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my_model

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  1. furry/baobai/baobai6/D_0.pth +3 -0
  2. furry/baobai/baobai6/D_1600.pth +3 -0
  3. furry/baobai/baobai6/D_2400.pth +3 -0
  4. furry/baobai/baobai6/D_3200.pth +3 -0
  5. furry/baobai/baobai6/G_0.pth +3 -0
  6. furry/baobai/baobai6/G_1600.pth +3 -0
  7. furry/baobai/baobai6/G_2400.pth +3 -0
  8. furry/baobai/baobai6/G_3200.pth +3 -0
  9. furry/baobai/baobai6/config.json +96 -0
  10. furry/baobai/baobai6/diffusion/config.yaml +48 -0
  11. furry/baobai/baobai6/diffusion/log_info.txt +433 -0
  12. furry/baobai/baobai6/diffusion/logs/events.out.tfevents.1684649704.dabec9d50fc6.33951.0 +3 -0
  13. furry/baobai/baobai6/diffusion/model_0.pt +3 -0
  14. furry/baobai/baobai6/diffusion/model_4000.pt +3 -0
  15. furry/baobai/baobai6/eval/events.out.tfevents.1684644188.dabec9d50fc6.7497.1 +3 -0
  16. furry/baobai/baobai6/events.out.tfevents.1684644188.dabec9d50fc6.7497.0 +3 -0
  17. furry/baobai/baobai6/githash +1 -0
  18. furry/baobai/baobai6/train.log +206 -0
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+ {
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+ },
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+ "data": {
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+ "training_files": "filelists/train.txt",
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+ "validation_files": "filelists/val.txt",
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furry/baobai/baobai6/diffusion/config.yaml ADDED
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+ data:
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+ block_size: 512
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+ cnhubertsoft_gate: 10
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+ duration: 1
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+ encoder: vec768l12
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+ encoder_hop_size: 320
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+ encoder_out_channels: 768
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+ encoder_sample_rate: 16000
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+ extensions:
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+ - wav
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+ sampling_rate: 44100
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+ training_files: filelists/train.txt
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+ validation_files: filelists/val.txt
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+ device: cuda
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+ env:
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+ expdir: logs/44k/diffusion
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+ gpu_id: 0
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+ infer:
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+ method: dpm-solver
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+ speedup: 10
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+ model:
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+ n_chans: 512
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+ n_hidden: 256
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+ n_layers: 20
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+ n_spk: 1
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+ type: Diffusion
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+ use_pitch_aug: true
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+ spk:
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+ baobai: 0
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+ train:
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+ amp_dtype: fp32
