, lr: 2.61e-03
2022-05-26 11:45:45,337 INFO [train.py:842] (2/4) Epoch 1, batch 4400, loss[loss=0.2409, simple_loss=0.437, pruned_loss=0.2243, over 7227.00 frames.], tot_loss[loss=0.2247, simple_loss=0.4057, pruned_loss=0.224, over 1427697.07 frames.], batch size: 21, lr: 2.60e-03
2022-05-26 11:46:23,886 INFO [train.py:842] (2/4) Epoch 1, batch 4450, loss[loss=0.2199, simple_loss=0.4006, pruned_loss=0.1955, over 7164.00 frames.], tot_loss[loss=0.2242, simple_loss=0.405, pruned_loss=0.2212, over 1429330.38 frames.], batch size: 19, lr: 2.59e-03
2022-05-26 11:47:02,770 INFO [train.py:842] (2/4) Epoch 1, batch 4500, loss[loss=0.2297, simple_loss=0.4143, pruned_loss=0.2251, over 7258.00 frames.], tot_loss[loss=0.2246, simple_loss=0.406, pruned_loss=0.2191, over 1431693.56 frames.], batch size: 19, lr: 2.59e-03
2022-05-26 11:47:41,422 INFO [train.py:842] (2/4) Epoch 1, batch 4550, loss[loss=0.221, simple_loss=0.3989, pruned_loss=0.2155, over 7075.00 frames.], tot_loss[loss=0.2239, simple_loss=0.4049, pruned_loss=0.2171, over 1429882.31 frames.], batch size: 18, lr: 2.58e-03
2022-05-26 11:48:20,132 INFO [train.py:842] (2/4) Epoch 1, batch 4600, loss[loss=0.1954, simple_loss=0.3568, pruned_loss=0.1696, over 7261.00 frames.], tot_loss[loss=0.2234, simple_loss=0.4043, pruned_loss=0.2148, over 1429543.28 frames.], batch size: 19, lr: 2.57e-03
2022-05-26 11:48:58,606 INFO [train.py:842] (2/4) Epoch 1, batch 4650, loss[loss=0.2041, simple_loss=0.3717, pruned_loss=0.1823, over 7078.00 frames.], tot_loss[loss=0.2222, simple_loss=0.4025, pruned_loss=0.211, over 1430094.05 frames.], batch size: 28, lr: 2.57e-03
2022-05-26 11:49:37,488 INFO [train.py:842] (2/4) Epoch 1, batch 4700, loss[loss=0.1791, simple_loss=0.3273, pruned_loss=0.1541, over 7282.00 frames.], tot_loss[loss=0.221, simple_loss=0.4004, pruned_loss=0.2086, over 1429675.91 frames.], batch size: 17, lr: 2.56e-03
2022-05-26 11:50:15,957 INFO [train.py:842] (2/4) Epoch 1, batch 4750, loss[loss=0.2795, simple_loss=0.4977, pruned_loss=0.306, over 4912.00 frames.], tot_loss[loss=0.2222, simple_loss=0.4027, pruned_loss=0.2096, over 1427308.10 frames.], batch size: 52, lr: 2.55e-03
2022-05-26 11:50:54,731 INFO [train.py:842] (2/4) Epoch 1, batch 4800, loss[loss=0.2358, simple_loss=0.4244, pruned_loss=0.2362, over 7447.00 frames.], tot_loss[loss=0.2225, simple_loss=0.4033, pruned_loss=0.2091, over 1429058.40 frames.], batch size: 20, lr: 2.55e-03
2022-05-26 11:51:33,198 INFO [train.py:842] (2/4) Epoch 1, batch 4850, loss[loss=0.2201, simple_loss=0.4, pruned_loss=0.2007, over 7258.00 frames.], tot_loss[loss=0.2213, simple_loss=0.4015, pruned_loss=0.2066, over 1427047.52 frames.], batch size: 19, lr: 2.54e-03
2022-05-26 11:52:11,922 INFO [train.py:842] (2/4) Epoch 1, batch 4900, loss[loss=0.2089, simple_loss=0.3787, pruned_loss=0.1959, over 7333.00 frames.], tot_loss[loss=0.2206, simple_loss=0.4004, pruned_loss=0.2044, over 1427803.62 frames.], batch size: 20, lr: 2.54e-03
2022-05-26 11:52:50,284 INFO [train.py:842] (2/4) Epoch 1, batch 4950, loss[loss=0.2118, simple_loss=0.3859, pruned_loss=0.1881, over 7339.00 frames.], tot_loss[loss=0.2207, simple_loss=0.4008, pruned_loss=0.2037, over 1423015.08 frames.], batch size: 19, lr: 2.53e-03
2022-05-26 11:53:29,132 INFO [train.py:842] (2/4) Epoch 1, batch 5000, loss[loss=0.1995, simple_loss=0.3659, pruned_loss=0.1654, over 7335.00 frames.], tot_loss[loss=0.2212, simple_loss=0.4018, pruned_loss=0.2034, over 1422468.45 frames.], batch size: 22, lr: 2.52e-03
2022-05-26 11:54:07,521 INFO [train.py:842] (2/4) Epoch 1, batch 5050, loss[loss=0.2854, simple_loss=0.507, pruned_loss=0.3188, over 7315.00 frames.], tot_loss[loss=0.2202, simple_loss=0.4002, pruned_loss=0.2016, over 1422511.40 frames.], batch size: 21, lr: 2.52e-03
2022-05-26 11:54:46,114 INFO [train.py:842] (2/4) Epoch 1, batch 5100, loss[loss=0.2037, simple_loss=0.3767, pruned_loss=0.1532, over 7203.00 frames.], tot_loss[loss=0.2196, simple_loss=0.3992, pruned_loss=0.1998, over 1421079.19 frames.], batch size: 22, lr: 2.51e-03
2022-05-26 11:55:24,650 INFO [train.py:842] (2/4) Epoch 1, batch 5150, loss[loss=0.2074, simple_loss=0.3789, pruned_loss=0.18, over 7428.00 frames.], tot_loss[loss=0.2181, simple_loss=0.397, pruned_loss=0.1969, over 1422650.62 frames.], batch size: 20, lr: 2.50e-03
2022-05-26 11:56:03,338 INFO [train.py:842] (2/4) Epoch 1, batch 5200, loss[loss=0.2363, simple_loss=0.4287, pruned_loss=0.2196, over 7310.00 frames.], tot_loss[loss=0.22, simple_loss=0.4, pruned_loss=0.1996, over 1421798.29 frames.], batch size: 25, lr: 2.50e-03
2022-05-26 11:56:41,767 INFO [train.py:842] (2/4) Epoch 1, batch 5250, loss[loss=0.2516, simple_loss=0.4536, pruned_loss=0.2485, over 5106.00 frames.], tot_loss[loss=0.2185, simple_loss=0.3976, pruned_loss=0.1967, over 1420194.44 frames.], batch size: 52, lr: 2.49e-03
2022-05-26 11:57:20,422 INFO [train.py:842] (2/4) Epoch 1, batch 5300, loss[loss=0.2021, simple_loss=0.3671, pruned_loss=0.1855, over 7285.00 frames.], tot_loss[loss=0.2181, simple_loss=0.397, pruned_loss=0.196, over 1417855.72 frames.], batch size: 17, lr: 2.49e-03
2022-05-26 11:57:58,735 INFO [train.py:842] (2/4) Epoch 1, batch 5350, loss[loss=0.226, simple_loss=0.4112, pruned_loss=0.2046, over 7377.00 frames.], tot_loss[loss=0.2175, simple_loss=0.3961, pruned_loss=0.1944, over 1415488.02 frames.], batch size: 23, lr: 2.48e-03
2022-05-26 11:58:37,511 INFO [train.py:842] (2/4) Epoch 1, batch 5400, loss[loss=0.2339, simple_loss=0.4263, pruned_loss=0.2079, over 7121.00 frames.], tot_loss[loss=0.2176, simple_loss=0.3964, pruned_loss=0.1944, over 1420821.44 frames.], batch size: 28, lr: 2.47e-03
2022-05-26 11:59:16,009 INFO [train.py:842] (2/4) Epoch 1, batch 5450, loss[loss=0.2061, simple_loss=0.3788, pruned_loss=0.1675, over 7146.00 frames.], tot_loss[loss=0.2171, simple_loss=0.3955, pruned_loss=0.1933, over 1422149.73 frames.], batch size: 20, lr: 2.47e-03
2022-05-26 11:59:54,853 INFO [train.py:842] (2/4) Epoch 1, batch 5500, loss[loss=0.2249, simple_loss=0.4051, pruned_loss=0.2231, over 4912.00 frames.], tot_loss[loss=0.2162, simple_loss=0.3939, pruned_loss=0.192, over 1420702.23 frames.], batch size: 53, lr: 2.46e-03
2022-05-26 12:00:33,677 INFO [train.py:842] (2/4) Epoch 1, batch 5550, loss[loss=0.203, simple_loss=0.3702, pruned_loss=0.1791, over 7213.00 frames.], tot_loss[loss=0.2149, simple_loss=0.3917, pruned_loss=0.1905, over 1422702.84 frames.], batch size: 16, lr: 2.45e-03
2022-05-26 12:01:12,579 INFO [train.py:842] (2/4) Epoch 1, batch 5600, loss[loss=0.229, simple_loss=0.4143, pruned_loss=0.2179, over 6523.00 frames.], tot_loss[loss=0.2161, simple_loss=0.3939, pruned_loss=0.1916, over 1424099.55 frames.], batch size: 38, lr: 2.45e-03
2022-05-26 12:01:51,147 INFO [train.py:842] (2/4) Epoch 1, batch 5650, loss[loss=0.2118, simple_loss=0.383, pruned_loss=0.2025, over 7293.00 frames.], tot_loss[loss=0.2161, simple_loss=0.3939, pruned_loss=0.1911, over 1421890.28 frames.], batch size: 17, lr: 2.44e-03
2022-05-26 12:02:29,825 INFO [train.py:842] (2/4) Epoch 1, batch 5700, loss[loss=0.1981, simple_loss=0.3623, pruned_loss=0.1696, over 7428.00 frames.], tot_loss[loss=0.2164, simple_loss=0.3945, pruned_loss=0.1915, over 1422124.06 frames.], batch size: 20, lr: 2.44e-03
2022-05-26 12:03:08,170 INFO [train.py:842] (2/4) Epoch 1, batch 5750, loss[loss=0.2015, simple_loss=0.3695, pruned_loss=0.1679, over 7282.00 frames.], tot_loss[loss=0.2162, simple_loss=0.3942, pruned_loss=0.1905, over 1423536.41 frames.], batch size: 18, lr: 2.43e-03
2022-05-26 12:03:47,051 INFO [train.py:842] (2/4) Epoch 1, batch 5800, loss[loss=0.2072, simple_loss=0.384, pruned_loss=0.1518, over 7224.00 frames.], tot_loss[loss=0.2154, simple_loss=0.3931, pruned_loss=0.1883, over 1428867.78 frames.], batch size: 22, lr: 2.42e-03
2022-05-26 12:04:25,393 INFO [train.py:842] (2/4) Epoch 1, batch 5850, loss[loss=0.2539, simple_loss=0.457, pruned_loss=0.254, over 7431.00 frames.], tot_loss[loss=0.2162, simple_loss=0.3945, pruned_loss=0.1897, over 1427436.12 frames.], batch size: 20, lr: 2.42e-03
2022-05-26 12:05:04,410 INFO [train.py:842] (2/4) Epoch 1, batch 5900, loss[loss=0.2135, simple_loss=0.3921, pruned_loss=0.1745, over 7317.00 frames.], tot_loss[loss=0.214, simple_loss=0.3906, pruned_loss=0.1867, over 1430174.65 frames.], batch size: 21, lr: 2.41e-03
2022-05-26 12:05:43,138 INFO [train.py:842] (2/4) Epoch 1, batch 5950, loss[loss=0.214, simple_loss=0.3881, pruned_loss=0.1994, over 7153.00 frames.], tot_loss[loss=0.2136, simple_loss=0.3901, pruned_loss=0.1858, over 1430791.39 frames.], batch size: 19, lr: 2.41e-03
2022-05-26 12:06:21,960 INFO [train.py:842] (2/4) Epoch 1, batch 6000, loss[loss=0.3961, simple_loss=0.3764, pruned_loss=0.2079, over 7089.00 frames.], tot_loss[loss=0.2141, simple_loss=0.3879, pruned_loss=0.1836, over 1427067.98 frames.], batch size: 26, lr: 2.40e-03
2022-05-26 12:06:21,961 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 12:06:31,825 INFO [train.py:871] (2/4) Epoch 1, validation: loss=0.2892, simple_loss=0.3436, pruned_loss=0.1174, over 868885.00 frames.
