2022-04-28 06:39:03,122 INFO [train.py:827] (1/8) Training started
2022-04-28 06:39:03,122 INFO [train.py:837] (1/8) Device: cuda:1
2022-04-28 06:39:03,161 INFO [train.py:846] (1/8) {'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, 'model_warm_step': 3000, 'env_info': {'k2-version': '1.14', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': '3b83183234d0f1d8391872630551c5af7c491ed2', 'k2-git-date': 'Tue Apr 12 08:26:41 2022', 'lhotse-version': '1.1.0.dev+missing.version.file', 'torch-version': '1.10.0+cu102', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'deeper-conformer', 'icefall-git-sha1': 'd79f5fe-dirty', 'icefall-git-date': 'Mon Apr 25 17:26:43 2022', 'icefall-path': '/ceph-fj/fangjun/open-source-2/icefall-deeper-conformer', 'k2-path': '/ceph-fj/fangjun/open-source-2/k2-multi-2/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-fj/fangjun/open-source-2/lhotse-multi-3/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-7-0309102938-68688b4cbd-xhtcg', 'IP address': '10.48.32.137'}, 'world_size': 8, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 40, 'start_epoch': 0, 'start_batch': 0, 'exp_dir': PosixPath('pruned_transducer_stateless4/exp-L'), '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, 'use_fp16': False, 'num_encoder_layers': 18, 'dim_feedforward': 2048, 'nhead': 8, 'encoder_dim': 512, 'decoder_dim': 512, 'joiner_dim': 512, '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}
2022-04-28 06:39:03,161 INFO [train.py:848] (1/8) About to create model
2022-04-28 06:39:03,691 INFO [train.py:852] (1/8) Number of model parameters: 118129516
2022-04-28 06:39:09,757 INFO [train.py:858] (1/8) Using DDP
2022-04-28 06:39:10,514 INFO [asr_datamodule.py:391] (1/8) About to get train-clean-100 cuts
2022-04-28 06:39:16,936 INFO [asr_datamodule.py:398] (1/8) About to get train-clean-360 cuts
2022-04-28 06:39:41,933 INFO [asr_datamodule.py:405] (1/8) About to get train-other-500 cuts
2022-04-28 06:40:23,580 INFO [asr_datamodule.py:209] (1/8) Enable MUSAN
2022-04-28 06:40:23,581 INFO [asr_datamodule.py:210] (1/8) About to get Musan cuts
2022-04-28 06:40:24,852 INFO [asr_datamodule.py:238] (1/8) Enable SpecAugment
2022-04-28 06:40:24,852 INFO [asr_datamodule.py:239] (1/8) Time warp factor: 80
2022-04-28 06:40:24,853 INFO [asr_datamodule.py:251] (1/8) Num frame mask: 10
2022-04-28 06:40:24,853 INFO [asr_datamodule.py:264] (1/8) About to create train dataset
2022-04-28 06:40:24,853 INFO [asr_datamodule.py:292] (1/8) Using BucketingSampler.
2022-04-28 06:40:29,450 INFO [asr_datamodule.py:308] (1/8) About to create train dataloader
2022-04-28 06:40:29,451 INFO [asr_datamodule.py:412] (1/8) About to get dev-clean cuts
2022-04-28 06:40:29,714 INFO [asr_datamodule.py:417] (1/8) About to get dev-other cuts
2022-04-28 06:40:29,841 INFO [asr_datamodule.py:339] (1/8) About to create dev dataset
2022-04-28 06:40:29,852 INFO [asr_datamodule.py:358] (1/8) About to create dev dataloader
2022-04-28 06:40:29,852 INFO [train.py:987] (1/8) Sanity check -- see if any of the batches in epoch 0 would cause OOM.
2022-04-28 06:40:42,725 INFO [distributed.py:874] (1/8) Reducer buckets have been rebuilt in this iteration.
2022-04-28 06:41:17,073 INFO [train.py:763] (1/8) Epoch 0, batch 0, loss[loss=0.6687, simple_loss=1.337, pruned_loss=7.08, over 7292.00 frames.], tot_loss[loss=0.6687, simple_loss=1.337, pruned_loss=7.08, over 7292.00 frames.], batch size: 17, lr: 3.00e-03
2022-04-28 06:42:23,563 INFO [train.py:763] (1/8) Epoch 0, batch 50, loss[loss=0.5195, simple_loss=1.039, pruned_loss=6.7, over 7175.00 frames.], tot_loss[loss=0.5693, simple_loss=1.139, pruned_loss=6.952, over 324695.83 frames.], batch size: 19, lr: 3.00e-03
2022-04-28 06:43:30,297 INFO [train.py:763] (1/8) Epoch 0, batch 100, loss[loss=0.4389, simple_loss=0.8779, pruned_loss=6.761, over 7014.00 frames.], tot_loss[loss=0.5101, simple_loss=1.02, pruned_loss=6.868, over 567421.65 frames.], batch size: 16, lr: 3.00e-03
2022-04-28 06:44:37,535 INFO [train.py:763] (1/8) Epoch 0, batch 150, loss[loss=0.351, simple_loss=0.702, pruned_loss=6.513, over 7002.00 frames.], tot_loss[loss=0.4754, simple_loss=0.9509, pruned_loss=6.855, over 758142.66 frames.], batch size: 16, lr: 3.00e-03
2022-04-28 06:45:44,956 INFO [train.py:763] (1/8) Epoch 0, batch 200, loss[loss=0.4166, simple_loss=0.8331, pruned_loss=6.876, over 7339.00 frames.], tot_loss[loss=0.4513, simple_loss=0.9027, pruned_loss=6.832, over 908266.56 frames.], batch size: 25, lr: 3.00e-03
2022-04-28 06:46:50,979 INFO [train.py:763] (1/8) Epoch 0, batch 250, loss[loss=0.4215, simple_loss=0.843, pruned_loss=6.816, over 7323.00 frames.], tot_loss[loss=0.4364, simple_loss=0.8728, pruned_loss=6.801, over 1016829.10 frames.], batch size: 21, lr: 3.00e-03
2022-04-28 06:47:58,724 INFO [train.py:763] (1/8) Epoch 0, batch 300, loss[loss=0.4289, simple_loss=0.8578, pruned_loss=6.751, over 7314.00 frames.], tot_loss[loss=0.4246, simple_loss=0.8491, pruned_loss=6.767, over 1109880.83 frames.], batch size: 25, lr: 3.00e-03
2022-04-28 06:49:06,197 INFO [train.py:763] (1/8) Epoch 0, batch 350, loss[loss=0.397, simple_loss=0.794, pruned_loss=6.672, over 7245.00 frames.], tot_loss[loss=0.4133, simple_loss=0.8266, pruned_loss=6.726, over 1179367.54 frames.], batch size: 19, lr: 3.00e-03
2022-04-28 06:50:12,116 INFO [train.py:763] (1/8) Epoch 0, batch 400, loss[loss=0.3603, simple_loss=0.7206, pruned_loss=6.587, over 7414.00 frames.], tot_loss[loss=0.4036, simple_loss=0.8071, pruned_loss=6.701, over 1231412.73 frames.], batch size: 21, lr: 3.00e-03
2022-04-28 06:51:17,805 INFO [train.py:763] (1/8) Epoch 0, batch 450, loss[loss=0.3322, simple_loss=0.6643, pruned_loss=6.661, over 7413.00 frames.], tot_loss[loss=0.3913, simple_loss=0.7826, pruned_loss=6.684, over 1267826.45 frames.], batch size: 21, lr: 2.99e-03
2022-04-28 06:52:24,499 INFO [train.py:763] (1/8) Epoch 0, batch 500, loss[loss=0.3215, simple_loss=0.6429, pruned_loss=6.732, over 7197.00 frames.], tot_loss[loss=0.3753, simple_loss=0.7506, pruned_loss=6.673, over 1303984.86 frames.], batch size: 22, lr: 2.99e-03
2022-04-28 06:53:29,995 INFO [train.py:763] (1/8) Epoch 0, batch 550, loss[loss=0.3079, simple_loss=0.6158, pruned_loss=6.699, over 7322.00 frames.], tot_loss[loss=0.3615, simple_loss=0.723, pruned_loss=6.677, over 1330353.96 frames.], batch size: 22, lr: 2.99e-03
2022-04-28 06:54:36,568 INFO [train.py:763] (1/8) Epoch 0, batch 600, loss[loss=0.3092, simple_loss=0.6184, pruned_loss=6.652, over 7117.00 frames.], tot_loss[loss=0.3456, simple_loss=0.6912, pruned_loss=6.667, over 1351033.81 frames.], batch size: 21, lr: 2.99e-03
2022-04-28 06:55:42,123 INFO [train.py:763] (1/8) Epoch 0, batch 650, loss[loss=0.2537, simple_loss=0.5074, pruned_loss=6.502, over 6997.00 frames.], tot_loss[loss=0.332, simple_loss=0.6639, pruned_loss=6.659, over 1369355.62 frames.], batch size: 16, lr: 2.99e-03
2022-04-28 06:56:47,773 INFO [train.py:763] (1/8) Epoch 0, batch 700, loss[loss=0.2635, simple_loss=0.527, pruned_loss=6.623, over 7193.00 frames.], tot_loss[loss=0.3166, simple_loss=0.6332, pruned_loss=6.638, over 1380415.06 frames.], batch size: 23, lr: 2.99e-03
2022-04-28 06:57:54,484 INFO [train.py:763] (1/8) Epoch 0, batch 750, loss[loss=0.2265, simple_loss=0.453, pruned_loss=6.458, over 7274.00 frames.], tot_loss[loss=0.3046, simple_loss=0.6092, pruned_loss=6.625, over 1391700.89 frames.], batch size: 17, lr: 2.98e-03
2022-04-28 06:59:01,267 INFO [train.py:763] (1/8) Epoch 0, batch 800, loss[loss=0.2808, simple_loss=0.5616, pruned_loss=6.684, over 7126.00 frames.], tot_loss[loss=0.2945, simple_loss=0.589, pruned_loss=6.615, over 1396997.67 frames.], batch size: 21, lr: 2.98e-03
2022-04-28 07:00:07,432 INFO [train.py:763] (1/8) Epoch 0, batch 850, loss[loss=0.2654, simple_loss=0.5309, pruned_loss=6.603, over 7217.00 frames.], tot_loss[loss=0.285, simple_loss=0.5701, pruned_loss=6.605, over 1402272.04 frames.], batch size: 21, lr: 2.98e-03
2022-04-28 07:01:13,427 INFO [train.py:763] (1/8) Epoch 0, batch 900, loss[loss=0.2743, simple_loss=0.5485, pruned_loss=6.729, over 7313.00 frames.], tot_loss[loss=0.2763, simple_loss=0.5526, pruned_loss=6.591, over 1407663.52 frames.], batch size: 21, lr: 2.98e-03
2022-04-28 07:02:19,010 INFO [train.py:763] (1/8) Epoch 0, batch 950, loss[loss=0.2137, simple_loss=0.4275, pruned_loss=6.485, over 7015.00 frames.], tot_loss[loss=0.2699, simple_loss=0.5398, pruned_loss=6.584, over 1404838.42 frames.], batch size: 16, lr: 2.97e-03
2022-04-28 07:03:26,141 INFO [train.py:763] (1/8) Epoch 0, batch 1000, loss[loss=0.1967, simple_loss=0.3934, pruned_loss=6.428, over 6983.00 frames.], tot_loss[loss=0.2633, simple_loss=0.5266, pruned_loss=6.577, over 1405651.28 frames.], batch size: 16, lr: 2.97e-03
2022-04-28 07:04:32,977 INFO [train.py:763] (1/8) Epoch 0, batch 1050, loss[loss=0.228, simple_loss=0.4561, pruned_loss=6.525, over 6990.00 frames.], tot_loss[loss=0.2592, simple_loss=0.5184, pruned_loss=6.575, over 1407666.50 frames.], batch size: 16, lr: 2.97e-03
2022-04-28 07:05:39,529 INFO [train.py:763] (1/8) Epoch 0, batch 1100, loss[loss=0.2532, simple_loss=0.5064, pruned_loss=6.708, over 7203.00 frames.], tot_loss[loss=0.2536, simple_loss=0.5071, pruned_loss=6.577, over 1411202.95 frames.], batch size: 22, lr: 2.96e-03
2022-04-28 07:06:46,911 INFO [train.py:763] (1/8) Epoch 0, batch 1150, loss[loss=0.2379, simple_loss=0.4758, pruned_loss=6.557, over 6663.00 frames.], tot_loss[loss=0.2486, simple_loss=0.4972, pruned_loss=6.574, over 1411754.79 frames.], batch size: 31, lr: 2.96e-03
2022-04-28 07:07:52,782 INFO [train.py:763] (1/8) Epoch 0, batch 1200, loss[loss=0.2187, simple_loss=0.4373, pruned_loss=6.61, over 7225.00 frames.], tot_loss[loss=0.2442, simple_loss=0.4884, pruned_loss=6.577, over 1420092.04 frames.], batch size: 26, lr: 2.96e-03
2022-04-28 07:08:58,129 INFO [train.py:763] (1/8) Epoch 0, batch 1250, loss[loss=0.2195, simple_loss=0.4391, pruned_loss=6.648, over 7382.00 frames.], tot_loss[loss=0.2406, simple_loss=0.4813, pruned_loss=6.578, over 1414141.99 frames.], batch size: 23, lr: 2.95e-03
2022-04-28 07:10:04,042 INFO [train.py:763] (1/8) Epoch 0, batch 1300, loss[loss=0.2279, simple_loss=0.4559, pruned_loss=6.736, over 7279.00 frames.], tot_loss[loss=0.2367, simple_loss=0.4734, pruned_loss=6.582, over 1422010.97 frames.], batch size: 24, lr: 2.95e-03
2022-04-28 07:11:09,799 INFO [train.py:763] (1/8) Epoch 0, batch 1350, loss[loss=0.2345, simple_loss=0.469, pruned_loss=6.578, over 7141.00 frames.], tot_loss[loss=0.2331, simple_loss=0.4662, pruned_loss=6.582, over 1423059.89 frames.], batch size: 20, lr: 2.95e-03
2022-04-28 07:12:15,113 INFO [train.py:763] (1/8) Epoch 0, batch 1400, loss[loss=0.211, simple_loss=0.4221, pruned_loss=6.507, over 7291.00 frames.], tot_loss[loss=0.2316, simple_loss=0.4633, pruned_loss=6.594, over 1419866.42 frames.], batch size: 24, lr: 2.94e-03
2022-04-28 07:13:21,016 INFO [train.py:763] (1/8) Epoch 0, batch 1450, loss[loss=0.21, simple_loss=0.42, pruned_loss=6.49, over 7141.00 frames.], tot_loss[loss=0.2284, simple_loss=0.4569, pruned_loss=6.586, over 1420223.07 frames.], batch size: 17, lr: 2.94e-03
2022-04-28 07:14:26,710 INFO [train.py:763] (1/8) Epoch 0, batch 1500, loss[loss=0.215, simple_loss=0.4301, pruned_loss=6.551, over 7302.00 frames.], tot_loss[loss=0.2259, simple_loss=0.4517, pruned_loss=6.577, over 1422738.31 frames.], batch size: 24, lr: 2.94e-03
2022-04-28 07:15:32,247 INFO [train.py:763] (1/8) Epoch 0, batch 1550, loss[loss=0.2295, simple_loss=0.459, pruned_loss=6.598, over 7112.00 frames.], tot_loss[loss=0.2231, simple_loss=0.4462, pruned_loss=6.574, over 1423145.74 frames.], batch size: 21, lr: 2.93e-03
2022-04-28 07:16:38,327 INFO [train.py:763] (1/8) Epoch 0, batch 1600, loss[loss=0.2173, simple_loss=0.4345, pruned_loss=6.585, over 7331.00 frames.], tot_loss[loss=0.221, simple_loss=0.4421, pruned_loss=6.568, over 1420311.29 frames.], batch size: 20, lr: 2.93e-03
2022-04-28 07:17:45,336 INFO [train.py:763] (1/8) Epoch 0, batch 1650, loss[loss=0.2054, simple_loss=0.4108, pruned_loss=6.547, over 7160.00 frames.], tot_loss[loss=0.2189, simple_loss=0.4378, pruned_loss=6.566, over 1421950.88 frames.], batch size: 18, lr: 2.92e-03
2022-04-28 07:18:51,995 INFO [train.py:763] (1/8) Epoch 0, batch 1700, loss[loss=0.2126, simple_loss=0.4252, pruned_loss=6.475, over 6552.00 frames.], tot_loss[loss=0.2182, simple_loss=0.4363, pruned_loss=6.568, over 1418109.57 frames.], batch size: 38, lr: 2.92e-03
2022-04-28 07:19:58,695 INFO [train.py:763] (1/8) Epoch 0, batch 1750, loss[loss=0.2127, simple_loss=0.4254, pruned_loss=6.445, over 6408.00 frames.], tot_loss[loss=0.2156, simple_loss=0.4312, pruned_loss=6.566, over 1418320.02 frames.], batch size: 38, lr: 2.91e-03
2022-04-28 07:21:06,365 INFO [train.py:763] (1/8) Epoch 0, batch 1800, loss[loss=0.2116, simple_loss=0.4232, pruned_loss=6.605, over 7068.00 frames.], tot_loss[loss=0.2139, simple_loss=0.4279, pruned_loss=6.567, over 1418200.49 frames.], batch size: 28, lr: 2.91e-03
2022-04-28 07:22:12,422 INFO [train.py:763] (1/8) Epoch 0, batch 1850, loss[loss=0.2287, simple_loss=0.4574, pruned_loss=6.486, over 5040.00 frames.], tot_loss[loss=0.2119, simple_loss=0.4238, pruned_loss=6.566, over 1419214.92 frames.], batch size: 52, lr: 2.91e-03
2022-04-28 07:23:18,918 INFO [train.py:763] (1/8) Epoch 0, batch 1900, loss[loss=0.2073, simple_loss=0.4146, pruned_loss=6.624, over 7258.00 frames.], tot_loss[loss=0.2104, simple_loss=0.4208, pruned_loss=6.569, over 1419056.63 frames.], batch size: 19, lr: 2.90e-03
2022-04-28 07:24:26,526 INFO [train.py:763] (1/8) Epoch 0, batch 1950, loss[loss=0.2132, simple_loss=0.4264, pruned_loss=6.569, over 7312.00 frames.], tot_loss[loss=0.209, simple_loss=0.418, pruned_loss=6.571, over 1422343.91 frames.], batch size: 21, lr: 2.90e-03
2022-04-28 07:25:34,070 INFO [train.py:763] (1/8) Epoch 0, batch 2000, loss[loss=0.1687, simple_loss=0.3374, pruned_loss=6.399, over 6767.00 frames.], tot_loss[loss=0.2079, simple_loss=0.4158, pruned_loss=6.569, over 1422592.58 frames.], batch size: 15, lr: 2.89e-03
2022-04-28 07:26:39,960 INFO [train.py:763] (1/8) Epoch 0, batch 2050, loss[loss=0.2045, simple_loss=0.409, pruned_loss=6.554, over 7163.00 frames.], tot_loss[loss=0.2065, simple_loss=0.4131, pruned_loss=6.566, over 1421555.22 frames.], batch size: 26, lr: 2.89e-03
2022-04-28 07:27:45,821 INFO [train.py:763] (1/8) Epoch 0, batch 2100, loss[loss=0.1996, simple_loss=0.3991, pruned_loss=6.48, over 7171.00 frames.], tot_loss[loss=0.2059, simple_loss=0.4118, pruned_loss=6.574, over 1418312.16 frames.], batch size: 18, lr: 2.88e-03
2022-04-28 07:28:51,549 INFO [train.py:763] (1/8) Epoch 0, batch 2150, loss[loss=0.2273, simple_loss=0.4547, pruned_loss=6.735, over 7326.00 frames.], tot_loss[loss=0.2053, simple_loss=0.4105, pruned_loss=6.578, over 1422340.34 frames.], batch size: 22, lr: 2.88e-03
2022-04-28 07:29:57,476 INFO [train.py:763] (1/8) Epoch 0, batch 2200, loss[loss=0.2148, simple_loss=0.4295, pruned_loss=6.69, over 7272.00 frames.], tot_loss[loss=0.204, simple_loss=0.408, pruned_loss=6.581, over 1422391.06 frames.], batch size: 25, lr: 2.87e-03
2022-04-28 07:31:03,288 INFO [train.py:763] (1/8) Epoch 0, batch 2250, loss[loss=0.2299, simple_loss=0.4599, pruned_loss=6.731, over 7221.00 frames.], tot_loss[loss=0.2023, simple_loss=0.4047, pruned_loss=6.58, over 1420308.87 frames.], batch size: 21, lr: 2.86e-03
2022-04-28 07:32:08,988 INFO [train.py:763] (1/8) Epoch 0, batch 2300, loss[loss=0.1936, simple_loss=0.3872, pruned_loss=6.531, over 7260.00 frames.], tot_loss[loss=0.2023, simple_loss=0.4046, pruned_loss=6.581, over 1415147.04 frames.], batch size: 19, lr: 2.86e-03
2022-04-28 07:33:14,407 INFO [train.py:763] (1/8) Epoch 0, batch 2350, loss[loss=0.2437, simple_loss=0.4875, pruned_loss=6.624, over 5299.00 frames.], tot_loss[loss=0.2018, simple_loss=0.4036, pruned_loss=6.587, over 1414557.55 frames.], batch size: 53, lr: 2.85e-03
2022-04-28 07:34:20,287 INFO [train.py:763] (1/8) Epoch 0, batch 2400, loss[loss=0.1868, simple_loss=0.3735, pruned_loss=6.538, over 7435.00 frames.], tot_loss[loss=0.2012, simple_loss=0.4024, pruned_loss=6.586, over 1411427.15 frames.], batch size: 20, lr: 2.85e-03
2022-04-28 07:35:25,715 INFO [train.py:763] (1/8) Epoch 0, batch 2450, loss[loss=0.2016, simple_loss=0.4031, pruned_loss=6.472, over 5110.00 frames.], tot_loss[loss=0.1999, simple_loss=0.3998, pruned_loss=6.585, over 1411459.75 frames.], batch size: 53, lr: 2.84e-03
2022-04-28 07:36:32,807 INFO [train.py:763] (1/8) Epoch 0, batch 2500, loss[loss=0.2082, simple_loss=0.4164, pruned_loss=6.663, over 7321.00 frames.], tot_loss[loss=0.1994, simple_loss=0.3987, pruned_loss=6.583, over 1416750.28 frames.], batch size: 20, lr: 2.84e-03
2022-04-28 07:37:40,455 INFO [train.py:763] (1/8) Epoch 0, batch 2550, loss[loss=0.1697, simple_loss=0.3394, pruned_loss=6.434, over 7402.00 frames.], tot_loss[loss=0.1995, simple_loss=0.3989, pruned_loss=6.591, over 1417887.16 frames.], batch size: 18, lr: 2.83e-03
2022-04-28 07:38:46,543 INFO [train.py:763] (1/8) Epoch 0, batch 2600, loss[loss=0.2062, simple_loss=0.4124, pruned_loss=6.749, over 7231.00 frames.], tot_loss[loss=0.1988, simple_loss=0.3977, pruned_loss=6.598, over 1421176.27 frames.], batch size: 20, lr: 2.83e-03
2022-04-28 07:39:52,334 INFO [train.py:763] (1/8) Epoch 0, batch 2650, loss[loss=0.1715, simple_loss=0.3429, pruned_loss=6.511, over 7230.00 frames.], tot_loss[loss=0.1969, simple_loss=0.3938, pruned_loss=6.596, over 1422379.95 frames.], batch size: 20, lr: 2.82e-03
2022-04-28 07:40:58,204 INFO [train.py:763] (1/8) Epoch 0, batch 2700, loss[loss=0.1861, simple_loss=0.3722, pruned_loss=6.671, over 7149.00 frames.], tot_loss[loss=0.1967, simple_loss=0.3934, pruned_loss=6.593, over 1421548.39 frames.], batch size: 20, lr: 2.81e-03
2022-04-28 07:42:03,316 INFO [train.py:763] (1/8) Epoch 0, batch 2750, loss[loss=0.1905, simple_loss=0.3809, pruned_loss=6.585, over 7325.00 frames.], tot_loss[loss=0.1963, simple_loss=0.3927, pruned_loss=6.6, over 1422375.15 frames.], batch size: 20, lr: 2.81e-03
2022-04-28 07:43:09,946 INFO [train.py:763] (1/8) Epoch 0, batch 2800, loss[loss=0.2077, simple_loss=0.4155, pruned_loss=6.665, over 7141.00 frames.], tot_loss[loss=0.1957, simple_loss=0.3914, pruned_loss=6.601, over 1421129.03 frames.], batch size: 20, lr: 2.80e-03
2022-04-28 07:44:16,827 INFO [train.py:763] (1/8) Epoch 0, batch 2850, loss[loss=0.1893, simple_loss=0.3787, pruned_loss=6.531, over 7374.00 frames.], tot_loss[loss=0.1949, simple_loss=0.3899, pruned_loss=6.6, over 1423945.08 frames.], batch size: 19, lr: 2.80e-03
2022-04-28 07:45:22,336 INFO [train.py:763] (1/8) Epoch 0, batch 2900, loss[loss=0.1785, simple_loss=0.3571, pruned_loss=6.666, over 7333.00 frames.], tot_loss[loss=0.1945, simple_loss=0.3889, pruned_loss=6.606, over 1420248.04 frames.], batch size: 20, lr: 2.79e-03
2022-04-28 07:46:27,653 INFO [train.py:763] (1/8) Epoch 0, batch 2950, loss[loss=0.2093, simple_loss=0.4185, pruned_loss=6.626, over 7141.00 frames.], tot_loss[loss=0.1943, simple_loss=0.3886, pruned_loss=6.606, over 1415923.02 frames.], batch size: 26, lr: 2.78e-03
2022-04-28 07:47:32,888 INFO [train.py:763] (1/8) Epoch 0, batch 3000, loss[loss=0.3626, simple_loss=0.4121, pruned_loss=1.565, over 7285.00 frames.], tot_loss[loss=0.2272, simple_loss=0.3882, pruned_loss=6.584, over 1419537.46 frames.], batch size: 17, lr: 2.78e-03
2022-04-28 07:47:32,889 INFO [train.py:783] (1/8) Computing validation loss
2022-04-28 07:47:50,998 INFO [train.py:792] (1/8) Epoch 0, validation: loss=2.072, simple_loss=0.4419, pruned_loss=1.851, over 698248.00 frames.
2022-04-28 07:48:57,673 INFO [train.py:763] (1/8) Epoch 0, batch 3050, loss[loss=0.3127, simple_loss=0.4347, pruned_loss=0.953, over 6255.00 frames.], tot_loss[loss=0.2518, simple_loss=0.3977, pruned_loss=5.399, over 1418605.88 frames.], batch size: 38, lr: 2.77e-03
2022-04-28 07:50:04,084 INFO [train.py:763] (1/8) Epoch 0, batch 3100, loss[loss=0.2602, simple_loss=0.4063, pruned_loss=0.5703, over 7418.00 frames.], tot_loss[loss=0.2526, simple_loss=0.3926, pruned_loss=4.336, over 1425235.07 frames.], batch size: 21, lr: 2.77e-03
2022-04-28 07:51:10,054 INFO [train.py:763] (1/8) Epoch 0, batch 3150, loss[loss=0.2392, simple_loss=0.4039, pruned_loss=0.3726, over 7414.00 frames.], tot_loss[loss=0.2474, simple_loss=0.3889, pruned_loss=3.463, over 1426431.36 frames.], batch size: 21, lr: 2.76e-03
2022-04-28 07:52:16,817 INFO [train.py:763] (1/8) Epoch 0, batch 3200, loss[loss=0.2273, simple_loss=0.398, pruned_loss=0.283, over 7291.00 frames.], tot_loss[loss=0.2416, simple_loss=0.3872, pruned_loss=2.771, over 1422358.52 frames.], batch size: 24, lr: 2.75e-03
2022-04-28 07:53:24,321 INFO [train.py:763] (1/8) Epoch 0, batch 3250, loss[loss=0.196, simple_loss=0.3528, pruned_loss=0.1957, over 7151.00 frames.], tot_loss[loss=0.2355, simple_loss=0.3849, pruned_loss=2.212, over 1422772.93 frames.], batch size: 20, lr: 2.75e-03
2022-04-28 07:54:30,947 INFO [train.py:763] (1/8) Epoch 0, batch 3300, loss[loss=0.2339, simple_loss=0.4187, pruned_loss=0.2459, over 7382.00 frames.], tot_loss[loss=0.2314, simple_loss=0.3849, pruned_loss=1.78, over 1419364.89 frames.], batch size: 23, lr: 2.74e-03
2022-04-28 07:55:37,618 INFO [train.py:763] (1/8) Epoch 0, batch 3350, loss[loss=0.2163, simple_loss=0.3875, pruned_loss=0.226, over 7271.00 frames.], tot_loss[loss=0.2273, simple_loss=0.3842, pruned_loss=1.431, over 1423886.42 frames.], batch size: 24, lr: 2.73e-03
2022-04-28 07:56:43,235 INFO [train.py:763] (1/8) Epoch 0, batch 3400, loss[loss=0.2066, simple_loss=0.3737, pruned_loss=0.1973, over 7252.00 frames.], tot_loss[loss=0.2249, simple_loss=0.3852, pruned_loss=1.162, over 1424121.23 frames.], batch size: 19, lr: 2.73e-03
2022-04-28 07:57:49,075 INFO [train.py:763] (1/8) Epoch 0, batch 3450, loss[loss=0.2133, simple_loss=0.3871, pruned_loss=0.1977, over 7315.00 frames.], tot_loss[loss=0.2221, simple_loss=0.3846, pruned_loss=0.9513, over 1424004.52 frames.], batch size: 25, lr: 2.72e-03
2022-04-28 07:58:54,327 INFO [train.py:763] (1/8) Epoch 0, batch 3500, loss[loss=0.2231, simple_loss=0.4037, pruned_loss=0.2127, over 7147.00 frames.], tot_loss[loss=0.2196, simple_loss=0.3841, pruned_loss=0.7848, over 1421977.00 frames.], batch size: 26, lr: 2.72e-03
2022-04-28 08:00:00,008 INFO [train.py:763] (1/8) Epoch 0, batch 3550, loss[loss=0.1941, simple_loss=0.3563, pruned_loss=0.1589, over 7216.00 frames.], tot_loss[loss=0.217, simple_loss=0.3825, pruned_loss=0.6537, over 1423324.34 frames.], batch size: 21, lr: 2.71e-03
2022-04-28 08:01:06,048 INFO [train.py:763] (1/8) Epoch 0, batch 3600, loss[loss=0.1995, simple_loss=0.3622, pruned_loss=0.1844, over 6996.00 frames.], tot_loss[loss=0.2153, simple_loss=0.3819, pruned_loss=0.5516, over 1421841.41 frames.], batch size: 16, lr: 2.70e-03
2022-04-28 08:02:21,060 INFO [train.py:763] (1/8) Epoch 0, batch 3650, loss[loss=0.2445, simple_loss=0.4403, pruned_loss=0.2436, over 7212.00 frames.], tot_loss[loss=0.2124, simple_loss=0.3791, pruned_loss=0.4683, over 1422535.17 frames.], batch size: 21, lr: 2.70e-03
2022-04-28 08:04:03,465 INFO [train.py:763] (1/8) Epoch 0, batch 3700, loss[loss=0.2107, simple_loss=0.3837, pruned_loss=0.1888, over 6672.00 frames.], tot_loss[loss=0.2101, simple_loss=0.3771, pruned_loss=0.4021, over 1426675.46 frames.], batch size: 31, lr: 2.69e-03
2022-04-28 08:05:34,887 INFO [train.py:763] (1/8) Epoch 0, batch 3750, loss[loss=0.1841, simple_loss=0.337, pruned_loss=0.156, over 7270.00 frames.], tot_loss[loss=0.2092, simple_loss=0.3768, pruned_loss=0.3536, over 1418852.79 frames.], batch size: 18, lr: 2.68e-03
2022-04-28 08:06:40,596 INFO [train.py:763] (1/8) Epoch 0, batch 3800, loss[loss=0.1787, simple_loss=0.3327, pruned_loss=0.1232, over 7127.00 frames.], tot_loss[loss=0.207, simple_loss=0.3744, pruned_loss=0.3114, over 1418881.71 frames.], batch size: 17, lr: 2.68e-03
2022-04-28 08:07:46,190 INFO [train.py:763] (1/8) Epoch 0, batch 3850, loss[loss=0.1685, simple_loss=0.3127, pruned_loss=0.1218, over 7152.00 frames.], tot_loss[loss=0.2055, simple_loss=0.3729, pruned_loss=0.2787, over 1424084.26 frames.], batch size: 17, lr: 2.67e-03
2022-04-28 08:08:52,445 INFO [train.py:763] (1/8) Epoch 0, batch 3900, loss[loss=0.1933, simple_loss=0.3502, pruned_loss=0.182, over 7212.00 frames.], tot_loss[loss=0.2055, simple_loss=0.3735, pruned_loss=0.256, over 1421118.06 frames.], batch size: 16, lr: 2.66e-03
2022-04-28 08:09:58,858 INFO [train.py:763] (1/8) Epoch 0, batch 3950, loss[loss=0.1855, simple_loss=0.3435, pruned_loss=0.1369, over 6842.00 frames.], tot_loss[loss=0.2044, simple_loss=0.3723, pruned_loss=0.2358, over 1418608.17 frames.], batch size: 15, lr: 2.66e-03
2022-04-28 08:11:04,203 INFO [train.py:763] (1/8) Epoch 0, batch 4000, loss[loss=0.2089, simple_loss=0.3836, pruned_loss=0.1705, over 7312.00 frames.], tot_loss[loss=0.2035, simple_loss=0.3716, pruned_loss=0.2189, over 1420523.21 frames.], batch size: 21, lr: 2.65e-03
2022-04-28 08:12:09,508 INFO [train.py:763] (1/8) Epoch 0, batch 4050, loss[loss=0.19, simple_loss=0.3544, pruned_loss=0.1278, over 7055.00 frames.], tot_loss[loss=0.2028, simple_loss=0.3708, pruned_loss=0.2059, over 1420657.61 frames.], batch size: 28, lr: 2.64e-03
2022-04-28 08:13:15,839 INFO [train.py:763] (1/8) Epoch 0, batch 4100, loss[loss=0.1924, simple_loss=0.3564, pruned_loss=0.1416, over 7260.00 frames.], tot_loss[loss=0.2018, simple_loss=0.3695, pruned_loss=0.1955, over 1420869.85 frames.], batch size: 19, lr: 2.64e-03
2022-04-28 08:14:22,419 INFO [train.py:763] (1/8) Epoch 0, batch 4150, loss[loss=0.1779, simple_loss=0.33, pruned_loss=0.1287, over 7062.00 frames.], tot_loss[loss=0.2011, simple_loss=0.3687, pruned_loss=0.1865, over 1425215.52 frames.], batch size: 18, lr: 2.63e-03
2022-04-28 08:15:27,427 INFO [train.py:763] (1/8) Epoch 0, batch 4200, loss[loss=0.1956, simple_loss=0.3625, pruned_loss=0.1438, over 7198.00 frames.], tot_loss[loss=0.2007, simple_loss=0.3685, pruned_loss=0.1796, over 1424683.25 frames.], batch size: 22, lr: 2.63e-03
2022-04-28 08:16:32,482 INFO [train.py:763] (1/8) Epoch 0, batch 4250, loss[loss=0.2191, simple_loss=0.4001, pruned_loss=0.1905, over 7434.00 frames.], tot_loss[loss=0.2019, simple_loss=0.3708, pruned_loss=0.1775, over 1423900.62 frames.], batch size: 20, lr: 2.62e-03
2022-04-28 08:17:38,264 INFO [train.py:763] (1/8) Epoch 0, batch 4300, loss[loss=0.2045, simple_loss=0.3786, pruned_loss=0.1518, over 7105.00 frames.], tot_loss[loss=0.2016, simple_loss=0.3705, pruned_loss=0.1725, over 1423231.27 frames.], batch size: 28, lr: 2.61e-03
2022-04-28 08:18:43,769 INFO [train.py:763] (1/8) Epoch 0, batch 4350, loss[loss=0.1975, simple_loss=0.3635, pruned_loss=0.1576, over 7442.00 frames.], tot_loss[loss=0.2011, simple_loss=0.3698, pruned_loss=0.1692, over 1426982.85 frames.], batch size: 20, lr: 2.61e-03
2022-04-28 08:19:48,916 INFO [train.py:763] (1/8) Epoch 0, batch 4400, loss[loss=0.1931, simple_loss=0.3561, pruned_loss=0.1511, over 7269.00 frames.], tot_loss[loss=0.2007, simple_loss=0.3694, pruned_loss=0.1653, over 1424974.29 frames.], batch size: 18, lr: 2.60e-03
2022-04-28 08:20:54,081 INFO [train.py:763] (1/8) Epoch 0, batch 4450, loss[loss=0.1915, simple_loss=0.3565, pruned_loss=0.1326, over 7436.00 frames.], tot_loss[loss=0.2009, simple_loss=0.3701, pruned_loss=0.1634, over 1424011.21 frames.], batch size: 20, lr: 2.59e-03
2022-04-28 08:21:59,570 INFO [train.py:763] (1/8) Epoch 0, batch 4500, loss[loss=0.2153, simple_loss=0.3957, pruned_loss=0.174, over 6438.00 frames.], tot_loss[loss=0.2009, simple_loss=0.3702, pruned_loss=0.1616, over 1414234.09 frames.], batch size: 38, lr: 2.59e-03
2022-04-28 08:23:05,615 INFO [train.py:763] (1/8) Epoch 0, batch 4550, loss[loss=0.2205, simple_loss=0.4021, pruned_loss=0.1949, over 4871.00 frames.], tot_loss[loss=0.2018, simple_loss=0.3718, pruned_loss=0.1613, over 1395398.29 frames.], batch size: 52, lr: 2.58e-03
2022-04-28 08:24:44,869 INFO [train.py:763] (1/8) Epoch 1, batch 0, loss[loss=0.2047, simple_loss=0.3778, pruned_loss=0.1583, over 7194.00 frames.], tot_loss[loss=0.2047, simple_loss=0.3778, pruned_loss=0.1583, over 7194.00 frames.], batch size: 26, lr: 2.56e-03
2022-04-28 08:25:50,517 INFO [train.py:763] (1/8) Epoch 1, batch 50, loss[loss=0.1975, simple_loss=0.3661, pruned_loss=0.1449, over 7239.00 frames.], tot_loss[loss=0.1979, simple_loss=0.3649, pruned_loss=0.1547, over 312189.11 frames.], batch size: 20, lr: 2.55e-03
2022-04-28 08:26:56,237 INFO [train.py:763] (1/8) Epoch 1, batch 100, loss[loss=0.1633, simple_loss=0.3063, pruned_loss=0.1014, over 7435.00 frames.], tot_loss[loss=0.1943, simple_loss=0.3591, pruned_loss=0.1474, over 560753.49 frames.], batch size: 20, lr: 2.54e-03
2022-04-28 08:28:01,395 INFO [train.py:763] (1/8) Epoch 1, batch 150, loss[loss=0.1861, simple_loss=0.3459, pruned_loss=0.1312, over 7324.00 frames.], tot_loss[loss=0.1944, simple_loss=0.3596, pruned_loss=0.1461, over 751182.36 frames.], batch size: 20, lr: 2.54e-03
2022-04-28 08:29:06,946 INFO [train.py:763] (1/8) Epoch 1, batch 200, loss[loss=0.1862, simple_loss=0.3464, pruned_loss=0.1299, over 7152.00 frames.], tot_loss[loss=0.1941, simple_loss=0.3591, pruned_loss=0.1448, over 901053.23 frames.], batch size: 19, lr: 2.53e-03
2022-04-28 08:30:12,403 INFO [train.py:763] (1/8) Epoch 1, batch 250, loss[loss=0.2127, simple_loss=0.3922, pruned_loss=0.1659, over 7362.00 frames.], tot_loss[loss=0.1948, simple_loss=0.3605, pruned_loss=0.1454, over 1015156.54 frames.], batch size: 23, lr: 2.53e-03
2022-04-28 08:31:17,599 INFO [train.py:763] (1/8) Epoch 1, batch 300, loss[loss=0.198, simple_loss=0.3646, pruned_loss=0.1565, over 7269.00 frames.], tot_loss[loss=0.1947, simple_loss=0.3605, pruned_loss=0.1444, over 1104838.92 frames.], batch size: 19, lr: 2.52e-03
2022-04-28 08:32:23,178 INFO [train.py:763] (1/8) Epoch 1, batch 350, loss[loss=0.196, simple_loss=0.3647, pruned_loss=0.1359, over 7225.00 frames.], tot_loss[loss=0.1948, simple_loss=0.3607, pruned_loss=0.1443, over 1173419.57 frames.], batch size: 21, lr: 2.51e-03
2022-04-28 08:33:29,294 INFO [train.py:763] (1/8) Epoch 1, batch 400, loss[loss=0.1875, simple_loss=0.3509, pruned_loss=0.1203, over 7148.00 frames.], tot_loss[loss=0.1946, simple_loss=0.3606, pruned_loss=0.1432, over 1229947.49 frames.], batch size: 20, lr: 2.51e-03
2022-04-28 08:34:36,147 INFO [train.py:763] (1/8) Epoch 1, batch 450, loss[loss=0.1802, simple_loss=0.3373, pruned_loss=0.1152, over 7161.00 frames.], tot_loss[loss=0.1943, simple_loss=0.3601, pruned_loss=0.1423, over 1274997.18 frames.], batch size: 19, lr: 2.50e-03
2022-04-28 08:35:42,350 INFO [train.py:763] (1/8) Epoch 1, batch 500, loss[loss=0.2062, simple_loss=0.3765, pruned_loss=0.1789, over 7160.00 frames.], tot_loss[loss=0.1949, simple_loss=0.3613, pruned_loss=0.1428, over 1307022.63 frames.], batch size: 18, lr: 2.49e-03
2022-04-28 08:36:48,848 INFO [train.py:763] (1/8) Epoch 1, batch 550, loss[loss=0.1656, simple_loss=0.3113, pruned_loss=0.09973, over 7360.00 frames.], tot_loss[loss=0.1943, simple_loss=0.3602, pruned_loss=0.1425, over 1332188.30 frames.], batch size: 19, lr: 2.49e-03
2022-04-28 08:37:55,692 INFO [train.py:763] (1/8) Epoch 1, batch 600, loss[loss=0.1769, simple_loss=0.3329, pruned_loss=0.1046, over 7378.00 frames.], tot_loss[loss=0.1945, simple_loss=0.3605, pruned_loss=0.1419, over 1353823.43 frames.], batch size: 23, lr: 2.48e-03
2022-04-28 08:39:01,283 INFO [train.py:763] (1/8) Epoch 1, batch 650, loss[loss=0.1607, simple_loss=0.2993, pruned_loss=0.1108, over 7270.00 frames.], tot_loss[loss=0.1931, simple_loss=0.3582, pruned_loss=0.1399, over 1367650.13 frames.], batch size: 18, lr: 2.48e-03
2022-04-28 08:40:06,979 INFO [train.py:763] (1/8) Epoch 1, batch 700, loss[loss=0.2368, simple_loss=0.4294, pruned_loss=0.221, over 4637.00 frames.], tot_loss[loss=0.193, simple_loss=0.3582, pruned_loss=0.1396, over 1378542.29 frames.], batch size: 52, lr: 2.47e-03
2022-04-28 08:41:12,396 INFO [train.py:763] (1/8) Epoch 1, batch 750, loss[loss=0.1677, simple_loss=0.3143, pruned_loss=0.1054, over 7257.00 frames.], tot_loss[loss=0.192, simple_loss=0.3564, pruned_loss=0.1379, over 1389846.29 frames.], batch size: 19, lr: 2.46e-03
2022-04-28 08:42:18,200 INFO [train.py:763] (1/8) Epoch 1, batch 800, loss[loss=0.1973, simple_loss=0.3653, pruned_loss=0.1464, over 7073.00 frames.], tot_loss[loss=0.1919, simple_loss=0.3562, pruned_loss=0.1377, over 1400212.58 frames.], batch size: 18, lr: 2.46e-03
2022-04-28 08:43:24,114 INFO [train.py:763] (1/8) Epoch 1, batch 850, loss[loss=0.1912, simple_loss=0.3569, pruned_loss=0.1273, over 7342.00 frames.], tot_loss[loss=0.1913, simple_loss=0.3553, pruned_loss=0.1361, over 1409421.42 frames.], batch size: 20, lr: 2.45e-03
2022-04-28 08:44:29,823 INFO [train.py:763] (1/8) Epoch 1, batch 900, loss[loss=0.1895, simple_loss=0.3528, pruned_loss=0.1313, over 7425.00 frames.], tot_loss[loss=0.1909, simple_loss=0.3547, pruned_loss=0.1355, over 1414551.68 frames.], batch size: 20, lr: 2.45e-03
2022-04-28 08:45:35,248 INFO [train.py:763] (1/8) Epoch 1, batch 950, loss[loss=0.186, simple_loss=0.3471, pruned_loss=0.1242, over 7249.00 frames.], tot_loss[loss=0.1915, simple_loss=0.3558, pruned_loss=0.1364, over 1416695.03 frames.], batch size: 19, lr: 2.44e-03
2022-04-28 08:46:40,816 INFO [train.py:763] (1/8) Epoch 1, batch 1000, loss[loss=0.1965, simple_loss=0.3646, pruned_loss=0.1422, over 6918.00 frames.], tot_loss[loss=0.1907, simple_loss=0.3544, pruned_loss=0.1353, over 1419045.51 frames.], batch size: 32, lr: 2.43e-03
2022-04-28 08:47:46,477 INFO [train.py:763] (1/8) Epoch 1, batch 1050, loss[loss=0.1974, simple_loss=0.3686, pruned_loss=0.1315, over 7424.00 frames.], tot_loss[loss=0.1908, simple_loss=0.3545, pruned_loss=0.1355, over 1420971.60 frames.], batch size: 20, lr: 2.43e-03
2022-04-28 08:48:51,692 INFO [train.py:763] (1/8) Epoch 1, batch 1100, loss[loss=0.1717, simple_loss=0.3209, pruned_loss=0.1131, over 7166.00 frames.], tot_loss[loss=0.1915, simple_loss=0.3557, pruned_loss=0.136, over 1421426.73 frames.], batch size: 18, lr: 2.42e-03
2022-04-28 08:49:57,303 INFO [train.py:763] (1/8) Epoch 1, batch 1150, loss[loss=0.198, simple_loss=0.3669, pruned_loss=0.1459, over 7228.00 frames.], tot_loss[loss=0.19, simple_loss=0.3533, pruned_loss=0.1338, over 1424640.73 frames.], batch size: 20, lr: 2.41e-03
2022-04-28 08:51:02,489 INFO [train.py:763] (1/8) Epoch 1, batch 1200, loss[loss=0.1974, simple_loss=0.3679, pruned_loss=0.1345, over 7041.00 frames.], tot_loss[loss=0.1891, simple_loss=0.3518, pruned_loss=0.1324, over 1423912.86 frames.], batch size: 28, lr: 2.41e-03
2022-04-28 08:52:07,804 INFO [train.py:763] (1/8) Epoch 1, batch 1250, loss[loss=0.1682, simple_loss=0.3166, pruned_loss=0.09942, over 7257.00 frames.], tot_loss[loss=0.1896, simple_loss=0.3527, pruned_loss=0.1322, over 1423378.18 frames.], batch size: 18, lr: 2.40e-03
2022-04-28 08:53:12,958 INFO [train.py:763] (1/8) Epoch 1, batch 1300, loss[loss=0.1911, simple_loss=0.3588, pruned_loss=0.1175, over 7223.00 frames.], tot_loss[loss=0.1899, simple_loss=0.3532, pruned_loss=0.1329, over 1417200.91 frames.], batch size: 21, lr: 2.40e-03
2022-04-28 08:54:18,349 INFO [train.py:763] (1/8) Epoch 1, batch 1350, loss[loss=0.1554, simple_loss=0.2871, pruned_loss=0.1186, over 7272.00 frames.], tot_loss[loss=0.1896, simple_loss=0.3526, pruned_loss=0.1326, over 1419782.89 frames.], batch size: 17, lr: 2.39e-03
2022-04-28 08:55:23,446 INFO [train.py:763] (1/8) Epoch 1, batch 1400, loss[loss=0.1986, simple_loss=0.3723, pruned_loss=0.1243, over 7232.00 frames.], tot_loss[loss=0.1905, simple_loss=0.3543, pruned_loss=0.1338, over 1418548.55 frames.], batch size: 21, lr: 2.39e-03
2022-04-28 08:56:28,947 INFO [train.py:763] (1/8) Epoch 1, batch 1450, loss[loss=0.3942, simple_loss=0.4154, pruned_loss=0.1865, over 7212.00 frames.], tot_loss[loss=0.2157, simple_loss=0.3556, pruned_loss=0.1362, over 1422157.14 frames.], batch size: 26, lr: 2.38e-03
2022-04-28 08:57:34,411 INFO [train.py:763] (1/8) Epoch 1, batch 1500, loss[loss=0.4076, simple_loss=0.4288, pruned_loss=0.1931, over 6366.00 frames.], tot_loss[loss=0.239, simple_loss=0.357, pruned_loss=0.137, over 1422160.80 frames.], batch size: 38, lr: 2.37e-03
2022-04-28 08:58:40,147 INFO [train.py:763] (1/8) Epoch 1, batch 1550, loss[loss=0.3001, simple_loss=0.3419, pruned_loss=0.1291, over 7436.00 frames.], tot_loss[loss=0.2574, simple_loss=0.359, pruned_loss=0.1373, over 1425018.71 frames.], batch size: 20, lr: 2.37e-03
2022-04-28 08:59:47,366 INFO [train.py:763] (1/8) Epoch 1, batch 1600, loss[loss=0.288, simple_loss=0.3289, pruned_loss=0.1235, over 7164.00 frames.], tot_loss[loss=0.2662, simple_loss=0.3566, pruned_loss=0.1342, over 1424487.43 frames.], batch size: 18, lr: 2.36e-03
2022-04-28 09:00:52,892 INFO [train.py:763] (1/8) Epoch 1, batch 1650, loss[loss=0.267, simple_loss=0.3208, pruned_loss=0.1067, over 7449.00 frames.], tot_loss[loss=0.2763, simple_loss=0.3569, pruned_loss=0.1338, over 1425196.71 frames.], batch size: 20, lr: 2.36e-03
2022-04-28 09:01:59,214 INFO [train.py:763] (1/8) Epoch 1, batch 1700, loss[loss=0.3095, simple_loss=0.363, pruned_loss=0.128, over 7420.00 frames.], tot_loss[loss=0.2821, simple_loss=0.3561, pruned_loss=0.1321, over 1423756.14 frames.], batch size: 21, lr: 2.35e-03
2022-04-28 09:03:06,112 INFO [train.py:763] (1/8) Epoch 1, batch 1750, loss[loss=0.2648, simple_loss=0.3111, pruned_loss=0.1093, over 7266.00 frames.], tot_loss[loss=0.2886, simple_loss=0.3574, pruned_loss=0.1317, over 1423540.88 frames.], batch size: 18, lr: 2.34e-03
2022-04-28 09:04:13,393 INFO [train.py:763] (1/8) Epoch 1, batch 1800, loss[loss=0.2825, simple_loss=0.3398, pruned_loss=0.1126, over 7352.00 frames.], tot_loss[loss=0.2917, simple_loss=0.3569, pruned_loss=0.1302, over 1424767.34 frames.], batch size: 19, lr: 2.34e-03
2022-04-28 09:05:20,642 INFO [train.py:763] (1/8) Epoch 1, batch 1850, loss[loss=0.2834, simple_loss=0.3404, pruned_loss=0.1132, over 7325.00 frames.], tot_loss[loss=0.2936, simple_loss=0.3559, pruned_loss=0.1288, over 1424958.42 frames.], batch size: 20, lr: 2.33e-03
2022-04-28 09:06:26,263 INFO [train.py:763] (1/8) Epoch 1, batch 1900, loss[loss=0.2223, simple_loss=0.2923, pruned_loss=0.07618, over 7000.00 frames.], tot_loss[loss=0.2948, simple_loss=0.3554, pruned_loss=0.1274, over 1428668.47 frames.], batch size: 16, lr: 2.33e-03
2022-04-28 09:07:32,757 INFO [train.py:763] (1/8) Epoch 1, batch 1950, loss[loss=0.2763, simple_loss=0.3209, pruned_loss=0.1158, over 7288.00 frames.], tot_loss[loss=0.2966, simple_loss=0.3556, pruned_loss=0.1268, over 1429459.91 frames.], batch size: 18, lr: 2.32e-03
2022-04-28 09:08:38,160 INFO [train.py:763] (1/8) Epoch 1, batch 2000, loss[loss=0.324, simple_loss=0.3844, pruned_loss=0.1318, over 7120.00 frames.], tot_loss[loss=0.3002, simple_loss=0.3574, pruned_loss=0.1277, over 1422980.82 frames.], batch size: 21, lr: 2.32e-03
2022-04-28 09:09:44,439 INFO [train.py:763] (1/8) Epoch 1, batch 2050, loss[loss=0.3149, simple_loss=0.3675, pruned_loss=0.1311, over 7156.00 frames.], tot_loss[loss=0.301, simple_loss=0.3572, pruned_loss=0.1273, over 1423483.71 frames.], batch size: 28, lr: 2.31e-03
2022-04-28 09:10:49,768 INFO [train.py:763] (1/8) Epoch 1, batch 2100, loss[loss=0.2827, simple_loss=0.3388, pruned_loss=0.1133, over 7409.00 frames.], tot_loss[loss=0.2999, simple_loss=0.3557, pruned_loss=0.1258, over 1425363.43 frames.], batch size: 18, lr: 2.31e-03
2022-04-28 09:11:55,363 INFO [train.py:763] (1/8) Epoch 1, batch 2150, loss[loss=0.3319, simple_loss=0.3876, pruned_loss=0.138, over 7412.00 frames.], tot_loss[loss=0.2986, simple_loss=0.3546, pruned_loss=0.1242, over 1424633.33 frames.], batch size: 21, lr: 2.30e-03
2022-04-28 09:13:01,255 INFO [train.py:763] (1/8) Epoch 1, batch 2200, loss[loss=0.3285, simple_loss=0.3899, pruned_loss=0.1336, over 7121.00 frames.], tot_loss[loss=0.298, simple_loss=0.3534, pruned_loss=0.1236, over 1423113.91 frames.], batch size: 21, lr: 2.29e-03
2022-04-28 09:14:06,868 INFO [train.py:763] (1/8) Epoch 1, batch 2250, loss[loss=0.2816, simple_loss=0.3528, pruned_loss=0.1052, over 7221.00 frames.], tot_loss[loss=0.2956, simple_loss=0.3514, pruned_loss=0.1217, over 1423742.06 frames.], batch size: 21, lr: 2.29e-03
2022-04-28 09:15:14,109 INFO [train.py:763] (1/8) Epoch 1, batch 2300, loss[loss=0.3855, simple_loss=0.4191, pruned_loss=0.1759, over 7212.00 frames.], tot_loss[loss=0.2966, simple_loss=0.3524, pruned_loss=0.1218, over 1425356.29 frames.], batch size: 22, lr: 2.28e-03
2022-04-28 09:16:21,354 INFO [train.py:763] (1/8) Epoch 1, batch 2350, loss[loss=0.3128, simple_loss=0.3617, pruned_loss=0.1319, over 7232.00 frames.], tot_loss[loss=0.2973, simple_loss=0.3529, pruned_loss=0.122, over 1423642.48 frames.], batch size: 20, lr: 2.28e-03
2022-04-28 09:17:26,502 INFO [train.py:763] (1/8) Epoch 1, batch 2400, loss[loss=0.3076, simple_loss=0.3563, pruned_loss=0.1295, over 7326.00 frames.], tot_loss[loss=0.2965, simple_loss=0.3526, pruned_loss=0.1211, over 1423570.15 frames.], batch size: 21, lr: 2.27e-03
2022-04-28 09:18:31,928 INFO [train.py:763] (1/8) Epoch 1, batch 2450, loss[loss=0.3217, simple_loss=0.3785, pruned_loss=0.1325, over 7319.00 frames.], tot_loss[loss=0.2965, simple_loss=0.3532, pruned_loss=0.1206, over 1426824.84 frames.], batch size: 21, lr: 2.27e-03
2022-04-28 09:19:37,097 INFO [train.py:763] (1/8) Epoch 1, batch 2500, loss[loss=0.297, simple_loss=0.3552, pruned_loss=0.1194, over 7176.00 frames.], tot_loss[loss=0.2973, simple_loss=0.3535, pruned_loss=0.121, over 1426984.82 frames.], batch size: 26, lr: 2.26e-03
2022-04-28 09:20:43,299 INFO [train.py:763] (1/8) Epoch 1, batch 2550, loss[loss=0.2588, simple_loss=0.3256, pruned_loss=0.09602, over 6999.00 frames.], tot_loss[loss=0.2974, simple_loss=0.3536, pruned_loss=0.121, over 1427043.76 frames.], batch size: 16, lr: 2.26e-03
2022-04-28 09:21:48,826 INFO [train.py:763] (1/8) Epoch 1, batch 2600, loss[loss=0.2987, simple_loss=0.37, pruned_loss=0.1137, over 7189.00 frames.], tot_loss[loss=0.2957, simple_loss=0.3527, pruned_loss=0.1197, over 1428744.38 frames.], batch size: 26, lr: 2.25e-03
2022-04-28 09:22:54,014 INFO [train.py:763] (1/8) Epoch 1, batch 2650, loss[loss=0.3418, simple_loss=0.3792, pruned_loss=0.1522, over 6457.00 frames.], tot_loss[loss=0.296, simple_loss=0.3528, pruned_loss=0.1198, over 1427430.63 frames.], batch size: 38, lr: 2.25e-03
2022-04-28 09:24:00,440 INFO [train.py:763] (1/8) Epoch 1, batch 2700, loss[loss=0.3508, simple_loss=0.3926, pruned_loss=0.1545, over 6833.00 frames.], tot_loss[loss=0.2947, simple_loss=0.3518, pruned_loss=0.119, over 1427067.13 frames.], batch size: 31, lr: 2.24e-03
2022-04-28 09:25:06,558 INFO [train.py:763] (1/8) Epoch 1, batch 2750, loss[loss=0.2986, simple_loss=0.3626, pruned_loss=0.1173, over 7297.00 frames.], tot_loss[loss=0.2937, simple_loss=0.3514, pruned_loss=0.1182, over 1424531.61 frames.], batch size: 24, lr: 2.24e-03
2022-04-28 09:26:12,249 INFO [train.py:763] (1/8) Epoch 1, batch 2800, loss[loss=0.2926, simple_loss=0.353, pruned_loss=0.1161, over 7201.00 frames.], tot_loss[loss=0.2931, simple_loss=0.3512, pruned_loss=0.1176, over 1426695.71 frames.], batch size: 23, lr: 2.23e-03
2022-04-28 09:27:17,544 INFO [train.py:763] (1/8) Epoch 1, batch 2850, loss[loss=0.3325, simple_loss=0.3818, pruned_loss=0.1416, over 7286.00 frames.], tot_loss[loss=0.2921, simple_loss=0.3506, pruned_loss=0.1169, over 1426690.13 frames.], batch size: 24, lr: 2.23e-03
2022-04-28 09:28:22,521 INFO [train.py:763] (1/8) Epoch 1, batch 2900, loss[loss=0.2684, simple_loss=0.3363, pruned_loss=0.1002, over 7235.00 frames.], tot_loss[loss=0.2942, simple_loss=0.3525, pruned_loss=0.118, over 1421381.99 frames.], batch size: 20, lr: 2.22e-03
2022-04-28 09:29:27,935 INFO [train.py:763] (1/8) Epoch 1, batch 2950, loss[loss=0.2741, simple_loss=0.3459, pruned_loss=0.1012, over 7232.00 frames.], tot_loss[loss=0.2924, simple_loss=0.351, pruned_loss=0.1169, over 1423142.67 frames.], batch size: 20, lr: 2.22e-03
2022-04-28 09:30:33,552 INFO [train.py:763] (1/8) Epoch 1, batch 3000, loss[loss=0.2685, simple_loss=0.3288, pruned_loss=0.1041, over 7271.00 frames.], tot_loss[loss=0.2904, simple_loss=0.3499, pruned_loss=0.1155, over 1425918.65 frames.], batch size: 17, lr: 2.21e-03
2022-04-28 09:30:33,553 INFO [train.py:783] (1/8) Computing validation loss
2022-04-28 09:30:49,513 INFO [train.py:792] (1/8) Epoch 1, validation: loss=0.217, simple_loss=0.3099, pruned_loss=0.06207, over 698248.00 frames.
2022-04-28 09:31:55,882 INFO [train.py:763] (1/8) Epoch 1, batch 3050, loss[loss=0.2405, simple_loss=0.3182, pruned_loss=0.08139, over 7278.00 frames.], tot_loss[loss=0.2895, simple_loss=0.3492, pruned_loss=0.1149, over 1421866.65 frames.], batch size: 18, lr: 2.20e-03
2022-04-28 09:33:01,968 INFO [train.py:763] (1/8) Epoch 1, batch 3100, loss[loss=0.3906, simple_loss=0.4113, pruned_loss=0.1849, over 5184.00 frames.], tot_loss[loss=0.2911, simple_loss=0.3501, pruned_loss=0.116, over 1421201.21 frames.], batch size: 52, lr: 2.20e-03
2022-04-28 09:34:07,383 INFO [train.py:763] (1/8) Epoch 1, batch 3150, loss[loss=0.255, simple_loss=0.3184, pruned_loss=0.09573, over 7210.00 frames.], tot_loss[loss=0.2891, simple_loss=0.3494, pruned_loss=0.1145, over 1424203.96 frames.], batch size: 16, lr: 2.19e-03
2022-04-28 09:35:13,545 INFO [train.py:763] (1/8) Epoch 1, batch 3200, loss[loss=0.3079, simple_loss=0.3568, pruned_loss=0.1295, over 4747.00 frames.], tot_loss[loss=0.291, simple_loss=0.3509, pruned_loss=0.1156, over 1412855.69 frames.], batch size: 52, lr: 2.19e-03
2022-04-28 09:36:19,398 INFO [train.py:763] (1/8) Epoch 1, batch 3250, loss[loss=0.2871, simple_loss=0.3652, pruned_loss=0.1045, over 7201.00 frames.], tot_loss[loss=0.2925, simple_loss=0.3523, pruned_loss=0.1164, over 1415881.09 frames.], batch size: 23, lr: 2.18e-03
2022-04-28 09:37:26,021 INFO [train.py:763] (1/8) Epoch 1, batch 3300, loss[loss=0.2912, simple_loss=0.3534, pruned_loss=0.1144, over 7207.00 frames.], tot_loss[loss=0.2917, simple_loss=0.3517, pruned_loss=0.1159, over 1420061.66 frames.], batch size: 22, lr: 2.18e-03
2022-04-28 09:38:31,143 INFO [train.py:763] (1/8) Epoch 1, batch 3350, loss[loss=0.3002, simple_loss=0.3578, pruned_loss=0.1213, over 7184.00 frames.], tot_loss[loss=0.2915, simple_loss=0.3522, pruned_loss=0.1154, over 1422621.76 frames.], batch size: 26, lr: 2.18e-03
2022-04-28 09:39:36,457 INFO [train.py:763] (1/8) Epoch 1, batch 3400, loss[loss=0.2192, simple_loss=0.2867, pruned_loss=0.07585, over 7120.00 frames.], tot_loss[loss=0.2902, simple_loss=0.351, pruned_loss=0.1147, over 1424482.32 frames.], batch size: 17, lr: 2.17e-03
2022-04-28 09:40:52,291 INFO [train.py:763] (1/8) Epoch 1, batch 3450, loss[loss=0.3597, simple_loss=0.4103, pruned_loss=0.1546, over 7276.00 frames.], tot_loss[loss=0.2908, simple_loss=0.3518, pruned_loss=0.1149, over 1427406.20 frames.], batch size: 24, lr: 2.17e-03
2022-04-28 09:41:59,070 INFO [train.py:763] (1/8) Epoch 1, batch 3500, loss[loss=0.3218, simple_loss=0.3875, pruned_loss=0.1281, over 6148.00 frames.], tot_loss[loss=0.292, simple_loss=0.3528, pruned_loss=0.1156, over 1424062.63 frames.], batch size: 37, lr: 2.16e-03
2022-04-28 09:43:05,802 INFO [train.py:763] (1/8) Epoch 1, batch 3550, loss[loss=0.2893, simple_loss=0.3587, pruned_loss=0.11, over 7296.00 frames.], tot_loss[loss=0.2908, simple_loss=0.352, pruned_loss=0.1148, over 1423537.33 frames.], batch size: 25, lr: 2.16e-03
2022-04-28 09:44:12,973 INFO [train.py:763] (1/8) Epoch 1, batch 3600, loss[loss=0.3271, simple_loss=0.376, pruned_loss=0.1392, over 7236.00 frames.], tot_loss[loss=0.2909, simple_loss=0.3523, pruned_loss=0.1148, over 1425080.37 frames.], batch size: 20, lr: 2.15e-03
2022-04-28 09:45:20,592 INFO [train.py:763] (1/8) Epoch 1, batch 3650, loss[loss=0.2564, simple_loss=0.3115, pruned_loss=0.1007, over 7230.00 frames.], tot_loss[loss=0.2895, simple_loss=0.3513, pruned_loss=0.1138, over 1427482.67 frames.], batch size: 16, lr: 2.15e-03
2022-04-28 09:46:27,937 INFO [train.py:763] (1/8) Epoch 1, batch 3700, loss[loss=0.2877, simple_loss=0.3435, pruned_loss=0.116, over 7165.00 frames.], tot_loss[loss=0.2888, simple_loss=0.3513, pruned_loss=0.1132, over 1429684.73 frames.], batch size: 19, lr: 2.14e-03
2022-04-28 09:47:33,410 INFO [train.py:763] (1/8) Epoch 1, batch 3750, loss[loss=0.2919, simple_loss=0.3576, pruned_loss=0.1131, over 7267.00 frames.], tot_loss[loss=0.2885, simple_loss=0.3509, pruned_loss=0.113, over 1430543.04 frames.], batch size: 24, lr: 2.14e-03
2022-04-28 09:48:38,885 INFO [train.py:763] (1/8) Epoch 1, batch 3800, loss[loss=0.2206, simple_loss=0.2853, pruned_loss=0.078, over 7258.00 frames.], tot_loss[loss=0.287, simple_loss=0.3496, pruned_loss=0.1122, over 1431535.01 frames.], batch size: 16, lr: 2.13e-03
2022-04-28 09:49:44,146 INFO [train.py:763] (1/8) Epoch 1, batch 3850, loss[loss=0.3292, simple_loss=0.3816, pruned_loss=0.1384, over 7208.00 frames.], tot_loss[loss=0.2856, simple_loss=0.3491, pruned_loss=0.111, over 1433372.68 frames.], batch size: 26, lr: 2.13e-03
2022-04-28 09:50:49,544 INFO [train.py:763] (1/8) Epoch 1, batch 3900, loss[loss=0.3143, simple_loss=0.3725, pruned_loss=0.128, over 7302.00 frames.], tot_loss[loss=0.2855, simple_loss=0.349, pruned_loss=0.1109, over 1431446.67 frames.], batch size: 24, lr: 2.12e-03
2022-04-28 09:51:55,498 INFO [train.py:763] (1/8) Epoch 1, batch 3950, loss[loss=0.3091, simple_loss=0.3754, pruned_loss=0.1214, over 7123.00 frames.], tot_loss[loss=0.284, simple_loss=0.3476, pruned_loss=0.1102, over 1429418.37 frames.], batch size: 21, lr: 2.12e-03
2022-04-28 09:53:01,242 INFO [train.py:763] (1/8) Epoch 1, batch 4000, loss[loss=0.2999, simple_loss=0.3609, pruned_loss=0.1195, over 7208.00 frames.], tot_loss[loss=0.2837, simple_loss=0.3471, pruned_loss=0.1101, over 1429182.68 frames.], batch size: 22, lr: 2.11e-03
2022-04-28 09:54:07,049 INFO [train.py:763] (1/8) Epoch 1, batch 4050, loss[loss=0.325, simple_loss=0.3722, pruned_loss=0.139, over 6950.00 frames.], tot_loss[loss=0.2861, simple_loss=0.3488, pruned_loss=0.1117, over 1427045.69 frames.], batch size: 31, lr: 2.11e-03
2022-04-28 09:55:12,316 INFO [train.py:763] (1/8) Epoch 1, batch 4100, loss[loss=0.3004, simple_loss=0.3534, pruned_loss=0.1237, over 7208.00 frames.], tot_loss[loss=0.2861, simple_loss=0.3488, pruned_loss=0.1117, over 1421079.17 frames.], batch size: 21, lr: 2.10e-03
2022-04-28 09:56:17,393 INFO [train.py:763] (1/8) Epoch 1, batch 4150, loss[loss=0.3181, simple_loss=0.3722, pruned_loss=0.132, over 6778.00 frames.], tot_loss[loss=0.284, simple_loss=0.3474, pruned_loss=0.1103, over 1420443.11 frames.], batch size: 31, lr: 2.10e-03
2022-04-28 09:57:22,850 INFO [train.py:763] (1/8) Epoch 1, batch 4200, loss[loss=0.2639, simple_loss=0.3233, pruned_loss=0.1022, over 7266.00 frames.], tot_loss[loss=0.2837, simple_loss=0.3469, pruned_loss=0.1103, over 1420250.18 frames.], batch size: 18, lr: 2.10e-03
2022-04-28 09:58:27,891 INFO [train.py:763] (1/8) Epoch 1, batch 4250, loss[loss=0.3386, simple_loss=0.3729, pruned_loss=0.1521, over 7268.00 frames.], tot_loss[loss=0.2845, simple_loss=0.3473, pruned_loss=0.1109, over 1415350.31 frames.], batch size: 18, lr: 2.09e-03
2022-04-28 09:59:34,312 INFO [train.py:763] (1/8) Epoch 1, batch 4300, loss[loss=0.2985, simple_loss=0.3605, pruned_loss=0.1183, over 7320.00 frames.], tot_loss[loss=0.2834, simple_loss=0.3466, pruned_loss=0.1101, over 1415068.01 frames.], batch size: 25, lr: 2.09e-03
2022-04-28 10:00:39,970 INFO [train.py:763] (1/8) Epoch 1, batch 4350, loss[loss=0.2289, simple_loss=0.2965, pruned_loss=0.08064, over 7002.00 frames.], tot_loss[loss=0.2831, simple_loss=0.3466, pruned_loss=0.1098, over 1416081.84 frames.], batch size: 16, lr: 2.08e-03
2022-04-28 10:01:45,336 INFO [train.py:763] (1/8) Epoch 1, batch 4400, loss[loss=0.2683, simple_loss=0.352, pruned_loss=0.09227, over 7310.00 frames.], tot_loss[loss=0.2831, simple_loss=0.3466, pruned_loss=0.1098, over 1410861.89 frames.], batch size: 21, lr: 2.08e-03
2022-04-28 10:02:50,267 INFO [train.py:763] (1/8) Epoch 1, batch 4450, loss[loss=0.3402, simple_loss=0.3835, pruned_loss=0.1485, over 6515.00 frames.], tot_loss[loss=0.2851, simple_loss=0.3486, pruned_loss=0.1108, over 1402767.80 frames.], batch size: 38, lr: 2.07e-03
2022-04-28 10:03:55,335 INFO [train.py:763] (1/8) Epoch 1, batch 4500, loss[loss=0.3433, simple_loss=0.3853, pruned_loss=0.1506, over 6479.00 frames.], tot_loss[loss=0.2849, simple_loss=0.3482, pruned_loss=0.1108, over 1388150.57 frames.], batch size: 38, lr: 2.07e-03
2022-04-28 10:04:59,435 INFO [train.py:763] (1/8) Epoch 1, batch 4550, loss[loss=0.3838, simple_loss=0.4137, pruned_loss=0.1769, over 4923.00 frames.], tot_loss[loss=0.2894, simple_loss=0.3514, pruned_loss=0.1137, over 1357874.86 frames.], batch size: 52, lr: 2.06e-03
2022-04-28 10:06:27,050 INFO [train.py:763] (1/8) Epoch 2, batch 0, loss[loss=0.2697, simple_loss=0.323, pruned_loss=0.1083, over 7276.00 frames.], tot_loss[loss=0.2697, simple_loss=0.323, pruned_loss=0.1083, over 7276.00 frames.], batch size: 17, lr: 2.02e-03
2022-04-28 10:07:33,519 INFO [train.py:763] (1/8) Epoch 2, batch 50, loss[loss=0.302, simple_loss=0.3685, pruned_loss=0.1178, over 7321.00 frames.], tot_loss[loss=0.2782, simple_loss=0.3428, pruned_loss=0.1068, over 321276.79 frames.], batch size: 25, lr: 2.02e-03
2022-04-28 10:08:39,165 INFO [train.py:763] (1/8) Epoch 2, batch 100, loss[loss=0.2421, simple_loss=0.2993, pruned_loss=0.0924, over 7006.00 frames.], tot_loss[loss=0.2759, simple_loss=0.3426, pruned_loss=0.1046, over 569743.12 frames.], batch size: 16, lr: 2.01e-03
2022-04-28 10:09:45,123 INFO [train.py:763] (1/8) Epoch 2, batch 150, loss[loss=0.335, simple_loss=0.3926, pruned_loss=0.1387, over 6762.00 frames.], tot_loss[loss=0.2725, simple_loss=0.3398, pruned_loss=0.1026, over 761594.78 frames.], batch size: 31, lr: 2.01e-03
2022-04-28 10:10:50,699 INFO [train.py:763] (1/8) Epoch 2, batch 200, loss[loss=0.2244, simple_loss=0.2814, pruned_loss=0.08373, over 6758.00 frames.], tot_loss[loss=0.2732, simple_loss=0.3407, pruned_loss=0.1028, over 900357.22 frames.], batch size: 15, lr: 2.00e-03
2022-04-28 10:11:56,042 INFO [train.py:763] (1/8) Epoch 2, batch 250, loss[loss=0.2699, simple_loss=0.3362, pruned_loss=0.1018, over 7354.00 frames.], tot_loss[loss=0.2766, simple_loss=0.3434, pruned_loss=0.1049, over 1010381.94 frames.], batch size: 19, lr: 2.00e-03
2022-04-28 10:13:01,577 INFO [train.py:763] (1/8) Epoch 2, batch 300, loss[loss=0.3042, simple_loss=0.3647, pruned_loss=0.1218, over 6729.00 frames.], tot_loss[loss=0.2769, simple_loss=0.344, pruned_loss=0.1049, over 1100739.96 frames.], batch size: 31, lr: 2.00e-03
2022-04-28 10:14:07,026 INFO [train.py:763] (1/8) Epoch 2, batch 350, loss[loss=0.252, simple_loss=0.3323, pruned_loss=0.08581, over 7310.00 frames.], tot_loss[loss=0.2765, simple_loss=0.3438, pruned_loss=0.1046, over 1172070.12 frames.], batch size: 21, lr: 1.99e-03
2022-04-28 10:15:12,737 INFO [train.py:763] (1/8) Epoch 2, batch 400, loss[loss=0.3065, simple_loss=0.3681, pruned_loss=0.1224, over 7282.00 frames.], tot_loss[loss=0.2773, simple_loss=0.3444, pruned_loss=0.1051, over 1223395.18 frames.], batch size: 24, lr: 1.99e-03
2022-04-28 10:16:17,703 INFO [train.py:763] (1/8) Epoch 2, batch 450, loss[loss=0.3176, simple_loss=0.388, pruned_loss=0.1236, over 7199.00 frames.], tot_loss[loss=0.2787, simple_loss=0.3459, pruned_loss=0.1058, over 1263606.09 frames.], batch size: 22, lr: 1.98e-03
2022-04-28 10:17:41,019 INFO [train.py:763] (1/8) Epoch 2, batch 500, loss[loss=0.2652, simple_loss=0.3257, pruned_loss=0.1023, over 7008.00 frames.], tot_loss[loss=0.2768, simple_loss=0.3444, pruned_loss=0.1046, over 1301390.45 frames.], batch size: 16, lr: 1.98e-03
2022-04-28 10:19:24,489 INFO [train.py:763] (1/8) Epoch 2, batch 550, loss[loss=0.2517, simple_loss=0.3309, pruned_loss=0.08621, over 7216.00 frames.], tot_loss[loss=0.274, simple_loss=0.3423, pruned_loss=0.1029, over 1331291.25 frames.], batch size: 21, lr: 1.98e-03
2022-04-28 10:20:31,147 INFO [train.py:763] (1/8) Epoch 2, batch 600, loss[loss=0.3405, simple_loss=0.406, pruned_loss=0.1375, over 7289.00 frames.], tot_loss[loss=0.2729, simple_loss=0.3412, pruned_loss=0.1023, over 1351901.34 frames.], batch size: 25, lr: 1.97e-03
2022-04-28 10:21:56,818 INFO [train.py:763] (1/8) Epoch 2, batch 650, loss[loss=0.3023, simple_loss=0.3613, pruned_loss=0.1216, over 7364.00 frames.], tot_loss[loss=0.2742, simple_loss=0.342, pruned_loss=0.1032, over 1366780.94 frames.], batch size: 19, lr: 1.97e-03
2022-04-28 10:23:03,991 INFO [train.py:763] (1/8) Epoch 2, batch 700, loss[loss=0.243, simple_loss=0.3243, pruned_loss=0.08081, over 7215.00 frames.], tot_loss[loss=0.2754, simple_loss=0.3428, pruned_loss=0.104, over 1376392.40 frames.], batch size: 21, lr: 1.96e-03
2022-04-28 10:24:09,347 INFO [train.py:763] (1/8) Epoch 2, batch 750, loss[loss=0.2604, simple_loss=0.3375, pruned_loss=0.09162, over 7170.00 frames.], tot_loss[loss=0.2755, simple_loss=0.343, pruned_loss=0.104, over 1390647.32 frames.], batch size: 23, lr: 1.96e-03
2022-04-28 10:25:14,622 INFO [train.py:763] (1/8) Epoch 2, batch 800, loss[loss=0.2795, simple_loss=0.3524, pruned_loss=0.1033, over 7204.00 frames.], tot_loss[loss=0.276, simple_loss=0.3437, pruned_loss=0.1041, over 1401501.33 frames.], batch size: 23, lr: 1.96e-03
2022-04-28 10:26:20,181 INFO [train.py:763] (1/8) Epoch 2, batch 850, loss[loss=0.2723, simple_loss=0.3448, pruned_loss=0.09995, over 7308.00 frames.], tot_loss[loss=0.2735, simple_loss=0.3416, pruned_loss=0.1027, over 1410036.21 frames.], batch size: 25, lr: 1.95e-03
2022-04-28 10:27:26,276 INFO [train.py:763] (1/8) Epoch 2, batch 900, loss[loss=0.2394, simple_loss=0.3161, pruned_loss=0.08134, over 7070.00 frames.], tot_loss[loss=0.2761, simple_loss=0.3437, pruned_loss=0.1042, over 1412850.36 frames.], batch size: 18, lr: 1.95e-03
2022-04-28 10:28:31,600 INFO [train.py:763] (1/8) Epoch 2, batch 950, loss[loss=0.2691, simple_loss=0.3466, pruned_loss=0.0958, over 7147.00 frames.], tot_loss[loss=0.2754, simple_loss=0.3433, pruned_loss=0.1037, over 1417773.81 frames.], batch size: 20, lr: 1.94e-03
2022-04-28 10:29:36,671 INFO [train.py:763] (1/8) Epoch 2, batch 1000, loss[loss=0.3321, simple_loss=0.3875, pruned_loss=0.1384, over 6740.00 frames.], tot_loss[loss=0.2755, simple_loss=0.3434, pruned_loss=0.1038, over 1416850.84 frames.], batch size: 31, lr: 1.94e-03
2022-04-28 10:30:41,952 INFO [train.py:763] (1/8) Epoch 2, batch 1050, loss[loss=0.2627, simple_loss=0.3313, pruned_loss=0.0971, over 7288.00 frames.], tot_loss[loss=0.2757, simple_loss=0.3436, pruned_loss=0.1039, over 1414558.15 frames.], batch size: 18, lr: 1.94e-03
2022-04-28 10:31:48,322 INFO [train.py:763] (1/8) Epoch 2, batch 1100, loss[loss=0.2503, simple_loss=0.3394, pruned_loss=0.08064, over 7220.00 frames.], tot_loss[loss=0.2759, simple_loss=0.3448, pruned_loss=0.1035, over 1419690.76 frames.], batch size: 21, lr: 1.93e-03
2022-04-28 10:32:55,818 INFO [train.py:763] (1/8) Epoch 2, batch 1150, loss[loss=0.3169, simple_loss=0.376, pruned_loss=0.1289, over 7234.00 frames.], tot_loss[loss=0.2757, simple_loss=0.3439, pruned_loss=0.1037, over 1420955.85 frames.], batch size: 20, lr: 1.93e-03
2022-04-28 10:34:03,563 INFO [train.py:763] (1/8) Epoch 2, batch 1200, loss[loss=0.2893, simple_loss=0.3392, pruned_loss=0.1197, over 7438.00 frames.], tot_loss[loss=0.2752, simple_loss=0.3436, pruned_loss=0.1034, over 1425149.51 frames.], batch size: 20, lr: 1.93e-03
2022-04-28 10:35:11,225 INFO [train.py:763] (1/8) Epoch 2, batch 1250, loss[loss=0.2893, simple_loss=0.3583, pruned_loss=0.1102, over 7414.00 frames.], tot_loss[loss=0.2736, simple_loss=0.342, pruned_loss=0.1026, over 1425649.48 frames.], batch size: 21, lr: 1.92e-03
2022-04-28 10:36:17,276 INFO [train.py:763] (1/8) Epoch 2, batch 1300, loss[loss=0.2562, simple_loss=0.3416, pruned_loss=0.08535, over 7318.00 frames.], tot_loss[loss=0.2716, simple_loss=0.3407, pruned_loss=0.1013, over 1426836.84 frames.], batch size: 21, lr: 1.92e-03
2022-04-28 10:37:22,331 INFO [train.py:763] (1/8) Epoch 2, batch 1350, loss[loss=0.2967, simple_loss=0.3601, pruned_loss=0.1167, over 7415.00 frames.], tot_loss[loss=0.2721, simple_loss=0.3416, pruned_loss=0.1013, over 1426523.04 frames.], batch size: 20, lr: 1.91e-03
2022-04-28 10:38:27,402 INFO [train.py:763] (1/8) Epoch 2, batch 1400, loss[loss=0.2529, simple_loss=0.3296, pruned_loss=0.0881, over 7156.00 frames.], tot_loss[loss=0.2737, simple_loss=0.343, pruned_loss=0.1022, over 1423540.17 frames.], batch size: 19, lr: 1.91e-03
2022-04-28 10:39:32,822 INFO [train.py:763] (1/8) Epoch 2, batch 1450, loss[loss=0.2764, simple_loss=0.3267, pruned_loss=0.1131, over 7144.00 frames.], tot_loss[loss=0.2733, simple_loss=0.3425, pruned_loss=0.102, over 1420044.31 frames.], batch size: 17, lr: 1.91e-03
2022-04-28 10:40:38,390 INFO [train.py:763] (1/8) Epoch 2, batch 1500, loss[loss=0.3107, simple_loss=0.3811, pruned_loss=0.1202, over 7312.00 frames.], tot_loss[loss=0.2734, simple_loss=0.3426, pruned_loss=0.1021, over 1417647.24 frames.], batch size: 21, lr: 1.90e-03
2022-04-28 10:41:43,973 INFO [train.py:763] (1/8) Epoch 2, batch 1550, loss[loss=0.2169, simple_loss=0.2997, pruned_loss=0.06702, over 7156.00 frames.], tot_loss[loss=0.2712, simple_loss=0.3416, pruned_loss=0.1004, over 1421675.83 frames.], batch size: 19, lr: 1.90e-03
2022-04-28 10:42:49,541 INFO [train.py:763] (1/8) Epoch 2, batch 1600, loss[loss=0.2056, simple_loss=0.2925, pruned_loss=0.05929, over 7168.00 frames.], tot_loss[loss=0.2689, simple_loss=0.3395, pruned_loss=0.09914, over 1424360.18 frames.], batch size: 19, lr: 1.90e-03
2022-04-28 10:43:56,343 INFO [train.py:763] (1/8) Epoch 2, batch 1650, loss[loss=0.2387, simple_loss=0.3136, pruned_loss=0.08195, over 7440.00 frames.], tot_loss[loss=0.2673, simple_loss=0.3378, pruned_loss=0.09839, over 1427057.30 frames.], batch size: 20, lr: 1.89e-03
2022-04-28 10:45:02,821 INFO [train.py:763] (1/8) Epoch 2, batch 1700, loss[loss=0.2768, simple_loss=0.3563, pruned_loss=0.09861, over 7161.00 frames.], tot_loss[loss=0.268, simple_loss=0.3384, pruned_loss=0.09882, over 1417659.78 frames.], batch size: 20, lr: 1.89e-03
2022-04-28 10:46:08,589 INFO [train.py:763] (1/8) Epoch 2, batch 1750, loss[loss=0.2802, simple_loss=0.3487, pruned_loss=0.1059, over 7230.00 frames.], tot_loss[loss=0.267, simple_loss=0.3377, pruned_loss=0.09813, over 1424998.32 frames.], batch size: 20, lr: 1.88e-03
2022-04-28 10:47:13,948 INFO [train.py:763] (1/8) Epoch 2, batch 1800, loss[loss=0.276, simple_loss=0.3429, pruned_loss=0.1046, over 7115.00 frames.], tot_loss[loss=0.2672, simple_loss=0.3374, pruned_loss=0.09846, over 1418182.85 frames.], batch size: 21, lr: 1.88e-03
2022-04-28 10:48:20,968 INFO [train.py:763] (1/8) Epoch 2, batch 1850, loss[loss=0.2762, simple_loss=0.3511, pruned_loss=0.1006, over 7405.00 frames.], tot_loss[loss=0.2657, simple_loss=0.3361, pruned_loss=0.09764, over 1418878.44 frames.], batch size: 21, lr: 1.88e-03
2022-04-28 10:49:26,579 INFO [train.py:763] (1/8) Epoch 2, batch 1900, loss[loss=0.2313, simple_loss=0.3085, pruned_loss=0.07705, over 7165.00 frames.], tot_loss[loss=0.2659, simple_loss=0.3363, pruned_loss=0.09779, over 1416660.05 frames.], batch size: 18, lr: 1.87e-03
2022-04-28 10:50:31,921 INFO [train.py:763] (1/8) Epoch 2, batch 1950, loss[loss=0.3501, simple_loss=0.3962, pruned_loss=0.152, over 6706.00 frames.], tot_loss[loss=0.2676, simple_loss=0.3372, pruned_loss=0.09898, over 1417437.67 frames.], batch size: 31, lr: 1.87e-03
2022-04-28 10:51:37,333 INFO [train.py:763] (1/8) Epoch 2, batch 2000, loss[loss=0.2542, simple_loss=0.3249, pruned_loss=0.09172, over 7165.00 frames.], tot_loss[loss=0.266, simple_loss=0.3364, pruned_loss=0.09781, over 1421665.88 frames.], batch size: 19, lr: 1.87e-03
2022-04-28 10:52:43,639 INFO [train.py:763] (1/8) Epoch 2, batch 2050, loss[loss=0.3243, simple_loss=0.3749, pruned_loss=0.1369, over 5208.00 frames.], tot_loss[loss=0.2685, simple_loss=0.3385, pruned_loss=0.09927, over 1421475.20 frames.], batch size: 52, lr: 1.86e-03
2022-04-28 10:53:49,750 INFO [train.py:763] (1/8) Epoch 2, batch 2100, loss[loss=0.2251, simple_loss=0.3141, pruned_loss=0.06812, over 7322.00 frames.], tot_loss[loss=0.2688, simple_loss=0.3391, pruned_loss=0.09923, over 1423918.38 frames.], batch size: 21, lr: 1.86e-03
2022-04-28 10:54:55,190 INFO [train.py:763] (1/8) Epoch 2, batch 2150, loss[loss=0.2996, simple_loss=0.3677, pruned_loss=0.1157, over 7250.00 frames.], tot_loss[loss=0.2683, simple_loss=0.3389, pruned_loss=0.09889, over 1425956.13 frames.], batch size: 20, lr: 1.86e-03
2022-04-28 10:56:00,715 INFO [train.py:763] (1/8) Epoch 2, batch 2200, loss[loss=0.2628, simple_loss=0.3362, pruned_loss=0.09475, over 7142.00 frames.], tot_loss[loss=0.268, simple_loss=0.3383, pruned_loss=0.0988, over 1425157.43 frames.], batch size: 20, lr: 1.85e-03
2022-04-28 10:57:05,938 INFO [train.py:763] (1/8) Epoch 2, batch 2250, loss[loss=0.266, simple_loss=0.3462, pruned_loss=0.09293, over 7317.00 frames.], tot_loss[loss=0.2682, simple_loss=0.3389, pruned_loss=0.09874, over 1424923.23 frames.], batch size: 20, lr: 1.85e-03
2022-04-28 10:58:11,384 INFO [train.py:763] (1/8) Epoch 2, batch 2300, loss[loss=0.2228, simple_loss=0.3136, pruned_loss=0.06605, over 7354.00 frames.], tot_loss[loss=0.2683, simple_loss=0.3388, pruned_loss=0.09894, over 1413394.10 frames.], batch size: 19, lr: 1.85e-03
2022-04-28 10:59:16,566 INFO [train.py:763] (1/8) Epoch 2, batch 2350, loss[loss=0.312, simple_loss=0.3724, pruned_loss=0.1258, over 7256.00 frames.], tot_loss[loss=0.2673, simple_loss=0.3379, pruned_loss=0.09834, over 1414697.56 frames.], batch size: 19, lr: 1.84e-03
2022-04-28 11:00:21,740 INFO [train.py:763] (1/8) Epoch 2, batch 2400, loss[loss=0.2449, simple_loss=0.3172, pruned_loss=0.08632, over 7248.00 frames.], tot_loss[loss=0.2663, simple_loss=0.3375, pruned_loss=0.09758, over 1417631.88 frames.], batch size: 19, lr: 1.84e-03
2022-04-28 11:01:26,803 INFO [train.py:763] (1/8) Epoch 2, batch 2450, loss[loss=0.3476, simple_loss=0.3941, pruned_loss=0.1506, over 7232.00 frames.], tot_loss[loss=0.2676, simple_loss=0.3387, pruned_loss=0.09826, over 1414693.66 frames.], batch size: 20, lr: 1.84e-03
2022-04-28 11:02:32,496 INFO [train.py:763] (1/8) Epoch 2, batch 2500, loss[loss=0.2647, simple_loss=0.3312, pruned_loss=0.09911, over 7165.00 frames.], tot_loss[loss=0.2676, simple_loss=0.3381, pruned_loss=0.0985, over 1413148.72 frames.], batch size: 19, lr: 1.83e-03
2022-04-28 11:03:38,313 INFO [train.py:763] (1/8) Epoch 2, batch 2550, loss[loss=0.3288, simple_loss=0.3844, pruned_loss=0.1366, over 7211.00 frames.], tot_loss[loss=0.2664, simple_loss=0.3371, pruned_loss=0.09789, over 1411764.02 frames.], batch size: 21, lr: 1.83e-03
2022-04-28 11:04:44,221 INFO [train.py:763] (1/8) Epoch 2, batch 2600, loss[loss=0.2925, simple_loss=0.3452, pruned_loss=0.1199, over 7269.00 frames.], tot_loss[loss=0.2657, simple_loss=0.3364, pruned_loss=0.09752, over 1417782.34 frames.], batch size: 18, lr: 1.83e-03
2022-04-28 11:05:50,133 INFO [train.py:763] (1/8) Epoch 2, batch 2650, loss[loss=0.2334, simple_loss=0.3117, pruned_loss=0.07758, over 7328.00 frames.], tot_loss[loss=0.2649, simple_loss=0.3357, pruned_loss=0.09701, over 1417639.02 frames.], batch size: 20, lr: 1.82e-03
2022-04-28 11:06:55,492 INFO [train.py:763] (1/8) Epoch 2, batch 2700, loss[loss=0.2529, simple_loss=0.3193, pruned_loss=0.09323, over 7071.00 frames.], tot_loss[loss=0.2639, simple_loss=0.3354, pruned_loss=0.09624, over 1418957.36 frames.], batch size: 18, lr: 1.82e-03
2022-04-28 11:08:01,949 INFO [train.py:763] (1/8) Epoch 2, batch 2750, loss[loss=0.3121, simple_loss=0.3797, pruned_loss=0.1222, over 7173.00 frames.], tot_loss[loss=0.2644, simple_loss=0.3356, pruned_loss=0.09653, over 1417732.58 frames.], batch size: 26, lr: 1.82e-03
2022-04-28 11:09:07,550 INFO [train.py:763] (1/8) Epoch 2, batch 2800, loss[loss=0.3485, simple_loss=0.387, pruned_loss=0.155, over 5237.00 frames.], tot_loss[loss=0.2622, simple_loss=0.3342, pruned_loss=0.09512, over 1418502.37 frames.], batch size: 53, lr: 1.81e-03
2022-04-28 11:10:13,390 INFO [train.py:763] (1/8) Epoch 2, batch 2850, loss[loss=0.2481, simple_loss=0.3266, pruned_loss=0.08478, over 7214.00 frames.], tot_loss[loss=0.2624, simple_loss=0.3343, pruned_loss=0.09521, over 1421458.34 frames.], batch size: 21, lr: 1.81e-03
2022-04-28 11:11:19,190 INFO [train.py:763] (1/8) Epoch 2, batch 2900, loss[loss=0.3669, simple_loss=0.4116, pruned_loss=0.1611, over 6283.00 frames.], tot_loss[loss=0.2619, simple_loss=0.3341, pruned_loss=0.09483, over 1417733.58 frames.], batch size: 37, lr: 1.81e-03
2022-04-28 11:12:24,869 INFO [train.py:763] (1/8) Epoch 2, batch 2950, loss[loss=0.2389, simple_loss=0.32, pruned_loss=0.07888, over 7160.00 frames.], tot_loss[loss=0.2637, simple_loss=0.3357, pruned_loss=0.09591, over 1416311.98 frames.], batch size: 26, lr: 1.80e-03
2022-04-28 11:13:30,378 INFO [train.py:763] (1/8) Epoch 2, batch 3000, loss[loss=0.266, simple_loss=0.3456, pruned_loss=0.09322, over 7331.00 frames.], tot_loss[loss=0.2634, simple_loss=0.3354, pruned_loss=0.09572, over 1419412.72 frames.], batch size: 22, lr: 1.80e-03
2022-04-28 11:13:30,379 INFO [train.py:783] (1/8) Computing validation loss
2022-04-28 11:13:45,774 INFO [train.py:792] (1/8) Epoch 2, validation: loss=0.2017, simple_loss=0.3052, pruned_loss=0.04915, over 698248.00 frames.
2022-04-28 11:14:51,524 INFO [train.py:763] (1/8) Epoch 2, batch 3050, loss[loss=0.2506, simple_loss=0.3325, pruned_loss=0.08438, over 7419.00 frames.], tot_loss[loss=0.2631, simple_loss=0.3353, pruned_loss=0.09544, over 1424900.61 frames.], batch size: 21, lr: 1.80e-03
2022-04-28 11:15:57,118 INFO [train.py:763] (1/8) Epoch 2, batch 3100, loss[loss=0.2564, simple_loss=0.3267, pruned_loss=0.09308, over 7274.00 frames.], tot_loss[loss=0.2634, simple_loss=0.3353, pruned_loss=0.09571, over 1428330.82 frames.], batch size: 18, lr: 1.79e-03
2022-04-28 11:17:02,754 INFO [train.py:763] (1/8) Epoch 2, batch 3150, loss[loss=0.2756, simple_loss=0.3475, pruned_loss=0.1019, over 7219.00 frames.], tot_loss[loss=0.2619, simple_loss=0.3341, pruned_loss=0.09483, over 1422901.57 frames.], batch size: 21, lr: 1.79e-03
2022-04-28 11:18:08,971 INFO [train.py:763] (1/8) Epoch 2, batch 3200, loss[loss=0.2908, simple_loss=0.3546, pruned_loss=0.1135, over 7377.00 frames.], tot_loss[loss=0.2641, simple_loss=0.3361, pruned_loss=0.09599, over 1425597.50 frames.], batch size: 23, lr: 1.79e-03
2022-04-28 11:19:14,936 INFO [train.py:763] (1/8) Epoch 2, batch 3250, loss[loss=0.2393, simple_loss=0.3186, pruned_loss=0.08003, over 7165.00 frames.], tot_loss[loss=0.2638, simple_loss=0.336, pruned_loss=0.09577, over 1426486.14 frames.], batch size: 19, lr: 1.79e-03
2022-04-28 11:20:20,952 INFO [train.py:763] (1/8) Epoch 2, batch 3300, loss[loss=0.292, simple_loss=0.3603, pruned_loss=0.1119, over 7158.00 frames.], tot_loss[loss=0.261, simple_loss=0.3341, pruned_loss=0.094, over 1428973.29 frames.], batch size: 26, lr: 1.78e-03
2022-04-28 11:21:25,809 INFO [train.py:763] (1/8) Epoch 2, batch 3350, loss[loss=0.268, simple_loss=0.3307, pruned_loss=0.1027, over 7288.00 frames.], tot_loss[loss=0.263, simple_loss=0.3357, pruned_loss=0.0951, over 1425732.24 frames.], batch size: 18, lr: 1.78e-03
2022-04-28 11:22:30,851 INFO [train.py:763] (1/8) Epoch 2, batch 3400, loss[loss=0.2625, simple_loss=0.328, pruned_loss=0.09852, over 7413.00 frames.], tot_loss[loss=0.265, simple_loss=0.3372, pruned_loss=0.09644, over 1423959.83 frames.], batch size: 18, lr: 1.78e-03
2022-04-28 11:23:36,217 INFO [train.py:763] (1/8) Epoch 2, batch 3450, loss[loss=0.2463, simple_loss=0.322, pruned_loss=0.0853, over 7248.00 frames.], tot_loss[loss=0.2635, simple_loss=0.3358, pruned_loss=0.0956, over 1420626.59 frames.], batch size: 19, lr: 1.77e-03
2022-04-28 11:24:41,580 INFO [train.py:763] (1/8) Epoch 2, batch 3500, loss[loss=0.2455, simple_loss=0.3309, pruned_loss=0.08003, over 7303.00 frames.], tot_loss[loss=0.261, simple_loss=0.3337, pruned_loss=0.09418, over 1422163.19 frames.], batch size: 25, lr: 1.77e-03
2022-04-28 11:25:47,025 INFO [train.py:763] (1/8) Epoch 2, batch 3550, loss[loss=0.2912, simple_loss=0.3581, pruned_loss=0.1122, over 7218.00 frames.], tot_loss[loss=0.2631, simple_loss=0.3352, pruned_loss=0.09547, over 1421193.27 frames.], batch size: 21, lr: 1.77e-03
2022-04-28 11:26:52,369 INFO [train.py:763] (1/8) Epoch 2, batch 3600, loss[loss=0.2757, simple_loss=0.3596, pruned_loss=0.09589, over 7310.00 frames.], tot_loss[loss=0.2613, simple_loss=0.3336, pruned_loss=0.0945, over 1422413.12 frames.], batch size: 24, lr: 1.76e-03
2022-04-28 11:27:57,954 INFO [train.py:763] (1/8) Epoch 2, batch 3650, loss[loss=0.3228, simple_loss=0.3799, pruned_loss=0.1328, over 7369.00 frames.], tot_loss[loss=0.2595, simple_loss=0.3322, pruned_loss=0.09344, over 1422114.17 frames.], batch size: 23, lr: 1.76e-03
2022-04-28 11:29:03,179 INFO [train.py:763] (1/8) Epoch 2, batch 3700, loss[loss=0.2218, simple_loss=0.298, pruned_loss=0.07275, over 7413.00 frames.], tot_loss[loss=0.26, simple_loss=0.3332, pruned_loss=0.09342, over 1416994.85 frames.], batch size: 18, lr: 1.76e-03
2022-04-28 11:30:08,699 INFO [train.py:763] (1/8) Epoch 2, batch 3750, loss[loss=0.1926, simple_loss=0.2694, pruned_loss=0.05791, over 7266.00 frames.], tot_loss[loss=0.2591, simple_loss=0.3327, pruned_loss=0.09278, over 1422569.28 frames.], batch size: 18, lr: 1.76e-03
2022-04-28 11:31:14,664 INFO [train.py:763] (1/8) Epoch 2, batch 3800, loss[loss=0.2376, simple_loss=0.3146, pruned_loss=0.08032, over 7152.00 frames.], tot_loss[loss=0.259, simple_loss=0.3323, pruned_loss=0.09289, over 1423825.31 frames.], batch size: 18, lr: 1.75e-03
2022-04-28 11:32:20,647 INFO [train.py:763] (1/8) Epoch 2, batch 3850, loss[loss=0.2286, simple_loss=0.3201, pruned_loss=0.0685, over 7340.00 frames.], tot_loss[loss=0.2599, simple_loss=0.3329, pruned_loss=0.09345, over 1422640.80 frames.], batch size: 22, lr: 1.75e-03
2022-04-28 11:33:26,576 INFO [train.py:763] (1/8) Epoch 2, batch 3900, loss[loss=0.316, simple_loss=0.3715, pruned_loss=0.1303, over 7336.00 frames.], tot_loss[loss=0.2596, simple_loss=0.3327, pruned_loss=0.09326, over 1424402.79 frames.], batch size: 20, lr: 1.75e-03
2022-04-28 11:34:31,994 INFO [train.py:763] (1/8) Epoch 2, batch 3950, loss[loss=0.2693, simple_loss=0.3402, pruned_loss=0.09915, over 7331.00 frames.], tot_loss[loss=0.2585, simple_loss=0.3321, pruned_loss=0.09246, over 1421241.52 frames.], batch size: 21, lr: 1.74e-03
2022-04-28 11:35:37,600 INFO [train.py:763] (1/8) Epoch 2, batch 4000, loss[loss=0.2532, simple_loss=0.3439, pruned_loss=0.08122, over 7330.00 frames.], tot_loss[loss=0.2574, simple_loss=0.3317, pruned_loss=0.09151, over 1425735.10 frames.], batch size: 22, lr: 1.74e-03
2022-04-28 11:36:44,078 INFO [train.py:763] (1/8) Epoch 2, batch 4050, loss[loss=0.3046, simple_loss=0.3845, pruned_loss=0.1124, over 7422.00 frames.], tot_loss[loss=0.2564, simple_loss=0.3306, pruned_loss=0.09105, over 1426685.45 frames.], batch size: 20, lr: 1.74e-03
2022-04-28 11:37:49,242 INFO [train.py:763] (1/8) Epoch 2, batch 4100, loss[loss=0.3008, simple_loss=0.3616, pruned_loss=0.12, over 7067.00 frames.], tot_loss[loss=0.2589, simple_loss=0.3323, pruned_loss=0.09269, over 1417112.68 frames.], batch size: 18, lr: 1.73e-03
2022-04-28 11:38:54,188 INFO [train.py:763] (1/8) Epoch 2, batch 4150, loss[loss=0.2657, simple_loss=0.3482, pruned_loss=0.09165, over 7109.00 frames.], tot_loss[loss=0.2583, simple_loss=0.3322, pruned_loss=0.0922, over 1422040.99 frames.], batch size: 21, lr: 1.73e-03
2022-04-28 11:40:00,864 INFO [train.py:763] (1/8) Epoch 2, batch 4200, loss[loss=0.2964, simple_loss=0.3705, pruned_loss=0.1112, over 6920.00 frames.], tot_loss[loss=0.2591, simple_loss=0.3327, pruned_loss=0.09276, over 1420714.10 frames.], batch size: 28, lr: 1.73e-03
2022-04-28 11:41:07,990 INFO [train.py:763] (1/8) Epoch 2, batch 4250, loss[loss=0.2536, simple_loss=0.3468, pruned_loss=0.08021, over 7214.00 frames.], tot_loss[loss=0.2581, simple_loss=0.3316, pruned_loss=0.09232, over 1421496.30 frames.], batch size: 22, lr: 1.73e-03
2022-04-28 11:42:14,754 INFO [train.py:763] (1/8) Epoch 2, batch 4300, loss[loss=0.2126, simple_loss=0.2917, pruned_loss=0.06676, over 7063.00 frames.], tot_loss[loss=0.2582, simple_loss=0.3321, pruned_loss=0.0921, over 1423555.65 frames.], batch size: 18, lr: 1.72e-03
2022-04-28 11:43:21,900 INFO [train.py:763] (1/8) Epoch 2, batch 4350, loss[loss=0.3066, simple_loss=0.3779, pruned_loss=0.1176, over 7133.00 frames.], tot_loss[loss=0.2585, simple_loss=0.3322, pruned_loss=0.09236, over 1425816.02 frames.], batch size: 20, lr: 1.72e-03
2022-04-28 11:44:27,745 INFO [train.py:763] (1/8) Epoch 2, batch 4400, loss[loss=0.2802, simple_loss=0.3609, pruned_loss=0.09972, over 7297.00 frames.], tot_loss[loss=0.2584, simple_loss=0.3318, pruned_loss=0.09244, over 1420024.96 frames.], batch size: 25, lr: 1.72e-03
2022-04-28 11:45:33,249 INFO [train.py:763] (1/8) Epoch 2, batch 4450, loss[loss=0.2584, simple_loss=0.3393, pruned_loss=0.08873, over 7322.00 frames.], tot_loss[loss=0.26, simple_loss=0.3336, pruned_loss=0.09326, over 1410911.35 frames.], batch size: 22, lr: 1.71e-03
2022-04-28 11:46:38,406 INFO [train.py:763] (1/8) Epoch 2, batch 4500, loss[loss=0.2513, simple_loss=0.337, pruned_loss=0.08287, over 7120.00 frames.], tot_loss[loss=0.261, simple_loss=0.3342, pruned_loss=0.09386, over 1404713.02 frames.], batch size: 21, lr: 1.71e-03
2022-04-28 11:47:42,632 INFO [train.py:763] (1/8) Epoch 2, batch 4550, loss[loss=0.3108, simple_loss=0.3674, pruned_loss=0.1271, over 6253.00 frames.], tot_loss[loss=0.2645, simple_loss=0.3369, pruned_loss=0.09603, over 1377150.95 frames.], batch size: 37, lr: 1.71e-03
2022-04-28 11:49:10,867 INFO [train.py:763] (1/8) Epoch 3, batch 0, loss[loss=0.2513, simple_loss=0.3279, pruned_loss=0.0874, over 7227.00 frames.], tot_loss[loss=0.2513, simple_loss=0.3279, pruned_loss=0.0874, over 7227.00 frames.], batch size: 23, lr: 1.66e-03
2022-04-28 11:50:17,405 INFO [train.py:763] (1/8) Epoch 3, batch 50, loss[loss=0.2346, simple_loss=0.3053, pruned_loss=0.082, over 7267.00 frames.], tot_loss[loss=0.2583, simple_loss=0.3323, pruned_loss=0.09217, over 318355.40 frames.], batch size: 17, lr: 1.66e-03
2022-04-28 11:51:23,920 INFO [train.py:763] (1/8) Epoch 3, batch 100, loss[loss=0.1947, simple_loss=0.2785, pruned_loss=0.05544, over 7279.00 frames.], tot_loss[loss=0.2542, simple_loss=0.3285, pruned_loss=0.08996, over 564884.69 frames.], batch size: 17, lr: 1.65e-03
2022-04-28 11:52:29,495 INFO [train.py:763] (1/8) Epoch 3, batch 150, loss[loss=0.2139, simple_loss=0.3031, pruned_loss=0.06231, over 7332.00 frames.], tot_loss[loss=0.2516, simple_loss=0.3265, pruned_loss=0.08835, over 755830.15 frames.], batch size: 22, lr: 1.65e-03
2022-04-28 11:53:34,975 INFO [train.py:763] (1/8) Epoch 3, batch 200, loss[loss=0.2916, simple_loss=0.3612, pruned_loss=0.111, over 7196.00 frames.], tot_loss[loss=0.252, simple_loss=0.3271, pruned_loss=0.08848, over 904456.78 frames.], batch size: 23, lr: 1.65e-03
2022-04-28 11:54:40,982 INFO [train.py:763] (1/8) Epoch 3, batch 250, loss[loss=0.2671, simple_loss=0.3508, pruned_loss=0.09172, over 7334.00 frames.], tot_loss[loss=0.2529, simple_loss=0.3286, pruned_loss=0.08857, over 1016329.58 frames.], batch size: 22, lr: 1.64e-03
2022-04-28 11:55:46,608 INFO [train.py:763] (1/8) Epoch 3, batch 300, loss[loss=0.252, simple_loss=0.3413, pruned_loss=0.08133, over 7382.00 frames.], tot_loss[loss=0.2514, simple_loss=0.3272, pruned_loss=0.0878, over 1110617.66 frames.], batch size: 23, lr: 1.64e-03
2022-04-28 11:56:52,027 INFO [train.py:763] (1/8) Epoch 3, batch 350, loss[loss=0.2418, simple_loss=0.325, pruned_loss=0.07932, over 7322.00 frames.], tot_loss[loss=0.251, simple_loss=0.3268, pruned_loss=0.08766, over 1182564.21 frames.], batch size: 21, lr: 1.64e-03
2022-04-28 11:57:57,844 INFO [train.py:763] (1/8) Epoch 3, batch 400, loss[loss=0.2458, simple_loss=0.3294, pruned_loss=0.08112, over 7226.00 frames.], tot_loss[loss=0.2504, simple_loss=0.3262, pruned_loss=0.0873, over 1232384.85 frames.], batch size: 20, lr: 1.64e-03
2022-04-28 11:59:03,272 INFO [train.py:763] (1/8) Epoch 3, batch 450, loss[loss=0.2567, simple_loss=0.3323, pruned_loss=0.09055, over 7143.00 frames.], tot_loss[loss=0.2503, simple_loss=0.326, pruned_loss=0.08726, over 1275538.94 frames.], batch size: 20, lr: 1.63e-03
2022-04-28 12:00:09,021 INFO [train.py:763] (1/8) Epoch 3, batch 500, loss[loss=0.2851, simple_loss=0.3535, pruned_loss=0.1083, over 7150.00 frames.], tot_loss[loss=0.2521, simple_loss=0.328, pruned_loss=0.08814, over 1305457.94 frames.], batch size: 19, lr: 1.63e-03
2022-04-28 12:01:14,926 INFO [train.py:763] (1/8) Epoch 3, batch 550, loss[loss=0.1958, simple_loss=0.2886, pruned_loss=0.0515, over 7151.00 frames.], tot_loss[loss=0.2523, simple_loss=0.3286, pruned_loss=0.08803, over 1330704.39 frames.], batch size: 18, lr: 1.63e-03
2022-04-28 12:02:20,843 INFO [train.py:763] (1/8) Epoch 3, batch 600, loss[loss=0.2713, simple_loss=0.3415, pruned_loss=0.1006, over 6319.00 frames.], tot_loss[loss=0.2533, simple_loss=0.329, pruned_loss=0.08883, over 1348179.12 frames.], batch size: 37, lr: 1.63e-03
2022-04-28 12:03:27,785 INFO [train.py:763] (1/8) Epoch 3, batch 650, loss[loss=0.2531, simple_loss=0.3243, pruned_loss=0.09092, over 7424.00 frames.], tot_loss[loss=0.2527, simple_loss=0.3288, pruned_loss=0.08833, over 1368483.38 frames.], batch size: 20, lr: 1.62e-03
2022-04-28 12:04:35,116 INFO [train.py:763] (1/8) Epoch 3, batch 700, loss[loss=0.2804, simple_loss=0.3516, pruned_loss=0.1046, over 7277.00 frames.], tot_loss[loss=0.2509, simple_loss=0.3275, pruned_loss=0.08719, over 1384960.09 frames.], batch size: 24, lr: 1.62e-03
2022-04-28 12:05:41,310 INFO [train.py:763] (1/8) Epoch 3, batch 750, loss[loss=0.2992, simple_loss=0.3666, pruned_loss=0.1159, over 7288.00 frames.], tot_loss[loss=0.251, simple_loss=0.3271, pruned_loss=0.0874, over 1393580.53 frames.], batch size: 24, lr: 1.62e-03
2022-04-28 12:06:46,992 INFO [train.py:763] (1/8) Epoch 3, batch 800, loss[loss=0.2426, simple_loss=0.3095, pruned_loss=0.08784, over 7264.00 frames.], tot_loss[loss=0.2534, simple_loss=0.3288, pruned_loss=0.08902, over 1397347.91 frames.], batch size: 19, lr: 1.62e-03
2022-04-28 12:07:53,459 INFO [train.py:763] (1/8) Epoch 3, batch 850, loss[loss=0.224, simple_loss=0.2965, pruned_loss=0.07582, over 7061.00 frames.], tot_loss[loss=0.2527, simple_loss=0.3288, pruned_loss=0.08836, over 1407645.71 frames.], batch size: 18, lr: 1.61e-03
2022-04-28 12:09:00,227 INFO [train.py:763] (1/8) Epoch 3, batch 900, loss[loss=0.2891, simple_loss=0.3536, pruned_loss=0.1122, over 7110.00 frames.], tot_loss[loss=0.2539, simple_loss=0.3296, pruned_loss=0.08911, over 1415638.32 frames.], batch size: 21, lr: 1.61e-03
2022-04-28 12:10:06,504 INFO [train.py:763] (1/8) Epoch 3, batch 950, loss[loss=0.2573, simple_loss=0.334, pruned_loss=0.09027, over 7160.00 frames.], tot_loss[loss=0.252, simple_loss=0.328, pruned_loss=0.08803, over 1420156.61 frames.], batch size: 26, lr: 1.61e-03
2022-04-28 12:11:12,747 INFO [train.py:763] (1/8) Epoch 3, batch 1000, loss[loss=0.224, simple_loss=0.3012, pruned_loss=0.07341, over 7264.00 frames.], tot_loss[loss=0.2518, simple_loss=0.3274, pruned_loss=0.08804, over 1420311.40 frames.], batch size: 18, lr: 1.61e-03
2022-04-28 12:12:18,771 INFO [train.py:763] (1/8) Epoch 3, batch 1050, loss[loss=0.2773, simple_loss=0.3476, pruned_loss=0.1035, over 6721.00 frames.], tot_loss[loss=0.2524, simple_loss=0.3279, pruned_loss=0.08847, over 1419499.01 frames.], batch size: 31, lr: 1.60e-03
2022-04-28 12:13:24,397 INFO [train.py:763] (1/8) Epoch 3, batch 1100, loss[loss=0.2421, simple_loss=0.3293, pruned_loss=0.07743, over 7417.00 frames.], tot_loss[loss=0.251, simple_loss=0.3269, pruned_loss=0.08752, over 1419742.14 frames.], batch size: 21, lr: 1.60e-03
2022-04-28 12:14:28,836 INFO [train.py:763] (1/8) Epoch 3, batch 1150, loss[loss=0.3017, simple_loss=0.3608, pruned_loss=0.1212, over 7311.00 frames.], tot_loss[loss=0.2528, simple_loss=0.3288, pruned_loss=0.08844, over 1416978.48 frames.], batch size: 21, lr: 1.60e-03
2022-04-28 12:15:35,086 INFO [train.py:763] (1/8) Epoch 3, batch 1200, loss[loss=0.2283, simple_loss=0.3091, pruned_loss=0.07379, over 7327.00 frames.], tot_loss[loss=0.2544, simple_loss=0.3303, pruned_loss=0.08923, over 1415558.36 frames.], batch size: 21, lr: 1.60e-03
2022-04-28 12:16:40,630 INFO [train.py:763] (1/8) Epoch 3, batch 1250, loss[loss=0.2084, simple_loss=0.2964, pruned_loss=0.06019, over 6834.00 frames.], tot_loss[loss=0.253, simple_loss=0.3295, pruned_loss=0.08827, over 1413398.28 frames.], batch size: 15, lr: 1.59e-03
2022-04-28 12:17:46,144 INFO [train.py:763] (1/8) Epoch 3, batch 1300, loss[loss=0.2508, simple_loss=0.331, pruned_loss=0.08527, over 7203.00 frames.], tot_loss[loss=0.2512, simple_loss=0.3279, pruned_loss=0.08724, over 1416489.58 frames.], batch size: 23, lr: 1.59e-03
2022-04-28 12:18:51,890 INFO [train.py:763] (1/8) Epoch 3, batch 1350, loss[loss=0.2319, simple_loss=0.3198, pruned_loss=0.072, over 7236.00 frames.], tot_loss[loss=0.2509, simple_loss=0.3277, pruned_loss=0.08704, over 1415881.39 frames.], batch size: 20, lr: 1.59e-03
2022-04-28 12:19:57,901 INFO [train.py:763] (1/8) Epoch 3, batch 1400, loss[loss=0.2562, simple_loss=0.3462, pruned_loss=0.08305, over 7202.00 frames.], tot_loss[loss=0.2502, simple_loss=0.3269, pruned_loss=0.08675, over 1419105.85 frames.], batch size: 22, lr: 1.59e-03
2022-04-28 12:21:03,056 INFO [train.py:763] (1/8) Epoch 3, batch 1450, loss[loss=0.2561, simple_loss=0.3448, pruned_loss=0.08373, over 7283.00 frames.], tot_loss[loss=0.2493, simple_loss=0.3266, pruned_loss=0.08601, over 1421423.27 frames.], batch size: 24, lr: 1.59e-03
2022-04-28 12:22:08,501 INFO [train.py:763] (1/8) Epoch 3, batch 1500, loss[loss=0.2536, simple_loss=0.3343, pruned_loss=0.08643, over 7283.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3258, pruned_loss=0.08565, over 1418914.64 frames.], batch size: 24, lr: 1.58e-03
2022-04-28 12:23:13,998 INFO [train.py:763] (1/8) Epoch 3, batch 1550, loss[loss=0.3077, simple_loss=0.3637, pruned_loss=0.1259, over 5179.00 frames.], tot_loss[loss=0.2488, simple_loss=0.3258, pruned_loss=0.08587, over 1418692.22 frames.], batch size: 52, lr: 1.58e-03
2022-04-28 12:24:20,148 INFO [train.py:763] (1/8) Epoch 3, batch 1600, loss[loss=0.2704, simple_loss=0.3489, pruned_loss=0.09597, over 7284.00 frames.], tot_loss[loss=0.2489, simple_loss=0.3261, pruned_loss=0.08585, over 1415106.12 frames.], batch size: 25, lr: 1.58e-03
2022-04-28 12:25:26,868 INFO [train.py:763] (1/8) Epoch 3, batch 1650, loss[loss=0.2221, simple_loss=0.3031, pruned_loss=0.07057, over 7335.00 frames.], tot_loss[loss=0.2473, simple_loss=0.3246, pruned_loss=0.08498, over 1416585.01 frames.], batch size: 20, lr: 1.58e-03
2022-04-28 12:26:34,038 INFO [train.py:763] (1/8) Epoch 3, batch 1700, loss[loss=0.2643, simple_loss=0.3497, pruned_loss=0.08939, over 7145.00 frames.], tot_loss[loss=0.2487, simple_loss=0.3265, pruned_loss=0.08542, over 1420034.04 frames.], batch size: 20, lr: 1.57e-03
2022-04-28 12:27:40,150 INFO [train.py:763] (1/8) Epoch 3, batch 1750, loss[loss=0.2495, simple_loss=0.3324, pruned_loss=0.08327, over 7189.00 frames.], tot_loss[loss=0.2496, simple_loss=0.3272, pruned_loss=0.08606, over 1419275.04 frames.], batch size: 22, lr: 1.57e-03
2022-04-28 12:28:45,188 INFO [train.py:763] (1/8) Epoch 3, batch 1800, loss[loss=0.2447, simple_loss=0.322, pruned_loss=0.08374, over 7214.00 frames.], tot_loss[loss=0.2511, simple_loss=0.3283, pruned_loss=0.08694, over 1421138.98 frames.], batch size: 21, lr: 1.57e-03
2022-04-28 12:29:50,459 INFO [train.py:763] (1/8) Epoch 3, batch 1850, loss[loss=0.22, simple_loss=0.2901, pruned_loss=0.07495, over 7132.00 frames.], tot_loss[loss=0.2501, simple_loss=0.328, pruned_loss=0.08608, over 1420351.94 frames.], batch size: 17, lr: 1.57e-03
2022-04-28 12:30:57,295 INFO [train.py:763] (1/8) Epoch 3, batch 1900, loss[loss=0.2287, simple_loss=0.308, pruned_loss=0.07463, over 7147.00 frames.], tot_loss[loss=0.2502, simple_loss=0.3279, pruned_loss=0.08627, over 1422881.43 frames.], batch size: 19, lr: 1.56e-03
2022-04-28 12:32:03,219 INFO [train.py:763] (1/8) Epoch 3, batch 1950, loss[loss=0.3001, simple_loss=0.365, pruned_loss=0.1176, over 6430.00 frames.], tot_loss[loss=0.2485, simple_loss=0.3268, pruned_loss=0.08508, over 1427611.72 frames.], batch size: 38, lr: 1.56e-03
2022-04-28 12:33:17,824 INFO [train.py:763] (1/8) Epoch 3, batch 2000, loss[loss=0.2587, simple_loss=0.3463, pruned_loss=0.08558, over 7104.00 frames.], tot_loss[loss=0.2497, simple_loss=0.3278, pruned_loss=0.08576, over 1424555.70 frames.], batch size: 21, lr: 1.56e-03
2022-04-28 12:35:10,045 INFO [train.py:763] (1/8) Epoch 3, batch 2050, loss[loss=0.2632, simple_loss=0.3343, pruned_loss=0.09607, over 6808.00 frames.], tot_loss[loss=0.2495, simple_loss=0.3275, pruned_loss=0.08571, over 1421993.28 frames.], batch size: 31, lr: 1.56e-03
2022-04-28 12:36:15,498 INFO [train.py:763] (1/8) Epoch 3, batch 2100, loss[loss=0.2531, simple_loss=0.3371, pruned_loss=0.0846, over 7328.00 frames.], tot_loss[loss=0.2475, simple_loss=0.3258, pruned_loss=0.08458, over 1419705.51 frames.], batch size: 21, lr: 1.56e-03
2022-04-28 12:37:29,640 INFO [train.py:763] (1/8) Epoch 3, batch 2150, loss[loss=0.2839, simple_loss=0.3554, pruned_loss=0.1062, over 7329.00 frames.], tot_loss[loss=0.2477, simple_loss=0.3258, pruned_loss=0.08483, over 1422664.25 frames.], batch size: 22, lr: 1.55e-03
2022-04-28 12:38:44,719 INFO [train.py:763] (1/8) Epoch 3, batch 2200, loss[loss=0.2583, simple_loss=0.344, pruned_loss=0.08635, over 7222.00 frames.], tot_loss[loss=0.2468, simple_loss=0.3252, pruned_loss=0.08425, over 1425168.67 frames.], batch size: 21, lr: 1.55e-03
2022-04-28 12:40:02,462 INFO [train.py:763] (1/8) Epoch 3, batch 2250, loss[loss=0.304, simple_loss=0.3469, pruned_loss=0.1306, over 5252.00 frames.], tot_loss[loss=0.2468, simple_loss=0.3251, pruned_loss=0.08428, over 1427180.95 frames.], batch size: 53, lr: 1.55e-03
2022-04-28 12:41:07,752 INFO [train.py:763] (1/8) Epoch 3, batch 2300, loss[loss=0.2336, simple_loss=0.3154, pruned_loss=0.07591, over 7159.00 frames.], tot_loss[loss=0.2466, simple_loss=0.3249, pruned_loss=0.08422, over 1430136.19 frames.], batch size: 19, lr: 1.55e-03
2022-04-28 12:42:14,640 INFO [train.py:763] (1/8) Epoch 3, batch 2350, loss[loss=0.2395, simple_loss=0.3249, pruned_loss=0.07704, over 7338.00 frames.], tot_loss[loss=0.2463, simple_loss=0.3243, pruned_loss=0.08413, over 1430965.29 frames.], batch size: 20, lr: 1.54e-03
2022-04-28 12:43:19,979 INFO [train.py:763] (1/8) Epoch 3, batch 2400, loss[loss=0.3033, simple_loss=0.382, pruned_loss=0.1123, over 7295.00 frames.], tot_loss[loss=0.2475, simple_loss=0.3255, pruned_loss=0.08479, over 1433742.83 frames.], batch size: 25, lr: 1.54e-03
2022-04-28 12:44:25,914 INFO [train.py:763] (1/8) Epoch 3, batch 2450, loss[loss=0.26, simple_loss=0.3434, pruned_loss=0.08836, over 7378.00 frames.], tot_loss[loss=0.2486, simple_loss=0.3265, pruned_loss=0.08536, over 1436829.21 frames.], batch size: 23, lr: 1.54e-03
2022-04-28 12:45:31,560 INFO [train.py:763] (1/8) Epoch 3, batch 2500, loss[loss=0.244, simple_loss=0.3129, pruned_loss=0.08755, over 7160.00 frames.], tot_loss[loss=0.2485, simple_loss=0.3263, pruned_loss=0.08535, over 1434337.28 frames.], batch size: 19, lr: 1.54e-03
2022-04-28 12:46:36,893 INFO [train.py:763] (1/8) Epoch 3, batch 2550, loss[loss=0.2655, simple_loss=0.3314, pruned_loss=0.09981, over 7409.00 frames.], tot_loss[loss=0.2502, simple_loss=0.3276, pruned_loss=0.08645, over 1426063.09 frames.], batch size: 18, lr: 1.54e-03
2022-04-28 12:47:42,408 INFO [train.py:763] (1/8) Epoch 3, batch 2600, loss[loss=0.2654, simple_loss=0.3472, pruned_loss=0.09176, over 7236.00 frames.], tot_loss[loss=0.2515, simple_loss=0.3284, pruned_loss=0.08735, over 1426249.48 frames.], batch size: 20, lr: 1.53e-03
2022-04-28 12:48:47,821 INFO [train.py:763] (1/8) Epoch 3, batch 2650, loss[loss=0.2254, simple_loss=0.2958, pruned_loss=0.07756, over 7016.00 frames.], tot_loss[loss=0.2522, simple_loss=0.3293, pruned_loss=0.08761, over 1420387.83 frames.], batch size: 16, lr: 1.53e-03
2022-04-28 12:49:52,900 INFO [train.py:763] (1/8) Epoch 3, batch 2700, loss[loss=0.2438, simple_loss=0.3056, pruned_loss=0.09101, over 6896.00 frames.], tot_loss[loss=0.2515, simple_loss=0.3291, pruned_loss=0.08699, over 1418436.17 frames.], batch size: 15, lr: 1.53e-03
2022-04-28 12:50:58,279 INFO [train.py:763] (1/8) Epoch 3, batch 2750, loss[loss=0.2668, simple_loss=0.3256, pruned_loss=0.104, over 7247.00 frames.], tot_loss[loss=0.2524, simple_loss=0.3301, pruned_loss=0.08737, over 1421581.73 frames.], batch size: 19, lr: 1.53e-03
2022-04-28 12:52:03,626 INFO [train.py:763] (1/8) Epoch 3, batch 2800, loss[loss=0.2172, simple_loss=0.3075, pruned_loss=0.06343, over 7162.00 frames.], tot_loss[loss=0.2509, simple_loss=0.3288, pruned_loss=0.08648, over 1423498.31 frames.], batch size: 19, lr: 1.53e-03
2022-04-28 12:53:09,247 INFO [train.py:763] (1/8) Epoch 3, batch 2850, loss[loss=0.2848, simple_loss=0.3563, pruned_loss=0.1066, over 4907.00 frames.], tot_loss[loss=0.249, simple_loss=0.3272, pruned_loss=0.08538, over 1421559.45 frames.], batch size: 52, lr: 1.52e-03
2022-04-28 12:54:14,535 INFO [train.py:763] (1/8) Epoch 3, batch 2900, loss[loss=0.2612, simple_loss=0.3356, pruned_loss=0.09346, over 6902.00 frames.], tot_loss[loss=0.2474, simple_loss=0.3258, pruned_loss=0.08449, over 1422342.17 frames.], batch size: 31, lr: 1.52e-03
2022-04-28 12:55:20,290 INFO [train.py:763] (1/8) Epoch 3, batch 2950, loss[loss=0.2425, simple_loss=0.3362, pruned_loss=0.07444, over 7088.00 frames.], tot_loss[loss=0.2475, simple_loss=0.326, pruned_loss=0.08447, over 1426633.37 frames.], batch size: 28, lr: 1.52e-03
2022-04-28 12:56:25,610 INFO [train.py:763] (1/8) Epoch 3, batch 3000, loss[loss=0.2399, simple_loss=0.3223, pruned_loss=0.07872, over 7155.00 frames.], tot_loss[loss=0.2473, simple_loss=0.3259, pruned_loss=0.08439, over 1424992.96 frames.], batch size: 20, lr: 1.52e-03
2022-04-28 12:56:25,611 INFO [train.py:783] (1/8) Computing validation loss
2022-04-28 12:56:40,877 INFO [train.py:792] (1/8) Epoch 3, validation: loss=0.1917, simple_loss=0.2967, pruned_loss=0.04336, over 698248.00 frames.
2022-04-28 12:57:46,581 INFO [train.py:763] (1/8) Epoch 3, batch 3050, loss[loss=0.2625, simple_loss=0.3528, pruned_loss=0.08613, over 7112.00 frames.], tot_loss[loss=0.2475, simple_loss=0.3262, pruned_loss=0.08439, over 1420020.45 frames.], batch size: 21, lr: 1.51e-03
2022-04-28 12:58:52,513 INFO [train.py:763] (1/8) Epoch 3, batch 3100, loss[loss=0.2002, simple_loss=0.2941, pruned_loss=0.05316, over 7284.00 frames.], tot_loss[loss=0.2464, simple_loss=0.325, pruned_loss=0.08394, over 1416957.91 frames.], batch size: 24, lr: 1.51e-03
2022-04-28 12:59:58,114 INFO [train.py:763] (1/8) Epoch 3, batch 3150, loss[loss=0.2541, simple_loss=0.3392, pruned_loss=0.0845, over 7288.00 frames.], tot_loss[loss=0.2449, simple_loss=0.3238, pruned_loss=0.08306, over 1421762.09 frames.], batch size: 25, lr: 1.51e-03
2022-04-28 13:01:03,462 INFO [train.py:763] (1/8) Epoch 3, batch 3200, loss[loss=0.23, simple_loss=0.3051, pruned_loss=0.07739, over 7062.00 frames.], tot_loss[loss=0.2437, simple_loss=0.3225, pruned_loss=0.08243, over 1423417.24 frames.], batch size: 18, lr: 1.51e-03
2022-04-28 13:02:09,452 INFO [train.py:763] (1/8) Epoch 3, batch 3250, loss[loss=0.2258, simple_loss=0.3002, pruned_loss=0.07567, over 7258.00 frames.], tot_loss[loss=0.2447, simple_loss=0.3234, pruned_loss=0.08299, over 1424017.21 frames.], batch size: 19, lr: 1.51e-03
2022-04-28 13:03:16,231 INFO [train.py:763] (1/8) Epoch 3, batch 3300, loss[loss=0.2575, simple_loss=0.3308, pruned_loss=0.0921, over 7211.00 frames.], tot_loss[loss=0.2455, simple_loss=0.3244, pruned_loss=0.08326, over 1422983.11 frames.], batch size: 23, lr: 1.50e-03
2022-04-28 13:04:22,926 INFO [train.py:763] (1/8) Epoch 3, batch 3350, loss[loss=0.2852, simple_loss=0.3615, pruned_loss=0.1044, over 6395.00 frames.], tot_loss[loss=0.2449, simple_loss=0.3235, pruned_loss=0.08315, over 1420480.08 frames.], batch size: 38, lr: 1.50e-03
2022-04-28 13:05:28,641 INFO [train.py:763] (1/8) Epoch 3, batch 3400, loss[loss=0.2398, simple_loss=0.3085, pruned_loss=0.08558, over 7014.00 frames.], tot_loss[loss=0.2458, simple_loss=0.324, pruned_loss=0.08377, over 1421350.32 frames.], batch size: 16, lr: 1.50e-03
2022-04-28 13:06:35,005 INFO [train.py:763] (1/8) Epoch 3, batch 3450, loss[loss=0.2041, simple_loss=0.289, pruned_loss=0.05964, over 7164.00 frames.], tot_loss[loss=0.2443, simple_loss=0.323, pruned_loss=0.08276, over 1426106.72 frames.], batch size: 18, lr: 1.50e-03
2022-04-28 13:07:42,191 INFO [train.py:763] (1/8) Epoch 3, batch 3500, loss[loss=0.244, simple_loss=0.3329, pruned_loss=0.07752, over 7390.00 frames.], tot_loss[loss=0.2439, simple_loss=0.3222, pruned_loss=0.08284, over 1427754.15 frames.], batch size: 23, lr: 1.50e-03
2022-04-28 13:08:48,563 INFO [train.py:763] (1/8) Epoch 3, batch 3550, loss[loss=0.2488, simple_loss=0.3304, pruned_loss=0.08359, over 7302.00 frames.], tot_loss[loss=0.2423, simple_loss=0.3208, pruned_loss=0.08195, over 1428778.04 frames.], batch size: 24, lr: 1.49e-03
2022-04-28 13:09:55,525 INFO [train.py:763] (1/8) Epoch 3, batch 3600, loss[loss=0.2366, simple_loss=0.2964, pruned_loss=0.0884, over 6991.00 frames.], tot_loss[loss=0.2436, simple_loss=0.3216, pruned_loss=0.08283, over 1426968.04 frames.], batch size: 16, lr: 1.49e-03
2022-04-28 13:11:02,049 INFO [train.py:763] (1/8) Epoch 3, batch 3650, loss[loss=0.2052, simple_loss=0.2768, pruned_loss=0.06673, over 7121.00 frames.], tot_loss[loss=0.2433, simple_loss=0.3217, pruned_loss=0.08244, over 1427523.23 frames.], batch size: 17, lr: 1.49e-03
2022-04-28 13:12:07,902 INFO [train.py:763] (1/8) Epoch 3, batch 3700, loss[loss=0.198, simple_loss=0.2755, pruned_loss=0.06026, over 6986.00 frames.], tot_loss[loss=0.2426, simple_loss=0.3209, pruned_loss=0.08213, over 1427952.54 frames.], batch size: 16, lr: 1.49e-03
2022-04-28 13:13:15,359 INFO [train.py:763] (1/8) Epoch 3, batch 3750, loss[loss=0.2417, simple_loss=0.3189, pruned_loss=0.08228, over 7434.00 frames.], tot_loss[loss=0.2418, simple_loss=0.32, pruned_loss=0.08176, over 1425771.64 frames.], batch size: 20, lr: 1.49e-03
2022-04-28 13:14:22,356 INFO [train.py:763] (1/8) Epoch 3, batch 3800, loss[loss=0.2286, simple_loss=0.3083, pruned_loss=0.07445, over 7056.00 frames.], tot_loss[loss=0.2419, simple_loss=0.3202, pruned_loss=0.08182, over 1421738.53 frames.], batch size: 18, lr: 1.48e-03
2022-04-28 13:15:29,711 INFO [train.py:763] (1/8) Epoch 3, batch 3850, loss[loss=0.2415, simple_loss=0.3096, pruned_loss=0.08674, over 7415.00 frames.], tot_loss[loss=0.2422, simple_loss=0.3205, pruned_loss=0.08197, over 1425551.86 frames.], batch size: 18, lr: 1.48e-03
2022-04-28 13:16:35,236 INFO [train.py:763] (1/8) Epoch 3, batch 3900, loss[loss=0.2891, simple_loss=0.3437, pruned_loss=0.1173, over 5259.00 frames.], tot_loss[loss=0.2412, simple_loss=0.3202, pruned_loss=0.0811, over 1426511.91 frames.], batch size: 52, lr: 1.48e-03
2022-04-28 13:17:41,251 INFO [train.py:763] (1/8) Epoch 3, batch 3950, loss[loss=0.2325, simple_loss=0.3028, pruned_loss=0.08108, over 6873.00 frames.], tot_loss[loss=0.2413, simple_loss=0.3202, pruned_loss=0.08122, over 1425781.25 frames.], batch size: 15, lr: 1.48e-03
2022-04-28 13:18:46,786 INFO [train.py:763] (1/8) Epoch 3, batch 4000, loss[loss=0.2485, simple_loss=0.3313, pruned_loss=0.0829, over 7218.00 frames.], tot_loss[loss=0.2415, simple_loss=0.3204, pruned_loss=0.08136, over 1418862.62 frames.], batch size: 21, lr: 1.48e-03
2022-04-28 13:19:52,129 INFO [train.py:763] (1/8) Epoch 3, batch 4050, loss[loss=0.2304, simple_loss=0.3189, pruned_loss=0.07099, over 7401.00 frames.], tot_loss[loss=0.2421, simple_loss=0.3212, pruned_loss=0.08155, over 1420603.36 frames.], batch size: 21, lr: 1.47e-03
2022-04-28 13:20:58,241 INFO [train.py:763] (1/8) Epoch 3, batch 4100, loss[loss=0.2979, simple_loss=0.3553, pruned_loss=0.1202, over 6342.00 frames.], tot_loss[loss=0.2426, simple_loss=0.3213, pruned_loss=0.08192, over 1422178.69 frames.], batch size: 38, lr: 1.47e-03
2022-04-28 13:22:04,069 INFO [train.py:763] (1/8) Epoch 3, batch 4150, loss[loss=0.2172, simple_loss=0.2943, pruned_loss=0.07008, over 6993.00 frames.], tot_loss[loss=0.2416, simple_loss=0.3205, pruned_loss=0.08131, over 1423879.77 frames.], batch size: 16, lr: 1.47e-03
2022-04-28 13:23:11,043 INFO [train.py:763] (1/8) Epoch 3, batch 4200, loss[loss=0.2245, simple_loss=0.3027, pruned_loss=0.07318, over 7167.00 frames.], tot_loss[loss=0.2419, simple_loss=0.3204, pruned_loss=0.0817, over 1422892.01 frames.], batch size: 19, lr: 1.47e-03
2022-04-28 13:24:18,329 INFO [train.py:763] (1/8) Epoch 3, batch 4250, loss[loss=0.2119, simple_loss=0.3003, pruned_loss=0.06171, over 7361.00 frames.], tot_loss[loss=0.2431, simple_loss=0.321, pruned_loss=0.08262, over 1415323.13 frames.], batch size: 19, lr: 1.47e-03
2022-04-28 13:25:24,087 INFO [train.py:763] (1/8) Epoch 3, batch 4300, loss[loss=0.2451, simple_loss=0.3205, pruned_loss=0.08482, over 7361.00 frames.], tot_loss[loss=0.2412, simple_loss=0.3193, pruned_loss=0.08153, over 1412927.55 frames.], batch size: 19, lr: 1.47e-03
2022-04-28 13:26:29,894 INFO [train.py:763] (1/8) Epoch 3, batch 4350, loss[loss=0.288, simple_loss=0.362, pruned_loss=0.107, over 6353.00 frames.], tot_loss[loss=0.2393, simple_loss=0.3176, pruned_loss=0.08051, over 1411862.01 frames.], batch size: 38, lr: 1.46e-03
2022-04-28 13:27:35,679 INFO [train.py:763] (1/8) Epoch 3, batch 4400, loss[loss=0.255, simple_loss=0.3221, pruned_loss=0.09397, over 7070.00 frames.], tot_loss[loss=0.2385, simple_loss=0.3165, pruned_loss=0.08029, over 1410272.60 frames.], batch size: 18, lr: 1.46e-03
2022-04-28 13:28:41,562 INFO [train.py:763] (1/8) Epoch 3, batch 4450, loss[loss=0.2441, simple_loss=0.3203, pruned_loss=0.08391, over 7375.00 frames.], tot_loss[loss=0.239, simple_loss=0.3164, pruned_loss=0.08085, over 1401372.11 frames.], batch size: 23, lr: 1.46e-03
2022-04-28 13:29:46,949 INFO [train.py:763] (1/8) Epoch 3, batch 4500, loss[loss=0.2435, simple_loss=0.3307, pruned_loss=0.07819, over 6361.00 frames.], tot_loss[loss=0.2388, simple_loss=0.3165, pruned_loss=0.08058, over 1396718.25 frames.], batch size: 38, lr: 1.46e-03
2022-04-28 13:30:51,039 INFO [train.py:763] (1/8) Epoch 3, batch 4550, loss[loss=0.2911, simple_loss=0.3527, pruned_loss=0.1147, over 4813.00 frames.], tot_loss[loss=0.2441, simple_loss=0.321, pruned_loss=0.08364, over 1361284.86 frames.], batch size: 52, lr: 1.46e-03
2022-04-28 13:32:20,223 INFO [train.py:763] (1/8) Epoch 4, batch 0, loss[loss=0.2434, simple_loss=0.3391, pruned_loss=0.07385, over 7219.00 frames.], tot_loss[loss=0.2434, simple_loss=0.3391, pruned_loss=0.07385, over 7219.00 frames.], batch size: 23, lr: 1.40e-03
2022-04-28 13:33:26,506 INFO [train.py:763] (1/8) Epoch 4, batch 50, loss[loss=0.2931, simple_loss=0.3655, pruned_loss=0.1104, over 7332.00 frames.], tot_loss[loss=0.2408, simple_loss=0.3205, pruned_loss=0.08054, over 320073.33 frames.], batch size: 22, lr: 1.40e-03
2022-04-28 13:34:31,939 INFO [train.py:763] (1/8) Epoch 4, batch 100, loss[loss=0.2429, simple_loss=0.328, pruned_loss=0.07893, over 7329.00 frames.], tot_loss[loss=0.2403, simple_loss=0.321, pruned_loss=0.07982, over 565739.41 frames.], batch size: 22, lr: 1.40e-03
2022-04-28 13:35:37,381 INFO [train.py:763] (1/8) Epoch 4, batch 150, loss[loss=0.2515, simple_loss=0.3336, pruned_loss=0.0847, over 5177.00 frames.], tot_loss[loss=0.2407, simple_loss=0.322, pruned_loss=0.07972, over 755112.75 frames.], batch size: 52, lr: 1.40e-03
2022-04-28 13:36:43,012 INFO [train.py:763] (1/8) Epoch 4, batch 200, loss[loss=0.2124, simple_loss=0.2974, pruned_loss=0.06369, over 7160.00 frames.], tot_loss[loss=0.2404, simple_loss=0.3216, pruned_loss=0.0796, over 903790.45 frames.], batch size: 19, lr: 1.40e-03
2022-04-28 13:37:48,978 INFO [train.py:763] (1/8) Epoch 4, batch 250, loss[loss=0.2317, simple_loss=0.3173, pruned_loss=0.07299, over 7342.00 frames.], tot_loss[loss=0.2433, simple_loss=0.3239, pruned_loss=0.08139, over 1020946.87 frames.], batch size: 22, lr: 1.39e-03
2022-04-28 13:38:55,652 INFO [train.py:763] (1/8) Epoch 4, batch 300, loss[loss=0.2345, simple_loss=0.3008, pruned_loss=0.0841, over 7263.00 frames.], tot_loss[loss=0.2405, simple_loss=0.3212, pruned_loss=0.07994, over 1112815.92 frames.], batch size: 17, lr: 1.39e-03
2022-04-28 13:40:02,793 INFO [train.py:763] (1/8) Epoch 4, batch 350, loss[loss=0.1954, simple_loss=0.2784, pruned_loss=0.0562, over 7163.00 frames.], tot_loss[loss=0.2386, simple_loss=0.3192, pruned_loss=0.07901, over 1180612.06 frames.], batch size: 19, lr: 1.39e-03
2022-04-28 13:41:09,481 INFO [train.py:763] (1/8) Epoch 4, batch 400, loss[loss=0.265, simple_loss=0.3516, pruned_loss=0.08918, over 7151.00 frames.], tot_loss[loss=0.2385, simple_loss=0.3191, pruned_loss=0.07895, over 1231527.01 frames.], batch size: 28, lr: 1.39e-03
2022-04-28 13:42:15,465 INFO [train.py:763] (1/8) Epoch 4, batch 450, loss[loss=0.2653, simple_loss=0.3341, pruned_loss=0.09822, over 7032.00 frames.], tot_loss[loss=0.2377, simple_loss=0.3182, pruned_loss=0.07858, over 1273668.68 frames.], batch size: 28, lr: 1.39e-03
2022-04-28 13:43:21,271 INFO [train.py:763] (1/8) Epoch 4, batch 500, loss[loss=0.2495, simple_loss=0.3308, pruned_loss=0.08416, over 7320.00 frames.], tot_loss[loss=0.2372, simple_loss=0.3177, pruned_loss=0.0784, over 1309304.18 frames.], batch size: 21, lr: 1.39e-03
2022-04-28 13:44:28,335 INFO [train.py:763] (1/8) Epoch 4, batch 550, loss[loss=0.2456, simple_loss=0.3304, pruned_loss=0.0804, over 6867.00 frames.], tot_loss[loss=0.2368, simple_loss=0.3176, pruned_loss=0.07799, over 1334117.86 frames.], batch size: 31, lr: 1.38e-03
2022-04-28 13:45:33,790 INFO [train.py:763] (1/8) Epoch 4, batch 600, loss[loss=0.2811, simple_loss=0.3365, pruned_loss=0.1129, over 7016.00 frames.], tot_loss[loss=0.2363, simple_loss=0.3171, pruned_loss=0.07779, over 1356421.20 frames.], batch size: 16, lr: 1.38e-03
2022-04-28 13:46:39,057 INFO [train.py:763] (1/8) Epoch 4, batch 650, loss[loss=0.2222, simple_loss=0.3084, pruned_loss=0.06799, over 7346.00 frames.], tot_loss[loss=0.2359, simple_loss=0.317, pruned_loss=0.07741, over 1371352.69 frames.], batch size: 20, lr: 1.38e-03
2022-04-28 13:47:44,004 INFO [train.py:763] (1/8) Epoch 4, batch 700, loss[loss=0.2928, simple_loss=0.3702, pruned_loss=0.1077, over 7302.00 frames.], tot_loss[loss=0.2356, simple_loss=0.3168, pruned_loss=0.07719, over 1380177.13 frames.], batch size: 25, lr: 1.38e-03
2022-04-28 13:48:49,478 INFO [train.py:763] (1/8) Epoch 4, batch 750, loss[loss=0.2148, simple_loss=0.2953, pruned_loss=0.06714, over 7074.00 frames.], tot_loss[loss=0.2362, simple_loss=0.3169, pruned_loss=0.07774, over 1384890.53 frames.], batch size: 18, lr: 1.38e-03
2022-04-28 13:49:55,002 INFO [train.py:763] (1/8) Epoch 4, batch 800, loss[loss=0.2141, simple_loss=0.2884, pruned_loss=0.06988, over 7439.00 frames.], tot_loss[loss=0.234, simple_loss=0.3151, pruned_loss=0.07645, over 1397339.51 frames.], batch size: 19, lr: 1.38e-03
2022-04-28 13:50:59,966 INFO [train.py:763] (1/8) Epoch 4, batch 850, loss[loss=0.2336, simple_loss=0.296, pruned_loss=0.08558, over 7056.00 frames.], tot_loss[loss=0.234, simple_loss=0.3152, pruned_loss=0.07643, over 1396060.26 frames.], batch size: 18, lr: 1.37e-03
2022-04-28 13:52:05,756 INFO [train.py:763] (1/8) Epoch 4, batch 900, loss[loss=0.2546, simple_loss=0.3307, pruned_loss=0.08927, over 7323.00 frames.], tot_loss[loss=0.2343, simple_loss=0.3152, pruned_loss=0.07672, over 1403146.39 frames.], batch size: 21, lr: 1.37e-03
2022-04-28 13:53:12,231 INFO [train.py:763] (1/8) Epoch 4, batch 950, loss[loss=0.2512, simple_loss=0.3344, pruned_loss=0.08399, over 7088.00 frames.], tot_loss[loss=0.2345, simple_loss=0.3155, pruned_loss=0.07672, over 1407590.14 frames.], batch size: 28, lr: 1.37e-03
2022-04-28 13:54:19,382 INFO [train.py:763] (1/8) Epoch 4, batch 1000, loss[loss=0.2582, simple_loss=0.3231, pruned_loss=0.09666, over 7063.00 frames.], tot_loss[loss=0.2333, simple_loss=0.3143, pruned_loss=0.0761, over 1411584.36 frames.], batch size: 18, lr: 1.37e-03
2022-04-28 13:55:24,901 INFO [train.py:763] (1/8) Epoch 4, batch 1050, loss[loss=0.2363, simple_loss=0.3263, pruned_loss=0.07313, over 7297.00 frames.], tot_loss[loss=0.2338, simple_loss=0.3149, pruned_loss=0.07636, over 1416839.94 frames.], batch size: 24, lr: 1.37e-03
2022-04-28 13:56:29,980 INFO [train.py:763] (1/8) Epoch 4, batch 1100, loss[loss=0.2239, simple_loss=0.3138, pruned_loss=0.06699, over 6393.00 frames.], tot_loss[loss=0.235, simple_loss=0.3161, pruned_loss=0.07692, over 1412044.86 frames.], batch size: 38, lr: 1.37e-03
2022-04-28 13:57:36,086 INFO [train.py:763] (1/8) Epoch 4, batch 1150, loss[loss=0.3028, simple_loss=0.373, pruned_loss=0.1163, over 7427.00 frames.], tot_loss[loss=0.2365, simple_loss=0.3174, pruned_loss=0.07777, over 1414533.08 frames.], batch size: 20, lr: 1.36e-03
2022-04-28 13:58:41,142 INFO [train.py:763] (1/8) Epoch 4, batch 1200, loss[loss=0.2639, simple_loss=0.3378, pruned_loss=0.09497, over 6613.00 frames.], tot_loss[loss=0.2362, simple_loss=0.317, pruned_loss=0.07767, over 1416958.57 frames.], batch size: 38, lr: 1.36e-03
2022-04-28 13:59:46,358 INFO [train.py:763] (1/8) Epoch 4, batch 1250, loss[loss=0.171, simple_loss=0.2602, pruned_loss=0.04085, over 7261.00 frames.], tot_loss[loss=0.2362, simple_loss=0.3169, pruned_loss=0.07773, over 1412935.82 frames.], batch size: 19, lr: 1.36e-03
2022-04-28 14:00:51,530 INFO [train.py:763] (1/8) Epoch 4, batch 1300, loss[loss=0.2392, simple_loss=0.3179, pruned_loss=0.08029, over 7339.00 frames.], tot_loss[loss=0.2351, simple_loss=0.3164, pruned_loss=0.07688, over 1416480.10 frames.], batch size: 20, lr: 1.36e-03
2022-04-28 14:01:57,421 INFO [train.py:763] (1/8) Epoch 4, batch 1350, loss[loss=0.1919, simple_loss=0.277, pruned_loss=0.05341, over 7142.00 frames.], tot_loss[loss=0.2345, simple_loss=0.3162, pruned_loss=0.07641, over 1423089.72 frames.], batch size: 17, lr: 1.36e-03
2022-04-28 14:03:02,788 INFO [train.py:763] (1/8) Epoch 4, batch 1400, loss[loss=0.2151, simple_loss=0.3153, pruned_loss=0.05743, over 7228.00 frames.], tot_loss[loss=0.2359, simple_loss=0.318, pruned_loss=0.07693, over 1418631.55 frames.], batch size: 20, lr: 1.36e-03
2022-04-28 14:04:07,963 INFO [train.py:763] (1/8) Epoch 4, batch 1450, loss[loss=0.2598, simple_loss=0.3115, pruned_loss=0.1041, over 7013.00 frames.], tot_loss[loss=0.2355, simple_loss=0.3174, pruned_loss=0.07676, over 1419291.81 frames.], batch size: 16, lr: 1.35e-03
2022-04-28 14:05:14,090 INFO [train.py:763] (1/8) Epoch 4, batch 1500, loss[loss=0.2212, simple_loss=0.3132, pruned_loss=0.06464, over 7322.00 frames.], tot_loss[loss=0.2347, simple_loss=0.3167, pruned_loss=0.07634, over 1422485.58 frames.], batch size: 20, lr: 1.35e-03
2022-04-28 14:06:19,706 INFO [train.py:763] (1/8) Epoch 4, batch 1550, loss[loss=0.2224, simple_loss=0.3096, pruned_loss=0.06758, over 7390.00 frames.], tot_loss[loss=0.2329, simple_loss=0.3151, pruned_loss=0.07537, over 1424399.24 frames.], batch size: 23, lr: 1.35e-03
2022-04-28 14:07:24,982 INFO [train.py:763] (1/8) Epoch 4, batch 1600, loss[loss=0.261, simple_loss=0.3444, pruned_loss=0.0888, over 7328.00 frames.], tot_loss[loss=0.2342, simple_loss=0.3161, pruned_loss=0.07615, over 1423761.44 frames.], batch size: 25, lr: 1.35e-03
2022-04-28 14:08:30,205 INFO [train.py:763] (1/8) Epoch 4, batch 1650, loss[loss=0.2471, simple_loss=0.3339, pruned_loss=0.0802, over 7119.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3151, pruned_loss=0.07519, over 1421669.50 frames.], batch size: 21, lr: 1.35e-03
2022-04-28 14:09:35,799 INFO [train.py:763] (1/8) Epoch 4, batch 1700, loss[loss=0.2465, simple_loss=0.3218, pruned_loss=0.08561, over 7336.00 frames.], tot_loss[loss=0.2317, simple_loss=0.314, pruned_loss=0.0747, over 1423547.66 frames.], batch size: 22, lr: 1.35e-03
2022-04-28 14:10:42,770 INFO [train.py:763] (1/8) Epoch 4, batch 1750, loss[loss=0.3168, simple_loss=0.3784, pruned_loss=0.1276, over 7312.00 frames.], tot_loss[loss=0.2322, simple_loss=0.3141, pruned_loss=0.07517, over 1423008.82 frames.], batch size: 24, lr: 1.34e-03
2022-04-28 14:11:49,092 INFO [train.py:763] (1/8) Epoch 4, batch 1800, loss[loss=0.2582, simple_loss=0.3346, pruned_loss=0.0909, over 7330.00 frames.], tot_loss[loss=0.2324, simple_loss=0.3145, pruned_loss=0.0752, over 1425923.29 frames.], batch size: 21, lr: 1.34e-03
2022-04-28 14:12:54,649 INFO [train.py:763] (1/8) Epoch 4, batch 1850, loss[loss=0.2197, simple_loss=0.3131, pruned_loss=0.06313, over 6376.00 frames.], tot_loss[loss=0.234, simple_loss=0.3157, pruned_loss=0.07614, over 1425865.09 frames.], batch size: 38, lr: 1.34e-03
2022-04-28 14:13:59,950 INFO [train.py:763] (1/8) Epoch 4, batch 1900, loss[loss=0.2279, simple_loss=0.3124, pruned_loss=0.07174, over 7124.00 frames.], tot_loss[loss=0.2349, simple_loss=0.3165, pruned_loss=0.07664, over 1427089.87 frames.], batch size: 21, lr: 1.34e-03
2022-04-28 14:15:05,361 INFO [train.py:763] (1/8) Epoch 4, batch 1950, loss[loss=0.2178, simple_loss=0.3001, pruned_loss=0.06777, over 7159.00 frames.], tot_loss[loss=0.2334, simple_loss=0.3153, pruned_loss=0.0757, over 1427423.92 frames.], batch size: 18, lr: 1.34e-03
2022-04-28 14:16:10,982 INFO [train.py:763] (1/8) Epoch 4, batch 2000, loss[loss=0.2474, simple_loss=0.3376, pruned_loss=0.07856, over 7319.00 frames.], tot_loss[loss=0.2338, simple_loss=0.3152, pruned_loss=0.07619, over 1425019.82 frames.], batch size: 25, lr: 1.34e-03
2022-04-28 14:17:16,770 INFO [train.py:763] (1/8) Epoch 4, batch 2050, loss[loss=0.2639, simple_loss=0.3414, pruned_loss=0.09322, over 7303.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3143, pruned_loss=0.07497, over 1429780.26 frames.], batch size: 24, lr: 1.34e-03
2022-04-28 14:18:22,258 INFO [train.py:763] (1/8) Epoch 4, batch 2100, loss[loss=0.1881, simple_loss=0.2666, pruned_loss=0.05474, over 7405.00 frames.], tot_loss[loss=0.231, simple_loss=0.3131, pruned_loss=0.07443, over 1433213.65 frames.], batch size: 18, lr: 1.33e-03
2022-04-28 14:19:27,835 INFO [train.py:763] (1/8) Epoch 4, batch 2150, loss[loss=0.2598, simple_loss=0.3279, pruned_loss=0.0959, over 7055.00 frames.], tot_loss[loss=0.2328, simple_loss=0.3148, pruned_loss=0.07541, over 1432593.82 frames.], batch size: 18, lr: 1.33e-03
2022-04-28 14:20:34,206 INFO [train.py:763] (1/8) Epoch 4, batch 2200, loss[loss=0.2632, simple_loss=0.3551, pruned_loss=0.08559, over 7319.00 frames.], tot_loss[loss=0.2322, simple_loss=0.3143, pruned_loss=0.07501, over 1434404.43 frames.], batch size: 22, lr: 1.33e-03
2022-04-28 14:21:39,759 INFO [train.py:763] (1/8) Epoch 4, batch 2250, loss[loss=0.252, simple_loss=0.3337, pruned_loss=0.08514, over 7369.00 frames.], tot_loss[loss=0.2326, simple_loss=0.3148, pruned_loss=0.07524, over 1431264.17 frames.], batch size: 23, lr: 1.33e-03
2022-04-28 14:22:45,297 INFO [train.py:763] (1/8) Epoch 4, batch 2300, loss[loss=0.1839, simple_loss=0.2649, pruned_loss=0.05151, over 7272.00 frames.], tot_loss[loss=0.232, simple_loss=0.3141, pruned_loss=0.07495, over 1429211.85 frames.], batch size: 17, lr: 1.33e-03
2022-04-28 14:23:50,801 INFO [train.py:763] (1/8) Epoch 4, batch 2350, loss[loss=0.2089, simple_loss=0.2883, pruned_loss=0.06475, over 7403.00 frames.], tot_loss[loss=0.2323, simple_loss=0.3149, pruned_loss=0.07489, over 1432598.51 frames.], batch size: 18, lr: 1.33e-03
2022-04-28 14:24:56,454 INFO [train.py:763] (1/8) Epoch 4, batch 2400, loss[loss=0.2428, simple_loss=0.3258, pruned_loss=0.07995, over 7220.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3145, pruned_loss=0.0748, over 1433935.80 frames.], batch size: 21, lr: 1.32e-03
2022-04-28 14:26:01,946 INFO [train.py:763] (1/8) Epoch 4, batch 2450, loss[loss=0.1988, simple_loss=0.2825, pruned_loss=0.05757, over 7276.00 frames.], tot_loss[loss=0.2317, simple_loss=0.314, pruned_loss=0.07468, over 1434063.01 frames.], batch size: 18, lr: 1.32e-03
2022-04-28 14:27:09,067 INFO [train.py:763] (1/8) Epoch 4, batch 2500, loss[loss=0.2626, simple_loss=0.3404, pruned_loss=0.0924, over 7211.00 frames.], tot_loss[loss=0.2317, simple_loss=0.3139, pruned_loss=0.07476, over 1432300.18 frames.], batch size: 22, lr: 1.32e-03
2022-04-28 14:28:14,996 INFO [train.py:763] (1/8) Epoch 4, batch 2550, loss[loss=0.2094, simple_loss=0.3073, pruned_loss=0.0558, over 7141.00 frames.], tot_loss[loss=0.2317, simple_loss=0.314, pruned_loss=0.07466, over 1432839.10 frames.], batch size: 20, lr: 1.32e-03
2022-04-28 14:29:20,317 INFO [train.py:763] (1/8) Epoch 4, batch 2600, loss[loss=0.2383, simple_loss=0.3138, pruned_loss=0.08137, over 7318.00 frames.], tot_loss[loss=0.2325, simple_loss=0.3146, pruned_loss=0.07515, over 1431344.48 frames.], batch size: 21, lr: 1.32e-03
2022-04-28 14:30:26,096 INFO [train.py:763] (1/8) Epoch 4, batch 2650, loss[loss=0.168, simple_loss=0.2576, pruned_loss=0.03917, over 7002.00 frames.], tot_loss[loss=0.2317, simple_loss=0.314, pruned_loss=0.07467, over 1429900.07 frames.], batch size: 16, lr: 1.32e-03
2022-04-28 14:31:31,706 INFO [train.py:763] (1/8) Epoch 4, batch 2700, loss[loss=0.2338, simple_loss=0.3076, pruned_loss=0.08001, over 7275.00 frames.], tot_loss[loss=0.2318, simple_loss=0.3143, pruned_loss=0.07469, over 1432007.89 frames.], batch size: 18, lr: 1.32e-03
2022-04-28 14:32:38,235 INFO [train.py:763] (1/8) Epoch 4, batch 2750, loss[loss=0.2782, simple_loss=0.3468, pruned_loss=0.1048, over 7362.00 frames.], tot_loss[loss=0.232, simple_loss=0.3142, pruned_loss=0.07487, over 1432458.70 frames.], batch size: 19, lr: 1.31e-03
2022-04-28 14:33:43,917 INFO [train.py:763] (1/8) Epoch 4, batch 2800, loss[loss=0.2039, simple_loss=0.2717, pruned_loss=0.06806, over 7133.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3139, pruned_loss=0.07511, over 1433152.61 frames.], batch size: 17, lr: 1.31e-03
2022-04-28 14:34:49,326 INFO [train.py:763] (1/8) Epoch 4, batch 2850, loss[loss=0.2558, simple_loss=0.3289, pruned_loss=0.09137, over 6770.00 frames.], tot_loss[loss=0.2321, simple_loss=0.3141, pruned_loss=0.07506, over 1430148.58 frames.], batch size: 31, lr: 1.31e-03
2022-04-28 14:35:55,985 INFO [train.py:763] (1/8) Epoch 4, batch 2900, loss[loss=0.2516, simple_loss=0.341, pruned_loss=0.08114, over 7285.00 frames.], tot_loss[loss=0.2335, simple_loss=0.3156, pruned_loss=0.0757, over 1427900.91 frames.], batch size: 24, lr: 1.31e-03
2022-04-28 14:37:01,945 INFO [train.py:763] (1/8) Epoch 4, batch 2950, loss[loss=0.25, simple_loss=0.3294, pruned_loss=0.08529, over 7330.00 frames.], tot_loss[loss=0.2319, simple_loss=0.3139, pruned_loss=0.07494, over 1428153.68 frames.], batch size: 22, lr: 1.31e-03
2022-04-28 14:38:07,793 INFO [train.py:763] (1/8) Epoch 4, batch 3000, loss[loss=0.2331, simple_loss=0.3289, pruned_loss=0.06865, over 7181.00 frames.], tot_loss[loss=0.2335, simple_loss=0.315, pruned_loss=0.07602, over 1423922.29 frames.], batch size: 26, lr: 1.31e-03
2022-04-28 14:38:07,793 INFO [train.py:783] (1/8) Computing validation loss
2022-04-28 14:38:23,246 INFO [train.py:792] (1/8) Epoch 4, validation: loss=0.1809, simple_loss=0.2865, pruned_loss=0.03766, over 698248.00 frames.