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+ batch_size: 48
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+ cache_all_data: true
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+ cache_device: cpu
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+ cache_fp16: true
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+ decay_step: 100000
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+ epochs: 100000
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+ gamma: 0.5
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+ interval_force_save: 10000
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+ interval_log: 10
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+ interval_val: 2000
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+ lr: 0.0002
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+ num_workers: 2
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+ save_opt: false
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+ weight_decay: 0
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+ vocoder:
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+ ckpt: pretrain/nsf_hifigan/model
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+ type: nsf-hifigan
furry/baobai/baobai6/diffusion/log_info.txt ADDED
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+ --- model size ---
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+ model: 55,192,704
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+ ======= start training =======
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+ epoch|batch_idx/num_batches|output_dir|batch/s|lr|time|step
5
+ epoch: 3 | 0/ 3 | logs/44k/diffusion | batch/s: 1.30 | lr: 0.0002 | loss: 0.032 | time: 0:00:07.9 | step: 10
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+ epoch: 6 | 1/ 3 | logs/44k/diffusion | batch/s: 2.41 | lr: 0.0002 | loss: 0.018 | time: 0:00:12.1 | step: 20
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+ epoch: 9 | 2/ 3 | logs/44k/diffusion | batch/s: 2.40 | lr: 0.0002 | loss: 0.061 | time: 0:00:16.1 | step: 30
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+ epoch: 13 | 0/ 3 | logs/44k/diffusion | batch/s: 2.40 | lr: 0.0002 | loss: 0.037 | time: 0:00:20.4 | step: 40
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+ epoch: 16 | 1/ 3 | logs/44k/diffusion | batch/s: 2.32 | lr: 0.0002 | loss: 0.008 | time: 0:00:24.8 | step: 50
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+ epoch: 19 | 2/ 3 | logs/44k/diffusion | batch/s: 2.31 | lr: 0.0002 | loss: 0.037 | time: 0:00:29.0 | step: 60
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+ epoch: 23 | 0/ 3 | logs/44k/diffusion | batch/s: 2.29 | lr: 0.0002 | loss: 0.038 | time: 0:00:33.4 | step: 70
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+ epoch: 26 | 1/ 3 | logs/44k/diffusion | batch/s: 2.25 | lr: 0.0002 | loss: 0.011 | time: 0:00:37.9 | step: 80
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+ epoch: 29 | 2/ 3 | logs/44k/diffusion | batch/s: 2.25 | lr: 0.0002 | loss: 0.006 | time: 0:00:42.2 | step: 90
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+ epoch: 33 | 0/ 3 | logs/44k/diffusion | batch/s: 2.27 | lr: 0.0002 | loss: 0.022 | time: 0:00:46.7 | step: 100
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+ epoch: 36 | 1/ 3 | logs/44k/diffusion | batch/s: 2.25 | lr: 0.0002 | loss: 0.020 | time: 0:00:51.2 | step: 110
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+ epoch: 39 | 2/ 3 | logs/44k/diffusion | batch/s: 2.32 | lr: 0.0002 | loss: 0.022 | time: 0:00:55.3 | step: 120
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+ epoch: 43 | 0/ 3 | logs/44k/diffusion | batch/s: 2.37 | lr: 0.0002 | loss: 0.024 | time: 0:00:59.7 | step: 130