2022-05-26 12:07:10,557 INFO [train.py:842] (2/4) Epoch 1, batch 6050, loss[loss=0.4044, simple_loss=0.4017, pruned_loss=0.2035, over 6770.00 frames.], tot_loss[loss=0.2587, simple_loss=0.3921, pruned_loss=0.1902, over 1423643.44 frames.], batch size: 15, lr: 2.39e-03
2022-05-26 12:07:49,766 INFO [train.py:842] (2/4) Epoch 1, batch 6100, loss[loss=0.2982, simple_loss=0.3261, pruned_loss=0.1351, over 6825.00 frames.], tot_loss[loss=0.2853, simple_loss=0.391, pruned_loss=0.1889, over 1426300.55 frames.], batch size: 15, lr: 2.39e-03
2022-05-26 12:08:28,486 INFO [train.py:842] (2/4) Epoch 1, batch 6150, loss[loss=0.4848, simple_loss=0.4505, pruned_loss=0.2595, over 7107.00 frames.], tot_loss[loss=0.3085, simple_loss=0.393, pruned_loss=0.1891, over 1426558.18 frames.], batch size: 21, lr: 2.38e-03
2022-05-26 12:09:07,159 INFO [train.py:842] (2/4) Epoch 1, batch 6200, loss[loss=0.378, simple_loss=0.3922, pruned_loss=0.1819, over 7326.00 frames.], tot_loss[loss=0.3229, simple_loss=0.3925, pruned_loss=0.1867, over 1426648.85 frames.], batch size: 22, lr: 2.38e-03
2022-05-26 12:09:45,799 INFO [train.py:842] (2/4) Epoch 1, batch 6250, loss[loss=0.3425, simple_loss=0.3728, pruned_loss=0.1561, over 7380.00 frames.], tot_loss[loss=0.3331, simple_loss=0.3911, pruned_loss=0.1841, over 1428801.42 frames.], batch size: 23, lr: 2.37e-03
2022-05-26 12:10:25,216 INFO [train.py:842] (2/4) Epoch 1, batch 6300, loss[loss=0.3433, simple_loss=0.3786, pruned_loss=0.154, over 7276.00 frames.], tot_loss[loss=0.3426, simple_loss=0.3907, pruned_loss=0.1836, over 1426265.15 frames.], batch size: 18, lr: 2.37e-03
2022-05-26 12:11:03,832 INFO [train.py:842] (2/4) Epoch 1, batch 6350, loss[loss=0.4051, simple_loss=0.4162, pruned_loss=0.1969, over 7147.00 frames.], tot_loss[loss=0.3499, simple_loss=0.391, pruned_loss=0.1827, over 1426883.57 frames.], batch size: 20, lr: 2.36e-03
2022-05-26 12:11:42,700 INFO [train.py:842] (2/4) Epoch 1, batch 6400, loss[loss=0.3709, simple_loss=0.3859, pruned_loss=0.1779, over 7361.00 frames.], tot_loss[loss=0.3569, simple_loss=0.3922, pruned_loss=0.1829, over 1426573.51 frames.], batch size: 19, lr: 2.35e-03
2022-05-26 12:12:21,091 INFO [train.py:842] (2/4) Epoch 1, batch 6450, loss[loss=0.3653, simple_loss=0.4005, pruned_loss=0.165, over 7123.00 frames.], tot_loss[loss=0.3607, simple_loss=0.3925, pruned_loss=0.1817, over 1426789.31 frames.], batch size: 21, lr: 2.35e-03
2022-05-26 12:12:59,891 INFO [train.py:842] (2/4) Epoch 1, batch 6500, loss[loss=0.3322, simple_loss=0.3541, pruned_loss=0.1551, over 7133.00 frames.], tot_loss[loss=0.3632, simple_loss=0.3919, pruned_loss=0.1806, over 1423385.57 frames.], batch size: 17, lr: 2.34e-03
2022-05-26 12:13:38,143 INFO [train.py:842] (2/4) Epoch 1, batch 6550, loss[loss=0.3251, simple_loss=0.378, pruned_loss=0.1361, over 7313.00 frames.], tot_loss[loss=0.3623, simple_loss=0.3906, pruned_loss=0.1774, over 1418475.54 frames.], batch size: 21, lr: 2.34e-03
2022-05-26 12:14:17,054 INFO [train.py:842] (2/4) Epoch 1, batch 6600, loss[loss=0.361, simple_loss=0.4017, pruned_loss=0.1601, over 7186.00 frames.], tot_loss[loss=0.3638, simple_loss=0.3905, pruned_loss=0.1767, over 1423835.56 frames.], batch size: 26, lr: 2.33e-03
2022-05-26 12:14:55,785 INFO [train.py:842] (2/4) Epoch 1, batch 6650, loss[loss=0.3325, simple_loss=0.3755, pruned_loss=0.1447, over 7074.00 frames.], tot_loss[loss=0.3684, simple_loss=0.3929, pruned_loss=0.1782, over 1422489.86 frames.], batch size: 18, lr: 2.33e-03
2022-05-26 12:15:34,734 INFO [train.py:842] (2/4) Epoch 1, batch 6700, loss[loss=0.5504, simple_loss=0.4992, pruned_loss=0.3008, over 5152.00 frames.], tot_loss[loss=0.3685, simple_loss=0.3928, pruned_loss=0.177, over 1424359.18 frames.], batch size: 52, lr: 2.32e-03
2022-05-26 12:16:13,264 INFO [train.py:842] (2/4) Epoch 1, batch 6750, loss[loss=0.3277, simple_loss=0.3771, pruned_loss=0.1392, over 7296.00 frames.], tot_loss[loss=0.3667, simple_loss=0.3914, pruned_loss=0.1748, over 1426669.84 frames.], batch size: 24, lr: 2.31e-03
2022-05-26 12:16:52,110 INFO [train.py:842] (2/4) Epoch 1, batch 6800, loss[loss=0.3374, simple_loss=0.3713, pruned_loss=0.1517, over 7427.00 frames.], tot_loss[loss=0.3659, simple_loss=0.3909, pruned_loss=0.1734, over 1428318.85 frames.], batch size: 20, lr: 2.31e-03
2022-05-26 12:17:30,583 INFO [train.py:842] (2/4) Epoch 1, batch 6850, loss[loss=0.2975, simple_loss=0.3454, pruned_loss=0.1248, over 7192.00 frames.], tot_loss[loss=0.3628, simple_loss=0.3887, pruned_loss=0.1707, over 1426723.86 frames.], batch size: 23, lr: 2.30e-03
2022-05-26 12:18:19,073 INFO [train.py:842] (2/4) Epoch 1, batch 6900, loss[loss=0.3725, simple_loss=0.41, pruned_loss=0.1675, over 7420.00 frames.], tot_loss[loss=0.3615, simple_loss=0.3873, pruned_loss=0.1697, over 1426990.82 frames.], batch size: 21, lr: 2.30e-03
2022-05-26 12:18:57,536 INFO [train.py:842] (2/4) Epoch 1, batch 6950, loss[loss=0.3657, simple_loss=0.3899, pruned_loss=0.1708, over 7270.00 frames.], tot_loss[loss=0.3649, simple_loss=0.3895, pruned_loss=0.1716, over 1422930.97 frames.], batch size: 18, lr: 2.29e-03
2022-05-26 12:19:36,275 INFO [train.py:842] (2/4) Epoch 1, batch 7000, loss[loss=0.3225, simple_loss=0.3552, pruned_loss=0.145, over 7163.00 frames.], tot_loss[loss=0.3643, simple_loss=0.3892, pruned_loss=0.1708, over 1420907.20 frames.], batch size: 18, lr: 2.29e-03
2022-05-26 12:20:14,722 INFO [train.py:842] (2/4) Epoch 1, batch 7050, loss[loss=0.3598, simple_loss=0.3836, pruned_loss=0.168, over 7149.00 frames.], tot_loss[loss=0.3639, simple_loss=0.3889, pruned_loss=0.1703, over 1422074.78 frames.], batch size: 19, lr: 2.28e-03
2022-05-26 12:20:53,598 INFO [train.py:842] (2/4) Epoch 1, batch 7100, loss[loss=0.3407, simple_loss=0.3786, pruned_loss=0.1514, over 7331.00 frames.], tot_loss[loss=0.3621, simple_loss=0.3877, pruned_loss=0.1689, over 1424534.91 frames.], batch size: 22, lr: 2.28e-03
2022-05-26 12:21:32,657 INFO [train.py:842] (2/4) Epoch 1, batch 7150, loss[loss=0.3917, simple_loss=0.4139, pruned_loss=0.1848, over 7210.00 frames.], tot_loss[loss=0.3617, simple_loss=0.3872, pruned_loss=0.1686, over 1419051.74 frames.], batch size: 22, lr: 2.27e-03
2022-05-26 12:22:11,491 INFO [train.py:842] (2/4) Epoch 1, batch 7200, loss[loss=0.3833, simple_loss=0.4099, pruned_loss=0.1784, over 7335.00 frames.], tot_loss[loss=0.3617, simple_loss=0.3881, pruned_loss=0.1681, over 1421303.77 frames.], batch size: 22, lr: 2.27e-03
2022-05-26 12:22:50,063 INFO [train.py:842] (2/4) Epoch 1, batch 7250, loss[loss=0.3557, simple_loss=0.3766, pruned_loss=0.1674, over 7063.00 frames.], tot_loss[loss=0.3623, simple_loss=0.3885, pruned_loss=0.1683, over 1417179.07 frames.], batch size: 18, lr: 2.26e-03
2022-05-26 12:23:28,692 INFO [train.py:842] (2/4) Epoch 1, batch 7300, loss[loss=0.3684, simple_loss=0.3976, pruned_loss=0.1696, over 7142.00 frames.], tot_loss[loss=0.3646, simple_loss=0.3904, pruned_loss=0.1697, over 1416661.34 frames.], batch size: 28, lr: 2.26e-03
2022-05-26 12:24:07,150 INFO [train.py:842] (2/4) Epoch 1, batch 7350, loss[loss=0.3053, simple_loss=0.3346, pruned_loss=0.1379, over 7196.00 frames.], tot_loss[loss=0.3606, simple_loss=0.3878, pruned_loss=0.1669, over 1416139.35 frames.], batch size: 16, lr: 2.25e-03
2022-05-26 12:24:45,814 INFO [train.py:842] (2/4) Epoch 1, batch 7400, loss[loss=0.3059, simple_loss=0.3408, pruned_loss=0.1355, over 7407.00 frames.], tot_loss[loss=0.3615, simple_loss=0.3885, pruned_loss=0.1674, over 1416336.84 frames.], batch size: 18, lr: 2.24e-03
2022-05-26 12:25:24,571 INFO [train.py:842] (2/4) Epoch 1, batch 7450, loss[loss=0.3151, simple_loss=0.3571, pruned_loss=0.1365, over 7390.00 frames.], tot_loss[loss=0.3616, simple_loss=0.3892, pruned_loss=0.1672, over 1424432.24 frames.], batch size: 18, lr: 2.24e-03
2022-05-26 12:26:03,336 INFO [train.py:842] (2/4) Epoch 1, batch 7500, loss[loss=0.4111, simple_loss=0.4157, pruned_loss=0.2033, over 7430.00 frames.], tot_loss[loss=0.3615, simple_loss=0.3892, pruned_loss=0.167, over 1421709.57 frames.], batch size: 20, lr: 2.23e-03
2022-05-26 12:26:41,950 INFO [train.py:842] (2/4) Epoch 1, batch 7550, loss[loss=0.3141, simple_loss=0.3535, pruned_loss=0.1374, over 7320.00 frames.], tot_loss[loss=0.3576, simple_loss=0.3862, pruned_loss=0.1646, over 1419974.59 frames.], batch size: 20, lr: 2.23e-03
2022-05-26 12:27:21,041 INFO [train.py:842] (2/4) Epoch 1, batch 7600, loss[loss=0.2937, simple_loss=0.3492, pruned_loss=0.1191, over 7398.00 frames.], tot_loss[loss=0.3534, simple_loss=0.3828, pruned_loss=0.1621, over 1423634.17 frames.], batch size: 21, lr: 2.22e-03
2022-05-26 12:28:28,318 INFO [train.py:842] (2/4) Epoch 1, batch 7650, loss[loss=0.4442, simple_loss=0.4329, pruned_loss=0.2277, over 7336.00 frames.], tot_loss[loss=0.3564, simple_loss=0.3854, pruned_loss=0.1638, over 1426965.08 frames.], batch size: 20, lr: 2.22e-03
2022-05-26 12:29:07,139 INFO [train.py:842] (2/4) Epoch 1, batch 7700, loss[loss=0.3403, simple_loss=0.3894, pruned_loss=0.1456, over 7221.00 frames.], tot_loss[loss=0.3565, simple_loss=0.3853, pruned_loss=0.1638, over 1424548.33 frames.], batch size: 20, lr: 2.21e-03
2022-05-26 12:29:46,009 INFO [train.py:842] (2/4) Epoch 1, batch 7750, loss[loss=0.3226, simple_loss=0.3562, pruned_loss=0.1445, over 7363.00 frames.], tot_loss[loss=0.3562, simple_loss=0.3854, pruned_loss=0.1636, over 1425735.77 frames.], batch size: 19, lr: 2.21e-03
2022-05-26 12:30:24,812 INFO [train.py:842] (2/4) Epoch 1, batch 7800, loss[loss=0.4322, simple_loss=0.4513, pruned_loss=0.2066, over 7050.00 frames.], tot_loss[loss=0.356, simple_loss=0.3857, pruned_loss=0.1632, over 1427870.61 frames.], batch size: 28, lr: 2.20e-03
2022-05-26 12:31:03,146 INFO [train.py:842] (2/4) Epoch 1, batch 7850, loss[loss=0.3964, simple_loss=0.4138, pruned_loss=0.1895, over 7278.00 frames.], tot_loss[loss=0.3554, simple_loss=0.3861, pruned_loss=0.1624, over 1430544.80 frames.], batch size: 24, lr: 2.20e-03