2022-04-28 14:39:28,674 INFO [train.py:763] (1/8) Epoch 4, batch 3050, loss[loss=0.223, simple_loss=0.3078, pruned_loss=0.06908, over 7212.00 frames.], tot_loss[loss=0.2327, simple_loss=0.3149, pruned_loss=0.07527, over 1428509.47 frames.], batch size: 22, lr: 1.31e-03
2022-04-28 14:40:34,110 INFO [train.py:763] (1/8) Epoch 4, batch 3100, loss[loss=0.2217, simple_loss=0.3204, pruned_loss=0.06156, over 7231.00 frames.], tot_loss[loss=0.2316, simple_loss=0.3144, pruned_loss=0.07438, over 1427172.44 frames.], batch size: 20, lr: 1.30e-03
2022-04-28 14:41:39,918 INFO [train.py:763] (1/8) Epoch 4, batch 3150, loss[loss=0.2314, simple_loss=0.3136, pruned_loss=0.07464, over 7339.00 frames.], tot_loss[loss=0.2313, simple_loss=0.3144, pruned_loss=0.07415, over 1428005.72 frames.], batch size: 25, lr: 1.30e-03
2022-04-28 14:42:46,505 INFO [train.py:763] (1/8) Epoch 4, batch 3200, loss[loss=0.2209, simple_loss=0.3059, pruned_loss=0.06793, over 7358.00 frames.], tot_loss[loss=0.2306, simple_loss=0.3139, pruned_loss=0.07367, over 1429029.60 frames.], batch size: 19, lr: 1.30e-03
2022-04-28 14:43:52,376 INFO [train.py:763] (1/8) Epoch 4, batch 3250, loss[loss=0.2236, simple_loss=0.306, pruned_loss=0.07059, over 7163.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3134, pruned_loss=0.07338, over 1427935.95 frames.], batch size: 18, lr: 1.30e-03
2022-04-28 14:44:57,964 INFO [train.py:763] (1/8) Epoch 4, batch 3300, loss[loss=0.2601, simple_loss=0.3479, pruned_loss=0.08612, over 7194.00 frames.], tot_loss[loss=0.2322, simple_loss=0.3151, pruned_loss=0.07471, over 1422730.14 frames.], batch size: 26, lr: 1.30e-03
2022-04-28 14:46:03,553 INFO [train.py:763] (1/8) Epoch 4, batch 3350, loss[loss=0.3199, simple_loss=0.3841, pruned_loss=0.1278, over 7121.00 frames.], tot_loss[loss=0.232, simple_loss=0.3151, pruned_loss=0.07448, over 1425511.06 frames.], batch size: 21, lr: 1.30e-03
2022-04-28 14:47:08,814 INFO [train.py:763] (1/8) Epoch 4, batch 3400, loss[loss=0.2424, simple_loss=0.3294, pruned_loss=0.07769, over 7240.00 frames.], tot_loss[loss=0.233, simple_loss=0.3159, pruned_loss=0.07506, over 1427538.80 frames.], batch size: 20, lr: 1.30e-03
2022-04-28 14:48:14,159 INFO [train.py:763] (1/8) Epoch 4, batch 3450, loss[loss=0.202, simple_loss=0.2991, pruned_loss=0.05249, over 7204.00 frames.], tot_loss[loss=0.2324, simple_loss=0.3149, pruned_loss=0.07495, over 1428236.41 frames.], batch size: 23, lr: 1.29e-03
2022-04-28 14:49:37,444 INFO [train.py:763] (1/8) Epoch 4, batch 3500, loss[loss=0.2369, simple_loss=0.3158, pruned_loss=0.07895, over 7320.00 frames.], tot_loss[loss=0.2317, simple_loss=0.3147, pruned_loss=0.07434, over 1430101.21 frames.], batch size: 20, lr: 1.29e-03
2022-04-28 14:50:52,144 INFO [train.py:763] (1/8) Epoch 4, batch 3550, loss[loss=0.2088, simple_loss=0.2999, pruned_loss=0.05882, over 7412.00 frames.], tot_loss[loss=0.2308, simple_loss=0.3139, pruned_loss=0.07389, over 1424716.25 frames.], batch size: 21, lr: 1.29e-03
2022-04-28 14:51:57,840 INFO [train.py:763] (1/8) Epoch 4, batch 3600, loss[loss=0.2194, simple_loss=0.3064, pruned_loss=0.06619, over 7256.00 frames.], tot_loss[loss=0.2304, simple_loss=0.3133, pruned_loss=0.07369, over 1421194.79 frames.], batch size: 19, lr: 1.29e-03
2022-04-28 14:53:23,230 INFO [train.py:763] (1/8) Epoch 4, batch 3650, loss[loss=0.2392, simple_loss=0.3163, pruned_loss=0.08111, over 6766.00 frames.], tot_loss[loss=0.2325, simple_loss=0.3152, pruned_loss=0.07494, over 1415252.57 frames.], batch size: 31, lr: 1.29e-03
2022-04-28 14:54:39,014 INFO [train.py:763] (1/8) Epoch 4, batch 3700, loss[loss=0.2734, simple_loss=0.3283, pruned_loss=0.1092, over 7166.00 frames.], tot_loss[loss=0.2296, simple_loss=0.3124, pruned_loss=0.07339, over 1419305.65 frames.], batch size: 18, lr: 1.29e-03
2022-04-28 14:55:53,480 INFO [train.py:763] (1/8) Epoch 4, batch 3750, loss[loss=0.2036, simple_loss=0.2831, pruned_loss=0.06203, over 6790.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3128, pruned_loss=0.07386, over 1420322.37 frames.], batch size: 15, lr: 1.29e-03
2022-04-28 14:56:59,176 INFO [train.py:763] (1/8) Epoch 4, batch 3800, loss[loss=0.2036, simple_loss=0.2812, pruned_loss=0.063, over 7285.00 frames.], tot_loss[loss=0.2307, simple_loss=0.3134, pruned_loss=0.07403, over 1421720.50 frames.], batch size: 18, lr: 1.28e-03
2022-04-28 14:58:05,503 INFO [train.py:763] (1/8) Epoch 4, batch 3850, loss[loss=0.2149, simple_loss=0.3098, pruned_loss=0.05997, over 7413.00 frames.], tot_loss[loss=0.2311, simple_loss=0.3138, pruned_loss=0.07425, over 1421747.15 frames.], batch size: 21, lr: 1.28e-03
2022-04-28 14:59:11,124 INFO [train.py:763] (1/8) Epoch 4, batch 3900, loss[loss=0.2259, simple_loss=0.3024, pruned_loss=0.0747, over 7158.00 frames.], tot_loss[loss=0.2296, simple_loss=0.3123, pruned_loss=0.07351, over 1418201.05 frames.], batch size: 18, lr: 1.28e-03
2022-04-28 15:00:16,491 INFO [train.py:763] (1/8) Epoch 4, batch 3950, loss[loss=0.2089, simple_loss=0.3031, pruned_loss=0.05736, over 7398.00 frames.], tot_loss[loss=0.2301, simple_loss=0.3126, pruned_loss=0.07374, over 1415204.52 frames.], batch size: 21, lr: 1.28e-03
2022-04-28 15:01:21,848 INFO [train.py:763] (1/8) Epoch 4, batch 4000, loss[loss=0.2232, simple_loss=0.3009, pruned_loss=0.07273, over 7424.00 frames.], tot_loss[loss=0.2288, simple_loss=0.3118, pruned_loss=0.07294, over 1418014.81 frames.], batch size: 20, lr: 1.28e-03
2022-04-28 15:02:27,495 INFO [train.py:763] (1/8) Epoch 4, batch 4050, loss[loss=0.2151, simple_loss=0.3072, pruned_loss=0.06145, over 7223.00 frames.], tot_loss[loss=0.2287, simple_loss=0.3114, pruned_loss=0.07299, over 1420622.02 frames.], batch size: 21, lr: 1.28e-03
2022-04-28 15:03:34,095 INFO [train.py:763] (1/8) Epoch 4, batch 4100, loss[loss=0.1928, simple_loss=0.2779, pruned_loss=0.05383, over 7279.00 frames.], tot_loss[loss=0.23, simple_loss=0.3129, pruned_loss=0.07354, over 1417260.48 frames.], batch size: 18, lr: 1.28e-03
2022-04-28 15:04:40,946 INFO [train.py:763] (1/8) Epoch 4, batch 4150, loss[loss=0.2272, simple_loss=0.3173, pruned_loss=0.06853, over 7189.00 frames.], tot_loss[loss=0.2302, simple_loss=0.3133, pruned_loss=0.07359, over 1416301.22 frames.], batch size: 22, lr: 1.27e-03
2022-04-28 15:05:47,254 INFO [train.py:763] (1/8) Epoch 4, batch 4200, loss[loss=0.2125, simple_loss=0.3001, pruned_loss=0.06243, over 7132.00 frames.], tot_loss[loss=0.2305, simple_loss=0.3133, pruned_loss=0.07388, over 1414695.98 frames.], batch size: 17, lr: 1.27e-03
2022-04-28 15:06:53,140 INFO [train.py:763] (1/8) Epoch 4, batch 4250, loss[loss=0.2059, simple_loss=0.2884, pruned_loss=0.06167, over 7055.00 frames.], tot_loss[loss=0.2322, simple_loss=0.3147, pruned_loss=0.07485, over 1415794.74 frames.], batch size: 18, lr: 1.27e-03
2022-04-28 15:07:59,469 INFO [train.py:763] (1/8) Epoch 4, batch 4300, loss[loss=0.1818, simple_loss=0.2713, pruned_loss=0.04614, over 7142.00 frames.], tot_loss[loss=0.2324, simple_loss=0.3152, pruned_loss=0.0748, over 1416082.49 frames.], batch size: 20, lr: 1.27e-03
2022-04-28 15:09:04,573 INFO [train.py:763] (1/8) Epoch 4, batch 4350, loss[loss=0.2629, simple_loss=0.3343, pruned_loss=0.09577, over 7413.00 frames.], tot_loss[loss=0.2336, simple_loss=0.3163, pruned_loss=0.0754, over 1414761.50 frames.], batch size: 21, lr: 1.27e-03
2022-04-28 15:10:09,736 INFO [train.py:763] (1/8) Epoch 4, batch 4400, loss[loss=0.2101, simple_loss=0.3031, pruned_loss=0.05852, over 7262.00 frames.], tot_loss[loss=0.2333, simple_loss=0.3162, pruned_loss=0.0752, over 1410936.30 frames.], batch size: 19, lr: 1.27e-03
2022-04-28 15:11:14,749 INFO [train.py:763] (1/8) Epoch 4, batch 4450, loss[loss=0.2563, simple_loss=0.3351, pruned_loss=0.08879, over 6813.00 frames.], tot_loss[loss=0.2333, simple_loss=0.316, pruned_loss=0.07528, over 1404830.95 frames.], batch size: 31, lr: 1.27e-03
2022-04-28 15:12:19,723 INFO [train.py:763] (1/8) Epoch 4, batch 4500, loss[loss=0.2728, simple_loss=0.3461, pruned_loss=0.09976, over 5060.00 frames.], tot_loss[loss=0.2356, simple_loss=0.3184, pruned_loss=0.0764, over 1394805.00 frames.], batch size: 53, lr: 1.27e-03
2022-04-28 15:13:25,331 INFO [train.py:763] (1/8) Epoch 4, batch 4550, loss[loss=0.3024, simple_loss=0.3557, pruned_loss=0.1245, over 5208.00 frames.], tot_loss[loss=0.2397, simple_loss=0.3207, pruned_loss=0.07935, over 1341348.81 frames.], batch size: 53, lr: 1.26e-03
2022-04-28 15:14:53,614 INFO [train.py:763] (1/8) Epoch 5, batch 0, loss[loss=0.1944, simple_loss=0.2791, pruned_loss=0.05488, over 7168.00 frames.], tot_loss[loss=0.1944, simple_loss=0.2791, pruned_loss=0.05488, over 7168.00 frames.], batch size: 19, lr: 1.21e-03
2022-04-28 15:15:59,877 INFO [train.py:763] (1/8) Epoch 5, batch 50, loss[loss=0.2731, simple_loss=0.3414, pruned_loss=0.1024, over 4936.00 frames.], tot_loss[loss=0.2281, simple_loss=0.313, pruned_loss=0.07161, over 318241.22 frames.], batch size: 52, lr: 1.21e-03
2022-04-28 15:17:05,489 INFO [train.py:763] (1/8) Epoch 5, batch 100, loss[loss=0.2246, simple_loss=0.3257, pruned_loss=0.06174, over 7143.00 frames.], tot_loss[loss=0.227, simple_loss=0.3125, pruned_loss=0.07074, over 561322.30 frames.], batch size: 20, lr: 1.21e-03
2022-04-28 15:18:11,203 INFO [train.py:763] (1/8) Epoch 5, batch 150, loss[loss=0.2508, simple_loss=0.3315, pruned_loss=0.08502, over 6789.00 frames.], tot_loss[loss=0.2258, simple_loss=0.3102, pruned_loss=0.07066, over 750359.04 frames.], batch size: 31, lr: 1.21e-03
2022-04-28 15:19:17,537 INFO [train.py:763] (1/8) Epoch 5, batch 200, loss[loss=0.2407, simple_loss=0.3117, pruned_loss=0.08486, over 7412.00 frames.], tot_loss[loss=0.2246, simple_loss=0.3093, pruned_loss=0.06989, over 899433.23 frames.], batch size: 18, lr: 1.21e-03
2022-04-28 15:20:23,013 INFO [train.py:763] (1/8) Epoch 5, batch 250, loss[loss=0.2585, simple_loss=0.3362, pruned_loss=0.09043, over 7329.00 frames.], tot_loss[loss=0.2239, simple_loss=0.3089, pruned_loss=0.06939, over 1019726.23 frames.], batch size: 22, lr: 1.21e-03
2022-04-28 15:21:29,012 INFO [train.py:763] (1/8) Epoch 5, batch 300, loss[loss=0.2074, simple_loss=0.3023, pruned_loss=0.05628, over 7227.00 frames.], tot_loss[loss=0.2232, simple_loss=0.3085, pruned_loss=0.06895, over 1112417.71 frames.], batch size: 20, lr: 1.21e-03
2022-04-28 15:22:35,196 INFO [train.py:763] (1/8) Epoch 5, batch 350, loss[loss=0.2093, simple_loss=0.3048, pruned_loss=0.05686, over 7328.00 frames.], tot_loss[loss=0.2226, simple_loss=0.3081, pruned_loss=0.06858, over 1184658.66 frames.], batch size: 20, lr: 1.20e-03
2022-04-28 15:23:40,934 INFO [train.py:763] (1/8) Epoch 5, batch 400, loss[loss=0.2568, simple_loss=0.3369, pruned_loss=0.0883, over 7354.00 frames.], tot_loss[loss=0.2255, simple_loss=0.3106, pruned_loss=0.07019, over 1236085.68 frames.], batch size: 23, lr: 1.20e-03
2022-04-28 15:24:46,898 INFO [train.py:763] (1/8) Epoch 5, batch 450, loss[loss=0.2306, simple_loss=0.3051, pruned_loss=0.07803, over 6744.00 frames.], tot_loss[loss=0.2257, simple_loss=0.3109, pruned_loss=0.07031, over 1278730.39 frames.], batch size: 15, lr: 1.20e-03
2022-04-28 15:25:52,440 INFO [train.py:763] (1/8) Epoch 5, batch 500, loss[loss=0.257, simple_loss=0.3284, pruned_loss=0.09283, over 4916.00 frames.], tot_loss[loss=0.2265, simple_loss=0.3114, pruned_loss=0.07079, over 1307733.08 frames.], batch size: 52, lr: 1.20e-03
2022-04-28 15:26:57,643 INFO [train.py:763] (1/8) Epoch 5, batch 550, loss[loss=0.225, simple_loss=0.3281, pruned_loss=0.06098, over 6483.00 frames.], tot_loss[loss=0.2255, simple_loss=0.3108, pruned_loss=0.0701, over 1331747.78 frames.], batch size: 38, lr: 1.20e-03
2022-04-28 15:28:04,518 INFO [train.py:763] (1/8) Epoch 5, batch 600, loss[loss=0.2309, simple_loss=0.3327, pruned_loss=0.06451, over 7137.00 frames.], tot_loss[loss=0.2249, simple_loss=0.3097, pruned_loss=0.07008, over 1351113.33 frames.], batch size: 20, lr: 1.20e-03
2022-04-28 15:29:09,667 INFO [train.py:763] (1/8) Epoch 5, batch 650, loss[loss=0.2384, simple_loss=0.3256, pruned_loss=0.07563, over 7409.00 frames.], tot_loss[loss=0.2248, simple_loss=0.3097, pruned_loss=0.06993, over 1366221.21 frames.], batch size: 21, lr: 1.20e-03
2022-04-28 15:30:15,010 INFO [train.py:763] (1/8) Epoch 5, batch 700, loss[loss=0.2053, simple_loss=0.2927, pruned_loss=0.05892, over 6795.00 frames.], tot_loss[loss=0.2254, simple_loss=0.3103, pruned_loss=0.07028, over 1378070.00 frames.], batch size: 15, lr: 1.20e-03
2022-04-28 15:31:20,298 INFO [train.py:763] (1/8) Epoch 5, batch 750, loss[loss=0.2474, simple_loss=0.3276, pruned_loss=0.08366, over 7220.00 frames.], tot_loss[loss=0.2265, simple_loss=0.3109, pruned_loss=0.07104, over 1387247.10 frames.], batch size: 21, lr: 1.19e-03
2022-04-28 15:32:25,891 INFO [train.py:763] (1/8) Epoch 5, batch 800, loss[loss=0.2249, simple_loss=0.3141, pruned_loss=0.0679, over 7233.00 frames.], tot_loss[loss=0.2258, simple_loss=0.3101, pruned_loss=0.07073, over 1398321.44 frames.], batch size: 21, lr: 1.19e-03
2022-04-28 15:33:31,213 INFO [train.py:763] (1/8) Epoch 5, batch 850, loss[loss=0.2351, simple_loss=0.3325, pruned_loss=0.06881, over 7190.00 frames.], tot_loss[loss=0.225, simple_loss=0.3097, pruned_loss=0.07013, over 1403897.86 frames.], batch size: 23, lr: 1.19e-03
2022-04-28 15:34:36,542 INFO [train.py:763] (1/8) Epoch 5, batch 900, loss[loss=0.2271, simple_loss=0.3143, pruned_loss=0.06998, over 7417.00 frames.], tot_loss[loss=0.2256, simple_loss=0.3104, pruned_loss=0.07042, over 1405299.57 frames.], batch size: 21, lr: 1.19e-03
2022-04-28 15:35:42,332 INFO [train.py:763] (1/8) Epoch 5, batch 950, loss[loss=0.2499, simple_loss=0.3109, pruned_loss=0.09445, over 7125.00 frames.], tot_loss[loss=0.2256, simple_loss=0.3103, pruned_loss=0.0704, over 1406460.35 frames.], batch size: 17, lr: 1.19e-03
2022-04-28 15:36:47,762 INFO [train.py:763] (1/8) Epoch 5, batch 1000, loss[loss=0.2095, simple_loss=0.307, pruned_loss=0.05596, over 7399.00 frames.], tot_loss[loss=0.2259, simple_loss=0.3106, pruned_loss=0.07058, over 1408926.27 frames.], batch size: 21, lr: 1.19e-03
2022-04-28 15:37:53,891 INFO [train.py:763] (1/8) Epoch 5, batch 1050, loss[loss=0.219, simple_loss=0.3088, pruned_loss=0.06464, over 7321.00 frames.], tot_loss[loss=0.2268, simple_loss=0.3111, pruned_loss=0.07125, over 1413153.83 frames.], batch size: 20, lr: 1.19e-03
2022-04-28 15:39:10,231 INFO [train.py:763] (1/8) Epoch 5, batch 1100, loss[loss=0.2044, simple_loss=0.3057, pruned_loss=0.05158, over 7323.00 frames.], tot_loss[loss=0.2273, simple_loss=0.3119, pruned_loss=0.0714, over 1408511.63 frames.], batch size: 21, lr: 1.19e-03
2022-04-28 15:40:16,765 INFO [train.py:763] (1/8) Epoch 5, batch 1150, loss[loss=0.2584, simple_loss=0.3394, pruned_loss=0.08871, over 7143.00 frames.], tot_loss[loss=0.2272, simple_loss=0.3122, pruned_loss=0.07108, over 1413837.22 frames.], batch size: 20, lr: 1.19e-03
2022-04-28 15:41:22,501 INFO [train.py:763] (1/8) Epoch 5, batch 1200, loss[loss=0.2251, simple_loss=0.3196, pruned_loss=0.06528, over 7183.00 frames.], tot_loss[loss=0.2258, simple_loss=0.3109, pruned_loss=0.07038, over 1415060.76 frames.], batch size: 26, lr: 1.18e-03
2022-04-28 15:42:28,993 INFO [train.py:763] (1/8) Epoch 5, batch 1250, loss[loss=0.2217, simple_loss=0.3084, pruned_loss=0.06747, over 7149.00 frames.], tot_loss[loss=0.2269, simple_loss=0.3114, pruned_loss=0.07116, over 1414116.02 frames.], batch size: 20, lr: 1.18e-03
2022-04-28 15:43:35,958 INFO [train.py:763] (1/8) Epoch 5, batch 1300, loss[loss=0.1733, simple_loss=0.2641, pruned_loss=0.04129, over 7356.00 frames.], tot_loss[loss=0.2269, simple_loss=0.311, pruned_loss=0.07143, over 1412013.14 frames.], batch size: 19, lr: 1.18e-03
2022-04-28 15:44:42,293 INFO [train.py:763] (1/8) Epoch 5, batch 1350, loss[loss=0.2532, simple_loss=0.3297, pruned_loss=0.08835, over 7068.00 frames.], tot_loss[loss=0.226, simple_loss=0.31, pruned_loss=0.07102, over 1414870.77 frames.], batch size: 28, lr: 1.18e-03
2022-04-28 15:45:48,503 INFO [train.py:763] (1/8) Epoch 5, batch 1400, loss[loss=0.1876, simple_loss=0.2729, pruned_loss=0.05111, over 7329.00 frames.], tot_loss[loss=0.2249, simple_loss=0.3092, pruned_loss=0.07028, over 1418800.31 frames.], batch size: 20, lr: 1.18e-03
2022-04-28 15:46:53,764 INFO [train.py:763] (1/8) Epoch 5, batch 1450, loss[loss=0.1883, simple_loss=0.2796, pruned_loss=0.04852, over 7437.00 frames.], tot_loss[loss=0.2244, simple_loss=0.3089, pruned_loss=0.0699, over 1419999.16 frames.], batch size: 20, lr: 1.18e-03
2022-04-28 15:47:59,047 INFO [train.py:763] (1/8) Epoch 5, batch 1500, loss[loss=0.2406, simple_loss=0.3251, pruned_loss=0.07805, over 7148.00 frames.], tot_loss[loss=0.2246, simple_loss=0.3092, pruned_loss=0.06998, over 1420241.58 frames.], batch size: 20, lr: 1.18e-03
2022-04-28 15:49:04,607 INFO [train.py:763] (1/8) Epoch 5, batch 1550, loss[loss=0.181, simple_loss=0.267, pruned_loss=0.04757, over 7284.00 frames.], tot_loss[loss=0.2257, simple_loss=0.3103, pruned_loss=0.07053, over 1422785.67 frames.], batch size: 17, lr: 1.18e-03
2022-04-28 15:50:09,902 INFO [train.py:763] (1/8) Epoch 5, batch 1600, loss[loss=0.2192, simple_loss=0.3064, pruned_loss=0.06599, over 7423.00 frames.], tot_loss[loss=0.2253, simple_loss=0.3101, pruned_loss=0.07022, over 1416431.73 frames.], batch size: 20, lr: 1.17e-03
2022-04-28 15:51:15,387 INFO [train.py:763] (1/8) Epoch 5, batch 1650, loss[loss=0.2384, simple_loss=0.3316, pruned_loss=0.07262, over 7286.00 frames.], tot_loss[loss=0.2244, simple_loss=0.3095, pruned_loss=0.06966, over 1415185.59 frames.], batch size: 25, lr: 1.17e-03
2022-04-28 15:52:21,475 INFO [train.py:763] (1/8) Epoch 5, batch 1700, loss[loss=0.231, simple_loss=0.3169, pruned_loss=0.07251, over 7213.00 frames.], tot_loss[loss=0.2237, simple_loss=0.3087, pruned_loss=0.06939, over 1414349.96 frames.], batch size: 22, lr: 1.17e-03
2022-04-28 15:53:26,973 INFO [train.py:763] (1/8) Epoch 5, batch 1750, loss[loss=0.2057, simple_loss=0.2836, pruned_loss=0.06388, over 7279.00 frames.], tot_loss[loss=0.2244, simple_loss=0.3094, pruned_loss=0.0697, over 1411208.74 frames.], batch size: 18, lr: 1.17e-03
2022-04-28 15:54:32,245 INFO [train.py:763] (1/8) Epoch 5, batch 1800, loss[loss=0.2627, simple_loss=0.3323, pruned_loss=0.09653, over 4990.00 frames.], tot_loss[loss=0.2253, simple_loss=0.3102, pruned_loss=0.07019, over 1412133.44 frames.], batch size: 52, lr: 1.17e-03
2022-04-28 15:55:37,877 INFO [train.py:763] (1/8) Epoch 5, batch 1850, loss[loss=0.1957, simple_loss=0.284, pruned_loss=0.05366, over 7168.00 frames.], tot_loss[loss=0.2253, simple_loss=0.3099, pruned_loss=0.07032, over 1416010.26 frames.], batch size: 18, lr: 1.17e-03
2022-04-28 15:56:43,238 INFO [train.py:763] (1/8) Epoch 5, batch 1900, loss[loss=0.1719, simple_loss=0.2567, pruned_loss=0.0436, over 7156.00 frames.], tot_loss[loss=0.2252, simple_loss=0.3099, pruned_loss=0.07027, over 1415226.03 frames.], batch size: 17, lr: 1.17e-03
2022-04-28 15:57:48,604 INFO [train.py:763] (1/8) Epoch 5, batch 1950, loss[loss=0.219, simple_loss=0.3105, pruned_loss=0.06373, over 7111.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3095, pruned_loss=0.06936, over 1420459.29 frames.], batch size: 21, lr: 1.17e-03
2022-04-28 15:58:54,742 INFO [train.py:763] (1/8) Epoch 5, batch 2000, loss[loss=0.2038, simple_loss=0.27, pruned_loss=0.0688, over 7265.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3093, pruned_loss=0.06945, over 1423476.13 frames.], batch size: 18, lr: 1.17e-03
2022-04-28 15:59:59,953 INFO [train.py:763] (1/8) Epoch 5, batch 2050, loss[loss=0.2496, simple_loss=0.3375, pruned_loss=0.08091, over 7089.00 frames.], tot_loss[loss=0.2238, simple_loss=0.3093, pruned_loss=0.06918, over 1423594.38 frames.], batch size: 28, lr: 1.16e-03
2022-04-28 16:01:06,583 INFO [train.py:763] (1/8) Epoch 5, batch 2100, loss[loss=0.2313, simple_loss=0.3142, pruned_loss=0.0742, over 6413.00 frames.], tot_loss[loss=0.2234, simple_loss=0.3089, pruned_loss=0.06897, over 1425201.62 frames.], batch size: 37, lr: 1.16e-03
2022-04-28 16:02:12,117 INFO [train.py:763] (1/8) Epoch 5, batch 2150, loss[loss=0.2635, simple_loss=0.3306, pruned_loss=0.09822, over 7144.00 frames.], tot_loss[loss=0.2243, simple_loss=0.3094, pruned_loss=0.06966, over 1430415.45 frames.], batch size: 20, lr: 1.16e-03
2022-04-28 16:03:17,457 INFO [train.py:763] (1/8) Epoch 5, batch 2200, loss[loss=0.2272, simple_loss=0.3139, pruned_loss=0.07027, over 7151.00 frames.], tot_loss[loss=0.2255, simple_loss=0.3102, pruned_loss=0.07034, over 1427646.75 frames.], batch size: 20, lr: 1.16e-03
2022-04-28 16:04:22,917 INFO [train.py:763] (1/8) Epoch 5, batch 2250, loss[loss=0.2004, simple_loss=0.2828, pruned_loss=0.05895, over 7359.00 frames.], tot_loss[loss=0.2256, simple_loss=0.3102, pruned_loss=0.07046, over 1425903.07 frames.], batch size: 19, lr: 1.16e-03
2022-04-28 16:05:29,058 INFO [train.py:763] (1/8) Epoch 5, batch 2300, loss[loss=0.2344, simple_loss=0.3176, pruned_loss=0.07563, over 7294.00 frames.], tot_loss[loss=0.2248, simple_loss=0.3098, pruned_loss=0.0699, over 1423094.26 frames.], batch size: 24, lr: 1.16e-03
2022-04-28 16:06:35,242 INFO [train.py:763] (1/8) Epoch 5, batch 2350, loss[loss=0.208, simple_loss=0.3056, pruned_loss=0.05524, over 7214.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3084, pruned_loss=0.0693, over 1422409.18 frames.], batch size: 21, lr: 1.16e-03
2022-04-28 16:07:41,475 INFO [train.py:763] (1/8) Epoch 5, batch 2400, loss[loss=0.2131, simple_loss=0.2954, pruned_loss=0.06543, over 7325.00 frames.], tot_loss[loss=0.2231, simple_loss=0.308, pruned_loss=0.06911, over 1422919.95 frames.], batch size: 20, lr: 1.16e-03
2022-04-28 16:08:47,664 INFO [train.py:763] (1/8) Epoch 5, batch 2450, loss[loss=0.1791, simple_loss=0.2592, pruned_loss=0.04947, over 6783.00 frames.], tot_loss[loss=0.2216, simple_loss=0.3068, pruned_loss=0.06819, over 1421607.25 frames.], batch size: 15, lr: 1.16e-03
2022-04-28 16:09:52,913 INFO [train.py:763] (1/8) Epoch 5, batch 2500, loss[loss=0.2569, simple_loss=0.3343, pruned_loss=0.08969, over 7340.00 frames.], tot_loss[loss=0.2226, simple_loss=0.3075, pruned_loss=0.06884, over 1420139.07 frames.], batch size: 22, lr: 1.15e-03
2022-04-28 16:10:59,309 INFO [train.py:763] (1/8) Epoch 5, batch 2550, loss[loss=0.2252, simple_loss=0.2953, pruned_loss=0.07749, over 6764.00 frames.], tot_loss[loss=0.2226, simple_loss=0.3074, pruned_loss=0.06893, over 1421410.76 frames.], batch size: 15, lr: 1.15e-03
2022-04-28 16:12:05,354 INFO [train.py:763] (1/8) Epoch 5, batch 2600, loss[loss=0.2427, simple_loss=0.3226, pruned_loss=0.08145, over 7308.00 frames.], tot_loss[loss=0.2228, simple_loss=0.308, pruned_loss=0.06881, over 1424578.13 frames.], batch size: 21, lr: 1.15e-03
2022-04-28 16:13:10,877 INFO [train.py:763] (1/8) Epoch 5, batch 2650, loss[loss=0.2524, simple_loss=0.3362, pruned_loss=0.08426, over 7280.00 frames.], tot_loss[loss=0.2233, simple_loss=0.3087, pruned_loss=0.06895, over 1422694.66 frames.], batch size: 25, lr: 1.15e-03
2022-04-28 16:14:16,437 INFO [train.py:763] (1/8) Epoch 5, batch 2700, loss[loss=0.2115, simple_loss=0.2947, pruned_loss=0.06417, over 7189.00 frames.], tot_loss[loss=0.2232, simple_loss=0.3087, pruned_loss=0.06889, over 1424900.64 frames.], batch size: 16, lr: 1.15e-03
2022-04-28 16:15:15,071 INFO [train.py:763] (1/8) Epoch 5, batch 2750, loss[loss=0.2195, simple_loss=0.2952, pruned_loss=0.07192, over 7228.00 frames.], tot_loss[loss=0.2231, simple_loss=0.3086, pruned_loss=0.06875, over 1422677.63 frames.], batch size: 20, lr: 1.15e-03
2022-04-28 16:16:11,915 INFO [train.py:763] (1/8) Epoch 5, batch 2800, loss[loss=0.2255, simple_loss=0.3108, pruned_loss=0.07008, over 7270.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3085, pruned_loss=0.06924, over 1420913.36 frames.], batch size: 18, lr: 1.15e-03
2022-04-28 16:17:08,597 INFO [train.py:763] (1/8) Epoch 5, batch 2850, loss[loss=0.1839, simple_loss=0.2693, pruned_loss=0.04927, over 7277.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3089, pruned_loss=0.06968, over 1418309.18 frames.], batch size: 17, lr: 1.15e-03
2022-04-28 16:18:06,396 INFO [train.py:763] (1/8) Epoch 5, batch 2900, loss[loss=0.2364, simple_loss=0.3156, pruned_loss=0.07861, over 6782.00 frames.], tot_loss[loss=0.2235, simple_loss=0.3085, pruned_loss=0.06921, over 1420261.22 frames.], batch size: 31, lr: 1.15e-03
2022-04-28 16:19:04,268 INFO [train.py:763] (1/8) Epoch 5, batch 2950, loss[loss=0.2307, simple_loss=0.3244, pruned_loss=0.06853, over 7146.00 frames.], tot_loss[loss=0.2231, simple_loss=0.3081, pruned_loss=0.06903, over 1420020.19 frames.], batch size: 20, lr: 1.14e-03
2022-04-28 16:19:58,110 INFO [train.py:763] (1/8) Epoch 5, batch 3000, loss[loss=0.2047, simple_loss=0.3009, pruned_loss=0.05422, over 7229.00 frames.], tot_loss[loss=0.2224, simple_loss=0.3079, pruned_loss=0.06844, over 1419554.64 frames.], batch size: 20, lr: 1.14e-03
2022-04-28 16:19:58,111 INFO [train.py:783] (1/8) Computing validation loss
2022-04-28 16:20:13,357 INFO [train.py:792] (1/8) Epoch 5, validation: loss=0.1791, simple_loss=0.2847, pruned_loss=0.03677, over 698248.00 frames.
2022-04-28 16:21:19,339 INFO [train.py:763] (1/8) Epoch 5, batch 3050, loss[loss=0.2409, simple_loss=0.3291, pruned_loss=0.0764, over 7222.00 frames.], tot_loss[loss=0.2212, simple_loss=0.3069, pruned_loss=0.0678, over 1425341.06 frames.], batch size: 23, lr: 1.14e-03
2022-04-28 16:22:24,938 INFO [train.py:763] (1/8) Epoch 5, batch 3100, loss[loss=0.2282, simple_loss=0.3175, pruned_loss=0.06943, over 7336.00 frames.], tot_loss[loss=0.2212, simple_loss=0.3062, pruned_loss=0.0681, over 1423689.32 frames.], batch size: 22, lr: 1.14e-03
2022-04-28 16:23:30,170 INFO [train.py:763] (1/8) Epoch 5, batch 3150, loss[loss=0.2247, simple_loss=0.3211, pruned_loss=0.06415, over 7212.00 frames.], tot_loss[loss=0.2222, simple_loss=0.3079, pruned_loss=0.0683, over 1423677.45 frames.], batch size: 23, lr: 1.14e-03
2022-04-28 16:24:36,688 INFO [train.py:763] (1/8) Epoch 5, batch 3200, loss[loss=0.218, simple_loss=0.3077, pruned_loss=0.06411, over 7222.00 frames.], tot_loss[loss=0.2229, simple_loss=0.3086, pruned_loss=0.06863, over 1425616.63 frames.], batch size: 21, lr: 1.14e-03
2022-04-28 16:25:42,633 INFO [train.py:763] (1/8) Epoch 5, batch 3250, loss[loss=0.2051, simple_loss=0.2904, pruned_loss=0.05995, over 7363.00 frames.], tot_loss[loss=0.2227, simple_loss=0.3085, pruned_loss=0.06849, over 1424694.79 frames.], batch size: 19, lr: 1.14e-03
2022-04-28 16:26:48,949 INFO [train.py:763] (1/8) Epoch 5, batch 3300, loss[loss=0.2449, simple_loss=0.3266, pruned_loss=0.08162, over 7210.00 frames.], tot_loss[loss=0.2223, simple_loss=0.3083, pruned_loss=0.06817, over 1420808.33 frames.], batch size: 23, lr: 1.14e-03
2022-04-28 16:27:54,260 INFO [train.py:763] (1/8) Epoch 5, batch 3350, loss[loss=0.2379, simple_loss=0.3134, pruned_loss=0.08117, over 7256.00 frames.], tot_loss[loss=0.2208, simple_loss=0.3072, pruned_loss=0.06721, over 1425509.54 frames.], batch size: 19, lr: 1.14e-03
2022-04-28 16:28:59,512 INFO [train.py:763] (1/8) Epoch 5, batch 3400, loss[loss=0.2167, simple_loss=0.3087, pruned_loss=0.06238, over 7272.00 frames.], tot_loss[loss=0.2208, simple_loss=0.3067, pruned_loss=0.06745, over 1425032.90 frames.], batch size: 24, lr: 1.14e-03
2022-04-28 16:30:05,194 INFO [train.py:763] (1/8) Epoch 5, batch 3450, loss[loss=0.2281, simple_loss=0.3271, pruned_loss=0.06459, over 7422.00 frames.], tot_loss[loss=0.223, simple_loss=0.3086, pruned_loss=0.06868, over 1427127.72 frames.], batch size: 21, lr: 1.13e-03
2022-04-28 16:31:11,005 INFO [train.py:763] (1/8) Epoch 5, batch 3500, loss[loss=0.2478, simple_loss=0.331, pruned_loss=0.08233, over 7198.00 frames.], tot_loss[loss=0.2223, simple_loss=0.3073, pruned_loss=0.06867, over 1424218.42 frames.], batch size: 22, lr: 1.13e-03
2022-04-28 16:32:16,130 INFO [train.py:763] (1/8) Epoch 5, batch 3550, loss[loss=0.2192, simple_loss=0.3083, pruned_loss=0.06505, over 7321.00 frames.], tot_loss[loss=0.2223, simple_loss=0.3076, pruned_loss=0.06854, over 1426948.26 frames.], batch size: 21, lr: 1.13e-03
2022-04-28 16:33:21,407 INFO [train.py:763] (1/8) Epoch 5, batch 3600, loss[loss=0.2139, simple_loss=0.2954, pruned_loss=0.06624, over 7170.00 frames.], tot_loss[loss=0.2228, simple_loss=0.3076, pruned_loss=0.06901, over 1428585.15 frames.], batch size: 18, lr: 1.13e-03
2022-04-28 16:34:27,111 INFO [train.py:763] (1/8) Epoch 5, batch 3650, loss[loss=0.2103, simple_loss=0.3015, pruned_loss=0.05959, over 7413.00 frames.], tot_loss[loss=0.2217, simple_loss=0.3067, pruned_loss=0.06838, over 1427372.51 frames.], batch size: 21, lr: 1.13e-03
2022-04-28 16:35:34,071 INFO [train.py:763] (1/8) Epoch 5, batch 3700, loss[loss=0.2191, simple_loss=0.2982, pruned_loss=0.07002, over 7225.00 frames.], tot_loss[loss=0.2219, simple_loss=0.3073, pruned_loss=0.06822, over 1426417.60 frames.], batch size: 20, lr: 1.13e-03
2022-04-28 16:36:39,371 INFO [train.py:763] (1/8) Epoch 5, batch 3750, loss[loss=0.2383, simple_loss=0.3385, pruned_loss=0.069, over 7381.00 frames.], tot_loss[loss=0.2209, simple_loss=0.3064, pruned_loss=0.06768, over 1424128.04 frames.], batch size: 23, lr: 1.13e-03
2022-04-28 16:37:46,344 INFO [train.py:763] (1/8) Epoch 5, batch 3800, loss[loss=0.2041, simple_loss=0.2958, pruned_loss=0.05617, over 7226.00 frames.], tot_loss[loss=0.2206, simple_loss=0.3058, pruned_loss=0.0677, over 1420545.64 frames.], batch size: 20, lr: 1.13e-03
2022-04-28 16:38:51,770 INFO [train.py:763] (1/8) Epoch 5, batch 3850, loss[loss=0.2016, simple_loss=0.2846, pruned_loss=0.0593, over 7432.00 frames.], tot_loss[loss=0.2211, simple_loss=0.3066, pruned_loss=0.06781, over 1420902.52 frames.], batch size: 20, lr: 1.13e-03
2022-04-28 16:39:57,100 INFO [train.py:763] (1/8) Epoch 5, batch 3900, loss[loss=0.1883, simple_loss=0.2616, pruned_loss=0.05752, over 7416.00 frames.], tot_loss[loss=0.2214, simple_loss=0.3064, pruned_loss=0.0682, over 1425495.94 frames.], batch size: 18, lr: 1.13e-03
2022-04-28 16:41:04,043 INFO [train.py:763] (1/8) Epoch 5, batch 3950, loss[loss=0.2152, simple_loss=0.3089, pruned_loss=0.06077, over 7301.00 frames.], tot_loss[loss=0.2205, simple_loss=0.3054, pruned_loss=0.06775, over 1424167.71 frames.], batch size: 24, lr: 1.12e-03
2022-04-28 16:42:10,956 INFO [train.py:763] (1/8) Epoch 5, batch 4000, loss[loss=0.2432, simple_loss=0.3209, pruned_loss=0.08279, over 7197.00 frames.], tot_loss[loss=0.2205, simple_loss=0.3061, pruned_loss=0.06743, over 1426374.31 frames.], batch size: 23, lr: 1.12e-03
2022-04-28 16:43:18,247 INFO [train.py:763] (1/8) Epoch 5, batch 4050, loss[loss=0.2635, simple_loss=0.3535, pruned_loss=0.08676, over 7268.00 frames.], tot_loss[loss=0.2202, simple_loss=0.3057, pruned_loss=0.06733, over 1426703.05 frames.], batch size: 24, lr: 1.12e-03
2022-04-28 16:44:25,534 INFO [train.py:763] (1/8) Epoch 5, batch 4100, loss[loss=0.1907, simple_loss=0.2743, pruned_loss=0.05352, over 7403.00 frames.], tot_loss[loss=0.2195, simple_loss=0.3048, pruned_loss=0.06709, over 1426408.26 frames.], batch size: 18, lr: 1.12e-03
2022-04-28 16:45:32,377 INFO [train.py:763] (1/8) Epoch 5, batch 4150, loss[loss=0.2459, simple_loss=0.3351, pruned_loss=0.07833, over 6759.00 frames.], tot_loss[loss=0.2191, simple_loss=0.3041, pruned_loss=0.06708, over 1426318.36 frames.], batch size: 31, lr: 1.12e-03
2022-04-28 16:46:39,133 INFO [train.py:763] (1/8) Epoch 5, batch 4200, loss[loss=0.2922, simple_loss=0.3612, pruned_loss=0.1116, over 7124.00 frames.], tot_loss[loss=0.2182, simple_loss=0.3033, pruned_loss=0.06658, over 1428189.97 frames.], batch size: 21, lr: 1.12e-03
2022-04-28 16:47:45,474 INFO [train.py:763] (1/8) Epoch 5, batch 4250, loss[loss=0.2512, simple_loss=0.3367, pruned_loss=0.08291, over 7391.00 frames.], tot_loss[loss=0.2188, simple_loss=0.3036, pruned_loss=0.06696, over 1430164.28 frames.], batch size: 23, lr: 1.12e-03
2022-04-28 16:48:52,195 INFO [train.py:763] (1/8) Epoch 5, batch 4300, loss[loss=0.1908, simple_loss=0.2822, pruned_loss=0.04977, over 7069.00 frames.], tot_loss[loss=0.2196, simple_loss=0.3042, pruned_loss=0.06755, over 1424065.65 frames.], batch size: 18, lr: 1.12e-03
2022-04-28 16:49:59,888 INFO [train.py:763] (1/8) Epoch 5, batch 4350, loss[loss=0.2188, simple_loss=0.314, pruned_loss=0.06184, over 7225.00 frames.], tot_loss[loss=0.2189, simple_loss=0.3042, pruned_loss=0.06681, over 1423999.11 frames.], batch size: 21, lr: 1.12e-03
2022-04-28 16:51:07,555 INFO [train.py:763] (1/8) Epoch 5, batch 4400, loss[loss=0.2119, simple_loss=0.3035, pruned_loss=0.06015, over 7415.00 frames.], tot_loss[loss=0.2183, simple_loss=0.3037, pruned_loss=0.06645, over 1423058.27 frames.], batch size: 20, lr: 1.12e-03
2022-04-28 16:52:13,249 INFO [train.py:763] (1/8) Epoch 5, batch 4450, loss[loss=0.1927, simple_loss=0.2824, pruned_loss=0.05155, over 7277.00 frames.], tot_loss[loss=0.2194, simple_loss=0.3043, pruned_loss=0.06726, over 1409856.69 frames.], batch size: 17, lr: 1.11e-03
2022-04-28 16:53:19,249 INFO [train.py:763] (1/8) Epoch 5, batch 4500, loss[loss=0.2206, simple_loss=0.3024, pruned_loss=0.06936, over 7229.00 frames.], tot_loss[loss=0.2179, simple_loss=0.3021, pruned_loss=0.06679, over 1409516.14 frames.], batch size: 20, lr: 1.11e-03
2022-04-28 16:54:23,898 INFO [train.py:763] (1/8) Epoch 5, batch 4550, loss[loss=0.3111, simple_loss=0.3816, pruned_loss=0.1203, over 4992.00 frames.], tot_loss[loss=0.2241, simple_loss=0.3065, pruned_loss=0.07082, over 1361907.85 frames.], batch size: 52, lr: 1.11e-03
2022-04-28 16:55:51,898 INFO [train.py:763] (1/8) Epoch 6, batch 0, loss[loss=0.1907, simple_loss=0.2696, pruned_loss=0.05589, over 7407.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2696, pruned_loss=0.05589, over 7407.00 frames.], batch size: 18, lr: 1.07e-03
2022-04-28 16:56:58,094 INFO [train.py:763] (1/8) Epoch 6, batch 50, loss[loss=0.1854, simple_loss=0.2782, pruned_loss=0.0463, over 7411.00 frames.], tot_loss[loss=0.2168, simple_loss=0.3015, pruned_loss=0.06609, over 322456.13 frames.], batch size: 18, lr: 1.07e-03
2022-04-28 16:58:04,026 INFO [train.py:763] (1/8) Epoch 6, batch 100, loss[loss=0.1862, simple_loss=0.2808, pruned_loss=0.04581, over 7152.00 frames.], tot_loss[loss=0.2143, simple_loss=0.3003, pruned_loss=0.06416, over 567977.56 frames.], batch size: 19, lr: 1.06e-03
2022-04-28 16:59:09,768 INFO [train.py:763] (1/8) Epoch 6, batch 150, loss[loss=0.1878, simple_loss=0.2749, pruned_loss=0.05033, over 7155.00 frames.], tot_loss[loss=0.2181, simple_loss=0.3037, pruned_loss=0.06622, over 757475.34 frames.], batch size: 19, lr: 1.06e-03
2022-04-28 17:00:15,498 INFO [train.py:763] (1/8) Epoch 6, batch 200, loss[loss=0.2299, simple_loss=0.3311, pruned_loss=0.06435, over 7381.00 frames.], tot_loss[loss=0.2163, simple_loss=0.3027, pruned_loss=0.06501, over 906235.19 frames.], batch size: 23, lr: 1.06e-03
2022-04-28 17:01:29,827 INFO [train.py:763] (1/8) Epoch 6, batch 250, loss[loss=0.1882, simple_loss=0.2777, pruned_loss=0.04936, over 7146.00 frames.], tot_loss[loss=0.2169, simple_loss=0.3039, pruned_loss=0.06494, over 1020125.82 frames.], batch size: 20, lr: 1.06e-03
2022-04-28 17:02:45,513 INFO [train.py:763] (1/8) Epoch 6, batch 300, loss[loss=0.1647, simple_loss=0.2456, pruned_loss=0.04192, over 6816.00 frames.], tot_loss[loss=0.2174, simple_loss=0.3038, pruned_loss=0.06546, over 1106705.98 frames.], batch size: 15, lr: 1.06e-03
2022-04-28 17:03:59,804 INFO [train.py:763] (1/8) Epoch 6, batch 350, loss[loss=0.2151, simple_loss=0.3035, pruned_loss=0.06331, over 7120.00 frames.], tot_loss[loss=0.216, simple_loss=0.3031, pruned_loss=0.06447, over 1177946.98 frames.], batch size: 21, lr: 1.06e-03
2022-04-28 17:05:05,097 INFO [train.py:763] (1/8) Epoch 6, batch 400, loss[loss=0.1956, simple_loss=0.2798, pruned_loss=0.05573, over 7172.00 frames.], tot_loss[loss=0.2163, simple_loss=0.3034, pruned_loss=0.06453, over 1230091.48 frames.], batch size: 18, lr: 1.06e-03
2022-04-28 17:06:20,538 INFO [train.py:763] (1/8) Epoch 6, batch 450, loss[loss=0.2075, simple_loss=0.2954, pruned_loss=0.0598, over 7355.00 frames.], tot_loss[loss=0.2174, simple_loss=0.3039, pruned_loss=0.06541, over 1275682.10 frames.], batch size: 19, lr: 1.06e-03
2022-04-28 17:07:44,115 INFO [train.py:763] (1/8) Epoch 6, batch 500, loss[loss=0.2171, simple_loss=0.3101, pruned_loss=0.0621, over 6477.00 frames.], tot_loss[loss=0.2181, simple_loss=0.3049, pruned_loss=0.06568, over 1305140.83 frames.], batch size: 38, lr: 1.06e-03
2022-04-28 17:08:59,112 INFO [train.py:763] (1/8) Epoch 6, batch 550, loss[loss=0.223, simple_loss=0.3155, pruned_loss=0.0653, over 7116.00 frames.], tot_loss[loss=0.2172, simple_loss=0.3039, pruned_loss=0.06532, over 1330086.64 frames.], batch size: 21, lr: 1.06e-03
2022-04-28 17:10:13,641 INFO [train.py:763] (1/8) Epoch 6, batch 600, loss[loss=0.2216, simple_loss=0.3162, pruned_loss=0.06354, over 7030.00 frames.], tot_loss[loss=0.2177, simple_loss=0.3045, pruned_loss=0.06541, over 1348960.30 frames.], batch size: 28, lr: 1.06e-03
2022-04-28 17:11:19,490 INFO [train.py:763] (1/8) Epoch 6, batch 650, loss[loss=0.2864, simple_loss=0.3574, pruned_loss=0.1077, over 5084.00 frames.], tot_loss[loss=0.2161, simple_loss=0.3028, pruned_loss=0.06463, over 1364326.93 frames.], batch size: 52, lr: 1.05e-03
2022-04-28 17:12:25,177 INFO [train.py:763] (1/8) Epoch 6, batch 700, loss[loss=0.1924, simple_loss=0.2872, pruned_loss=0.04875, over 7162.00 frames.], tot_loss[loss=0.2146, simple_loss=0.3015, pruned_loss=0.06384, over 1378323.53 frames.], batch size: 18, lr: 1.05e-03
2022-04-28 17:13:31,498 INFO [train.py:763] (1/8) Epoch 6, batch 750, loss[loss=0.242, simple_loss=0.3271, pruned_loss=0.07851, over 6787.00 frames.], tot_loss[loss=0.2146, simple_loss=0.3015, pruned_loss=0.06381, over 1391190.00 frames.], batch size: 31, lr: 1.05e-03
2022-04-28 17:14:37,092 INFO [train.py:763] (1/8) Epoch 6, batch 800, loss[loss=0.2107, simple_loss=0.3002, pruned_loss=0.06065, over 7319.00 frames.], tot_loss[loss=0.2152, simple_loss=0.3019, pruned_loss=0.06427, over 1391438.44 frames.], batch size: 20, lr: 1.05e-03
2022-04-28 17:15:43,492 INFO [train.py:763] (1/8) Epoch 6, batch 850, loss[loss=0.2077, simple_loss=0.2999, pruned_loss=0.0577, over 7315.00 frames.], tot_loss[loss=0.2146, simple_loss=0.3013, pruned_loss=0.06397, over 1397590.25 frames.], batch size: 24, lr: 1.05e-03
2022-04-28 17:16:48,955 INFO [train.py:763] (1/8) Epoch 6, batch 900, loss[loss=0.2662, simple_loss=0.3319, pruned_loss=0.1003, over 7382.00 frames.], tot_loss[loss=0.217, simple_loss=0.3033, pruned_loss=0.06531, over 1403428.32 frames.], batch size: 23, lr: 1.05e-03
2022-04-28 17:17:54,044 INFO [train.py:763] (1/8) Epoch 6, batch 950, loss[loss=0.2203, simple_loss=0.306, pruned_loss=0.06727, over 7375.00 frames.], tot_loss[loss=0.2167, simple_loss=0.3036, pruned_loss=0.06494, over 1407153.86 frames.], batch size: 23, lr: 1.05e-03
2022-04-28 17:18:59,562 INFO [train.py:763] (1/8) Epoch 6, batch 1000, loss[loss=0.2255, simple_loss=0.3103, pruned_loss=0.07037, over 7378.00 frames.], tot_loss[loss=0.2165, simple_loss=0.3029, pruned_loss=0.06502, over 1407430.19 frames.], batch size: 23, lr: 1.05e-03
2022-04-28 17:20:06,059 INFO [train.py:763] (1/8) Epoch 6, batch 1050, loss[loss=0.2097, simple_loss=0.2986, pruned_loss=0.06035, over 7160.00 frames.], tot_loss[loss=0.2163, simple_loss=0.3027, pruned_loss=0.0649, over 1414680.66 frames.], batch size: 19, lr: 1.05e-03
2022-04-28 17:21:12,155 INFO [train.py:763] (1/8) Epoch 6, batch 1100, loss[loss=0.2365, simple_loss=0.3235, pruned_loss=0.07472, over 7293.00 frames.], tot_loss[loss=0.2164, simple_loss=0.3028, pruned_loss=0.06493, over 1418280.44 frames.], batch size: 25, lr: 1.05e-03
2022-04-28 17:22:18,726 INFO [train.py:763] (1/8) Epoch 6, batch 1150, loss[loss=0.1539, simple_loss=0.2432, pruned_loss=0.0323, over 7143.00 frames.], tot_loss[loss=0.2167, simple_loss=0.3035, pruned_loss=0.06492, over 1417300.86 frames.], batch size: 17, lr: 1.05e-03
2022-04-28 17:23:26,112 INFO [train.py:763] (1/8) Epoch 6, batch 1200, loss[loss=0.1983, simple_loss=0.2728, pruned_loss=0.06186, over 7199.00 frames.], tot_loss[loss=0.2177, simple_loss=0.3041, pruned_loss=0.06563, over 1412974.59 frames.], batch size: 16, lr: 1.04e-03
2022-04-28 17:24:33,314 INFO [train.py:763] (1/8) Epoch 6, batch 1250, loss[loss=0.222, simple_loss=0.3085, pruned_loss=0.0678, over 7230.00 frames.], tot_loss[loss=0.2168, simple_loss=0.303, pruned_loss=0.06526, over 1414551.89 frames.], batch size: 20, lr: 1.04e-03
2022-04-28 17:25:39,227 INFO [train.py:763] (1/8) Epoch 6, batch 1300, loss[loss=0.2251, simple_loss=0.3038, pruned_loss=0.0732, over 7279.00 frames.], tot_loss[loss=0.2167, simple_loss=0.3031, pruned_loss=0.06512, over 1415677.65 frames.], batch size: 17, lr: 1.04e-03
2022-04-28 17:26:44,443 INFO [train.py:763] (1/8) Epoch 6, batch 1350, loss[loss=0.2208, simple_loss=0.3001, pruned_loss=0.07077, over 7404.00 frames.], tot_loss[loss=0.2173, simple_loss=0.304, pruned_loss=0.06532, over 1421320.94 frames.], batch size: 21, lr: 1.04e-03
2022-04-28 17:27:49,618 INFO [train.py:763] (1/8) Epoch 6, batch 1400, loss[loss=0.1835, simple_loss=0.2771, pruned_loss=0.04493, over 7142.00 frames.], tot_loss[loss=0.2179, simple_loss=0.3047, pruned_loss=0.06553, over 1420108.88 frames.], batch size: 19, lr: 1.04e-03
2022-04-28 17:28:55,363 INFO [train.py:763] (1/8) Epoch 6, batch 1450, loss[loss=0.2057, simple_loss=0.2904, pruned_loss=0.06054, over 6766.00 frames.], tot_loss[loss=0.2163, simple_loss=0.304, pruned_loss=0.06435, over 1420665.34 frames.], batch size: 31, lr: 1.04e-03
2022-04-28 17:30:00,748 INFO [train.py:763] (1/8) Epoch 6, batch 1500, loss[loss=0.2469, simple_loss=0.3287, pruned_loss=0.08256, over 7409.00 frames.], tot_loss[loss=0.2164, simple_loss=0.3039, pruned_loss=0.0644, over 1423497.73 frames.], batch size: 21, lr: 1.04e-03
2022-04-28 17:31:05,972 INFO [train.py:763] (1/8) Epoch 6, batch 1550, loss[loss=0.2088, simple_loss=0.303, pruned_loss=0.05724, over 7184.00 frames.], tot_loss[loss=0.2158, simple_loss=0.3033, pruned_loss=0.06419, over 1417290.00 frames.], batch size: 26, lr: 1.04e-03
2022-04-28 17:32:11,539 INFO [train.py:763] (1/8) Epoch 6, batch 1600, loss[loss=0.1934, simple_loss=0.2921, pruned_loss=0.04735, over 7107.00 frames.], tot_loss[loss=0.2149, simple_loss=0.3026, pruned_loss=0.06358, over 1423620.31 frames.], batch size: 21, lr: 1.04e-03
2022-04-28 17:33:16,937 INFO [train.py:763] (1/8) Epoch 6, batch 1650, loss[loss=0.1922, simple_loss=0.2809, pruned_loss=0.05178, over 7064.00 frames.], tot_loss[loss=0.215, simple_loss=0.3024, pruned_loss=0.06381, over 1417573.38 frames.], batch size: 18, lr: 1.04e-03
2022-04-28 17:34:24,085 INFO [train.py:763] (1/8) Epoch 6, batch 1700, loss[loss=0.2321, simple_loss=0.3123, pruned_loss=0.07599, over 7196.00 frames.], tot_loss[loss=0.2145, simple_loss=0.3015, pruned_loss=0.06373, over 1417170.00 frames.], batch size: 22, lr: 1.04e-03
2022-04-28 17:35:30,120 INFO [train.py:763] (1/8) Epoch 6, batch 1750, loss[loss=0.225, simple_loss=0.3248, pruned_loss=0.06265, over 7344.00 frames.], tot_loss[loss=0.2159, simple_loss=0.3029, pruned_loss=0.06438, over 1412397.40 frames.], batch size: 22, lr: 1.04e-03
2022-04-28 17:36:35,224 INFO [train.py:763] (1/8) Epoch 6, batch 1800, loss[loss=0.2038, simple_loss=0.2961, pruned_loss=0.0558, over 7266.00 frames.], tot_loss[loss=0.2164, simple_loss=0.304, pruned_loss=0.0644, over 1415083.87 frames.], batch size: 25, lr: 1.03e-03
2022-04-28 17:37:41,011 INFO [train.py:763] (1/8) Epoch 6, batch 1850, loss[loss=0.2063, simple_loss=0.2788, pruned_loss=0.0669, over 6987.00 frames.], tot_loss[loss=0.2162, simple_loss=0.3033, pruned_loss=0.06453, over 1417492.83 frames.], batch size: 16, lr: 1.03e-03
2022-04-28 17:38:46,201 INFO [train.py:763] (1/8) Epoch 6, batch 1900, loss[loss=0.2208, simple_loss=0.2972, pruned_loss=0.07219, over 7453.00 frames.], tot_loss[loss=0.2155, simple_loss=0.3028, pruned_loss=0.06413, over 1414764.72 frames.], batch size: 19, lr: 1.03e-03
2022-04-28 17:39:52,667 INFO [train.py:763] (1/8) Epoch 6, batch 1950, loss[loss=0.2163, simple_loss=0.3006, pruned_loss=0.06599, over 7277.00 frames.], tot_loss[loss=0.2153, simple_loss=0.3022, pruned_loss=0.06423, over 1418130.23 frames.], batch size: 18, lr: 1.03e-03
2022-04-28 17:40:59,181 INFO [train.py:763] (1/8) Epoch 6, batch 2000, loss[loss=0.2176, simple_loss=0.3126, pruned_loss=0.06133, over 7345.00 frames.], tot_loss[loss=0.2149, simple_loss=0.302, pruned_loss=0.06387, over 1418708.45 frames.], batch size: 25, lr: 1.03e-03
2022-04-28 17:42:06,055 INFO [train.py:763] (1/8) Epoch 6, batch 2050, loss[loss=0.1981, simple_loss=0.2874, pruned_loss=0.05438, over 7299.00 frames.], tot_loss[loss=0.2155, simple_loss=0.3027, pruned_loss=0.06421, over 1415947.61 frames.], batch size: 24, lr: 1.03e-03
2022-04-28 17:43:12,552 INFO [train.py:763] (1/8) Epoch 6, batch 2100, loss[loss=0.1906, simple_loss=0.2759, pruned_loss=0.05261, over 7006.00 frames.], tot_loss[loss=0.2154, simple_loss=0.3026, pruned_loss=0.06416, over 1418568.31 frames.], batch size: 16, lr: 1.03e-03
2022-04-28 17:44:19,359 INFO [train.py:763] (1/8) Epoch 6, batch 2150, loss[loss=0.2123, simple_loss=0.3047, pruned_loss=0.06, over 7413.00 frames.], tot_loss[loss=0.2152, simple_loss=0.3025, pruned_loss=0.06393, over 1423748.08 frames.], batch size: 21, lr: 1.03e-03
2022-04-28 17:45:25,694 INFO [train.py:763] (1/8) Epoch 6, batch 2200, loss[loss=0.1921, simple_loss=0.2804, pruned_loss=0.05187, over 7128.00 frames.], tot_loss[loss=0.2149, simple_loss=0.3021, pruned_loss=0.06381, over 1421212.30 frames.], batch size: 17, lr: 1.03e-03
2022-04-28 17:46:32,098 INFO [train.py:763] (1/8) Epoch 6, batch 2250, loss[loss=0.1806, simple_loss=0.2651, pruned_loss=0.04811, over 7282.00 frames.], tot_loss[loss=0.2156, simple_loss=0.3026, pruned_loss=0.06433, over 1416209.06 frames.], batch size: 17, lr: 1.03e-03
2022-04-28 17:47:38,686 INFO [train.py:763] (1/8) Epoch 6, batch 2300, loss[loss=0.2643, simple_loss=0.3532, pruned_loss=0.08772, over 7196.00 frames.], tot_loss[loss=0.2156, simple_loss=0.3026, pruned_loss=0.06433, over 1419226.94 frames.], batch size: 23, lr: 1.03e-03
2022-04-28 17:48:44,943 INFO [train.py:763] (1/8) Epoch 6, batch 2350, loss[loss=0.2085, simple_loss=0.3076, pruned_loss=0.05474, over 7409.00 frames.], tot_loss[loss=0.2158, simple_loss=0.3027, pruned_loss=0.06442, over 1417756.20 frames.], batch size: 21, lr: 1.02e-03
2022-04-28 17:49:50,848 INFO [train.py:763] (1/8) Epoch 6, batch 2400, loss[loss=0.211, simple_loss=0.2819, pruned_loss=0.07005, over 7276.00 frames.], tot_loss[loss=0.2147, simple_loss=0.3017, pruned_loss=0.06384, over 1421275.02 frames.], batch size: 18, lr: 1.02e-03
2022-04-28 17:50:56,968 INFO [train.py:763] (1/8) Epoch 6, batch 2450, loss[loss=0.2031, simple_loss=0.3083, pruned_loss=0.0489, over 7410.00 frames.], tot_loss[loss=0.2162, simple_loss=0.3029, pruned_loss=0.06471, over 1417168.77 frames.], batch size: 21, lr: 1.02e-03
2022-04-28 17:52:02,801 INFO [train.py:763] (1/8) Epoch 6, batch 2500, loss[loss=0.218, simple_loss=0.3153, pruned_loss=0.06039, over 7325.00 frames.], tot_loss[loss=0.2183, simple_loss=0.3046, pruned_loss=0.066, over 1416865.12 frames.], batch size: 21, lr: 1.02e-03
2022-04-28 17:53:08,641 INFO [train.py:763] (1/8) Epoch 6, batch 2550, loss[loss=0.257, simple_loss=0.3294, pruned_loss=0.0923, over 7434.00 frames.], tot_loss[loss=0.2179, simple_loss=0.3047, pruned_loss=0.06557, over 1423427.13 frames.], batch size: 20, lr: 1.02e-03
2022-04-28 17:54:14,768 INFO [train.py:763] (1/8) Epoch 6, batch 2600, loss[loss=0.2038, simple_loss=0.2915, pruned_loss=0.05805, over 7156.00 frames.], tot_loss[loss=0.2176, simple_loss=0.3043, pruned_loss=0.06546, over 1417314.59 frames.], batch size: 18, lr: 1.02e-03
2022-04-28 17:55:21,045 INFO [train.py:763] (1/8) Epoch 6, batch 2650, loss[loss=0.1851, simple_loss=0.2772, pruned_loss=0.0465, over 7167.00 frames.], tot_loss[loss=0.2172, simple_loss=0.3038, pruned_loss=0.06528, over 1417818.41 frames.], batch size: 18, lr: 1.02e-03
2022-04-28 17:56:26,531 INFO [train.py:763] (1/8) Epoch 6, batch 2700, loss[loss=0.1654, simple_loss=0.2581, pruned_loss=0.03637, over 6828.00 frames.], tot_loss[loss=0.2155, simple_loss=0.3024, pruned_loss=0.06431, over 1419642.74 frames.], batch size: 15, lr: 1.02e-03
2022-04-28 17:57:32,628 INFO [train.py:763] (1/8) Epoch 6, batch 2750, loss[loss=0.1911, simple_loss=0.2762, pruned_loss=0.05305, over 7405.00 frames.], tot_loss[loss=0.2156, simple_loss=0.303, pruned_loss=0.0641, over 1419981.51 frames.], batch size: 18, lr: 1.02e-03
2022-04-28 17:58:39,120 INFO [train.py:763] (1/8) Epoch 6, batch 2800, loss[loss=0.2112, simple_loss=0.289, pruned_loss=0.06672, over 6992.00 frames.], tot_loss[loss=0.2148, simple_loss=0.3021, pruned_loss=0.06377, over 1417583.25 frames.], batch size: 16, lr: 1.02e-03
2022-04-28 17:59:46,048 INFO [train.py:763] (1/8) Epoch 6, batch 2850, loss[loss=0.1979, simple_loss=0.2905, pruned_loss=0.05267, over 7316.00 frames.], tot_loss[loss=0.2139, simple_loss=0.3008, pruned_loss=0.06345, over 1422417.12 frames.], batch size: 21, lr: 1.02e-03
2022-04-28 18:00:52,207 INFO [train.py:763] (1/8) Epoch 6, batch 2900, loss[loss=0.3117, simple_loss=0.3744, pruned_loss=0.1245, over 5294.00 frames.], tot_loss[loss=0.2129, simple_loss=0.3005, pruned_loss=0.06265, over 1425107.00 frames.], batch size: 52, lr: 1.02e-03
2022-04-28 18:01:57,561 INFO [train.py:763] (1/8) Epoch 6, batch 2950, loss[loss=0.242, simple_loss=0.3231, pruned_loss=0.08049, over 7290.00 frames.], tot_loss[loss=0.2135, simple_loss=0.3015, pruned_loss=0.06277, over 1424974.54 frames.], batch size: 25, lr: 1.01e-03
2022-04-28 18:03:03,519 INFO [train.py:763] (1/8) Epoch 6, batch 3000, loss[loss=0.2407, simple_loss=0.3175, pruned_loss=0.082, over 7182.00 frames.], tot_loss[loss=0.2135, simple_loss=0.3013, pruned_loss=0.06283, over 1426594.78 frames.], batch size: 26, lr: 1.01e-03
2022-04-28 18:03:03,520 INFO [train.py:783] (1/8) Computing validation loss
2022-04-28 18:03:18,818 INFO [train.py:792] (1/8) Epoch 6, validation: loss=0.1749, simple_loss=0.2793, pruned_loss=0.03525, over 698248.00 frames.
2022-04-28 18:04:24,348 INFO [train.py:763] (1/8) Epoch 6, batch 3050, loss[loss=0.2281, simple_loss=0.3221, pruned_loss=0.06707, over 7156.00 frames.], tot_loss[loss=0.215, simple_loss=0.3027, pruned_loss=0.06359, over 1426730.30 frames.], batch size: 26, lr: 1.01e-03
2022-04-28 18:05:30,258 INFO [train.py:763] (1/8) Epoch 6, batch 3100, loss[loss=0.2733, simple_loss=0.3388, pruned_loss=0.1039, over 7193.00 frames.], tot_loss[loss=0.2154, simple_loss=0.3032, pruned_loss=0.06376, over 1424892.52 frames.], batch size: 26, lr: 1.01e-03
2022-04-28 18:06:36,920 INFO [train.py:763] (1/8) Epoch 6, batch 3150, loss[loss=0.2232, simple_loss=0.3241, pruned_loss=0.06111, over 7057.00 frames.], tot_loss[loss=0.2139, simple_loss=0.302, pruned_loss=0.06286, over 1428333.48 frames.], batch size: 28, lr: 1.01e-03
2022-04-28 18:07:42,732 INFO [train.py:763] (1/8) Epoch 6, batch 3200, loss[loss=0.1977, simple_loss=0.2899, pruned_loss=0.05278, over 7332.00 frames.], tot_loss[loss=0.2152, simple_loss=0.3033, pruned_loss=0.06352, over 1424327.22 frames.], batch size: 22, lr: 1.01e-03
2022-04-28 18:08:48,608 INFO [train.py:763] (1/8) Epoch 6, batch 3250, loss[loss=0.2221, simple_loss=0.3116, pruned_loss=0.06628, over 7091.00 frames.], tot_loss[loss=0.2144, simple_loss=0.3024, pruned_loss=0.06322, over 1424268.18 frames.], batch size: 28, lr: 1.01e-03
2022-04-28 18:09:54,855 INFO [train.py:763] (1/8) Epoch 6, batch 3300, loss[loss=0.1977, simple_loss=0.3013, pruned_loss=0.047, over 7158.00 frames.], tot_loss[loss=0.215, simple_loss=0.303, pruned_loss=0.06345, over 1418871.35 frames.], batch size: 20, lr: 1.01e-03
2022-04-28 18:11:00,641 INFO [train.py:763] (1/8) Epoch 6, batch 3350, loss[loss=0.2043, simple_loss=0.287, pruned_loss=0.0608, over 7161.00 frames.], tot_loss[loss=0.2165, simple_loss=0.3043, pruned_loss=0.06432, over 1419906.68 frames.], batch size: 19, lr: 1.01e-03
2022-04-28 18:12:05,973 INFO [train.py:763] (1/8) Epoch 6, batch 3400, loss[loss=0.2384, simple_loss=0.329, pruned_loss=0.07392, over 7113.00 frames.], tot_loss[loss=0.2168, simple_loss=0.3044, pruned_loss=0.0646, over 1422645.44 frames.], batch size: 21, lr: 1.01e-03
2022-04-28 18:13:11,470 INFO [train.py:763] (1/8) Epoch 6, batch 3450, loss[loss=0.2446, simple_loss=0.3295, pruned_loss=0.07988, over 7309.00 frames.], tot_loss[loss=0.2166, simple_loss=0.3044, pruned_loss=0.0644, over 1420177.17 frames.], batch size: 24, lr: 1.01e-03
2022-04-28 18:14:16,740 INFO [train.py:763] (1/8) Epoch 6, batch 3500, loss[loss=0.2319, simple_loss=0.3289, pruned_loss=0.06743, over 7205.00 frames.], tot_loss[loss=0.217, simple_loss=0.3048, pruned_loss=0.0646, over 1422708.99 frames.], batch size: 21, lr: 1.01e-03
2022-04-28 18:15:22,307 INFO [train.py:763] (1/8) Epoch 6, batch 3550, loss[loss=0.2197, simple_loss=0.3141, pruned_loss=0.06268, over 7380.00 frames.], tot_loss[loss=0.2171, simple_loss=0.3045, pruned_loss=0.0648, over 1423923.02 frames.], batch size: 23, lr: 1.01e-03
2022-04-28 18:16:27,535 INFO [train.py:763] (1/8) Epoch 6, batch 3600, loss[loss=0.2009, simple_loss=0.2936, pruned_loss=0.05408, over 7227.00 frames.], tot_loss[loss=0.2166, simple_loss=0.3043, pruned_loss=0.06444, over 1425816.22 frames.], batch size: 21, lr: 1.00e-03
2022-04-28 18:17:32,792 INFO [train.py:763] (1/8) Epoch 6, batch 3650, loss[loss=0.2177, simple_loss=0.3071, pruned_loss=0.06416, over 7140.00 frames.], tot_loss[loss=0.2161, simple_loss=0.3041, pruned_loss=0.06404, over 1422761.47 frames.], batch size: 28, lr: 1.00e-03
2022-04-28 18:18:39,443 INFO [train.py:763] (1/8) Epoch 6, batch 3700, loss[loss=0.19, simple_loss=0.2762, pruned_loss=0.0519, over 7440.00 frames.], tot_loss[loss=0.2156, simple_loss=0.3031, pruned_loss=0.06409, over 1424083.15 frames.], batch size: 20, lr: 1.00e-03
2022-04-28 18:19:44,869 INFO [train.py:763] (1/8) Epoch 6, batch 3750, loss[loss=0.2816, simple_loss=0.3472, pruned_loss=0.108, over 5132.00 frames.], tot_loss[loss=0.2162, simple_loss=0.3028, pruned_loss=0.0648, over 1424218.32 frames.], batch size: 52, lr: 1.00e-03
2022-04-28 18:20:50,221 INFO [train.py:763] (1/8) Epoch 6, batch 3800, loss[loss=0.2145, simple_loss=0.2949, pruned_loss=0.067, over 7372.00 frames.], tot_loss[loss=0.2155, simple_loss=0.3028, pruned_loss=0.06416, over 1421392.57 frames.], batch size: 19, lr: 1.00e-03
2022-04-28 18:21:56,430 INFO [train.py:763] (1/8) Epoch 6, batch 3850, loss[loss=0.2132, simple_loss=0.2998, pruned_loss=0.06329, over 7125.00 frames.], tot_loss[loss=0.2149, simple_loss=0.3017, pruned_loss=0.06404, over 1423929.12 frames.], batch size: 17, lr: 1.00e-03
2022-04-28 18:23:02,742 INFO [train.py:763] (1/8) Epoch 6, batch 3900, loss[loss=0.201, simple_loss=0.2862, pruned_loss=0.05783, over 7172.00 frames.], tot_loss[loss=0.2136, simple_loss=0.3006, pruned_loss=0.06331, over 1424602.02 frames.], batch size: 18, lr: 1.00e-03
2022-04-28 18:24:08,624 INFO [train.py:763] (1/8) Epoch 6, batch 3950, loss[loss=0.2201, simple_loss=0.3101, pruned_loss=0.06501, over 7332.00 frames.], tot_loss[loss=0.2119, simple_loss=0.2993, pruned_loss=0.06227, over 1427070.49 frames.], batch size: 22, lr: 9.99e-04
2022-04-28 18:25:14,070 INFO [train.py:763] (1/8) Epoch 6, batch 4000, loss[loss=0.3, simple_loss=0.3612, pruned_loss=0.1194, over 6876.00 frames.], tot_loss[loss=0.2112, simple_loss=0.2989, pruned_loss=0.06177, over 1431682.74 frames.], batch size: 31, lr: 9.98e-04
2022-04-28 18:26:19,661 INFO [train.py:763] (1/8) Epoch 6, batch 4050, loss[loss=0.2011, simple_loss=0.2896, pruned_loss=0.05625, over 7163.00 frames.], tot_loss[loss=0.2116, simple_loss=0.2993, pruned_loss=0.06191, over 1430119.91 frames.], batch size: 18, lr: 9.98e-04
2022-04-28 18:27:25,497 INFO [train.py:763] (1/8) Epoch 6, batch 4100, loss[loss=0.2235, simple_loss=0.3101, pruned_loss=0.06848, over 7098.00 frames.], tot_loss[loss=0.2121, simple_loss=0.2996, pruned_loss=0.06231, over 1425438.31 frames.], batch size: 21, lr: 9.97e-04
2022-04-28 18:28:32,062 INFO [train.py:763] (1/8) Epoch 6, batch 4150, loss[loss=0.2196, simple_loss=0.3095, pruned_loss=0.0649, over 7183.00 frames.], tot_loss[loss=0.2111, simple_loss=0.2991, pruned_loss=0.06159, over 1425651.20 frames.], batch size: 23, lr: 9.96e-04
2022-04-28 18:29:37,830 INFO [train.py:763] (1/8) Epoch 6, batch 4200, loss[loss=0.2255, simple_loss=0.3055, pruned_loss=0.07274, over 7292.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2986, pruned_loss=0.06095, over 1427855.21 frames.], batch size: 17, lr: 9.95e-04
2022-04-28 18:30:43,249 INFO [train.py:763] (1/8) Epoch 6, batch 4250, loss[loss=0.197, simple_loss=0.2922, pruned_loss=0.0509, over 7435.00 frames.], tot_loss[loss=0.2111, simple_loss=0.2991, pruned_loss=0.06153, over 1422410.67 frames.], batch size: 20, lr: 9.95e-04
2022-04-28 18:31:48,727 INFO [train.py:763] (1/8) Epoch 6, batch 4300, loss[loss=0.2489, simple_loss=0.332, pruned_loss=0.08288, over 7235.00 frames.], tot_loss[loss=0.2134, simple_loss=0.3013, pruned_loss=0.06273, over 1416899.19 frames.], batch size: 20, lr: 9.94e-04
2022-04-28 18:32:54,885 INFO [train.py:763] (1/8) Epoch 6, batch 4350, loss[loss=0.2156, simple_loss=0.3052, pruned_loss=0.06304, over 6575.00 frames.], tot_loss[loss=0.213, simple_loss=0.3011, pruned_loss=0.06246, over 1411083.76 frames.], batch size: 38, lr: 9.93e-04
2022-04-28 18:34:00,597 INFO [train.py:763] (1/8) Epoch 6, batch 4400, loss[loss=0.2264, simple_loss=0.3184, pruned_loss=0.06725, over 6735.00 frames.], tot_loss[loss=0.2129, simple_loss=0.301, pruned_loss=0.06239, over 1412142.44 frames.], batch size: 31, lr: 9.92e-04
2022-04-28 18:35:07,312 INFO [train.py:763] (1/8) Epoch 6, batch 4450, loss[loss=0.2304, simple_loss=0.3231, pruned_loss=0.06883, over 7210.00 frames.], tot_loss[loss=0.2135, simple_loss=0.3018, pruned_loss=0.06263, over 1406841.38 frames.], batch size: 22, lr: 9.92e-04
2022-04-28 18:36:23,319 INFO [train.py:763] (1/8) Epoch 6, batch 4500, loss[loss=0.2139, simple_loss=0.3025, pruned_loss=0.06268, over 7192.00 frames.], tot_loss[loss=0.2155, simple_loss=0.3035, pruned_loss=0.06372, over 1404433.93 frames.], batch size: 22, lr: 9.91e-04
2022-04-28 18:37:28,288 INFO [train.py:763] (1/8) Epoch 6, batch 4550, loss[loss=0.2921, simple_loss=0.3611, pruned_loss=0.1115, over 5245.00 frames.], tot_loss[loss=0.2182, simple_loss=0.3059, pruned_loss=0.06526, over 1390020.26 frames.], batch size: 52, lr: 9.90e-04
2022-04-28 18:38:57,444 INFO [train.py:763] (1/8) Epoch 7, batch 0, loss[loss=0.2391, simple_loss=0.3419, pruned_loss=0.06811, over 7326.00 frames.], tot_loss[loss=0.2391, simple_loss=0.3419, pruned_loss=0.06811, over 7326.00 frames.], batch size: 22, lr: 9.49e-04
2022-04-28 18:40:02,640 INFO [train.py:763] (1/8) Epoch 7, batch 50, loss[loss=0.2089, simple_loss=0.284, pruned_loss=0.06694, over 7124.00 frames.], tot_loss[loss=0.2175, simple_loss=0.3063, pruned_loss=0.06441, over 320829.26 frames.], batch size: 17, lr: 9.48e-04
2022-04-28 18:41:07,850 INFO [train.py:763] (1/8) Epoch 7, batch 100, loss[loss=0.2008, simple_loss=0.2969, pruned_loss=0.05236, over 7302.00 frames.], tot_loss[loss=0.2131, simple_loss=0.3036, pruned_loss=0.06128, over 568921.19 frames.], batch size: 25, lr: 9.48e-04
2022-04-28 18:42:13,272 INFO [train.py:763] (1/8) Epoch 7, batch 150, loss[loss=0.234, simple_loss=0.3204, pruned_loss=0.07381, over 7107.00 frames.], tot_loss[loss=0.2113, simple_loss=0.3007, pruned_loss=0.06099, over 759068.65 frames.], batch size: 21, lr: 9.47e-04
2022-04-28 18:43:19,110 INFO [train.py:763] (1/8) Epoch 7, batch 200, loss[loss=0.2407, simple_loss=0.3327, pruned_loss=0.07435, over 7221.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2999, pruned_loss=0.06037, over 907429.59 frames.], batch size: 22, lr: 9.46e-04
2022-04-28 18:44:24,613 INFO [train.py:763] (1/8) Epoch 7, batch 250, loss[loss=0.1961, simple_loss=0.2854, pruned_loss=0.05341, over 7120.00 frames.], tot_loss[loss=0.2096, simple_loss=0.2992, pruned_loss=0.05997, over 1020924.38 frames.], batch size: 21, lr: 9.46e-04
2022-04-28 18:45:29,824 INFO [train.py:763] (1/8) Epoch 7, batch 300, loss[loss=0.2342, simple_loss=0.3092, pruned_loss=0.07964, over 7064.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2997, pruned_loss=0.05999, over 1106836.39 frames.], batch size: 18, lr: 9.45e-04
2022-04-28 18:46:35,548 INFO [train.py:763] (1/8) Epoch 7, batch 350, loss[loss=0.2207, simple_loss=0.3083, pruned_loss=0.06654, over 7109.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2989, pruned_loss=0.05997, over 1177863.14 frames.], batch size: 21, lr: 9.44e-04
2022-04-28 18:47:40,824 INFO [train.py:763] (1/8) Epoch 7, batch 400, loss[loss=0.2928, simple_loss=0.3546, pruned_loss=0.1155, over 5114.00 frames.], tot_loss[loss=0.2101, simple_loss=0.2994, pruned_loss=0.06038, over 1230676.68 frames.], batch size: 52, lr: 9.43e-04
2022-04-28 18:48:46,396 INFO [train.py:763] (1/8) Epoch 7, batch 450, loss[loss=0.1839, simple_loss=0.2713, pruned_loss=0.04821, over 6814.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2986, pruned_loss=0.06022, over 1272187.33 frames.], batch size: 15, lr: 9.43e-04
2022-04-28 18:49:51,764 INFO [train.py:763] (1/8) Epoch 7, batch 500, loss[loss=0.2202, simple_loss=0.3125, pruned_loss=0.06396, over 7206.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2971, pruned_loss=0.05906, over 1304938.98 frames.], batch size: 23, lr: 9.42e-04
2022-04-28 18:50:57,360 INFO [train.py:763] (1/8) Epoch 7, batch 550, loss[loss=0.2289, simple_loss=0.3019, pruned_loss=0.07797, over 7208.00 frames.], tot_loss[loss=0.2083, simple_loss=0.298, pruned_loss=0.05931, over 1333070.66 frames.], batch size: 23, lr: 9.41e-04
2022-04-28 18:52:02,631 INFO [train.py:763] (1/8) Epoch 7, batch 600, loss[loss=0.1952, simple_loss=0.2898, pruned_loss=0.05032, over 7230.00 frames.], tot_loss[loss=0.2087, simple_loss=0.2983, pruned_loss=0.05949, over 1353053.88 frames.], batch size: 21, lr: 9.41e-04
2022-04-28 18:53:08,461 INFO [train.py:763] (1/8) Epoch 7, batch 650, loss[loss=0.2038, simple_loss=0.2917, pruned_loss=0.05795, over 7256.00 frames.], tot_loss[loss=0.2081, simple_loss=0.298, pruned_loss=0.05907, over 1369188.01 frames.], batch size: 19, lr: 9.40e-04
2022-04-28 18:54:13,818 INFO [train.py:763] (1/8) Epoch 7, batch 700, loss[loss=0.2727, simple_loss=0.3407, pruned_loss=0.1024, over 4985.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2999, pruned_loss=0.06034, over 1376446.97 frames.], batch size: 52, lr: 9.39e-04
2022-04-28 18:55:19,476 INFO [train.py:763] (1/8) Epoch 7, batch 750, loss[loss=0.1995, simple_loss=0.2759, pruned_loss=0.06151, over 7357.00 frames.], tot_loss[loss=0.2098, simple_loss=0.2989, pruned_loss=0.06031, over 1384913.06 frames.], batch size: 19, lr: 9.39e-04
2022-04-28 18:56:26,108 INFO [train.py:763] (1/8) Epoch 7, batch 800, loss[loss=0.1937, simple_loss=0.2965, pruned_loss=0.04546, over 6489.00 frames.], tot_loss[loss=0.2119, simple_loss=0.301, pruned_loss=0.06143, over 1389515.25 frames.], batch size: 38, lr: 9.38e-04
2022-04-28 18:57:33,277 INFO [train.py:763] (1/8) Epoch 7, batch 850, loss[loss=0.1821, simple_loss=0.27, pruned_loss=0.04707, over 7394.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2976, pruned_loss=0.05952, over 1398408.47 frames.], batch size: 18, lr: 9.37e-04
2022-04-28 18:58:40,226 INFO [train.py:763] (1/8) Epoch 7, batch 900, loss[loss=0.2054, simple_loss=0.3025, pruned_loss=0.0541, over 6702.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2974, pruned_loss=0.06013, over 1398441.87 frames.], batch size: 31, lr: 9.36e-04
2022-04-28 18:59:46,946 INFO [train.py:763] (1/8) Epoch 7, batch 950, loss[loss=0.176, simple_loss=0.2747, pruned_loss=0.03865, over 7231.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2987, pruned_loss=0.06021, over 1404830.92 frames.], batch size: 20, lr: 9.36e-04
2022-04-28 19:00:52,051 INFO [train.py:763] (1/8) Epoch 7, batch 1000, loss[loss=0.193, simple_loss=0.283, pruned_loss=0.05146, over 7216.00 frames.], tot_loss[loss=0.2105, simple_loss=0.2996, pruned_loss=0.06072, over 1409189.84 frames.], batch size: 21, lr: 9.35e-04
2022-04-28 19:01:58,577 INFO [train.py:763] (1/8) Epoch 7, batch 1050, loss[loss=0.1712, simple_loss=0.2592, pruned_loss=0.04166, over 7130.00 frames.], tot_loss[loss=0.2112, simple_loss=0.3001, pruned_loss=0.0611, over 1406969.76 frames.], batch size: 17, lr: 9.34e-04
2022-04-28 19:03:05,227 INFO [train.py:763] (1/8) Epoch 7, batch 1100, loss[loss=0.2454, simple_loss=0.3304, pruned_loss=0.08018, over 7204.00 frames.], tot_loss[loss=0.2099, simple_loss=0.2987, pruned_loss=0.0605, over 1410589.82 frames.], batch size: 22, lr: 9.34e-04
2022-04-28 19:04:11,929 INFO [train.py:763] (1/8) Epoch 7, batch 1150, loss[loss=0.3105, simple_loss=0.3586, pruned_loss=0.1312, over 5159.00 frames.], tot_loss[loss=0.2096, simple_loss=0.299, pruned_loss=0.06014, over 1416480.34 frames.], batch size: 52, lr: 9.33e-04
2022-04-28 19:05:18,438 INFO [train.py:763] (1/8) Epoch 7, batch 1200, loss[loss=0.2245, simple_loss=0.3154, pruned_loss=0.06685, over 7140.00 frames.], tot_loss[loss=0.2088, simple_loss=0.2982, pruned_loss=0.05971, over 1421098.92 frames.], batch size: 20, lr: 9.32e-04
2022-04-28 19:06:24,031 INFO [train.py:763] (1/8) Epoch 7, batch 1250, loss[loss=0.1891, simple_loss=0.2668, pruned_loss=0.05565, over 7296.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2966, pruned_loss=0.05956, over 1420196.46 frames.], batch size: 18, lr: 9.32e-04
2022-04-28 19:07:30,157 INFO [train.py:763] (1/8) Epoch 7, batch 1300, loss[loss=0.2087, simple_loss=0.306, pruned_loss=0.05573, over 7146.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2981, pruned_loss=0.06044, over 1416973.18 frames.], batch size: 20, lr: 9.31e-04
2022-04-28 19:08:35,480 INFO [train.py:763] (1/8) Epoch 7, batch 1350, loss[loss=0.1978, simple_loss=0.2865, pruned_loss=0.05458, over 7157.00 frames.], tot_loss[loss=0.2108, simple_loss=0.2992, pruned_loss=0.06122, over 1416120.25 frames.], batch size: 19, lr: 9.30e-04
2022-04-28 19:09:41,318 INFO [train.py:763] (1/8) Epoch 7, batch 1400, loss[loss=0.1769, simple_loss=0.265, pruned_loss=0.04442, over 7286.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2984, pruned_loss=0.06023, over 1416782.28 frames.], batch size: 18, lr: 9.30e-04
2022-04-28 19:10:48,152 INFO [train.py:763] (1/8) Epoch 7, batch 1450, loss[loss=0.1946, simple_loss=0.2818, pruned_loss=0.05371, over 7168.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2983, pruned_loss=0.06003, over 1416897.02 frames.], batch size: 18, lr: 9.29e-04
2022-04-28 19:11:54,399 INFO [train.py:763] (1/8) Epoch 7, batch 1500, loss[loss=0.1853, simple_loss=0.2758, pruned_loss=0.04744, over 7421.00 frames.], tot_loss[loss=0.2093, simple_loss=0.2979, pruned_loss=0.06037, over 1416252.68 frames.], batch size: 18, lr: 9.28e-04
2022-04-28 19:12:59,476 INFO [train.py:763] (1/8) Epoch 7, batch 1550, loss[loss=0.22, simple_loss=0.3105, pruned_loss=0.06472, over 7212.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2993, pruned_loss=0.06071, over 1421636.68 frames.], batch size: 22, lr: 9.28e-04
2022-04-28 19:14:04,513 INFO [train.py:763] (1/8) Epoch 7, batch 1600, loss[loss=0.2201, simple_loss=0.3185, pruned_loss=0.06082, over 6186.00 frames.], tot_loss[loss=0.2117, simple_loss=0.3007, pruned_loss=0.06141, over 1421888.18 frames.], batch size: 37, lr: 9.27e-04
2022-04-28 19:15:09,643 INFO [train.py:763] (1/8) Epoch 7, batch 1650, loss[loss=0.2153, simple_loss=0.3115, pruned_loss=0.05952, over 7259.00 frames.], tot_loss[loss=0.2112, simple_loss=0.3006, pruned_loss=0.06091, over 1420334.55 frames.], batch size: 24, lr: 9.26e-04
2022-04-28 19:16:15,823 INFO [train.py:763] (1/8) Epoch 7, batch 1700, loss[loss=0.2317, simple_loss=0.3332, pruned_loss=0.06514, over 7319.00 frames.], tot_loss[loss=0.2116, simple_loss=0.3008, pruned_loss=0.06123, over 1420749.02 frames.], batch size: 21, lr: 9.26e-04
2022-04-28 19:17:22,174 INFO [train.py:763] (1/8) Epoch 7, batch 1750, loss[loss=0.1868, simple_loss=0.2772, pruned_loss=0.04818, over 7346.00 frames.], tot_loss[loss=0.2111, simple_loss=0.3002, pruned_loss=0.06102, over 1421063.62 frames.], batch size: 22, lr: 9.25e-04
2022-04-28 19:18:45,817 INFO [train.py:763] (1/8) Epoch 7, batch 1800, loss[loss=0.1936, simple_loss=0.2994, pruned_loss=0.04386, over 7335.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2984, pruned_loss=0.06051, over 1421970.88 frames.], batch size: 22, lr: 9.24e-04
2022-04-28 19:19:59,991 INFO [train.py:763] (1/8) Epoch 7, batch 1850, loss[loss=0.1971, simple_loss=0.2818, pruned_loss=0.0562, over 7227.00 frames.], tot_loss[loss=0.2095, simple_loss=0.2984, pruned_loss=0.06029, over 1423715.27 frames.], batch size: 20, lr: 9.24e-04
2022-04-28 19:21:23,367 INFO [train.py:763] (1/8) Epoch 7, batch 1900, loss[loss=0.1973, simple_loss=0.2958, pruned_loss=0.04936, over 7279.00 frames.], tot_loss[loss=0.2093, simple_loss=0.298, pruned_loss=0.06027, over 1421793.16 frames.], batch size: 25, lr: 9.23e-04
2022-04-28 19:22:40,068 INFO [train.py:763] (1/8) Epoch 7, batch 1950, loss[loss=0.2259, simple_loss=0.2898, pruned_loss=0.08099, over 7002.00 frames.], tot_loss[loss=0.2097, simple_loss=0.2982, pruned_loss=0.06062, over 1426427.61 frames.], batch size: 16, lr: 9.22e-04
2022-04-28 19:23:47,452 INFO [train.py:763] (1/8) Epoch 7, batch 2000, loss[loss=0.2174, simple_loss=0.3118, pruned_loss=0.06146, over 7122.00 frames.], tot_loss[loss=0.2096, simple_loss=0.298, pruned_loss=0.06055, over 1426967.89 frames.], batch size: 21, lr: 9.22e-04
2022-04-28 19:25:02,868 INFO [train.py:763] (1/8) Epoch 7, batch 2050, loss[loss=0.2634, simple_loss=0.3303, pruned_loss=0.09832, over 5250.00 frames.], tot_loss[loss=0.2103, simple_loss=0.2987, pruned_loss=0.06094, over 1420802.73 frames.], batch size: 52, lr: 9.21e-04
2022-04-28 19:26:07,936 INFO [train.py:763] (1/8) Epoch 7, batch 2100, loss[loss=0.2029, simple_loss=0.297, pruned_loss=0.05439, over 7230.00 frames.], tot_loss[loss=0.2111, simple_loss=0.2995, pruned_loss=0.06142, over 1416313.33 frames.], batch size: 20, lr: 9.20e-04
2022-04-28 19:27:22,247 INFO [train.py:763] (1/8) Epoch 7, batch 2150, loss[loss=0.2382, simple_loss=0.3146, pruned_loss=0.08091, over 7218.00 frames.], tot_loss[loss=0.2104, simple_loss=0.2988, pruned_loss=0.06099, over 1418372.57 frames.], batch size: 22, lr: 9.20e-04
2022-04-28 19:28:27,688 INFO [train.py:763] (1/8) Epoch 7, batch 2200, loss[loss=0.2087, simple_loss=0.3078, pruned_loss=0.05486, over 7278.00 frames.], tot_loss[loss=0.2084, simple_loss=0.2967, pruned_loss=0.06005, over 1415880.71 frames.], batch size: 24, lr: 9.19e-04
2022-04-28 19:29:32,842 INFO [train.py:763] (1/8) Epoch 7, batch 2250, loss[loss=0.1988, simple_loss=0.3003, pruned_loss=0.04861, over 7206.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2965, pruned_loss=0.06027, over 1411174.74 frames.], batch size: 23, lr: 9.18e-04
2022-04-28 19:30:38,173 INFO [train.py:763] (1/8) Epoch 7, batch 2300, loss[loss=0.1805, simple_loss=0.2642, pruned_loss=0.04843, over 7415.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2974, pruned_loss=0.06053, over 1411596.11 frames.], batch size: 18, lr: 9.18e-04
2022-04-28 19:31:43,912 INFO [train.py:763] (1/8) Epoch 7, batch 2350, loss[loss=0.1806, simple_loss=0.2691, pruned_loss=0.04606, over 7076.00 frames.], tot_loss[loss=0.2084, simple_loss=0.297, pruned_loss=0.05994, over 1411243.24 frames.], batch size: 18, lr: 9.17e-04
2022-04-28 19:32:50,592 INFO [train.py:763] (1/8) Epoch 7, batch 2400, loss[loss=0.1745, simple_loss=0.2796, pruned_loss=0.03473, over 7266.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2969, pruned_loss=0.06012, over 1415696.31 frames.], batch size: 19, lr: 9.16e-04
2022-04-28 19:33:55,905 INFO [train.py:763] (1/8) Epoch 7, batch 2450, loss[loss=0.26, simple_loss=0.3385, pruned_loss=0.09073, over 7309.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2972, pruned_loss=0.05984, over 1422346.15 frames.], batch size: 24, lr: 9.16e-04
2022-04-28 19:35:01,301 INFO [train.py:763] (1/8) Epoch 7, batch 2500, loss[loss=0.2278, simple_loss=0.3092, pruned_loss=0.07322, over 7321.00 frames.], tot_loss[loss=0.2094, simple_loss=0.2981, pruned_loss=0.06038, over 1420829.71 frames.], batch size: 21, lr: 9.15e-04
2022-04-28 19:36:06,925 INFO [train.py:763] (1/8) Epoch 7, batch 2550, loss[loss=0.2141, simple_loss=0.2971, pruned_loss=0.06552, over 7352.00 frames.], tot_loss[loss=0.2091, simple_loss=0.2976, pruned_loss=0.06027, over 1424740.68 frames.], batch size: 19, lr: 9.14e-04
2022-04-28 19:37:12,483 INFO [train.py:763] (1/8) Epoch 7, batch 2600, loss[loss=0.1721, simple_loss=0.2677, pruned_loss=0.03831, over 7197.00 frames.], tot_loss[loss=0.2083, simple_loss=0.2971, pruned_loss=0.0597, over 1425496.93 frames.], batch size: 16, lr: 9.14e-04
2022-04-28 19:38:17,714 INFO [train.py:763] (1/8) Epoch 7, batch 2650, loss[loss=0.2112, simple_loss=0.3017, pruned_loss=0.06032, over 7118.00 frames.], tot_loss[loss=0.2085, simple_loss=0.2977, pruned_loss=0.05964, over 1426305.46 frames.], batch size: 21, lr: 9.13e-04
2022-04-28 19:39:23,648 INFO [train.py:763] (1/8) Epoch 7, batch 2700, loss[loss=0.1839, simple_loss=0.2693, pruned_loss=0.04922, over 6825.00 frames.], tot_loss[loss=0.2073, simple_loss=0.2967, pruned_loss=0.05892, over 1428810.69 frames.], batch size: 15, lr: 9.12e-04
2022-04-28 19:40:30,716 INFO [train.py:763] (1/8) Epoch 7, batch 2750, loss[loss=0.1593, simple_loss=0.2482, pruned_loss=0.03514, over 6992.00 frames.], tot_loss[loss=0.2065, simple_loss=0.2958, pruned_loss=0.05854, over 1427653.20 frames.], batch size: 16, lr: 9.12e-04
2022-04-28 19:41:36,689 INFO [train.py:763] (1/8) Epoch 7, batch 2800, loss[loss=0.2144, simple_loss=0.2951, pruned_loss=0.06688, over 7143.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2972, pruned_loss=0.05912, over 1428133.90 frames.], batch size: 20, lr: 9.11e-04
2022-04-28 19:42:43,485 INFO [train.py:763] (1/8) Epoch 7, batch 2850, loss[loss=0.2015, simple_loss=0.2871, pruned_loss=0.05797, over 7210.00 frames.], tot_loss[loss=0.2069, simple_loss=0.2967, pruned_loss=0.05857, over 1427554.34 frames.], batch size: 22, lr: 9.11e-04
2022-04-28 19:43:49,293 INFO [train.py:763] (1/8) Epoch 7, batch 2900, loss[loss=0.1857, simple_loss=0.2738, pruned_loss=0.04887, over 7155.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2968, pruned_loss=0.05826, over 1426868.90 frames.], batch size: 17, lr: 9.10e-04
2022-04-28 19:44:55,755 INFO [train.py:763] (1/8) Epoch 7, batch 2950, loss[loss=0.1757, simple_loss=0.2735, pruned_loss=0.03895, over 7063.00 frames.], tot_loss[loss=0.207, simple_loss=0.2965, pruned_loss=0.05877, over 1426015.28 frames.], batch size: 18, lr: 9.09e-04
2022-04-28 19:46:01,158 INFO [train.py:763] (1/8) Epoch 7, batch 3000, loss[loss=0.2577, simple_loss=0.3269, pruned_loss=0.0942, over 4947.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2973, pruned_loss=0.05907, over 1421869.32 frames.], batch size: 52, lr: 9.09e-04
2022-04-28 19:46:01,159 INFO [train.py:783] (1/8) Computing validation loss
2022-04-28 19:46:16,423 INFO [train.py:792] (1/8) Epoch 7, validation: loss=0.1713, simple_loss=0.2754, pruned_loss=0.03361, over 698248.00 frames.
2022-04-28 19:47:23,038 INFO [train.py:763] (1/8) Epoch 7, batch 3050, loss[loss=0.2157, simple_loss=0.3028, pruned_loss=0.06428, over 6394.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2968, pruned_loss=0.05955, over 1415890.59 frames.], batch size: 37, lr: 9.08e-04
2022-04-28 19:48:28,733 INFO [train.py:763] (1/8) Epoch 7, batch 3100, loss[loss=0.2088, simple_loss=0.2929, pruned_loss=0.06235, over 7256.00 frames.], tot_loss[loss=0.2072, simple_loss=0.2961, pruned_loss=0.05913, over 1420634.36 frames.], batch size: 19, lr: 9.07e-04
2022-04-28 19:49:34,314 INFO [train.py:763] (1/8) Epoch 7, batch 3150, loss[loss=0.1974, simple_loss=0.2933, pruned_loss=0.05073, over 7439.00 frames.], tot_loss[loss=0.2061, simple_loss=0.295, pruned_loss=0.05864, over 1422261.82 frames.], batch size: 20, lr: 9.07e-04
2022-04-28 19:50:39,919 INFO [train.py:763] (1/8) Epoch 7, batch 3200, loss[loss=0.1776, simple_loss=0.2694, pruned_loss=0.04292, over 7426.00 frames.], tot_loss[loss=0.2051, simple_loss=0.2941, pruned_loss=0.05804, over 1424827.24 frames.], batch size: 20, lr: 9.06e-04
2022-04-28 19:51:45,166 INFO [train.py:763] (1/8) Epoch 7, batch 3250, loss[loss=0.2079, simple_loss=0.2981, pruned_loss=0.05879, over 7047.00 frames.], tot_loss[loss=0.2067, simple_loss=0.2958, pruned_loss=0.05877, over 1423967.05 frames.], batch size: 28, lr: 9.05e-04
2022-04-28 19:52:50,675 INFO [train.py:763] (1/8) Epoch 7, batch 3300, loss[loss=0.2508, simple_loss=0.3324, pruned_loss=0.08459, over 6818.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2962, pruned_loss=0.05875, over 1422436.12 frames.], batch size: 31, lr: 9.05e-04
2022-04-28 19:53:56,155 INFO [train.py:763] (1/8) Epoch 7, batch 3350, loss[loss=0.177, simple_loss=0.2748, pruned_loss=0.03958, over 7435.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2963, pruned_loss=0.05897, over 1421540.91 frames.], batch size: 20, lr: 9.04e-04
2022-04-28 19:55:01,741 INFO [train.py:763] (1/8) Epoch 7, batch 3400, loss[loss=0.2208, simple_loss=0.3131, pruned_loss=0.06424, over 6749.00 frames.], tot_loss[loss=0.2079, simple_loss=0.2968, pruned_loss=0.05947, over 1418863.37 frames.], batch size: 31, lr: 9.04e-04
2022-04-28 19:56:08,385 INFO [train.py:763] (1/8) Epoch 7, batch 3450, loss[loss=0.1866, simple_loss=0.2717, pruned_loss=0.05076, over 7431.00 frames.], tot_loss[loss=0.209, simple_loss=0.298, pruned_loss=0.06004, over 1422709.71 frames.], batch size: 18, lr: 9.03e-04
2022-04-28 19:57:15,787 INFO [train.py:763] (1/8) Epoch 7, batch 3500, loss[loss=0.2261, simple_loss=0.315, pruned_loss=0.06862, over 7362.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2984, pruned_loss=0.06006, over 1422155.59 frames.], batch size: 23, lr: 9.02e-04
2022-04-28 19:58:22,785 INFO [train.py:763] (1/8) Epoch 7, batch 3550, loss[loss=0.2145, simple_loss=0.2983, pruned_loss=0.06534, over 7261.00 frames.], tot_loss[loss=0.2087, simple_loss=0.2979, pruned_loss=0.05977, over 1422995.38 frames.], batch size: 19, lr: 9.02e-04
2022-04-28 19:59:29,956 INFO [train.py:763] (1/8) Epoch 7, batch 3600, loss[loss=0.2097, simple_loss=0.2899, pruned_loss=0.06477, over 7277.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2964, pruned_loss=0.05921, over 1421236.66 frames.], batch size: 17, lr: 9.01e-04
2022-04-28 20:00:37,041 INFO [train.py:763] (1/8) Epoch 7, batch 3650, loss[loss=0.2541, simple_loss=0.3233, pruned_loss=0.09247, over 7406.00 frames.], tot_loss[loss=0.2092, simple_loss=0.2981, pruned_loss=0.06015, over 1416228.54 frames.], batch size: 21, lr: 9.01e-04
2022-04-28 20:01:42,541 INFO [train.py:763] (1/8) Epoch 7, batch 3700, loss[loss=0.2452, simple_loss=0.3227, pruned_loss=0.08383, over 7220.00 frames.], tot_loss[loss=0.2076, simple_loss=0.2969, pruned_loss=0.05913, over 1419887.80 frames.], batch size: 21, lr: 9.00e-04
2022-04-28 20:02:49,191 INFO [train.py:763] (1/8) Epoch 7, batch 3750, loss[loss=0.2012, simple_loss=0.2974, pruned_loss=0.05252, over 7164.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2969, pruned_loss=0.059, over 1416693.84 frames.], batch size: 19, lr: 8.99e-04
2022-04-28 20:03:54,761 INFO [train.py:763] (1/8) Epoch 7, batch 3800, loss[loss=0.2195, simple_loss=0.3099, pruned_loss=0.06454, over 7286.00 frames.], tot_loss[loss=0.2077, simple_loss=0.2977, pruned_loss=0.05885, over 1420304.29 frames.], batch size: 24, lr: 8.99e-04
2022-04-28 20:05:00,507 INFO [train.py:763] (1/8) Epoch 7, batch 3850, loss[loss=0.2373, simple_loss=0.3283, pruned_loss=0.07311, over 7224.00 frames.], tot_loss[loss=0.2086, simple_loss=0.2989, pruned_loss=0.05914, over 1417450.36 frames.], batch size: 21, lr: 8.98e-04
2022-04-28 20:06:06,735 INFO [train.py:763] (1/8) Epoch 7, batch 3900, loss[loss=0.2237, simple_loss=0.3101, pruned_loss=0.06866, over 7426.00 frames.], tot_loss[loss=0.2078, simple_loss=0.2978, pruned_loss=0.05892, over 1421844.44 frames.], batch size: 20, lr: 8.97e-04
2022-04-28 20:07:13,247 INFO [train.py:763] (1/8) Epoch 7, batch 3950, loss[loss=0.192, simple_loss=0.2749, pruned_loss=0.05457, over 6992.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2968, pruned_loss=0.05825, over 1424385.59 frames.], batch size: 16, lr: 8.97e-04
2022-04-28 20:08:18,735 INFO [train.py:763] (1/8) Epoch 7, batch 4000, loss[loss=0.2169, simple_loss=0.3126, pruned_loss=0.06063, over 7141.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2969, pruned_loss=0.0586, over 1423288.11 frames.], batch size: 20, lr: 8.96e-04
2022-04-28 20:09:23,869 INFO [train.py:763] (1/8) Epoch 7, batch 4050, loss[loss=0.1819, simple_loss=0.2779, pruned_loss=0.04292, over 7412.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2961, pruned_loss=0.05822, over 1425965.23 frames.], batch size: 21, lr: 8.96e-04
2022-04-28 20:10:29,412 INFO [train.py:763] (1/8) Epoch 7, batch 4100, loss[loss=0.1939, simple_loss=0.2657, pruned_loss=0.06105, over 7283.00 frames.], tot_loss[loss=0.207, simple_loss=0.2964, pruned_loss=0.05882, over 1419203.68 frames.], batch size: 17, lr: 8.95e-04
2022-04-28 20:11:34,141 INFO [train.py:763] (1/8) Epoch 7, batch 4150, loss[loss=0.1813, simple_loss=0.2801, pruned_loss=0.04128, over 7346.00 frames.], tot_loss[loss=0.2074, simple_loss=0.2972, pruned_loss=0.05884, over 1413646.16 frames.], batch size: 22, lr: 8.94e-04
2022-04-28 20:12:39,365 INFO [train.py:763] (1/8) Epoch 7, batch 4200, loss[loss=0.2008, simple_loss=0.2964, pruned_loss=0.05259, over 7142.00 frames.], tot_loss[loss=0.2071, simple_loss=0.2972, pruned_loss=0.05846, over 1415910.01 frames.], batch size: 20, lr: 8.94e-04
2022-04-28 20:13:44,888 INFO [train.py:763] (1/8) Epoch 7, batch 4250, loss[loss=0.2428, simple_loss=0.3338, pruned_loss=0.07592, over 7213.00 frames.], tot_loss[loss=0.2075, simple_loss=0.2975, pruned_loss=0.05873, over 1419757.28 frames.], batch size: 22, lr: 8.93e-04
2022-04-28 20:14:50,389 INFO [train.py:763] (1/8) Epoch 7, batch 4300, loss[loss=0.2194, simple_loss=0.3085, pruned_loss=0.06509, over 7320.00 frames.], tot_loss[loss=0.2074, simple_loss=0.297, pruned_loss=0.05887, over 1419234.06 frames.], batch size: 21, lr: 8.93e-04
2022-04-28 20:15:55,684 INFO [train.py:763] (1/8) Epoch 7, batch 4350, loss[loss=0.2109, simple_loss=0.3015, pruned_loss=0.06013, over 7113.00 frames.], tot_loss[loss=0.2066, simple_loss=0.2962, pruned_loss=0.05849, over 1414795.81 frames.], batch size: 21, lr: 8.92e-04
2022-04-28 20:17:01,780 INFO [train.py:763] (1/8) Epoch 7, batch 4400, loss[loss=0.2111, simple_loss=0.3074, pruned_loss=0.05745, over 7102.00 frames.], tot_loss[loss=0.206, simple_loss=0.2955, pruned_loss=0.05828, over 1417024.62 frames.], batch size: 28, lr: 8.91e-04
2022-04-28 20:18:08,979 INFO [train.py:763] (1/8) Epoch 7, batch 4450, loss[loss=0.2174, simple_loss=0.3003, pruned_loss=0.06723, over 7322.00 frames.], tot_loss[loss=0.2061, simple_loss=0.2953, pruned_loss=0.05846, over 1417485.17 frames.], batch size: 20, lr: 8.91e-04
2022-04-28 20:19:16,355 INFO [train.py:763] (1/8) Epoch 7, batch 4500, loss[loss=0.2145, simple_loss=0.2936, pruned_loss=0.06772, over 7157.00 frames.], tot_loss[loss=0.2046, simple_loss=0.2938, pruned_loss=0.05771, over 1415528.37 frames.], batch size: 18, lr: 8.90e-04
2022-04-28 20:20:24,250 INFO [train.py:763] (1/8) Epoch 7, batch 4550, loss[loss=0.1686, simple_loss=0.2584, pruned_loss=0.03934, over 7288.00 frames.], tot_loss[loss=0.2055, simple_loss=0.2936, pruned_loss=0.05869, over 1399479.84 frames.], batch size: 17, lr: 8.90e-04
2022-04-28 20:21:52,808 INFO [train.py:763] (1/8) Epoch 8, batch 0, loss[loss=0.1994, simple_loss=0.3017, pruned_loss=0.04859, over 7203.00 frames.], tot_loss[loss=0.1994, simple_loss=0.3017, pruned_loss=0.04859, over 7203.00 frames.], batch size: 23, lr: 8.54e-04
2022-04-28 20:22:58,561 INFO [train.py:763] (1/8) Epoch 8, batch 50, loss[loss=0.1825, simple_loss=0.2724, pruned_loss=0.04633, over 7024.00 frames.], tot_loss[loss=0.2089, simple_loss=0.2999, pruned_loss=0.05896, over 319289.86 frames.], batch size: 28, lr: 8.53e-04
2022-04-28 20:24:03,943 INFO [train.py:763] (1/8) Epoch 8, batch 100, loss[loss=0.2272, simple_loss=0.3186, pruned_loss=0.06789, over 7233.00 frames.], tot_loss[loss=0.204, simple_loss=0.2958, pruned_loss=0.05609, over 566674.81 frames.], batch size: 20, lr: 8.53e-04
2022-04-28 20:25:10,090 INFO [train.py:763] (1/8) Epoch 8, batch 150, loss[loss=0.2446, simple_loss=0.316, pruned_loss=0.08662, over 5153.00 frames.], tot_loss[loss=0.2058, simple_loss=0.2967, pruned_loss=0.05746, over 753903.15 frames.], batch size: 52, lr: 8.52e-04
2022-04-28 20:26:16,004 INFO [train.py:763] (1/8) Epoch 8, batch 200, loss[loss=0.2073, simple_loss=0.3016, pruned_loss=0.05652, over 7186.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2956, pruned_loss=0.05645, over 901870.92 frames.], batch size: 22, lr: 8.51e-04
2022-04-28 20:27:21,273 INFO [train.py:763] (1/8) Epoch 8, batch 250, loss[loss=0.1946, simple_loss=0.2785, pruned_loss=0.05537, over 7429.00 frames.], tot_loss[loss=0.205, simple_loss=0.2959, pruned_loss=0.05704, over 1018843.73 frames.], batch size: 20, lr: 8.51e-04
2022-04-28 20:28:27,034 INFO [train.py:763] (1/8) Epoch 8, batch 300, loss[loss=0.2031, simple_loss=0.2973, pruned_loss=0.05442, over 7346.00 frames.], tot_loss[loss=0.2068, simple_loss=0.2977, pruned_loss=0.05791, over 1103961.83 frames.], batch size: 22, lr: 8.50e-04
2022-04-28 20:29:32,796 INFO [train.py:763] (1/8) Epoch 8, batch 350, loss[loss=0.1902, simple_loss=0.2813, pruned_loss=0.0495, over 7160.00 frames.], tot_loss[loss=0.206, simple_loss=0.2968, pruned_loss=0.05761, over 1177613.00 frames.], batch size: 19, lr: 8.50e-04
2022-04-28 20:30:38,285 INFO [train.py:763] (1/8) Epoch 8, batch 400, loss[loss=0.1744, simple_loss=0.2575, pruned_loss=0.04567, over 7133.00 frames.], tot_loss[loss=0.2056, simple_loss=0.2961, pruned_loss=0.05755, over 1236876.33 frames.], batch size: 17, lr: 8.49e-04
2022-04-28 20:31:43,711 INFO [train.py:763] (1/8) Epoch 8, batch 450, loss[loss=0.1763, simple_loss=0.2749, pruned_loss=0.03881, over 7261.00 frames.], tot_loss[loss=0.2044, simple_loss=0.2949, pruned_loss=0.0569, over 1278418.52 frames.], batch size: 19, lr: 8.49e-04
2022-04-28 20:32:50,561 INFO [train.py:763] (1/8) Epoch 8, batch 500, loss[loss=0.1588, simple_loss=0.2505, pruned_loss=0.03356, over 7398.00 frames.], tot_loss[loss=0.205, simple_loss=0.2954, pruned_loss=0.05731, over 1311548.36 frames.], batch size: 18, lr: 8.48e-04
2022-04-28 20:33:57,711 INFO [train.py:763] (1/8) Epoch 8, batch 550, loss[loss=0.1815, simple_loss=0.2739, pruned_loss=0.0446, over 7070.00 frames.], tot_loss[loss=0.2043, simple_loss=0.2947, pruned_loss=0.05701, over 1339607.10 frames.], batch size: 18, lr: 8.48e-04
2022-04-28 20:35:03,801 INFO [train.py:763] (1/8) Epoch 8, batch 600, loss[loss=0.2072, simple_loss=0.2946, pruned_loss=0.05991, over 7075.00 frames.], tot_loss[loss=0.204, simple_loss=0.2945, pruned_loss=0.05675, over 1360859.77 frames.], batch size: 18, lr: 8.47e-04
2022-04-28 20:36:09,108 INFO [train.py:763] (1/8) Epoch 8, batch 650, loss[loss=0.1801, simple_loss=0.2792, pruned_loss=0.04056, over 7360.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2938, pruned_loss=0.05653, over 1374272.95 frames.], batch size: 19, lr: 8.46e-04
2022-04-28 20:37:14,551 INFO [train.py:763] (1/8) Epoch 8, batch 700, loss[loss=0.1815, simple_loss=0.27, pruned_loss=0.0465, over 7435.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2942, pruned_loss=0.05636, over 1386519.04 frames.], batch size: 20, lr: 8.46e-04
2022-04-28 20:38:20,314 INFO [train.py:763] (1/8) Epoch 8, batch 750, loss[loss=0.1866, simple_loss=0.2673, pruned_loss=0.05297, over 7174.00 frames.], tot_loss[loss=0.204, simple_loss=0.2946, pruned_loss=0.05671, over 1390128.30 frames.], batch size: 18, lr: 8.45e-04
2022-04-28 20:39:25,912 INFO [train.py:763] (1/8) Epoch 8, batch 800, loss[loss=0.2171, simple_loss=0.3033, pruned_loss=0.06539, over 7397.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2936, pruned_loss=0.05633, over 1396312.31 frames.], batch size: 23, lr: 8.45e-04
2022-04-28 20:40:32,528 INFO [train.py:763] (1/8) Epoch 8, batch 850, loss[loss=0.1931, simple_loss=0.2889, pruned_loss=0.04867, over 7310.00 frames.], tot_loss[loss=0.204, simple_loss=0.2945, pruned_loss=0.05676, over 1402204.23 frames.], batch size: 21, lr: 8.44e-04
2022-04-28 20:41:39,526 INFO [train.py:763] (1/8) Epoch 8, batch 900, loss[loss=0.2226, simple_loss=0.3141, pruned_loss=0.06555, over 7225.00 frames.], tot_loss[loss=0.2042, simple_loss=0.2948, pruned_loss=0.05686, over 1411654.92 frames.], batch size: 21, lr: 8.44e-04
2022-04-28 20:42:46,673 INFO [train.py:763] (1/8) Epoch 8, batch 950, loss[loss=0.1838, simple_loss=0.2771, pruned_loss=0.04529, over 7326.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2946, pruned_loss=0.05675, over 1409833.33 frames.], batch size: 20, lr: 8.43e-04
2022-04-28 20:43:53,791 INFO [train.py:763] (1/8) Epoch 8, batch 1000, loss[loss=0.2134, simple_loss=0.2959, pruned_loss=0.06543, over 7431.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2943, pruned_loss=0.05647, over 1414838.12 frames.], batch size: 20, lr: 8.43e-04
2022-04-28 20:45:00,936 INFO [train.py:763] (1/8) Epoch 8, batch 1050, loss[loss=0.1861, simple_loss=0.2759, pruned_loss=0.04812, over 7260.00 frames.], tot_loss[loss=0.2032, simple_loss=0.294, pruned_loss=0.05615, over 1419748.00 frames.], batch size: 19, lr: 8.42e-04
2022-04-28 20:46:07,160 INFO [train.py:763] (1/8) Epoch 8, batch 1100, loss[loss=0.1724, simple_loss=0.2585, pruned_loss=0.0431, over 7259.00 frames.], tot_loss[loss=0.2041, simple_loss=0.295, pruned_loss=0.05662, over 1422466.03 frames.], batch size: 17, lr: 8.41e-04
2022-04-28 20:47:12,899 INFO [train.py:763] (1/8) Epoch 8, batch 1150, loss[loss=0.2135, simple_loss=0.3066, pruned_loss=0.06013, over 7312.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2936, pruned_loss=0.05634, over 1422303.93 frames.], batch size: 25, lr: 8.41e-04
2022-04-28 20:48:18,240 INFO [train.py:763] (1/8) Epoch 8, batch 1200, loss[loss=0.2007, simple_loss=0.2976, pruned_loss=0.05192, over 7421.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2937, pruned_loss=0.05654, over 1421890.61 frames.], batch size: 20, lr: 8.40e-04
2022-04-28 20:49:23,427 INFO [train.py:763] (1/8) Epoch 8, batch 1250, loss[loss=0.1937, simple_loss=0.2813, pruned_loss=0.05308, over 7185.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2936, pruned_loss=0.05676, over 1417701.58 frames.], batch size: 16, lr: 8.40e-04
2022-04-28 20:50:29,920 INFO [train.py:763] (1/8) Epoch 8, batch 1300, loss[loss=0.228, simple_loss=0.3224, pruned_loss=0.06684, over 7148.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2941, pruned_loss=0.0571, over 1414149.39 frames.], batch size: 19, lr: 8.39e-04
2022-04-28 20:51:37,148 INFO [train.py:763] (1/8) Epoch 8, batch 1350, loss[loss=0.223, simple_loss=0.3169, pruned_loss=0.06459, over 7434.00 frames.], tot_loss[loss=0.204, simple_loss=0.2939, pruned_loss=0.05701, over 1418509.68 frames.], batch size: 20, lr: 8.39e-04
2022-04-28 20:52:43,211 INFO [train.py:763] (1/8) Epoch 8, batch 1400, loss[loss=0.1917, simple_loss=0.2821, pruned_loss=0.05063, over 7227.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2935, pruned_loss=0.05658, over 1415576.08 frames.], batch size: 21, lr: 8.38e-04
2022-04-28 20:53:48,892 INFO [train.py:763] (1/8) Epoch 8, batch 1450, loss[loss=0.2401, simple_loss=0.3302, pruned_loss=0.07495, over 7318.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2928, pruned_loss=0.05604, over 1420463.68 frames.], batch size: 21, lr: 8.38e-04
2022-04-28 20:54:55,523 INFO [train.py:763] (1/8) Epoch 8, batch 1500, loss[loss=0.1931, simple_loss=0.2797, pruned_loss=0.05321, over 7231.00 frames.], tot_loss[loss=0.202, simple_loss=0.2925, pruned_loss=0.05579, over 1422901.52 frames.], batch size: 20, lr: 8.37e-04
2022-04-28 20:56:02,359 INFO [train.py:763] (1/8) Epoch 8, batch 1550, loss[loss=0.2303, simple_loss=0.3171, pruned_loss=0.07181, over 7198.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2912, pruned_loss=0.05516, over 1421819.52 frames.], batch size: 22, lr: 8.37e-04
2022-04-28 20:57:08,583 INFO [train.py:763] (1/8) Epoch 8, batch 1600, loss[loss=0.2121, simple_loss=0.3075, pruned_loss=0.05838, over 7059.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2926, pruned_loss=0.05593, over 1420204.18 frames.], batch size: 18, lr: 8.36e-04
2022-04-28 20:58:15,579 INFO [train.py:763] (1/8) Epoch 8, batch 1650, loss[loss=0.1794, simple_loss=0.277, pruned_loss=0.04083, over 7102.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2937, pruned_loss=0.05686, over 1420631.45 frames.], batch size: 21, lr: 8.35e-04
2022-04-28 20:59:22,335 INFO [train.py:763] (1/8) Epoch 8, batch 1700, loss[loss=0.1934, simple_loss=0.2908, pruned_loss=0.04796, over 7150.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2943, pruned_loss=0.05677, over 1418925.76 frames.], batch size: 20, lr: 8.35e-04
2022-04-28 21:00:28,777 INFO [train.py:763] (1/8) Epoch 8, batch 1750, loss[loss=0.1955, simple_loss=0.2992, pruned_loss=0.04596, over 7330.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2937, pruned_loss=0.05631, over 1420960.61 frames.], batch size: 21, lr: 8.34e-04
2022-04-28 21:01:33,979 INFO [train.py:763] (1/8) Epoch 8, batch 1800, loss[loss=0.1851, simple_loss=0.2746, pruned_loss=0.04778, over 7243.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2935, pruned_loss=0.05643, over 1418064.44 frames.], batch size: 20, lr: 8.34e-04
2022-04-28 21:02:39,282 INFO [train.py:763] (1/8) Epoch 8, batch 1850, loss[loss=0.1926, simple_loss=0.2867, pruned_loss=0.04927, over 7242.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2944, pruned_loss=0.05662, over 1421774.02 frames.], batch size: 20, lr: 8.33e-04
2022-04-28 21:03:44,673 INFO [train.py:763] (1/8) Epoch 8, batch 1900, loss[loss=0.1993, simple_loss=0.2973, pruned_loss=0.05067, over 7150.00 frames.], tot_loss[loss=0.2039, simple_loss=0.2946, pruned_loss=0.05661, over 1420345.55 frames.], batch size: 19, lr: 8.33e-04
2022-04-28 21:04:50,204 INFO [train.py:763] (1/8) Epoch 8, batch 1950, loss[loss=0.1816, simple_loss=0.2888, pruned_loss=0.03715, over 7118.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2944, pruned_loss=0.05609, over 1421030.62 frames.], batch size: 21, lr: 8.32e-04
2022-04-28 21:05:55,497 INFO [train.py:763] (1/8) Epoch 8, batch 2000, loss[loss=0.2152, simple_loss=0.311, pruned_loss=0.05976, over 7269.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2933, pruned_loss=0.05568, over 1422208.57 frames.], batch size: 24, lr: 8.32e-04
2022-04-28 21:07:00,732 INFO [train.py:763] (1/8) Epoch 8, batch 2050, loss[loss=0.1905, simple_loss=0.2708, pruned_loss=0.05507, over 7270.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2936, pruned_loss=0.05604, over 1421458.75 frames.], batch size: 17, lr: 8.31e-04
2022-04-28 21:08:05,936 INFO [train.py:763] (1/8) Epoch 8, batch 2100, loss[loss=0.2003, simple_loss=0.2954, pruned_loss=0.05261, over 7259.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2941, pruned_loss=0.05637, over 1423517.34 frames.], batch size: 19, lr: 8.31e-04
2022-04-28 21:09:08,023 INFO [train.py:763] (1/8) Epoch 8, batch 2150, loss[loss=0.2061, simple_loss=0.2978, pruned_loss=0.05717, over 7068.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2933, pruned_loss=0.05612, over 1425443.47 frames.], batch size: 18, lr: 8.30e-04
2022-04-28 21:10:14,556 INFO [train.py:763] (1/8) Epoch 8, batch 2200, loss[loss=0.2081, simple_loss=0.2806, pruned_loss=0.06778, over 7272.00 frames.], tot_loss[loss=0.2026, simple_loss=0.2929, pruned_loss=0.05614, over 1423299.23 frames.], batch size: 17, lr: 8.30e-04
2022-04-28 21:11:21,396 INFO [train.py:763] (1/8) Epoch 8, batch 2250, loss[loss=0.1663, simple_loss=0.2645, pruned_loss=0.03407, over 7165.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2929, pruned_loss=0.05599, over 1423699.21 frames.], batch size: 18, lr: 8.29e-04
2022-04-28 21:12:26,807 INFO [train.py:763] (1/8) Epoch 8, batch 2300, loss[loss=0.1817, simple_loss=0.2816, pruned_loss=0.04094, over 7142.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2941, pruned_loss=0.05657, over 1424765.85 frames.], batch size: 20, lr: 8.29e-04
2022-04-28 21:13:32,125 INFO [train.py:763] (1/8) Epoch 8, batch 2350, loss[loss=0.2309, simple_loss=0.3235, pruned_loss=0.06921, over 6804.00 frames.], tot_loss[loss=0.2039, simple_loss=0.294, pruned_loss=0.05691, over 1423821.84 frames.], batch size: 31, lr: 8.28e-04
2022-04-28 21:14:37,450 INFO [train.py:763] (1/8) Epoch 8, batch 2400, loss[loss=0.2129, simple_loss=0.2869, pruned_loss=0.06947, over 7266.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2929, pruned_loss=0.05621, over 1424850.74 frames.], batch size: 18, lr: 8.28e-04
2022-04-28 21:15:42,877 INFO [train.py:763] (1/8) Epoch 8, batch 2450, loss[loss=0.1796, simple_loss=0.265, pruned_loss=0.04708, over 7397.00 frames.], tot_loss[loss=0.2026, simple_loss=0.293, pruned_loss=0.0561, over 1425812.01 frames.], batch size: 18, lr: 8.27e-04
2022-04-28 21:16:48,163 INFO [train.py:763] (1/8) Epoch 8, batch 2500, loss[loss=0.213, simple_loss=0.3072, pruned_loss=0.0594, over 7200.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2927, pruned_loss=0.05617, over 1425057.10 frames.], batch size: 22, lr: 8.27e-04
2022-04-28 21:17:53,461 INFO [train.py:763] (1/8) Epoch 8, batch 2550, loss[loss=0.1847, simple_loss=0.27, pruned_loss=0.04974, over 7129.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2924, pruned_loss=0.05621, over 1422055.18 frames.], batch size: 17, lr: 8.26e-04
2022-04-28 21:18:58,782 INFO [train.py:763] (1/8) Epoch 8, batch 2600, loss[loss=0.2134, simple_loss=0.2982, pruned_loss=0.06427, over 7359.00 frames.], tot_loss[loss=0.2041, simple_loss=0.2939, pruned_loss=0.05718, over 1419042.39 frames.], batch size: 23, lr: 8.25e-04
2022-04-28 21:20:03,878 INFO [train.py:763] (1/8) Epoch 8, batch 2650, loss[loss=0.2725, simple_loss=0.3493, pruned_loss=0.09791, over 4872.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2937, pruned_loss=0.05692, over 1417152.69 frames.], batch size: 52, lr: 8.25e-04
2022-04-28 21:21:09,313 INFO [train.py:763] (1/8) Epoch 8, batch 2700, loss[loss=0.2388, simple_loss=0.3238, pruned_loss=0.07689, over 7335.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2937, pruned_loss=0.05657, over 1418939.18 frames.], batch size: 22, lr: 8.24e-04
2022-04-28 21:22:14,615 INFO [train.py:763] (1/8) Epoch 8, batch 2750, loss[loss=0.1998, simple_loss=0.2848, pruned_loss=0.05738, over 7337.00 frames.], tot_loss[loss=0.2022, simple_loss=0.2925, pruned_loss=0.05591, over 1423437.39 frames.], batch size: 20, lr: 8.24e-04
2022-04-28 21:23:20,616 INFO [train.py:763] (1/8) Epoch 8, batch 2800, loss[loss=0.2412, simple_loss=0.3268, pruned_loss=0.0778, over 7199.00 frames.], tot_loss[loss=0.2025, simple_loss=0.2929, pruned_loss=0.05608, over 1426026.13 frames.], batch size: 22, lr: 8.23e-04
2022-04-28 21:24:26,772 INFO [train.py:763] (1/8) Epoch 8, batch 2850, loss[loss=0.1972, simple_loss=0.2913, pruned_loss=0.05152, over 7149.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2923, pruned_loss=0.05532, over 1427971.40 frames.], batch size: 19, lr: 8.23e-04
2022-04-28 21:25:32,042 INFO [train.py:763] (1/8) Epoch 8, batch 2900, loss[loss=0.1655, simple_loss=0.2753, pruned_loss=0.02785, over 7323.00 frames.], tot_loss[loss=0.2006, simple_loss=0.292, pruned_loss=0.05459, over 1427094.88 frames.], batch size: 21, lr: 8.22e-04
2022-04-28 21:26:37,469 INFO [train.py:763] (1/8) Epoch 8, batch 2950, loss[loss=0.1612, simple_loss=0.2515, pruned_loss=0.03546, over 7277.00 frames.], tot_loss[loss=0.2015, simple_loss=0.2927, pruned_loss=0.05509, over 1423199.20 frames.], batch size: 18, lr: 8.22e-04
2022-04-28 21:27:43,084 INFO [train.py:763] (1/8) Epoch 8, batch 3000, loss[loss=0.2141, simple_loss=0.3108, pruned_loss=0.05866, over 7288.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2922, pruned_loss=0.05511, over 1421474.93 frames.], batch size: 24, lr: 8.21e-04
2022-04-28 21:27:43,085 INFO [train.py:783] (1/8) Computing validation loss
2022-04-28 21:27:58,488 INFO [train.py:792] (1/8) Epoch 8, validation: loss=0.1715, simple_loss=0.2766, pruned_loss=0.03324, over 698248.00 frames.