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+ epoch: 46 | 1/ 3 | logs/44k/diffusion | batch/s: 2.33 | lr: 0.0002 | loss: 0.014 | time: 0:01:04.0 | step: 140
19
+ epoch: 49 | 2/ 3 | logs/44k/diffusion | batch/s: 2.40 | lr: 0.0002 | loss: 0.030 | time: 0:01:08.0 | step: 150
20
+ epoch: 53 | 0/ 3 | logs/44k/diffusion | batch/s: 2.42 | lr: 0.0002 | loss: 0.054 | time: 0:01:12.3 | step: 160
21
+ epoch: 56 | 1/ 3 | logs/44k/diffusion | batch/s: 2.39 | lr: 0.0002 | loss: 0.011 | time: 0:01:16.5 | step: 170
22
+ epoch: 59 | 2/ 3 | logs/44k/diffusion | batch/s: 2.41 | lr: 0.0002 | loss: 0.016 | time: 0:01:20.5 | step: 180
23
+ epoch: 63 | 0/ 3 | logs/44k/diffusion | batch/s: 2.42 | lr: 0.0002 | loss: 0.018 | time: 0:01:24.7 | step: 190
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+ epoch: 66 | 1/ 3 | logs/44k/diffusion | batch/s: 2.39 | lr: 0.0002 | loss: 0.025 | time: 0:01:28.9 | step: 200
25
+ epoch: 69 | 2/ 3 | logs/44k/diffusion | batch/s: 2.40 | lr: 0.0002 | loss: 0.036 | time: 0:01:33.0 | step: 210
26
+ epoch: 73 | 0/ 3 | logs/44k/diffusion | batch/s: 2.40 | lr: 0.0002 | loss: 0.041 | time: 0:01:37.2 | step: 220
27
+ epoch: 76 | 1/ 3 | logs/44k/diffusion | batch/s: 2.36 | lr: 0.0002 | loss: 0.024 | time: 0:01:41.5 | step: 230
28
+ epoch: 79 | 2/ 3 | logs/44k/diffusion | batch/s: 2.37 | lr: 0.0002 | loss: 0.013 | time: 0:01:45.6 | step: 240
29
+ epoch: 83 | 0/ 3 | logs/44k/diffusion | batch/s: 2.36 | lr: 0.0002 | loss: 0.005 | time: 0:01:49.9 | step: 250
30
+ epoch: 86 | 1/ 3 | logs/44k/diffusion | batch/s: 2.32 | lr: 0.0002 | loss: 0.031 | time: 0:01:54.2 | step: 260
31
+ epoch: 89 | 2/ 3 | logs/44k/diffusion | batch/s: 2.35 | lr: 0.0002 | loss: 0.019 | time: 0:01:58.4 | step: 270
32
+ epoch: 93 | 0/ 3 | logs/44k/diffusion | batch/s: 2.36 | lr: 0.0002 | loss: 0.015 | time: 0:02:02.7 | step: 280
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+ epoch: 96 | 1/ 3 | logs/44k/diffusion | batch/s: 2.31 | lr: 0.0002 | loss: 0.017 | time: 0:02:07.0 | step: 290
34
+ epoch: 99 | 2/ 3 | logs/44k/diffusion | batch/s: 2.35 | lr: 0.0002 | loss: 0.012 | time: 0:02:11.2 | step: 300
35
+ epoch: 103 | 0/ 3 | logs/44k/diffusion | batch/s: 2.38 | lr: 0.0002 | loss: 0.019 | time: 0:02:15.5 | step: 310
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+ epoch: 106 | 1/ 3 | logs/44k/diffusion | batch/s: 2.34 | lr: 0.0002 | loss: 0.006 | time: 0:02:19.8 | step: 320
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+ epoch: 109 | 2/ 3 | logs/44k/diffusion | batch/s: 2.37 | lr: 0.0002 | loss: 0.006 | time: 0:02:23.9 | step: 330
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+ epoch: 113 | 0/ 3 | logs/44k/diffusion | batch/s: 2.39 | lr: 0.0002 | loss: 0.035 | time: 0:02:28.2 | step: 340
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+ epoch: 116 | 1/ 3 | logs/44k/diffusion | batch/s: 2.36 | lr: 0.0002 | loss: 0.008 | time: 0:02:32.5 | step: 350
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+ epoch: 119 | 2/ 3 | logs/44k/diffusion | batch/s: 2.38 | lr: 0.0002 | loss: 0.025 | time: 0:02:36.5 | step: 360
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+ epoch: 123 | 0/ 3 | logs/44k/diffusion | batch/s: 2.39 | lr: 0.0002 | loss: 0.010 | time: 0:02:40.8 | step: 370
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+ epoch: 126 | 1/ 3 | logs/44k/diffusion | batch/s: 2.35 | lr: 0.0002 | loss: 0.022 | time: 0:02:45.1 | step: 380
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+ epoch: 129 | 2/ 3 | logs/44k/diffusion | batch/s: 2.38 | lr: 0.0002 | loss: 0.046 | time: 0:02:49.1 | step: 390
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+ epoch: 133 | 0/ 3 | logs/44k/diffusion | batch/s: 2.39 | lr: 0.0002 | loss: 0.013 | time: 0:02:53.5 | step: 400
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+ epoch: 136 | 1/ 3 | logs/44k/diffusion | batch/s: 2.33 | lr: 0.0002 | loss: 0.023 | time: 0:02:57.7 | step: 410