2022-05-26 12:31:41,887 INFO [train.py:842] (2/4) Epoch 1, batch 7900, loss[loss=0.3635, simple_loss=0.3854, pruned_loss=0.1707, over 7421.00 frames.], tot_loss[loss=0.3589, simple_loss=0.3882, pruned_loss=0.1648, over 1426870.69 frames.], batch size: 20, lr: 2.19e-03
2022-05-26 12:32:20,417 INFO [train.py:842] (2/4) Epoch 1, batch 7950, loss[loss=0.4018, simple_loss=0.422, pruned_loss=0.1908, over 6600.00 frames.], tot_loss[loss=0.3558, simple_loss=0.3853, pruned_loss=0.1632, over 1422776.95 frames.], batch size: 38, lr: 2.19e-03
2022-05-26 12:33:01,914 INFO [train.py:842] (2/4) Epoch 1, batch 8000, loss[loss=0.3211, simple_loss=0.3484, pruned_loss=0.1469, over 7145.00 frames.], tot_loss[loss=0.3529, simple_loss=0.3837, pruned_loss=0.1611, over 1425701.26 frames.], batch size: 17, lr: 2.18e-03
2022-05-26 12:33:40,595 INFO [train.py:842] (2/4) Epoch 1, batch 8050, loss[loss=0.2947, simple_loss=0.3369, pruned_loss=0.1262, over 7144.00 frames.], tot_loss[loss=0.352, simple_loss=0.3832, pruned_loss=0.1605, over 1429894.34 frames.], batch size: 17, lr: 2.18e-03
2022-05-26 12:34:19,297 INFO [train.py:842] (2/4) Epoch 1, batch 8100, loss[loss=0.3521, simple_loss=0.3737, pruned_loss=0.1652, over 7258.00 frames.], tot_loss[loss=0.3517, simple_loss=0.3831, pruned_loss=0.1601, over 1428721.46 frames.], batch size: 19, lr: 2.17e-03
2022-05-26 12:34:57,730 INFO [train.py:842] (2/4) Epoch 1, batch 8150, loss[loss=0.3919, simple_loss=0.4257, pruned_loss=0.1791, over 7205.00 frames.], tot_loss[loss=0.3569, simple_loss=0.387, pruned_loss=0.1634, over 1423314.63 frames.], batch size: 22, lr: 2.17e-03
2022-05-26 12:35:36,455 INFO [train.py:842] (2/4) Epoch 1, batch 8200, loss[loss=0.3517, simple_loss=0.3783, pruned_loss=0.1626, over 7172.00 frames.], tot_loss[loss=0.3534, simple_loss=0.385, pruned_loss=0.1609, over 1421239.06 frames.], batch size: 18, lr: 2.16e-03
2022-05-26 12:36:15,274 INFO [train.py:842] (2/4) Epoch 1, batch 8250, loss[loss=0.336, simple_loss=0.3693, pruned_loss=0.1514, over 7256.00 frames.], tot_loss[loss=0.354, simple_loss=0.3845, pruned_loss=0.1618, over 1421967.53 frames.], batch size: 19, lr: 2.16e-03
2022-05-26 12:36:53,989 INFO [train.py:842] (2/4) Epoch 1, batch 8300, loss[loss=0.4806, simple_loss=0.4758, pruned_loss=0.2427, over 6716.00 frames.], tot_loss[loss=0.3542, simple_loss=0.3851, pruned_loss=0.1616, over 1421593.56 frames.], batch size: 31, lr: 2.15e-03
2022-05-26 12:37:32,673 INFO [train.py:842] (2/4) Epoch 1, batch 8350, loss[loss=0.2792, simple_loss=0.3291, pruned_loss=0.1146, over 7284.00 frames.], tot_loss[loss=0.3506, simple_loss=0.3826, pruned_loss=0.1593, over 1424765.26 frames.], batch size: 18, lr: 2.15e-03
2022-05-26 12:38:11,583 INFO [train.py:842] (2/4) Epoch 1, batch 8400, loss[loss=0.375, simple_loss=0.4149, pruned_loss=0.1675, over 7292.00 frames.], tot_loss[loss=0.3495, simple_loss=0.3823, pruned_loss=0.1583, over 1424061.59 frames.], batch size: 25, lr: 2.15e-03
2022-05-26 12:38:49,958 INFO [train.py:842] (2/4) Epoch 1, batch 8450, loss[loss=0.3406, simple_loss=0.3843, pruned_loss=0.1485, over 7118.00 frames.], tot_loss[loss=0.3472, simple_loss=0.3812, pruned_loss=0.1566, over 1423697.80 frames.], batch size: 21, lr: 2.14e-03
2022-05-26 12:39:28,659 INFO [train.py:842] (2/4) Epoch 1, batch 8500, loss[loss=0.3321, simple_loss=0.3761, pruned_loss=0.1441, over 7152.00 frames.], tot_loss[loss=0.3483, simple_loss=0.3821, pruned_loss=0.1573, over 1422789.74 frames.], batch size: 20, lr: 2.14e-03
2022-05-26 12:40:07,534 INFO [train.py:842] (2/4) Epoch 1, batch 8550, loss[loss=0.4087, simple_loss=0.4221, pruned_loss=0.1977, over 7155.00 frames.], tot_loss[loss=0.3456, simple_loss=0.38, pruned_loss=0.1555, over 1424630.29 frames.], batch size: 18, lr: 2.13e-03
2022-05-26 12:40:46,238 INFO [train.py:842] (2/4) Epoch 1, batch 8600, loss[loss=0.3424, simple_loss=0.3788, pruned_loss=0.153, over 7063.00 frames.], tot_loss[loss=0.3488, simple_loss=0.3821, pruned_loss=0.1578, over 1421021.15 frames.], batch size: 18, lr: 2.13e-03
2022-05-26 12:41:24,617 INFO [train.py:842] (2/4) Epoch 1, batch 8650, loss[loss=0.3058, simple_loss=0.3672, pruned_loss=0.1222, over 7325.00 frames.], tot_loss[loss=0.3499, simple_loss=0.3833, pruned_loss=0.1582, over 1413581.81 frames.], batch size: 21, lr: 2.12e-03
2022-05-26 12:42:03,384 INFO [train.py:842] (2/4) Epoch 1, batch 8700, loss[loss=0.3003, simple_loss=0.3352, pruned_loss=0.1327, over 7145.00 frames.], tot_loss[loss=0.3506, simple_loss=0.3842, pruned_loss=0.1585, over 1410789.67 frames.], batch size: 17, lr: 2.12e-03
2022-05-26 12:42:41,796 INFO [train.py:842] (2/4) Epoch 1, batch 8750, loss[loss=0.3491, simple_loss=0.3892, pruned_loss=0.1546, over 6672.00 frames.], tot_loss[loss=0.3527, simple_loss=0.3857, pruned_loss=0.1599, over 1412728.29 frames.], batch size: 31, lr: 2.11e-03
2022-05-26 12:43:20,372 INFO [train.py:842] (2/4) Epoch 1, batch 8800, loss[loss=0.298, simple_loss=0.3481, pruned_loss=0.124, over 6794.00 frames.], tot_loss[loss=0.3484, simple_loss=0.3829, pruned_loss=0.1569, over 1416195.97 frames.], batch size: 31, lr: 2.11e-03
2022-05-26 12:43:58,596 INFO [train.py:842] (2/4) Epoch 1, batch 8850, loss[loss=0.5767, simple_loss=0.5313, pruned_loss=0.311, over 4719.00 frames.], tot_loss[loss=0.3511, simple_loss=0.385, pruned_loss=0.1586, over 1411483.04 frames.], batch size: 52, lr: 2.10e-03
2022-05-26 12:44:37,288 INFO [train.py:842] (2/4) Epoch 1, batch 8900, loss[loss=0.3234, simple_loss=0.3579, pruned_loss=0.1444, over 6984.00 frames.], tot_loss[loss=0.3513, simple_loss=0.3852, pruned_loss=0.1588, over 1402973.48 frames.], batch size: 16, lr: 2.10e-03
2022-05-26 12:45:15,601 INFO [train.py:842] (2/4) Epoch 1, batch 8950, loss[loss=0.3206, simple_loss=0.3772, pruned_loss=0.1321, over 7313.00 frames.], tot_loss[loss=0.3506, simple_loss=0.3853, pruned_loss=0.1579, over 1405404.31 frames.], batch size: 21, lr: 2.10e-03
2022-05-26 12:45:54,260 INFO [train.py:842] (2/4) Epoch 1, batch 9000, loss[loss=0.3897, simple_loss=0.4018, pruned_loss=0.1888, over 5252.00 frames.], tot_loss[loss=0.3518, simple_loss=0.3863, pruned_loss=0.1587, over 1399089.45 frames.], batch size: 52, lr: 2.09e-03
2022-05-26 12:45:54,260 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 12:46:03,568 INFO [train.py:871] (2/4) Epoch 1, validation: loss=0.2508, simple_loss=0.3369, pruned_loss=0.08236, over 868885.00 frames.
2022-05-26 12:46:41,379 INFO [train.py:842] (2/4) Epoch 1, batch 9050, loss[loss=0.3581, simple_loss=0.3855, pruned_loss=0.1653, over 5468.00 frames.], tot_loss[loss=0.3534, simple_loss=0.3877, pruned_loss=0.1595, over 1387939.13 frames.], batch size: 52, lr: 2.09e-03
2022-05-26 12:47:18,742 INFO [train.py:842] (2/4) Epoch 1, batch 9100, loss[loss=0.382, simple_loss=0.4193, pruned_loss=0.1723, over 4972.00 frames.], tot_loss[loss=0.3568, simple_loss=0.3905, pruned_loss=0.1615, over 1344992.14 frames.], batch size: 52, lr: 2.08e-03
2022-05-26 12:47:56,195 INFO [train.py:842] (2/4) Epoch 1, batch 9150, loss[loss=0.374, simple_loss=0.4012, pruned_loss=0.1734, over 5146.00 frames.], tot_loss[loss=0.3639, simple_loss=0.3948, pruned_loss=0.1665, over 1286292.09 frames.], batch size: 52, lr: 2.08e-03
2022-05-26 12:48:47,790 INFO [train.py:842] (2/4) Epoch 2, batch 0, loss[loss=0.417, simple_loss=0.4321, pruned_loss=0.2009, over 7186.00 frames.], tot_loss[loss=0.417, simple_loss=0.4321, pruned_loss=0.2009, over 7186.00 frames.], batch size: 26, lr: 2.06e-03
2022-05-26 12:49:27,344 INFO [train.py:842] (2/4) Epoch 2, batch 50, loss[loss=0.2862, simple_loss=0.343, pruned_loss=0.1147, over 7234.00 frames.], tot_loss[loss=0.3374, simple_loss=0.3754, pruned_loss=0.1498, over 312324.82 frames.], batch size: 20, lr: 2.06e-03
2022-05-26 12:50:06,217 INFO [train.py:842] (2/4) Epoch 2, batch 100, loss[loss=0.4156, simple_loss=0.4319, pruned_loss=0.1997, over 7427.00 frames.], tot_loss[loss=0.3457, simple_loss=0.3796, pruned_loss=0.156, over 560210.94 frames.], batch size: 20, lr: 2.05e-03
2022-05-26 12:50:45,170 INFO [train.py:842] (2/4) Epoch 2, batch 150, loss[loss=0.3196, simple_loss=0.3802, pruned_loss=0.1295, over 7327.00 frames.], tot_loss[loss=0.3413, simple_loss=0.378, pruned_loss=0.1523, over 751075.15 frames.], batch size: 20, lr: 2.05e-03
2022-05-26 12:51:23,759 INFO [train.py:842] (2/4) Epoch 2, batch 200, loss[loss=0.419, simple_loss=0.4197, pruned_loss=0.2091, over 7166.00 frames.], tot_loss[loss=0.3399, simple_loss=0.3769, pruned_loss=0.1514, over 901016.37 frames.], batch size: 19, lr: 2.04e-03
2022-05-26 12:52:03,051 INFO [train.py:842] (2/4) Epoch 2, batch 250, loss[loss=0.3512, simple_loss=0.3811, pruned_loss=0.1607, over 7387.00 frames.], tot_loss[loss=0.3418, simple_loss=0.378, pruned_loss=0.1528, over 1015787.87 frames.], batch size: 23, lr: 2.04e-03
2022-05-26 12:52:42,055 INFO [train.py:842] (2/4) Epoch 2, batch 300, loss[loss=0.3652, simple_loss=0.3875, pruned_loss=0.1715, over 7270.00 frames.], tot_loss[loss=0.3423, simple_loss=0.3788, pruned_loss=0.1529, over 1104881.58 frames.], batch size: 19, lr: 2.03e-03
2022-05-26 12:53:21,154 INFO [train.py:842] (2/4) Epoch 2, batch 350, loss[loss=0.3387, simple_loss=0.3733, pruned_loss=0.1521, over 7218.00 frames.], tot_loss[loss=0.3365, simple_loss=0.3749, pruned_loss=0.149, over 1174002.75 frames.], batch size: 21, lr: 2.03e-03
2022-05-26 12:53:59,748 INFO [train.py:842] (2/4) Epoch 2, batch 400, loss[loss=0.3891, simple_loss=0.4112, pruned_loss=0.1835, over 7142.00 frames.], tot_loss[loss=0.3369, simple_loss=0.3756, pruned_loss=0.1491, over 1230889.42 frames.], batch size: 20, lr: 2.03e-03
2022-05-26 12:54:38,379 INFO [train.py:842] (2/4) Epoch 2, batch 450, loss[loss=0.3874, simple_loss=0.42, pruned_loss=0.1774, over 7159.00 frames.], tot_loss[loss=0.3358, simple_loss=0.3752, pruned_loss=0.1482, over 1276071.47 frames.], batch size: 19, lr: 2.02e-03
2022-05-26 12:55:16,799 INFO [train.py:842] (2/4) Epoch 2, batch 500, loss[loss=0.2966, simple_loss=0.3338, pruned_loss=0.1297, over 7183.00 frames.], tot_loss[loss=0.3336, simple_loss=0.374, pruned_loss=0.1466, over 1307951.72 frames.], batch size: 18, lr: 2.02e-03