2022-04-28 21:29:04,151 INFO [train.py:763] (1/8) Epoch 8, batch 3050, loss[loss=0.1741, simple_loss=0.2711, pruned_loss=0.03856, over 7326.00 frames.], tot_loss[loss=0.2017, simple_loss=0.2923, pruned_loss=0.05555, over 1418167.95 frames.], batch size: 20, lr: 8.21e-04
2022-04-28 21:30:09,330 INFO [train.py:763] (1/8) Epoch 8, batch 3100, loss[loss=0.2261, simple_loss=0.309, pruned_loss=0.07167, over 6763.00 frames.], tot_loss[loss=0.2036, simple_loss=0.2942, pruned_loss=0.05645, over 1413429.37 frames.], batch size: 31, lr: 8.20e-04
2022-04-28 21:31:14,880 INFO [train.py:763] (1/8) Epoch 8, batch 3150, loss[loss=0.2209, simple_loss=0.3136, pruned_loss=0.06407, over 7157.00 frames.], tot_loss[loss=0.2031, simple_loss=0.2937, pruned_loss=0.0562, over 1416776.20 frames.], batch size: 19, lr: 8.20e-04
2022-04-28 21:32:20,533 INFO [train.py:763] (1/8) Epoch 8, batch 3200, loss[loss=0.2069, simple_loss=0.2929, pruned_loss=0.06044, over 7153.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2936, pruned_loss=0.05609, over 1420601.62 frames.], batch size: 20, lr: 8.19e-04
2022-04-28 21:33:34,630 INFO [train.py:763] (1/8) Epoch 8, batch 3250, loss[loss=0.2424, simple_loss=0.3169, pruned_loss=0.0839, over 4872.00 frames.], tot_loss[loss=0.2038, simple_loss=0.2946, pruned_loss=0.05651, over 1419057.89 frames.], batch size: 52, lr: 8.19e-04
2022-04-28 21:34:51,670 INFO [train.py:763] (1/8) Epoch 8, batch 3300, loss[loss=0.1948, simple_loss=0.296, pruned_loss=0.04683, over 7201.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2937, pruned_loss=0.05653, over 1419145.79 frames.], batch size: 22, lr: 8.18e-04
2022-04-28 21:36:05,889 INFO [train.py:763] (1/8) Epoch 8, batch 3350, loss[loss=0.182, simple_loss=0.2771, pruned_loss=0.04343, over 7263.00 frames.], tot_loss[loss=0.2028, simple_loss=0.2934, pruned_loss=0.05607, over 1422538.72 frames.], batch size: 19, lr: 8.18e-04
2022-04-28 21:37:39,077 INFO [train.py:763] (1/8) Epoch 8, batch 3400, loss[loss=0.2194, simple_loss=0.317, pruned_loss=0.0609, over 6800.00 frames.], tot_loss[loss=0.2034, simple_loss=0.2941, pruned_loss=0.05629, over 1421021.96 frames.], batch size: 31, lr: 8.17e-04
2022-04-28 21:38:45,185 INFO [train.py:763] (1/8) Epoch 8, batch 3450, loss[loss=0.1728, simple_loss=0.2545, pruned_loss=0.04559, over 7399.00 frames.], tot_loss[loss=0.203, simple_loss=0.2936, pruned_loss=0.0562, over 1423639.00 frames.], batch size: 18, lr: 8.17e-04
2022-04-28 21:40:00,474 INFO [train.py:763] (1/8) Epoch 8, batch 3500, loss[loss=0.1608, simple_loss=0.2509, pruned_loss=0.03535, over 7160.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2933, pruned_loss=0.0563, over 1424303.93 frames.], batch size: 19, lr: 8.16e-04
2022-04-28 21:41:15,117 INFO [train.py:763] (1/8) Epoch 8, batch 3550, loss[loss=0.202, simple_loss=0.2932, pruned_loss=0.05541, over 7163.00 frames.], tot_loss[loss=0.2024, simple_loss=0.2929, pruned_loss=0.05597, over 1426339.50 frames.], batch size: 18, lr: 8.16e-04
2022-04-28 21:42:20,506 INFO [train.py:763] (1/8) Epoch 8, batch 3600, loss[loss=0.1745, simple_loss=0.2666, pruned_loss=0.04117, over 7279.00 frames.], tot_loss[loss=0.2035, simple_loss=0.2941, pruned_loss=0.05646, over 1424189.82 frames.], batch size: 18, lr: 8.15e-04
2022-04-28 21:43:26,012 INFO [train.py:763] (1/8) Epoch 8, batch 3650, loss[loss=0.1811, simple_loss=0.2686, pruned_loss=0.04682, over 7140.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2935, pruned_loss=0.05616, over 1425715.06 frames.], batch size: 17, lr: 8.15e-04
2022-04-28 21:44:39,930 INFO [train.py:763] (1/8) Epoch 8, batch 3700, loss[loss=0.2198, simple_loss=0.3123, pruned_loss=0.06367, over 7276.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2933, pruned_loss=0.05567, over 1426486.41 frames.], batch size: 25, lr: 8.14e-04
2022-04-28 21:45:45,258 INFO [train.py:763] (1/8) Epoch 8, batch 3750, loss[loss=0.1991, simple_loss=0.2909, pruned_loss=0.05364, over 7427.00 frames.], tot_loss[loss=0.2033, simple_loss=0.2944, pruned_loss=0.05606, over 1426038.57 frames.], batch size: 20, lr: 8.14e-04
2022-04-28 21:46:51,549 INFO [train.py:763] (1/8) Epoch 8, batch 3800, loss[loss=0.204, simple_loss=0.2932, pruned_loss=0.05745, over 7412.00 frames.], tot_loss[loss=0.2037, simple_loss=0.2946, pruned_loss=0.05634, over 1427537.44 frames.], batch size: 18, lr: 8.13e-04
2022-04-28 21:47:57,461 INFO [train.py:763] (1/8) Epoch 8, batch 3850, loss[loss=0.172, simple_loss=0.2611, pruned_loss=0.04144, over 7295.00 frames.], tot_loss[loss=0.2029, simple_loss=0.2942, pruned_loss=0.05581, over 1429712.78 frames.], batch size: 17, lr: 8.13e-04
2022-04-28 21:49:03,316 INFO [train.py:763] (1/8) Epoch 8, batch 3900, loss[loss=0.2441, simple_loss=0.3277, pruned_loss=0.08023, over 4959.00 frames.], tot_loss[loss=0.2032, simple_loss=0.2949, pruned_loss=0.05574, over 1427255.07 frames.], batch size: 52, lr: 8.12e-04
2022-04-28 21:50:08,722 INFO [train.py:763] (1/8) Epoch 8, batch 3950, loss[loss=0.2013, simple_loss=0.3036, pruned_loss=0.04954, over 6771.00 frames.], tot_loss[loss=0.202, simple_loss=0.2934, pruned_loss=0.05526, over 1427784.40 frames.], batch size: 31, lr: 8.12e-04
2022-04-28 21:51:14,796 INFO [train.py:763] (1/8) Epoch 8, batch 4000, loss[loss=0.2066, simple_loss=0.3017, pruned_loss=0.05578, over 7218.00 frames.], tot_loss[loss=0.2023, simple_loss=0.2941, pruned_loss=0.05528, over 1427030.78 frames.], batch size: 21, lr: 8.11e-04
2022-04-28 21:52:21,952 INFO [train.py:763] (1/8) Epoch 8, batch 4050, loss[loss=0.1689, simple_loss=0.2537, pruned_loss=0.04205, over 7409.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2933, pruned_loss=0.05549, over 1426438.35 frames.], batch size: 18, lr: 8.11e-04
2022-04-28 21:53:28,739 INFO [train.py:763] (1/8) Epoch 8, batch 4100, loss[loss=0.184, simple_loss=0.272, pruned_loss=0.04802, over 7118.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2929, pruned_loss=0.05529, over 1427157.18 frames.], batch size: 17, lr: 8.10e-04
2022-04-28 21:54:34,088 INFO [train.py:763] (1/8) Epoch 8, batch 4150, loss[loss=0.2084, simple_loss=0.3031, pruned_loss=0.05689, over 7130.00 frames.], tot_loss[loss=0.2019, simple_loss=0.2927, pruned_loss=0.05557, over 1422438.44 frames.], batch size: 28, lr: 8.10e-04
2022-04-28 21:55:39,788 INFO [train.py:763] (1/8) Epoch 8, batch 4200, loss[loss=0.2105, simple_loss=0.3061, pruned_loss=0.05743, over 7328.00 frames.], tot_loss[loss=0.2006, simple_loss=0.2914, pruned_loss=0.05494, over 1424275.45 frames.], batch size: 20, lr: 8.09e-04
2022-04-28 21:56:45,196 INFO [train.py:763] (1/8) Epoch 8, batch 4250, loss[loss=0.1949, simple_loss=0.2667, pruned_loss=0.06155, over 7144.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2917, pruned_loss=0.05505, over 1419890.55 frames.], batch size: 17, lr: 8.09e-04
2022-04-28 21:57:50,931 INFO [train.py:763] (1/8) Epoch 8, batch 4300, loss[loss=0.1864, simple_loss=0.2902, pruned_loss=0.04129, over 7405.00 frames.], tot_loss[loss=0.2012, simple_loss=0.2917, pruned_loss=0.05533, over 1415073.25 frames.], batch size: 21, lr: 8.08e-04
2022-04-28 21:58:56,621 INFO [train.py:763] (1/8) Epoch 8, batch 4350, loss[loss=0.1825, simple_loss=0.2692, pruned_loss=0.04788, over 7279.00 frames.], tot_loss[loss=0.2008, simple_loss=0.291, pruned_loss=0.05527, over 1420291.26 frames.], batch size: 17, lr: 8.08e-04
2022-04-28 22:00:02,321 INFO [train.py:763] (1/8) Epoch 8, batch 4400, loss[loss=0.2293, simple_loss=0.322, pruned_loss=0.0683, over 7034.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2904, pruned_loss=0.05504, over 1416211.86 frames.], batch size: 28, lr: 8.07e-04
2022-04-28 22:01:09,618 INFO [train.py:763] (1/8) Epoch 8, batch 4450, loss[loss=0.2029, simple_loss=0.3047, pruned_loss=0.05053, over 7030.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2891, pruned_loss=0.05483, over 1410479.01 frames.], batch size: 28, lr: 8.07e-04
2022-04-28 22:02:15,953 INFO [train.py:763] (1/8) Epoch 8, batch 4500, loss[loss=0.218, simple_loss=0.3139, pruned_loss=0.06105, over 7067.00 frames.], tot_loss[loss=0.2009, simple_loss=0.2902, pruned_loss=0.05584, over 1392075.56 frames.], batch size: 28, lr: 8.07e-04
2022-04-28 22:03:19,879 INFO [train.py:763] (1/8) Epoch 8, batch 4550, loss[loss=0.1983, simple_loss=0.2901, pruned_loss=0.05322, over 6288.00 frames.], tot_loss[loss=0.2063, simple_loss=0.2952, pruned_loss=0.05873, over 1351199.77 frames.], batch size: 37, lr: 8.06e-04
2022-04-28 22:04:39,796 INFO [train.py:763] (1/8) Epoch 9, batch 0, loss[loss=0.1751, simple_loss=0.2728, pruned_loss=0.03871, over 7408.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2728, pruned_loss=0.03871, over 7408.00 frames.], batch size: 21, lr: 7.75e-04
2022-04-28 22:05:45,911 INFO [train.py:763] (1/8) Epoch 9, batch 50, loss[loss=0.2073, simple_loss=0.3025, pruned_loss=0.05606, over 7189.00 frames.], tot_loss[loss=0.2021, simple_loss=0.2934, pruned_loss=0.05536, over 321344.42 frames.], batch size: 23, lr: 7.74e-04
2022-04-28 22:06:51,597 INFO [train.py:763] (1/8) Epoch 9, batch 100, loss[loss=0.2433, simple_loss=0.3237, pruned_loss=0.08146, over 5100.00 frames.], tot_loss[loss=0.2016, simple_loss=0.2916, pruned_loss=0.05581, over 556914.33 frames.], batch size: 52, lr: 7.74e-04
2022-04-28 22:07:57,279 INFO [train.py:763] (1/8) Epoch 9, batch 150, loss[loss=0.17, simple_loss=0.2668, pruned_loss=0.03657, over 7441.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2897, pruned_loss=0.05375, over 750262.63 frames.], batch size: 20, lr: 7.73e-04
2022-04-28 22:09:03,714 INFO [train.py:763] (1/8) Epoch 9, batch 200, loss[loss=0.1653, simple_loss=0.269, pruned_loss=0.0308, over 7434.00 frames.], tot_loss[loss=0.1973, simple_loss=0.2891, pruned_loss=0.05275, over 898068.36 frames.], batch size: 20, lr: 7.73e-04
2022-04-28 22:10:10,398 INFO [train.py:763] (1/8) Epoch 9, batch 250, loss[loss=0.1784, simple_loss=0.263, pruned_loss=0.04693, over 7158.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2901, pruned_loss=0.05256, over 1010499.04 frames.], batch size: 18, lr: 7.72e-04
2022-04-28 22:11:16,229 INFO [train.py:763] (1/8) Epoch 9, batch 300, loss[loss=0.1764, simple_loss=0.2702, pruned_loss=0.04133, over 7324.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2906, pruned_loss=0.05323, over 1104647.80 frames.], batch size: 20, lr: 7.72e-04
2022-04-28 22:12:21,602 INFO [train.py:763] (1/8) Epoch 9, batch 350, loss[loss=0.1976, simple_loss=0.2954, pruned_loss=0.04991, over 7203.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2909, pruned_loss=0.05364, over 1173819.58 frames.], batch size: 23, lr: 7.71e-04
2022-04-28 22:13:26,943 INFO [train.py:763] (1/8) Epoch 9, batch 400, loss[loss=0.2007, simple_loss=0.2911, pruned_loss=0.05516, over 7152.00 frames.], tot_loss[loss=0.1994, simple_loss=0.2914, pruned_loss=0.05368, over 1223362.47 frames.], batch size: 26, lr: 7.71e-04
2022-04-28 22:14:32,125 INFO [train.py:763] (1/8) Epoch 9, batch 450, loss[loss=0.1829, simple_loss=0.2827, pruned_loss=0.04152, over 6486.00 frames.], tot_loss[loss=0.1996, simple_loss=0.292, pruned_loss=0.05362, over 1261843.83 frames.], batch size: 38, lr: 7.71e-04
2022-04-28 22:15:37,755 INFO [train.py:763] (1/8) Epoch 9, batch 500, loss[loss=0.1873, simple_loss=0.2871, pruned_loss=0.04376, over 7147.00 frames.], tot_loss[loss=0.1995, simple_loss=0.2919, pruned_loss=0.05357, over 1296939.60 frames.], batch size: 19, lr: 7.70e-04
2022-04-28 22:16:43,392 INFO [train.py:763] (1/8) Epoch 9, batch 550, loss[loss=0.1487, simple_loss=0.2403, pruned_loss=0.02859, over 7132.00 frames.], tot_loss[loss=0.198, simple_loss=0.2905, pruned_loss=0.05277, over 1324969.96 frames.], batch size: 17, lr: 7.70e-04
2022-04-28 22:17:49,458 INFO [train.py:763] (1/8) Epoch 9, batch 600, loss[loss=0.1678, simple_loss=0.2628, pruned_loss=0.03641, over 7276.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2899, pruned_loss=0.0528, over 1346603.28 frames.], batch size: 18, lr: 7.69e-04
2022-04-28 22:18:54,912 INFO [train.py:763] (1/8) Epoch 9, batch 650, loss[loss=0.2156, simple_loss=0.301, pruned_loss=0.06506, over 7172.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2904, pruned_loss=0.05295, over 1363079.74 frames.], batch size: 26, lr: 7.69e-04
2022-04-28 22:20:00,484 INFO [train.py:763] (1/8) Epoch 9, batch 700, loss[loss=0.2004, simple_loss=0.2907, pruned_loss=0.05508, over 7312.00 frames.], tot_loss[loss=0.198, simple_loss=0.29, pruned_loss=0.05301, over 1376779.29 frames.], batch size: 25, lr: 7.68e-04
2022-04-28 22:21:06,841 INFO [train.py:763] (1/8) Epoch 9, batch 750, loss[loss=0.1783, simple_loss=0.2801, pruned_loss=0.03825, over 7431.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2905, pruned_loss=0.05347, over 1386986.97 frames.], batch size: 20, lr: 7.68e-04
2022-04-28 22:22:12,198 INFO [train.py:763] (1/8) Epoch 9, batch 800, loss[loss=0.2247, simple_loss=0.3213, pruned_loss=0.06407, over 7289.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2902, pruned_loss=0.05335, over 1393538.42 frames.], batch size: 24, lr: 7.67e-04
2022-04-28 22:23:17,412 INFO [train.py:763] (1/8) Epoch 9, batch 850, loss[loss=0.2233, simple_loss=0.3111, pruned_loss=0.06778, over 6387.00 frames.], tot_loss[loss=0.199, simple_loss=0.291, pruned_loss=0.05348, over 1396488.98 frames.], batch size: 37, lr: 7.67e-04
2022-04-28 22:24:22,755 INFO [train.py:763] (1/8) Epoch 9, batch 900, loss[loss=0.1947, simple_loss=0.2911, pruned_loss=0.04917, over 7315.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2911, pruned_loss=0.05353, over 1406505.20 frames.], batch size: 21, lr: 7.66e-04
2022-04-28 22:25:27,959 INFO [train.py:763] (1/8) Epoch 9, batch 950, loss[loss=0.2377, simple_loss=0.3089, pruned_loss=0.08325, over 7147.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2924, pruned_loss=0.05423, over 1406402.28 frames.], batch size: 26, lr: 7.66e-04
2022-04-28 22:26:33,998 INFO [train.py:763] (1/8) Epoch 9, batch 1000, loss[loss=0.2252, simple_loss=0.3229, pruned_loss=0.06375, over 7335.00 frames.], tot_loss[loss=0.2003, simple_loss=0.292, pruned_loss=0.05426, over 1414155.94 frames.], batch size: 20, lr: 7.66e-04
2022-04-28 22:27:40,362 INFO [train.py:763] (1/8) Epoch 9, batch 1050, loss[loss=0.2284, simple_loss=0.3184, pruned_loss=0.06919, over 7000.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2917, pruned_loss=0.05428, over 1416333.00 frames.], batch size: 28, lr: 7.65e-04
2022-04-28 22:28:46,001 INFO [train.py:763] (1/8) Epoch 9, batch 1100, loss[loss=0.212, simple_loss=0.309, pruned_loss=0.05753, over 7044.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2929, pruned_loss=0.05484, over 1416851.69 frames.], batch size: 28, lr: 7.65e-04
2022-04-28 22:29:52,329 INFO [train.py:763] (1/8) Epoch 9, batch 1150, loss[loss=0.1766, simple_loss=0.276, pruned_loss=0.03858, over 7330.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2932, pruned_loss=0.05472, over 1421310.68 frames.], batch size: 20, lr: 7.64e-04
2022-04-28 22:30:57,644 INFO [train.py:763] (1/8) Epoch 9, batch 1200, loss[loss=0.2083, simple_loss=0.2925, pruned_loss=0.06199, over 7185.00 frames.], tot_loss[loss=0.2013, simple_loss=0.2933, pruned_loss=0.05464, over 1420442.43 frames.], batch size: 23, lr: 7.64e-04
2022-04-28 22:32:04,403 INFO [train.py:763] (1/8) Epoch 9, batch 1250, loss[loss=0.1741, simple_loss=0.2596, pruned_loss=0.04429, over 7297.00 frames.], tot_loss[loss=0.2018, simple_loss=0.2936, pruned_loss=0.05505, over 1419098.11 frames.], batch size: 17, lr: 7.63e-04
2022-04-28 22:33:11,155 INFO [train.py:763] (1/8) Epoch 9, batch 1300, loss[loss=0.1698, simple_loss=0.2587, pruned_loss=0.04047, over 6998.00 frames.], tot_loss[loss=0.2002, simple_loss=0.2918, pruned_loss=0.05427, over 1416518.58 frames.], batch size: 16, lr: 7.63e-04
2022-04-28 22:34:16,571 INFO [train.py:763] (1/8) Epoch 9, batch 1350, loss[loss=0.2003, simple_loss=0.296, pruned_loss=0.05228, over 7323.00 frames.], tot_loss[loss=0.2, simple_loss=0.2916, pruned_loss=0.05423, over 1415134.12 frames.], batch size: 21, lr: 7.62e-04
2022-04-28 22:35:21,675 INFO [train.py:763] (1/8) Epoch 9, batch 1400, loss[loss=0.1881, simple_loss=0.2847, pruned_loss=0.04577, over 7118.00 frames.], tot_loss[loss=0.2008, simple_loss=0.2931, pruned_loss=0.05428, over 1418110.47 frames.], batch size: 21, lr: 7.62e-04
2022-04-28 22:36:27,459 INFO [train.py:763] (1/8) Epoch 9, batch 1450, loss[loss=0.1902, simple_loss=0.2769, pruned_loss=0.05169, over 7289.00 frames.], tot_loss[loss=0.2003, simple_loss=0.2925, pruned_loss=0.05405, over 1418894.23 frames.], batch size: 25, lr: 7.62e-04
2022-04-28 22:37:33,360 INFO [train.py:763] (1/8) Epoch 9, batch 1500, loss[loss=0.2143, simple_loss=0.3105, pruned_loss=0.05907, over 5010.00 frames.], tot_loss[loss=0.1995, simple_loss=0.292, pruned_loss=0.05352, over 1415732.14 frames.], batch size: 54, lr: 7.61e-04
2022-04-28 22:38:38,708 INFO [train.py:763] (1/8) Epoch 9, batch 1550, loss[loss=0.1837, simple_loss=0.2811, pruned_loss=0.04311, over 7364.00 frames.], tot_loss[loss=0.1987, simple_loss=0.2913, pruned_loss=0.05308, over 1419306.05 frames.], batch size: 19, lr: 7.61e-04
2022-04-28 22:39:43,989 INFO [train.py:763] (1/8) Epoch 9, batch 1600, loss[loss=0.2105, simple_loss=0.2972, pruned_loss=0.06193, over 7252.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2904, pruned_loss=0.053, over 1417782.47 frames.], batch size: 19, lr: 7.60e-04
2022-04-28 22:40:50,097 INFO [train.py:763] (1/8) Epoch 9, batch 1650, loss[loss=0.199, simple_loss=0.2995, pruned_loss=0.0493, over 7417.00 frames.], tot_loss[loss=0.1977, simple_loss=0.2899, pruned_loss=0.05273, over 1415822.58 frames.], batch size: 21, lr: 7.60e-04
2022-04-28 22:41:56,341 INFO [train.py:763] (1/8) Epoch 9, batch 1700, loss[loss=0.2302, simple_loss=0.3175, pruned_loss=0.07143, over 7299.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2903, pruned_loss=0.05298, over 1413774.90 frames.], batch size: 24, lr: 7.59e-04
2022-04-28 22:43:01,518 INFO [train.py:763] (1/8) Epoch 9, batch 1750, loss[loss=0.1835, simple_loss=0.2694, pruned_loss=0.04878, over 7183.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2907, pruned_loss=0.05345, over 1406329.28 frames.], batch size: 16, lr: 7.59e-04
2022-04-28 22:44:07,086 INFO [train.py:763] (1/8) Epoch 9, batch 1800, loss[loss=0.21, simple_loss=0.2876, pruned_loss=0.06618, over 7367.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2902, pruned_loss=0.05319, over 1410426.19 frames.], batch size: 19, lr: 7.59e-04
2022-04-28 22:45:14,102 INFO [train.py:763] (1/8) Epoch 9, batch 1850, loss[loss=0.1913, simple_loss=0.2776, pruned_loss=0.05248, over 7350.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2911, pruned_loss=0.05369, over 1411577.84 frames.], batch size: 19, lr: 7.58e-04
2022-04-28 22:46:21,653 INFO [train.py:763] (1/8) Epoch 9, batch 1900, loss[loss=0.1721, simple_loss=0.2614, pruned_loss=0.04137, over 7281.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2897, pruned_loss=0.05325, over 1415695.83 frames.], batch size: 18, lr: 7.58e-04
2022-04-28 22:47:28,652 INFO [train.py:763] (1/8) Epoch 9, batch 1950, loss[loss=0.1953, simple_loss=0.2909, pruned_loss=0.04992, over 7206.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2896, pruned_loss=0.05334, over 1415753.22 frames.], batch size: 23, lr: 7.57e-04
2022-04-28 22:48:34,054 INFO [train.py:763] (1/8) Epoch 9, batch 2000, loss[loss=0.2034, simple_loss=0.3034, pruned_loss=0.0517, over 7236.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2893, pruned_loss=0.05316, over 1418092.56 frames.], batch size: 20, lr: 7.57e-04
2022-04-28 22:49:39,699 INFO [train.py:763] (1/8) Epoch 9, batch 2050, loss[loss=0.1894, simple_loss=0.2841, pruned_loss=0.04737, over 7191.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2898, pruned_loss=0.05322, over 1419899.18 frames.], batch size: 23, lr: 7.56e-04
2022-04-28 22:50:45,163 INFO [train.py:763] (1/8) Epoch 9, batch 2100, loss[loss=0.1782, simple_loss=0.2746, pruned_loss=0.04085, over 7143.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2894, pruned_loss=0.0531, over 1424514.65 frames.], batch size: 20, lr: 7.56e-04
2022-04-28 22:51:50,836 INFO [train.py:763] (1/8) Epoch 9, batch 2150, loss[loss=0.1672, simple_loss=0.2516, pruned_loss=0.04142, over 7403.00 frames.], tot_loss[loss=0.1981, simple_loss=0.2889, pruned_loss=0.05361, over 1426616.25 frames.], batch size: 18, lr: 7.56e-04
2022-04-28 22:52:56,057 INFO [train.py:763] (1/8) Epoch 9, batch 2200, loss[loss=0.1914, simple_loss=0.287, pruned_loss=0.04788, over 6407.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2894, pruned_loss=0.05361, over 1425885.97 frames.], batch size: 37, lr: 7.55e-04
2022-04-28 22:54:01,586 INFO [train.py:763] (1/8) Epoch 9, batch 2250, loss[loss=0.1709, simple_loss=0.2705, pruned_loss=0.03567, over 7325.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2891, pruned_loss=0.05304, over 1428109.72 frames.], batch size: 21, lr: 7.55e-04
2022-04-28 22:55:07,218 INFO [train.py:763] (1/8) Epoch 9, batch 2300, loss[loss=0.1811, simple_loss=0.2804, pruned_loss=0.04092, over 7145.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2894, pruned_loss=0.05296, over 1426417.37 frames.], batch size: 20, lr: 7.54e-04
2022-04-28 22:56:13,146 INFO [train.py:763] (1/8) Epoch 9, batch 2350, loss[loss=0.2398, simple_loss=0.3209, pruned_loss=0.07939, over 7211.00 frames.], tot_loss[loss=0.1974, simple_loss=0.2889, pruned_loss=0.05292, over 1426001.69 frames.], batch size: 22, lr: 7.54e-04
2022-04-28 22:57:18,359 INFO [train.py:763] (1/8) Epoch 9, batch 2400, loss[loss=0.1777, simple_loss=0.2676, pruned_loss=0.04395, over 7280.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2885, pruned_loss=0.05246, over 1427767.88 frames.], batch size: 18, lr: 7.53e-04
2022-04-28 22:58:24,894 INFO [train.py:763] (1/8) Epoch 9, batch 2450, loss[loss=0.1922, simple_loss=0.2787, pruned_loss=0.05283, over 7069.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2881, pruned_loss=0.05192, over 1430312.95 frames.], batch size: 18, lr: 7.53e-04
2022-04-28 22:59:30,587 INFO [train.py:763] (1/8) Epoch 9, batch 2500, loss[loss=0.2064, simple_loss=0.299, pruned_loss=0.05695, over 7317.00 frames.], tot_loss[loss=0.1957, simple_loss=0.2875, pruned_loss=0.05193, over 1428477.32 frames.], batch size: 21, lr: 7.53e-04
2022-04-28 23:00:35,848 INFO [train.py:763] (1/8) Epoch 9, batch 2550, loss[loss=0.1948, simple_loss=0.2933, pruned_loss=0.04813, over 7232.00 frames.], tot_loss[loss=0.1968, simple_loss=0.2884, pruned_loss=0.0526, over 1425843.38 frames.], batch size: 21, lr: 7.52e-04
2022-04-28 23:01:42,060 INFO [train.py:763] (1/8) Epoch 9, batch 2600, loss[loss=0.2143, simple_loss=0.2952, pruned_loss=0.06673, over 7181.00 frames.], tot_loss[loss=0.1977, simple_loss=0.289, pruned_loss=0.05321, over 1429045.57 frames.], batch size: 26, lr: 7.52e-04
2022-04-28 23:02:47,159 INFO [train.py:763] (1/8) Epoch 9, batch 2650, loss[loss=0.2153, simple_loss=0.3012, pruned_loss=0.06467, over 7334.00 frames.], tot_loss[loss=0.1982, simple_loss=0.2897, pruned_loss=0.05332, over 1425195.77 frames.], batch size: 22, lr: 7.51e-04
2022-04-28 23:03:53,444 INFO [train.py:763] (1/8) Epoch 9, batch 2700, loss[loss=0.1811, simple_loss=0.2813, pruned_loss=0.04047, over 6804.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2885, pruned_loss=0.0526, over 1425250.27 frames.], batch size: 31, lr: 7.51e-04
2022-04-28 23:04:58,883 INFO [train.py:763] (1/8) Epoch 9, batch 2750, loss[loss=0.2022, simple_loss=0.2916, pruned_loss=0.05645, over 6797.00 frames.], tot_loss[loss=0.1962, simple_loss=0.288, pruned_loss=0.05224, over 1423334.79 frames.], batch size: 31, lr: 7.50e-04
2022-04-28 23:06:04,504 INFO [train.py:763] (1/8) Epoch 9, batch 2800, loss[loss=0.2288, simple_loss=0.3154, pruned_loss=0.07113, over 7369.00 frames.], tot_loss[loss=0.1958, simple_loss=0.2876, pruned_loss=0.05204, over 1428408.38 frames.], batch size: 23, lr: 7.50e-04
2022-04-28 23:07:09,870 INFO [train.py:763] (1/8) Epoch 9, batch 2850, loss[loss=0.2286, simple_loss=0.3199, pruned_loss=0.06871, over 7325.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2885, pruned_loss=0.0525, over 1426310.42 frames.], batch size: 22, lr: 7.50e-04
2022-04-28 23:08:15,554 INFO [train.py:763] (1/8) Epoch 9, batch 2900, loss[loss=0.1908, simple_loss=0.2765, pruned_loss=0.05253, over 7112.00 frames.], tot_loss[loss=0.197, simple_loss=0.2884, pruned_loss=0.05283, over 1425372.60 frames.], batch size: 21, lr: 7.49e-04
2022-04-28 23:09:22,015 INFO [train.py:763] (1/8) Epoch 9, batch 2950, loss[loss=0.1655, simple_loss=0.2458, pruned_loss=0.04261, over 7267.00 frames.], tot_loss[loss=0.1968, simple_loss=0.288, pruned_loss=0.05278, over 1425984.50 frames.], batch size: 18, lr: 7.49e-04
2022-04-28 23:10:28,990 INFO [train.py:763] (1/8) Epoch 9, batch 3000, loss[loss=0.1757, simple_loss=0.2584, pruned_loss=0.04648, over 7274.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2877, pruned_loss=0.05259, over 1426455.15 frames.], batch size: 17, lr: 7.48e-04
2022-04-28 23:10:28,991 INFO [train.py:783] (1/8) Computing validation loss
2022-04-28 23:10:44,551 INFO [train.py:792] (1/8) Epoch 9, validation: loss=0.1713, simple_loss=0.276, pruned_loss=0.03324, over 698248.00 frames.
2022-04-28 23:11:50,376 INFO [train.py:763] (1/8) Epoch 9, batch 3050, loss[loss=0.2052, simple_loss=0.3009, pruned_loss=0.05471, over 7152.00 frames.], tot_loss[loss=0.1968, simple_loss=0.288, pruned_loss=0.05283, over 1426357.61 frames.], batch size: 19, lr: 7.48e-04
2022-04-28 23:12:55,854 INFO [train.py:763] (1/8) Epoch 9, batch 3100, loss[loss=0.1798, simple_loss=0.2746, pruned_loss=0.04247, over 7126.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2882, pruned_loss=0.05217, over 1429038.53 frames.], batch size: 21, lr: 7.47e-04
2022-04-28 23:14:01,347 INFO [train.py:763] (1/8) Epoch 9, batch 3150, loss[loss=0.2038, simple_loss=0.3028, pruned_loss=0.05241, over 7330.00 frames.], tot_loss[loss=0.1956, simple_loss=0.2881, pruned_loss=0.05158, over 1424870.73 frames.], batch size: 21, lr: 7.47e-04
2022-04-28 23:15:07,616 INFO [train.py:763] (1/8) Epoch 9, batch 3200, loss[loss=0.2384, simple_loss=0.3191, pruned_loss=0.07882, over 7233.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2871, pruned_loss=0.05109, over 1425589.65 frames.], batch size: 20, lr: 7.47e-04
2022-04-28 23:16:13,885 INFO [train.py:763] (1/8) Epoch 9, batch 3250, loss[loss=0.1812, simple_loss=0.2835, pruned_loss=0.03946, over 7420.00 frames.], tot_loss[loss=0.196, simple_loss=0.2888, pruned_loss=0.05162, over 1427533.82 frames.], batch size: 21, lr: 7.46e-04
2022-04-28 23:17:19,390 INFO [train.py:763] (1/8) Epoch 9, batch 3300, loss[loss=0.1784, simple_loss=0.2796, pruned_loss=0.03854, over 7204.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2891, pruned_loss=0.05213, over 1428445.46 frames.], batch size: 22, lr: 7.46e-04
2022-04-28 23:18:25,153 INFO [train.py:763] (1/8) Epoch 9, batch 3350, loss[loss=0.216, simple_loss=0.2991, pruned_loss=0.06644, over 7194.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2897, pruned_loss=0.05207, over 1430264.48 frames.], batch size: 23, lr: 7.45e-04
2022-04-28 23:19:31,229 INFO [train.py:763] (1/8) Epoch 9, batch 3400, loss[loss=0.1492, simple_loss=0.2325, pruned_loss=0.03295, over 7269.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2896, pruned_loss=0.05208, over 1425899.61 frames.], batch size: 17, lr: 7.45e-04
2022-04-28 23:20:36,539 INFO [train.py:763] (1/8) Epoch 9, batch 3450, loss[loss=0.1884, simple_loss=0.2844, pruned_loss=0.04617, over 7280.00 frames.], tot_loss[loss=0.1972, simple_loss=0.2896, pruned_loss=0.05238, over 1424932.04 frames.], batch size: 24, lr: 7.45e-04
2022-04-28 23:21:42,130 INFO [train.py:763] (1/8) Epoch 9, batch 3500, loss[loss=0.2059, simple_loss=0.3027, pruned_loss=0.05455, over 7422.00 frames.], tot_loss[loss=0.1978, simple_loss=0.2903, pruned_loss=0.05265, over 1424706.66 frames.], batch size: 21, lr: 7.44e-04
2022-04-28 23:22:49,853 INFO [train.py:763] (1/8) Epoch 9, batch 3550, loss[loss=0.1946, simple_loss=0.2884, pruned_loss=0.05036, over 7011.00 frames.], tot_loss[loss=0.1965, simple_loss=0.2889, pruned_loss=0.05207, over 1427424.61 frames.], batch size: 28, lr: 7.44e-04
2022-04-28 23:23:55,515 INFO [train.py:763] (1/8) Epoch 9, batch 3600, loss[loss=0.2074, simple_loss=0.3054, pruned_loss=0.05475, over 7024.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2891, pruned_loss=0.05233, over 1427248.21 frames.], batch size: 28, lr: 7.43e-04
2022-04-28 23:25:02,071 INFO [train.py:763] (1/8) Epoch 9, batch 3650, loss[loss=0.1777, simple_loss=0.2737, pruned_loss=0.04087, over 7071.00 frames.], tot_loss[loss=0.1967, simple_loss=0.2889, pruned_loss=0.05227, over 1423389.92 frames.], batch size: 18, lr: 7.43e-04
2022-04-28 23:26:07,306 INFO [train.py:763] (1/8) Epoch 9, batch 3700, loss[loss=0.1867, simple_loss=0.2758, pruned_loss=0.04876, over 7277.00 frames.], tot_loss[loss=0.1969, simple_loss=0.2893, pruned_loss=0.0522, over 1425411.95 frames.], batch size: 17, lr: 7.43e-04
2022-04-28 23:27:12,604 INFO [train.py:763] (1/8) Epoch 9, batch 3750, loss[loss=0.2011, simple_loss=0.2847, pruned_loss=0.05875, over 7162.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2907, pruned_loss=0.05307, over 1428188.17 frames.], batch size: 19, lr: 7.42e-04
2022-04-28 23:28:17,823 INFO [train.py:763] (1/8) Epoch 9, batch 3800, loss[loss=0.1816, simple_loss=0.2781, pruned_loss=0.0426, over 7416.00 frames.], tot_loss[loss=0.1985, simple_loss=0.2907, pruned_loss=0.0531, over 1426305.49 frames.], batch size: 20, lr: 7.42e-04
2022-04-28 23:29:23,009 INFO [train.py:763] (1/8) Epoch 9, batch 3850, loss[loss=0.219, simple_loss=0.3068, pruned_loss=0.0656, over 7071.00 frames.], tot_loss[loss=0.2001, simple_loss=0.2922, pruned_loss=0.05403, over 1425384.11 frames.], batch size: 18, lr: 7.41e-04
2022-04-28 23:30:28,552 INFO [train.py:763] (1/8) Epoch 9, batch 3900, loss[loss=0.2168, simple_loss=0.3115, pruned_loss=0.06111, over 7162.00 frames.], tot_loss[loss=0.1991, simple_loss=0.2912, pruned_loss=0.05354, over 1427363.19 frames.], batch size: 19, lr: 7.41e-04
2022-04-28 23:31:35,176 INFO [train.py:763] (1/8) Epoch 9, batch 3950, loss[loss=0.2738, simple_loss=0.357, pruned_loss=0.09535, over 5113.00 frames.], tot_loss[loss=0.1983, simple_loss=0.2903, pruned_loss=0.05312, over 1422327.60 frames.], batch size: 52, lr: 7.41e-04
2022-04-28 23:32:42,030 INFO [train.py:763] (1/8) Epoch 9, batch 4000, loss[loss=0.2284, simple_loss=0.3022, pruned_loss=0.07731, over 7263.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2908, pruned_loss=0.0535, over 1423824.39 frames.], batch size: 19, lr: 7.40e-04
2022-04-28 23:33:47,287 INFO [train.py:763] (1/8) Epoch 9, batch 4050, loss[loss=0.1811, simple_loss=0.2648, pruned_loss=0.04868, over 7132.00 frames.], tot_loss[loss=0.1988, simple_loss=0.2906, pruned_loss=0.0535, over 1424478.83 frames.], batch size: 17, lr: 7.40e-04
2022-04-28 23:34:53,526 INFO [train.py:763] (1/8) Epoch 9, batch 4100, loss[loss=0.2023, simple_loss=0.3003, pruned_loss=0.05217, over 7329.00 frames.], tot_loss[loss=0.1976, simple_loss=0.2898, pruned_loss=0.05272, over 1425992.70 frames.], batch size: 21, lr: 7.39e-04
2022-04-28 23:35:59,479 INFO [train.py:763] (1/8) Epoch 9, batch 4150, loss[loss=0.1758, simple_loss=0.2591, pruned_loss=0.04623, over 7402.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2894, pruned_loss=0.05238, over 1425711.49 frames.], batch size: 18, lr: 7.39e-04
2022-04-28 23:37:04,698 INFO [train.py:763] (1/8) Epoch 9, batch 4200, loss[loss=0.197, simple_loss=0.2988, pruned_loss=0.04764, over 7283.00 frames.], tot_loss[loss=0.1971, simple_loss=0.2896, pruned_loss=0.05232, over 1427359.92 frames.], batch size: 24, lr: 7.39e-04
2022-04-28 23:38:10,555 INFO [train.py:763] (1/8) Epoch 9, batch 4250, loss[loss=0.1697, simple_loss=0.2598, pruned_loss=0.03985, over 7279.00 frames.], tot_loss[loss=0.1976, simple_loss=0.29, pruned_loss=0.05259, over 1423973.58 frames.], batch size: 17, lr: 7.38e-04
2022-04-28 23:39:16,463 INFO [train.py:763] (1/8) Epoch 9, batch 4300, loss[loss=0.2318, simple_loss=0.3294, pruned_loss=0.06707, over 7295.00 frames.], tot_loss[loss=0.197, simple_loss=0.2897, pruned_loss=0.05213, over 1418534.02 frames.], batch size: 24, lr: 7.38e-04
2022-04-28 23:40:22,451 INFO [train.py:763] (1/8) Epoch 9, batch 4350, loss[loss=0.2414, simple_loss=0.3169, pruned_loss=0.08301, over 4985.00 frames.], tot_loss[loss=0.1979, simple_loss=0.2908, pruned_loss=0.05252, over 1408603.35 frames.], batch size: 52, lr: 7.37e-04
2022-04-28 23:41:28,472 INFO [train.py:763] (1/8) Epoch 9, batch 4400, loss[loss=0.2016, simple_loss=0.2965, pruned_loss=0.05335, over 7196.00 frames.], tot_loss[loss=0.1984, simple_loss=0.2913, pruned_loss=0.05275, over 1412189.21 frames.], batch size: 22, lr: 7.37e-04
2022-04-28 23:42:35,221 INFO [train.py:763] (1/8) Epoch 9, batch 4450, loss[loss=0.2115, simple_loss=0.2993, pruned_loss=0.06187, over 5216.00 frames.], tot_loss[loss=0.2004, simple_loss=0.2926, pruned_loss=0.05413, over 1396309.25 frames.], batch size: 53, lr: 7.37e-04
2022-04-28 23:43:41,392 INFO [train.py:763] (1/8) Epoch 9, batch 4500, loss[loss=0.2171, simple_loss=0.3025, pruned_loss=0.06579, over 7143.00 frames.], tot_loss[loss=0.1993, simple_loss=0.2913, pruned_loss=0.05371, over 1393236.96 frames.], batch size: 20, lr: 7.36e-04
2022-04-28 23:44:47,990 INFO [train.py:763] (1/8) Epoch 9, batch 4550, loss[loss=0.2455, simple_loss=0.3284, pruned_loss=0.08129, over 7159.00 frames.], tot_loss[loss=0.1999, simple_loss=0.291, pruned_loss=0.05441, over 1374702.96 frames.], batch size: 26, lr: 7.36e-04
2022-04-28 23:46:26,279 INFO [train.py:763] (1/8) Epoch 10, batch 0, loss[loss=0.2375, simple_loss=0.3201, pruned_loss=0.07748, over 7437.00 frames.], tot_loss[loss=0.2375, simple_loss=0.3201, pruned_loss=0.07748, over 7437.00 frames.], batch size: 20, lr: 7.08e-04
2022-04-28 23:47:32,324 INFO [train.py:763] (1/8) Epoch 10, batch 50, loss[loss=0.1837, simple_loss=0.2962, pruned_loss=0.03561, over 7430.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2928, pruned_loss=0.04979, over 322684.91 frames.], batch size: 20, lr: 7.08e-04
2022-04-28 23:48:38,908 INFO [train.py:763] (1/8) Epoch 10, batch 100, loss[loss=0.1638, simple_loss=0.2549, pruned_loss=0.0363, over 7284.00 frames.], tot_loss[loss=0.1957, simple_loss=0.29, pruned_loss=0.05073, over 567291.56 frames.], batch size: 18, lr: 7.08e-04
2022-04-28 23:49:55,133 INFO [train.py:763] (1/8) Epoch 10, batch 150, loss[loss=0.1651, simple_loss=0.2553, pruned_loss=0.03742, over 7224.00 frames.], tot_loss[loss=0.1964, simple_loss=0.2908, pruned_loss=0.05093, over 760372.89 frames.], batch size: 16, lr: 7.07e-04
2022-04-28 23:51:18,546 INFO [train.py:763] (1/8) Epoch 10, batch 200, loss[loss=0.1674, simple_loss=0.2549, pruned_loss=0.03996, over 7415.00 frames.], tot_loss[loss=0.1953, simple_loss=0.2897, pruned_loss=0.05049, over 908053.91 frames.], batch size: 18, lr: 7.07e-04
2022-04-28 23:52:32,863 INFO [train.py:763] (1/8) Epoch 10, batch 250, loss[loss=0.2418, simple_loss=0.3322, pruned_loss=0.07574, over 6350.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2885, pruned_loss=0.0505, over 1023558.26 frames.], batch size: 38, lr: 7.06e-04
2022-04-28 23:53:48,225 INFO [train.py:763] (1/8) Epoch 10, batch 300, loss[loss=0.2381, simple_loss=0.3161, pruned_loss=0.08007, over 5099.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2866, pruned_loss=0.04922, over 1114980.67 frames.], batch size: 52, lr: 7.06e-04
2022-04-28 23:54:53,617 INFO [train.py:763] (1/8) Epoch 10, batch 350, loss[loss=0.2449, simple_loss=0.3346, pruned_loss=0.07757, over 6785.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2862, pruned_loss=0.04927, over 1187528.34 frames.], batch size: 31, lr: 7.06e-04
2022-04-28 23:56:17,500 INFO [train.py:763] (1/8) Epoch 10, batch 400, loss[loss=0.1985, simple_loss=0.2874, pruned_loss=0.05484, over 7427.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2863, pruned_loss=0.04935, over 1241895.73 frames.], batch size: 20, lr: 7.05e-04
2022-04-28 23:57:23,252 INFO [train.py:763] (1/8) Epoch 10, batch 450, loss[loss=0.1961, simple_loss=0.2945, pruned_loss=0.0489, over 7234.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2846, pruned_loss=0.04911, over 1282231.65 frames.], batch size: 20, lr: 7.05e-04
2022-04-28 23:58:37,637 INFO [train.py:763] (1/8) Epoch 10, batch 500, loss[loss=0.168, simple_loss=0.254, pruned_loss=0.04102, over 7330.00 frames.], tot_loss[loss=0.191, simple_loss=0.2841, pruned_loss=0.04896, over 1317313.90 frames.], batch size: 20, lr: 7.04e-04
2022-04-28 23:59:42,723 INFO [train.py:763] (1/8) Epoch 10, batch 550, loss[loss=0.1857, simple_loss=0.2703, pruned_loss=0.05056, over 7060.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2843, pruned_loss=0.04879, over 1342120.81 frames.], batch size: 18, lr: 7.04e-04
2022-04-29 00:00:47,811 INFO [train.py:763] (1/8) Epoch 10, batch 600, loss[loss=0.1915, simple_loss=0.2683, pruned_loss=0.05734, over 6989.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2847, pruned_loss=0.04926, over 1360944.84 frames.], batch size: 16, lr: 7.04e-04
2022-04-29 00:01:53,006 INFO [train.py:763] (1/8) Epoch 10, batch 650, loss[loss=0.1679, simple_loss=0.2587, pruned_loss=0.03858, over 7124.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2852, pruned_loss=0.04974, over 1366548.67 frames.], batch size: 17, lr: 7.03e-04
2022-04-29 00:02:58,042 INFO [train.py:763] (1/8) Epoch 10, batch 700, loss[loss=0.167, simple_loss=0.2564, pruned_loss=0.03882, over 7253.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2864, pruned_loss=0.05008, over 1377060.44 frames.], batch size: 16, lr: 7.03e-04
2022-04-29 00:04:03,193 INFO [train.py:763] (1/8) Epoch 10, batch 750, loss[loss=0.2142, simple_loss=0.3176, pruned_loss=0.05541, over 7148.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2866, pruned_loss=0.04996, over 1384164.64 frames.], batch size: 20, lr: 7.03e-04
2022-04-29 00:05:08,472 INFO [train.py:763] (1/8) Epoch 10, batch 800, loss[loss=0.2101, simple_loss=0.3046, pruned_loss=0.05782, over 7164.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2868, pruned_loss=0.05014, over 1396176.86 frames.], batch size: 26, lr: 7.02e-04
2022-04-29 00:06:13,824 INFO [train.py:763] (1/8) Epoch 10, batch 850, loss[loss=0.1948, simple_loss=0.2925, pruned_loss=0.0485, over 7334.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2865, pruned_loss=0.05033, over 1400631.09 frames.], batch size: 20, lr: 7.02e-04
2022-04-29 00:07:19,242 INFO [train.py:763] (1/8) Epoch 10, batch 900, loss[loss=0.1598, simple_loss=0.2575, pruned_loss=0.03106, over 7429.00 frames.], tot_loss[loss=0.1942, simple_loss=0.2872, pruned_loss=0.05059, over 1408880.40 frames.], batch size: 20, lr: 7.02e-04
2022-04-29 00:08:24,540 INFO [train.py:763] (1/8) Epoch 10, batch 950, loss[loss=0.1782, simple_loss=0.2632, pruned_loss=0.0466, over 6998.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2869, pruned_loss=0.05025, over 1410634.63 frames.], batch size: 16, lr: 7.01e-04
2022-04-29 00:09:29,919 INFO [train.py:763] (1/8) Epoch 10, batch 1000, loss[loss=0.2539, simple_loss=0.337, pruned_loss=0.08541, over 7276.00 frames.], tot_loss[loss=0.194, simple_loss=0.2865, pruned_loss=0.05071, over 1414453.04 frames.], batch size: 25, lr: 7.01e-04
2022-04-29 00:10:35,517 INFO [train.py:763] (1/8) Epoch 10, batch 1050, loss[loss=0.1637, simple_loss=0.2585, pruned_loss=0.03448, over 7250.00 frames.], tot_loss[loss=0.195, simple_loss=0.2878, pruned_loss=0.05111, over 1409143.02 frames.], batch size: 19, lr: 7.00e-04
2022-04-29 00:11:41,109 INFO [train.py:763] (1/8) Epoch 10, batch 1100, loss[loss=0.1969, simple_loss=0.2745, pruned_loss=0.05962, over 7170.00 frames.], tot_loss[loss=0.1948, simple_loss=0.2877, pruned_loss=0.05094, over 1413797.95 frames.], batch size: 18, lr: 7.00e-04
2022-04-29 00:12:46,585 INFO [train.py:763] (1/8) Epoch 10, batch 1150, loss[loss=0.2069, simple_loss=0.2927, pruned_loss=0.06054, over 7061.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2865, pruned_loss=0.05067, over 1417907.19 frames.], batch size: 18, lr: 7.00e-04
2022-04-29 00:13:53,259 INFO [train.py:763] (1/8) Epoch 10, batch 1200, loss[loss=0.1664, simple_loss=0.2475, pruned_loss=0.04261, over 6797.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2861, pruned_loss=0.0509, over 1420574.35 frames.], batch size: 15, lr: 6.99e-04
2022-04-29 00:14:58,979 INFO [train.py:763] (1/8) Epoch 10, batch 1250, loss[loss=0.1536, simple_loss=0.2364, pruned_loss=0.03538, over 7128.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2852, pruned_loss=0.05011, over 1423642.81 frames.], batch size: 17, lr: 6.99e-04
2022-04-29 00:16:04,761 INFO [train.py:763] (1/8) Epoch 10, batch 1300, loss[loss=0.1593, simple_loss=0.2687, pruned_loss=0.025, over 7317.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2848, pruned_loss=0.04956, over 1419646.25 frames.], batch size: 21, lr: 6.99e-04
2022-04-29 00:17:11,807 INFO [train.py:763] (1/8) Epoch 10, batch 1350, loss[loss=0.1987, simple_loss=0.2946, pruned_loss=0.05138, over 7325.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2854, pruned_loss=0.04999, over 1423790.70 frames.], batch size: 21, lr: 6.98e-04
2022-04-29 00:18:18,354 INFO [train.py:763] (1/8) Epoch 10, batch 1400, loss[loss=0.1761, simple_loss=0.2708, pruned_loss=0.04072, over 7170.00 frames.], tot_loss[loss=0.1924, simple_loss=0.2849, pruned_loss=0.04997, over 1426961.12 frames.], batch size: 19, lr: 6.98e-04
2022-04-29 00:19:25,279 INFO [train.py:763] (1/8) Epoch 10, batch 1450, loss[loss=0.1732, simple_loss=0.2652, pruned_loss=0.0406, over 7254.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2851, pruned_loss=0.04967, over 1427345.51 frames.], batch size: 17, lr: 6.97e-04
2022-04-29 00:20:30,750 INFO [train.py:763] (1/8) Epoch 10, batch 1500, loss[loss=0.206, simple_loss=0.3088, pruned_loss=0.05162, over 7030.00 frames.], tot_loss[loss=0.1925, simple_loss=0.2855, pruned_loss=0.04974, over 1425466.06 frames.], batch size: 28, lr: 6.97e-04
2022-04-29 00:21:36,429 INFO [train.py:763] (1/8) Epoch 10, batch 1550, loss[loss=0.1877, simple_loss=0.2764, pruned_loss=0.04947, over 7429.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2861, pruned_loss=0.05011, over 1423829.01 frames.], batch size: 20, lr: 6.97e-04
2022-04-29 00:22:41,606 INFO [train.py:763] (1/8) Epoch 10, batch 1600, loss[loss=0.2062, simple_loss=0.3101, pruned_loss=0.0511, over 6729.00 frames.], tot_loss[loss=0.1934, simple_loss=0.286, pruned_loss=0.05039, over 1418488.50 frames.], batch size: 31, lr: 6.96e-04
2022-04-29 00:23:47,727 INFO [train.py:763] (1/8) Epoch 10, batch 1650, loss[loss=0.165, simple_loss=0.2538, pruned_loss=0.03803, over 6813.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2856, pruned_loss=0.05034, over 1417282.59 frames.], batch size: 15, lr: 6.96e-04
2022-04-29 00:24:52,735 INFO [train.py:763] (1/8) Epoch 10, batch 1700, loss[loss=0.1659, simple_loss=0.2498, pruned_loss=0.04098, over 6801.00 frames.], tot_loss[loss=0.1939, simple_loss=0.2866, pruned_loss=0.05056, over 1416574.51 frames.], batch size: 15, lr: 6.96e-04
2022-04-29 00:25:58,396 INFO [train.py:763] (1/8) Epoch 10, batch 1750, loss[loss=0.1872, simple_loss=0.29, pruned_loss=0.04221, over 7118.00 frames.], tot_loss[loss=0.193, simple_loss=0.2855, pruned_loss=0.05027, over 1413098.49 frames.], batch size: 21, lr: 6.95e-04
2022-04-29 00:27:03,848 INFO [train.py:763] (1/8) Epoch 10, batch 1800, loss[loss=0.2168, simple_loss=0.3046, pruned_loss=0.06449, over 5471.00 frames.], tot_loss[loss=0.193, simple_loss=0.2858, pruned_loss=0.05007, over 1413198.22 frames.], batch size: 53, lr: 6.95e-04
2022-04-29 00:28:10,765 INFO [train.py:763] (1/8) Epoch 10, batch 1850, loss[loss=0.2055, simple_loss=0.3034, pruned_loss=0.05382, over 6494.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2864, pruned_loss=0.05047, over 1417813.48 frames.], batch size: 38, lr: 6.95e-04
2022-04-29 00:29:17,821 INFO [train.py:763] (1/8) Epoch 10, batch 1900, loss[loss=0.2006, simple_loss=0.3036, pruned_loss=0.0488, over 7319.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2864, pruned_loss=0.05038, over 1422037.04 frames.], batch size: 21, lr: 6.94e-04
2022-04-29 00:30:24,812 INFO [train.py:763] (1/8) Epoch 10, batch 1950, loss[loss=0.1973, simple_loss=0.2965, pruned_loss=0.04902, over 7356.00 frames.], tot_loss[loss=0.1935, simple_loss=0.2863, pruned_loss=0.0503, over 1421641.29 frames.], batch size: 19, lr: 6.94e-04
2022-04-29 00:31:31,793 INFO [train.py:763] (1/8) Epoch 10, batch 2000, loss[loss=0.1866, simple_loss=0.2706, pruned_loss=0.05127, over 7169.00 frames.], tot_loss[loss=0.194, simple_loss=0.287, pruned_loss=0.05056, over 1423255.98 frames.], batch size: 18, lr: 6.93e-04
2022-04-29 00:32:38,660 INFO [train.py:763] (1/8) Epoch 10, batch 2050, loss[loss=0.1795, simple_loss=0.2681, pruned_loss=0.04543, over 7279.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2865, pruned_loss=0.05015, over 1424387.86 frames.], batch size: 17, lr: 6.93e-04
2022-04-29 00:33:45,455 INFO [train.py:763] (1/8) Epoch 10, batch 2100, loss[loss=0.2169, simple_loss=0.3026, pruned_loss=0.06563, over 7380.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2866, pruned_loss=0.05025, over 1424761.62 frames.], batch size: 23, lr: 6.93e-04
2022-04-29 00:35:01,070 INFO [train.py:763] (1/8) Epoch 10, batch 2150, loss[loss=0.1945, simple_loss=0.2841, pruned_loss=0.05246, over 7155.00 frames.], tot_loss[loss=0.1928, simple_loss=0.2859, pruned_loss=0.0498, over 1425211.13 frames.], batch size: 18, lr: 6.92e-04
2022-04-29 00:36:06,568 INFO [train.py:763] (1/8) Epoch 10, batch 2200, loss[loss=0.1859, simple_loss=0.2802, pruned_loss=0.0458, over 7238.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2858, pruned_loss=0.04999, over 1423822.84 frames.], batch size: 20, lr: 6.92e-04
2022-04-29 00:37:11,925 INFO [train.py:763] (1/8) Epoch 10, batch 2250, loss[loss=0.1679, simple_loss=0.2784, pruned_loss=0.02874, over 7336.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2867, pruned_loss=0.05035, over 1427064.93 frames.], batch size: 22, lr: 6.92e-04
2022-04-29 00:38:17,409 INFO [train.py:763] (1/8) Epoch 10, batch 2300, loss[loss=0.2012, simple_loss=0.3073, pruned_loss=0.04756, over 7173.00 frames.], tot_loss[loss=0.1943, simple_loss=0.2871, pruned_loss=0.05079, over 1427489.33 frames.], batch size: 26, lr: 6.91e-04
2022-04-29 00:39:22,702 INFO [train.py:763] (1/8) Epoch 10, batch 2350, loss[loss=0.1984, simple_loss=0.2938, pruned_loss=0.05144, over 6957.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2866, pruned_loss=0.05037, over 1429921.52 frames.], batch size: 32, lr: 6.91e-04
2022-04-29 00:40:27,869 INFO [train.py:763] (1/8) Epoch 10, batch 2400, loss[loss=0.211, simple_loss=0.3112, pruned_loss=0.05545, over 7311.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2863, pruned_loss=0.0506, over 1423088.84 frames.], batch size: 21, lr: 6.91e-04
2022-04-29 00:41:33,297 INFO [train.py:763] (1/8) Epoch 10, batch 2450, loss[loss=0.1867, simple_loss=0.2773, pruned_loss=0.04804, over 7006.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2852, pruned_loss=0.05027, over 1423717.82 frames.], batch size: 16, lr: 6.90e-04
2022-04-29 00:42:38,512 INFO [train.py:763] (1/8) Epoch 10, batch 2500, loss[loss=0.204, simple_loss=0.2933, pruned_loss=0.05736, over 7165.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2862, pruned_loss=0.05066, over 1422258.69 frames.], batch size: 19, lr: 6.90e-04
2022-04-29 00:43:44,250 INFO [train.py:763] (1/8) Epoch 10, batch 2550, loss[loss=0.1602, simple_loss=0.2486, pruned_loss=0.03587, over 7177.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2855, pruned_loss=0.04987, over 1426299.16 frames.], batch size: 16, lr: 6.90e-04
2022-04-29 00:44:51,066 INFO [train.py:763] (1/8) Epoch 10, batch 2600, loss[loss=0.2203, simple_loss=0.2968, pruned_loss=0.07187, over 7371.00 frames.], tot_loss[loss=0.192, simple_loss=0.2849, pruned_loss=0.04949, over 1427336.29 frames.], batch size: 23, lr: 6.89e-04
2022-04-29 00:45:56,181 INFO [train.py:763] (1/8) Epoch 10, batch 2650, loss[loss=0.1602, simple_loss=0.2418, pruned_loss=0.03933, over 7014.00 frames.], tot_loss[loss=0.1934, simple_loss=0.286, pruned_loss=0.05041, over 1422570.04 frames.], batch size: 16, lr: 6.89e-04
2022-04-29 00:47:01,611 INFO [train.py:763] (1/8) Epoch 10, batch 2700, loss[loss=0.1987, simple_loss=0.3031, pruned_loss=0.04718, over 7414.00 frames.], tot_loss[loss=0.193, simple_loss=0.2861, pruned_loss=0.04993, over 1425789.13 frames.], batch size: 21, lr: 6.89e-04
2022-04-29 00:48:08,165 INFO [train.py:763] (1/8) Epoch 10, batch 2750, loss[loss=0.1748, simple_loss=0.2638, pruned_loss=0.04288, over 7296.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2845, pruned_loss=0.04937, over 1424143.65 frames.], batch size: 18, lr: 6.88e-04
2022-04-29 00:49:13,512 INFO [train.py:763] (1/8) Epoch 10, batch 2800, loss[loss=0.1906, simple_loss=0.2915, pruned_loss=0.04489, over 7153.00 frames.], tot_loss[loss=0.1919, simple_loss=0.285, pruned_loss=0.04937, over 1423543.99 frames.], batch size: 19, lr: 6.88e-04
2022-04-29 00:50:19,052 INFO [train.py:763] (1/8) Epoch 10, batch 2850, loss[loss=0.2022, simple_loss=0.3054, pruned_loss=0.04947, over 7324.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2854, pruned_loss=0.04957, over 1424660.25 frames.], batch size: 21, lr: 6.87e-04
2022-04-29 00:51:24,555 INFO [train.py:763] (1/8) Epoch 10, batch 2900, loss[loss=0.2054, simple_loss=0.3094, pruned_loss=0.0507, over 7206.00 frames.], tot_loss[loss=0.1926, simple_loss=0.2858, pruned_loss=0.04972, over 1427341.16 frames.], batch size: 23, lr: 6.87e-04
2022-04-29 00:52:30,306 INFO [train.py:763] (1/8) Epoch 10, batch 2950, loss[loss=0.2103, simple_loss=0.3033, pruned_loss=0.05871, over 7194.00 frames.], tot_loss[loss=0.1938, simple_loss=0.287, pruned_loss=0.05028, over 1425187.14 frames.], batch size: 22, lr: 6.87e-04
2022-04-29 00:53:36,007 INFO [train.py:763] (1/8) Epoch 10, batch 3000, loss[loss=0.1657, simple_loss=0.2636, pruned_loss=0.03393, over 7159.00 frames.], tot_loss[loss=0.1937, simple_loss=0.287, pruned_loss=0.05017, over 1423825.66 frames.], batch size: 18, lr: 6.86e-04
2022-04-29 00:53:36,008 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 00:53:51,272 INFO [train.py:792] (1/8) Epoch 10, validation: loss=0.1689, simple_loss=0.2722, pruned_loss=0.03283, over 698248.00 frames.
2022-04-29 00:54:57,773 INFO [train.py:763] (1/8) Epoch 10, batch 3050, loss[loss=0.1846, simple_loss=0.2841, pruned_loss=0.04259, over 7182.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2863, pruned_loss=0.05014, over 1427611.79 frames.], batch size: 26, lr: 6.86e-04
2022-04-29 00:56:03,585 INFO [train.py:763] (1/8) Epoch 10, batch 3100, loss[loss=0.1854, simple_loss=0.2714, pruned_loss=0.04967, over 7411.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2863, pruned_loss=0.05042, over 1425596.91 frames.], batch size: 18, lr: 6.86e-04
2022-04-29 00:57:10,794 INFO [train.py:763] (1/8) Epoch 10, batch 3150, loss[loss=0.1551, simple_loss=0.2493, pruned_loss=0.03047, over 7280.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2857, pruned_loss=0.05005, over 1427891.67 frames.], batch size: 18, lr: 6.85e-04
2022-04-29 00:58:16,971 INFO [train.py:763] (1/8) Epoch 10, batch 3200, loss[loss=0.1601, simple_loss=0.2473, pruned_loss=0.03643, over 7153.00 frames.], tot_loss[loss=0.192, simple_loss=0.2848, pruned_loss=0.04961, over 1429767.30 frames.], batch size: 18, lr: 6.85e-04
2022-04-29 00:59:22,560 INFO [train.py:763] (1/8) Epoch 10, batch 3250, loss[loss=0.1702, simple_loss=0.2687, pruned_loss=0.03586, over 7069.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2848, pruned_loss=0.04927, over 1431401.73 frames.], batch size: 18, lr: 6.85e-04
2022-04-29 01:00:29,376 INFO [train.py:763] (1/8) Epoch 10, batch 3300, loss[loss=0.206, simple_loss=0.2973, pruned_loss=0.05739, over 6583.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2855, pruned_loss=0.05013, over 1431225.66 frames.], batch size: 38, lr: 6.84e-04
2022-04-29 01:01:36,451 INFO [train.py:763] (1/8) Epoch 10, batch 3350, loss[loss=0.1799, simple_loss=0.2867, pruned_loss=0.03656, over 7120.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2863, pruned_loss=0.05088, over 1425322.94 frames.], batch size: 21, lr: 6.84e-04
2022-04-29 01:02:41,921 INFO [train.py:763] (1/8) Epoch 10, batch 3400, loss[loss=0.1743, simple_loss=0.2575, pruned_loss=0.0456, over 6993.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2865, pruned_loss=0.05081, over 1421641.32 frames.], batch size: 16, lr: 6.84e-04
2022-04-29 01:03:47,410 INFO [train.py:763] (1/8) Epoch 10, batch 3450, loss[loss=0.1958, simple_loss=0.2919, pruned_loss=0.04983, over 7108.00 frames.], tot_loss[loss=0.1938, simple_loss=0.2866, pruned_loss=0.05054, over 1424539.11 frames.], batch size: 21, lr: 6.83e-04
2022-04-29 01:04:52,721 INFO [train.py:763] (1/8) Epoch 10, batch 3500, loss[loss=0.1596, simple_loss=0.2495, pruned_loss=0.03481, over 7398.00 frames.], tot_loss[loss=0.1941, simple_loss=0.2864, pruned_loss=0.05088, over 1424988.20 frames.], batch size: 18, lr: 6.83e-04
2022-04-29 01:05:58,215 INFO [train.py:763] (1/8) Epoch 10, batch 3550, loss[loss=0.2103, simple_loss=0.2899, pruned_loss=0.06529, over 6414.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2856, pruned_loss=0.05036, over 1423934.81 frames.], batch size: 38, lr: 6.83e-04
2022-04-29 01:07:03,431 INFO [train.py:763] (1/8) Epoch 10, batch 3600, loss[loss=0.1862, simple_loss=0.2748, pruned_loss=0.04881, over 6329.00 frames.], tot_loss[loss=0.1934, simple_loss=0.2857, pruned_loss=0.0505, over 1419917.77 frames.], batch size: 37, lr: 6.82e-04
2022-04-29 01:08:09,034 INFO [train.py:763] (1/8) Epoch 10, batch 3650, loss[loss=0.1779, simple_loss=0.2753, pruned_loss=0.04023, over 7117.00 frames.], tot_loss[loss=0.1931, simple_loss=0.2858, pruned_loss=0.05023, over 1422234.29 frames.], batch size: 21, lr: 6.82e-04
2022-04-29 01:09:14,317 INFO [train.py:763] (1/8) Epoch 10, batch 3700, loss[loss=0.2206, simple_loss=0.3188, pruned_loss=0.06113, over 7118.00 frames.], tot_loss[loss=0.1933, simple_loss=0.2862, pruned_loss=0.05021, over 1418591.54 frames.], batch size: 21, lr: 6.82e-04
2022-04-29 01:10:20,242 INFO [train.py:763] (1/8) Epoch 10, batch 3750, loss[loss=0.1847, simple_loss=0.2802, pruned_loss=0.04463, over 7433.00 frames.], tot_loss[loss=0.1932, simple_loss=0.2863, pruned_loss=0.05008, over 1424379.87 frames.], batch size: 20, lr: 6.81e-04
2022-04-29 01:11:26,039 INFO [train.py:763] (1/8) Epoch 10, batch 3800, loss[loss=0.2302, simple_loss=0.3211, pruned_loss=0.06964, over 7325.00 frames.], tot_loss[loss=0.1936, simple_loss=0.2864, pruned_loss=0.05036, over 1422873.71 frames.], batch size: 24, lr: 6.81e-04
2022-04-29 01:12:32,917 INFO [train.py:763] (1/8) Epoch 10, batch 3850, loss[loss=0.2302, simple_loss=0.3179, pruned_loss=0.07129, over 7218.00 frames.], tot_loss[loss=0.1929, simple_loss=0.2856, pruned_loss=0.05004, over 1426743.01 frames.], batch size: 22, lr: 6.81e-04
2022-04-29 01:13:40,343 INFO [train.py:763] (1/8) Epoch 10, batch 3900, loss[loss=0.2149, simple_loss=0.3018, pruned_loss=0.06399, over 7383.00 frames.], tot_loss[loss=0.1919, simple_loss=0.2847, pruned_loss=0.0495, over 1427045.62 frames.], batch size: 23, lr: 6.80e-04
2022-04-29 01:14:47,716 INFO [train.py:763] (1/8) Epoch 10, batch 3950, loss[loss=0.2078, simple_loss=0.3058, pruned_loss=0.05484, over 7432.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2837, pruned_loss=0.04891, over 1426143.39 frames.], batch size: 20, lr: 6.80e-04
2022-04-29 01:15:53,612 INFO [train.py:763] (1/8) Epoch 10, batch 4000, loss[loss=0.2209, simple_loss=0.3147, pruned_loss=0.0635, over 7218.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2838, pruned_loss=0.04938, over 1418161.15 frames.], batch size: 21, lr: 6.80e-04
2022-04-29 01:17:00,541 INFO [train.py:763] (1/8) Epoch 10, batch 4050, loss[loss=0.2004, simple_loss=0.3055, pruned_loss=0.04765, over 7200.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2851, pruned_loss=0.05008, over 1418449.91 frames.], batch size: 22, lr: 6.79e-04
2022-04-29 01:18:07,363 INFO [train.py:763] (1/8) Epoch 10, batch 4100, loss[loss=0.185, simple_loss=0.2873, pruned_loss=0.04132, over 7205.00 frames.], tot_loss[loss=0.1927, simple_loss=0.2852, pruned_loss=0.05014, over 1417936.90 frames.], batch size: 22, lr: 6.79e-04
2022-04-29 01:19:14,029 INFO [train.py:763] (1/8) Epoch 10, batch 4150, loss[loss=0.1808, simple_loss=0.2765, pruned_loss=0.0426, over 6798.00 frames.], tot_loss[loss=0.1933, simple_loss=0.286, pruned_loss=0.0503, over 1415457.31 frames.], batch size: 31, lr: 6.79e-04
2022-04-29 01:20:19,800 INFO [train.py:763] (1/8) Epoch 10, batch 4200, loss[loss=0.2039, simple_loss=0.2857, pruned_loss=0.06102, over 7071.00 frames.], tot_loss[loss=0.195, simple_loss=0.2879, pruned_loss=0.05111, over 1416393.44 frames.], batch size: 28, lr: 6.78e-04
2022-04-29 01:21:26,028 INFO [train.py:763] (1/8) Epoch 10, batch 4250, loss[loss=0.2187, simple_loss=0.3083, pruned_loss=0.0646, over 5147.00 frames.], tot_loss[loss=0.1937, simple_loss=0.2863, pruned_loss=0.0505, over 1415391.13 frames.], batch size: 52, lr: 6.78e-04
2022-04-29 01:22:31,075 INFO [train.py:763] (1/8) Epoch 10, batch 4300, loss[loss=0.2119, simple_loss=0.3035, pruned_loss=0.06012, over 5278.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2873, pruned_loss=0.05108, over 1412067.97 frames.], batch size: 53, lr: 6.78e-04
2022-04-29 01:23:36,197 INFO [train.py:763] (1/8) Epoch 10, batch 4350, loss[loss=0.1868, simple_loss=0.2844, pruned_loss=0.04461, over 7238.00 frames.], tot_loss[loss=0.1952, simple_loss=0.2879, pruned_loss=0.05128, over 1410396.78 frames.], batch size: 20, lr: 6.77e-04
2022-04-29 01:24:41,259 INFO [train.py:763] (1/8) Epoch 10, batch 4400, loss[loss=0.188, simple_loss=0.2842, pruned_loss=0.04588, over 7205.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2889, pruned_loss=0.05149, over 1415651.23 frames.], batch size: 22, lr: 6.77e-04
2022-04-29 01:25:46,576 INFO [train.py:763] (1/8) Epoch 10, batch 4450, loss[loss=0.1705, simple_loss=0.2753, pruned_loss=0.0329, over 7231.00 frames.], tot_loss[loss=0.1963, simple_loss=0.2895, pruned_loss=0.05155, over 1418307.29 frames.], batch size: 20, lr: 6.77e-04
2022-04-29 01:26:52,300 INFO [train.py:763] (1/8) Epoch 10, batch 4500, loss[loss=0.2649, simple_loss=0.3361, pruned_loss=0.09691, over 4762.00 frames.], tot_loss[loss=0.1986, simple_loss=0.2918, pruned_loss=0.05273, over 1410557.20 frames.], batch size: 52, lr: 6.76e-04
2022-04-29 01:27:57,101 INFO [train.py:763] (1/8) Epoch 10, batch 4550, loss[loss=0.2337, simple_loss=0.3178, pruned_loss=0.07482, over 4787.00 frames.], tot_loss[loss=0.2027, simple_loss=0.2944, pruned_loss=0.05551, over 1345863.85 frames.], batch size: 52, lr: 6.76e-04
2022-04-29 01:29:26,055 INFO [train.py:763] (1/8) Epoch 11, batch 0, loss[loss=0.1961, simple_loss=0.3021, pruned_loss=0.04502, over 7408.00 frames.], tot_loss[loss=0.1961, simple_loss=0.3021, pruned_loss=0.04502, over 7408.00 frames.], batch size: 21, lr: 6.52e-04
2022-04-29 01:30:32,265 INFO [train.py:763] (1/8) Epoch 11, batch 50, loss[loss=0.1804, simple_loss=0.2744, pruned_loss=0.04316, over 5226.00 frames.], tot_loss[loss=0.1887, simple_loss=0.283, pruned_loss=0.04718, over 319294.08 frames.], batch size: 52, lr: 6.52e-04
2022-04-29 01:31:38,377 INFO [train.py:763] (1/8) Epoch 11, batch 100, loss[loss=0.161, simple_loss=0.256, pruned_loss=0.03298, over 6341.00 frames.], tot_loss[loss=0.1917, simple_loss=0.2858, pruned_loss=0.04885, over 558865.59 frames.], batch size: 38, lr: 6.51e-04
2022-04-29 01:32:44,336 INFO [train.py:763] (1/8) Epoch 11, batch 150, loss[loss=0.1908, simple_loss=0.2639, pruned_loss=0.05884, over 7262.00 frames.], tot_loss[loss=0.1947, simple_loss=0.2888, pruned_loss=0.05032, over 749210.37 frames.], batch size: 17, lr: 6.51e-04
2022-04-29 01:33:50,249 INFO [train.py:763] (1/8) Epoch 11, batch 200, loss[loss=0.2145, simple_loss=0.3172, pruned_loss=0.05595, over 7216.00 frames.], tot_loss[loss=0.1962, simple_loss=0.2895, pruned_loss=0.05145, over 896436.66 frames.], batch size: 22, lr: 6.51e-04
2022-04-29 01:34:55,808 INFO [train.py:763] (1/8) Epoch 11, batch 250, loss[loss=0.196, simple_loss=0.2838, pruned_loss=0.05414, over 6785.00 frames.], tot_loss[loss=0.193, simple_loss=0.2868, pruned_loss=0.04962, over 1014483.50 frames.], batch size: 31, lr: 6.50e-04
2022-04-29 01:36:01,202 INFO [train.py:763] (1/8) Epoch 11, batch 300, loss[loss=0.2153, simple_loss=0.3086, pruned_loss=0.061, over 7206.00 frames.], tot_loss[loss=0.1921, simple_loss=0.2861, pruned_loss=0.04903, over 1099541.77 frames.], batch size: 22, lr: 6.50e-04
2022-04-29 01:37:06,905 INFO [train.py:763] (1/8) Epoch 11, batch 350, loss[loss=0.1819, simple_loss=0.2836, pruned_loss=0.04007, over 7345.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2853, pruned_loss=0.0485, over 1166346.37 frames.], batch size: 22, lr: 6.50e-04
2022-04-29 01:38:12,674 INFO [train.py:763] (1/8) Epoch 11, batch 400, loss[loss=0.2018, simple_loss=0.3066, pruned_loss=0.04847, over 7336.00 frames.], tot_loss[loss=0.1908, simple_loss=0.285, pruned_loss=0.04828, over 1221441.25 frames.], batch size: 22, lr: 6.49e-04
2022-04-29 01:39:18,302 INFO [train.py:763] (1/8) Epoch 11, batch 450, loss[loss=0.1834, simple_loss=0.279, pruned_loss=0.04389, over 7168.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2845, pruned_loss=0.04785, over 1269752.76 frames.], batch size: 19, lr: 6.49e-04
2022-04-29 01:40:24,054 INFO [train.py:763] (1/8) Epoch 11, batch 500, loss[loss=0.2223, simple_loss=0.3214, pruned_loss=0.06163, over 7376.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2852, pruned_loss=0.04848, over 1303597.95 frames.], batch size: 23, lr: 6.49e-04
2022-04-29 01:41:30,079 INFO [train.py:763] (1/8) Epoch 11, batch 550, loss[loss=0.1839, simple_loss=0.2927, pruned_loss=0.03757, over 7416.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2838, pruned_loss=0.04796, over 1329679.71 frames.], batch size: 21, lr: 6.48e-04
2022-04-29 01:42:36,718 INFO [train.py:763] (1/8) Epoch 11, batch 600, loss[loss=0.2013, simple_loss=0.3007, pruned_loss=0.05101, over 7327.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2843, pruned_loss=0.0483, over 1348984.79 frames.], batch size: 22, lr: 6.48e-04
2022-04-29 01:43:44,063 INFO [train.py:763] (1/8) Epoch 11, batch 650, loss[loss=0.2106, simple_loss=0.3061, pruned_loss=0.05751, over 7382.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2836, pruned_loss=0.04802, over 1370019.50 frames.], batch size: 23, lr: 6.48e-04
2022-04-29 01:44:51,066 INFO [train.py:763] (1/8) Epoch 11, batch 700, loss[loss=0.2206, simple_loss=0.3205, pruned_loss=0.06033, over 7313.00 frames.], tot_loss[loss=0.19, simple_loss=0.2836, pruned_loss=0.04814, over 1380327.31 frames.], batch size: 24, lr: 6.47e-04
2022-04-29 01:45:57,536 INFO [train.py:763] (1/8) Epoch 11, batch 750, loss[loss=0.1809, simple_loss=0.2725, pruned_loss=0.04465, over 7313.00 frames.], tot_loss[loss=0.1913, simple_loss=0.2851, pruned_loss=0.04878, over 1386504.65 frames.], batch size: 20, lr: 6.47e-04
2022-04-29 01:47:03,478 INFO [train.py:763] (1/8) Epoch 11, batch 800, loss[loss=0.1788, simple_loss=0.2726, pruned_loss=0.04251, over 7409.00 frames.], tot_loss[loss=0.1912, simple_loss=0.2852, pruned_loss=0.04864, over 1400097.47 frames.], batch size: 18, lr: 6.47e-04
2022-04-29 01:48:08,962 INFO [train.py:763] (1/8) Epoch 11, batch 850, loss[loss=0.1956, simple_loss=0.2974, pruned_loss=0.04694, over 6747.00 frames.], tot_loss[loss=0.1923, simple_loss=0.2863, pruned_loss=0.0491, over 1403652.01 frames.], batch size: 31, lr: 6.46e-04
2022-04-29 01:49:14,789 INFO [train.py:763] (1/8) Epoch 11, batch 900, loss[loss=0.1715, simple_loss=0.2813, pruned_loss=0.03079, over 7342.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2865, pruned_loss=0.04894, over 1408264.18 frames.], batch size: 22, lr: 6.46e-04
2022-04-29 01:50:20,604 INFO [train.py:763] (1/8) Epoch 11, batch 950, loss[loss=0.1664, simple_loss=0.266, pruned_loss=0.03336, over 7432.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2857, pruned_loss=0.04849, over 1413151.05 frames.], batch size: 20, lr: 6.46e-04
2022-04-29 01:51:27,136 INFO [train.py:763] (1/8) Epoch 11, batch 1000, loss[loss=0.1991, simple_loss=0.2997, pruned_loss=0.04923, over 7152.00 frames.], tot_loss[loss=0.1922, simple_loss=0.2866, pruned_loss=0.04887, over 1415585.84 frames.], batch size: 19, lr: 6.46e-04
2022-04-29 01:52:32,488 INFO [train.py:763] (1/8) Epoch 11, batch 1050, loss[loss=0.1614, simple_loss=0.2567, pruned_loss=0.03307, over 6990.00 frames.], tot_loss[loss=0.1926, simple_loss=0.287, pruned_loss=0.04916, over 1415011.66 frames.], batch size: 16, lr: 6.45e-04
2022-04-29 01:53:38,688 INFO [train.py:763] (1/8) Epoch 11, batch 1100, loss[loss=0.1737, simple_loss=0.2758, pruned_loss=0.03586, over 7153.00 frames.], tot_loss[loss=0.1916, simple_loss=0.2862, pruned_loss=0.04853, over 1418130.13 frames.], batch size: 19, lr: 6.45e-04
2022-04-29 01:54:45,797 INFO [train.py:763] (1/8) Epoch 11, batch 1150, loss[loss=0.2815, simple_loss=0.3421, pruned_loss=0.1104, over 5159.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2848, pruned_loss=0.04805, over 1420774.51 frames.], batch size: 52, lr: 6.45e-04
2022-04-29 01:55:51,956 INFO [train.py:763] (1/8) Epoch 11, batch 1200, loss[loss=0.1736, simple_loss=0.2802, pruned_loss=0.03349, over 7115.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2851, pruned_loss=0.0481, over 1423036.38 frames.], batch size: 21, lr: 6.44e-04
2022-04-29 01:56:57,796 INFO [train.py:763] (1/8) Epoch 11, batch 1250, loss[loss=0.1262, simple_loss=0.2126, pruned_loss=0.01996, over 6984.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2845, pruned_loss=0.04794, over 1424200.10 frames.], batch size: 16, lr: 6.44e-04
2022-04-29 01:58:03,703 INFO [train.py:763] (1/8) Epoch 11, batch 1300, loss[loss=0.1915, simple_loss=0.2841, pruned_loss=0.04948, over 7330.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2843, pruned_loss=0.04746, over 1425867.39 frames.], batch size: 20, lr: 6.44e-04
2022-04-29 01:59:10,165 INFO [train.py:763] (1/8) Epoch 11, batch 1350, loss[loss=0.1718, simple_loss=0.2747, pruned_loss=0.03439, over 7320.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2846, pruned_loss=0.04799, over 1422602.06 frames.], batch size: 21, lr: 6.43e-04
2022-04-29 02:00:15,526 INFO [train.py:763] (1/8) Epoch 11, batch 1400, loss[loss=0.1843, simple_loss=0.2853, pruned_loss=0.04167, over 7315.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2834, pruned_loss=0.04789, over 1420545.55 frames.], batch size: 21, lr: 6.43e-04
2022-04-29 02:01:21,165 INFO [train.py:763] (1/8) Epoch 11, batch 1450, loss[loss=0.1601, simple_loss=0.2479, pruned_loss=0.03613, over 7063.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2836, pruned_loss=0.04809, over 1420688.29 frames.], batch size: 18, lr: 6.43e-04
2022-04-29 02:02:28,454 INFO [train.py:763] (1/8) Epoch 11, batch 1500, loss[loss=0.2187, simple_loss=0.3111, pruned_loss=0.06317, over 7205.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2834, pruned_loss=0.0479, over 1425208.57 frames.], batch size: 23, lr: 6.42e-04
2022-04-29 02:03:33,958 INFO [train.py:763] (1/8) Epoch 11, batch 1550, loss[loss=0.1928, simple_loss=0.2972, pruned_loss=0.04418, over 7229.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2832, pruned_loss=0.04807, over 1425288.04 frames.], batch size: 20, lr: 6.42e-04
2022-04-29 02:04:39,634 INFO [train.py:763] (1/8) Epoch 11, batch 1600, loss[loss=0.1488, simple_loss=0.2483, pruned_loss=0.02462, over 7363.00 frames.], tot_loss[loss=0.1903, simple_loss=0.284, pruned_loss=0.04827, over 1426077.95 frames.], batch size: 19, lr: 6.42e-04
2022-04-29 02:06:04,016 INFO [train.py:763] (1/8) Epoch 11, batch 1650, loss[loss=0.1833, simple_loss=0.2875, pruned_loss=0.03953, over 7386.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2842, pruned_loss=0.04823, over 1426130.25 frames.], batch size: 23, lr: 6.42e-04
2022-04-29 02:07:17,966 INFO [train.py:763] (1/8) Epoch 11, batch 1700, loss[loss=0.1992, simple_loss=0.2939, pruned_loss=0.05228, over 7215.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2849, pruned_loss=0.04844, over 1427575.55 frames.], batch size: 21, lr: 6.41e-04
2022-04-29 02:08:33,274 INFO [train.py:763] (1/8) Epoch 11, batch 1750, loss[loss=0.2006, simple_loss=0.2882, pruned_loss=0.05648, over 7147.00 frames.], tot_loss[loss=0.192, simple_loss=0.2858, pruned_loss=0.04908, over 1427606.08 frames.], batch size: 26, lr: 6.41e-04
2022-04-29 02:09:47,985 INFO [train.py:763] (1/8) Epoch 11, batch 1800, loss[loss=0.151, simple_loss=0.243, pruned_loss=0.02951, over 6989.00 frames.], tot_loss[loss=0.1915, simple_loss=0.2851, pruned_loss=0.04891, over 1428640.98 frames.], batch size: 16, lr: 6.41e-04
2022-04-29 02:11:03,170 INFO [train.py:763] (1/8) Epoch 11, batch 1850, loss[loss=0.1927, simple_loss=0.2881, pruned_loss=0.04862, over 7159.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2843, pruned_loss=0.04895, over 1426949.78 frames.], batch size: 26, lr: 6.40e-04
2022-04-29 02:12:18,081 INFO [train.py:763] (1/8) Epoch 11, batch 1900, loss[loss=0.1851, simple_loss=0.283, pruned_loss=0.04356, over 7440.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2842, pruned_loss=0.04858, over 1429313.02 frames.], batch size: 20, lr: 6.40e-04
2022-04-29 02:13:32,348 INFO [train.py:763] (1/8) Epoch 11, batch 1950, loss[loss=0.189, simple_loss=0.2628, pruned_loss=0.05755, over 7022.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2836, pruned_loss=0.04851, over 1427602.35 frames.], batch size: 16, lr: 6.40e-04
2022-04-29 02:14:38,124 INFO [train.py:763] (1/8) Epoch 11, batch 2000, loss[loss=0.1927, simple_loss=0.2907, pruned_loss=0.04738, over 6515.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2839, pruned_loss=0.04844, over 1425079.07 frames.], batch size: 37, lr: 6.39e-04
2022-04-29 02:15:44,446 INFO [train.py:763] (1/8) Epoch 11, batch 2050, loss[loss=0.2031, simple_loss=0.3017, pruned_loss=0.05227, over 7374.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2839, pruned_loss=0.04849, over 1423338.03 frames.], batch size: 23, lr: 6.39e-04
2022-04-29 02:16:50,750 INFO [train.py:763] (1/8) Epoch 11, batch 2100, loss[loss=0.1924, simple_loss=0.2924, pruned_loss=0.04616, over 6783.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2839, pruned_loss=0.04813, over 1427034.64 frames.], batch size: 31, lr: 6.39e-04
2022-04-29 02:17:57,123 INFO [train.py:763] (1/8) Epoch 11, batch 2150, loss[loss=0.1773, simple_loss=0.2676, pruned_loss=0.04351, over 7199.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2838, pruned_loss=0.0483, over 1423499.05 frames.], batch size: 16, lr: 6.38e-04
2022-04-29 02:19:03,271 INFO [train.py:763] (1/8) Epoch 11, batch 2200, loss[loss=0.1941, simple_loss=0.2949, pruned_loss=0.04664, over 7413.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2838, pruned_loss=0.04802, over 1427356.78 frames.], batch size: 20, lr: 6.38e-04
2022-04-29 02:20:09,534 INFO [train.py:763] (1/8) Epoch 11, batch 2250, loss[loss=0.1892, simple_loss=0.2715, pruned_loss=0.05343, over 7150.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2834, pruned_loss=0.04801, over 1426628.88 frames.], batch size: 17, lr: 6.38e-04
2022-04-29 02:21:16,307 INFO [train.py:763] (1/8) Epoch 11, batch 2300, loss[loss=0.2035, simple_loss=0.2921, pruned_loss=0.05745, over 7369.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2846, pruned_loss=0.04853, over 1424650.35 frames.], batch size: 19, lr: 6.38e-04
2022-04-29 02:22:22,089 INFO [train.py:763] (1/8) Epoch 11, batch 2350, loss[loss=0.207, simple_loss=0.2998, pruned_loss=0.05706, over 7311.00 frames.], tot_loss[loss=0.19, simple_loss=0.2835, pruned_loss=0.04827, over 1425682.03 frames.], batch size: 24, lr: 6.37e-04
2022-04-29 02:23:28,142 INFO [train.py:763] (1/8) Epoch 11, batch 2400, loss[loss=0.1652, simple_loss=0.2725, pruned_loss=0.0289, over 7110.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2836, pruned_loss=0.04785, over 1428005.57 frames.], batch size: 21, lr: 6.37e-04
2022-04-29 02:24:33,620 INFO [train.py:763] (1/8) Epoch 11, batch 2450, loss[loss=0.2088, simple_loss=0.3089, pruned_loss=0.05437, over 7241.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2834, pruned_loss=0.04784, over 1425589.99 frames.], batch size: 20, lr: 6.37e-04
2022-04-29 02:25:39,226 INFO [train.py:763] (1/8) Epoch 11, batch 2500, loss[loss=0.1872, simple_loss=0.2698, pruned_loss=0.05224, over 7077.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2832, pruned_loss=0.04806, over 1424637.72 frames.], batch size: 18, lr: 6.36e-04
2022-04-29 02:26:45,645 INFO [train.py:763] (1/8) Epoch 11, batch 2550, loss[loss=0.1607, simple_loss=0.2553, pruned_loss=0.03301, over 7277.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2845, pruned_loss=0.04838, over 1428116.92 frames.], batch size: 17, lr: 6.36e-04
2022-04-29 02:27:50,866 INFO [train.py:763] (1/8) Epoch 11, batch 2600, loss[loss=0.214, simple_loss=0.3076, pruned_loss=0.06026, over 7277.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2846, pruned_loss=0.04866, over 1422581.25 frames.], batch size: 24, lr: 6.36e-04
2022-04-29 02:28:56,394 INFO [train.py:763] (1/8) Epoch 11, batch 2650, loss[loss=0.1707, simple_loss=0.2745, pruned_loss=0.03352, over 7260.00 frames.], tot_loss[loss=0.1906, simple_loss=0.2842, pruned_loss=0.04845, over 1419497.50 frames.], batch size: 19, lr: 6.36e-04
2022-04-29 02:30:03,348 INFO [train.py:763] (1/8) Epoch 11, batch 2700, loss[loss=0.2678, simple_loss=0.3576, pruned_loss=0.08902, over 7302.00 frames.], tot_loss[loss=0.1903, simple_loss=0.2842, pruned_loss=0.04825, over 1423227.15 frames.], batch size: 25, lr: 6.35e-04
2022-04-29 02:31:08,820 INFO [train.py:763] (1/8) Epoch 11, batch 2750, loss[loss=0.1879, simple_loss=0.2769, pruned_loss=0.04939, over 7430.00 frames.], tot_loss[loss=0.1893, simple_loss=0.2832, pruned_loss=0.04775, over 1426173.84 frames.], batch size: 20, lr: 6.35e-04
2022-04-29 02:32:14,647 INFO [train.py:763] (1/8) Epoch 11, batch 2800, loss[loss=0.2025, simple_loss=0.3008, pruned_loss=0.05205, over 7115.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2838, pruned_loss=0.04824, over 1427193.57 frames.], batch size: 21, lr: 6.35e-04
2022-04-29 02:33:21,113 INFO [train.py:763] (1/8) Epoch 11, batch 2850, loss[loss=0.2118, simple_loss=0.3099, pruned_loss=0.05686, over 7330.00 frames.], tot_loss[loss=0.189, simple_loss=0.2829, pruned_loss=0.04752, over 1429684.19 frames.], batch size: 21, lr: 6.34e-04
2022-04-29 02:34:28,403 INFO [train.py:763] (1/8) Epoch 11, batch 2900, loss[loss=0.2056, simple_loss=0.3141, pruned_loss=0.04852, over 7281.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2843, pruned_loss=0.04837, over 1425400.80 frames.], batch size: 24, lr: 6.34e-04
2022-04-29 02:35:35,070 INFO [train.py:763] (1/8) Epoch 11, batch 2950, loss[loss=0.2058, simple_loss=0.2957, pruned_loss=0.05799, over 7233.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2836, pruned_loss=0.04789, over 1421257.13 frames.], batch size: 21, lr: 6.34e-04
2022-04-29 02:36:40,644 INFO [train.py:763] (1/8) Epoch 11, batch 3000, loss[loss=0.2132, simple_loss=0.313, pruned_loss=0.05667, over 7306.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2836, pruned_loss=0.04811, over 1422455.73 frames.], batch size: 25, lr: 6.33e-04
2022-04-29 02:36:40,645 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 02:36:55,964 INFO [train.py:792] (1/8) Epoch 11, validation: loss=0.1677, simple_loss=0.2702, pruned_loss=0.03262, over 698248.00 frames.
2022-04-29 02:38:01,323 INFO [train.py:763] (1/8) Epoch 11, batch 3050, loss[loss=0.2088, simple_loss=0.3158, pruned_loss=0.05092, over 7388.00 frames.], tot_loss[loss=0.191, simple_loss=0.285, pruned_loss=0.04851, over 1420503.82 frames.], batch size: 23, lr: 6.33e-04
2022-04-29 02:39:06,997 INFO [train.py:763] (1/8) Epoch 11, batch 3100, loss[loss=0.1607, simple_loss=0.2655, pruned_loss=0.02789, over 7325.00 frames.], tot_loss[loss=0.1904, simple_loss=0.284, pruned_loss=0.04837, over 1421853.48 frames.], batch size: 20, lr: 6.33e-04
2022-04-29 02:40:14,523 INFO [train.py:763] (1/8) Epoch 11, batch 3150, loss[loss=0.1961, simple_loss=0.2847, pruned_loss=0.0537, over 7365.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2837, pruned_loss=0.04797, over 1424261.34 frames.], batch size: 23, lr: 6.33e-04
2022-04-29 02:41:19,856 INFO [train.py:763] (1/8) Epoch 11, batch 3200, loss[loss=0.1601, simple_loss=0.2589, pruned_loss=0.03068, over 7126.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2833, pruned_loss=0.0477, over 1423498.16 frames.], batch size: 21, lr: 6.32e-04
2022-04-29 02:42:26,204 INFO [train.py:763] (1/8) Epoch 11, batch 3250, loss[loss=0.1845, simple_loss=0.2866, pruned_loss=0.04118, over 7425.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2834, pruned_loss=0.04788, over 1424981.01 frames.], batch size: 21, lr: 6.32e-04
2022-04-29 02:43:31,316 INFO [train.py:763] (1/8) Epoch 11, batch 3300, loss[loss=0.1697, simple_loss=0.2616, pruned_loss=0.03888, over 7427.00 frames.], tot_loss[loss=0.1911, simple_loss=0.285, pruned_loss=0.04862, over 1425640.59 frames.], batch size: 17, lr: 6.32e-04
2022-04-29 02:44:36,748 INFO [train.py:763] (1/8) Epoch 11, batch 3350, loss[loss=0.1929, simple_loss=0.2777, pruned_loss=0.05406, over 7269.00 frames.], tot_loss[loss=0.1907, simple_loss=0.285, pruned_loss=0.0482, over 1426459.41 frames.], batch size: 18, lr: 6.31e-04
2022-04-29 02:45:42,396 INFO [train.py:763] (1/8) Epoch 11, batch 3400, loss[loss=0.2173, simple_loss=0.3091, pruned_loss=0.06276, over 6116.00 frames.], tot_loss[loss=0.1914, simple_loss=0.286, pruned_loss=0.0484, over 1420636.65 frames.], batch size: 37, lr: 6.31e-04
2022-04-29 02:46:49,527 INFO [train.py:763] (1/8) Epoch 11, batch 3450, loss[loss=0.1713, simple_loss=0.2761, pruned_loss=0.03328, over 7115.00 frames.], tot_loss[loss=0.19, simple_loss=0.2843, pruned_loss=0.04784, over 1417630.30 frames.], batch size: 21, lr: 6.31e-04
2022-04-29 02:47:56,122 INFO [train.py:763] (1/8) Epoch 11, batch 3500, loss[loss=0.1804, simple_loss=0.2835, pruned_loss=0.03864, over 7324.00 frames.], tot_loss[loss=0.1898, simple_loss=0.2845, pruned_loss=0.04754, over 1423515.40 frames.], batch size: 21, lr: 6.31e-04
2022-04-29 02:49:02,210 INFO [train.py:763] (1/8) Epoch 11, batch 3550, loss[loss=0.1765, simple_loss=0.2601, pruned_loss=0.04647, over 6995.00 frames.], tot_loss[loss=0.1899, simple_loss=0.2842, pruned_loss=0.0478, over 1421981.27 frames.], batch size: 16, lr: 6.30e-04
2022-04-29 02:50:08,007 INFO [train.py:763] (1/8) Epoch 11, batch 3600, loss[loss=0.194, simple_loss=0.291, pruned_loss=0.04849, over 7232.00 frames.], tot_loss[loss=0.1903, simple_loss=0.285, pruned_loss=0.04776, over 1424102.40 frames.], batch size: 20, lr: 6.30e-04
2022-04-29 02:51:13,359 INFO [train.py:763] (1/8) Epoch 11, batch 3650, loss[loss=0.1803, simple_loss=0.2755, pruned_loss=0.04253, over 7440.00 frames.], tot_loss[loss=0.1896, simple_loss=0.2844, pruned_loss=0.04746, over 1423821.55 frames.], batch size: 20, lr: 6.30e-04
2022-04-29 02:52:20,064 INFO [train.py:763] (1/8) Epoch 11, batch 3700, loss[loss=0.2101, simple_loss=0.2993, pruned_loss=0.06046, over 6751.00 frames.], tot_loss[loss=0.1901, simple_loss=0.2844, pruned_loss=0.04793, over 1420371.23 frames.], batch size: 31, lr: 6.29e-04
2022-04-29 02:53:25,479 INFO [train.py:763] (1/8) Epoch 11, batch 3750, loss[loss=0.2047, simple_loss=0.3028, pruned_loss=0.05336, over 7377.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2829, pruned_loss=0.04724, over 1425100.97 frames.], batch size: 23, lr: 6.29e-04
2022-04-29 02:54:30,949 INFO [train.py:763] (1/8) Epoch 11, batch 3800, loss[loss=0.1938, simple_loss=0.2867, pruned_loss=0.05047, over 7176.00 frames.], tot_loss[loss=0.1887, simple_loss=0.2825, pruned_loss=0.04747, over 1427730.23 frames.], batch size: 26, lr: 6.29e-04
2022-04-29 02:55:36,107 INFO [train.py:763] (1/8) Epoch 11, batch 3850, loss[loss=0.1772, simple_loss=0.2805, pruned_loss=0.03693, over 7122.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2826, pruned_loss=0.04747, over 1428400.61 frames.], batch size: 21, lr: 6.29e-04
2022-04-29 02:56:41,383 INFO [train.py:763] (1/8) Epoch 11, batch 3900, loss[loss=0.2071, simple_loss=0.3063, pruned_loss=0.05391, over 7432.00 frames.], tot_loss[loss=0.1897, simple_loss=0.2835, pruned_loss=0.04795, over 1428919.50 frames.], batch size: 20, lr: 6.28e-04
2022-04-29 02:57:46,958 INFO [train.py:763] (1/8) Epoch 11, batch 3950, loss[loss=0.2001, simple_loss=0.2983, pruned_loss=0.05096, over 7236.00 frames.], tot_loss[loss=0.1895, simple_loss=0.2833, pruned_loss=0.04789, over 1430476.94 frames.], batch size: 20, lr: 6.28e-04
2022-04-29 02:58:52,092 INFO [train.py:763] (1/8) Epoch 11, batch 4000, loss[loss=0.1904, simple_loss=0.2846, pruned_loss=0.04813, over 7410.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2841, pruned_loss=0.04875, over 1425631.43 frames.], batch size: 21, lr: 6.28e-04
2022-04-29 02:59:57,356 INFO [train.py:763] (1/8) Epoch 11, batch 4050, loss[loss=0.2025, simple_loss=0.2946, pruned_loss=0.05516, over 7432.00 frames.], tot_loss[loss=0.1909, simple_loss=0.2841, pruned_loss=0.04879, over 1424863.62 frames.], batch size: 20, lr: 6.27e-04
2022-04-29 03:01:03,190 INFO [train.py:763] (1/8) Epoch 11, batch 4100, loss[loss=0.1912, simple_loss=0.2906, pruned_loss=0.04588, over 7316.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2837, pruned_loss=0.04835, over 1421057.44 frames.], batch size: 20, lr: 6.27e-04
2022-04-29 03:02:08,243 INFO [train.py:763] (1/8) Epoch 11, batch 4150, loss[loss=0.1876, simple_loss=0.2797, pruned_loss=0.0477, over 7239.00 frames.], tot_loss[loss=0.1902, simple_loss=0.284, pruned_loss=0.04819, over 1421272.56 frames.], batch size: 20, lr: 6.27e-04
2022-04-29 03:03:14,695 INFO [train.py:763] (1/8) Epoch 11, batch 4200, loss[loss=0.2019, simple_loss=0.302, pruned_loss=0.05085, over 7348.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2842, pruned_loss=0.04842, over 1421530.39 frames.], batch size: 22, lr: 6.27e-04
2022-04-29 03:04:21,492 INFO [train.py:763] (1/8) Epoch 11, batch 4250, loss[loss=0.1691, simple_loss=0.256, pruned_loss=0.04108, over 7406.00 frames.], tot_loss[loss=0.189, simple_loss=0.2828, pruned_loss=0.04764, over 1424320.96 frames.], batch size: 18, lr: 6.26e-04
2022-04-29 03:05:27,592 INFO [train.py:763] (1/8) Epoch 11, batch 4300, loss[loss=0.1992, simple_loss=0.2915, pruned_loss=0.05347, over 7229.00 frames.], tot_loss[loss=0.1895, simple_loss=0.283, pruned_loss=0.04799, over 1417706.05 frames.], batch size: 20, lr: 6.26e-04
2022-04-29 03:06:35,253 INFO [train.py:763] (1/8) Epoch 11, batch 4350, loss[loss=0.206, simple_loss=0.3054, pruned_loss=0.05333, over 7200.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2814, pruned_loss=0.04742, over 1419421.89 frames.], batch size: 22, lr: 6.26e-04
2022-04-29 03:07:41,463 INFO [train.py:763] (1/8) Epoch 11, batch 4400, loss[loss=0.2069, simple_loss=0.3096, pruned_loss=0.05216, over 7318.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2813, pruned_loss=0.04726, over 1418015.13 frames.], batch size: 21, lr: 6.25e-04
2022-04-29 03:08:47,769 INFO [train.py:763] (1/8) Epoch 11, batch 4450, loss[loss=0.2303, simple_loss=0.3197, pruned_loss=0.07039, over 6289.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2804, pruned_loss=0.04751, over 1406398.49 frames.], batch size: 38, lr: 6.25e-04
2022-04-29 03:09:54,257 INFO [train.py:763] (1/8) Epoch 11, batch 4500, loss[loss=0.1842, simple_loss=0.2842, pruned_loss=0.04212, over 6607.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2804, pruned_loss=0.04806, over 1391609.31 frames.], batch size: 40, lr: 6.25e-04
2022-04-29 03:10:59,840 INFO [train.py:763] (1/8) Epoch 11, batch 4550, loss[loss=0.2073, simple_loss=0.2966, pruned_loss=0.05897, over 5151.00 frames.], tot_loss[loss=0.1911, simple_loss=0.2824, pruned_loss=0.04987, over 1352570.04 frames.], batch size: 52, lr: 6.25e-04
2022-04-29 03:12:38,229 INFO [train.py:763] (1/8) Epoch 12, batch 0, loss[loss=0.1804, simple_loss=0.2804, pruned_loss=0.0402, over 7150.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2804, pruned_loss=0.0402, over 7150.00 frames.], batch size: 20, lr: 6.03e-04
2022-04-29 03:13:44,621 INFO [train.py:763] (1/8) Epoch 12, batch 50, loss[loss=0.1879, simple_loss=0.2888, pruned_loss=0.04352, over 7239.00 frames.], tot_loss[loss=0.1907, simple_loss=0.2856, pruned_loss=0.04793, over 318314.23 frames.], batch size: 20, lr: 6.03e-04
2022-04-29 03:14:50,360 INFO [train.py:763] (1/8) Epoch 12, batch 100, loss[loss=0.2014, simple_loss=0.309, pruned_loss=0.04686, over 7199.00 frames.], tot_loss[loss=0.1905, simple_loss=0.2858, pruned_loss=0.04761, over 564411.77 frames.], batch size: 23, lr: 6.03e-04
2022-04-29 03:15:56,446 INFO [train.py:763] (1/8) Epoch 12, batch 150, loss[loss=0.1758, simple_loss=0.2725, pruned_loss=0.03955, over 7152.00 frames.], tot_loss[loss=0.1914, simple_loss=0.2868, pruned_loss=0.048, over 753711.34 frames.], batch size: 20, lr: 6.03e-04
2022-04-29 03:17:02,801 INFO [train.py:763] (1/8) Epoch 12, batch 200, loss[loss=0.1945, simple_loss=0.2924, pruned_loss=0.04832, over 7148.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2852, pruned_loss=0.04761, over 899927.84 frames.], batch size: 20, lr: 6.02e-04
2022-04-29 03:18:09,055 INFO [train.py:763] (1/8) Epoch 12, batch 250, loss[loss=0.1902, simple_loss=0.2767, pruned_loss=0.05187, over 6795.00 frames.], tot_loss[loss=0.1897, simple_loss=0.285, pruned_loss=0.04718, over 1013477.28 frames.], batch size: 15, lr: 6.02e-04
2022-04-29 03:19:15,281 INFO [train.py:763] (1/8) Epoch 12, batch 300, loss[loss=0.1958, simple_loss=0.2904, pruned_loss=0.05057, over 7145.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2833, pruned_loss=0.04681, over 1104949.59 frames.], batch size: 20, lr: 6.02e-04
2022-04-29 03:20:20,570 INFO [train.py:763] (1/8) Epoch 12, batch 350, loss[loss=0.2114, simple_loss=0.3089, pruned_loss=0.05691, over 7073.00 frames.], tot_loss[loss=0.1889, simple_loss=0.284, pruned_loss=0.04693, over 1177382.83 frames.], batch size: 28, lr: 6.01e-04
2022-04-29 03:21:26,172 INFO [train.py:763] (1/8) Epoch 12, batch 400, loss[loss=0.1659, simple_loss=0.26, pruned_loss=0.03587, over 7356.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2834, pruned_loss=0.04684, over 1234809.52 frames.], batch size: 19, lr: 6.01e-04
2022-04-29 03:22:31,836 INFO [train.py:763] (1/8) Epoch 12, batch 450, loss[loss=0.1648, simple_loss=0.2791, pruned_loss=0.02525, over 7317.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2829, pruned_loss=0.04668, over 1278368.55 frames.], batch size: 21, lr: 6.01e-04
2022-04-29 03:23:38,035 INFO [train.py:763] (1/8) Epoch 12, batch 500, loss[loss=0.1992, simple_loss=0.2874, pruned_loss=0.0555, over 6306.00 frames.], tot_loss[loss=0.187, simple_loss=0.2811, pruned_loss=0.04643, over 1312039.92 frames.], batch size: 37, lr: 6.01e-04
2022-04-29 03:24:43,947 INFO [train.py:763] (1/8) Epoch 12, batch 550, loss[loss=0.1934, simple_loss=0.2944, pruned_loss=0.04624, over 7399.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2806, pruned_loss=0.04592, over 1334061.64 frames.], batch size: 23, lr: 6.00e-04
2022-04-29 03:25:49,963 INFO [train.py:763] (1/8) Epoch 12, batch 600, loss[loss=0.1688, simple_loss=0.2477, pruned_loss=0.04495, over 6794.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2796, pruned_loss=0.04597, over 1347339.95 frames.], batch size: 15, lr: 6.00e-04
2022-04-29 03:26:55,895 INFO [train.py:763] (1/8) Epoch 12, batch 650, loss[loss=0.1944, simple_loss=0.2903, pruned_loss=0.0493, over 7300.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2803, pruned_loss=0.04604, over 1366539.52 frames.], batch size: 18, lr: 6.00e-04
2022-04-29 03:28:02,296 INFO [train.py:763] (1/8) Epoch 12, batch 700, loss[loss=0.1396, simple_loss=0.2208, pruned_loss=0.02922, over 6804.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2811, pruned_loss=0.04597, over 1383926.89 frames.], batch size: 15, lr: 6.00e-04
2022-04-29 03:29:07,992 INFO [train.py:763] (1/8) Epoch 12, batch 750, loss[loss=0.2141, simple_loss=0.3064, pruned_loss=0.06087, over 7215.00 frames.], tot_loss[loss=0.187, simple_loss=0.2819, pruned_loss=0.04603, over 1395823.68 frames.], batch size: 23, lr: 5.99e-04
2022-04-29 03:30:14,228 INFO [train.py:763] (1/8) Epoch 12, batch 800, loss[loss=0.2158, simple_loss=0.3098, pruned_loss=0.06086, over 7215.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2817, pruned_loss=0.04564, over 1405709.60 frames.], batch size: 22, lr: 5.99e-04
2022-04-29 03:31:20,656 INFO [train.py:763] (1/8) Epoch 12, batch 850, loss[loss=0.163, simple_loss=0.2517, pruned_loss=0.03711, over 7141.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2828, pruned_loss=0.04609, over 1411306.60 frames.], batch size: 17, lr: 5.99e-04
2022-04-29 03:32:27,842 INFO [train.py:763] (1/8) Epoch 12, batch 900, loss[loss=0.1803, simple_loss=0.2834, pruned_loss=0.03865, over 7313.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2821, pruned_loss=0.046, over 1413766.04 frames.], batch size: 20, lr: 5.99e-04
2022-04-29 03:33:44,137 INFO [train.py:763] (1/8) Epoch 12, batch 950, loss[loss=0.1753, simple_loss=0.287, pruned_loss=0.03187, over 7180.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2829, pruned_loss=0.04627, over 1414789.78 frames.], batch size: 26, lr: 5.98e-04
2022-04-29 03:34:49,709 INFO [train.py:763] (1/8) Epoch 12, batch 1000, loss[loss=0.191, simple_loss=0.2797, pruned_loss=0.05117, over 6428.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2834, pruned_loss=0.04636, over 1415985.88 frames.], batch size: 38, lr: 5.98e-04
2022-04-29 03:35:56,175 INFO [train.py:763] (1/8) Epoch 12, batch 1050, loss[loss=0.2202, simple_loss=0.3023, pruned_loss=0.06904, over 7249.00 frames.], tot_loss[loss=0.1879, simple_loss=0.2831, pruned_loss=0.04638, over 1417473.06 frames.], batch size: 19, lr: 5.98e-04
2022-04-29 03:37:02,297 INFO [train.py:763] (1/8) Epoch 12, batch 1100, loss[loss=0.1721, simple_loss=0.2724, pruned_loss=0.03589, over 7384.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2823, pruned_loss=0.04612, over 1423427.81 frames.], batch size: 23, lr: 5.97e-04
2022-04-29 03:38:08,854 INFO [train.py:763] (1/8) Epoch 12, batch 1150, loss[loss=0.1921, simple_loss=0.2791, pruned_loss=0.05255, over 7331.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2818, pruned_loss=0.04621, over 1425650.12 frames.], batch size: 20, lr: 5.97e-04
2022-04-29 03:39:15,122 INFO [train.py:763] (1/8) Epoch 12, batch 1200, loss[loss=0.2555, simple_loss=0.3318, pruned_loss=0.08959, over 4832.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2825, pruned_loss=0.04657, over 1421742.91 frames.], batch size: 53, lr: 5.97e-04
2022-04-29 03:40:21,631 INFO [train.py:763] (1/8) Epoch 12, batch 1250, loss[loss=0.175, simple_loss=0.2766, pruned_loss=0.0367, over 7155.00 frames.], tot_loss[loss=0.188, simple_loss=0.2826, pruned_loss=0.04668, over 1419041.65 frames.], batch size: 19, lr: 5.97e-04
2022-04-29 03:41:28,266 INFO [train.py:763] (1/8) Epoch 12, batch 1300, loss[loss=0.168, simple_loss=0.2616, pruned_loss=0.03717, over 7056.00 frames.], tot_loss[loss=0.1865, simple_loss=0.281, pruned_loss=0.046, over 1419974.71 frames.], batch size: 18, lr: 5.96e-04
2022-04-29 03:42:33,926 INFO [train.py:763] (1/8) Epoch 12, batch 1350, loss[loss=0.2066, simple_loss=0.2968, pruned_loss=0.05819, over 5423.00 frames.], tot_loss[loss=0.188, simple_loss=0.2828, pruned_loss=0.04658, over 1418112.97 frames.], batch size: 53, lr: 5.96e-04
2022-04-29 03:43:39,826 INFO [train.py:763] (1/8) Epoch 12, batch 1400, loss[loss=0.2075, simple_loss=0.3017, pruned_loss=0.0566, over 7286.00 frames.], tot_loss[loss=0.188, simple_loss=0.2829, pruned_loss=0.04656, over 1417997.67 frames.], batch size: 25, lr: 5.96e-04
2022-04-29 03:44:45,260 INFO [train.py:763] (1/8) Epoch 12, batch 1450, loss[loss=0.1819, simple_loss=0.29, pruned_loss=0.03687, over 7322.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2833, pruned_loss=0.04677, over 1415650.33 frames.], batch size: 21, lr: 5.96e-04
2022-04-29 03:45:51,847 INFO [train.py:763] (1/8) Epoch 12, batch 1500, loss[loss=0.2178, simple_loss=0.3063, pruned_loss=0.06468, over 7191.00 frames.], tot_loss[loss=0.1884, simple_loss=0.283, pruned_loss=0.04688, over 1418706.41 frames.], batch size: 23, lr: 5.95e-04
2022-04-29 03:46:59,223 INFO [train.py:763] (1/8) Epoch 12, batch 1550, loss[loss=0.1975, simple_loss=0.3055, pruned_loss=0.04477, over 7039.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2824, pruned_loss=0.04612, over 1420175.96 frames.], batch size: 28, lr: 5.95e-04
2022-04-29 03:48:05,681 INFO [train.py:763] (1/8) Epoch 12, batch 1600, loss[loss=0.1882, simple_loss=0.2845, pruned_loss=0.04589, over 7307.00 frames.], tot_loss[loss=0.1885, simple_loss=0.2831, pruned_loss=0.0469, over 1419227.20 frames.], batch size: 25, lr: 5.95e-04
2022-04-29 03:49:11,827 INFO [train.py:763] (1/8) Epoch 12, batch 1650, loss[loss=0.1963, simple_loss=0.2902, pruned_loss=0.05122, over 7300.00 frames.], tot_loss[loss=0.1877, simple_loss=0.2825, pruned_loss=0.04648, over 1422288.16 frames.], batch size: 24, lr: 5.95e-04
2022-04-29 03:50:17,600 INFO [train.py:763] (1/8) Epoch 12, batch 1700, loss[loss=0.1791, simple_loss=0.2779, pruned_loss=0.04018, over 7127.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2816, pruned_loss=0.04595, over 1417808.08 frames.], batch size: 17, lr: 5.94e-04
2022-04-29 03:51:23,275 INFO [train.py:763] (1/8) Epoch 12, batch 1750, loss[loss=0.225, simple_loss=0.3217, pruned_loss=0.06409, over 7188.00 frames.], tot_loss[loss=0.1862, simple_loss=0.2808, pruned_loss=0.04575, over 1421706.41 frames.], batch size: 26, lr: 5.94e-04
2022-04-29 03:52:29,188 INFO [train.py:763] (1/8) Epoch 12, batch 1800, loss[loss=0.1874, simple_loss=0.2645, pruned_loss=0.05516, over 7003.00 frames.], tot_loss[loss=0.1866, simple_loss=0.281, pruned_loss=0.04612, over 1427017.44 frames.], batch size: 16, lr: 5.94e-04
2022-04-29 03:53:35,396 INFO [train.py:763] (1/8) Epoch 12, batch 1850, loss[loss=0.1969, simple_loss=0.2935, pruned_loss=0.05018, over 7324.00 frames.], tot_loss[loss=0.1853, simple_loss=0.28, pruned_loss=0.04527, over 1427586.10 frames.], batch size: 22, lr: 5.94e-04
2022-04-29 03:54:41,516 INFO [train.py:763] (1/8) Epoch 12, batch 1900, loss[loss=0.1731, simple_loss=0.2676, pruned_loss=0.03936, over 7229.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2806, pruned_loss=0.04562, over 1428447.98 frames.], batch size: 20, lr: 5.93e-04
2022-04-29 03:55:47,351 INFO [train.py:763] (1/8) Epoch 12, batch 1950, loss[loss=0.1593, simple_loss=0.2459, pruned_loss=0.03632, over 7281.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2804, pruned_loss=0.04552, over 1428493.09 frames.], batch size: 17, lr: 5.93e-04
2022-04-29 03:56:53,845 INFO [train.py:763] (1/8) Epoch 12, batch 2000, loss[loss=0.1511, simple_loss=0.2446, pruned_loss=0.0288, over 7009.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2795, pruned_loss=0.04544, over 1428740.54 frames.], batch size: 16, lr: 5.93e-04
2022-04-29 03:57:59,757 INFO [train.py:763] (1/8) Epoch 12, batch 2050, loss[loss=0.1885, simple_loss=0.28, pruned_loss=0.04849, over 7158.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2788, pruned_loss=0.04543, over 1422597.54 frames.], batch size: 19, lr: 5.93e-04
2022-04-29 03:59:05,463 INFO [train.py:763] (1/8) Epoch 12, batch 2100, loss[loss=0.1669, simple_loss=0.2665, pruned_loss=0.03371, over 7154.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2798, pruned_loss=0.04565, over 1423116.05 frames.], batch size: 19, lr: 5.92e-04
2022-04-29 04:00:11,330 INFO [train.py:763] (1/8) Epoch 12, batch 2150, loss[loss=0.1536, simple_loss=0.2373, pruned_loss=0.03502, over 7269.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2809, pruned_loss=0.04584, over 1422501.53 frames.], batch size: 18, lr: 5.92e-04
2022-04-29 04:01:17,160 INFO [train.py:763] (1/8) Epoch 12, batch 2200, loss[loss=0.175, simple_loss=0.2762, pruned_loss=0.03691, over 7327.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2801, pruned_loss=0.04543, over 1422917.39 frames.], batch size: 20, lr: 5.92e-04
2022-04-29 04:02:23,226 INFO [train.py:763] (1/8) Epoch 12, batch 2250, loss[loss=0.1772, simple_loss=0.2747, pruned_loss=0.03986, over 7079.00 frames.], tot_loss[loss=0.1855, simple_loss=0.2803, pruned_loss=0.04536, over 1421676.31 frames.], batch size: 28, lr: 5.91e-04
2022-04-29 04:03:29,734 INFO [train.py:763] (1/8) Epoch 12, batch 2300, loss[loss=0.1657, simple_loss=0.2751, pruned_loss=0.02809, over 7114.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2801, pruned_loss=0.04509, over 1425552.91 frames.], batch size: 21, lr: 5.91e-04
2022-04-29 04:04:36,296 INFO [train.py:763] (1/8) Epoch 12, batch 2350, loss[loss=0.2303, simple_loss=0.3176, pruned_loss=0.0715, over 7157.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2807, pruned_loss=0.04542, over 1427038.68 frames.], batch size: 19, lr: 5.91e-04
2022-04-29 04:05:42,053 INFO [train.py:763] (1/8) Epoch 12, batch 2400, loss[loss=0.1628, simple_loss=0.2443, pruned_loss=0.0406, over 7142.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2815, pruned_loss=0.0458, over 1426929.48 frames.], batch size: 17, lr: 5.91e-04
2022-04-29 04:06:47,899 INFO [train.py:763] (1/8) Epoch 12, batch 2450, loss[loss=0.1825, simple_loss=0.2801, pruned_loss=0.04248, over 7217.00 frames.], tot_loss[loss=0.1873, simple_loss=0.2821, pruned_loss=0.04628, over 1426207.28 frames.], batch size: 21, lr: 5.90e-04
2022-04-29 04:07:54,991 INFO [train.py:763] (1/8) Epoch 12, batch 2500, loss[loss=0.1732, simple_loss=0.2664, pruned_loss=0.04002, over 7282.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2827, pruned_loss=0.04651, over 1427394.55 frames.], batch size: 18, lr: 5.90e-04
2022-04-29 04:09:01,292 INFO [train.py:763] (1/8) Epoch 12, batch 2550, loss[loss=0.1636, simple_loss=0.2585, pruned_loss=0.03431, over 7244.00 frames.], tot_loss[loss=0.188, simple_loss=0.2829, pruned_loss=0.04652, over 1428810.70 frames.], batch size: 16, lr: 5.90e-04
2022-04-29 04:10:08,003 INFO [train.py:763] (1/8) Epoch 12, batch 2600, loss[loss=0.1666, simple_loss=0.2524, pruned_loss=0.04035, over 7226.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2825, pruned_loss=0.04691, over 1424650.28 frames.], batch size: 16, lr: 5.90e-04
2022-04-29 04:11:13,659 INFO [train.py:763] (1/8) Epoch 12, batch 2650, loss[loss=0.1684, simple_loss=0.2521, pruned_loss=0.04229, over 7010.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2831, pruned_loss=0.04708, over 1422755.25 frames.], batch size: 16, lr: 5.89e-04
2022-04-29 04:12:19,550 INFO [train.py:763] (1/8) Epoch 12, batch 2700, loss[loss=0.1807, simple_loss=0.2656, pruned_loss=0.04788, over 7012.00 frames.], tot_loss[loss=0.1877, simple_loss=0.282, pruned_loss=0.04675, over 1423747.21 frames.], batch size: 16, lr: 5.89e-04
2022-04-29 04:13:25,130 INFO [train.py:763] (1/8) Epoch 12, batch 2750, loss[loss=0.1887, simple_loss=0.2913, pruned_loss=0.04304, over 7110.00 frames.], tot_loss[loss=0.1871, simple_loss=0.2815, pruned_loss=0.04634, over 1420906.27 frames.], batch size: 21, lr: 5.89e-04
2022-04-29 04:14:30,845 INFO [train.py:763] (1/8) Epoch 12, batch 2800, loss[loss=0.1519, simple_loss=0.2485, pruned_loss=0.02766, over 7134.00 frames.], tot_loss[loss=0.1873, simple_loss=0.282, pruned_loss=0.04632, over 1420431.87 frames.], batch size: 17, lr: 5.89e-04
2022-04-29 04:15:37,560 INFO [train.py:763] (1/8) Epoch 12, batch 2850, loss[loss=0.174, simple_loss=0.2693, pruned_loss=0.0394, over 7374.00 frames.], tot_loss[loss=0.1872, simple_loss=0.2819, pruned_loss=0.04624, over 1426711.71 frames.], batch size: 23, lr: 5.88e-04
2022-04-29 04:16:43,206 INFO [train.py:763] (1/8) Epoch 12, batch 2900, loss[loss=0.178, simple_loss=0.2755, pruned_loss=0.04021, over 7347.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2829, pruned_loss=0.0467, over 1424695.34 frames.], batch size: 19, lr: 5.88e-04
2022-04-29 04:17:49,204 INFO [train.py:763] (1/8) Epoch 12, batch 2950, loss[loss=0.1702, simple_loss=0.2649, pruned_loss=0.03774, over 7114.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2819, pruned_loss=0.04653, over 1427165.17 frames.], batch size: 21, lr: 5.88e-04
2022-04-29 04:18:54,866 INFO [train.py:763] (1/8) Epoch 12, batch 3000, loss[loss=0.1409, simple_loss=0.2263, pruned_loss=0.02769, over 7298.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2826, pruned_loss=0.04678, over 1428188.10 frames.], batch size: 17, lr: 5.88e-04
2022-04-29 04:18:54,867 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 04:19:10,345 INFO [train.py:792] (1/8) Epoch 12, validation: loss=0.1673, simple_loss=0.27, pruned_loss=0.03225, over 698248.00 frames.
2022-04-29 04:20:16,204 INFO [train.py:763] (1/8) Epoch 12, batch 3050, loss[loss=0.168, simple_loss=0.2528, pruned_loss=0.04154, over 7120.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2821, pruned_loss=0.04677, over 1429330.83 frames.], batch size: 17, lr: 5.87e-04
2022-04-29 04:21:32,152 INFO [train.py:763] (1/8) Epoch 12, batch 3100, loss[loss=0.1815, simple_loss=0.2834, pruned_loss=0.03978, over 7123.00 frames.], tot_loss[loss=0.1876, simple_loss=0.2817, pruned_loss=0.04678, over 1427924.96 frames.], batch size: 21, lr: 5.87e-04
2022-04-29 04:22:37,472 INFO [train.py:763] (1/8) Epoch 12, batch 3150, loss[loss=0.1934, simple_loss=0.2906, pruned_loss=0.04809, over 7326.00 frames.], tot_loss[loss=0.1889, simple_loss=0.2832, pruned_loss=0.04732, over 1424719.48 frames.], batch size: 25, lr: 5.87e-04
2022-04-29 04:23:52,369 INFO [train.py:763] (1/8) Epoch 12, batch 3200, loss[loss=0.2414, simple_loss=0.3183, pruned_loss=0.08224, over 5120.00 frames.], tot_loss[loss=0.1885, simple_loss=0.283, pruned_loss=0.04697, over 1425879.45 frames.], batch size: 52, lr: 5.87e-04
2022-04-29 04:25:17,143 INFO [train.py:763] (1/8) Epoch 12, batch 3250, loss[loss=0.1791, simple_loss=0.2591, pruned_loss=0.0495, over 7312.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2828, pruned_loss=0.0467, over 1428254.42 frames.], batch size: 17, lr: 5.86e-04
2022-04-29 04:26:23,033 INFO [train.py:763] (1/8) Epoch 12, batch 3300, loss[loss=0.1797, simple_loss=0.2733, pruned_loss=0.04304, over 7323.00 frames.], tot_loss[loss=0.1878, simple_loss=0.2823, pruned_loss=0.04658, over 1428753.65 frames.], batch size: 20, lr: 5.86e-04
2022-04-29 04:27:37,932 INFO [train.py:763] (1/8) Epoch 12, batch 3350, loss[loss=0.2014, simple_loss=0.2924, pruned_loss=0.05518, over 6989.00 frames.], tot_loss[loss=0.188, simple_loss=0.2826, pruned_loss=0.04674, over 1421896.29 frames.], batch size: 16, lr: 5.86e-04
2022-04-29 04:29:03,562 INFO [train.py:763] (1/8) Epoch 12, batch 3400, loss[loss=0.1825, simple_loss=0.2875, pruned_loss=0.03876, over 7376.00 frames.], tot_loss[loss=0.1884, simple_loss=0.2829, pruned_loss=0.04695, over 1425005.25 frames.], batch size: 23, lr: 5.86e-04
2022-04-29 04:30:18,595 INFO [train.py:763] (1/8) Epoch 12, batch 3450, loss[loss=0.162, simple_loss=0.2464, pruned_loss=0.03877, over 7418.00 frames.], tot_loss[loss=0.1894, simple_loss=0.2833, pruned_loss=0.04776, over 1414560.11 frames.], batch size: 18, lr: 5.85e-04
2022-04-29 04:31:24,820 INFO [train.py:763] (1/8) Epoch 12, batch 3500, loss[loss=0.2022, simple_loss=0.3105, pruned_loss=0.04694, over 6782.00 frames.], tot_loss[loss=0.1888, simple_loss=0.2833, pruned_loss=0.04711, over 1416418.77 frames.], batch size: 31, lr: 5.85e-04
2022-04-29 04:32:31,884 INFO [train.py:763] (1/8) Epoch 12, batch 3550, loss[loss=0.1575, simple_loss=0.247, pruned_loss=0.03396, over 7018.00 frames.], tot_loss[loss=0.1882, simple_loss=0.2832, pruned_loss=0.04665, over 1421406.90 frames.], batch size: 16, lr: 5.85e-04
2022-04-29 04:33:38,544 INFO [train.py:763] (1/8) Epoch 12, batch 3600, loss[loss=0.1698, simple_loss=0.264, pruned_loss=0.0378, over 7279.00 frames.], tot_loss[loss=0.1881, simple_loss=0.2831, pruned_loss=0.04658, over 1421846.05 frames.], batch size: 18, lr: 5.85e-04
2022-04-29 04:34:44,019 INFO [train.py:763] (1/8) Epoch 12, batch 3650, loss[loss=0.2196, simple_loss=0.3258, pruned_loss=0.05667, over 7422.00 frames.], tot_loss[loss=0.1875, simple_loss=0.2824, pruned_loss=0.04631, over 1425114.96 frames.], batch size: 21, lr: 5.84e-04
2022-04-29 04:35:49,777 INFO [train.py:763] (1/8) Epoch 12, batch 3700, loss[loss=0.175, simple_loss=0.2602, pruned_loss=0.04492, over 7271.00 frames.], tot_loss[loss=0.1869, simple_loss=0.2812, pruned_loss=0.04635, over 1425494.05 frames.], batch size: 19, lr: 5.84e-04
2022-04-29 04:36:55,381 INFO [train.py:763] (1/8) Epoch 12, batch 3750, loss[loss=0.1903, simple_loss=0.2912, pruned_loss=0.04467, over 7419.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2804, pruned_loss=0.04592, over 1425509.32 frames.], batch size: 21, lr: 5.84e-04
2022-04-29 04:38:01,435 INFO [train.py:763] (1/8) Epoch 12, batch 3800, loss[loss=0.173, simple_loss=0.2705, pruned_loss=0.03777, over 7040.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2811, pruned_loss=0.04614, over 1429541.94 frames.], batch size: 28, lr: 5.84e-04
2022-04-29 04:39:06,788 INFO [train.py:763] (1/8) Epoch 12, batch 3850, loss[loss=0.1898, simple_loss=0.2894, pruned_loss=0.04509, over 7208.00 frames.], tot_loss[loss=0.1874, simple_loss=0.2819, pruned_loss=0.04649, over 1426687.02 frames.], batch size: 22, lr: 5.83e-04
2022-04-29 04:40:13,129 INFO [train.py:763] (1/8) Epoch 12, batch 3900, loss[loss=0.2073, simple_loss=0.2975, pruned_loss=0.0585, over 7323.00 frames.], tot_loss[loss=0.186, simple_loss=0.2803, pruned_loss=0.04587, over 1424941.92 frames.], batch size: 24, lr: 5.83e-04
2022-04-29 04:41:18,535 INFO [train.py:763] (1/8) Epoch 12, batch 3950, loss[loss=0.1741, simple_loss=0.2709, pruned_loss=0.03864, over 7208.00 frames.], tot_loss[loss=0.186, simple_loss=0.2801, pruned_loss=0.0459, over 1424163.47 frames.], batch size: 23, lr: 5.83e-04
2022-04-29 04:42:24,204 INFO [train.py:763] (1/8) Epoch 12, batch 4000, loss[loss=0.17, simple_loss=0.2577, pruned_loss=0.04116, over 7134.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2803, pruned_loss=0.04616, over 1423656.84 frames.], batch size: 17, lr: 5.83e-04
2022-04-29 04:43:29,489 INFO [train.py:763] (1/8) Epoch 12, batch 4050, loss[loss=0.1966, simple_loss=0.2974, pruned_loss=0.04787, over 7231.00 frames.], tot_loss[loss=0.1856, simple_loss=0.2799, pruned_loss=0.0456, over 1425589.19 frames.], batch size: 20, lr: 5.82e-04
2022-04-29 04:44:35,688 INFO [train.py:763] (1/8) Epoch 12, batch 4100, loss[loss=0.2252, simple_loss=0.3194, pruned_loss=0.06547, over 7140.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2797, pruned_loss=0.04547, over 1424839.39 frames.], batch size: 20, lr: 5.82e-04
2022-04-29 04:45:41,162 INFO [train.py:763] (1/8) Epoch 12, batch 4150, loss[loss=0.1837, simple_loss=0.2769, pruned_loss=0.04528, over 7435.00 frames.], tot_loss[loss=0.1857, simple_loss=0.2804, pruned_loss=0.04551, over 1419479.16 frames.], batch size: 20, lr: 5.82e-04
2022-04-29 04:46:48,354 INFO [train.py:763] (1/8) Epoch 12, batch 4200, loss[loss=0.1825, simple_loss=0.2855, pruned_loss=0.03968, over 7150.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2798, pruned_loss=0.04531, over 1421339.57 frames.], batch size: 20, lr: 5.82e-04
2022-04-29 04:47:54,431 INFO [train.py:763] (1/8) Epoch 12, batch 4250, loss[loss=0.1898, simple_loss=0.2881, pruned_loss=0.04579, over 7163.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2799, pruned_loss=0.04539, over 1419477.30 frames.], batch size: 26, lr: 5.81e-04
2022-04-29 04:49:00,803 INFO [train.py:763] (1/8) Epoch 12, batch 4300, loss[loss=0.1957, simple_loss=0.2916, pruned_loss=0.04989, over 7430.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2806, pruned_loss=0.04554, over 1417124.27 frames.], batch size: 20, lr: 5.81e-04
2022-04-29 04:50:06,807 INFO [train.py:763] (1/8) Epoch 12, batch 4350, loss[loss=0.1579, simple_loss=0.2546, pruned_loss=0.03065, over 6991.00 frames.], tot_loss[loss=0.1861, simple_loss=0.2805, pruned_loss=0.04583, over 1411971.80 frames.], batch size: 16, lr: 5.81e-04
2022-04-29 04:51:13,414 INFO [train.py:763] (1/8) Epoch 12, batch 4400, loss[loss=0.246, simple_loss=0.3209, pruned_loss=0.08549, over 4862.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2791, pruned_loss=0.04523, over 1409680.90 frames.], batch size: 52, lr: 5.81e-04
2022-04-29 04:52:19,270 INFO [train.py:763] (1/8) Epoch 12, batch 4450, loss[loss=0.2, simple_loss=0.3072, pruned_loss=0.04634, over 7276.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2787, pruned_loss=0.04475, over 1407202.62 frames.], batch size: 24, lr: 5.81e-04
2022-04-29 04:53:25,177 INFO [train.py:763] (1/8) Epoch 12, batch 4500, loss[loss=0.2162, simple_loss=0.3099, pruned_loss=0.06121, over 7413.00 frames.], tot_loss[loss=0.1864, simple_loss=0.281, pruned_loss=0.04587, over 1388774.16 frames.], batch size: 21, lr: 5.80e-04
2022-04-29 04:54:31,139 INFO [train.py:763] (1/8) Epoch 12, batch 4550, loss[loss=0.2143, simple_loss=0.2972, pruned_loss=0.0657, over 5171.00 frames.], tot_loss[loss=0.1892, simple_loss=0.2837, pruned_loss=0.04735, over 1352901.23 frames.], batch size: 52, lr: 5.80e-04
2022-04-29 04:56:09,894 INFO [train.py:763] (1/8) Epoch 13, batch 0, loss[loss=0.1745, simple_loss=0.2711, pruned_loss=0.03892, over 7391.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2711, pruned_loss=0.03892, over 7391.00 frames.], batch size: 23, lr: 5.61e-04
2022-04-29 04:57:15,971 INFO [train.py:763] (1/8) Epoch 13, batch 50, loss[loss=0.302, simple_loss=0.3634, pruned_loss=0.1203, over 7118.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2768, pruned_loss=0.04586, over 322836.70 frames.], batch size: 21, lr: 5.61e-04
2022-04-29 04:58:22,263 INFO [train.py:763] (1/8) Epoch 13, batch 100, loss[loss=0.2208, simple_loss=0.3278, pruned_loss=0.05696, over 7134.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2779, pruned_loss=0.04448, over 572522.20 frames.], batch size: 20, lr: 5.61e-04
2022-04-29 04:59:28,141 INFO [train.py:763] (1/8) Epoch 13, batch 150, loss[loss=0.1631, simple_loss=0.2491, pruned_loss=0.03856, over 6993.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2753, pruned_loss=0.04352, over 762979.68 frames.], batch size: 16, lr: 5.61e-04
2022-04-29 05:00:33,585 INFO [train.py:763] (1/8) Epoch 13, batch 200, loss[loss=0.1756, simple_loss=0.2756, pruned_loss=0.03781, over 7211.00 frames.], tot_loss[loss=0.1827, simple_loss=0.277, pruned_loss=0.04424, over 909455.67 frames.], batch size: 22, lr: 5.60e-04
2022-04-29 05:01:39,401 INFO [train.py:763] (1/8) Epoch 13, batch 250, loss[loss=0.192, simple_loss=0.2951, pruned_loss=0.04445, over 7198.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2772, pruned_loss=0.04366, over 1025427.57 frames.], batch size: 22, lr: 5.60e-04
2022-04-29 05:02:44,821 INFO [train.py:763] (1/8) Epoch 13, batch 300, loss[loss=0.1835, simple_loss=0.2783, pruned_loss=0.04439, over 7408.00 frames.], tot_loss[loss=0.1835, simple_loss=0.279, pruned_loss=0.04396, over 1111565.78 frames.], batch size: 21, lr: 5.60e-04
2022-04-29 05:03:50,335 INFO [train.py:763] (1/8) Epoch 13, batch 350, loss[loss=0.175, simple_loss=0.263, pruned_loss=0.04354, over 7428.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2789, pruned_loss=0.04448, over 1179991.25 frames.], batch size: 20, lr: 5.60e-04
2022-04-29 05:04:55,868 INFO [train.py:763] (1/8) Epoch 13, batch 400, loss[loss=0.1925, simple_loss=0.2884, pruned_loss=0.04824, over 7071.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2788, pruned_loss=0.04438, over 1231156.35 frames.], batch size: 28, lr: 5.59e-04
2022-04-29 05:06:01,967 INFO [train.py:763] (1/8) Epoch 13, batch 450, loss[loss=0.1607, simple_loss=0.2663, pruned_loss=0.0275, over 6292.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2795, pruned_loss=0.04417, over 1273125.36 frames.], batch size: 37, lr: 5.59e-04
2022-04-29 05:07:07,984 INFO [train.py:763] (1/8) Epoch 13, batch 500, loss[loss=0.1936, simple_loss=0.287, pruned_loss=0.05007, over 7073.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2779, pruned_loss=0.04364, over 1299784.81 frames.], batch size: 28, lr: 5.59e-04
2022-04-29 05:08:13,588 INFO [train.py:763] (1/8) Epoch 13, batch 550, loss[loss=0.1911, simple_loss=0.2854, pruned_loss=0.04838, over 6481.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2786, pruned_loss=0.04397, over 1325942.86 frames.], batch size: 38, lr: 5.59e-04
2022-04-29 05:09:19,617 INFO [train.py:763] (1/8) Epoch 13, batch 600, loss[loss=0.186, simple_loss=0.2835, pruned_loss=0.04423, over 7312.00 frames.], tot_loss[loss=0.182, simple_loss=0.2774, pruned_loss=0.04335, over 1348796.40 frames.], batch size: 21, lr: 5.59e-04
2022-04-29 05:10:25,762 INFO [train.py:763] (1/8) Epoch 13, batch 650, loss[loss=0.2012, simple_loss=0.2979, pruned_loss=0.0523, over 7064.00 frames.], tot_loss[loss=0.1827, simple_loss=0.278, pruned_loss=0.04374, over 1361078.33 frames.], batch size: 18, lr: 5.58e-04
2022-04-29 05:11:32,548 INFO [train.py:763] (1/8) Epoch 13, batch 700, loss[loss=0.1522, simple_loss=0.2451, pruned_loss=0.02968, over 7255.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2786, pruned_loss=0.04434, over 1376609.54 frames.], batch size: 18, lr: 5.58e-04
2022-04-29 05:12:37,744 INFO [train.py:763] (1/8) Epoch 13, batch 750, loss[loss=0.211, simple_loss=0.3246, pruned_loss=0.04868, over 7179.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2786, pruned_loss=0.044, over 1382986.29 frames.], batch size: 23, lr: 5.58e-04
2022-04-29 05:13:44,375 INFO [train.py:763] (1/8) Epoch 13, batch 800, loss[loss=0.2158, simple_loss=0.3146, pruned_loss=0.05848, over 7309.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2798, pruned_loss=0.04453, over 1392235.93 frames.], batch size: 25, lr: 5.58e-04
2022-04-29 05:14:50,890 INFO [train.py:763] (1/8) Epoch 13, batch 850, loss[loss=0.1979, simple_loss=0.2946, pruned_loss=0.05064, over 7215.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2789, pruned_loss=0.04429, over 1399803.73 frames.], batch size: 21, lr: 5.57e-04
2022-04-29 05:15:57,547 INFO [train.py:763] (1/8) Epoch 13, batch 900, loss[loss=0.1813, simple_loss=0.2692, pruned_loss=0.04669, over 7171.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2792, pruned_loss=0.04461, over 1402846.99 frames.], batch size: 18, lr: 5.57e-04
2022-04-29 05:17:04,245 INFO [train.py:763] (1/8) Epoch 13, batch 950, loss[loss=0.185, simple_loss=0.2846, pruned_loss=0.04265, over 7232.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2795, pruned_loss=0.04453, over 1403868.52 frames.], batch size: 21, lr: 5.57e-04
2022-04-29 05:18:11,092 INFO [train.py:763] (1/8) Epoch 13, batch 1000, loss[loss=0.1765, simple_loss=0.2653, pruned_loss=0.04384, over 7206.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2786, pruned_loss=0.04437, over 1410691.07 frames.], batch size: 22, lr: 5.57e-04
2022-04-29 05:19:17,014 INFO [train.py:763] (1/8) Epoch 13, batch 1050, loss[loss=0.2161, simple_loss=0.3095, pruned_loss=0.0614, over 7414.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2791, pruned_loss=0.04449, over 1411397.24 frames.], batch size: 21, lr: 5.56e-04
2022-04-29 05:20:22,744 INFO [train.py:763] (1/8) Epoch 13, batch 1100, loss[loss=0.1791, simple_loss=0.2767, pruned_loss=0.04076, over 6746.00 frames.], tot_loss[loss=0.184, simple_loss=0.2789, pruned_loss=0.04451, over 1410871.48 frames.], batch size: 31, lr: 5.56e-04
2022-04-29 05:21:28,694 INFO [train.py:763] (1/8) Epoch 13, batch 1150, loss[loss=0.1892, simple_loss=0.2919, pruned_loss=0.0432, over 7342.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2801, pruned_loss=0.04501, over 1410821.65 frames.], batch size: 22, lr: 5.56e-04
2022-04-29 05:22:34,607 INFO [train.py:763] (1/8) Epoch 13, batch 1200, loss[loss=0.1848, simple_loss=0.2771, pruned_loss=0.04624, over 5341.00 frames.], tot_loss[loss=0.1851, simple_loss=0.2801, pruned_loss=0.04502, over 1410374.03 frames.], batch size: 52, lr: 5.56e-04
2022-04-29 05:23:40,299 INFO [train.py:763] (1/8) Epoch 13, batch 1250, loss[loss=0.1427, simple_loss=0.2348, pruned_loss=0.02534, over 7427.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2806, pruned_loss=0.04512, over 1414761.60 frames.], batch size: 20, lr: 5.56e-04
2022-04-29 05:24:45,575 INFO [train.py:763] (1/8) Epoch 13, batch 1300, loss[loss=0.178, simple_loss=0.2746, pruned_loss=0.04065, over 7264.00 frames.], tot_loss[loss=0.185, simple_loss=0.2806, pruned_loss=0.04469, over 1417751.30 frames.], batch size: 19, lr: 5.55e-04
2022-04-29 05:25:51,457 INFO [train.py:763] (1/8) Epoch 13, batch 1350, loss[loss=0.1532, simple_loss=0.2441, pruned_loss=0.03116, over 7277.00 frames.], tot_loss[loss=0.185, simple_loss=0.28, pruned_loss=0.04496, over 1421531.76 frames.], batch size: 18, lr: 5.55e-04
2022-04-29 05:26:57,107 INFO [train.py:763] (1/8) Epoch 13, batch 1400, loss[loss=0.1566, simple_loss=0.2551, pruned_loss=0.02907, over 7157.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2809, pruned_loss=0.04543, over 1417791.22 frames.], batch size: 18, lr: 5.55e-04
2022-04-29 05:28:02,590 INFO [train.py:763] (1/8) Epoch 13, batch 1450, loss[loss=0.142, simple_loss=0.2291, pruned_loss=0.0274, over 7290.00 frames.], tot_loss[loss=0.185, simple_loss=0.2805, pruned_loss=0.04475, over 1421534.23 frames.], batch size: 17, lr: 5.55e-04
2022-04-29 05:29:08,107 INFO [train.py:763] (1/8) Epoch 13, batch 1500, loss[loss=0.1562, simple_loss=0.2477, pruned_loss=0.03238, over 7263.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2797, pruned_loss=0.04452, over 1423232.41 frames.], batch size: 17, lr: 5.54e-04
2022-04-29 05:30:14,044 INFO [train.py:763] (1/8) Epoch 13, batch 1550, loss[loss=0.2071, simple_loss=0.3004, pruned_loss=0.05687, over 6427.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2804, pruned_loss=0.04466, over 1419117.35 frames.], batch size: 38, lr: 5.54e-04
2022-04-29 05:31:19,477 INFO [train.py:763] (1/8) Epoch 13, batch 1600, loss[loss=0.1787, simple_loss=0.2821, pruned_loss=0.03763, over 7424.00 frames.], tot_loss[loss=0.1858, simple_loss=0.2812, pruned_loss=0.04516, over 1418005.43 frames.], batch size: 21, lr: 5.54e-04
2022-04-29 05:32:25,607 INFO [train.py:763] (1/8) Epoch 13, batch 1650, loss[loss=0.198, simple_loss=0.3024, pruned_loss=0.04679, over 7238.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2813, pruned_loss=0.04525, over 1419731.35 frames.], batch size: 20, lr: 5.54e-04
2022-04-29 05:33:31,238 INFO [train.py:763] (1/8) Epoch 13, batch 1700, loss[loss=0.2003, simple_loss=0.3097, pruned_loss=0.04538, over 6298.00 frames.], tot_loss[loss=0.1854, simple_loss=0.2813, pruned_loss=0.04482, over 1418929.25 frames.], batch size: 37, lr: 5.54e-04
2022-04-29 05:34:36,770 INFO [train.py:763] (1/8) Epoch 13, batch 1750, loss[loss=0.1563, simple_loss=0.2518, pruned_loss=0.03045, over 7275.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2798, pruned_loss=0.04423, over 1420916.61 frames.], batch size: 17, lr: 5.53e-04
2022-04-29 05:35:42,700 INFO [train.py:763] (1/8) Epoch 13, batch 1800, loss[loss=0.2077, simple_loss=0.2906, pruned_loss=0.06238, over 7146.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2803, pruned_loss=0.04444, over 1426358.89 frames.], batch size: 20, lr: 5.53e-04
2022-04-29 05:36:48,185 INFO [train.py:763] (1/8) Epoch 13, batch 1850, loss[loss=0.2297, simple_loss=0.3223, pruned_loss=0.06857, over 7311.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2807, pruned_loss=0.04486, over 1426167.73 frames.], batch size: 25, lr: 5.53e-04
2022-04-29 05:37:54,118 INFO [train.py:763] (1/8) Epoch 13, batch 1900, loss[loss=0.2127, simple_loss=0.3067, pruned_loss=0.05934, over 6356.00 frames.], tot_loss[loss=0.1859, simple_loss=0.281, pruned_loss=0.0454, over 1422390.19 frames.], batch size: 37, lr: 5.53e-04
2022-04-29 05:39:00,690 INFO [train.py:763] (1/8) Epoch 13, batch 1950, loss[loss=0.173, simple_loss=0.2776, pruned_loss=0.03422, over 7257.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2816, pruned_loss=0.04562, over 1423711.82 frames.], batch size: 19, lr: 5.52e-04
2022-04-29 05:40:07,431 INFO [train.py:763] (1/8) Epoch 13, batch 2000, loss[loss=0.194, simple_loss=0.3089, pruned_loss=0.0396, over 7325.00 frames.], tot_loss[loss=0.1863, simple_loss=0.2817, pruned_loss=0.04548, over 1425262.98 frames.], batch size: 22, lr: 5.52e-04
2022-04-29 05:41:13,023 INFO [train.py:763] (1/8) Epoch 13, batch 2050, loss[loss=0.1818, simple_loss=0.286, pruned_loss=0.0388, over 7375.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2808, pruned_loss=0.04481, over 1426771.06 frames.], batch size: 23, lr: 5.52e-04
2022-04-29 05:42:18,152 INFO [train.py:763] (1/8) Epoch 13, batch 2100, loss[loss=0.1589, simple_loss=0.2603, pruned_loss=0.02873, over 7217.00 frames.], tot_loss[loss=0.1851, simple_loss=0.281, pruned_loss=0.0446, over 1426720.48 frames.], batch size: 20, lr: 5.52e-04
2022-04-29 05:43:24,241 INFO [train.py:763] (1/8) Epoch 13, batch 2150, loss[loss=0.2065, simple_loss=0.3009, pruned_loss=0.05606, over 7206.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2809, pruned_loss=0.0444, over 1428882.94 frames.], batch size: 26, lr: 5.52e-04
2022-04-29 05:44:29,757 INFO [train.py:763] (1/8) Epoch 13, batch 2200, loss[loss=0.1654, simple_loss=0.2615, pruned_loss=0.03469, over 7429.00 frames.], tot_loss[loss=0.1851, simple_loss=0.281, pruned_loss=0.04459, over 1427560.08 frames.], batch size: 20, lr: 5.51e-04
2022-04-29 05:45:35,366 INFO [train.py:763] (1/8) Epoch 13, batch 2250, loss[loss=0.1896, simple_loss=0.2949, pruned_loss=0.04214, over 7227.00 frames.], tot_loss[loss=0.1852, simple_loss=0.2812, pruned_loss=0.04458, over 1427707.22 frames.], batch size: 20, lr: 5.51e-04
2022-04-29 05:46:41,459 INFO [train.py:763] (1/8) Epoch 13, batch 2300, loss[loss=0.1803, simple_loss=0.2833, pruned_loss=0.03868, over 7035.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2796, pruned_loss=0.04395, over 1428293.44 frames.], batch size: 28, lr: 5.51e-04
2022-04-29 05:47:46,891 INFO [train.py:763] (1/8) Epoch 13, batch 2350, loss[loss=0.2093, simple_loss=0.2925, pruned_loss=0.06307, over 5229.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2798, pruned_loss=0.04437, over 1427345.33 frames.], batch size: 54, lr: 5.51e-04
2022-04-29 05:48:52,769 INFO [train.py:763] (1/8) Epoch 13, batch 2400, loss[loss=0.1724, simple_loss=0.2571, pruned_loss=0.04386, over 7284.00 frames.], tot_loss[loss=0.1844, simple_loss=0.2798, pruned_loss=0.0445, over 1427904.95 frames.], batch size: 17, lr: 5.50e-04
2022-04-29 05:49:58,371 INFO [train.py:763] (1/8) Epoch 13, batch 2450, loss[loss=0.2243, simple_loss=0.3159, pruned_loss=0.06632, over 7000.00 frames.], tot_loss[loss=0.185, simple_loss=0.2806, pruned_loss=0.04475, over 1430687.73 frames.], batch size: 32, lr: 5.50e-04
2022-04-29 05:51:03,649 INFO [train.py:763] (1/8) Epoch 13, batch 2500, loss[loss=0.1483, simple_loss=0.2438, pruned_loss=0.02637, over 7287.00 frames.], tot_loss[loss=0.1859, simple_loss=0.2813, pruned_loss=0.04521, over 1427193.46 frames.], batch size: 17, lr: 5.50e-04
2022-04-29 05:52:08,889 INFO [train.py:763] (1/8) Epoch 13, batch 2550, loss[loss=0.1993, simple_loss=0.2905, pruned_loss=0.0541, over 7289.00 frames.], tot_loss[loss=0.1864, simple_loss=0.2814, pruned_loss=0.04566, over 1422974.29 frames.], batch size: 25, lr: 5.50e-04
2022-04-29 05:53:14,607 INFO [train.py:763] (1/8) Epoch 13, batch 2600, loss[loss=0.1848, simple_loss=0.2894, pruned_loss=0.04013, over 7407.00 frames.], tot_loss[loss=0.1864, simple_loss=0.281, pruned_loss=0.04585, over 1419219.26 frames.], batch size: 21, lr: 5.50e-04
2022-04-29 05:54:20,019 INFO [train.py:763] (1/8) Epoch 13, batch 2650, loss[loss=0.1989, simple_loss=0.2925, pruned_loss=0.05266, over 7112.00 frames.], tot_loss[loss=0.1865, simple_loss=0.2813, pruned_loss=0.04587, over 1417343.66 frames.], batch size: 21, lr: 5.49e-04
2022-04-29 05:55:25,827 INFO [train.py:763] (1/8) Epoch 13, batch 2700, loss[loss=0.1554, simple_loss=0.2553, pruned_loss=0.0277, over 6999.00 frames.], tot_loss[loss=0.1853, simple_loss=0.2803, pruned_loss=0.04516, over 1422514.96 frames.], batch size: 16, lr: 5.49e-04
2022-04-29 05:56:31,325 INFO [train.py:763] (1/8) Epoch 13, batch 2750, loss[loss=0.2023, simple_loss=0.3069, pruned_loss=0.04889, over 7289.00 frames.], tot_loss[loss=0.1848, simple_loss=0.28, pruned_loss=0.0448, over 1427859.16 frames.], batch size: 24, lr: 5.49e-04
2022-04-29 05:57:36,853 INFO [train.py:763] (1/8) Epoch 13, batch 2800, loss[loss=0.1684, simple_loss=0.2593, pruned_loss=0.03874, over 7149.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2789, pruned_loss=0.04449, over 1425947.74 frames.], batch size: 17, lr: 5.49e-04
2022-04-29 05:58:42,722 INFO [train.py:763] (1/8) Epoch 13, batch 2850, loss[loss=0.202, simple_loss=0.2987, pruned_loss=0.05262, over 7410.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2784, pruned_loss=0.04416, over 1427344.46 frames.], batch size: 21, lr: 5.48e-04
2022-04-29 05:59:48,435 INFO [train.py:763] (1/8) Epoch 13, batch 2900, loss[loss=0.168, simple_loss=0.2734, pruned_loss=0.03128, over 7116.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2793, pruned_loss=0.0445, over 1428012.06 frames.], batch size: 21, lr: 5.48e-04
2022-04-29 06:00:53,879 INFO [train.py:763] (1/8) Epoch 13, batch 2950, loss[loss=0.2203, simple_loss=0.3214, pruned_loss=0.05959, over 7196.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2796, pruned_loss=0.04446, over 1428443.61 frames.], batch size: 23, lr: 5.48e-04
2022-04-29 06:01:59,746 INFO [train.py:763] (1/8) Epoch 13, batch 3000, loss[loss=0.2024, simple_loss=0.3155, pruned_loss=0.04467, over 7296.00 frames.], tot_loss[loss=0.1833, simple_loss=0.2786, pruned_loss=0.04404, over 1429994.85 frames.], batch size: 24, lr: 5.48e-04
2022-04-29 06:01:59,747 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 06:02:15,158 INFO [train.py:792] (1/8) Epoch 13, validation: loss=0.1677, simple_loss=0.2714, pruned_loss=0.03198, over 698248.00 frames.