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+ epoch: 139 | 2/ 3 | logs/44k/diffusion | batch/s: 2.38 | lr: 0.0002 | loss: 0.020 | time: 0:03:01.8 | step: 420
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+ epoch: 143 | 0/ 3 | logs/44k/diffusion | batch/s: 2.37 | lr: 0.0002 | loss: 0.009 | time: 0:03:06.2 | step: 430
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+ epoch: 146 | 1/ 3 | logs/44k/diffusion | batch/s: 2.31 | lr: 0.0002 | loss: 0.006 | time: 0:03:10.5 | step: 440
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+ epoch: 149 | 2/ 3 | logs/44k/diffusion | batch/s: 2.34 | lr: 0.0002 | loss: 0.012 | time: 0:03:14.6 | step: 450
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+ epoch: 153 | 0/ 3 | logs/44k/diffusion | batch/s: 2.37 | lr: 0.0002 | loss: 0.018 | time: 0:03:19.0 | step: 460
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+ epoch: 159 | 2/ 3 | logs/44k/diffusion | batch/s: 2.35 | lr: 0.0002 | loss: 0.010 | time: 0:03:27.4 | step: 480
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+ epoch: 163 | 0/ 3 | logs/44k/diffusion | batch/s: 2.37 | lr: 0.0002 | loss: 0.021 | time: 0:03:31.7 | step: 490
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+ epoch: 166 | 1/ 3 | logs/44k/diffusion | batch/s: 2.35 | lr: 0.0002 | loss: 0.009 | time: 0:03:36.0 | step: 500
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+ epoch: 169 | 2/ 3 | logs/44k/diffusion | batch/s: 2.37 | lr: 0.0002 | loss: 0.005 | time: 0:03:40.1 | step: 510
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+ epoch: 173 | 0/ 3 | logs/44k/diffusion | batch/s: 2.39 | lr: 0.0002 | loss: 0.017 | time: 0:03:44.4 | step: 520
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+ epoch: 179 | 2/ 3 | logs/44k/diffusion | batch/s: 2.37 | lr: 0.0002 | loss: 0.006 | time: 0:03:52.8 | step: 540
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+ epoch: 183 | 0/ 3 | logs/44k/diffusion | batch/s: 2.39 | lr: 0.0002 | loss: 0.026 | time: 0:03:57.1 | step: 550
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+ epoch: 186 | 1/ 3 | logs/44k/diffusion | batch/s: 2.34 | lr: 0.0002 | loss: 0.007 | time: 0:04:01.3 | step: 560
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+ epoch: 189 | 2/ 3 | logs/44k/diffusion | batch/s: 2.37 | lr: 0.0002 | loss: 0.014 | time: 0:04:05.5 | step: 570
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+ epoch: 216 | 1/ 3 | logs/44k/diffusion | batch/s: 2.34 | lr: 0.0002 | loss: 0.004 | time: 0:04:39.4 | step: 650
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+ epoch: 219 | 2/ 3 | logs/44k/diffusion | batch/s: 2.38 | lr: 0.0002 | loss: 0.026 | time: 0:04:43.5 | step: 660
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+ epoch: 223 | 0/ 3 | logs/44k/diffusion | batch/s: 2.39 | lr: 0.0002 | loss: 0.027 | time: 0:04:47.8 | step: 670
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+ epoch: 226 | 1/ 3 | logs/44k/diffusion | batch/s: 2.36 | lr: 0.0002 | loss: 0.038 | time: 0:04:52.1 | step: 680
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+ epoch: 229 | 2/ 3 | logs/44k/diffusion | batch/s: 2.38 | lr: 0.0002 | loss: 0.034 | time: 0:04:56.1 | step: 690
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+ epoch: 233 | 0/ 3 | logs/44k/diffusion | batch/s: 2.40 | lr: 0.0002 | loss: 0.012 | time: 0:05:00.4 | step: 700
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+ epoch: 583 | 0/ 3 | logs/44k/diffusion | batch/s: 2.37 | lr: 0.0002 | loss: 0.007 | time: 0:12:25.3 | step: 1750
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+ epoch: 666 | 1/ 3 | logs/44k/diffusion | batch/s: 2.33 | lr: 0.0002 | loss: 0.038 | time: 0:14:11.4 | step: 2000
205
+ --- <validation> ---
206
+ loss: 0.025.