2022-05-26 12:55:56,046 INFO [train.py:842] (2/4) Epoch 2, batch 550, loss[loss=0.3128, simple_loss=0.345, pruned_loss=0.1403, over 7347.00 frames.], tot_loss[loss=0.3346, simple_loss=0.3744, pruned_loss=0.1474, over 1332808.02 frames.], batch size: 19, lr: 2.01e-03
2022-05-26 12:56:34,291 INFO [train.py:842] (2/4) Epoch 2, batch 600, loss[loss=0.346, simple_loss=0.38, pruned_loss=0.1561, over 7398.00 frames.], tot_loss[loss=0.3371, simple_loss=0.3767, pruned_loss=0.1487, over 1354032.62 frames.], batch size: 23, lr: 2.01e-03
2022-05-26 12:57:13,119 INFO [train.py:842] (2/4) Epoch 2, batch 650, loss[loss=0.3105, simple_loss=0.3468, pruned_loss=0.1371, over 7271.00 frames.], tot_loss[loss=0.3334, simple_loss=0.3735, pruned_loss=0.1467, over 1368213.90 frames.], batch size: 18, lr: 2.01e-03
2022-05-26 12:57:51,836 INFO [train.py:842] (2/4) Epoch 2, batch 700, loss[loss=0.4465, simple_loss=0.4334, pruned_loss=0.2298, over 5203.00 frames.], tot_loss[loss=0.3306, simple_loss=0.3715, pruned_loss=0.1448, over 1380117.64 frames.], batch size: 53, lr: 2.00e-03
2022-05-26 12:58:30,909 INFO [train.py:842] (2/4) Epoch 2, batch 750, loss[loss=0.2934, simple_loss=0.3515, pruned_loss=0.1176, over 7255.00 frames.], tot_loss[loss=0.3343, simple_loss=0.3738, pruned_loss=0.1474, over 1391635.39 frames.], batch size: 19, lr: 2.00e-03
2022-05-26 12:59:09,531 INFO [train.py:842] (2/4) Epoch 2, batch 800, loss[loss=0.3262, simple_loss=0.3661, pruned_loss=0.1432, over 7060.00 frames.], tot_loss[loss=0.3352, simple_loss=0.3748, pruned_loss=0.1478, over 1401356.14 frames.], batch size: 18, lr: 1.99e-03
2022-05-26 12:59:48,503 INFO [train.py:842] (2/4) Epoch 2, batch 850, loss[loss=0.3852, simple_loss=0.4099, pruned_loss=0.1802, over 7329.00 frames.], tot_loss[loss=0.3324, simple_loss=0.3731, pruned_loss=0.1458, over 1408430.79 frames.], batch size: 20, lr: 1.99e-03
2022-05-26 13:00:27,143 INFO [train.py:842] (2/4) Epoch 2, batch 900, loss[loss=0.2886, simple_loss=0.3432, pruned_loss=0.117, over 7428.00 frames.], tot_loss[loss=0.3312, simple_loss=0.3723, pruned_loss=0.145, over 1412458.51 frames.], batch size: 20, lr: 1.99e-03
2022-05-26 13:01:06,368 INFO [train.py:842] (2/4) Epoch 2, batch 950, loss[loss=0.2896, simple_loss=0.3323, pruned_loss=0.1235, over 7251.00 frames.], tot_loss[loss=0.3309, simple_loss=0.3727, pruned_loss=0.1445, over 1415187.68 frames.], batch size: 19, lr: 1.98e-03
2022-05-26 13:01:45,058 INFO [train.py:842] (2/4) Epoch 2, batch 1000, loss[loss=0.3285, simple_loss=0.3713, pruned_loss=0.1428, over 6851.00 frames.], tot_loss[loss=0.3306, simple_loss=0.3726, pruned_loss=0.1443, over 1417102.04 frames.], batch size: 31, lr: 1.98e-03
2022-05-26 13:02:24,223 INFO [train.py:842] (2/4) Epoch 2, batch 1050, loss[loss=0.3069, simple_loss=0.3645, pruned_loss=0.1247, over 7428.00 frames.], tot_loss[loss=0.3304, simple_loss=0.3724, pruned_loss=0.1442, over 1419696.01 frames.], batch size: 20, lr: 1.97e-03
2022-05-26 13:03:02,483 INFO [train.py:842] (2/4) Epoch 2, batch 1100, loss[loss=0.2804, simple_loss=0.3223, pruned_loss=0.1192, over 7171.00 frames.], tot_loss[loss=0.3324, simple_loss=0.374, pruned_loss=0.1454, over 1421020.59 frames.], batch size: 18, lr: 1.97e-03
2022-05-26 13:03:41,563 INFO [train.py:842] (2/4) Epoch 2, batch 1150, loss[loss=0.3343, simple_loss=0.3838, pruned_loss=0.1424, over 7234.00 frames.], tot_loss[loss=0.3301, simple_loss=0.3721, pruned_loss=0.144, over 1424918.57 frames.], batch size: 20, lr: 1.97e-03
2022-05-26 13:04:19,992 INFO [train.py:842] (2/4) Epoch 2, batch 1200, loss[loss=0.413, simple_loss=0.4232, pruned_loss=0.2014, over 7012.00 frames.], tot_loss[loss=0.3294, simple_loss=0.3714, pruned_loss=0.1437, over 1423602.27 frames.], batch size: 28, lr: 1.96e-03
2022-05-26 13:04:58,738 INFO [train.py:842] (2/4) Epoch 2, batch 1250, loss[loss=0.2585, simple_loss=0.3123, pruned_loss=0.1023, over 7284.00 frames.], tot_loss[loss=0.3304, simple_loss=0.3724, pruned_loss=0.1442, over 1423002.53 frames.], batch size: 18, lr: 1.96e-03
2022-05-26 13:05:37,175 INFO [train.py:842] (2/4) Epoch 2, batch 1300, loss[loss=0.3276, simple_loss=0.3785, pruned_loss=0.1383, over 7212.00 frames.], tot_loss[loss=0.3291, simple_loss=0.3716, pruned_loss=0.1433, over 1416893.03 frames.], batch size: 21, lr: 1.95e-03
2022-05-26 13:06:15,946 INFO [train.py:842] (2/4) Epoch 2, batch 1350, loss[loss=0.3599, simple_loss=0.3787, pruned_loss=0.1706, over 7277.00 frames.], tot_loss[loss=0.33, simple_loss=0.3721, pruned_loss=0.1439, over 1419980.20 frames.], batch size: 17, lr: 1.95e-03
2022-05-26 13:06:54,263 INFO [train.py:842] (2/4) Epoch 2, batch 1400, loss[loss=0.3561, simple_loss=0.4089, pruned_loss=0.1517, over 7209.00 frames.], tot_loss[loss=0.3321, simple_loss=0.3735, pruned_loss=0.1454, over 1417966.83 frames.], batch size: 21, lr: 1.95e-03
2022-05-26 13:07:33,450 INFO [train.py:842] (2/4) Epoch 2, batch 1450, loss[loss=0.3307, simple_loss=0.3776, pruned_loss=0.1418, over 7187.00 frames.], tot_loss[loss=0.3326, simple_loss=0.3737, pruned_loss=0.1457, over 1421816.97 frames.], batch size: 26, lr: 1.94e-03
2022-05-26 13:08:12,021 INFO [train.py:842] (2/4) Epoch 2, batch 1500, loss[loss=0.3977, simple_loss=0.4169, pruned_loss=0.1893, over 6554.00 frames.], tot_loss[loss=0.3323, simple_loss=0.3736, pruned_loss=0.1455, over 1421318.16 frames.], batch size: 38, lr: 1.94e-03
2022-05-26 13:08:50,744 INFO [train.py:842] (2/4) Epoch 2, batch 1550, loss[loss=0.3315, simple_loss=0.3789, pruned_loss=0.1421, over 7422.00 frames.], tot_loss[loss=0.3317, simple_loss=0.3736, pruned_loss=0.1449, over 1424804.86 frames.], batch size: 20, lr: 1.94e-03
2022-05-26 13:09:29,434 INFO [train.py:842] (2/4) Epoch 2, batch 1600, loss[loss=0.2713, simple_loss=0.3242, pruned_loss=0.1092, over 7178.00 frames.], tot_loss[loss=0.3297, simple_loss=0.3717, pruned_loss=0.1439, over 1424087.19 frames.], batch size: 18, lr: 1.93e-03
2022-05-26 13:10:08,261 INFO [train.py:842] (2/4) Epoch 2, batch 1650, loss[loss=0.3227, simple_loss=0.3614, pruned_loss=0.142, over 7427.00 frames.], tot_loss[loss=0.3284, simple_loss=0.371, pruned_loss=0.143, over 1423667.95 frames.], batch size: 20, lr: 1.93e-03
2022-05-26 13:10:46,848 INFO [train.py:842] (2/4) Epoch 2, batch 1700, loss[loss=0.3217, simple_loss=0.3684, pruned_loss=0.1375, over 7416.00 frames.], tot_loss[loss=0.3266, simple_loss=0.3702, pruned_loss=0.1415, over 1422450.54 frames.], batch size: 21, lr: 1.92e-03
2022-05-26 13:11:25,779 INFO [train.py:842] (2/4) Epoch 2, batch 1750, loss[loss=0.2833, simple_loss=0.3289, pruned_loss=0.1188, over 7278.00 frames.], tot_loss[loss=0.3264, simple_loss=0.3704, pruned_loss=0.1412, over 1422469.04 frames.], batch size: 18, lr: 1.92e-03
2022-05-26 13:12:04,200 INFO [train.py:842] (2/4) Epoch 2, batch 1800, loss[loss=0.3407, simple_loss=0.3783, pruned_loss=0.1515, over 7349.00 frames.], tot_loss[loss=0.3282, simple_loss=0.3717, pruned_loss=0.1424, over 1424032.29 frames.], batch size: 19, lr: 1.92e-03
2022-05-26 13:12:43,080 INFO [train.py:842] (2/4) Epoch 2, batch 1850, loss[loss=0.2503, simple_loss=0.322, pruned_loss=0.08931, over 7309.00 frames.], tot_loss[loss=0.3235, simple_loss=0.3684, pruned_loss=0.1393, over 1424356.17 frames.], batch size: 20, lr: 1.91e-03
2022-05-26 13:13:21,382 INFO [train.py:842] (2/4) Epoch 2, batch 1900, loss[loss=0.3197, simple_loss=0.3471, pruned_loss=0.1462, over 6992.00 frames.], tot_loss[loss=0.3234, simple_loss=0.3687, pruned_loss=0.1391, over 1428315.37 frames.], batch size: 16, lr: 1.91e-03
2022-05-26 13:14:00,097 INFO [train.py:842] (2/4) Epoch 2, batch 1950, loss[loss=0.3802, simple_loss=0.3939, pruned_loss=0.1833, over 7296.00 frames.], tot_loss[loss=0.3253, simple_loss=0.3703, pruned_loss=0.1401, over 1428509.27 frames.], batch size: 18, lr: 1.91e-03
2022-05-26 13:14:38,211 INFO [train.py:842] (2/4) Epoch 2, batch 2000, loss[loss=0.3445, simple_loss=0.3863, pruned_loss=0.1513, over 7107.00 frames.], tot_loss[loss=0.3287, simple_loss=0.3726, pruned_loss=0.1424, over 1423005.87 frames.], batch size: 21, lr: 1.90e-03
2022-05-26 13:15:17,087 INFO [train.py:842] (2/4) Epoch 2, batch 2050, loss[loss=0.3861, simple_loss=0.4223, pruned_loss=0.175, over 7097.00 frames.], tot_loss[loss=0.3281, simple_loss=0.3718, pruned_loss=0.1422, over 1423932.53 frames.], batch size: 28, lr: 1.90e-03
2022-05-26 13:15:55,559 INFO [train.py:842] (2/4) Epoch 2, batch 2100, loss[loss=0.3476, simple_loss=0.3632, pruned_loss=0.166, over 7410.00 frames.], tot_loss[loss=0.3289, simple_loss=0.3722, pruned_loss=0.1427, over 1424634.48 frames.], batch size: 18, lr: 1.90e-03
2022-05-26 13:16:34,411 INFO [train.py:842] (2/4) Epoch 2, batch 2150, loss[loss=0.3741, simple_loss=0.4126, pruned_loss=0.1678, over 7406.00 frames.], tot_loss[loss=0.3291, simple_loss=0.3723, pruned_loss=0.1429, over 1423971.09 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 13:17:12,972 INFO [train.py:842] (2/4) Epoch 2, batch 2200, loss[loss=0.3614, simple_loss=0.3887, pruned_loss=0.1671, over 7109.00 frames.], tot_loss[loss=0.3252, simple_loss=0.3692, pruned_loss=0.1406, over 1423301.29 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 13:17:52,117 INFO [train.py:842] (2/4) Epoch 2, batch 2250, loss[loss=0.2727, simple_loss=0.348, pruned_loss=0.09873, over 7215.00 frames.], tot_loss[loss=0.3237, simple_loss=0.3679, pruned_loss=0.1398, over 1424822.51 frames.], batch size: 21, lr: 1.89e-03
2022-05-26 13:18:30,733 INFO [train.py:842] (2/4) Epoch 2, batch 2300, loss[loss=0.3343, simple_loss=0.3842, pruned_loss=0.1421, over 7200.00 frames.], tot_loss[loss=0.3227, simple_loss=0.3673, pruned_loss=0.139, over 1424963.61 frames.], batch size: 22, lr: 1.88e-03
2022-05-26 13:19:09,884 INFO [train.py:842] (2/4) Epoch 2, batch 2350, loss[loss=0.2621, simple_loss=0.3379, pruned_loss=0.09316, over 7229.00 frames.], tot_loss[loss=0.3229, simple_loss=0.3678, pruned_loss=0.1391, over 1422108.61 frames.], batch size: 20, lr: 1.88e-03