2022-04-29 06:03:21,966 INFO [train.py:763] (1/8) Epoch 13, batch 3050, loss[loss=0.1671, simple_loss=0.2625, pruned_loss=0.03589, over 7276.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2786, pruned_loss=0.04436, over 1430478.16 frames.], batch size: 17, lr: 5.48e-04
2022-04-29 06:04:29,181 INFO [train.py:763] (1/8) Epoch 13, batch 3100, loss[loss=0.1863, simple_loss=0.2831, pruned_loss=0.04478, over 7210.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2794, pruned_loss=0.04457, over 1430993.68 frames.], batch size: 23, lr: 5.47e-04
2022-04-29 06:05:35,704 INFO [train.py:763] (1/8) Epoch 13, batch 3150, loss[loss=0.2531, simple_loss=0.332, pruned_loss=0.08707, over 4731.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2782, pruned_loss=0.0441, over 1429366.99 frames.], batch size: 52, lr: 5.47e-04
2022-04-29 06:06:41,344 INFO [train.py:763] (1/8) Epoch 13, batch 3200, loss[loss=0.1825, simple_loss=0.2915, pruned_loss=0.03673, over 7319.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2786, pruned_loss=0.04436, over 1429018.93 frames.], batch size: 22, lr: 5.47e-04
2022-04-29 06:07:46,883 INFO [train.py:763] (1/8) Epoch 13, batch 3250, loss[loss=0.2232, simple_loss=0.3165, pruned_loss=0.0649, over 7188.00 frames.], tot_loss[loss=0.184, simple_loss=0.2792, pruned_loss=0.04444, over 1426817.06 frames.], batch size: 26, lr: 5.47e-04
2022-04-29 06:08:52,446 INFO [train.py:763] (1/8) Epoch 13, batch 3300, loss[loss=0.1534, simple_loss=0.2503, pruned_loss=0.02825, over 7160.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2794, pruned_loss=0.04442, over 1423547.87 frames.], batch size: 18, lr: 5.46e-04
2022-04-29 06:09:57,827 INFO [train.py:763] (1/8) Epoch 13, batch 3350, loss[loss=0.1708, simple_loss=0.2606, pruned_loss=0.04047, over 7411.00 frames.], tot_loss[loss=0.1835, simple_loss=0.2789, pruned_loss=0.04403, over 1425528.74 frames.], batch size: 18, lr: 5.46e-04
2022-04-29 06:11:03,347 INFO [train.py:763] (1/8) Epoch 13, batch 3400, loss[loss=0.1852, simple_loss=0.2754, pruned_loss=0.04747, over 7164.00 frames.], tot_loss[loss=0.1836, simple_loss=0.2791, pruned_loss=0.04401, over 1426826.52 frames.], batch size: 18, lr: 5.46e-04
2022-04-29 06:12:10,252 INFO [train.py:763] (1/8) Epoch 13, batch 3450, loss[loss=0.172, simple_loss=0.2787, pruned_loss=0.0327, over 7111.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2797, pruned_loss=0.04437, over 1426219.24 frames.], batch size: 21, lr: 5.46e-04
2022-04-29 06:13:16,583 INFO [train.py:763] (1/8) Epoch 13, batch 3500, loss[loss=0.2103, simple_loss=0.3114, pruned_loss=0.05463, over 7334.00 frames.], tot_loss[loss=0.184, simple_loss=0.2794, pruned_loss=0.0443, over 1427969.39 frames.], batch size: 22, lr: 5.46e-04
2022-04-29 06:14:22,079 INFO [train.py:763] (1/8) Epoch 13, batch 3550, loss[loss=0.2229, simple_loss=0.3197, pruned_loss=0.06304, over 7331.00 frames.], tot_loss[loss=0.1842, simple_loss=0.2798, pruned_loss=0.04428, over 1428196.82 frames.], batch size: 21, lr: 5.45e-04
2022-04-29 06:15:27,778 INFO [train.py:763] (1/8) Epoch 13, batch 3600, loss[loss=0.1684, simple_loss=0.2622, pruned_loss=0.03726, over 7355.00 frames.], tot_loss[loss=0.183, simple_loss=0.2784, pruned_loss=0.04381, over 1431304.35 frames.], batch size: 19, lr: 5.45e-04
2022-04-29 06:16:33,706 INFO [train.py:763] (1/8) Epoch 13, batch 3650, loss[loss=0.1783, simple_loss=0.2868, pruned_loss=0.03485, over 7240.00 frames.], tot_loss[loss=0.1831, simple_loss=0.2787, pruned_loss=0.04375, over 1430110.09 frames.], batch size: 20, lr: 5.45e-04
2022-04-29 06:17:39,180 INFO [train.py:763] (1/8) Epoch 13, batch 3700, loss[loss=0.1932, simple_loss=0.2851, pruned_loss=0.05064, over 7302.00 frames.], tot_loss[loss=0.1839, simple_loss=0.2791, pruned_loss=0.04437, over 1421581.23 frames.], batch size: 24, lr: 5.45e-04
2022-04-29 06:18:44,836 INFO [train.py:763] (1/8) Epoch 13, batch 3750, loss[loss=0.2131, simple_loss=0.3018, pruned_loss=0.06213, over 4889.00 frames.], tot_loss[loss=0.1849, simple_loss=0.2799, pruned_loss=0.04498, over 1420687.82 frames.], batch size: 52, lr: 5.45e-04
2022-04-29 06:19:51,470 INFO [train.py:763] (1/8) Epoch 13, batch 3800, loss[loss=0.1523, simple_loss=0.2334, pruned_loss=0.03562, over 7000.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2795, pruned_loss=0.04489, over 1419844.66 frames.], batch size: 16, lr: 5.44e-04
2022-04-29 06:20:57,072 INFO [train.py:763] (1/8) Epoch 13, batch 3850, loss[loss=0.1946, simple_loss=0.293, pruned_loss=0.04808, over 7206.00 frames.], tot_loss[loss=0.1843, simple_loss=0.2796, pruned_loss=0.04451, over 1420117.74 frames.], batch size: 22, lr: 5.44e-04
2022-04-29 06:22:02,333 INFO [train.py:763] (1/8) Epoch 13, batch 3900, loss[loss=0.1864, simple_loss=0.2896, pruned_loss=0.04155, over 7318.00 frames.], tot_loss[loss=0.1844, simple_loss=0.28, pruned_loss=0.04437, over 1422044.44 frames.], batch size: 21, lr: 5.44e-04
2022-04-29 06:23:08,129 INFO [train.py:763] (1/8) Epoch 13, batch 3950, loss[loss=0.2379, simple_loss=0.3125, pruned_loss=0.08163, over 5034.00 frames.], tot_loss[loss=0.1841, simple_loss=0.2794, pruned_loss=0.04445, over 1420744.68 frames.], batch size: 53, lr: 5.44e-04
2022-04-29 06:24:13,268 INFO [train.py:763] (1/8) Epoch 13, batch 4000, loss[loss=0.1999, simple_loss=0.3028, pruned_loss=0.04848, over 7339.00 frames.], tot_loss[loss=0.1846, simple_loss=0.2803, pruned_loss=0.0445, over 1422063.59 frames.], batch size: 22, lr: 5.43e-04
2022-04-29 06:25:19,011 INFO [train.py:763] (1/8) Epoch 13, batch 4050, loss[loss=0.1626, simple_loss=0.2515, pruned_loss=0.03687, over 6856.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2788, pruned_loss=0.04397, over 1423503.98 frames.], batch size: 15, lr: 5.43e-04
2022-04-29 06:26:24,353 INFO [train.py:763] (1/8) Epoch 13, batch 4100, loss[loss=0.2102, simple_loss=0.3091, pruned_loss=0.05566, over 6684.00 frames.], tot_loss[loss=0.1837, simple_loss=0.2788, pruned_loss=0.04429, over 1420199.81 frames.], batch size: 31, lr: 5.43e-04
2022-04-29 06:27:29,932 INFO [train.py:763] (1/8) Epoch 13, batch 4150, loss[loss=0.1841, simple_loss=0.2866, pruned_loss=0.04084, over 7231.00 frames.], tot_loss[loss=0.1836, simple_loss=0.279, pruned_loss=0.04412, over 1420964.98 frames.], batch size: 21, lr: 5.43e-04
2022-04-29 06:28:36,035 INFO [train.py:763] (1/8) Epoch 13, batch 4200, loss[loss=0.2002, simple_loss=0.2721, pruned_loss=0.06413, over 7285.00 frames.], tot_loss[loss=0.1832, simple_loss=0.278, pruned_loss=0.04422, over 1421970.83 frames.], batch size: 17, lr: 5.43e-04
2022-04-29 06:29:41,276 INFO [train.py:763] (1/8) Epoch 13, batch 4250, loss[loss=0.1789, simple_loss=0.2834, pruned_loss=0.03719, over 6482.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2781, pruned_loss=0.04434, over 1417138.86 frames.], batch size: 38, lr: 5.42e-04
2022-04-29 06:30:47,744 INFO [train.py:763] (1/8) Epoch 13, batch 4300, loss[loss=0.2123, simple_loss=0.3022, pruned_loss=0.06119, over 7222.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2784, pruned_loss=0.04457, over 1412304.67 frames.], batch size: 21, lr: 5.42e-04
2022-04-29 06:31:53,159 INFO [train.py:763] (1/8) Epoch 13, batch 4350, loss[loss=0.1635, simple_loss=0.2539, pruned_loss=0.03652, over 6815.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2787, pruned_loss=0.04442, over 1408535.57 frames.], batch size: 15, lr: 5.42e-04
2022-04-29 06:33:10,015 INFO [train.py:763] (1/8) Epoch 13, batch 4400, loss[loss=0.2084, simple_loss=0.3002, pruned_loss=0.05829, over 7146.00 frames.], tot_loss[loss=0.1838, simple_loss=0.2784, pruned_loss=0.0446, over 1402708.01 frames.], batch size: 20, lr: 5.42e-04
2022-04-29 06:34:14,930 INFO [train.py:763] (1/8) Epoch 13, batch 4450, loss[loss=0.2376, simple_loss=0.308, pruned_loss=0.08356, over 4783.00 frames.], tot_loss[loss=0.184, simple_loss=0.2789, pruned_loss=0.04455, over 1393289.77 frames.], batch size: 55, lr: 5.42e-04
2022-04-29 06:35:30,493 INFO [train.py:763] (1/8) Epoch 13, batch 4500, loss[loss=0.2386, simple_loss=0.3165, pruned_loss=0.08036, over 4979.00 frames.], tot_loss[loss=0.1848, simple_loss=0.2796, pruned_loss=0.045, over 1378659.15 frames.], batch size: 52, lr: 5.41e-04
2022-04-29 06:36:35,407 INFO [train.py:763] (1/8) Epoch 13, batch 4550, loss[loss=0.1887, simple_loss=0.2911, pruned_loss=0.04313, over 6729.00 frames.], tot_loss[loss=0.1867, simple_loss=0.2809, pruned_loss=0.04621, over 1368500.97 frames.], batch size: 31, lr: 5.41e-04
2022-04-29 06:38:13,962 INFO [train.py:763] (1/8) Epoch 14, batch 0, loss[loss=0.1886, simple_loss=0.2849, pruned_loss=0.04615, over 7098.00 frames.], tot_loss[loss=0.1886, simple_loss=0.2849, pruned_loss=0.04615, over 7098.00 frames.], batch size: 28, lr: 5.25e-04
2022-04-29 06:39:20,734 INFO [train.py:763] (1/8) Epoch 14, batch 50, loss[loss=0.2185, simple_loss=0.3028, pruned_loss=0.06712, over 5503.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2776, pruned_loss=0.04316, over 321912.59 frames.], batch size: 52, lr: 5.24e-04
2022-04-29 06:40:45,783 INFO [train.py:763] (1/8) Epoch 14, batch 100, loss[loss=0.1472, simple_loss=0.2404, pruned_loss=0.02702, over 7156.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2762, pruned_loss=0.0418, over 568562.36 frames.], batch size: 18, lr: 5.24e-04
2022-04-29 06:41:59,830 INFO [train.py:763] (1/8) Epoch 14, batch 150, loss[loss=0.18, simple_loss=0.2925, pruned_loss=0.03373, over 7108.00 frames.], tot_loss[loss=0.1806, simple_loss=0.278, pruned_loss=0.04156, over 758942.14 frames.], batch size: 21, lr: 5.24e-04
2022-04-29 06:43:06,513 INFO [train.py:763] (1/8) Epoch 14, batch 200, loss[loss=0.1914, simple_loss=0.2843, pruned_loss=0.04927, over 7335.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2786, pruned_loss=0.04256, over 902792.74 frames.], batch size: 20, lr: 5.24e-04
2022-04-29 06:44:23,224 INFO [train.py:763] (1/8) Epoch 14, batch 250, loss[loss=0.1842, simple_loss=0.2919, pruned_loss=0.03828, over 6167.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2792, pruned_loss=0.04275, over 1019157.05 frames.], batch size: 37, lr: 5.24e-04
2022-04-29 06:45:48,392 INFO [train.py:763] (1/8) Epoch 14, batch 300, loss[loss=0.1447, simple_loss=0.2426, pruned_loss=0.02339, over 7137.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2778, pruned_loss=0.04251, over 1108875.73 frames.], batch size: 17, lr: 5.23e-04
2022-04-29 06:46:55,901 INFO [train.py:763] (1/8) Epoch 14, batch 350, loss[loss=0.1853, simple_loss=0.2731, pruned_loss=0.04876, over 6855.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2772, pruned_loss=0.04273, over 1170456.32 frames.], batch size: 15, lr: 5.23e-04
2022-04-29 06:48:03,002 INFO [train.py:763] (1/8) Epoch 14, batch 400, loss[loss=0.1855, simple_loss=0.2894, pruned_loss=0.04075, over 7146.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2772, pruned_loss=0.04298, over 1226600.21 frames.], batch size: 20, lr: 5.23e-04
2022-04-29 06:49:01,685 INFO [train.py:763] (1/8) Epoch 14, batch 450, loss[loss=0.2084, simple_loss=0.2997, pruned_loss=0.05855, over 7164.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2773, pruned_loss=0.04307, over 1271826.21 frames.], batch size: 19, lr: 5.23e-04
2022-04-29 06:50:05,439 INFO [train.py:763] (1/8) Epoch 14, batch 500, loss[loss=0.1787, simple_loss=0.2818, pruned_loss=0.0378, over 7433.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2784, pruned_loss=0.0436, over 1303076.31 frames.], batch size: 20, lr: 5.23e-04
2022-04-29 06:51:07,459 INFO [train.py:763] (1/8) Epoch 14, batch 550, loss[loss=0.1687, simple_loss=0.2631, pruned_loss=0.03714, over 7280.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2776, pruned_loss=0.04311, over 1332104.24 frames.], batch size: 18, lr: 5.22e-04
2022-04-29 06:52:12,671 INFO [train.py:763] (1/8) Epoch 14, batch 600, loss[loss=0.1672, simple_loss=0.2667, pruned_loss=0.03389, over 7237.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2767, pruned_loss=0.04249, over 1355378.45 frames.], batch size: 20, lr: 5.22e-04
2022-04-29 06:53:18,167 INFO [train.py:763] (1/8) Epoch 14, batch 650, loss[loss=0.1731, simple_loss=0.2772, pruned_loss=0.03456, over 7348.00 frames.], tot_loss[loss=0.181, simple_loss=0.2775, pruned_loss=0.04228, over 1370012.30 frames.], batch size: 22, lr: 5.22e-04
2022-04-29 06:54:23,431 INFO [train.py:763] (1/8) Epoch 14, batch 700, loss[loss=0.1791, simple_loss=0.2846, pruned_loss=0.03685, over 7323.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2779, pruned_loss=0.04284, over 1383132.85 frames.], batch size: 20, lr: 5.22e-04
2022-04-29 06:55:28,865 INFO [train.py:763] (1/8) Epoch 14, batch 750, loss[loss=0.1691, simple_loss=0.2754, pruned_loss=0.03137, over 7337.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2777, pruned_loss=0.04271, over 1390966.17 frames.], batch size: 22, lr: 5.22e-04
2022-04-29 06:56:34,177 INFO [train.py:763] (1/8) Epoch 14, batch 800, loss[loss=0.1663, simple_loss=0.2673, pruned_loss=0.03267, over 7328.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2772, pruned_loss=0.04246, over 1399822.97 frames.], batch size: 22, lr: 5.21e-04
2022-04-29 06:57:40,706 INFO [train.py:763] (1/8) Epoch 14, batch 850, loss[loss=0.1612, simple_loss=0.2576, pruned_loss=0.03235, over 7121.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2776, pruned_loss=0.04265, over 1402809.36 frames.], batch size: 17, lr: 5.21e-04
2022-04-29 06:58:46,053 INFO [train.py:763] (1/8) Epoch 14, batch 900, loss[loss=0.2047, simple_loss=0.2864, pruned_loss=0.06153, over 7264.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2779, pruned_loss=0.04324, over 1396714.15 frames.], batch size: 19, lr: 5.21e-04
2022-04-29 06:59:51,292 INFO [train.py:763] (1/8) Epoch 14, batch 950, loss[loss=0.216, simple_loss=0.3127, pruned_loss=0.05967, over 7334.00 frames.], tot_loss[loss=0.1819, simple_loss=0.278, pruned_loss=0.04295, over 1405444.25 frames.], batch size: 22, lr: 5.21e-04
2022-04-29 07:00:56,952 INFO [train.py:763] (1/8) Epoch 14, batch 1000, loss[loss=0.1906, simple_loss=0.2869, pruned_loss=0.0471, over 7018.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2786, pruned_loss=0.04333, over 1405691.08 frames.], batch size: 28, lr: 5.21e-04
2022-04-29 07:02:02,196 INFO [train.py:763] (1/8) Epoch 14, batch 1050, loss[loss=0.1647, simple_loss=0.2594, pruned_loss=0.035, over 7287.00 frames.], tot_loss[loss=0.1816, simple_loss=0.2776, pruned_loss=0.04275, over 1411377.89 frames.], batch size: 18, lr: 5.20e-04
2022-04-29 07:03:07,568 INFO [train.py:763] (1/8) Epoch 14, batch 1100, loss[loss=0.1529, simple_loss=0.2458, pruned_loss=0.03002, over 7263.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2776, pruned_loss=0.04304, over 1415314.77 frames.], batch size: 17, lr: 5.20e-04
2022-04-29 07:04:13,187 INFO [train.py:763] (1/8) Epoch 14, batch 1150, loss[loss=0.1462, simple_loss=0.2455, pruned_loss=0.02343, over 7408.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2773, pruned_loss=0.04306, over 1421022.82 frames.], batch size: 21, lr: 5.20e-04
2022-04-29 07:05:18,947 INFO [train.py:763] (1/8) Epoch 14, batch 1200, loss[loss=0.1733, simple_loss=0.276, pruned_loss=0.03527, over 7434.00 frames.], tot_loss[loss=0.182, simple_loss=0.2776, pruned_loss=0.04319, over 1422346.87 frames.], batch size: 20, lr: 5.20e-04
2022-04-29 07:06:24,245 INFO [train.py:763] (1/8) Epoch 14, batch 1250, loss[loss=0.1635, simple_loss=0.262, pruned_loss=0.0325, over 7347.00 frames.], tot_loss[loss=0.1812, simple_loss=0.2774, pruned_loss=0.04249, over 1425221.84 frames.], batch size: 19, lr: 5.20e-04
2022-04-29 07:07:29,932 INFO [train.py:763] (1/8) Epoch 14, batch 1300, loss[loss=0.184, simple_loss=0.2836, pruned_loss=0.04222, over 6445.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2774, pruned_loss=0.04267, over 1419325.65 frames.], batch size: 37, lr: 5.19e-04
2022-04-29 07:08:35,853 INFO [train.py:763] (1/8) Epoch 14, batch 1350, loss[loss=0.1776, simple_loss=0.2593, pruned_loss=0.04791, over 6988.00 frames.], tot_loss[loss=0.182, simple_loss=0.2778, pruned_loss=0.04311, over 1420615.10 frames.], batch size: 16, lr: 5.19e-04
2022-04-29 07:09:40,886 INFO [train.py:763] (1/8) Epoch 14, batch 1400, loss[loss=0.1843, simple_loss=0.2745, pruned_loss=0.04708, over 7300.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2773, pruned_loss=0.04321, over 1420275.48 frames.], batch size: 24, lr: 5.19e-04
2022-04-29 07:10:46,113 INFO [train.py:763] (1/8) Epoch 14, batch 1450, loss[loss=0.1793, simple_loss=0.2794, pruned_loss=0.03955, over 7385.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2781, pruned_loss=0.04315, over 1417361.81 frames.], batch size: 23, lr: 5.19e-04
2022-04-29 07:11:52,455 INFO [train.py:763] (1/8) Epoch 14, batch 1500, loss[loss=0.1582, simple_loss=0.2576, pruned_loss=0.02943, over 7153.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2788, pruned_loss=0.04342, over 1411980.92 frames.], batch size: 20, lr: 5.19e-04
2022-04-29 07:12:59,675 INFO [train.py:763] (1/8) Epoch 14, batch 1550, loss[loss=0.2005, simple_loss=0.299, pruned_loss=0.051, over 7115.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2776, pruned_loss=0.04269, over 1416840.81 frames.], batch size: 21, lr: 5.18e-04
2022-04-29 07:14:06,936 INFO [train.py:763] (1/8) Epoch 14, batch 1600, loss[loss=0.1791, simple_loss=0.277, pruned_loss=0.04061, over 7417.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2771, pruned_loss=0.04251, over 1419509.26 frames.], batch size: 21, lr: 5.18e-04
2022-04-29 07:15:13,438 INFO [train.py:763] (1/8) Epoch 14, batch 1650, loss[loss=0.2008, simple_loss=0.2997, pruned_loss=0.05097, over 7189.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2773, pruned_loss=0.04243, over 1425252.55 frames.], batch size: 23, lr: 5.18e-04
2022-04-29 07:16:19,624 INFO [train.py:763] (1/8) Epoch 14, batch 1700, loss[loss=0.1869, simple_loss=0.2852, pruned_loss=0.04427, over 7301.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2762, pruned_loss=0.04242, over 1428820.99 frames.], batch size: 25, lr: 5.18e-04
2022-04-29 07:17:25,761 INFO [train.py:763] (1/8) Epoch 14, batch 1750, loss[loss=0.1891, simple_loss=0.3004, pruned_loss=0.03893, over 7069.00 frames.], tot_loss[loss=0.1817, simple_loss=0.2774, pruned_loss=0.04305, over 1431634.11 frames.], batch size: 28, lr: 5.18e-04
2022-04-29 07:18:30,996 INFO [train.py:763] (1/8) Epoch 14, batch 1800, loss[loss=0.1522, simple_loss=0.2421, pruned_loss=0.03117, over 7281.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2772, pruned_loss=0.04286, over 1428196.70 frames.], batch size: 17, lr: 5.17e-04
2022-04-29 07:19:36,651 INFO [train.py:763] (1/8) Epoch 14, batch 1850, loss[loss=0.2064, simple_loss=0.286, pruned_loss=0.06339, over 7165.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2776, pruned_loss=0.04338, over 1432089.11 frames.], batch size: 18, lr: 5.17e-04
2022-04-29 07:20:42,275 INFO [train.py:763] (1/8) Epoch 14, batch 1900, loss[loss=0.1693, simple_loss=0.2668, pruned_loss=0.03587, over 7447.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2776, pruned_loss=0.04368, over 1431518.33 frames.], batch size: 22, lr: 5.17e-04
2022-04-29 07:21:47,862 INFO [train.py:763] (1/8) Epoch 14, batch 1950, loss[loss=0.2031, simple_loss=0.2952, pruned_loss=0.05548, over 7265.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2775, pruned_loss=0.0434, over 1431751.31 frames.], batch size: 18, lr: 5.17e-04
2022-04-29 07:22:53,274 INFO [train.py:763] (1/8) Epoch 14, batch 2000, loss[loss=0.2182, simple_loss=0.3138, pruned_loss=0.06136, over 6450.00 frames.], tot_loss[loss=0.1828, simple_loss=0.278, pruned_loss=0.04379, over 1427556.89 frames.], batch size: 38, lr: 5.17e-04
2022-04-29 07:23:58,400 INFO [train.py:763] (1/8) Epoch 14, batch 2050, loss[loss=0.1808, simple_loss=0.2825, pruned_loss=0.03951, over 7292.00 frames.], tot_loss[loss=0.1827, simple_loss=0.2785, pruned_loss=0.04342, over 1429023.90 frames.], batch size: 25, lr: 5.16e-04
2022-04-29 07:25:03,740 INFO [train.py:763] (1/8) Epoch 14, batch 2100, loss[loss=0.1547, simple_loss=0.2497, pruned_loss=0.02985, over 7415.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2782, pruned_loss=0.04328, over 1422246.93 frames.], batch size: 18, lr: 5.16e-04
2022-04-29 07:26:09,018 INFO [train.py:763] (1/8) Epoch 14, batch 2150, loss[loss=0.2096, simple_loss=0.3032, pruned_loss=0.05798, over 7213.00 frames.], tot_loss[loss=0.182, simple_loss=0.2779, pruned_loss=0.04308, over 1420427.63 frames.], batch size: 22, lr: 5.16e-04
2022-04-29 07:27:14,550 INFO [train.py:763] (1/8) Epoch 14, batch 2200, loss[loss=0.1911, simple_loss=0.2952, pruned_loss=0.04346, over 7434.00 frames.], tot_loss[loss=0.1822, simple_loss=0.278, pruned_loss=0.04321, over 1420406.00 frames.], batch size: 20, lr: 5.16e-04
2022-04-29 07:28:19,750 INFO [train.py:763] (1/8) Epoch 14, batch 2250, loss[loss=0.1966, simple_loss=0.2917, pruned_loss=0.05075, over 7185.00 frames.], tot_loss[loss=0.182, simple_loss=0.2779, pruned_loss=0.04305, over 1422253.52 frames.], batch size: 28, lr: 5.16e-04
2022-04-29 07:29:24,984 INFO [train.py:763] (1/8) Epoch 14, batch 2300, loss[loss=0.1801, simple_loss=0.2622, pruned_loss=0.04905, over 6840.00 frames.], tot_loss[loss=0.182, simple_loss=0.2779, pruned_loss=0.0431, over 1420913.45 frames.], batch size: 15, lr: 5.15e-04
2022-04-29 07:30:30,166 INFO [train.py:763] (1/8) Epoch 14, batch 2350, loss[loss=0.1387, simple_loss=0.2337, pruned_loss=0.02184, over 7389.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2776, pruned_loss=0.04328, over 1423833.63 frames.], batch size: 18, lr: 5.15e-04
2022-04-29 07:31:35,492 INFO [train.py:763] (1/8) Epoch 14, batch 2400, loss[loss=0.1686, simple_loss=0.2509, pruned_loss=0.0431, over 7419.00 frames.], tot_loss[loss=0.1826, simple_loss=0.2783, pruned_loss=0.04346, over 1421911.54 frames.], batch size: 18, lr: 5.15e-04
2022-04-29 07:32:40,929 INFO [train.py:763] (1/8) Epoch 14, batch 2450, loss[loss=0.2013, simple_loss=0.2926, pruned_loss=0.05498, over 7406.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2789, pruned_loss=0.04331, over 1423095.37 frames.], batch size: 21, lr: 5.15e-04
2022-04-29 07:33:46,234 INFO [train.py:763] (1/8) Epoch 14, batch 2500, loss[loss=0.1787, simple_loss=0.2877, pruned_loss=0.03481, over 7316.00 frames.], tot_loss[loss=0.1832, simple_loss=0.2796, pruned_loss=0.04337, over 1425170.05 frames.], batch size: 21, lr: 5.15e-04
2022-04-29 07:34:51,431 INFO [train.py:763] (1/8) Epoch 14, batch 2550, loss[loss=0.1687, simple_loss=0.2658, pruned_loss=0.03576, over 7153.00 frames.], tot_loss[loss=0.1829, simple_loss=0.279, pruned_loss=0.04343, over 1427840.31 frames.], batch size: 18, lr: 5.14e-04
2022-04-29 07:35:56,546 INFO [train.py:763] (1/8) Epoch 14, batch 2600, loss[loss=0.2033, simple_loss=0.297, pruned_loss=0.05479, over 7210.00 frames.], tot_loss[loss=0.1832, simple_loss=0.279, pruned_loss=0.0437, over 1421039.96 frames.], batch size: 23, lr: 5.14e-04
2022-04-29 07:37:01,619 INFO [train.py:763] (1/8) Epoch 14, batch 2650, loss[loss=0.1799, simple_loss=0.278, pruned_loss=0.04097, over 7307.00 frames.], tot_loss[loss=0.1834, simple_loss=0.2792, pruned_loss=0.04376, over 1421504.84 frames.], batch size: 25, lr: 5.14e-04
2022-04-29 07:38:06,935 INFO [train.py:763] (1/8) Epoch 14, batch 2700, loss[loss=0.2095, simple_loss=0.3099, pruned_loss=0.05453, over 7324.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2782, pruned_loss=0.04326, over 1423983.31 frames.], batch size: 21, lr: 5.14e-04
2022-04-29 07:39:12,133 INFO [train.py:763] (1/8) Epoch 14, batch 2750, loss[loss=0.2005, simple_loss=0.297, pruned_loss=0.05203, over 7274.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2785, pruned_loss=0.04325, over 1425122.45 frames.], batch size: 24, lr: 5.14e-04
2022-04-29 07:40:17,443 INFO [train.py:763] (1/8) Epoch 14, batch 2800, loss[loss=0.1922, simple_loss=0.287, pruned_loss=0.04869, over 7146.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2774, pruned_loss=0.04267, over 1427986.23 frames.], batch size: 20, lr: 5.14e-04
2022-04-29 07:41:22,765 INFO [train.py:763] (1/8) Epoch 14, batch 2850, loss[loss=0.2, simple_loss=0.2903, pruned_loss=0.05486, over 6780.00 frames.], tot_loss[loss=0.1815, simple_loss=0.2777, pruned_loss=0.04262, over 1427729.68 frames.], batch size: 15, lr: 5.13e-04
2022-04-29 07:42:28,525 INFO [train.py:763] (1/8) Epoch 14, batch 2900, loss[loss=0.1972, simple_loss=0.2903, pruned_loss=0.05205, over 7363.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2777, pruned_loss=0.04304, over 1423693.68 frames.], batch size: 23, lr: 5.13e-04
2022-04-29 07:43:34,055 INFO [train.py:763] (1/8) Epoch 14, batch 2950, loss[loss=0.203, simple_loss=0.2857, pruned_loss=0.06017, over 7430.00 frames.], tot_loss[loss=0.1821, simple_loss=0.2777, pruned_loss=0.04328, over 1424953.23 frames.], batch size: 20, lr: 5.13e-04
2022-04-29 07:44:39,581 INFO [train.py:763] (1/8) Epoch 14, batch 3000, loss[loss=0.1644, simple_loss=0.2646, pruned_loss=0.03217, over 7168.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2772, pruned_loss=0.04329, over 1423529.75 frames.], batch size: 19, lr: 5.13e-04
2022-04-29 07:44:39,582 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 07:44:54,980 INFO [train.py:792] (1/8) Epoch 14, validation: loss=0.1687, simple_loss=0.2708, pruned_loss=0.03331, over 698248.00 frames.
2022-04-29 07:46:00,329 INFO [train.py:763] (1/8) Epoch 14, batch 3050, loss[loss=0.1617, simple_loss=0.2437, pruned_loss=0.03985, over 6823.00 frames.], tot_loss[loss=0.1822, simple_loss=0.2778, pruned_loss=0.04329, over 1425956.36 frames.], batch size: 15, lr: 5.13e-04
2022-04-29 07:47:05,874 INFO [train.py:763] (1/8) Epoch 14, batch 3100, loss[loss=0.1773, simple_loss=0.2689, pruned_loss=0.04283, over 7336.00 frames.], tot_loss[loss=0.1825, simple_loss=0.2784, pruned_loss=0.04327, over 1422405.50 frames.], batch size: 20, lr: 5.12e-04
2022-04-29 07:48:12,212 INFO [train.py:763] (1/8) Epoch 14, batch 3150, loss[loss=0.1703, simple_loss=0.2491, pruned_loss=0.04575, over 7278.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2767, pruned_loss=0.04227, over 1427361.50 frames.], batch size: 17, lr: 5.12e-04
2022-04-29 07:49:18,809 INFO [train.py:763] (1/8) Epoch 14, batch 3200, loss[loss=0.1792, simple_loss=0.2755, pruned_loss=0.04148, over 7074.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2758, pruned_loss=0.04175, over 1427753.81 frames.], batch size: 28, lr: 5.12e-04
2022-04-29 07:50:24,260 INFO [train.py:763] (1/8) Epoch 14, batch 3250, loss[loss=0.164, simple_loss=0.2609, pruned_loss=0.03355, over 7057.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2761, pruned_loss=0.0421, over 1428263.42 frames.], batch size: 18, lr: 5.12e-04
2022-04-29 07:51:29,738 INFO [train.py:763] (1/8) Epoch 14, batch 3300, loss[loss=0.1966, simple_loss=0.2728, pruned_loss=0.06017, over 7263.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2755, pruned_loss=0.0425, over 1427529.27 frames.], batch size: 17, lr: 5.12e-04
2022-04-29 07:52:35,057 INFO [train.py:763] (1/8) Epoch 14, batch 3350, loss[loss=0.2357, simple_loss=0.3253, pruned_loss=0.07302, over 7206.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2766, pruned_loss=0.04276, over 1427139.02 frames.], batch size: 23, lr: 5.11e-04
2022-04-29 07:53:40,778 INFO [train.py:763] (1/8) Epoch 14, batch 3400, loss[loss=0.2025, simple_loss=0.3017, pruned_loss=0.05167, over 7225.00 frames.], tot_loss[loss=0.1813, simple_loss=0.2772, pruned_loss=0.04275, over 1424233.24 frames.], batch size: 21, lr: 5.11e-04
2022-04-29 07:54:45,991 INFO [train.py:763] (1/8) Epoch 14, batch 3450, loss[loss=0.1773, simple_loss=0.278, pruned_loss=0.03829, over 7013.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2784, pruned_loss=0.04327, over 1420781.88 frames.], batch size: 28, lr: 5.11e-04
2022-04-29 07:55:51,601 INFO [train.py:763] (1/8) Epoch 14, batch 3500, loss[loss=0.177, simple_loss=0.2741, pruned_loss=0.03992, over 7181.00 frames.], tot_loss[loss=0.1818, simple_loss=0.2775, pruned_loss=0.04302, over 1425950.83 frames.], batch size: 26, lr: 5.11e-04
2022-04-29 07:56:57,021 INFO [train.py:763] (1/8) Epoch 14, batch 3550, loss[loss=0.1944, simple_loss=0.305, pruned_loss=0.04192, over 7227.00 frames.], tot_loss[loss=0.1824, simple_loss=0.2782, pruned_loss=0.04333, over 1427768.25 frames.], batch size: 20, lr: 5.11e-04
2022-04-29 07:58:03,507 INFO [train.py:763] (1/8) Epoch 14, batch 3600, loss[loss=0.1943, simple_loss=0.2882, pruned_loss=0.05019, over 7325.00 frames.], tot_loss[loss=0.183, simple_loss=0.2784, pruned_loss=0.04373, over 1424762.44 frames.], batch size: 21, lr: 5.11e-04
2022-04-29 07:59:08,916 INFO [train.py:763] (1/8) Epoch 14, batch 3650, loss[loss=0.1602, simple_loss=0.2607, pruned_loss=0.02983, over 7265.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2786, pruned_loss=0.04355, over 1424718.61 frames.], batch size: 19, lr: 5.10e-04
2022-04-29 08:00:14,235 INFO [train.py:763] (1/8) Epoch 14, batch 3700, loss[loss=0.1558, simple_loss=0.2589, pruned_loss=0.02635, over 7433.00 frames.], tot_loss[loss=0.183, simple_loss=0.2787, pruned_loss=0.04363, over 1421561.66 frames.], batch size: 20, lr: 5.10e-04
2022-04-29 08:01:19,993 INFO [train.py:763] (1/8) Epoch 14, batch 3750, loss[loss=0.2031, simple_loss=0.2967, pruned_loss=0.05479, over 5442.00 frames.], tot_loss[loss=0.1828, simple_loss=0.2785, pruned_loss=0.04355, over 1423360.25 frames.], batch size: 52, lr: 5.10e-04
2022-04-29 08:02:27,023 INFO [train.py:763] (1/8) Epoch 14, batch 3800, loss[loss=0.1991, simple_loss=0.2871, pruned_loss=0.05553, over 7064.00 frames.], tot_loss[loss=0.1823, simple_loss=0.2782, pruned_loss=0.04315, over 1425050.17 frames.], batch size: 18, lr: 5.10e-04
2022-04-29 08:03:33,821 INFO [train.py:763] (1/8) Epoch 14, batch 3850, loss[loss=0.1963, simple_loss=0.3006, pruned_loss=0.04599, over 7224.00 frames.], tot_loss[loss=0.182, simple_loss=0.2785, pruned_loss=0.0427, over 1427416.43 frames.], batch size: 20, lr: 5.10e-04
2022-04-29 08:04:40,265 INFO [train.py:763] (1/8) Epoch 14, batch 3900, loss[loss=0.1889, simple_loss=0.2855, pruned_loss=0.04615, over 7253.00 frames.], tot_loss[loss=0.1806, simple_loss=0.2768, pruned_loss=0.04214, over 1424684.69 frames.], batch size: 19, lr: 5.09e-04
2022-04-29 08:05:46,497 INFO [train.py:763] (1/8) Epoch 14, batch 3950, loss[loss=0.1577, simple_loss=0.2651, pruned_loss=0.02515, over 7360.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2768, pruned_loss=0.04242, over 1421916.18 frames.], batch size: 19, lr: 5.09e-04
2022-04-29 08:06:52,811 INFO [train.py:763] (1/8) Epoch 14, batch 4000, loss[loss=0.1922, simple_loss=0.2893, pruned_loss=0.04755, over 7226.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2772, pruned_loss=0.04283, over 1422672.67 frames.], batch size: 21, lr: 5.09e-04
2022-04-29 08:07:57,992 INFO [train.py:763] (1/8) Epoch 14, batch 4050, loss[loss=0.1975, simple_loss=0.3009, pruned_loss=0.047, over 7209.00 frames.], tot_loss[loss=0.1814, simple_loss=0.2778, pruned_loss=0.04253, over 1426885.05 frames.], batch size: 21, lr: 5.09e-04
2022-04-29 08:09:03,251 INFO [train.py:763] (1/8) Epoch 14, batch 4100, loss[loss=0.1817, simple_loss=0.2836, pruned_loss=0.03994, over 7195.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2775, pruned_loss=0.04313, over 1417965.53 frames.], batch size: 23, lr: 5.09e-04
2022-04-29 08:10:08,497 INFO [train.py:763] (1/8) Epoch 14, batch 4150, loss[loss=0.2266, simple_loss=0.3146, pruned_loss=0.06934, over 5088.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2776, pruned_loss=0.04311, over 1411929.38 frames.], batch size: 54, lr: 5.08e-04
2022-04-29 08:11:13,731 INFO [train.py:763] (1/8) Epoch 14, batch 4200, loss[loss=0.1807, simple_loss=0.2783, pruned_loss=0.04149, over 7238.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2763, pruned_loss=0.04269, over 1410289.85 frames.], batch size: 20, lr: 5.08e-04
2022-04-29 08:12:19,793 INFO [train.py:763] (1/8) Epoch 14, batch 4250, loss[loss=0.1695, simple_loss=0.2692, pruned_loss=0.03492, over 7069.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2762, pruned_loss=0.04237, over 1408331.77 frames.], batch size: 18, lr: 5.08e-04
2022-04-29 08:13:25,927 INFO [train.py:763] (1/8) Epoch 14, batch 4300, loss[loss=0.1593, simple_loss=0.2543, pruned_loss=0.03215, over 7172.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2768, pruned_loss=0.04231, over 1404735.85 frames.], batch size: 16, lr: 5.08e-04
2022-04-29 08:14:30,946 INFO [train.py:763] (1/8) Epoch 14, batch 4350, loss[loss=0.1756, simple_loss=0.2759, pruned_loss=0.03771, over 7322.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2774, pruned_loss=0.04242, over 1409304.48 frames.], batch size: 21, lr: 5.08e-04
2022-04-29 08:15:37,006 INFO [train.py:763] (1/8) Epoch 14, batch 4400, loss[loss=0.1482, simple_loss=0.2454, pruned_loss=0.02552, over 7165.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2757, pruned_loss=0.04196, over 1411653.97 frames.], batch size: 19, lr: 5.08e-04
2022-04-29 08:16:42,687 INFO [train.py:763] (1/8) Epoch 14, batch 4450, loss[loss=0.1898, simple_loss=0.2905, pruned_loss=0.04456, over 7174.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2746, pruned_loss=0.04177, over 1405361.00 frames.], batch size: 18, lr: 5.07e-04
2022-04-29 08:17:47,610 INFO [train.py:763] (1/8) Epoch 14, batch 4500, loss[loss=0.1831, simple_loss=0.2729, pruned_loss=0.04667, over 7068.00 frames.], tot_loss[loss=0.18, simple_loss=0.2755, pruned_loss=0.0423, over 1395766.17 frames.], batch size: 18, lr: 5.07e-04
2022-04-29 08:18:51,943 INFO [train.py:763] (1/8) Epoch 14, batch 4550, loss[loss=0.2186, simple_loss=0.31, pruned_loss=0.06358, over 5022.00 frames.], tot_loss[loss=0.1821, simple_loss=0.277, pruned_loss=0.0436, over 1369225.82 frames.], batch size: 52, lr: 5.07e-04
2022-04-29 08:20:20,832 INFO [train.py:763] (1/8) Epoch 15, batch 0, loss[loss=0.1908, simple_loss=0.2891, pruned_loss=0.04619, over 7291.00 frames.], tot_loss[loss=0.1908, simple_loss=0.2891, pruned_loss=0.04619, over 7291.00 frames.], batch size: 24, lr: 4.92e-04
2022-04-29 08:21:27,545 INFO [train.py:763] (1/8) Epoch 15, batch 50, loss[loss=0.165, simple_loss=0.2563, pruned_loss=0.03682, over 7395.00 frames.], tot_loss[loss=0.1791, simple_loss=0.276, pruned_loss=0.04111, over 321492.26 frames.], batch size: 18, lr: 4.92e-04
2022-04-29 08:22:33,675 INFO [train.py:763] (1/8) Epoch 15, batch 100, loss[loss=0.156, simple_loss=0.2461, pruned_loss=0.03296, over 7330.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2734, pruned_loss=0.04022, over 564939.64 frames.], batch size: 20, lr: 4.92e-04
2022-04-29 08:23:40,360 INFO [train.py:763] (1/8) Epoch 15, batch 150, loss[loss=0.1819, simple_loss=0.289, pruned_loss=0.03738, over 7154.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2742, pruned_loss=0.04094, over 755208.44 frames.], batch size: 20, lr: 4.92e-04
2022-04-29 08:24:46,767 INFO [train.py:763] (1/8) Epoch 15, batch 200, loss[loss=0.1795, simple_loss=0.2741, pruned_loss=0.04245, over 7115.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2739, pruned_loss=0.04086, over 898430.60 frames.], batch size: 21, lr: 4.91e-04
2022-04-29 08:25:52,225 INFO [train.py:763] (1/8) Epoch 15, batch 250, loss[loss=0.1686, simple_loss=0.2624, pruned_loss=0.03738, over 7157.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2739, pruned_loss=0.04123, over 1015060.17 frames.], batch size: 19, lr: 4.91e-04
2022-04-29 08:26:57,838 INFO [train.py:763] (1/8) Epoch 15, batch 300, loss[loss=0.1856, simple_loss=0.2806, pruned_loss=0.0453, over 7154.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2738, pruned_loss=0.04128, over 1109470.28 frames.], batch size: 19, lr: 4.91e-04
2022-04-29 08:28:03,220 INFO [train.py:763] (1/8) Epoch 15, batch 350, loss[loss=0.1612, simple_loss=0.2533, pruned_loss=0.03452, over 7280.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2737, pruned_loss=0.04127, over 1180213.56 frames.], batch size: 18, lr: 4.91e-04
2022-04-29 08:29:08,689 INFO [train.py:763] (1/8) Epoch 15, batch 400, loss[loss=0.1638, simple_loss=0.2536, pruned_loss=0.03699, over 7248.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2746, pruned_loss=0.04101, over 1233856.79 frames.], batch size: 19, lr: 4.91e-04
2022-04-29 08:30:14,239 INFO [train.py:763] (1/8) Epoch 15, batch 450, loss[loss=0.1638, simple_loss=0.2596, pruned_loss=0.03401, over 7427.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2745, pruned_loss=0.04103, over 1280950.76 frames.], batch size: 20, lr: 4.91e-04
2022-04-29 08:31:19,781 INFO [train.py:763] (1/8) Epoch 15, batch 500, loss[loss=0.2129, simple_loss=0.3106, pruned_loss=0.05762, over 7185.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2763, pruned_loss=0.04171, over 1317903.05 frames.], batch size: 23, lr: 4.90e-04
2022-04-29 08:32:25,945 INFO [train.py:763] (1/8) Epoch 15, batch 550, loss[loss=0.1692, simple_loss=0.2624, pruned_loss=0.03801, over 7282.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2748, pruned_loss=0.04111, over 1345109.78 frames.], batch size: 18, lr: 4.90e-04
2022-04-29 08:33:31,108 INFO [train.py:763] (1/8) Epoch 15, batch 600, loss[loss=0.1699, simple_loss=0.271, pruned_loss=0.03444, over 7160.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2754, pruned_loss=0.04149, over 1361788.08 frames.], batch size: 19, lr: 4.90e-04
2022-04-29 08:34:36,398 INFO [train.py:763] (1/8) Epoch 15, batch 650, loss[loss=0.1855, simple_loss=0.2799, pruned_loss=0.0455, over 6524.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2755, pruned_loss=0.04168, over 1374109.65 frames.], batch size: 38, lr: 4.90e-04
2022-04-29 08:35:42,058 INFO [train.py:763] (1/8) Epoch 15, batch 700, loss[loss=0.2007, simple_loss=0.3019, pruned_loss=0.04973, over 7087.00 frames.], tot_loss[loss=0.1795, simple_loss=0.276, pruned_loss=0.04153, over 1386363.53 frames.], batch size: 28, lr: 4.90e-04
2022-04-29 08:36:47,192 INFO [train.py:763] (1/8) Epoch 15, batch 750, loss[loss=0.1867, simple_loss=0.2714, pruned_loss=0.05105, over 7162.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2761, pruned_loss=0.04142, over 1395122.44 frames.], batch size: 19, lr: 4.89e-04
2022-04-29 08:37:53,214 INFO [train.py:763] (1/8) Epoch 15, batch 800, loss[loss=0.1874, simple_loss=0.2842, pruned_loss=0.04528, over 7260.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2759, pruned_loss=0.04145, over 1402578.05 frames.], batch size: 19, lr: 4.89e-04
2022-04-29 08:39:00,103 INFO [train.py:763] (1/8) Epoch 15, batch 850, loss[loss=0.1616, simple_loss=0.2686, pruned_loss=0.02732, over 7141.00 frames.], tot_loss[loss=0.1792, simple_loss=0.276, pruned_loss=0.0412, over 1404922.81 frames.], batch size: 20, lr: 4.89e-04
2022-04-29 08:40:05,802 INFO [train.py:763] (1/8) Epoch 15, batch 900, loss[loss=0.1605, simple_loss=0.2563, pruned_loss=0.03238, over 7360.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2768, pruned_loss=0.04209, over 1404419.34 frames.], batch size: 19, lr: 4.89e-04
2022-04-29 08:41:11,038 INFO [train.py:763] (1/8) Epoch 15, batch 950, loss[loss=0.1757, simple_loss=0.2736, pruned_loss=0.03888, over 7435.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2758, pruned_loss=0.04187, over 1407134.89 frames.], batch size: 20, lr: 4.89e-04
2022-04-29 08:42:16,442 INFO [train.py:763] (1/8) Epoch 15, batch 1000, loss[loss=0.1913, simple_loss=0.2829, pruned_loss=0.04989, over 7286.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2755, pruned_loss=0.04158, over 1413265.69 frames.], batch size: 25, lr: 4.89e-04
2022-04-29 08:43:21,669 INFO [train.py:763] (1/8) Epoch 15, batch 1050, loss[loss=0.1879, simple_loss=0.2863, pruned_loss=0.04478, over 7320.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2765, pruned_loss=0.04187, over 1418283.64 frames.], batch size: 20, lr: 4.88e-04
2022-04-29 08:44:28,812 INFO [train.py:763] (1/8) Epoch 15, batch 1100, loss[loss=0.145, simple_loss=0.2433, pruned_loss=0.02338, over 7362.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2757, pruned_loss=0.04131, over 1422098.99 frames.], batch size: 19, lr: 4.88e-04
2022-04-29 08:45:35,099 INFO [train.py:763] (1/8) Epoch 15, batch 1150, loss[loss=0.215, simple_loss=0.3032, pruned_loss=0.06339, over 5130.00 frames.], tot_loss[loss=0.179, simple_loss=0.2753, pruned_loss=0.04136, over 1422277.00 frames.], batch size: 52, lr: 4.88e-04
2022-04-29 08:46:40,371 INFO [train.py:763] (1/8) Epoch 15, batch 1200, loss[loss=0.2109, simple_loss=0.3014, pruned_loss=0.06024, over 7117.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2754, pruned_loss=0.04166, over 1418413.96 frames.], batch size: 21, lr: 4.88e-04
2022-04-29 08:47:45,860 INFO [train.py:763] (1/8) Epoch 15, batch 1250, loss[loss=0.1538, simple_loss=0.2406, pruned_loss=0.03348, over 7182.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2752, pruned_loss=0.04193, over 1419471.74 frames.], batch size: 16, lr: 4.88e-04
2022-04-29 08:48:51,148 INFO [train.py:763] (1/8) Epoch 15, batch 1300, loss[loss=0.1686, simple_loss=0.2701, pruned_loss=0.03352, over 7206.00 frames.], tot_loss[loss=0.1799, simple_loss=0.276, pruned_loss=0.04192, over 1425546.22 frames.], batch size: 22, lr: 4.88e-04
2022-04-29 08:49:56,770 INFO [train.py:763] (1/8) Epoch 15, batch 1350, loss[loss=0.1607, simple_loss=0.2562, pruned_loss=0.03256, over 7160.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2759, pruned_loss=0.04196, over 1418394.69 frames.], batch size: 19, lr: 4.87e-04
2022-04-29 08:51:13,199 INFO [train.py:763] (1/8) Epoch 15, batch 1400, loss[loss=0.1841, simple_loss=0.2894, pruned_loss=0.03945, over 7348.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2765, pruned_loss=0.04187, over 1416493.00 frames.], batch size: 22, lr: 4.87e-04
2022-04-29 08:52:20,208 INFO [train.py:763] (1/8) Epoch 15, batch 1450, loss[loss=0.1781, simple_loss=0.2918, pruned_loss=0.03217, over 7423.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2764, pruned_loss=0.04149, over 1422491.76 frames.], batch size: 21, lr: 4.87e-04
2022-04-29 08:53:25,684 INFO [train.py:763] (1/8) Epoch 15, batch 1500, loss[loss=0.208, simple_loss=0.2989, pruned_loss=0.05851, over 7199.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2764, pruned_loss=0.04159, over 1422774.40 frames.], batch size: 23, lr: 4.87e-04
2022-04-29 08:54:40,089 INFO [train.py:763] (1/8) Epoch 15, batch 1550, loss[loss=0.1381, simple_loss=0.2282, pruned_loss=0.02404, over 7227.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2758, pruned_loss=0.0417, over 1421475.31 frames.], batch size: 16, lr: 4.87e-04
2022-04-29 08:56:03,995 INFO [train.py:763] (1/8) Epoch 15, batch 1600, loss[loss=0.1629, simple_loss=0.251, pruned_loss=0.03743, over 7240.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2762, pruned_loss=0.0418, over 1424002.40 frames.], batch size: 16, lr: 4.87e-04
2022-04-29 08:57:19,954 INFO [train.py:763] (1/8) Epoch 15, batch 1650, loss[loss=0.185, simple_loss=0.2795, pruned_loss=0.04524, over 7128.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2762, pruned_loss=0.0418, over 1425597.58 frames.], batch size: 20, lr: 4.86e-04
2022-04-29 08:58:25,680 INFO [train.py:763] (1/8) Epoch 15, batch 1700, loss[loss=0.1454, simple_loss=0.2369, pruned_loss=0.02696, over 7400.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2747, pruned_loss=0.0412, over 1425641.91 frames.], batch size: 18, lr: 4.86e-04
2022-04-29 08:59:40,115 INFO [train.py:763] (1/8) Epoch 15, batch 1750, loss[loss=0.1847, simple_loss=0.2918, pruned_loss=0.03881, over 7378.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2754, pruned_loss=0.04168, over 1425258.60 frames.], batch size: 23, lr: 4.86e-04
2022-04-29 09:00:47,095 INFO [train.py:763] (1/8) Epoch 15, batch 1800, loss[loss=0.1599, simple_loss=0.2621, pruned_loss=0.02885, over 7362.00 frames.], tot_loss[loss=0.18, simple_loss=0.2763, pruned_loss=0.04188, over 1423330.16 frames.], batch size: 19, lr: 4.86e-04
2022-04-29 09:02:11,303 INFO [train.py:763] (1/8) Epoch 15, batch 1850, loss[loss=0.1854, simple_loss=0.2862, pruned_loss=0.04223, over 7150.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2752, pruned_loss=0.04177, over 1425942.27 frames.], batch size: 20, lr: 4.86e-04
2022-04-29 09:03:16,747 INFO [train.py:763] (1/8) Epoch 15, batch 1900, loss[loss=0.2018, simple_loss=0.2937, pruned_loss=0.0549, over 7303.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2751, pruned_loss=0.04182, over 1429832.49 frames.], batch size: 25, lr: 4.86e-04
2022-04-29 09:04:23,831 INFO [train.py:763] (1/8) Epoch 15, batch 1950, loss[loss=0.1675, simple_loss=0.2716, pruned_loss=0.03173, over 7215.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2759, pruned_loss=0.04179, over 1430544.02 frames.], batch size: 23, lr: 4.85e-04
2022-04-29 09:05:29,696 INFO [train.py:763] (1/8) Epoch 15, batch 2000, loss[loss=0.2462, simple_loss=0.3256, pruned_loss=0.08341, over 4812.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2764, pruned_loss=0.04213, over 1423605.35 frames.], batch size: 53, lr: 4.85e-04
2022-04-29 09:06:36,280 INFO [train.py:763] (1/8) Epoch 15, batch 2050, loss[loss=0.1734, simple_loss=0.2727, pruned_loss=0.037, over 6398.00 frames.], tot_loss[loss=0.1805, simple_loss=0.277, pruned_loss=0.04202, over 1422598.48 frames.], batch size: 37, lr: 4.85e-04
2022-04-29 09:07:41,964 INFO [train.py:763] (1/8) Epoch 15, batch 2100, loss[loss=0.1666, simple_loss=0.2764, pruned_loss=0.02842, over 7117.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2765, pruned_loss=0.04186, over 1424003.50 frames.], batch size: 21, lr: 4.85e-04
2022-04-29 09:08:48,746 INFO [train.py:763] (1/8) Epoch 15, batch 2150, loss[loss=0.1564, simple_loss=0.2511, pruned_loss=0.03091, over 7263.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2769, pruned_loss=0.04185, over 1419485.54 frames.], batch size: 19, lr: 4.85e-04
2022-04-29 09:09:53,838 INFO [train.py:763] (1/8) Epoch 15, batch 2200, loss[loss=0.1695, simple_loss=0.2696, pruned_loss=0.03476, over 7206.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2767, pruned_loss=0.04205, over 1416687.99 frames.], batch size: 22, lr: 4.84e-04
2022-04-29 09:10:59,454 INFO [train.py:763] (1/8) Epoch 15, batch 2250, loss[loss=0.1845, simple_loss=0.2862, pruned_loss=0.04143, over 7424.00 frames.], tot_loss[loss=0.1811, simple_loss=0.2772, pruned_loss=0.04251, over 1418672.83 frames.], batch size: 21, lr: 4.84e-04
2022-04-29 09:12:05,739 INFO [train.py:763] (1/8) Epoch 15, batch 2300, loss[loss=0.1908, simple_loss=0.2869, pruned_loss=0.04741, over 7195.00 frames.], tot_loss[loss=0.1807, simple_loss=0.277, pruned_loss=0.04219, over 1419817.68 frames.], batch size: 23, lr: 4.84e-04
2022-04-29 09:13:13,274 INFO [train.py:763] (1/8) Epoch 15, batch 2350, loss[loss=0.1972, simple_loss=0.3034, pruned_loss=0.04554, over 7292.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2765, pruned_loss=0.04191, over 1422993.50 frames.], batch size: 25, lr: 4.84e-04
2022-04-29 09:14:19,339 INFO [train.py:763] (1/8) Epoch 15, batch 2400, loss[loss=0.1793, simple_loss=0.2819, pruned_loss=0.03833, over 7294.00 frames.], tot_loss[loss=0.1787, simple_loss=0.275, pruned_loss=0.04123, over 1426493.91 frames.], batch size: 25, lr: 4.84e-04
2022-04-29 09:15:24,433 INFO [train.py:763] (1/8) Epoch 15, batch 2450, loss[loss=0.2125, simple_loss=0.3096, pruned_loss=0.05769, over 6750.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2749, pruned_loss=0.04121, over 1424956.06 frames.], batch size: 31, lr: 4.84e-04
2022-04-29 09:16:31,164 INFO [train.py:763] (1/8) Epoch 15, batch 2500, loss[loss=0.1636, simple_loss=0.2693, pruned_loss=0.02896, over 7233.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2751, pruned_loss=0.04124, over 1427216.79 frames.], batch size: 21, lr: 4.83e-04
2022-04-29 09:17:37,362 INFO [train.py:763] (1/8) Epoch 15, batch 2550, loss[loss=0.1801, simple_loss=0.2823, pruned_loss=0.03892, over 7147.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2746, pruned_loss=0.04111, over 1423990.23 frames.], batch size: 20, lr: 4.83e-04
2022-04-29 09:18:44,501 INFO [train.py:763] (1/8) Epoch 15, batch 2600, loss[loss=0.1906, simple_loss=0.2839, pruned_loss=0.04864, over 7366.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2755, pruned_loss=0.0418, over 1422564.89 frames.], batch size: 19, lr: 4.83e-04
2022-04-29 09:19:51,209 INFO [train.py:763] (1/8) Epoch 15, batch 2650, loss[loss=0.2105, simple_loss=0.3078, pruned_loss=0.05657, over 7387.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2757, pruned_loss=0.04175, over 1422644.81 frames.], batch size: 23, lr: 4.83e-04
2022-04-29 09:20:56,493 INFO [train.py:763] (1/8) Epoch 15, batch 2700, loss[loss=0.1991, simple_loss=0.2986, pruned_loss=0.04984, over 7201.00 frames.], tot_loss[loss=0.1799, simple_loss=0.2758, pruned_loss=0.04194, over 1419501.49 frames.], batch size: 26, lr: 4.83e-04
2022-04-29 09:22:02,816 INFO [train.py:763] (1/8) Epoch 15, batch 2750, loss[loss=0.1917, simple_loss=0.2767, pruned_loss=0.05331, over 7274.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2767, pruned_loss=0.04195, over 1423255.18 frames.], batch size: 18, lr: 4.83e-04
2022-04-29 09:23:10,085 INFO [train.py:763] (1/8) Epoch 15, batch 2800, loss[loss=0.1805, simple_loss=0.2759, pruned_loss=0.04251, over 7222.00 frames.], tot_loss[loss=0.1801, simple_loss=0.277, pruned_loss=0.04166, over 1425809.76 frames.], batch size: 21, lr: 4.82e-04
2022-04-29 09:24:17,264 INFO [train.py:763] (1/8) Epoch 15, batch 2850, loss[loss=0.1559, simple_loss=0.2484, pruned_loss=0.03173, over 7150.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2768, pruned_loss=0.04175, over 1424937.84 frames.], batch size: 18, lr: 4.82e-04
2022-04-29 09:25:24,199 INFO [train.py:763] (1/8) Epoch 15, batch 2900, loss[loss=0.1689, simple_loss=0.2654, pruned_loss=0.03625, over 7157.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2773, pruned_loss=0.04183, over 1427989.36 frames.], batch size: 18, lr: 4.82e-04
2022-04-29 09:26:29,781 INFO [train.py:763] (1/8) Epoch 15, batch 2950, loss[loss=0.1805, simple_loss=0.2879, pruned_loss=0.03655, over 7330.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2771, pruned_loss=0.04183, over 1423975.49 frames.], batch size: 22, lr: 4.82e-04
2022-04-29 09:27:35,043 INFO [train.py:763] (1/8) Epoch 15, batch 3000, loss[loss=0.1835, simple_loss=0.2879, pruned_loss=0.03949, over 7411.00 frames.], tot_loss[loss=0.1808, simple_loss=0.2777, pruned_loss=0.0419, over 1428255.26 frames.], batch size: 21, lr: 4.82e-04
2022-04-29 09:27:35,044 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 09:27:50,494 INFO [train.py:792] (1/8) Epoch 15, validation: loss=0.1668, simple_loss=0.2684, pruned_loss=0.03254, over 698248.00 frames.
2022-04-29 09:28:57,620 INFO [train.py:763] (1/8) Epoch 15, batch 3050, loss[loss=0.1735, simple_loss=0.2587, pruned_loss=0.04419, over 7421.00 frames.], tot_loss[loss=0.1809, simple_loss=0.2778, pruned_loss=0.04206, over 1426031.69 frames.], batch size: 18, lr: 4.82e-04
2022-04-29 09:30:04,539 INFO [train.py:763] (1/8) Epoch 15, batch 3100, loss[loss=0.1652, simple_loss=0.2634, pruned_loss=0.03351, over 7206.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2771, pruned_loss=0.04177, over 1426128.97 frames.], batch size: 23, lr: 4.81e-04
2022-04-29 09:31:11,565 INFO [train.py:763] (1/8) Epoch 15, batch 3150, loss[loss=0.1646, simple_loss=0.2576, pruned_loss=0.03578, over 7158.00 frames.], tot_loss[loss=0.1803, simple_loss=0.2768, pruned_loss=0.0419, over 1424376.69 frames.], batch size: 18, lr: 4.81e-04
2022-04-29 09:32:29,190 INFO [train.py:763] (1/8) Epoch 15, batch 3200, loss[loss=0.1908, simple_loss=0.2978, pruned_loss=0.04188, over 7289.00 frames.], tot_loss[loss=0.1807, simple_loss=0.2777, pruned_loss=0.04182, over 1424426.03 frames.], batch size: 24, lr: 4.81e-04
2022-04-29 09:33:36,693 INFO [train.py:763] (1/8) Epoch 15, batch 3250, loss[loss=0.184, simple_loss=0.2835, pruned_loss=0.04224, over 7327.00 frames.], tot_loss[loss=0.1792, simple_loss=0.276, pruned_loss=0.04117, over 1425477.66 frames.], batch size: 21, lr: 4.81e-04
2022-04-29 09:34:43,458 INFO [train.py:763] (1/8) Epoch 15, batch 3300, loss[loss=0.2314, simple_loss=0.3165, pruned_loss=0.07314, over 7343.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2764, pruned_loss=0.04116, over 1429472.49 frames.], batch size: 25, lr: 4.81e-04
2022-04-29 09:35:50,324 INFO [train.py:763] (1/8) Epoch 15, batch 3350, loss[loss=0.1645, simple_loss=0.2698, pruned_loss=0.02959, over 7235.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2764, pruned_loss=0.04089, over 1431939.98 frames.], batch size: 20, lr: 4.81e-04
2022-04-29 09:36:57,530 INFO [train.py:763] (1/8) Epoch 15, batch 3400, loss[loss=0.167, simple_loss=0.2703, pruned_loss=0.03185, over 7082.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2769, pruned_loss=0.04118, over 1429127.42 frames.], batch size: 28, lr: 4.80e-04
2022-04-29 09:38:05,023 INFO [train.py:763] (1/8) Epoch 15, batch 3450, loss[loss=0.1946, simple_loss=0.2929, pruned_loss=0.04815, over 7360.00 frames.], tot_loss[loss=0.1795, simple_loss=0.2764, pruned_loss=0.04134, over 1430522.94 frames.], batch size: 19, lr: 4.80e-04
2022-04-29 09:39:11,457 INFO [train.py:763] (1/8) Epoch 15, batch 3500, loss[loss=0.1869, simple_loss=0.2836, pruned_loss=0.04509, over 7317.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2768, pruned_loss=0.04171, over 1428416.81 frames.], batch size: 21, lr: 4.80e-04
2022-04-29 09:40:16,434 INFO [train.py:763] (1/8) Epoch 15, batch 3550, loss[loss=0.1911, simple_loss=0.2906, pruned_loss=0.04584, over 7162.00 frames.], tot_loss[loss=0.1804, simple_loss=0.2771, pruned_loss=0.04187, over 1424723.50 frames.], batch size: 26, lr: 4.80e-04
2022-04-29 09:41:21,620 INFO [train.py:763] (1/8) Epoch 15, batch 3600, loss[loss=0.1891, simple_loss=0.2917, pruned_loss=0.04324, over 7332.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2767, pruned_loss=0.04135, over 1426488.31 frames.], batch size: 21, lr: 4.80e-04
2022-04-29 09:42:26,932 INFO [train.py:763] (1/8) Epoch 15, batch 3650, loss[loss=0.1729, simple_loss=0.2652, pruned_loss=0.04031, over 7289.00 frames.], tot_loss[loss=0.18, simple_loss=0.2772, pruned_loss=0.04145, over 1426101.76 frames.], batch size: 18, lr: 4.80e-04
2022-04-29 09:43:33,155 INFO [train.py:763] (1/8) Epoch 15, batch 3700, loss[loss=0.144, simple_loss=0.2328, pruned_loss=0.02758, over 6810.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2767, pruned_loss=0.04146, over 1424107.12 frames.], batch size: 15, lr: 4.79e-04
2022-04-29 09:44:39,837 INFO [train.py:763] (1/8) Epoch 15, batch 3750, loss[loss=0.2261, simple_loss=0.32, pruned_loss=0.06612, over 7290.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2765, pruned_loss=0.0419, over 1422096.56 frames.], batch size: 25, lr: 4.79e-04
2022-04-29 09:45:46,799 INFO [train.py:763] (1/8) Epoch 15, batch 3800, loss[loss=0.1446, simple_loss=0.2419, pruned_loss=0.02371, over 7134.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2765, pruned_loss=0.0419, over 1425981.91 frames.], batch size: 17, lr: 4.79e-04
2022-04-29 09:46:53,783 INFO [train.py:763] (1/8) Epoch 15, batch 3850, loss[loss=0.1579, simple_loss=0.2498, pruned_loss=0.03301, over 7295.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2762, pruned_loss=0.04159, over 1421594.74 frames.], batch size: 18, lr: 4.79e-04
2022-04-29 09:48:00,488 INFO [train.py:763] (1/8) Epoch 15, batch 3900, loss[loss=0.1922, simple_loss=0.2833, pruned_loss=0.05055, over 7226.00 frames.], tot_loss[loss=0.1805, simple_loss=0.2766, pruned_loss=0.04216, over 1423326.22 frames.], batch size: 21, lr: 4.79e-04
2022-04-29 09:49:06,578 INFO [train.py:763] (1/8) Epoch 15, batch 3950, loss[loss=0.1603, simple_loss=0.2603, pruned_loss=0.0302, over 7234.00 frames.], tot_loss[loss=0.18, simple_loss=0.2761, pruned_loss=0.04198, over 1422758.79 frames.], batch size: 20, lr: 4.79e-04
2022-04-29 09:50:13,628 INFO [train.py:763] (1/8) Epoch 15, batch 4000, loss[loss=0.1746, simple_loss=0.2832, pruned_loss=0.033, over 7311.00 frames.], tot_loss[loss=0.1801, simple_loss=0.2762, pruned_loss=0.04197, over 1420190.61 frames.], batch size: 21, lr: 4.79e-04
2022-04-29 09:51:19,315 INFO [train.py:763] (1/8) Epoch 15, batch 4050, loss[loss=0.1885, simple_loss=0.2784, pruned_loss=0.04929, over 7161.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2753, pruned_loss=0.04119, over 1418649.65 frames.], batch size: 18, lr: 4.78e-04
2022-04-29 09:52:24,921 INFO [train.py:763] (1/8) Epoch 15, batch 4100, loss[loss=0.1581, simple_loss=0.2514, pruned_loss=0.03241, over 7168.00 frames.], tot_loss[loss=0.178, simple_loss=0.2741, pruned_loss=0.04091, over 1424429.06 frames.], batch size: 18, lr: 4.78e-04
2022-04-29 09:53:30,116 INFO [train.py:763] (1/8) Epoch 15, batch 4150, loss[loss=0.1705, simple_loss=0.2717, pruned_loss=0.03466, over 7018.00 frames.], tot_loss[loss=0.1786, simple_loss=0.2744, pruned_loss=0.04136, over 1418709.28 frames.], batch size: 28, lr: 4.78e-04
2022-04-29 09:54:36,327 INFO [train.py:763] (1/8) Epoch 15, batch 4200, loss[loss=0.1665, simple_loss=0.2571, pruned_loss=0.03799, over 7024.00 frames.], tot_loss[loss=0.1792, simple_loss=0.275, pruned_loss=0.04169, over 1418259.77 frames.], batch size: 16, lr: 4.78e-04
2022-04-29 09:55:43,466 INFO [train.py:763] (1/8) Epoch 15, batch 4250, loss[loss=0.182, simple_loss=0.2702, pruned_loss=0.04692, over 7172.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2744, pruned_loss=0.0416, over 1416318.94 frames.], batch size: 18, lr: 4.78e-04
2022-04-29 09:56:48,657 INFO [train.py:763] (1/8) Epoch 15, batch 4300, loss[loss=0.168, simple_loss=0.2772, pruned_loss=0.02943, over 6740.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2746, pruned_loss=0.04138, over 1411701.10 frames.], batch size: 31, lr: 4.78e-04
2022-04-29 09:57:53,919 INFO [train.py:763] (1/8) Epoch 15, batch 4350, loss[loss=0.1893, simple_loss=0.2803, pruned_loss=0.04913, over 7169.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2749, pruned_loss=0.04133, over 1415311.64 frames.], batch size: 18, lr: 4.77e-04
2022-04-29 09:59:00,570 INFO [train.py:763] (1/8) Epoch 15, batch 4400, loss[loss=0.1664, simple_loss=0.2715, pruned_loss=0.03066, over 7110.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2751, pruned_loss=0.04134, over 1415926.50 frames.], batch size: 21, lr: 4.77e-04
2022-04-29 10:00:06,758 INFO [train.py:763] (1/8) Epoch 15, batch 4450, loss[loss=0.1815, simple_loss=0.2726, pruned_loss=0.04517, over 7205.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2753, pruned_loss=0.04153, over 1410520.92 frames.], batch size: 22, lr: 4.77e-04
2022-04-29 10:01:11,551 INFO [train.py:763] (1/8) Epoch 15, batch 4500, loss[loss=0.1598, simple_loss=0.2444, pruned_loss=0.03757, over 7135.00 frames.], tot_loss[loss=0.1791, simple_loss=0.2755, pruned_loss=0.04137, over 1400457.30 frames.], batch size: 17, lr: 4.77e-04
2022-04-29 10:02:15,682 INFO [train.py:763] (1/8) Epoch 15, batch 4550, loss[loss=0.2052, simple_loss=0.2925, pruned_loss=0.05898, over 5214.00 frames.], tot_loss[loss=0.1829, simple_loss=0.2786, pruned_loss=0.04367, over 1350652.22 frames.], batch size: 52, lr: 4.77e-04
2022-04-29 10:03:53,496 INFO [train.py:763] (1/8) Epoch 16, batch 0, loss[loss=0.204, simple_loss=0.2994, pruned_loss=0.05425, over 7118.00 frames.], tot_loss[loss=0.204, simple_loss=0.2994, pruned_loss=0.05425, over 7118.00 frames.], batch size: 21, lr: 4.63e-04
2022-04-29 10:04:59,092 INFO [train.py:763] (1/8) Epoch 16, batch 50, loss[loss=0.1888, simple_loss=0.2796, pruned_loss=0.04903, over 7341.00 frames.], tot_loss[loss=0.1866, simple_loss=0.2828, pruned_loss=0.04518, over 316901.34 frames.], batch size: 21, lr: 4.63e-04
2022-04-29 10:06:04,339 INFO [train.py:763] (1/8) Epoch 16, batch 100, loss[loss=0.1839, simple_loss=0.2885, pruned_loss=0.03964, over 7143.00 frames.], tot_loss[loss=0.1819, simple_loss=0.2782, pruned_loss=0.04284, over 559004.34 frames.], batch size: 20, lr: 4.63e-04
2022-04-29 10:07:09,681 INFO [train.py:763] (1/8) Epoch 16, batch 150, loss[loss=0.1561, simple_loss=0.244, pruned_loss=0.03406, over 6985.00 frames.], tot_loss[loss=0.1797, simple_loss=0.2763, pruned_loss=0.04152, over 746989.87 frames.], batch size: 16, lr: 4.63e-04
2022-04-29 10:08:15,063 INFO [train.py:763] (1/8) Epoch 16, batch 200, loss[loss=0.1576, simple_loss=0.2487, pruned_loss=0.03329, over 7143.00 frames.], tot_loss[loss=0.1799, simple_loss=0.277, pruned_loss=0.04136, over 896413.40 frames.], batch size: 17, lr: 4.63e-04
2022-04-29 10:09:20,554 INFO [train.py:763] (1/8) Epoch 16, batch 250, loss[loss=0.1794, simple_loss=0.2696, pruned_loss=0.04459, over 7255.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2765, pruned_loss=0.04133, over 1016267.56 frames.], batch size: 19, lr: 4.63e-04
2022-04-29 10:10:25,847 INFO [train.py:763] (1/8) Epoch 16, batch 300, loss[loss=0.1689, simple_loss=0.2632, pruned_loss=0.03728, over 7055.00 frames.], tot_loss[loss=0.1802, simple_loss=0.2773, pruned_loss=0.04156, over 1101978.76 frames.], batch size: 18, lr: 4.62e-04
2022-04-29 10:11:32,020 INFO [train.py:763] (1/8) Epoch 16, batch 350, loss[loss=0.192, simple_loss=0.2814, pruned_loss=0.05134, over 6774.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2758, pruned_loss=0.04145, over 1172050.70 frames.], batch size: 15, lr: 4.62e-04
2022-04-29 10:12:37,987 INFO [train.py:763] (1/8) Epoch 16, batch 400, loss[loss=0.2246, simple_loss=0.3025, pruned_loss=0.07339, over 4976.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2759, pruned_loss=0.04129, over 1227642.14 frames.], batch size: 53, lr: 4.62e-04
2022-04-29 10:13:43,445 INFO [train.py:763] (1/8) Epoch 16, batch 450, loss[loss=0.1637, simple_loss=0.2662, pruned_loss=0.0306, over 7348.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2762, pruned_loss=0.0413, over 1268868.94 frames.], batch size: 19, lr: 4.62e-04
2022-04-29 10:14:49,054 INFO [train.py:763] (1/8) Epoch 16, batch 500, loss[loss=0.1719, simple_loss=0.2665, pruned_loss=0.03867, over 7165.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2752, pruned_loss=0.04092, over 1301581.48 frames.], batch size: 18, lr: 4.62e-04
2022-04-29 10:15:54,719 INFO [train.py:763] (1/8) Epoch 16, batch 550, loss[loss=0.1544, simple_loss=0.2521, pruned_loss=0.02833, over 7120.00 frames.], tot_loss[loss=0.178, simple_loss=0.2744, pruned_loss=0.04084, over 1327295.52 frames.], batch size: 17, lr: 4.62e-04
2022-04-29 10:17:00,205 INFO [train.py:763] (1/8) Epoch 16, batch 600, loss[loss=0.1724, simple_loss=0.271, pruned_loss=0.03691, over 7013.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2752, pruned_loss=0.04157, over 1342678.03 frames.], batch size: 28, lr: 4.62e-04
2022-04-29 10:18:05,530 INFO [train.py:763] (1/8) Epoch 16, batch 650, loss[loss=0.168, simple_loss=0.2648, pruned_loss=0.03562, over 7337.00 frames.], tot_loss[loss=0.1789, simple_loss=0.275, pruned_loss=0.04141, over 1360942.30 frames.], batch size: 20, lr: 4.61e-04
2022-04-29 10:19:10,729 INFO [train.py:763] (1/8) Epoch 16, batch 700, loss[loss=0.1542, simple_loss=0.2491, pruned_loss=0.02968, over 7253.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2756, pruned_loss=0.04135, over 1367671.35 frames.], batch size: 19, lr: 4.61e-04
2022-04-29 10:20:16,739 INFO [train.py:763] (1/8) Epoch 16, batch 750, loss[loss=0.1722, simple_loss=0.269, pruned_loss=0.03769, over 7151.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2758, pruned_loss=0.04143, over 1376609.14 frames.], batch size: 20, lr: 4.61e-04
2022-04-29 10:21:21,857 INFO [train.py:763] (1/8) Epoch 16, batch 800, loss[loss=0.1751, simple_loss=0.2774, pruned_loss=0.03637, over 7158.00 frames.], tot_loss[loss=0.1796, simple_loss=0.2764, pruned_loss=0.04144, over 1387567.42 frames.], batch size: 19, lr: 4.61e-04
2022-04-29 10:22:27,307 INFO [train.py:763] (1/8) Epoch 16, batch 850, loss[loss=0.184, simple_loss=0.2877, pruned_loss=0.04013, over 6215.00 frames.], tot_loss[loss=0.1787, simple_loss=0.2753, pruned_loss=0.04103, over 1395734.13 frames.], batch size: 37, lr: 4.61e-04
2022-04-29 10:23:32,964 INFO [train.py:763] (1/8) Epoch 16, batch 900, loss[loss=0.1853, simple_loss=0.2802, pruned_loss=0.04518, over 7329.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2751, pruned_loss=0.04053, over 1407696.05 frames.], batch size: 20, lr: 4.61e-04
2022-04-29 10:24:38,446 INFO [train.py:763] (1/8) Epoch 16, batch 950, loss[loss=0.1653, simple_loss=0.2548, pruned_loss=0.03791, over 7139.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2744, pruned_loss=0.04016, over 1412509.87 frames.], batch size: 17, lr: 4.60e-04
2022-04-29 10:25:44,698 INFO [train.py:763] (1/8) Epoch 16, batch 1000, loss[loss=0.1564, simple_loss=0.2616, pruned_loss=0.02563, over 7123.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2744, pruned_loss=0.04005, over 1417467.70 frames.], batch size: 21, lr: 4.60e-04
2022-04-29 10:26:51,175 INFO [train.py:763] (1/8) Epoch 16, batch 1050, loss[loss=0.1983, simple_loss=0.3008, pruned_loss=0.04784, over 7341.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2734, pruned_loss=0.03988, over 1421709.39 frames.], batch size: 22, lr: 4.60e-04
2022-04-29 10:27:57,453 INFO [train.py:763] (1/8) Epoch 16, batch 1100, loss[loss=0.1871, simple_loss=0.2829, pruned_loss=0.04565, over 7290.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2735, pruned_loss=0.0396, over 1422543.44 frames.], batch size: 24, lr: 4.60e-04
2022-04-29 10:29:02,472 INFO [train.py:763] (1/8) Epoch 16, batch 1150, loss[loss=0.1778, simple_loss=0.2734, pruned_loss=0.04109, over 7254.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2742, pruned_loss=0.03972, over 1423308.78 frames.], batch size: 24, lr: 4.60e-04
2022-04-29 10:30:08,050 INFO [train.py:763] (1/8) Epoch 16, batch 1200, loss[loss=0.2131, simple_loss=0.3067, pruned_loss=0.05976, over 7286.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2736, pruned_loss=0.03957, over 1419822.01 frames.], batch size: 25, lr: 4.60e-04
2022-04-29 10:31:13,265 INFO [train.py:763] (1/8) Epoch 16, batch 1250, loss[loss=0.1576, simple_loss=0.2496, pruned_loss=0.03279, over 7301.00 frames.], tot_loss[loss=0.178, simple_loss=0.2749, pruned_loss=0.04051, over 1415320.36 frames.], batch size: 18, lr: 4.60e-04
2022-04-29 10:32:19,086 INFO [train.py:763] (1/8) Epoch 16, batch 1300, loss[loss=0.18, simple_loss=0.2872, pruned_loss=0.03644, over 7348.00 frames.], tot_loss[loss=0.1782, simple_loss=0.275, pruned_loss=0.04069, over 1413231.42 frames.], batch size: 22, lr: 4.59e-04
2022-04-29 10:33:25,809 INFO [train.py:763] (1/8) Epoch 16, batch 1350, loss[loss=0.1629, simple_loss=0.2542, pruned_loss=0.03579, over 6999.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2753, pruned_loss=0.04068, over 1418485.04 frames.], batch size: 16, lr: 4.59e-04
2022-04-29 10:34:32,888 INFO [train.py:763] (1/8) Epoch 16, batch 1400, loss[loss=0.1866, simple_loss=0.2886, pruned_loss=0.0423, over 7138.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2739, pruned_loss=0.04048, over 1419200.63 frames.], batch size: 20, lr: 4.59e-04
2022-04-29 10:35:38,351 INFO [train.py:763] (1/8) Epoch 16, batch 1450, loss[loss=0.2095, simple_loss=0.3045, pruned_loss=0.05729, over 7346.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2746, pruned_loss=0.04045, over 1418615.23 frames.], batch size: 22, lr: 4.59e-04
2022-04-29 10:36:43,996 INFO [train.py:763] (1/8) Epoch 16, batch 1500, loss[loss=0.1777, simple_loss=0.2707, pruned_loss=0.04239, over 7264.00 frames.], tot_loss[loss=0.177, simple_loss=0.2735, pruned_loss=0.04022, over 1424382.71 frames.], batch size: 19, lr: 4.59e-04
2022-04-29 10:37:49,277 INFO [train.py:763] (1/8) Epoch 16, batch 1550, loss[loss=0.1777, simple_loss=0.2754, pruned_loss=0.03995, over 7212.00 frames.], tot_loss[loss=0.1772, simple_loss=0.2737, pruned_loss=0.04039, over 1422589.15 frames.], batch size: 21, lr: 4.59e-04
2022-04-29 10:38:55,269 INFO [train.py:763] (1/8) Epoch 16, batch 1600, loss[loss=0.1825, simple_loss=0.2764, pruned_loss=0.04428, over 7416.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2734, pruned_loss=0.04012, over 1427165.77 frames.], batch size: 20, lr: 4.58e-04
2022-04-29 10:40:00,454 INFO [train.py:763] (1/8) Epoch 16, batch 1650, loss[loss=0.1964, simple_loss=0.2961, pruned_loss=0.04831, over 7413.00 frames.], tot_loss[loss=0.1771, simple_loss=0.274, pruned_loss=0.04017, over 1429091.99 frames.], batch size: 21, lr: 4.58e-04
2022-04-29 10:41:05,542 INFO [train.py:763] (1/8) Epoch 16, batch 1700, loss[loss=0.2699, simple_loss=0.3333, pruned_loss=0.1032, over 4596.00 frames.], tot_loss[loss=0.1793, simple_loss=0.2757, pruned_loss=0.04151, over 1422805.68 frames.], batch size: 52, lr: 4.58e-04
2022-04-29 10:42:10,600 INFO [train.py:763] (1/8) Epoch 16, batch 1750, loss[loss=0.1925, simple_loss=0.2927, pruned_loss=0.04616, over 7376.00 frames.], tot_loss[loss=0.1798, simple_loss=0.2763, pruned_loss=0.04161, over 1414455.69 frames.], batch size: 23, lr: 4.58e-04
2022-04-29 10:43:15,523 INFO [train.py:763] (1/8) Epoch 16, batch 1800, loss[loss=0.1928, simple_loss=0.2827, pruned_loss=0.05142, over 7203.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2758, pruned_loss=0.04084, over 1415924.41 frames.], batch size: 23, lr: 4.58e-04
2022-04-29 10:44:20,684 INFO [train.py:763] (1/8) Epoch 16, batch 1850, loss[loss=0.2004, simple_loss=0.3031, pruned_loss=0.04883, over 6261.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2757, pruned_loss=0.04062, over 1416621.91 frames.], batch size: 37, lr: 4.58e-04
2022-04-29 10:45:26,186 INFO [train.py:763] (1/8) Epoch 16, batch 1900, loss[loss=0.1733, simple_loss=0.2639, pruned_loss=0.04132, over 7421.00 frames.], tot_loss[loss=0.1789, simple_loss=0.276, pruned_loss=0.04088, over 1420865.75 frames.], batch size: 20, lr: 4.58e-04
2022-04-29 10:46:31,346 INFO [train.py:763] (1/8) Epoch 16, batch 1950, loss[loss=0.198, simple_loss=0.2918, pruned_loss=0.05211, over 7325.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2755, pruned_loss=0.04074, over 1423210.65 frames.], batch size: 21, lr: 4.57e-04
2022-04-29 10:47:36,625 INFO [train.py:763] (1/8) Epoch 16, batch 2000, loss[loss=0.1904, simple_loss=0.2789, pruned_loss=0.05098, over 7259.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2752, pruned_loss=0.04027, over 1424703.87 frames.], batch size: 19, lr: 4.57e-04
2022-04-29 10:48:44,156 INFO [train.py:763] (1/8) Epoch 16, batch 2050, loss[loss=0.1675, simple_loss=0.2593, pruned_loss=0.03787, over 7410.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2739, pruned_loss=0.04015, over 1428247.83 frames.], batch size: 18, lr: 4.57e-04
2022-04-29 10:49:51,119 INFO [train.py:763] (1/8) Epoch 16, batch 2100, loss[loss=0.1866, simple_loss=0.291, pruned_loss=0.04112, over 7413.00 frames.], tot_loss[loss=0.1768, simple_loss=0.274, pruned_loss=0.03979, over 1428318.26 frames.], batch size: 21, lr: 4.57e-04
2022-04-29 10:50:57,994 INFO [train.py:763] (1/8) Epoch 16, batch 2150, loss[loss=0.1699, simple_loss=0.2619, pruned_loss=0.03894, over 7340.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2747, pruned_loss=0.04007, over 1424549.72 frames.], batch size: 19, lr: 4.57e-04
2022-04-29 10:52:04,703 INFO [train.py:763] (1/8) Epoch 16, batch 2200, loss[loss=0.1737, simple_loss=0.2749, pruned_loss=0.03626, over 7325.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2747, pruned_loss=0.03994, over 1421916.41 frames.], batch size: 22, lr: 4.57e-04
2022-04-29 10:53:10,679 INFO [train.py:763] (1/8) Epoch 16, batch 2250, loss[loss=0.1699, simple_loss=0.263, pruned_loss=0.03843, over 7406.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2755, pruned_loss=0.04055, over 1424502.08 frames.], batch size: 21, lr: 4.56e-04
2022-04-29 10:54:16,234 INFO [train.py:763] (1/8) Epoch 16, batch 2300, loss[loss=0.196, simple_loss=0.2997, pruned_loss=0.04613, over 7298.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2754, pruned_loss=0.04067, over 1423244.20 frames.], batch size: 24, lr: 4.56e-04
2022-04-29 10:55:22,548 INFO [train.py:763] (1/8) Epoch 16, batch 2350, loss[loss=0.1764, simple_loss=0.279, pruned_loss=0.03684, over 7387.00 frames.], tot_loss[loss=0.177, simple_loss=0.274, pruned_loss=0.04002, over 1426969.99 frames.], batch size: 23, lr: 4.56e-04
2022-04-29 10:56:28,585 INFO [train.py:763] (1/8) Epoch 16, batch 2400, loss[loss=0.1668, simple_loss=0.2558, pruned_loss=0.03891, over 7011.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2737, pruned_loss=0.03978, over 1424525.85 frames.], batch size: 16, lr: 4.56e-04
2022-04-29 10:57:34,906 INFO [train.py:763] (1/8) Epoch 16, batch 2450, loss[loss=0.1875, simple_loss=0.282, pruned_loss=0.04652, over 7335.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2736, pruned_loss=0.04017, over 1424094.02 frames.], batch size: 22, lr: 4.56e-04
2022-04-29 10:58:41,492 INFO [train.py:763] (1/8) Epoch 16, batch 2500, loss[loss=0.1995, simple_loss=0.2955, pruned_loss=0.05177, over 7221.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2733, pruned_loss=0.03997, over 1424146.12 frames.], batch size: 21, lr: 4.56e-04
2022-04-29 10:59:48,421 INFO [train.py:763] (1/8) Epoch 16, batch 2550, loss[loss=0.1874, simple_loss=0.2927, pruned_loss=0.04103, over 7212.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2726, pruned_loss=0.03989, over 1419320.98 frames.], batch size: 21, lr: 4.56e-04
2022-04-29 11:00:54,058 INFO [train.py:763] (1/8) Epoch 16, batch 2600, loss[loss=0.1646, simple_loss=0.2722, pruned_loss=0.02851, over 7074.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2748, pruned_loss=0.04078, over 1421577.70 frames.], batch size: 28, lr: 4.55e-04
2022-04-29 11:01:59,327 INFO [train.py:763] (1/8) Epoch 16, batch 2650, loss[loss=0.1599, simple_loss=0.2687, pruned_loss=0.02559, over 7348.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2749, pruned_loss=0.04095, over 1419883.64 frames.], batch size: 19, lr: 4.55e-04
2022-04-29 11:03:04,681 INFO [train.py:763] (1/8) Epoch 16, batch 2700, loss[loss=0.1873, simple_loss=0.2943, pruned_loss=0.04019, over 7331.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2741, pruned_loss=0.04069, over 1423533.99 frames.], batch size: 22, lr: 4.55e-04
2022-04-29 11:04:10,089 INFO [train.py:763] (1/8) Epoch 16, batch 2750, loss[loss=0.1907, simple_loss=0.2918, pruned_loss=0.04475, over 7163.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2741, pruned_loss=0.0406, over 1422953.49 frames.], batch size: 19, lr: 4.55e-04
2022-04-29 11:05:15,587 INFO [train.py:763] (1/8) Epoch 16, batch 2800, loss[loss=0.21, simple_loss=0.3011, pruned_loss=0.05945, over 5220.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2738, pruned_loss=0.04055, over 1421881.08 frames.], batch size: 52, lr: 4.55e-04
2022-04-29 11:06:20,605 INFO [train.py:763] (1/8) Epoch 16, batch 2850, loss[loss=0.1629, simple_loss=0.2644, pruned_loss=0.03076, over 7317.00 frames.], tot_loss[loss=0.1779, simple_loss=0.2744, pruned_loss=0.04065, over 1422256.11 frames.], batch size: 21, lr: 4.55e-04
2022-04-29 11:07:35,852 INFO [train.py:763] (1/8) Epoch 16, batch 2900, loss[loss=0.187, simple_loss=0.29, pruned_loss=0.04194, over 7232.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2742, pruned_loss=0.04073, over 1419103.70 frames.], batch size: 20, lr: 4.55e-04
2022-04-29 11:08:42,369 INFO [train.py:763] (1/8) Epoch 16, batch 2950, loss[loss=0.1597, simple_loss=0.2537, pruned_loss=0.0328, over 7285.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2751, pruned_loss=0.04101, over 1419279.82 frames.], batch size: 18, lr: 4.54e-04
2022-04-29 11:09:49,125 INFO [train.py:763] (1/8) Epoch 16, batch 3000, loss[loss=0.1706, simple_loss=0.2674, pruned_loss=0.03686, over 7143.00 frames.], tot_loss[loss=0.1782, simple_loss=0.2752, pruned_loss=0.04066, over 1424058.39 frames.], batch size: 20, lr: 4.54e-04
2022-04-29 11:09:49,126 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 11:10:05,042 INFO [train.py:792] (1/8) Epoch 16, validation: loss=0.1677, simple_loss=0.2693, pruned_loss=0.03309, over 698248.00 frames.
2022-04-29 11:11:10,308 INFO [train.py:763] (1/8) Epoch 16, batch 3050, loss[loss=0.1911, simple_loss=0.2938, pruned_loss=0.04421, over 6463.00 frames.], tot_loss[loss=0.178, simple_loss=0.275, pruned_loss=0.04051, over 1423403.13 frames.], batch size: 38, lr: 4.54e-04
2022-04-29 11:12:42,600 INFO [train.py:763] (1/8) Epoch 16, batch 3100, loss[loss=0.1508, simple_loss=0.2617, pruned_loss=0.0199, over 7319.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2759, pruned_loss=0.04089, over 1419821.53 frames.], batch size: 25, lr: 4.54e-04
2022-04-29 11:13:48,020 INFO [train.py:763] (1/8) Epoch 16, batch 3150, loss[loss=0.1771, simple_loss=0.2653, pruned_loss=0.04449, over 7339.00 frames.], tot_loss[loss=0.1788, simple_loss=0.2757, pruned_loss=0.04099, over 1418266.02 frames.], batch size: 20, lr: 4.54e-04
2022-04-29 11:15:03,458 INFO [train.py:763] (1/8) Epoch 16, batch 3200, loss[loss=0.1561, simple_loss=0.2558, pruned_loss=0.02818, over 7366.00 frames.], tot_loss[loss=0.1794, simple_loss=0.2764, pruned_loss=0.04124, over 1418264.89 frames.], batch size: 19, lr: 4.54e-04
2022-04-29 11:16:27,096 INFO [train.py:763] (1/8) Epoch 16, batch 3250, loss[loss=0.1755, simple_loss=0.2683, pruned_loss=0.04136, over 7062.00 frames.], tot_loss[loss=0.1784, simple_loss=0.2752, pruned_loss=0.04083, over 1423563.70 frames.], batch size: 18, lr: 4.54e-04
2022-04-29 11:17:32,420 INFO [train.py:763] (1/8) Epoch 16, batch 3300, loss[loss=0.2017, simple_loss=0.3047, pruned_loss=0.04941, over 7171.00 frames.], tot_loss[loss=0.1785, simple_loss=0.2754, pruned_loss=0.04084, over 1424473.20 frames.], batch size: 19, lr: 4.53e-04
2022-04-29 11:18:47,353 INFO [train.py:763] (1/8) Epoch 16, batch 3350, loss[loss=0.183, simple_loss=0.2849, pruned_loss=0.04055, over 7350.00 frames.], tot_loss[loss=0.1792, simple_loss=0.276, pruned_loss=0.04123, over 1425582.86 frames.], batch size: 22, lr: 4.53e-04
2022-04-29 11:19:53,999 INFO [train.py:763] (1/8) Epoch 16, batch 3400, loss[loss=0.2023, simple_loss=0.3005, pruned_loss=0.052, over 7151.00 frames.], tot_loss[loss=0.1789, simple_loss=0.2755, pruned_loss=0.04117, over 1423549.16 frames.], batch size: 20, lr: 4.53e-04
2022-04-29 11:21:00,495 INFO [train.py:763] (1/8) Epoch 16, batch 3450, loss[loss=0.1827, simple_loss=0.2742, pruned_loss=0.04555, over 7322.00 frames.], tot_loss[loss=0.1777, simple_loss=0.274, pruned_loss=0.04066, over 1424674.27 frames.], batch size: 20, lr: 4.53e-04
2022-04-29 11:22:05,840 INFO [train.py:763] (1/8) Epoch 16, batch 3500, loss[loss=0.175, simple_loss=0.2718, pruned_loss=0.03913, over 7200.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2736, pruned_loss=0.0403, over 1423993.51 frames.], batch size: 22, lr: 4.53e-04
2022-04-29 11:23:10,998 INFO [train.py:763] (1/8) Epoch 16, batch 3550, loss[loss=0.177, simple_loss=0.2782, pruned_loss=0.03785, over 7112.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2742, pruned_loss=0.04003, over 1426286.64 frames.], batch size: 21, lr: 4.53e-04
2022-04-29 11:24:16,265 INFO [train.py:763] (1/8) Epoch 16, batch 3600, loss[loss=0.1603, simple_loss=0.2563, pruned_loss=0.03215, over 7286.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2749, pruned_loss=0.04019, over 1427499.73 frames.], batch size: 18, lr: 4.52e-04
2022-04-29 11:25:21,852 INFO [train.py:763] (1/8) Epoch 16, batch 3650, loss[loss=0.1957, simple_loss=0.2861, pruned_loss=0.05268, over 7314.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2738, pruned_loss=0.03983, over 1431100.40 frames.], batch size: 21, lr: 4.52e-04
2022-04-29 11:26:27,134 INFO [train.py:763] (1/8) Epoch 16, batch 3700, loss[loss=0.1738, simple_loss=0.2853, pruned_loss=0.03117, over 7141.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2733, pruned_loss=0.03971, over 1431033.83 frames.], batch size: 20, lr: 4.52e-04
2022-04-29 11:27:34,286 INFO [train.py:763] (1/8) Epoch 16, batch 3750, loss[loss=0.1651, simple_loss=0.2654, pruned_loss=0.03238, over 6334.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2735, pruned_loss=0.04006, over 1427574.41 frames.], batch size: 37, lr: 4.52e-04
2022-04-29 11:28:40,552 INFO [train.py:763] (1/8) Epoch 16, batch 3800, loss[loss=0.1917, simple_loss=0.2966, pruned_loss=0.04336, over 6464.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2739, pruned_loss=0.03997, over 1426157.36 frames.], batch size: 37, lr: 4.52e-04
2022-04-29 11:29:46,870 INFO [train.py:763] (1/8) Epoch 16, batch 3850, loss[loss=0.1471, simple_loss=0.2375, pruned_loss=0.02831, over 6996.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2737, pruned_loss=0.03955, over 1425951.50 frames.], batch size: 16, lr: 4.52e-04
2022-04-29 11:30:53,555 INFO [train.py:763] (1/8) Epoch 16, batch 3900, loss[loss=0.1649, simple_loss=0.2665, pruned_loss=0.03169, over 7202.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2723, pruned_loss=0.03932, over 1428116.17 frames.], batch size: 22, lr: 4.52e-04
2022-04-29 11:32:00,327 INFO [train.py:763] (1/8) Epoch 16, batch 3950, loss[loss=0.1925, simple_loss=0.2866, pruned_loss=0.04919, over 7206.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2743, pruned_loss=0.04037, over 1428664.48 frames.], batch size: 23, lr: 4.51e-04
2022-04-29 11:33:05,769 INFO [train.py:763] (1/8) Epoch 16, batch 4000, loss[loss=0.1774, simple_loss=0.2768, pruned_loss=0.03895, over 7273.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2737, pruned_loss=0.04025, over 1429119.08 frames.], batch size: 18, lr: 4.51e-04
2022-04-29 11:34:12,297 INFO [train.py:763] (1/8) Epoch 16, batch 4050, loss[loss=0.1799, simple_loss=0.2771, pruned_loss=0.04136, over 6713.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2735, pruned_loss=0.04005, over 1424989.82 frames.], batch size: 31, lr: 4.51e-04
2022-04-29 11:35:18,250 INFO [train.py:763] (1/8) Epoch 16, batch 4100, loss[loss=0.1818, simple_loss=0.2845, pruned_loss=0.03957, over 6505.00 frames.], tot_loss[loss=0.1781, simple_loss=0.2748, pruned_loss=0.04073, over 1423779.30 frames.], batch size: 38, lr: 4.51e-04
2022-04-29 11:36:24,675 INFO [train.py:763] (1/8) Epoch 16, batch 4150, loss[loss=0.1416, simple_loss=0.2379, pruned_loss=0.02264, over 7131.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2734, pruned_loss=0.0401, over 1422609.26 frames.], batch size: 17, lr: 4.51e-04
2022-04-29 11:37:30,199 INFO [train.py:763] (1/8) Epoch 16, batch 4200, loss[loss=0.1754, simple_loss=0.2722, pruned_loss=0.03928, over 7164.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2734, pruned_loss=0.03976, over 1422188.42 frames.], batch size: 26, lr: 4.51e-04
2022-04-29 11:38:36,626 INFO [train.py:763] (1/8) Epoch 16, batch 4250, loss[loss=0.1629, simple_loss=0.2527, pruned_loss=0.03658, over 7301.00 frames.], tot_loss[loss=0.177, simple_loss=0.274, pruned_loss=0.03998, over 1423057.75 frames.], batch size: 18, lr: 4.51e-04
2022-04-29 11:39:43,732 INFO [train.py:763] (1/8) Epoch 16, batch 4300, loss[loss=0.1997, simple_loss=0.2904, pruned_loss=0.05452, over 7069.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2734, pruned_loss=0.04016, over 1422026.20 frames.], batch size: 18, lr: 4.50e-04
2022-04-29 11:40:49,811 INFO [train.py:763] (1/8) Epoch 16, batch 4350, loss[loss=0.1811, simple_loss=0.2648, pruned_loss=0.04872, over 7171.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2731, pruned_loss=0.04019, over 1421352.43 frames.], batch size: 18, lr: 4.50e-04
2022-04-29 11:41:55,138 INFO [train.py:763] (1/8) Epoch 16, batch 4400, loss[loss=0.1816, simple_loss=0.2919, pruned_loss=0.03562, over 7226.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2733, pruned_loss=0.03994, over 1419607.18 frames.], batch size: 21, lr: 4.50e-04
2022-04-29 11:43:00,293 INFO [train.py:763] (1/8) Epoch 16, batch 4450, loss[loss=0.1677, simple_loss=0.2486, pruned_loss=0.04338, over 7122.00 frames.], tot_loss[loss=0.1775, simple_loss=0.2743, pruned_loss=0.04035, over 1415504.49 frames.], batch size: 17, lr: 4.50e-04
2022-04-29 11:44:06,063 INFO [train.py:763] (1/8) Epoch 16, batch 4500, loss[loss=0.1728, simple_loss=0.264, pruned_loss=0.04083, over 7246.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2739, pruned_loss=0.04075, over 1414684.19 frames.], batch size: 20, lr: 4.50e-04
2022-04-29 11:45:13,646 INFO [train.py:763] (1/8) Epoch 16, batch 4550, loss[loss=0.2128, simple_loss=0.2995, pruned_loss=0.06307, over 5281.00 frames.], tot_loss[loss=0.1783, simple_loss=0.2737, pruned_loss=0.04148, over 1380933.42 frames.], batch size: 52, lr: 4.50e-04
2022-04-29 11:46:42,220 INFO [train.py:763] (1/8) Epoch 17, batch 0, loss[loss=0.1856, simple_loss=0.279, pruned_loss=0.04609, over 7234.00 frames.], tot_loss[loss=0.1856, simple_loss=0.279, pruned_loss=0.04609, over 7234.00 frames.], batch size: 20, lr: 4.38e-04
2022-04-29 11:47:48,724 INFO [train.py:763] (1/8) Epoch 17, batch 50, loss[loss=0.1615, simple_loss=0.2356, pruned_loss=0.04366, over 7000.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2698, pruned_loss=0.03795, over 323385.31 frames.], batch size: 16, lr: 4.38e-04
2022-04-29 11:48:54,533 INFO [train.py:763] (1/8) Epoch 17, batch 100, loss[loss=0.1583, simple_loss=0.249, pruned_loss=0.03381, over 7158.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2727, pruned_loss=0.03902, over 564556.24 frames.], batch size: 18, lr: 4.37e-04
2022-04-29 11:50:00,284 INFO [train.py:763] (1/8) Epoch 17, batch 150, loss[loss=0.2045, simple_loss=0.3032, pruned_loss=0.05292, over 7139.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2731, pruned_loss=0.03916, over 751383.08 frames.], batch size: 20, lr: 4.37e-04
2022-04-29 11:51:07,234 INFO [train.py:763] (1/8) Epoch 17, batch 200, loss[loss=0.1808, simple_loss=0.2633, pruned_loss=0.04917, over 7168.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2738, pruned_loss=0.03983, over 901901.79 frames.], batch size: 18, lr: 4.37e-04
2022-04-29 11:52:14,162 INFO [train.py:763] (1/8) Epoch 17, batch 250, loss[loss=0.182, simple_loss=0.2804, pruned_loss=0.04175, over 6713.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2745, pruned_loss=0.0401, over 1019663.81 frames.], batch size: 31, lr: 4.37e-04
2022-04-29 11:53:19,798 INFO [train.py:763] (1/8) Epoch 17, batch 300, loss[loss=0.1928, simple_loss=0.2906, pruned_loss=0.04754, over 7067.00 frames.], tot_loss[loss=0.1776, simple_loss=0.2748, pruned_loss=0.0402, over 1104159.15 frames.], batch size: 28, lr: 4.37e-04
2022-04-29 11:54:25,515 INFO [train.py:763] (1/8) Epoch 17, batch 350, loss[loss=0.17, simple_loss=0.2786, pruned_loss=0.03067, over 7333.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2731, pruned_loss=0.03968, over 1172274.74 frames.], batch size: 22, lr: 4.37e-04
2022-04-29 11:55:31,572 INFO [train.py:763] (1/8) Epoch 17, batch 400, loss[loss=0.1838, simple_loss=0.2692, pruned_loss=0.04919, over 6806.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2735, pruned_loss=0.03971, over 1232185.54 frames.], batch size: 15, lr: 4.37e-04
2022-04-29 11:56:37,248 INFO [train.py:763] (1/8) Epoch 17, batch 450, loss[loss=0.1755, simple_loss=0.2815, pruned_loss=0.03471, over 7206.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2741, pruned_loss=0.03936, over 1275597.08 frames.], batch size: 22, lr: 4.36e-04
2022-04-29 11:57:42,948 INFO [train.py:763] (1/8) Epoch 17, batch 500, loss[loss=0.171, simple_loss=0.2759, pruned_loss=0.03308, over 7342.00 frames.], tot_loss[loss=0.1761, simple_loss=0.2738, pruned_loss=0.03922, over 1313326.49 frames.], batch size: 22, lr: 4.36e-04
2022-04-29 11:58:48,660 INFO [train.py:763] (1/8) Epoch 17, batch 550, loss[loss=0.1595, simple_loss=0.2542, pruned_loss=0.03237, over 7144.00 frames.], tot_loss[loss=0.1755, simple_loss=0.273, pruned_loss=0.03907, over 1339996.35 frames.], batch size: 17, lr: 4.36e-04
2022-04-29 11:59:54,495 INFO [train.py:763] (1/8) Epoch 17, batch 600, loss[loss=0.178, simple_loss=0.2784, pruned_loss=0.03881, over 6336.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2743, pruned_loss=0.03982, over 1357230.50 frames.], batch size: 37, lr: 4.36e-04
2022-04-29 12:01:00,139 INFO [train.py:763] (1/8) Epoch 17, batch 650, loss[loss=0.197, simple_loss=0.2842, pruned_loss=0.0549, over 5030.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2742, pruned_loss=0.03979, over 1369509.23 frames.], batch size: 53, lr: 4.36e-04
2022-04-29 12:02:07,659 INFO [train.py:763] (1/8) Epoch 17, batch 700, loss[loss=0.1813, simple_loss=0.2765, pruned_loss=0.04303, over 7307.00 frames.], tot_loss[loss=0.1759, simple_loss=0.2731, pruned_loss=0.03929, over 1380617.53 frames.], batch size: 21, lr: 4.36e-04
2022-04-29 12:03:15,581 INFO [train.py:763] (1/8) Epoch 17, batch 750, loss[loss=0.1742, simple_loss=0.2682, pruned_loss=0.04012, over 7412.00 frames.], tot_loss[loss=0.1748, simple_loss=0.272, pruned_loss=0.03883, over 1391342.81 frames.], batch size: 18, lr: 4.36e-04
2022-04-29 12:04:22,597 INFO [train.py:763] (1/8) Epoch 17, batch 800, loss[loss=0.226, simple_loss=0.3133, pruned_loss=0.06942, over 7319.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2724, pruned_loss=0.03903, over 1403114.71 frames.], batch size: 21, lr: 4.36e-04
2022-04-29 12:05:28,619 INFO [train.py:763] (1/8) Epoch 17, batch 850, loss[loss=0.2038, simple_loss=0.3051, pruned_loss=0.0512, over 7409.00 frames.], tot_loss[loss=0.175, simple_loss=0.2723, pruned_loss=0.03885, over 1407031.37 frames.], batch size: 21, lr: 4.35e-04
2022-04-29 12:06:34,119 INFO [train.py:763] (1/8) Epoch 17, batch 900, loss[loss=0.1702, simple_loss=0.2651, pruned_loss=0.03765, over 7178.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2743, pruned_loss=0.03999, over 1406859.99 frames.], batch size: 22, lr: 4.35e-04
2022-04-29 12:07:40,033 INFO [train.py:763] (1/8) Epoch 17, batch 950, loss[loss=0.1935, simple_loss=0.2967, pruned_loss=0.04518, over 7269.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2739, pruned_loss=0.03965, over 1409682.51 frames.], batch size: 19, lr: 4.35e-04
2022-04-29 12:08:46,272 INFO [train.py:763] (1/8) Epoch 17, batch 1000, loss[loss=0.1916, simple_loss=0.2996, pruned_loss=0.04177, over 7264.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2732, pruned_loss=0.03908, over 1414488.56 frames.], batch size: 24, lr: 4.35e-04
2022-04-29 12:09:52,067 INFO [train.py:763] (1/8) Epoch 17, batch 1050, loss[loss=0.1832, simple_loss=0.2674, pruned_loss=0.04948, over 7294.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2725, pruned_loss=0.03853, over 1416545.51 frames.], batch size: 17, lr: 4.35e-04
2022-04-29 12:10:57,968 INFO [train.py:763] (1/8) Epoch 17, batch 1100, loss[loss=0.2074, simple_loss=0.3002, pruned_loss=0.05728, over 7282.00 frames.], tot_loss[loss=0.176, simple_loss=0.2734, pruned_loss=0.03933, over 1420562.50 frames.], batch size: 25, lr: 4.35e-04
2022-04-29 12:12:04,944 INFO [train.py:763] (1/8) Epoch 17, batch 1150, loss[loss=0.1838, simple_loss=0.287, pruned_loss=0.04026, over 7378.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2739, pruned_loss=0.03958, over 1419140.41 frames.], batch size: 23, lr: 4.35e-04
2022-04-29 12:13:12,220 INFO [train.py:763] (1/8) Epoch 17, batch 1200, loss[loss=0.1769, simple_loss=0.267, pruned_loss=0.04346, over 7278.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2741, pruned_loss=0.03974, over 1417322.10 frames.], batch size: 18, lr: 4.34e-04
2022-04-29 12:14:19,344 INFO [train.py:763] (1/8) Epoch 17, batch 1250, loss[loss=0.1749, simple_loss=0.2635, pruned_loss=0.04314, over 7415.00 frames.], tot_loss[loss=0.1765, simple_loss=0.2738, pruned_loss=0.03959, over 1419748.04 frames.], batch size: 21, lr: 4.34e-04
2022-04-29 12:15:25,173 INFO [train.py:763] (1/8) Epoch 17, batch 1300, loss[loss=0.1491, simple_loss=0.2465, pruned_loss=0.02589, over 7168.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2734, pruned_loss=0.0397, over 1420269.42 frames.], batch size: 26, lr: 4.34e-04
2022-04-29 12:16:30,501 INFO [train.py:763] (1/8) Epoch 17, batch 1350, loss[loss=0.1472, simple_loss=0.2414, pruned_loss=0.02647, over 6998.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2738, pruned_loss=0.03984, over 1422833.22 frames.], batch size: 16, lr: 4.34e-04
2022-04-29 12:17:36,046 INFO [train.py:763] (1/8) Epoch 17, batch 1400, loss[loss=0.1796, simple_loss=0.286, pruned_loss=0.03661, over 7117.00 frames.], tot_loss[loss=0.177, simple_loss=0.2745, pruned_loss=0.03977, over 1423962.92 frames.], batch size: 21, lr: 4.34e-04
2022-04-29 12:18:41,487 INFO [train.py:763] (1/8) Epoch 17, batch 1450, loss[loss=0.185, simple_loss=0.283, pruned_loss=0.0435, over 7151.00 frames.], tot_loss[loss=0.1774, simple_loss=0.275, pruned_loss=0.03992, over 1422194.71 frames.], batch size: 20, lr: 4.34e-04
2022-04-29 12:19:47,538 INFO [train.py:763] (1/8) Epoch 17, batch 1500, loss[loss=0.2016, simple_loss=0.2887, pruned_loss=0.05721, over 7321.00 frames.], tot_loss[loss=0.1777, simple_loss=0.2752, pruned_loss=0.04009, over 1415876.66 frames.], batch size: 25, lr: 4.34e-04
2022-04-29 12:20:53,497 INFO [train.py:763] (1/8) Epoch 17, batch 1550, loss[loss=0.174, simple_loss=0.2622, pruned_loss=0.0429, over 7140.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2748, pruned_loss=0.0399, over 1422810.98 frames.], batch size: 19, lr: 4.33e-04
2022-04-29 12:21:59,195 INFO [train.py:763] (1/8) Epoch 17, batch 1600, loss[loss=0.167, simple_loss=0.2695, pruned_loss=0.03223, over 7420.00 frames.], tot_loss[loss=0.1775, simple_loss=0.275, pruned_loss=0.04001, over 1423141.44 frames.], batch size: 20, lr: 4.33e-04
2022-04-29 12:23:04,503 INFO [train.py:763] (1/8) Epoch 17, batch 1650, loss[loss=0.1402, simple_loss=0.2263, pruned_loss=0.02707, over 7290.00 frames.], tot_loss[loss=0.1775, simple_loss=0.275, pruned_loss=0.03999, over 1422635.38 frames.], batch size: 17, lr: 4.33e-04
2022-04-29 12:24:09,898 INFO [train.py:763] (1/8) Epoch 17, batch 1700, loss[loss=0.1874, simple_loss=0.2843, pruned_loss=0.04521, over 7360.00 frames.], tot_loss[loss=0.177, simple_loss=0.2747, pruned_loss=0.03966, over 1425260.69 frames.], batch size: 19, lr: 4.33e-04
2022-04-29 12:25:15,252 INFO [train.py:763] (1/8) Epoch 17, batch 1750, loss[loss=0.1747, simple_loss=0.2746, pruned_loss=0.03741, over 7320.00 frames.], tot_loss[loss=0.1768, simple_loss=0.2742, pruned_loss=0.03968, over 1426456.68 frames.], batch size: 21, lr: 4.33e-04
2022-04-29 12:26:20,535 INFO [train.py:763] (1/8) Epoch 17, batch 1800, loss[loss=0.1767, simple_loss=0.2633, pruned_loss=0.04505, over 7235.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2738, pruned_loss=0.0398, over 1430592.42 frames.], batch size: 20, lr: 4.33e-04
2022-04-29 12:27:26,283 INFO [train.py:763] (1/8) Epoch 17, batch 1850, loss[loss=0.1842, simple_loss=0.2773, pruned_loss=0.04554, over 5107.00 frames.], tot_loss[loss=0.1757, simple_loss=0.2722, pruned_loss=0.03959, over 1428122.36 frames.], batch size: 53, lr: 4.33e-04
2022-04-29 12:28:31,339 INFO [train.py:763] (1/8) Epoch 17, batch 1900, loss[loss=0.1594, simple_loss=0.2598, pruned_loss=0.02955, over 7328.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2735, pruned_loss=0.0396, over 1427965.22 frames.], batch size: 21, lr: 4.33e-04
2022-04-29 12:29:36,729 INFO [train.py:763] (1/8) Epoch 17, batch 1950, loss[loss=0.1658, simple_loss=0.2751, pruned_loss=0.0282, over 7317.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2741, pruned_loss=0.03985, over 1424439.01 frames.], batch size: 21, lr: 4.32e-04
2022-04-29 12:30:42,615 INFO [train.py:763] (1/8) Epoch 17, batch 2000, loss[loss=0.2085, simple_loss=0.2868, pruned_loss=0.06513, over 5308.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2731, pruned_loss=0.0397, over 1425527.95 frames.], batch size: 52, lr: 4.32e-04
2022-04-29 12:31:59,160 INFO [train.py:763] (1/8) Epoch 17, batch 2050, loss[loss=0.1962, simple_loss=0.2964, pruned_loss=0.04797, over 7109.00 frames.], tot_loss[loss=0.176, simple_loss=0.2727, pruned_loss=0.03967, over 1420490.13 frames.], batch size: 21, lr: 4.32e-04
2022-04-29 12:33:04,642 INFO [train.py:763] (1/8) Epoch 17, batch 2100, loss[loss=0.2043, simple_loss=0.3124, pruned_loss=0.04813, over 6680.00 frames.], tot_loss[loss=0.1755, simple_loss=0.2723, pruned_loss=0.03934, over 1416050.04 frames.], batch size: 31, lr: 4.32e-04
2022-04-29 12:34:11,526 INFO [train.py:763] (1/8) Epoch 17, batch 2150, loss[loss=0.1892, simple_loss=0.2967, pruned_loss=0.0408, over 7220.00 frames.], tot_loss[loss=0.175, simple_loss=0.2721, pruned_loss=0.03896, over 1418158.31 frames.], batch size: 21, lr: 4.32e-04
2022-04-29 12:35:18,268 INFO [train.py:763] (1/8) Epoch 17, batch 2200, loss[loss=0.1717, simple_loss=0.2543, pruned_loss=0.04453, over 6786.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2724, pruned_loss=0.03906, over 1421729.19 frames.], batch size: 15, lr: 4.32e-04
2022-04-29 12:36:23,940 INFO [train.py:763] (1/8) Epoch 17, batch 2250, loss[loss=0.158, simple_loss=0.2389, pruned_loss=0.03851, over 7020.00 frames.], tot_loss[loss=0.175, simple_loss=0.2722, pruned_loss=0.0389, over 1424666.81 frames.], batch size: 16, lr: 4.32e-04
2022-04-29 12:37:31,403 INFO [train.py:763] (1/8) Epoch 17, batch 2300, loss[loss=0.1602, simple_loss=0.2659, pruned_loss=0.02721, over 7140.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2727, pruned_loss=0.03908, over 1427166.20 frames.], batch size: 20, lr: 4.31e-04
2022-04-29 12:38:38,622 INFO [train.py:763] (1/8) Epoch 17, batch 2350, loss[loss=0.2062, simple_loss=0.3084, pruned_loss=0.05199, over 7165.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2724, pruned_loss=0.03906, over 1426473.13 frames.], batch size: 26, lr: 4.31e-04
2022-04-29 12:39:44,062 INFO [train.py:763] (1/8) Epoch 17, batch 2400, loss[loss=0.2022, simple_loss=0.301, pruned_loss=0.05165, over 6487.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2723, pruned_loss=0.03892, over 1425261.59 frames.], batch size: 38, lr: 4.31e-04
2022-04-29 12:40:49,289 INFO [train.py:763] (1/8) Epoch 17, batch 2450, loss[loss=0.1722, simple_loss=0.2722, pruned_loss=0.03612, over 7151.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2713, pruned_loss=0.03899, over 1426410.85 frames.], batch size: 19, lr: 4.31e-04
2022-04-29 12:41:54,332 INFO [train.py:763] (1/8) Epoch 17, batch 2500, loss[loss=0.1812, simple_loss=0.2924, pruned_loss=0.03496, over 7115.00 frames.], tot_loss[loss=0.1767, simple_loss=0.273, pruned_loss=0.04015, over 1419399.49 frames.], batch size: 21, lr: 4.31e-04
2022-04-29 12:42:59,723 INFO [train.py:763] (1/8) Epoch 17, batch 2550, loss[loss=0.1992, simple_loss=0.3053, pruned_loss=0.04657, over 7316.00 frames.], tot_loss[loss=0.1764, simple_loss=0.273, pruned_loss=0.03992, over 1419738.89 frames.], batch size: 21, lr: 4.31e-04
2022-04-29 12:44:04,849 INFO [train.py:763] (1/8) Epoch 17, batch 2600, loss[loss=0.1732, simple_loss=0.2709, pruned_loss=0.03779, over 6778.00 frames.], tot_loss[loss=0.1762, simple_loss=0.273, pruned_loss=0.03967, over 1419073.47 frames.], batch size: 15, lr: 4.31e-04
2022-04-29 12:45:10,699 INFO [train.py:763] (1/8) Epoch 17, batch 2650, loss[loss=0.1729, simple_loss=0.2729, pruned_loss=0.03643, over 7359.00 frames.], tot_loss[loss=0.176, simple_loss=0.2727, pruned_loss=0.03965, over 1420021.93 frames.], batch size: 19, lr: 4.31e-04
2022-04-29 12:46:17,007 INFO [train.py:763] (1/8) Epoch 17, batch 2700, loss[loss=0.1551, simple_loss=0.2561, pruned_loss=0.02708, over 7291.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2728, pruned_loss=0.03998, over 1419817.13 frames.], batch size: 18, lr: 4.30e-04
2022-04-29 12:47:22,081 INFO [train.py:763] (1/8) Epoch 17, batch 2750, loss[loss=0.181, simple_loss=0.2816, pruned_loss=0.04018, over 7137.00 frames.], tot_loss[loss=0.1763, simple_loss=0.2728, pruned_loss=0.0399, over 1417884.74 frames.], batch size: 20, lr: 4.30e-04
2022-04-29 12:48:28,857 INFO [train.py:763] (1/8) Epoch 17, batch 2800, loss[loss=0.1993, simple_loss=0.2978, pruned_loss=0.05042, over 7321.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2724, pruned_loss=0.03998, over 1417372.48 frames.], batch size: 21, lr: 4.30e-04
2022-04-29 12:49:34,423 INFO [train.py:763] (1/8) Epoch 17, batch 2850, loss[loss=0.1836, simple_loss=0.279, pruned_loss=0.04414, over 7294.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2729, pruned_loss=0.03972, over 1420325.42 frames.], batch size: 25, lr: 4.30e-04
2022-04-29 12:50:39,887 INFO [train.py:763] (1/8) Epoch 17, batch 2900, loss[loss=0.1973, simple_loss=0.3064, pruned_loss=0.04416, over 7199.00 frames.], tot_loss[loss=0.1773, simple_loss=0.274, pruned_loss=0.04032, over 1422488.50 frames.], batch size: 22, lr: 4.30e-04
2022-04-29 12:51:46,362 INFO [train.py:763] (1/8) Epoch 17, batch 2950, loss[loss=0.1747, simple_loss=0.2766, pruned_loss=0.03641, over 6223.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2735, pruned_loss=0.03962, over 1418793.37 frames.], batch size: 37, lr: 4.30e-04
2022-04-29 12:52:52,635 INFO [train.py:763] (1/8) Epoch 17, batch 3000, loss[loss=0.2085, simple_loss=0.3098, pruned_loss=0.05358, over 7293.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2739, pruned_loss=0.0393, over 1417397.11 frames.], batch size: 25, lr: 4.30e-04
2022-04-29 12:52:52,636 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 12:53:07,981 INFO [train.py:792] (1/8) Epoch 17, validation: loss=0.167, simple_loss=0.268, pruned_loss=0.03296, over 698248.00 frames.
2022-04-29 12:54:13,315 INFO [train.py:763] (1/8) Epoch 17, batch 3050, loss[loss=0.1667, simple_loss=0.2714, pruned_loss=0.03098, over 7128.00 frames.], tot_loss[loss=0.1767, simple_loss=0.2742, pruned_loss=0.03962, over 1417131.83 frames.], batch size: 21, lr: 4.29e-04
2022-04-29 12:55:18,434 INFO [train.py:763] (1/8) Epoch 17, batch 3100, loss[loss=0.1594, simple_loss=0.2733, pruned_loss=0.02279, over 7230.00 frames.], tot_loss[loss=0.1774, simple_loss=0.275, pruned_loss=0.0399, over 1418393.96 frames.], batch size: 20, lr: 4.29e-04
2022-04-29 12:56:23,980 INFO [train.py:763] (1/8) Epoch 17, batch 3150, loss[loss=0.1802, simple_loss=0.2675, pruned_loss=0.04651, over 7247.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2741, pruned_loss=0.03959, over 1421493.35 frames.], batch size: 19, lr: 4.29e-04
2022-04-29 12:57:29,296 INFO [train.py:763] (1/8) Epoch 17, batch 3200, loss[loss=0.1939, simple_loss=0.2895, pruned_loss=0.0491, over 6765.00 frames.], tot_loss[loss=0.1766, simple_loss=0.2739, pruned_loss=0.03966, over 1419207.81 frames.], batch size: 31, lr: 4.29e-04
2022-04-29 12:58:34,631 INFO [train.py:763] (1/8) Epoch 17, batch 3250, loss[loss=0.1865, simple_loss=0.2794, pruned_loss=0.04685, over 7381.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2725, pruned_loss=0.03889, over 1422612.83 frames.], batch size: 23, lr: 4.29e-04
2022-04-29 12:59:42,205 INFO [train.py:763] (1/8) Epoch 17, batch 3300, loss[loss=0.1634, simple_loss=0.2561, pruned_loss=0.0353, over 7167.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2716, pruned_loss=0.03843, over 1427417.30 frames.], batch size: 18, lr: 4.29e-04
2022-04-29 13:00:47,852 INFO [train.py:763] (1/8) Epoch 17, batch 3350, loss[loss=0.1613, simple_loss=0.2487, pruned_loss=0.03694, over 7422.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2719, pruned_loss=0.03842, over 1427246.58 frames.], batch size: 18, lr: 4.29e-04
2022-04-29 13:01:54,344 INFO [train.py:763] (1/8) Epoch 17, batch 3400, loss[loss=0.1903, simple_loss=0.2965, pruned_loss=0.04201, over 7375.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2722, pruned_loss=0.03874, over 1430438.64 frames.], batch size: 23, lr: 4.29e-04
2022-04-29 13:02:59,883 INFO [train.py:763] (1/8) Epoch 17, batch 3450, loss[loss=0.1629, simple_loss=0.2432, pruned_loss=0.04129, over 7413.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2725, pruned_loss=0.03887, over 1430573.47 frames.], batch size: 18, lr: 4.28e-04
2022-04-29 13:04:05,572 INFO [train.py:763] (1/8) Epoch 17, batch 3500, loss[loss=0.1987, simple_loss=0.2913, pruned_loss=0.05303, over 6205.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2718, pruned_loss=0.03878, over 1432851.08 frames.], batch size: 37, lr: 4.28e-04
2022-04-29 13:05:11,602 INFO [train.py:763] (1/8) Epoch 17, batch 3550, loss[loss=0.1733, simple_loss=0.2736, pruned_loss=0.03644, over 7199.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2717, pruned_loss=0.03871, over 1431562.05 frames.], batch size: 23, lr: 4.28e-04
2022-04-29 13:06:17,356 INFO [train.py:763] (1/8) Epoch 17, batch 3600, loss[loss=0.2144, simple_loss=0.3094, pruned_loss=0.05976, over 7219.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2716, pruned_loss=0.03872, over 1432380.63 frames.], batch size: 21, lr: 4.28e-04
2022-04-29 13:07:22,977 INFO [train.py:763] (1/8) Epoch 17, batch 3650, loss[loss=0.1654, simple_loss=0.2693, pruned_loss=0.0307, over 7341.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2715, pruned_loss=0.03853, over 1423567.32 frames.], batch size: 22, lr: 4.28e-04
2022-04-29 13:08:28,133 INFO [train.py:763] (1/8) Epoch 17, batch 3700, loss[loss=0.1586, simple_loss=0.2477, pruned_loss=0.03475, over 6982.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2727, pruned_loss=0.03895, over 1424812.42 frames.], batch size: 16, lr: 4.28e-04
2022-04-29 13:09:33,325 INFO [train.py:763] (1/8) Epoch 17, batch 3750, loss[loss=0.1705, simple_loss=0.2756, pruned_loss=0.03268, over 7293.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2728, pruned_loss=0.03868, over 1426574.56 frames.], batch size: 25, lr: 4.28e-04
2022-04-29 13:10:39,693 INFO [train.py:763] (1/8) Epoch 17, batch 3800, loss[loss=0.177, simple_loss=0.2812, pruned_loss=0.03641, over 7360.00 frames.], tot_loss[loss=0.1752, simple_loss=0.273, pruned_loss=0.03874, over 1425620.79 frames.], batch size: 19, lr: 4.28e-04
2022-04-29 13:11:45,016 INFO [train.py:763] (1/8) Epoch 17, batch 3850, loss[loss=0.169, simple_loss=0.2656, pruned_loss=0.03614, over 7415.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2724, pruned_loss=0.03862, over 1424492.88 frames.], batch size: 18, lr: 4.27e-04
2022-04-29 13:12:50,424 INFO [train.py:763] (1/8) Epoch 17, batch 3900, loss[loss=0.1866, simple_loss=0.2944, pruned_loss=0.03944, over 7122.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2728, pruned_loss=0.03878, over 1420753.23 frames.], batch size: 21, lr: 4.27e-04
2022-04-29 13:13:55,778 INFO [train.py:763] (1/8) Epoch 17, batch 3950, loss[loss=0.186, simple_loss=0.2816, pruned_loss=0.04519, over 7107.00 frames.], tot_loss[loss=0.1746, simple_loss=0.272, pruned_loss=0.03855, over 1422343.88 frames.], batch size: 28, lr: 4.27e-04
2022-04-29 13:15:01,125 INFO [train.py:763] (1/8) Epoch 17, batch 4000, loss[loss=0.1618, simple_loss=0.2559, pruned_loss=0.0338, over 6828.00 frames.], tot_loss[loss=0.175, simple_loss=0.2725, pruned_loss=0.03876, over 1423791.54 frames.], batch size: 15, lr: 4.27e-04
2022-04-29 13:16:06,981 INFO [train.py:763] (1/8) Epoch 17, batch 4050, loss[loss=0.1878, simple_loss=0.2789, pruned_loss=0.04834, over 7119.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2729, pruned_loss=0.03915, over 1426939.08 frames.], batch size: 28, lr: 4.27e-04
2022-04-29 13:17:12,349 INFO [train.py:763] (1/8) Epoch 17, batch 4100, loss[loss=0.1768, simple_loss=0.2807, pruned_loss=0.03645, over 7145.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2729, pruned_loss=0.03933, over 1423596.41 frames.], batch size: 20, lr: 4.27e-04
2022-04-29 13:18:18,022 INFO [train.py:763] (1/8) Epoch 17, batch 4150, loss[loss=0.1805, simple_loss=0.2805, pruned_loss=0.04028, over 7330.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2726, pruned_loss=0.03911, over 1422801.94 frames.], batch size: 20, lr: 4.27e-04
2022-04-29 13:19:24,062 INFO [train.py:763] (1/8) Epoch 17, batch 4200, loss[loss=0.1866, simple_loss=0.2775, pruned_loss=0.04783, over 6997.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2716, pruned_loss=0.0389, over 1422552.15 frames.], batch size: 16, lr: 4.26e-04
2022-04-29 13:20:29,203 INFO [train.py:763] (1/8) Epoch 17, batch 4250, loss[loss=0.1826, simple_loss=0.2764, pruned_loss=0.0444, over 6872.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2715, pruned_loss=0.03887, over 1418291.80 frames.], batch size: 32, lr: 4.26e-04
2022-04-29 13:21:35,160 INFO [train.py:763] (1/8) Epoch 17, batch 4300, loss[loss=0.144, simple_loss=0.2293, pruned_loss=0.0293, over 6990.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2704, pruned_loss=0.03893, over 1419062.33 frames.], batch size: 16, lr: 4.26e-04
2022-04-29 13:22:49,724 INFO [train.py:763] (1/8) Epoch 17, batch 4350, loss[loss=0.1793, simple_loss=0.2847, pruned_loss=0.03692, over 7217.00 frames.], tot_loss[loss=0.1751, simple_loss=0.2714, pruned_loss=0.03944, over 1406790.70 frames.], batch size: 21, lr: 4.26e-04
2022-04-29 13:23:54,551 INFO [train.py:763] (1/8) Epoch 17, batch 4400, loss[loss=0.1877, simple_loss=0.2761, pruned_loss=0.04965, over 7074.00 frames.], tot_loss[loss=0.1762, simple_loss=0.2726, pruned_loss=0.03988, over 1401906.62 frames.], batch size: 18, lr: 4.26e-04
2022-04-29 13:24:59,616 INFO [train.py:763] (1/8) Epoch 17, batch 4450, loss[loss=0.196, simple_loss=0.2892, pruned_loss=0.0514, over 6377.00 frames.], tot_loss[loss=0.1774, simple_loss=0.2741, pruned_loss=0.04034, over 1392519.43 frames.], batch size: 37, lr: 4.26e-04
2022-04-29 13:26:04,072 INFO [train.py:763] (1/8) Epoch 17, batch 4500, loss[loss=0.1532, simple_loss=0.2398, pruned_loss=0.03326, over 6988.00 frames.], tot_loss[loss=0.1778, simple_loss=0.2746, pruned_loss=0.04054, over 1380260.34 frames.], batch size: 16, lr: 4.26e-04
2022-04-29 13:27:09,434 INFO [train.py:763] (1/8) Epoch 17, batch 4550, loss[loss=0.1561, simple_loss=0.2499, pruned_loss=0.03117, over 7161.00 frames.], tot_loss[loss=0.1773, simple_loss=0.2739, pruned_loss=0.04037, over 1368521.77 frames.], batch size: 19, lr: 4.26e-04
2022-04-29 13:29:06,465 INFO [train.py:763] (1/8) Epoch 18, batch 0, loss[loss=0.183, simple_loss=0.2888, pruned_loss=0.03855, over 7299.00 frames.], tot_loss[loss=0.183, simple_loss=0.2888, pruned_loss=0.03855, over 7299.00 frames.], batch size: 25, lr: 4.15e-04
2022-04-29 13:30:22,086 INFO [train.py:763] (1/8) Epoch 18, batch 50, loss[loss=0.2072, simple_loss=0.2961, pruned_loss=0.05914, over 7335.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2714, pruned_loss=0.03782, over 324759.81 frames.], batch size: 22, lr: 4.15e-04
2022-04-29 13:31:37,248 INFO [train.py:763] (1/8) Epoch 18, batch 100, loss[loss=0.1749, simple_loss=0.2885, pruned_loss=0.03065, over 7348.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2717, pruned_loss=0.03738, over 574616.80 frames.], batch size: 22, lr: 4.14e-04
2022-04-29 13:32:51,551 INFO [train.py:763] (1/8) Epoch 18, batch 150, loss[loss=0.1727, simple_loss=0.2797, pruned_loss=0.03288, over 7222.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2699, pruned_loss=0.0374, over 764496.83 frames.], batch size: 21, lr: 4.14e-04
2022-04-29 13:33:57,482 INFO [train.py:763] (1/8) Epoch 18, batch 200, loss[loss=0.1623, simple_loss=0.2559, pruned_loss=0.0344, over 7275.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2704, pruned_loss=0.03834, over 909764.88 frames.], batch size: 17, lr: 4.14e-04
2022-04-29 13:35:11,768 INFO [train.py:763] (1/8) Epoch 18, batch 250, loss[loss=0.1982, simple_loss=0.2884, pruned_loss=0.054, over 6759.00 frames.], tot_loss[loss=0.174, simple_loss=0.271, pruned_loss=0.03856, over 1025821.28 frames.], batch size: 31, lr: 4.14e-04
2022-04-29 13:36:17,271 INFO [train.py:763] (1/8) Epoch 18, batch 300, loss[loss=0.1906, simple_loss=0.2915, pruned_loss=0.04485, over 7239.00 frames.], tot_loss[loss=0.1735, simple_loss=0.271, pruned_loss=0.03805, over 1116027.73 frames.], batch size: 20, lr: 4.14e-04
2022-04-29 13:37:24,206 INFO [train.py:763] (1/8) Epoch 18, batch 350, loss[loss=0.174, simple_loss=0.2718, pruned_loss=0.03812, over 6859.00 frames.], tot_loss[loss=0.173, simple_loss=0.2704, pruned_loss=0.03786, over 1182633.94 frames.], batch size: 31, lr: 4.14e-04
2022-04-29 13:38:31,273 INFO [train.py:763] (1/8) Epoch 18, batch 400, loss[loss=0.1675, simple_loss=0.2616, pruned_loss=0.03672, over 7061.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2711, pruned_loss=0.03821, over 1233661.17 frames.], batch size: 18, lr: 4.14e-04
2022-04-29 13:39:38,717 INFO [train.py:763] (1/8) Epoch 18, batch 450, loss[loss=0.1868, simple_loss=0.2885, pruned_loss=0.04251, over 7335.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2721, pruned_loss=0.03828, over 1275330.84 frames.], batch size: 22, lr: 4.14e-04
2022-04-29 13:40:45,465 INFO [train.py:763] (1/8) Epoch 18, batch 500, loss[loss=0.1535, simple_loss=0.2406, pruned_loss=0.03316, over 7128.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2726, pruned_loss=0.03848, over 1306418.05 frames.], batch size: 17, lr: 4.13e-04
2022-04-29 13:41:52,278 INFO [train.py:763] (1/8) Epoch 18, batch 550, loss[loss=0.1755, simple_loss=0.2601, pruned_loss=0.04547, over 7272.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2731, pruned_loss=0.03877, over 1336343.71 frames.], batch size: 17, lr: 4.13e-04
2022-04-29 13:42:57,722 INFO [train.py:763] (1/8) Epoch 18, batch 600, loss[loss=0.1554, simple_loss=0.2511, pruned_loss=0.02986, over 7280.00 frames.], tot_loss[loss=0.1752, simple_loss=0.273, pruned_loss=0.03872, over 1356614.31 frames.], batch size: 18, lr: 4.13e-04
2022-04-29 13:44:04,377 INFO [train.py:763] (1/8) Epoch 18, batch 650, loss[loss=0.1887, simple_loss=0.2879, pruned_loss=0.04471, over 7118.00 frames.], tot_loss[loss=0.1739, simple_loss=0.272, pruned_loss=0.03788, over 1374937.81 frames.], batch size: 21, lr: 4.13e-04
2022-04-29 13:45:09,471 INFO [train.py:763] (1/8) Epoch 18, batch 700, loss[loss=0.1812, simple_loss=0.2738, pruned_loss=0.04431, over 5049.00 frames.], tot_loss[loss=0.174, simple_loss=0.272, pruned_loss=0.03806, over 1385325.13 frames.], batch size: 52, lr: 4.13e-04
2022-04-29 13:46:15,214 INFO [train.py:763] (1/8) Epoch 18, batch 750, loss[loss=0.1585, simple_loss=0.2688, pruned_loss=0.02405, over 7171.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2711, pruned_loss=0.03777, over 1394071.49 frames.], batch size: 19, lr: 4.13e-04
2022-04-29 13:47:20,148 INFO [train.py:763] (1/8) Epoch 18, batch 800, loss[loss=0.175, simple_loss=0.2846, pruned_loss=0.03268, over 6666.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2727, pruned_loss=0.03845, over 1397666.80 frames.], batch size: 31, lr: 4.13e-04
2022-04-29 13:48:26,402 INFO [train.py:763] (1/8) Epoch 18, batch 850, loss[loss=0.1699, simple_loss=0.2617, pruned_loss=0.03904, over 7074.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2733, pruned_loss=0.03864, over 1405035.64 frames.], batch size: 18, lr: 4.13e-04
2022-04-29 13:49:33,106 INFO [train.py:763] (1/8) Epoch 18, batch 900, loss[loss=0.1857, simple_loss=0.2758, pruned_loss=0.0478, over 7199.00 frames.], tot_loss[loss=0.176, simple_loss=0.274, pruned_loss=0.03906, over 1410653.39 frames.], batch size: 16, lr: 4.12e-04
2022-04-29 13:50:38,404 INFO [train.py:763] (1/8) Epoch 18, batch 950, loss[loss=0.1933, simple_loss=0.2965, pruned_loss=0.04501, over 7368.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2725, pruned_loss=0.03848, over 1413448.08 frames.], batch size: 23, lr: 4.12e-04
2022-04-29 13:51:45,513 INFO [train.py:763] (1/8) Epoch 18, batch 1000, loss[loss=0.1861, simple_loss=0.2823, pruned_loss=0.04492, over 7159.00 frames.], tot_loss[loss=0.1742, simple_loss=0.272, pruned_loss=0.03823, over 1420006.17 frames.], batch size: 20, lr: 4.12e-04
2022-04-29 13:52:52,989 INFO [train.py:763] (1/8) Epoch 18, batch 1050, loss[loss=0.1989, simple_loss=0.2924, pruned_loss=0.05265, over 7283.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2716, pruned_loss=0.03825, over 1417644.03 frames.], batch size: 25, lr: 4.12e-04
2022-04-29 13:53:58,530 INFO [train.py:763] (1/8) Epoch 18, batch 1100, loss[loss=0.1741, simple_loss=0.2785, pruned_loss=0.03479, over 7327.00 frames.], tot_loss[loss=0.1735, simple_loss=0.271, pruned_loss=0.03803, over 1418486.78 frames.], batch size: 20, lr: 4.12e-04
2022-04-29 13:55:03,937 INFO [train.py:763] (1/8) Epoch 18, batch 1150, loss[loss=0.1876, simple_loss=0.2792, pruned_loss=0.04793, over 7284.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2706, pruned_loss=0.03799, over 1418794.89 frames.], batch size: 24, lr: 4.12e-04
2022-04-29 13:56:09,831 INFO [train.py:763] (1/8) Epoch 18, batch 1200, loss[loss=0.1896, simple_loss=0.2752, pruned_loss=0.05202, over 4682.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2707, pruned_loss=0.03822, over 1413455.48 frames.], batch size: 52, lr: 4.12e-04
2022-04-29 13:57:15,050 INFO [train.py:763] (1/8) Epoch 18, batch 1250, loss[loss=0.1888, simple_loss=0.2855, pruned_loss=0.0461, over 7134.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2708, pruned_loss=0.03797, over 1413848.58 frames.], batch size: 21, lr: 4.12e-04
2022-04-29 13:58:20,082 INFO [train.py:763] (1/8) Epoch 18, batch 1300, loss[loss=0.1738, simple_loss=0.2724, pruned_loss=0.03762, over 7154.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2713, pruned_loss=0.03804, over 1413515.99 frames.], batch size: 19, lr: 4.12e-04
2022-04-29 13:59:25,398 INFO [train.py:763] (1/8) Epoch 18, batch 1350, loss[loss=0.2029, simple_loss=0.2984, pruned_loss=0.05372, over 7011.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2722, pruned_loss=0.03861, over 1412454.49 frames.], batch size: 28, lr: 4.11e-04
2022-04-29 14:00:32,446 INFO [train.py:763] (1/8) Epoch 18, batch 1400, loss[loss=0.1637, simple_loss=0.2484, pruned_loss=0.0395, over 7071.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2716, pruned_loss=0.0386, over 1411110.45 frames.], batch size: 18, lr: 4.11e-04
2022-04-29 14:01:39,694 INFO [train.py:763] (1/8) Epoch 18, batch 1450, loss[loss=0.1575, simple_loss=0.2596, pruned_loss=0.02771, over 7315.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2712, pruned_loss=0.03794, over 1418613.58 frames.], batch size: 21, lr: 4.11e-04
2022-04-29 14:02:45,982 INFO [train.py:763] (1/8) Epoch 18, batch 1500, loss[loss=0.1519, simple_loss=0.2393, pruned_loss=0.03223, over 7256.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2715, pruned_loss=0.03791, over 1422027.95 frames.], batch size: 19, lr: 4.11e-04
2022-04-29 14:03:53,118 INFO [train.py:763] (1/8) Epoch 18, batch 1550, loss[loss=0.1973, simple_loss=0.2899, pruned_loss=0.05231, over 7408.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2716, pruned_loss=0.03844, over 1425309.85 frames.], batch size: 21, lr: 4.11e-04
2022-04-29 14:04:58,306 INFO [train.py:763] (1/8) Epoch 18, batch 1600, loss[loss=0.1954, simple_loss=0.2851, pruned_loss=0.05281, over 7199.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2703, pruned_loss=0.03805, over 1424212.25 frames.], batch size: 22, lr: 4.11e-04
2022-04-29 14:06:03,947 INFO [train.py:763] (1/8) Epoch 18, batch 1650, loss[loss=0.1535, simple_loss=0.2508, pruned_loss=0.02809, over 7174.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2706, pruned_loss=0.03802, over 1422798.85 frames.], batch size: 18, lr: 4.11e-04
2022-04-29 14:07:10,554 INFO [train.py:763] (1/8) Epoch 18, batch 1700, loss[loss=0.1668, simple_loss=0.2615, pruned_loss=0.03607, over 7167.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2715, pruned_loss=0.03841, over 1423608.15 frames.], batch size: 18, lr: 4.11e-04
2022-04-29 14:08:17,583 INFO [train.py:763] (1/8) Epoch 18, batch 1750, loss[loss=0.1693, simple_loss=0.2689, pruned_loss=0.03487, over 7141.00 frames.], tot_loss[loss=0.1756, simple_loss=0.2728, pruned_loss=0.03915, over 1416191.73 frames.], batch size: 20, lr: 4.10e-04
2022-04-29 14:09:24,695 INFO [train.py:763] (1/8) Epoch 18, batch 1800, loss[loss=0.1695, simple_loss=0.2717, pruned_loss=0.03368, over 7265.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2747, pruned_loss=0.03957, over 1416050.23 frames.], batch size: 19, lr: 4.10e-04
2022-04-29 14:10:32,225 INFO [train.py:763] (1/8) Epoch 18, batch 1850, loss[loss=0.1977, simple_loss=0.2849, pruned_loss=0.05526, over 7304.00 frames.], tot_loss[loss=0.1761, simple_loss=0.274, pruned_loss=0.03909, over 1421880.60 frames.], batch size: 24, lr: 4.10e-04
2022-04-29 14:11:39,560 INFO [train.py:763] (1/8) Epoch 18, batch 1900, loss[loss=0.1973, simple_loss=0.2909, pruned_loss=0.05184, over 7089.00 frames.], tot_loss[loss=0.1764, simple_loss=0.2743, pruned_loss=0.0393, over 1419515.80 frames.], batch size: 28, lr: 4.10e-04
2022-04-29 14:12:46,672 INFO [train.py:763] (1/8) Epoch 18, batch 1950, loss[loss=0.1358, simple_loss=0.2301, pruned_loss=0.02076, over 6988.00 frames.], tot_loss[loss=0.1758, simple_loss=0.2738, pruned_loss=0.03885, over 1420266.99 frames.], batch size: 16, lr: 4.10e-04
2022-04-29 14:13:51,989 INFO [train.py:763] (1/8) Epoch 18, batch 2000, loss[loss=0.1588, simple_loss=0.2674, pruned_loss=0.02511, over 7144.00 frames.], tot_loss[loss=0.175, simple_loss=0.2729, pruned_loss=0.03854, over 1423613.97 frames.], batch size: 20, lr: 4.10e-04
2022-04-29 14:14:57,423 INFO [train.py:763] (1/8) Epoch 18, batch 2050, loss[loss=0.1949, simple_loss=0.3051, pruned_loss=0.04239, over 7281.00 frames.], tot_loss[loss=0.1743, simple_loss=0.2722, pruned_loss=0.03822, over 1423999.98 frames.], batch size: 25, lr: 4.10e-04
2022-04-29 14:16:02,572 INFO [train.py:763] (1/8) Epoch 18, batch 2100, loss[loss=0.1428, simple_loss=0.247, pruned_loss=0.01937, over 7162.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2717, pruned_loss=0.03773, over 1424237.72 frames.], batch size: 19, lr: 4.10e-04
2022-04-29 14:17:08,134 INFO [train.py:763] (1/8) Epoch 18, batch 2150, loss[loss=0.1861, simple_loss=0.2998, pruned_loss=0.03627, over 7223.00 frames.], tot_loss[loss=0.174, simple_loss=0.2718, pruned_loss=0.03811, over 1420493.36 frames.], batch size: 21, lr: 4.09e-04
2022-04-29 14:18:13,397 INFO [train.py:763] (1/8) Epoch 18, batch 2200, loss[loss=0.1732, simple_loss=0.2734, pruned_loss=0.03647, over 7116.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2713, pruned_loss=0.03797, over 1424646.74 frames.], batch size: 21, lr: 4.09e-04
2022-04-29 14:19:18,569 INFO [train.py:763] (1/8) Epoch 18, batch 2250, loss[loss=0.1856, simple_loss=0.2824, pruned_loss=0.04439, over 6545.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2711, pruned_loss=0.03811, over 1423998.56 frames.], batch size: 38, lr: 4.09e-04
2022-04-29 14:20:23,887 INFO [train.py:763] (1/8) Epoch 18, batch 2300, loss[loss=0.1997, simple_loss=0.2945, pruned_loss=0.05247, over 7365.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2706, pruned_loss=0.03803, over 1425576.48 frames.], batch size: 23, lr: 4.09e-04
2022-04-29 14:21:28,907 INFO [train.py:763] (1/8) Epoch 18, batch 2350, loss[loss=0.1527, simple_loss=0.2315, pruned_loss=0.03693, over 7272.00 frames.], tot_loss[loss=0.1737, simple_loss=0.271, pruned_loss=0.03822, over 1423285.39 frames.], batch size: 17, lr: 4.09e-04
2022-04-29 14:22:34,035 INFO [train.py:763] (1/8) Epoch 18, batch 2400, loss[loss=0.18, simple_loss=0.2826, pruned_loss=0.03865, over 7146.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2719, pruned_loss=0.03875, over 1420170.38 frames.], batch size: 20, lr: 4.09e-04
2022-04-29 14:23:41,076 INFO [train.py:763] (1/8) Epoch 18, batch 2450, loss[loss=0.18, simple_loss=0.2765, pruned_loss=0.04176, over 7145.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2716, pruned_loss=0.03846, over 1423034.34 frames.], batch size: 20, lr: 4.09e-04
2022-04-29 14:24:46,855 INFO [train.py:763] (1/8) Epoch 18, batch 2500, loss[loss=0.1561, simple_loss=0.2609, pruned_loss=0.02568, over 7187.00 frames.], tot_loss[loss=0.1737, simple_loss=0.271, pruned_loss=0.03818, over 1421759.40 frames.], batch size: 26, lr: 4.09e-04
2022-04-29 14:25:51,851 INFO [train.py:763] (1/8) Epoch 18, batch 2550, loss[loss=0.2308, simple_loss=0.3154, pruned_loss=0.07317, over 7285.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2711, pruned_loss=0.03862, over 1421384.27 frames.], batch size: 24, lr: 4.08e-04
2022-04-29 14:26:57,010 INFO [train.py:763] (1/8) Epoch 18, batch 2600, loss[loss=0.1379, simple_loss=0.2227, pruned_loss=0.02661, over 7013.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2719, pruned_loss=0.03864, over 1425003.78 frames.], batch size: 16, lr: 4.08e-04
2022-04-29 14:28:02,330 INFO [train.py:763] (1/8) Epoch 18, batch 2650, loss[loss=0.2278, simple_loss=0.3196, pruned_loss=0.06801, over 7273.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2727, pruned_loss=0.03899, over 1426906.19 frames.], batch size: 24, lr: 4.08e-04
2022-04-29 14:29:08,095 INFO [train.py:763] (1/8) Epoch 18, batch 2700, loss[loss=0.2205, simple_loss=0.3071, pruned_loss=0.06699, over 7284.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2716, pruned_loss=0.03854, over 1431081.54 frames.], batch size: 25, lr: 4.08e-04
2022-04-29 14:30:14,902 INFO [train.py:763] (1/8) Epoch 18, batch 2750, loss[loss=0.1545, simple_loss=0.2606, pruned_loss=0.02422, over 7409.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2717, pruned_loss=0.03853, over 1430665.32 frames.], batch size: 21, lr: 4.08e-04
2022-04-29 14:31:21,337 INFO [train.py:763] (1/8) Epoch 18, batch 2800, loss[loss=0.177, simple_loss=0.2749, pruned_loss=0.03959, over 7064.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2704, pruned_loss=0.03833, over 1431046.47 frames.], batch size: 18, lr: 4.08e-04
2022-04-29 14:32:26,506 INFO [train.py:763] (1/8) Epoch 18, batch 2850, loss[loss=0.1595, simple_loss=0.2557, pruned_loss=0.03162, over 7154.00 frames.], tot_loss[loss=0.174, simple_loss=0.2709, pruned_loss=0.03854, over 1427621.97 frames.], batch size: 19, lr: 4.08e-04
2022-04-29 14:33:31,779 INFO [train.py:763] (1/8) Epoch 18, batch 2900, loss[loss=0.1921, simple_loss=0.2908, pruned_loss=0.04667, over 7206.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2705, pruned_loss=0.03832, over 1424245.74 frames.], batch size: 26, lr: 4.08e-04
2022-04-29 14:34:37,290 INFO [train.py:763] (1/8) Epoch 18, batch 2950, loss[loss=0.1598, simple_loss=0.2474, pruned_loss=0.03604, over 7273.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2706, pruned_loss=0.038, over 1429951.98 frames.], batch size: 17, lr: 4.08e-04
2022-04-29 14:35:43,263 INFO [train.py:763] (1/8) Epoch 18, batch 3000, loss[loss=0.1935, simple_loss=0.2898, pruned_loss=0.04862, over 5116.00 frames.], tot_loss[loss=0.1726, simple_loss=0.27, pruned_loss=0.03762, over 1429695.56 frames.], batch size: 53, lr: 4.07e-04
2022-04-29 14:35:43,264 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 14:35:58,559 INFO [train.py:792] (1/8) Epoch 18, validation: loss=0.1668, simple_loss=0.2671, pruned_loss=0.03324, over 698248.00 frames.
2022-04-29 14:37:05,448 INFO [train.py:763] (1/8) Epoch 18, batch 3050, loss[loss=0.1833, simple_loss=0.2894, pruned_loss=0.03861, over 7205.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2713, pruned_loss=0.03798, over 1430683.33 frames.], batch size: 23, lr: 4.07e-04
2022-04-29 14:38:12,644 INFO [train.py:763] (1/8) Epoch 18, batch 3100, loss[loss=0.1693, simple_loss=0.2746, pruned_loss=0.03201, over 6654.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2717, pruned_loss=0.03825, over 1431697.43 frames.], batch size: 38, lr: 4.07e-04
2022-04-29 14:39:19,391 INFO [train.py:763] (1/8) Epoch 18, batch 3150, loss[loss=0.1631, simple_loss=0.2404, pruned_loss=0.04285, over 7266.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2722, pruned_loss=0.03853, over 1429132.18 frames.], batch size: 18, lr: 4.07e-04
2022-04-29 14:40:26,377 INFO [train.py:763] (1/8) Epoch 18, batch 3200, loss[loss=0.1888, simple_loss=0.2877, pruned_loss=0.04496, over 7160.00 frames.], tot_loss[loss=0.1742, simple_loss=0.2719, pruned_loss=0.03825, over 1427409.94 frames.], batch size: 19, lr: 4.07e-04
2022-04-29 14:41:32,518 INFO [train.py:763] (1/8) Epoch 18, batch 3250, loss[loss=0.1601, simple_loss=0.2564, pruned_loss=0.03184, over 7362.00 frames.], tot_loss[loss=0.175, simple_loss=0.273, pruned_loss=0.03849, over 1424481.53 frames.], batch size: 19, lr: 4.07e-04
2022-04-29 14:42:37,739 INFO [train.py:763] (1/8) Epoch 18, batch 3300, loss[loss=0.1675, simple_loss=0.2816, pruned_loss=0.02665, over 6338.00 frames.], tot_loss[loss=0.1747, simple_loss=0.2728, pruned_loss=0.03827, over 1424461.05 frames.], batch size: 37, lr: 4.07e-04
2022-04-29 14:43:43,236 INFO [train.py:763] (1/8) Epoch 18, batch 3350, loss[loss=0.1914, simple_loss=0.2921, pruned_loss=0.04533, over 7132.00 frames.], tot_loss[loss=0.1741, simple_loss=0.2723, pruned_loss=0.03793, over 1423472.02 frames.], batch size: 21, lr: 4.07e-04
2022-04-29 14:44:48,482 INFO [train.py:763] (1/8) Epoch 18, batch 3400, loss[loss=0.1509, simple_loss=0.2454, pruned_loss=0.02822, over 7257.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2717, pruned_loss=0.03748, over 1424092.42 frames.], batch size: 18, lr: 4.06e-04
2022-04-29 14:45:53,980 INFO [train.py:763] (1/8) Epoch 18, batch 3450, loss[loss=0.1599, simple_loss=0.2604, pruned_loss=0.02969, over 7361.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2697, pruned_loss=0.03733, over 1420364.19 frames.], batch size: 19, lr: 4.06e-04
2022-04-29 14:46:59,196 INFO [train.py:763] (1/8) Epoch 18, batch 3500, loss[loss=0.1657, simple_loss=0.2571, pruned_loss=0.03715, over 7279.00 frames.], tot_loss[loss=0.1724, simple_loss=0.27, pruned_loss=0.03745, over 1423087.55 frames.], batch size: 18, lr: 4.06e-04
2022-04-29 14:48:04,600 INFO [train.py:763] (1/8) Epoch 18, batch 3550, loss[loss=0.1359, simple_loss=0.2197, pruned_loss=0.02611, over 7134.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2698, pruned_loss=0.03769, over 1423122.36 frames.], batch size: 17, lr: 4.06e-04
2022-04-29 14:49:09,816 INFO [train.py:763] (1/8) Epoch 18, batch 3600, loss[loss=0.1835, simple_loss=0.2749, pruned_loss=0.04602, over 7195.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2702, pruned_loss=0.038, over 1420246.03 frames.], batch size: 23, lr: 4.06e-04
2022-04-29 14:50:14,980 INFO [train.py:763] (1/8) Epoch 18, batch 3650, loss[loss=0.1795, simple_loss=0.2752, pruned_loss=0.04186, over 7329.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2705, pruned_loss=0.03786, over 1413459.34 frames.], batch size: 20, lr: 4.06e-04
2022-04-29 14:51:20,200 INFO [train.py:763] (1/8) Epoch 18, batch 3700, loss[loss=0.1724, simple_loss=0.2811, pruned_loss=0.03191, over 7414.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2724, pruned_loss=0.03895, over 1415383.95 frames.], batch size: 21, lr: 4.06e-04
2022-04-29 14:52:25,582 INFO [train.py:763] (1/8) Epoch 18, batch 3750, loss[loss=0.1916, simple_loss=0.2909, pruned_loss=0.04617, over 7363.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2725, pruned_loss=0.03898, over 1411610.99 frames.], batch size: 23, lr: 4.06e-04
2022-04-29 14:53:30,893 INFO [train.py:763] (1/8) Epoch 18, batch 3800, loss[loss=0.1738, simple_loss=0.267, pruned_loss=0.04032, over 7371.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2728, pruned_loss=0.03878, over 1417178.77 frames.], batch size: 19, lr: 4.06e-04
2022-04-29 14:54:36,406 INFO [train.py:763] (1/8) Epoch 18, batch 3850, loss[loss=0.1439, simple_loss=0.2405, pruned_loss=0.02369, over 7158.00 frames.], tot_loss[loss=0.1743, simple_loss=0.272, pruned_loss=0.03832, over 1415425.28 frames.], batch size: 18, lr: 4.05e-04
2022-04-29 14:55:41,213 INFO [train.py:763] (1/8) Epoch 18, batch 3900, loss[loss=0.1765, simple_loss=0.2862, pruned_loss=0.03341, over 7115.00 frames.], tot_loss[loss=0.1749, simple_loss=0.2728, pruned_loss=0.03851, over 1413430.05 frames.], batch size: 21, lr: 4.05e-04
2022-04-29 14:56:46,299 INFO [train.py:763] (1/8) Epoch 18, batch 3950, loss[loss=0.1678, simple_loss=0.2643, pruned_loss=0.03568, over 7172.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2734, pruned_loss=0.03864, over 1416324.00 frames.], batch size: 18, lr: 4.05e-04
2022-04-29 14:57:51,526 INFO [train.py:763] (1/8) Epoch 18, batch 4000, loss[loss=0.2163, simple_loss=0.3061, pruned_loss=0.06321, over 5150.00 frames.], tot_loss[loss=0.1752, simple_loss=0.2731, pruned_loss=0.0386, over 1417336.44 frames.], batch size: 52, lr: 4.05e-04
2022-04-29 14:58:57,191 INFO [train.py:763] (1/8) Epoch 18, batch 4050, loss[loss=0.1471, simple_loss=0.2415, pruned_loss=0.02636, over 7282.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2716, pruned_loss=0.038, over 1415792.25 frames.], batch size: 16, lr: 4.05e-04
2022-04-29 15:00:03,350 INFO [train.py:763] (1/8) Epoch 18, batch 4100, loss[loss=0.2024, simple_loss=0.2975, pruned_loss=0.05368, over 5038.00 frames.], tot_loss[loss=0.1746, simple_loss=0.2723, pruned_loss=0.03843, over 1415521.39 frames.], batch size: 52, lr: 4.05e-04
2022-04-29 15:01:09,076 INFO [train.py:763] (1/8) Epoch 18, batch 4150, loss[loss=0.1783, simple_loss=0.2724, pruned_loss=0.04214, over 7373.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2712, pruned_loss=0.03821, over 1420801.67 frames.], batch size: 23, lr: 4.05e-04
2022-04-29 15:02:16,179 INFO [train.py:763] (1/8) Epoch 18, batch 4200, loss[loss=0.2048, simple_loss=0.2974, pruned_loss=0.05612, over 7184.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2714, pruned_loss=0.03815, over 1419118.40 frames.], batch size: 23, lr: 4.05e-04
2022-04-29 15:03:23,609 INFO [train.py:763] (1/8) Epoch 18, batch 4250, loss[loss=0.1462, simple_loss=0.2354, pruned_loss=0.02847, over 6832.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2712, pruned_loss=0.03795, over 1419153.65 frames.], batch size: 15, lr: 4.04e-04
2022-04-29 15:04:28,930 INFO [train.py:763] (1/8) Epoch 18, batch 4300, loss[loss=0.1635, simple_loss=0.2683, pruned_loss=0.02936, over 7131.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2712, pruned_loss=0.0378, over 1419059.43 frames.], batch size: 26, lr: 4.04e-04
2022-04-29 15:05:35,078 INFO [train.py:763] (1/8) Epoch 18, batch 4350, loss[loss=0.1556, simple_loss=0.2515, pruned_loss=0.0298, over 7160.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2709, pruned_loss=0.03815, over 1416260.38 frames.], batch size: 18, lr: 4.04e-04
2022-04-29 15:06:42,524 INFO [train.py:763] (1/8) Epoch 18, batch 4400, loss[loss=0.1958, simple_loss=0.2985, pruned_loss=0.0465, over 6329.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2705, pruned_loss=0.03812, over 1412576.70 frames.], batch size: 37, lr: 4.04e-04
2022-04-29 15:07:48,910 INFO [train.py:763] (1/8) Epoch 18, batch 4450, loss[loss=0.1612, simple_loss=0.2519, pruned_loss=0.03523, over 6768.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2701, pruned_loss=0.03838, over 1407909.52 frames.], batch size: 15, lr: 4.04e-04
2022-04-29 15:08:55,424 INFO [train.py:763] (1/8) Epoch 18, batch 4500, loss[loss=0.1573, simple_loss=0.2602, pruned_loss=0.02722, over 7152.00 frames.], tot_loss[loss=0.1745, simple_loss=0.2712, pruned_loss=0.03894, over 1395235.22 frames.], batch size: 20, lr: 4.04e-04
2022-04-29 15:10:01,685 INFO [train.py:763] (1/8) Epoch 18, batch 4550, loss[loss=0.1638, simple_loss=0.2681, pruned_loss=0.0298, over 6401.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2707, pruned_loss=0.03947, over 1365601.21 frames.], batch size: 37, lr: 4.04e-04
2022-04-29 15:11:30,592 INFO [train.py:763] (1/8) Epoch 19, batch 0, loss[loss=0.1739, simple_loss=0.27, pruned_loss=0.03889, over 7359.00 frames.], tot_loss[loss=0.1739, simple_loss=0.27, pruned_loss=0.03889, over 7359.00 frames.], batch size: 19, lr: 3.94e-04
2022-04-29 15:12:36,739 INFO [train.py:763] (1/8) Epoch 19, batch 50, loss[loss=0.1458, simple_loss=0.2464, pruned_loss=0.02258, over 7296.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2719, pruned_loss=0.0377, over 321592.73 frames.], batch size: 18, lr: 3.94e-04
2022-04-29 15:13:42,678 INFO [train.py:763] (1/8) Epoch 19, batch 100, loss[loss=0.2227, simple_loss=0.302, pruned_loss=0.07166, over 5270.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2712, pruned_loss=0.03755, over 566239.59 frames.], batch size: 52, lr: 3.94e-04
2022-04-29 15:14:48,875 INFO [train.py:763] (1/8) Epoch 19, batch 150, loss[loss=0.1782, simple_loss=0.2886, pruned_loss=0.03389, over 7310.00 frames.], tot_loss[loss=0.173, simple_loss=0.2722, pruned_loss=0.03686, over 756800.20 frames.], batch size: 21, lr: 3.94e-04
2022-04-29 15:15:54,340 INFO [train.py:763] (1/8) Epoch 19, batch 200, loss[loss=0.1839, simple_loss=0.2931, pruned_loss=0.03737, over 7342.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2717, pruned_loss=0.03723, over 904543.61 frames.], batch size: 22, lr: 3.93e-04
2022-04-29 15:17:00,298 INFO [train.py:763] (1/8) Epoch 19, batch 250, loss[loss=0.178, simple_loss=0.277, pruned_loss=0.03952, over 7339.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2692, pruned_loss=0.03652, over 1023641.10 frames.], batch size: 22, lr: 3.93e-04
2022-04-29 15:18:06,649 INFO [train.py:763] (1/8) Epoch 19, batch 300, loss[loss=0.181, simple_loss=0.2829, pruned_loss=0.03956, over 7207.00 frames.], tot_loss[loss=0.1712, simple_loss=0.27, pruned_loss=0.03621, over 1113428.54 frames.], batch size: 23, lr: 3.93e-04
2022-04-29 15:19:12,752 INFO [train.py:763] (1/8) Epoch 19, batch 350, loss[loss=0.1704, simple_loss=0.2688, pruned_loss=0.03601, over 7150.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2711, pruned_loss=0.03675, over 1185903.45 frames.], batch size: 20, lr: 3.93e-04
2022-04-29 15:20:18,121 INFO [train.py:763] (1/8) Epoch 19, batch 400, loss[loss=0.1723, simple_loss=0.2649, pruned_loss=0.03989, over 7139.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2725, pruned_loss=0.03746, over 1238795.19 frames.], batch size: 20, lr: 3.93e-04
2022-04-29 15:21:23,455 INFO [train.py:763] (1/8) Epoch 19, batch 450, loss[loss=0.1924, simple_loss=0.2922, pruned_loss=0.04628, over 7370.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2728, pruned_loss=0.03727, over 1276509.52 frames.], batch size: 23, lr: 3.93e-04
2022-04-29 15:22:28,664 INFO [train.py:763] (1/8) Epoch 19, batch 500, loss[loss=0.1743, simple_loss=0.2822, pruned_loss=0.03319, over 7226.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2727, pruned_loss=0.0373, over 1309065.18 frames.], batch size: 21, lr: 3.93e-04
2022-04-29 15:23:34,243 INFO [train.py:763] (1/8) Epoch 19, batch 550, loss[loss=0.1643, simple_loss=0.2664, pruned_loss=0.0311, over 6694.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2711, pruned_loss=0.03678, over 1334730.04 frames.], batch size: 31, lr: 3.93e-04
2022-04-29 15:24:40,466 INFO [train.py:763] (1/8) Epoch 19, batch 600, loss[loss=0.152, simple_loss=0.2494, pruned_loss=0.02733, over 7163.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2695, pruned_loss=0.03663, over 1356021.80 frames.], batch size: 18, lr: 3.93e-04
2022-04-29 15:25:45,941 INFO [train.py:763] (1/8) Epoch 19, batch 650, loss[loss=0.1472, simple_loss=0.2401, pruned_loss=0.02719, over 7164.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2701, pruned_loss=0.03652, over 1369742.40 frames.], batch size: 18, lr: 3.92e-04
2022-04-29 15:26:51,169 INFO [train.py:763] (1/8) Epoch 19, batch 700, loss[loss=0.1978, simple_loss=0.2849, pruned_loss=0.05531, over 7229.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2712, pruned_loss=0.03695, over 1383659.40 frames.], batch size: 20, lr: 3.92e-04
2022-04-29 15:27:56,783 INFO [train.py:763] (1/8) Epoch 19, batch 750, loss[loss=0.1985, simple_loss=0.2883, pruned_loss=0.05434, over 7310.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2706, pruned_loss=0.03708, over 1393882.12 frames.], batch size: 25, lr: 3.92e-04
2022-04-29 15:29:03,457 INFO [train.py:763] (1/8) Epoch 19, batch 800, loss[loss=0.1529, simple_loss=0.2385, pruned_loss=0.03368, over 7411.00 frames.], tot_loss[loss=0.173, simple_loss=0.2708, pruned_loss=0.03762, over 1403558.10 frames.], batch size: 18, lr: 3.92e-04
2022-04-29 15:30:19,514 INFO [train.py:763] (1/8) Epoch 19, batch 850, loss[loss=0.1633, simple_loss=0.2572, pruned_loss=0.03473, over 7070.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2707, pruned_loss=0.03758, over 1410827.95 frames.], batch size: 28, lr: 3.92e-04
2022-04-29 15:31:25,289 INFO [train.py:763] (1/8) Epoch 19, batch 900, loss[loss=0.1656, simple_loss=0.2714, pruned_loss=0.02989, over 7360.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2695, pruned_loss=0.0371, over 1416570.96 frames.], batch size: 19, lr: 3.92e-04
2022-04-29 15:32:30,746 INFO [train.py:763] (1/8) Epoch 19, batch 950, loss[loss=0.174, simple_loss=0.2709, pruned_loss=0.03852, over 7233.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2702, pruned_loss=0.03746, over 1419805.35 frames.], batch size: 20, lr: 3.92e-04
2022-04-29 15:33:36,032 INFO [train.py:763] (1/8) Epoch 19, batch 1000, loss[loss=0.1749, simple_loss=0.2683, pruned_loss=0.04076, over 7314.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2704, pruned_loss=0.03759, over 1420928.65 frames.], batch size: 24, lr: 3.92e-04
2022-04-29 15:34:41,369 INFO [train.py:763] (1/8) Epoch 19, batch 1050, loss[loss=0.165, simple_loss=0.2648, pruned_loss=0.0326, over 7199.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2706, pruned_loss=0.03761, over 1420046.95 frames.], batch size: 22, lr: 3.92e-04
2022-04-29 15:35:47,010 INFO [train.py:763] (1/8) Epoch 19, batch 1100, loss[loss=0.2033, simple_loss=0.2939, pruned_loss=0.05629, over 7203.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2707, pruned_loss=0.0379, over 1416043.51 frames.], batch size: 22, lr: 3.91e-04
2022-04-29 15:36:52,331 INFO [train.py:763] (1/8) Epoch 19, batch 1150, loss[loss=0.2042, simple_loss=0.3131, pruned_loss=0.04769, over 7294.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2717, pruned_loss=0.03804, over 1420420.37 frames.], batch size: 24, lr: 3.91e-04
2022-04-29 15:38:08,753 INFO [train.py:763] (1/8) Epoch 19, batch 1200, loss[loss=0.1944, simple_loss=0.2945, pruned_loss=0.04712, over 7324.00 frames.], tot_loss[loss=0.173, simple_loss=0.2708, pruned_loss=0.03758, over 1424862.42 frames.], batch size: 22, lr: 3.91e-04
2022-04-29 15:39:14,189 INFO [train.py:763] (1/8) Epoch 19, batch 1250, loss[loss=0.1446, simple_loss=0.2428, pruned_loss=0.02319, over 7136.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2699, pruned_loss=0.03733, over 1425644.63 frames.], batch size: 17, lr: 3.91e-04
2022-04-29 15:40:19,873 INFO [train.py:763] (1/8) Epoch 19, batch 1300, loss[loss=0.1863, simple_loss=0.2878, pruned_loss=0.04238, over 7126.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2696, pruned_loss=0.03713, over 1427123.04 frames.], batch size: 21, lr: 3.91e-04
2022-04-29 15:41:25,078 INFO [train.py:763] (1/8) Epoch 19, batch 1350, loss[loss=0.2068, simple_loss=0.3033, pruned_loss=0.05513, over 7211.00 frames.], tot_loss[loss=0.1732, simple_loss=0.271, pruned_loss=0.03765, over 1429313.87 frames.], batch size: 22, lr: 3.91e-04
2022-04-29 15:42:30,862 INFO [train.py:763] (1/8) Epoch 19, batch 1400, loss[loss=0.1653, simple_loss=0.268, pruned_loss=0.03131, over 7151.00 frames.], tot_loss[loss=0.1736, simple_loss=0.2714, pruned_loss=0.03792, over 1431127.20 frames.], batch size: 26, lr: 3.91e-04
2022-04-29 15:43:46,244 INFO [train.py:763] (1/8) Epoch 19, batch 1450, loss[loss=0.1948, simple_loss=0.2912, pruned_loss=0.04919, over 7149.00 frames.], tot_loss[loss=0.1737, simple_loss=0.2715, pruned_loss=0.03794, over 1429153.82 frames.], batch size: 26, lr: 3.91e-04
2022-04-29 15:45:09,719 INFO [train.py:763] (1/8) Epoch 19, batch 1500, loss[loss=0.2218, simple_loss=0.3072, pruned_loss=0.0682, over 7383.00 frames.], tot_loss[loss=0.1748, simple_loss=0.2725, pruned_loss=0.03857, over 1428135.90 frames.], batch size: 23, lr: 3.91e-04
2022-04-29 15:46:15,428 INFO [train.py:763] (1/8) Epoch 19, batch 1550, loss[loss=0.1656, simple_loss=0.261, pruned_loss=0.03512, over 7426.00 frames.], tot_loss[loss=0.1735, simple_loss=0.2709, pruned_loss=0.03806, over 1429646.28 frames.], batch size: 20, lr: 3.91e-04
2022-04-29 15:47:30,075 INFO [train.py:763] (1/8) Epoch 19, batch 1600, loss[loss=0.1736, simple_loss=0.2746, pruned_loss=0.03632, over 7327.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2714, pruned_loss=0.03812, over 1424000.20 frames.], batch size: 22, lr: 3.90e-04
2022-04-29 15:48:53,934 INFO [train.py:763] (1/8) Epoch 19, batch 1650, loss[loss=0.1834, simple_loss=0.2876, pruned_loss=0.03964, over 7223.00 frames.], tot_loss[loss=0.1744, simple_loss=0.2724, pruned_loss=0.03817, over 1421985.46 frames.], batch size: 23, lr: 3.90e-04
2022-04-29 15:50:08,827 INFO [train.py:763] (1/8) Epoch 19, batch 1700, loss[loss=0.141, simple_loss=0.2343, pruned_loss=0.02381, over 7159.00 frames.], tot_loss[loss=0.1739, simple_loss=0.2718, pruned_loss=0.03803, over 1421283.84 frames.], batch size: 19, lr: 3.90e-04
2022-04-29 15:51:14,399 INFO [train.py:763] (1/8) Epoch 19, batch 1750, loss[loss=0.1755, simple_loss=0.2791, pruned_loss=0.03597, over 7339.00 frames.], tot_loss[loss=0.1738, simple_loss=0.272, pruned_loss=0.03781, over 1427140.95 frames.], batch size: 22, lr: 3.90e-04
2022-04-29 15:52:19,998 INFO [train.py:763] (1/8) Epoch 19, batch 1800, loss[loss=0.1538, simple_loss=0.2538, pruned_loss=0.02693, over 7260.00 frames.], tot_loss[loss=0.1732, simple_loss=0.2717, pruned_loss=0.03739, over 1425696.75 frames.], batch size: 25, lr: 3.90e-04
2022-04-29 15:53:25,556 INFO [train.py:763] (1/8) Epoch 19, batch 1850, loss[loss=0.161, simple_loss=0.2481, pruned_loss=0.03698, over 7065.00 frames.], tot_loss[loss=0.1734, simple_loss=0.2712, pruned_loss=0.03776, over 1428411.63 frames.], batch size: 18, lr: 3.90e-04
2022-04-29 15:54:30,870 INFO [train.py:763] (1/8) Epoch 19, batch 1900, loss[loss=0.1613, simple_loss=0.2644, pruned_loss=0.02913, over 7235.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2716, pruned_loss=0.0375, over 1429037.48 frames.], batch size: 20, lr: 3.90e-04
2022-04-29 15:55:38,244 INFO [train.py:763] (1/8) Epoch 19, batch 1950, loss[loss=0.162, simple_loss=0.2645, pruned_loss=0.02971, over 6560.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2704, pruned_loss=0.03714, over 1429871.47 frames.], batch size: 38, lr: 3.90e-04
2022-04-29 15:56:45,557 INFO [train.py:763] (1/8) Epoch 19, batch 2000, loss[loss=0.1535, simple_loss=0.2597, pruned_loss=0.02362, over 7238.00 frames.], tot_loss[loss=0.1722, simple_loss=0.27, pruned_loss=0.03725, over 1430898.06 frames.], batch size: 20, lr: 3.90e-04
2022-04-29 15:57:52,837 INFO [train.py:763] (1/8) Epoch 19, batch 2050, loss[loss=0.1821, simple_loss=0.2824, pruned_loss=0.0409, over 7215.00 frames.], tot_loss[loss=0.172, simple_loss=0.2693, pruned_loss=0.0373, over 1430329.39 frames.], batch size: 21, lr: 3.89e-04
2022-04-29 15:58:58,691 INFO [train.py:763] (1/8) Epoch 19, batch 2100, loss[loss=0.1654, simple_loss=0.2664, pruned_loss=0.03221, over 7424.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2688, pruned_loss=0.03688, over 1432989.64 frames.], batch size: 20, lr: 3.89e-04
2022-04-29 16:00:05,504 INFO [train.py:763] (1/8) Epoch 19, batch 2150, loss[loss=0.1526, simple_loss=0.2429, pruned_loss=0.03117, over 7208.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2682, pruned_loss=0.03682, over 1426210.94 frames.], batch size: 22, lr: 3.89e-04
2022-04-29 16:01:11,302 INFO [train.py:763] (1/8) Epoch 19, batch 2200, loss[loss=0.1557, simple_loss=0.2476, pruned_loss=0.03186, over 6836.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2688, pruned_loss=0.03732, over 1421597.27 frames.], batch size: 15, lr: 3.89e-04
2022-04-29 16:02:17,295 INFO [train.py:763] (1/8) Epoch 19, batch 2250, loss[loss=0.1628, simple_loss=0.2612, pruned_loss=0.03221, over 7144.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2689, pruned_loss=0.0372, over 1424449.12 frames.], batch size: 20, lr: 3.89e-04
2022-04-29 16:03:23,074 INFO [train.py:763] (1/8) Epoch 19, batch 2300, loss[loss=0.2074, simple_loss=0.3063, pruned_loss=0.05425, over 7371.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2682, pruned_loss=0.037, over 1424483.37 frames.], batch size: 23, lr: 3.89e-04
2022-04-29 16:04:28,768 INFO [train.py:763] (1/8) Epoch 19, batch 2350, loss[loss=0.1768, simple_loss=0.2869, pruned_loss=0.03335, over 7316.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2694, pruned_loss=0.03708, over 1423434.20 frames.], batch size: 21, lr: 3.89e-04
2022-04-29 16:05:34,123 INFO [train.py:763] (1/8) Epoch 19, batch 2400, loss[loss=0.1556, simple_loss=0.2568, pruned_loss=0.02715, over 7431.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2692, pruned_loss=0.03718, over 1424825.51 frames.], batch size: 20, lr: 3.89e-04
2022-04-29 16:06:39,693 INFO [train.py:763] (1/8) Epoch 19, batch 2450, loss[loss=0.1682, simple_loss=0.272, pruned_loss=0.03225, over 7040.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2691, pruned_loss=0.03695, over 1427813.79 frames.], batch size: 28, lr: 3.89e-04
2022-04-29 16:07:45,461 INFO [train.py:763] (1/8) Epoch 19, batch 2500, loss[loss=0.1793, simple_loss=0.283, pruned_loss=0.03782, over 7122.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2691, pruned_loss=0.03692, over 1426314.33 frames.], batch size: 26, lr: 3.88e-04
2022-04-29 16:08:50,996 INFO [train.py:763] (1/8) Epoch 19, batch 2550, loss[loss=0.1656, simple_loss=0.2715, pruned_loss=0.02984, over 7335.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2698, pruned_loss=0.03733, over 1425518.24 frames.], batch size: 20, lr: 3.88e-04
2022-04-29 16:09:56,807 INFO [train.py:763] (1/8) Epoch 19, batch 2600, loss[loss=0.2271, simple_loss=0.3156, pruned_loss=0.06928, over 6782.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2703, pruned_loss=0.03766, over 1426361.38 frames.], batch size: 31, lr: 3.88e-04
2022-04-29 16:11:03,364 INFO [train.py:763] (1/8) Epoch 19, batch 2650, loss[loss=0.1611, simple_loss=0.2525, pruned_loss=0.03488, over 7002.00 frames.], tot_loss[loss=0.1725, simple_loss=0.27, pruned_loss=0.03749, over 1427192.71 frames.], batch size: 16, lr: 3.88e-04
2022-04-29 16:12:10,010 INFO [train.py:763] (1/8) Epoch 19, batch 2700, loss[loss=0.1785, simple_loss=0.2801, pruned_loss=0.03843, over 7381.00 frames.], tot_loss[loss=0.1714, simple_loss=0.269, pruned_loss=0.03695, over 1428086.19 frames.], batch size: 23, lr: 3.88e-04
2022-04-29 16:13:17,137 INFO [train.py:763] (1/8) Epoch 19, batch 2750, loss[loss=0.1713, simple_loss=0.2737, pruned_loss=0.03442, over 7210.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2697, pruned_loss=0.03702, over 1427146.64 frames.], batch size: 23, lr: 3.88e-04
2022-04-29 16:14:22,705 INFO [train.py:763] (1/8) Epoch 19, batch 2800, loss[loss=0.146, simple_loss=0.2428, pruned_loss=0.02457, over 7159.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2695, pruned_loss=0.03697, over 1431286.46 frames.], batch size: 18, lr: 3.88e-04
2022-04-29 16:15:28,760 INFO [train.py:763] (1/8) Epoch 19, batch 2850, loss[loss=0.1854, simple_loss=0.2883, pruned_loss=0.04122, over 7408.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2695, pruned_loss=0.0368, over 1433213.58 frames.], batch size: 21, lr: 3.88e-04
2022-04-29 16:16:34,846 INFO [train.py:763] (1/8) Epoch 19, batch 2900, loss[loss=0.1817, simple_loss=0.271, pruned_loss=0.04618, over 7205.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2694, pruned_loss=0.03678, over 1428479.73 frames.], batch size: 26, lr: 3.88e-04
2022-04-29 16:17:40,405 INFO [train.py:763] (1/8) Epoch 19, batch 2950, loss[loss=0.1444, simple_loss=0.2475, pruned_loss=0.02067, over 7225.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2701, pruned_loss=0.03718, over 1432238.00 frames.], batch size: 20, lr: 3.87e-04
2022-04-29 16:18:45,955 INFO [train.py:763] (1/8) Epoch 19, batch 3000, loss[loss=0.1872, simple_loss=0.294, pruned_loss=0.04016, over 7391.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2713, pruned_loss=0.03766, over 1431493.53 frames.], batch size: 23, lr: 3.87e-04
2022-04-29 16:18:45,956 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 16:19:01,554 INFO [train.py:792] (1/8) Epoch 19, validation: loss=0.1668, simple_loss=0.2663, pruned_loss=0.03363, over 698248.00 frames.