207
+ epoch: 669 | 2/ 3 | logs/44k/diffusion | batch/s: 0.46 | lr: 0.0002 | loss: 0.008 | time: 0:14:33.1 | step: 2010
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+ epoch: 673 | 0/ 3 | logs/44k/diffusion | batch/s: 2.41 | lr: 0.0002 | loss: 0.014 | time: 0:14:37.3 | step: 2020
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+ epoch: 676 | 1/ 3 | logs/44k/diffusion | batch/s: 2.33 | lr: 0.0002 | loss: 0.013 | time: 0:14:41.6 | step: 2030
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+ epoch: 679 | 2/ 3 | logs/44k/diffusion | batch/s: 2.34 | lr: 0.0002 | loss: 0.008 | time: 0:14:45.8 | step: 2040
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+ epoch: 683 | 0/ 3 | logs/44k/diffusion | batch/s: 2.35 | lr: 0.0002 | loss: 0.026 | time: 0:14:50.2 | step: 2050
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+ epoch: 686 | 1/ 3 | logs/44k/diffusion | batch/s: 2.29 | lr: 0.0002 | loss: 0.019 | time: 0:14:54.5 | step: 2060
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+ epoch: 693 | 0/ 3 | logs/44k/diffusion | batch/s: 2.34 | lr: 0.0002 | loss: 0.016 | time: 0:15:03.2 | step: 2080
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+ epoch: 696 | 1/ 3 | logs/44k/diffusion | batch/s: 2.31 | lr: 0.0002 | loss: 0.014 | time: 0:15:07.5 | step: 2090
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+ epoch: 699 | 2/ 3 | logs/44k/diffusion | batch/s: 2.34 | lr: 0.0002 | loss: 0.008 | time: 0:15:11.6 | step: 2100
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+ epoch: 703 | 0/ 3 | logs/44k/diffusion | batch/s: 2.37 | lr: 0.0002 | loss: 0.004 | time: 0:15:16.0 | step: 2110
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+ epoch: 706 | 1/ 3 | logs/44k/diffusion | batch/s: 2.34 | lr: 0.0002 | loss: 0.004 | time: 0:15:20.3 | step: 2120
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+ epoch: 709 | 2/ 3 | logs/44k/diffusion | batch/s: 2.36 | lr: 0.0002 | loss: 0.012 | time: 0:15:24.3 | step: 2130
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+ epoch: 713 | 0/ 3 | logs/44k/diffusion | batch/s: 2.41 | lr: 0.0002 | loss: 0.010 | time: 0:15:28.6 | step: 2140
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+ epoch: 716 | 1/ 3 | logs/44k/diffusion | batch/s: 2.35 | lr: 0.0002 | loss: 0.007 | time: 0:15:32.9 | step: 2150
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+ --- <validation> ---
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+ loss: 0.055.
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+ 2023-05-21 04:43:08,221 44k INFO {'train': {'log_interval': 200, 'eval_interval': 800, 'seed': 1234, 'epochs': 10000, 'learning_rate': 0.0001, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 6, 'fp16_run': False, 'lr_decay': 0.999875, 'segment_size': 10240, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0, 'use_sr': True, 'max_speclen': 512, 'port': '8001', 'keep_ckpts': 3, 'all_in_mem': False}, 'data': {'training_files': 'filelists/train.txt', 'validation_files': 'filelists/val.txt', 'max_wav_value': 32768.0, 'sampling_rate': 44100, 'filter_length': 2048, 'hop_length': 512, 'win_length': 2048, 'n_mel_channels': 80, 'mel_fmin': 0.0, 'mel_fmax': 22050}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0.1, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [8, 8, 2, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4, 4], 'n_layers_q': 3, 'use_spectral_norm': False, 'gin_channels': 768, 'ssl_dim': 768, 'n_speakers': 1, 'speech_encoder': 'vec768l12', 'speaker_embedding': False}, 'spk': {'baobai': 0}, 'model_dir': './logs/44k'}