2022-05-26 13:19:48,354 INFO [train.py:842] (2/4) Epoch 2, batch 2400, loss[loss=0.3528, simple_loss=0.3961, pruned_loss=0.1548, over 7319.00 frames.], tot_loss[loss=0.322, simple_loss=0.3669, pruned_loss=0.1386, over 1421868.25 frames.], batch size: 21, lr: 1.87e-03
2022-05-26 13:20:27,510 INFO [train.py:842] (2/4) Epoch 2, batch 2450, loss[loss=0.3291, simple_loss=0.377, pruned_loss=0.1405, over 7312.00 frames.], tot_loss[loss=0.3239, simple_loss=0.3686, pruned_loss=0.1396, over 1425775.44 frames.], batch size: 21, lr: 1.87e-03
2022-05-26 13:21:06,101 INFO [train.py:842] (2/4) Epoch 2, batch 2500, loss[loss=0.3727, simple_loss=0.4081, pruned_loss=0.1687, over 7141.00 frames.], tot_loss[loss=0.3223, simple_loss=0.3674, pruned_loss=0.1386, over 1426143.21 frames.], batch size: 26, lr: 1.87e-03
2022-05-26 13:21:44,924 INFO [train.py:842] (2/4) Epoch 2, batch 2550, loss[loss=0.2419, simple_loss=0.2902, pruned_loss=0.09681, over 6989.00 frames.], tot_loss[loss=0.3208, simple_loss=0.3664, pruned_loss=0.1376, over 1426357.67 frames.], batch size: 16, lr: 1.86e-03
2022-05-26 13:22:23,545 INFO [train.py:842] (2/4) Epoch 2, batch 2600, loss[loss=0.3656, simple_loss=0.4036, pruned_loss=0.1638, over 7134.00 frames.], tot_loss[loss=0.3213, simple_loss=0.3665, pruned_loss=0.1381, over 1428748.18 frames.], batch size: 26, lr: 1.86e-03
2022-05-26 13:23:02,366 INFO [train.py:842] (2/4) Epoch 2, batch 2650, loss[loss=0.2885, simple_loss=0.3532, pruned_loss=0.1119, over 6666.00 frames.], tot_loss[loss=0.3203, simple_loss=0.3661, pruned_loss=0.1373, over 1427821.12 frames.], batch size: 38, lr: 1.86e-03
2022-05-26 13:23:41,052 INFO [train.py:842] (2/4) Epoch 2, batch 2700, loss[loss=0.3308, simple_loss=0.3745, pruned_loss=0.1435, over 6721.00 frames.], tot_loss[loss=0.3185, simple_loss=0.3646, pruned_loss=0.1361, over 1427350.30 frames.], batch size: 31, lr: 1.85e-03
2022-05-26 13:24:20,362 INFO [train.py:842] (2/4) Epoch 2, batch 2750, loss[loss=0.3449, simple_loss=0.4053, pruned_loss=0.1423, over 7299.00 frames.], tot_loss[loss=0.3194, simple_loss=0.3653, pruned_loss=0.1367, over 1424020.04 frames.], batch size: 24, lr: 1.85e-03
2022-05-26 13:24:58,803 INFO [train.py:842] (2/4) Epoch 2, batch 2800, loss[loss=0.2938, simple_loss=0.3562, pruned_loss=0.1157, over 7211.00 frames.], tot_loss[loss=0.3207, simple_loss=0.3662, pruned_loss=0.1376, over 1427283.47 frames.], batch size: 23, lr: 1.85e-03
2022-05-26 13:25:37,893 INFO [train.py:842] (2/4) Epoch 2, batch 2850, loss[loss=0.2814, simple_loss=0.3464, pruned_loss=0.1083, over 7275.00 frames.], tot_loss[loss=0.3205, simple_loss=0.3662, pruned_loss=0.1374, over 1426634.68 frames.], batch size: 24, lr: 1.84e-03
2022-05-26 13:26:16,447 INFO [train.py:842] (2/4) Epoch 2, batch 2900, loss[loss=0.3442, simple_loss=0.3974, pruned_loss=0.1455, over 7229.00 frames.], tot_loss[loss=0.3233, simple_loss=0.3685, pruned_loss=0.1391, over 1422140.75 frames.], batch size: 20, lr: 1.84e-03
2022-05-26 13:26:55,513 INFO [train.py:842] (2/4) Epoch 2, batch 2950, loss[loss=0.2775, simple_loss=0.3474, pruned_loss=0.1038, over 7230.00 frames.], tot_loss[loss=0.3227, simple_loss=0.368, pruned_loss=0.1388, over 1423672.06 frames.], batch size: 20, lr: 1.84e-03
2022-05-26 13:27:34,165 INFO [train.py:842] (2/4) Epoch 2, batch 3000, loss[loss=0.2938, simple_loss=0.3387, pruned_loss=0.1245, over 7275.00 frames.], tot_loss[loss=0.3193, simple_loss=0.3657, pruned_loss=0.1365, over 1426973.15 frames.], batch size: 17, lr: 1.84e-03
2022-05-26 13:27:34,166 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 13:27:43,282 INFO [train.py:871] (2/4) Epoch 2, validation: loss=0.238, simple_loss=0.3286, pruned_loss=0.07369, over 868885.00 frames.
2022-05-26 13:28:22,611 INFO [train.py:842] (2/4) Epoch 2, batch 3050, loss[loss=0.2862, simple_loss=0.332, pruned_loss=0.1202, over 7281.00 frames.], tot_loss[loss=0.3197, simple_loss=0.3658, pruned_loss=0.1368, over 1421864.65 frames.], batch size: 18, lr: 1.83e-03
2022-05-26 13:29:00,952 INFO [train.py:842] (2/4) Epoch 2, batch 3100, loss[loss=0.4376, simple_loss=0.4442, pruned_loss=0.2155, over 5245.00 frames.], tot_loss[loss=0.3202, simple_loss=0.3661, pruned_loss=0.1371, over 1422048.19 frames.], batch size: 52, lr: 1.83e-03
2022-05-26 13:29:39,781 INFO [train.py:842] (2/4) Epoch 2, batch 3150, loss[loss=0.2937, simple_loss=0.324, pruned_loss=0.1317, over 6783.00 frames.], tot_loss[loss=0.3182, simple_loss=0.365, pruned_loss=0.1357, over 1424231.86 frames.], batch size: 15, lr: 1.83e-03
2022-05-26 13:30:18,094 INFO [train.py:842] (2/4) Epoch 2, batch 3200, loss[loss=0.351, simple_loss=0.3879, pruned_loss=0.1571, over 5110.00 frames.], tot_loss[loss=0.3203, simple_loss=0.3663, pruned_loss=0.1372, over 1414187.62 frames.], batch size: 52, lr: 1.82e-03
2022-05-26 13:30:56,890 INFO [train.py:842] (2/4) Epoch 2, batch 3250, loss[loss=0.2845, simple_loss=0.3542, pruned_loss=0.1074, over 7189.00 frames.], tot_loss[loss=0.3219, simple_loss=0.3675, pruned_loss=0.1382, over 1416487.06 frames.], batch size: 23, lr: 1.82e-03
2022-05-26 13:31:35,491 INFO [train.py:842] (2/4) Epoch 2, batch 3300, loss[loss=0.3147, simple_loss=0.3599, pruned_loss=0.1347, over 7208.00 frames.], tot_loss[loss=0.3188, simple_loss=0.3654, pruned_loss=0.1361, over 1421203.77 frames.], batch size: 22, lr: 1.82e-03
2022-05-26 13:32:14,252 INFO [train.py:842] (2/4) Epoch 2, batch 3350, loss[loss=0.3578, simple_loss=0.394, pruned_loss=0.1608, over 7211.00 frames.], tot_loss[loss=0.3196, simple_loss=0.3664, pruned_loss=0.1364, over 1423942.00 frames.], batch size: 26, lr: 1.81e-03
2022-05-26 13:32:52,898 INFO [train.py:842] (2/4) Epoch 2, batch 3400, loss[loss=0.2355, simple_loss=0.3019, pruned_loss=0.08448, over 7146.00 frames.], tot_loss[loss=0.3181, simple_loss=0.3654, pruned_loss=0.1354, over 1425532.83 frames.], batch size: 17, lr: 1.81e-03
2022-05-26 13:33:31,547 INFO [train.py:842] (2/4) Epoch 2, batch 3450, loss[loss=0.2995, simple_loss=0.3571, pruned_loss=0.121, over 7299.00 frames.], tot_loss[loss=0.3161, simple_loss=0.3641, pruned_loss=0.1341, over 1427515.14 frames.], batch size: 24, lr: 1.81e-03
2022-05-26 13:34:10,040 INFO [train.py:842] (2/4) Epoch 2, batch 3500, loss[loss=0.305, simple_loss=0.3518, pruned_loss=0.1291, over 6264.00 frames.], tot_loss[loss=0.3167, simple_loss=0.3644, pruned_loss=0.1344, over 1424072.96 frames.], batch size: 37, lr: 1.80e-03
2022-05-26 13:34:48,896 INFO [train.py:842] (2/4) Epoch 2, batch 3550, loss[loss=0.3611, simple_loss=0.4015, pruned_loss=0.1603, over 7292.00 frames.], tot_loss[loss=0.3168, simple_loss=0.3646, pruned_loss=0.1345, over 1424506.85 frames.], batch size: 25, lr: 1.80e-03
2022-05-26 13:35:27,116 INFO [train.py:842] (2/4) Epoch 2, batch 3600, loss[loss=0.3793, simple_loss=0.4171, pruned_loss=0.1708, over 7245.00 frames.], tot_loss[loss=0.3154, simple_loss=0.3643, pruned_loss=0.1332, over 1426266.67 frames.], batch size: 20, lr: 1.80e-03
2022-05-26 13:36:06,091 INFO [train.py:842] (2/4) Epoch 2, batch 3650, loss[loss=0.3287, simple_loss=0.3546, pruned_loss=0.1514, over 6828.00 frames.], tot_loss[loss=0.3135, simple_loss=0.3626, pruned_loss=0.1322, over 1428402.48 frames.], batch size: 15, lr: 1.79e-03
2022-05-26 13:36:44,543 INFO [train.py:842] (2/4) Epoch 2, batch 3700, loss[loss=0.2865, simple_loss=0.3543, pruned_loss=0.1094, over 7159.00 frames.], tot_loss[loss=0.3121, simple_loss=0.3623, pruned_loss=0.131, over 1429659.19 frames.], batch size: 19, lr: 1.79e-03
2022-05-26 13:37:23,446 INFO [train.py:842] (2/4) Epoch 2, batch 3750, loss[loss=0.3333, simple_loss=0.3701, pruned_loss=0.1483, over 7303.00 frames.], tot_loss[loss=0.3155, simple_loss=0.3647, pruned_loss=0.1332, over 1430593.11 frames.], batch size: 24, lr: 1.79e-03
2022-05-26 13:38:02,063 INFO [train.py:842] (2/4) Epoch 2, batch 3800, loss[loss=0.2802, simple_loss=0.3305, pruned_loss=0.1149, over 6988.00 frames.], tot_loss[loss=0.3153, simple_loss=0.3642, pruned_loss=0.1332, over 1431154.57 frames.], batch size: 16, lr: 1.79e-03
2022-05-26 13:38:40,878 INFO [train.py:842] (2/4) Epoch 2, batch 3850, loss[loss=0.2728, simple_loss=0.3489, pruned_loss=0.09833, over 7199.00 frames.], tot_loss[loss=0.316, simple_loss=0.3648, pruned_loss=0.1336, over 1431244.83 frames.], batch size: 22, lr: 1.78e-03
2022-05-26 13:39:19,559 INFO [train.py:842] (2/4) Epoch 2, batch 3900, loss[loss=0.3876, simple_loss=0.4148, pruned_loss=0.1802, over 6605.00 frames.], tot_loss[loss=0.317, simple_loss=0.3653, pruned_loss=0.1344, over 1433181.24 frames.], batch size: 38, lr: 1.78e-03
2022-05-26 13:39:58,481 INFO [train.py:842] (2/4) Epoch 2, batch 3950, loss[loss=0.3753, simple_loss=0.4198, pruned_loss=0.1654, over 7324.00 frames.], tot_loss[loss=0.3156, simple_loss=0.3637, pruned_loss=0.1338, over 1431069.80 frames.], batch size: 21, lr: 1.78e-03
2022-05-26 13:40:36,943 INFO [train.py:842] (2/4) Epoch 2, batch 4000, loss[loss=0.3886, simple_loss=0.4151, pruned_loss=0.181, over 4483.00 frames.], tot_loss[loss=0.3147, simple_loss=0.3632, pruned_loss=0.1331, over 1431323.36 frames.], batch size: 52, lr: 1.77e-03
2022-05-26 13:41:15,589 INFO [train.py:842] (2/4) Epoch 2, batch 4050, loss[loss=0.4028, simple_loss=0.433, pruned_loss=0.1862, over 6689.00 frames.], tot_loss[loss=0.3157, simple_loss=0.3638, pruned_loss=0.1338, over 1426258.26 frames.], batch size: 31, lr: 1.77e-03
2022-05-26 13:41:54,111 INFO [train.py:842] (2/4) Epoch 2, batch 4100, loss[loss=0.3049, simple_loss=0.3612, pruned_loss=0.1243, over 7061.00 frames.], tot_loss[loss=0.3172, simple_loss=0.3646, pruned_loss=0.1348, over 1429102.37 frames.], batch size: 28, lr: 1.77e-03
2022-05-26 13:42:32,939 INFO [train.py:842] (2/4) Epoch 2, batch 4150, loss[loss=0.2797, simple_loss=0.355, pruned_loss=0.1022, over 7188.00 frames.], tot_loss[loss=0.3166, simple_loss=0.3641, pruned_loss=0.1346, over 1426610.38 frames.], batch size: 26, lr: 1.76e-03
2022-05-26 13:43:11,631 INFO [train.py:842] (2/4) Epoch 2, batch 4200, loss[loss=0.2812, simple_loss=0.3285, pruned_loss=0.1169, over 7000.00 frames.], tot_loss[loss=0.3168, simple_loss=0.3643, pruned_loss=0.1346, over 1425715.03 frames.], batch size: 16, lr: 1.76e-03