2022-04-29 16:20:06,919 INFO [train.py:763] (1/8) Epoch 19, batch 3050, loss[loss=0.1643, simple_loss=0.2559, pruned_loss=0.03632, over 7155.00 frames.], tot_loss[loss=0.1738, simple_loss=0.2716, pruned_loss=0.03805, over 1433153.47 frames.], batch size: 19, lr: 3.87e-04
2022-04-29 16:21:12,180 INFO [train.py:763] (1/8) Epoch 19, batch 3100, loss[loss=0.1898, simple_loss=0.2866, pruned_loss=0.04648, over 7113.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2713, pruned_loss=0.03762, over 1431923.36 frames.], batch size: 21, lr: 3.87e-04
2022-04-29 16:22:17,531 INFO [train.py:763] (1/8) Epoch 19, batch 3150, loss[loss=0.1599, simple_loss=0.2627, pruned_loss=0.02854, over 7285.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2712, pruned_loss=0.03772, over 1432754.29 frames.], batch size: 18, lr: 3.87e-04
2022-04-29 16:23:23,019 INFO [train.py:763] (1/8) Epoch 19, batch 3200, loss[loss=0.196, simple_loss=0.2926, pruned_loss=0.04968, over 6939.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2702, pruned_loss=0.03759, over 1432813.36 frames.], batch size: 32, lr: 3.87e-04
2022-04-29 16:24:28,065 INFO [train.py:763] (1/8) Epoch 19, batch 3250, loss[loss=0.1651, simple_loss=0.2739, pruned_loss=0.02818, over 7068.00 frames.], tot_loss[loss=0.1733, simple_loss=0.2709, pruned_loss=0.03787, over 1428873.77 frames.], batch size: 18, lr: 3.87e-04
2022-04-29 16:25:34,718 INFO [train.py:763] (1/8) Epoch 19, batch 3300, loss[loss=0.164, simple_loss=0.2503, pruned_loss=0.03879, over 7142.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2706, pruned_loss=0.03762, over 1427060.73 frames.], batch size: 17, lr: 3.87e-04
2022-04-29 16:26:41,782 INFO [train.py:763] (1/8) Epoch 19, batch 3350, loss[loss=0.1609, simple_loss=0.2742, pruned_loss=0.02379, over 7147.00 frames.], tot_loss[loss=0.1723, simple_loss=0.27, pruned_loss=0.03726, over 1427273.57 frames.], batch size: 20, lr: 3.87e-04
2022-04-29 16:27:47,541 INFO [train.py:763] (1/8) Epoch 19, batch 3400, loss[loss=0.1717, simple_loss=0.2565, pruned_loss=0.04341, over 7274.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2697, pruned_loss=0.03694, over 1426667.77 frames.], batch size: 17, lr: 3.87e-04
2022-04-29 16:28:53,014 INFO [train.py:763] (1/8) Epoch 19, batch 3450, loss[loss=0.1767, simple_loss=0.2667, pruned_loss=0.04331, over 7241.00 frames.], tot_loss[loss=0.1718, simple_loss=0.27, pruned_loss=0.03685, over 1425941.01 frames.], batch size: 20, lr: 3.86e-04
2022-04-29 16:29:58,521 INFO [train.py:763] (1/8) Epoch 19, batch 3500, loss[loss=0.1396, simple_loss=0.24, pruned_loss=0.01962, over 7263.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2691, pruned_loss=0.03636, over 1424825.97 frames.], batch size: 19, lr: 3.86e-04
2022-04-29 16:31:03,659 INFO [train.py:763] (1/8) Epoch 19, batch 3550, loss[loss=0.162, simple_loss=0.264, pruned_loss=0.02997, over 7111.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2703, pruned_loss=0.03678, over 1426899.20 frames.], batch size: 21, lr: 3.86e-04
2022-04-29 16:32:09,190 INFO [train.py:763] (1/8) Epoch 19, batch 3600, loss[loss=0.1816, simple_loss=0.2833, pruned_loss=0.03992, over 7192.00 frames.], tot_loss[loss=0.1728, simple_loss=0.2706, pruned_loss=0.03756, over 1430059.02 frames.], batch size: 23, lr: 3.86e-04
2022-04-29 16:33:15,436 INFO [train.py:763] (1/8) Epoch 19, batch 3650, loss[loss=0.1641, simple_loss=0.2764, pruned_loss=0.02592, over 7325.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2704, pruned_loss=0.03766, over 1430471.57 frames.], batch size: 21, lr: 3.86e-04
2022-04-29 16:34:21,091 INFO [train.py:763] (1/8) Epoch 19, batch 3700, loss[loss=0.1694, simple_loss=0.2573, pruned_loss=0.04072, over 7169.00 frames.], tot_loss[loss=0.173, simple_loss=0.2707, pruned_loss=0.03765, over 1432173.88 frames.], batch size: 18, lr: 3.86e-04
2022-04-29 16:35:26,774 INFO [train.py:763] (1/8) Epoch 19, batch 3750, loss[loss=0.1862, simple_loss=0.2885, pruned_loss=0.04195, over 7043.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2698, pruned_loss=0.03725, over 1426161.83 frames.], batch size: 28, lr: 3.86e-04
2022-04-29 16:36:32,305 INFO [train.py:763] (1/8) Epoch 19, batch 3800, loss[loss=0.1396, simple_loss=0.2428, pruned_loss=0.01822, over 7322.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2695, pruned_loss=0.03758, over 1422420.60 frames.], batch size: 20, lr: 3.86e-04
2022-04-29 16:37:37,905 INFO [train.py:763] (1/8) Epoch 19, batch 3850, loss[loss=0.1591, simple_loss=0.242, pruned_loss=0.03816, over 7284.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2694, pruned_loss=0.03746, over 1420031.98 frames.], batch size: 17, lr: 3.86e-04
2022-04-29 16:38:44,164 INFO [train.py:763] (1/8) Epoch 19, batch 3900, loss[loss=0.1587, simple_loss=0.254, pruned_loss=0.03168, over 7119.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2704, pruned_loss=0.03752, over 1417580.90 frames.], batch size: 21, lr: 3.85e-04
2022-04-29 16:39:50,747 INFO [train.py:763] (1/8) Epoch 19, batch 3950, loss[loss=0.1897, simple_loss=0.2951, pruned_loss=0.04217, over 7333.00 frames.], tot_loss[loss=0.1733, simple_loss=0.271, pruned_loss=0.03779, over 1411700.39 frames.], batch size: 20, lr: 3.85e-04
2022-04-29 16:40:57,111 INFO [train.py:763] (1/8) Epoch 19, batch 4000, loss[loss=0.1626, simple_loss=0.2505, pruned_loss=0.03736, over 7166.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2706, pruned_loss=0.03762, over 1409755.05 frames.], batch size: 18, lr: 3.85e-04
2022-04-29 16:42:03,325 INFO [train.py:763] (1/8) Epoch 19, batch 4050, loss[loss=0.1812, simple_loss=0.2801, pruned_loss=0.04113, over 7339.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2701, pruned_loss=0.03767, over 1406270.99 frames.], batch size: 20, lr: 3.85e-04
2022-04-29 16:43:09,189 INFO [train.py:763] (1/8) Epoch 19, batch 4100, loss[loss=0.1783, simple_loss=0.2698, pruned_loss=0.04343, over 7282.00 frames.], tot_loss[loss=0.1723, simple_loss=0.2691, pruned_loss=0.03769, over 1406522.45 frames.], batch size: 18, lr: 3.85e-04
2022-04-29 16:44:14,859 INFO [train.py:763] (1/8) Epoch 19, batch 4150, loss[loss=0.1604, simple_loss=0.252, pruned_loss=0.03438, over 7062.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2673, pruned_loss=0.03698, over 1410622.87 frames.], batch size: 18, lr: 3.85e-04
2022-04-29 16:45:20,203 INFO [train.py:763] (1/8) Epoch 19, batch 4200, loss[loss=0.1673, simple_loss=0.261, pruned_loss=0.03678, over 6782.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2683, pruned_loss=0.03714, over 1405775.92 frames.], batch size: 15, lr: 3.85e-04
2022-04-29 16:46:26,001 INFO [train.py:763] (1/8) Epoch 19, batch 4250, loss[loss=0.2209, simple_loss=0.3122, pruned_loss=0.06477, over 7199.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2675, pruned_loss=0.03685, over 1403384.71 frames.], batch size: 23, lr: 3.85e-04
2022-04-29 16:47:31,494 INFO [train.py:763] (1/8) Epoch 19, batch 4300, loss[loss=0.1784, simple_loss=0.2893, pruned_loss=0.03373, over 7228.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2682, pruned_loss=0.03666, over 1400214.76 frames.], batch size: 21, lr: 3.85e-04
2022-04-29 16:48:37,207 INFO [train.py:763] (1/8) Epoch 19, batch 4350, loss[loss=0.2143, simple_loss=0.3074, pruned_loss=0.06056, over 5114.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2679, pruned_loss=0.03664, over 1403148.28 frames.], batch size: 53, lr: 3.84e-04
2022-04-29 16:49:42,591 INFO [train.py:763] (1/8) Epoch 19, batch 4400, loss[loss=0.1734, simple_loss=0.2659, pruned_loss=0.04051, over 7168.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2676, pruned_loss=0.03642, over 1399388.30 frames.], batch size: 19, lr: 3.84e-04
2022-04-29 16:50:47,787 INFO [train.py:763] (1/8) Epoch 19, batch 4450, loss[loss=0.1461, simple_loss=0.2325, pruned_loss=0.02987, over 7245.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2676, pruned_loss=0.03683, over 1392323.83 frames.], batch size: 16, lr: 3.84e-04
2022-04-29 16:51:52,273 INFO [train.py:763] (1/8) Epoch 19, batch 4500, loss[loss=0.2013, simple_loss=0.293, pruned_loss=0.05483, over 7185.00 frames.], tot_loss[loss=0.172, simple_loss=0.2693, pruned_loss=0.03737, over 1385517.24 frames.], batch size: 23, lr: 3.84e-04
2022-04-29 16:52:57,055 INFO [train.py:763] (1/8) Epoch 19, batch 4550, loss[loss=0.1508, simple_loss=0.2552, pruned_loss=0.02317, over 6078.00 frames.], tot_loss[loss=0.1754, simple_loss=0.2723, pruned_loss=0.03929, over 1338857.27 frames.], batch size: 37, lr: 3.84e-04
2022-04-29 16:54:25,843 INFO [train.py:763] (1/8) Epoch 20, batch 0, loss[loss=0.1904, simple_loss=0.2724, pruned_loss=0.05418, over 7006.00 frames.], tot_loss[loss=0.1904, simple_loss=0.2724, pruned_loss=0.05418, over 7006.00 frames.], batch size: 16, lr: 3.75e-04
2022-04-29 16:55:32,592 INFO [train.py:763] (1/8) Epoch 20, batch 50, loss[loss=0.172, simple_loss=0.2696, pruned_loss=0.03719, over 6394.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2676, pruned_loss=0.03666, over 323507.03 frames.], batch size: 38, lr: 3.75e-04
2022-04-29 16:56:38,001 INFO [train.py:763] (1/8) Epoch 20, batch 100, loss[loss=0.1764, simple_loss=0.265, pruned_loss=0.04394, over 6824.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2674, pruned_loss=0.03557, over 566887.58 frames.], batch size: 15, lr: 3.75e-04
2022-04-29 16:57:44,562 INFO [train.py:763] (1/8) Epoch 20, batch 150, loss[loss=0.1581, simple_loss=0.2561, pruned_loss=0.03004, over 7156.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2685, pruned_loss=0.03601, over 756243.63 frames.], batch size: 18, lr: 3.75e-04
2022-04-29 16:58:49,750 INFO [train.py:763] (1/8) Epoch 20, batch 200, loss[loss=0.2089, simple_loss=0.303, pruned_loss=0.05742, over 6829.00 frames.], tot_loss[loss=0.1731, simple_loss=0.2713, pruned_loss=0.0375, over 901710.04 frames.], batch size: 31, lr: 3.75e-04
2022-04-29 16:59:55,578 INFO [train.py:763] (1/8) Epoch 20, batch 250, loss[loss=0.1741, simple_loss=0.2688, pruned_loss=0.03969, over 7152.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2707, pruned_loss=0.0374, over 1013659.43 frames.], batch size: 19, lr: 3.75e-04
2022-04-29 17:01:00,762 INFO [train.py:763] (1/8) Epoch 20, batch 300, loss[loss=0.1784, simple_loss=0.2722, pruned_loss=0.0423, over 7280.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2705, pruned_loss=0.03729, over 1102693.16 frames.], batch size: 18, lr: 3.75e-04
2022-04-29 17:02:05,606 INFO [train.py:763] (1/8) Epoch 20, batch 350, loss[loss=0.1372, simple_loss=0.232, pruned_loss=0.02117, over 7257.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2704, pruned_loss=0.03689, over 1170693.80 frames.], batch size: 19, lr: 3.74e-04
2022-04-29 17:03:10,956 INFO [train.py:763] (1/8) Epoch 20, batch 400, loss[loss=0.1282, simple_loss=0.2232, pruned_loss=0.01663, over 7072.00 frames.], tot_loss[loss=0.171, simple_loss=0.2693, pruned_loss=0.03634, over 1229380.35 frames.], batch size: 18, lr: 3.74e-04
2022-04-29 17:04:16,937 INFO [train.py:763] (1/8) Epoch 20, batch 450, loss[loss=0.1668, simple_loss=0.2561, pruned_loss=0.03877, over 7072.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2696, pruned_loss=0.03662, over 1271731.99 frames.], batch size: 18, lr: 3.74e-04
2022-04-29 17:05:22,377 INFO [train.py:763] (1/8) Epoch 20, batch 500, loss[loss=0.1648, simple_loss=0.2575, pruned_loss=0.03605, over 7043.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2697, pruned_loss=0.03645, over 1310411.81 frames.], batch size: 28, lr: 3.74e-04
2022-04-29 17:06:27,715 INFO [train.py:763] (1/8) Epoch 20, batch 550, loss[loss=0.1582, simple_loss=0.2481, pruned_loss=0.03414, over 6833.00 frames.], tot_loss[loss=0.1708, simple_loss=0.269, pruned_loss=0.03626, over 1336436.36 frames.], batch size: 15, lr: 3.74e-04
2022-04-29 17:07:34,454 INFO [train.py:763] (1/8) Epoch 20, batch 600, loss[loss=0.1884, simple_loss=0.2792, pruned_loss=0.04883, over 7210.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2697, pruned_loss=0.03636, over 1355452.33 frames.], batch size: 22, lr: 3.74e-04
2022-04-29 17:08:41,618 INFO [train.py:763] (1/8) Epoch 20, batch 650, loss[loss=0.152, simple_loss=0.2471, pruned_loss=0.02841, over 7145.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2673, pruned_loss=0.03555, over 1369900.26 frames.], batch size: 17, lr: 3.74e-04
2022-04-29 17:09:47,492 INFO [train.py:763] (1/8) Epoch 20, batch 700, loss[loss=0.168, simple_loss=0.2661, pruned_loss=0.03495, over 7234.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2681, pruned_loss=0.03581, over 1380005.24 frames.], batch size: 20, lr: 3.74e-04
2022-04-29 17:10:53,616 INFO [train.py:763] (1/8) Epoch 20, batch 750, loss[loss=0.1463, simple_loss=0.2363, pruned_loss=0.0282, over 7414.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2688, pruned_loss=0.03639, over 1385244.50 frames.], batch size: 18, lr: 3.74e-04
2022-04-29 17:11:58,916 INFO [train.py:763] (1/8) Epoch 20, batch 800, loss[loss=0.1819, simple_loss=0.2807, pruned_loss=0.04155, over 7228.00 frames.], tot_loss[loss=0.171, simple_loss=0.2688, pruned_loss=0.03659, over 1384758.86 frames.], batch size: 20, lr: 3.73e-04
2022-04-29 17:13:05,456 INFO [train.py:763] (1/8) Epoch 20, batch 850, loss[loss=0.1888, simple_loss=0.2903, pruned_loss=0.04366, over 7314.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2676, pruned_loss=0.03611, over 1391183.06 frames.], batch size: 25, lr: 3.73e-04
2022-04-29 17:14:10,905 INFO [train.py:763] (1/8) Epoch 20, batch 900, loss[loss=0.1718, simple_loss=0.2738, pruned_loss=0.03494, over 7229.00 frames.], tot_loss[loss=0.1696, simple_loss=0.267, pruned_loss=0.03617, over 1399628.47 frames.], batch size: 20, lr: 3.73e-04
2022-04-29 17:15:15,946 INFO [train.py:763] (1/8) Epoch 20, batch 950, loss[loss=0.171, simple_loss=0.2756, pruned_loss=0.03313, over 7328.00 frames.], tot_loss[loss=0.17, simple_loss=0.2675, pruned_loss=0.03624, over 1406192.52 frames.], batch size: 22, lr: 3.73e-04
2022-04-29 17:16:21,950 INFO [train.py:763] (1/8) Epoch 20, batch 1000, loss[loss=0.209, simple_loss=0.304, pruned_loss=0.05699, over 7204.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2687, pruned_loss=0.03645, over 1405226.88 frames.], batch size: 23, lr: 3.73e-04
2022-04-29 17:17:26,876 INFO [train.py:763] (1/8) Epoch 20, batch 1050, loss[loss=0.151, simple_loss=0.2519, pruned_loss=0.02507, over 7398.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2693, pruned_loss=0.03658, over 1406381.55 frames.], batch size: 21, lr: 3.73e-04
2022-04-29 17:18:32,319 INFO [train.py:763] (1/8) Epoch 20, batch 1100, loss[loss=0.1631, simple_loss=0.2519, pruned_loss=0.03715, over 7219.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2689, pruned_loss=0.03699, over 1408211.80 frames.], batch size: 16, lr: 3.73e-04
2022-04-29 17:19:37,613 INFO [train.py:763] (1/8) Epoch 20, batch 1150, loss[loss=0.1881, simple_loss=0.2916, pruned_loss=0.04228, over 7293.00 frames.], tot_loss[loss=0.1724, simple_loss=0.2699, pruned_loss=0.03747, over 1413302.81 frames.], batch size: 24, lr: 3.73e-04
2022-04-29 17:20:42,596 INFO [train.py:763] (1/8) Epoch 20, batch 1200, loss[loss=0.1731, simple_loss=0.266, pruned_loss=0.04011, over 7290.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2701, pruned_loss=0.03677, over 1416540.27 frames.], batch size: 18, lr: 3.73e-04
2022-04-29 17:21:47,930 INFO [train.py:763] (1/8) Epoch 20, batch 1250, loss[loss=0.1837, simple_loss=0.2858, pruned_loss=0.04077, over 7287.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2688, pruned_loss=0.03645, over 1418188.87 frames.], batch size: 24, lr: 3.73e-04
2022-04-29 17:22:53,224 INFO [train.py:763] (1/8) Epoch 20, batch 1300, loss[loss=0.1441, simple_loss=0.2439, pruned_loss=0.02209, over 7054.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2677, pruned_loss=0.03626, over 1417007.57 frames.], batch size: 18, lr: 3.72e-04
2022-04-29 17:23:59,017 INFO [train.py:763] (1/8) Epoch 20, batch 1350, loss[loss=0.2092, simple_loss=0.3042, pruned_loss=0.05707, over 7340.00 frames.], tot_loss[loss=0.1702, simple_loss=0.268, pruned_loss=0.03624, over 1424155.17 frames.], batch size: 22, lr: 3.72e-04
2022-04-29 17:25:04,572 INFO [train.py:763] (1/8) Epoch 20, batch 1400, loss[loss=0.1821, simple_loss=0.2789, pruned_loss=0.04265, over 7370.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2683, pruned_loss=0.03648, over 1426418.47 frames.], batch size: 23, lr: 3.72e-04
2022-04-29 17:26:11,036 INFO [train.py:763] (1/8) Epoch 20, batch 1450, loss[loss=0.2062, simple_loss=0.3118, pruned_loss=0.05026, over 5290.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2683, pruned_loss=0.03665, over 1420838.22 frames.], batch size: 52, lr: 3.72e-04
2022-04-29 17:27:17,682 INFO [train.py:763] (1/8) Epoch 20, batch 1500, loss[loss=0.1972, simple_loss=0.2965, pruned_loss=0.04901, over 7320.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2697, pruned_loss=0.03721, over 1418874.24 frames.], batch size: 22, lr: 3.72e-04
2022-04-29 17:28:24,676 INFO [train.py:763] (1/8) Epoch 20, batch 1550, loss[loss=0.1865, simple_loss=0.2938, pruned_loss=0.03967, over 6843.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2701, pruned_loss=0.03743, over 1420269.71 frames.], batch size: 31, lr: 3.72e-04
2022-04-29 17:29:31,791 INFO [train.py:763] (1/8) Epoch 20, batch 1600, loss[loss=0.1666, simple_loss=0.2747, pruned_loss=0.0292, over 7335.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2706, pruned_loss=0.03721, over 1422311.35 frames.], batch size: 22, lr: 3.72e-04
2022-04-29 17:30:38,859 INFO [train.py:763] (1/8) Epoch 20, batch 1650, loss[loss=0.1625, simple_loss=0.268, pruned_loss=0.02846, over 7328.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2709, pruned_loss=0.03731, over 1424224.19 frames.], batch size: 20, lr: 3.72e-04
2022-04-29 17:31:46,136 INFO [train.py:763] (1/8) Epoch 20, batch 1700, loss[loss=0.1795, simple_loss=0.2772, pruned_loss=0.04093, over 7333.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2707, pruned_loss=0.03718, over 1423459.67 frames.], batch size: 22, lr: 3.72e-04
2022-04-29 17:32:52,730 INFO [train.py:763] (1/8) Epoch 20, batch 1750, loss[loss=0.1831, simple_loss=0.2627, pruned_loss=0.05176, over 7396.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2704, pruned_loss=0.03698, over 1423581.62 frames.], batch size: 18, lr: 3.72e-04
2022-04-29 17:33:59,655 INFO [train.py:763] (1/8) Epoch 20, batch 1800, loss[loss=0.1798, simple_loss=0.2919, pruned_loss=0.03385, over 7180.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2696, pruned_loss=0.03663, over 1424932.69 frames.], batch size: 23, lr: 3.71e-04
2022-04-29 17:35:06,942 INFO [train.py:763] (1/8) Epoch 20, batch 1850, loss[loss=0.1421, simple_loss=0.2468, pruned_loss=0.01867, over 7408.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2693, pruned_loss=0.03679, over 1423516.67 frames.], batch size: 18, lr: 3.71e-04
2022-04-29 17:36:12,557 INFO [train.py:763] (1/8) Epoch 20, batch 1900, loss[loss=0.1509, simple_loss=0.2515, pruned_loss=0.02517, over 7158.00 frames.], tot_loss[loss=0.1719, simple_loss=0.2695, pruned_loss=0.03709, over 1424659.17 frames.], batch size: 19, lr: 3.71e-04
2022-04-29 17:37:18,016 INFO [train.py:763] (1/8) Epoch 20, batch 1950, loss[loss=0.1517, simple_loss=0.247, pruned_loss=0.02821, over 7260.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2691, pruned_loss=0.03658, over 1428007.70 frames.], batch size: 19, lr: 3.71e-04
2022-04-29 17:38:24,301 INFO [train.py:763] (1/8) Epoch 20, batch 2000, loss[loss=0.1611, simple_loss=0.2679, pruned_loss=0.02715, over 6723.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2685, pruned_loss=0.0363, over 1424108.79 frames.], batch size: 31, lr: 3.71e-04
2022-04-29 17:39:29,413 INFO [train.py:763] (1/8) Epoch 20, batch 2050, loss[loss=0.1743, simple_loss=0.2882, pruned_loss=0.0302, over 7223.00 frames.], tot_loss[loss=0.171, simple_loss=0.2691, pruned_loss=0.03649, over 1423793.92 frames.], batch size: 21, lr: 3.71e-04
2022-04-29 17:40:35,600 INFO [train.py:763] (1/8) Epoch 20, batch 2100, loss[loss=0.1621, simple_loss=0.2586, pruned_loss=0.03285, over 7064.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2689, pruned_loss=0.03626, over 1422849.21 frames.], batch size: 18, lr: 3.71e-04
2022-04-29 17:41:42,815 INFO [train.py:763] (1/8) Epoch 20, batch 2150, loss[loss=0.1519, simple_loss=0.2423, pruned_loss=0.03078, over 7261.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2688, pruned_loss=0.03629, over 1422732.10 frames.], batch size: 16, lr: 3.71e-04
2022-04-29 17:42:48,992 INFO [train.py:763] (1/8) Epoch 20, batch 2200, loss[loss=0.1882, simple_loss=0.2884, pruned_loss=0.04405, over 7203.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2676, pruned_loss=0.03575, over 1425688.04 frames.], batch size: 22, lr: 3.71e-04
2022-04-29 17:43:54,361 INFO [train.py:763] (1/8) Epoch 20, batch 2250, loss[loss=0.1651, simple_loss=0.2569, pruned_loss=0.03665, over 7204.00 frames.], tot_loss[loss=0.1708, simple_loss=0.269, pruned_loss=0.03632, over 1426767.23 frames.], batch size: 22, lr: 3.71e-04
2022-04-29 17:45:01,606 INFO [train.py:763] (1/8) Epoch 20, batch 2300, loss[loss=0.2044, simple_loss=0.2938, pruned_loss=0.05755, over 4813.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2681, pruned_loss=0.03621, over 1423340.23 frames.], batch size: 53, lr: 3.71e-04
2022-04-29 17:46:08,267 INFO [train.py:763] (1/8) Epoch 20, batch 2350, loss[loss=0.1907, simple_loss=0.2868, pruned_loss=0.0473, over 7286.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2698, pruned_loss=0.03715, over 1418694.31 frames.], batch size: 24, lr: 3.70e-04
2022-04-29 17:47:15,536 INFO [train.py:763] (1/8) Epoch 20, batch 2400, loss[loss=0.1849, simple_loss=0.2796, pruned_loss=0.0451, over 7203.00 frames.], tot_loss[loss=0.1712, simple_loss=0.269, pruned_loss=0.0367, over 1421434.06 frames.], batch size: 23, lr: 3.70e-04
2022-04-29 17:48:22,379 INFO [train.py:763] (1/8) Epoch 20, batch 2450, loss[loss=0.1614, simple_loss=0.2655, pruned_loss=0.02867, over 7152.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2686, pruned_loss=0.03615, over 1422779.76 frames.], batch size: 19, lr: 3.70e-04
2022-04-29 17:49:29,423 INFO [train.py:763] (1/8) Epoch 20, batch 2500, loss[loss=0.165, simple_loss=0.2701, pruned_loss=0.03001, over 7416.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2682, pruned_loss=0.0362, over 1423398.05 frames.], batch size: 21, lr: 3.70e-04
2022-04-29 17:50:36,099 INFO [train.py:763] (1/8) Epoch 20, batch 2550, loss[loss=0.2163, simple_loss=0.2984, pruned_loss=0.06706, over 5029.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2696, pruned_loss=0.03673, over 1421211.34 frames.], batch size: 52, lr: 3.70e-04
2022-04-29 17:51:41,444 INFO [train.py:763] (1/8) Epoch 20, batch 2600, loss[loss=0.1454, simple_loss=0.2438, pruned_loss=0.02345, over 7067.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2701, pruned_loss=0.03677, over 1421899.57 frames.], batch size: 18, lr: 3.70e-04
2022-04-29 17:52:58,239 INFO [train.py:763] (1/8) Epoch 20, batch 2650, loss[loss=0.1463, simple_loss=0.2442, pruned_loss=0.02413, over 7320.00 frames.], tot_loss[loss=0.172, simple_loss=0.2702, pruned_loss=0.03693, over 1417073.18 frames.], batch size: 20, lr: 3.70e-04
2022-04-29 17:54:04,061 INFO [train.py:763] (1/8) Epoch 20, batch 2700, loss[loss=0.1455, simple_loss=0.2297, pruned_loss=0.03066, over 7406.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2693, pruned_loss=0.03657, over 1420747.23 frames.], batch size: 18, lr: 3.70e-04
2022-04-29 17:55:10,582 INFO [train.py:763] (1/8) Epoch 20, batch 2750, loss[loss=0.1705, simple_loss=0.2606, pruned_loss=0.04023, over 7159.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2695, pruned_loss=0.03647, over 1422194.42 frames.], batch size: 18, lr: 3.70e-04
2022-04-29 17:56:15,901 INFO [train.py:763] (1/8) Epoch 20, batch 2800, loss[loss=0.2, simple_loss=0.2989, pruned_loss=0.05049, over 7386.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2694, pruned_loss=0.03658, over 1425370.47 frames.], batch size: 23, lr: 3.70e-04
2022-04-29 17:57:21,241 INFO [train.py:763] (1/8) Epoch 20, batch 2850, loss[loss=0.1906, simple_loss=0.2823, pruned_loss=0.04942, over 7217.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2692, pruned_loss=0.03656, over 1420483.92 frames.], batch size: 23, lr: 3.69e-04
2022-04-29 17:58:26,457 INFO [train.py:763] (1/8) Epoch 20, batch 2900, loss[loss=0.1899, simple_loss=0.2752, pruned_loss=0.05228, over 7062.00 frames.], tot_loss[loss=0.1712, simple_loss=0.269, pruned_loss=0.0367, over 1415837.90 frames.], batch size: 28, lr: 3.69e-04
2022-04-29 17:59:31,727 INFO [train.py:763] (1/8) Epoch 20, batch 2950, loss[loss=0.1684, simple_loss=0.2616, pruned_loss=0.03755, over 7356.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2694, pruned_loss=0.03664, over 1415047.45 frames.], batch size: 19, lr: 3.69e-04
2022-04-29 18:01:03,484 INFO [train.py:763] (1/8) Epoch 20, batch 3000, loss[loss=0.1703, simple_loss=0.2754, pruned_loss=0.03258, over 6785.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2692, pruned_loss=0.03674, over 1414926.03 frames.], batch size: 31, lr: 3.69e-04
2022-04-29 18:01:03,485 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 18:01:18,758 INFO [train.py:792] (1/8) Epoch 20, validation: loss=0.1672, simple_loss=0.2663, pruned_loss=0.03407, over 698248.00 frames.
2022-04-29 18:02:33,643 INFO [train.py:763] (1/8) Epoch 20, batch 3050, loss[loss=0.1481, simple_loss=0.2466, pruned_loss=0.02482, over 7272.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2689, pruned_loss=0.03677, over 1415654.28 frames.], batch size: 18, lr: 3.69e-04
2022-04-29 18:03:49,733 INFO [train.py:763] (1/8) Epoch 20, batch 3100, loss[loss=0.1722, simple_loss=0.2701, pruned_loss=0.0371, over 7384.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2699, pruned_loss=0.03722, over 1413675.49 frames.], batch size: 23, lr: 3.69e-04
2022-04-29 18:05:13,900 INFO [train.py:763] (1/8) Epoch 20, batch 3150, loss[loss=0.1808, simple_loss=0.2731, pruned_loss=0.0443, over 7290.00 frames.], tot_loss[loss=0.1722, simple_loss=0.2697, pruned_loss=0.03734, over 1419024.80 frames.], batch size: 24, lr: 3.69e-04
2022-04-29 18:06:18,922 INFO [train.py:763] (1/8) Epoch 20, batch 3200, loss[loss=0.1898, simple_loss=0.2932, pruned_loss=0.04318, over 7314.00 frames.], tot_loss[loss=0.173, simple_loss=0.271, pruned_loss=0.03753, over 1423224.25 frames.], batch size: 21, lr: 3.69e-04
2022-04-29 18:07:24,049 INFO [train.py:763] (1/8) Epoch 20, batch 3250, loss[loss=0.1607, simple_loss=0.2607, pruned_loss=0.03033, over 7064.00 frames.], tot_loss[loss=0.1726, simple_loss=0.2704, pruned_loss=0.03743, over 1421642.76 frames.], batch size: 18, lr: 3.69e-04
2022-04-29 18:08:29,710 INFO [train.py:763] (1/8) Epoch 20, batch 3300, loss[loss=0.145, simple_loss=0.2418, pruned_loss=0.02408, over 7134.00 frames.], tot_loss[loss=0.1715, simple_loss=0.2696, pruned_loss=0.03671, over 1423012.38 frames.], batch size: 17, lr: 3.69e-04
2022-04-29 18:09:35,970 INFO [train.py:763] (1/8) Epoch 20, batch 3350, loss[loss=0.1955, simple_loss=0.3077, pruned_loss=0.04163, over 7234.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2697, pruned_loss=0.03668, over 1419750.17 frames.], batch size: 20, lr: 3.68e-04
2022-04-29 18:10:42,809 INFO [train.py:763] (1/8) Epoch 20, batch 3400, loss[loss=0.1572, simple_loss=0.2623, pruned_loss=0.02598, over 6624.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2698, pruned_loss=0.03676, over 1416672.46 frames.], batch size: 38, lr: 3.68e-04
2022-04-29 18:11:49,526 INFO [train.py:763] (1/8) Epoch 20, batch 3450, loss[loss=0.1823, simple_loss=0.3035, pruned_loss=0.03057, over 7319.00 frames.], tot_loss[loss=0.1725, simple_loss=0.2706, pruned_loss=0.03716, over 1414887.11 frames.], batch size: 21, lr: 3.68e-04
2022-04-29 18:12:54,737 INFO [train.py:763] (1/8) Epoch 20, batch 3500, loss[loss=0.1861, simple_loss=0.2954, pruned_loss=0.03839, over 7015.00 frames.], tot_loss[loss=0.1727, simple_loss=0.2707, pruned_loss=0.03739, over 1409934.45 frames.], batch size: 28, lr: 3.68e-04
2022-04-29 18:14:00,242 INFO [train.py:763] (1/8) Epoch 20, batch 3550, loss[loss=0.1702, simple_loss=0.2505, pruned_loss=0.04499, over 7280.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2696, pruned_loss=0.03689, over 1413427.80 frames.], batch size: 17, lr: 3.68e-04
2022-04-29 18:15:05,500 INFO [train.py:763] (1/8) Epoch 20, batch 3600, loss[loss=0.164, simple_loss=0.2746, pruned_loss=0.02675, over 7383.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2698, pruned_loss=0.03685, over 1411106.99 frames.], batch size: 23, lr: 3.68e-04
2022-04-29 18:16:10,759 INFO [train.py:763] (1/8) Epoch 20, batch 3650, loss[loss=0.1959, simple_loss=0.2986, pruned_loss=0.04659, over 7136.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2695, pruned_loss=0.03668, over 1412775.07 frames.], batch size: 26, lr: 3.68e-04
2022-04-29 18:17:15,967 INFO [train.py:763] (1/8) Epoch 20, batch 3700, loss[loss=0.1667, simple_loss=0.2718, pruned_loss=0.0308, over 7323.00 frames.], tot_loss[loss=0.171, simple_loss=0.2693, pruned_loss=0.03634, over 1413414.42 frames.], batch size: 21, lr: 3.68e-04
2022-04-29 18:18:22,129 INFO [train.py:763] (1/8) Epoch 20, batch 3750, loss[loss=0.1686, simple_loss=0.2659, pruned_loss=0.0356, over 7317.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2681, pruned_loss=0.03568, over 1416742.19 frames.], batch size: 25, lr: 3.68e-04
2022-04-29 18:19:27,281 INFO [train.py:763] (1/8) Epoch 20, batch 3800, loss[loss=0.1767, simple_loss=0.2754, pruned_loss=0.039, over 7104.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2681, pruned_loss=0.03587, over 1417117.99 frames.], batch size: 26, lr: 3.68e-04
2022-04-29 18:20:33,279 INFO [train.py:763] (1/8) Epoch 20, batch 3850, loss[loss=0.1899, simple_loss=0.2895, pruned_loss=0.04517, over 7329.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2685, pruned_loss=0.03589, over 1418459.82 frames.], batch size: 20, lr: 3.68e-04
2022-04-29 18:21:38,666 INFO [train.py:763] (1/8) Epoch 20, batch 3900, loss[loss=0.1955, simple_loss=0.2954, pruned_loss=0.04782, over 7255.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2692, pruned_loss=0.03634, over 1421800.09 frames.], batch size: 19, lr: 3.67e-04
2022-04-29 18:22:44,410 INFO [train.py:763] (1/8) Epoch 20, batch 3950, loss[loss=0.1464, simple_loss=0.2409, pruned_loss=0.0259, over 7392.00 frames.], tot_loss[loss=0.1714, simple_loss=0.27, pruned_loss=0.03646, over 1416797.10 frames.], batch size: 18, lr: 3.67e-04
2022-04-29 18:23:51,277 INFO [train.py:763] (1/8) Epoch 20, batch 4000, loss[loss=0.1817, simple_loss=0.2768, pruned_loss=0.04335, over 7360.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2695, pruned_loss=0.03612, over 1420929.12 frames.], batch size: 19, lr: 3.67e-04
2022-04-29 18:24:58,613 INFO [train.py:763] (1/8) Epoch 20, batch 4050, loss[loss=0.2139, simple_loss=0.3028, pruned_loss=0.0625, over 4902.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2682, pruned_loss=0.03571, over 1418097.94 frames.], batch size: 52, lr: 3.67e-04
2022-04-29 18:26:05,422 INFO [train.py:763] (1/8) Epoch 20, batch 4100, loss[loss=0.1766, simple_loss=0.2856, pruned_loss=0.03381, over 7218.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2692, pruned_loss=0.0362, over 1409672.87 frames.], batch size: 21, lr: 3.67e-04
2022-04-29 18:27:10,991 INFO [train.py:763] (1/8) Epoch 20, batch 4150, loss[loss=0.1712, simple_loss=0.2584, pruned_loss=0.04199, over 7073.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2696, pruned_loss=0.03603, over 1411442.58 frames.], batch size: 18, lr: 3.67e-04
2022-04-29 18:28:16,324 INFO [train.py:763] (1/8) Epoch 20, batch 4200, loss[loss=0.1799, simple_loss=0.2886, pruned_loss=0.03553, over 6803.00 frames.], tot_loss[loss=0.1718, simple_loss=0.2705, pruned_loss=0.03657, over 1410560.99 frames.], batch size: 31, lr: 3.67e-04
2022-04-29 18:29:32,305 INFO [train.py:763] (1/8) Epoch 20, batch 4250, loss[loss=0.1847, simple_loss=0.2976, pruned_loss=0.03587, over 7218.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2695, pruned_loss=0.03595, over 1415261.11 frames.], batch size: 21, lr: 3.67e-04
2022-04-29 18:30:38,993 INFO [train.py:763] (1/8) Epoch 20, batch 4300, loss[loss=0.1667, simple_loss=0.2742, pruned_loss=0.02957, over 7317.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2696, pruned_loss=0.0359, over 1416298.44 frames.], batch size: 24, lr: 3.67e-04
2022-04-29 18:31:45,007 INFO [train.py:763] (1/8) Epoch 20, batch 4350, loss[loss=0.1739, simple_loss=0.2907, pruned_loss=0.02855, over 7227.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2704, pruned_loss=0.03641, over 1416007.65 frames.], batch size: 21, lr: 3.67e-04
2022-04-29 18:32:52,213 INFO [train.py:763] (1/8) Epoch 20, batch 4400, loss[loss=0.1711, simple_loss=0.2596, pruned_loss=0.04131, over 7158.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2704, pruned_loss=0.03636, over 1415240.79 frames.], batch size: 18, lr: 3.66e-04
2022-04-29 18:33:58,452 INFO [train.py:763] (1/8) Epoch 20, batch 4450, loss[loss=0.1502, simple_loss=0.2533, pruned_loss=0.02352, over 6986.00 frames.], tot_loss[loss=0.1712, simple_loss=0.27, pruned_loss=0.03623, over 1407703.35 frames.], batch size: 16, lr: 3.66e-04
2022-04-29 18:35:05,722 INFO [train.py:763] (1/8) Epoch 20, batch 4500, loss[loss=0.1522, simple_loss=0.2435, pruned_loss=0.03045, over 6997.00 frames.], tot_loss[loss=0.1714, simple_loss=0.2703, pruned_loss=0.03626, over 1410740.51 frames.], batch size: 16, lr: 3.66e-04
2022-04-29 18:36:13,270 INFO [train.py:763] (1/8) Epoch 20, batch 4550, loss[loss=0.2175, simple_loss=0.3023, pruned_loss=0.06638, over 5166.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2694, pruned_loss=0.03648, over 1394593.58 frames.], batch size: 52, lr: 3.66e-04
2022-04-29 18:37:42,386 INFO [train.py:763] (1/8) Epoch 21, batch 0, loss[loss=0.1792, simple_loss=0.2835, pruned_loss=0.03749, over 7299.00 frames.], tot_loss[loss=0.1792, simple_loss=0.2835, pruned_loss=0.03749, over 7299.00 frames.], batch size: 25, lr: 3.58e-04
2022-04-29 18:38:48,207 INFO [train.py:763] (1/8) Epoch 21, batch 50, loss[loss=0.1323, simple_loss=0.2291, pruned_loss=0.01776, over 7167.00 frames.], tot_loss[loss=0.1717, simple_loss=0.2713, pruned_loss=0.03604, over 318506.41 frames.], batch size: 18, lr: 3.58e-04
2022-04-29 18:39:53,570 INFO [train.py:763] (1/8) Epoch 21, batch 100, loss[loss=0.192, simple_loss=0.2976, pruned_loss=0.04323, over 7113.00 frames.], tot_loss[loss=0.17, simple_loss=0.2685, pruned_loss=0.03574, over 564768.09 frames.], batch size: 21, lr: 3.58e-04
2022-04-29 18:41:00,341 INFO [train.py:763] (1/8) Epoch 21, batch 150, loss[loss=0.1955, simple_loss=0.3013, pruned_loss=0.04487, over 7321.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2689, pruned_loss=0.0357, over 754903.10 frames.], batch size: 21, lr: 3.58e-04
2022-04-29 18:42:07,756 INFO [train.py:763] (1/8) Epoch 21, batch 200, loss[loss=0.1494, simple_loss=0.2596, pruned_loss=0.0196, over 7342.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2701, pruned_loss=0.03658, over 902374.97 frames.], batch size: 22, lr: 3.58e-04
2022-04-29 18:43:14,297 INFO [train.py:763] (1/8) Epoch 21, batch 250, loss[loss=0.1544, simple_loss=0.2557, pruned_loss=0.02656, over 7256.00 frames.], tot_loss[loss=0.171, simple_loss=0.2694, pruned_loss=0.03632, over 1016536.07 frames.], batch size: 19, lr: 3.57e-04
2022-04-29 18:44:19,570 INFO [train.py:763] (1/8) Epoch 21, batch 300, loss[loss=0.1814, simple_loss=0.2775, pruned_loss=0.04261, over 7239.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2687, pruned_loss=0.03592, over 1108537.73 frames.], batch size: 20, lr: 3.57e-04
2022-04-29 18:45:25,082 INFO [train.py:763] (1/8) Epoch 21, batch 350, loss[loss=0.1469, simple_loss=0.2543, pruned_loss=0.01982, over 7147.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2678, pruned_loss=0.03546, over 1178912.45 frames.], batch size: 19, lr: 3.57e-04
2022-04-29 18:46:30,618 INFO [train.py:763] (1/8) Epoch 21, batch 400, loss[loss=0.1806, simple_loss=0.2787, pruned_loss=0.04131, over 7233.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2682, pruned_loss=0.0355, over 1231576.63 frames.], batch size: 21, lr: 3.57e-04
2022-04-29 18:47:36,040 INFO [train.py:763] (1/8) Epoch 21, batch 450, loss[loss=0.207, simple_loss=0.305, pruned_loss=0.0545, over 5066.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2678, pruned_loss=0.0352, over 1275180.22 frames.], batch size: 55, lr: 3.57e-04
2022-04-29 18:48:41,847 INFO [train.py:763] (1/8) Epoch 21, batch 500, loss[loss=0.1936, simple_loss=0.2933, pruned_loss=0.04693, over 7317.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2693, pruned_loss=0.03572, over 1310502.70 frames.], batch size: 25, lr: 3.57e-04
2022-04-29 18:49:47,435 INFO [train.py:763] (1/8) Epoch 21, batch 550, loss[loss=0.1541, simple_loss=0.2463, pruned_loss=0.03088, over 7439.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2702, pruned_loss=0.03653, over 1332872.05 frames.], batch size: 20, lr: 3.57e-04
2022-04-29 18:50:53,640 INFO [train.py:763] (1/8) Epoch 21, batch 600, loss[loss=0.1801, simple_loss=0.2797, pruned_loss=0.04022, over 7338.00 frames.], tot_loss[loss=0.1708, simple_loss=0.269, pruned_loss=0.03628, over 1354260.77 frames.], batch size: 22, lr: 3.57e-04
2022-04-29 18:51:58,877 INFO [train.py:763] (1/8) Epoch 21, batch 650, loss[loss=0.1668, simple_loss=0.2757, pruned_loss=0.0289, over 7330.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2693, pruned_loss=0.03615, over 1370884.75 frames.], batch size: 22, lr: 3.57e-04
2022-04-29 18:53:04,511 INFO [train.py:763] (1/8) Epoch 21, batch 700, loss[loss=0.1892, simple_loss=0.2865, pruned_loss=0.04592, over 7310.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2689, pruned_loss=0.03623, over 1378484.89 frames.], batch size: 25, lr: 3.57e-04
2022-04-29 18:54:10,368 INFO [train.py:763] (1/8) Epoch 21, batch 750, loss[loss=0.1714, simple_loss=0.2637, pruned_loss=0.03952, over 7161.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2688, pruned_loss=0.03683, over 1387255.12 frames.], batch size: 18, lr: 3.57e-04
2022-04-29 18:55:16,596 INFO [train.py:763] (1/8) Epoch 21, batch 800, loss[loss=0.1909, simple_loss=0.3053, pruned_loss=0.03828, over 7286.00 frames.], tot_loss[loss=0.171, simple_loss=0.2692, pruned_loss=0.03636, over 1399959.21 frames.], batch size: 25, lr: 3.56e-04
2022-04-29 18:56:22,303 INFO [train.py:763] (1/8) Epoch 21, batch 850, loss[loss=0.1653, simple_loss=0.2598, pruned_loss=0.03543, over 7403.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2693, pruned_loss=0.03629, over 1405405.62 frames.], batch size: 18, lr: 3.56e-04
2022-04-29 18:57:27,449 INFO [train.py:763] (1/8) Epoch 21, batch 900, loss[loss=0.186, simple_loss=0.2847, pruned_loss=0.04365, over 6361.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2692, pruned_loss=0.0366, over 1409537.36 frames.], batch size: 37, lr: 3.56e-04
2022-04-29 18:58:32,834 INFO [train.py:763] (1/8) Epoch 21, batch 950, loss[loss=0.1323, simple_loss=0.2223, pruned_loss=0.02115, over 7277.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2687, pruned_loss=0.03636, over 1411833.58 frames.], batch size: 18, lr: 3.56e-04
2022-04-29 18:59:38,148 INFO [train.py:763] (1/8) Epoch 21, batch 1000, loss[loss=0.1644, simple_loss=0.2682, pruned_loss=0.03032, over 7157.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2696, pruned_loss=0.03649, over 1412082.70 frames.], batch size: 19, lr: 3.56e-04
2022-04-29 19:00:44,770 INFO [train.py:763] (1/8) Epoch 21, batch 1050, loss[loss=0.1745, simple_loss=0.2749, pruned_loss=0.03703, over 7324.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2686, pruned_loss=0.03618, over 1415246.40 frames.], batch size: 22, lr: 3.56e-04
2022-04-29 19:01:50,753 INFO [train.py:763] (1/8) Epoch 21, batch 1100, loss[loss=0.1901, simple_loss=0.2923, pruned_loss=0.04398, over 6326.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2687, pruned_loss=0.03629, over 1418970.81 frames.], batch size: 37, lr: 3.56e-04
2022-04-29 19:02:56,400 INFO [train.py:763] (1/8) Epoch 21, batch 1150, loss[loss=0.1519, simple_loss=0.2609, pruned_loss=0.02145, over 7262.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2684, pruned_loss=0.03626, over 1419811.58 frames.], batch size: 19, lr: 3.56e-04
2022-04-29 19:04:02,097 INFO [train.py:763] (1/8) Epoch 21, batch 1200, loss[loss=0.1891, simple_loss=0.2947, pruned_loss=0.04173, over 7302.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2686, pruned_loss=0.03659, over 1420748.56 frames.], batch size: 25, lr: 3.56e-04
2022-04-29 19:05:07,718 INFO [train.py:763] (1/8) Epoch 21, batch 1250, loss[loss=0.143, simple_loss=0.2319, pruned_loss=0.02708, over 7001.00 frames.], tot_loss[loss=0.1707, simple_loss=0.2688, pruned_loss=0.03628, over 1419810.76 frames.], batch size: 16, lr: 3.56e-04
2022-04-29 19:06:13,269 INFO [train.py:763] (1/8) Epoch 21, batch 1300, loss[loss=0.1792, simple_loss=0.2831, pruned_loss=0.03762, over 7159.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2688, pruned_loss=0.03621, over 1418639.54 frames.], batch size: 19, lr: 3.56e-04
2022-04-29 19:07:19,446 INFO [train.py:763] (1/8) Epoch 21, batch 1350, loss[loss=0.1843, simple_loss=0.2868, pruned_loss=0.04092, over 7418.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2682, pruned_loss=0.03619, over 1422878.79 frames.], batch size: 21, lr: 3.55e-04
2022-04-29 19:08:24,893 INFO [train.py:763] (1/8) Epoch 21, batch 1400, loss[loss=0.1665, simple_loss=0.2745, pruned_loss=0.02929, over 7196.00 frames.], tot_loss[loss=0.17, simple_loss=0.2678, pruned_loss=0.03606, over 1419404.57 frames.], batch size: 22, lr: 3.55e-04
2022-04-29 19:09:30,408 INFO [train.py:763] (1/8) Epoch 21, batch 1450, loss[loss=0.1645, simple_loss=0.2674, pruned_loss=0.03083, over 7436.00 frames.], tot_loss[loss=0.17, simple_loss=0.2681, pruned_loss=0.03591, over 1424739.75 frames.], batch size: 20, lr: 3.55e-04
2022-04-29 19:10:36,216 INFO [train.py:763] (1/8) Epoch 21, batch 1500, loss[loss=0.172, simple_loss=0.2791, pruned_loss=0.03241, over 7232.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2674, pruned_loss=0.03586, over 1426868.77 frames.], batch size: 20, lr: 3.55e-04
2022-04-29 19:11:42,021 INFO [train.py:763] (1/8) Epoch 21, batch 1550, loss[loss=0.1561, simple_loss=0.2617, pruned_loss=0.02526, over 7236.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2678, pruned_loss=0.036, over 1429725.39 frames.], batch size: 20, lr: 3.55e-04
2022-04-29 19:12:47,947 INFO [train.py:763] (1/8) Epoch 21, batch 1600, loss[loss=0.1261, simple_loss=0.2185, pruned_loss=0.01684, over 6862.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2684, pruned_loss=0.03601, over 1430481.13 frames.], batch size: 15, lr: 3.55e-04
2022-04-29 19:13:54,883 INFO [train.py:763] (1/8) Epoch 21, batch 1650, loss[loss=0.1731, simple_loss=0.2782, pruned_loss=0.03397, over 6800.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2684, pruned_loss=0.0357, over 1431862.68 frames.], batch size: 31, lr: 3.55e-04
2022-04-29 19:15:01,798 INFO [train.py:763] (1/8) Epoch 21, batch 1700, loss[loss=0.17, simple_loss=0.2667, pruned_loss=0.03671, over 7330.00 frames.], tot_loss[loss=0.1684, simple_loss=0.267, pruned_loss=0.03493, over 1434030.16 frames.], batch size: 22, lr: 3.55e-04
2022-04-29 19:16:08,176 INFO [train.py:763] (1/8) Epoch 21, batch 1750, loss[loss=0.1578, simple_loss=0.2619, pruned_loss=0.02684, over 7227.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2675, pruned_loss=0.03505, over 1432749.00 frames.], batch size: 20, lr: 3.55e-04
2022-04-29 19:17:14,196 INFO [train.py:763] (1/8) Epoch 21, batch 1800, loss[loss=0.1477, simple_loss=0.2374, pruned_loss=0.02901, over 7259.00 frames.], tot_loss[loss=0.1688, simple_loss=0.267, pruned_loss=0.03526, over 1431145.86 frames.], batch size: 17, lr: 3.55e-04
2022-04-29 19:18:19,473 INFO [train.py:763] (1/8) Epoch 21, batch 1850, loss[loss=0.1851, simple_loss=0.2843, pruned_loss=0.04291, over 6252.00 frames.], tot_loss[loss=0.169, simple_loss=0.2672, pruned_loss=0.0354, over 1426937.61 frames.], batch size: 37, lr: 3.55e-04
2022-04-29 19:19:25,203 INFO [train.py:763] (1/8) Epoch 21, batch 1900, loss[loss=0.223, simple_loss=0.3144, pruned_loss=0.06578, over 5438.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2669, pruned_loss=0.03534, over 1424880.80 frames.], batch size: 52, lr: 3.54e-04
2022-04-29 19:20:31,906 INFO [train.py:763] (1/8) Epoch 21, batch 1950, loss[loss=0.1611, simple_loss=0.2495, pruned_loss=0.03637, over 7284.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2668, pruned_loss=0.03521, over 1425542.00 frames.], batch size: 17, lr: 3.54e-04
2022-04-29 19:21:37,659 INFO [train.py:763] (1/8) Epoch 21, batch 2000, loss[loss=0.1817, simple_loss=0.2846, pruned_loss=0.03943, over 7324.00 frames.], tot_loss[loss=0.1697, simple_loss=0.268, pruned_loss=0.03571, over 1427529.97 frames.], batch size: 20, lr: 3.54e-04
2022-04-29 19:22:44,041 INFO [train.py:763] (1/8) Epoch 21, batch 2050, loss[loss=0.1404, simple_loss=0.2325, pruned_loss=0.02415, over 7273.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2678, pruned_loss=0.03548, over 1428550.98 frames.], batch size: 17, lr: 3.54e-04
2022-04-29 19:23:50,502 INFO [train.py:763] (1/8) Epoch 21, batch 2100, loss[loss=0.1424, simple_loss=0.2329, pruned_loss=0.026, over 7402.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2677, pruned_loss=0.03527, over 1426908.97 frames.], batch size: 18, lr: 3.54e-04
2022-04-29 19:24:56,268 INFO [train.py:763] (1/8) Epoch 21, batch 2150, loss[loss=0.1675, simple_loss=0.2578, pruned_loss=0.03861, over 7153.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2665, pruned_loss=0.03484, over 1423000.84 frames.], batch size: 18, lr: 3.54e-04
2022-04-29 19:26:02,248 INFO [train.py:763] (1/8) Epoch 21, batch 2200, loss[loss=0.1665, simple_loss=0.2747, pruned_loss=0.02915, over 7113.00 frames.], tot_loss[loss=0.168, simple_loss=0.2668, pruned_loss=0.03459, over 1425468.21 frames.], batch size: 21, lr: 3.54e-04
2022-04-29 19:27:08,591 INFO [train.py:763] (1/8) Epoch 21, batch 2250, loss[loss=0.1549, simple_loss=0.2393, pruned_loss=0.03526, over 6783.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2678, pruned_loss=0.03539, over 1423174.27 frames.], batch size: 15, lr: 3.54e-04
2022-04-29 19:28:14,981 INFO [train.py:763] (1/8) Epoch 21, batch 2300, loss[loss=0.1786, simple_loss=0.2738, pruned_loss=0.04174, over 5249.00 frames.], tot_loss[loss=0.1695, simple_loss=0.268, pruned_loss=0.03544, over 1424341.61 frames.], batch size: 52, lr: 3.54e-04
2022-04-29 19:29:21,491 INFO [train.py:763] (1/8) Epoch 21, batch 2350, loss[loss=0.1805, simple_loss=0.2858, pruned_loss=0.03763, over 6523.00 frames.], tot_loss[loss=0.169, simple_loss=0.2672, pruned_loss=0.03542, over 1426965.59 frames.], batch size: 38, lr: 3.54e-04
2022-04-29 19:30:28,261 INFO [train.py:763] (1/8) Epoch 21, batch 2400, loss[loss=0.1597, simple_loss=0.2443, pruned_loss=0.03753, over 7135.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2674, pruned_loss=0.03566, over 1426875.23 frames.], batch size: 17, lr: 3.54e-04
2022-04-29 19:31:33,881 INFO [train.py:763] (1/8) Epoch 21, batch 2450, loss[loss=0.1641, simple_loss=0.2546, pruned_loss=0.03683, over 7280.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2675, pruned_loss=0.03537, over 1425798.94 frames.], batch size: 17, lr: 3.54e-04
2022-04-29 19:32:39,517 INFO [train.py:763] (1/8) Epoch 21, batch 2500, loss[loss=0.1692, simple_loss=0.2789, pruned_loss=0.02977, over 7414.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2679, pruned_loss=0.03555, over 1422506.91 frames.], batch size: 21, lr: 3.53e-04
2022-04-29 19:33:46,125 INFO [train.py:763] (1/8) Epoch 21, batch 2550, loss[loss=0.1932, simple_loss=0.286, pruned_loss=0.05022, over 7078.00 frames.], tot_loss[loss=0.17, simple_loss=0.2682, pruned_loss=0.03592, over 1421505.16 frames.], batch size: 18, lr: 3.53e-04
2022-04-29 19:34:52,130 INFO [train.py:763] (1/8) Epoch 21, batch 2600, loss[loss=0.1719, simple_loss=0.2681, pruned_loss=0.03786, over 7157.00 frames.], tot_loss[loss=0.1707, simple_loss=0.269, pruned_loss=0.03619, over 1418010.49 frames.], batch size: 19, lr: 3.53e-04
2022-04-29 19:35:58,115 INFO [train.py:763] (1/8) Epoch 21, batch 2650, loss[loss=0.1686, simple_loss=0.2705, pruned_loss=0.03332, over 7258.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2676, pruned_loss=0.03569, over 1422108.52 frames.], batch size: 19, lr: 3.53e-04
2022-04-29 19:37:03,416 INFO [train.py:763] (1/8) Epoch 21, batch 2700, loss[loss=0.1403, simple_loss=0.2401, pruned_loss=0.02029, over 7173.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2674, pruned_loss=0.03571, over 1421115.21 frames.], batch size: 18, lr: 3.53e-04
2022-04-29 19:38:08,438 INFO [train.py:763] (1/8) Epoch 21, batch 2750, loss[loss=0.1443, simple_loss=0.2391, pruned_loss=0.02477, over 7066.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2677, pruned_loss=0.03591, over 1420628.87 frames.], batch size: 18, lr: 3.53e-04
2022-04-29 19:39:13,884 INFO [train.py:763] (1/8) Epoch 21, batch 2800, loss[loss=0.1475, simple_loss=0.2424, pruned_loss=0.02625, over 7279.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2682, pruned_loss=0.03606, over 1421002.55 frames.], batch size: 18, lr: 3.53e-04
2022-04-29 19:40:19,365 INFO [train.py:763] (1/8) Epoch 21, batch 2850, loss[loss=0.1541, simple_loss=0.2605, pruned_loss=0.02389, over 7159.00 frames.], tot_loss[loss=0.17, simple_loss=0.2679, pruned_loss=0.03603, over 1419206.86 frames.], batch size: 19, lr: 3.53e-04
2022-04-29 19:41:24,553 INFO [train.py:763] (1/8) Epoch 21, batch 2900, loss[loss=0.1523, simple_loss=0.2554, pruned_loss=0.02463, over 7163.00 frames.], tot_loss[loss=0.17, simple_loss=0.2681, pruned_loss=0.03588, over 1421407.23 frames.], batch size: 19, lr: 3.53e-04
2022-04-29 19:42:30,254 INFO [train.py:763] (1/8) Epoch 21, batch 2950, loss[loss=0.1582, simple_loss=0.2644, pruned_loss=0.02598, over 7416.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2678, pruned_loss=0.03574, over 1421169.06 frames.], batch size: 21, lr: 3.53e-04
2022-04-29 19:43:36,682 INFO [train.py:763] (1/8) Epoch 21, batch 3000, loss[loss=0.1552, simple_loss=0.2605, pruned_loss=0.02491, over 7161.00 frames.], tot_loss[loss=0.169, simple_loss=0.2673, pruned_loss=0.03531, over 1424870.95 frames.], batch size: 18, lr: 3.53e-04
2022-04-29 19:43:36,683 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 19:43:52,055 INFO [train.py:792] (1/8) Epoch 21, validation: loss=0.1676, simple_loss=0.2672, pruned_loss=0.03398, over 698248.00 frames.
2022-04-29 19:44:57,937 INFO [train.py:763] (1/8) Epoch 21, batch 3050, loss[loss=0.1685, simple_loss=0.2654, pruned_loss=0.03582, over 7090.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2672, pruned_loss=0.03556, over 1426626.03 frames.], batch size: 28, lr: 3.52e-04
2022-04-29 19:46:03,953 INFO [train.py:763] (1/8) Epoch 21, batch 3100, loss[loss=0.2231, simple_loss=0.3091, pruned_loss=0.06853, over 4918.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2665, pruned_loss=0.03548, over 1426963.22 frames.], batch size: 52, lr: 3.52e-04
2022-04-29 19:47:10,172 INFO [train.py:763] (1/8) Epoch 21, batch 3150, loss[loss=0.1734, simple_loss=0.2824, pruned_loss=0.03215, over 7412.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2659, pruned_loss=0.03499, over 1424225.37 frames.], batch size: 21, lr: 3.52e-04
2022-04-29 19:48:15,882 INFO [train.py:763] (1/8) Epoch 21, batch 3200, loss[loss=0.1573, simple_loss=0.2457, pruned_loss=0.03446, over 7059.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2662, pruned_loss=0.03497, over 1425984.81 frames.], batch size: 18, lr: 3.52e-04
2022-04-29 19:49:21,830 INFO [train.py:763] (1/8) Epoch 21, batch 3250, loss[loss=0.1458, simple_loss=0.2357, pruned_loss=0.02796, over 6994.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2675, pruned_loss=0.03531, over 1427230.32 frames.], batch size: 16, lr: 3.52e-04
2022-04-29 19:50:27,763 INFO [train.py:763] (1/8) Epoch 21, batch 3300, loss[loss=0.1698, simple_loss=0.2752, pruned_loss=0.0322, over 7428.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2682, pruned_loss=0.03541, over 1430788.95 frames.], batch size: 20, lr: 3.52e-04
2022-04-29 19:51:34,061 INFO [train.py:763] (1/8) Epoch 21, batch 3350, loss[loss=0.146, simple_loss=0.2385, pruned_loss=0.02679, over 7368.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2681, pruned_loss=0.03543, over 1429371.73 frames.], batch size: 19, lr: 3.52e-04
2022-04-29 19:52:40,198 INFO [train.py:763] (1/8) Epoch 21, batch 3400, loss[loss=0.1615, simple_loss=0.2536, pruned_loss=0.03472, over 7125.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2682, pruned_loss=0.03562, over 1425790.29 frames.], batch size: 17, lr: 3.52e-04
2022-04-29 19:53:45,691 INFO [train.py:763] (1/8) Epoch 21, batch 3450, loss[loss=0.1871, simple_loss=0.2921, pruned_loss=0.04102, over 7341.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2688, pruned_loss=0.03596, over 1427383.55 frames.], batch size: 22, lr: 3.52e-04
2022-04-29 19:54:51,959 INFO [train.py:763] (1/8) Epoch 21, batch 3500, loss[loss=0.175, simple_loss=0.285, pruned_loss=0.03256, over 7326.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2685, pruned_loss=0.03617, over 1429696.04 frames.], batch size: 22, lr: 3.52e-04
2022-04-29 19:55:58,076 INFO [train.py:763] (1/8) Epoch 21, batch 3550, loss[loss=0.1661, simple_loss=0.2682, pruned_loss=0.03196, over 6791.00 frames.], tot_loss[loss=0.1716, simple_loss=0.2699, pruned_loss=0.0366, over 1427373.12 frames.], batch size: 31, lr: 3.52e-04
2022-04-29 19:57:04,813 INFO [train.py:763] (1/8) Epoch 21, batch 3600, loss[loss=0.1308, simple_loss=0.2266, pruned_loss=0.01756, over 7289.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2687, pruned_loss=0.03604, over 1422957.95 frames.], batch size: 17, lr: 3.51e-04
2022-04-29 19:58:10,365 INFO [train.py:763] (1/8) Epoch 21, batch 3650, loss[loss=0.1854, simple_loss=0.2835, pruned_loss=0.0436, over 7376.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2696, pruned_loss=0.03636, over 1425322.29 frames.], batch size: 23, lr: 3.51e-04
2022-04-29 19:59:15,682 INFO [train.py:763] (1/8) Epoch 21, batch 3700, loss[loss=0.1738, simple_loss=0.2793, pruned_loss=0.03412, over 7225.00 frames.], tot_loss[loss=0.1707, simple_loss=0.269, pruned_loss=0.03619, over 1427617.54 frames.], batch size: 21, lr: 3.51e-04
2022-04-29 20:00:21,230 INFO [train.py:763] (1/8) Epoch 21, batch 3750, loss[loss=0.1541, simple_loss=0.2486, pruned_loss=0.02979, over 7003.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2685, pruned_loss=0.03581, over 1431153.45 frames.], batch size: 16, lr: 3.51e-04
2022-04-29 20:01:26,920 INFO [train.py:763] (1/8) Epoch 21, batch 3800, loss[loss=0.2049, simple_loss=0.282, pruned_loss=0.06393, over 5163.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2676, pruned_loss=0.03573, over 1425380.90 frames.], batch size: 52, lr: 3.51e-04
2022-04-29 20:02:32,210 INFO [train.py:763] (1/8) Epoch 21, batch 3850, loss[loss=0.2005, simple_loss=0.2986, pruned_loss=0.0512, over 7236.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2678, pruned_loss=0.03547, over 1428049.01 frames.], batch size: 20, lr: 3.51e-04
2022-04-29 20:03:37,823 INFO [train.py:763] (1/8) Epoch 21, batch 3900, loss[loss=0.1918, simple_loss=0.2878, pruned_loss=0.04787, over 6330.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2669, pruned_loss=0.03517, over 1427551.69 frames.], batch size: 38, lr: 3.51e-04
2022-04-29 20:04:43,329 INFO [train.py:763] (1/8) Epoch 21, batch 3950, loss[loss=0.1865, simple_loss=0.2724, pruned_loss=0.05031, over 7285.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2661, pruned_loss=0.03455, over 1425861.64 frames.], batch size: 17, lr: 3.51e-04
2022-04-29 20:05:50,731 INFO [train.py:763] (1/8) Epoch 21, batch 4000, loss[loss=0.1897, simple_loss=0.2919, pruned_loss=0.0438, over 7312.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2679, pruned_loss=0.03566, over 1424905.71 frames.], batch size: 21, lr: 3.51e-04
2022-04-29 20:06:57,084 INFO [train.py:763] (1/8) Epoch 21, batch 4050, loss[loss=0.1337, simple_loss=0.2362, pruned_loss=0.01563, over 7374.00 frames.], tot_loss[loss=0.169, simple_loss=0.2671, pruned_loss=0.03542, over 1423862.64 frames.], batch size: 19, lr: 3.51e-04
2022-04-29 20:08:02,549 INFO [train.py:763] (1/8) Epoch 21, batch 4100, loss[loss=0.17, simple_loss=0.2712, pruned_loss=0.03439, over 7328.00 frames.], tot_loss[loss=0.169, simple_loss=0.267, pruned_loss=0.03546, over 1425147.32 frames.], batch size: 20, lr: 3.51e-04
2022-04-29 20:09:08,413 INFO [train.py:763] (1/8) Epoch 21, batch 4150, loss[loss=0.1489, simple_loss=0.2376, pruned_loss=0.03013, over 7065.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2666, pruned_loss=0.03547, over 1420259.14 frames.], batch size: 18, lr: 3.51e-04
2022-04-29 20:10:23,428 INFO [train.py:763] (1/8) Epoch 21, batch 4200, loss[loss=0.169, simple_loss=0.2631, pruned_loss=0.03748, over 7155.00 frames.], tot_loss[loss=0.1694, simple_loss=0.267, pruned_loss=0.03585, over 1415646.33 frames.], batch size: 20, lr: 3.50e-04
2022-04-29 20:11:28,555 INFO [train.py:763] (1/8) Epoch 21, batch 4250, loss[loss=0.1841, simple_loss=0.2887, pruned_loss=0.03977, over 6599.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2674, pruned_loss=0.03575, over 1409221.04 frames.], batch size: 31, lr: 3.50e-04
2022-04-29 20:12:34,518 INFO [train.py:763] (1/8) Epoch 21, batch 4300, loss[loss=0.1683, simple_loss=0.2802, pruned_loss=0.02822, over 7286.00 frames.], tot_loss[loss=0.17, simple_loss=0.2683, pruned_loss=0.03581, over 1411199.11 frames.], batch size: 24, lr: 3.50e-04
2022-04-29 20:13:40,099 INFO [train.py:763] (1/8) Epoch 21, batch 4350, loss[loss=0.1792, simple_loss=0.2829, pruned_loss=0.0377, over 7331.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2684, pruned_loss=0.03574, over 1408243.75 frames.], batch size: 22, lr: 3.50e-04
2022-04-29 20:14:45,317 INFO [train.py:763] (1/8) Epoch 21, batch 4400, loss[loss=0.1719, simple_loss=0.2736, pruned_loss=0.03506, over 7118.00 frames.], tot_loss[loss=0.1705, simple_loss=0.269, pruned_loss=0.03607, over 1403120.77 frames.], batch size: 21, lr: 3.50e-04
2022-04-29 20:15:50,787 INFO [train.py:763] (1/8) Epoch 21, batch 4450, loss[loss=0.1794, simple_loss=0.2849, pruned_loss=0.03695, over 7340.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2698, pruned_loss=0.03639, over 1399120.08 frames.], batch size: 22, lr: 3.50e-04
2022-04-29 20:17:22,857 INFO [train.py:763] (1/8) Epoch 21, batch 4500, loss[loss=0.1772, simple_loss=0.275, pruned_loss=0.03969, over 7114.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2714, pruned_loss=0.03717, over 1389502.03 frames.], batch size: 28, lr: 3.50e-04
2022-04-29 20:18:27,309 INFO [train.py:763] (1/8) Epoch 21, batch 4550, loss[loss=0.2077, simple_loss=0.2892, pruned_loss=0.06312, over 5223.00 frames.], tot_loss[loss=0.1753, simple_loss=0.2736, pruned_loss=0.03851, over 1347270.38 frames.], batch size: 52, lr: 3.50e-04
2022-04-29 20:20:15,477 INFO [train.py:763] (1/8) Epoch 22, batch 0, loss[loss=0.1633, simple_loss=0.2515, pruned_loss=0.03753, over 7189.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2515, pruned_loss=0.03753, over 7189.00 frames.], batch size: 16, lr: 3.42e-04
2022-04-29 20:21:30,524 INFO [train.py:763] (1/8) Epoch 22, batch 50, loss[loss=0.164, simple_loss=0.2584, pruned_loss=0.03478, over 7164.00 frames.], tot_loss[loss=0.1729, simple_loss=0.2697, pruned_loss=0.03802, over 319350.67 frames.], batch size: 19, lr: 3.42e-04
2022-04-29 20:22:35,941 INFO [train.py:763] (1/8) Epoch 22, batch 100, loss[loss=0.1529, simple_loss=0.2602, pruned_loss=0.02283, over 7299.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2683, pruned_loss=0.03495, over 565713.29 frames.], batch size: 18, lr: 3.42e-04
2022-04-29 20:23:41,420 INFO [train.py:763] (1/8) Epoch 22, batch 150, loss[loss=0.1778, simple_loss=0.278, pruned_loss=0.03875, over 7266.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2707, pruned_loss=0.03585, over 753273.56 frames.], batch size: 24, lr: 3.42e-04
2022-04-29 20:24:46,881 INFO [train.py:763] (1/8) Epoch 22, batch 200, loss[loss=0.1519, simple_loss=0.2461, pruned_loss=0.02884, over 6192.00 frames.], tot_loss[loss=0.1713, simple_loss=0.2706, pruned_loss=0.03602, over 901423.96 frames.], batch size: 37, lr: 3.42e-04
2022-04-29 20:25:52,441 INFO [train.py:763] (1/8) Epoch 22, batch 250, loss[loss=0.1725, simple_loss=0.2797, pruned_loss=0.0327, over 7193.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2699, pruned_loss=0.03582, over 1016424.01 frames.], batch size: 23, lr: 3.42e-04
2022-04-29 20:26:58,033 INFO [train.py:763] (1/8) Epoch 22, batch 300, loss[loss=0.174, simple_loss=0.2686, pruned_loss=0.03969, over 7144.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2691, pruned_loss=0.0359, over 1101952.59 frames.], batch size: 19, lr: 3.42e-04
2022-04-29 20:28:05,352 INFO [train.py:763] (1/8) Epoch 22, batch 350, loss[loss=0.1913, simple_loss=0.3009, pruned_loss=0.04086, over 7339.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2682, pruned_loss=0.03523, over 1176431.04 frames.], batch size: 22, lr: 3.42e-04
2022-04-29 20:29:12,804 INFO [train.py:763] (1/8) Epoch 22, batch 400, loss[loss=0.17, simple_loss=0.2822, pruned_loss=0.02893, over 7191.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2683, pruned_loss=0.0356, over 1230155.67 frames.], batch size: 23, lr: 3.42e-04
2022-04-29 20:30:18,162 INFO [train.py:763] (1/8) Epoch 22, batch 450, loss[loss=0.2095, simple_loss=0.3127, pruned_loss=0.05317, over 7265.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2694, pruned_loss=0.03586, over 1271404.66 frames.], batch size: 24, lr: 3.42e-04
2022-04-29 20:31:24,302 INFO [train.py:763] (1/8) Epoch 22, batch 500, loss[loss=0.1557, simple_loss=0.2467, pruned_loss=0.03236, over 6777.00 frames.], tot_loss[loss=0.1703, simple_loss=0.2692, pruned_loss=0.03569, over 1307057.47 frames.], batch size: 15, lr: 3.41e-04
2022-04-29 20:32:31,785 INFO [train.py:763] (1/8) Epoch 22, batch 550, loss[loss=0.1848, simple_loss=0.2847, pruned_loss=0.04244, over 7286.00 frames.], tot_loss[loss=0.17, simple_loss=0.2686, pruned_loss=0.03573, over 1337254.81 frames.], batch size: 24, lr: 3.41e-04
2022-04-29 20:33:39,037 INFO [train.py:763] (1/8) Epoch 22, batch 600, loss[loss=0.1918, simple_loss=0.2816, pruned_loss=0.05098, over 7110.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2692, pruned_loss=0.03601, over 1359816.82 frames.], batch size: 21, lr: 3.41e-04
2022-04-29 20:34:44,740 INFO [train.py:763] (1/8) Epoch 22, batch 650, loss[loss=0.1662, simple_loss=0.2667, pruned_loss=0.03284, over 6896.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2696, pruned_loss=0.03575, over 1374146.88 frames.], batch size: 31, lr: 3.41e-04
2022-04-29 20:35:51,883 INFO [train.py:763] (1/8) Epoch 22, batch 700, loss[loss=0.1921, simple_loss=0.2902, pruned_loss=0.04704, over 5172.00 frames.], tot_loss[loss=0.1701, simple_loss=0.2689, pruned_loss=0.03568, over 1380725.47 frames.], batch size: 53, lr: 3.41e-04
2022-04-29 20:36:59,160 INFO [train.py:763] (1/8) Epoch 22, batch 750, loss[loss=0.1847, simple_loss=0.2816, pruned_loss=0.04395, over 7215.00 frames.], tot_loss[loss=0.171, simple_loss=0.2698, pruned_loss=0.03612, over 1392638.03 frames.], batch size: 23, lr: 3.41e-04
2022-04-29 20:38:05,931 INFO [train.py:763] (1/8) Epoch 22, batch 800, loss[loss=0.1664, simple_loss=0.2715, pruned_loss=0.03063, over 7355.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2691, pruned_loss=0.03587, over 1396522.40 frames.], batch size: 19, lr: 3.41e-04
2022-04-29 20:39:11,694 INFO [train.py:763] (1/8) Epoch 22, batch 850, loss[loss=0.1729, simple_loss=0.2739, pruned_loss=0.03598, over 7424.00 frames.], tot_loss[loss=0.1702, simple_loss=0.2689, pruned_loss=0.03578, over 1404331.66 frames.], batch size: 20, lr: 3.41e-04
2022-04-29 20:40:16,908 INFO [train.py:763] (1/8) Epoch 22, batch 900, loss[loss=0.1691, simple_loss=0.2717, pruned_loss=0.03324, over 7166.00 frames.], tot_loss[loss=0.1706, simple_loss=0.2691, pruned_loss=0.03606, over 1408758.44 frames.], batch size: 19, lr: 3.41e-04
2022-04-29 20:41:22,120 INFO [train.py:763] (1/8) Epoch 22, batch 950, loss[loss=0.167, simple_loss=0.2671, pruned_loss=0.03345, over 7022.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2687, pruned_loss=0.03541, over 1410179.99 frames.], batch size: 28, lr: 3.41e-04
2022-04-29 20:42:27,343 INFO [train.py:763] (1/8) Epoch 22, batch 1000, loss[loss=0.1579, simple_loss=0.2591, pruned_loss=0.0284, over 7359.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2687, pruned_loss=0.03529, over 1417193.06 frames.], batch size: 19, lr: 3.41e-04
2022-04-29 20:43:32,804 INFO [train.py:763] (1/8) Epoch 22, batch 1050, loss[loss=0.1955, simple_loss=0.2959, pruned_loss=0.0476, over 4862.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2678, pruned_loss=0.03524, over 1417755.04 frames.], batch size: 53, lr: 3.41e-04
2022-04-29 20:44:37,787 INFO [train.py:763] (1/8) Epoch 22, batch 1100, loss[loss=0.1365, simple_loss=0.2215, pruned_loss=0.02579, over 7278.00 frames.], tot_loss[loss=0.1698, simple_loss=0.269, pruned_loss=0.03531, over 1417239.22 frames.], batch size: 17, lr: 3.40e-04
2022-04-29 20:45:43,153 INFO [train.py:763] (1/8) Epoch 22, batch 1150, loss[loss=0.1655, simple_loss=0.2618, pruned_loss=0.03461, over 7422.00 frames.], tot_loss[loss=0.1698, simple_loss=0.269, pruned_loss=0.03533, over 1421451.97 frames.], batch size: 20, lr: 3.40e-04
2022-04-29 20:46:49,110 INFO [train.py:763] (1/8) Epoch 22, batch 1200, loss[loss=0.1448, simple_loss=0.2344, pruned_loss=0.02764, over 7288.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2683, pruned_loss=0.03523, over 1421628.61 frames.], batch size: 18, lr: 3.40e-04
2022-04-29 20:47:55,635 INFO [train.py:763] (1/8) Epoch 22, batch 1250, loss[loss=0.1602, simple_loss=0.2503, pruned_loss=0.03498, over 6841.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2676, pruned_loss=0.03488, over 1425297.84 frames.], batch size: 15, lr: 3.40e-04
2022-04-29 20:49:00,848 INFO [train.py:763] (1/8) Epoch 22, batch 1300, loss[loss=0.1603, simple_loss=0.2646, pruned_loss=0.02796, over 7204.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2675, pruned_loss=0.03464, over 1427724.92 frames.], batch size: 23, lr: 3.40e-04
2022-04-29 20:50:07,452 INFO [train.py:763] (1/8) Epoch 22, batch 1350, loss[loss=0.1605, simple_loss=0.2473, pruned_loss=0.03682, over 7276.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2665, pruned_loss=0.03465, over 1428161.30 frames.], batch size: 18, lr: 3.40e-04
2022-04-29 20:51:13,803 INFO [train.py:763] (1/8) Epoch 22, batch 1400, loss[loss=0.1879, simple_loss=0.2947, pruned_loss=0.0406, over 7104.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2673, pruned_loss=0.03511, over 1428393.25 frames.], batch size: 21, lr: 3.40e-04
2022-04-29 20:52:19,583 INFO [train.py:763] (1/8) Epoch 22, batch 1450, loss[loss=0.1357, simple_loss=0.2331, pruned_loss=0.01911, over 7419.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2668, pruned_loss=0.035, over 1421889.92 frames.], batch size: 18, lr: 3.40e-04
2022-04-29 20:53:25,449 INFO [train.py:763] (1/8) Epoch 22, batch 1500, loss[loss=0.1811, simple_loss=0.2924, pruned_loss=0.03491, over 7038.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2658, pruned_loss=0.03463, over 1422950.80 frames.], batch size: 28, lr: 3.40e-04
2022-04-29 20:54:31,378 INFO [train.py:763] (1/8) Epoch 22, batch 1550, loss[loss=0.1412, simple_loss=0.2356, pruned_loss=0.02338, over 7357.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2661, pruned_loss=0.03482, over 1414460.23 frames.], batch size: 19, lr: 3.40e-04
2022-04-29 20:55:37,830 INFO [train.py:763] (1/8) Epoch 22, batch 1600, loss[loss=0.1943, simple_loss=0.2987, pruned_loss=0.04491, over 7221.00 frames.], tot_loss[loss=0.1686, simple_loss=0.267, pruned_loss=0.03508, over 1411794.72 frames.], batch size: 21, lr: 3.40e-04
2022-04-29 20:56:43,433 INFO [train.py:763] (1/8) Epoch 22, batch 1650, loss[loss=0.153, simple_loss=0.257, pruned_loss=0.02452, over 7371.00 frames.], tot_loss[loss=0.169, simple_loss=0.2676, pruned_loss=0.03522, over 1414373.26 frames.], batch size: 23, lr: 3.40e-04
2022-04-29 20:57:48,935 INFO [train.py:763] (1/8) Epoch 22, batch 1700, loss[loss=0.1534, simple_loss=0.245, pruned_loss=0.03091, over 7422.00 frames.], tot_loss[loss=0.1697, simple_loss=0.2682, pruned_loss=0.03561, over 1415538.02 frames.], batch size: 18, lr: 3.39e-04
2022-04-29 20:58:54,070 INFO [train.py:763] (1/8) Epoch 22, batch 1750, loss[loss=0.1924, simple_loss=0.2835, pruned_loss=0.05063, over 7188.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2688, pruned_loss=0.03595, over 1414477.23 frames.], batch size: 26, lr: 3.39e-04
2022-04-29 20:59:59,907 INFO [train.py:763] (1/8) Epoch 22, batch 1800, loss[loss=0.2108, simple_loss=0.3172, pruned_loss=0.05217, over 5153.00 frames.], tot_loss[loss=0.1705, simple_loss=0.2689, pruned_loss=0.03609, over 1410886.59 frames.], batch size: 52, lr: 3.39e-04
2022-04-29 21:01:05,540 INFO [train.py:763] (1/8) Epoch 22, batch 1850, loss[loss=0.1866, simple_loss=0.2836, pruned_loss=0.04477, over 7421.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2679, pruned_loss=0.03544, over 1416428.94 frames.], batch size: 20, lr: 3.39e-04
2022-04-29 21:02:10,914 INFO [train.py:763] (1/8) Epoch 22, batch 1900, loss[loss=0.1707, simple_loss=0.2741, pruned_loss=0.03364, over 7154.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2669, pruned_loss=0.03485, over 1420282.86 frames.], batch size: 20, lr: 3.39e-04
2022-04-29 21:03:17,152 INFO [train.py:763] (1/8) Epoch 22, batch 1950, loss[loss=0.1581, simple_loss=0.2632, pruned_loss=0.02649, over 7154.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2667, pruned_loss=0.03509, over 1417340.13 frames.], batch size: 20, lr: 3.39e-04
2022-04-29 21:04:22,497 INFO [train.py:763] (1/8) Epoch 22, batch 2000, loss[loss=0.1759, simple_loss=0.2679, pruned_loss=0.04195, over 7256.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2684, pruned_loss=0.03536, over 1421216.79 frames.], batch size: 19, lr: 3.39e-04
2022-04-29 21:05:28,498 INFO [train.py:763] (1/8) Epoch 22, batch 2050, loss[loss=0.1719, simple_loss=0.2681, pruned_loss=0.03784, over 7230.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2677, pruned_loss=0.03502, over 1425272.76 frames.], batch size: 20, lr: 3.39e-04
2022-04-29 21:06:35,597 INFO [train.py:763] (1/8) Epoch 22, batch 2100, loss[loss=0.2157, simple_loss=0.3004, pruned_loss=0.06549, over 7208.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2676, pruned_loss=0.0349, over 1419463.21 frames.], batch size: 23, lr: 3.39e-04
2022-04-29 21:07:42,147 INFO [train.py:763] (1/8) Epoch 22, batch 2150, loss[loss=0.1671, simple_loss=0.2616, pruned_loss=0.03628, over 7156.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2674, pruned_loss=0.03497, over 1419469.47 frames.], batch size: 19, lr: 3.39e-04
2022-04-29 21:08:47,295 INFO [train.py:763] (1/8) Epoch 22, batch 2200, loss[loss=0.1502, simple_loss=0.2587, pruned_loss=0.02082, over 7143.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2673, pruned_loss=0.03509, over 1414662.94 frames.], batch size: 20, lr: 3.39e-04
2022-04-29 21:09:53,559 INFO [train.py:763] (1/8) Epoch 22, batch 2250, loss[loss=0.17, simple_loss=0.2657, pruned_loss=0.03714, over 7154.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2685, pruned_loss=0.03571, over 1410939.33 frames.], batch size: 19, lr: 3.39e-04
2022-04-29 21:11:00,716 INFO [train.py:763] (1/8) Epoch 22, batch 2300, loss[loss=0.1533, simple_loss=0.2649, pruned_loss=0.02084, over 7320.00 frames.], tot_loss[loss=0.169, simple_loss=0.2675, pruned_loss=0.03527, over 1412666.72 frames.], batch size: 21, lr: 3.38e-04
2022-04-29 21:12:07,637 INFO [train.py:763] (1/8) Epoch 22, batch 2350, loss[loss=0.1743, simple_loss=0.2892, pruned_loss=0.02968, over 7337.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2682, pruned_loss=0.03555, over 1414580.62 frames.], batch size: 22, lr: 3.38e-04
2022-04-29 21:13:14,356 INFO [train.py:763] (1/8) Epoch 22, batch 2400, loss[loss=0.2245, simple_loss=0.3183, pruned_loss=0.06534, over 7301.00 frames.], tot_loss[loss=0.1704, simple_loss=0.2691, pruned_loss=0.03584, over 1417245.48 frames.], batch size: 24, lr: 3.38e-04
2022-04-29 21:14:19,601 INFO [train.py:763] (1/8) Epoch 22, batch 2450, loss[loss=0.2086, simple_loss=0.2945, pruned_loss=0.06135, over 7210.00 frames.], tot_loss[loss=0.1711, simple_loss=0.2702, pruned_loss=0.03603, over 1421516.79 frames.], batch size: 22, lr: 3.38e-04
2022-04-29 21:15:24,880 INFO [train.py:763] (1/8) Epoch 22, batch 2500, loss[loss=0.1492, simple_loss=0.2581, pruned_loss=0.02011, over 6515.00 frames.], tot_loss[loss=0.1694, simple_loss=0.268, pruned_loss=0.03546, over 1419745.01 frames.], batch size: 38, lr: 3.38e-04
2022-04-29 21:16:30,046 INFO [train.py:763] (1/8) Epoch 22, batch 2550, loss[loss=0.1476, simple_loss=0.2498, pruned_loss=0.02273, over 7380.00 frames.], tot_loss[loss=0.1697, simple_loss=0.268, pruned_loss=0.03568, over 1420808.39 frames.], batch size: 23, lr: 3.38e-04
2022-04-29 21:17:35,650 INFO [train.py:763] (1/8) Epoch 22, batch 2600, loss[loss=0.1549, simple_loss=0.2522, pruned_loss=0.02879, over 7343.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2676, pruned_loss=0.03553, over 1425102.38 frames.], batch size: 22, lr: 3.38e-04
2022-04-29 21:18:41,155 INFO [train.py:763] (1/8) Epoch 22, batch 2650, loss[loss=0.1629, simple_loss=0.2709, pruned_loss=0.02745, over 7288.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2658, pruned_loss=0.03492, over 1422175.86 frames.], batch size: 25, lr: 3.38e-04
2022-04-29 21:19:46,642 INFO [train.py:763] (1/8) Epoch 22, batch 2700, loss[loss=0.1567, simple_loss=0.2613, pruned_loss=0.0261, over 7154.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2661, pruned_loss=0.0348, over 1421961.74 frames.], batch size: 19, lr: 3.38e-04
2022-04-29 21:20:54,006 INFO [train.py:763] (1/8) Epoch 22, batch 2750, loss[loss=0.1571, simple_loss=0.2471, pruned_loss=0.03353, over 7156.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2665, pruned_loss=0.03544, over 1419438.19 frames.], batch size: 18, lr: 3.38e-04
2022-04-29 21:22:00,023 INFO [train.py:763] (1/8) Epoch 22, batch 2800, loss[loss=0.1438, simple_loss=0.2331, pruned_loss=0.02719, over 7161.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2665, pruned_loss=0.03564, over 1418767.32 frames.], batch size: 18, lr: 3.38e-04
2022-04-29 21:23:05,436 INFO [train.py:763] (1/8) Epoch 22, batch 2850, loss[loss=0.1907, simple_loss=0.291, pruned_loss=0.04519, over 7069.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2664, pruned_loss=0.03508, over 1420537.60 frames.], batch size: 28, lr: 3.38e-04
2022-04-29 21:24:10,662 INFO [train.py:763] (1/8) Epoch 22, batch 2900, loss[loss=0.1785, simple_loss=0.2831, pruned_loss=0.03694, over 7284.00 frames.], tot_loss[loss=0.1686, simple_loss=0.267, pruned_loss=0.03515, over 1422621.98 frames.], batch size: 25, lr: 3.37e-04
2022-04-29 21:25:15,971 INFO [train.py:763] (1/8) Epoch 22, batch 2950, loss[loss=0.173, simple_loss=0.2745, pruned_loss=0.03572, over 7222.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2676, pruned_loss=0.03536, over 1423211.49 frames.], batch size: 22, lr: 3.37e-04
2022-04-29 21:26:20,975 INFO [train.py:763] (1/8) Epoch 22, batch 3000, loss[loss=0.1483, simple_loss=0.2365, pruned_loss=0.03001, over 7004.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2676, pruned_loss=0.03545, over 1423461.56 frames.], batch size: 16, lr: 3.37e-04
2022-04-29 21:26:20,976 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 21:26:36,379 INFO [train.py:792] (1/8) Epoch 22, validation: loss=0.1681, simple_loss=0.2667, pruned_loss=0.03474, over 698248.00 frames.
2022-04-29 21:27:41,667 INFO [train.py:763] (1/8) Epoch 22, batch 3050, loss[loss=0.1428, simple_loss=0.2345, pruned_loss=0.02552, over 7155.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2671, pruned_loss=0.03528, over 1425879.64 frames.], batch size: 19, lr: 3.37e-04
2022-04-29 21:28:58,460 INFO [train.py:763] (1/8) Epoch 22, batch 3100, loss[loss=0.1597, simple_loss=0.2702, pruned_loss=0.02454, over 7236.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2668, pruned_loss=0.03518, over 1424957.06 frames.], batch size: 20, lr: 3.37e-04
2022-04-29 21:30:03,941 INFO [train.py:763] (1/8) Epoch 22, batch 3150, loss[loss=0.172, simple_loss=0.2673, pruned_loss=0.03836, over 7322.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2668, pruned_loss=0.0349, over 1426952.83 frames.], batch size: 20, lr: 3.37e-04
2022-04-29 21:31:09,274 INFO [train.py:763] (1/8) Epoch 22, batch 3200, loss[loss=0.1737, simple_loss=0.2794, pruned_loss=0.034, over 7110.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2666, pruned_loss=0.03451, over 1427798.98 frames.], batch size: 21, lr: 3.37e-04
2022-04-29 21:32:14,547 INFO [train.py:763] (1/8) Epoch 22, batch 3250, loss[loss=0.1836, simple_loss=0.2818, pruned_loss=0.04269, over 6337.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2675, pruned_loss=0.03495, over 1422438.03 frames.], batch size: 38, lr: 3.37e-04
2022-04-29 21:33:19,827 INFO [train.py:763] (1/8) Epoch 22, batch 3300, loss[loss=0.1914, simple_loss=0.3018, pruned_loss=0.04048, over 7289.00 frames.], tot_loss[loss=0.1696, simple_loss=0.2685, pruned_loss=0.03534, over 1422992.72 frames.], batch size: 24, lr: 3.37e-04
2022-04-29 21:34:25,355 INFO [train.py:763] (1/8) Epoch 22, batch 3350, loss[loss=0.1739, simple_loss=0.272, pruned_loss=0.03794, over 7158.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2666, pruned_loss=0.03484, over 1427247.82 frames.], batch size: 26, lr: 3.37e-04
2022-04-29 21:35:30,550 INFO [train.py:763] (1/8) Epoch 22, batch 3400, loss[loss=0.1511, simple_loss=0.2473, pruned_loss=0.02749, over 7160.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2667, pruned_loss=0.03482, over 1428156.55 frames.], batch size: 19, lr: 3.37e-04
2022-04-29 21:36:36,029 INFO [train.py:763] (1/8) Epoch 22, batch 3450, loss[loss=0.1381, simple_loss=0.2305, pruned_loss=0.02281, over 6805.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2664, pruned_loss=0.03473, over 1430070.41 frames.], batch size: 15, lr: 3.37e-04
2022-04-29 21:37:41,466 INFO [train.py:763] (1/8) Epoch 22, batch 3500, loss[loss=0.1618, simple_loss=0.2465, pruned_loss=0.03858, over 6759.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2662, pruned_loss=0.0345, over 1430667.08 frames.], batch size: 15, lr: 3.37e-04
2022-04-29 21:38:46,764 INFO [train.py:763] (1/8) Epoch 22, batch 3550, loss[loss=0.1395, simple_loss=0.2342, pruned_loss=0.02239, over 7410.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2667, pruned_loss=0.03484, over 1431485.52 frames.], batch size: 18, lr: 3.36e-04
2022-04-29 21:39:52,004 INFO [train.py:763] (1/8) Epoch 22, batch 3600, loss[loss=0.1395, simple_loss=0.2309, pruned_loss=0.024, over 7282.00 frames.], tot_loss[loss=0.168, simple_loss=0.267, pruned_loss=0.03456, over 1432012.27 frames.], batch size: 17, lr: 3.36e-04
2022-04-29 21:40:57,415 INFO [train.py:763] (1/8) Epoch 22, batch 3650, loss[loss=0.1752, simple_loss=0.2734, pruned_loss=0.03851, over 6407.00 frames.], tot_loss[loss=0.1681, simple_loss=0.267, pruned_loss=0.03463, over 1431626.35 frames.], batch size: 37, lr: 3.36e-04
2022-04-29 21:42:03,751 INFO [train.py:763] (1/8) Epoch 22, batch 3700, loss[loss=0.1793, simple_loss=0.2749, pruned_loss=0.04188, over 7162.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2668, pruned_loss=0.03432, over 1430446.29 frames.], batch size: 19, lr: 3.36e-04
2022-04-29 21:43:09,191 INFO [train.py:763] (1/8) Epoch 22, batch 3750, loss[loss=0.1517, simple_loss=0.2461, pruned_loss=0.02865, over 7283.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2675, pruned_loss=0.03494, over 1427818.27 frames.], batch size: 17, lr: 3.36e-04
2022-04-29 21:44:14,438 INFO [train.py:763] (1/8) Epoch 22, batch 3800, loss[loss=0.178, simple_loss=0.2834, pruned_loss=0.03636, over 7370.00 frames.], tot_loss[loss=0.169, simple_loss=0.2678, pruned_loss=0.03511, over 1429314.04 frames.], batch size: 23, lr: 3.36e-04
2022-04-29 21:45:19,890 INFO [train.py:763] (1/8) Epoch 22, batch 3850, loss[loss=0.1668, simple_loss=0.2625, pruned_loss=0.03557, over 7057.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2667, pruned_loss=0.03478, over 1430176.18 frames.], batch size: 28, lr: 3.36e-04
2022-04-29 21:46:26,371 INFO [train.py:763] (1/8) Epoch 22, batch 3900, loss[loss=0.1967, simple_loss=0.295, pruned_loss=0.04922, over 7129.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2665, pruned_loss=0.03504, over 1430596.53 frames.], batch size: 21, lr: 3.36e-04
2022-04-29 21:47:31,491 INFO [train.py:763] (1/8) Epoch 22, batch 3950, loss[loss=0.1592, simple_loss=0.2575, pruned_loss=0.03049, over 7158.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2662, pruned_loss=0.0346, over 1430322.70 frames.], batch size: 19, lr: 3.36e-04
2022-04-29 21:48:36,596 INFO [train.py:763] (1/8) Epoch 22, batch 4000, loss[loss=0.1656, simple_loss=0.255, pruned_loss=0.03812, over 7259.00 frames.], tot_loss[loss=0.1676, simple_loss=0.266, pruned_loss=0.0346, over 1427010.96 frames.], batch size: 17, lr: 3.36e-04
2022-04-29 21:49:42,509 INFO [train.py:763] (1/8) Epoch 22, batch 4050, loss[loss=0.1404, simple_loss=0.2339, pruned_loss=0.02351, over 6824.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2669, pruned_loss=0.03432, over 1422017.97 frames.], batch size: 15, lr: 3.36e-04
2022-04-29 21:50:49,117 INFO [train.py:763] (1/8) Epoch 22, batch 4100, loss[loss=0.1458, simple_loss=0.2378, pruned_loss=0.02687, over 7203.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2657, pruned_loss=0.03425, over 1418818.17 frames.], batch size: 16, lr: 3.36e-04
2022-04-29 21:51:54,117 INFO [train.py:763] (1/8) Epoch 22, batch 4150, loss[loss=0.157, simple_loss=0.2598, pruned_loss=0.02713, over 7324.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2668, pruned_loss=0.03464, over 1418043.32 frames.], batch size: 21, lr: 3.35e-04
2022-04-29 21:52:59,300 INFO [train.py:763] (1/8) Epoch 22, batch 4200, loss[loss=0.1483, simple_loss=0.2416, pruned_loss=0.0275, over 6989.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2672, pruned_loss=0.03454, over 1422562.88 frames.], batch size: 16, lr: 3.35e-04
2022-04-29 21:54:05,490 INFO [train.py:763] (1/8) Epoch 22, batch 4250, loss[loss=0.1501, simple_loss=0.2568, pruned_loss=0.02167, over 7236.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2674, pruned_loss=0.03454, over 1423929.21 frames.], batch size: 20, lr: 3.35e-04
2022-04-29 21:55:12,486 INFO [train.py:763] (1/8) Epoch 22, batch 4300, loss[loss=0.1652, simple_loss=0.2631, pruned_loss=0.0337, over 7157.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2664, pruned_loss=0.03458, over 1420847.41 frames.], batch size: 18, lr: 3.35e-04
2022-04-29 21:56:19,737 INFO [train.py:763] (1/8) Epoch 22, batch 4350, loss[loss=0.1446, simple_loss=0.2389, pruned_loss=0.0252, over 7196.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2655, pruned_loss=0.03444, over 1422174.46 frames.], batch size: 16, lr: 3.35e-04
2022-04-29 21:57:26,789 INFO [train.py:763] (1/8) Epoch 22, batch 4400, loss[loss=0.1508, simple_loss=0.2551, pruned_loss=0.02329, over 7060.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2658, pruned_loss=0.03464, over 1419066.63 frames.], batch size: 18, lr: 3.35e-04
2022-04-29 21:58:31,943 INFO [train.py:763] (1/8) Epoch 22, batch 4450, loss[loss=0.1945, simple_loss=0.2817, pruned_loss=0.05361, over 4642.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2665, pruned_loss=0.03498, over 1412700.51 frames.], batch size: 53, lr: 3.35e-04
2022-04-29 21:59:36,917 INFO [train.py:763] (1/8) Epoch 22, batch 4500, loss[loss=0.1761, simple_loss=0.2718, pruned_loss=0.04019, over 7064.00 frames.], tot_loss[loss=0.1692, simple_loss=0.2674, pruned_loss=0.0355, over 1411814.65 frames.], batch size: 18, lr: 3.35e-04
2022-04-29 22:00:41,211 INFO [train.py:763] (1/8) Epoch 22, batch 4550, loss[loss=0.206, simple_loss=0.2999, pruned_loss=0.05607, over 4865.00 frames.], tot_loss[loss=0.1721, simple_loss=0.2699, pruned_loss=0.03714, over 1354126.35 frames.], batch size: 52, lr: 3.35e-04
2022-04-29 22:02:00,636 INFO [train.py:763] (1/8) Epoch 23, batch 0, loss[loss=0.1491, simple_loss=0.2387, pruned_loss=0.02973, over 6836.00 frames.], tot_loss[loss=0.1491, simple_loss=0.2387, pruned_loss=0.02973, over 6836.00 frames.], batch size: 15, lr: 3.28e-04
2022-04-29 22:03:02,942 INFO [train.py:763] (1/8) Epoch 23, batch 50, loss[loss=0.1498, simple_loss=0.2417, pruned_loss=0.02895, over 7274.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2679, pruned_loss=0.03515, over 315399.80 frames.], batch size: 17, lr: 3.28e-04
2022-04-29 22:04:05,007 INFO [train.py:763] (1/8) Epoch 23, batch 100, loss[loss=0.188, simple_loss=0.2805, pruned_loss=0.04773, over 7325.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2669, pruned_loss=0.03397, over 566634.42 frames.], batch size: 20, lr: 3.28e-04
2022-04-29 22:05:10,554 INFO [train.py:763] (1/8) Epoch 23, batch 150, loss[loss=0.1599, simple_loss=0.2631, pruned_loss=0.0283, over 7371.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2673, pruned_loss=0.03452, over 752483.40 frames.], batch size: 23, lr: 3.28e-04
2022-04-29 22:06:15,910 INFO [train.py:763] (1/8) Epoch 23, batch 200, loss[loss=0.1714, simple_loss=0.2749, pruned_loss=0.03398, over 7205.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2675, pruned_loss=0.0346, over 903241.33 frames.], batch size: 22, lr: 3.28e-04
2022-04-29 22:07:21,262 INFO [train.py:763] (1/8) Epoch 23, batch 250, loss[loss=0.1677, simple_loss=0.2803, pruned_loss=0.02757, over 7410.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2669, pruned_loss=0.03476, over 1016079.37 frames.], batch size: 21, lr: 3.28e-04
2022-04-29 22:08:27,017 INFO [train.py:763] (1/8) Epoch 23, batch 300, loss[loss=0.1664, simple_loss=0.2702, pruned_loss=0.03129, over 7141.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2652, pruned_loss=0.03374, over 1107481.88 frames.], batch size: 20, lr: 3.27e-04
2022-04-29 22:09:32,879 INFO [train.py:763] (1/8) Epoch 23, batch 350, loss[loss=0.1709, simple_loss=0.282, pruned_loss=0.02988, over 7292.00 frames.], tot_loss[loss=0.167, simple_loss=0.2659, pruned_loss=0.03403, over 1179328.40 frames.], batch size: 25, lr: 3.27e-04
2022-04-29 22:10:38,044 INFO [train.py:763] (1/8) Epoch 23, batch 400, loss[loss=0.1582, simple_loss=0.2552, pruned_loss=0.03054, over 7300.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2663, pruned_loss=0.03424, over 1231209.69 frames.], batch size: 24, lr: 3.27e-04
2022-04-29 22:11:43,824 INFO [train.py:763] (1/8) Epoch 23, batch 450, loss[loss=0.1517, simple_loss=0.258, pruned_loss=0.02271, over 7147.00 frames.], tot_loss[loss=0.166, simple_loss=0.2652, pruned_loss=0.03343, over 1276253.32 frames.], batch size: 20, lr: 3.27e-04
2022-04-29 22:12:49,134 INFO [train.py:763] (1/8) Epoch 23, batch 500, loss[loss=0.1487, simple_loss=0.2459, pruned_loss=0.02574, over 7362.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2654, pruned_loss=0.03345, over 1307828.72 frames.], batch size: 19, lr: 3.27e-04
2022-04-29 22:13:54,751 INFO [train.py:763] (1/8) Epoch 23, batch 550, loss[loss=0.1845, simple_loss=0.2876, pruned_loss=0.04075, over 7205.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2658, pruned_loss=0.03357, over 1336918.51 frames.], batch size: 22, lr: 3.27e-04
2022-04-29 22:15:00,600 INFO [train.py:763] (1/8) Epoch 23, batch 600, loss[loss=0.1775, simple_loss=0.2768, pruned_loss=0.03906, over 7354.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2655, pruned_loss=0.0339, over 1354235.86 frames.], batch size: 19, lr: 3.27e-04
2022-04-29 22:16:06,054 INFO [train.py:763] (1/8) Epoch 23, batch 650, loss[loss=0.1526, simple_loss=0.2539, pruned_loss=0.02563, over 7353.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2651, pruned_loss=0.03396, over 1364330.27 frames.], batch size: 19, lr: 3.27e-04
2022-04-29 22:17:12,009 INFO [train.py:763] (1/8) Epoch 23, batch 700, loss[loss=0.1863, simple_loss=0.2837, pruned_loss=0.04439, over 7171.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2643, pruned_loss=0.03382, over 1381222.85 frames.], batch size: 26, lr: 3.27e-04
2022-04-29 22:18:17,839 INFO [train.py:763] (1/8) Epoch 23, batch 750, loss[loss=0.1659, simple_loss=0.2586, pruned_loss=0.03655, over 7028.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2651, pruned_loss=0.03381, over 1392119.56 frames.], batch size: 16, lr: 3.27e-04
2022-04-29 22:19:23,431 INFO [train.py:763] (1/8) Epoch 23, batch 800, loss[loss=0.1749, simple_loss=0.2652, pruned_loss=0.04235, over 7252.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2647, pruned_loss=0.0338, over 1399681.60 frames.], batch size: 19, lr: 3.27e-04
2022-04-29 22:20:28,943 INFO [train.py:763] (1/8) Epoch 23, batch 850, loss[loss=0.162, simple_loss=0.2717, pruned_loss=0.02616, over 6751.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2643, pruned_loss=0.03362, over 1405407.06 frames.], batch size: 31, lr: 3.27e-04
2022-04-29 22:21:34,329 INFO [train.py:763] (1/8) Epoch 23, batch 900, loss[loss=0.1609, simple_loss=0.2604, pruned_loss=0.03063, over 7428.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2645, pruned_loss=0.03381, over 1411201.99 frames.], batch size: 20, lr: 3.27e-04
2022-04-29 22:22:49,568 INFO [train.py:763] (1/8) Epoch 23, batch 950, loss[loss=0.1504, simple_loss=0.2522, pruned_loss=0.02423, over 6272.00 frames.], tot_loss[loss=0.1657, simple_loss=0.264, pruned_loss=0.03368, over 1415918.53 frames.], batch size: 38, lr: 3.26e-04
2022-04-29 22:23:55,237 INFO [train.py:763] (1/8) Epoch 23, batch 1000, loss[loss=0.1843, simple_loss=0.2902, pruned_loss=0.03915, over 7325.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2648, pruned_loss=0.03405, over 1418073.77 frames.], batch size: 21, lr: 3.26e-04
2022-04-29 22:25:00,702 INFO [train.py:763] (1/8) Epoch 23, batch 1050, loss[loss=0.1611, simple_loss=0.2595, pruned_loss=0.03131, over 7238.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2659, pruned_loss=0.03474, over 1411847.39 frames.], batch size: 20, lr: 3.26e-04
2022-04-29 22:26:07,027 INFO [train.py:763] (1/8) Epoch 23, batch 1100, loss[loss=0.1661, simple_loss=0.2674, pruned_loss=0.03242, over 7137.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2664, pruned_loss=0.03502, over 1411238.98 frames.], batch size: 20, lr: 3.26e-04
2022-04-29 22:27:12,598 INFO [train.py:763] (1/8) Epoch 23, batch 1150, loss[loss=0.1527, simple_loss=0.2683, pruned_loss=0.0186, over 6394.00 frames.], tot_loss[loss=0.167, simple_loss=0.265, pruned_loss=0.03452, over 1415218.56 frames.], batch size: 38, lr: 3.26e-04
2022-04-29 22:28:17,832 INFO [train.py:763] (1/8) Epoch 23, batch 1200, loss[loss=0.1629, simple_loss=0.2541, pruned_loss=0.03587, over 7159.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2657, pruned_loss=0.03475, over 1417235.11 frames.], batch size: 18, lr: 3.26e-04
2022-04-29 22:29:23,307 INFO [train.py:763] (1/8) Epoch 23, batch 1250, loss[loss=0.1614, simple_loss=0.2562, pruned_loss=0.03336, over 7331.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2656, pruned_loss=0.03483, over 1418288.90 frames.], batch size: 20, lr: 3.26e-04
2022-04-29 22:30:28,892 INFO [train.py:763] (1/8) Epoch 23, batch 1300, loss[loss=0.1591, simple_loss=0.2658, pruned_loss=0.02616, over 6601.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2653, pruned_loss=0.03474, over 1419577.05 frames.], batch size: 31, lr: 3.26e-04
2022-04-29 22:31:51,694 INFO [train.py:763] (1/8) Epoch 23, batch 1350, loss[loss=0.1703, simple_loss=0.2489, pruned_loss=0.04587, over 7405.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2661, pruned_loss=0.0348, over 1425078.35 frames.], batch size: 18, lr: 3.26e-04
2022-04-29 22:32:57,243 INFO [train.py:763] (1/8) Epoch 23, batch 1400, loss[loss=0.1871, simple_loss=0.2874, pruned_loss=0.04339, over 7174.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2661, pruned_loss=0.03475, over 1423503.11 frames.], batch size: 26, lr: 3.26e-04
2022-04-29 22:34:20,486 INFO [train.py:763] (1/8) Epoch 23, batch 1450, loss[loss=0.1562, simple_loss=0.2478, pruned_loss=0.03228, over 7147.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2668, pruned_loss=0.03472, over 1421986.57 frames.], batch size: 20, lr: 3.26e-04
2022-04-29 22:35:53,262 INFO [train.py:763] (1/8) Epoch 23, batch 1500, loss[loss=0.1987, simple_loss=0.2998, pruned_loss=0.04882, over 7150.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2664, pruned_loss=0.03451, over 1420026.47 frames.], batch size: 20, lr: 3.26e-04
2022-04-29 22:36:59,419 INFO [train.py:763] (1/8) Epoch 23, batch 1550, loss[loss=0.1718, simple_loss=0.2795, pruned_loss=0.0321, over 6739.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2653, pruned_loss=0.03403, over 1419877.30 frames.], batch size: 31, lr: 3.26e-04
2022-04-29 22:38:04,559 INFO [train.py:763] (1/8) Epoch 23, batch 1600, loss[loss=0.1535, simple_loss=0.2534, pruned_loss=0.02678, over 7324.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2657, pruned_loss=0.0342, over 1421709.19 frames.], batch size: 20, lr: 3.25e-04
2022-04-29 22:39:10,552 INFO [train.py:763] (1/8) Epoch 23, batch 1650, loss[loss=0.1494, simple_loss=0.2364, pruned_loss=0.03114, over 6790.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2668, pruned_loss=0.03471, over 1414670.70 frames.], batch size: 15, lr: 3.25e-04
2022-04-29 22:40:17,830 INFO [train.py:763] (1/8) Epoch 23, batch 1700, loss[loss=0.1668, simple_loss=0.2798, pruned_loss=0.02686, over 7325.00 frames.], tot_loss[loss=0.168, simple_loss=0.267, pruned_loss=0.03449, over 1418751.55 frames.], batch size: 21, lr: 3.25e-04
2022-04-29 22:41:24,853 INFO [train.py:763] (1/8) Epoch 23, batch 1750, loss[loss=0.1447, simple_loss=0.2441, pruned_loss=0.02266, over 7071.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2672, pruned_loss=0.03479, over 1420227.01 frames.], batch size: 18, lr: 3.25e-04
2022-04-29 22:42:30,370 INFO [train.py:763] (1/8) Epoch 23, batch 1800, loss[loss=0.1742, simple_loss=0.2687, pruned_loss=0.03979, over 7327.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2671, pruned_loss=0.03468, over 1420674.34 frames.], batch size: 22, lr: 3.25e-04
2022-04-29 22:43:35,679 INFO [train.py:763] (1/8) Epoch 23, batch 1850, loss[loss=0.1753, simple_loss=0.2869, pruned_loss=0.03186, over 7330.00 frames.], tot_loss[loss=0.1681, simple_loss=0.267, pruned_loss=0.03458, over 1424673.26 frames.], batch size: 24, lr: 3.25e-04
2022-04-29 22:44:41,102 INFO [train.py:763] (1/8) Epoch 23, batch 1900, loss[loss=0.1816, simple_loss=0.2905, pruned_loss=0.03638, over 7039.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2672, pruned_loss=0.03485, over 1423119.77 frames.], batch size: 28, lr: 3.25e-04
2022-04-29 22:45:46,546 INFO [train.py:763] (1/8) Epoch 23, batch 1950, loss[loss=0.1711, simple_loss=0.2816, pruned_loss=0.03035, over 7110.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2673, pruned_loss=0.03454, over 1423743.73 frames.], batch size: 21, lr: 3.25e-04
2022-04-29 22:46:52,057 INFO [train.py:763] (1/8) Epoch 23, batch 2000, loss[loss=0.1921, simple_loss=0.2853, pruned_loss=0.04944, over 5226.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2682, pruned_loss=0.03498, over 1422880.27 frames.], batch size: 52, lr: 3.25e-04
2022-04-29 22:47:58,953 INFO [train.py:763] (1/8) Epoch 23, batch 2050, loss[loss=0.1577, simple_loss=0.2693, pruned_loss=0.02306, over 7424.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2684, pruned_loss=0.03507, over 1422719.64 frames.], batch size: 20, lr: 3.25e-04
2022-04-29 22:49:05,148 INFO [train.py:763] (1/8) Epoch 23, batch 2100, loss[loss=0.1459, simple_loss=0.2386, pruned_loss=0.02658, over 7009.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2682, pruned_loss=0.03526, over 1424266.11 frames.], batch size: 16, lr: 3.25e-04
2022-04-29 22:50:10,654 INFO [train.py:763] (1/8) Epoch 23, batch 2150, loss[loss=0.1771, simple_loss=0.2828, pruned_loss=0.03566, over 5341.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2673, pruned_loss=0.03478, over 1421495.91 frames.], batch size: 52, lr: 3.25e-04
2022-04-29 22:51:16,164 INFO [train.py:763] (1/8) Epoch 23, batch 2200, loss[loss=0.1358, simple_loss=0.2306, pruned_loss=0.02053, over 7149.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2672, pruned_loss=0.03477, over 1420178.33 frames.], batch size: 17, lr: 3.25e-04
2022-04-29 22:52:21,331 INFO [train.py:763] (1/8) Epoch 23, batch 2250, loss[loss=0.142, simple_loss=0.2434, pruned_loss=0.02034, over 7319.00 frames.], tot_loss[loss=0.1687, simple_loss=0.2677, pruned_loss=0.03479, over 1409760.91 frames.], batch size: 25, lr: 3.24e-04
2022-04-29 22:53:28,271 INFO [train.py:763] (1/8) Epoch 23, batch 2300, loss[loss=0.1339, simple_loss=0.2215, pruned_loss=0.02315, over 7282.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2667, pruned_loss=0.03435, over 1416859.38 frames.], batch size: 17, lr: 3.24e-04
2022-04-29 22:54:34,464 INFO [train.py:763] (1/8) Epoch 23, batch 2350, loss[loss=0.1624, simple_loss=0.2643, pruned_loss=0.03026, over 7329.00 frames.], tot_loss[loss=0.168, simple_loss=0.2671, pruned_loss=0.03444, over 1418955.54 frames.], batch size: 22, lr: 3.24e-04
2022-04-29 22:55:39,710 INFO [train.py:763] (1/8) Epoch 23, batch 2400, loss[loss=0.1484, simple_loss=0.2331, pruned_loss=0.03191, over 6801.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2676, pruned_loss=0.03435, over 1421644.99 frames.], batch size: 15, lr: 3.24e-04
2022-04-29 22:56:45,972 INFO [train.py:763] (1/8) Epoch 23, batch 2450, loss[loss=0.1896, simple_loss=0.2978, pruned_loss=0.04071, over 7227.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2677, pruned_loss=0.03434, over 1419011.29 frames.], batch size: 20, lr: 3.24e-04
2022-04-29 22:57:51,398 INFO [train.py:763] (1/8) Epoch 23, batch 2500, loss[loss=0.1729, simple_loss=0.2751, pruned_loss=0.03534, over 7320.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2669, pruned_loss=0.03418, over 1418659.82 frames.], batch size: 21, lr: 3.24e-04
2022-04-29 22:58:56,885 INFO [train.py:763] (1/8) Epoch 23, batch 2550, loss[loss=0.2045, simple_loss=0.2988, pruned_loss=0.05512, over 4930.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2659, pruned_loss=0.03396, over 1413636.17 frames.], batch size: 52, lr: 3.24e-04
2022-04-29 23:00:02,920 INFO [train.py:763] (1/8) Epoch 23, batch 2600, loss[loss=0.1673, simple_loss=0.2662, pruned_loss=0.03423, over 7288.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2675, pruned_loss=0.03444, over 1417577.01 frames.], batch size: 18, lr: 3.24e-04
2022-04-29 23:01:08,567 INFO [train.py:763] (1/8) Epoch 23, batch 2650, loss[loss=0.1557, simple_loss=0.2628, pruned_loss=0.02428, over 7321.00 frames.], tot_loss[loss=0.168, simple_loss=0.2673, pruned_loss=0.03436, over 1417003.31 frames.], batch size: 21, lr: 3.24e-04
2022-04-29 23:02:14,022 INFO [train.py:763] (1/8) Epoch 23, batch 2700, loss[loss=0.1669, simple_loss=0.2845, pruned_loss=0.02464, over 7342.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2671, pruned_loss=0.03436, over 1421584.44 frames.], batch size: 22, lr: 3.24e-04
2022-04-29 23:03:19,897 INFO [train.py:763] (1/8) Epoch 23, batch 2750, loss[loss=0.1692, simple_loss=0.2673, pruned_loss=0.03557, over 7415.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2662, pruned_loss=0.03369, over 1425141.05 frames.], batch size: 21, lr: 3.24e-04
2022-04-29 23:04:25,095 INFO [train.py:763] (1/8) Epoch 23, batch 2800, loss[loss=0.1593, simple_loss=0.2609, pruned_loss=0.02885, over 7245.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2674, pruned_loss=0.03452, over 1421109.71 frames.], batch size: 20, lr: 3.24e-04
2022-04-29 23:05:30,273 INFO [train.py:763] (1/8) Epoch 23, batch 2850, loss[loss=0.1575, simple_loss=0.2581, pruned_loss=0.02843, over 7357.00 frames.], tot_loss[loss=0.1695, simple_loss=0.2688, pruned_loss=0.03505, over 1422238.48 frames.], batch size: 19, lr: 3.24e-04
2022-04-29 23:06:35,472 INFO [train.py:763] (1/8) Epoch 23, batch 2900, loss[loss=0.19, simple_loss=0.2968, pruned_loss=0.04162, over 7304.00 frames.], tot_loss[loss=0.1699, simple_loss=0.2693, pruned_loss=0.03524, over 1421621.14 frames.], batch size: 25, lr: 3.24e-04
2022-04-29 23:07:40,685 INFO [train.py:763] (1/8) Epoch 23, batch 2950, loss[loss=0.1842, simple_loss=0.2786, pruned_loss=0.04494, over 7284.00 frames.], tot_loss[loss=0.1698, simple_loss=0.2691, pruned_loss=0.03527, over 1425667.30 frames.], batch size: 17, lr: 3.23e-04
2022-04-29 23:08:45,890 INFO [train.py:763] (1/8) Epoch 23, batch 3000, loss[loss=0.1901, simple_loss=0.287, pruned_loss=0.04657, over 7120.00 frames.], tot_loss[loss=0.1694, simple_loss=0.2684, pruned_loss=0.03519, over 1421313.76 frames.], batch size: 21, lr: 3.23e-04
2022-04-29 23:08:45,891 INFO [train.py:783] (1/8) Computing validation loss
2022-04-29 23:09:01,228 INFO [train.py:792] (1/8) Epoch 23, validation: loss=0.1683, simple_loss=0.2665, pruned_loss=0.03509, over 698248.00 frames.
2022-04-29 23:10:07,037 INFO [train.py:763] (1/8) Epoch 23, batch 3050, loss[loss=0.1636, simple_loss=0.2595, pruned_loss=0.03384, over 7271.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2677, pruned_loss=0.03493, over 1416923.58 frames.], batch size: 18, lr: 3.23e-04
2022-04-29 23:11:12,527 INFO [train.py:763] (1/8) Epoch 23, batch 3100, loss[loss=0.1383, simple_loss=0.2379, pruned_loss=0.01939, over 6766.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2669, pruned_loss=0.0345, over 1420336.71 frames.], batch size: 31, lr: 3.23e-04
2022-04-29 23:12:19,058 INFO [train.py:763] (1/8) Epoch 23, batch 3150, loss[loss=0.1254, simple_loss=0.2186, pruned_loss=0.01613, over 7418.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2665, pruned_loss=0.03446, over 1422083.99 frames.], batch size: 17, lr: 3.23e-04
2022-04-29 23:13:26,791 INFO [train.py:763] (1/8) Epoch 23, batch 3200, loss[loss=0.1667, simple_loss=0.275, pruned_loss=0.02922, over 7311.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2665, pruned_loss=0.03403, over 1426286.43 frames.], batch size: 21, lr: 3.23e-04
2022-04-29 23:14:33,554 INFO [train.py:763] (1/8) Epoch 23, batch 3250, loss[loss=0.1553, simple_loss=0.2464, pruned_loss=0.03211, over 7172.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2659, pruned_loss=0.03436, over 1428584.72 frames.], batch size: 18, lr: 3.23e-04
2022-04-29 23:15:38,818 INFO [train.py:763] (1/8) Epoch 23, batch 3300, loss[loss=0.1908, simple_loss=0.2914, pruned_loss=0.04509, over 7298.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2666, pruned_loss=0.0346, over 1428308.20 frames.], batch size: 24, lr: 3.23e-04
2022-04-29 23:16:45,587 INFO [train.py:763] (1/8) Epoch 23, batch 3350, loss[loss=0.1734, simple_loss=0.2763, pruned_loss=0.03521, over 7291.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2672, pruned_loss=0.03484, over 1423798.02 frames.], batch size: 24, lr: 3.23e-04
2022-04-29 23:17:51,524 INFO [train.py:763] (1/8) Epoch 23, batch 3400, loss[loss=0.1642, simple_loss=0.2634, pruned_loss=0.03252, over 7372.00 frames.], tot_loss[loss=0.1679, simple_loss=0.267, pruned_loss=0.03443, over 1427994.14 frames.], batch size: 19, lr: 3.23e-04
2022-04-29 23:18:56,726 INFO [train.py:763] (1/8) Epoch 23, batch 3450, loss[loss=0.1691, simple_loss=0.2671, pruned_loss=0.03551, over 7345.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2675, pruned_loss=0.03453, over 1423000.57 frames.], batch size: 22, lr: 3.23e-04
2022-04-29 23:20:02,251 INFO [train.py:763] (1/8) Epoch 23, batch 3500, loss[loss=0.1329, simple_loss=0.2227, pruned_loss=0.02152, over 6795.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2662, pruned_loss=0.03431, over 1422245.36 frames.], batch size: 15, lr: 3.23e-04
2022-04-29 23:21:08,254 INFO [train.py:763] (1/8) Epoch 23, batch 3550, loss[loss=0.1976, simple_loss=0.2928, pruned_loss=0.05122, over 7119.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2662, pruned_loss=0.03454, over 1423312.91 frames.], batch size: 21, lr: 3.23e-04
2022-04-29 23:22:13,612 INFO [train.py:763] (1/8) Epoch 23, batch 3600, loss[loss=0.1604, simple_loss=0.2655, pruned_loss=0.02763, over 7068.00 frames.], tot_loss[loss=0.1683, simple_loss=0.267, pruned_loss=0.03476, over 1423481.50 frames.], batch size: 18, lr: 3.22e-04
2022-04-29 23:23:18,840 INFO [train.py:763] (1/8) Epoch 23, batch 3650, loss[loss=0.1414, simple_loss=0.2428, pruned_loss=0.02005, over 7350.00 frames.], tot_loss[loss=0.1693, simple_loss=0.2685, pruned_loss=0.035, over 1424045.08 frames.], batch size: 19, lr: 3.22e-04
2022-04-29 23:24:24,041 INFO [train.py:763] (1/8) Epoch 23, batch 3700, loss[loss=0.1747, simple_loss=0.2831, pruned_loss=0.03311, over 6416.00 frames.], tot_loss[loss=0.1691, simple_loss=0.2686, pruned_loss=0.03485, over 1420755.93 frames.], batch size: 37, lr: 3.22e-04
2022-04-29 23:25:30,843 INFO [train.py:763] (1/8) Epoch 23, batch 3750, loss[loss=0.157, simple_loss=0.2559, pruned_loss=0.02902, over 7278.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2679, pruned_loss=0.03463, over 1422117.36 frames.], batch size: 18, lr: 3.22e-04
2022-04-29 23:26:37,726 INFO [train.py:763] (1/8) Epoch 23, batch 3800, loss[loss=0.1561, simple_loss=0.2524, pruned_loss=0.02987, over 7425.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2668, pruned_loss=0.03439, over 1424296.63 frames.], batch size: 20, lr: 3.22e-04
2022-04-29 23:27:43,263 INFO [train.py:763] (1/8) Epoch 23, batch 3850, loss[loss=0.2254, simple_loss=0.3109, pruned_loss=0.06997, over 5110.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2665, pruned_loss=0.03435, over 1420945.89 frames.], batch size: 53, lr: 3.22e-04
2022-04-29 23:28:48,634 INFO [train.py:763] (1/8) Epoch 23, batch 3900, loss[loss=0.1627, simple_loss=0.2698, pruned_loss=0.02785, over 6755.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2668, pruned_loss=0.03435, over 1417304.28 frames.], batch size: 31, lr: 3.22e-04
2022-04-29 23:29:53,689 INFO [train.py:763] (1/8) Epoch 23, batch 3950, loss[loss=0.1389, simple_loss=0.2287, pruned_loss=0.02461, over 7148.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2665, pruned_loss=0.03433, over 1417407.64 frames.], batch size: 17, lr: 3.22e-04
2022-04-29 23:30:59,586 INFO [train.py:763] (1/8) Epoch 23, batch 4000, loss[loss=0.1786, simple_loss=0.2845, pruned_loss=0.03633, over 7213.00 frames.], tot_loss[loss=0.1677, simple_loss=0.267, pruned_loss=0.03426, over 1415475.13 frames.], batch size: 22, lr: 3.22e-04
2022-04-29 23:32:05,446 INFO [train.py:763] (1/8) Epoch 23, batch 4050, loss[loss=0.2176, simple_loss=0.2945, pruned_loss=0.0704, over 4831.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2667, pruned_loss=0.03428, over 1416833.01 frames.], batch size: 52, lr: 3.22e-04
2022-04-29 23:33:10,717 INFO [train.py:763] (1/8) Epoch 23, batch 4100, loss[loss=0.1518, simple_loss=0.2426, pruned_loss=0.03054, over 7284.00 frames.], tot_loss[loss=0.1676, simple_loss=0.2668, pruned_loss=0.03421, over 1416929.37 frames.], batch size: 18, lr: 3.22e-04
2022-04-29 23:34:16,153 INFO [train.py:763] (1/8) Epoch 23, batch 4150, loss[loss=0.1491, simple_loss=0.2399, pruned_loss=0.0292, over 6999.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2667, pruned_loss=0.03414, over 1418375.65 frames.], batch size: 16, lr: 3.22e-04
2022-04-29 23:35:21,251 INFO [train.py:763] (1/8) Epoch 23, batch 4200, loss[loss=0.1412, simple_loss=0.2284, pruned_loss=0.02702, over 7272.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2673, pruned_loss=0.03471, over 1418943.17 frames.], batch size: 18, lr: 3.22e-04
2022-04-29 23:36:26,915 INFO [train.py:763] (1/8) Epoch 23, batch 4250, loss[loss=0.1775, simple_loss=0.2813, pruned_loss=0.03686, over 7373.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2665, pruned_loss=0.03443, over 1417064.61 frames.], batch size: 23, lr: 3.22e-04
2022-04-29 23:37:32,237 INFO [train.py:763] (1/8) Epoch 23, batch 4300, loss[loss=0.1795, simple_loss=0.2622, pruned_loss=0.04843, over 6809.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2662, pruned_loss=0.03482, over 1415922.72 frames.], batch size: 15, lr: 3.21e-04
2022-04-29 23:38:37,630 INFO [train.py:763] (1/8) Epoch 23, batch 4350, loss[loss=0.1902, simple_loss=0.2914, pruned_loss=0.04446, over 6697.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2667, pruned_loss=0.03484, over 1413166.01 frames.], batch size: 31, lr: 3.21e-04
2022-04-29 23:39:43,230 INFO [train.py:763] (1/8) Epoch 23, batch 4400, loss[loss=0.1671, simple_loss=0.2707, pruned_loss=0.03181, over 6388.00 frames.], tot_loss[loss=0.1684, simple_loss=0.2669, pruned_loss=0.03495, over 1406371.52 frames.], batch size: 37, lr: 3.21e-04
2022-04-29 23:40:48,374 INFO [train.py:763] (1/8) Epoch 23, batch 4450, loss[loss=0.1908, simple_loss=0.294, pruned_loss=0.04376, over 6354.00 frames.], tot_loss[loss=0.1677, simple_loss=0.266, pruned_loss=0.03472, over 1408754.30 frames.], batch size: 37, lr: 3.21e-04
2022-04-29 23:41:53,042 INFO [train.py:763] (1/8) Epoch 23, batch 4500, loss[loss=0.1616, simple_loss=0.2643, pruned_loss=0.02941, over 6346.00 frames.], tot_loss[loss=0.1681, simple_loss=0.2665, pruned_loss=0.03481, over 1396168.12 frames.], batch size: 37, lr: 3.21e-04
2022-04-29 23:42:58,310 INFO [train.py:763] (1/8) Epoch 23, batch 4550, loss[loss=0.1812, simple_loss=0.2852, pruned_loss=0.03858, over 7297.00 frames.], tot_loss[loss=0.1689, simple_loss=0.2672, pruned_loss=0.03529, over 1386042.80 frames.], batch size: 24, lr: 3.21e-04
2022-04-29 23:44:17,934 INFO [train.py:763] (1/8) Epoch 24, batch 0, loss[loss=0.1959, simple_loss=0.2919, pruned_loss=0.04997, over 7069.00 frames.], tot_loss[loss=0.1959, simple_loss=0.2919, pruned_loss=0.04997, over 7069.00 frames.], batch size: 18, lr: 3.15e-04
2022-04-29 23:45:23,858 INFO [train.py:763] (1/8) Epoch 24, batch 50, loss[loss=0.1492, simple_loss=0.2475, pruned_loss=0.02546, over 7256.00 frames.], tot_loss[loss=0.169, simple_loss=0.2673, pruned_loss=0.03532, over 322134.90 frames.], batch size: 19, lr: 3.15e-04
2022-04-29 23:46:30,362 INFO [train.py:763] (1/8) Epoch 24, batch 100, loss[loss=0.1506, simple_loss=0.2515, pruned_loss=0.02489, over 7329.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2656, pruned_loss=0.03442, over 570468.22 frames.], batch size: 20, lr: 3.15e-04
2022-04-29 23:47:35,974 INFO [train.py:763] (1/8) Epoch 24, batch 150, loss[loss=0.1641, simple_loss=0.2628, pruned_loss=0.03271, over 7308.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2659, pruned_loss=0.03377, over 761445.38 frames.], batch size: 21, lr: 3.14e-04
2022-04-29 23:48:41,593 INFO [train.py:763] (1/8) Epoch 24, batch 200, loss[loss=0.1521, simple_loss=0.2442, pruned_loss=0.02996, over 6799.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2655, pruned_loss=0.03394, over 906360.31 frames.], batch size: 15, lr: 3.14e-04
2022-04-29 23:49:46,882 INFO [train.py:763] (1/8) Epoch 24, batch 250, loss[loss=0.1865, simple_loss=0.2886, pruned_loss=0.04222, over 7232.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2662, pruned_loss=0.03427, over 1018404.30 frames.], batch size: 20, lr: 3.14e-04
2022-04-29 23:50:52,234 INFO [train.py:763] (1/8) Epoch 24, batch 300, loss[loss=0.1639, simple_loss=0.254, pruned_loss=0.03689, over 7158.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2665, pruned_loss=0.03416, over 1112360.20 frames.], batch size: 19, lr: 3.14e-04
2022-04-29 23:51:57,520 INFO [train.py:763] (1/8) Epoch 24, batch 350, loss[loss=0.1791, simple_loss=0.2884, pruned_loss=0.03495, over 7197.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2659, pruned_loss=0.03428, over 1181335.92 frames.], batch size: 23, lr: 3.14e-04
2022-04-29 23:53:03,341 INFO [train.py:763] (1/8) Epoch 24, batch 400, loss[loss=0.1651, simple_loss=0.2731, pruned_loss=0.02853, over 7231.00 frames.], tot_loss[loss=0.166, simple_loss=0.2647, pruned_loss=0.0337, over 1236505.60 frames.], batch size: 20, lr: 3.14e-04
2022-04-29 23:54:08,671 INFO [train.py:763] (1/8) Epoch 24, batch 450, loss[loss=0.1764, simple_loss=0.2835, pruned_loss=0.03465, over 7027.00 frames.], tot_loss[loss=0.166, simple_loss=0.2648, pruned_loss=0.03365, over 1277141.88 frames.], batch size: 28, lr: 3.14e-04
2022-04-29 23:55:14,211 INFO [train.py:763] (1/8) Epoch 24, batch 500, loss[loss=0.1626, simple_loss=0.2591, pruned_loss=0.03305, over 7168.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2652, pruned_loss=0.03349, over 1312730.95 frames.], batch size: 18, lr: 3.14e-04
2022-04-29 23:56:20,427 INFO [train.py:763] (1/8) Epoch 24, batch 550, loss[loss=0.181, simple_loss=0.2723, pruned_loss=0.04488, over 7166.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2654, pruned_loss=0.03311, over 1340185.60 frames.], batch size: 18, lr: 3.14e-04
2022-04-29 23:57:26,719 INFO [train.py:763] (1/8) Epoch 24, batch 600, loss[loss=0.1897, simple_loss=0.2925, pruned_loss=0.04349, over 7203.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2662, pruned_loss=0.03365, over 1358904.13 frames.], batch size: 23, lr: 3.14e-04
2022-04-29 23:58:32,095 INFO [train.py:763] (1/8) Epoch 24, batch 650, loss[loss=0.1408, simple_loss=0.236, pruned_loss=0.0228, over 7285.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2649, pruned_loss=0.03371, over 1370556.06 frames.], batch size: 17, lr: 3.14e-04
2022-04-29 23:59:38,741 INFO [train.py:763] (1/8) Epoch 24, batch 700, loss[loss=0.1369, simple_loss=0.2287, pruned_loss=0.02257, over 6815.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2657, pruned_loss=0.03388, over 1386665.10 frames.], batch size: 15, lr: 3.14e-04
2022-04-30 00:00:44,926 INFO [train.py:763] (1/8) Epoch 24, batch 750, loss[loss=0.1683, simple_loss=0.2766, pruned_loss=0.02996, over 7228.00 frames.], tot_loss[loss=0.167, simple_loss=0.2658, pruned_loss=0.03413, over 1397603.69 frames.], batch size: 20, lr: 3.14e-04
2022-04-30 00:01:50,608 INFO [train.py:763] (1/8) Epoch 24, batch 800, loss[loss=0.174, simple_loss=0.2755, pruned_loss=0.03626, over 7411.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2661, pruned_loss=0.03435, over 1405396.14 frames.], batch size: 21, lr: 3.14e-04
2022-04-30 00:02:56,129 INFO [train.py:763] (1/8) Epoch 24, batch 850, loss[loss=0.1626, simple_loss=0.2759, pruned_loss=0.02462, over 7317.00 frames.], tot_loss[loss=0.1677, simple_loss=0.2665, pruned_loss=0.03442, over 1407527.92 frames.], batch size: 21, lr: 3.13e-04
2022-04-30 00:04:01,369 INFO [train.py:763] (1/8) Epoch 24, batch 900, loss[loss=0.1949, simple_loss=0.3028, pruned_loss=0.04352, over 7289.00 frames.], tot_loss[loss=0.1688, simple_loss=0.2678, pruned_loss=0.03492, over 1410450.46 frames.], batch size: 25, lr: 3.13e-04
2022-04-30 00:05:07,032 INFO [train.py:763] (1/8) Epoch 24, batch 950, loss[loss=0.1972, simple_loss=0.2835, pruned_loss=0.05542, over 4790.00 frames.], tot_loss[loss=0.1686, simple_loss=0.2674, pruned_loss=0.03486, over 1405597.29 frames.], batch size: 52, lr: 3.13e-04
2022-04-30 00:06:12,844 INFO [train.py:763] (1/8) Epoch 24, batch 1000, loss[loss=0.181, simple_loss=0.28, pruned_loss=0.04095, over 7417.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2677, pruned_loss=0.03466, over 1412661.11 frames.], batch size: 21, lr: 3.13e-04
2022-04-30 00:07:18,485 INFO [train.py:763] (1/8) Epoch 24, batch 1050, loss[loss=0.1729, simple_loss=0.2667, pruned_loss=0.03952, over 7332.00 frames.], tot_loss[loss=0.1679, simple_loss=0.2674, pruned_loss=0.03416, over 1419564.00 frames.], batch size: 20, lr: 3.13e-04
2022-04-30 00:08:23,984 INFO [train.py:763] (1/8) Epoch 24, batch 1100, loss[loss=0.1798, simple_loss=0.2863, pruned_loss=0.03665, over 7339.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2667, pruned_loss=0.0339, over 1421781.91 frames.], batch size: 22, lr: 3.13e-04
2022-04-30 00:09:29,776 INFO [train.py:763] (1/8) Epoch 24, batch 1150, loss[loss=0.1977, simple_loss=0.3003, pruned_loss=0.04759, over 7209.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2667, pruned_loss=0.03417, over 1424668.95 frames.], batch size: 23, lr: 3.13e-04
2022-04-30 00:10:35,406 INFO [train.py:763] (1/8) Epoch 24, batch 1200, loss[loss=0.1806, simple_loss=0.275, pruned_loss=0.04308, over 7380.00 frames.], tot_loss[loss=0.1668, simple_loss=0.266, pruned_loss=0.03377, over 1424654.20 frames.], batch size: 23, lr: 3.13e-04
2022-04-30 00:11:41,672 INFO [train.py:763] (1/8) Epoch 24, batch 1250, loss[loss=0.153, simple_loss=0.2573, pruned_loss=0.02433, over 7141.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2663, pruned_loss=0.03434, over 1423122.34 frames.], batch size: 20, lr: 3.13e-04
2022-04-30 00:12:47,620 INFO [train.py:763] (1/8) Epoch 24, batch 1300, loss[loss=0.15, simple_loss=0.2419, pruned_loss=0.02903, over 6821.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2658, pruned_loss=0.03422, over 1422320.04 frames.], batch size: 15, lr: 3.13e-04
2022-04-30 00:13:53,404 INFO [train.py:763] (1/8) Epoch 24, batch 1350, loss[loss=0.1511, simple_loss=0.2587, pruned_loss=0.02172, over 6567.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2651, pruned_loss=0.03403, over 1421658.61 frames.], batch size: 38, lr: 3.13e-04
2022-04-30 00:14:58,833 INFO [train.py:763] (1/8) Epoch 24, batch 1400, loss[loss=0.1298, simple_loss=0.2187, pruned_loss=0.02038, over 7278.00 frames.], tot_loss[loss=0.167, simple_loss=0.2657, pruned_loss=0.03418, over 1426431.44 frames.], batch size: 17, lr: 3.13e-04
2022-04-30 00:16:04,288 INFO [train.py:763] (1/8) Epoch 24, batch 1450, loss[loss=0.1675, simple_loss=0.2761, pruned_loss=0.02943, over 7148.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2659, pruned_loss=0.03418, over 1422726.94 frames.], batch size: 20, lr: 3.13e-04
2022-04-30 00:17:11,224 INFO [train.py:763] (1/8) Epoch 24, batch 1500, loss[loss=0.1788, simple_loss=0.2803, pruned_loss=0.03862, over 6965.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2653, pruned_loss=0.03384, over 1422111.16 frames.], batch size: 32, lr: 3.13e-04
2022-04-30 00:18:17,537 INFO [train.py:763] (1/8) Epoch 24, batch 1550, loss[loss=0.1525, simple_loss=0.2475, pruned_loss=0.02874, over 7268.00 frames.], tot_loss[loss=0.1675, simple_loss=0.2668, pruned_loss=0.0341, over 1423191.26 frames.], batch size: 18, lr: 3.12e-04
2022-04-30 00:19:23,705 INFO [train.py:763] (1/8) Epoch 24, batch 1600, loss[loss=0.1435, simple_loss=0.2403, pruned_loss=0.02336, over 7257.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2664, pruned_loss=0.034, over 1421232.25 frames.], batch size: 16, lr: 3.12e-04
2022-04-30 00:20:29,913 INFO [train.py:763] (1/8) Epoch 24, batch 1650, loss[loss=0.1607, simple_loss=0.2629, pruned_loss=0.02926, over 7215.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2656, pruned_loss=0.03364, over 1422286.17 frames.], batch size: 21, lr: 3.12e-04
2022-04-30 00:21:35,719 INFO [train.py:763] (1/8) Epoch 24, batch 1700, loss[loss=0.172, simple_loss=0.2727, pruned_loss=0.0356, over 7388.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2657, pruned_loss=0.03385, over 1419933.11 frames.], batch size: 23, lr: 3.12e-04
2022-04-30 00:22:40,920 INFO [train.py:763] (1/8) Epoch 24, batch 1750, loss[loss=0.1632, simple_loss=0.2486, pruned_loss=0.03893, over 7146.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2664, pruned_loss=0.03424, over 1422461.23 frames.], batch size: 17, lr: 3.12e-04
2022-04-30 00:23:47,085 INFO [train.py:763] (1/8) Epoch 24, batch 1800, loss[loss=0.145, simple_loss=0.2419, pruned_loss=0.02408, over 7009.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2666, pruned_loss=0.03382, over 1422827.25 frames.], batch size: 16, lr: 3.12e-04
2022-04-30 00:24:52,828 INFO [train.py:763] (1/8) Epoch 24, batch 1850, loss[loss=0.1551, simple_loss=0.236, pruned_loss=0.03717, over 7207.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2662, pruned_loss=0.03374, over 1419920.59 frames.], batch size: 16, lr: 3.12e-04
2022-04-30 00:26:09,449 INFO [train.py:763] (1/8) Epoch 24, batch 1900, loss[loss=0.1637, simple_loss=0.2656, pruned_loss=0.03093, over 7329.00 frames.], tot_loss[loss=0.1666, simple_loss=0.266, pruned_loss=0.03358, over 1421718.00 frames.], batch size: 25, lr: 3.12e-04
2022-04-30 00:27:15,223 INFO [train.py:763] (1/8) Epoch 24, batch 1950, loss[loss=0.1441, simple_loss=0.2432, pruned_loss=0.02251, over 7255.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2654, pruned_loss=0.03407, over 1423424.41 frames.], batch size: 19, lr: 3.12e-04
2022-04-30 00:28:21,027 INFO [train.py:763] (1/8) Epoch 24, batch 2000, loss[loss=0.1378, simple_loss=0.2367, pruned_loss=0.01946, over 7159.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2646, pruned_loss=0.03356, over 1423921.81 frames.], batch size: 18, lr: 3.12e-04
2022-04-30 00:29:27,104 INFO [train.py:763] (1/8) Epoch 24, batch 2050, loss[loss=0.174, simple_loss=0.2744, pruned_loss=0.03678, over 7312.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2636, pruned_loss=0.03337, over 1426947.32 frames.], batch size: 21, lr: 3.12e-04
2022-04-30 00:30:32,482 INFO [train.py:763] (1/8) Epoch 24, batch 2100, loss[loss=0.1496, simple_loss=0.2503, pruned_loss=0.0245, over 7258.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2635, pruned_loss=0.03311, over 1423149.89 frames.], batch size: 19, lr: 3.12e-04
2022-04-30 00:31:37,973 INFO [train.py:763] (1/8) Epoch 24, batch 2150, loss[loss=0.1767, simple_loss=0.2889, pruned_loss=0.03227, over 7438.00 frames.], tot_loss[loss=0.166, simple_loss=0.2651, pruned_loss=0.03347, over 1421595.82 frames.], batch size: 20, lr: 3.12e-04
2022-04-30 00:32:43,332 INFO [train.py:763] (1/8) Epoch 24, batch 2200, loss[loss=0.1425, simple_loss=0.2295, pruned_loss=0.02778, over 6779.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2644, pruned_loss=0.03316, over 1421117.75 frames.], batch size: 15, lr: 3.12e-04
2022-04-30 00:33:49,447 INFO [train.py:763] (1/8) Epoch 24, batch 2250, loss[loss=0.1585, simple_loss=0.2607, pruned_loss=0.02813, over 7073.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2643, pruned_loss=0.03354, over 1417533.80 frames.], batch size: 18, lr: 3.12e-04
2022-04-30 00:34:55,312 INFO [train.py:763] (1/8) Epoch 24, batch 2300, loss[loss=0.1566, simple_loss=0.2443, pruned_loss=0.03447, over 6805.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2645, pruned_loss=0.03361, over 1418151.57 frames.], batch size: 15, lr: 3.11e-04
2022-04-30 00:36:01,133 INFO [train.py:763] (1/8) Epoch 24, batch 2350, loss[loss=0.1748, simple_loss=0.2767, pruned_loss=0.03644, over 7314.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2641, pruned_loss=0.03353, over 1418738.74 frames.], batch size: 21, lr: 3.11e-04
2022-04-30 00:37:06,711 INFO [train.py:763] (1/8) Epoch 24, batch 2400, loss[loss=0.1873, simple_loss=0.2803, pruned_loss=0.04715, over 7351.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2656, pruned_loss=0.03386, over 1423873.47 frames.], batch size: 19, lr: 3.11e-04
2022-04-30 00:38:21,816 INFO [train.py:763] (1/8) Epoch 24, batch 2450, loss[loss=0.1596, simple_loss=0.2491, pruned_loss=0.03501, over 7139.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2656, pruned_loss=0.0337, over 1423126.53 frames.], batch size: 17, lr: 3.11e-04
2022-04-30 00:39:27,185 INFO [train.py:763] (1/8) Epoch 24, batch 2500, loss[loss=0.2049, simple_loss=0.2983, pruned_loss=0.05577, over 7410.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2659, pruned_loss=0.03427, over 1423436.68 frames.], batch size: 21, lr: 3.11e-04
2022-04-30 00:40:32,702 INFO [train.py:763] (1/8) Epoch 24, batch 2550, loss[loss=0.1661, simple_loss=0.2677, pruned_loss=0.03227, over 7427.00 frames.], tot_loss[loss=0.1672, simple_loss=0.266, pruned_loss=0.03416, over 1424635.05 frames.], batch size: 20, lr: 3.11e-04
2022-04-30 00:41:38,101 INFO [train.py:763] (1/8) Epoch 24, batch 2600, loss[loss=0.1449, simple_loss=0.2391, pruned_loss=0.02535, over 7146.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2657, pruned_loss=0.03421, over 1420550.24 frames.], batch size: 17, lr: 3.11e-04
2022-04-30 00:42:43,679 INFO [train.py:763] (1/8) Epoch 24, batch 2650, loss[loss=0.1766, simple_loss=0.2767, pruned_loss=0.03822, over 7202.00 frames.], tot_loss[loss=0.167, simple_loss=0.266, pruned_loss=0.03394, over 1423133.64 frames.], batch size: 22, lr: 3.11e-04
2022-04-30 00:43:49,271 INFO [train.py:763] (1/8) Epoch 24, batch 2700, loss[loss=0.1552, simple_loss=0.2525, pruned_loss=0.02891, over 7059.00 frames.], tot_loss[loss=0.166, simple_loss=0.2652, pruned_loss=0.0334, over 1425299.92 frames.], batch size: 18, lr: 3.11e-04
2022-04-30 00:44:54,688 INFO [train.py:763] (1/8) Epoch 24, batch 2750, loss[loss=0.1711, simple_loss=0.2734, pruned_loss=0.03441, over 7137.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2644, pruned_loss=0.0334, over 1420813.33 frames.], batch size: 20, lr: 3.11e-04
2022-04-30 00:46:00,208 INFO [train.py:763] (1/8) Epoch 24, batch 2800, loss[loss=0.1779, simple_loss=0.2752, pruned_loss=0.04033, over 7260.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2642, pruned_loss=0.03357, over 1420832.62 frames.], batch size: 19, lr: 3.11e-04
2022-04-30 00:47:22,975 INFO [train.py:763] (1/8) Epoch 24, batch 2850, loss[loss=0.137, simple_loss=0.2407, pruned_loss=0.01664, over 7431.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2646, pruned_loss=0.03338, over 1420219.59 frames.], batch size: 20, lr: 3.11e-04
2022-04-30 00:48:28,452 INFO [train.py:763] (1/8) Epoch 24, batch 2900, loss[loss=0.1633, simple_loss=0.2662, pruned_loss=0.03021, over 7204.00 frames.], tot_loss[loss=0.1664, simple_loss=0.266, pruned_loss=0.03345, over 1420482.07 frames.], batch size: 23, lr: 3.11e-04
2022-04-30 00:49:52,261 INFO [train.py:763] (1/8) Epoch 24, batch 2950, loss[loss=0.194, simple_loss=0.2926, pruned_loss=0.04768, over 7114.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2659, pruned_loss=0.03344, over 1425661.75 frames.], batch size: 21, lr: 3.11e-04
2022-04-30 00:51:06,863 INFO [train.py:763] (1/8) Epoch 24, batch 3000, loss[loss=0.1768, simple_loss=0.2854, pruned_loss=0.03407, over 6704.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2646, pruned_loss=0.03316, over 1428250.75 frames.], batch size: 31, lr: 3.10e-04
2022-04-30 00:51:06,864 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 00:51:22,143 INFO [train.py:792] (1/8) Epoch 24, validation: loss=0.1679, simple_loss=0.2653, pruned_loss=0.03523, over 698248.00 frames.