2
+ 2023-05-21 04:43:16,737 44k INFO emb_g.weight is not in the checkpoint
3
+ 2023-05-21 04:43:16,823 44k INFO Loaded checkpoint './logs/44k/G_0.pth' (iteration 0)
4
+ 2023-05-21 04:43:18,005 44k INFO Loaded checkpoint './logs/44k/D_0.pth' (iteration 0)
5
+ 2023-05-21 04:44:18,923 44k INFO ====> Epoch: 1, cost 70.71 s
6
+ 2023-05-21 04:44:52,034 44k INFO ====> Epoch: 2, cost 33.11 s
7
+ 2023-05-21 04:45:25,029 44k INFO ====> Epoch: 3, cost 33.00 s
8
+ 2023-05-21 04:45:58,832 44k INFO ====> Epoch: 4, cost 33.80 s
9
+ 2023-05-21 04:46:32,881 44k INFO ====> Epoch: 5, cost 34.05 s
10
+ 2023-05-21 04:47:07,159 44k INFO ====> Epoch: 6, cost 34.28 s
11
+ 2023-05-21 04:47:41,279 44k INFO ====> Epoch: 7, cost 34.12 s
12
+ 2023-05-21 04:48:15,771 44k INFO ====> Epoch: 8, cost 34.49 s
13
+ 2023-05-21 04:48:51,407 44k INFO ====> Epoch: 9, cost 35.64 s
14
+ 2023-05-21 04:49:13,163 44k INFO Train Epoch: 10 [48%]
15
+ 2023-05-21 04:49:13,170 44k INFO Losses: [2.5445778369903564, 2.83388090133667, 15.380541801452637, 18.88252830505371, 1.092060923576355], step: 200, lr: 9.98875562335968e-05, reference_loss: 40.73358917236328
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+ 2023-05-21 04:49:26,768 44k INFO ====> Epoch: 10, cost 35.36 s
17
+ 2023-05-21 04:50:00,043 44k INFO ====> Epoch: 11, cost 33.27 s
18
+ 2023-05-21 04:50:32,887 44k INFO ====> Epoch: 12, cost 32.84 s
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+ 2023-05-21 04:51:05,646 44k INFO ====> Epoch: 13, cost 32.76 s
20
+ 2023-05-21 04:51:38,556 44k INFO ====> Epoch: 14, cost 32.91 s
21
+ 2023-05-21 04:52:11,877 44k INFO ====> Epoch: 15, cost 33.32 s
22
+ 2023-05-21 04:52:45,053 44k INFO ====> Epoch: 16, cost 33.18 s
23
+ 2023-05-21 04:53:18,379 44k INFO ====> Epoch: 17, cost 33.33 s
24
+ 2023-05-21 04:53:51,413 44k INFO ====> Epoch: 18, cost 33.03 s
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+ 2023-05-21 04:54:24,092 44k INFO ====> Epoch: 19, cost 32.68 s
26
+ 2023-05-21 04:54:34,638 44k INFO Train Epoch: 20 [0%]
27
+ 2023-05-21 04:54:34,639 44k INFO Losses: [2.2672250270843506, 3.07883882522583, 15.199647903442383, 18.87508201599121, 1.056928277015686], step: 400, lr: 9.976276699833672e-05, reference_loss: 40.477718353271484
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+ 2023-05-21 04:54:58,369 44k INFO ====> Epoch: 20, cost 34.28 s
29
+ 2023-05-21 04:55:31,807 44k INFO ====> Epoch: 21, cost 33.44 s
30
+ 2023-05-21 04:56:04,832 44k INFO ====> Epoch: 22, cost 33.03 s
31
+ 2023-05-21 04:56:37,683 44k INFO ====> Epoch: 23, cost 32.85 s
32
+ 2023-05-21 04:57:11,401 44k INFO ====> Epoch: 24, cost 33.72 s
33
+ 2023-05-21 04:57:45,362 44k INFO ====> Epoch: 25, cost 33.96 s
34
+ 2023-05-21 04:58:19,556 44k INFO ====> Epoch: 26, cost 34.19 s
35
+ 2023-05-21 04:58:53,777 44k INFO ====> Epoch: 27, cost 34.22 s
36
+ 2023-05-21 04:59:28,417 44k INFO ====> Epoch: 28, cost 34.64 s
37
+ 2023-05-21 04:59:52,227 44k INFO Train Epoch: 29 [52%]
38
+ 2023-05-21 04:59:52,228 44k INFO Losses: [2.6102354526519775, 2.5509371757507324, 15.050143241882324, 18.5263729095459, 0.6427398324012756], step: 600, lr: 9.965058998565574e-05, reference_loss: 39.38042449951172
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+ 2023-05-21 05:00:04,491 44k INFO ====> Epoch: 29, cost 36.07 s