2022-05-26 13:43:50,287 INFO [train.py:842] (2/4) Epoch 2, batch 4250, loss[loss=0.2961, simple_loss=0.3555, pruned_loss=0.1184, over 7196.00 frames.], tot_loss[loss=0.3153, simple_loss=0.3634, pruned_loss=0.1335, over 1423976.80 frames.], batch size: 22, lr: 1.76e-03
2022-05-26 13:44:28,859 INFO [train.py:842] (2/4) Epoch 2, batch 4300, loss[loss=0.2779, simple_loss=0.3508, pruned_loss=0.1025, over 7338.00 frames.], tot_loss[loss=0.3144, simple_loss=0.3627, pruned_loss=0.1331, over 1425634.27 frames.], batch size: 22, lr: 1.76e-03
2022-05-26 13:45:07,447 INFO [train.py:842] (2/4) Epoch 2, batch 4350, loss[loss=0.3035, simple_loss=0.3497, pruned_loss=0.1286, over 7152.00 frames.], tot_loss[loss=0.3141, simple_loss=0.3627, pruned_loss=0.1327, over 1422192.96 frames.], batch size: 19, lr: 1.75e-03
2022-05-26 13:45:45,797 INFO [train.py:842] (2/4) Epoch 2, batch 4400, loss[loss=0.2686, simple_loss=0.343, pruned_loss=0.09709, over 7284.00 frames.], tot_loss[loss=0.3133, simple_loss=0.3622, pruned_loss=0.1322, over 1423255.17 frames.], batch size: 24, lr: 1.75e-03
2022-05-26 13:46:25,232 INFO [train.py:842] (2/4) Epoch 2, batch 4450, loss[loss=0.2362, simple_loss=0.2938, pruned_loss=0.08927, over 7395.00 frames.], tot_loss[loss=0.3133, simple_loss=0.3622, pruned_loss=0.1323, over 1423558.94 frames.], batch size: 18, lr: 1.75e-03
2022-05-26 13:47:03,778 INFO [train.py:842] (2/4) Epoch 2, batch 4500, loss[loss=0.3236, simple_loss=0.3741, pruned_loss=0.1366, over 7315.00 frames.], tot_loss[loss=0.3155, simple_loss=0.3637, pruned_loss=0.1337, over 1425798.40 frames.], batch size: 20, lr: 1.74e-03
2022-05-26 13:47:42,544 INFO [train.py:842] (2/4) Epoch 2, batch 4550, loss[loss=0.3951, simple_loss=0.4123, pruned_loss=0.1889, over 7276.00 frames.], tot_loss[loss=0.3151, simple_loss=0.3631, pruned_loss=0.1335, over 1426419.07 frames.], batch size: 18, lr: 1.74e-03
2022-05-26 13:48:20,814 INFO [train.py:842] (2/4) Epoch 2, batch 4600, loss[loss=0.3389, simple_loss=0.3828, pruned_loss=0.1475, over 7202.00 frames.], tot_loss[loss=0.3142, simple_loss=0.3624, pruned_loss=0.133, over 1420816.07 frames.], batch size: 22, lr: 1.74e-03
2022-05-26 13:48:59,680 INFO [train.py:842] (2/4) Epoch 2, batch 4650, loss[loss=0.3167, simple_loss=0.3608, pruned_loss=0.1363, over 7297.00 frames.], tot_loss[loss=0.3121, simple_loss=0.3613, pruned_loss=0.1314, over 1424332.80 frames.], batch size: 25, lr: 1.74e-03
2022-05-26 13:49:38,508 INFO [train.py:842] (2/4) Epoch 2, batch 4700, loss[loss=0.4187, simple_loss=0.4406, pruned_loss=0.1984, over 7322.00 frames.], tot_loss[loss=0.3125, simple_loss=0.3616, pruned_loss=0.1317, over 1424232.14 frames.], batch size: 21, lr: 1.73e-03
2022-05-26 13:50:16,914 INFO [train.py:842] (2/4) Epoch 2, batch 4750, loss[loss=0.3274, simple_loss=0.3662, pruned_loss=0.1443, over 7418.00 frames.], tot_loss[loss=0.3143, simple_loss=0.3629, pruned_loss=0.1329, over 1417413.12 frames.], batch size: 21, lr: 1.73e-03
2022-05-26 13:50:55,347 INFO [train.py:842] (2/4) Epoch 2, batch 4800, loss[loss=0.3187, simple_loss=0.3736, pruned_loss=0.1319, over 7303.00 frames.], tot_loss[loss=0.3126, simple_loss=0.3619, pruned_loss=0.1317, over 1415715.40 frames.], batch size: 24, lr: 1.73e-03
2022-05-26 13:51:34,134 INFO [train.py:842] (2/4) Epoch 2, batch 4850, loss[loss=0.2773, simple_loss=0.3333, pruned_loss=0.1106, over 7159.00 frames.], tot_loss[loss=0.3132, simple_loss=0.3624, pruned_loss=0.132, over 1415803.40 frames.], batch size: 18, lr: 1.73e-03
2022-05-26 13:52:12,637 INFO [train.py:842] (2/4) Epoch 2, batch 4900, loss[loss=0.2685, simple_loss=0.3133, pruned_loss=0.1118, over 7265.00 frames.], tot_loss[loss=0.3095, simple_loss=0.3602, pruned_loss=0.1294, over 1418069.27 frames.], batch size: 17, lr: 1.72e-03
2022-05-26 13:52:51,828 INFO [train.py:842] (2/4) Epoch 2, batch 4950, loss[loss=0.3154, simple_loss=0.3654, pruned_loss=0.1326, over 7241.00 frames.], tot_loss[loss=0.3072, simple_loss=0.3582, pruned_loss=0.1281, over 1420298.96 frames.], batch size: 20, lr: 1.72e-03
2022-05-26 13:53:30,406 INFO [train.py:842] (2/4) Epoch 2, batch 5000, loss[loss=0.311, simple_loss=0.3507, pruned_loss=0.1356, over 7273.00 frames.], tot_loss[loss=0.3079, simple_loss=0.3587, pruned_loss=0.1285, over 1422893.24 frames.], batch size: 17, lr: 1.72e-03
2022-05-26 13:54:08,957 INFO [train.py:842] (2/4) Epoch 2, batch 5050, loss[loss=0.3305, simple_loss=0.367, pruned_loss=0.147, over 7413.00 frames.], tot_loss[loss=0.3103, simple_loss=0.3605, pruned_loss=0.13, over 1416402.75 frames.], batch size: 21, lr: 1.71e-03
2022-05-26 13:54:47,653 INFO [train.py:842] (2/4) Epoch 2, batch 5100, loss[loss=0.2932, simple_loss=0.3576, pruned_loss=0.1144, over 7156.00 frames.], tot_loss[loss=0.3091, simple_loss=0.36, pruned_loss=0.1291, over 1419992.62 frames.], batch size: 19, lr: 1.71e-03
2022-05-26 13:55:26,653 INFO [train.py:842] (2/4) Epoch 2, batch 5150, loss[loss=0.3363, simple_loss=0.3906, pruned_loss=0.141, over 7225.00 frames.], tot_loss[loss=0.31, simple_loss=0.3604, pruned_loss=0.1298, over 1421088.66 frames.], batch size: 21, lr: 1.71e-03
2022-05-26 13:56:05,012 INFO [train.py:842] (2/4) Epoch 2, batch 5200, loss[loss=0.3145, simple_loss=0.3748, pruned_loss=0.1271, over 7297.00 frames.], tot_loss[loss=0.309, simple_loss=0.3602, pruned_loss=0.1289, over 1422255.38 frames.], batch size: 25, lr: 1.71e-03
2022-05-26 13:56:43,792 INFO [train.py:842] (2/4) Epoch 2, batch 5250, loss[loss=0.2902, simple_loss=0.3465, pruned_loss=0.117, over 6781.00 frames.], tot_loss[loss=0.3085, simple_loss=0.3597, pruned_loss=0.1286, over 1425003.87 frames.], batch size: 31, lr: 1.70e-03
2022-05-26 13:57:22,560 INFO [train.py:842] (2/4) Epoch 2, batch 5300, loss[loss=0.3597, simple_loss=0.4009, pruned_loss=0.1593, over 7379.00 frames.], tot_loss[loss=0.3092, simple_loss=0.3601, pruned_loss=0.1291, over 1421722.82 frames.], batch size: 23, lr: 1.70e-03
2022-05-26 13:58:01,623 INFO [train.py:842] (2/4) Epoch 2, batch 5350, loss[loss=0.2206, simple_loss=0.2881, pruned_loss=0.07655, over 7365.00 frames.], tot_loss[loss=0.3074, simple_loss=0.3583, pruned_loss=0.1283, over 1419352.01 frames.], batch size: 19, lr: 1.70e-03
2022-05-26 13:58:40,204 INFO [train.py:842] (2/4) Epoch 2, batch 5400, loss[loss=0.3162, simple_loss=0.3617, pruned_loss=0.1354, over 6525.00 frames.], tot_loss[loss=0.3065, simple_loss=0.3575, pruned_loss=0.1278, over 1420373.41 frames.], batch size: 38, lr: 1.70e-03
2022-05-26 13:59:19,542 INFO [train.py:842] (2/4) Epoch 2, batch 5450, loss[loss=0.2678, simple_loss=0.3219, pruned_loss=0.1069, over 6821.00 frames.], tot_loss[loss=0.3055, simple_loss=0.3564, pruned_loss=0.1273, over 1421473.64 frames.], batch size: 15, lr: 1.69e-03
2022-05-26 13:59:58,104 INFO [train.py:842] (2/4) Epoch 2, batch 5500, loss[loss=0.2966, simple_loss=0.3493, pruned_loss=0.1219, over 7134.00 frames.], tot_loss[loss=0.3061, simple_loss=0.3569, pruned_loss=0.1277, over 1423660.42 frames.], batch size: 17, lr: 1.69e-03
2022-05-26 14:00:37,017 INFO [train.py:842] (2/4) Epoch 2, batch 5550, loss[loss=0.2395, simple_loss=0.3103, pruned_loss=0.08434, over 6984.00 frames.], tot_loss[loss=0.3053, simple_loss=0.3561, pruned_loss=0.1273, over 1424205.91 frames.], batch size: 16, lr: 1.69e-03
2022-05-26 14:01:15,380 INFO [train.py:842] (2/4) Epoch 2, batch 5600, loss[loss=0.2842, simple_loss=0.358, pruned_loss=0.1052, over 7309.00 frames.], tot_loss[loss=0.3053, simple_loss=0.3568, pruned_loss=0.1269, over 1424723.58 frames.], batch size: 24, lr: 1.69e-03
2022-05-26 14:01:54,145 INFO [train.py:842] (2/4) Epoch 2, batch 5650, loss[loss=0.3192, simple_loss=0.3753, pruned_loss=0.1315, over 7205.00 frames.], tot_loss[loss=0.3066, simple_loss=0.3581, pruned_loss=0.1276, over 1425579.90 frames.], batch size: 23, lr: 1.68e-03
2022-05-26 14:02:32,781 INFO [train.py:842] (2/4) Epoch 2, batch 5700, loss[loss=0.2344, simple_loss=0.2985, pruned_loss=0.08518, over 7296.00 frames.], tot_loss[loss=0.3062, simple_loss=0.3572, pruned_loss=0.1276, over 1424592.99 frames.], batch size: 18, lr: 1.68e-03
2022-05-26 14:03:11,573 INFO [train.py:842] (2/4) Epoch 2, batch 5750, loss[loss=0.3802, simple_loss=0.417, pruned_loss=0.1717, over 7312.00 frames.], tot_loss[loss=0.306, simple_loss=0.3571, pruned_loss=0.1275, over 1422557.67 frames.], batch size: 21, lr: 1.68e-03
2022-05-26 14:03:50,308 INFO [train.py:842] (2/4) Epoch 2, batch 5800, loss[loss=0.3021, simple_loss=0.3583, pruned_loss=0.123, over 7159.00 frames.], tot_loss[loss=0.304, simple_loss=0.3559, pruned_loss=0.126, over 1426718.11 frames.], batch size: 26, lr: 1.68e-03
2022-05-26 14:04:29,364 INFO [train.py:842] (2/4) Epoch 2, batch 5850, loss[loss=0.3621, simple_loss=0.4051, pruned_loss=0.1596, over 7416.00 frames.], tot_loss[loss=0.3062, simple_loss=0.3579, pruned_loss=0.1273, over 1422592.24 frames.], batch size: 21, lr: 1.67e-03
2022-05-26 14:05:07,952 INFO [train.py:842] (2/4) Epoch 2, batch 5900, loss[loss=0.3462, simple_loss=0.3687, pruned_loss=0.1619, over 7294.00 frames.], tot_loss[loss=0.305, simple_loss=0.3565, pruned_loss=0.1267, over 1425126.25 frames.], batch size: 17, lr: 1.67e-03
2022-05-26 14:05:46,677 INFO [train.py:842] (2/4) Epoch 2, batch 5950, loss[loss=0.3748, simple_loss=0.4237, pruned_loss=0.1629, over 7207.00 frames.], tot_loss[loss=0.3051, simple_loss=0.3568, pruned_loss=0.1267, over 1423968.37 frames.], batch size: 22, lr: 1.67e-03
2022-05-26 14:06:25,158 INFO [train.py:842] (2/4) Epoch 2, batch 6000, loss[loss=0.3377, simple_loss=0.3846, pruned_loss=0.1454, over 7412.00 frames.], tot_loss[loss=0.3071, simple_loss=0.3585, pruned_loss=0.1278, over 1420396.97 frames.], batch size: 21, lr: 1.67e-03
2022-05-26 14:06:25,158 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 14:06:34,412 INFO [train.py:871] (2/4) Epoch 2, validation: loss=0.2262, simple_loss=0.3196, pruned_loss=0.0664, over 868885.00 frames.