2022-04-30 00:52:37,061 INFO [train.py:763] (1/8) Epoch 24, batch 3050, loss[loss=0.179, simple_loss=0.2916, pruned_loss=0.0332, over 7115.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2649, pruned_loss=0.03336, over 1429129.39 frames.], batch size: 21, lr: 3.10e-04
2022-04-30 00:53:42,762 INFO [train.py:763] (1/8) Epoch 24, batch 3100, loss[loss=0.1413, simple_loss=0.2352, pruned_loss=0.02372, over 6834.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2637, pruned_loss=0.03322, over 1429142.37 frames.], batch size: 15, lr: 3.10e-04
2022-04-30 00:54:48,069 INFO [train.py:763] (1/8) Epoch 24, batch 3150, loss[loss=0.1597, simple_loss=0.2559, pruned_loss=0.03175, over 7252.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2637, pruned_loss=0.03303, over 1430292.60 frames.], batch size: 19, lr: 3.10e-04
2022-04-30 00:55:53,499 INFO [train.py:763] (1/8) Epoch 24, batch 3200, loss[loss=0.1897, simple_loss=0.2804, pruned_loss=0.04953, over 4839.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2631, pruned_loss=0.03306, over 1429282.00 frames.], batch size: 53, lr: 3.10e-04
2022-04-30 00:56:59,204 INFO [train.py:763] (1/8) Epoch 24, batch 3250, loss[loss=0.1636, simple_loss=0.2556, pruned_loss=0.03576, over 7226.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2641, pruned_loss=0.03348, over 1426564.32 frames.], batch size: 20, lr: 3.10e-04
2022-04-30 00:58:05,426 INFO [train.py:763] (1/8) Epoch 24, batch 3300, loss[loss=0.1543, simple_loss=0.2584, pruned_loss=0.02506, over 7162.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2645, pruned_loss=0.03343, over 1425691.09 frames.], batch size: 19, lr: 3.10e-04
2022-04-30 00:59:11,083 INFO [train.py:763] (1/8) Epoch 24, batch 3350, loss[loss=0.1622, simple_loss=0.2586, pruned_loss=0.03295, over 7258.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2653, pruned_loss=0.03371, over 1423272.52 frames.], batch size: 19, lr: 3.10e-04
2022-04-30 01:00:16,803 INFO [train.py:763] (1/8) Epoch 24, batch 3400, loss[loss=0.1596, simple_loss=0.2467, pruned_loss=0.03626, over 7268.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2644, pruned_loss=0.03344, over 1425167.64 frames.], batch size: 17, lr: 3.10e-04
2022-04-30 01:01:22,327 INFO [train.py:763] (1/8) Epoch 24, batch 3450, loss[loss=0.1785, simple_loss=0.2731, pruned_loss=0.04197, over 7218.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2649, pruned_loss=0.03386, over 1421859.87 frames.], batch size: 21, lr: 3.10e-04
2022-04-30 01:02:27,609 INFO [train.py:763] (1/8) Epoch 24, batch 3500, loss[loss=0.1436, simple_loss=0.2382, pruned_loss=0.02446, over 7139.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2651, pruned_loss=0.03419, over 1422797.62 frames.], batch size: 17, lr: 3.10e-04
2022-04-30 01:03:33,196 INFO [train.py:763] (1/8) Epoch 24, batch 3550, loss[loss=0.1766, simple_loss=0.2722, pruned_loss=0.04047, over 7325.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2654, pruned_loss=0.03395, over 1424917.49 frames.], batch size: 20, lr: 3.10e-04
2022-04-30 01:04:38,395 INFO [train.py:763] (1/8) Epoch 24, batch 3600, loss[loss=0.1657, simple_loss=0.263, pruned_loss=0.03421, over 7209.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2651, pruned_loss=0.03352, over 1423989.72 frames.], batch size: 23, lr: 3.10e-04
2022-04-30 01:05:45,315 INFO [train.py:763] (1/8) Epoch 24, batch 3650, loss[loss=0.1687, simple_loss=0.2673, pruned_loss=0.03503, over 6457.00 frames.], tot_loss[loss=0.165, simple_loss=0.2642, pruned_loss=0.03291, over 1419925.55 frames.], batch size: 38, lr: 3.10e-04
2022-04-30 01:06:51,860 INFO [train.py:763] (1/8) Epoch 24, batch 3700, loss[loss=0.1557, simple_loss=0.2629, pruned_loss=0.02422, over 7433.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2637, pruned_loss=0.03267, over 1422418.79 frames.], batch size: 20, lr: 3.10e-04
2022-04-30 01:07:57,541 INFO [train.py:763] (1/8) Epoch 24, batch 3750, loss[loss=0.1726, simple_loss=0.2736, pruned_loss=0.03583, over 7385.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2641, pruned_loss=0.03287, over 1424226.17 frames.], batch size: 23, lr: 3.09e-04
2022-04-30 01:09:02,950 INFO [train.py:763] (1/8) Epoch 24, batch 3800, loss[loss=0.2032, simple_loss=0.2988, pruned_loss=0.05382, over 4985.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2648, pruned_loss=0.03382, over 1421954.15 frames.], batch size: 52, lr: 3.09e-04
2022-04-30 01:10:08,027 INFO [train.py:763] (1/8) Epoch 24, batch 3850, loss[loss=0.153, simple_loss=0.2475, pruned_loss=0.02925, over 7278.00 frames.], tot_loss[loss=0.167, simple_loss=0.2657, pruned_loss=0.03411, over 1421390.56 frames.], batch size: 18, lr: 3.09e-04
2022-04-30 01:11:13,748 INFO [train.py:763] (1/8) Epoch 24, batch 3900, loss[loss=0.1626, simple_loss=0.2629, pruned_loss=0.03109, over 7257.00 frames.], tot_loss[loss=0.1672, simple_loss=0.2659, pruned_loss=0.03423, over 1420834.34 frames.], batch size: 19, lr: 3.09e-04
2022-04-30 01:12:19,227 INFO [train.py:763] (1/8) Epoch 24, batch 3950, loss[loss=0.1681, simple_loss=0.2643, pruned_loss=0.03596, over 7425.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2664, pruned_loss=0.03456, over 1422916.42 frames.], batch size: 18, lr: 3.09e-04
2022-04-30 01:13:24,349 INFO [train.py:763] (1/8) Epoch 24, batch 4000, loss[loss=0.1625, simple_loss=0.2703, pruned_loss=0.02734, over 7320.00 frames.], tot_loss[loss=0.1683, simple_loss=0.2666, pruned_loss=0.03495, over 1422352.43 frames.], batch size: 21, lr: 3.09e-04
2022-04-30 01:14:29,871 INFO [train.py:763] (1/8) Epoch 24, batch 4050, loss[loss=0.1483, simple_loss=0.2475, pruned_loss=0.0246, over 7447.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2655, pruned_loss=0.03431, over 1421378.10 frames.], batch size: 20, lr: 3.09e-04
2022-04-30 01:15:36,735 INFO [train.py:763] (1/8) Epoch 24, batch 4100, loss[loss=0.1705, simple_loss=0.2711, pruned_loss=0.03498, over 6442.00 frames.], tot_loss[loss=0.1674, simple_loss=0.2659, pruned_loss=0.03442, over 1421863.61 frames.], batch size: 38, lr: 3.09e-04
2022-04-30 01:16:43,482 INFO [train.py:763] (1/8) Epoch 24, batch 4150, loss[loss=0.1628, simple_loss=0.2684, pruned_loss=0.02861, over 7230.00 frames.], tot_loss[loss=0.1671, simple_loss=0.2655, pruned_loss=0.03431, over 1419006.27 frames.], batch size: 21, lr: 3.09e-04
2022-04-30 01:17:50,167 INFO [train.py:763] (1/8) Epoch 24, batch 4200, loss[loss=0.1717, simple_loss=0.2745, pruned_loss=0.03446, over 7224.00 frames.], tot_loss[loss=0.1678, simple_loss=0.2669, pruned_loss=0.03436, over 1420319.27 frames.], batch size: 23, lr: 3.09e-04
2022-04-30 01:18:56,565 INFO [train.py:763] (1/8) Epoch 24, batch 4250, loss[loss=0.1904, simple_loss=0.2942, pruned_loss=0.04332, over 6532.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2667, pruned_loss=0.03393, over 1414806.58 frames.], batch size: 38, lr: 3.09e-04
2022-04-30 01:20:02,370 INFO [train.py:763] (1/8) Epoch 24, batch 4300, loss[loss=0.1383, simple_loss=0.2385, pruned_loss=0.01905, over 7161.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2657, pruned_loss=0.03356, over 1414940.74 frames.], batch size: 19, lr: 3.09e-04
2022-04-30 01:21:09,410 INFO [train.py:763] (1/8) Epoch 24, batch 4350, loss[loss=0.1729, simple_loss=0.2839, pruned_loss=0.03093, over 7307.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2646, pruned_loss=0.03308, over 1415185.25 frames.], batch size: 25, lr: 3.09e-04
2022-04-30 01:22:16,102 INFO [train.py:763] (1/8) Epoch 24, batch 4400, loss[loss=0.1663, simple_loss=0.2727, pruned_loss=0.02998, over 7268.00 frames.], tot_loss[loss=0.166, simple_loss=0.2655, pruned_loss=0.03329, over 1414421.29 frames.], batch size: 24, lr: 3.09e-04
2022-04-30 01:23:21,721 INFO [train.py:763] (1/8) Epoch 24, batch 4450, loss[loss=0.1732, simple_loss=0.2745, pruned_loss=0.036, over 7296.00 frames.], tot_loss[loss=0.167, simple_loss=0.2666, pruned_loss=0.03369, over 1406069.71 frames.], batch size: 25, lr: 3.09e-04
2022-04-30 01:24:28,204 INFO [train.py:763] (1/8) Epoch 24, batch 4500, loss[loss=0.1681, simple_loss=0.266, pruned_loss=0.03514, over 5231.00 frames.], tot_loss[loss=0.1685, simple_loss=0.2683, pruned_loss=0.03438, over 1389738.71 frames.], batch size: 55, lr: 3.08e-04
2022-04-30 01:25:32,949 INFO [train.py:763] (1/8) Epoch 24, batch 4550, loss[loss=0.1937, simple_loss=0.2817, pruned_loss=0.05289, over 5403.00 frames.], tot_loss[loss=0.1709, simple_loss=0.2706, pruned_loss=0.03554, over 1352160.02 frames.], batch size: 52, lr: 3.08e-04
2022-04-30 01:26:52,284 INFO [train.py:763] (1/8) Epoch 25, batch 0, loss[loss=0.1769, simple_loss=0.2873, pruned_loss=0.03319, over 7216.00 frames.], tot_loss[loss=0.1769, simple_loss=0.2873, pruned_loss=0.03319, over 7216.00 frames.], batch size: 21, lr: 3.02e-04
2022-04-30 01:27:58,471 INFO [train.py:763] (1/8) Epoch 25, batch 50, loss[loss=0.1693, simple_loss=0.2669, pruned_loss=0.03584, over 7324.00 frames.], tot_loss[loss=0.165, simple_loss=0.2633, pruned_loss=0.03337, over 323111.38 frames.], batch size: 21, lr: 3.02e-04
2022-04-30 01:29:03,629 INFO [train.py:763] (1/8) Epoch 25, batch 100, loss[loss=0.1936, simple_loss=0.2902, pruned_loss=0.04846, over 5249.00 frames.], tot_loss[loss=0.1653, simple_loss=0.265, pruned_loss=0.03277, over 567251.14 frames.], batch size: 52, lr: 3.02e-04
2022-04-30 01:30:08,881 INFO [train.py:763] (1/8) Epoch 25, batch 150, loss[loss=0.1581, simple_loss=0.2526, pruned_loss=0.03175, over 7267.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2667, pruned_loss=0.03331, over 760524.19 frames.], batch size: 17, lr: 3.02e-04
2022-04-30 01:31:14,494 INFO [train.py:763] (1/8) Epoch 25, batch 200, loss[loss=0.1852, simple_loss=0.2876, pruned_loss=0.04133, over 7381.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2654, pruned_loss=0.03312, over 907557.41 frames.], batch size: 23, lr: 3.02e-04
2022-04-30 01:32:20,360 INFO [train.py:763] (1/8) Epoch 25, batch 250, loss[loss=0.1814, simple_loss=0.2806, pruned_loss=0.04112, over 7217.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2654, pruned_loss=0.03357, over 1019661.15 frames.], batch size: 22, lr: 3.02e-04
2022-04-30 01:33:26,236 INFO [train.py:763] (1/8) Epoch 25, batch 300, loss[loss=0.1587, simple_loss=0.2553, pruned_loss=0.031, over 7328.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2654, pruned_loss=0.03358, over 1106450.50 frames.], batch size: 20, lr: 3.02e-04
2022-04-30 01:34:31,516 INFO [train.py:763] (1/8) Epoch 25, batch 350, loss[loss=0.1569, simple_loss=0.2512, pruned_loss=0.0313, over 7168.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2639, pruned_loss=0.03288, over 1175601.98 frames.], batch size: 18, lr: 3.02e-04
2022-04-30 01:35:36,789 INFO [train.py:763] (1/8) Epoch 25, batch 400, loss[loss=0.1603, simple_loss=0.253, pruned_loss=0.03377, over 7411.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2643, pruned_loss=0.03306, over 1233388.02 frames.], batch size: 18, lr: 3.02e-04
2022-04-30 01:36:42,353 INFO [train.py:763] (1/8) Epoch 25, batch 450, loss[loss=0.169, simple_loss=0.2797, pruned_loss=0.02918, over 7415.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2644, pruned_loss=0.03307, over 1274188.55 frames.], batch size: 21, lr: 3.02e-04
2022-04-30 01:37:47,500 INFO [train.py:763] (1/8) Epoch 25, batch 500, loss[loss=0.188, simple_loss=0.2841, pruned_loss=0.04598, over 7371.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2652, pruned_loss=0.03331, over 1302793.53 frames.], batch size: 23, lr: 3.02e-04
2022-04-30 01:38:52,808 INFO [train.py:763] (1/8) Epoch 25, batch 550, loss[loss=0.1622, simple_loss=0.2703, pruned_loss=0.02709, over 7235.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2647, pruned_loss=0.03285, over 1329180.18 frames.], batch size: 20, lr: 3.02e-04
2022-04-30 01:39:58,985 INFO [train.py:763] (1/8) Epoch 25, batch 600, loss[loss=0.1599, simple_loss=0.2709, pruned_loss=0.02452, over 6999.00 frames.], tot_loss[loss=0.1657, simple_loss=0.265, pruned_loss=0.03319, over 1347268.08 frames.], batch size: 28, lr: 3.02e-04
2022-04-30 01:41:04,680 INFO [train.py:763] (1/8) Epoch 25, batch 650, loss[loss=0.1529, simple_loss=0.2594, pruned_loss=0.02316, over 7339.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2643, pruned_loss=0.03338, over 1361847.98 frames.], batch size: 20, lr: 3.02e-04
2022-04-30 01:42:10,707 INFO [train.py:763] (1/8) Epoch 25, batch 700, loss[loss=0.164, simple_loss=0.2687, pruned_loss=0.02969, over 7146.00 frames.], tot_loss[loss=0.1651, simple_loss=0.264, pruned_loss=0.03307, over 1375115.51 frames.], batch size: 20, lr: 3.02e-04
2022-04-30 01:43:16,096 INFO [train.py:763] (1/8) Epoch 25, batch 750, loss[loss=0.1719, simple_loss=0.2666, pruned_loss=0.03857, over 7425.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2646, pruned_loss=0.03307, over 1390804.27 frames.], batch size: 20, lr: 3.01e-04
2022-04-30 01:44:20,963 INFO [train.py:763] (1/8) Epoch 25, batch 800, loss[loss=0.1738, simple_loss=0.2764, pruned_loss=0.03562, over 6888.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2643, pruned_loss=0.03278, over 1396385.51 frames.], batch size: 31, lr: 3.01e-04
2022-04-30 01:45:26,280 INFO [train.py:763] (1/8) Epoch 25, batch 850, loss[loss=0.2001, simple_loss=0.3041, pruned_loss=0.04803, over 7110.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2651, pruned_loss=0.03271, over 1406732.50 frames.], batch size: 21, lr: 3.01e-04
2022-04-30 01:46:33,099 INFO [train.py:763] (1/8) Epoch 25, batch 900, loss[loss=0.1561, simple_loss=0.2324, pruned_loss=0.03992, over 6787.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2652, pruned_loss=0.03286, over 1406325.27 frames.], batch size: 15, lr: 3.01e-04
2022-04-30 01:47:40,153 INFO [train.py:763] (1/8) Epoch 25, batch 950, loss[loss=0.1354, simple_loss=0.225, pruned_loss=0.02286, over 7261.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2647, pruned_loss=0.03293, over 1412661.65 frames.], batch size: 17, lr: 3.01e-04
2022-04-30 01:48:46,806 INFO [train.py:763] (1/8) Epoch 25, batch 1000, loss[loss=0.1944, simple_loss=0.2907, pruned_loss=0.04902, over 7123.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2658, pruned_loss=0.03348, over 1411930.30 frames.], batch size: 21, lr: 3.01e-04
2022-04-30 01:49:52,612 INFO [train.py:763] (1/8) Epoch 25, batch 1050, loss[loss=0.1998, simple_loss=0.289, pruned_loss=0.05529, over 5405.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2663, pruned_loss=0.03329, over 1412583.71 frames.], batch size: 53, lr: 3.01e-04
2022-04-30 01:50:59,148 INFO [train.py:763] (1/8) Epoch 25, batch 1100, loss[loss=0.154, simple_loss=0.2613, pruned_loss=0.02336, over 7115.00 frames.], tot_loss[loss=0.1663, simple_loss=0.266, pruned_loss=0.03325, over 1413568.81 frames.], batch size: 21, lr: 3.01e-04
2022-04-30 01:52:04,512 INFO [train.py:763] (1/8) Epoch 25, batch 1150, loss[loss=0.1871, simple_loss=0.2814, pruned_loss=0.04643, over 7376.00 frames.], tot_loss[loss=0.166, simple_loss=0.2655, pruned_loss=0.03324, over 1417179.44 frames.], batch size: 23, lr: 3.01e-04
2022-04-30 01:53:10,907 INFO [train.py:763] (1/8) Epoch 25, batch 1200, loss[loss=0.1778, simple_loss=0.2609, pruned_loss=0.04732, over 7129.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2657, pruned_loss=0.0336, over 1420792.83 frames.], batch size: 17, lr: 3.01e-04
2022-04-30 01:54:16,905 INFO [train.py:763] (1/8) Epoch 25, batch 1250, loss[loss=0.1623, simple_loss=0.2576, pruned_loss=0.03346, over 7315.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2656, pruned_loss=0.03396, over 1422911.75 frames.], batch size: 21, lr: 3.01e-04
2022-04-30 01:55:23,798 INFO [train.py:763] (1/8) Epoch 25, batch 1300, loss[loss=0.1644, simple_loss=0.2652, pruned_loss=0.03176, over 7421.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2657, pruned_loss=0.03404, over 1426051.01 frames.], batch size: 20, lr: 3.01e-04
2022-04-30 01:56:30,374 INFO [train.py:763] (1/8) Epoch 25, batch 1350, loss[loss=0.167, simple_loss=0.2717, pruned_loss=0.03113, over 7326.00 frames.], tot_loss[loss=0.1668, simple_loss=0.266, pruned_loss=0.03385, over 1426647.52 frames.], batch size: 21, lr: 3.01e-04
2022-04-30 01:57:36,863 INFO [train.py:763] (1/8) Epoch 25, batch 1400, loss[loss=0.1735, simple_loss=0.2778, pruned_loss=0.03462, over 7328.00 frames.], tot_loss[loss=0.167, simple_loss=0.2662, pruned_loss=0.03392, over 1426678.56 frames.], batch size: 22, lr: 3.01e-04
2022-04-30 01:58:42,268 INFO [train.py:763] (1/8) Epoch 25, batch 1450, loss[loss=0.1477, simple_loss=0.2452, pruned_loss=0.0251, over 7018.00 frames.], tot_loss[loss=0.167, simple_loss=0.2661, pruned_loss=0.03392, over 1428908.82 frames.], batch size: 16, lr: 3.01e-04
2022-04-30 01:59:49,360 INFO [train.py:763] (1/8) Epoch 25, batch 1500, loss[loss=0.1587, simple_loss=0.2726, pruned_loss=0.02242, over 7213.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2658, pruned_loss=0.03392, over 1428013.67 frames.], batch size: 21, lr: 3.00e-04
2022-04-30 02:00:55,046 INFO [train.py:763] (1/8) Epoch 25, batch 1550, loss[loss=0.1584, simple_loss=0.252, pruned_loss=0.03244, over 7150.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2654, pruned_loss=0.03357, over 1427195.02 frames.], batch size: 17, lr: 3.00e-04
2022-04-30 02:02:00,072 INFO [train.py:763] (1/8) Epoch 25, batch 1600, loss[loss=0.1806, simple_loss=0.2896, pruned_loss=0.03579, over 7145.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2668, pruned_loss=0.0339, over 1424043.19 frames.], batch size: 20, lr: 3.00e-04
2022-04-30 02:03:05,633 INFO [train.py:763] (1/8) Epoch 25, batch 1650, loss[loss=0.1821, simple_loss=0.2825, pruned_loss=0.04085, over 7167.00 frames.], tot_loss[loss=0.166, simple_loss=0.2653, pruned_loss=0.03336, over 1425177.87 frames.], batch size: 28, lr: 3.00e-04
2022-04-30 02:04:10,609 INFO [train.py:763] (1/8) Epoch 25, batch 1700, loss[loss=0.1725, simple_loss=0.2791, pruned_loss=0.0329, over 7308.00 frames.], tot_loss[loss=0.1654, simple_loss=0.265, pruned_loss=0.03295, over 1425354.16 frames.], batch size: 21, lr: 3.00e-04
2022-04-30 02:05:15,840 INFO [train.py:763] (1/8) Epoch 25, batch 1750, loss[loss=0.1591, simple_loss=0.2458, pruned_loss=0.03622, over 7142.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2643, pruned_loss=0.03293, over 1424333.15 frames.], batch size: 17, lr: 3.00e-04
2022-04-30 02:06:21,039 INFO [train.py:763] (1/8) Epoch 25, batch 1800, loss[loss=0.1641, simple_loss=0.2696, pruned_loss=0.02932, over 7143.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2643, pruned_loss=0.03319, over 1420891.64 frames.], batch size: 20, lr: 3.00e-04
2022-04-30 02:07:26,296 INFO [train.py:763] (1/8) Epoch 25, batch 1850, loss[loss=0.1561, simple_loss=0.2587, pruned_loss=0.02673, over 7442.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2646, pruned_loss=0.03335, over 1422117.23 frames.], batch size: 20, lr: 3.00e-04
2022-04-30 02:08:31,445 INFO [train.py:763] (1/8) Epoch 25, batch 1900, loss[loss=0.1286, simple_loss=0.2238, pruned_loss=0.01672, over 7137.00 frames.], tot_loss[loss=0.166, simple_loss=0.2647, pruned_loss=0.0336, over 1422939.92 frames.], batch size: 17, lr: 3.00e-04
2022-04-30 02:09:36,777 INFO [train.py:763] (1/8) Epoch 25, batch 1950, loss[loss=0.1763, simple_loss=0.2742, pruned_loss=0.03922, over 5017.00 frames.], tot_loss[loss=0.166, simple_loss=0.2647, pruned_loss=0.03369, over 1421205.18 frames.], batch size: 52, lr: 3.00e-04
2022-04-30 02:10:42,028 INFO [train.py:763] (1/8) Epoch 25, batch 2000, loss[loss=0.1737, simple_loss=0.2746, pruned_loss=0.03639, over 7148.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2645, pruned_loss=0.03351, over 1417902.64 frames.], batch size: 19, lr: 3.00e-04
2022-04-30 02:11:47,908 INFO [train.py:763] (1/8) Epoch 25, batch 2050, loss[loss=0.1495, simple_loss=0.2541, pruned_loss=0.0224, over 7332.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2639, pruned_loss=0.03336, over 1419025.84 frames.], batch size: 20, lr: 3.00e-04
2022-04-30 02:12:54,272 INFO [train.py:763] (1/8) Epoch 25, batch 2100, loss[loss=0.1804, simple_loss=0.2735, pruned_loss=0.04368, over 7210.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2643, pruned_loss=0.03319, over 1418048.57 frames.], batch size: 22, lr: 3.00e-04
2022-04-30 02:13:59,522 INFO [train.py:763] (1/8) Epoch 25, batch 2150, loss[loss=0.1349, simple_loss=0.2396, pruned_loss=0.01506, over 7177.00 frames.], tot_loss[loss=0.1668, simple_loss=0.2663, pruned_loss=0.03364, over 1419496.28 frames.], batch size: 18, lr: 3.00e-04
2022-04-30 02:15:05,483 INFO [train.py:763] (1/8) Epoch 25, batch 2200, loss[loss=0.1931, simple_loss=0.2869, pruned_loss=0.04962, over 7143.00 frames.], tot_loss[loss=0.167, simple_loss=0.2666, pruned_loss=0.03369, over 1421420.12 frames.], batch size: 28, lr: 3.00e-04
2022-04-30 02:16:11,385 INFO [train.py:763] (1/8) Epoch 25, batch 2250, loss[loss=0.2011, simple_loss=0.2951, pruned_loss=0.05352, over 7377.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2655, pruned_loss=0.03355, over 1423978.91 frames.], batch size: 23, lr: 3.00e-04
2022-04-30 02:17:16,596 INFO [train.py:763] (1/8) Epoch 25, batch 2300, loss[loss=0.1543, simple_loss=0.2513, pruned_loss=0.02859, over 7060.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2656, pruned_loss=0.03346, over 1424210.84 frames.], batch size: 18, lr: 2.99e-04
2022-04-30 02:18:23,381 INFO [train.py:763] (1/8) Epoch 25, batch 2350, loss[loss=0.158, simple_loss=0.2589, pruned_loss=0.02852, over 7266.00 frames.], tot_loss[loss=0.166, simple_loss=0.265, pruned_loss=0.03349, over 1424455.44 frames.], batch size: 19, lr: 2.99e-04
2022-04-30 02:19:30,573 INFO [train.py:763] (1/8) Epoch 25, batch 2400, loss[loss=0.1935, simple_loss=0.2863, pruned_loss=0.0504, over 7388.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2648, pruned_loss=0.0334, over 1422752.48 frames.], batch size: 23, lr: 2.99e-04
2022-04-30 02:20:35,957 INFO [train.py:763] (1/8) Epoch 25, batch 2450, loss[loss=0.1857, simple_loss=0.2901, pruned_loss=0.04067, over 6799.00 frames.], tot_loss[loss=0.1669, simple_loss=0.2657, pruned_loss=0.03409, over 1422278.10 frames.], batch size: 31, lr: 2.99e-04
2022-04-30 02:21:42,822 INFO [train.py:763] (1/8) Epoch 25, batch 2500, loss[loss=0.1361, simple_loss=0.2307, pruned_loss=0.02077, over 7356.00 frames.], tot_loss[loss=0.166, simple_loss=0.2645, pruned_loss=0.03371, over 1422721.47 frames.], batch size: 19, lr: 2.99e-04
2022-04-30 02:22:48,786 INFO [train.py:763] (1/8) Epoch 25, batch 2550, loss[loss=0.1602, simple_loss=0.2499, pruned_loss=0.03522, over 7408.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2638, pruned_loss=0.03338, over 1425396.57 frames.], batch size: 18, lr: 2.99e-04
2022-04-30 02:23:56,376 INFO [train.py:763] (1/8) Epoch 25, batch 2600, loss[loss=0.1572, simple_loss=0.2576, pruned_loss=0.02841, over 7147.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2646, pruned_loss=0.03386, over 1423947.39 frames.], batch size: 19, lr: 2.99e-04
2022-04-30 02:25:02,540 INFO [train.py:763] (1/8) Epoch 25, batch 2650, loss[loss=0.1559, simple_loss=0.2574, pruned_loss=0.02715, over 7132.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2647, pruned_loss=0.03372, over 1419477.32 frames.], batch size: 28, lr: 2.99e-04
2022-04-30 02:26:07,756 INFO [train.py:763] (1/8) Epoch 25, batch 2700, loss[loss=0.147, simple_loss=0.2381, pruned_loss=0.028, over 7260.00 frames.], tot_loss[loss=0.1667, simple_loss=0.2654, pruned_loss=0.03401, over 1420238.60 frames.], batch size: 19, lr: 2.99e-04
2022-04-30 02:27:12,939 INFO [train.py:763] (1/8) Epoch 25, batch 2750, loss[loss=0.1718, simple_loss=0.2694, pruned_loss=0.03711, over 7279.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2649, pruned_loss=0.03387, over 1413459.98 frames.], batch size: 25, lr: 2.99e-04
2022-04-30 02:28:19,369 INFO [train.py:763] (1/8) Epoch 25, batch 2800, loss[loss=0.1599, simple_loss=0.2454, pruned_loss=0.03716, over 7284.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2645, pruned_loss=0.03352, over 1416207.76 frames.], batch size: 18, lr: 2.99e-04
2022-04-30 02:29:24,929 INFO [train.py:763] (1/8) Epoch 25, batch 2850, loss[loss=0.1702, simple_loss=0.2774, pruned_loss=0.03146, over 7412.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2638, pruned_loss=0.03318, over 1411800.72 frames.], batch size: 21, lr: 2.99e-04
2022-04-30 02:30:30,620 INFO [train.py:763] (1/8) Epoch 25, batch 2900, loss[loss=0.1609, simple_loss=0.2555, pruned_loss=0.03321, over 7137.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2641, pruned_loss=0.03316, over 1418228.17 frames.], batch size: 20, lr: 2.99e-04
2022-04-30 02:31:35,882 INFO [train.py:763] (1/8) Epoch 25, batch 2950, loss[loss=0.1579, simple_loss=0.2499, pruned_loss=0.03298, over 7327.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2647, pruned_loss=0.03331, over 1418580.04 frames.], batch size: 20, lr: 2.99e-04
2022-04-30 02:32:41,161 INFO [train.py:763] (1/8) Epoch 25, batch 3000, loss[loss=0.168, simple_loss=0.2699, pruned_loss=0.03306, over 6549.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2638, pruned_loss=0.03281, over 1423451.43 frames.], batch size: 38, lr: 2.99e-04
2022-04-30 02:32:41,162 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 02:32:56,272 INFO [train.py:792] (1/8) Epoch 25, validation: loss=0.1697, simple_loss=0.2684, pruned_loss=0.03548, over 698248.00 frames.
2022-04-30 02:34:02,072 INFO [train.py:763] (1/8) Epoch 25, batch 3050, loss[loss=0.1731, simple_loss=0.279, pruned_loss=0.03364, over 7330.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2649, pruned_loss=0.03333, over 1422667.64 frames.], batch size: 22, lr: 2.99e-04
2022-04-30 02:35:09,271 INFO [train.py:763] (1/8) Epoch 25, batch 3100, loss[loss=0.1597, simple_loss=0.2544, pruned_loss=0.03245, over 7258.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2645, pruned_loss=0.03312, over 1419598.99 frames.], batch size: 19, lr: 2.98e-04
2022-04-30 02:36:16,360 INFO [train.py:763] (1/8) Epoch 25, batch 3150, loss[loss=0.1367, simple_loss=0.2278, pruned_loss=0.02278, over 7127.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2645, pruned_loss=0.03328, over 1418947.01 frames.], batch size: 17, lr: 2.98e-04
2022-04-30 02:37:22,257 INFO [train.py:763] (1/8) Epoch 25, batch 3200, loss[loss=0.1594, simple_loss=0.2604, pruned_loss=0.02917, over 7162.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2648, pruned_loss=0.03296, over 1421236.24 frames.], batch size: 19, lr: 2.98e-04
2022-04-30 02:38:29,211 INFO [train.py:763] (1/8) Epoch 25, batch 3250, loss[loss=0.1542, simple_loss=0.2394, pruned_loss=0.03449, over 7277.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2635, pruned_loss=0.03275, over 1424489.97 frames.], batch size: 18, lr: 2.98e-04
2022-04-30 02:39:35,756 INFO [train.py:763] (1/8) Epoch 25, batch 3300, loss[loss=0.162, simple_loss=0.2645, pruned_loss=0.02972, over 7179.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2649, pruned_loss=0.03328, over 1417802.74 frames.], batch size: 26, lr: 2.98e-04
2022-04-30 02:40:42,706 INFO [train.py:763] (1/8) Epoch 25, batch 3350, loss[loss=0.1732, simple_loss=0.2769, pruned_loss=0.03474, over 7308.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2649, pruned_loss=0.03342, over 1414757.96 frames.], batch size: 21, lr: 2.98e-04
2022-04-30 02:41:49,871 INFO [train.py:763] (1/8) Epoch 25, batch 3400, loss[loss=0.1665, simple_loss=0.2764, pruned_loss=0.02827, over 6567.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2643, pruned_loss=0.03339, over 1419434.75 frames.], batch size: 38, lr: 2.98e-04
2022-04-30 02:42:55,393 INFO [train.py:763] (1/8) Epoch 25, batch 3450, loss[loss=0.1603, simple_loss=0.2551, pruned_loss=0.03275, over 7158.00 frames.], tot_loss[loss=0.1653, simple_loss=0.264, pruned_loss=0.0333, over 1419474.17 frames.], batch size: 18, lr: 2.98e-04
2022-04-30 02:44:00,602 INFO [train.py:763] (1/8) Epoch 25, batch 3500, loss[loss=0.1666, simple_loss=0.272, pruned_loss=0.03057, over 7391.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2647, pruned_loss=0.03331, over 1418652.22 frames.], batch size: 23, lr: 2.98e-04
2022-04-30 02:45:06,555 INFO [train.py:763] (1/8) Epoch 25, batch 3550, loss[loss=0.1488, simple_loss=0.2579, pruned_loss=0.01985, over 7408.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2647, pruned_loss=0.03321, over 1420752.20 frames.], batch size: 21, lr: 2.98e-04
2022-04-30 02:46:12,316 INFO [train.py:763] (1/8) Epoch 25, batch 3600, loss[loss=0.1542, simple_loss=0.2503, pruned_loss=0.02904, over 7221.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2646, pruned_loss=0.03342, over 1425481.88 frames.], batch size: 23, lr: 2.98e-04
2022-04-30 02:47:18,087 INFO [train.py:763] (1/8) Epoch 25, batch 3650, loss[loss=0.1439, simple_loss=0.2383, pruned_loss=0.02474, over 7249.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2646, pruned_loss=0.03333, over 1426868.81 frames.], batch size: 19, lr: 2.98e-04
2022-04-30 02:48:25,753 INFO [train.py:763] (1/8) Epoch 25, batch 3700, loss[loss=0.1917, simple_loss=0.2832, pruned_loss=0.05009, over 7077.00 frames.], tot_loss[loss=0.1654, simple_loss=0.264, pruned_loss=0.0334, over 1423958.01 frames.], batch size: 18, lr: 2.98e-04
2022-04-30 02:49:32,857 INFO [train.py:763] (1/8) Epoch 25, batch 3750, loss[loss=0.1644, simple_loss=0.2608, pruned_loss=0.03397, over 7147.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2642, pruned_loss=0.03304, over 1422343.11 frames.], batch size: 19, lr: 2.98e-04
2022-04-30 02:50:38,247 INFO [train.py:763] (1/8) Epoch 25, batch 3800, loss[loss=0.1813, simple_loss=0.2744, pruned_loss=0.0441, over 6408.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2638, pruned_loss=0.03302, over 1419754.79 frames.], batch size: 37, lr: 2.98e-04
2022-04-30 02:51:43,563 INFO [train.py:763] (1/8) Epoch 25, batch 3850, loss[loss=0.153, simple_loss=0.2609, pruned_loss=0.02253, over 7142.00 frames.], tot_loss[loss=0.1653, simple_loss=0.264, pruned_loss=0.03332, over 1418225.79 frames.], batch size: 20, lr: 2.97e-04
2022-04-30 02:52:57,810 INFO [train.py:763] (1/8) Epoch 25, batch 3900, loss[loss=0.1376, simple_loss=0.2329, pruned_loss=0.02113, over 7416.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2646, pruned_loss=0.03312, over 1420259.75 frames.], batch size: 18, lr: 2.97e-04
2022-04-30 02:54:03,674 INFO [train.py:763] (1/8) Epoch 25, batch 3950, loss[loss=0.1584, simple_loss=0.2586, pruned_loss=0.0291, over 7236.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2644, pruned_loss=0.03313, over 1424747.78 frames.], batch size: 20, lr: 2.97e-04
2022-04-30 02:55:09,638 INFO [train.py:763] (1/8) Epoch 25, batch 4000, loss[loss=0.14, simple_loss=0.2506, pruned_loss=0.01468, over 7438.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2643, pruned_loss=0.03297, over 1417994.49 frames.], batch size: 20, lr: 2.97e-04
2022-04-30 02:56:14,886 INFO [train.py:763] (1/8) Epoch 25, batch 4050, loss[loss=0.1705, simple_loss=0.2713, pruned_loss=0.03485, over 7419.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2643, pruned_loss=0.03301, over 1419583.82 frames.], batch size: 21, lr: 2.97e-04
2022-04-30 02:57:21,067 INFO [train.py:763] (1/8) Epoch 25, batch 4100, loss[loss=0.1682, simple_loss=0.2621, pruned_loss=0.03715, over 7413.00 frames.], tot_loss[loss=0.165, simple_loss=0.2641, pruned_loss=0.03295, over 1418216.46 frames.], batch size: 21, lr: 2.97e-04
2022-04-30 02:58:26,415 INFO [train.py:763] (1/8) Epoch 25, batch 4150, loss[loss=0.162, simple_loss=0.2616, pruned_loss=0.03115, over 7263.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2647, pruned_loss=0.03303, over 1423415.07 frames.], batch size: 19, lr: 2.97e-04
2022-04-30 02:59:32,218 INFO [train.py:763] (1/8) Epoch 25, batch 4200, loss[loss=0.1796, simple_loss=0.275, pruned_loss=0.04211, over 7105.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2648, pruned_loss=0.03315, over 1419708.90 frames.], batch size: 28, lr: 2.97e-04
2022-04-30 03:00:37,739 INFO [train.py:763] (1/8) Epoch 25, batch 4250, loss[loss=0.1365, simple_loss=0.2305, pruned_loss=0.02126, over 7158.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2641, pruned_loss=0.03284, over 1419319.50 frames.], batch size: 18, lr: 2.97e-04
2022-04-30 03:01:43,168 INFO [train.py:763] (1/8) Epoch 25, batch 4300, loss[loss=0.1965, simple_loss=0.2853, pruned_loss=0.05388, over 7195.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2647, pruned_loss=0.03324, over 1422788.10 frames.], batch size: 26, lr: 2.97e-04
2022-04-30 03:03:06,198 INFO [train.py:763] (1/8) Epoch 25, batch 4350, loss[loss=0.1704, simple_loss=0.2709, pruned_loss=0.03493, over 7227.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2644, pruned_loss=0.03321, over 1415726.22 frames.], batch size: 20, lr: 2.97e-04
2022-04-30 03:04:20,099 INFO [train.py:763] (1/8) Epoch 25, batch 4400, loss[loss=0.1489, simple_loss=0.2395, pruned_loss=0.02912, over 7449.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2651, pruned_loss=0.03279, over 1415470.10 frames.], batch size: 19, lr: 2.97e-04
2022-04-30 03:05:34,208 INFO [train.py:763] (1/8) Epoch 25, batch 4450, loss[loss=0.1764, simple_loss=0.2744, pruned_loss=0.03917, over 7299.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2649, pruned_loss=0.03279, over 1414628.36 frames.], batch size: 24, lr: 2.97e-04
2022-04-30 03:06:39,191 INFO [train.py:763] (1/8) Epoch 25, batch 4500, loss[loss=0.1566, simple_loss=0.264, pruned_loss=0.02455, over 7315.00 frames.], tot_loss[loss=0.1664, simple_loss=0.2657, pruned_loss=0.03358, over 1399255.69 frames.], batch size: 20, lr: 2.97e-04
2022-04-30 03:08:11,349 INFO [train.py:763] (1/8) Epoch 25, batch 4550, loss[loss=0.1886, simple_loss=0.2796, pruned_loss=0.04885, over 5179.00 frames.], tot_loss[loss=0.1673, simple_loss=0.2662, pruned_loss=0.03424, over 1388643.97 frames.], batch size: 52, lr: 2.97e-04
2022-04-30 03:09:39,540 INFO [train.py:763] (1/8) Epoch 26, batch 0, loss[loss=0.1677, simple_loss=0.255, pruned_loss=0.04024, over 7176.00 frames.], tot_loss[loss=0.1677, simple_loss=0.255, pruned_loss=0.04024, over 7176.00 frames.], batch size: 18, lr: 2.91e-04
2022-04-30 03:10:45,448 INFO [train.py:763] (1/8) Epoch 26, batch 50, loss[loss=0.152, simple_loss=0.2407, pruned_loss=0.03169, over 7280.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2639, pruned_loss=0.03392, over 318774.87 frames.], batch size: 17, lr: 2.91e-04
2022-04-30 03:11:50,709 INFO [train.py:763] (1/8) Epoch 26, batch 100, loss[loss=0.1434, simple_loss=0.2443, pruned_loss=0.02119, over 7282.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2623, pruned_loss=0.03227, over 562113.49 frames.], batch size: 17, lr: 2.91e-04
2022-04-30 03:12:56,048 INFO [train.py:763] (1/8) Epoch 26, batch 150, loss[loss=0.1668, simple_loss=0.2795, pruned_loss=0.02705, over 6447.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2621, pruned_loss=0.03181, over 750619.27 frames.], batch size: 38, lr: 2.91e-04
2022-04-30 03:14:01,246 INFO [train.py:763] (1/8) Epoch 26, batch 200, loss[loss=0.1882, simple_loss=0.2908, pruned_loss=0.04276, over 7190.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2638, pruned_loss=0.03291, over 892994.01 frames.], batch size: 26, lr: 2.91e-04
2022-04-30 03:15:07,040 INFO [train.py:763] (1/8) Epoch 26, batch 250, loss[loss=0.1656, simple_loss=0.2609, pruned_loss=0.03515, over 6397.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2651, pruned_loss=0.03335, over 1005394.99 frames.], batch size: 38, lr: 2.91e-04
2022-04-30 03:16:13,120 INFO [train.py:763] (1/8) Epoch 26, batch 300, loss[loss=0.1765, simple_loss=0.2755, pruned_loss=0.03872, over 6518.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2647, pruned_loss=0.03277, over 1100274.91 frames.], batch size: 38, lr: 2.91e-04
2022-04-30 03:17:18,445 INFO [train.py:763] (1/8) Epoch 26, batch 350, loss[loss=0.1536, simple_loss=0.257, pruned_loss=0.02509, over 6824.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2638, pruned_loss=0.03282, over 1168141.55 frames.], batch size: 32, lr: 2.91e-04
2022-04-30 03:18:23,747 INFO [train.py:763] (1/8) Epoch 26, batch 400, loss[loss=0.1618, simple_loss=0.264, pruned_loss=0.02977, over 7151.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2641, pruned_loss=0.03282, over 1228608.85 frames.], batch size: 20, lr: 2.91e-04
2022-04-30 03:19:29,468 INFO [train.py:763] (1/8) Epoch 26, batch 450, loss[loss=0.1486, simple_loss=0.2539, pruned_loss=0.02166, over 7243.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2644, pruned_loss=0.03248, over 1276172.99 frames.], batch size: 20, lr: 2.91e-04
2022-04-30 03:20:34,848 INFO [train.py:763] (1/8) Epoch 26, batch 500, loss[loss=0.1779, simple_loss=0.264, pruned_loss=0.04594, over 5026.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2637, pruned_loss=0.03232, over 1308098.79 frames.], batch size: 52, lr: 2.91e-04
2022-04-30 03:21:40,167 INFO [train.py:763] (1/8) Epoch 26, batch 550, loss[loss=0.1775, simple_loss=0.276, pruned_loss=0.03951, over 7195.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2641, pruned_loss=0.03239, over 1332395.19 frames.], batch size: 22, lr: 2.90e-04
2022-04-30 03:22:45,576 INFO [train.py:763] (1/8) Epoch 26, batch 600, loss[loss=0.1534, simple_loss=0.249, pruned_loss=0.02894, over 7261.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2647, pruned_loss=0.03281, over 1355222.79 frames.], batch size: 19, lr: 2.90e-04
2022-04-30 03:23:51,103 INFO [train.py:763] (1/8) Epoch 26, batch 650, loss[loss=0.1398, simple_loss=0.2319, pruned_loss=0.02385, over 7280.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2647, pruned_loss=0.033, over 1371590.34 frames.], batch size: 18, lr: 2.90e-04
2022-04-30 03:24:56,236 INFO [train.py:763] (1/8) Epoch 26, batch 700, loss[loss=0.1597, simple_loss=0.2685, pruned_loss=0.02549, over 7102.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2648, pruned_loss=0.03267, over 1380996.59 frames.], batch size: 21, lr: 2.90e-04
2022-04-30 03:26:12,123 INFO [train.py:763] (1/8) Epoch 26, batch 750, loss[loss=0.1654, simple_loss=0.2679, pruned_loss=0.03141, over 7150.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2651, pruned_loss=0.03287, over 1389738.03 frames.], batch size: 20, lr: 2.90e-04
2022-04-30 03:27:17,950 INFO [train.py:763] (1/8) Epoch 26, batch 800, loss[loss=0.1544, simple_loss=0.2597, pruned_loss=0.02455, over 7232.00 frames.], tot_loss[loss=0.165, simple_loss=0.2647, pruned_loss=0.03264, over 1395614.83 frames.], batch size: 20, lr: 2.90e-04
2022-04-30 03:28:23,823 INFO [train.py:763] (1/8) Epoch 26, batch 850, loss[loss=0.162, simple_loss=0.2564, pruned_loss=0.03386, over 4863.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2653, pruned_loss=0.03261, over 1398540.05 frames.], batch size: 53, lr: 2.90e-04
2022-04-30 03:29:29,377 INFO [train.py:763] (1/8) Epoch 26, batch 900, loss[loss=0.1586, simple_loss=0.2326, pruned_loss=0.04225, over 7413.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2647, pruned_loss=0.03278, over 1408636.67 frames.], batch size: 18, lr: 2.90e-04
2022-04-30 03:30:35,250 INFO [train.py:763] (1/8) Epoch 26, batch 950, loss[loss=0.1473, simple_loss=0.2415, pruned_loss=0.02653, over 6820.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2649, pruned_loss=0.03282, over 1409021.67 frames.], batch size: 15, lr: 2.90e-04
2022-04-30 03:31:40,713 INFO [train.py:763] (1/8) Epoch 26, batch 1000, loss[loss=0.1835, simple_loss=0.2887, pruned_loss=0.03917, over 7278.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2655, pruned_loss=0.0329, over 1412351.04 frames.], batch size: 24, lr: 2.90e-04
2022-04-30 03:32:46,138 INFO [train.py:763] (1/8) Epoch 26, batch 1050, loss[loss=0.1937, simple_loss=0.2954, pruned_loss=0.04599, over 7200.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2659, pruned_loss=0.03286, over 1417331.33 frames.], batch size: 23, lr: 2.90e-04
2022-04-30 03:33:51,496 INFO [train.py:763] (1/8) Epoch 26, batch 1100, loss[loss=0.1859, simple_loss=0.2864, pruned_loss=0.04274, over 7210.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2651, pruned_loss=0.03273, over 1421595.57 frames.], batch size: 22, lr: 2.90e-04
2022-04-30 03:34:56,892 INFO [train.py:763] (1/8) Epoch 26, batch 1150, loss[loss=0.1521, simple_loss=0.2512, pruned_loss=0.02647, over 7167.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2657, pruned_loss=0.03297, over 1422888.30 frames.], batch size: 19, lr: 2.90e-04
2022-04-30 03:36:02,471 INFO [train.py:763] (1/8) Epoch 26, batch 1200, loss[loss=0.1693, simple_loss=0.2739, pruned_loss=0.0324, over 7308.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2657, pruned_loss=0.03295, over 1426626.70 frames.], batch size: 24, lr: 2.90e-04
2022-04-30 03:37:08,326 INFO [train.py:763] (1/8) Epoch 26, batch 1250, loss[loss=0.175, simple_loss=0.2777, pruned_loss=0.0361, over 6344.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2654, pruned_loss=0.03273, over 1426246.51 frames.], batch size: 37, lr: 2.90e-04
2022-04-30 03:38:14,025 INFO [train.py:763] (1/8) Epoch 26, batch 1300, loss[loss=0.1419, simple_loss=0.2329, pruned_loss=0.0255, over 7276.00 frames.], tot_loss[loss=0.165, simple_loss=0.2651, pruned_loss=0.03246, over 1423383.36 frames.], batch size: 18, lr: 2.90e-04
2022-04-30 03:39:20,365 INFO [train.py:763] (1/8) Epoch 26, batch 1350, loss[loss=0.1404, simple_loss=0.2415, pruned_loss=0.01959, over 7423.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2641, pruned_loss=0.0327, over 1426595.50 frames.], batch size: 18, lr: 2.89e-04
2022-04-30 03:40:25,492 INFO [train.py:763] (1/8) Epoch 26, batch 1400, loss[loss=0.1659, simple_loss=0.2691, pruned_loss=0.0314, over 7213.00 frames.], tot_loss[loss=0.165, simple_loss=0.2638, pruned_loss=0.03305, over 1419291.91 frames.], batch size: 23, lr: 2.89e-04
2022-04-30 03:41:30,974 INFO [train.py:763] (1/8) Epoch 26, batch 1450, loss[loss=0.1473, simple_loss=0.2338, pruned_loss=0.03036, over 7284.00 frames.], tot_loss[loss=0.1652, simple_loss=0.264, pruned_loss=0.03319, over 1422014.44 frames.], batch size: 18, lr: 2.89e-04
2022-04-30 03:42:36,426 INFO [train.py:763] (1/8) Epoch 26, batch 1500, loss[loss=0.22, simple_loss=0.2948, pruned_loss=0.0726, over 4798.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2638, pruned_loss=0.03303, over 1418047.12 frames.], batch size: 52, lr: 2.89e-04
2022-04-30 03:43:42,571 INFO [train.py:763] (1/8) Epoch 26, batch 1550, loss[loss=0.1561, simple_loss=0.2636, pruned_loss=0.02431, over 7110.00 frames.], tot_loss[loss=0.1645, simple_loss=0.264, pruned_loss=0.03252, over 1420962.70 frames.], batch size: 21, lr: 2.89e-04
2022-04-30 03:44:49,278 INFO [train.py:763] (1/8) Epoch 26, batch 1600, loss[loss=0.1558, simple_loss=0.2537, pruned_loss=0.02897, over 7261.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2628, pruned_loss=0.03216, over 1425319.16 frames.], batch size: 19, lr: 2.89e-04
2022-04-30 03:45:54,875 INFO [train.py:763] (1/8) Epoch 26, batch 1650, loss[loss=0.1742, simple_loss=0.2845, pruned_loss=0.03194, over 7131.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2634, pruned_loss=0.03272, over 1429052.36 frames.], batch size: 26, lr: 2.89e-04
2022-04-30 03:47:00,374 INFO [train.py:763] (1/8) Epoch 26, batch 1700, loss[loss=0.1807, simple_loss=0.2866, pruned_loss=0.03743, over 7337.00 frames.], tot_loss[loss=0.164, simple_loss=0.2631, pruned_loss=0.03246, over 1430478.45 frames.], batch size: 22, lr: 2.89e-04
2022-04-30 03:48:06,017 INFO [train.py:763] (1/8) Epoch 26, batch 1750, loss[loss=0.192, simple_loss=0.2916, pruned_loss=0.04614, over 7167.00 frames.], tot_loss[loss=0.165, simple_loss=0.2641, pruned_loss=0.03298, over 1430725.25 frames.], batch size: 26, lr: 2.89e-04
2022-04-30 03:49:13,271 INFO [train.py:763] (1/8) Epoch 26, batch 1800, loss[loss=0.1809, simple_loss=0.2818, pruned_loss=0.04004, over 7132.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2643, pruned_loss=0.03317, over 1428056.42 frames.], batch size: 21, lr: 2.89e-04
2022-04-30 03:50:19,934 INFO [train.py:763] (1/8) Epoch 26, batch 1850, loss[loss=0.1955, simple_loss=0.2893, pruned_loss=0.05084, over 4998.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2642, pruned_loss=0.03296, over 1429026.91 frames.], batch size: 53, lr: 2.89e-04
2022-04-30 03:51:25,627 INFO [train.py:763] (1/8) Epoch 26, batch 1900, loss[loss=0.1676, simple_loss=0.2636, pruned_loss=0.0358, over 7357.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2629, pruned_loss=0.03248, over 1427831.82 frames.], batch size: 19, lr: 2.89e-04
2022-04-30 03:52:30,899 INFO [train.py:763] (1/8) Epoch 26, batch 1950, loss[loss=0.1559, simple_loss=0.2597, pruned_loss=0.02604, over 6397.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2629, pruned_loss=0.03239, over 1424789.28 frames.], batch size: 38, lr: 2.89e-04
2022-04-30 03:53:36,218 INFO [train.py:763] (1/8) Epoch 26, batch 2000, loss[loss=0.1656, simple_loss=0.2625, pruned_loss=0.03436, over 6732.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2617, pruned_loss=0.03208, over 1423380.10 frames.], batch size: 31, lr: 2.89e-04
2022-04-30 03:54:41,492 INFO [train.py:763] (1/8) Epoch 26, batch 2050, loss[loss=0.1949, simple_loss=0.3101, pruned_loss=0.03991, over 7120.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2632, pruned_loss=0.03266, over 1426343.38 frames.], batch size: 26, lr: 2.89e-04
2022-04-30 03:55:48,141 INFO [train.py:763] (1/8) Epoch 26, batch 2100, loss[loss=0.161, simple_loss=0.2633, pruned_loss=0.0294, over 7212.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2631, pruned_loss=0.03261, over 1424328.00 frames.], batch size: 22, lr: 2.89e-04
2022-04-30 03:56:54,314 INFO [train.py:763] (1/8) Epoch 26, batch 2150, loss[loss=0.1803, simple_loss=0.2819, pruned_loss=0.03932, over 7297.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2649, pruned_loss=0.0335, over 1427815.74 frames.], batch size: 25, lr: 2.89e-04
2022-04-30 03:57:59,844 INFO [train.py:763] (1/8) Epoch 26, batch 2200, loss[loss=0.1551, simple_loss=0.255, pruned_loss=0.02761, over 7227.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2652, pruned_loss=0.03351, over 1426954.68 frames.], batch size: 20, lr: 2.88e-04
2022-04-30 03:59:06,002 INFO [train.py:763] (1/8) Epoch 26, batch 2250, loss[loss=0.1493, simple_loss=0.2365, pruned_loss=0.03108, over 6997.00 frames.], tot_loss[loss=0.1658, simple_loss=0.265, pruned_loss=0.03325, over 1431978.07 frames.], batch size: 16, lr: 2.88e-04
2022-04-30 04:00:11,170 INFO [train.py:763] (1/8) Epoch 26, batch 2300, loss[loss=0.1258, simple_loss=0.2128, pruned_loss=0.01941, over 7136.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2648, pruned_loss=0.03298, over 1433363.00 frames.], batch size: 17, lr: 2.88e-04
2022-04-30 04:01:17,205 INFO [train.py:763] (1/8) Epoch 26, batch 2350, loss[loss=0.1459, simple_loss=0.2573, pruned_loss=0.0172, over 7155.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2657, pruned_loss=0.03337, over 1431759.42 frames.], batch size: 20, lr: 2.88e-04
2022-04-30 04:02:24,606 INFO [train.py:763] (1/8) Epoch 26, batch 2400, loss[loss=0.198, simple_loss=0.294, pruned_loss=0.05102, over 7313.00 frames.], tot_loss[loss=0.1657, simple_loss=0.2652, pruned_loss=0.03311, over 1432739.08 frames.], batch size: 24, lr: 2.88e-04
2022-04-30 04:03:31,273 INFO [train.py:763] (1/8) Epoch 26, batch 2450, loss[loss=0.1597, simple_loss=0.253, pruned_loss=0.03317, over 7230.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2645, pruned_loss=0.03298, over 1435446.56 frames.], batch size: 20, lr: 2.88e-04
2022-04-30 04:04:36,617 INFO [train.py:763] (1/8) Epoch 26, batch 2500, loss[loss=0.1632, simple_loss=0.2634, pruned_loss=0.0315, over 7219.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2647, pruned_loss=0.03296, over 1437330.77 frames.], batch size: 21, lr: 2.88e-04
2022-04-30 04:05:41,764 INFO [train.py:763] (1/8) Epoch 26, batch 2550, loss[loss=0.1639, simple_loss=0.2577, pruned_loss=0.03512, over 6823.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2644, pruned_loss=0.03273, over 1434986.91 frames.], batch size: 31, lr: 2.88e-04
2022-04-30 04:06:47,198 INFO [train.py:763] (1/8) Epoch 26, batch 2600, loss[loss=0.1658, simple_loss=0.2557, pruned_loss=0.03794, over 6794.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2639, pruned_loss=0.03293, over 1435471.68 frames.], batch size: 15, lr: 2.88e-04
2022-04-30 04:07:52,614 INFO [train.py:763] (1/8) Epoch 26, batch 2650, loss[loss=0.1829, simple_loss=0.2843, pruned_loss=0.04077, over 7314.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2642, pruned_loss=0.03317, over 1431056.08 frames.], batch size: 24, lr: 2.88e-04
2022-04-30 04:08:58,040 INFO [train.py:763] (1/8) Epoch 26, batch 2700, loss[loss=0.1894, simple_loss=0.2895, pruned_loss=0.04464, over 7339.00 frames.], tot_loss[loss=0.165, simple_loss=0.2639, pruned_loss=0.03303, over 1428937.78 frames.], batch size: 22, lr: 2.88e-04
2022-04-30 04:10:03,923 INFO [train.py:763] (1/8) Epoch 26, batch 2750, loss[loss=0.1419, simple_loss=0.2411, pruned_loss=0.02137, over 7145.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2632, pruned_loss=0.0326, over 1427624.68 frames.], batch size: 19, lr: 2.88e-04
2022-04-30 04:11:09,744 INFO [train.py:763] (1/8) Epoch 26, batch 2800, loss[loss=0.1707, simple_loss=0.2704, pruned_loss=0.03552, over 7300.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2642, pruned_loss=0.0326, over 1427153.07 frames.], batch size: 25, lr: 2.88e-04
2022-04-30 04:12:16,485 INFO [train.py:763] (1/8) Epoch 26, batch 2850, loss[loss=0.1365, simple_loss=0.2385, pruned_loss=0.01724, over 7263.00 frames.], tot_loss[loss=0.165, simple_loss=0.2646, pruned_loss=0.03267, over 1426537.69 frames.], batch size: 19, lr: 2.88e-04
2022-04-30 04:13:21,767 INFO [train.py:763] (1/8) Epoch 26, batch 2900, loss[loss=0.1477, simple_loss=0.252, pruned_loss=0.02173, over 7167.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2647, pruned_loss=0.03282, over 1425557.37 frames.], batch size: 19, lr: 2.88e-04
2022-04-30 04:14:26,917 INFO [train.py:763] (1/8) Epoch 26, batch 2950, loss[loss=0.154, simple_loss=0.2673, pruned_loss=0.02033, over 7117.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2656, pruned_loss=0.03339, over 1419873.30 frames.], batch size: 21, lr: 2.88e-04
2022-04-30 04:15:32,482 INFO [train.py:763] (1/8) Epoch 26, batch 3000, loss[loss=0.1729, simple_loss=0.2716, pruned_loss=0.03709, over 7409.00 frames.], tot_loss[loss=0.166, simple_loss=0.2656, pruned_loss=0.03323, over 1418962.51 frames.], batch size: 21, lr: 2.88e-04
2022-04-30 04:15:32,483 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 04:15:47,843 INFO [train.py:792] (1/8) Epoch 26, validation: loss=0.1682, simple_loss=0.2653, pruned_loss=0.03549, over 698248.00 frames.
2022-04-30 04:16:54,020 INFO [train.py:763] (1/8) Epoch 26, batch 3050, loss[loss=0.1618, simple_loss=0.274, pruned_loss=0.02483, over 7107.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2649, pruned_loss=0.03334, over 1410713.94 frames.], batch size: 21, lr: 2.87e-04
2022-04-30 04:17:59,869 INFO [train.py:763] (1/8) Epoch 26, batch 3100, loss[loss=0.1709, simple_loss=0.2744, pruned_loss=0.03374, over 7317.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2651, pruned_loss=0.03331, over 1416897.33 frames.], batch size: 21, lr: 2.87e-04
2022-04-30 04:19:05,971 INFO [train.py:763] (1/8) Epoch 26, batch 3150, loss[loss=0.1871, simple_loss=0.2766, pruned_loss=0.04877, over 7216.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2655, pruned_loss=0.03357, over 1417471.07 frames.], batch size: 22, lr: 2.87e-04
2022-04-30 04:20:11,638 INFO [train.py:763] (1/8) Epoch 26, batch 3200, loss[loss=0.1889, simple_loss=0.2889, pruned_loss=0.04443, over 7212.00 frames.], tot_loss[loss=0.1667, simple_loss=0.266, pruned_loss=0.03369, over 1419480.42 frames.], batch size: 23, lr: 2.87e-04
2022-04-30 04:21:17,156 INFO [train.py:763] (1/8) Epoch 26, batch 3250, loss[loss=0.1698, simple_loss=0.2736, pruned_loss=0.03303, over 6640.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2659, pruned_loss=0.0336, over 1420083.44 frames.], batch size: 39, lr: 2.87e-04
2022-04-30 04:22:22,721 INFO [train.py:763] (1/8) Epoch 26, batch 3300, loss[loss=0.1584, simple_loss=0.2632, pruned_loss=0.02686, over 6757.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2653, pruned_loss=0.03328, over 1419933.25 frames.], batch size: 31, lr: 2.87e-04
2022-04-30 04:23:27,752 INFO [train.py:763] (1/8) Epoch 26, batch 3350, loss[loss=0.1631, simple_loss=0.2697, pruned_loss=0.02823, over 7330.00 frames.], tot_loss[loss=0.1665, simple_loss=0.266, pruned_loss=0.03351, over 1420290.24 frames.], batch size: 22, lr: 2.87e-04
2022-04-30 04:24:33,265 INFO [train.py:763] (1/8) Epoch 26, batch 3400, loss[loss=0.1684, simple_loss=0.2764, pruned_loss=0.03017, over 7151.00 frames.], tot_loss[loss=0.1663, simple_loss=0.2655, pruned_loss=0.03353, over 1417430.07 frames.], batch size: 20, lr: 2.87e-04
2022-04-30 04:25:38,625 INFO [train.py:763] (1/8) Epoch 26, batch 3450, loss[loss=0.1661, simple_loss=0.2667, pruned_loss=0.03278, over 7341.00 frames.], tot_loss[loss=0.1661, simple_loss=0.266, pruned_loss=0.03315, over 1420900.02 frames.], batch size: 22, lr: 2.87e-04
2022-04-30 04:26:44,097 INFO [train.py:763] (1/8) Epoch 26, batch 3500, loss[loss=0.1488, simple_loss=0.2446, pruned_loss=0.02653, over 7245.00 frames.], tot_loss[loss=0.1655, simple_loss=0.265, pruned_loss=0.03301, over 1423657.23 frames.], batch size: 16, lr: 2.87e-04
2022-04-30 04:27:49,684 INFO [train.py:763] (1/8) Epoch 26, batch 3550, loss[loss=0.1757, simple_loss=0.2741, pruned_loss=0.0386, over 5528.00 frames.], tot_loss[loss=0.1648, simple_loss=0.264, pruned_loss=0.03281, over 1418529.70 frames.], batch size: 52, lr: 2.87e-04
2022-04-30 04:28:54,782 INFO [train.py:763] (1/8) Epoch 26, batch 3600, loss[loss=0.1554, simple_loss=0.2581, pruned_loss=0.02633, over 7158.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2645, pruned_loss=0.03305, over 1416003.82 frames.], batch size: 19, lr: 2.87e-04
2022-04-30 04:30:00,884 INFO [train.py:763] (1/8) Epoch 26, batch 3650, loss[loss=0.1611, simple_loss=0.2537, pruned_loss=0.03425, over 7075.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2639, pruned_loss=0.03291, over 1414946.81 frames.], batch size: 18, lr: 2.87e-04
2022-04-30 04:31:07,244 INFO [train.py:763] (1/8) Epoch 26, batch 3700, loss[loss=0.1541, simple_loss=0.2454, pruned_loss=0.03143, over 7286.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2639, pruned_loss=0.03323, over 1413511.90 frames.], batch size: 18, lr: 2.87e-04
2022-04-30 04:32:12,926 INFO [train.py:763] (1/8) Epoch 26, batch 3750, loss[loss=0.1728, simple_loss=0.2682, pruned_loss=0.03864, over 7226.00 frames.], tot_loss[loss=0.164, simple_loss=0.2624, pruned_loss=0.03275, over 1417628.32 frames.], batch size: 21, lr: 2.87e-04
2022-04-30 04:33:19,995 INFO [train.py:763] (1/8) Epoch 26, batch 3800, loss[loss=0.1705, simple_loss=0.2794, pruned_loss=0.03076, over 7329.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2623, pruned_loss=0.03272, over 1421594.57 frames.], batch size: 20, lr: 2.87e-04
2022-04-30 04:34:26,370 INFO [train.py:763] (1/8) Epoch 26, batch 3850, loss[loss=0.1354, simple_loss=0.2268, pruned_loss=0.02198, over 7398.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2633, pruned_loss=0.03272, over 1414754.67 frames.], batch size: 18, lr: 2.87e-04
2022-04-30 04:35:31,743 INFO [train.py:763] (1/8) Epoch 26, batch 3900, loss[loss=0.1696, simple_loss=0.2737, pruned_loss=0.03276, over 7070.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2642, pruned_loss=0.03299, over 1415393.74 frames.], batch size: 28, lr: 2.86e-04
2022-04-30 04:36:37,007 INFO [train.py:763] (1/8) Epoch 26, batch 3950, loss[loss=0.1421, simple_loss=0.2398, pruned_loss=0.02216, over 7360.00 frames.], tot_loss[loss=0.1655, simple_loss=0.2648, pruned_loss=0.0331, over 1419160.07 frames.], batch size: 19, lr: 2.86e-04
2022-04-30 04:37:42,771 INFO [train.py:763] (1/8) Epoch 26, batch 4000, loss[loss=0.1938, simple_loss=0.2958, pruned_loss=0.04591, over 7135.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2638, pruned_loss=0.03283, over 1424719.07 frames.], batch size: 28, lr: 2.86e-04
2022-04-30 04:38:48,118 INFO [train.py:763] (1/8) Epoch 26, batch 4050, loss[loss=0.1742, simple_loss=0.2681, pruned_loss=0.04009, over 7345.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2636, pruned_loss=0.0326, over 1426320.03 frames.], batch size: 20, lr: 2.86e-04
2022-04-30 04:39:53,363 INFO [train.py:763] (1/8) Epoch 26, batch 4100, loss[loss=0.1722, simple_loss=0.2732, pruned_loss=0.03561, over 7323.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2626, pruned_loss=0.03226, over 1424617.48 frames.], batch size: 20, lr: 2.86e-04
2022-04-30 04:40:58,510 INFO [train.py:763] (1/8) Epoch 26, batch 4150, loss[loss=0.1663, simple_loss=0.2658, pruned_loss=0.03337, over 7104.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2633, pruned_loss=0.03244, over 1421363.50 frames.], batch size: 21, lr: 2.86e-04
2022-04-30 04:42:03,899 INFO [train.py:763] (1/8) Epoch 26, batch 4200, loss[loss=0.1482, simple_loss=0.2478, pruned_loss=0.02426, over 7332.00 frames.], tot_loss[loss=0.164, simple_loss=0.2632, pruned_loss=0.03239, over 1422820.46 frames.], batch size: 22, lr: 2.86e-04
2022-04-30 04:43:08,777 INFO [train.py:763] (1/8) Epoch 26, batch 4250, loss[loss=0.1976, simple_loss=0.3052, pruned_loss=0.04494, over 7413.00 frames.], tot_loss[loss=0.1652, simple_loss=0.2647, pruned_loss=0.0328, over 1415649.06 frames.], batch size: 21, lr: 2.86e-04
2022-04-30 04:44:14,517 INFO [train.py:763] (1/8) Epoch 26, batch 4300, loss[loss=0.1634, simple_loss=0.2774, pruned_loss=0.02473, over 6693.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2649, pruned_loss=0.03285, over 1414067.74 frames.], batch size: 31, lr: 2.86e-04
2022-04-30 04:45:19,673 INFO [train.py:763] (1/8) Epoch 26, batch 4350, loss[loss=0.1369, simple_loss=0.2257, pruned_loss=0.02402, over 7017.00 frames.], tot_loss[loss=0.1653, simple_loss=0.2647, pruned_loss=0.03297, over 1413153.45 frames.], batch size: 16, lr: 2.86e-04
2022-04-30 04:46:24,703 INFO [train.py:763] (1/8) Epoch 26, batch 4400, loss[loss=0.1632, simple_loss=0.2711, pruned_loss=0.02759, over 6353.00 frames.], tot_loss[loss=0.1665, simple_loss=0.2658, pruned_loss=0.03355, over 1400215.14 frames.], batch size: 37, lr: 2.86e-04
2022-04-30 04:47:29,333 INFO [train.py:763] (1/8) Epoch 26, batch 4450, loss[loss=0.164, simple_loss=0.2734, pruned_loss=0.02729, over 7345.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2653, pruned_loss=0.03346, over 1396018.21 frames.], batch size: 22, lr: 2.86e-04
2022-04-30 04:48:34,527 INFO [train.py:763] (1/8) Epoch 26, batch 4500, loss[loss=0.1724, simple_loss=0.2695, pruned_loss=0.0377, over 7162.00 frames.], tot_loss[loss=0.1666, simple_loss=0.2654, pruned_loss=0.0339, over 1385452.88 frames.], batch size: 18, lr: 2.86e-04
2022-04-30 04:49:39,406 INFO [train.py:763] (1/8) Epoch 26, batch 4550, loss[loss=0.2406, simple_loss=0.3214, pruned_loss=0.07987, over 5045.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2642, pruned_loss=0.03402, over 1369407.12 frames.], batch size: 52, lr: 2.86e-04
2022-04-30 04:51:07,355 INFO [train.py:763] (1/8) Epoch 27, batch 0, loss[loss=0.1576, simple_loss=0.2496, pruned_loss=0.03277, over 7264.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2496, pruned_loss=0.03277, over 7264.00 frames.], batch size: 19, lr: 2.81e-04
2022-04-30 04:52:13,082 INFO [train.py:763] (1/8) Epoch 27, batch 50, loss[loss=0.1607, simple_loss=0.2605, pruned_loss=0.03042, over 7264.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2642, pruned_loss=0.03206, over 321502.71 frames.], batch size: 19, lr: 2.81e-04
2022-04-30 04:53:19,206 INFO [train.py:763] (1/8) Epoch 27, batch 100, loss[loss=0.1588, simple_loss=0.2575, pruned_loss=0.03004, over 7143.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2645, pruned_loss=0.03136, over 565018.47 frames.], batch size: 20, lr: 2.80e-04
2022-04-30 04:54:25,258 INFO [train.py:763] (1/8) Epoch 27, batch 150, loss[loss=0.1588, simple_loss=0.2614, pruned_loss=0.0281, over 6464.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2647, pruned_loss=0.03227, over 753288.05 frames.], batch size: 37, lr: 2.80e-04
2022-04-30 04:55:31,374 INFO [train.py:763] (1/8) Epoch 27, batch 200, loss[loss=0.1805, simple_loss=0.2849, pruned_loss=0.03808, over 7221.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2639, pruned_loss=0.03219, over 898976.19 frames.], batch size: 23, lr: 2.80e-04
2022-04-30 04:56:38,001 INFO [train.py:763] (1/8) Epoch 27, batch 250, loss[loss=0.1787, simple_loss=0.2774, pruned_loss=0.03997, over 7277.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2642, pruned_loss=0.03201, over 1014863.98 frames.], batch size: 24, lr: 2.80e-04
2022-04-30 04:57:44,218 INFO [train.py:763] (1/8) Epoch 27, batch 300, loss[loss=0.1766, simple_loss=0.2816, pruned_loss=0.03584, over 6820.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2651, pruned_loss=0.03222, over 1104317.84 frames.], batch size: 31, lr: 2.80e-04
2022-04-30 04:58:50,090 INFO [train.py:763] (1/8) Epoch 27, batch 350, loss[loss=0.1493, simple_loss=0.2546, pruned_loss=0.02203, over 7156.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2647, pruned_loss=0.03183, over 1176511.64 frames.], batch size: 19, lr: 2.80e-04
2022-04-30 04:59:56,368 INFO [train.py:763] (1/8) Epoch 27, batch 400, loss[loss=0.1491, simple_loss=0.2435, pruned_loss=0.02738, over 7144.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2649, pruned_loss=0.03242, over 1232961.19 frames.], batch size: 17, lr: 2.80e-04
2022-04-30 05:01:02,254 INFO [train.py:763] (1/8) Epoch 27, batch 450, loss[loss=0.1853, simple_loss=0.2861, pruned_loss=0.0423, over 7344.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2645, pruned_loss=0.03244, over 1269819.89 frames.], batch size: 25, lr: 2.80e-04
2022-04-30 05:02:08,165 INFO [train.py:763] (1/8) Epoch 27, batch 500, loss[loss=0.1809, simple_loss=0.2932, pruned_loss=0.03429, over 7309.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2651, pruned_loss=0.0326, over 1307153.07 frames.], batch size: 21, lr: 2.80e-04
2022-04-30 05:03:14,021 INFO [train.py:763] (1/8) Epoch 27, batch 550, loss[loss=0.1719, simple_loss=0.2666, pruned_loss=0.0386, over 7048.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2649, pruned_loss=0.03294, over 1329250.32 frames.], batch size: 18, lr: 2.80e-04
2022-04-30 05:04:19,655 INFO [train.py:763] (1/8) Epoch 27, batch 600, loss[loss=0.1606, simple_loss=0.2578, pruned_loss=0.03172, over 7324.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2634, pruned_loss=0.03195, over 1347781.31 frames.], batch size: 20, lr: 2.80e-04
2022-04-30 05:05:24,799 INFO [train.py:763] (1/8) Epoch 27, batch 650, loss[loss=0.1492, simple_loss=0.255, pruned_loss=0.02174, over 7055.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2638, pruned_loss=0.03206, over 1365747.47 frames.], batch size: 28, lr: 2.80e-04
2022-04-30 05:06:40,258 INFO [train.py:763] (1/8) Epoch 27, batch 700, loss[loss=0.1453, simple_loss=0.2408, pruned_loss=0.02489, over 7064.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2633, pruned_loss=0.03185, over 1379740.49 frames.], batch size: 18, lr: 2.80e-04
2022-04-30 05:07:46,092 INFO [train.py:763] (1/8) Epoch 27, batch 750, loss[loss=0.1613, simple_loss=0.2709, pruned_loss=0.02589, over 7228.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2626, pruned_loss=0.03181, over 1391120.87 frames.], batch size: 21, lr: 2.80e-04
2022-04-30 05:08:51,497 INFO [train.py:763] (1/8) Epoch 27, batch 800, loss[loss=0.1973, simple_loss=0.294, pruned_loss=0.0503, over 7051.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2626, pruned_loss=0.03184, over 1397635.01 frames.], batch size: 28, lr: 2.80e-04
2022-04-30 05:09:56,917 INFO [train.py:763] (1/8) Epoch 27, batch 850, loss[loss=0.1702, simple_loss=0.2782, pruned_loss=0.03104, over 7292.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2633, pruned_loss=0.03206, over 1405074.81 frames.], batch size: 25, lr: 2.80e-04
2022-04-30 05:11:02,105 INFO [train.py:763] (1/8) Epoch 27, batch 900, loss[loss=0.1495, simple_loss=0.2466, pruned_loss=0.02614, over 7002.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2643, pruned_loss=0.03242, over 1407220.55 frames.], batch size: 16, lr: 2.80e-04
2022-04-30 05:12:07,291 INFO [train.py:763] (1/8) Epoch 27, batch 950, loss[loss=0.1663, simple_loss=0.2642, pruned_loss=0.03423, over 7152.00 frames.], tot_loss[loss=0.165, simple_loss=0.2645, pruned_loss=0.03277, over 1409313.11 frames.], batch size: 18, lr: 2.80e-04
2022-04-30 05:13:12,793 INFO [train.py:763] (1/8) Epoch 27, batch 1000, loss[loss=0.158, simple_loss=0.2608, pruned_loss=0.02766, over 7425.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2641, pruned_loss=0.03259, over 1415026.33 frames.], batch size: 20, lr: 2.79e-04
2022-04-30 05:14:18,814 INFO [train.py:763] (1/8) Epoch 27, batch 1050, loss[loss=0.169, simple_loss=0.2731, pruned_loss=0.03247, over 7414.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2647, pruned_loss=0.03299, over 1414916.28 frames.], batch size: 21, lr: 2.79e-04
2022-04-30 05:15:25,041 INFO [train.py:763] (1/8) Epoch 27, batch 1100, loss[loss=0.169, simple_loss=0.2646, pruned_loss=0.03672, over 7056.00 frames.], tot_loss[loss=0.1662, simple_loss=0.2658, pruned_loss=0.0333, over 1414445.39 frames.], batch size: 18, lr: 2.79e-04
2022-04-30 05:16:31,239 INFO [train.py:763] (1/8) Epoch 27, batch 1150, loss[loss=0.17, simple_loss=0.2708, pruned_loss=0.0346, over 7200.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2646, pruned_loss=0.03282, over 1419834.89 frames.], batch size: 23, lr: 2.79e-04
2022-04-30 05:17:47,506 INFO [train.py:763] (1/8) Epoch 27, batch 1200, loss[loss=0.1371, simple_loss=0.2276, pruned_loss=0.02327, over 7147.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2634, pruned_loss=0.03239, over 1424559.79 frames.], batch size: 17, lr: 2.79e-04
2022-04-30 05:19:01,884 INFO [train.py:763] (1/8) Epoch 27, batch 1250, loss[loss=0.1307, simple_loss=0.2267, pruned_loss=0.01741, over 7146.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2633, pruned_loss=0.03217, over 1422556.44 frames.], batch size: 17, lr: 2.79e-04
2022-04-30 05:20:26,019 INFO [train.py:763] (1/8) Epoch 27, batch 1300, loss[loss=0.1371, simple_loss=0.2324, pruned_loss=0.02094, over 7269.00 frames.], tot_loss[loss=0.164, simple_loss=0.2634, pruned_loss=0.03232, over 1418757.58 frames.], batch size: 18, lr: 2.79e-04
2022-04-30 05:21:31,873 INFO [train.py:763] (1/8) Epoch 27, batch 1350, loss[loss=0.1665, simple_loss=0.2586, pruned_loss=0.03727, over 7356.00 frames.], tot_loss[loss=0.1639, simple_loss=0.263, pruned_loss=0.03245, over 1419082.93 frames.], batch size: 19, lr: 2.79e-04
2022-04-30 05:22:37,302 INFO [train.py:763] (1/8) Epoch 27, batch 1400, loss[loss=0.1603, simple_loss=0.262, pruned_loss=0.02924, over 7050.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2632, pruned_loss=0.03278, over 1418657.96 frames.], batch size: 18, lr: 2.79e-04
2022-04-30 05:24:10,241 INFO [train.py:763] (1/8) Epoch 27, batch 1450, loss[loss=0.1428, simple_loss=0.2409, pruned_loss=0.02238, over 7330.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2623, pruned_loss=0.03253, over 1421592.82 frames.], batch size: 20, lr: 2.79e-04
2022-04-30 05:25:16,093 INFO [train.py:763] (1/8) Epoch 27, batch 1500, loss[loss=0.1628, simple_loss=0.275, pruned_loss=0.02527, over 7120.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2629, pruned_loss=0.03232, over 1423485.89 frames.], batch size: 21, lr: 2.79e-04
2022-04-30 05:26:21,997 INFO [train.py:763] (1/8) Epoch 27, batch 1550, loss[loss=0.1633, simple_loss=0.2485, pruned_loss=0.0391, over 6776.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2638, pruned_loss=0.03286, over 1420757.79 frames.], batch size: 15, lr: 2.79e-04
2022-04-30 05:27:29,033 INFO [train.py:763] (1/8) Epoch 27, batch 1600, loss[loss=0.1525, simple_loss=0.2593, pruned_loss=0.0229, over 7415.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2632, pruned_loss=0.03229, over 1425385.39 frames.], batch size: 21, lr: 2.79e-04
2022-04-30 05:28:35,019 INFO [train.py:763] (1/8) Epoch 27, batch 1650, loss[loss=0.1429, simple_loss=0.2525, pruned_loss=0.01667, over 7067.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2636, pruned_loss=0.03247, over 1426542.02 frames.], batch size: 18, lr: 2.79e-04
2022-04-30 05:29:41,341 INFO [train.py:763] (1/8) Epoch 27, batch 1700, loss[loss=0.1623, simple_loss=0.2598, pruned_loss=0.03243, over 7346.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2641, pruned_loss=0.03272, over 1428012.47 frames.], batch size: 19, lr: 2.79e-04
2022-04-30 05:30:48,489 INFO [train.py:763] (1/8) Epoch 27, batch 1750, loss[loss=0.2, simple_loss=0.2953, pruned_loss=0.05236, over 6779.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2633, pruned_loss=0.03249, over 1429878.64 frames.], batch size: 31, lr: 2.79e-04
2022-04-30 05:31:54,570 INFO [train.py:763] (1/8) Epoch 27, batch 1800, loss[loss=0.1722, simple_loss=0.2878, pruned_loss=0.02836, over 7234.00 frames.], tot_loss[loss=0.1646, simple_loss=0.264, pruned_loss=0.03264, over 1429606.51 frames.], batch size: 20, lr: 2.79e-04
2022-04-30 05:33:00,690 INFO [train.py:763] (1/8) Epoch 27, batch 1850, loss[loss=0.1437, simple_loss=0.2425, pruned_loss=0.0225, over 7158.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2638, pruned_loss=0.03229, over 1431931.18 frames.], batch size: 19, lr: 2.79e-04
2022-04-30 05:34:06,839 INFO [train.py:763] (1/8) Epoch 27, batch 1900, loss[loss=0.147, simple_loss=0.2409, pruned_loss=0.02658, over 7279.00 frames.], tot_loss[loss=0.165, simple_loss=0.2647, pruned_loss=0.03267, over 1431556.96 frames.], batch size: 17, lr: 2.78e-04
2022-04-30 05:35:13,648 INFO [train.py:763] (1/8) Epoch 27, batch 1950, loss[loss=0.1672, simple_loss=0.2753, pruned_loss=0.02954, over 6503.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2641, pruned_loss=0.03264, over 1426186.09 frames.], batch size: 38, lr: 2.78e-04
2022-04-30 05:36:20,335 INFO [train.py:763] (1/8) Epoch 27, batch 2000, loss[loss=0.191, simple_loss=0.2968, pruned_loss=0.04264, over 7224.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2642, pruned_loss=0.03273, over 1425623.12 frames.], batch size: 21, lr: 2.78e-04
2022-04-30 05:37:26,470 INFO [train.py:763] (1/8) Epoch 27, batch 2050, loss[loss=0.1815, simple_loss=0.2673, pruned_loss=0.04782, over 7195.00 frames.], tot_loss[loss=0.1661, simple_loss=0.2654, pruned_loss=0.03341, over 1424242.87 frames.], batch size: 23, lr: 2.78e-04
2022-04-30 05:38:32,945 INFO [train.py:763] (1/8) Epoch 27, batch 2100, loss[loss=0.1785, simple_loss=0.2806, pruned_loss=0.03817, over 7297.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2647, pruned_loss=0.03275, over 1423998.51 frames.], batch size: 25, lr: 2.78e-04
2022-04-30 05:39:38,768 INFO [train.py:763] (1/8) Epoch 27, batch 2150, loss[loss=0.1446, simple_loss=0.2403, pruned_loss=0.02444, over 7138.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2643, pruned_loss=0.03204, over 1422697.74 frames.], batch size: 17, lr: 2.78e-04
2022-04-30 05:40:44,411 INFO [train.py:763] (1/8) Epoch 27, batch 2200, loss[loss=0.1748, simple_loss=0.2723, pruned_loss=0.03867, over 7284.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2641, pruned_loss=0.03214, over 1422118.48 frames.], batch size: 24, lr: 2.78e-04
2022-04-30 05:41:50,157 INFO [train.py:763] (1/8) Epoch 27, batch 2250, loss[loss=0.1612, simple_loss=0.2683, pruned_loss=0.02703, over 7322.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2635, pruned_loss=0.03176, over 1424414.82 frames.], batch size: 22, lr: 2.78e-04
2022-04-30 05:42:56,032 INFO [train.py:763] (1/8) Epoch 27, batch 2300, loss[loss=0.1616, simple_loss=0.2792, pruned_loss=0.02196, over 7136.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2638, pruned_loss=0.03153, over 1422033.53 frames.], batch size: 20, lr: 2.78e-04
2022-04-30 05:44:01,765 INFO [train.py:763] (1/8) Epoch 27, batch 2350, loss[loss=0.1604, simple_loss=0.2499, pruned_loss=0.03546, over 7163.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2642, pruned_loss=0.03182, over 1419417.14 frames.], batch size: 19, lr: 2.78e-04
2022-04-30 05:45:08,043 INFO [train.py:763] (1/8) Epoch 27, batch 2400, loss[loss=0.1808, simple_loss=0.2886, pruned_loss=0.03653, over 7189.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2643, pruned_loss=0.03213, over 1421947.48 frames.], batch size: 23, lr: 2.78e-04
2022-04-30 05:46:14,164 INFO [train.py:763] (1/8) Epoch 27, batch 2450, loss[loss=0.1757, simple_loss=0.2775, pruned_loss=0.03696, over 6446.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2633, pruned_loss=0.03171, over 1423405.69 frames.], batch size: 37, lr: 2.78e-04
2022-04-30 05:47:19,802 INFO [train.py:763] (1/8) Epoch 27, batch 2500, loss[loss=0.1462, simple_loss=0.2319, pruned_loss=0.03022, over 6763.00 frames.], tot_loss[loss=0.164, simple_loss=0.2635, pruned_loss=0.03225, over 1419781.74 frames.], batch size: 15, lr: 2.78e-04
2022-04-30 05:48:25,886 INFO [train.py:763] (1/8) Epoch 27, batch 2550, loss[loss=0.1588, simple_loss=0.2542, pruned_loss=0.0317, over 7255.00 frames.], tot_loss[loss=0.1635, simple_loss=0.263, pruned_loss=0.032, over 1420591.44 frames.], batch size: 19, lr: 2.78e-04
2022-04-30 05:49:31,725 INFO [train.py:763] (1/8) Epoch 27, batch 2600, loss[loss=0.1623, simple_loss=0.2624, pruned_loss=0.03113, over 7225.00 frames.], tot_loss[loss=0.164, simple_loss=0.2634, pruned_loss=0.03235, over 1420859.38 frames.], batch size: 20, lr: 2.78e-04
2022-04-30 05:50:37,396 INFO [train.py:763] (1/8) Epoch 27, batch 2650, loss[loss=0.1453, simple_loss=0.2344, pruned_loss=0.02811, over 6999.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2639, pruned_loss=0.03256, over 1419836.52 frames.], batch size: 16, lr: 2.78e-04
2022-04-30 05:51:42,952 INFO [train.py:763] (1/8) Epoch 27, batch 2700, loss[loss=0.161, simple_loss=0.2651, pruned_loss=0.02843, over 7307.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2649, pruned_loss=0.03296, over 1421873.26 frames.], batch size: 21, lr: 2.78e-04
2022-04-30 05:52:49,036 INFO [train.py:763] (1/8) Epoch 27, batch 2750, loss[loss=0.1427, simple_loss=0.2466, pruned_loss=0.01944, over 7260.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2653, pruned_loss=0.03318, over 1419672.33 frames.], batch size: 19, lr: 2.78e-04
2022-04-30 05:53:54,755 INFO [train.py:763] (1/8) Epoch 27, batch 2800, loss[loss=0.1491, simple_loss=0.2497, pruned_loss=0.0242, over 7226.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2646, pruned_loss=0.03281, over 1416310.52 frames.], batch size: 20, lr: 2.77e-04
2022-04-30 05:55:00,519 INFO [train.py:763] (1/8) Epoch 27, batch 2850, loss[loss=0.1447, simple_loss=0.2495, pruned_loss=0.02001, over 7126.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2643, pruned_loss=0.03251, over 1420716.85 frames.], batch size: 17, lr: 2.77e-04
2022-04-30 05:56:06,157 INFO [train.py:763] (1/8) Epoch 27, batch 2900, loss[loss=0.1921, simple_loss=0.2968, pruned_loss=0.04369, over 7332.00 frames.], tot_loss[loss=0.1654, simple_loss=0.2653, pruned_loss=0.03279, over 1420356.09 frames.], batch size: 25, lr: 2.77e-04
2022-04-30 05:57:11,709 INFO [train.py:763] (1/8) Epoch 27, batch 2950, loss[loss=0.1645, simple_loss=0.2655, pruned_loss=0.03175, over 7228.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2652, pruned_loss=0.03302, over 1423521.08 frames.], batch size: 23, lr: 2.77e-04
2022-04-30 05:58:18,058 INFO [train.py:763] (1/8) Epoch 27, batch 3000, loss[loss=0.1985, simple_loss=0.2992, pruned_loss=0.04893, over 7031.00 frames.], tot_loss[loss=0.165, simple_loss=0.2648, pruned_loss=0.03257, over 1425342.97 frames.], batch size: 28, lr: 2.77e-04
2022-04-30 05:58:18,059 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 05:58:33,165 INFO [train.py:792] (1/8) Epoch 27, validation: loss=0.1686, simple_loss=0.2648, pruned_loss=0.03621, over 698248.00 frames.
2022-04-30 05:59:40,073 INFO [train.py:763] (1/8) Epoch 27, batch 3050, loss[loss=0.1308, simple_loss=0.2225, pruned_loss=0.01958, over 7138.00 frames.], tot_loss[loss=0.1649, simple_loss=0.2648, pruned_loss=0.03253, over 1427154.42 frames.], batch size: 17, lr: 2.77e-04
2022-04-30 06:00:45,833 INFO [train.py:763] (1/8) Epoch 27, batch 3100, loss[loss=0.1732, simple_loss=0.267, pruned_loss=0.03967, over 7389.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2636, pruned_loss=0.03226, over 1425820.89 frames.], batch size: 23, lr: 2.77e-04
2022-04-30 06:01:51,940 INFO [train.py:763] (1/8) Epoch 27, batch 3150, loss[loss=0.1476, simple_loss=0.2467, pruned_loss=0.02428, over 7407.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2635, pruned_loss=0.03238, over 1424063.94 frames.], batch size: 18, lr: 2.77e-04
2022-04-30 06:02:58,130 INFO [train.py:763] (1/8) Epoch 27, batch 3200, loss[loss=0.1722, simple_loss=0.2755, pruned_loss=0.03446, over 7311.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2635, pruned_loss=0.03213, over 1424516.71 frames.], batch size: 21, lr: 2.77e-04
2022-04-30 06:04:04,075 INFO [train.py:763] (1/8) Epoch 27, batch 3250, loss[loss=0.1786, simple_loss=0.2725, pruned_loss=0.04238, over 7162.00 frames.], tot_loss[loss=0.164, simple_loss=0.263, pruned_loss=0.03252, over 1424374.34 frames.], batch size: 18, lr: 2.77e-04
2022-04-30 06:05:10,045 INFO [train.py:763] (1/8) Epoch 27, batch 3300, loss[loss=0.1469, simple_loss=0.2338, pruned_loss=0.02998, over 6991.00 frames.], tot_loss[loss=0.165, simple_loss=0.264, pruned_loss=0.03299, over 1423330.98 frames.], batch size: 16, lr: 2.77e-04
2022-04-30 06:06:16,455 INFO [train.py:763] (1/8) Epoch 27, batch 3350, loss[loss=0.1815, simple_loss=0.2772, pruned_loss=0.04289, over 7377.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2638, pruned_loss=0.03295, over 1420812.53 frames.], batch size: 23, lr: 2.77e-04
2022-04-30 06:07:23,073 INFO [train.py:763] (1/8) Epoch 27, batch 3400, loss[loss=0.1702, simple_loss=0.2679, pruned_loss=0.03622, over 7316.00 frames.], tot_loss[loss=0.165, simple_loss=0.2641, pruned_loss=0.03296, over 1422646.59 frames.], batch size: 20, lr: 2.77e-04
2022-04-30 06:08:29,076 INFO [train.py:763] (1/8) Epoch 27, batch 3450, loss[loss=0.1909, simple_loss=0.2922, pruned_loss=0.04478, over 7206.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2639, pruned_loss=0.03271, over 1423622.14 frames.], batch size: 22, lr: 2.77e-04
2022-04-30 06:09:34,961 INFO [train.py:763] (1/8) Epoch 27, batch 3500, loss[loss=0.1786, simple_loss=0.2777, pruned_loss=0.03973, over 7068.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2634, pruned_loss=0.03269, over 1422676.21 frames.], batch size: 18, lr: 2.77e-04
2022-04-30 06:10:40,826 INFO [train.py:763] (1/8) Epoch 27, batch 3550, loss[loss=0.1718, simple_loss=0.2827, pruned_loss=0.03042, over 7339.00 frames.], tot_loss[loss=0.1645, simple_loss=0.264, pruned_loss=0.03253, over 1423643.05 frames.], batch size: 22, lr: 2.77e-04
2022-04-30 06:11:46,445 INFO [train.py:763] (1/8) Epoch 27, batch 3600, loss[loss=0.1536, simple_loss=0.2497, pruned_loss=0.02873, over 7066.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2644, pruned_loss=0.03244, over 1422576.55 frames.], batch size: 18, lr: 2.77e-04
2022-04-30 06:12:52,025 INFO [train.py:763] (1/8) Epoch 27, batch 3650, loss[loss=0.1697, simple_loss=0.2687, pruned_loss=0.03532, over 7416.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2632, pruned_loss=0.03223, over 1423523.70 frames.], batch size: 21, lr: 2.77e-04
2022-04-30 06:13:58,380 INFO [train.py:763] (1/8) Epoch 27, batch 3700, loss[loss=0.14, simple_loss=0.245, pruned_loss=0.01754, over 7433.00 frames.], tot_loss[loss=0.1636, simple_loss=0.263, pruned_loss=0.03212, over 1422915.72 frames.], batch size: 20, lr: 2.77e-04
2022-04-30 06:15:04,062 INFO [train.py:763] (1/8) Epoch 27, batch 3750, loss[loss=0.1823, simple_loss=0.2723, pruned_loss=0.04618, over 5037.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2635, pruned_loss=0.03245, over 1419498.47 frames.], batch size: 52, lr: 2.76e-04
2022-04-30 06:16:10,307 INFO [train.py:763] (1/8) Epoch 27, batch 3800, loss[loss=0.1616, simple_loss=0.2531, pruned_loss=0.03506, over 7269.00 frames.], tot_loss[loss=0.164, simple_loss=0.2635, pruned_loss=0.03229, over 1421448.65 frames.], batch size: 17, lr: 2.76e-04
2022-04-30 06:17:16,528 INFO [train.py:763] (1/8) Epoch 27, batch 3850, loss[loss=0.1584, simple_loss=0.2666, pruned_loss=0.0251, over 7169.00 frames.], tot_loss[loss=0.164, simple_loss=0.2633, pruned_loss=0.03235, over 1425589.44 frames.], batch size: 19, lr: 2.76e-04
2022-04-30 06:18:22,908 INFO [train.py:763] (1/8) Epoch 27, batch 3900, loss[loss=0.1658, simple_loss=0.2662, pruned_loss=0.03267, over 7202.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2635, pruned_loss=0.03236, over 1424076.79 frames.], batch size: 22, lr: 2.76e-04
2022-04-30 06:19:28,539 INFO [train.py:763] (1/8) Epoch 27, batch 3950, loss[loss=0.1707, simple_loss=0.2697, pruned_loss=0.03584, over 7207.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2625, pruned_loss=0.03166, over 1425091.17 frames.], batch size: 22, lr: 2.76e-04
2022-04-30 06:20:34,796 INFO [train.py:763] (1/8) Epoch 27, batch 4000, loss[loss=0.1614, simple_loss=0.266, pruned_loss=0.0284, over 6739.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2614, pruned_loss=0.03141, over 1421542.72 frames.], batch size: 31, lr: 2.76e-04
2022-04-30 06:21:40,917 INFO [train.py:763] (1/8) Epoch 27, batch 4050, loss[loss=0.2036, simple_loss=0.2943, pruned_loss=0.0564, over 5284.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2628, pruned_loss=0.03224, over 1415650.76 frames.], batch size: 52, lr: 2.76e-04
2022-04-30 06:22:47,107 INFO [train.py:763] (1/8) Epoch 27, batch 4100, loss[loss=0.1577, simple_loss=0.2427, pruned_loss=0.03633, over 7144.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2629, pruned_loss=0.03251, over 1417669.02 frames.], batch size: 17, lr: 2.76e-04
2022-04-30 06:24:03,944 INFO [train.py:763] (1/8) Epoch 27, batch 4150, loss[loss=0.1569, simple_loss=0.2607, pruned_loss=0.02656, over 7156.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2637, pruned_loss=0.03244, over 1422434.87 frames.], batch size: 19, lr: 2.76e-04
2022-04-30 06:25:09,374 INFO [train.py:763] (1/8) Epoch 27, batch 4200, loss[loss=0.2297, simple_loss=0.3065, pruned_loss=0.07645, over 4923.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2644, pruned_loss=0.03291, over 1416290.93 frames.], batch size: 53, lr: 2.76e-04
2022-04-30 06:26:15,106 INFO [train.py:763] (1/8) Epoch 27, batch 4250, loss[loss=0.1405, simple_loss=0.2368, pruned_loss=0.02213, over 7063.00 frames.], tot_loss[loss=0.1648, simple_loss=0.2643, pruned_loss=0.03266, over 1414510.51 frames.], batch size: 18, lr: 2.76e-04
2022-04-30 06:27:21,138 INFO [train.py:763] (1/8) Epoch 27, batch 4300, loss[loss=0.1294, simple_loss=0.213, pruned_loss=0.02288, over 7135.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2634, pruned_loss=0.03262, over 1417018.61 frames.], batch size: 17, lr: 2.76e-04
2022-04-30 06:28:27,401 INFO [train.py:763] (1/8) Epoch 27, batch 4350, loss[loss=0.1631, simple_loss=0.2722, pruned_loss=0.02697, over 7216.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2634, pruned_loss=0.0327, over 1417143.96 frames.], batch size: 21, lr: 2.76e-04
2022-04-30 06:29:33,355 INFO [train.py:763] (1/8) Epoch 27, batch 4400, loss[loss=0.1853, simple_loss=0.2858, pruned_loss=0.04234, over 6415.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2633, pruned_loss=0.03294, over 1408112.89 frames.], batch size: 38, lr: 2.76e-04
2022-04-30 06:30:39,367 INFO [train.py:763] (1/8) Epoch 27, batch 4450, loss[loss=0.1369, simple_loss=0.2304, pruned_loss=0.02166, over 6803.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2635, pruned_loss=0.03287, over 1402588.47 frames.], batch size: 15, lr: 2.76e-04
2022-04-30 06:31:44,896 INFO [train.py:763] (1/8) Epoch 27, batch 4500, loss[loss=0.1342, simple_loss=0.2339, pruned_loss=0.01727, over 7212.00 frames.], tot_loss[loss=0.1647, simple_loss=0.2636, pruned_loss=0.03289, over 1389111.29 frames.], batch size: 21, lr: 2.76e-04
2022-04-30 06:32:50,034 INFO [train.py:763] (1/8) Epoch 27, batch 4550, loss[loss=0.1744, simple_loss=0.2722, pruned_loss=0.0383, over 6429.00 frames.], tot_loss[loss=0.1656, simple_loss=0.2641, pruned_loss=0.03354, over 1359130.36 frames.], batch size: 38, lr: 2.76e-04
2022-04-30 06:34:19,195 INFO [train.py:763] (1/8) Epoch 28, batch 0, loss[loss=0.1602, simple_loss=0.2571, pruned_loss=0.03165, over 7097.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2571, pruned_loss=0.03165, over 7097.00 frames.], batch size: 28, lr: 2.71e-04
2022-04-30 06:35:24,833 INFO [train.py:763] (1/8) Epoch 28, batch 50, loss[loss=0.1912, simple_loss=0.2819, pruned_loss=0.05028, over 7305.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2621, pruned_loss=0.03068, over 323128.36 frames.], batch size: 24, lr: 2.71e-04
2022-04-30 06:36:31,682 INFO [train.py:763] (1/8) Epoch 28, batch 100, loss[loss=0.1867, simple_loss=0.279, pruned_loss=0.04717, over 7326.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2631, pruned_loss=0.03221, over 569103.39 frames.], batch size: 21, lr: 2.71e-04
2022-04-30 06:37:37,371 INFO [train.py:763] (1/8) Epoch 28, batch 150, loss[loss=0.1757, simple_loss=0.2874, pruned_loss=0.032, over 7237.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2626, pruned_loss=0.03187, over 759090.60 frames.], batch size: 20, lr: 2.71e-04
2022-04-30 06:38:43,639 INFO [train.py:763] (1/8) Epoch 28, batch 200, loss[loss=0.1531, simple_loss=0.2527, pruned_loss=0.02672, over 7053.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2622, pruned_loss=0.03159, over 908153.02 frames.], batch size: 18, lr: 2.71e-04
2022-04-30 06:39:49,239 INFO [train.py:763] (1/8) Epoch 28, batch 250, loss[loss=0.1976, simple_loss=0.2904, pruned_loss=0.05247, over 5161.00 frames.], tot_loss[loss=0.162, simple_loss=0.2611, pruned_loss=0.03145, over 1019571.76 frames.], batch size: 52, lr: 2.71e-04
2022-04-30 06:40:54,482 INFO [train.py:763] (1/8) Epoch 28, batch 300, loss[loss=0.1676, simple_loss=0.2597, pruned_loss=0.03777, over 7166.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2619, pruned_loss=0.03171, over 1109089.55 frames.], batch size: 18, lr: 2.70e-04
2022-04-30 06:41:59,622 INFO [train.py:763] (1/8) Epoch 28, batch 350, loss[loss=0.1542, simple_loss=0.2499, pruned_loss=0.02926, over 7067.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2622, pruned_loss=0.03149, over 1180323.79 frames.], batch size: 18, lr: 2.70e-04
2022-04-30 06:43:05,885 INFO [train.py:763] (1/8) Epoch 28, batch 400, loss[loss=0.1411, simple_loss=0.2354, pruned_loss=0.02344, over 7147.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2625, pruned_loss=0.03138, over 1236206.06 frames.], batch size: 20, lr: 2.70e-04
2022-04-30 06:44:12,428 INFO [train.py:763] (1/8) Epoch 28, batch 450, loss[loss=0.1587, simple_loss=0.2628, pruned_loss=0.02726, over 7112.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2627, pruned_loss=0.03158, over 1282007.63 frames.], batch size: 21, lr: 2.70e-04
2022-04-30 06:45:17,936 INFO [train.py:763] (1/8) Epoch 28, batch 500, loss[loss=0.188, simple_loss=0.282, pruned_loss=0.04701, over 5173.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2631, pruned_loss=0.03175, over 1309749.52 frames.], batch size: 52, lr: 2.70e-04
2022-04-30 06:46:23,647 INFO [train.py:763] (1/8) Epoch 28, batch 550, loss[loss=0.152, simple_loss=0.2564, pruned_loss=0.02382, over 7212.00 frames.], tot_loss[loss=0.164, simple_loss=0.2637, pruned_loss=0.03215, over 1331953.60 frames.], batch size: 21, lr: 2.70e-04
2022-04-30 06:47:29,778 INFO [train.py:763] (1/8) Epoch 28, batch 600, loss[loss=0.1454, simple_loss=0.2519, pruned_loss=0.01949, over 7257.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2632, pruned_loss=0.03201, over 1349962.75 frames.], batch size: 19, lr: 2.70e-04
2022-04-30 06:48:35,459 INFO [train.py:763] (1/8) Epoch 28, batch 650, loss[loss=0.1592, simple_loss=0.265, pruned_loss=0.02667, over 7074.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2631, pruned_loss=0.03193, over 1368301.04 frames.], batch size: 18, lr: 2.70e-04
2022-04-30 06:49:42,649 INFO [train.py:763] (1/8) Epoch 28, batch 700, loss[loss=0.2208, simple_loss=0.3084, pruned_loss=0.06661, over 5062.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2642, pruned_loss=0.03203, over 1376500.07 frames.], batch size: 54, lr: 2.70e-04
2022-04-30 06:50:48,227 INFO [train.py:763] (1/8) Epoch 28, batch 750, loss[loss=0.1632, simple_loss=0.2682, pruned_loss=0.02913, over 7430.00 frames.], tot_loss[loss=0.1636, simple_loss=0.264, pruned_loss=0.0316, over 1382819.44 frames.], batch size: 20, lr: 2.70e-04
2022-04-30 06:51:53,707 INFO [train.py:763] (1/8) Epoch 28, batch 800, loss[loss=0.1607, simple_loss=0.2694, pruned_loss=0.02603, over 7109.00 frames.], tot_loss[loss=0.164, simple_loss=0.2646, pruned_loss=0.03167, over 1389344.16 frames.], batch size: 21, lr: 2.70e-04
2022-04-30 06:52:59,916 INFO [train.py:763] (1/8) Epoch 28, batch 850, loss[loss=0.1649, simple_loss=0.2706, pruned_loss=0.02961, over 6229.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2643, pruned_loss=0.03176, over 1393759.88 frames.], batch size: 37, lr: 2.70e-04
2022-04-30 06:54:06,450 INFO [train.py:763] (1/8) Epoch 28, batch 900, loss[loss=0.1765, simple_loss=0.2736, pruned_loss=0.03969, over 6820.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2636, pruned_loss=0.03194, over 1400148.96 frames.], batch size: 31, lr: 2.70e-04
2022-04-30 06:55:12,069 INFO [train.py:763] (1/8) Epoch 28, batch 950, loss[loss=0.1779, simple_loss=0.2684, pruned_loss=0.04372, over 7209.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2637, pruned_loss=0.03228, over 1409157.50 frames.], batch size: 22, lr: 2.70e-04
2022-04-30 06:56:17,977 INFO [train.py:763] (1/8) Epoch 28, batch 1000, loss[loss=0.1437, simple_loss=0.233, pruned_loss=0.02725, over 6751.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2626, pruned_loss=0.03207, over 1415132.48 frames.], batch size: 15, lr: 2.70e-04
2022-04-30 06:57:23,496 INFO [train.py:763] (1/8) Epoch 28, batch 1050, loss[loss=0.1681, simple_loss=0.2634, pruned_loss=0.03635, over 7410.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2628, pruned_loss=0.03135, over 1420667.40 frames.], batch size: 21, lr: 2.70e-04
2022-04-30 06:58:29,247 INFO [train.py:763] (1/8) Epoch 28, batch 1100, loss[loss=0.154, simple_loss=0.247, pruned_loss=0.03051, over 7277.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2627, pruned_loss=0.0315, over 1422849.16 frames.], batch size: 17, lr: 2.70e-04
2022-04-30 06:59:35,655 INFO [train.py:763] (1/8) Epoch 28, batch 1150, loss[loss=0.1657, simple_loss=0.267, pruned_loss=0.03221, over 7097.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2628, pruned_loss=0.03154, over 1421879.69 frames.], batch size: 28, lr: 2.70e-04
2022-04-30 07:00:40,810 INFO [train.py:763] (1/8) Epoch 28, batch 1200, loss[loss=0.1764, simple_loss=0.2783, pruned_loss=0.03722, over 7083.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2636, pruned_loss=0.03161, over 1424013.70 frames.], batch size: 28, lr: 2.70e-04
2022-04-30 07:01:47,022 INFO [train.py:763] (1/8) Epoch 28, batch 1250, loss[loss=0.1888, simple_loss=0.285, pruned_loss=0.04631, over 7215.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2632, pruned_loss=0.03179, over 1418997.81 frames.], batch size: 22, lr: 2.70e-04
2022-04-30 07:02:52,926 INFO [train.py:763] (1/8) Epoch 28, batch 1300, loss[loss=0.1736, simple_loss=0.274, pruned_loss=0.03654, over 7153.00 frames.], tot_loss[loss=0.163, simple_loss=0.2622, pruned_loss=0.03186, over 1420985.90 frames.], batch size: 20, lr: 2.69e-04
2022-04-30 07:03:58,472 INFO [train.py:763] (1/8) Epoch 28, batch 1350, loss[loss=0.1762, simple_loss=0.2826, pruned_loss=0.03493, over 7118.00 frames.], tot_loss[loss=0.1629, simple_loss=0.262, pruned_loss=0.03196, over 1425912.40 frames.], batch size: 21, lr: 2.69e-04
2022-04-30 07:05:04,525 INFO [train.py:763] (1/8) Epoch 28, batch 1400, loss[loss=0.1547, simple_loss=0.2417, pruned_loss=0.03389, over 7275.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2618, pruned_loss=0.03167, over 1427459.23 frames.], batch size: 17, lr: 2.69e-04
2022-04-30 07:06:10,011 INFO [train.py:763] (1/8) Epoch 28, batch 1450, loss[loss=0.1652, simple_loss=0.2696, pruned_loss=0.0304, over 7293.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2614, pruned_loss=0.0315, over 1431104.57 frames.], batch size: 24, lr: 2.69e-04
2022-04-30 07:07:16,028 INFO [train.py:763] (1/8) Epoch 28, batch 1500, loss[loss=0.1473, simple_loss=0.2496, pruned_loss=0.02255, over 7327.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2622, pruned_loss=0.03163, over 1428378.31 frames.], batch size: 20, lr: 2.69e-04
2022-04-30 07:08:21,693 INFO [train.py:763] (1/8) Epoch 28, batch 1550, loss[loss=0.1857, simple_loss=0.2851, pruned_loss=0.04316, over 7224.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2619, pruned_loss=0.03144, over 1430128.00 frames.], batch size: 21, lr: 2.69e-04
2022-04-30 07:09:26,974 INFO [train.py:763] (1/8) Epoch 28, batch 1600, loss[loss=0.1328, simple_loss=0.2256, pruned_loss=0.01999, over 6823.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2618, pruned_loss=0.03124, over 1426410.49 frames.], batch size: 15, lr: 2.69e-04
2022-04-30 07:10:32,954 INFO [train.py:763] (1/8) Epoch 28, batch 1650, loss[loss=0.1705, simple_loss=0.2564, pruned_loss=0.04234, over 7217.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2614, pruned_loss=0.03139, over 1429229.29 frames.], batch size: 16, lr: 2.69e-04
2022-04-30 07:11:39,860 INFO [train.py:763] (1/8) Epoch 28, batch 1700, loss[loss=0.1404, simple_loss=0.2327, pruned_loss=0.02403, over 7260.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2615, pruned_loss=0.03109, over 1431266.98 frames.], batch size: 19, lr: 2.69e-04
2022-04-30 07:12:45,211 INFO [train.py:763] (1/8) Epoch 28, batch 1750, loss[loss=0.1469, simple_loss=0.248, pruned_loss=0.02288, over 7122.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2612, pruned_loss=0.03086, over 1433545.49 frames.], batch size: 21, lr: 2.69e-04
2022-04-30 07:13:50,836 INFO [train.py:763] (1/8) Epoch 28, batch 1800, loss[loss=0.1557, simple_loss=0.248, pruned_loss=0.03166, over 7008.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2608, pruned_loss=0.03113, over 1423101.13 frames.], batch size: 16, lr: 2.69e-04
2022-04-30 07:14:56,956 INFO [train.py:763] (1/8) Epoch 28, batch 1850, loss[loss=0.1419, simple_loss=0.2365, pruned_loss=0.02363, over 7401.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2622, pruned_loss=0.0316, over 1425705.66 frames.], batch size: 18, lr: 2.69e-04
2022-04-30 07:16:02,988 INFO [train.py:763] (1/8) Epoch 28, batch 1900, loss[loss=0.1552, simple_loss=0.2542, pruned_loss=0.02805, over 7126.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2621, pruned_loss=0.03144, over 1426642.66 frames.], batch size: 26, lr: 2.69e-04
2022-04-30 07:17:09,677 INFO [train.py:763] (1/8) Epoch 28, batch 1950, loss[loss=0.1896, simple_loss=0.2874, pruned_loss=0.04592, over 7285.00 frames.], tot_loss[loss=0.163, simple_loss=0.2622, pruned_loss=0.03183, over 1428613.69 frames.], batch size: 25, lr: 2.69e-04
2022-04-30 07:18:15,510 INFO [train.py:763] (1/8) Epoch 28, batch 2000, loss[loss=0.2004, simple_loss=0.2932, pruned_loss=0.05377, over 7195.00 frames.], tot_loss[loss=0.163, simple_loss=0.2621, pruned_loss=0.03194, over 1431773.31 frames.], batch size: 23, lr: 2.69e-04
2022-04-30 07:19:21,142 INFO [train.py:763] (1/8) Epoch 28, batch 2050, loss[loss=0.1604, simple_loss=0.2634, pruned_loss=0.02869, over 7320.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2625, pruned_loss=0.03223, over 1425190.06 frames.], batch size: 21, lr: 2.69e-04
2022-04-30 07:20:26,743 INFO [train.py:763] (1/8) Epoch 28, batch 2100, loss[loss=0.203, simple_loss=0.3019, pruned_loss=0.05206, over 7308.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2619, pruned_loss=0.03184, over 1426442.95 frames.], batch size: 25, lr: 2.69e-04
2022-04-30 07:21:33,842 INFO [train.py:763] (1/8) Epoch 28, batch 2150, loss[loss=0.1701, simple_loss=0.2606, pruned_loss=0.03984, over 7215.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2623, pruned_loss=0.03198, over 1428081.04 frames.], batch size: 21, lr: 2.69e-04
2022-04-30 07:22:48,759 INFO [train.py:763] (1/8) Epoch 28, batch 2200, loss[loss=0.1975, simple_loss=0.2998, pruned_loss=0.04762, over 7330.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2622, pruned_loss=0.0317, over 1422520.14 frames.], batch size: 25, lr: 2.69e-04
2022-04-30 07:23:56,113 INFO [train.py:763] (1/8) Epoch 28, batch 2250, loss[loss=0.1585, simple_loss=0.2615, pruned_loss=0.02775, over 7111.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2626, pruned_loss=0.03196, over 1426427.03 frames.], batch size: 21, lr: 2.68e-04
2022-04-30 07:25:01,834 INFO [train.py:763] (1/8) Epoch 28, batch 2300, loss[loss=0.1527, simple_loss=0.253, pruned_loss=0.02618, over 7291.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2627, pruned_loss=0.03214, over 1427823.71 frames.], batch size: 24, lr: 2.68e-04
2022-04-30 07:26:07,547 INFO [train.py:763] (1/8) Epoch 28, batch 2350, loss[loss=0.1585, simple_loss=0.2572, pruned_loss=0.02986, over 7060.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2628, pruned_loss=0.03238, over 1424854.99 frames.], batch size: 18, lr: 2.68e-04
2022-04-30 07:27:14,914 INFO [train.py:763] (1/8) Epoch 28, batch 2400, loss[loss=0.1311, simple_loss=0.2193, pruned_loss=0.0214, over 7357.00 frames.], tot_loss[loss=0.163, simple_loss=0.2617, pruned_loss=0.03208, over 1426363.79 frames.], batch size: 19, lr: 2.68e-04
2022-04-30 07:28:20,436 INFO [train.py:763] (1/8) Epoch 28, batch 2450, loss[loss=0.1485, simple_loss=0.2578, pruned_loss=0.01959, over 7120.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2629, pruned_loss=0.03242, over 1416885.54 frames.], batch size: 21, lr: 2.68e-04
2022-04-30 07:29:26,088 INFO [train.py:763] (1/8) Epoch 28, batch 2500, loss[loss=0.1323, simple_loss=0.2214, pruned_loss=0.02157, over 7417.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2624, pruned_loss=0.03212, over 1420389.95 frames.], batch size: 18, lr: 2.68e-04
2022-04-30 07:30:32,240 INFO [train.py:763] (1/8) Epoch 28, batch 2550, loss[loss=0.1671, simple_loss=0.2603, pruned_loss=0.03695, over 7150.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2623, pruned_loss=0.03209, over 1418183.57 frames.], batch size: 18, lr: 2.68e-04
2022-04-30 07:31:37,897 INFO [train.py:763] (1/8) Epoch 28, batch 2600, loss[loss=0.2001, simple_loss=0.2937, pruned_loss=0.0532, over 7205.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2621, pruned_loss=0.03205, over 1416596.72 frames.], batch size: 23, lr: 2.68e-04
2022-04-30 07:32:43,455 INFO [train.py:763] (1/8) Epoch 28, batch 2650, loss[loss=0.1471, simple_loss=0.2337, pruned_loss=0.03021, over 7430.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2626, pruned_loss=0.03247, over 1419547.99 frames.], batch size: 18, lr: 2.68e-04
2022-04-30 07:33:59,643 INFO [train.py:763] (1/8) Epoch 28, batch 2700, loss[loss=0.167, simple_loss=0.2618, pruned_loss=0.03611, over 5213.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2619, pruned_loss=0.03253, over 1419860.14 frames.], batch size: 53, lr: 2.68e-04
2022-04-30 07:35:13,948 INFO [train.py:763] (1/8) Epoch 28, batch 2750, loss[loss=0.1663, simple_loss=0.2678, pruned_loss=0.03242, over 7316.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2625, pruned_loss=0.03286, over 1415550.16 frames.], batch size: 21, lr: 2.68e-04
2022-04-30 07:36:28,354 INFO [train.py:763] (1/8) Epoch 28, batch 2800, loss[loss=0.1793, simple_loss=0.2785, pruned_loss=0.04, over 7330.00 frames.], tot_loss[loss=0.1639, simple_loss=0.263, pruned_loss=0.03237, over 1417638.65 frames.], batch size: 22, lr: 2.68e-04
2022-04-30 07:37:44,242 INFO [train.py:763] (1/8) Epoch 28, batch 2850, loss[loss=0.1406, simple_loss=0.2408, pruned_loss=0.02014, over 7255.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2624, pruned_loss=0.03236, over 1417944.88 frames.], batch size: 19, lr: 2.68e-04
2022-04-30 07:38:58,497 INFO [train.py:763] (1/8) Epoch 28, batch 2900, loss[loss=0.1727, simple_loss=0.2651, pruned_loss=0.04019, over 7278.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2624, pruned_loss=0.03221, over 1417151.13 frames.], batch size: 17, lr: 2.68e-04
2022-04-30 07:40:13,606 INFO [train.py:763] (1/8) Epoch 28, batch 2950, loss[loss=0.1311, simple_loss=0.2202, pruned_loss=0.02101, over 7128.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2618, pruned_loss=0.03207, over 1417050.88 frames.], batch size: 17, lr: 2.68e-04
2022-04-30 07:41:27,528 INFO [train.py:763] (1/8) Epoch 28, batch 3000, loss[loss=0.1394, simple_loss=0.2412, pruned_loss=0.01875, over 7235.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2624, pruned_loss=0.03202, over 1418390.75 frames.], batch size: 20, lr: 2.68e-04
2022-04-30 07:41:27,529 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 07:41:44,121 INFO [train.py:792] (1/8) Epoch 28, validation: loss=0.1685, simple_loss=0.2656, pruned_loss=0.03573, over 698248.00 frames.
2022-04-30 07:42:49,822 INFO [train.py:763] (1/8) Epoch 28, batch 3050, loss[loss=0.1702, simple_loss=0.2658, pruned_loss=0.03732, over 7149.00 frames.], tot_loss[loss=0.163, simple_loss=0.2624, pruned_loss=0.03182, over 1421485.08 frames.], batch size: 18, lr: 2.68e-04
2022-04-30 07:43:55,526 INFO [train.py:763] (1/8) Epoch 28, batch 3100, loss[loss=0.1366, simple_loss=0.2252, pruned_loss=0.024, over 7266.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2613, pruned_loss=0.03159, over 1418735.00 frames.], batch size: 18, lr: 2.68e-04
2022-04-30 07:45:01,627 INFO [train.py:763] (1/8) Epoch 28, batch 3150, loss[loss=0.1925, simple_loss=0.297, pruned_loss=0.04406, over 7216.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2629, pruned_loss=0.03211, over 1422629.12 frames.], batch size: 21, lr: 2.68e-04
2022-04-30 07:46:07,723 INFO [train.py:763] (1/8) Epoch 28, batch 3200, loss[loss=0.1686, simple_loss=0.279, pruned_loss=0.02914, over 7111.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2632, pruned_loss=0.0323, over 1422507.45 frames.], batch size: 21, lr: 2.68e-04
2022-04-30 07:47:14,367 INFO [train.py:763] (1/8) Epoch 28, batch 3250, loss[loss=0.136, simple_loss=0.2235, pruned_loss=0.02429, over 7159.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2625, pruned_loss=0.03231, over 1421393.16 frames.], batch size: 16, lr: 2.67e-04
2022-04-30 07:48:20,827 INFO [train.py:763] (1/8) Epoch 28, batch 3300, loss[loss=0.1495, simple_loss=0.2518, pruned_loss=0.02354, over 7220.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2639, pruned_loss=0.03269, over 1421106.99 frames.], batch size: 21, lr: 2.67e-04
2022-04-30 07:49:26,929 INFO [train.py:763] (1/8) Epoch 28, batch 3350, loss[loss=0.1864, simple_loss=0.2957, pruned_loss=0.03856, over 7098.00 frames.], tot_loss[loss=0.1643, simple_loss=0.2635, pruned_loss=0.0325, over 1418547.94 frames.], batch size: 28, lr: 2.67e-04
2022-04-30 07:50:33,792 INFO [train.py:763] (1/8) Epoch 28, batch 3400, loss[loss=0.1492, simple_loss=0.2412, pruned_loss=0.02859, over 7062.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2634, pruned_loss=0.03277, over 1417253.81 frames.], batch size: 18, lr: 2.67e-04
2022-04-30 07:51:39,845 INFO [train.py:763] (1/8) Epoch 28, batch 3450, loss[loss=0.1435, simple_loss=0.2348, pruned_loss=0.02612, over 7269.00 frames.], tot_loss[loss=0.1646, simple_loss=0.2635, pruned_loss=0.03291, over 1419952.85 frames.], batch size: 17, lr: 2.67e-04
2022-04-30 07:52:45,405 INFO [train.py:763] (1/8) Epoch 28, batch 3500, loss[loss=0.1633, simple_loss=0.2713, pruned_loss=0.02766, over 6701.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2634, pruned_loss=0.03242, over 1419181.11 frames.], batch size: 31, lr: 2.67e-04
2022-04-30 07:53:50,880 INFO [train.py:763] (1/8) Epoch 28, batch 3550, loss[loss=0.1564, simple_loss=0.2618, pruned_loss=0.02549, over 7291.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2623, pruned_loss=0.03208, over 1422802.56 frames.], batch size: 18, lr: 2.67e-04
2022-04-30 07:54:56,700 INFO [train.py:763] (1/8) Epoch 28, batch 3600, loss[loss=0.14, simple_loss=0.2386, pruned_loss=0.0207, over 7266.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2628, pruned_loss=0.03222, over 1423180.84 frames.], batch size: 16, lr: 2.67e-04
2022-04-30 07:56:02,359 INFO [train.py:763] (1/8) Epoch 28, batch 3650, loss[loss=0.1674, simple_loss=0.2692, pruned_loss=0.03286, over 7343.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2624, pruned_loss=0.0321, over 1426241.34 frames.], batch size: 22, lr: 2.67e-04
2022-04-30 07:57:08,117 INFO [train.py:763] (1/8) Epoch 28, batch 3700, loss[loss=0.2201, simple_loss=0.3118, pruned_loss=0.06418, over 7207.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2617, pruned_loss=0.0318, over 1426300.22 frames.], batch size: 23, lr: 2.67e-04
2022-04-30 07:58:13,560 INFO [train.py:763] (1/8) Epoch 28, batch 3750, loss[loss=0.1983, simple_loss=0.295, pruned_loss=0.05081, over 5320.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2618, pruned_loss=0.03175, over 1426208.75 frames.], batch size: 52, lr: 2.67e-04
2022-04-30 07:59:19,061 INFO [train.py:763] (1/8) Epoch 28, batch 3800, loss[loss=0.1532, simple_loss=0.2483, pruned_loss=0.02909, over 7433.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2623, pruned_loss=0.03149, over 1426362.77 frames.], batch size: 20, lr: 2.67e-04
2022-04-30 08:00:24,612 INFO [train.py:763] (1/8) Epoch 28, batch 3850, loss[loss=0.1619, simple_loss=0.2593, pruned_loss=0.03224, over 7394.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2629, pruned_loss=0.03188, over 1427260.45 frames.], batch size: 23, lr: 2.67e-04
2022-04-30 08:01:31,043 INFO [train.py:763] (1/8) Epoch 28, batch 3900, loss[loss=0.1618, simple_loss=0.2556, pruned_loss=0.03403, over 7266.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2634, pruned_loss=0.03199, over 1430300.68 frames.], batch size: 24, lr: 2.67e-04
2022-04-30 08:02:37,685 INFO [train.py:763] (1/8) Epoch 28, batch 3950, loss[loss=0.143, simple_loss=0.2347, pruned_loss=0.0257, over 7418.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2643, pruned_loss=0.03221, over 1431066.89 frames.], batch size: 18, lr: 2.67e-04
2022-04-30 08:03:44,063 INFO [train.py:763] (1/8) Epoch 28, batch 4000, loss[loss=0.1767, simple_loss=0.2734, pruned_loss=0.03997, over 7336.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2642, pruned_loss=0.03224, over 1430758.01 frames.], batch size: 22, lr: 2.67e-04
2022-04-30 08:04:50,782 INFO [train.py:763] (1/8) Epoch 28, batch 4050, loss[loss=0.1459, simple_loss=0.2386, pruned_loss=0.02664, over 7287.00 frames.], tot_loss[loss=0.1649, simple_loss=0.265, pruned_loss=0.03242, over 1429494.53 frames.], batch size: 17, lr: 2.67e-04
2022-04-30 08:05:55,978 INFO [train.py:763] (1/8) Epoch 28, batch 4100, loss[loss=0.1828, simple_loss=0.2789, pruned_loss=0.04336, over 7329.00 frames.], tot_loss[loss=0.1647, simple_loss=0.265, pruned_loss=0.03225, over 1429813.18 frames.], batch size: 22, lr: 2.67e-04
2022-04-30 08:07:02,629 INFO [train.py:763] (1/8) Epoch 28, batch 4150, loss[loss=0.1541, simple_loss=0.2567, pruned_loss=0.02579, over 7322.00 frames.], tot_loss[loss=0.1639, simple_loss=0.2642, pruned_loss=0.0318, over 1424111.74 frames.], batch size: 21, lr: 2.67e-04
2022-04-30 08:08:09,141 INFO [train.py:763] (1/8) Epoch 28, batch 4200, loss[loss=0.1508, simple_loss=0.2501, pruned_loss=0.02569, over 7263.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2644, pruned_loss=0.032, over 1421082.41 frames.], batch size: 19, lr: 2.66e-04
2022-04-30 08:09:14,665 INFO [train.py:763] (1/8) Epoch 28, batch 4250, loss[loss=0.1722, simple_loss=0.2727, pruned_loss=0.03584, over 6924.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2634, pruned_loss=0.03168, over 1420930.96 frames.], batch size: 32, lr: 2.66e-04
2022-04-30 08:10:19,663 INFO [train.py:763] (1/8) Epoch 28, batch 4300, loss[loss=0.1392, simple_loss=0.2408, pruned_loss=0.01879, over 7157.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2633, pruned_loss=0.03183, over 1417651.02 frames.], batch size: 18, lr: 2.66e-04
2022-04-30 08:11:24,965 INFO [train.py:763] (1/8) Epoch 28, batch 4350, loss[loss=0.1658, simple_loss=0.2733, pruned_loss=0.02913, over 7319.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2627, pruned_loss=0.03143, over 1419843.42 frames.], batch size: 21, lr: 2.66e-04
2022-04-30 08:12:30,142 INFO [train.py:763] (1/8) Epoch 28, batch 4400, loss[loss=0.1834, simple_loss=0.2872, pruned_loss=0.03977, over 7304.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2641, pruned_loss=0.03213, over 1410734.13 frames.], batch size: 24, lr: 2.66e-04
2022-04-30 08:13:35,272 INFO [train.py:763] (1/8) Epoch 28, batch 4450, loss[loss=0.1574, simple_loss=0.2644, pruned_loss=0.02518, over 6437.00 frames.], tot_loss[loss=0.1644, simple_loss=0.2643, pruned_loss=0.03227, over 1403025.36 frames.], batch size: 37, lr: 2.66e-04
2022-04-30 08:14:40,128 INFO [train.py:763] (1/8) Epoch 28, batch 4500, loss[loss=0.1745, simple_loss=0.2818, pruned_loss=0.03364, over 7210.00 frames.], tot_loss[loss=0.1651, simple_loss=0.2649, pruned_loss=0.03265, over 1379457.62 frames.], batch size: 22, lr: 2.66e-04
2022-04-30 08:15:45,361 INFO [train.py:763] (1/8) Epoch 28, batch 4550, loss[loss=0.1974, simple_loss=0.2886, pruned_loss=0.05312, over 5046.00 frames.], tot_loss[loss=0.1659, simple_loss=0.2658, pruned_loss=0.03304, over 1360669.63 frames.], batch size: 52, lr: 2.66e-04
2022-04-30 08:17:05,891 INFO [train.py:763] (1/8) Epoch 29, batch 0, loss[loss=0.1543, simple_loss=0.2548, pruned_loss=0.02688, over 7333.00 frames.], tot_loss[loss=0.1543, simple_loss=0.2548, pruned_loss=0.02688, over 7333.00 frames.], batch size: 20, lr: 2.62e-04
2022-04-30 08:18:11,689 INFO [train.py:763] (1/8) Epoch 29, batch 50, loss[loss=0.1417, simple_loss=0.2264, pruned_loss=0.02851, over 7278.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2606, pruned_loss=0.03039, over 324637.79 frames.], batch size: 18, lr: 2.62e-04
2022-04-30 08:19:17,262 INFO [train.py:763] (1/8) Epoch 29, batch 100, loss[loss=0.1397, simple_loss=0.238, pruned_loss=0.02073, over 7288.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2613, pruned_loss=0.03121, over 572540.59 frames.], batch size: 17, lr: 2.62e-04
2022-04-30 08:20:22,569 INFO [train.py:763] (1/8) Epoch 29, batch 150, loss[loss=0.175, simple_loss=0.2787, pruned_loss=0.03562, over 7306.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2615, pruned_loss=0.03187, over 750515.45 frames.], batch size: 24, lr: 2.62e-04
2022-04-30 08:21:28,003 INFO [train.py:763] (1/8) Epoch 29, batch 200, loss[loss=0.1502, simple_loss=0.2398, pruned_loss=0.03033, over 7343.00 frames.], tot_loss[loss=0.163, simple_loss=0.2623, pruned_loss=0.03187, over 899539.14 frames.], batch size: 19, lr: 2.61e-04
2022-04-30 08:22:33,081 INFO [train.py:763] (1/8) Epoch 29, batch 250, loss[loss=0.1261, simple_loss=0.2228, pruned_loss=0.01467, over 7181.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2634, pruned_loss=0.03202, over 1017130.69 frames.], batch size: 16, lr: 2.61e-04
2022-04-30 08:23:39,496 INFO [train.py:763] (1/8) Epoch 29, batch 300, loss[loss=0.1455, simple_loss=0.2341, pruned_loss=0.02842, over 7275.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2638, pruned_loss=0.03221, over 1109637.28 frames.], batch size: 18, lr: 2.61e-04
2022-04-30 08:24:46,637 INFO [train.py:763] (1/8) Epoch 29, batch 350, loss[loss=0.1682, simple_loss=0.2754, pruned_loss=0.03053, over 7325.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2634, pruned_loss=0.03242, over 1182828.87 frames.], batch size: 20, lr: 2.61e-04
2022-04-30 08:25:52,370 INFO [train.py:763] (1/8) Epoch 29, batch 400, loss[loss=0.1946, simple_loss=0.2884, pruned_loss=0.05041, over 7329.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2631, pruned_loss=0.03216, over 1238213.15 frames.], batch size: 24, lr: 2.61e-04
2022-04-30 08:26:57,828 INFO [train.py:763] (1/8) Epoch 29, batch 450, loss[loss=0.165, simple_loss=0.2743, pruned_loss=0.02785, over 7420.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2622, pruned_loss=0.03154, over 1280220.49 frames.], batch size: 21, lr: 2.61e-04
2022-04-30 08:28:03,213 INFO [train.py:763] (1/8) Epoch 29, batch 500, loss[loss=0.1427, simple_loss=0.2373, pruned_loss=0.02402, over 7314.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2624, pruned_loss=0.03166, over 1308691.77 frames.], batch size: 20, lr: 2.61e-04
2022-04-30 08:29:08,674 INFO [train.py:763] (1/8) Epoch 29, batch 550, loss[loss=0.1772, simple_loss=0.2823, pruned_loss=0.036, over 7274.00 frames.], tot_loss[loss=0.163, simple_loss=0.2623, pruned_loss=0.03182, over 1335819.62 frames.], batch size: 24, lr: 2.61e-04
2022-04-30 08:30:14,686 INFO [train.py:763] (1/8) Epoch 29, batch 600, loss[loss=0.1944, simple_loss=0.2961, pruned_loss=0.04633, over 7210.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2619, pruned_loss=0.03159, over 1351331.62 frames.], batch size: 22, lr: 2.61e-04
2022-04-30 08:31:20,872 INFO [train.py:763] (1/8) Epoch 29, batch 650, loss[loss=0.1646, simple_loss=0.2603, pruned_loss=0.03442, over 7075.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2626, pruned_loss=0.03189, over 1366191.58 frames.], batch size: 18, lr: 2.61e-04
2022-04-30 08:32:27,047 INFO [train.py:763] (1/8) Epoch 29, batch 700, loss[loss=0.1483, simple_loss=0.2566, pruned_loss=0.02005, over 7329.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2632, pruned_loss=0.0319, over 1374821.61 frames.], batch size: 20, lr: 2.61e-04
2022-04-30 08:33:32,281 INFO [train.py:763] (1/8) Epoch 29, batch 750, loss[loss=0.1534, simple_loss=0.2556, pruned_loss=0.02555, over 7233.00 frames.], tot_loss[loss=0.1634, simple_loss=0.263, pruned_loss=0.03195, over 1380879.40 frames.], batch size: 20, lr: 2.61e-04
2022-04-30 08:34:37,539 INFO [train.py:763] (1/8) Epoch 29, batch 800, loss[loss=0.158, simple_loss=0.2575, pruned_loss=0.02929, over 7344.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2623, pruned_loss=0.03219, over 1387437.15 frames.], batch size: 22, lr: 2.61e-04
2022-04-30 08:35:43,024 INFO [train.py:763] (1/8) Epoch 29, batch 850, loss[loss=0.1598, simple_loss=0.2476, pruned_loss=0.03597, over 7061.00 frames.], tot_loss[loss=0.162, simple_loss=0.2609, pruned_loss=0.03155, over 1397014.98 frames.], batch size: 18, lr: 2.61e-04
2022-04-30 08:36:48,529 INFO [train.py:763] (1/8) Epoch 29, batch 900, loss[loss=0.1465, simple_loss=0.2507, pruned_loss=0.02119, over 7217.00 frames.], tot_loss[loss=0.1621, simple_loss=0.261, pruned_loss=0.03164, over 1401394.36 frames.], batch size: 21, lr: 2.61e-04
2022-04-30 08:37:53,908 INFO [train.py:763] (1/8) Epoch 29, batch 950, loss[loss=0.1571, simple_loss=0.2553, pruned_loss=0.02944, over 7114.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2618, pruned_loss=0.03174, over 1407104.14 frames.], batch size: 21, lr: 2.61e-04
2022-04-30 08:38:59,973 INFO [train.py:763] (1/8) Epoch 29, batch 1000, loss[loss=0.1778, simple_loss=0.2898, pruned_loss=0.03289, over 7141.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2636, pruned_loss=0.03186, over 1410816.24 frames.], batch size: 20, lr: 2.61e-04
2022-04-30 08:40:06,268 INFO [train.py:763] (1/8) Epoch 29, batch 1050, loss[loss=0.1584, simple_loss=0.2462, pruned_loss=0.03533, over 7282.00 frames.], tot_loss[loss=0.164, simple_loss=0.2638, pruned_loss=0.03208, over 1407790.48 frames.], batch size: 18, lr: 2.61e-04
2022-04-30 08:41:11,504 INFO [train.py:763] (1/8) Epoch 29, batch 1100, loss[loss=0.1713, simple_loss=0.2701, pruned_loss=0.03626, over 7327.00 frames.], tot_loss[loss=0.1645, simple_loss=0.2647, pruned_loss=0.03214, over 1417178.21 frames.], batch size: 21, lr: 2.61e-04
2022-04-30 08:42:16,627 INFO [train.py:763] (1/8) Epoch 29, batch 1150, loss[loss=0.1325, simple_loss=0.2295, pruned_loss=0.01774, over 6998.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2645, pruned_loss=0.03195, over 1418764.32 frames.], batch size: 16, lr: 2.61e-04
2022-04-30 08:43:21,909 INFO [train.py:763] (1/8) Epoch 29, batch 1200, loss[loss=0.1617, simple_loss=0.2572, pruned_loss=0.0331, over 7167.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2639, pruned_loss=0.03136, over 1422995.64 frames.], batch size: 19, lr: 2.61e-04
2022-04-30 08:44:27,476 INFO [train.py:763] (1/8) Epoch 29, batch 1250, loss[loss=0.1794, simple_loss=0.2854, pruned_loss=0.03675, over 5198.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2633, pruned_loss=0.03167, over 1418446.49 frames.], batch size: 52, lr: 2.60e-04
2022-04-30 08:45:34,623 INFO [train.py:763] (1/8) Epoch 29, batch 1300, loss[loss=0.1697, simple_loss=0.2723, pruned_loss=0.03355, over 7332.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2628, pruned_loss=0.03141, over 1419916.83 frames.], batch size: 22, lr: 2.60e-04
2022-04-30 08:46:42,212 INFO [train.py:763] (1/8) Epoch 29, batch 1350, loss[loss=0.1625, simple_loss=0.2672, pruned_loss=0.02893, over 6290.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2623, pruned_loss=0.03144, over 1420050.25 frames.], batch size: 37, lr: 2.60e-04
2022-04-30 08:47:48,987 INFO [train.py:763] (1/8) Epoch 29, batch 1400, loss[loss=0.1517, simple_loss=0.2383, pruned_loss=0.03255, over 7219.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2617, pruned_loss=0.03127, over 1420579.19 frames.], batch size: 16, lr: 2.60e-04
2022-04-30 08:48:56,273 INFO [train.py:763] (1/8) Epoch 29, batch 1450, loss[loss=0.1575, simple_loss=0.2637, pruned_loss=0.02566, over 7122.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2621, pruned_loss=0.03138, over 1419183.59 frames.], batch size: 21, lr: 2.60e-04
2022-04-30 08:50:03,376 INFO [train.py:763] (1/8) Epoch 29, batch 1500, loss[loss=0.1501, simple_loss=0.2533, pruned_loss=0.02339, over 7264.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2624, pruned_loss=0.0316, over 1418608.82 frames.], batch size: 19, lr: 2.60e-04
2022-04-30 08:51:09,976 INFO [train.py:763] (1/8) Epoch 29, batch 1550, loss[loss=0.1676, simple_loss=0.2668, pruned_loss=0.03419, over 7212.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2627, pruned_loss=0.03181, over 1418705.05 frames.], batch size: 23, lr: 2.60e-04
2022-04-30 08:52:16,970 INFO [train.py:763] (1/8) Epoch 29, batch 1600, loss[loss=0.1578, simple_loss=0.262, pruned_loss=0.02683, over 7320.00 frames.], tot_loss[loss=0.1635, simple_loss=0.263, pruned_loss=0.03203, over 1419336.44 frames.], batch size: 21, lr: 2.60e-04
2022-04-30 08:53:22,973 INFO [train.py:763] (1/8) Epoch 29, batch 1650, loss[loss=0.1748, simple_loss=0.2816, pruned_loss=0.03407, over 7172.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2621, pruned_loss=0.03166, over 1423362.88 frames.], batch size: 26, lr: 2.60e-04
2022-04-30 08:54:28,290 INFO [train.py:763] (1/8) Epoch 29, batch 1700, loss[loss=0.1731, simple_loss=0.2645, pruned_loss=0.04087, over 7125.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2617, pruned_loss=0.03121, over 1426148.48 frames.], batch size: 17, lr: 2.60e-04
2022-04-30 08:55:35,254 INFO [train.py:763] (1/8) Epoch 29, batch 1750, loss[loss=0.152, simple_loss=0.2514, pruned_loss=0.0263, over 7154.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2618, pruned_loss=0.03123, over 1421942.16 frames.], batch size: 20, lr: 2.60e-04
2022-04-30 08:56:42,194 INFO [train.py:763] (1/8) Epoch 29, batch 1800, loss[loss=0.1827, simple_loss=0.2909, pruned_loss=0.03718, over 5053.00 frames.], tot_loss[loss=0.162, simple_loss=0.2619, pruned_loss=0.03109, over 1419587.83 frames.], batch size: 52, lr: 2.60e-04
2022-04-30 08:57:49,262 INFO [train.py:763] (1/8) Epoch 29, batch 1850, loss[loss=0.1825, simple_loss=0.2838, pruned_loss=0.0406, over 7116.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2611, pruned_loss=0.03095, over 1423591.29 frames.], batch size: 21, lr: 2.60e-04
2022-04-30 08:58:55,864 INFO [train.py:763] (1/8) Epoch 29, batch 1900, loss[loss=0.1332, simple_loss=0.224, pruned_loss=0.02115, over 7223.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2614, pruned_loss=0.03082, over 1426337.16 frames.], batch size: 16, lr: 2.60e-04
2022-04-30 09:00:01,477 INFO [train.py:763] (1/8) Epoch 29, batch 1950, loss[loss=0.1712, simple_loss=0.2602, pruned_loss=0.04105, over 7270.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2619, pruned_loss=0.03117, over 1427474.98 frames.], batch size: 17, lr: 2.60e-04
2022-04-30 09:01:06,697 INFO [train.py:763] (1/8) Epoch 29, batch 2000, loss[loss=0.1619, simple_loss=0.2697, pruned_loss=0.02709, over 7329.00 frames.], tot_loss[loss=0.162, simple_loss=0.262, pruned_loss=0.03102, over 1429041.00 frames.], batch size: 22, lr: 2.60e-04
2022-04-30 09:02:12,105 INFO [train.py:763] (1/8) Epoch 29, batch 2050, loss[loss=0.2035, simple_loss=0.3068, pruned_loss=0.05013, over 7197.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2622, pruned_loss=0.03137, over 1429340.66 frames.], batch size: 23, lr: 2.60e-04
2022-04-30 09:03:17,246 INFO [train.py:763] (1/8) Epoch 29, batch 2100, loss[loss=0.1571, simple_loss=0.2623, pruned_loss=0.02596, over 7147.00 frames.], tot_loss[loss=0.1635, simple_loss=0.2633, pruned_loss=0.03182, over 1428322.25 frames.], batch size: 20, lr: 2.60e-04
2022-04-30 09:04:22,316 INFO [train.py:763] (1/8) Epoch 29, batch 2150, loss[loss=0.1402, simple_loss=0.2275, pruned_loss=0.02643, over 7126.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2631, pruned_loss=0.03204, over 1427500.59 frames.], batch size: 17, lr: 2.60e-04
2022-04-30 09:05:27,759 INFO [train.py:763] (1/8) Epoch 29, batch 2200, loss[loss=0.1845, simple_loss=0.2896, pruned_loss=0.03968, over 7282.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2627, pruned_loss=0.03193, over 1421884.39 frames.], batch size: 24, lr: 2.60e-04
2022-04-30 09:06:32,908 INFO [train.py:763] (1/8) Epoch 29, batch 2250, loss[loss=0.1765, simple_loss=0.2821, pruned_loss=0.03548, over 7165.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2627, pruned_loss=0.0315, over 1421536.67 frames.], batch size: 26, lr: 2.59e-04
2022-04-30 09:07:38,517 INFO [train.py:763] (1/8) Epoch 29, batch 2300, loss[loss=0.1855, simple_loss=0.2818, pruned_loss=0.04464, over 7331.00 frames.], tot_loss[loss=0.1631, simple_loss=0.263, pruned_loss=0.03163, over 1418475.20 frames.], batch size: 20, lr: 2.59e-04
2022-04-30 09:08:43,786 INFO [train.py:763] (1/8) Epoch 29, batch 2350, loss[loss=0.1643, simple_loss=0.2664, pruned_loss=0.03105, over 7332.00 frames.], tot_loss[loss=0.163, simple_loss=0.2628, pruned_loss=0.0316, over 1420380.68 frames.], batch size: 22, lr: 2.59e-04
2022-04-30 09:09:49,527 INFO [train.py:763] (1/8) Epoch 29, batch 2400, loss[loss=0.1847, simple_loss=0.2913, pruned_loss=0.03908, over 7294.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2632, pruned_loss=0.03173, over 1422546.72 frames.], batch size: 25, lr: 2.59e-04
2022-04-30 09:10:55,174 INFO [train.py:763] (1/8) Epoch 29, batch 2450, loss[loss=0.1617, simple_loss=0.2711, pruned_loss=0.02614, over 7132.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2619, pruned_loss=0.03138, over 1426514.28 frames.], batch size: 20, lr: 2.59e-04
2022-04-30 09:12:00,711 INFO [train.py:763] (1/8) Epoch 29, batch 2500, loss[loss=0.1482, simple_loss=0.2334, pruned_loss=0.03152, over 7213.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2614, pruned_loss=0.03107, over 1430909.53 frames.], batch size: 16, lr: 2.59e-04
2022-04-30 09:13:06,079 INFO [train.py:763] (1/8) Epoch 29, batch 2550, loss[loss=0.1415, simple_loss=0.2342, pruned_loss=0.02436, over 7402.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2612, pruned_loss=0.03114, over 1428238.59 frames.], batch size: 18, lr: 2.59e-04
2022-04-30 09:14:11,173 INFO [train.py:763] (1/8) Epoch 29, batch 2600, loss[loss=0.1618, simple_loss=0.2749, pruned_loss=0.02437, over 7119.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2614, pruned_loss=0.03111, over 1427659.12 frames.], batch size: 21, lr: 2.59e-04
2022-04-30 09:15:16,452 INFO [train.py:763] (1/8) Epoch 29, batch 2650, loss[loss=0.1531, simple_loss=0.2446, pruned_loss=0.03078, over 7146.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2608, pruned_loss=0.03083, over 1430281.61 frames.], batch size: 17, lr: 2.59e-04
2022-04-30 09:16:21,499 INFO [train.py:763] (1/8) Epoch 29, batch 2700, loss[loss=0.1874, simple_loss=0.288, pruned_loss=0.04343, over 7113.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2623, pruned_loss=0.03159, over 1430070.98 frames.], batch size: 21, lr: 2.59e-04
2022-04-30 09:17:27,765 INFO [train.py:763] (1/8) Epoch 29, batch 2750, loss[loss=0.1656, simple_loss=0.2625, pruned_loss=0.03439, over 7239.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2628, pruned_loss=0.03183, over 1425852.17 frames.], batch size: 20, lr: 2.59e-04
2022-04-30 09:18:33,543 INFO [train.py:763] (1/8) Epoch 29, batch 2800, loss[loss=0.165, simple_loss=0.2793, pruned_loss=0.02534, over 7336.00 frames.], tot_loss[loss=0.1632, simple_loss=0.263, pruned_loss=0.03164, over 1424074.49 frames.], batch size: 22, lr: 2.59e-04
2022-04-30 09:19:39,944 INFO [train.py:763] (1/8) Epoch 29, batch 2850, loss[loss=0.1533, simple_loss=0.2635, pruned_loss=0.02155, over 7234.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2623, pruned_loss=0.03141, over 1418132.84 frames.], batch size: 20, lr: 2.59e-04
2022-04-30 09:20:45,378 INFO [train.py:763] (1/8) Epoch 29, batch 2900, loss[loss=0.1302, simple_loss=0.222, pruned_loss=0.01922, over 7006.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2619, pruned_loss=0.0315, over 1421917.38 frames.], batch size: 16, lr: 2.59e-04
2022-04-30 09:22:01,683 INFO [train.py:763] (1/8) Epoch 29, batch 2950, loss[loss=0.1682, simple_loss=0.2674, pruned_loss=0.03446, over 6542.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2616, pruned_loss=0.03137, over 1422864.07 frames.], batch size: 38, lr: 2.59e-04
2022-04-30 09:23:07,150 INFO [train.py:763] (1/8) Epoch 29, batch 3000, loss[loss=0.174, simple_loss=0.2756, pruned_loss=0.03626, over 7131.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2619, pruned_loss=0.03148, over 1425667.40 frames.], batch size: 21, lr: 2.59e-04
2022-04-30 09:23:07,151 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 09:23:22,371 INFO [train.py:792] (1/8) Epoch 29, validation: loss=0.1693, simple_loss=0.2664, pruned_loss=0.03606, over 698248.00 frames.