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+ 2023-05-21 05:00:39,358 44k INFO ====> Epoch: 30, cost 34.87 s
41
+ 2023-05-21 05:01:14,944 44k INFO ====> Epoch: 31, cost 35.59 s
42
+ 2023-05-21 05:01:50,616 44k INFO ====> Epoch: 32, cost 35.67 s
43
+ 2023-05-21 05:02:24,588 44k INFO ====> Epoch: 33, cost 33.97 s
44
+ 2023-05-21 05:02:58,697 44k INFO ====> Epoch: 34, cost 34.11 s
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+ 2023-05-21 05:03:31,929 44k INFO ====> Epoch: 35, cost 33.23 s
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+ 2023-05-21 05:04:04,532 44k INFO ====> Epoch: 36, cost 32.60 s
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+ 2023-05-21 05:04:37,191 44k INFO ====> Epoch: 37, cost 32.66 s
48
+ 2023-05-21 05:05:09,501 44k INFO ====> Epoch: 38, cost 32.31 s
49
+ 2023-05-21 05:05:20,677 44k INFO Train Epoch: 39 [5%]
50
+ 2023-05-21 05:05:20,678 44k INFO Losses: [2.1589901447296143, 2.6669301986694336, 16.74024772644043, 18.339496612548828, 0.8423145413398743], step: 800, lr: 9.952609679164422e-05, reference_loss: 40.74797821044922
51
+ 2023-05-21 05:05:38,923 44k INFO Saving model and optimizer state at iteration 39 to ./logs/44k/G_800.pth
52
+ 2023-05-21 05:05:43,429 44k INFO Saving model and optimizer state at iteration 39 to ./logs/44k/D_800.pth
53
+ 2023-05-21 05:06:08,013 44k INFO ====> Epoch: 39, cost 58.51 s
54
+ 2023-05-21 05:06:43,198 44k INFO ====> Epoch: 40, cost 35.18 s
55
+ 2023-05-21 05:07:18,870 44k INFO ====> Epoch: 41, cost 35.67 s
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+ 2023-05-21 05:07:53,211 44k INFO ====> Epoch: 42, cost 34.34 s
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+ 2023-05-21 05:08:26,741 44k INFO ====> Epoch: 43, cost 33.53 s
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+ 2023-05-21 05:09:00,756 44k INFO ====> Epoch: 44, cost 34.01 s
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+ 2023-05-21 05:09:34,017 44k INFO ====> Epoch: 45, cost 33.26 s
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+ 2023-05-21 05:10:06,712 44k INFO ====> Epoch: 46, cost 32.69 s
61
+ 2023-05-21 05:10:39,216 44k INFO ====> Epoch: 47, cost 32.50 s
62
+ 2023-05-21 05:11:01,524 44k INFO Train Epoch: 48 [57%]
63
+ 2023-05-21 05:11:01,528 44k INFO Losses: [2.5245370864868164, 2.759749174118042, 15.53822135925293, 18.24738121032715, 0.8092263340950012], step: 1000, lr: 9.941418589985758e-05, reference_loss: 39.87911605834961
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+ 2023-05-21 05:11:12,263 44k INFO ====> Epoch: 48, cost 33.05 s
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+ 2023-05-21 05:11:44,742 44k INFO ====> Epoch: 49, cost 32.48 s
66
+ 2023-05-21 05:12:16,612 44k INFO ====> Epoch: 50, cost 31.87 s
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+ 2023-05-21 05:12:48,642 44k INFO ====> Epoch: 51, cost 32.03 s
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+ 2023-05-21 05:13:20,754 44k INFO ====> Epoch: 52, cost 32.11 s
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+ 2023-05-21 05:13:53,229 44k INFO ====> Epoch: 53, cost 32.47 s
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+ 2023-05-21 05:14:25,437 44k INFO ====> Epoch: 54, cost 32.21 s
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+ 2023-05-21 05:14:58,124 44k INFO ====> Epoch: 55, cost 32.69 s