2022-05-26 14:07:13,292 INFO [train.py:842] (2/4) Epoch 2, batch 6050, loss[loss=0.2993, simple_loss=0.357, pruned_loss=0.1208, over 7206.00 frames.], tot_loss[loss=0.3075, simple_loss=0.3586, pruned_loss=0.1282, over 1424709.34 frames.], batch size: 23, lr: 1.66e-03
2022-05-26 14:07:51,787 INFO [train.py:842] (2/4) Epoch 2, batch 6100, loss[loss=0.2997, simple_loss=0.359, pruned_loss=0.1201, over 7369.00 frames.], tot_loss[loss=0.3069, simple_loss=0.3585, pruned_loss=0.1277, over 1426325.20 frames.], batch size: 23, lr: 1.66e-03
2022-05-26 14:08:30,698 INFO [train.py:842] (2/4) Epoch 2, batch 6150, loss[loss=0.3338, simple_loss=0.3868, pruned_loss=0.1404, over 7003.00 frames.], tot_loss[loss=0.3052, simple_loss=0.3571, pruned_loss=0.1267, over 1426082.95 frames.], batch size: 28, lr: 1.66e-03
2022-05-26 14:09:09,176 INFO [train.py:842] (2/4) Epoch 2, batch 6200, loss[loss=0.2646, simple_loss=0.3285, pruned_loss=0.1003, over 6753.00 frames.], tot_loss[loss=0.3064, simple_loss=0.3579, pruned_loss=0.1274, over 1424811.47 frames.], batch size: 31, lr: 1.66e-03
2022-05-26 14:09:47,987 INFO [train.py:842] (2/4) Epoch 2, batch 6250, loss[loss=0.2853, simple_loss=0.3403, pruned_loss=0.1152, over 7106.00 frames.], tot_loss[loss=0.3067, simple_loss=0.3582, pruned_loss=0.1276, over 1427485.41 frames.], batch size: 21, lr: 1.65e-03
2022-05-26 14:10:27,105 INFO [train.py:842] (2/4) Epoch 2, batch 6300, loss[loss=0.3098, simple_loss=0.37, pruned_loss=0.1248, over 7173.00 frames.], tot_loss[loss=0.3041, simple_loss=0.3564, pruned_loss=0.1259, over 1431298.10 frames.], batch size: 26, lr: 1.65e-03
2022-05-26 14:11:05,661 INFO [train.py:842] (2/4) Epoch 2, batch 6350, loss[loss=0.3061, simple_loss=0.3479, pruned_loss=0.1322, over 6294.00 frames.], tot_loss[loss=0.3051, simple_loss=0.3575, pruned_loss=0.1263, over 1430170.82 frames.], batch size: 37, lr: 1.65e-03
2022-05-26 14:11:44,201 INFO [train.py:842] (2/4) Epoch 2, batch 6400, loss[loss=0.3377, simple_loss=0.3827, pruned_loss=0.1463, over 6795.00 frames.], tot_loss[loss=0.3041, simple_loss=0.3563, pruned_loss=0.1259, over 1424750.03 frames.], batch size: 31, lr: 1.65e-03
2022-05-26 14:12:23,048 INFO [train.py:842] (2/4) Epoch 2, batch 6450, loss[loss=0.2565, simple_loss=0.3141, pruned_loss=0.09945, over 7408.00 frames.], tot_loss[loss=0.3021, simple_loss=0.3551, pruned_loss=0.1246, over 1424349.42 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 14:13:01,662 INFO [train.py:842] (2/4) Epoch 2, batch 6500, loss[loss=0.2858, simple_loss=0.343, pruned_loss=0.1143, over 7195.00 frames.], tot_loss[loss=0.3034, simple_loss=0.3558, pruned_loss=0.1255, over 1423286.17 frames.], batch size: 22, lr: 1.64e-03
2022-05-26 14:13:40,430 INFO [train.py:842] (2/4) Epoch 2, batch 6550, loss[loss=0.2637, simple_loss=0.3149, pruned_loss=0.1062, over 7072.00 frames.], tot_loss[loss=0.3042, simple_loss=0.3567, pruned_loss=0.1259, over 1420096.36 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 14:14:18,980 INFO [train.py:842] (2/4) Epoch 2, batch 6600, loss[loss=0.2712, simple_loss=0.3221, pruned_loss=0.1102, over 7298.00 frames.], tot_loss[loss=0.306, simple_loss=0.3577, pruned_loss=0.1272, over 1420307.27 frames.], batch size: 18, lr: 1.64e-03
2022-05-26 14:14:57,584 INFO [train.py:842] (2/4) Epoch 2, batch 6650, loss[loss=0.2964, simple_loss=0.362, pruned_loss=0.1154, over 7193.00 frames.], tot_loss[loss=0.3066, simple_loss=0.3581, pruned_loss=0.1275, over 1413181.74 frames.], batch size: 23, lr: 1.63e-03
2022-05-26 14:15:36,120 INFO [train.py:842] (2/4) Epoch 2, batch 6700, loss[loss=0.2652, simple_loss=0.3246, pruned_loss=0.1029, over 7274.00 frames.], tot_loss[loss=0.3033, simple_loss=0.3557, pruned_loss=0.1254, over 1419667.22 frames.], batch size: 17, lr: 1.63e-03
2022-05-26 14:16:14,706 INFO [train.py:842] (2/4) Epoch 2, batch 6750, loss[loss=0.3273, simple_loss=0.3778, pruned_loss=0.1384, over 7222.00 frames.], tot_loss[loss=0.3032, simple_loss=0.3563, pruned_loss=0.125, over 1422429.12 frames.], batch size: 20, lr: 1.63e-03
2022-05-26 14:16:53,059 INFO [train.py:842] (2/4) Epoch 2, batch 6800, loss[loss=0.2985, simple_loss=0.3607, pruned_loss=0.1182, over 7130.00 frames.], tot_loss[loss=0.3007, simple_loss=0.3548, pruned_loss=0.1233, over 1424640.23 frames.], batch size: 21, lr: 1.63e-03
2022-05-26 14:17:34,587 INFO [train.py:842] (2/4) Epoch 2, batch 6850, loss[loss=0.3184, simple_loss=0.3628, pruned_loss=0.137, over 7329.00 frames.], tot_loss[loss=0.3007, simple_loss=0.355, pruned_loss=0.1232, over 1421308.87 frames.], batch size: 20, lr: 1.63e-03
2022-05-26 14:18:13,245 INFO [train.py:842] (2/4) Epoch 2, batch 6900, loss[loss=0.3167, simple_loss=0.3641, pruned_loss=0.1347, over 7438.00 frames.], tot_loss[loss=0.3003, simple_loss=0.3549, pruned_loss=0.1229, over 1421414.44 frames.], batch size: 20, lr: 1.62e-03
2022-05-26 14:18:52,139 INFO [train.py:842] (2/4) Epoch 2, batch 6950, loss[loss=0.2225, simple_loss=0.2915, pruned_loss=0.07671, over 7284.00 frames.], tot_loss[loss=0.2987, simple_loss=0.3527, pruned_loss=0.1223, over 1421262.56 frames.], batch size: 18, lr: 1.62e-03
2022-05-26 14:19:30,566 INFO [train.py:842] (2/4) Epoch 2, batch 7000, loss[loss=0.2883, simple_loss=0.3527, pruned_loss=0.1119, over 7316.00 frames.], tot_loss[loss=0.2996, simple_loss=0.353, pruned_loss=0.1231, over 1423409.55 frames.], batch size: 21, lr: 1.62e-03
2022-05-26 14:20:09,754 INFO [train.py:842] (2/4) Epoch 2, batch 7050, loss[loss=0.4874, simple_loss=0.47, pruned_loss=0.2524, over 5305.00 frames.], tot_loss[loss=0.2988, simple_loss=0.3524, pruned_loss=0.1226, over 1425588.80 frames.], batch size: 53, lr: 1.62e-03
2022-05-26 14:20:48,374 INFO [train.py:842] (2/4) Epoch 2, batch 7100, loss[loss=0.284, simple_loss=0.3547, pruned_loss=0.1066, over 7102.00 frames.], tot_loss[loss=0.3006, simple_loss=0.354, pruned_loss=0.1237, over 1425292.31 frames.], batch size: 21, lr: 1.61e-03
2022-05-26 14:21:26,952 INFO [train.py:842] (2/4) Epoch 2, batch 7150, loss[loss=0.2648, simple_loss=0.333, pruned_loss=0.09831, over 7416.00 frames.], tot_loss[loss=0.3013, simple_loss=0.3547, pruned_loss=0.124, over 1422247.85 frames.], batch size: 21, lr: 1.61e-03
2022-05-26 14:22:05,293 INFO [train.py:842] (2/4) Epoch 2, batch 7200, loss[loss=0.2395, simple_loss=0.2984, pruned_loss=0.09032, over 6984.00 frames.], tot_loss[loss=0.3024, simple_loss=0.3554, pruned_loss=0.1247, over 1420616.02 frames.], batch size: 16, lr: 1.61e-03
2022-05-26 14:22:44,437 INFO [train.py:842] (2/4) Epoch 2, batch 7250, loss[loss=0.3135, simple_loss=0.3574, pruned_loss=0.1348, over 7227.00 frames.], tot_loss[loss=0.3024, simple_loss=0.3554, pruned_loss=0.1247, over 1425739.97 frames.], batch size: 20, lr: 1.61e-03
2022-05-26 14:23:22,967 INFO [train.py:842] (2/4) Epoch 2, batch 7300, loss[loss=0.389, simple_loss=0.4142, pruned_loss=0.1819, over 7222.00 frames.], tot_loss[loss=0.3045, simple_loss=0.3573, pruned_loss=0.1258, over 1427990.37 frames.], batch size: 21, lr: 1.60e-03
2022-05-26 14:24:01,873 INFO [train.py:842] (2/4) Epoch 2, batch 7350, loss[loss=0.4012, simple_loss=0.42, pruned_loss=0.1911, over 5367.00 frames.], tot_loss[loss=0.3037, simple_loss=0.3561, pruned_loss=0.1256, over 1424508.21 frames.], batch size: 52, lr: 1.60e-03
2022-05-26 14:24:40,513 INFO [train.py:842] (2/4) Epoch 2, batch 7400, loss[loss=0.3001, simple_loss=0.3455, pruned_loss=0.1273, over 7015.00 frames.], tot_loss[loss=0.303, simple_loss=0.3555, pruned_loss=0.1252, over 1424109.13 frames.], batch size: 16, lr: 1.60e-03
2022-05-26 14:25:19,330 INFO [train.py:842] (2/4) Epoch 2, batch 7450, loss[loss=0.2359, simple_loss=0.3126, pruned_loss=0.07958, over 7364.00 frames.], tot_loss[loss=0.3025, simple_loss=0.3549, pruned_loss=0.1251, over 1419080.47 frames.], batch size: 19, lr: 1.60e-03
2022-05-26 14:25:57,996 INFO [train.py:842] (2/4) Epoch 2, batch 7500, loss[loss=0.2845, simple_loss=0.3646, pruned_loss=0.1022, over 7219.00 frames.], tot_loss[loss=0.3013, simple_loss=0.3544, pruned_loss=0.1241, over 1419567.37 frames.], batch size: 21, lr: 1.60e-03
2022-05-26 14:26:36,917 INFO [train.py:842] (2/4) Epoch 2, batch 7550, loss[loss=0.338, simple_loss=0.3941, pruned_loss=0.141, over 7405.00 frames.], tot_loss[loss=0.3016, simple_loss=0.3548, pruned_loss=0.1242, over 1421156.60 frames.], batch size: 21, lr: 1.59e-03
2022-05-26 14:27:15,662 INFO [train.py:842] (2/4) Epoch 2, batch 7600, loss[loss=0.3568, simple_loss=0.3851, pruned_loss=0.1642, over 4913.00 frames.], tot_loss[loss=0.2999, simple_loss=0.3531, pruned_loss=0.1233, over 1420924.38 frames.], batch size: 52, lr: 1.59e-03