2022-04-30 09:24:27,452 INFO [train.py:763] (1/8) Epoch 29, batch 3050, loss[loss=0.1592, simple_loss=0.2677, pruned_loss=0.02532, over 7115.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2619, pruned_loss=0.03134, over 1427021.83 frames.], batch size: 21, lr: 2.59e-04
2022-04-30 09:25:32,592 INFO [train.py:763] (1/8) Epoch 29, batch 3100, loss[loss=0.1658, simple_loss=0.2802, pruned_loss=0.02566, over 7424.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2619, pruned_loss=0.03135, over 1427190.60 frames.], batch size: 21, lr: 2.59e-04
2022-04-30 09:26:38,420 INFO [train.py:763] (1/8) Epoch 29, batch 3150, loss[loss=0.1803, simple_loss=0.2749, pruned_loss=0.04287, over 7158.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2612, pruned_loss=0.0315, over 1422844.88 frames.], batch size: 18, lr: 2.59e-04
2022-04-30 09:27:44,831 INFO [train.py:763] (1/8) Epoch 29, batch 3200, loss[loss=0.1472, simple_loss=0.238, pruned_loss=0.02821, over 7260.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2606, pruned_loss=0.03142, over 1425780.57 frames.], batch size: 19, lr: 2.59e-04
2022-04-30 09:28:51,940 INFO [train.py:763] (1/8) Epoch 29, batch 3250, loss[loss=0.152, simple_loss=0.2492, pruned_loss=0.02746, over 7097.00 frames.], tot_loss[loss=0.162, simple_loss=0.2609, pruned_loss=0.03158, over 1420743.12 frames.], batch size: 28, lr: 2.59e-04
2022-04-30 09:29:57,730 INFO [train.py:763] (1/8) Epoch 29, batch 3300, loss[loss=0.1706, simple_loss=0.2699, pruned_loss=0.03562, over 7338.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2615, pruned_loss=0.03159, over 1424184.33 frames.], batch size: 20, lr: 2.58e-04
2022-04-30 09:31:03,710 INFO [train.py:763] (1/8) Epoch 29, batch 3350, loss[loss=0.133, simple_loss=0.2256, pruned_loss=0.02017, over 7304.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2606, pruned_loss=0.03106, over 1428482.12 frames.], batch size: 17, lr: 2.58e-04
2022-04-30 09:32:09,338 INFO [train.py:763] (1/8) Epoch 29, batch 3400, loss[loss=0.2156, simple_loss=0.3148, pruned_loss=0.05824, over 4827.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2605, pruned_loss=0.03111, over 1424397.80 frames.], batch size: 52, lr: 2.58e-04
2022-04-30 09:33:15,079 INFO [train.py:763] (1/8) Epoch 29, batch 3450, loss[loss=0.1799, simple_loss=0.2796, pruned_loss=0.04012, over 7327.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2613, pruned_loss=0.03126, over 1421348.85 frames.], batch size: 24, lr: 2.58e-04
2022-04-30 09:34:21,146 INFO [train.py:763] (1/8) Epoch 29, batch 3500, loss[loss=0.177, simple_loss=0.2809, pruned_loss=0.0365, over 7209.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2622, pruned_loss=0.03151, over 1423069.36 frames.], batch size: 26, lr: 2.58e-04
2022-04-30 09:35:26,534 INFO [train.py:763] (1/8) Epoch 29, batch 3550, loss[loss=0.1728, simple_loss=0.27, pruned_loss=0.03778, over 7166.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2618, pruned_loss=0.03162, over 1423014.52 frames.], batch size: 18, lr: 2.58e-04
2022-04-30 09:36:32,237 INFO [train.py:763] (1/8) Epoch 29, batch 3600, loss[loss=0.1782, simple_loss=0.2694, pruned_loss=0.04346, over 7246.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2623, pruned_loss=0.03202, over 1427616.25 frames.], batch size: 19, lr: 2.58e-04
2022-04-30 09:37:46,876 INFO [train.py:763] (1/8) Epoch 29, batch 3650, loss[loss=0.1778, simple_loss=0.2784, pruned_loss=0.03859, over 6857.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2623, pruned_loss=0.03242, over 1429764.74 frames.], batch size: 31, lr: 2.58e-04
2022-04-30 09:38:52,211 INFO [train.py:763] (1/8) Epoch 29, batch 3700, loss[loss=0.1583, simple_loss=0.2506, pruned_loss=0.03293, over 7259.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2623, pruned_loss=0.03209, over 1429983.36 frames.], batch size: 17, lr: 2.58e-04
2022-04-30 09:39:59,119 INFO [train.py:763] (1/8) Epoch 29, batch 3750, loss[loss=0.1616, simple_loss=0.2707, pruned_loss=0.02621, over 7103.00 frames.], tot_loss[loss=0.1623, simple_loss=0.262, pruned_loss=0.03132, over 1433241.30 frames.], batch size: 28, lr: 2.58e-04
2022-04-30 09:41:05,832 INFO [train.py:763] (1/8) Epoch 29, batch 3800, loss[loss=0.2031, simple_loss=0.3026, pruned_loss=0.05181, over 7202.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2635, pruned_loss=0.0316, over 1424417.48 frames.], batch size: 22, lr: 2.58e-04
2022-04-30 09:42:11,177 INFO [train.py:763] (1/8) Epoch 29, batch 3850, loss[loss=0.1241, simple_loss=0.2202, pruned_loss=0.01398, over 6796.00 frames.], tot_loss[loss=0.162, simple_loss=0.2621, pruned_loss=0.03096, over 1425096.23 frames.], batch size: 15, lr: 2.58e-04
2022-04-30 09:43:16,814 INFO [train.py:763] (1/8) Epoch 29, batch 3900, loss[loss=0.1387, simple_loss=0.2354, pruned_loss=0.02103, over 7141.00 frames.], tot_loss[loss=0.1619, simple_loss=0.262, pruned_loss=0.0309, over 1425181.89 frames.], batch size: 17, lr: 2.58e-04
2022-04-30 09:44:22,551 INFO [train.py:763] (1/8) Epoch 29, batch 3950, loss[loss=0.1653, simple_loss=0.27, pruned_loss=0.03025, over 7375.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2627, pruned_loss=0.03123, over 1420374.85 frames.], batch size: 23, lr: 2.58e-04
2022-04-30 09:45:27,972 INFO [train.py:763] (1/8) Epoch 29, batch 4000, loss[loss=0.1555, simple_loss=0.256, pruned_loss=0.0275, over 7292.00 frames.], tot_loss[loss=0.164, simple_loss=0.2643, pruned_loss=0.03187, over 1419306.04 frames.], batch size: 25, lr: 2.58e-04
2022-04-30 09:46:33,248 INFO [train.py:763] (1/8) Epoch 29, batch 4050, loss[loss=0.1748, simple_loss=0.2803, pruned_loss=0.03462, over 7115.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2634, pruned_loss=0.03192, over 1420310.24 frames.], batch size: 28, lr: 2.58e-04
2022-04-30 09:47:39,263 INFO [train.py:763] (1/8) Epoch 29, batch 4100, loss[loss=0.1651, simple_loss=0.2854, pruned_loss=0.0224, over 7322.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2631, pruned_loss=0.03163, over 1422412.08 frames.], batch size: 21, lr: 2.58e-04
2022-04-30 09:48:45,601 INFO [train.py:763] (1/8) Epoch 29, batch 4150, loss[loss=0.1612, simple_loss=0.258, pruned_loss=0.03226, over 7210.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2623, pruned_loss=0.03145, over 1422640.73 frames.], batch size: 21, lr: 2.58e-04
2022-04-30 09:50:00,121 INFO [train.py:763] (1/8) Epoch 29, batch 4200, loss[loss=0.1521, simple_loss=0.2605, pruned_loss=0.02188, over 7441.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2628, pruned_loss=0.0313, over 1423867.07 frames.], batch size: 20, lr: 2.58e-04
2022-04-30 09:51:13,963 INFO [train.py:763] (1/8) Epoch 29, batch 4250, loss[loss=0.1754, simple_loss=0.2747, pruned_loss=0.03801, over 7384.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2641, pruned_loss=0.03173, over 1418239.95 frames.], batch size: 23, lr: 2.58e-04
2022-04-30 09:52:28,882 INFO [train.py:763] (1/8) Epoch 29, batch 4300, loss[loss=0.1424, simple_loss=0.2278, pruned_loss=0.02848, over 7285.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2637, pruned_loss=0.03176, over 1421311.02 frames.], batch size: 17, lr: 2.58e-04
2022-04-30 09:53:43,987 INFO [train.py:763] (1/8) Epoch 29, batch 4350, loss[loss=0.1714, simple_loss=0.2649, pruned_loss=0.03896, over 7223.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2616, pruned_loss=0.03132, over 1422866.05 frames.], batch size: 20, lr: 2.58e-04
2022-04-30 09:54:58,496 INFO [train.py:763] (1/8) Epoch 29, batch 4400, loss[loss=0.186, simple_loss=0.2918, pruned_loss=0.04007, over 7237.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2612, pruned_loss=0.03126, over 1418947.00 frames.], batch size: 20, lr: 2.57e-04
2022-04-30 09:56:12,785 INFO [train.py:763] (1/8) Epoch 29, batch 4450, loss[loss=0.1646, simple_loss=0.2689, pruned_loss=0.03013, over 6314.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2606, pruned_loss=0.03084, over 1413858.47 frames.], batch size: 37, lr: 2.57e-04
2022-04-30 09:57:17,981 INFO [train.py:763] (1/8) Epoch 29, batch 4500, loss[loss=0.2195, simple_loss=0.2998, pruned_loss=0.06954, over 5224.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2624, pruned_loss=0.03159, over 1399183.42 frames.], batch size: 54, lr: 2.57e-04
2022-04-30 09:58:32,303 INFO [train.py:763] (1/8) Epoch 29, batch 4550, loss[loss=0.1599, simple_loss=0.2807, pruned_loss=0.01956, over 4807.00 frames.], tot_loss[loss=0.1654, simple_loss=0.265, pruned_loss=0.03291, over 1358124.48 frames.], batch size: 53, lr: 2.57e-04
2022-04-30 10:00:01,326 INFO [train.py:763] (1/8) Epoch 30, batch 0, loss[loss=0.1708, simple_loss=0.2776, pruned_loss=0.03197, over 7333.00 frames.], tot_loss[loss=0.1708, simple_loss=0.2776, pruned_loss=0.03197, over 7333.00 frames.], batch size: 20, lr: 2.53e-04
2022-04-30 10:01:06,992 INFO [train.py:763] (1/8) Epoch 30, batch 50, loss[loss=0.1649, simple_loss=0.2618, pruned_loss=0.03403, over 7260.00 frames.], tot_loss[loss=0.1658, simple_loss=0.2649, pruned_loss=0.03329, over 317129.20 frames.], batch size: 19, lr: 2.53e-04
2022-04-30 10:02:12,184 INFO [train.py:763] (1/8) Epoch 30, batch 100, loss[loss=0.1806, simple_loss=0.2787, pruned_loss=0.0413, over 7389.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2635, pruned_loss=0.03199, over 561429.38 frames.], batch size: 23, lr: 2.53e-04
2022-04-30 10:03:17,805 INFO [train.py:763] (1/8) Epoch 30, batch 150, loss[loss=0.1731, simple_loss=0.2776, pruned_loss=0.03434, over 7220.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2615, pruned_loss=0.03072, over 756802.64 frames.], batch size: 22, lr: 2.53e-04
2022-04-30 10:04:23,870 INFO [train.py:763] (1/8) Epoch 30, batch 200, loss[loss=0.1798, simple_loss=0.2688, pruned_loss=0.04542, over 4848.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2607, pruned_loss=0.03111, over 901668.67 frames.], batch size: 52, lr: 2.53e-04
2022-04-30 10:05:29,994 INFO [train.py:763] (1/8) Epoch 30, batch 250, loss[loss=0.1694, simple_loss=0.2707, pruned_loss=0.03404, over 7304.00 frames.], tot_loss[loss=0.1622, simple_loss=0.262, pruned_loss=0.03118, over 1015356.79 frames.], batch size: 25, lr: 2.53e-04
2022-04-30 10:06:35,954 INFO [train.py:763] (1/8) Epoch 30, batch 300, loss[loss=0.1471, simple_loss=0.2572, pruned_loss=0.01855, over 7325.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2624, pruned_loss=0.03139, over 1107188.29 frames.], batch size: 21, lr: 2.53e-04
2022-04-30 10:07:41,453 INFO [train.py:763] (1/8) Epoch 30, batch 350, loss[loss=0.1399, simple_loss=0.2494, pruned_loss=0.01517, over 7160.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2627, pruned_loss=0.03119, over 1174472.05 frames.], batch size: 18, lr: 2.53e-04
2022-04-30 10:08:46,854 INFO [train.py:763] (1/8) Epoch 30, batch 400, loss[loss=0.1695, simple_loss=0.2613, pruned_loss=0.03888, over 7216.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2629, pruned_loss=0.03126, over 1224875.12 frames.], batch size: 21, lr: 2.53e-04
2022-04-30 10:09:52,320 INFO [train.py:763] (1/8) Epoch 30, batch 450, loss[loss=0.1828, simple_loss=0.2818, pruned_loss=0.04187, over 7144.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2623, pruned_loss=0.03093, over 1266531.74 frames.], batch size: 26, lr: 2.53e-04
2022-04-30 10:10:57,856 INFO [train.py:763] (1/8) Epoch 30, batch 500, loss[loss=0.1455, simple_loss=0.2299, pruned_loss=0.03053, over 7292.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2615, pruned_loss=0.03086, over 1301334.23 frames.], batch size: 17, lr: 2.53e-04
2022-04-30 10:12:03,591 INFO [train.py:763] (1/8) Epoch 30, batch 550, loss[loss=0.15, simple_loss=0.2589, pruned_loss=0.02054, over 7408.00 frames.], tot_loss[loss=0.162, simple_loss=0.2623, pruned_loss=0.03082, over 1327636.69 frames.], batch size: 21, lr: 2.53e-04
2022-04-30 10:13:09,439 INFO [train.py:763] (1/8) Epoch 30, batch 600, loss[loss=0.1749, simple_loss=0.2703, pruned_loss=0.03977, over 7071.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2637, pruned_loss=0.03155, over 1347162.55 frames.], batch size: 18, lr: 2.53e-04
2022-04-30 10:14:15,862 INFO [train.py:763] (1/8) Epoch 30, batch 650, loss[loss=0.1748, simple_loss=0.2794, pruned_loss=0.03509, over 7147.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2632, pruned_loss=0.03122, over 1368266.70 frames.], batch size: 20, lr: 2.53e-04
2022-04-30 10:15:21,893 INFO [train.py:763] (1/8) Epoch 30, batch 700, loss[loss=0.1688, simple_loss=0.2448, pruned_loss=0.04636, over 7250.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2623, pruned_loss=0.03131, over 1379322.74 frames.], batch size: 16, lr: 2.52e-04
2022-04-30 10:16:28,668 INFO [train.py:763] (1/8) Epoch 30, batch 750, loss[loss=0.1785, simple_loss=0.2824, pruned_loss=0.03734, over 7230.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2622, pruned_loss=0.03132, over 1386725.24 frames.], batch size: 20, lr: 2.52e-04
2022-04-30 10:17:34,225 INFO [train.py:763] (1/8) Epoch 30, batch 800, loss[loss=0.1621, simple_loss=0.2647, pruned_loss=0.0298, over 7322.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2629, pruned_loss=0.03176, over 1394953.35 frames.], batch size: 20, lr: 2.52e-04
2022-04-30 10:18:39,959 INFO [train.py:763] (1/8) Epoch 30, batch 850, loss[loss=0.1587, simple_loss=0.2548, pruned_loss=0.03128, over 7422.00 frames.], tot_loss[loss=0.163, simple_loss=0.2623, pruned_loss=0.03185, over 1398392.05 frames.], batch size: 20, lr: 2.52e-04
2022-04-30 10:19:45,738 INFO [train.py:763] (1/8) Epoch 30, batch 900, loss[loss=0.1497, simple_loss=0.2467, pruned_loss=0.02629, over 7208.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2622, pruned_loss=0.0316, over 1404275.24 frames.], batch size: 16, lr: 2.52e-04
2022-04-30 10:20:52,495 INFO [train.py:763] (1/8) Epoch 30, batch 950, loss[loss=0.1499, simple_loss=0.2575, pruned_loss=0.02117, over 7044.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2615, pruned_loss=0.03142, over 1405808.43 frames.], batch size: 28, lr: 2.52e-04
2022-04-30 10:21:58,499 INFO [train.py:763] (1/8) Epoch 30, batch 1000, loss[loss=0.1536, simple_loss=0.2623, pruned_loss=0.02238, over 7339.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2611, pruned_loss=0.03133, over 1408344.86 frames.], batch size: 22, lr: 2.52e-04
2022-04-30 10:23:03,970 INFO [train.py:763] (1/8) Epoch 30, batch 1050, loss[loss=0.1602, simple_loss=0.2695, pruned_loss=0.02546, over 7075.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2616, pruned_loss=0.03167, over 1410965.55 frames.], batch size: 28, lr: 2.52e-04
2022-04-30 10:24:09,735 INFO [train.py:763] (1/8) Epoch 30, batch 1100, loss[loss=0.1682, simple_loss=0.2696, pruned_loss=0.03347, over 7070.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2608, pruned_loss=0.03136, over 1414856.75 frames.], batch size: 18, lr: 2.52e-04
2022-04-30 10:25:15,758 INFO [train.py:763] (1/8) Epoch 30, batch 1150, loss[loss=0.1582, simple_loss=0.2451, pruned_loss=0.03567, over 7058.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2602, pruned_loss=0.03113, over 1416484.45 frames.], batch size: 18, lr: 2.52e-04
2022-04-30 10:26:21,663 INFO [train.py:763] (1/8) Epoch 30, batch 1200, loss[loss=0.1659, simple_loss=0.2714, pruned_loss=0.03024, over 7199.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2606, pruned_loss=0.03162, over 1417964.70 frames.], batch size: 22, lr: 2.52e-04
2022-04-30 10:27:27,464 INFO [train.py:763] (1/8) Epoch 30, batch 1250, loss[loss=0.1366, simple_loss=0.2365, pruned_loss=0.01842, over 7397.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2611, pruned_loss=0.03163, over 1417919.40 frames.], batch size: 18, lr: 2.52e-04
2022-04-30 10:28:33,935 INFO [train.py:763] (1/8) Epoch 30, batch 1300, loss[loss=0.1638, simple_loss=0.2661, pruned_loss=0.03075, over 7137.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2603, pruned_loss=0.03115, over 1417094.19 frames.], batch size: 26, lr: 2.52e-04
2022-04-30 10:29:40,213 INFO [train.py:763] (1/8) Epoch 30, batch 1350, loss[loss=0.1534, simple_loss=0.2506, pruned_loss=0.02813, over 7126.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2617, pruned_loss=0.03131, over 1414006.72 frames.], batch size: 17, lr: 2.52e-04
2022-04-30 10:30:45,701 INFO [train.py:763] (1/8) Epoch 30, batch 1400, loss[loss=0.1759, simple_loss=0.2773, pruned_loss=0.03723, over 7327.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2619, pruned_loss=0.03116, over 1418295.30 frames.], batch size: 22, lr: 2.52e-04
2022-04-30 10:31:51,067 INFO [train.py:763] (1/8) Epoch 30, batch 1450, loss[loss=0.1495, simple_loss=0.2639, pruned_loss=0.01754, over 7146.00 frames.], tot_loss[loss=0.162, simple_loss=0.2621, pruned_loss=0.03096, over 1419238.42 frames.], batch size: 20, lr: 2.52e-04
2022-04-30 10:32:56,515 INFO [train.py:763] (1/8) Epoch 30, batch 1500, loss[loss=0.1835, simple_loss=0.2887, pruned_loss=0.03915, over 7307.00 frames.], tot_loss[loss=0.1624, simple_loss=0.263, pruned_loss=0.03093, over 1425196.76 frames.], batch size: 25, lr: 2.52e-04
2022-04-30 10:34:02,179 INFO [train.py:763] (1/8) Epoch 30, batch 1550, loss[loss=0.1803, simple_loss=0.2837, pruned_loss=0.03848, over 7282.00 frames.], tot_loss[loss=0.161, simple_loss=0.2612, pruned_loss=0.03037, over 1426599.17 frames.], batch size: 25, lr: 2.52e-04
2022-04-30 10:35:07,677 INFO [train.py:763] (1/8) Epoch 30, batch 1600, loss[loss=0.1641, simple_loss=0.2666, pruned_loss=0.0308, over 7261.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2602, pruned_loss=0.03033, over 1427872.59 frames.], batch size: 19, lr: 2.52e-04
2022-04-30 10:36:13,949 INFO [train.py:763] (1/8) Epoch 30, batch 1650, loss[loss=0.1701, simple_loss=0.2735, pruned_loss=0.03332, over 7118.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2612, pruned_loss=0.03047, over 1428466.05 frames.], batch size: 21, lr: 2.52e-04
2022-04-30 10:37:20,415 INFO [train.py:763] (1/8) Epoch 30, batch 1700, loss[loss=0.1627, simple_loss=0.2751, pruned_loss=0.02512, over 7304.00 frames.], tot_loss[loss=0.16, simple_loss=0.2594, pruned_loss=0.03032, over 1425276.63 frames.], batch size: 24, lr: 2.52e-04
2022-04-30 10:38:27,157 INFO [train.py:763] (1/8) Epoch 30, batch 1750, loss[loss=0.1814, simple_loss=0.2787, pruned_loss=0.04198, over 7393.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2602, pruned_loss=0.03061, over 1427628.45 frames.], batch size: 23, lr: 2.52e-04
2022-04-30 10:39:33,031 INFO [train.py:763] (1/8) Epoch 30, batch 1800, loss[loss=0.1535, simple_loss=0.2495, pruned_loss=0.02877, over 7438.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2603, pruned_loss=0.03094, over 1423931.69 frames.], batch size: 20, lr: 2.51e-04
2022-04-30 10:40:38,992 INFO [train.py:763] (1/8) Epoch 30, batch 1850, loss[loss=0.1383, simple_loss=0.2309, pruned_loss=0.02286, over 7118.00 frames.], tot_loss[loss=0.161, simple_loss=0.2603, pruned_loss=0.03089, over 1422640.64 frames.], batch size: 17, lr: 2.51e-04
2022-04-30 10:41:45,825 INFO [train.py:763] (1/8) Epoch 30, batch 1900, loss[loss=0.152, simple_loss=0.2439, pruned_loss=0.03009, over 7325.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2599, pruned_loss=0.03063, over 1426553.86 frames.], batch size: 20, lr: 2.51e-04
2022-04-30 10:42:51,809 INFO [train.py:763] (1/8) Epoch 30, batch 1950, loss[loss=0.1608, simple_loss=0.2696, pruned_loss=0.02595, over 7371.00 frames.], tot_loss[loss=0.16, simple_loss=0.2595, pruned_loss=0.03027, over 1425801.56 frames.], batch size: 23, lr: 2.51e-04
2022-04-30 10:43:59,474 INFO [train.py:763] (1/8) Epoch 30, batch 2000, loss[loss=0.1642, simple_loss=0.2568, pruned_loss=0.03581, over 7163.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2589, pruned_loss=0.03023, over 1427352.91 frames.], batch size: 18, lr: 2.51e-04
2022-04-30 10:45:05,770 INFO [train.py:763] (1/8) Epoch 30, batch 2050, loss[loss=0.192, simple_loss=0.2925, pruned_loss=0.04571, over 7222.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2585, pruned_loss=0.03019, over 1424706.39 frames.], batch size: 22, lr: 2.51e-04
2022-04-30 10:46:11,291 INFO [train.py:763] (1/8) Epoch 30, batch 2100, loss[loss=0.1807, simple_loss=0.276, pruned_loss=0.04265, over 7160.00 frames.], tot_loss[loss=0.161, simple_loss=0.2605, pruned_loss=0.03079, over 1423129.72 frames.], batch size: 19, lr: 2.51e-04
2022-04-30 10:47:17,301 INFO [train.py:763] (1/8) Epoch 30, batch 2150, loss[loss=0.1531, simple_loss=0.2484, pruned_loss=0.02893, over 7156.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2598, pruned_loss=0.03028, over 1426657.02 frames.], batch size: 18, lr: 2.51e-04
2022-04-30 10:48:22,847 INFO [train.py:763] (1/8) Epoch 30, batch 2200, loss[loss=0.1554, simple_loss=0.2552, pruned_loss=0.02775, over 7068.00 frames.], tot_loss[loss=0.1612, simple_loss=0.261, pruned_loss=0.03067, over 1428379.26 frames.], batch size: 18, lr: 2.51e-04
2022-04-30 10:49:28,454 INFO [train.py:763] (1/8) Epoch 30, batch 2250, loss[loss=0.1841, simple_loss=0.2908, pruned_loss=0.03868, over 7204.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2624, pruned_loss=0.03091, over 1427901.12 frames.], batch size: 23, lr: 2.51e-04
2022-04-30 10:50:34,514 INFO [train.py:763] (1/8) Epoch 30, batch 2300, loss[loss=0.154, simple_loss=0.2526, pruned_loss=0.02766, over 7247.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2621, pruned_loss=0.03105, over 1429960.93 frames.], batch size: 19, lr: 2.51e-04
2022-04-30 10:51:40,502 INFO [train.py:763] (1/8) Epoch 30, batch 2350, loss[loss=0.1558, simple_loss=0.2512, pruned_loss=0.03018, over 7065.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2623, pruned_loss=0.03132, over 1430020.93 frames.], batch size: 18, lr: 2.51e-04
2022-04-30 10:52:46,203 INFO [train.py:763] (1/8) Epoch 30, batch 2400, loss[loss=0.152, simple_loss=0.2673, pruned_loss=0.01833, over 7216.00 frames.], tot_loss[loss=0.1624, simple_loss=0.262, pruned_loss=0.0314, over 1428590.94 frames.], batch size: 21, lr: 2.51e-04
2022-04-30 10:53:51,751 INFO [train.py:763] (1/8) Epoch 30, batch 2450, loss[loss=0.1609, simple_loss=0.2728, pruned_loss=0.02445, over 7230.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2629, pruned_loss=0.03112, over 1424313.42 frames.], batch size: 21, lr: 2.51e-04
2022-04-30 10:54:57,032 INFO [train.py:763] (1/8) Epoch 30, batch 2500, loss[loss=0.1641, simple_loss=0.2668, pruned_loss=0.03075, over 7321.00 frames.], tot_loss[loss=0.162, simple_loss=0.2622, pruned_loss=0.03093, over 1426979.57 frames.], batch size: 22, lr: 2.51e-04
2022-04-30 10:56:03,557 INFO [train.py:763] (1/8) Epoch 30, batch 2550, loss[loss=0.1757, simple_loss=0.2828, pruned_loss=0.0343, over 7180.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2622, pruned_loss=0.03111, over 1429127.63 frames.], batch size: 23, lr: 2.51e-04
2022-04-30 10:57:09,406 INFO [train.py:763] (1/8) Epoch 30, batch 2600, loss[loss=0.1532, simple_loss=0.244, pruned_loss=0.03124, over 7397.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2621, pruned_loss=0.03146, over 1428256.73 frames.], batch size: 18, lr: 2.51e-04
2022-04-30 10:58:15,103 INFO [train.py:763] (1/8) Epoch 30, batch 2650, loss[loss=0.1479, simple_loss=0.2587, pruned_loss=0.01855, over 7410.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2621, pruned_loss=0.03132, over 1425390.74 frames.], batch size: 21, lr: 2.51e-04
2022-04-30 10:59:20,429 INFO [train.py:763] (1/8) Epoch 30, batch 2700, loss[loss=0.1773, simple_loss=0.2788, pruned_loss=0.03794, over 7308.00 frames.], tot_loss[loss=0.163, simple_loss=0.2629, pruned_loss=0.03155, over 1419009.95 frames.], batch size: 25, lr: 2.51e-04
2022-04-30 11:00:26,175 INFO [train.py:763] (1/8) Epoch 30, batch 2750, loss[loss=0.16, simple_loss=0.2634, pruned_loss=0.0283, over 7146.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2623, pruned_loss=0.03119, over 1419841.35 frames.], batch size: 20, lr: 2.51e-04
2022-04-30 11:01:31,734 INFO [train.py:763] (1/8) Epoch 30, batch 2800, loss[loss=0.1619, simple_loss=0.2623, pruned_loss=0.03081, over 7163.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2623, pruned_loss=0.03137, over 1422016.51 frames.], batch size: 18, lr: 2.51e-04
2022-04-30 11:02:36,837 INFO [train.py:763] (1/8) Epoch 30, batch 2850, loss[loss=0.1664, simple_loss=0.2751, pruned_loss=0.02883, over 7185.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2627, pruned_loss=0.03129, over 1420080.67 frames.], batch size: 22, lr: 2.51e-04
2022-04-30 11:03:42,111 INFO [train.py:763] (1/8) Epoch 30, batch 2900, loss[loss=0.1608, simple_loss=0.2729, pruned_loss=0.0243, over 7098.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2624, pruned_loss=0.0312, over 1425037.47 frames.], batch size: 21, lr: 2.51e-04
2022-04-30 11:04:47,458 INFO [train.py:763] (1/8) Epoch 30, batch 2950, loss[loss=0.1756, simple_loss=0.2603, pruned_loss=0.04541, over 7272.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2619, pruned_loss=0.0309, over 1424483.83 frames.], batch size: 19, lr: 2.50e-04
2022-04-30 11:05:53,065 INFO [train.py:763] (1/8) Epoch 30, batch 3000, loss[loss=0.1504, simple_loss=0.259, pruned_loss=0.02086, over 7337.00 frames.], tot_loss[loss=0.1612, simple_loss=0.261, pruned_loss=0.03074, over 1424332.58 frames.], batch size: 20, lr: 2.50e-04
2022-04-30 11:05:53,066 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 11:06:08,153 INFO [train.py:792] (1/8) Epoch 30, validation: loss=0.1701, simple_loss=0.2661, pruned_loss=0.03704, over 698248.00 frames.
2022-04-30 11:07:13,673 INFO [train.py:763] (1/8) Epoch 30, batch 3050, loss[loss=0.1651, simple_loss=0.2477, pruned_loss=0.04128, over 7001.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2617, pruned_loss=0.03129, over 1423569.80 frames.], batch size: 16, lr: 2.50e-04
2022-04-30 11:08:19,231 INFO [train.py:763] (1/8) Epoch 30, batch 3100, loss[loss=0.1643, simple_loss=0.2641, pruned_loss=0.0322, over 7297.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2612, pruned_loss=0.03101, over 1427261.26 frames.], batch size: 25, lr: 2.50e-04
2022-04-30 11:09:24,925 INFO [train.py:763] (1/8) Epoch 30, batch 3150, loss[loss=0.1585, simple_loss=0.2492, pruned_loss=0.03387, over 6989.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2614, pruned_loss=0.03108, over 1425753.52 frames.], batch size: 16, lr: 2.50e-04
2022-04-30 11:10:31,193 INFO [train.py:763] (1/8) Epoch 30, batch 3200, loss[loss=0.156, simple_loss=0.2626, pruned_loss=0.02467, over 7202.00 frames.], tot_loss[loss=0.1616, simple_loss=0.261, pruned_loss=0.03104, over 1417472.77 frames.], batch size: 23, lr: 2.50e-04
2022-04-30 11:11:37,936 INFO [train.py:763] (1/8) Epoch 30, batch 3250, loss[loss=0.179, simple_loss=0.2908, pruned_loss=0.03359, over 7151.00 frames.], tot_loss[loss=0.163, simple_loss=0.2624, pruned_loss=0.03181, over 1416663.73 frames.], batch size: 20, lr: 2.50e-04
2022-04-30 11:12:45,386 INFO [train.py:763] (1/8) Epoch 30, batch 3300, loss[loss=0.1421, simple_loss=0.2373, pruned_loss=0.02349, over 7277.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2619, pruned_loss=0.03165, over 1422756.21 frames.], batch size: 17, lr: 2.50e-04
2022-04-30 11:13:51,984 INFO [train.py:763] (1/8) Epoch 30, batch 3350, loss[loss=0.1798, simple_loss=0.2897, pruned_loss=0.03493, over 7226.00 frames.], tot_loss[loss=0.162, simple_loss=0.2615, pruned_loss=0.03124, over 1421139.13 frames.], batch size: 21, lr: 2.50e-04
2022-04-30 11:14:57,141 INFO [train.py:763] (1/8) Epoch 30, batch 3400, loss[loss=0.1775, simple_loss=0.2806, pruned_loss=0.03721, over 7323.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2605, pruned_loss=0.03053, over 1421714.51 frames.], batch size: 25, lr: 2.50e-04
2022-04-30 11:16:02,380 INFO [train.py:763] (1/8) Epoch 30, batch 3450, loss[loss=0.172, simple_loss=0.2735, pruned_loss=0.03524, over 6354.00 frames.], tot_loss[loss=0.162, simple_loss=0.262, pruned_loss=0.03104, over 1426110.33 frames.], batch size: 37, lr: 2.50e-04
2022-04-30 11:17:08,605 INFO [train.py:763] (1/8) Epoch 30, batch 3500, loss[loss=0.1832, simple_loss=0.2884, pruned_loss=0.039, over 7362.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2622, pruned_loss=0.03104, over 1427969.71 frames.], batch size: 23, lr: 2.50e-04
2022-04-30 11:18:14,699 INFO [train.py:763] (1/8) Epoch 30, batch 3550, loss[loss=0.1531, simple_loss=0.2437, pruned_loss=0.03121, over 7423.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2625, pruned_loss=0.0312, over 1428951.43 frames.], batch size: 20, lr: 2.50e-04
2022-04-30 11:19:20,436 INFO [train.py:763] (1/8) Epoch 30, batch 3600, loss[loss=0.1606, simple_loss=0.2644, pruned_loss=0.0284, over 7292.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2634, pruned_loss=0.03167, over 1423969.72 frames.], batch size: 24, lr: 2.50e-04
2022-04-30 11:20:25,886 INFO [train.py:763] (1/8) Epoch 30, batch 3650, loss[loss=0.1448, simple_loss=0.2367, pruned_loss=0.0265, over 7147.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2631, pruned_loss=0.03128, over 1422797.06 frames.], batch size: 17, lr: 2.50e-04
2022-04-30 11:21:32,099 INFO [train.py:763] (1/8) Epoch 30, batch 3700, loss[loss=0.1479, simple_loss=0.237, pruned_loss=0.02943, over 7269.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2615, pruned_loss=0.03066, over 1425198.99 frames.], batch size: 17, lr: 2.50e-04
2022-04-30 11:22:38,024 INFO [train.py:763] (1/8) Epoch 30, batch 3750, loss[loss=0.1493, simple_loss=0.2536, pruned_loss=0.02255, over 7265.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2618, pruned_loss=0.0312, over 1423303.80 frames.], batch size: 19, lr: 2.50e-04
2022-04-30 11:23:45,243 INFO [train.py:763] (1/8) Epoch 30, batch 3800, loss[loss=0.1307, simple_loss=0.2312, pruned_loss=0.01511, over 7300.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2612, pruned_loss=0.03099, over 1425749.16 frames.], batch size: 18, lr: 2.50e-04
2022-04-30 11:24:50,558 INFO [train.py:763] (1/8) Epoch 30, batch 3850, loss[loss=0.1353, simple_loss=0.2336, pruned_loss=0.01849, over 7069.00 frames.], tot_loss[loss=0.1623, simple_loss=0.262, pruned_loss=0.03129, over 1425155.57 frames.], batch size: 18, lr: 2.50e-04
2022-04-30 11:25:56,095 INFO [train.py:763] (1/8) Epoch 30, batch 3900, loss[loss=0.1519, simple_loss=0.2525, pruned_loss=0.02561, over 7265.00 frames.], tot_loss[loss=0.1619, simple_loss=0.262, pruned_loss=0.03087, over 1428869.78 frames.], batch size: 24, lr: 2.50e-04
2022-04-30 11:27:01,584 INFO [train.py:763] (1/8) Epoch 30, batch 3950, loss[loss=0.144, simple_loss=0.2469, pruned_loss=0.02055, over 7361.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2616, pruned_loss=0.03076, over 1428847.06 frames.], batch size: 19, lr: 2.50e-04
2022-04-30 11:28:06,970 INFO [train.py:763] (1/8) Epoch 30, batch 4000, loss[loss=0.155, simple_loss=0.2571, pruned_loss=0.02645, over 7163.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2623, pruned_loss=0.03121, over 1427345.70 frames.], batch size: 18, lr: 2.50e-04
2022-04-30 11:29:11,956 INFO [train.py:763] (1/8) Epoch 30, batch 4050, loss[loss=0.1785, simple_loss=0.2699, pruned_loss=0.04357, over 7279.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2625, pruned_loss=0.03127, over 1426364.89 frames.], batch size: 24, lr: 2.49e-04
2022-04-30 11:30:18,167 INFO [train.py:763] (1/8) Epoch 30, batch 4100, loss[loss=0.1474, simple_loss=0.2409, pruned_loss=0.02697, over 7156.00 frames.], tot_loss[loss=0.163, simple_loss=0.2629, pruned_loss=0.03154, over 1427619.57 frames.], batch size: 19, lr: 2.49e-04
2022-04-30 11:31:24,157 INFO [train.py:763] (1/8) Epoch 30, batch 4150, loss[loss=0.1718, simple_loss=0.2782, pruned_loss=0.03271, over 7113.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2627, pruned_loss=0.0309, over 1429688.03 frames.], batch size: 21, lr: 2.49e-04
2022-04-30 11:32:29,729 INFO [train.py:763] (1/8) Epoch 30, batch 4200, loss[loss=0.1586, simple_loss=0.2523, pruned_loss=0.0324, over 6798.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2621, pruned_loss=0.03105, over 1430962.91 frames.], batch size: 15, lr: 2.49e-04
2022-04-30 11:33:35,005 INFO [train.py:763] (1/8) Epoch 30, batch 4250, loss[loss=0.1837, simple_loss=0.2764, pruned_loss=0.04553, over 7130.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2623, pruned_loss=0.03128, over 1426897.80 frames.], batch size: 26, lr: 2.49e-04
2022-04-30 11:34:41,233 INFO [train.py:763] (1/8) Epoch 30, batch 4300, loss[loss=0.1705, simple_loss=0.2714, pruned_loss=0.03479, over 7329.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2618, pruned_loss=0.03118, over 1429375.99 frames.], batch size: 24, lr: 2.49e-04
2022-04-30 11:35:46,140 INFO [train.py:763] (1/8) Epoch 30, batch 4350, loss[loss=0.1655, simple_loss=0.2738, pruned_loss=0.02858, over 7123.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2625, pruned_loss=0.03111, over 1421278.98 frames.], batch size: 21, lr: 2.49e-04
2022-04-30 11:36:51,029 INFO [train.py:763] (1/8) Epoch 30, batch 4400, loss[loss=0.155, simple_loss=0.2659, pruned_loss=0.02206, over 7126.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2622, pruned_loss=0.03096, over 1411475.91 frames.], batch size: 21, lr: 2.49e-04
2022-04-30 11:37:56,307 INFO [train.py:763] (1/8) Epoch 30, batch 4450, loss[loss=0.1431, simple_loss=0.2593, pruned_loss=0.01348, over 6265.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2619, pruned_loss=0.03068, over 1409975.69 frames.], batch size: 37, lr: 2.49e-04
2022-04-30 11:39:02,207 INFO [train.py:763] (1/8) Epoch 30, batch 4500, loss[loss=0.1661, simple_loss=0.2718, pruned_loss=0.03015, over 6486.00 frames.], tot_loss[loss=0.1634, simple_loss=0.2635, pruned_loss=0.0316, over 1386211.68 frames.], batch size: 38, lr: 2.49e-04
2022-04-30 11:40:07,226 INFO [train.py:763] (1/8) Epoch 30, batch 4550, loss[loss=0.1756, simple_loss=0.2765, pruned_loss=0.03737, over 5389.00 frames.], tot_loss[loss=0.165, simple_loss=0.2648, pruned_loss=0.03258, over 1356187.20 frames.], batch size: 52, lr: 2.49e-04
2022-04-30 11:41:35,691 INFO [train.py:763] (1/8) Epoch 31, batch 0, loss[loss=0.1989, simple_loss=0.2818, pruned_loss=0.05795, over 5146.00 frames.], tot_loss[loss=0.1989, simple_loss=0.2818, pruned_loss=0.05795, over 5146.00 frames.], batch size: 52, lr: 2.45e-04
2022-04-30 11:42:41,157 INFO [train.py:763] (1/8) Epoch 31, batch 50, loss[loss=0.1786, simple_loss=0.2777, pruned_loss=0.03972, over 6340.00 frames.], tot_loss[loss=0.1682, simple_loss=0.2684, pruned_loss=0.03398, over 319844.58 frames.], batch size: 37, lr: 2.45e-04
2022-04-30 11:43:46,468 INFO [train.py:763] (1/8) Epoch 31, batch 100, loss[loss=0.1595, simple_loss=0.2579, pruned_loss=0.03056, over 7287.00 frames.], tot_loss[loss=0.1638, simple_loss=0.264, pruned_loss=0.0318, over 566639.97 frames.], batch size: 25, lr: 2.45e-04
2022-04-30 11:44:52,569 INFO [train.py:763] (1/8) Epoch 31, batch 150, loss[loss=0.1675, simple_loss=0.2704, pruned_loss=0.03233, over 7177.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2618, pruned_loss=0.03026, over 758133.46 frames.], batch size: 26, lr: 2.45e-04
2022-04-30 11:45:58,814 INFO [train.py:763] (1/8) Epoch 31, batch 200, loss[loss=0.1441, simple_loss=0.2325, pruned_loss=0.02787, over 7424.00 frames.], tot_loss[loss=0.1608, simple_loss=0.261, pruned_loss=0.03031, over 904358.44 frames.], batch size: 17, lr: 2.45e-04
2022-04-30 11:47:04,080 INFO [train.py:763] (1/8) Epoch 31, batch 250, loss[loss=0.2006, simple_loss=0.3086, pruned_loss=0.04628, over 7273.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2609, pruned_loss=0.03033, over 1024100.04 frames.], batch size: 24, lr: 2.45e-04
2022-04-30 11:48:09,433 INFO [train.py:763] (1/8) Epoch 31, batch 300, loss[loss=0.1959, simple_loss=0.2996, pruned_loss=0.04614, over 7312.00 frames.], tot_loss[loss=0.162, simple_loss=0.2621, pruned_loss=0.03099, over 1114663.33 frames.], batch size: 24, lr: 2.45e-04
2022-04-30 11:49:14,694 INFO [train.py:763] (1/8) Epoch 31, batch 350, loss[loss=0.1659, simple_loss=0.268, pruned_loss=0.03188, over 7095.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2619, pruned_loss=0.03077, over 1182059.00 frames.], batch size: 28, lr: 2.45e-04
2022-04-30 11:50:20,234 INFO [train.py:763] (1/8) Epoch 31, batch 400, loss[loss=0.2072, simple_loss=0.2958, pruned_loss=0.05934, over 7140.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2618, pruned_loss=0.03083, over 1236832.36 frames.], batch size: 26, lr: 2.45e-04
2022-04-30 11:51:25,628 INFO [train.py:763] (1/8) Epoch 31, batch 450, loss[loss=0.1556, simple_loss=0.267, pruned_loss=0.0221, over 7322.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2616, pruned_loss=0.03064, over 1277667.70 frames.], batch size: 21, lr: 2.45e-04
2022-04-30 11:52:41,061 INFO [train.py:763] (1/8) Epoch 31, batch 500, loss[loss=0.1616, simple_loss=0.2683, pruned_loss=0.02749, over 7338.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2616, pruned_loss=0.03057, over 1313375.79 frames.], batch size: 22, lr: 2.45e-04
2022-04-30 11:53:47,761 INFO [train.py:763] (1/8) Epoch 31, batch 550, loss[loss=0.1725, simple_loss=0.2731, pruned_loss=0.03597, over 7343.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2619, pruned_loss=0.03066, over 1341377.97 frames.], batch size: 22, lr: 2.45e-04
2022-04-30 11:54:53,975 INFO [train.py:763] (1/8) Epoch 31, batch 600, loss[loss=0.1392, simple_loss=0.2295, pruned_loss=0.02439, over 7145.00 frames.], tot_loss[loss=0.1608, simple_loss=0.261, pruned_loss=0.03028, over 1363947.72 frames.], batch size: 17, lr: 2.45e-04
2022-04-30 11:55:59,908 INFO [train.py:763] (1/8) Epoch 31, batch 650, loss[loss=0.1591, simple_loss=0.2471, pruned_loss=0.03558, over 7004.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2606, pruned_loss=0.03031, over 1380154.56 frames.], batch size: 16, lr: 2.45e-04
2022-04-30 11:57:06,461 INFO [train.py:763] (1/8) Epoch 31, batch 700, loss[loss=0.1817, simple_loss=0.2772, pruned_loss=0.0431, over 7206.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2616, pruned_loss=0.03042, over 1388867.75 frames.], batch size: 23, lr: 2.45e-04
2022-04-30 11:58:13,269 INFO [train.py:763] (1/8) Epoch 31, batch 750, loss[loss=0.1592, simple_loss=0.2681, pruned_loss=0.02513, over 7125.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2616, pruned_loss=0.03045, over 1397549.56 frames.], batch size: 21, lr: 2.44e-04
2022-04-30 11:59:18,734 INFO [train.py:763] (1/8) Epoch 31, batch 800, loss[loss=0.1562, simple_loss=0.2441, pruned_loss=0.03414, over 7272.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2608, pruned_loss=0.03006, over 1402677.42 frames.], batch size: 18, lr: 2.44e-04
2022-04-30 12:00:24,035 INFO [train.py:763] (1/8) Epoch 31, batch 850, loss[loss=0.1984, simple_loss=0.3032, pruned_loss=0.04679, over 7297.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2621, pruned_loss=0.03029, over 1409313.55 frames.], batch size: 25, lr: 2.44e-04
2022-04-30 12:01:28,729 INFO [train.py:763] (1/8) Epoch 31, batch 900, loss[loss=0.1678, simple_loss=0.2651, pruned_loss=0.0353, over 7342.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2633, pruned_loss=0.03056, over 1411895.71 frames.], batch size: 22, lr: 2.44e-04
2022-04-30 12:02:34,058 INFO [train.py:763] (1/8) Epoch 31, batch 950, loss[loss=0.1457, simple_loss=0.2361, pruned_loss=0.02769, over 7195.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2619, pruned_loss=0.03053, over 1413730.23 frames.], batch size: 16, lr: 2.44e-04
2022-04-30 12:03:39,303 INFO [train.py:763] (1/8) Epoch 31, batch 1000, loss[loss=0.16, simple_loss=0.2616, pruned_loss=0.02914, over 7417.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2614, pruned_loss=0.03058, over 1417168.39 frames.], batch size: 20, lr: 2.44e-04
2022-04-30 12:04:53,738 INFO [train.py:763] (1/8) Epoch 31, batch 1050, loss[loss=0.1787, simple_loss=0.2773, pruned_loss=0.04003, over 7240.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2616, pruned_loss=0.03068, over 1421544.21 frames.], batch size: 20, lr: 2.44e-04
2022-04-30 12:05:59,155 INFO [train.py:763] (1/8) Epoch 31, batch 1100, loss[loss=0.1714, simple_loss=0.2828, pruned_loss=0.03003, over 7216.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2613, pruned_loss=0.03076, over 1419592.90 frames.], batch size: 22, lr: 2.44e-04
2022-04-30 12:07:23,561 INFO [train.py:763] (1/8) Epoch 31, batch 1150, loss[loss=0.1367, simple_loss=0.2229, pruned_loss=0.0253, over 7132.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2616, pruned_loss=0.03076, over 1422674.96 frames.], batch size: 17, lr: 2.44e-04
2022-04-30 12:08:30,097 INFO [train.py:763] (1/8) Epoch 31, batch 1200, loss[loss=0.1661, simple_loss=0.2744, pruned_loss=0.02885, over 7417.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2608, pruned_loss=0.03036, over 1425058.57 frames.], batch size: 21, lr: 2.44e-04
2022-04-30 12:09:54,553 INFO [train.py:763] (1/8) Epoch 31, batch 1250, loss[loss=0.1593, simple_loss=0.2575, pruned_loss=0.03061, over 7178.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2612, pruned_loss=0.03048, over 1419202.80 frames.], batch size: 23, lr: 2.44e-04
2022-04-30 12:11:00,225 INFO [train.py:763] (1/8) Epoch 31, batch 1300, loss[loss=0.1849, simple_loss=0.2906, pruned_loss=0.03958, over 7150.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2613, pruned_loss=0.03073, over 1424178.30 frames.], batch size: 20, lr: 2.44e-04
2022-04-30 12:12:14,866 INFO [train.py:763] (1/8) Epoch 31, batch 1350, loss[loss=0.1571, simple_loss=0.258, pruned_loss=0.02806, over 7342.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2614, pruned_loss=0.03103, over 1422454.31 frames.], batch size: 20, lr: 2.44e-04
2022-04-30 12:13:22,476 INFO [train.py:763] (1/8) Epoch 31, batch 1400, loss[loss=0.1531, simple_loss=0.2546, pruned_loss=0.02578, over 7241.00 frames.], tot_loss[loss=0.16, simple_loss=0.2596, pruned_loss=0.03016, over 1422604.53 frames.], batch size: 20, lr: 2.44e-04
2022-04-30 12:14:38,780 INFO [train.py:763] (1/8) Epoch 31, batch 1450, loss[loss=0.1714, simple_loss=0.2733, pruned_loss=0.03473, over 7325.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2601, pruned_loss=0.03005, over 1424563.81 frames.], batch size: 20, lr: 2.44e-04
2022-04-30 12:15:46,110 INFO [train.py:763] (1/8) Epoch 31, batch 1500, loss[loss=0.171, simple_loss=0.2723, pruned_loss=0.03488, over 5070.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2595, pruned_loss=0.02975, over 1423220.27 frames.], batch size: 52, lr: 2.44e-04
2022-04-30 12:16:51,627 INFO [train.py:763] (1/8) Epoch 31, batch 1550, loss[loss=0.1353, simple_loss=0.2249, pruned_loss=0.02287, over 7404.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2594, pruned_loss=0.02996, over 1423141.26 frames.], batch size: 18, lr: 2.44e-04
2022-04-30 12:17:56,938 INFO [train.py:763] (1/8) Epoch 31, batch 1600, loss[loss=0.1879, simple_loss=0.2806, pruned_loss=0.04766, over 7199.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2596, pruned_loss=0.03, over 1418364.30 frames.], batch size: 23, lr: 2.44e-04
2022-04-30 12:19:02,303 INFO [train.py:763] (1/8) Epoch 31, batch 1650, loss[loss=0.1528, simple_loss=0.2523, pruned_loss=0.02666, over 7412.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2603, pruned_loss=0.03033, over 1417345.12 frames.], batch size: 21, lr: 2.44e-04
2022-04-30 12:20:07,949 INFO [train.py:763] (1/8) Epoch 31, batch 1700, loss[loss=0.1577, simple_loss=0.2594, pruned_loss=0.02799, over 7121.00 frames.], tot_loss[loss=0.161, simple_loss=0.2606, pruned_loss=0.03072, over 1411919.69 frames.], batch size: 21, lr: 2.44e-04
2022-04-30 12:21:14,750 INFO [train.py:763] (1/8) Epoch 31, batch 1750, loss[loss=0.2149, simple_loss=0.294, pruned_loss=0.06788, over 5224.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2616, pruned_loss=0.03101, over 1410070.74 frames.], batch size: 52, lr: 2.44e-04
2022-04-30 12:22:33,259 INFO [train.py:763] (1/8) Epoch 31, batch 1800, loss[loss=0.187, simple_loss=0.2872, pruned_loss=0.04341, over 7229.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2624, pruned_loss=0.03105, over 1411728.08 frames.], batch size: 20, lr: 2.44e-04
2022-04-30 12:23:40,150 INFO [train.py:763] (1/8) Epoch 31, batch 1850, loss[loss=0.1482, simple_loss=0.2343, pruned_loss=0.03109, over 7004.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2628, pruned_loss=0.03164, over 1406080.74 frames.], batch size: 16, lr: 2.44e-04
2022-04-30 12:24:46,002 INFO [train.py:763] (1/8) Epoch 31, batch 1900, loss[loss=0.1554, simple_loss=0.246, pruned_loss=0.03243, over 7361.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2619, pruned_loss=0.03129, over 1412193.97 frames.], batch size: 19, lr: 2.44e-04
2022-04-30 12:25:51,348 INFO [train.py:763] (1/8) Epoch 31, batch 1950, loss[loss=0.1568, simple_loss=0.2565, pruned_loss=0.02854, over 7363.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2616, pruned_loss=0.03136, over 1418314.33 frames.], batch size: 19, lr: 2.43e-04
2022-04-30 12:26:56,755 INFO [train.py:763] (1/8) Epoch 31, batch 2000, loss[loss=0.133, simple_loss=0.2329, pruned_loss=0.01659, over 7289.00 frames.], tot_loss[loss=0.162, simple_loss=0.2616, pruned_loss=0.03116, over 1420069.05 frames.], batch size: 18, lr: 2.43e-04
2022-04-30 12:28:01,917 INFO [train.py:763] (1/8) Epoch 31, batch 2050, loss[loss=0.1786, simple_loss=0.2902, pruned_loss=0.03349, over 7137.00 frames.], tot_loss[loss=0.162, simple_loss=0.2614, pruned_loss=0.03128, over 1416366.31 frames.], batch size: 20, lr: 2.43e-04
2022-04-30 12:29:07,872 INFO [train.py:763] (1/8) Epoch 31, batch 2100, loss[loss=0.1423, simple_loss=0.2414, pruned_loss=0.02155, over 7212.00 frames.], tot_loss[loss=0.163, simple_loss=0.2627, pruned_loss=0.03164, over 1416585.98 frames.], batch size: 16, lr: 2.43e-04
2022-04-30 12:30:13,151 INFO [train.py:763] (1/8) Epoch 31, batch 2150, loss[loss=0.1588, simple_loss=0.2659, pruned_loss=0.02582, over 7207.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2627, pruned_loss=0.03148, over 1420434.21 frames.], batch size: 21, lr: 2.43e-04
2022-04-30 12:31:18,646 INFO [train.py:763] (1/8) Epoch 31, batch 2200, loss[loss=0.1642, simple_loss=0.27, pruned_loss=0.02924, over 7138.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2624, pruned_loss=0.0313, over 1423250.27 frames.], batch size: 26, lr: 2.43e-04
2022-04-30 12:32:23,983 INFO [train.py:763] (1/8) Epoch 31, batch 2250, loss[loss=0.168, simple_loss=0.2574, pruned_loss=0.03925, over 7061.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2621, pruned_loss=0.03128, over 1424355.39 frames.], batch size: 18, lr: 2.43e-04
2022-04-30 12:33:30,742 INFO [train.py:763] (1/8) Epoch 31, batch 2300, loss[loss=0.1671, simple_loss=0.2628, pruned_loss=0.03573, over 7331.00 frames.], tot_loss[loss=0.1623, simple_loss=0.262, pruned_loss=0.03129, over 1421435.84 frames.], batch size: 22, lr: 2.43e-04
2022-04-30 12:34:36,635 INFO [train.py:763] (1/8) Epoch 31, batch 2350, loss[loss=0.1251, simple_loss=0.2166, pruned_loss=0.01682, over 7275.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2627, pruned_loss=0.03135, over 1425176.80 frames.], batch size: 17, lr: 2.43e-04
2022-04-30 12:35:41,754 INFO [train.py:763] (1/8) Epoch 31, batch 2400, loss[loss=0.1725, simple_loss=0.2718, pruned_loss=0.03655, over 7327.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2627, pruned_loss=0.03149, over 1421395.61 frames.], batch size: 20, lr: 2.43e-04
2022-04-30 12:36:47,280 INFO [train.py:763] (1/8) Epoch 31, batch 2450, loss[loss=0.1676, simple_loss=0.2727, pruned_loss=0.03128, over 7173.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2621, pruned_loss=0.03141, over 1422541.30 frames.], batch size: 26, lr: 2.43e-04
2022-04-30 12:37:52,779 INFO [train.py:763] (1/8) Epoch 31, batch 2500, loss[loss=0.1547, simple_loss=0.2444, pruned_loss=0.03245, over 7262.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2622, pruned_loss=0.03135, over 1424592.47 frames.], batch size: 17, lr: 2.43e-04
2022-04-30 12:38:58,016 INFO [train.py:763] (1/8) Epoch 31, batch 2550, loss[loss=0.1819, simple_loss=0.2883, pruned_loss=0.03777, over 7329.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2622, pruned_loss=0.03143, over 1422517.71 frames.], batch size: 20, lr: 2.43e-04
2022-04-30 12:40:03,277 INFO [train.py:763] (1/8) Epoch 31, batch 2600, loss[loss=0.1352, simple_loss=0.233, pruned_loss=0.01872, over 7139.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2622, pruned_loss=0.03131, over 1420808.22 frames.], batch size: 17, lr: 2.43e-04
2022-04-30 12:41:08,491 INFO [train.py:763] (1/8) Epoch 31, batch 2650, loss[loss=0.1798, simple_loss=0.285, pruned_loss=0.03734, over 7154.00 frames.], tot_loss[loss=0.162, simple_loss=0.2619, pruned_loss=0.03106, over 1423097.73 frames.], batch size: 26, lr: 2.43e-04
2022-04-30 12:42:15,310 INFO [train.py:763] (1/8) Epoch 31, batch 2700, loss[loss=0.1571, simple_loss=0.2617, pruned_loss=0.02621, over 7316.00 frames.], tot_loss[loss=0.162, simple_loss=0.2617, pruned_loss=0.03108, over 1422607.92 frames.], batch size: 20, lr: 2.43e-04
2022-04-30 12:43:20,588 INFO [train.py:763] (1/8) Epoch 31, batch 2750, loss[loss=0.1537, simple_loss=0.2664, pruned_loss=0.02053, over 7029.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2617, pruned_loss=0.03065, over 1424906.53 frames.], batch size: 28, lr: 2.43e-04
2022-04-30 12:44:27,119 INFO [train.py:763] (1/8) Epoch 31, batch 2800, loss[loss=0.1453, simple_loss=0.2356, pruned_loss=0.02753, over 7405.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2613, pruned_loss=0.03096, over 1424290.52 frames.], batch size: 18, lr: 2.43e-04
2022-04-30 12:45:34,093 INFO [train.py:763] (1/8) Epoch 31, batch 2850, loss[loss=0.162, simple_loss=0.2658, pruned_loss=0.02907, over 6501.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2614, pruned_loss=0.0311, over 1420801.91 frames.], batch size: 38, lr: 2.43e-04
2022-04-30 12:46:39,726 INFO [train.py:763] (1/8) Epoch 31, batch 2900, loss[loss=0.1476, simple_loss=0.2489, pruned_loss=0.02318, over 7228.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2624, pruned_loss=0.03112, over 1424483.99 frames.], batch size: 20, lr: 2.43e-04
2022-04-30 12:47:44,747 INFO [train.py:763] (1/8) Epoch 31, batch 2950, loss[loss=0.1793, simple_loss=0.2812, pruned_loss=0.03867, over 7194.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2631, pruned_loss=0.03117, over 1416696.85 frames.], batch size: 23, lr: 2.43e-04
2022-04-30 12:48:50,668 INFO [train.py:763] (1/8) Epoch 31, batch 3000, loss[loss=0.1653, simple_loss=0.2629, pruned_loss=0.03382, over 7438.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2634, pruned_loss=0.03141, over 1417947.39 frames.], batch size: 20, lr: 2.43e-04
2022-04-30 12:48:50,669 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 12:49:05,872 INFO [train.py:792] (1/8) Epoch 31, validation: loss=0.1686, simple_loss=0.2652, pruned_loss=0.03603, over 698248.00 frames.
2022-04-30 12:50:12,206 INFO [train.py:763] (1/8) Epoch 31, batch 3050, loss[loss=0.1707, simple_loss=0.2768, pruned_loss=0.03225, over 7268.00 frames.], tot_loss[loss=0.1638, simple_loss=0.2635, pruned_loss=0.03206, over 1421701.56 frames.], batch size: 25, lr: 2.43e-04
2022-04-30 12:51:18,204 INFO [train.py:763] (1/8) Epoch 31, batch 3100, loss[loss=0.1568, simple_loss=0.2591, pruned_loss=0.02728, over 7063.00 frames.], tot_loss[loss=0.1632, simple_loss=0.2629, pruned_loss=0.03176, over 1424991.04 frames.], batch size: 28, lr: 2.42e-04
2022-04-30 12:52:23,638 INFO [train.py:763] (1/8) Epoch 31, batch 3150, loss[loss=0.1378, simple_loss=0.2263, pruned_loss=0.02464, over 7283.00 frames.], tot_loss[loss=0.1624, simple_loss=0.2621, pruned_loss=0.0314, over 1423049.73 frames.], batch size: 17, lr: 2.42e-04
2022-04-30 12:53:29,084 INFO [train.py:763] (1/8) Epoch 31, batch 3200, loss[loss=0.1594, simple_loss=0.2674, pruned_loss=0.02566, over 7109.00 frames.], tot_loss[loss=0.1628, simple_loss=0.2626, pruned_loss=0.03146, over 1425415.59 frames.], batch size: 21, lr: 2.42e-04
2022-04-30 12:54:36,152 INFO [train.py:763] (1/8) Epoch 31, batch 3250, loss[loss=0.1792, simple_loss=0.2802, pruned_loss=0.0391, over 7350.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2626, pruned_loss=0.03129, over 1426829.54 frames.], batch size: 22, lr: 2.42e-04
2022-04-30 12:55:42,934 INFO [train.py:763] (1/8) Epoch 31, batch 3300, loss[loss=0.1655, simple_loss=0.2737, pruned_loss=0.02866, over 7420.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2622, pruned_loss=0.03121, over 1423023.38 frames.], batch size: 20, lr: 2.42e-04
2022-04-30 12:56:50,142 INFO [train.py:763] (1/8) Epoch 31, batch 3350, loss[loss=0.1513, simple_loss=0.2618, pruned_loss=0.02039, over 7322.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2608, pruned_loss=0.03075, over 1425344.93 frames.], batch size: 21, lr: 2.42e-04
2022-04-30 12:57:56,831 INFO [train.py:763] (1/8) Epoch 31, batch 3400, loss[loss=0.1718, simple_loss=0.277, pruned_loss=0.03327, over 7327.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2619, pruned_loss=0.03136, over 1422596.84 frames.], batch size: 20, lr: 2.42e-04
2022-04-30 12:59:03,243 INFO [train.py:763] (1/8) Epoch 31, batch 3450, loss[loss=0.1696, simple_loss=0.2755, pruned_loss=0.0319, over 7213.00 frames.], tot_loss[loss=0.163, simple_loss=0.2627, pruned_loss=0.03162, over 1425738.46 frames.], batch size: 22, lr: 2.42e-04
2022-04-30 13:00:08,921 INFO [train.py:763] (1/8) Epoch 31, batch 3500, loss[loss=0.189, simple_loss=0.2889, pruned_loss=0.04451, over 7304.00 frames.], tot_loss[loss=0.163, simple_loss=0.2631, pruned_loss=0.03144, over 1429077.33 frames.], batch size: 24, lr: 2.42e-04
2022-04-30 13:01:14,841 INFO [train.py:763] (1/8) Epoch 31, batch 3550, loss[loss=0.224, simple_loss=0.3124, pruned_loss=0.06777, over 7376.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2621, pruned_loss=0.03146, over 1432306.38 frames.], batch size: 23, lr: 2.42e-04
2022-04-30 13:02:21,335 INFO [train.py:763] (1/8) Epoch 31, batch 3600, loss[loss=0.1757, simple_loss=0.2862, pruned_loss=0.03267, over 6435.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2614, pruned_loss=0.0315, over 1429551.74 frames.], batch size: 38, lr: 2.42e-04
2022-04-30 13:03:26,535 INFO [train.py:763] (1/8) Epoch 31, batch 3650, loss[loss=0.158, simple_loss=0.2579, pruned_loss=0.02901, over 7239.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2619, pruned_loss=0.03121, over 1429404.50 frames.], batch size: 20, lr: 2.42e-04
2022-04-30 13:04:32,081 INFO [train.py:763] (1/8) Epoch 31, batch 3700, loss[loss=0.146, simple_loss=0.2381, pruned_loss=0.02693, over 7134.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2611, pruned_loss=0.03101, over 1430782.22 frames.], batch size: 17, lr: 2.42e-04
2022-04-30 13:05:36,813 INFO [train.py:763] (1/8) Epoch 31, batch 3750, loss[loss=0.1791, simple_loss=0.2842, pruned_loss=0.03705, over 7201.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2619, pruned_loss=0.03115, over 1424594.42 frames.], batch size: 23, lr: 2.42e-04
2022-04-30 13:06:42,585 INFO [train.py:763] (1/8) Epoch 31, batch 3800, loss[loss=0.1668, simple_loss=0.2651, pruned_loss=0.03424, over 7380.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2625, pruned_loss=0.03098, over 1425605.90 frames.], batch size: 23, lr: 2.42e-04
2022-04-30 13:07:47,976 INFO [train.py:763] (1/8) Epoch 31, batch 3850, loss[loss=0.1446, simple_loss=0.2416, pruned_loss=0.02377, over 7432.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2618, pruned_loss=0.03057, over 1427366.41 frames.], batch size: 20, lr: 2.42e-04
2022-04-30 13:08:53,256 INFO [train.py:763] (1/8) Epoch 31, batch 3900, loss[loss=0.1576, simple_loss=0.2603, pruned_loss=0.02749, over 7161.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2615, pruned_loss=0.03044, over 1429129.36 frames.], batch size: 18, lr: 2.42e-04
2022-04-30 13:09:58,648 INFO [train.py:763] (1/8) Epoch 31, batch 3950, loss[loss=0.1662, simple_loss=0.2798, pruned_loss=0.02634, over 7229.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2616, pruned_loss=0.03075, over 1424558.88 frames.], batch size: 21, lr: 2.42e-04
2022-04-30 13:11:04,247 INFO [train.py:763] (1/8) Epoch 31, batch 4000, loss[loss=0.1496, simple_loss=0.2351, pruned_loss=0.03201, over 7425.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2615, pruned_loss=0.03109, over 1421799.93 frames.], batch size: 18, lr: 2.42e-04
2022-04-30 13:12:09,631 INFO [train.py:763] (1/8) Epoch 31, batch 4050, loss[loss=0.173, simple_loss=0.2837, pruned_loss=0.03112, over 7367.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2618, pruned_loss=0.03101, over 1420050.45 frames.], batch size: 23, lr: 2.42e-04
2022-04-30 13:13:15,760 INFO [train.py:763] (1/8) Epoch 31, batch 4100, loss[loss=0.1827, simple_loss=0.2841, pruned_loss=0.04065, over 7194.00 frames.], tot_loss[loss=0.1627, simple_loss=0.2629, pruned_loss=0.03121, over 1418260.58 frames.], batch size: 22, lr: 2.42e-04
2022-04-30 13:14:21,773 INFO [train.py:763] (1/8) Epoch 31, batch 4150, loss[loss=0.1866, simple_loss=0.2859, pruned_loss=0.04364, over 7206.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2624, pruned_loss=0.03114, over 1421919.55 frames.], batch size: 21, lr: 2.42e-04
2022-04-30 13:15:28,688 INFO [train.py:763] (1/8) Epoch 31, batch 4200, loss[loss=0.1629, simple_loss=0.2656, pruned_loss=0.03008, over 7332.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2611, pruned_loss=0.0311, over 1422372.88 frames.], batch size: 20, lr: 2.42e-04
2022-04-30 13:16:35,572 INFO [train.py:763] (1/8) Epoch 31, batch 4250, loss[loss=0.1575, simple_loss=0.2598, pruned_loss=0.02762, over 7245.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2609, pruned_loss=0.03106, over 1421030.73 frames.], batch size: 19, lr: 2.42e-04
2022-04-30 13:17:40,850 INFO [train.py:763] (1/8) Epoch 31, batch 4300, loss[loss=0.1515, simple_loss=0.2454, pruned_loss=0.02879, over 7404.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2605, pruned_loss=0.03058, over 1420431.64 frames.], batch size: 18, lr: 2.42e-04
2022-04-30 13:18:46,141 INFO [train.py:763] (1/8) Epoch 31, batch 4350, loss[loss=0.1565, simple_loss=0.2535, pruned_loss=0.02976, over 7165.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2609, pruned_loss=0.03088, over 1420591.44 frames.], batch size: 18, lr: 2.41e-04
2022-04-30 13:19:51,341 INFO [train.py:763] (1/8) Epoch 31, batch 4400, loss[loss=0.1644, simple_loss=0.2613, pruned_loss=0.03371, over 7307.00 frames.], tot_loss[loss=0.162, simple_loss=0.2615, pruned_loss=0.03124, over 1406578.34 frames.], batch size: 25, lr: 2.41e-04
2022-04-30 13:20:56,965 INFO [train.py:763] (1/8) Epoch 31, batch 4450, loss[loss=0.1426, simple_loss=0.2334, pruned_loss=0.02585, over 7245.00 frames.], tot_loss[loss=0.162, simple_loss=0.2616, pruned_loss=0.0312, over 1403328.08 frames.], batch size: 16, lr: 2.41e-04
2022-04-30 13:22:02,213 INFO [train.py:763] (1/8) Epoch 31, batch 4500, loss[loss=0.1662, simple_loss=0.2761, pruned_loss=0.0281, over 6720.00 frames.], tot_loss[loss=0.1633, simple_loss=0.2629, pruned_loss=0.03187, over 1394491.72 frames.], batch size: 31, lr: 2.41e-04
2022-04-30 13:23:07,076 INFO [train.py:763] (1/8) Epoch 31, batch 4550, loss[loss=0.1893, simple_loss=0.2888, pruned_loss=0.04485, over 4859.00 frames.], tot_loss[loss=0.1641, simple_loss=0.2629, pruned_loss=0.03264, over 1355245.76 frames.], batch size: 52, lr: 2.41e-04
2022-04-30 13:24:35,147 INFO [train.py:763] (1/8) Epoch 32, batch 0, loss[loss=0.1902, simple_loss=0.2919, pruned_loss=0.04426, over 6857.00 frames.], tot_loss[loss=0.1902, simple_loss=0.2919, pruned_loss=0.04426, over 6857.00 frames.], batch size: 31, lr: 2.38e-04
2022-04-30 13:25:38,916 INFO [train.py:763] (1/8) Epoch 32, batch 50, loss[loss=0.166, simple_loss=0.2627, pruned_loss=0.03466, over 5269.00 frames.], tot_loss[loss=0.1601, simple_loss=0.261, pruned_loss=0.02963, over 314393.54 frames.], batch size: 53, lr: 2.38e-04
2022-04-30 13:26:41,318 INFO [train.py:763] (1/8) Epoch 32, batch 100, loss[loss=0.1754, simple_loss=0.2888, pruned_loss=0.03098, over 6335.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2605, pruned_loss=0.0294, over 558614.34 frames.], batch size: 37, lr: 2.38e-04
2022-04-30 13:27:47,090 INFO [train.py:763] (1/8) Epoch 32, batch 150, loss[loss=0.1717, simple_loss=0.2803, pruned_loss=0.03156, over 7196.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2621, pruned_loss=0.02929, over 751154.24 frames.], batch size: 23, lr: 2.37e-04
2022-04-30 13:28:52,458 INFO [train.py:763] (1/8) Epoch 32, batch 200, loss[loss=0.1477, simple_loss=0.2366, pruned_loss=0.02945, over 7001.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2612, pruned_loss=0.02959, over 893918.69 frames.], batch size: 16, lr: 2.37e-04
2022-04-30 13:29:57,592 INFO [train.py:763] (1/8) Epoch 32, batch 250, loss[loss=0.1548, simple_loss=0.2673, pruned_loss=0.02116, over 7232.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2618, pruned_loss=0.03027, over 1009437.21 frames.], batch size: 20, lr: 2.37e-04
2022-04-30 13:31:03,085 INFO [train.py:763] (1/8) Epoch 32, batch 300, loss[loss=0.1677, simple_loss=0.2759, pruned_loss=0.02976, over 6779.00 frames.], tot_loss[loss=0.1622, simple_loss=0.2629, pruned_loss=0.03077, over 1092865.88 frames.], batch size: 31, lr: 2.37e-04
2022-04-30 13:32:10,106 INFO [train.py:763] (1/8) Epoch 32, batch 350, loss[loss=0.1639, simple_loss=0.2512, pruned_loss=0.03827, over 7415.00 frames.], tot_loss[loss=0.162, simple_loss=0.2622, pruned_loss=0.03091, over 1163476.21 frames.], batch size: 18, lr: 2.37e-04
2022-04-30 13:33:15,977 INFO [train.py:763] (1/8) Epoch 32, batch 400, loss[loss=0.1489, simple_loss=0.2443, pruned_loss=0.02674, over 7430.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2614, pruned_loss=0.03085, over 1221592.53 frames.], batch size: 20, lr: 2.37e-04
2022-04-30 13:34:21,564 INFO [train.py:763] (1/8) Epoch 32, batch 450, loss[loss=0.1596, simple_loss=0.272, pruned_loss=0.02356, over 6597.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2604, pruned_loss=0.03052, over 1263647.01 frames.], batch size: 31, lr: 2.37e-04
2022-04-30 13:35:26,872 INFO [train.py:763] (1/8) Epoch 32, batch 500, loss[loss=0.1856, simple_loss=0.291, pruned_loss=0.04015, over 7199.00 frames.], tot_loss[loss=0.161, simple_loss=0.261, pruned_loss=0.03052, over 1301563.12 frames.], batch size: 23, lr: 2.37e-04
2022-04-30 13:36:32,824 INFO [train.py:763] (1/8) Epoch 32, batch 550, loss[loss=0.1637, simple_loss=0.2652, pruned_loss=0.03108, over 7320.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2621, pruned_loss=0.03073, over 1330457.89 frames.], batch size: 21, lr: 2.37e-04
2022-04-30 13:37:38,142 INFO [train.py:763] (1/8) Epoch 32, batch 600, loss[loss=0.1796, simple_loss=0.2807, pruned_loss=0.03923, over 7288.00 frames.], tot_loss[loss=0.1616, simple_loss=0.262, pruned_loss=0.03058, over 1347842.25 frames.], batch size: 24, lr: 2.37e-04
2022-04-30 13:38:43,400 INFO [train.py:763] (1/8) Epoch 32, batch 650, loss[loss=0.1582, simple_loss=0.2615, pruned_loss=0.02746, over 7183.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2613, pruned_loss=0.03029, over 1364821.96 frames.], batch size: 26, lr: 2.37e-04
2022-04-30 13:39:48,619 INFO [train.py:763] (1/8) Epoch 32, batch 700, loss[loss=0.1424, simple_loss=0.2331, pruned_loss=0.02582, over 7147.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2616, pruned_loss=0.03056, over 1374707.48 frames.], batch size: 17, lr: 2.37e-04
2022-04-30 13:40:55,073 INFO [train.py:763] (1/8) Epoch 32, batch 750, loss[loss=0.1683, simple_loss=0.2803, pruned_loss=0.02817, over 7220.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2616, pruned_loss=0.03068, over 1380563.19 frames.], batch size: 21, lr: 2.37e-04
2022-04-30 13:42:02,249 INFO [train.py:763] (1/8) Epoch 32, batch 800, loss[loss=0.1415, simple_loss=0.2447, pruned_loss=0.01911, over 7430.00 frames.], tot_loss[loss=0.1608, simple_loss=0.26, pruned_loss=0.03074, over 1391946.30 frames.], batch size: 20, lr: 2.37e-04
2022-04-30 13:43:08,537 INFO [train.py:763] (1/8) Epoch 32, batch 850, loss[loss=0.1765, simple_loss=0.2888, pruned_loss=0.03208, over 7392.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2606, pruned_loss=0.03049, over 1399353.82 frames.], batch size: 23, lr: 2.37e-04
2022-04-30 13:44:14,303 INFO [train.py:763] (1/8) Epoch 32, batch 900, loss[loss=0.1548, simple_loss=0.2531, pruned_loss=0.02825, over 7204.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2591, pruned_loss=0.03013, over 1408707.55 frames.], batch size: 23, lr: 2.37e-04
2022-04-30 13:45:21,094 INFO [train.py:763] (1/8) Epoch 32, batch 950, loss[loss=0.1361, simple_loss=0.2391, pruned_loss=0.01656, over 7424.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2596, pruned_loss=0.0303, over 1413564.86 frames.], batch size: 20, lr: 2.37e-04
2022-04-30 13:46:27,362 INFO [train.py:763] (1/8) Epoch 32, batch 1000, loss[loss=0.1655, simple_loss=0.2737, pruned_loss=0.02866, over 7202.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2589, pruned_loss=0.03001, over 1413176.60 frames.], batch size: 23, lr: 2.37e-04
2022-04-30 13:47:33,314 INFO [train.py:763] (1/8) Epoch 32, batch 1050, loss[loss=0.1749, simple_loss=0.2872, pruned_loss=0.03129, over 7083.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2604, pruned_loss=0.03027, over 1412848.48 frames.], batch size: 28, lr: 2.37e-04
2022-04-30 13:48:38,618 INFO [train.py:763] (1/8) Epoch 32, batch 1100, loss[loss=0.1741, simple_loss=0.2699, pruned_loss=0.03913, over 7329.00 frames.], tot_loss[loss=0.1601, simple_loss=0.26, pruned_loss=0.03009, over 1418026.75 frames.], batch size: 24, lr: 2.37e-04
2022-04-30 13:49:45,240 INFO [train.py:763] (1/8) Epoch 32, batch 1150, loss[loss=0.17, simple_loss=0.2656, pruned_loss=0.03724, over 7214.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2596, pruned_loss=0.02977, over 1418951.71 frames.], batch size: 23, lr: 2.37e-04
2022-04-30 13:50:50,720 INFO [train.py:763] (1/8) Epoch 32, batch 1200, loss[loss=0.1795, simple_loss=0.285, pruned_loss=0.03698, over 7156.00 frames.], tot_loss[loss=0.161, simple_loss=0.2612, pruned_loss=0.0304, over 1421639.05 frames.], batch size: 26, lr: 2.37e-04
2022-04-30 13:51:56,747 INFO [train.py:763] (1/8) Epoch 32, batch 1250, loss[loss=0.1627, simple_loss=0.27, pruned_loss=0.02766, over 6414.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2618, pruned_loss=0.03083, over 1421251.54 frames.], batch size: 38, lr: 2.37e-04
2022-04-30 13:53:02,500 INFO [train.py:763] (1/8) Epoch 32, batch 1300, loss[loss=0.1671, simple_loss=0.2743, pruned_loss=0.02997, over 7224.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2608, pruned_loss=0.03049, over 1421969.80 frames.], batch size: 21, lr: 2.37e-04
2022-04-30 13:54:10,199 INFO [train.py:763] (1/8) Epoch 32, batch 1350, loss[loss=0.1509, simple_loss=0.2408, pruned_loss=0.03046, over 7297.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2606, pruned_loss=0.03043, over 1420999.08 frames.], batch size: 17, lr: 2.37e-04
2022-04-30 13:55:17,149 INFO [train.py:763] (1/8) Epoch 32, batch 1400, loss[loss=0.1614, simple_loss=0.2739, pruned_loss=0.0244, over 7144.00 frames.], tot_loss[loss=0.1602, simple_loss=0.26, pruned_loss=0.03021, over 1422031.31 frames.], batch size: 20, lr: 2.36e-04
2022-04-30 13:56:22,419 INFO [train.py:763] (1/8) Epoch 32, batch 1450, loss[loss=0.1671, simple_loss=0.269, pruned_loss=0.03262, over 6809.00 frames.], tot_loss[loss=0.1601, simple_loss=0.26, pruned_loss=0.03014, over 1425242.23 frames.], batch size: 31, lr: 2.36e-04
2022-04-30 13:57:27,824 INFO [train.py:763] (1/8) Epoch 32, batch 1500, loss[loss=0.1614, simple_loss=0.2683, pruned_loss=0.02722, over 4962.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2607, pruned_loss=0.03056, over 1422434.59 frames.], batch size: 52, lr: 2.36e-04
2022-04-30 13:58:33,077 INFO [train.py:763] (1/8) Epoch 32, batch 1550, loss[loss=0.1573, simple_loss=0.2708, pruned_loss=0.02195, over 7223.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2613, pruned_loss=0.03076, over 1418175.59 frames.], batch size: 21, lr: 2.36e-04
2022-04-30 13:59:38,325 INFO [train.py:763] (1/8) Epoch 32, batch 1600, loss[loss=0.1492, simple_loss=0.2555, pruned_loss=0.02147, over 7415.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2608, pruned_loss=0.0308, over 1420661.20 frames.], batch size: 21, lr: 2.36e-04
2022-04-30 14:00:43,685 INFO [train.py:763] (1/8) Epoch 32, batch 1650, loss[loss=0.1672, simple_loss=0.2659, pruned_loss=0.03424, over 7222.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2613, pruned_loss=0.03107, over 1421681.65 frames.], batch size: 21, lr: 2.36e-04
2022-04-30 14:01:48,788 INFO [train.py:763] (1/8) Epoch 32, batch 1700, loss[loss=0.1545, simple_loss=0.2621, pruned_loss=0.02347, over 7265.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2612, pruned_loss=0.03081, over 1424364.86 frames.], batch size: 24, lr: 2.36e-04
2022-04-30 14:02:54,184 INFO [train.py:763] (1/8) Epoch 32, batch 1750, loss[loss=0.1732, simple_loss=0.2785, pruned_loss=0.03394, over 7030.00 frames.], tot_loss[loss=0.1626, simple_loss=0.263, pruned_loss=0.03111, over 1417026.01 frames.], batch size: 28, lr: 2.36e-04
2022-04-30 14:03:59,640 INFO [train.py:763] (1/8) Epoch 32, batch 1800, loss[loss=0.15, simple_loss=0.2449, pruned_loss=0.02753, over 7254.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2616, pruned_loss=0.03053, over 1420053.21 frames.], batch size: 19, lr: 2.36e-04
2022-04-30 14:05:06,115 INFO [train.py:763] (1/8) Epoch 32, batch 1850, loss[loss=0.1635, simple_loss=0.2636, pruned_loss=0.03165, over 7319.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2612, pruned_loss=0.0303, over 1423229.36 frames.], batch size: 21, lr: 2.36e-04
2022-04-30 14:06:21,242 INFO [train.py:763] (1/8) Epoch 32, batch 1900, loss[loss=0.1929, simple_loss=0.2989, pruned_loss=0.04345, over 7375.00 frames.], tot_loss[loss=0.161, simple_loss=0.2611, pruned_loss=0.03041, over 1425747.02 frames.], batch size: 23, lr: 2.36e-04
2022-04-30 14:07:26,669 INFO [train.py:763] (1/8) Epoch 32, batch 1950, loss[loss=0.1497, simple_loss=0.2548, pruned_loss=0.02223, over 7290.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2608, pruned_loss=0.03033, over 1424047.70 frames.], batch size: 24, lr: 2.36e-04
2022-04-30 14:08:33,678 INFO [train.py:763] (1/8) Epoch 32, batch 2000, loss[loss=0.1554, simple_loss=0.2675, pruned_loss=0.02162, over 6548.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2605, pruned_loss=0.03036, over 1425586.23 frames.], batch size: 38, lr: 2.36e-04
2022-04-30 14:09:39,827 INFO [train.py:763] (1/8) Epoch 32, batch 2050, loss[loss=0.1498, simple_loss=0.2444, pruned_loss=0.02758, over 7159.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2607, pruned_loss=0.03057, over 1426044.94 frames.], batch size: 18, lr: 2.36e-04
2022-04-30 14:10:45,530 INFO [train.py:763] (1/8) Epoch 32, batch 2100, loss[loss=0.1486, simple_loss=0.2443, pruned_loss=0.02644, over 7165.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2605, pruned_loss=0.03053, over 1428002.05 frames.], batch size: 19, lr: 2.36e-04
2022-04-30 14:11:52,554 INFO [train.py:763] (1/8) Epoch 32, batch 2150, loss[loss=0.1473, simple_loss=0.2374, pruned_loss=0.02862, over 7425.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2606, pruned_loss=0.0305, over 1429092.22 frames.], batch size: 18, lr: 2.36e-04
2022-04-30 14:12:58,733 INFO [train.py:763] (1/8) Epoch 32, batch 2200, loss[loss=0.1888, simple_loss=0.2908, pruned_loss=0.0434, over 5306.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2614, pruned_loss=0.03087, over 1423718.52 frames.], batch size: 52, lr: 2.36e-04
2022-04-30 14:14:05,699 INFO [train.py:763] (1/8) Epoch 32, batch 2250, loss[loss=0.1813, simple_loss=0.2799, pruned_loss=0.04134, over 7210.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2612, pruned_loss=0.03092, over 1420980.30 frames.], batch size: 26, lr: 2.36e-04
2022-04-30 14:15:12,723 INFO [train.py:763] (1/8) Epoch 32, batch 2300, loss[loss=0.1925, simple_loss=0.2847, pruned_loss=0.05015, over 7193.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2606, pruned_loss=0.0308, over 1419369.00 frames.], batch size: 22, lr: 2.36e-04
2022-04-30 14:16:18,545 INFO [train.py:763] (1/8) Epoch 32, batch 2350, loss[loss=0.1464, simple_loss=0.2357, pruned_loss=0.02854, over 6808.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2596, pruned_loss=0.03062, over 1421976.14 frames.], batch size: 15, lr: 2.36e-04
2022-04-30 14:17:25,996 INFO [train.py:763] (1/8) Epoch 32, batch 2400, loss[loss=0.1529, simple_loss=0.2526, pruned_loss=0.02664, over 7420.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2589, pruned_loss=0.03017, over 1423486.10 frames.], batch size: 20, lr: 2.36e-04
2022-04-30 14:18:32,876 INFO [train.py:763] (1/8) Epoch 32, batch 2450, loss[loss=0.1494, simple_loss=0.2519, pruned_loss=0.02343, over 7250.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2592, pruned_loss=0.03023, over 1425729.30 frames.], batch size: 19, lr: 2.36e-04
2022-04-30 14:19:38,453 INFO [train.py:763] (1/8) Epoch 32, batch 2500, loss[loss=0.1695, simple_loss=0.273, pruned_loss=0.03298, over 7325.00 frames.], tot_loss[loss=0.1603, simple_loss=0.26, pruned_loss=0.03033, over 1427120.22 frames.], batch size: 21, lr: 2.36e-04
2022-04-30 14:20:45,068 INFO [train.py:763] (1/8) Epoch 32, batch 2550, loss[loss=0.1699, simple_loss=0.2651, pruned_loss=0.03735, over 7378.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2601, pruned_loss=0.03056, over 1427360.79 frames.], batch size: 23, lr: 2.36e-04
2022-04-30 14:21:59,921 INFO [train.py:763] (1/8) Epoch 32, batch 2600, loss[loss=0.1801, simple_loss=0.2843, pruned_loss=0.03793, over 7195.00 frames.], tot_loss[loss=0.1615, simple_loss=0.261, pruned_loss=0.03099, over 1427949.46 frames.], batch size: 23, lr: 2.36e-04
2022-04-30 14:23:23,012 INFO [train.py:763] (1/8) Epoch 32, batch 2650, loss[loss=0.136, simple_loss=0.2301, pruned_loss=0.02092, over 6787.00 frames.], tot_loss[loss=0.1623, simple_loss=0.2619, pruned_loss=0.03135, over 1423203.89 frames.], batch size: 15, lr: 2.35e-04
2022-04-30 14:24:36,937 INFO [train.py:763] (1/8) Epoch 32, batch 2700, loss[loss=0.1496, simple_loss=0.2554, pruned_loss=0.02191, over 7431.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2611, pruned_loss=0.03063, over 1424845.95 frames.], batch size: 20, lr: 2.35e-04
2022-04-30 14:25:51,353 INFO [train.py:763] (1/8) Epoch 32, batch 2750, loss[loss=0.1547, simple_loss=0.2446, pruned_loss=0.03236, over 7276.00 frames.], tot_loss[loss=0.1612, simple_loss=0.261, pruned_loss=0.03075, over 1425834.53 frames.], batch size: 18, lr: 2.35e-04
2022-04-30 14:26:57,660 INFO [train.py:763] (1/8) Epoch 32, batch 2800, loss[loss=0.1868, simple_loss=0.289, pruned_loss=0.04226, over 7204.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2603, pruned_loss=0.03053, over 1425136.86 frames.], batch size: 23, lr: 2.35e-04
2022-04-30 14:28:12,042 INFO [train.py:763] (1/8) Epoch 32, batch 2850, loss[loss=0.1701, simple_loss=0.2765, pruned_loss=0.03188, over 7330.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2601, pruned_loss=0.03033, over 1426146.69 frames.], batch size: 21, lr: 2.35e-04
2022-04-30 14:29:27,081 INFO [train.py:763] (1/8) Epoch 32, batch 2900, loss[loss=0.1617, simple_loss=0.2684, pruned_loss=0.02747, over 7281.00 frames.], tot_loss[loss=0.161, simple_loss=0.2609, pruned_loss=0.03057, over 1425386.57 frames.], batch size: 25, lr: 2.35e-04
2022-04-30 14:30:33,972 INFO [train.py:763] (1/8) Epoch 32, batch 2950, loss[loss=0.1399, simple_loss=0.2379, pruned_loss=0.02094, over 7435.00 frames.], tot_loss[loss=0.161, simple_loss=0.2612, pruned_loss=0.03038, over 1427689.13 frames.], batch size: 20, lr: 2.35e-04
2022-04-30 14:31:40,119 INFO [train.py:763] (1/8) Epoch 32, batch 3000, loss[loss=0.1487, simple_loss=0.2506, pruned_loss=0.02339, over 7058.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2607, pruned_loss=0.0302, over 1426531.14 frames.], batch size: 18, lr: 2.35e-04
2022-04-30 14:31:40,120 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 14:31:55,319 INFO [train.py:792] (1/8) Epoch 32, validation: loss=0.1696, simple_loss=0.2645, pruned_loss=0.0374, over 698248.00 frames.
2022-04-30 14:33:01,764 INFO [train.py:763] (1/8) Epoch 32, batch 3050, loss[loss=0.1564, simple_loss=0.2656, pruned_loss=0.02358, over 6271.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2601, pruned_loss=0.03003, over 1422965.52 frames.], batch size: 37, lr: 2.35e-04
2022-04-30 14:34:07,505 INFO [train.py:763] (1/8) Epoch 32, batch 3100, loss[loss=0.1636, simple_loss=0.2589, pruned_loss=0.03416, over 7384.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2605, pruned_loss=0.02996, over 1424064.03 frames.], batch size: 23, lr: 2.35e-04
2022-04-30 14:35:13,884 INFO [train.py:763] (1/8) Epoch 32, batch 3150, loss[loss=0.1398, simple_loss=0.2397, pruned_loss=0.01993, over 7459.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2599, pruned_loss=0.0299, over 1421876.14 frames.], batch size: 19, lr: 2.35e-04
2022-04-30 14:36:20,358 INFO [train.py:763] (1/8) Epoch 32, batch 3200, loss[loss=0.1427, simple_loss=0.23, pruned_loss=0.02771, over 6813.00 frames.], tot_loss[loss=0.1603, simple_loss=0.26, pruned_loss=0.03034, over 1421428.26 frames.], batch size: 15, lr: 2.35e-04
2022-04-30 14:37:25,786 INFO [train.py:763] (1/8) Epoch 32, batch 3250, loss[loss=0.1338, simple_loss=0.2251, pruned_loss=0.02126, over 7290.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2604, pruned_loss=0.03061, over 1418507.47 frames.], batch size: 18, lr: 2.35e-04
2022-04-30 14:38:31,355 INFO [train.py:763] (1/8) Epoch 32, batch 3300, loss[loss=0.1799, simple_loss=0.2843, pruned_loss=0.03778, over 7236.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2601, pruned_loss=0.03055, over 1424232.30 frames.], batch size: 20, lr: 2.35e-04
2022-04-30 14:39:37,083 INFO [train.py:763] (1/8) Epoch 32, batch 3350, loss[loss=0.1543, simple_loss=0.2598, pruned_loss=0.02443, over 7325.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2602, pruned_loss=0.0304, over 1427927.69 frames.], batch size: 21, lr: 2.35e-04
2022-04-30 14:40:43,411 INFO [train.py:763] (1/8) Epoch 32, batch 3400, loss[loss=0.1701, simple_loss=0.2556, pruned_loss=0.04226, over 7273.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2602, pruned_loss=0.03036, over 1427866.59 frames.], batch size: 18, lr: 2.35e-04
2022-04-30 14:41:50,116 INFO [train.py:763] (1/8) Epoch 32, batch 3450, loss[loss=0.1672, simple_loss=0.263, pruned_loss=0.03564, over 7332.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2614, pruned_loss=0.03078, over 1431142.09 frames.], batch size: 20, lr: 2.35e-04
2022-04-30 14:42:56,323 INFO [train.py:763] (1/8) Epoch 32, batch 3500, loss[loss=0.1585, simple_loss=0.264, pruned_loss=0.02646, over 7387.00 frames.], tot_loss[loss=0.162, simple_loss=0.2622, pruned_loss=0.03088, over 1427840.20 frames.], batch size: 23, lr: 2.35e-04
2022-04-30 14:44:01,645 INFO [train.py:763] (1/8) Epoch 32, batch 3550, loss[loss=0.1485, simple_loss=0.2387, pruned_loss=0.02915, over 7409.00 frames.], tot_loss[loss=0.1625, simple_loss=0.2625, pruned_loss=0.03122, over 1426253.83 frames.], batch size: 18, lr: 2.35e-04
2022-04-30 14:45:06,993 INFO [train.py:763] (1/8) Epoch 32, batch 3600, loss[loss=0.1497, simple_loss=0.2493, pruned_loss=0.02506, over 7314.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2624, pruned_loss=0.03095, over 1424203.40 frames.], batch size: 20, lr: 2.35e-04
2022-04-30 14:46:12,675 INFO [train.py:763] (1/8) Epoch 32, batch 3650, loss[loss=0.138, simple_loss=0.2424, pruned_loss=0.01681, over 7325.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2615, pruned_loss=0.03063, over 1423093.47 frames.], batch size: 20, lr: 2.35e-04
2022-04-30 14:47:18,399 INFO [train.py:763] (1/8) Epoch 32, batch 3700, loss[loss=0.1369, simple_loss=0.2277, pruned_loss=0.02308, over 7281.00 frames.], tot_loss[loss=0.162, simple_loss=0.2624, pruned_loss=0.0308, over 1426350.12 frames.], batch size: 17, lr: 2.35e-04
2022-04-30 14:48:25,077 INFO [train.py:763] (1/8) Epoch 32, batch 3750, loss[loss=0.147, simple_loss=0.2593, pruned_loss=0.01735, over 7225.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2617, pruned_loss=0.03061, over 1426627.37 frames.], batch size: 21, lr: 2.35e-04
2022-04-30 14:49:30,593 INFO [train.py:763] (1/8) Epoch 32, batch 3800, loss[loss=0.1651, simple_loss=0.2603, pruned_loss=0.03491, over 7195.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2605, pruned_loss=0.03012, over 1427061.57 frames.], batch size: 23, lr: 2.35e-04
2022-04-30 14:50:35,838 INFO [train.py:763] (1/8) Epoch 32, batch 3850, loss[loss=0.1439, simple_loss=0.2548, pruned_loss=0.01645, over 7311.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2604, pruned_loss=0.03008, over 1428266.51 frames.], batch size: 21, lr: 2.35e-04
2022-04-30 14:51:41,190 INFO [train.py:763] (1/8) Epoch 32, batch 3900, loss[loss=0.1532, simple_loss=0.2515, pruned_loss=0.02746, over 6827.00 frames.], tot_loss[loss=0.161, simple_loss=0.2615, pruned_loss=0.0303, over 1428699.98 frames.], batch size: 15, lr: 2.35e-04
2022-04-30 14:52:46,605 INFO [train.py:763] (1/8) Epoch 32, batch 3950, loss[loss=0.1418, simple_loss=0.2405, pruned_loss=0.02155, over 7404.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2626, pruned_loss=0.03049, over 1431589.84 frames.], batch size: 18, lr: 2.34e-04
2022-04-30 14:53:52,265 INFO [train.py:763] (1/8) Epoch 32, batch 4000, loss[loss=0.1535, simple_loss=0.2642, pruned_loss=0.0214, over 6258.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2616, pruned_loss=0.03012, over 1432223.03 frames.], batch size: 37, lr: 2.34e-04
2022-04-30 14:54:57,658 INFO [train.py:763] (1/8) Epoch 32, batch 4050, loss[loss=0.1309, simple_loss=0.2191, pruned_loss=0.0214, over 7276.00 frames.], tot_loss[loss=0.161, simple_loss=0.2613, pruned_loss=0.03032, over 1428041.38 frames.], batch size: 18, lr: 2.34e-04
2022-04-30 14:56:02,792 INFO [train.py:763] (1/8) Epoch 32, batch 4100, loss[loss=0.1813, simple_loss=0.2863, pruned_loss=0.03821, over 7161.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2611, pruned_loss=0.03018, over 1422501.21 frames.], batch size: 26, lr: 2.34e-04
2022-04-30 14:57:08,462 INFO [train.py:763] (1/8) Epoch 32, batch 4150, loss[loss=0.1429, simple_loss=0.2383, pruned_loss=0.02373, over 7240.00 frames.], tot_loss[loss=0.16, simple_loss=0.2605, pruned_loss=0.02972, over 1422466.91 frames.], batch size: 16, lr: 2.34e-04
2022-04-30 14:58:14,267 INFO [train.py:763] (1/8) Epoch 32, batch 4200, loss[loss=0.1652, simple_loss=0.2607, pruned_loss=0.03487, over 7261.00 frames.], tot_loss[loss=0.16, simple_loss=0.2602, pruned_loss=0.02986, over 1420715.87 frames.], batch size: 19, lr: 2.34e-04
2022-04-30 14:59:19,679 INFO [train.py:763] (1/8) Epoch 32, batch 4250, loss[loss=0.1645, simple_loss=0.2587, pruned_loss=0.03513, over 7427.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2597, pruned_loss=0.02992, over 1421545.89 frames.], batch size: 20, lr: 2.34e-04
2022-04-30 15:00:26,350 INFO [train.py:763] (1/8) Epoch 32, batch 4300, loss[loss=0.1732, simple_loss=0.2836, pruned_loss=0.03136, over 6759.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2605, pruned_loss=0.02967, over 1419344.09 frames.], batch size: 31, lr: 2.34e-04
2022-04-30 15:01:32,992 INFO [train.py:763] (1/8) Epoch 32, batch 4350, loss[loss=0.1578, simple_loss=0.2506, pruned_loss=0.03256, over 7216.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2599, pruned_loss=0.02948, over 1414628.88 frames.], batch size: 21, lr: 2.34e-04
2022-04-30 15:02:38,276 INFO [train.py:763] (1/8) Epoch 32, batch 4400, loss[loss=0.1756, simple_loss=0.2831, pruned_loss=0.03403, over 7150.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2597, pruned_loss=0.02962, over 1414370.06 frames.], batch size: 20, lr: 2.34e-04
2022-04-30 15:03:43,363 INFO [train.py:763] (1/8) Epoch 32, batch 4450, loss[loss=0.1775, simple_loss=0.2789, pruned_loss=0.03802, over 7344.00 frames.], tot_loss[loss=0.1593, simple_loss=0.26, pruned_loss=0.02935, over 1408331.12 frames.], batch size: 22, lr: 2.34e-04
2022-04-30 15:04:48,245 INFO [train.py:763] (1/8) Epoch 32, batch 4500, loss[loss=0.1469, simple_loss=0.246, pruned_loss=0.02392, over 7155.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2604, pruned_loss=0.02966, over 1397358.84 frames.], batch size: 20, lr: 2.34e-04
2022-04-30 15:05:53,071 INFO [train.py:763] (1/8) Epoch 32, batch 4550, loss[loss=0.1727, simple_loss=0.2615, pruned_loss=0.04193, over 5061.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2609, pruned_loss=0.02998, over 1375338.57 frames.], batch size: 52, lr: 2.34e-04
2022-04-30 15:07:21,084 INFO [train.py:763] (1/8) Epoch 33, batch 0, loss[loss=0.1771, simple_loss=0.2802, pruned_loss=0.03701, over 7438.00 frames.], tot_loss[loss=0.1771, simple_loss=0.2802, pruned_loss=0.03701, over 7438.00 frames.], batch size: 20, lr: 2.31e-04
2022-04-30 15:08:26,672 INFO [train.py:763] (1/8) Epoch 33, batch 50, loss[loss=0.1551, simple_loss=0.2565, pruned_loss=0.02687, over 7059.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2569, pruned_loss=0.03013, over 324506.92 frames.], batch size: 28, lr: 2.30e-04
2022-04-30 15:09:31,884 INFO [train.py:763] (1/8) Epoch 33, batch 100, loss[loss=0.1991, simple_loss=0.2991, pruned_loss=0.04952, over 7116.00 frames.], tot_loss[loss=0.16, simple_loss=0.2597, pruned_loss=0.03013, over 565986.78 frames.], batch size: 21, lr: 2.30e-04
2022-04-30 15:10:37,373 INFO [train.py:763] (1/8) Epoch 33, batch 150, loss[loss=0.1512, simple_loss=0.2415, pruned_loss=0.03047, over 7074.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2588, pruned_loss=0.02994, over 756559.23 frames.], batch size: 18, lr: 2.30e-04
2022-04-30 15:11:42,896 INFO [train.py:763] (1/8) Epoch 33, batch 200, loss[loss=0.155, simple_loss=0.248, pruned_loss=0.03101, over 7284.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2587, pruned_loss=0.02996, over 906532.38 frames.], batch size: 17, lr: 2.30e-04
2022-04-30 15:12:48,573 INFO [train.py:763] (1/8) Epoch 33, batch 250, loss[loss=0.1768, simple_loss=0.2749, pruned_loss=0.03939, over 5178.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2584, pruned_loss=0.03032, over 1013783.12 frames.], batch size: 52, lr: 2.30e-04
2022-04-30 15:13:55,817 INFO [train.py:763] (1/8) Epoch 33, batch 300, loss[loss=0.168, simple_loss=0.2721, pruned_loss=0.03198, over 7379.00 frames.], tot_loss[loss=0.1601, simple_loss=0.259, pruned_loss=0.03058, over 1103773.06 frames.], batch size: 23, lr: 2.30e-04
2022-04-30 15:15:01,947 INFO [train.py:763] (1/8) Epoch 33, batch 350, loss[loss=0.1366, simple_loss=0.2272, pruned_loss=0.02295, over 7132.00 frames.], tot_loss[loss=0.161, simple_loss=0.2608, pruned_loss=0.03057, over 1168287.20 frames.], batch size: 17, lr: 2.30e-04
2022-04-30 15:16:08,892 INFO [train.py:763] (1/8) Epoch 33, batch 400, loss[loss=0.2014, simple_loss=0.3014, pruned_loss=0.05076, over 7410.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2597, pruned_loss=0.03032, over 1229750.02 frames.], batch size: 21, lr: 2.30e-04
2022-04-30 15:17:14,698 INFO [train.py:763] (1/8) Epoch 33, batch 450, loss[loss=0.1454, simple_loss=0.2364, pruned_loss=0.02721, over 7420.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2605, pruned_loss=0.03053, over 1274673.12 frames.], batch size: 18, lr: 2.30e-04
2022-04-30 15:18:21,051 INFO [train.py:763] (1/8) Epoch 33, batch 500, loss[loss=0.1772, simple_loss=0.2812, pruned_loss=0.03663, over 7286.00 frames.], tot_loss[loss=0.1618, simple_loss=0.2616, pruned_loss=0.03098, over 1306170.23 frames.], batch size: 24, lr: 2.30e-04
2022-04-30 15:19:26,290 INFO [train.py:763] (1/8) Epoch 33, batch 550, loss[loss=0.1768, simple_loss=0.2862, pruned_loss=0.0337, over 6379.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2615, pruned_loss=0.0309, over 1329908.67 frames.], batch size: 38, lr: 2.30e-04
2022-04-30 15:20:43,084 INFO [train.py:763] (1/8) Epoch 33, batch 600, loss[loss=0.1807, simple_loss=0.2948, pruned_loss=0.03327, over 7289.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2614, pruned_loss=0.03072, over 1352461.18 frames.], batch size: 25, lr: 2.30e-04
2022-04-30 15:21:48,329 INFO [train.py:763] (1/8) Epoch 33, batch 650, loss[loss=0.1429, simple_loss=0.2419, pruned_loss=0.02197, over 7157.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2617, pruned_loss=0.03051, over 1371044.38 frames.], batch size: 18, lr: 2.30e-04
2022-04-30 15:22:53,611 INFO [train.py:763] (1/8) Epoch 33, batch 700, loss[loss=0.1462, simple_loss=0.2373, pruned_loss=0.02749, over 7135.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2601, pruned_loss=0.03017, over 1377718.83 frames.], batch size: 17, lr: 2.30e-04
2022-04-30 15:23:58,783 INFO [train.py:763] (1/8) Epoch 33, batch 750, loss[loss=0.1594, simple_loss=0.2707, pruned_loss=0.02408, over 7199.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2606, pruned_loss=0.0299, over 1388998.41 frames.], batch size: 23, lr: 2.30e-04
2022-04-30 15:25:05,620 INFO [train.py:763] (1/8) Epoch 33, batch 800, loss[loss=0.1524, simple_loss=0.2441, pruned_loss=0.03033, over 7279.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2608, pruned_loss=0.03007, over 1394969.37 frames.], batch size: 18, lr: 2.30e-04
2022-04-30 15:26:11,923 INFO [train.py:763] (1/8) Epoch 33, batch 850, loss[loss=0.1643, simple_loss=0.2628, pruned_loss=0.03292, over 6576.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2609, pruned_loss=0.02993, over 1405025.78 frames.], batch size: 38, lr: 2.30e-04
2022-04-30 15:27:17,420 INFO [train.py:763] (1/8) Epoch 33, batch 900, loss[loss=0.2148, simple_loss=0.3107, pruned_loss=0.05946, over 4965.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2607, pruned_loss=0.02996, over 1410124.40 frames.], batch size: 52, lr: 2.30e-04
2022-04-30 15:28:22,822 INFO [train.py:763] (1/8) Epoch 33, batch 950, loss[loss=0.144, simple_loss=0.2395, pruned_loss=0.02424, over 7289.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2606, pruned_loss=0.03011, over 1407865.32 frames.], batch size: 18, lr: 2.30e-04
2022-04-30 15:29:28,253 INFO [train.py:763] (1/8) Epoch 33, batch 1000, loss[loss=0.1644, simple_loss=0.2731, pruned_loss=0.02786, over 7440.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2602, pruned_loss=0.03012, over 1408769.18 frames.], batch size: 20, lr: 2.30e-04
2022-04-30 15:30:33,669 INFO [train.py:763] (1/8) Epoch 33, batch 1050, loss[loss=0.1405, simple_loss=0.2422, pruned_loss=0.01938, over 7162.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2608, pruned_loss=0.03047, over 1414865.87 frames.], batch size: 19, lr: 2.30e-04
2022-04-30 15:31:40,467 INFO [train.py:763] (1/8) Epoch 33, batch 1100, loss[loss=0.1669, simple_loss=0.2722, pruned_loss=0.03074, over 6305.00 frames.], tot_loss[loss=0.1614, simple_loss=0.2614, pruned_loss=0.03068, over 1412928.79 frames.], batch size: 37, lr: 2.30e-04
2022-04-30 15:32:45,927 INFO [train.py:763] (1/8) Epoch 33, batch 1150, loss[loss=0.1633, simple_loss=0.2629, pruned_loss=0.0319, over 7438.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2604, pruned_loss=0.0306, over 1415890.02 frames.], batch size: 20, lr: 2.30e-04
2022-04-30 15:33:51,342 INFO [train.py:763] (1/8) Epoch 33, batch 1200, loss[loss=0.1535, simple_loss=0.258, pruned_loss=0.02447, over 7210.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2608, pruned_loss=0.03069, over 1420414.26 frames.], batch size: 23, lr: 2.30e-04
2022-04-30 15:34:56,631 INFO [train.py:763] (1/8) Epoch 33, batch 1250, loss[loss=0.1685, simple_loss=0.2748, pruned_loss=0.03107, over 7328.00 frames.], tot_loss[loss=0.161, simple_loss=0.2605, pruned_loss=0.03069, over 1418727.60 frames.], batch size: 22, lr: 2.30e-04
2022-04-30 15:36:02,615 INFO [train.py:763] (1/8) Epoch 33, batch 1300, loss[loss=0.1633, simple_loss=0.2734, pruned_loss=0.02658, over 7262.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2602, pruned_loss=0.03106, over 1417911.13 frames.], batch size: 26, lr: 2.30e-04
2022-04-30 15:37:09,763 INFO [train.py:763] (1/8) Epoch 33, batch 1350, loss[loss=0.1802, simple_loss=0.2785, pruned_loss=0.04093, over 7223.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2595, pruned_loss=0.03053, over 1419809.65 frames.], batch size: 21, lr: 2.29e-04
2022-04-30 15:38:16,825 INFO [train.py:763] (1/8) Epoch 33, batch 1400, loss[loss=0.1462, simple_loss=0.2502, pruned_loss=0.0211, over 7253.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2582, pruned_loss=0.03007, over 1422362.67 frames.], batch size: 19, lr: 2.29e-04
2022-04-30 15:39:22,842 INFO [train.py:763] (1/8) Epoch 33, batch 1450, loss[loss=0.1644, simple_loss=0.2678, pruned_loss=0.03047, over 7409.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2583, pruned_loss=0.0295, over 1425913.29 frames.], batch size: 21, lr: 2.29e-04
2022-04-30 15:40:28,338 INFO [train.py:763] (1/8) Epoch 33, batch 1500, loss[loss=0.1885, simple_loss=0.2866, pruned_loss=0.04523, over 7366.00 frames.], tot_loss[loss=0.1601, simple_loss=0.26, pruned_loss=0.03006, over 1424336.06 frames.], batch size: 23, lr: 2.29e-04
2022-04-30 15:41:33,824 INFO [train.py:763] (1/8) Epoch 33, batch 1550, loss[loss=0.1505, simple_loss=0.2474, pruned_loss=0.02681, over 7300.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2601, pruned_loss=0.03032, over 1422263.39 frames.], batch size: 24, lr: 2.29e-04
2022-04-30 15:42:39,066 INFO [train.py:763] (1/8) Epoch 33, batch 1600, loss[loss=0.1482, simple_loss=0.2518, pruned_loss=0.02236, over 7314.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2606, pruned_loss=0.0303, over 1423940.01 frames.], batch size: 20, lr: 2.29e-04
2022-04-30 15:43:46,168 INFO [train.py:763] (1/8) Epoch 33, batch 1650, loss[loss=0.1641, simple_loss=0.2631, pruned_loss=0.03255, over 7215.00 frames.], tot_loss[loss=0.161, simple_loss=0.261, pruned_loss=0.03054, over 1423615.51 frames.], batch size: 22, lr: 2.29e-04
2022-04-30 15:44:53,518 INFO [train.py:763] (1/8) Epoch 33, batch 1700, loss[loss=0.1964, simple_loss=0.2987, pruned_loss=0.047, over 7375.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2615, pruned_loss=0.03035, over 1427142.37 frames.], batch size: 23, lr: 2.29e-04
2022-04-30 15:46:00,130 INFO [train.py:763] (1/8) Epoch 33, batch 1750, loss[loss=0.1803, simple_loss=0.2759, pruned_loss=0.04235, over 7104.00 frames.], tot_loss[loss=0.1619, simple_loss=0.2624, pruned_loss=0.03072, over 1422368.51 frames.], batch size: 28, lr: 2.29e-04
2022-04-30 15:47:05,293 INFO [train.py:763] (1/8) Epoch 33, batch 1800, loss[loss=0.1247, simple_loss=0.2161, pruned_loss=0.01665, over 7278.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2615, pruned_loss=0.03043, over 1423198.96 frames.], batch size: 17, lr: 2.29e-04
2022-04-30 15:48:11,896 INFO [train.py:763] (1/8) Epoch 33, batch 1850, loss[loss=0.1679, simple_loss=0.287, pruned_loss=0.02444, over 7333.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2612, pruned_loss=0.03029, over 1414607.06 frames.], batch size: 21, lr: 2.29e-04
2022-04-30 15:49:17,341 INFO [train.py:763] (1/8) Epoch 33, batch 1900, loss[loss=0.1472, simple_loss=0.2528, pruned_loss=0.02085, over 6774.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2614, pruned_loss=0.03036, over 1410043.08 frames.], batch size: 31, lr: 2.29e-04
2022-04-30 15:50:23,825 INFO [train.py:763] (1/8) Epoch 33, batch 1950, loss[loss=0.1635, simple_loss=0.2498, pruned_loss=0.03863, over 6976.00 frames.], tot_loss[loss=0.1616, simple_loss=0.2616, pruned_loss=0.03077, over 1416109.79 frames.], batch size: 16, lr: 2.29e-04
2022-04-30 15:51:31,073 INFO [train.py:763] (1/8) Epoch 33, batch 2000, loss[loss=0.1618, simple_loss=0.2573, pruned_loss=0.03312, over 7397.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2613, pruned_loss=0.03031, over 1420970.82 frames.], batch size: 18, lr: 2.29e-04
2022-04-30 15:52:37,440 INFO [train.py:763] (1/8) Epoch 33, batch 2050, loss[loss=0.1727, simple_loss=0.2794, pruned_loss=0.03306, over 7147.00 frames.], tot_loss[loss=0.161, simple_loss=0.2612, pruned_loss=0.03035, over 1420662.88 frames.], batch size: 26, lr: 2.29e-04
2022-04-30 15:53:42,708 INFO [train.py:763] (1/8) Epoch 33, batch 2100, loss[loss=0.1636, simple_loss=0.2602, pruned_loss=0.03351, over 7182.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2614, pruned_loss=0.03021, over 1423919.64 frames.], batch size: 23, lr: 2.29e-04
2022-04-30 15:54:47,940 INFO [train.py:763] (1/8) Epoch 33, batch 2150, loss[loss=0.1861, simple_loss=0.2855, pruned_loss=0.04333, over 7290.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2604, pruned_loss=0.02998, over 1423704.84 frames.], batch size: 24, lr: 2.29e-04
2022-04-30 15:55:53,179 INFO [train.py:763] (1/8) Epoch 33, batch 2200, loss[loss=0.141, simple_loss=0.2518, pruned_loss=0.01513, over 7316.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2616, pruned_loss=0.03013, over 1426599.91 frames.], batch size: 21, lr: 2.29e-04
2022-04-30 15:56:58,873 INFO [train.py:763] (1/8) Epoch 33, batch 2250, loss[loss=0.1392, simple_loss=0.2309, pruned_loss=0.02373, over 7278.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2609, pruned_loss=0.03013, over 1422447.15 frames.], batch size: 18, lr: 2.29e-04
2022-04-30 15:58:05,270 INFO [train.py:763] (1/8) Epoch 33, batch 2300, loss[loss=0.1561, simple_loss=0.2468, pruned_loss=0.03269, over 7154.00 frames.], tot_loss[loss=0.1615, simple_loss=0.2621, pruned_loss=0.03044, over 1423322.03 frames.], batch size: 19, lr: 2.29e-04
2022-04-30 15:59:10,706 INFO [train.py:763] (1/8) Epoch 33, batch 2350, loss[loss=0.1641, simple_loss=0.2583, pruned_loss=0.035, over 7153.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2605, pruned_loss=0.03027, over 1424423.38 frames.], batch size: 19, lr: 2.29e-04
2022-04-30 16:00:16,788 INFO [train.py:763] (1/8) Epoch 33, batch 2400, loss[loss=0.1644, simple_loss=0.2633, pruned_loss=0.03272, over 7380.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2601, pruned_loss=0.0302, over 1425286.64 frames.], batch size: 23, lr: 2.29e-04
2022-04-30 16:01:22,889 INFO [train.py:763] (1/8) Epoch 33, batch 2450, loss[loss=0.1588, simple_loss=0.2717, pruned_loss=0.02301, over 7226.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2609, pruned_loss=0.03044, over 1419172.35 frames.], batch size: 21, lr: 2.29e-04
2022-04-30 16:02:28,044 INFO [train.py:763] (1/8) Epoch 33, batch 2500, loss[loss=0.1604, simple_loss=0.2557, pruned_loss=0.03256, over 6997.00 frames.], tot_loss[loss=0.1609, simple_loss=0.261, pruned_loss=0.03043, over 1417672.87 frames.], batch size: 16, lr: 2.29e-04
2022-04-30 16:03:33,222 INFO [train.py:763] (1/8) Epoch 33, batch 2550, loss[loss=0.199, simple_loss=0.2924, pruned_loss=0.05287, over 7331.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2607, pruned_loss=0.03047, over 1419462.10 frames.], batch size: 22, lr: 2.29e-04
2022-04-30 16:04:38,851 INFO [train.py:763] (1/8) Epoch 33, batch 2600, loss[loss=0.1451, simple_loss=0.2438, pruned_loss=0.02326, over 7070.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2607, pruned_loss=0.03048, over 1419215.08 frames.], batch size: 18, lr: 2.29e-04
2022-04-30 16:05:45,687 INFO [train.py:763] (1/8) Epoch 33, batch 2650, loss[loss=0.1427, simple_loss=0.246, pruned_loss=0.0197, over 7345.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2597, pruned_loss=0.02986, over 1420439.38 frames.], batch size: 22, lr: 2.29e-04
2022-04-30 16:06:52,532 INFO [train.py:763] (1/8) Epoch 33, batch 2700, loss[loss=0.1345, simple_loss=0.2278, pruned_loss=0.02061, over 7291.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2593, pruned_loss=0.02954, over 1425115.97 frames.], batch size: 18, lr: 2.28e-04
2022-04-30 16:07:59,667 INFO [train.py:763] (1/8) Epoch 33, batch 2750, loss[loss=0.1621, simple_loss=0.2739, pruned_loss=0.02513, over 7311.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2592, pruned_loss=0.02926, over 1424138.93 frames.], batch size: 21, lr: 2.28e-04
2022-04-30 16:09:06,753 INFO [train.py:763] (1/8) Epoch 33, batch 2800, loss[loss=0.1223, simple_loss=0.2106, pruned_loss=0.01696, over 7400.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2598, pruned_loss=0.02939, over 1429156.15 frames.], batch size: 18, lr: 2.28e-04
2022-04-30 16:10:13,295 INFO [train.py:763] (1/8) Epoch 33, batch 2850, loss[loss=0.1738, simple_loss=0.2786, pruned_loss=0.03451, over 7204.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2596, pruned_loss=0.02903, over 1429986.23 frames.], batch size: 23, lr: 2.28e-04
2022-04-30 16:11:18,328 INFO [train.py:763] (1/8) Epoch 33, batch 2900, loss[loss=0.1418, simple_loss=0.2439, pruned_loss=0.01982, over 7145.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2592, pruned_loss=0.02913, over 1426470.77 frames.], batch size: 20, lr: 2.28e-04
2022-04-30 16:12:24,334 INFO [train.py:763] (1/8) Epoch 33, batch 2950, loss[loss=0.1622, simple_loss=0.2592, pruned_loss=0.03259, over 7142.00 frames.], tot_loss[loss=0.158, simple_loss=0.2581, pruned_loss=0.02898, over 1426553.88 frames.], batch size: 20, lr: 2.28e-04
2022-04-30 16:13:31,370 INFO [train.py:763] (1/8) Epoch 33, batch 3000, loss[loss=0.1816, simple_loss=0.2797, pruned_loss=0.04178, over 7363.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2585, pruned_loss=0.02926, over 1426675.52 frames.], batch size: 19, lr: 2.28e-04
2022-04-30 16:13:31,371 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 16:13:46,765 INFO [train.py:792] (1/8) Epoch 33, validation: loss=0.1701, simple_loss=0.2653, pruned_loss=0.03746, over 698248.00 frames.
2022-04-30 16:14:51,745 INFO [train.py:763] (1/8) Epoch 33, batch 3050, loss[loss=0.1568, simple_loss=0.2563, pruned_loss=0.02871, over 7358.00 frames.], tot_loss[loss=0.1597, simple_loss=0.26, pruned_loss=0.02971, over 1427336.07 frames.], batch size: 19, lr: 2.28e-04
2022-04-30 16:15:58,063 INFO [train.py:763] (1/8) Epoch 33, batch 3100, loss[loss=0.121, simple_loss=0.2126, pruned_loss=0.01474, over 6797.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2599, pruned_loss=0.02959, over 1428402.39 frames.], batch size: 15, lr: 2.28e-04
2022-04-30 16:17:04,951 INFO [train.py:763] (1/8) Epoch 33, batch 3150, loss[loss=0.1373, simple_loss=0.2302, pruned_loss=0.0222, over 7290.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2592, pruned_loss=0.02912, over 1428419.32 frames.], batch size: 17, lr: 2.28e-04
2022-04-30 16:18:11,843 INFO [train.py:763] (1/8) Epoch 33, batch 3200, loss[loss=0.1799, simple_loss=0.272, pruned_loss=0.04392, over 4906.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2596, pruned_loss=0.02955, over 1423844.20 frames.], batch size: 52, lr: 2.28e-04
2022-04-30 16:19:17,471 INFO [train.py:763] (1/8) Epoch 33, batch 3250, loss[loss=0.177, simple_loss=0.2624, pruned_loss=0.0458, over 7126.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2593, pruned_loss=0.0298, over 1421795.44 frames.], batch size: 17, lr: 2.28e-04
2022-04-30 16:20:22,898 INFO [train.py:763] (1/8) Epoch 33, batch 3300, loss[loss=0.1719, simple_loss=0.2652, pruned_loss=0.03931, over 7109.00 frames.], tot_loss[loss=0.16, simple_loss=0.2595, pruned_loss=0.03026, over 1418644.38 frames.], batch size: 28, lr: 2.28e-04
2022-04-30 16:21:28,690 INFO [train.py:763] (1/8) Epoch 33, batch 3350, loss[loss=0.156, simple_loss=0.26, pruned_loss=0.02598, over 7140.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2586, pruned_loss=0.02992, over 1421749.95 frames.], batch size: 20, lr: 2.28e-04
2022-04-30 16:22:44,380 INFO [train.py:763] (1/8) Epoch 33, batch 3400, loss[loss=0.1614, simple_loss=0.2654, pruned_loss=0.0287, over 7202.00 frames.], tot_loss[loss=0.16, simple_loss=0.2597, pruned_loss=0.0302, over 1422343.35 frames.], batch size: 23, lr: 2.28e-04
2022-04-30 16:23:50,289 INFO [train.py:763] (1/8) Epoch 33, batch 3450, loss[loss=0.1438, simple_loss=0.2369, pruned_loss=0.02532, over 7005.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2587, pruned_loss=0.02998, over 1427561.95 frames.], batch size: 16, lr: 2.28e-04
2022-04-30 16:24:55,492 INFO [train.py:763] (1/8) Epoch 33, batch 3500, loss[loss=0.1699, simple_loss=0.2761, pruned_loss=0.03181, over 7184.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2608, pruned_loss=0.03025, over 1429626.15 frames.], batch size: 23, lr: 2.28e-04
2022-04-30 16:26:01,147 INFO [train.py:763] (1/8) Epoch 33, batch 3550, loss[loss=0.1325, simple_loss=0.234, pruned_loss=0.01549, over 7288.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2598, pruned_loss=0.02994, over 1431242.54 frames.], batch size: 17, lr: 2.28e-04
2022-04-30 16:27:06,626 INFO [train.py:763] (1/8) Epoch 33, batch 3600, loss[loss=0.1841, simple_loss=0.2686, pruned_loss=0.0498, over 7324.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2602, pruned_loss=0.03038, over 1433806.37 frames.], batch size: 21, lr: 2.28e-04
2022-04-30 16:28:13,475 INFO [train.py:763] (1/8) Epoch 33, batch 3650, loss[loss=0.1709, simple_loss=0.2758, pruned_loss=0.03303, over 6387.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2603, pruned_loss=0.03017, over 1428647.81 frames.], batch size: 37, lr: 2.28e-04
2022-04-30 16:29:20,523 INFO [train.py:763] (1/8) Epoch 33, batch 3700, loss[loss=0.1591, simple_loss=0.2686, pruned_loss=0.0248, over 7242.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2589, pruned_loss=0.03014, over 1423769.68 frames.], batch size: 20, lr: 2.28e-04
2022-04-30 16:30:26,038 INFO [train.py:763] (1/8) Epoch 33, batch 3750, loss[loss=0.1624, simple_loss=0.2645, pruned_loss=0.03012, over 7290.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2586, pruned_loss=0.03001, over 1420807.61 frames.], batch size: 24, lr: 2.28e-04
2022-04-30 16:31:31,699 INFO [train.py:763] (1/8) Epoch 33, batch 3800, loss[loss=0.1467, simple_loss=0.2468, pruned_loss=0.02332, over 7146.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2592, pruned_loss=0.02986, over 1424960.68 frames.], batch size: 20, lr: 2.28e-04
2022-04-30 16:32:38,580 INFO [train.py:763] (1/8) Epoch 33, batch 3850, loss[loss=0.1759, simple_loss=0.2822, pruned_loss=0.03481, over 7220.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2591, pruned_loss=0.02983, over 1426933.44 frames.], batch size: 23, lr: 2.28e-04
2022-04-30 16:33:45,460 INFO [train.py:763] (1/8) Epoch 33, batch 3900, loss[loss=0.1695, simple_loss=0.2669, pruned_loss=0.03603, over 7196.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2592, pruned_loss=0.02957, over 1425303.41 frames.], batch size: 23, lr: 2.28e-04
2022-04-30 16:34:52,421 INFO [train.py:763] (1/8) Epoch 33, batch 3950, loss[loss=0.1682, simple_loss=0.2699, pruned_loss=0.0332, over 7321.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2593, pruned_loss=0.02944, over 1422799.26 frames.], batch size: 20, lr: 2.28e-04
2022-04-30 16:35:59,190 INFO [train.py:763] (1/8) Epoch 33, batch 4000, loss[loss=0.1507, simple_loss=0.2441, pruned_loss=0.02864, over 7077.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2594, pruned_loss=0.02958, over 1423007.93 frames.], batch size: 18, lr: 2.28e-04
2022-04-30 16:37:13,142 INFO [train.py:763] (1/8) Epoch 33, batch 4050, loss[loss=0.172, simple_loss=0.2745, pruned_loss=0.03469, over 7193.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2604, pruned_loss=0.02997, over 1418206.61 frames.], batch size: 26, lr: 2.27e-04
2022-04-30 16:38:27,100 INFO [train.py:763] (1/8) Epoch 33, batch 4100, loss[loss=0.1702, simple_loss=0.2708, pruned_loss=0.03477, over 6330.00 frames.], tot_loss[loss=0.1606, simple_loss=0.261, pruned_loss=0.03011, over 1419534.36 frames.], batch size: 37, lr: 2.27e-04
2022-04-30 16:39:41,392 INFO [train.py:763] (1/8) Epoch 33, batch 4150, loss[loss=0.1398, simple_loss=0.2398, pruned_loss=0.0199, over 7399.00 frames.], tot_loss[loss=0.161, simple_loss=0.2613, pruned_loss=0.03034, over 1418579.13 frames.], batch size: 18, lr: 2.27e-04
2022-04-30 16:40:55,329 INFO [train.py:763] (1/8) Epoch 33, batch 4200, loss[loss=0.1557, simple_loss=0.264, pruned_loss=0.02363, over 7241.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2611, pruned_loss=0.03014, over 1421655.88 frames.], batch size: 20, lr: 2.27e-04
2022-04-30 16:42:02,047 INFO [train.py:763] (1/8) Epoch 33, batch 4250, loss[loss=0.1394, simple_loss=0.2282, pruned_loss=0.02529, over 7122.00 frames.], tot_loss[loss=0.1612, simple_loss=0.2617, pruned_loss=0.03034, over 1420324.87 frames.], batch size: 17, lr: 2.27e-04
2022-04-30 16:43:17,916 INFO [train.py:763] (1/8) Epoch 33, batch 4300, loss[loss=0.1399, simple_loss=0.2343, pruned_loss=0.02274, over 6987.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2618, pruned_loss=0.03036, over 1420363.28 frames.], batch size: 16, lr: 2.27e-04
2022-04-30 16:44:24,670 INFO [train.py:763] (1/8) Epoch 33, batch 4350, loss[loss=0.1417, simple_loss=0.2293, pruned_loss=0.02709, over 6764.00 frames.], tot_loss[loss=0.1626, simple_loss=0.2632, pruned_loss=0.03099, over 1416602.83 frames.], batch size: 15, lr: 2.27e-04
2022-04-30 16:45:48,492 INFO [train.py:763] (1/8) Epoch 33, batch 4400, loss[loss=0.13, simple_loss=0.2272, pruned_loss=0.01643, over 7176.00 frames.], tot_loss[loss=0.1621, simple_loss=0.2624, pruned_loss=0.03089, over 1416991.92 frames.], batch size: 18, lr: 2.27e-04
2022-04-30 16:46:53,553 INFO [train.py:763] (1/8) Epoch 33, batch 4450, loss[loss=0.1571, simple_loss=0.2573, pruned_loss=0.02842, over 7193.00 frames.], tot_loss[loss=0.163, simple_loss=0.2627, pruned_loss=0.03164, over 1402556.77 frames.], batch size: 23, lr: 2.27e-04
2022-04-30 16:48:00,201 INFO [train.py:763] (1/8) Epoch 33, batch 4500, loss[loss=0.1893, simple_loss=0.2752, pruned_loss=0.0517, over 5193.00 frames.], tot_loss[loss=0.1629, simple_loss=0.2626, pruned_loss=0.03156, over 1393486.54 frames.], batch size: 52, lr: 2.27e-04
2022-04-30 16:49:05,829 INFO [train.py:763] (1/8) Epoch 33, batch 4550, loss[loss=0.2232, simple_loss=0.3048, pruned_loss=0.07078, over 4827.00 frames.], tot_loss[loss=0.165, simple_loss=0.265, pruned_loss=0.03251, over 1352726.08 frames.], batch size: 52, lr: 2.27e-04
2022-04-30 16:50:25,377 INFO [train.py:763] (1/8) Epoch 34, batch 0, loss[loss=0.1637, simple_loss=0.2703, pruned_loss=0.02855, over 7240.00 frames.], tot_loss[loss=0.1637, simple_loss=0.2703, pruned_loss=0.02855, over 7240.00 frames.], batch size: 20, lr: 2.24e-04
2022-04-30 16:51:31,603 INFO [train.py:763] (1/8) Epoch 34, batch 50, loss[loss=0.1856, simple_loss=0.2977, pruned_loss=0.03675, over 7297.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2591, pruned_loss=0.02961, over 318314.76 frames.], batch size: 24, lr: 2.24e-04
2022-04-30 16:52:37,602 INFO [train.py:763] (1/8) Epoch 34, batch 100, loss[loss=0.2027, simple_loss=0.3034, pruned_loss=0.05105, over 7126.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2593, pruned_loss=0.02952, over 567447.16 frames.], batch size: 26, lr: 2.24e-04
2022-04-30 16:53:43,310 INFO [train.py:763] (1/8) Epoch 34, batch 150, loss[loss=0.1964, simple_loss=0.2865, pruned_loss=0.05321, over 7392.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2591, pruned_loss=0.02906, over 760304.25 frames.], batch size: 23, lr: 2.24e-04
2022-04-30 16:54:49,441 INFO [train.py:763] (1/8) Epoch 34, batch 200, loss[loss=0.1478, simple_loss=0.2452, pruned_loss=0.02517, over 7063.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2596, pruned_loss=0.02992, over 909887.51 frames.], batch size: 18, lr: 2.24e-04
2022-04-30 16:55:56,554 INFO [train.py:763] (1/8) Epoch 34, batch 250, loss[loss=0.1539, simple_loss=0.2643, pruned_loss=0.02181, over 7237.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2604, pruned_loss=0.03024, over 1027264.22 frames.], batch size: 20, lr: 2.24e-04
2022-04-30 16:57:03,053 INFO [train.py:763] (1/8) Epoch 34, batch 300, loss[loss=0.1525, simple_loss=0.2505, pruned_loss=0.02725, over 7154.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2593, pruned_loss=0.02985, over 1113350.82 frames.], batch size: 19, lr: 2.24e-04
2022-04-30 16:58:08,937 INFO [train.py:763] (1/8) Epoch 34, batch 350, loss[loss=0.1748, simple_loss=0.2813, pruned_loss=0.03414, over 7185.00 frames.], tot_loss[loss=0.159, simple_loss=0.2592, pruned_loss=0.02938, over 1185707.47 frames.], batch size: 23, lr: 2.24e-04
2022-04-30 16:59:14,457 INFO [train.py:763] (1/8) Epoch 34, batch 400, loss[loss=0.1781, simple_loss=0.2734, pruned_loss=0.04137, over 7330.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2605, pruned_loss=0.03, over 1239938.27 frames.], batch size: 20, lr: 2.24e-04
2022-04-30 17:00:20,017 INFO [train.py:763] (1/8) Epoch 34, batch 450, loss[loss=0.1763, simple_loss=0.2885, pruned_loss=0.03203, over 6770.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2596, pruned_loss=0.03007, over 1284930.01 frames.], batch size: 31, lr: 2.24e-04
2022-04-30 17:01:26,960 INFO [train.py:763] (1/8) Epoch 34, batch 500, loss[loss=0.1694, simple_loss=0.2714, pruned_loss=0.0337, over 7325.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2594, pruned_loss=0.03005, over 1315190.86 frames.], batch size: 20, lr: 2.23e-04
2022-04-30 17:02:32,702 INFO [train.py:763] (1/8) Epoch 34, batch 550, loss[loss=0.144, simple_loss=0.2397, pruned_loss=0.02415, over 7057.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2593, pruned_loss=0.03019, over 1335346.58 frames.], batch size: 18, lr: 2.23e-04
2022-04-30 17:03:38,761 INFO [train.py:763] (1/8) Epoch 34, batch 600, loss[loss=0.175, simple_loss=0.2818, pruned_loss=0.0341, over 7347.00 frames.], tot_loss[loss=0.161, simple_loss=0.2608, pruned_loss=0.03056, over 1354317.55 frames.], batch size: 22, lr: 2.23e-04
2022-04-30 17:04:44,664 INFO [train.py:763] (1/8) Epoch 34, batch 650, loss[loss=0.1262, simple_loss=0.2291, pruned_loss=0.01161, over 7164.00 frames.], tot_loss[loss=0.1613, simple_loss=0.2614, pruned_loss=0.03053, over 1373011.01 frames.], batch size: 18, lr: 2.23e-04
2022-04-30 17:05:50,786 INFO [train.py:763] (1/8) Epoch 34, batch 700, loss[loss=0.159, simple_loss=0.2605, pruned_loss=0.02875, over 7256.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2608, pruned_loss=0.03039, over 1386585.79 frames.], batch size: 17, lr: 2.23e-04
2022-04-30 17:06:58,021 INFO [train.py:763] (1/8) Epoch 34, batch 750, loss[loss=0.1553, simple_loss=0.2552, pruned_loss=0.02775, over 7266.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2595, pruned_loss=0.03009, over 1394247.58 frames.], batch size: 19, lr: 2.23e-04
2022-04-30 17:08:04,369 INFO [train.py:763] (1/8) Epoch 34, batch 800, loss[loss=0.1565, simple_loss=0.2658, pruned_loss=0.02358, over 7232.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2597, pruned_loss=0.0298, over 1403040.84 frames.], batch size: 21, lr: 2.23e-04
2022-04-30 17:09:09,694 INFO [train.py:763] (1/8) Epoch 34, batch 850, loss[loss=0.1791, simple_loss=0.2864, pruned_loss=0.03594, over 7295.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2607, pruned_loss=0.03013, over 1403090.08 frames.], batch size: 24, lr: 2.23e-04
2022-04-30 17:10:15,222 INFO [train.py:763] (1/8) Epoch 34, batch 900, loss[loss=0.1608, simple_loss=0.2547, pruned_loss=0.03347, over 5125.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2599, pruned_loss=0.02973, over 1406547.15 frames.], batch size: 53, lr: 2.23e-04
2022-04-30 17:11:21,164 INFO [train.py:763] (1/8) Epoch 34, batch 950, loss[loss=0.1554, simple_loss=0.2596, pruned_loss=0.02565, over 7247.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2592, pruned_loss=0.02958, over 1409923.52 frames.], batch size: 19, lr: 2.23e-04
2022-04-30 17:12:27,411 INFO [train.py:763] (1/8) Epoch 34, batch 1000, loss[loss=0.1782, simple_loss=0.2813, pruned_loss=0.03751, over 6876.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2592, pruned_loss=0.02955, over 1411588.96 frames.], batch size: 31, lr: 2.23e-04
2022-04-30 17:13:34,598 INFO [train.py:763] (1/8) Epoch 34, batch 1050, loss[loss=0.1714, simple_loss=0.2746, pruned_loss=0.03411, over 7425.00 frames.], tot_loss[loss=0.159, simple_loss=0.259, pruned_loss=0.02948, over 1415670.74 frames.], batch size: 21, lr: 2.23e-04
2022-04-30 17:14:40,035 INFO [train.py:763] (1/8) Epoch 34, batch 1100, loss[loss=0.1563, simple_loss=0.2604, pruned_loss=0.02606, over 7346.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2589, pruned_loss=0.0295, over 1420164.56 frames.], batch size: 19, lr: 2.23e-04
2022-04-30 17:15:45,149 INFO [train.py:763] (1/8) Epoch 34, batch 1150, loss[loss=0.1869, simple_loss=0.2815, pruned_loss=0.04611, over 7197.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2592, pruned_loss=0.02927, over 1421636.93 frames.], batch size: 23, lr: 2.23e-04
2022-04-30 17:16:50,471 INFO [train.py:763] (1/8) Epoch 34, batch 1200, loss[loss=0.163, simple_loss=0.2563, pruned_loss=0.03479, over 7278.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2591, pruned_loss=0.02925, over 1425420.62 frames.], batch size: 18, lr: 2.23e-04
2022-04-30 17:17:56,081 INFO [train.py:763] (1/8) Epoch 34, batch 1250, loss[loss=0.1764, simple_loss=0.2772, pruned_loss=0.03782, over 7337.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2596, pruned_loss=0.02947, over 1424218.05 frames.], batch size: 22, lr: 2.23e-04
2022-04-30 17:19:02,071 INFO [train.py:763] (1/8) Epoch 34, batch 1300, loss[loss=0.1739, simple_loss=0.2786, pruned_loss=0.03459, over 7052.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2607, pruned_loss=0.02986, over 1420014.11 frames.], batch size: 28, lr: 2.23e-04
2022-04-30 17:20:07,319 INFO [train.py:763] (1/8) Epoch 34, batch 1350, loss[loss=0.1584, simple_loss=0.2663, pruned_loss=0.02527, over 7127.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2608, pruned_loss=0.02973, over 1423099.09 frames.], batch size: 28, lr: 2.23e-04
2022-04-30 17:21:12,462 INFO [train.py:763] (1/8) Epoch 34, batch 1400, loss[loss=0.15, simple_loss=0.2499, pruned_loss=0.02502, over 7317.00 frames.], tot_loss[loss=0.1608, simple_loss=0.261, pruned_loss=0.03028, over 1420981.41 frames.], batch size: 20, lr: 2.23e-04
2022-04-30 17:22:17,955 INFO [train.py:763] (1/8) Epoch 34, batch 1450, loss[loss=0.168, simple_loss=0.2609, pruned_loss=0.03752, over 7254.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2603, pruned_loss=0.03007, over 1418444.65 frames.], batch size: 19, lr: 2.23e-04
2022-04-30 17:23:24,433 INFO [train.py:763] (1/8) Epoch 34, batch 1500, loss[loss=0.1603, simple_loss=0.2492, pruned_loss=0.03571, over 7136.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2606, pruned_loss=0.03001, over 1419499.40 frames.], batch size: 17, lr: 2.23e-04
2022-04-30 17:24:29,698 INFO [train.py:763] (1/8) Epoch 34, batch 1550, loss[loss=0.177, simple_loss=0.2903, pruned_loss=0.03187, over 7224.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2613, pruned_loss=0.03023, over 1419428.17 frames.], batch size: 21, lr: 2.23e-04
2022-04-30 17:25:36,471 INFO [train.py:763] (1/8) Epoch 34, batch 1600, loss[loss=0.1626, simple_loss=0.2685, pruned_loss=0.0284, over 7148.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2607, pruned_loss=0.02979, over 1421382.58 frames.], batch size: 28, lr: 2.23e-04
2022-04-30 17:26:43,358 INFO [train.py:763] (1/8) Epoch 34, batch 1650, loss[loss=0.1308, simple_loss=0.2238, pruned_loss=0.01888, over 7406.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2599, pruned_loss=0.02951, over 1427010.54 frames.], batch size: 18, lr: 2.23e-04
2022-04-30 17:27:48,834 INFO [train.py:763] (1/8) Epoch 34, batch 1700, loss[loss=0.1702, simple_loss=0.2572, pruned_loss=0.04166, over 5204.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2596, pruned_loss=0.02962, over 1426527.97 frames.], batch size: 52, lr: 2.23e-04
2022-04-30 17:28:54,321 INFO [train.py:763] (1/8) Epoch 34, batch 1750, loss[loss=0.1475, simple_loss=0.2453, pruned_loss=0.02485, over 7154.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2593, pruned_loss=0.02948, over 1425111.12 frames.], batch size: 18, lr: 2.23e-04
2022-04-30 17:29:59,723 INFO [train.py:763] (1/8) Epoch 34, batch 1800, loss[loss=0.1852, simple_loss=0.2765, pruned_loss=0.04699, over 7300.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2589, pruned_loss=0.02927, over 1428858.13 frames.], batch size: 25, lr: 2.23e-04
2022-04-30 17:31:04,981 INFO [train.py:763] (1/8) Epoch 34, batch 1850, loss[loss=0.1784, simple_loss=0.2726, pruned_loss=0.04206, over 7053.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2591, pruned_loss=0.02968, over 1425462.48 frames.], batch size: 18, lr: 2.23e-04
2022-04-30 17:32:10,318 INFO [train.py:763] (1/8) Epoch 34, batch 1900, loss[loss=0.1731, simple_loss=0.2672, pruned_loss=0.03954, over 7392.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2588, pruned_loss=0.02973, over 1424714.13 frames.], batch size: 23, lr: 2.22e-04
2022-04-30 17:33:15,840 INFO [train.py:763] (1/8) Epoch 34, batch 1950, loss[loss=0.1282, simple_loss=0.234, pruned_loss=0.01124, over 7168.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2596, pruned_loss=0.02984, over 1423860.56 frames.], batch size: 18, lr: 2.22e-04
2022-04-30 17:34:22,079 INFO [train.py:763] (1/8) Epoch 34, batch 2000, loss[loss=0.1679, simple_loss=0.2669, pruned_loss=0.03448, over 6434.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2594, pruned_loss=0.02984, over 1419287.86 frames.], batch size: 38, lr: 2.22e-04
2022-04-30 17:35:27,874 INFO [train.py:763] (1/8) Epoch 34, batch 2050, loss[loss=0.1567, simple_loss=0.2705, pruned_loss=0.02143, over 7112.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2595, pruned_loss=0.0299, over 1421011.10 frames.], batch size: 21, lr: 2.22e-04
2022-04-30 17:36:33,092 INFO [train.py:763] (1/8) Epoch 34, batch 2100, loss[loss=0.1669, simple_loss=0.2741, pruned_loss=0.02984, over 7416.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2605, pruned_loss=0.03003, over 1423972.23 frames.], batch size: 21, lr: 2.22e-04
2022-04-30 17:37:40,125 INFO [train.py:763] (1/8) Epoch 34, batch 2150, loss[loss=0.1683, simple_loss=0.2682, pruned_loss=0.03416, over 6412.00 frames.], tot_loss[loss=0.1598, simple_loss=0.26, pruned_loss=0.02978, over 1427306.07 frames.], batch size: 37, lr: 2.22e-04
2022-04-30 17:38:46,194 INFO [train.py:763] (1/8) Epoch 34, batch 2200, loss[loss=0.1434, simple_loss=0.2544, pruned_loss=0.01622, over 7426.00 frames.], tot_loss[loss=0.16, simple_loss=0.2599, pruned_loss=0.03006, over 1424355.95 frames.], batch size: 20, lr: 2.22e-04
2022-04-30 17:39:51,383 INFO [train.py:763] (1/8) Epoch 34, batch 2250, loss[loss=0.1522, simple_loss=0.2433, pruned_loss=0.03061, over 7279.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2595, pruned_loss=0.02965, over 1422100.97 frames.], batch size: 18, lr: 2.22e-04
2022-04-30 17:40:56,557 INFO [train.py:763] (1/8) Epoch 34, batch 2300, loss[loss=0.1772, simple_loss=0.2701, pruned_loss=0.04216, over 7239.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2599, pruned_loss=0.02992, over 1419056.95 frames.], batch size: 26, lr: 2.22e-04
2022-04-30 17:42:01,778 INFO [train.py:763] (1/8) Epoch 34, batch 2350, loss[loss=0.1802, simple_loss=0.2769, pruned_loss=0.04178, over 7051.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2599, pruned_loss=0.02938, over 1417680.20 frames.], batch size: 28, lr: 2.22e-04
2022-04-30 17:43:08,014 INFO [train.py:763] (1/8) Epoch 34, batch 2400, loss[loss=0.1383, simple_loss=0.2367, pruned_loss=0.01998, over 7016.00 frames.], tot_loss[loss=0.1592, simple_loss=0.26, pruned_loss=0.02917, over 1422730.44 frames.], batch size: 16, lr: 2.22e-04
2022-04-30 17:44:15,063 INFO [train.py:763] (1/8) Epoch 34, batch 2450, loss[loss=0.1582, simple_loss=0.2546, pruned_loss=0.03086, over 7430.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2594, pruned_loss=0.029, over 1423092.40 frames.], batch size: 20, lr: 2.22e-04
2022-04-30 17:45:22,379 INFO [train.py:763] (1/8) Epoch 34, batch 2500, loss[loss=0.1797, simple_loss=0.284, pruned_loss=0.0377, over 6406.00 frames.], tot_loss[loss=0.1583, simple_loss=0.259, pruned_loss=0.0288, over 1424314.45 frames.], batch size: 37, lr: 2.22e-04
2022-04-30 17:46:28,712 INFO [train.py:763] (1/8) Epoch 34, batch 2550, loss[loss=0.134, simple_loss=0.2351, pruned_loss=0.01644, over 7119.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2591, pruned_loss=0.0292, over 1424013.47 frames.], batch size: 21, lr: 2.22e-04
2022-04-30 17:47:35,756 INFO [train.py:763] (1/8) Epoch 34, batch 2600, loss[loss=0.1932, simple_loss=0.2836, pruned_loss=0.05144, over 7211.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2589, pruned_loss=0.02939, over 1424068.03 frames.], batch size: 22, lr: 2.22e-04
2022-04-30 17:48:40,938 INFO [train.py:763] (1/8) Epoch 34, batch 2650, loss[loss=0.1828, simple_loss=0.2816, pruned_loss=0.04195, over 7182.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2595, pruned_loss=0.02971, over 1422293.01 frames.], batch size: 23, lr: 2.22e-04
2022-04-30 17:49:46,278 INFO [train.py:763] (1/8) Epoch 34, batch 2700, loss[loss=0.1746, simple_loss=0.2843, pruned_loss=0.03243, over 7114.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2595, pruned_loss=0.0296, over 1423795.36 frames.], batch size: 21, lr: 2.22e-04
2022-04-30 17:50:51,546 INFO [train.py:763] (1/8) Epoch 34, batch 2750, loss[loss=0.1683, simple_loss=0.2748, pruned_loss=0.03095, over 7314.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2595, pruned_loss=0.02958, over 1423515.00 frames.], batch size: 21, lr: 2.22e-04
2022-04-30 17:51:57,728 INFO [train.py:763] (1/8) Epoch 34, batch 2800, loss[loss=0.163, simple_loss=0.2704, pruned_loss=0.02784, over 7341.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2602, pruned_loss=0.02962, over 1424816.51 frames.], batch size: 20, lr: 2.22e-04
2022-04-30 17:53:04,497 INFO [train.py:763] (1/8) Epoch 34, batch 2850, loss[loss=0.1403, simple_loss=0.2376, pruned_loss=0.02152, over 7156.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2603, pruned_loss=0.02957, over 1423414.75 frames.], batch size: 19, lr: 2.22e-04
2022-04-30 17:54:11,623 INFO [train.py:763] (1/8) Epoch 34, batch 2900, loss[loss=0.1542, simple_loss=0.2678, pruned_loss=0.02033, over 6333.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2608, pruned_loss=0.02991, over 1422649.27 frames.], batch size: 37, lr: 2.22e-04
2022-04-30 17:55:17,492 INFO [train.py:763] (1/8) Epoch 34, batch 2950, loss[loss=0.1583, simple_loss=0.2541, pruned_loss=0.03126, over 7162.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2617, pruned_loss=0.03008, over 1417274.45 frames.], batch size: 16, lr: 2.22e-04
2022-04-30 17:56:22,954 INFO [train.py:763] (1/8) Epoch 34, batch 3000, loss[loss=0.171, simple_loss=0.2771, pruned_loss=0.03241, over 7363.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2608, pruned_loss=0.02941, over 1421160.06 frames.], batch size: 23, lr: 2.22e-04
2022-04-30 17:56:22,955 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 17:56:38,271 INFO [train.py:792] (1/8) Epoch 34, validation: loss=0.1686, simple_loss=0.2638, pruned_loss=0.03669, over 698248.00 frames.
2022-04-30 17:57:44,332 INFO [train.py:763] (1/8) Epoch 34, batch 3050, loss[loss=0.1633, simple_loss=0.2683, pruned_loss=0.02915, over 7236.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2614, pruned_loss=0.02945, over 1423735.06 frames.], batch size: 20, lr: 2.22e-04
2022-04-30 17:58:51,170 INFO [train.py:763] (1/8) Epoch 34, batch 3100, loss[loss=0.1605, simple_loss=0.2657, pruned_loss=0.02768, over 7382.00 frames.], tot_loss[loss=0.16, simple_loss=0.2611, pruned_loss=0.02951, over 1420761.57 frames.], batch size: 23, lr: 2.22e-04
2022-04-30 17:59:56,668 INFO [train.py:763] (1/8) Epoch 34, batch 3150, loss[loss=0.1518, simple_loss=0.2475, pruned_loss=0.02805, over 7204.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2598, pruned_loss=0.02953, over 1422801.01 frames.], batch size: 22, lr: 2.22e-04
2022-04-30 18:01:02,259 INFO [train.py:763] (1/8) Epoch 34, batch 3200, loss[loss=0.1474, simple_loss=0.2472, pruned_loss=0.02378, over 7203.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2603, pruned_loss=0.02961, over 1427274.09 frames.], batch size: 22, lr: 2.22e-04
2022-04-30 18:02:09,378 INFO [train.py:763] (1/8) Epoch 34, batch 3250, loss[loss=0.1478, simple_loss=0.2471, pruned_loss=0.02429, over 7435.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2605, pruned_loss=0.0297, over 1425351.04 frames.], batch size: 20, lr: 2.22e-04
2022-04-30 18:03:15,759 INFO [train.py:763] (1/8) Epoch 34, batch 3300, loss[loss=0.1574, simple_loss=0.2552, pruned_loss=0.02977, over 7427.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2606, pruned_loss=0.02977, over 1426449.18 frames.], batch size: 20, lr: 2.22e-04
2022-04-30 18:04:21,124 INFO [train.py:763] (1/8) Epoch 34, batch 3350, loss[loss=0.163, simple_loss=0.2628, pruned_loss=0.0316, over 7423.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2602, pruned_loss=0.02964, over 1430218.10 frames.], batch size: 20, lr: 2.21e-04
2022-04-30 18:05:26,506 INFO [train.py:763] (1/8) Epoch 34, batch 3400, loss[loss=0.1593, simple_loss=0.2528, pruned_loss=0.03292, over 7278.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2598, pruned_loss=0.02972, over 1426389.26 frames.], batch size: 18, lr: 2.21e-04
2022-04-30 18:06:31,934 INFO [train.py:763] (1/8) Epoch 34, batch 3450, loss[loss=0.1364, simple_loss=0.2379, pruned_loss=0.01743, over 6994.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2602, pruned_loss=0.02971, over 1429463.00 frames.], batch size: 16, lr: 2.21e-04
2022-04-30 18:07:37,449 INFO [train.py:763] (1/8) Epoch 34, batch 3500, loss[loss=0.1536, simple_loss=0.2546, pruned_loss=0.02633, over 7326.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2593, pruned_loss=0.02952, over 1427774.32 frames.], batch size: 22, lr: 2.21e-04
2022-04-30 18:08:42,509 INFO [train.py:763] (1/8) Epoch 34, batch 3550, loss[loss=0.1749, simple_loss=0.2768, pruned_loss=0.03648, over 6791.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2594, pruned_loss=0.02978, over 1420302.68 frames.], batch size: 31, lr: 2.21e-04
2022-04-30 18:09:48,191 INFO [train.py:763] (1/8) Epoch 34, batch 3600, loss[loss=0.1471, simple_loss=0.2499, pruned_loss=0.02212, over 7201.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2594, pruned_loss=0.02965, over 1419139.02 frames.], batch size: 22, lr: 2.21e-04
2022-04-30 18:10:55,326 INFO [train.py:763] (1/8) Epoch 34, batch 3650, loss[loss=0.1746, simple_loss=0.2751, pruned_loss=0.03706, over 7291.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2601, pruned_loss=0.02984, over 1420604.73 frames.], batch size: 25, lr: 2.21e-04
2022-04-30 18:12:01,492 INFO [train.py:763] (1/8) Epoch 34, batch 3700, loss[loss=0.1665, simple_loss=0.2729, pruned_loss=0.03003, over 6403.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2594, pruned_loss=0.02947, over 1420205.69 frames.], batch size: 38, lr: 2.21e-04
2022-04-30 18:13:06,703 INFO [train.py:763] (1/8) Epoch 34, batch 3750, loss[loss=0.1785, simple_loss=0.2685, pruned_loss=0.04422, over 5047.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2595, pruned_loss=0.02964, over 1417856.91 frames.], batch size: 53, lr: 2.21e-04
2022-04-30 18:14:11,988 INFO [train.py:763] (1/8) Epoch 34, batch 3800, loss[loss=0.1469, simple_loss=0.2522, pruned_loss=0.02082, over 6744.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2596, pruned_loss=0.02943, over 1418238.91 frames.], batch size: 31, lr: 2.21e-04
2022-04-30 18:15:17,334 INFO [train.py:763] (1/8) Epoch 34, batch 3850, loss[loss=0.18, simple_loss=0.2891, pruned_loss=0.03549, over 7287.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2595, pruned_loss=0.02957, over 1421179.96 frames.], batch size: 24, lr: 2.21e-04
2022-04-30 18:16:23,812 INFO [train.py:763] (1/8) Epoch 34, batch 3900, loss[loss=0.1401, simple_loss=0.2346, pruned_loss=0.02278, over 6805.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2603, pruned_loss=0.0299, over 1417664.91 frames.], batch size: 15, lr: 2.21e-04
2022-04-30 18:17:30,967 INFO [train.py:763] (1/8) Epoch 34, batch 3950, loss[loss=0.1654, simple_loss=0.2601, pruned_loss=0.0354, over 7123.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2596, pruned_loss=0.03003, over 1417579.11 frames.], batch size: 17, lr: 2.21e-04
2022-04-30 18:18:37,956 INFO [train.py:763] (1/8) Epoch 34, batch 4000, loss[loss=0.1575, simple_loss=0.2446, pruned_loss=0.03521, over 6994.00 frames.], tot_loss[loss=0.1602, simple_loss=0.26, pruned_loss=0.03018, over 1417183.74 frames.], batch size: 16, lr: 2.21e-04
2022-04-30 18:19:54,711 INFO [train.py:763] (1/8) Epoch 34, batch 4050, loss[loss=0.1669, simple_loss=0.2713, pruned_loss=0.03127, over 6421.00 frames.], tot_loss[loss=0.16, simple_loss=0.2604, pruned_loss=0.02982, over 1419930.26 frames.], batch size: 37, lr: 2.21e-04
2022-04-30 18:21:01,772 INFO [train.py:763] (1/8) Epoch 34, batch 4100, loss[loss=0.1524, simple_loss=0.2648, pruned_loss=0.02005, over 7218.00 frames.], tot_loss[loss=0.1596, simple_loss=0.26, pruned_loss=0.02958, over 1425748.63 frames.], batch size: 21, lr: 2.21e-04
2022-04-30 18:22:08,649 INFO [train.py:763] (1/8) Epoch 34, batch 4150, loss[loss=0.1541, simple_loss=0.2646, pruned_loss=0.02176, over 7323.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2599, pruned_loss=0.0299, over 1424203.27 frames.], batch size: 21, lr: 2.21e-04
2022-04-30 18:23:15,040 INFO [train.py:763] (1/8) Epoch 34, batch 4200, loss[loss=0.1533, simple_loss=0.2476, pruned_loss=0.02946, over 7320.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2601, pruned_loss=0.03005, over 1421978.87 frames.], batch size: 21, lr: 2.21e-04
2022-04-30 18:24:20,537 INFO [train.py:763] (1/8) Epoch 34, batch 4250, loss[loss=0.1404, simple_loss=0.2335, pruned_loss=0.02361, over 7292.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2598, pruned_loss=0.02987, over 1426257.65 frames.], batch size: 17, lr: 2.21e-04
2022-04-30 18:25:25,962 INFO [train.py:763] (1/8) Epoch 34, batch 4300, loss[loss=0.1624, simple_loss=0.2644, pruned_loss=0.03024, over 7211.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2592, pruned_loss=0.02982, over 1417723.63 frames.], batch size: 26, lr: 2.21e-04
2022-04-30 18:26:32,709 INFO [train.py:763] (1/8) Epoch 34, batch 4350, loss[loss=0.1683, simple_loss=0.2693, pruned_loss=0.03367, over 7318.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2592, pruned_loss=0.02997, over 1414137.39 frames.], batch size: 24, lr: 2.21e-04
2022-04-30 18:27:38,204 INFO [train.py:763] (1/8) Epoch 34, batch 4400, loss[loss=0.1503, simple_loss=0.2512, pruned_loss=0.02474, over 7165.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2599, pruned_loss=0.02986, over 1409516.82 frames.], batch size: 19, lr: 2.21e-04
2022-04-30 18:28:42,656 INFO [train.py:763] (1/8) Epoch 34, batch 4450, loss[loss=0.1795, simple_loss=0.293, pruned_loss=0.03299, over 6760.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2604, pruned_loss=0.03003, over 1393746.14 frames.], batch size: 31, lr: 2.21e-04
2022-04-30 18:29:47,243 INFO [train.py:763] (1/8) Epoch 34, batch 4500, loss[loss=0.1656, simple_loss=0.2691, pruned_loss=0.03109, over 7136.00 frames.], tot_loss[loss=0.161, simple_loss=0.2609, pruned_loss=0.0305, over 1380061.98 frames.], batch size: 26, lr: 2.21e-04
2022-04-30 18:30:51,772 INFO [train.py:763] (1/8) Epoch 34, batch 4550, loss[loss=0.1966, simple_loss=0.2957, pruned_loss=0.04875, over 5019.00 frames.], tot_loss[loss=0.1631, simple_loss=0.2631, pruned_loss=0.03155, over 1354593.89 frames.], batch size: 52, lr: 2.21e-04
2022-04-30 18:32:11,386 INFO [train.py:763] (1/8) Epoch 35, batch 0, loss[loss=0.149, simple_loss=0.2439, pruned_loss=0.02699, over 7336.00 frames.], tot_loss[loss=0.149, simple_loss=0.2439, pruned_loss=0.02699, over 7336.00 frames.], batch size: 20, lr: 2.18e-04
2022-04-30 18:33:17,373 INFO [train.py:763] (1/8) Epoch 35, batch 50, loss[loss=0.1671, simple_loss=0.2661, pruned_loss=0.03403, over 7440.00 frames.], tot_loss[loss=0.1642, simple_loss=0.2637, pruned_loss=0.0324, over 317214.75 frames.], batch size: 20, lr: 2.18e-04
2022-04-30 18:34:22,743 INFO [train.py:763] (1/8) Epoch 35, batch 100, loss[loss=0.1785, simple_loss=0.2697, pruned_loss=0.04365, over 5228.00 frames.], tot_loss[loss=0.162, simple_loss=0.262, pruned_loss=0.03097, over 562850.69 frames.], batch size: 52, lr: 2.17e-04
2022-04-30 18:35:28,401 INFO [train.py:763] (1/8) Epoch 35, batch 150, loss[loss=0.1812, simple_loss=0.286, pruned_loss=0.0382, over 7221.00 frames.], tot_loss[loss=0.16, simple_loss=0.2603, pruned_loss=0.02991, over 752079.41 frames.], batch size: 20, lr: 2.17e-04
2022-04-30 18:36:34,085 INFO [train.py:763] (1/8) Epoch 35, batch 200, loss[loss=0.1589, simple_loss=0.2677, pruned_loss=0.02504, over 7308.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2597, pruned_loss=0.02986, over 902766.24 frames.], batch size: 21, lr: 2.17e-04
2022-04-30 18:37:50,841 INFO [train.py:763] (1/8) Epoch 35, batch 250, loss[loss=0.1354, simple_loss=0.2345, pruned_loss=0.01816, over 7154.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2591, pruned_loss=0.02972, over 1022297.37 frames.], batch size: 19, lr: 2.17e-04
2022-04-30 18:38:58,243 INFO [train.py:763] (1/8) Epoch 35, batch 300, loss[loss=0.1623, simple_loss=0.2696, pruned_loss=0.02745, over 7149.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2591, pruned_loss=0.02963, over 1107157.05 frames.], batch size: 26, lr: 2.17e-04
2022-04-30 18:40:05,543 INFO [train.py:763] (1/8) Epoch 35, batch 350, loss[loss=0.1592, simple_loss=0.2671, pruned_loss=0.02559, over 6762.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2595, pruned_loss=0.02942, over 1176734.03 frames.], batch size: 31, lr: 2.17e-04
2022-04-30 18:41:12,760 INFO [train.py:763] (1/8) Epoch 35, batch 400, loss[loss=0.1675, simple_loss=0.2633, pruned_loss=0.0359, over 7200.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2599, pruned_loss=0.02976, over 1232507.25 frames.], batch size: 22, lr: 2.17e-04
2022-04-30 18:42:19,848 INFO [train.py:763] (1/8) Epoch 35, batch 450, loss[loss=0.1804, simple_loss=0.2856, pruned_loss=0.03758, over 7163.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2606, pruned_loss=0.02966, over 1279570.56 frames.], batch size: 26, lr: 2.17e-04
2022-04-30 18:43:25,153 INFO [train.py:763] (1/8) Epoch 35, batch 500, loss[loss=0.1707, simple_loss=0.2773, pruned_loss=0.03199, over 7200.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2609, pruned_loss=0.02965, over 1311524.38 frames.], batch size: 23, lr: 2.17e-04
2022-04-30 18:44:30,966 INFO [train.py:763] (1/8) Epoch 35, batch 550, loss[loss=0.1419, simple_loss=0.247, pruned_loss=0.01844, over 7432.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2608, pruned_loss=0.02924, over 1337389.94 frames.], batch size: 20, lr: 2.17e-04
2022-04-30 18:45:37,226 INFO [train.py:763] (1/8) Epoch 35, batch 600, loss[loss=0.1821, simple_loss=0.2826, pruned_loss=0.04085, over 7226.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2604, pruned_loss=0.02941, over 1360056.47 frames.], batch size: 23, lr: 2.17e-04
2022-04-30 18:46:44,904 INFO [train.py:763] (1/8) Epoch 35, batch 650, loss[loss=0.1461, simple_loss=0.2418, pruned_loss=0.02519, over 7165.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2589, pruned_loss=0.02913, over 1374480.32 frames.], batch size: 19, lr: 2.17e-04
2022-04-30 18:47:52,734 INFO [train.py:763] (1/8) Epoch 35, batch 700, loss[loss=0.1466, simple_loss=0.2406, pruned_loss=0.02629, over 7273.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2585, pruned_loss=0.02932, over 1385312.64 frames.], batch size: 19, lr: 2.17e-04
2022-04-30 18:48:58,270 INFO [train.py:763] (1/8) Epoch 35, batch 750, loss[loss=0.1805, simple_loss=0.283, pruned_loss=0.03899, over 7340.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2593, pruned_loss=0.02941, over 1385408.04 frames.], batch size: 20, lr: 2.17e-04
2022-04-30 18:50:03,736 INFO [train.py:763] (1/8) Epoch 35, batch 800, loss[loss=0.1648, simple_loss=0.267, pruned_loss=0.03127, over 7408.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2597, pruned_loss=0.02927, over 1393603.61 frames.], batch size: 21, lr: 2.17e-04
2022-04-30 18:51:09,200 INFO [train.py:763] (1/8) Epoch 35, batch 850, loss[loss=0.1615, simple_loss=0.2715, pruned_loss=0.02571, over 7219.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2598, pruned_loss=0.02948, over 1395611.44 frames.], batch size: 21, lr: 2.17e-04
2022-04-30 18:52:23,428 INFO [train.py:763] (1/8) Epoch 35, batch 900, loss[loss=0.163, simple_loss=0.2637, pruned_loss=0.03111, over 6787.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2599, pruned_loss=0.02944, over 1402033.10 frames.], batch size: 31, lr: 2.17e-04
2022-04-30 18:53:37,764 INFO [train.py:763] (1/8) Epoch 35, batch 950, loss[loss=0.1461, simple_loss=0.2322, pruned_loss=0.03001, over 6995.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2602, pruned_loss=0.02973, over 1405196.70 frames.], batch size: 16, lr: 2.17e-04
2022-04-30 18:54:42,875 INFO [train.py:763] (1/8) Epoch 35, batch 1000, loss[loss=0.1716, simple_loss=0.2621, pruned_loss=0.04052, over 7280.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2596, pruned_loss=0.02953, over 1407014.15 frames.], batch size: 17, lr: 2.17e-04
2022-04-30 18:55:57,240 INFO [train.py:763] (1/8) Epoch 35, batch 1050, loss[loss=0.1526, simple_loss=0.252, pruned_loss=0.02663, over 7352.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2593, pruned_loss=0.0294, over 1407241.17 frames.], batch size: 19, lr: 2.17e-04
2022-04-30 18:57:20,305 INFO [train.py:763] (1/8) Epoch 35, batch 1100, loss[loss=0.1863, simple_loss=0.2904, pruned_loss=0.04111, over 7208.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2604, pruned_loss=0.02991, over 1407973.75 frames.], batch size: 22, lr: 2.17e-04
2022-04-30 18:58:25,986 INFO [train.py:763] (1/8) Epoch 35, batch 1150, loss[loss=0.1675, simple_loss=0.2668, pruned_loss=0.03417, over 7285.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2595, pruned_loss=0.0297, over 1412776.99 frames.], batch size: 24, lr: 2.17e-04
2022-04-30 18:59:32,075 INFO [train.py:763] (1/8) Epoch 35, batch 1200, loss[loss=0.1355, simple_loss=0.2314, pruned_loss=0.01977, over 7283.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2605, pruned_loss=0.03011, over 1408563.64 frames.], batch size: 17, lr: 2.17e-04
2022-04-30 19:00:55,254 INFO [train.py:763] (1/8) Epoch 35, batch 1250, loss[loss=0.1395, simple_loss=0.2297, pruned_loss=0.02462, over 6999.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2602, pruned_loss=0.03003, over 1409905.89 frames.], batch size: 16, lr: 2.17e-04
2022-04-30 19:02:00,727 INFO [train.py:763] (1/8) Epoch 35, batch 1300, loss[loss=0.1253, simple_loss=0.2225, pruned_loss=0.014, over 7151.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2606, pruned_loss=0.03002, over 1414136.17 frames.], batch size: 17, lr: 2.17e-04
2022-04-30 19:03:07,772 INFO [train.py:763] (1/8) Epoch 35, batch 1350, loss[loss=0.1402, simple_loss=0.2358, pruned_loss=0.02233, over 7257.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2594, pruned_loss=0.02956, over 1418976.56 frames.], batch size: 19, lr: 2.17e-04
2022-04-30 19:04:12,913 INFO [train.py:763] (1/8) Epoch 35, batch 1400, loss[loss=0.131, simple_loss=0.2288, pruned_loss=0.01659, over 7001.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2608, pruned_loss=0.02995, over 1417233.36 frames.], batch size: 16, lr: 2.17e-04
2022-04-30 19:05:18,837 INFO [train.py:763] (1/8) Epoch 35, batch 1450, loss[loss=0.1322, simple_loss=0.2234, pruned_loss=0.02049, over 6811.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2599, pruned_loss=0.02949, over 1414169.70 frames.], batch size: 15, lr: 2.17e-04
2022-04-30 19:06:24,730 INFO [train.py:763] (1/8) Epoch 35, batch 1500, loss[loss=0.166, simple_loss=0.2778, pruned_loss=0.02714, over 7319.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2601, pruned_loss=0.02965, over 1418195.12 frames.], batch size: 21, lr: 2.17e-04
2022-04-30 19:07:30,578 INFO [train.py:763] (1/8) Epoch 35, batch 1550, loss[loss=0.1391, simple_loss=0.2455, pruned_loss=0.01635, over 7241.00 frames.], tot_loss[loss=0.1593, simple_loss=0.26, pruned_loss=0.0293, over 1420321.19 frames.], batch size: 20, lr: 2.17e-04
2022-04-30 19:08:36,022 INFO [train.py:763] (1/8) Epoch 35, batch 1600, loss[loss=0.1662, simple_loss=0.2698, pruned_loss=0.03132, over 7387.00 frames.], tot_loss[loss=0.1585, simple_loss=0.259, pruned_loss=0.02902, over 1420325.42 frames.], batch size: 23, lr: 2.16e-04
2022-04-30 19:09:42,622 INFO [train.py:763] (1/8) Epoch 35, batch 1650, loss[loss=0.146, simple_loss=0.2463, pruned_loss=0.02285, over 7159.00 frames.], tot_loss[loss=0.159, simple_loss=0.2597, pruned_loss=0.02915, over 1421817.03 frames.], batch size: 19, lr: 2.16e-04
2022-04-30 19:10:49,564 INFO [train.py:763] (1/8) Epoch 35, batch 1700, loss[loss=0.1771, simple_loss=0.2866, pruned_loss=0.03376, over 7278.00 frames.], tot_loss[loss=0.1603, simple_loss=0.261, pruned_loss=0.02975, over 1423667.70 frames.], batch size: 25, lr: 2.16e-04
2022-04-30 19:11:56,503 INFO [train.py:763] (1/8) Epoch 35, batch 1750, loss[loss=0.1255, simple_loss=0.2183, pruned_loss=0.01636, over 7276.00 frames.], tot_loss[loss=0.1606, simple_loss=0.2612, pruned_loss=0.03, over 1419831.35 frames.], batch size: 18, lr: 2.16e-04
2022-04-30 19:13:03,557 INFO [train.py:763] (1/8) Epoch 35, batch 1800, loss[loss=0.2161, simple_loss=0.3039, pruned_loss=0.06415, over 7188.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2618, pruned_loss=0.02996, over 1421993.63 frames.], batch size: 23, lr: 2.16e-04
2022-04-30 19:14:09,383 INFO [train.py:763] (1/8) Epoch 35, batch 1850, loss[loss=0.1985, simple_loss=0.2877, pruned_loss=0.05461, over 7120.00 frames.], tot_loss[loss=0.1611, simple_loss=0.2619, pruned_loss=0.03016, over 1424200.68 frames.], batch size: 21, lr: 2.16e-04
2022-04-30 19:15:15,151 INFO [train.py:763] (1/8) Epoch 35, batch 1900, loss[loss=0.1486, simple_loss=0.2566, pruned_loss=0.0203, over 6720.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2615, pruned_loss=0.0301, over 1424972.84 frames.], batch size: 31, lr: 2.16e-04
2022-04-30 19:16:21,440 INFO [train.py:763] (1/8) Epoch 35, batch 1950, loss[loss=0.1558, simple_loss=0.2669, pruned_loss=0.0223, over 7236.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2602, pruned_loss=0.03015, over 1422958.17 frames.], batch size: 20, lr: 2.16e-04
2022-04-30 19:17:27,461 INFO [train.py:763] (1/8) Epoch 35, batch 2000, loss[loss=0.1511, simple_loss=0.2443, pruned_loss=0.029, over 7021.00 frames.], tot_loss[loss=0.161, simple_loss=0.2614, pruned_loss=0.03031, over 1420307.24 frames.], batch size: 16, lr: 2.16e-04
2022-04-30 19:18:34,497 INFO [train.py:763] (1/8) Epoch 35, batch 2050, loss[loss=0.1805, simple_loss=0.2788, pruned_loss=0.04114, over 7319.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2608, pruned_loss=0.02999, over 1425500.82 frames.], batch size: 21, lr: 2.16e-04
2022-04-30 19:19:40,351 INFO [train.py:763] (1/8) Epoch 35, batch 2100, loss[loss=0.1702, simple_loss=0.2653, pruned_loss=0.03754, over 7419.00 frames.], tot_loss[loss=0.1596, simple_loss=0.26, pruned_loss=0.02961, over 1424128.03 frames.], batch size: 21, lr: 2.16e-04
2022-04-30 19:20:47,326 INFO [train.py:763] (1/8) Epoch 35, batch 2150, loss[loss=0.1477, simple_loss=0.2403, pruned_loss=0.02755, over 7252.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2589, pruned_loss=0.02946, over 1427166.47 frames.], batch size: 19, lr: 2.16e-04
2022-04-30 19:21:54,057 INFO [train.py:763] (1/8) Epoch 35, batch 2200, loss[loss=0.1538, simple_loss=0.2446, pruned_loss=0.03151, over 7411.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2589, pruned_loss=0.02935, over 1426384.56 frames.], batch size: 18, lr: 2.16e-04
2022-04-30 19:23:01,261 INFO [train.py:763] (1/8) Epoch 35, batch 2250, loss[loss=0.1672, simple_loss=0.2703, pruned_loss=0.03208, over 7330.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2596, pruned_loss=0.02932, over 1423212.26 frames.], batch size: 22, lr: 2.16e-04
2022-04-30 19:24:07,988 INFO [train.py:763] (1/8) Epoch 35, batch 2300, loss[loss=0.1281, simple_loss=0.2204, pruned_loss=0.01792, over 7129.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2583, pruned_loss=0.02906, over 1425677.93 frames.], batch size: 17, lr: 2.16e-04
2022-04-30 19:25:12,949 INFO [train.py:763] (1/8) Epoch 35, batch 2350, loss[loss=0.1883, simple_loss=0.2817, pruned_loss=0.04746, over 4971.00 frames.], tot_loss[loss=0.159, simple_loss=0.2592, pruned_loss=0.02944, over 1423936.72 frames.], batch size: 52, lr: 2.16e-04
2022-04-30 19:26:18,892 INFO [train.py:763] (1/8) Epoch 35, batch 2400, loss[loss=0.146, simple_loss=0.2448, pruned_loss=0.02365, over 7406.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2589, pruned_loss=0.02885, over 1427187.59 frames.], batch size: 18, lr: 2.16e-04
2022-04-30 19:27:24,046 INFO [train.py:763] (1/8) Epoch 35, batch 2450, loss[loss=0.172, simple_loss=0.2763, pruned_loss=0.03381, over 7171.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2592, pruned_loss=0.0292, over 1422206.18 frames.], batch size: 18, lr: 2.16e-04
2022-04-30 19:28:30,198 INFO [train.py:763] (1/8) Epoch 35, batch 2500, loss[loss=0.1528, simple_loss=0.2576, pruned_loss=0.02398, over 7139.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2595, pruned_loss=0.02946, over 1425833.94 frames.], batch size: 20, lr: 2.16e-04
2022-04-30 19:29:36,782 INFO [train.py:763] (1/8) Epoch 35, batch 2550, loss[loss=0.1527, simple_loss=0.2515, pruned_loss=0.02691, over 7361.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2605, pruned_loss=0.02988, over 1423903.60 frames.], batch size: 19, lr: 2.16e-04
2022-04-30 19:30:41,921 INFO [train.py:763] (1/8) Epoch 35, batch 2600, loss[loss=0.1575, simple_loss=0.2497, pruned_loss=0.03269, over 7164.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2606, pruned_loss=0.02983, over 1424416.85 frames.], batch size: 19, lr: 2.16e-04
2022-04-30 19:31:47,694 INFO [train.py:763] (1/8) Epoch 35, batch 2650, loss[loss=0.2039, simple_loss=0.2981, pruned_loss=0.05485, over 4897.00 frames.], tot_loss[loss=0.16, simple_loss=0.2604, pruned_loss=0.02973, over 1422558.75 frames.], batch size: 52, lr: 2.16e-04
2022-04-30 19:32:53,212 INFO [train.py:763] (1/8) Epoch 35, batch 2700, loss[loss=0.1623, simple_loss=0.2639, pruned_loss=0.03037, over 7307.00 frames.], tot_loss[loss=0.1603, simple_loss=0.2604, pruned_loss=0.03006, over 1424138.09 frames.], batch size: 21, lr: 2.16e-04
2022-04-30 19:33:59,267 INFO [train.py:763] (1/8) Epoch 35, batch 2750, loss[loss=0.1897, simple_loss=0.2794, pruned_loss=0.04998, over 7113.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2607, pruned_loss=0.03017, over 1426189.50 frames.], batch size: 21, lr: 2.16e-04
2022-04-30 19:35:05,456 INFO [train.py:763] (1/8) Epoch 35, batch 2800, loss[loss=0.1527, simple_loss=0.2513, pruned_loss=0.02704, over 7198.00 frames.], tot_loss[loss=0.159, simple_loss=0.2588, pruned_loss=0.02956, over 1427576.37 frames.], batch size: 22, lr: 2.16e-04
2022-04-30 19:36:12,139 INFO [train.py:763] (1/8) Epoch 35, batch 2850, loss[loss=0.1375, simple_loss=0.2285, pruned_loss=0.02326, over 7283.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2581, pruned_loss=0.02952, over 1428130.81 frames.], batch size: 17, lr: 2.16e-04
2022-04-30 19:37:18,075 INFO [train.py:763] (1/8) Epoch 35, batch 2900, loss[loss=0.1434, simple_loss=0.2436, pruned_loss=0.02163, over 7244.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2572, pruned_loss=0.02959, over 1426617.69 frames.], batch size: 19, lr: 2.16e-04
2022-04-30 19:38:23,359 INFO [train.py:763] (1/8) Epoch 35, batch 2950, loss[loss=0.1386, simple_loss=0.235, pruned_loss=0.02112, over 7158.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2578, pruned_loss=0.02945, over 1424794.19 frames.], batch size: 18, lr: 2.16e-04
2022-04-30 19:39:28,870 INFO [train.py:763] (1/8) Epoch 35, batch 3000, loss[loss=0.1452, simple_loss=0.2411, pruned_loss=0.02464, over 7163.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2589, pruned_loss=0.02981, over 1421615.99 frames.], batch size: 19, lr: 2.16e-04
2022-04-30 19:39:28,872 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 19:39:43,929 INFO [train.py:792] (1/8) Epoch 35, validation: loss=0.1681, simple_loss=0.2634, pruned_loss=0.03644, over 698248.00 frames.
2022-04-30 19:40:49,415 INFO [train.py:763] (1/8) Epoch 35, batch 3050, loss[loss=0.1644, simple_loss=0.2833, pruned_loss=0.02276, over 7282.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2596, pruned_loss=0.02995, over 1424260.08 frames.], batch size: 24, lr: 2.16e-04
2022-04-30 19:41:55,472 INFO [train.py:763] (1/8) Epoch 35, batch 3100, loss[loss=0.1741, simple_loss=0.2761, pruned_loss=0.0361, over 7280.00 frames.], tot_loss[loss=0.16, simple_loss=0.2601, pruned_loss=0.02995, over 1428497.21 frames.], batch size: 25, lr: 2.15e-04
2022-04-30 19:43:02,575 INFO [train.py:763] (1/8) Epoch 35, batch 3150, loss[loss=0.1821, simple_loss=0.2809, pruned_loss=0.04166, over 7378.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2597, pruned_loss=0.03003, over 1427075.81 frames.], batch size: 23, lr: 2.15e-04
2022-04-30 19:44:09,426 INFO [train.py:763] (1/8) Epoch 35, batch 3200, loss[loss=0.1369, simple_loss=0.2266, pruned_loss=0.02362, over 7154.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2594, pruned_loss=0.02986, over 1420498.82 frames.], batch size: 17, lr: 2.15e-04
2022-04-30 19:45:15,566 INFO [train.py:763] (1/8) Epoch 35, batch 3250, loss[loss=0.1885, simple_loss=0.2875, pruned_loss=0.04475, over 4669.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2594, pruned_loss=0.02993, over 1418003.33 frames.], batch size: 52, lr: 2.15e-04
2022-04-30 19:46:21,012 INFO [train.py:763] (1/8) Epoch 35, batch 3300, loss[loss=0.1707, simple_loss=0.257, pruned_loss=0.04218, over 7198.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2596, pruned_loss=0.02979, over 1421920.23 frames.], batch size: 23, lr: 2.15e-04
2022-04-30 19:47:26,298 INFO [train.py:763] (1/8) Epoch 35, batch 3350, loss[loss=0.1675, simple_loss=0.2742, pruned_loss=0.03042, over 7198.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2599, pruned_loss=0.02988, over 1425335.55 frames.], batch size: 23, lr: 2.15e-04
2022-04-30 19:48:32,221 INFO [train.py:763] (1/8) Epoch 35, batch 3400, loss[loss=0.1644, simple_loss=0.2734, pruned_loss=0.02773, over 7262.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2594, pruned_loss=0.02953, over 1424415.72 frames.], batch size: 19, lr: 2.15e-04
2022-04-30 19:49:37,615 INFO [train.py:763] (1/8) Epoch 35, batch 3450, loss[loss=0.1568, simple_loss=0.2396, pruned_loss=0.03703, over 7257.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2593, pruned_loss=0.0295, over 1422434.16 frames.], batch size: 17, lr: 2.15e-04
2022-04-30 19:50:43,216 INFO [train.py:763] (1/8) Epoch 35, batch 3500, loss[loss=0.1786, simple_loss=0.2864, pruned_loss=0.03536, over 7415.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2592, pruned_loss=0.02947, over 1419111.86 frames.], batch size: 21, lr: 2.15e-04
2022-04-30 19:51:48,956 INFO [train.py:763] (1/8) Epoch 35, batch 3550, loss[loss=0.1808, simple_loss=0.2934, pruned_loss=0.03412, over 7099.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2599, pruned_loss=0.02962, over 1423002.17 frames.], batch size: 28, lr: 2.15e-04
2022-04-30 19:52:54,516 INFO [train.py:763] (1/8) Epoch 35, batch 3600, loss[loss=0.1558, simple_loss=0.2561, pruned_loss=0.02778, over 7295.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2604, pruned_loss=0.02963, over 1421426.18 frames.], batch size: 25, lr: 2.15e-04
2022-04-30 19:54:00,481 INFO [train.py:763] (1/8) Epoch 35, batch 3650, loss[loss=0.1858, simple_loss=0.2824, pruned_loss=0.04457, over 7303.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2607, pruned_loss=0.02975, over 1424054.80 frames.], batch size: 24, lr: 2.15e-04
2022-04-30 19:55:05,857 INFO [train.py:763] (1/8) Epoch 35, batch 3700, loss[loss=0.1534, simple_loss=0.2623, pruned_loss=0.02224, over 7114.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2601, pruned_loss=0.02958, over 1427286.87 frames.], batch size: 21, lr: 2.15e-04
2022-04-30 19:56:11,428 INFO [train.py:763] (1/8) Epoch 35, batch 3750, loss[loss=0.171, simple_loss=0.2625, pruned_loss=0.03972, over 7330.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2594, pruned_loss=0.02942, over 1426905.07 frames.], batch size: 22, lr: 2.15e-04
2022-04-30 19:57:16,644 INFO [train.py:763] (1/8) Epoch 35, batch 3800, loss[loss=0.1689, simple_loss=0.2556, pruned_loss=0.04112, over 7367.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2607, pruned_loss=0.02962, over 1428660.78 frames.], batch size: 19, lr: 2.15e-04
2022-04-30 19:58:21,845 INFO [train.py:763] (1/8) Epoch 35, batch 3850, loss[loss=0.1368, simple_loss=0.23, pruned_loss=0.02186, over 6989.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2609, pruned_loss=0.02977, over 1424016.85 frames.], batch size: 16, lr: 2.15e-04
2022-04-30 19:59:27,366 INFO [train.py:763] (1/8) Epoch 35, batch 3900, loss[loss=0.1611, simple_loss=0.268, pruned_loss=0.02711, over 7191.00 frames.], tot_loss[loss=0.1596, simple_loss=0.26, pruned_loss=0.0296, over 1425563.34 frames.], batch size: 23, lr: 2.15e-04
2022-04-30 20:00:33,655 INFO [train.py:763] (1/8) Epoch 35, batch 3950, loss[loss=0.1667, simple_loss=0.272, pruned_loss=0.03073, over 6800.00 frames.], tot_loss[loss=0.1608, simple_loss=0.2613, pruned_loss=0.03013, over 1424094.46 frames.], batch size: 31, lr: 2.15e-04
2022-04-30 20:01:41,041 INFO [train.py:763] (1/8) Epoch 35, batch 4000, loss[loss=0.17, simple_loss=0.2667, pruned_loss=0.03663, over 7009.00 frames.], tot_loss[loss=0.1617, simple_loss=0.2623, pruned_loss=0.03054, over 1423879.14 frames.], batch size: 28, lr: 2.15e-04
2022-04-30 20:02:46,170 INFO [train.py:763] (1/8) Epoch 35, batch 4050, loss[loss=0.1585, simple_loss=0.2667, pruned_loss=0.02518, over 7224.00 frames.], tot_loss[loss=0.16, simple_loss=0.2605, pruned_loss=0.02979, over 1426528.85 frames.], batch size: 21, lr: 2.15e-04
2022-04-30 20:03:51,697 INFO [train.py:763] (1/8) Epoch 35, batch 4100, loss[loss=0.1302, simple_loss=0.2237, pruned_loss=0.01839, over 7120.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2608, pruned_loss=0.03001, over 1426685.77 frames.], batch size: 17, lr: 2.15e-04
2022-04-30 20:04:57,464 INFO [train.py:763] (1/8) Epoch 35, batch 4150, loss[loss=0.1798, simple_loss=0.2814, pruned_loss=0.03904, over 7195.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2599, pruned_loss=0.0297, over 1418605.80 frames.], batch size: 23, lr: 2.15e-04
2022-04-30 20:06:03,156 INFO [train.py:763] (1/8) Epoch 35, batch 4200, loss[loss=0.1498, simple_loss=0.2535, pruned_loss=0.02308, over 7242.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2598, pruned_loss=0.02989, over 1416204.61 frames.], batch size: 20, lr: 2.15e-04
2022-04-30 20:07:09,099 INFO [train.py:763] (1/8) Epoch 35, batch 4250, loss[loss=0.184, simple_loss=0.2873, pruned_loss=0.04031, over 7214.00 frames.], tot_loss[loss=0.1598, simple_loss=0.26, pruned_loss=0.02984, over 1415054.09 frames.], batch size: 22, lr: 2.15e-04
2022-04-30 20:08:14,300 INFO [train.py:763] (1/8) Epoch 35, batch 4300, loss[loss=0.1977, simple_loss=0.2914, pruned_loss=0.05199, over 7196.00 frames.], tot_loss[loss=0.1599, simple_loss=0.26, pruned_loss=0.02985, over 1410853.18 frames.], batch size: 22, lr: 2.15e-04
2022-04-30 20:09:20,405 INFO [train.py:763] (1/8) Epoch 35, batch 4350, loss[loss=0.1513, simple_loss=0.2514, pruned_loss=0.02561, over 7435.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2582, pruned_loss=0.02937, over 1411216.55 frames.], batch size: 20, lr: 2.15e-04
2022-04-30 20:10:26,505 INFO [train.py:763] (1/8) Epoch 35, batch 4400, loss[loss=0.1514, simple_loss=0.2526, pruned_loss=0.02508, over 7367.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2572, pruned_loss=0.02891, over 1415497.85 frames.], batch size: 19, lr: 2.15e-04
2022-04-30 20:11:33,064 INFO [train.py:763] (1/8) Epoch 35, batch 4450, loss[loss=0.1593, simple_loss=0.2598, pruned_loss=0.02941, over 7220.00 frames.], tot_loss[loss=0.1568, simple_loss=0.256, pruned_loss=0.02874, over 1405054.13 frames.], batch size: 21, lr: 2.15e-04
2022-04-30 20:12:39,709 INFO [train.py:763] (1/8) Epoch 35, batch 4500, loss[loss=0.1483, simple_loss=0.2457, pruned_loss=0.02541, over 7217.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2571, pruned_loss=0.02912, over 1393348.11 frames.], batch size: 21, lr: 2.15e-04
2022-04-30 20:13:46,214 INFO [train.py:763] (1/8) Epoch 35, batch 4550, loss[loss=0.155, simple_loss=0.2496, pruned_loss=0.0302, over 7263.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2592, pruned_loss=0.03061, over 1356039.12 frames.], batch size: 19, lr: 2.15e-04
2022-04-30 20:15:13,845 INFO [train.py:763] (1/8) Epoch 36, batch 0, loss[loss=0.1581, simple_loss=0.2529, pruned_loss=0.03162, over 7346.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2529, pruned_loss=0.03162, over 7346.00 frames.], batch size: 22, lr: 2.12e-04
2022-04-30 20:16:19,178 INFO [train.py:763] (1/8) Epoch 36, batch 50, loss[loss=0.1211, simple_loss=0.2162, pruned_loss=0.01303, over 7074.00 frames.], tot_loss[loss=0.1613, simple_loss=0.262, pruned_loss=0.03034, over 321196.29 frames.], batch size: 18, lr: 2.12e-04
2022-04-30 20:17:24,379 INFO [train.py:763] (1/8) Epoch 36, batch 100, loss[loss=0.1489, simple_loss=0.2596, pruned_loss=0.0191, over 7319.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2608, pruned_loss=0.02952, over 566884.63 frames.], batch size: 20, lr: 2.12e-04
2022-04-30 20:18:29,496 INFO [train.py:763] (1/8) Epoch 36, batch 150, loss[loss=0.1605, simple_loss=0.2564, pruned_loss=0.03225, over 7025.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2607, pruned_loss=0.02968, over 754635.25 frames.], batch size: 28, lr: 2.11e-04
2022-04-30 20:19:34,473 INFO [train.py:763] (1/8) Epoch 36, batch 200, loss[loss=0.1492, simple_loss=0.2597, pruned_loss=0.01938, over 7328.00 frames.], tot_loss[loss=0.1609, simple_loss=0.2621, pruned_loss=0.02985, over 905967.42 frames.], batch size: 21, lr: 2.11e-04
2022-04-30 20:20:39,729 INFO [train.py:763] (1/8) Epoch 36, batch 250, loss[loss=0.1445, simple_loss=0.2417, pruned_loss=0.02366, over 7267.00 frames.], tot_loss[loss=0.1604, simple_loss=0.261, pruned_loss=0.02987, over 1018274.69 frames.], batch size: 19, lr: 2.11e-04
2022-04-30 20:21:45,225 INFO [train.py:763] (1/8) Epoch 36, batch 300, loss[loss=0.1839, simple_loss=0.2859, pruned_loss=0.04092, over 7337.00 frames.], tot_loss[loss=0.16, simple_loss=0.2605, pruned_loss=0.02979, over 1104541.60 frames.], batch size: 22, lr: 2.11e-04
2022-04-30 20:22:50,516 INFO [train.py:763] (1/8) Epoch 36, batch 350, loss[loss=0.1501, simple_loss=0.2511, pruned_loss=0.02459, over 7162.00 frames.], tot_loss[loss=0.16, simple_loss=0.2608, pruned_loss=0.02966, over 1173665.05 frames.], batch size: 18, lr: 2.11e-04
2022-04-30 20:23:55,932 INFO [train.py:763] (1/8) Epoch 36, batch 400, loss[loss=0.1549, simple_loss=0.2679, pruned_loss=0.02098, over 7234.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2599, pruned_loss=0.02912, over 1232803.33 frames.], batch size: 20, lr: 2.11e-04
2022-04-30 20:25:01,047 INFO [train.py:763] (1/8) Epoch 36, batch 450, loss[loss=0.1584, simple_loss=0.2568, pruned_loss=0.02998, over 7147.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2601, pruned_loss=0.0291, over 1277177.23 frames.], batch size: 20, lr: 2.11e-04
2022-04-30 20:26:07,075 INFO [train.py:763] (1/8) Epoch 36, batch 500, loss[loss=0.1418, simple_loss=0.245, pruned_loss=0.01926, over 7239.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2601, pruned_loss=0.02959, over 1306498.75 frames.], batch size: 20, lr: 2.11e-04
2022-04-30 20:27:14,406 INFO [train.py:763] (1/8) Epoch 36, batch 550, loss[loss=0.165, simple_loss=0.2607, pruned_loss=0.03467, over 7069.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2603, pruned_loss=0.02959, over 1322788.80 frames.], batch size: 18, lr: 2.11e-04
2022-04-30 20:28:22,077 INFO [train.py:763] (1/8) Epoch 36, batch 600, loss[loss=0.1567, simple_loss=0.2616, pruned_loss=0.02589, over 7445.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2589, pruned_loss=0.02924, over 1348069.09 frames.], batch size: 20, lr: 2.11e-04
2022-04-30 20:29:29,896 INFO [train.py:763] (1/8) Epoch 36, batch 650, loss[loss=0.1377, simple_loss=0.2353, pruned_loss=0.0201, over 7136.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2572, pruned_loss=0.0288, over 1367433.91 frames.], batch size: 17, lr: 2.11e-04
2022-04-30 20:30:35,968 INFO [train.py:763] (1/8) Epoch 36, batch 700, loss[loss=0.1604, simple_loss=0.2627, pruned_loss=0.02904, over 7229.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2573, pruned_loss=0.02877, over 1380160.66 frames.], batch size: 20, lr: 2.11e-04
2022-04-30 20:31:41,363 INFO [train.py:763] (1/8) Epoch 36, batch 750, loss[loss=0.162, simple_loss=0.2556, pruned_loss=0.03424, over 7160.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2577, pruned_loss=0.02887, over 1388651.08 frames.], batch size: 19, lr: 2.11e-04
2022-04-30 20:32:47,557 INFO [train.py:763] (1/8) Epoch 36, batch 800, loss[loss=0.1394, simple_loss=0.2291, pruned_loss=0.02482, over 7419.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2573, pruned_loss=0.02922, over 1398942.24 frames.], batch size: 18, lr: 2.11e-04
2022-04-30 20:33:53,516 INFO [train.py:763] (1/8) Epoch 36, batch 850, loss[loss=0.1473, simple_loss=0.2414, pruned_loss=0.02661, over 7260.00 frames.], tot_loss[loss=0.1584, simple_loss=0.258, pruned_loss=0.02946, over 1398078.13 frames.], batch size: 19, lr: 2.11e-04
2022-04-30 20:34:59,124 INFO [train.py:763] (1/8) Epoch 36, batch 900, loss[loss=0.1476, simple_loss=0.25, pruned_loss=0.0226, over 7070.00 frames.], tot_loss[loss=0.158, simple_loss=0.2578, pruned_loss=0.02915, over 1407063.53 frames.], batch size: 18, lr: 2.11e-04
2022-04-30 20:36:04,436 INFO [train.py:763] (1/8) Epoch 36, batch 950, loss[loss=0.1334, simple_loss=0.2177, pruned_loss=0.02449, over 7292.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2575, pruned_loss=0.02912, over 1410605.55 frames.], batch size: 17, lr: 2.11e-04
2022-04-30 20:37:09,729 INFO [train.py:763] (1/8) Epoch 36, batch 1000, loss[loss=0.1871, simple_loss=0.292, pruned_loss=0.04105, over 6855.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2579, pruned_loss=0.02935, over 1412942.19 frames.], batch size: 31, lr: 2.11e-04
2022-04-30 20:38:15,272 INFO [train.py:763] (1/8) Epoch 36, batch 1050, loss[loss=0.1888, simple_loss=0.2968, pruned_loss=0.04042, over 7391.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2576, pruned_loss=0.02917, over 1417319.09 frames.], batch size: 23, lr: 2.11e-04
2022-04-30 20:39:20,504 INFO [train.py:763] (1/8) Epoch 36, batch 1100, loss[loss=0.1593, simple_loss=0.2731, pruned_loss=0.0228, over 7224.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2576, pruned_loss=0.02928, over 1419456.82 frames.], batch size: 21, lr: 2.11e-04
2022-04-30 20:40:26,423 INFO [train.py:763] (1/8) Epoch 36, batch 1150, loss[loss=0.1737, simple_loss=0.2628, pruned_loss=0.04237, over 4751.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2584, pruned_loss=0.02969, over 1417758.58 frames.], batch size: 52, lr: 2.11e-04
2022-04-30 20:41:32,756 INFO [train.py:763] (1/8) Epoch 36, batch 1200, loss[loss=0.1545, simple_loss=0.2657, pruned_loss=0.02164, over 7153.00 frames.], tot_loss[loss=0.1602, simple_loss=0.26, pruned_loss=0.03018, over 1419773.86 frames.], batch size: 20, lr: 2.11e-04
2022-04-30 20:42:37,806 INFO [train.py:763] (1/8) Epoch 36, batch 1250, loss[loss=0.1684, simple_loss=0.2693, pruned_loss=0.03376, over 7211.00 frames.], tot_loss[loss=0.1605, simple_loss=0.2607, pruned_loss=0.03015, over 1420552.09 frames.], batch size: 22, lr: 2.11e-04
2022-04-30 20:43:42,980 INFO [train.py:763] (1/8) Epoch 36, batch 1300, loss[loss=0.1406, simple_loss=0.2325, pruned_loss=0.0243, over 7137.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2609, pruned_loss=0.0297, over 1422383.28 frames.], batch size: 17, lr: 2.11e-04
2022-04-30 20:44:48,209 INFO [train.py:763] (1/8) Epoch 36, batch 1350, loss[loss=0.1595, simple_loss=0.2557, pruned_loss=0.03165, over 7079.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2606, pruned_loss=0.02959, over 1417825.93 frames.], batch size: 18, lr: 2.11e-04
2022-04-30 20:45:54,979 INFO [train.py:763] (1/8) Epoch 36, batch 1400, loss[loss=0.1319, simple_loss=0.2254, pruned_loss=0.01926, over 6987.00 frames.], tot_loss[loss=0.1596, simple_loss=0.26, pruned_loss=0.02955, over 1417970.62 frames.], batch size: 16, lr: 2.11e-04
2022-04-30 20:47:00,116 INFO [train.py:763] (1/8) Epoch 36, batch 1450, loss[loss=0.198, simple_loss=0.2812, pruned_loss=0.05735, over 7273.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2596, pruned_loss=0.02968, over 1419893.27 frames.], batch size: 24, lr: 2.11e-04
2022-04-30 20:48:05,223 INFO [train.py:763] (1/8) Epoch 36, batch 1500, loss[loss=0.1662, simple_loss=0.2675, pruned_loss=0.03248, over 7293.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2593, pruned_loss=0.02954, over 1416131.96 frames.], batch size: 24, lr: 2.11e-04
2022-04-30 20:49:10,949 INFO [train.py:763] (1/8) Epoch 36, batch 1550, loss[loss=0.1467, simple_loss=0.2495, pruned_loss=0.02192, over 6730.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2593, pruned_loss=0.02949, over 1410412.16 frames.], batch size: 31, lr: 2.11e-04
2022-04-30 20:50:16,847 INFO [train.py:763] (1/8) Epoch 36, batch 1600, loss[loss=0.186, simple_loss=0.2738, pruned_loss=0.04914, over 7384.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2588, pruned_loss=0.02909, over 1410845.72 frames.], batch size: 23, lr: 2.11e-04
2022-04-30 20:51:24,011 INFO [train.py:763] (1/8) Epoch 36, batch 1650, loss[loss=0.1696, simple_loss=0.271, pruned_loss=0.03413, over 7200.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2588, pruned_loss=0.02925, over 1414416.63 frames.], batch size: 22, lr: 2.11e-04
2022-04-30 20:52:38,229 INFO [train.py:763] (1/8) Epoch 36, batch 1700, loss[loss=0.1343, simple_loss=0.2342, pruned_loss=0.01718, over 7149.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2592, pruned_loss=0.02933, over 1412980.73 frames.], batch size: 19, lr: 2.11e-04
2022-04-30 20:53:43,582 INFO [train.py:763] (1/8) Epoch 36, batch 1750, loss[loss=0.1617, simple_loss=0.2605, pruned_loss=0.03144, over 7355.00 frames.], tot_loss[loss=0.1589, simple_loss=0.259, pruned_loss=0.02942, over 1407925.79 frames.], batch size: 19, lr: 2.10e-04
2022-04-30 20:54:48,729 INFO [train.py:763] (1/8) Epoch 36, batch 1800, loss[loss=0.1805, simple_loss=0.288, pruned_loss=0.03645, over 7284.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2595, pruned_loss=0.02964, over 1410777.37 frames.], batch size: 24, lr: 2.10e-04
2022-04-30 20:55:54,035 INFO [train.py:763] (1/8) Epoch 36, batch 1850, loss[loss=0.145, simple_loss=0.2375, pruned_loss=0.02626, over 7267.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2593, pruned_loss=0.02988, over 1410841.77 frames.], batch size: 19, lr: 2.10e-04
2022-04-30 20:56:59,658 INFO [train.py:763] (1/8) Epoch 36, batch 1900, loss[loss=0.1585, simple_loss=0.2527, pruned_loss=0.03215, over 6855.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2599, pruned_loss=0.02997, over 1417028.54 frames.], batch size: 31, lr: 2.10e-04
2022-04-30 20:58:07,233 INFO [train.py:763] (1/8) Epoch 36, batch 1950, loss[loss=0.1555, simple_loss=0.2593, pruned_loss=0.02586, over 7214.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2595, pruned_loss=0.03009, over 1420734.51 frames.], batch size: 21, lr: 2.10e-04
2022-04-30 20:59:14,630 INFO [train.py:763] (1/8) Epoch 36, batch 2000, loss[loss=0.1497, simple_loss=0.2472, pruned_loss=0.0261, over 7402.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2596, pruned_loss=0.02981, over 1417697.17 frames.], batch size: 21, lr: 2.10e-04
2022-04-30 21:00:22,192 INFO [train.py:763] (1/8) Epoch 36, batch 2050, loss[loss=0.1768, simple_loss=0.2778, pruned_loss=0.03787, over 7230.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2597, pruned_loss=0.02994, over 1420389.25 frames.], batch size: 20, lr: 2.10e-04
2022-04-30 21:01:28,530 INFO [train.py:763] (1/8) Epoch 36, batch 2100, loss[loss=0.1668, simple_loss=0.2719, pruned_loss=0.03081, over 7145.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2596, pruned_loss=0.02962, over 1420553.10 frames.], batch size: 20, lr: 2.10e-04
2022-04-30 21:02:35,085 INFO [train.py:763] (1/8) Epoch 36, batch 2150, loss[loss=0.1519, simple_loss=0.2595, pruned_loss=0.02215, over 7414.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2601, pruned_loss=0.02984, over 1418888.32 frames.], batch size: 21, lr: 2.10e-04
2022-04-30 21:03:42,383 INFO [train.py:763] (1/8) Epoch 36, batch 2200, loss[loss=0.1429, simple_loss=0.2342, pruned_loss=0.02581, over 7256.00 frames.], tot_loss[loss=0.16, simple_loss=0.2601, pruned_loss=0.02995, over 1419991.24 frames.], batch size: 19, lr: 2.10e-04
2022-04-30 21:04:49,054 INFO [train.py:763] (1/8) Epoch 36, batch 2250, loss[loss=0.1726, simple_loss=0.2903, pruned_loss=0.02743, over 7155.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2609, pruned_loss=0.03025, over 1420735.85 frames.], batch size: 20, lr: 2.10e-04
2022-04-30 21:05:54,012 INFO [train.py:763] (1/8) Epoch 36, batch 2300, loss[loss=0.1672, simple_loss=0.279, pruned_loss=0.02768, over 7208.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2606, pruned_loss=0.02994, over 1420278.11 frames.], batch size: 23, lr: 2.10e-04
2022-04-30 21:06:59,112 INFO [train.py:763] (1/8) Epoch 36, batch 2350, loss[loss=0.1289, simple_loss=0.217, pruned_loss=0.02042, over 7279.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2606, pruned_loss=0.03014, over 1413335.60 frames.], batch size: 17, lr: 2.10e-04
2022-04-30 21:08:06,479 INFO [train.py:763] (1/8) Epoch 36, batch 2400, loss[loss=0.182, simple_loss=0.2904, pruned_loss=0.03681, over 7304.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2611, pruned_loss=0.0302, over 1419092.42 frames.], batch size: 25, lr: 2.10e-04
2022-04-30 21:09:12,594 INFO [train.py:763] (1/8) Epoch 36, batch 2450, loss[loss=0.1769, simple_loss=0.2813, pruned_loss=0.03624, over 7176.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2605, pruned_loss=0.02984, over 1424587.21 frames.], batch size: 26, lr: 2.10e-04
2022-04-30 21:10:36,032 INFO [train.py:763] (1/8) Epoch 36, batch 2500, loss[loss=0.1666, simple_loss=0.2712, pruned_loss=0.03098, over 7162.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2598, pruned_loss=0.02972, over 1427148.59 frames.], batch size: 19, lr: 2.10e-04
2022-04-30 21:11:41,252 INFO [train.py:763] (1/8) Epoch 36, batch 2550, loss[loss=0.1751, simple_loss=0.2756, pruned_loss=0.03729, over 7277.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2591, pruned_loss=0.02926, over 1428089.14 frames.], batch size: 24, lr: 2.10e-04
2022-04-30 21:12:55,222 INFO [train.py:763] (1/8) Epoch 36, batch 2600, loss[loss=0.1318, simple_loss=0.2309, pruned_loss=0.01634, over 6823.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2593, pruned_loss=0.0295, over 1424488.46 frames.], batch size: 15, lr: 2.10e-04
2022-04-30 21:14:18,381 INFO [train.py:763] (1/8) Epoch 36, batch 2650, loss[loss=0.1625, simple_loss=0.2656, pruned_loss=0.02966, over 7202.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2596, pruned_loss=0.02965, over 1427467.65 frames.], batch size: 22, lr: 2.10e-04
2022-04-30 21:15:32,420 INFO [train.py:763] (1/8) Epoch 36, batch 2700, loss[loss=0.1592, simple_loss=0.2623, pruned_loss=0.02805, over 6363.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2589, pruned_loss=0.02947, over 1423418.03 frames.], batch size: 38, lr: 2.10e-04
2022-04-30 21:16:46,238 INFO [train.py:763] (1/8) Epoch 36, batch 2750, loss[loss=0.2027, simple_loss=0.2806, pruned_loss=0.06242, over 5010.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2591, pruned_loss=0.0292, over 1423959.34 frames.], batch size: 52, lr: 2.10e-04
2022-04-30 21:17:52,033 INFO [train.py:763] (1/8) Epoch 36, batch 2800, loss[loss=0.1405, simple_loss=0.2402, pruned_loss=0.02041, over 7276.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2585, pruned_loss=0.02933, over 1428530.90 frames.], batch size: 18, lr: 2.10e-04
2022-04-30 21:19:07,512 INFO [train.py:763] (1/8) Epoch 36, batch 2850, loss[loss=0.1616, simple_loss=0.2696, pruned_loss=0.02683, over 6623.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2594, pruned_loss=0.02966, over 1428151.09 frames.], batch size: 38, lr: 2.10e-04
2022-04-30 21:20:12,992 INFO [train.py:763] (1/8) Epoch 36, batch 2900, loss[loss=0.124, simple_loss=0.218, pruned_loss=0.01499, over 6997.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2593, pruned_loss=0.02902, over 1429931.30 frames.], batch size: 16, lr: 2.10e-04
2022-04-30 21:21:20,751 INFO [train.py:763] (1/8) Epoch 36, batch 2950, loss[loss=0.1352, simple_loss=0.2429, pruned_loss=0.01372, over 7420.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2591, pruned_loss=0.02913, over 1426445.17 frames.], batch size: 20, lr: 2.10e-04
2022-04-30 21:22:27,897 INFO [train.py:763] (1/8) Epoch 36, batch 3000, loss[loss=0.1548, simple_loss=0.2659, pruned_loss=0.02186, over 7226.00 frames.], tot_loss[loss=0.1593, simple_loss=0.2598, pruned_loss=0.02937, over 1422748.54 frames.], batch size: 21, lr: 2.10e-04
2022-04-30 21:22:27,898 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 21:22:43,065 INFO [train.py:792] (1/8) Epoch 36, validation: loss=0.1683, simple_loss=0.2628, pruned_loss=0.03692, over 698248.00 frames.
2022-04-30 21:23:48,278 INFO [train.py:763] (1/8) Epoch 36, batch 3050, loss[loss=0.1431, simple_loss=0.2379, pruned_loss=0.02419, over 7196.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2591, pruned_loss=0.0292, over 1421352.94 frames.], batch size: 16, lr: 2.10e-04
2022-04-30 21:24:54,039 INFO [train.py:763] (1/8) Epoch 36, batch 3100, loss[loss=0.173, simple_loss=0.2701, pruned_loss=0.03797, over 7079.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2591, pruned_loss=0.02908, over 1419503.75 frames.], batch size: 18, lr: 2.10e-04
2022-04-30 21:26:01,267 INFO [train.py:763] (1/8) Epoch 36, batch 3150, loss[loss=0.1517, simple_loss=0.2481, pruned_loss=0.02764, over 7002.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2592, pruned_loss=0.0292, over 1417413.08 frames.], batch size: 16, lr: 2.10e-04
2022-04-30 21:27:07,746 INFO [train.py:763] (1/8) Epoch 36, batch 3200, loss[loss=0.1668, simple_loss=0.2656, pruned_loss=0.034, over 4870.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2592, pruned_loss=0.02903, over 1417682.29 frames.], batch size: 52, lr: 2.10e-04
2022-04-30 21:28:14,746 INFO [train.py:763] (1/8) Epoch 36, batch 3250, loss[loss=0.1749, simple_loss=0.2753, pruned_loss=0.03723, over 7184.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2594, pruned_loss=0.02898, over 1416689.15 frames.], batch size: 22, lr: 2.10e-04
2022-04-30 21:29:20,184 INFO [train.py:763] (1/8) Epoch 36, batch 3300, loss[loss=0.1549, simple_loss=0.2584, pruned_loss=0.02568, over 7414.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2598, pruned_loss=0.0293, over 1413611.86 frames.], batch size: 21, lr: 2.10e-04
2022-04-30 21:30:25,144 INFO [train.py:763] (1/8) Epoch 36, batch 3350, loss[loss=0.1934, simple_loss=0.2924, pruned_loss=0.04721, over 7401.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2601, pruned_loss=0.02935, over 1410763.04 frames.], batch size: 23, lr: 2.09e-04
2022-04-30 21:31:31,784 INFO [train.py:763] (1/8) Epoch 36, batch 3400, loss[loss=0.1467, simple_loss=0.2396, pruned_loss=0.02686, over 7139.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2599, pruned_loss=0.02947, over 1416232.32 frames.], batch size: 17, lr: 2.09e-04
2022-04-30 21:32:37,221 INFO [train.py:763] (1/8) Epoch 36, batch 3450, loss[loss=0.1261, simple_loss=0.2137, pruned_loss=0.01923, over 7267.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2588, pruned_loss=0.02893, over 1419192.60 frames.], batch size: 17, lr: 2.09e-04
2022-04-30 21:33:42,448 INFO [train.py:763] (1/8) Epoch 36, batch 3500, loss[loss=0.1706, simple_loss=0.2679, pruned_loss=0.03666, over 7351.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2599, pruned_loss=0.02947, over 1417174.31 frames.], batch size: 19, lr: 2.09e-04
2022-04-30 21:34:47,627 INFO [train.py:763] (1/8) Epoch 36, batch 3550, loss[loss=0.1779, simple_loss=0.2575, pruned_loss=0.04911, over 6770.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2597, pruned_loss=0.02953, over 1414578.02 frames.], batch size: 15, lr: 2.09e-04
2022-04-30 21:35:54,817 INFO [train.py:763] (1/8) Epoch 36, batch 3600, loss[loss=0.1524, simple_loss=0.2397, pruned_loss=0.03249, over 7005.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2588, pruned_loss=0.02928, over 1421402.62 frames.], batch size: 16, lr: 2.09e-04
2022-04-30 21:37:01,773 INFO [train.py:763] (1/8) Epoch 36, batch 3650, loss[loss=0.1436, simple_loss=0.2554, pruned_loss=0.01591, over 7150.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2586, pruned_loss=0.02881, over 1423522.68 frames.], batch size: 19, lr: 2.09e-04
2022-04-30 21:38:08,983 INFO [train.py:763] (1/8) Epoch 36, batch 3700, loss[loss=0.1621, simple_loss=0.2623, pruned_loss=0.03094, over 7243.00 frames.], tot_loss[loss=0.158, simple_loss=0.2582, pruned_loss=0.02895, over 1426912.99 frames.], batch size: 20, lr: 2.09e-04
2022-04-30 21:39:14,210 INFO [train.py:763] (1/8) Epoch 36, batch 3750, loss[loss=0.1977, simple_loss=0.3034, pruned_loss=0.04597, over 7296.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2588, pruned_loss=0.02897, over 1423567.20 frames.], batch size: 24, lr: 2.09e-04
2022-04-30 21:40:19,606 INFO [train.py:763] (1/8) Epoch 36, batch 3800, loss[loss=0.1391, simple_loss=0.2311, pruned_loss=0.02357, over 7262.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2584, pruned_loss=0.02889, over 1424773.92 frames.], batch size: 17, lr: 2.09e-04
2022-04-30 21:41:25,003 INFO [train.py:763] (1/8) Epoch 36, batch 3850, loss[loss=0.194, simple_loss=0.2977, pruned_loss=0.0452, over 4857.00 frames.], tot_loss[loss=0.158, simple_loss=0.2582, pruned_loss=0.02896, over 1423773.42 frames.], batch size: 52, lr: 2.09e-04
2022-04-30 21:42:30,256 INFO [train.py:763] (1/8) Epoch 36, batch 3900, loss[loss=0.1585, simple_loss=0.2566, pruned_loss=0.03016, over 7321.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2581, pruned_loss=0.02915, over 1425264.50 frames.], batch size: 20, lr: 2.09e-04
2022-04-30 21:43:35,819 INFO [train.py:763] (1/8) Epoch 36, batch 3950, loss[loss=0.1334, simple_loss=0.2359, pruned_loss=0.01542, over 7276.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2584, pruned_loss=0.02924, over 1426078.93 frames.], batch size: 18, lr: 2.09e-04
2022-04-30 21:44:41,517 INFO [train.py:763] (1/8) Epoch 36, batch 4000, loss[loss=0.1436, simple_loss=0.2436, pruned_loss=0.02177, over 7156.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2585, pruned_loss=0.02912, over 1427015.81 frames.], batch size: 20, lr: 2.09e-04
2022-04-30 21:45:48,403 INFO [train.py:763] (1/8) Epoch 36, batch 4050, loss[loss=0.1549, simple_loss=0.2715, pruned_loss=0.01919, over 7145.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2585, pruned_loss=0.02918, over 1426562.77 frames.], batch size: 20, lr: 2.09e-04
2022-04-30 21:46:54,630 INFO [train.py:763] (1/8) Epoch 36, batch 4100, loss[loss=0.1799, simple_loss=0.2814, pruned_loss=0.03916, over 7302.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2591, pruned_loss=0.02893, over 1424063.65 frames.], batch size: 25, lr: 2.09e-04
2022-04-30 21:48:00,285 INFO [train.py:763] (1/8) Epoch 36, batch 4150, loss[loss=0.159, simple_loss=0.2635, pruned_loss=0.02732, over 7226.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2582, pruned_loss=0.02867, over 1425858.77 frames.], batch size: 21, lr: 2.09e-04
2022-04-30 21:49:06,725 INFO [train.py:763] (1/8) Epoch 36, batch 4200, loss[loss=0.1623, simple_loss=0.2624, pruned_loss=0.03107, over 7327.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2581, pruned_loss=0.02855, over 1428502.02 frames.], batch size: 22, lr: 2.09e-04
2022-04-30 21:50:13,178 INFO [train.py:763] (1/8) Epoch 36, batch 4250, loss[loss=0.155, simple_loss=0.2461, pruned_loss=0.03194, over 7201.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2577, pruned_loss=0.02871, over 1431006.37 frames.], batch size: 22, lr: 2.09e-04
2022-04-30 21:51:18,747 INFO [train.py:763] (1/8) Epoch 36, batch 4300, loss[loss=0.15, simple_loss=0.2591, pruned_loss=0.02041, over 7332.00 frames.], tot_loss[loss=0.158, simple_loss=0.2584, pruned_loss=0.02887, over 1425919.09 frames.], batch size: 20, lr: 2.09e-04
2022-04-30 21:52:24,304 INFO [train.py:763] (1/8) Epoch 36, batch 4350, loss[loss=0.1723, simple_loss=0.2799, pruned_loss=0.03235, over 7320.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2589, pruned_loss=0.02908, over 1430534.13 frames.], batch size: 22, lr: 2.09e-04
2022-04-30 21:53:30,947 INFO [train.py:763] (1/8) Epoch 36, batch 4400, loss[loss=0.1591, simple_loss=0.2697, pruned_loss=0.02424, over 7331.00 frames.], tot_loss[loss=0.1588, simple_loss=0.259, pruned_loss=0.02925, over 1422758.83 frames.], batch size: 22, lr: 2.09e-04
2022-04-30 21:54:38,267 INFO [train.py:763] (1/8) Epoch 36, batch 4450, loss[loss=0.138, simple_loss=0.2286, pruned_loss=0.02368, over 7408.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2587, pruned_loss=0.02906, over 1421104.47 frames.], batch size: 18, lr: 2.09e-04
2022-04-30 21:55:43,445 INFO [train.py:763] (1/8) Epoch 36, batch 4500, loss[loss=0.1394, simple_loss=0.2348, pruned_loss=0.02197, over 7270.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2587, pruned_loss=0.02889, over 1415865.68 frames.], batch size: 18, lr: 2.09e-04
2022-04-30 21:56:47,988 INFO [train.py:763] (1/8) Epoch 36, batch 4550, loss[loss=0.1672, simple_loss=0.2757, pruned_loss=0.02932, over 6234.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2601, pruned_loss=0.02941, over 1392796.47 frames.], batch size: 37, lr: 2.09e-04
2022-04-30 21:58:07,228 INFO [train.py:763] (1/8) Epoch 37, batch 0, loss[loss=0.1295, simple_loss=0.2383, pruned_loss=0.01031, over 7365.00 frames.], tot_loss[loss=0.1295, simple_loss=0.2383, pruned_loss=0.01031, over 7365.00 frames.], batch size: 19, lr: 2.06e-04
2022-04-30 21:59:13,879 INFO [train.py:763] (1/8) Epoch 37, batch 50, loss[loss=0.1481, simple_loss=0.2559, pruned_loss=0.02019, over 6436.00 frames.], tot_loss[loss=0.1532, simple_loss=0.2537, pruned_loss=0.02633, over 322563.42 frames.], batch size: 38, lr: 2.06e-04
2022-04-30 22:00:20,503 INFO [train.py:763] (1/8) Epoch 37, batch 100, loss[loss=0.148, simple_loss=0.2517, pruned_loss=0.02217, over 7243.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2578, pruned_loss=0.02849, over 559152.85 frames.], batch size: 19, lr: 2.06e-04
2022-04-30 22:01:27,279 INFO [train.py:763] (1/8) Epoch 37, batch 150, loss[loss=0.1682, simple_loss=0.2662, pruned_loss=0.03511, over 7362.00 frames.], tot_loss[loss=0.158, simple_loss=0.2588, pruned_loss=0.0286, over 747335.74 frames.], batch size: 23, lr: 2.06e-04
2022-04-30 22:02:34,174 INFO [train.py:763] (1/8) Epoch 37, batch 200, loss[loss=0.1599, simple_loss=0.2697, pruned_loss=0.02505, over 7407.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2585, pruned_loss=0.02824, over 895405.59 frames.], batch size: 21, lr: 2.06e-04
2022-04-30 22:03:39,630 INFO [train.py:763] (1/8) Epoch 37, batch 250, loss[loss=0.1775, simple_loss=0.2822, pruned_loss=0.03637, over 7356.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2586, pruned_loss=0.02843, over 1013588.48 frames.], batch size: 19, lr: 2.06e-04
2022-04-30 22:04:45,206 INFO [train.py:763] (1/8) Epoch 37, batch 300, loss[loss=0.1659, simple_loss=0.2702, pruned_loss=0.03077, over 7227.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2585, pruned_loss=0.02863, over 1104483.80 frames.], batch size: 20, lr: 2.06e-04
2022-04-30 22:05:51,655 INFO [train.py:763] (1/8) Epoch 37, batch 350, loss[loss=0.1259, simple_loss=0.2226, pruned_loss=0.0146, over 7262.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2583, pruned_loss=0.02821, over 1171745.13 frames.], batch size: 19, lr: 2.06e-04
2022-04-30 22:06:57,559 INFO [train.py:763] (1/8) Epoch 37, batch 400, loss[loss=0.1471, simple_loss=0.2384, pruned_loss=0.02797, over 7258.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2577, pruned_loss=0.02807, over 1232407.03 frames.], batch size: 17, lr: 2.06e-04
2022-04-30 22:08:03,013 INFO [train.py:763] (1/8) Epoch 37, batch 450, loss[loss=0.1602, simple_loss=0.2716, pruned_loss=0.02434, over 7112.00 frames.], tot_loss[loss=0.1562, simple_loss=0.257, pruned_loss=0.02768, over 1275799.25 frames.], batch size: 21, lr: 2.06e-04
2022-04-30 22:09:09,215 INFO [train.py:763] (1/8) Epoch 37, batch 500, loss[loss=0.137, simple_loss=0.2353, pruned_loss=0.01933, over 7280.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2561, pruned_loss=0.02762, over 1311919.16 frames.], batch size: 18, lr: 2.06e-04
2022-04-30 22:10:16,143 INFO [train.py:763] (1/8) Epoch 37, batch 550, loss[loss=0.152, simple_loss=0.25, pruned_loss=0.02698, over 7334.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2571, pruned_loss=0.02796, over 1335956.86 frames.], batch size: 20, lr: 2.06e-04
2022-04-30 22:11:22,961 INFO [train.py:763] (1/8) Epoch 37, batch 600, loss[loss=0.1582, simple_loss=0.264, pruned_loss=0.02614, over 7380.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2583, pruned_loss=0.02818, over 1357569.10 frames.], batch size: 23, lr: 2.06e-04
2022-04-30 22:12:30,631 INFO [train.py:763] (1/8) Epoch 37, batch 650, loss[loss=0.1518, simple_loss=0.2619, pruned_loss=0.02078, over 7329.00 frames.], tot_loss[loss=0.158, simple_loss=0.2591, pruned_loss=0.02845, over 1374095.62 frames.], batch size: 22, lr: 2.06e-04
2022-04-30 22:13:38,153 INFO [train.py:763] (1/8) Epoch 37, batch 700, loss[loss=0.153, simple_loss=0.2494, pruned_loss=0.02832, over 7166.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2588, pruned_loss=0.02834, over 1386164.48 frames.], batch size: 18, lr: 2.06e-04
2022-04-30 22:14:45,729 INFO [train.py:763] (1/8) Epoch 37, batch 750, loss[loss=0.1687, simple_loss=0.2771, pruned_loss=0.03017, over 7396.00 frames.], tot_loss[loss=0.158, simple_loss=0.2592, pruned_loss=0.02838, over 1400673.69 frames.], batch size: 23, lr: 2.05e-04
2022-04-30 22:15:51,458 INFO [train.py:763] (1/8) Epoch 37, batch 800, loss[loss=0.1492, simple_loss=0.2397, pruned_loss=0.02942, over 7408.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2594, pruned_loss=0.02847, over 1408183.73 frames.], batch size: 18, lr: 2.05e-04
2022-04-30 22:16:56,736 INFO [train.py:763] (1/8) Epoch 37, batch 850, loss[loss=0.1684, simple_loss=0.2709, pruned_loss=0.03294, over 7359.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2595, pruned_loss=0.0281, over 1411325.98 frames.], batch size: 19, lr: 2.05e-04
2022-04-30 22:18:02,418 INFO [train.py:763] (1/8) Epoch 37, batch 900, loss[loss=0.1732, simple_loss=0.2777, pruned_loss=0.03441, over 7283.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2589, pruned_loss=0.02822, over 1412564.77 frames.], batch size: 24, lr: 2.05e-04
2022-04-30 22:19:07,694 INFO [train.py:763] (1/8) Epoch 37, batch 950, loss[loss=0.1406, simple_loss=0.2452, pruned_loss=0.01802, over 7261.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2595, pruned_loss=0.0287, over 1417884.64 frames.], batch size: 19, lr: 2.05e-04
2022-04-30 22:20:12,874 INFO [train.py:763] (1/8) Epoch 37, batch 1000, loss[loss=0.1719, simple_loss=0.2688, pruned_loss=0.03748, over 7194.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2603, pruned_loss=0.02925, over 1420021.91 frames.], batch size: 22, lr: 2.05e-04
2022-04-30 22:21:18,157 INFO [train.py:763] (1/8) Epoch 37, batch 1050, loss[loss=0.1692, simple_loss=0.2756, pruned_loss=0.03138, over 7325.00 frames.], tot_loss[loss=0.159, simple_loss=0.2599, pruned_loss=0.02901, over 1421054.64 frames.], batch size: 20, lr: 2.05e-04
2022-04-30 22:22:25,732 INFO [train.py:763] (1/8) Epoch 37, batch 1100, loss[loss=0.141, simple_loss=0.2405, pruned_loss=0.02072, over 6810.00 frames.], tot_loss[loss=0.1591, simple_loss=0.26, pruned_loss=0.0291, over 1423952.37 frames.], batch size: 15, lr: 2.05e-04
2022-04-30 22:23:31,641 INFO [train.py:763] (1/8) Epoch 37, batch 1150, loss[loss=0.1502, simple_loss=0.2412, pruned_loss=0.02958, over 7287.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2599, pruned_loss=0.02868, over 1420528.90 frames.], batch size: 18, lr: 2.05e-04
2022-04-30 22:24:36,966 INFO [train.py:763] (1/8) Epoch 37, batch 1200, loss[loss=0.1588, simple_loss=0.2577, pruned_loss=0.0299, over 7157.00 frames.], tot_loss[loss=0.1595, simple_loss=0.2609, pruned_loss=0.02903, over 1422308.30 frames.], batch size: 26, lr: 2.05e-04
2022-04-30 22:25:43,864 INFO [train.py:763] (1/8) Epoch 37, batch 1250, loss[loss=0.1499, simple_loss=0.2617, pruned_loss=0.01904, over 6466.00 frames.], tot_loss[loss=0.1597, simple_loss=0.2608, pruned_loss=0.02927, over 1426514.75 frames.], batch size: 38, lr: 2.05e-04
2022-04-30 22:26:50,670 INFO [train.py:763] (1/8) Epoch 37, batch 1300, loss[loss=0.1498, simple_loss=0.2377, pruned_loss=0.03093, over 7292.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2608, pruned_loss=0.02953, over 1425608.96 frames.], batch size: 17, lr: 2.05e-04
2022-04-30 22:27:56,059 INFO [train.py:763] (1/8) Epoch 37, batch 1350, loss[loss=0.1649, simple_loss=0.2687, pruned_loss=0.03056, over 7124.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2591, pruned_loss=0.02917, over 1419936.57 frames.], batch size: 21, lr: 2.05e-04
2022-04-30 22:29:02,056 INFO [train.py:763] (1/8) Epoch 37, batch 1400, loss[loss=0.1676, simple_loss=0.2756, pruned_loss=0.02982, over 7282.00 frames.], tot_loss[loss=0.1588, simple_loss=0.259, pruned_loss=0.02927, over 1420302.85 frames.], batch size: 24, lr: 2.05e-04
2022-04-30 22:30:07,327 INFO [train.py:763] (1/8) Epoch 37, batch 1450, loss[loss=0.1705, simple_loss=0.2653, pruned_loss=0.03782, over 7211.00 frames.], tot_loss[loss=0.159, simple_loss=0.2593, pruned_loss=0.02932, over 1425275.58 frames.], batch size: 22, lr: 2.05e-04
2022-04-30 22:31:13,181 INFO [train.py:763] (1/8) Epoch 37, batch 1500, loss[loss=0.1636, simple_loss=0.2673, pruned_loss=0.02997, over 7299.00 frames.], tot_loss[loss=0.159, simple_loss=0.2593, pruned_loss=0.02935, over 1426005.96 frames.], batch size: 25, lr: 2.05e-04
2022-04-30 22:32:18,520 INFO [train.py:763] (1/8) Epoch 37, batch 1550, loss[loss=0.1693, simple_loss=0.2672, pruned_loss=0.03566, over 7251.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2595, pruned_loss=0.02962, over 1423060.13 frames.], batch size: 20, lr: 2.05e-04
2022-04-30 22:33:23,882 INFO [train.py:763] (1/8) Epoch 37, batch 1600, loss[loss=0.1424, simple_loss=0.2386, pruned_loss=0.02312, over 7262.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2596, pruned_loss=0.02939, over 1425547.22 frames.], batch size: 19, lr: 2.05e-04
2022-04-30 22:34:29,218 INFO [train.py:763] (1/8) Epoch 37, batch 1650, loss[loss=0.1868, simple_loss=0.2886, pruned_loss=0.0425, over 7045.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2595, pruned_loss=0.02943, over 1425266.55 frames.], batch size: 28, lr: 2.05e-04
2022-04-30 22:35:34,591 INFO [train.py:763] (1/8) Epoch 37, batch 1700, loss[loss=0.1399, simple_loss=0.2423, pruned_loss=0.01877, over 7152.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2583, pruned_loss=0.02895, over 1424016.91 frames.], batch size: 18, lr: 2.05e-04
2022-04-30 22:36:40,244 INFO [train.py:763] (1/8) Epoch 37, batch 1750, loss[loss=0.1779, simple_loss=0.2753, pruned_loss=0.04024, over 5015.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2583, pruned_loss=0.02892, over 1422060.74 frames.], batch size: 52, lr: 2.05e-04
2022-04-30 22:37:45,568 INFO [train.py:763] (1/8) Epoch 37, batch 1800, loss[loss=0.1558, simple_loss=0.2541, pruned_loss=0.02879, over 7330.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2573, pruned_loss=0.02862, over 1419961.90 frames.], batch size: 20, lr: 2.05e-04
2022-04-30 22:38:50,828 INFO [train.py:763] (1/8) Epoch 37, batch 1850, loss[loss=0.1418, simple_loss=0.2371, pruned_loss=0.02328, over 7292.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2582, pruned_loss=0.02917, over 1422337.11 frames.], batch size: 18, lr: 2.05e-04
2022-04-30 22:39:57,179 INFO [train.py:763] (1/8) Epoch 37, batch 1900, loss[loss=0.1471, simple_loss=0.2434, pruned_loss=0.02542, over 7194.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2588, pruned_loss=0.02937, over 1425228.60 frames.], batch size: 16, lr: 2.05e-04
2022-04-30 22:41:04,597 INFO [train.py:763] (1/8) Epoch 37, batch 1950, loss[loss=0.1514, simple_loss=0.2457, pruned_loss=0.02861, over 7259.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2591, pruned_loss=0.02918, over 1427536.77 frames.], batch size: 19, lr: 2.05e-04
2022-04-30 22:42:12,273 INFO [train.py:763] (1/8) Epoch 37, batch 2000, loss[loss=0.1455, simple_loss=0.2372, pruned_loss=0.02688, over 7405.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2596, pruned_loss=0.02956, over 1426248.09 frames.], batch size: 18, lr: 2.05e-04
2022-04-30 22:43:17,400 INFO [train.py:763] (1/8) Epoch 37, batch 2050, loss[loss=0.1617, simple_loss=0.2579, pruned_loss=0.03274, over 7254.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2605, pruned_loss=0.02996, over 1423318.06 frames.], batch size: 19, lr: 2.05e-04
2022-04-30 22:44:22,373 INFO [train.py:763] (1/8) Epoch 37, batch 2100, loss[loss=0.171, simple_loss=0.2691, pruned_loss=0.03644, over 7143.00 frames.], tot_loss[loss=0.16, simple_loss=0.2603, pruned_loss=0.02979, over 1417978.57 frames.], batch size: 26, lr: 2.05e-04
2022-04-30 22:45:27,586 INFO [train.py:763] (1/8) Epoch 37, batch 2150, loss[loss=0.1797, simple_loss=0.2713, pruned_loss=0.04401, over 7072.00 frames.], tot_loss[loss=0.1598, simple_loss=0.26, pruned_loss=0.02976, over 1417420.57 frames.], batch size: 18, lr: 2.05e-04
2022-04-30 22:46:32,466 INFO [train.py:763] (1/8) Epoch 37, batch 2200, loss[loss=0.1568, simple_loss=0.2557, pruned_loss=0.02897, over 7065.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2606, pruned_loss=0.02954, over 1418644.43 frames.], batch size: 18, lr: 2.05e-04
2022-04-30 22:47:37,561 INFO [train.py:763] (1/8) Epoch 37, batch 2250, loss[loss=0.1786, simple_loss=0.2799, pruned_loss=0.03867, over 6449.00 frames.], tot_loss[loss=0.1602, simple_loss=0.2609, pruned_loss=0.02974, over 1417656.84 frames.], batch size: 38, lr: 2.05e-04
2022-04-30 22:48:44,664 INFO [train.py:763] (1/8) Epoch 37, batch 2300, loss[loss=0.168, simple_loss=0.2736, pruned_loss=0.03122, over 7074.00 frames.], tot_loss[loss=0.1607, simple_loss=0.2615, pruned_loss=0.02997, over 1422025.81 frames.], batch size: 18, lr: 2.05e-04
2022-04-30 22:49:50,007 INFO [train.py:763] (1/8) Epoch 37, batch 2350, loss[loss=0.1548, simple_loss=0.2542, pruned_loss=0.0277, over 7332.00 frames.], tot_loss[loss=0.1603, simple_loss=0.261, pruned_loss=0.02981, over 1419910.47 frames.], batch size: 20, lr: 2.05e-04
2022-04-30 22:50:55,500 INFO [train.py:763] (1/8) Epoch 37, batch 2400, loss[loss=0.1413, simple_loss=0.235, pruned_loss=0.02386, over 7395.00 frames.], tot_loss[loss=0.159, simple_loss=0.2595, pruned_loss=0.02926, over 1424882.04 frames.], batch size: 18, lr: 2.05e-04
2022-04-30 22:52:02,204 INFO [train.py:763] (1/8) Epoch 37, batch 2450, loss[loss=0.1524, simple_loss=0.248, pruned_loss=0.02837, over 7327.00 frames.], tot_loss[loss=0.159, simple_loss=0.2596, pruned_loss=0.02919, over 1426340.73 frames.], batch size: 20, lr: 2.04e-04
2022-04-30 22:53:07,499 INFO [train.py:763] (1/8) Epoch 37, batch 2500, loss[loss=0.1712, simple_loss=0.2682, pruned_loss=0.03714, over 7169.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2601, pruned_loss=0.02982, over 1426754.78 frames.], batch size: 18, lr: 2.04e-04
2022-04-30 22:54:13,275 INFO [train.py:763] (1/8) Epoch 37, batch 2550, loss[loss=0.1613, simple_loss=0.2587, pruned_loss=0.03193, over 7172.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2597, pruned_loss=0.02953, over 1424166.70 frames.], batch size: 18, lr: 2.04e-04
2022-04-30 22:55:19,537 INFO [train.py:763] (1/8) Epoch 37, batch 2600, loss[loss=0.1591, simple_loss=0.2593, pruned_loss=0.02948, over 7432.00 frames.], tot_loss[loss=0.159, simple_loss=0.259, pruned_loss=0.02949, over 1423285.19 frames.], batch size: 20, lr: 2.04e-04
2022-04-30 22:56:24,704 INFO [train.py:763] (1/8) Epoch 37, batch 2650, loss[loss=0.1782, simple_loss=0.2752, pruned_loss=0.04056, over 7197.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2593, pruned_loss=0.02993, over 1424525.43 frames.], batch size: 23, lr: 2.04e-04
2022-04-30 22:57:30,426 INFO [train.py:763] (1/8) Epoch 37, batch 2700, loss[loss=0.1576, simple_loss=0.2631, pruned_loss=0.02602, over 7242.00 frames.], tot_loss[loss=0.159, simple_loss=0.2587, pruned_loss=0.02967, over 1424059.23 frames.], batch size: 20, lr: 2.04e-04
2022-04-30 22:58:35,699 INFO [train.py:763] (1/8) Epoch 37, batch 2750, loss[loss=0.1515, simple_loss=0.2439, pruned_loss=0.02957, over 7367.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2587, pruned_loss=0.02945, over 1424881.22 frames.], batch size: 19, lr: 2.04e-04
2022-04-30 22:59:42,057 INFO [train.py:763] (1/8) Epoch 37, batch 2800, loss[loss=0.1646, simple_loss=0.2631, pruned_loss=0.0331, over 7292.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2587, pruned_loss=0.02946, over 1422538.65 frames.], batch size: 24, lr: 2.04e-04
2022-04-30 23:00:49,137 INFO [train.py:763] (1/8) Epoch 37, batch 2850, loss[loss=0.1648, simple_loss=0.2659, pruned_loss=0.03191, over 7421.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2586, pruned_loss=0.02948, over 1423078.33 frames.], batch size: 21, lr: 2.04e-04
2022-04-30 23:01:56,134 INFO [train.py:763] (1/8) Epoch 37, batch 2900, loss[loss=0.1393, simple_loss=0.2321, pruned_loss=0.02325, over 7148.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2582, pruned_loss=0.0293, over 1423484.50 frames.], batch size: 17, lr: 2.04e-04
2022-04-30 23:03:03,258 INFO [train.py:763] (1/8) Epoch 37, batch 2950, loss[loss=0.1443, simple_loss=0.2285, pruned_loss=0.03007, over 7413.00 frames.], tot_loss[loss=0.158, simple_loss=0.2579, pruned_loss=0.02907, over 1428196.08 frames.], batch size: 18, lr: 2.04e-04
2022-04-30 23:04:10,177 INFO [train.py:763] (1/8) Epoch 37, batch 3000, loss[loss=0.1593, simple_loss=0.2675, pruned_loss=0.02554, over 7201.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2576, pruned_loss=0.02898, over 1427259.48 frames.], batch size: 23, lr: 2.04e-04
2022-04-30 23:04:10,178 INFO [train.py:783] (1/8) Computing validation loss
2022-04-30 23:04:25,432 INFO [train.py:792] (1/8) Epoch 37, validation: loss=0.1692, simple_loss=0.2632, pruned_loss=0.03757, over 698248.00 frames.
2022-04-30 23:05:32,514 INFO [train.py:763] (1/8) Epoch 37, batch 3050, loss[loss=0.1402, simple_loss=0.2371, pruned_loss=0.02168, over 7175.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2579, pruned_loss=0.02894, over 1428093.49 frames.], batch size: 18, lr: 2.04e-04
2022-04-30 23:06:38,292 INFO [train.py:763] (1/8) Epoch 37, batch 3100, loss[loss=0.1764, simple_loss=0.2775, pruned_loss=0.03764, over 7198.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2585, pruned_loss=0.02904, over 1420240.73 frames.], batch size: 22, lr: 2.04e-04
2022-04-30 23:07:53,027 INFO [train.py:763] (1/8) Epoch 37, batch 3150, loss[loss=0.1613, simple_loss=0.2642, pruned_loss=0.02914, over 7373.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2586, pruned_loss=0.02936, over 1419385.66 frames.], batch size: 23, lr: 2.04e-04
2022-04-30 23:08:58,937 INFO [train.py:763] (1/8) Epoch 37, batch 3200, loss[loss=0.1639, simple_loss=0.2747, pruned_loss=0.02658, over 7120.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2587, pruned_loss=0.02924, over 1424058.45 frames.], batch size: 21, lr: 2.04e-04
2022-04-30 23:10:06,287 INFO [train.py:763] (1/8) Epoch 37, batch 3250, loss[loss=0.1621, simple_loss=0.2553, pruned_loss=0.03449, over 7265.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2587, pruned_loss=0.02929, over 1425582.94 frames.], batch size: 18, lr: 2.04e-04
2022-04-30 23:11:13,122 INFO [train.py:763] (1/8) Epoch 37, batch 3300, loss[loss=0.1763, simple_loss=0.2822, pruned_loss=0.03524, over 7234.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2585, pruned_loss=0.02922, over 1424749.50 frames.], batch size: 20, lr: 2.04e-04
2022-04-30 23:12:18,273 INFO [train.py:763] (1/8) Epoch 37, batch 3350, loss[loss=0.1835, simple_loss=0.2805, pruned_loss=0.0433, over 7205.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2595, pruned_loss=0.0293, over 1425612.72 frames.], batch size: 22, lr: 2.04e-04
2022-04-30 23:13:23,565 INFO [train.py:763] (1/8) Epoch 37, batch 3400, loss[loss=0.1675, simple_loss=0.2705, pruned_loss=0.03222, over 6708.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2594, pruned_loss=0.02907, over 1429726.45 frames.], batch size: 31, lr: 2.04e-04
2022-04-30 23:14:28,970 INFO [train.py:763] (1/8) Epoch 37, batch 3450, loss[loss=0.1692, simple_loss=0.2689, pruned_loss=0.03476, over 7437.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2599, pruned_loss=0.02927, over 1430925.20 frames.], batch size: 20, lr: 2.04e-04
2022-04-30 23:15:34,296 INFO [train.py:763] (1/8) Epoch 37, batch 3500, loss[loss=0.1653, simple_loss=0.2712, pruned_loss=0.02964, over 7238.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2595, pruned_loss=0.02935, over 1429283.21 frames.], batch size: 20, lr: 2.04e-04
2022-04-30 23:16:39,546 INFO [train.py:763] (1/8) Epoch 37, batch 3550, loss[loss=0.1756, simple_loss=0.2763, pruned_loss=0.03747, over 7154.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2607, pruned_loss=0.02929, over 1430053.81 frames.], batch size: 20, lr: 2.04e-04
2022-04-30 23:17:44,639 INFO [train.py:763] (1/8) Epoch 37, batch 3600, loss[loss=0.169, simple_loss=0.2699, pruned_loss=0.03402, over 6730.00 frames.], tot_loss[loss=0.1596, simple_loss=0.2606, pruned_loss=0.02934, over 1428012.31 frames.], batch size: 31, lr: 2.04e-04
2022-04-30 23:18:50,217 INFO [train.py:763] (1/8) Epoch 37, batch 3650, loss[loss=0.157, simple_loss=0.2617, pruned_loss=0.02613, over 7080.00 frames.], tot_loss[loss=0.1594, simple_loss=0.2603, pruned_loss=0.02928, over 1430966.68 frames.], batch size: 28, lr: 2.04e-04
2022-04-30 23:19:55,912 INFO [train.py:763] (1/8) Epoch 37, batch 3700, loss[loss=0.179, simple_loss=0.2789, pruned_loss=0.03953, over 7282.00 frames.], tot_loss[loss=0.1599, simple_loss=0.2603, pruned_loss=0.02975, over 1423429.86 frames.], batch size: 24, lr: 2.04e-04
2022-04-30 23:21:00,946 INFO [train.py:763] (1/8) Epoch 37, batch 3750, loss[loss=0.1606, simple_loss=0.267, pruned_loss=0.02703, over 7167.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2603, pruned_loss=0.02967, over 1418771.59 frames.], batch size: 19, lr: 2.04e-04
2022-04-30 23:22:07,061 INFO [train.py:763] (1/8) Epoch 37, batch 3800, loss[loss=0.173, simple_loss=0.2674, pruned_loss=0.03935, over 7358.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2592, pruned_loss=0.02923, over 1418667.89 frames.], batch size: 23, lr: 2.04e-04
2022-04-30 23:23:12,317 INFO [train.py:763] (1/8) Epoch 37, batch 3850, loss[loss=0.1672, simple_loss=0.2751, pruned_loss=0.02959, over 7094.00 frames.], tot_loss[loss=0.1586, simple_loss=0.259, pruned_loss=0.02912, over 1420585.46 frames.], batch size: 21, lr: 2.04e-04
2022-04-30 23:24:18,020 INFO [train.py:763] (1/8) Epoch 37, batch 3900, loss[loss=0.1575, simple_loss=0.259, pruned_loss=0.02804, over 7321.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2589, pruned_loss=0.02938, over 1422107.79 frames.], batch size: 20, lr: 2.04e-04
2022-04-30 23:25:32,658 INFO [train.py:763] (1/8) Epoch 37, batch 3950, loss[loss=0.1658, simple_loss=0.2595, pruned_loss=0.03608, over 7206.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2585, pruned_loss=0.02923, over 1416851.44 frames.], batch size: 22, lr: 2.04e-04
2022-04-30 23:26:37,876 INFO [train.py:763] (1/8) Epoch 37, batch 4000, loss[loss=0.15, simple_loss=0.2549, pruned_loss=0.02259, over 7155.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2587, pruned_loss=0.02922, over 1417608.96 frames.], batch size: 19, lr: 2.04e-04
2022-04-30 23:28:01,970 INFO [train.py:763] (1/8) Epoch 37, batch 4050, loss[loss=0.1421, simple_loss=0.2392, pruned_loss=0.02251, over 7277.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2581, pruned_loss=0.02899, over 1411577.50 frames.], batch size: 17, lr: 2.04e-04
2022-04-30 23:29:07,114 INFO [train.py:763] (1/8) Epoch 37, batch 4100, loss[loss=0.1642, simple_loss=0.2677, pruned_loss=0.03038, over 7216.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2586, pruned_loss=0.02902, over 1413505.36 frames.], batch size: 21, lr: 2.04e-04
2022-04-30 23:30:21,706 INFO [train.py:763] (1/8) Epoch 37, batch 4150, loss[loss=0.162, simple_loss=0.2595, pruned_loss=0.0323, over 7254.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2576, pruned_loss=0.02898, over 1412658.43 frames.], batch size: 19, lr: 2.03e-04
2022-04-30 23:31:36,369 INFO [train.py:763] (1/8) Epoch 37, batch 4200, loss[loss=0.1579, simple_loss=0.2564, pruned_loss=0.0297, over 7315.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2574, pruned_loss=0.0289, over 1413747.03 frames.], batch size: 24, lr: 2.03e-04
2022-04-30 23:32:51,951 INFO [train.py:763] (1/8) Epoch 37, batch 4250, loss[loss=0.1468, simple_loss=0.257, pruned_loss=0.01826, over 7233.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2574, pruned_loss=0.02885, over 1415010.49 frames.], batch size: 20, lr: 2.03e-04
2022-04-30 23:33:58,668 INFO [train.py:763] (1/8) Epoch 37, batch 4300, loss[loss=0.1813, simple_loss=0.2823, pruned_loss=0.0401, over 4885.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2562, pruned_loss=0.02854, over 1412315.96 frames.], batch size: 53, lr: 2.03e-04
2022-04-30 23:35:04,834 INFO [train.py:763] (1/8) Epoch 37, batch 4350, loss[loss=0.1353, simple_loss=0.2344, pruned_loss=0.01808, over 7014.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2551, pruned_loss=0.02817, over 1415249.14 frames.], batch size: 16, lr: 2.03e-04
2022-04-30 23:36:10,337 INFO [train.py:763] (1/8) Epoch 37, batch 4400, loss[loss=0.1293, simple_loss=0.2292, pruned_loss=0.0147, over 6774.00 frames.], tot_loss[loss=0.1555, simple_loss=0.255, pruned_loss=0.02807, over 1415364.88 frames.], batch size: 15, lr: 2.03e-04
2022-04-30 23:37:17,176 INFO [train.py:763] (1/8) Epoch 37, batch 4450, loss[loss=0.1591, simple_loss=0.2479, pruned_loss=0.03521, over 6784.00 frames.], tot_loss[loss=0.1557, simple_loss=0.255, pruned_loss=0.02822, over 1407164.86 frames.], batch size: 15, lr: 2.03e-04
2022-04-30 23:38:22,785 INFO [train.py:763] (1/8) Epoch 37, batch 4500, loss[loss=0.1628, simple_loss=0.266, pruned_loss=0.02981, over 6498.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2554, pruned_loss=0.02862, over 1383528.12 frames.], batch size: 38, lr: 2.03e-04
2022-04-30 23:39:28,656 INFO [train.py:763] (1/8) Epoch 37, batch 4550, loss[loss=0.1835, simple_loss=0.2862, pruned_loss=0.04047, over 5399.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2545, pruned_loss=0.02868, over 1357995.57 frames.], batch size: 52, lr: 2.03e-04
2022-04-30 23:40:56,599 INFO [train.py:763] (1/8) Epoch 38, batch 0, loss[loss=0.1712, simple_loss=0.2804, pruned_loss=0.031, over 7250.00 frames.], tot_loss[loss=0.1712, simple_loss=0.2804, pruned_loss=0.031, over 7250.00 frames.], batch size: 19, lr: 2.01e-04
2022-04-30 23:42:03,204 INFO [train.py:763] (1/8) Epoch 38, batch 50, loss[loss=0.1755, simple_loss=0.28, pruned_loss=0.03549, over 7144.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2603, pruned_loss=0.02855, over 320300.00 frames.], batch size: 20, lr: 2.01e-04
2022-04-30 23:43:10,049 INFO [train.py:763] (1/8) Epoch 38, batch 100, loss[loss=0.1587, simple_loss=0.2732, pruned_loss=0.02209, over 6798.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2601, pruned_loss=0.02862, over 565997.76 frames.], batch size: 31, lr: 2.01e-04
2022-04-30 23:44:16,790 INFO [train.py:763] (1/8) Epoch 38, batch 150, loss[loss=0.1579, simple_loss=0.2558, pruned_loss=0.03002, over 7167.00 frames.], tot_loss[loss=0.1576, simple_loss=0.258, pruned_loss=0.02862, over 755262.21 frames.], batch size: 18, lr: 2.01e-04
2022-04-30 23:45:22,772 INFO [train.py:763] (1/8) Epoch 38, batch 200, loss[loss=0.1626, simple_loss=0.2595, pruned_loss=0.03289, over 7429.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2595, pruned_loss=0.02937, over 901333.06 frames.], batch size: 20, lr: 2.00e-04
2022-04-30 23:46:29,135 INFO [train.py:763] (1/8) Epoch 38, batch 250, loss[loss=0.1808, simple_loss=0.2812, pruned_loss=0.0402, over 6281.00 frames.], tot_loss[loss=0.1598, simple_loss=0.2603, pruned_loss=0.02962, over 1017337.20 frames.], batch size: 37, lr: 2.00e-04
2022-04-30 23:47:35,364 INFO [train.py:763] (1/8) Epoch 38, batch 300, loss[loss=0.169, simple_loss=0.2656, pruned_loss=0.03626, over 7442.00 frames.], tot_loss[loss=0.1591, simple_loss=0.2596, pruned_loss=0.02932, over 1112260.68 frames.], batch size: 20, lr: 2.00e-04
2022-04-30 23:48:41,445 INFO [train.py:763] (1/8) Epoch 38, batch 350, loss[loss=0.176, simple_loss=0.2681, pruned_loss=0.04197, over 7291.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2583, pruned_loss=0.02906, over 1178936.62 frames.], batch size: 24, lr: 2.00e-04
2022-04-30 23:49:47,444 INFO [train.py:763] (1/8) Epoch 38, batch 400, loss[loss=0.1417, simple_loss=0.2417, pruned_loss=0.02087, over 7220.00 frames.], tot_loss[loss=0.158, simple_loss=0.2581, pruned_loss=0.0289, over 1228315.96 frames.], batch size: 21, lr: 2.00e-04
2022-04-30 23:50:53,870 INFO [train.py:763] (1/8) Epoch 38, batch 450, loss[loss=0.1695, simple_loss=0.2728, pruned_loss=0.03307, over 7194.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2577, pruned_loss=0.02872, over 1274112.84 frames.], batch size: 23, lr: 2.00e-04
2022-04-30 23:52:00,161 INFO [train.py:763] (1/8) Epoch 38, batch 500, loss[loss=0.161, simple_loss=0.2626, pruned_loss=0.02969, over 7137.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2575, pruned_loss=0.02878, over 1301555.97 frames.], batch size: 20, lr: 2.00e-04
2022-04-30 23:53:06,412 INFO [train.py:763] (1/8) Epoch 38, batch 550, loss[loss=0.1615, simple_loss=0.2664, pruned_loss=0.02828, over 7421.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2572, pruned_loss=0.02847, over 1326801.39 frames.], batch size: 20, lr: 2.00e-04
2022-04-30 23:54:12,145 INFO [train.py:763] (1/8) Epoch 38, batch 600, loss[loss=0.1587, simple_loss=0.2541, pruned_loss=0.0317, over 7159.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2582, pruned_loss=0.02862, over 1345439.86 frames.], batch size: 18, lr: 2.00e-04
2022-04-30 23:55:17,886 INFO [train.py:763] (1/8) Epoch 38, batch 650, loss[loss=0.1317, simple_loss=0.2322, pruned_loss=0.01559, over 7296.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2581, pruned_loss=0.02855, over 1364978.89 frames.], batch size: 17, lr: 2.00e-04
2022-04-30 23:56:23,408 INFO [train.py:763] (1/8) Epoch 38, batch 700, loss[loss=0.1687, simple_loss=0.2526, pruned_loss=0.04235, over 6828.00 frames.], tot_loss[loss=0.157, simple_loss=0.2573, pruned_loss=0.02838, over 1377144.17 frames.], batch size: 15, lr: 2.00e-04
2022-04-30 23:57:28,962 INFO [train.py:763] (1/8) Epoch 38, batch 750, loss[loss=0.1434, simple_loss=0.2499, pruned_loss=0.01844, over 6570.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2564, pruned_loss=0.02772, over 1386078.87 frames.], batch size: 38, lr: 2.00e-04
2022-04-30 23:58:35,117 INFO [train.py:763] (1/8) Epoch 38, batch 800, loss[loss=0.167, simple_loss=0.2622, pruned_loss=0.03592, over 7237.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2566, pruned_loss=0.02794, over 1398910.79 frames.], batch size: 20, lr: 2.00e-04
2022-04-30 23:59:41,182 INFO [train.py:763] (1/8) Epoch 38, batch 850, loss[loss=0.1643, simple_loss=0.2687, pruned_loss=0.02994, over 7090.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2565, pruned_loss=0.02763, over 1405013.90 frames.], batch size: 28, lr: 2.00e-04
2022-05-01 00:00:47,047 INFO [train.py:763] (1/8) Epoch 38, batch 900, loss[loss=0.1639, simple_loss=0.2704, pruned_loss=0.02867, over 7414.00 frames.], tot_loss[loss=0.1564, simple_loss=0.257, pruned_loss=0.02785, over 1403930.44 frames.], batch size: 21, lr: 2.00e-04
2022-05-01 00:01:52,974 INFO [train.py:763] (1/8) Epoch 38, batch 950, loss[loss=0.1384, simple_loss=0.2312, pruned_loss=0.02283, over 7126.00 frames.], tot_loss[loss=0.1576, simple_loss=0.258, pruned_loss=0.02855, over 1404713.54 frames.], batch size: 17, lr: 2.00e-04
2022-05-01 00:02:58,568 INFO [train.py:763] (1/8) Epoch 38, batch 1000, loss[loss=0.1414, simple_loss=0.2462, pruned_loss=0.01825, over 7360.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2579, pruned_loss=0.02847, over 1408198.30 frames.], batch size: 19, lr: 2.00e-04
2022-05-01 00:04:03,994 INFO [train.py:763] (1/8) Epoch 38, batch 1050, loss[loss=0.1737, simple_loss=0.272, pruned_loss=0.03771, over 6857.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2573, pruned_loss=0.02859, over 1410659.55 frames.], batch size: 31, lr: 2.00e-04
2022-05-01 00:05:09,983 INFO [train.py:763] (1/8) Epoch 38, batch 1100, loss[loss=0.1766, simple_loss=0.2812, pruned_loss=0.03606, over 7381.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2569, pruned_loss=0.02834, over 1414886.31 frames.], batch size: 23, lr: 2.00e-04
2022-05-01 00:06:15,677 INFO [train.py:763] (1/8) Epoch 38, batch 1150, loss[loss=0.1386, simple_loss=0.2339, pruned_loss=0.02165, over 7279.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2562, pruned_loss=0.02826, over 1418447.85 frames.], batch size: 18, lr: 2.00e-04
2022-05-01 00:07:21,224 INFO [train.py:763] (1/8) Epoch 38, batch 1200, loss[loss=0.1605, simple_loss=0.258, pruned_loss=0.03144, over 6740.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2565, pruned_loss=0.02831, over 1420016.38 frames.], batch size: 31, lr: 2.00e-04
2022-05-01 00:08:27,091 INFO [train.py:763] (1/8) Epoch 38, batch 1250, loss[loss=0.1481, simple_loss=0.2415, pruned_loss=0.02735, over 7423.00 frames.], tot_loss[loss=0.1565, simple_loss=0.2565, pruned_loss=0.02824, over 1421420.47 frames.], batch size: 20, lr: 2.00e-04
2022-05-01 00:09:34,184 INFO [train.py:763] (1/8) Epoch 38, batch 1300, loss[loss=0.1436, simple_loss=0.2299, pruned_loss=0.02864, over 7269.00 frames.], tot_loss[loss=0.1557, simple_loss=0.2556, pruned_loss=0.02795, over 1425048.58 frames.], batch size: 17, lr: 2.00e-04
2022-05-01 00:10:39,850 INFO [train.py:763] (1/8) Epoch 38, batch 1350, loss[loss=0.1519, simple_loss=0.2568, pruned_loss=0.02347, over 7315.00 frames.], tot_loss[loss=0.1566, simple_loss=0.2568, pruned_loss=0.02818, over 1425654.80 frames.], batch size: 20, lr: 2.00e-04
2022-05-01 00:11:45,175 INFO [train.py:763] (1/8) Epoch 38, batch 1400, loss[loss=0.1485, simple_loss=0.2446, pruned_loss=0.02623, over 7162.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2571, pruned_loss=0.02838, over 1426006.74 frames.], batch size: 19, lr: 2.00e-04
2022-05-01 00:12:50,401 INFO [train.py:763] (1/8) Epoch 38, batch 1450, loss[loss=0.2057, simple_loss=0.2945, pruned_loss=0.05838, over 7296.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2589, pruned_loss=0.02883, over 1426053.32 frames.], batch size: 25, lr: 2.00e-04
2022-05-01 00:13:55,955 INFO [train.py:763] (1/8) Epoch 38, batch 1500, loss[loss=0.1769, simple_loss=0.2778, pruned_loss=0.03795, over 7126.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2595, pruned_loss=0.02897, over 1424397.25 frames.], batch size: 21, lr: 2.00e-04
2022-05-01 00:15:03,016 INFO [train.py:763] (1/8) Epoch 38, batch 1550, loss[loss=0.1575, simple_loss=0.2653, pruned_loss=0.02484, over 7209.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2583, pruned_loss=0.02873, over 1423891.81 frames.], batch size: 22, lr: 2.00e-04
2022-05-01 00:16:09,258 INFO [train.py:763] (1/8) Epoch 38, batch 1600, loss[loss=0.1651, simple_loss=0.2673, pruned_loss=0.03139, over 6772.00 frames.], tot_loss[loss=0.158, simple_loss=0.2582, pruned_loss=0.02884, over 1426358.37 frames.], batch size: 31, lr: 2.00e-04
2022-05-01 00:17:15,070 INFO [train.py:763] (1/8) Epoch 38, batch 1650, loss[loss=0.1633, simple_loss=0.2705, pruned_loss=0.028, over 7227.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2581, pruned_loss=0.02879, over 1425318.89 frames.], batch size: 21, lr: 2.00e-04
2022-05-01 00:18:31,382 INFO [train.py:763] (1/8) Epoch 38, batch 1700, loss[loss=0.1734, simple_loss=0.272, pruned_loss=0.03737, over 7070.00 frames.], tot_loss[loss=0.158, simple_loss=0.2588, pruned_loss=0.02863, over 1426815.65 frames.], batch size: 28, lr: 2.00e-04
2022-05-01 00:19:36,538 INFO [train.py:763] (1/8) Epoch 38, batch 1750, loss[loss=0.156, simple_loss=0.2624, pruned_loss=0.0248, over 7437.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2594, pruned_loss=0.02861, over 1426147.78 frames.], batch size: 20, lr: 2.00e-04
2022-05-01 00:20:42,262 INFO [train.py:763] (1/8) Epoch 38, batch 1800, loss[loss=0.158, simple_loss=0.2586, pruned_loss=0.02874, over 7194.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2596, pruned_loss=0.02871, over 1424044.09 frames.], batch size: 23, lr: 2.00e-04
2022-05-01 00:21:47,719 INFO [train.py:763] (1/8) Epoch 38, batch 1850, loss[loss=0.1395, simple_loss=0.241, pruned_loss=0.01904, over 7144.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2588, pruned_loss=0.02873, over 1421652.44 frames.], batch size: 19, lr: 2.00e-04
2022-05-01 00:22:54,645 INFO [train.py:763] (1/8) Epoch 38, batch 1900, loss[loss=0.1296, simple_loss=0.2223, pruned_loss=0.01846, over 7280.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2586, pruned_loss=0.02922, over 1425033.49 frames.], batch size: 18, lr: 2.00e-04
2022-05-01 00:24:00,340 INFO [train.py:763] (1/8) Epoch 38, batch 1950, loss[loss=0.1708, simple_loss=0.2749, pruned_loss=0.03334, over 7325.00 frames.], tot_loss[loss=0.1587, simple_loss=0.259, pruned_loss=0.02919, over 1425948.96 frames.], batch size: 21, lr: 1.99e-04
2022-05-01 00:25:06,438 INFO [train.py:763] (1/8) Epoch 38, batch 2000, loss[loss=0.1277, simple_loss=0.2327, pruned_loss=0.01136, over 7260.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2588, pruned_loss=0.02888, over 1424479.84 frames.], batch size: 19, lr: 1.99e-04
2022-05-01 00:26:13,053 INFO [train.py:763] (1/8) Epoch 38, batch 2050, loss[loss=0.1708, simple_loss=0.2643, pruned_loss=0.03863, over 7323.00 frames.], tot_loss[loss=0.1586, simple_loss=0.259, pruned_loss=0.02909, over 1422059.23 frames.], batch size: 20, lr: 1.99e-04
2022-05-01 00:27:18,298 INFO [train.py:763] (1/8) Epoch 38, batch 2100, loss[loss=0.1418, simple_loss=0.2347, pruned_loss=0.02441, over 7246.00 frames.], tot_loss[loss=0.1584, simple_loss=0.259, pruned_loss=0.02885, over 1423019.39 frames.], batch size: 16, lr: 1.99e-04
2022-05-01 00:28:25,362 INFO [train.py:763] (1/8) Epoch 38, batch 2150, loss[loss=0.1586, simple_loss=0.2561, pruned_loss=0.03053, over 7265.00 frames.], tot_loss[loss=0.158, simple_loss=0.2587, pruned_loss=0.02867, over 1422057.45 frames.], batch size: 19, lr: 1.99e-04
2022-05-01 00:29:31,358 INFO [train.py:763] (1/8) Epoch 38, batch 2200, loss[loss=0.1735, simple_loss=0.2709, pruned_loss=0.03802, over 7208.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2594, pruned_loss=0.02882, over 1422361.52 frames.], batch size: 22, lr: 1.99e-04
2022-05-01 00:30:38,810 INFO [train.py:763] (1/8) Epoch 38, batch 2250, loss[loss=0.1708, simple_loss=0.2684, pruned_loss=0.03663, over 7151.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2584, pruned_loss=0.02857, over 1424589.20 frames.], batch size: 20, lr: 1.99e-04
2022-05-01 00:31:44,024 INFO [train.py:763] (1/8) Epoch 38, batch 2300, loss[loss=0.1512, simple_loss=0.2482, pruned_loss=0.02707, over 7155.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2587, pruned_loss=0.02874, over 1424264.79 frames.], batch size: 19, lr: 1.99e-04
2022-05-01 00:32:50,157 INFO [train.py:763] (1/8) Epoch 38, batch 2350, loss[loss=0.1747, simple_loss=0.2811, pruned_loss=0.03412, over 7241.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2581, pruned_loss=0.02885, over 1426066.08 frames.], batch size: 20, lr: 1.99e-04
2022-05-01 00:33:55,523 INFO [train.py:763] (1/8) Epoch 38, batch 2400, loss[loss=0.162, simple_loss=0.2697, pruned_loss=0.02714, over 7151.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2578, pruned_loss=0.02886, over 1428358.02 frames.], batch size: 20, lr: 1.99e-04
2022-05-01 00:35:01,018 INFO [train.py:763] (1/8) Epoch 38, batch 2450, loss[loss=0.1495, simple_loss=0.2403, pruned_loss=0.02933, over 7406.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2569, pruned_loss=0.02841, over 1429118.98 frames.], batch size: 18, lr: 1.99e-04
2022-05-01 00:36:06,993 INFO [train.py:763] (1/8) Epoch 38, batch 2500, loss[loss=0.1457, simple_loss=0.2405, pruned_loss=0.02543, over 7405.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2568, pruned_loss=0.02831, over 1427514.81 frames.], batch size: 18, lr: 1.99e-04
2022-05-01 00:37:12,684 INFO [train.py:763] (1/8) Epoch 38, batch 2550, loss[loss=0.1486, simple_loss=0.2517, pruned_loss=0.02278, over 7428.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2571, pruned_loss=0.02831, over 1432092.02 frames.], batch size: 20, lr: 1.99e-04
2022-05-01 00:38:18,035 INFO [train.py:763] (1/8) Epoch 38, batch 2600, loss[loss=0.1712, simple_loss=0.276, pruned_loss=0.03323, over 7217.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2577, pruned_loss=0.02842, over 1429586.51 frames.], batch size: 26, lr: 1.99e-04
2022-05-01 00:39:23,361 INFO [train.py:763] (1/8) Epoch 38, batch 2650, loss[loss=0.153, simple_loss=0.2555, pruned_loss=0.02524, over 7071.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2572, pruned_loss=0.02811, over 1430471.02 frames.], batch size: 28, lr: 1.99e-04
2022-05-01 00:40:27,515 INFO [train.py:763] (1/8) Epoch 38, batch 2700, loss[loss=0.1918, simple_loss=0.2897, pruned_loss=0.0469, over 7296.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2571, pruned_loss=0.02817, over 1428665.11 frames.], batch size: 25, lr: 1.99e-04
2022-05-01 00:41:33,241 INFO [train.py:763] (1/8) Epoch 38, batch 2750, loss[loss=0.1463, simple_loss=0.2493, pruned_loss=0.02165, over 7158.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2576, pruned_loss=0.02842, over 1429334.82 frames.], batch size: 19, lr: 1.99e-04
2022-05-01 00:42:38,765 INFO [train.py:763] (1/8) Epoch 38, batch 2800, loss[loss=0.1754, simple_loss=0.2734, pruned_loss=0.03872, over 7331.00 frames.], tot_loss[loss=0.157, simple_loss=0.2576, pruned_loss=0.02816, over 1426314.53 frames.], batch size: 22, lr: 1.99e-04
2022-05-01 00:43:44,126 INFO [train.py:763] (1/8) Epoch 38, batch 2850, loss[loss=0.151, simple_loss=0.2577, pruned_loss=0.02216, over 6310.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2577, pruned_loss=0.02845, over 1426632.20 frames.], batch size: 38, lr: 1.99e-04
2022-05-01 00:44:49,673 INFO [train.py:763] (1/8) Epoch 38, batch 2900, loss[loss=0.1491, simple_loss=0.2654, pruned_loss=0.01643, over 7320.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2578, pruned_loss=0.02817, over 1425152.32 frames.], batch size: 21, lr: 1.99e-04
2022-05-01 00:45:55,139 INFO [train.py:763] (1/8) Epoch 38, batch 2950, loss[loss=0.1695, simple_loss=0.2726, pruned_loss=0.03324, over 7338.00 frames.], tot_loss[loss=0.157, simple_loss=0.2576, pruned_loss=0.02818, over 1428468.06 frames.], batch size: 22, lr: 1.99e-04
2022-05-01 00:47:00,414 INFO [train.py:763] (1/8) Epoch 38, batch 3000, loss[loss=0.1826, simple_loss=0.2963, pruned_loss=0.0344, over 7236.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2589, pruned_loss=0.02843, over 1429457.43 frames.], batch size: 20, lr: 1.99e-04
2022-05-01 00:47:00,415 INFO [train.py:783] (1/8) Computing validation loss
2022-05-01 00:47:15,873 INFO [train.py:792] (1/8) Epoch 38, validation: loss=0.1707, simple_loss=0.2648, pruned_loss=0.03834, over 698248.00 frames.
2022-05-01 00:48:21,029 INFO [train.py:763] (1/8) Epoch 38, batch 3050, loss[loss=0.1509, simple_loss=0.2468, pruned_loss=0.02744, over 7117.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2588, pruned_loss=0.02846, over 1425607.67 frames.], batch size: 17, lr: 1.99e-04
2022-05-01 00:49:26,202 INFO [train.py:763] (1/8) Epoch 38, batch 3100, loss[loss=0.1752, simple_loss=0.2824, pruned_loss=0.03397, over 6517.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2591, pruned_loss=0.02873, over 1417745.29 frames.], batch size: 38, lr: 1.99e-04
2022-05-01 00:50:31,505 INFO [train.py:763] (1/8) Epoch 38, batch 3150, loss[loss=0.1698, simple_loss=0.2714, pruned_loss=0.03411, over 7408.00 frames.], tot_loss[loss=0.159, simple_loss=0.2601, pruned_loss=0.02898, over 1423644.21 frames.], batch size: 21, lr: 1.99e-04
2022-05-01 00:51:36,873 INFO [train.py:763] (1/8) Epoch 38, batch 3200, loss[loss=0.1818, simple_loss=0.2892, pruned_loss=0.03721, over 6582.00 frames.], tot_loss[loss=0.1589, simple_loss=0.26, pruned_loss=0.02889, over 1424779.89 frames.], batch size: 38, lr: 1.99e-04
2022-05-01 00:52:42,220 INFO [train.py:763] (1/8) Epoch 38, batch 3250, loss[loss=0.1493, simple_loss=0.2532, pruned_loss=0.02267, over 6486.00 frames.], tot_loss[loss=0.1586, simple_loss=0.2598, pruned_loss=0.02874, over 1424674.63 frames.], batch size: 38, lr: 1.99e-04
2022-05-01 00:53:47,529 INFO [train.py:763] (1/8) Epoch 38, batch 3300, loss[loss=0.1479, simple_loss=0.2483, pruned_loss=0.0237, over 7157.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2594, pruned_loss=0.02848, over 1424700.91 frames.], batch size: 19, lr: 1.99e-04
2022-05-01 00:54:52,912 INFO [train.py:763] (1/8) Epoch 38, batch 3350, loss[loss=0.1434, simple_loss=0.2407, pruned_loss=0.02305, over 7134.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2588, pruned_loss=0.02824, over 1425856.69 frames.], batch size: 17, lr: 1.99e-04
2022-05-01 00:55:59,020 INFO [train.py:763] (1/8) Epoch 38, batch 3400, loss[loss=0.1485, simple_loss=0.2461, pruned_loss=0.02544, over 7352.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2585, pruned_loss=0.02825, over 1426859.60 frames.], batch size: 19, lr: 1.99e-04
2022-05-01 00:57:06,506 INFO [train.py:763] (1/8) Epoch 38, batch 3450, loss[loss=0.1696, simple_loss=0.2762, pruned_loss=0.03148, over 7205.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2584, pruned_loss=0.02865, over 1419008.96 frames.], batch size: 23, lr: 1.99e-04
2022-05-01 00:58:13,607 INFO [train.py:763] (1/8) Epoch 38, batch 3500, loss[loss=0.1422, simple_loss=0.2388, pruned_loss=0.02285, over 7161.00 frames.], tot_loss[loss=0.1586, simple_loss=0.259, pruned_loss=0.02908, over 1420787.21 frames.], batch size: 19, lr: 1.99e-04
2022-05-01 00:59:19,212 INFO [train.py:763] (1/8) Epoch 38, batch 3550, loss[loss=0.1779, simple_loss=0.2899, pruned_loss=0.033, over 7329.00 frames.], tot_loss[loss=0.1589, simple_loss=0.2587, pruned_loss=0.02948, over 1423978.14 frames.], batch size: 22, lr: 1.99e-04
2022-05-01 01:00:25,363 INFO [train.py:763] (1/8) Epoch 38, batch 3600, loss[loss=0.133, simple_loss=0.2352, pruned_loss=0.01545, over 7276.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2585, pruned_loss=0.02919, over 1424819.98 frames.], batch size: 18, lr: 1.99e-04
2022-05-01 01:01:30,571 INFO [train.py:763] (1/8) Epoch 38, batch 3650, loss[loss=0.1564, simple_loss=0.2587, pruned_loss=0.02709, over 7039.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2589, pruned_loss=0.02922, over 1426108.81 frames.], batch size: 28, lr: 1.99e-04
2022-05-01 01:02:35,701 INFO [train.py:763] (1/8) Epoch 38, batch 3700, loss[loss=0.1653, simple_loss=0.2702, pruned_loss=0.03018, over 6237.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2589, pruned_loss=0.02893, over 1423008.64 frames.], batch size: 37, lr: 1.99e-04
2022-05-01 01:03:41,345 INFO [train.py:763] (1/8) Epoch 38, batch 3750, loss[loss=0.1693, simple_loss=0.2698, pruned_loss=0.03444, over 7186.00 frames.], tot_loss[loss=0.158, simple_loss=0.2582, pruned_loss=0.02891, over 1416472.92 frames.], batch size: 23, lr: 1.98e-04
2022-05-01 01:04:46,826 INFO [train.py:763] (1/8) Epoch 38, batch 3800, loss[loss=0.1632, simple_loss=0.2605, pruned_loss=0.03295, over 7358.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2583, pruned_loss=0.0287, over 1422543.78 frames.], batch size: 19, lr: 1.98e-04
2022-05-01 01:05:52,024 INFO [train.py:763] (1/8) Epoch 38, batch 3850, loss[loss=0.2447, simple_loss=0.3234, pruned_loss=0.083, over 4982.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2585, pruned_loss=0.02918, over 1419823.42 frames.], batch size: 52, lr: 1.98e-04
2022-05-01 01:06:57,232 INFO [train.py:763] (1/8) Epoch 38, batch 3900, loss[loss=0.1543, simple_loss=0.2541, pruned_loss=0.02721, over 7037.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2589, pruned_loss=0.02927, over 1419978.21 frames.], batch size: 28, lr: 1.98e-04
2022-05-01 01:08:02,831 INFO [train.py:763] (1/8) Epoch 38, batch 3950, loss[loss=0.1754, simple_loss=0.2826, pruned_loss=0.03406, over 7320.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2588, pruned_loss=0.02883, over 1422006.21 frames.], batch size: 25, lr: 1.98e-04
2022-05-01 01:09:08,084 INFO [train.py:763] (1/8) Epoch 38, batch 4000, loss[loss=0.1488, simple_loss=0.2453, pruned_loss=0.02618, over 6764.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2593, pruned_loss=0.02876, over 1423828.39 frames.], batch size: 31, lr: 1.98e-04
2022-05-01 01:10:13,451 INFO [train.py:763] (1/8) Epoch 38, batch 4050, loss[loss=0.1888, simple_loss=0.2931, pruned_loss=0.04228, over 6791.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2597, pruned_loss=0.02857, over 1422980.87 frames.], batch size: 31, lr: 1.98e-04
2022-05-01 01:11:18,887 INFO [train.py:763] (1/8) Epoch 38, batch 4100, loss[loss=0.1685, simple_loss=0.2766, pruned_loss=0.03024, over 7221.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2585, pruned_loss=0.02841, over 1421972.39 frames.], batch size: 21, lr: 1.98e-04
2022-05-01 01:12:24,227 INFO [train.py:763] (1/8) Epoch 38, batch 4150, loss[loss=0.1438, simple_loss=0.2462, pruned_loss=0.02069, over 7229.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2585, pruned_loss=0.02816, over 1420307.74 frames.], batch size: 21, lr: 1.98e-04
2022-05-01 01:13:30,534 INFO [train.py:763] (1/8) Epoch 38, batch 4200, loss[loss=0.148, simple_loss=0.2557, pruned_loss=0.02018, over 6687.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2583, pruned_loss=0.0281, over 1419981.75 frames.], batch size: 31, lr: 1.98e-04
2022-05-01 01:14:35,833 INFO [train.py:763] (1/8) Epoch 38, batch 4250, loss[loss=0.1477, simple_loss=0.2408, pruned_loss=0.02734, over 7145.00 frames.], tot_loss[loss=0.1573, simple_loss=0.2577, pruned_loss=0.02846, over 1416967.69 frames.], batch size: 17, lr: 1.98e-04
2022-05-01 01:15:41,252 INFO [train.py:763] (1/8) Epoch 38, batch 4300, loss[loss=0.1733, simple_loss=0.2743, pruned_loss=0.03613, over 7305.00 frames.], tot_loss[loss=0.1582, simple_loss=0.2591, pruned_loss=0.02866, over 1418139.56 frames.], batch size: 25, lr: 1.98e-04
2022-05-01 01:16:46,654 INFO [train.py:763] (1/8) Epoch 38, batch 4350, loss[loss=0.1241, simple_loss=0.2225, pruned_loss=0.0128, over 7439.00 frames.], tot_loss[loss=0.159, simple_loss=0.26, pruned_loss=0.02902, over 1413629.16 frames.], batch size: 20, lr: 1.98e-04
2022-05-01 01:17:51,727 INFO [train.py:763] (1/8) Epoch 38, batch 4400, loss[loss=0.1379, simple_loss=0.2381, pruned_loss=0.01884, over 7341.00 frames.], tot_loss[loss=0.1601, simple_loss=0.2613, pruned_loss=0.02943, over 1412066.97 frames.], batch size: 22, lr: 1.98e-04
2022-05-01 01:18:57,802 INFO [train.py:763] (1/8) Epoch 38, batch 4450, loss[loss=0.1468, simple_loss=0.23, pruned_loss=0.03177, over 7002.00 frames.], tot_loss[loss=0.1607, simple_loss=0.262, pruned_loss=0.02975, over 1400176.80 frames.], batch size: 16, lr: 1.98e-04
2022-05-01 01:20:03,887 INFO [train.py:763] (1/8) Epoch 38, batch 4500, loss[loss=0.16, simple_loss=0.2586, pruned_loss=0.0307, over 7159.00 frames.], tot_loss[loss=0.161, simple_loss=0.262, pruned_loss=0.02997, over 1389267.27 frames.], batch size: 18, lr: 1.98e-04
2022-05-01 01:21:09,321 INFO [train.py:763] (1/8) Epoch 38, batch 4550, loss[loss=0.1942, simple_loss=0.3057, pruned_loss=0.04128, over 4952.00 frames.], tot_loss[loss=0.1636, simple_loss=0.2643, pruned_loss=0.03149, over 1349990.66 frames.], batch size: 53, lr: 1.98e-04
2022-05-01 01:22:39,305 INFO [train.py:763] (1/8) Epoch 39, batch 0, loss[loss=0.173, simple_loss=0.2796, pruned_loss=0.03322, over 7302.00 frames.], tot_loss[loss=0.173, simple_loss=0.2796, pruned_loss=0.03322, over 7302.00 frames.], batch size: 24, lr: 1.96e-04
2022-05-01 01:23:45,000 INFO [train.py:763] (1/8) Epoch 39, batch 50, loss[loss=0.1242, simple_loss=0.2213, pruned_loss=0.01351, over 7285.00 frames.], tot_loss[loss=0.1604, simple_loss=0.2598, pruned_loss=0.03051, over 317507.18 frames.], batch size: 17, lr: 1.95e-04
2022-05-01 01:24:50,354 INFO [train.py:763] (1/8) Epoch 39, batch 100, loss[loss=0.1693, simple_loss=0.2665, pruned_loss=0.03604, over 7362.00 frames.], tot_loss[loss=0.1587, simple_loss=0.2586, pruned_loss=0.02938, over 562838.47 frames.], batch size: 19, lr: 1.95e-04
2022-05-01 01:25:56,217 INFO [train.py:763] (1/8) Epoch 39, batch 150, loss[loss=0.157, simple_loss=0.2589, pruned_loss=0.0275, over 7228.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2553, pruned_loss=0.02862, over 755623.23 frames.], batch size: 20, lr: 1.95e-04
2022-05-01 01:27:01,294 INFO [train.py:763] (1/8) Epoch 39, batch 200, loss[loss=0.1353, simple_loss=0.2342, pruned_loss=0.01817, over 7408.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2582, pruned_loss=0.02851, over 903341.42 frames.], batch size: 18, lr: 1.95e-04
2022-05-01 01:28:06,659 INFO [train.py:763] (1/8) Epoch 39, batch 250, loss[loss=0.163, simple_loss=0.2683, pruned_loss=0.02882, over 7126.00 frames.], tot_loss[loss=0.1574, simple_loss=0.258, pruned_loss=0.02847, over 1015946.57 frames.], batch size: 21, lr: 1.95e-04
2022-05-01 01:29:11,527 INFO [train.py:763] (1/8) Epoch 39, batch 300, loss[loss=0.1786, simple_loss=0.2712, pruned_loss=0.04304, over 7293.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2581, pruned_loss=0.02818, over 1106649.97 frames.], batch size: 24, lr: 1.95e-04
2022-05-01 01:30:16,874 INFO [train.py:763] (1/8) Epoch 39, batch 350, loss[loss=0.1474, simple_loss=0.2578, pruned_loss=0.01851, over 7146.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2585, pruned_loss=0.02836, over 1171120.54 frames.], batch size: 20, lr: 1.95e-04
2022-05-01 01:31:22,219 INFO [train.py:763] (1/8) Epoch 39, batch 400, loss[loss=0.1861, simple_loss=0.2831, pruned_loss=0.04454, over 7190.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2586, pruned_loss=0.02832, over 1228851.10 frames.], batch size: 26, lr: 1.95e-04
2022-05-01 01:32:27,453 INFO [train.py:763] (1/8) Epoch 39, batch 450, loss[loss=0.1855, simple_loss=0.2843, pruned_loss=0.04331, over 7302.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2582, pruned_loss=0.02846, over 1273425.85 frames.], batch size: 25, lr: 1.95e-04
2022-05-01 01:33:32,866 INFO [train.py:763] (1/8) Epoch 39, batch 500, loss[loss=0.1488, simple_loss=0.2501, pruned_loss=0.02369, over 7317.00 frames.], tot_loss[loss=0.157, simple_loss=0.2574, pruned_loss=0.02828, over 1305636.75 frames.], batch size: 21, lr: 1.95e-04
2022-05-01 01:34:38,279 INFO [train.py:763] (1/8) Epoch 39, batch 550, loss[loss=0.1604, simple_loss=0.2676, pruned_loss=0.02654, over 7237.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2573, pruned_loss=0.02858, over 1326951.91 frames.], batch size: 20, lr: 1.95e-04
2022-05-01 01:35:43,498 INFO [train.py:763] (1/8) Epoch 39, batch 600, loss[loss=0.1493, simple_loss=0.2435, pruned_loss=0.02758, over 7255.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2572, pruned_loss=0.02851, over 1348413.79 frames.], batch size: 19, lr: 1.95e-04
2022-05-01 01:36:48,742 INFO [train.py:763] (1/8) Epoch 39, batch 650, loss[loss=0.1622, simple_loss=0.2758, pruned_loss=0.02434, over 7231.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2571, pruned_loss=0.02833, over 1367187.72 frames.], batch size: 20, lr: 1.95e-04
2022-05-01 01:37:53,921 INFO [train.py:763] (1/8) Epoch 39, batch 700, loss[loss=0.1444, simple_loss=0.2303, pruned_loss=0.02918, over 7281.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2577, pruned_loss=0.02852, over 1380854.54 frames.], batch size: 18, lr: 1.95e-04
2022-05-01 01:38:59,263 INFO [train.py:763] (1/8) Epoch 39, batch 750, loss[loss=0.1444, simple_loss=0.2344, pruned_loss=0.02722, over 7364.00 frames.], tot_loss[loss=0.1565, simple_loss=0.257, pruned_loss=0.02804, over 1386606.32 frames.], batch size: 19, lr: 1.95e-04
2022-05-01 01:40:04,491 INFO [train.py:763] (1/8) Epoch 39, batch 800, loss[loss=0.1611, simple_loss=0.2622, pruned_loss=0.02998, over 7437.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2576, pruned_loss=0.02815, over 1396201.85 frames.], batch size: 22, lr: 1.95e-04
2022-05-01 01:41:18,504 INFO [train.py:763] (1/8) Epoch 39, batch 850, loss[loss=0.1393, simple_loss=0.2341, pruned_loss=0.02226, over 7137.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2586, pruned_loss=0.02827, over 1403135.78 frames.], batch size: 17, lr: 1.95e-04
2022-05-01 01:42:32,263 INFO [train.py:763] (1/8) Epoch 39, batch 900, loss[loss=0.1599, simple_loss=0.2685, pruned_loss=0.02563, over 7197.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2589, pruned_loss=0.02838, over 1408549.02 frames.], batch size: 23, lr: 1.95e-04
2022-05-01 01:43:55,210 INFO [train.py:763] (1/8) Epoch 39, batch 950, loss[loss=0.1546, simple_loss=0.2623, pruned_loss=0.0234, over 5185.00 frames.], tot_loss[loss=0.158, simple_loss=0.259, pruned_loss=0.02856, over 1411409.71 frames.], batch size: 52, lr: 1.95e-04
2022-05-01 01:45:01,221 INFO [train.py:763] (1/8) Epoch 39, batch 1000, loss[loss=0.1771, simple_loss=0.2821, pruned_loss=0.03606, over 7118.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2591, pruned_loss=0.02882, over 1410431.02 frames.], batch size: 21, lr: 1.95e-04
2022-05-01 01:46:06,270 INFO [train.py:763] (1/8) Epoch 39, batch 1050, loss[loss=0.1728, simple_loss=0.2784, pruned_loss=0.0336, over 7227.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2591, pruned_loss=0.02879, over 1409775.15 frames.], batch size: 21, lr: 1.95e-04
2022-05-01 01:47:29,491 INFO [train.py:763] (1/8) Epoch 39, batch 1100, loss[loss=0.136, simple_loss=0.2336, pruned_loss=0.01916, over 7170.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2582, pruned_loss=0.02859, over 1408023.22 frames.], batch size: 18, lr: 1.95e-04
2022-05-01 01:48:43,941 INFO [train.py:763] (1/8) Epoch 39, batch 1150, loss[loss=0.1477, simple_loss=0.2513, pruned_loss=0.02207, over 6762.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2576, pruned_loss=0.02835, over 1415443.43 frames.], batch size: 31, lr: 1.95e-04
2022-05-01 01:49:48,902 INFO [train.py:763] (1/8) Epoch 39, batch 1200, loss[loss=0.1552, simple_loss=0.2594, pruned_loss=0.0255, over 6425.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2579, pruned_loss=0.02855, over 1417954.56 frames.], batch size: 38, lr: 1.95e-04
2022-05-01 01:50:54,367 INFO [train.py:763] (1/8) Epoch 39, batch 1250, loss[loss=0.1749, simple_loss=0.2847, pruned_loss=0.03257, over 7295.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2581, pruned_loss=0.02844, over 1422157.09 frames.], batch size: 25, lr: 1.95e-04
2022-05-01 01:51:59,439 INFO [train.py:763] (1/8) Epoch 39, batch 1300, loss[loss=0.1586, simple_loss=0.2719, pruned_loss=0.02265, over 7427.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2577, pruned_loss=0.02805, over 1423006.29 frames.], batch size: 20, lr: 1.95e-04
2022-05-01 01:53:04,814 INFO [train.py:763] (1/8) Epoch 39, batch 1350, loss[loss=0.1653, simple_loss=0.2713, pruned_loss=0.02972, over 6616.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2579, pruned_loss=0.02848, over 1421494.99 frames.], batch size: 38, lr: 1.95e-04
2022-05-01 01:54:11,087 INFO [train.py:763] (1/8) Epoch 39, batch 1400, loss[loss=0.16, simple_loss=0.2683, pruned_loss=0.02585, over 6534.00 frames.], tot_loss[loss=0.1572, simple_loss=0.258, pruned_loss=0.02825, over 1423583.41 frames.], batch size: 38, lr: 1.95e-04
2022-05-01 01:55:16,351 INFO [train.py:763] (1/8) Epoch 39, batch 1450, loss[loss=0.1818, simple_loss=0.2747, pruned_loss=0.04446, over 7206.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2577, pruned_loss=0.02791, over 1425580.31 frames.], batch size: 23, lr: 1.95e-04
2022-05-01 01:56:21,443 INFO [train.py:763] (1/8) Epoch 39, batch 1500, loss[loss=0.128, simple_loss=0.2176, pruned_loss=0.01923, over 7130.00 frames.], tot_loss[loss=0.1572, simple_loss=0.258, pruned_loss=0.02819, over 1426199.91 frames.], batch size: 17, lr: 1.95e-04
2022-05-01 01:57:28,666 INFO [train.py:763] (1/8) Epoch 39, batch 1550, loss[loss=0.1662, simple_loss=0.2681, pruned_loss=0.03208, over 7182.00 frames.], tot_loss[loss=0.157, simple_loss=0.2577, pruned_loss=0.02812, over 1424362.95 frames.], batch size: 23, lr: 1.95e-04
2022-05-01 01:58:35,228 INFO [train.py:763] (1/8) Epoch 39, batch 1600, loss[loss=0.1625, simple_loss=0.2667, pruned_loss=0.02913, over 7166.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2584, pruned_loss=0.02829, over 1426758.70 frames.], batch size: 28, lr: 1.95e-04
2022-05-01 01:59:41,355 INFO [train.py:763] (1/8) Epoch 39, batch 1650, loss[loss=0.1872, simple_loss=0.2883, pruned_loss=0.04303, over 4930.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2582, pruned_loss=0.02831, over 1420925.01 frames.], batch size: 52, lr: 1.95e-04
2022-05-01 02:00:47,161 INFO [train.py:763] (1/8) Epoch 39, batch 1700, loss[loss=0.168, simple_loss=0.2489, pruned_loss=0.0436, over 7011.00 frames.], tot_loss[loss=0.158, simple_loss=0.2582, pruned_loss=0.02889, over 1413206.57 frames.], batch size: 16, lr: 1.95e-04
2022-05-01 02:01:53,359 INFO [train.py:763] (1/8) Epoch 39, batch 1750, loss[loss=0.1651, simple_loss=0.2794, pruned_loss=0.02541, over 7315.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2581, pruned_loss=0.0287, over 1414459.39 frames.], batch size: 21, lr: 1.95e-04
2022-05-01 02:02:58,280 INFO [train.py:763] (1/8) Epoch 39, batch 1800, loss[loss=0.1691, simple_loss=0.2768, pruned_loss=0.03071, over 7338.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2593, pruned_loss=0.02882, over 1417439.79 frames.], batch size: 22, lr: 1.95e-04
2022-05-01 02:04:03,600 INFO [train.py:763] (1/8) Epoch 39, batch 1850, loss[loss=0.1672, simple_loss=0.2653, pruned_loss=0.03454, over 7078.00 frames.], tot_loss[loss=0.158, simple_loss=0.2587, pruned_loss=0.02862, over 1420274.23 frames.], batch size: 18, lr: 1.95e-04
2022-05-01 02:05:08,883 INFO [train.py:763] (1/8) Epoch 39, batch 1900, loss[loss=0.1682, simple_loss=0.261, pruned_loss=0.03776, over 7150.00 frames.], tot_loss[loss=0.1575, simple_loss=0.2585, pruned_loss=0.02829, over 1423639.03 frames.], batch size: 19, lr: 1.94e-04
2022-05-01 02:06:14,309 INFO [train.py:763] (1/8) Epoch 39, batch 1950, loss[loss=0.1784, simple_loss=0.271, pruned_loss=0.04291, over 5167.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2589, pruned_loss=0.02865, over 1418209.14 frames.], batch size: 53, lr: 1.94e-04
2022-05-01 02:07:19,645 INFO [train.py:763] (1/8) Epoch 39, batch 2000, loss[loss=0.1584, simple_loss=0.262, pruned_loss=0.02746, over 7069.00 frames.], tot_loss[loss=0.158, simple_loss=0.2587, pruned_loss=0.02867, over 1422161.36 frames.], batch size: 18, lr: 1.94e-04
2022-05-01 02:08:24,808 INFO [train.py:763] (1/8) Epoch 39, batch 2050, loss[loss=0.1563, simple_loss=0.2584, pruned_loss=0.02709, over 7428.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2585, pruned_loss=0.02849, over 1425832.46 frames.], batch size: 20, lr: 1.94e-04
2022-05-01 02:09:30,529 INFO [train.py:763] (1/8) Epoch 39, batch 2100, loss[loss=0.1422, simple_loss=0.2378, pruned_loss=0.02335, over 7424.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2587, pruned_loss=0.0288, over 1424689.34 frames.], batch size: 18, lr: 1.94e-04
2022-05-01 02:10:35,927 INFO [train.py:763] (1/8) Epoch 39, batch 2150, loss[loss=0.1676, simple_loss=0.2814, pruned_loss=0.0269, over 7148.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2584, pruned_loss=0.02863, over 1428479.80 frames.], batch size: 20, lr: 1.94e-04
2022-05-01 02:11:43,127 INFO [train.py:763] (1/8) Epoch 39, batch 2200, loss[loss=0.1657, simple_loss=0.257, pruned_loss=0.03714, over 7234.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2577, pruned_loss=0.02828, over 1430431.90 frames.], batch size: 20, lr: 1.94e-04
2022-05-01 02:12:48,268 INFO [train.py:763] (1/8) Epoch 39, batch 2250, loss[loss=0.1721, simple_loss=0.273, pruned_loss=0.03564, over 7207.00 frames.], tot_loss[loss=0.157, simple_loss=0.2575, pruned_loss=0.02824, over 1428688.40 frames.], batch size: 22, lr: 1.94e-04
2022-05-01 02:13:53,527 INFO [train.py:763] (1/8) Epoch 39, batch 2300, loss[loss=0.1285, simple_loss=0.2241, pruned_loss=0.01647, over 7441.00 frames.], tot_loss[loss=0.1568, simple_loss=0.257, pruned_loss=0.02826, over 1426049.63 frames.], batch size: 20, lr: 1.94e-04
2022-05-01 02:15:00,685 INFO [train.py:763] (1/8) Epoch 39, batch 2350, loss[loss=0.1646, simple_loss=0.2733, pruned_loss=0.02795, over 7346.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2559, pruned_loss=0.0282, over 1425454.41 frames.], batch size: 22, lr: 1.94e-04
2022-05-01 02:16:07,734 INFO [train.py:763] (1/8) Epoch 39, batch 2400, loss[loss=0.1793, simple_loss=0.2739, pruned_loss=0.04231, over 7215.00 frames.], tot_loss[loss=0.1559, simple_loss=0.2555, pruned_loss=0.02817, over 1426386.28 frames.], batch size: 22, lr: 1.94e-04
2022-05-01 02:17:13,327 INFO [train.py:763] (1/8) Epoch 39, batch 2450, loss[loss=0.1758, simple_loss=0.282, pruned_loss=0.03482, over 7030.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2566, pruned_loss=0.02839, over 1421216.51 frames.], batch size: 28, lr: 1.94e-04
2022-05-01 02:18:19,516 INFO [train.py:763] (1/8) Epoch 39, batch 2500, loss[loss=0.1468, simple_loss=0.2506, pruned_loss=0.02156, over 7414.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2565, pruned_loss=0.02868, over 1418642.32 frames.], batch size: 21, lr: 1.94e-04
2022-05-01 02:19:24,720 INFO [train.py:763] (1/8) Epoch 39, batch 2550, loss[loss=0.1528, simple_loss=0.2623, pruned_loss=0.02161, over 7070.00 frames.], tot_loss[loss=0.157, simple_loss=0.2571, pruned_loss=0.02848, over 1418469.90 frames.], batch size: 28, lr: 1.94e-04
2022-05-01 02:20:31,562 INFO [train.py:763] (1/8) Epoch 39, batch 2600, loss[loss=0.1531, simple_loss=0.2626, pruned_loss=0.0218, over 7336.00 frames.], tot_loss[loss=0.157, simple_loss=0.257, pruned_loss=0.02855, over 1418631.91 frames.], batch size: 22, lr: 1.94e-04
2022-05-01 02:21:37,533 INFO [train.py:763] (1/8) Epoch 39, batch 2650, loss[loss=0.1427, simple_loss=0.2401, pruned_loss=0.02259, over 7178.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2577, pruned_loss=0.0286, over 1420949.25 frames.], batch size: 18, lr: 1.94e-04
2022-05-01 02:22:43,385 INFO [train.py:763] (1/8) Epoch 39, batch 2700, loss[loss=0.1459, simple_loss=0.2543, pruned_loss=0.0187, over 7226.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2588, pruned_loss=0.02907, over 1422837.09 frames.], batch size: 26, lr: 1.94e-04
2022-05-01 02:23:48,610 INFO [train.py:763] (1/8) Epoch 39, batch 2750, loss[loss=0.172, simple_loss=0.2813, pruned_loss=0.03137, over 7286.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2582, pruned_loss=0.02851, over 1425991.28 frames.], batch size: 24, lr: 1.94e-04
2022-05-01 02:24:53,687 INFO [train.py:763] (1/8) Epoch 39, batch 2800, loss[loss=0.1427, simple_loss=0.2493, pruned_loss=0.01808, over 7071.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2584, pruned_loss=0.02865, over 1423038.27 frames.], batch size: 18, lr: 1.94e-04
2022-05-01 02:25:58,653 INFO [train.py:763] (1/8) Epoch 39, batch 2850, loss[loss=0.1364, simple_loss=0.2403, pruned_loss=0.0162, over 6598.00 frames.], tot_loss[loss=0.1579, simple_loss=0.2584, pruned_loss=0.02869, over 1419485.51 frames.], batch size: 38, lr: 1.94e-04
2022-05-01 02:27:03,579 INFO [train.py:763] (1/8) Epoch 39, batch 2900, loss[loss=0.1429, simple_loss=0.2424, pruned_loss=0.0217, over 7060.00 frames.], tot_loss[loss=0.1577, simple_loss=0.2582, pruned_loss=0.02863, over 1419833.99 frames.], batch size: 18, lr: 1.94e-04
2022-05-01 02:28:08,499 INFO [train.py:763] (1/8) Epoch 39, batch 2950, loss[loss=0.1695, simple_loss=0.2746, pruned_loss=0.03221, over 7296.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2594, pruned_loss=0.02877, over 1419442.87 frames.], batch size: 24, lr: 1.94e-04
2022-05-01 02:29:13,388 INFO [train.py:763] (1/8) Epoch 39, batch 3000, loss[loss=0.1726, simple_loss=0.2829, pruned_loss=0.03114, over 7345.00 frames.], tot_loss[loss=0.1592, simple_loss=0.2599, pruned_loss=0.02922, over 1413349.22 frames.], batch size: 22, lr: 1.94e-04
2022-05-01 02:29:13,389 INFO [train.py:783] (1/8) Computing validation loss
2022-05-01 02:29:28,415 INFO [train.py:792] (1/8) Epoch 39, validation: loss=0.1688, simple_loss=0.2638, pruned_loss=0.03694, over 698248.00 frames.
2022-05-01 02:30:33,956 INFO [train.py:763] (1/8) Epoch 39, batch 3050, loss[loss=0.1575, simple_loss=0.2579, pruned_loss=0.02856, over 7354.00 frames.], tot_loss[loss=0.159, simple_loss=0.2595, pruned_loss=0.02924, over 1414706.86 frames.], batch size: 19, lr: 1.94e-04
2022-05-01 02:31:41,153 INFO [train.py:763] (1/8) Epoch 39, batch 3100, loss[loss=0.1824, simple_loss=0.2862, pruned_loss=0.03924, over 7174.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2596, pruned_loss=0.02899, over 1417522.54 frames.], batch size: 26, lr: 1.94e-04
2022-05-01 02:32:47,801 INFO [train.py:763] (1/8) Epoch 39, batch 3150, loss[loss=0.1723, simple_loss=0.2707, pruned_loss=0.03694, over 7151.00 frames.], tot_loss[loss=0.1588, simple_loss=0.2598, pruned_loss=0.02891, over 1421106.88 frames.], batch size: 20, lr: 1.94e-04
2022-05-01 02:33:53,385 INFO [train.py:763] (1/8) Epoch 39, batch 3200, loss[loss=0.1816, simple_loss=0.2761, pruned_loss=0.04349, over 5077.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2595, pruned_loss=0.02859, over 1420950.14 frames.], batch size: 52, lr: 1.94e-04
2022-05-01 02:34:58,487 INFO [train.py:763] (1/8) Epoch 39, batch 3250, loss[loss=0.1649, simple_loss=0.2679, pruned_loss=0.03098, over 7377.00 frames.], tot_loss[loss=0.159, simple_loss=0.2603, pruned_loss=0.02887, over 1420067.08 frames.], batch size: 23, lr: 1.94e-04
2022-05-01 02:36:03,618 INFO [train.py:763] (1/8) Epoch 39, batch 3300, loss[loss=0.1624, simple_loss=0.2802, pruned_loss=0.02235, over 7105.00 frames.], tot_loss[loss=0.1584, simple_loss=0.2592, pruned_loss=0.02881, over 1418839.85 frames.], batch size: 21, lr: 1.94e-04
2022-05-01 02:37:08,798 INFO [train.py:763] (1/8) Epoch 39, batch 3350, loss[loss=0.1501, simple_loss=0.2577, pruned_loss=0.02127, over 7109.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2584, pruned_loss=0.02836, over 1417356.94 frames.], batch size: 21, lr: 1.94e-04
2022-05-01 02:38:14,797 INFO [train.py:763] (1/8) Epoch 39, batch 3400, loss[loss=0.1723, simple_loss=0.2761, pruned_loss=0.03429, over 7161.00 frames.], tot_loss[loss=0.1564, simple_loss=0.2573, pruned_loss=0.02778, over 1417482.85 frames.], batch size: 19, lr: 1.94e-04
2022-05-01 02:39:20,469 INFO [train.py:763] (1/8) Epoch 39, batch 3450, loss[loss=0.1459, simple_loss=0.2402, pruned_loss=0.02586, over 7275.00 frames.], tot_loss[loss=0.1572, simple_loss=0.2578, pruned_loss=0.02826, over 1416135.84 frames.], batch size: 17, lr: 1.94e-04
2022-05-01 02:40:25,649 INFO [train.py:763] (1/8) Epoch 39, batch 3500, loss[loss=0.1593, simple_loss=0.2679, pruned_loss=0.02531, over 7327.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2584, pruned_loss=0.02842, over 1418213.28 frames.], batch size: 21, lr: 1.94e-04
2022-05-01 02:41:31,487 INFO [train.py:763] (1/8) Epoch 39, batch 3550, loss[loss=0.1393, simple_loss=0.2383, pruned_loss=0.0202, over 7059.00 frames.], tot_loss[loss=0.1567, simple_loss=0.2569, pruned_loss=0.02824, over 1419252.80 frames.], batch size: 18, lr: 1.94e-04
2022-05-01 02:42:37,765 INFO [train.py:763] (1/8) Epoch 39, batch 3600, loss[loss=0.1772, simple_loss=0.2765, pruned_loss=0.03891, over 5195.00 frames.], tot_loss[loss=0.1576, simple_loss=0.2576, pruned_loss=0.02873, over 1416417.02 frames.], batch size: 54, lr: 1.94e-04
2022-05-01 02:43:44,822 INFO [train.py:763] (1/8) Epoch 39, batch 3650, loss[loss=0.1745, simple_loss=0.2849, pruned_loss=0.03205, over 6631.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2586, pruned_loss=0.02878, over 1418399.37 frames.], batch size: 38, lr: 1.94e-04
2022-05-01 02:44:50,053 INFO [train.py:763] (1/8) Epoch 39, batch 3700, loss[loss=0.1503, simple_loss=0.2411, pruned_loss=0.0298, over 7146.00 frames.], tot_loss[loss=0.1581, simple_loss=0.2587, pruned_loss=0.02873, over 1422227.43 frames.], batch size: 17, lr: 1.94e-04
2022-05-01 02:45:55,095 INFO [train.py:763] (1/8) Epoch 39, batch 3750, loss[loss=0.1568, simple_loss=0.2511, pruned_loss=0.03123, over 7350.00 frames.], tot_loss[loss=0.1585, simple_loss=0.2592, pruned_loss=0.02888, over 1419103.25 frames.], batch size: 19, lr: 1.93e-04
2022-05-01 02:47:00,705 INFO [train.py:763] (1/8) Epoch 39, batch 3800, loss[loss=0.1296, simple_loss=0.217, pruned_loss=0.02108, over 7015.00 frames.], tot_loss[loss=0.1578, simple_loss=0.2584, pruned_loss=0.02864, over 1422950.17 frames.], batch size: 16, lr: 1.93e-04
2022-05-01 02:48:07,767 INFO [train.py:763] (1/8) Epoch 39, batch 3850, loss[loss=0.1519, simple_loss=0.2537, pruned_loss=0.02507, over 7407.00 frames.], tot_loss[loss=0.157, simple_loss=0.2575, pruned_loss=0.02829, over 1420110.41 frames.], batch size: 21, lr: 1.93e-04
2022-05-01 02:49:13,629 INFO [train.py:763] (1/8) Epoch 39, batch 3900, loss[loss=0.1728, simple_loss=0.2856, pruned_loss=0.02998, over 7197.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2575, pruned_loss=0.02841, over 1421208.42 frames.], batch size: 23, lr: 1.93e-04
2022-05-01 02:50:20,006 INFO [train.py:763] (1/8) Epoch 39, batch 3950, loss[loss=0.1419, simple_loss=0.2432, pruned_loss=0.02026, over 7068.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2568, pruned_loss=0.02869, over 1416216.46 frames.], batch size: 18, lr: 1.93e-04
2022-05-01 02:51:25,345 INFO [train.py:763] (1/8) Epoch 39, batch 4000, loss[loss=0.127, simple_loss=0.2211, pruned_loss=0.01641, over 7134.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2573, pruned_loss=0.0288, over 1416685.80 frames.], batch size: 17, lr: 1.93e-04
2022-05-01 02:52:30,794 INFO [train.py:763] (1/8) Epoch 39, batch 4050, loss[loss=0.1799, simple_loss=0.2932, pruned_loss=0.03331, over 7211.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2574, pruned_loss=0.02873, over 1420995.96 frames.], batch size: 22, lr: 1.93e-04
2022-05-01 02:53:35,943 INFO [train.py:763] (1/8) Epoch 39, batch 4100, loss[loss=0.167, simple_loss=0.2588, pruned_loss=0.03758, over 7228.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2574, pruned_loss=0.02844, over 1421175.78 frames.], batch size: 20, lr: 1.93e-04
2022-05-01 02:54:41,368 INFO [train.py:763] (1/8) Epoch 39, batch 4150, loss[loss=0.1746, simple_loss=0.2615, pruned_loss=0.04384, over 7285.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2577, pruned_loss=0.0286, over 1423522.32 frames.], batch size: 18, lr: 1.93e-04
2022-05-01 02:55:46,808 INFO [train.py:763] (1/8) Epoch 39, batch 4200, loss[loss=0.1458, simple_loss=0.2318, pruned_loss=0.02988, over 7154.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2573, pruned_loss=0.02828, over 1424260.96 frames.], batch size: 18, lr: 1.93e-04
2022-05-01 02:56:52,115 INFO [train.py:763] (1/8) Epoch 39, batch 4250, loss[loss=0.1463, simple_loss=0.2446, pruned_loss=0.02398, over 7313.00 frames.], tot_loss[loss=0.1574, simple_loss=0.2578, pruned_loss=0.02848, over 1418860.01 frames.], batch size: 21, lr: 1.93e-04
2022-05-01 02:57:57,422 INFO [train.py:763] (1/8) Epoch 39, batch 4300, loss[loss=0.1531, simple_loss=0.2486, pruned_loss=0.02885, over 7161.00 frames.], tot_loss[loss=0.1571, simple_loss=0.2574, pruned_loss=0.02841, over 1419093.19 frames.], batch size: 18, lr: 1.93e-04
2022-05-01 02:59:02,810 INFO [train.py:763] (1/8) Epoch 39, batch 4350, loss[loss=0.1517, simple_loss=0.2464, pruned_loss=0.02854, over 7327.00 frames.], tot_loss[loss=0.1568, simple_loss=0.2572, pruned_loss=0.02814, over 1420128.08 frames.], batch size: 20, lr: 1.93e-04
2022-05-01 03:00:09,027 INFO [train.py:763] (1/8) Epoch 39, batch 4400, loss[loss=0.1431, simple_loss=0.2467, pruned_loss=0.01975, over 6802.00 frames.], tot_loss[loss=0.1563, simple_loss=0.2569, pruned_loss=0.02787, over 1421401.84 frames.], batch size: 31, lr: 1.93e-04
2022-05-01 03:01:14,004 INFO [train.py:763] (1/8) Epoch 39, batch 4450, loss[loss=0.1553, simple_loss=0.2523, pruned_loss=0.02909, over 7166.00 frames.], tot_loss[loss=0.1569, simple_loss=0.2573, pruned_loss=0.02824, over 1408593.08 frames.], batch size: 18, lr: 1.93e-04
2022-05-01 03:02:19,220 INFO [train.py:763] (1/8) Epoch 39, batch 4500, loss[loss=0.1707, simple_loss=0.2874, pruned_loss=0.02697, over 7222.00 frames.], tot_loss[loss=0.1583, simple_loss=0.2588, pruned_loss=0.02888, over 1401074.22 frames.], batch size: 21, lr: 1.93e-04
2022-05-01 03:03:25,868 INFO [train.py:763] (1/8) Epoch 39, batch 4550, loss[loss=0.1554, simple_loss=0.2437, pruned_loss=0.03353, over 6762.00 frames.], tot_loss[loss=0.1562, simple_loss=0.2555, pruned_loss=0.0284, over 1393034.96 frames.], batch size: 15, lr: 1.93e-04
2022-05-01 03:04:15,396 INFO [train.py:971] (1/8) Done!