2022-05-26 14:27:54,288 INFO [train.py:842] (2/4) Epoch 2, batch 7650, loss[loss=0.3263, simple_loss=0.369, pruned_loss=0.1418, over 7409.00 frames.], tot_loss[loss=0.2982, simple_loss=0.352, pruned_loss=0.1222, over 1421842.91 frames.], batch size: 21, lr: 1.59e-03
2022-05-26 14:28:32,786 INFO [train.py:842] (2/4) Epoch 2, batch 7700, loss[loss=0.3115, simple_loss=0.3714, pruned_loss=0.1258, over 7339.00 frames.], tot_loss[loss=0.2981, simple_loss=0.3518, pruned_loss=0.1223, over 1423144.39 frames.], batch size: 22, lr: 1.59e-03
2022-05-26 14:29:11,476 INFO [train.py:842] (2/4) Epoch 2, batch 7750, loss[loss=0.3233, simple_loss=0.3803, pruned_loss=0.1332, over 7039.00 frames.], tot_loss[loss=0.2978, simple_loss=0.352, pruned_loss=0.1218, over 1424122.10 frames.], batch size: 28, lr: 1.59e-03
2022-05-26 14:29:50,000 INFO [train.py:842] (2/4) Epoch 2, batch 7800, loss[loss=0.3993, simple_loss=0.4155, pruned_loss=0.1915, over 7139.00 frames.], tot_loss[loss=0.2993, simple_loss=0.3531, pruned_loss=0.1227, over 1423708.02 frames.], batch size: 20, lr: 1.58e-03
2022-05-26 14:30:28,805 INFO [train.py:842] (2/4) Epoch 2, batch 7850, loss[loss=0.2838, simple_loss=0.353, pruned_loss=0.1072, over 7316.00 frames.], tot_loss[loss=0.298, simple_loss=0.3523, pruned_loss=0.1219, over 1423636.68 frames.], batch size: 21, lr: 1.58e-03
2022-05-26 14:31:07,198 INFO [train.py:842] (2/4) Epoch 2, batch 7900, loss[loss=0.3853, simple_loss=0.4105, pruned_loss=0.1801, over 5012.00 frames.], tot_loss[loss=0.2978, simple_loss=0.3522, pruned_loss=0.1217, over 1425539.29 frames.], batch size: 52, lr: 1.58e-03
2022-05-26 14:31:46,043 INFO [train.py:842] (2/4) Epoch 2, batch 7950, loss[loss=0.2528, simple_loss=0.3146, pruned_loss=0.09549, over 7164.00 frames.], tot_loss[loss=0.2978, simple_loss=0.3522, pruned_loss=0.1218, over 1427748.92 frames.], batch size: 18, lr: 1.58e-03
2022-05-26 14:32:24,256 INFO [train.py:842] (2/4) Epoch 2, batch 8000, loss[loss=0.2966, simple_loss=0.3629, pruned_loss=0.1151, over 7232.00 frames.], tot_loss[loss=0.2984, simple_loss=0.3529, pruned_loss=0.122, over 1426965.04 frames.], batch size: 21, lr: 1.57e-03
2022-05-26 14:33:02,889 INFO [train.py:842] (2/4) Epoch 2, batch 8050, loss[loss=0.3715, simple_loss=0.4142, pruned_loss=0.1644, over 6388.00 frames.], tot_loss[loss=0.3005, simple_loss=0.3543, pruned_loss=0.1234, over 1424652.37 frames.], batch size: 37, lr: 1.57e-03
2022-05-26 14:33:41,415 INFO [train.py:842] (2/4) Epoch 2, batch 8100, loss[loss=0.2952, simple_loss=0.3416, pruned_loss=0.1244, over 7163.00 frames.], tot_loss[loss=0.2998, simple_loss=0.3536, pruned_loss=0.123, over 1427027.64 frames.], batch size: 26, lr: 1.57e-03
2022-05-26 14:34:20,547 INFO [train.py:842] (2/4) Epoch 2, batch 8150, loss[loss=0.3494, simple_loss=0.3838, pruned_loss=0.1575, over 7063.00 frames.], tot_loss[loss=0.3003, simple_loss=0.3535, pruned_loss=0.1235, over 1428197.20 frames.], batch size: 18, lr: 1.57e-03
2022-05-26 14:34:58,967 INFO [train.py:842] (2/4) Epoch 2, batch 8200, loss[loss=0.2706, simple_loss=0.3265, pruned_loss=0.1074, over 7289.00 frames.], tot_loss[loss=0.3003, simple_loss=0.3537, pruned_loss=0.1235, over 1423434.32 frames.], batch size: 18, lr: 1.57e-03
2022-05-26 14:35:38,140 INFO [train.py:842] (2/4) Epoch 2, batch 8250, loss[loss=0.2865, simple_loss=0.3358, pruned_loss=0.1186, over 7058.00 frames.], tot_loss[loss=0.2974, simple_loss=0.3512, pruned_loss=0.1218, over 1420623.98 frames.], batch size: 28, lr: 1.56e-03
2022-05-26 14:36:16,475 INFO [train.py:842] (2/4) Epoch 2, batch 8300, loss[loss=0.3144, simple_loss=0.3732, pruned_loss=0.1278, over 7145.00 frames.], tot_loss[loss=0.298, simple_loss=0.3522, pruned_loss=0.1219, over 1418915.58 frames.], batch size: 20, lr: 1.56e-03
2022-05-26 14:36:55,265 INFO [train.py:842] (2/4) Epoch 2, batch 8350, loss[loss=0.4046, simple_loss=0.4279, pruned_loss=0.1906, over 5062.00 frames.], tot_loss[loss=0.2973, simple_loss=0.3518, pruned_loss=0.1214, over 1417418.48 frames.], batch size: 53, lr: 1.56e-03
2022-05-26 14:37:33,581 INFO [train.py:842] (2/4) Epoch 2, batch 8400, loss[loss=0.2423, simple_loss=0.305, pruned_loss=0.08985, over 7117.00 frames.], tot_loss[loss=0.2981, simple_loss=0.3526, pruned_loss=0.1217, over 1418021.72 frames.], batch size: 17, lr: 1.56e-03
2022-05-26 14:38:12,075 INFO [train.py:842] (2/4) Epoch 2, batch 8450, loss[loss=0.2827, simple_loss=0.353, pruned_loss=0.1062, over 7189.00 frames.], tot_loss[loss=0.2976, simple_loss=0.3528, pruned_loss=0.1212, over 1413648.58 frames.], batch size: 22, lr: 1.56e-03
2022-05-26 14:38:50,512 INFO [train.py:842] (2/4) Epoch 2, batch 8500, loss[loss=0.2997, simple_loss=0.3514, pruned_loss=0.124, over 7125.00 frames.], tot_loss[loss=0.2997, simple_loss=0.3542, pruned_loss=0.1226, over 1417615.95 frames.], batch size: 17, lr: 1.55e-03
2022-05-26 14:39:29,185 INFO [train.py:842] (2/4) Epoch 2, batch 8550, loss[loss=0.2414, simple_loss=0.3173, pruned_loss=0.08273, over 7354.00 frames.], tot_loss[loss=0.2991, simple_loss=0.3543, pruned_loss=0.1219, over 1423287.35 frames.], batch size: 19, lr: 1.55e-03
2022-05-26 14:40:07,844 INFO [train.py:842] (2/4) Epoch 2, batch 8600, loss[loss=0.3135, simple_loss=0.3655, pruned_loss=0.1307, over 6384.00 frames.], tot_loss[loss=0.2974, simple_loss=0.3524, pruned_loss=0.1212, over 1420856.84 frames.], batch size: 38, lr: 1.55e-03
2022-05-26 14:40:46,959 INFO [train.py:842] (2/4) Epoch 2, batch 8650, loss[loss=0.329, simple_loss=0.3752, pruned_loss=0.1414, over 7145.00 frames.], tot_loss[loss=0.2963, simple_loss=0.3515, pruned_loss=0.1206, over 1422843.69 frames.], batch size: 20, lr: 1.55e-03
2022-05-26 14:41:25,736 INFO [train.py:842] (2/4) Epoch 2, batch 8700, loss[loss=0.2403, simple_loss=0.3073, pruned_loss=0.0867, over 7069.00 frames.], tot_loss[loss=0.2942, simple_loss=0.3496, pruned_loss=0.1194, over 1421339.00 frames.], batch size: 18, lr: 1.55e-03
2022-05-26 14:42:04,176 INFO [train.py:842] (2/4) Epoch 2, batch 8750, loss[loss=0.23, simple_loss=0.2969, pruned_loss=0.08156, over 7156.00 frames.], tot_loss[loss=0.2941, simple_loss=0.3495, pruned_loss=0.1193, over 1420259.03 frames.], batch size: 18, lr: 1.54e-03
2022-05-26 14:42:42,568 INFO [train.py:842] (2/4) Epoch 2, batch 8800, loss[loss=0.3067, simple_loss=0.3682, pruned_loss=0.1226, over 7331.00 frames.], tot_loss[loss=0.2959, simple_loss=0.3504, pruned_loss=0.1207, over 1412729.38 frames.], batch size: 22, lr: 1.54e-03
2022-05-26 14:43:21,145 INFO [train.py:842] (2/4) Epoch 2, batch 8850, loss[loss=0.3794, simple_loss=0.4241, pruned_loss=0.1673, over 7296.00 frames.], tot_loss[loss=0.2975, simple_loss=0.3515, pruned_loss=0.1217, over 1410443.92 frames.], batch size: 24, lr: 1.54e-03
2022-05-26 14:43:59,277 INFO [train.py:842] (2/4) Epoch 2, batch 8900, loss[loss=0.2974, simple_loss=0.3592, pruned_loss=0.1178, over 6772.00 frames.], tot_loss[loss=0.2974, simple_loss=0.3515, pruned_loss=0.1216, over 1400972.25 frames.], batch size: 31, lr: 1.54e-03
2022-05-26 14:44:37,751 INFO [train.py:842] (2/4) Epoch 2, batch 8950, loss[loss=0.2734, simple_loss=0.3427, pruned_loss=0.1021, over 7106.00 frames.], tot_loss[loss=0.2985, simple_loss=0.3528, pruned_loss=0.1221, over 1400664.00 frames.], batch size: 21, lr: 1.54e-03
2022-05-26 14:45:16,055 INFO [train.py:842] (2/4) Epoch 2, batch 9000, loss[loss=0.377, simple_loss=0.4057, pruned_loss=0.1741, over 7275.00 frames.], tot_loss[loss=0.3, simple_loss=0.3539, pruned_loss=0.1231, over 1396835.82 frames.], batch size: 18, lr: 1.53e-03
2022-05-26 14:45:16,055 INFO [train.py:862] (2/4) Computing validation loss
2022-05-26 14:45:25,235 INFO [train.py:871] (2/4) Epoch 2, validation: loss=0.2179, simple_loss=0.3144, pruned_loss=0.06069, over 868885.00 frames.
2022-05-26 14:46:03,593 INFO [train.py:842] (2/4) Epoch 2, batch 9050, loss[loss=0.246, simple_loss=0.3137, pruned_loss=0.08921, over 7266.00 frames.], tot_loss[loss=0.3018, simple_loss=0.3551, pruned_loss=0.1242, over 1381754.41 frames.], batch size: 18, lr: 1.53e-03
2022-05-26 14:46:40,966 INFO [train.py:842] (2/4) Epoch 2, batch 9100, loss[loss=0.3191, simple_loss=0.3553, pruned_loss=0.1415, over 4915.00 frames.], tot_loss[loss=0.307, simple_loss=0.3587, pruned_loss=0.1277, over 1327044.20 frames.], batch size: 53, lr: 1.53e-03
2022-05-26 14:47:18,817 INFO [train.py:842] (2/4) Epoch 2, batch 9150, loss[loss=0.3048, simple_loss=0.3533, pruned_loss=0.1281, over 4816.00 frames.], tot_loss[loss=0.3144, simple_loss=0.3636, pruned_loss=0.1327, over 1257783.86 frames.], batch size: 52, lr: 1.53e-03