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+ 2023-05-21 05:15:30,617 44k INFO ====> Epoch: 56, cost 32.49 s
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+ 2023-05-21 05:16:03,136 44k INFO ====> Epoch: 57, cost 32.52 s
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+ 2023-05-21 05:16:15,483 44k INFO Train Epoch: 58 [10%]
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+ 2023-05-21 05:16:15,484 44k INFO Losses: [2.4595236778259277, 2.8447391986846924, 16.389728546142578, 18.046951293945312, 0.5579648613929749], step: 1200, lr: 9.928998804478705e-05, reference_loss: 40.29890823364258
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+ 2023-05-21 05:16:36,572 44k INFO ====> Epoch: 58, cost 33.44 s
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+ 2023-05-21 05:17:09,212 44k INFO ====> Epoch: 59, cost 32.64 s
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+ 2023-05-21 05:17:41,885 44k INFO ====> Epoch: 60, cost 32.67 s
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+ 2023-05-21 05:18:15,056 44k INFO ====> Epoch: 61, cost 33.17 s
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+ 2023-05-21 05:18:48,935 44k INFO ====> Epoch: 62, cost 33.88 s
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+ 2023-05-21 05:19:23,333 44k INFO ====> Epoch: 63, cost 34.40 s
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+ 2023-05-21 05:19:57,813 44k INFO ====> Epoch: 64, cost 34.48 s
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+ 2023-05-21 05:20:32,971 44k INFO ====> Epoch: 65, cost 35.16 s
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+ 2023-05-21 05:21:08,468 44k INFO ====> Epoch: 66, cost 35.50 s
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+ 2023-05-21 05:21:33,538 44k INFO Train Epoch: 67 [62%]
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+ 2023-05-21 05:21:33,543 44k INFO Losses: [2.5141336917877197, 2.321308135986328, 13.527164459228516, 17.738521575927734, 0.9825926423072815], step: 1400, lr: 9.917834264256819e-05, reference_loss: 37.08372116088867
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+ 2023-05-21 05:21:44,291 44k INFO ====> Epoch: 67, cost 35.82 s
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+ 2023-05-21 05:22:18,049 44k INFO ====> Epoch: 68, cost 33.76 s
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+ 2023-05-21 05:22:51,054 44k INFO ====> Epoch: 69, cost 33.01 s
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+ 2023-05-21 05:23:23,243 44k INFO ====> Epoch: 70, cost 32.19 s
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+ 2023-05-21 05:23:55,543 44k INFO ====> Epoch: 71, cost 32.30 s
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+ 2023-05-21 05:24:27,802 44k INFO ====> Epoch: 72, cost 32.26 s
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+ 2023-05-21 05:24:59,750 44k INFO ====> Epoch: 73, cost 31.95 s
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+ 2023-05-21 05:25:31,765 44k INFO ====> Epoch: 74, cost 32.01 s
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+ 2023-05-21 05:26:03,995 44k INFO ====> Epoch: 75, cost 32.23 s
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+ 2023-05-21 05:26:36,249 44k INFO ====> Epoch: 76, cost 32.25 s
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+ 2023-05-21 05:26:49,329 44k INFO Train Epoch: 77 [14%]
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+ 2023-05-21 05:26:49,330 44k INFO Losses: [2.3342432975769043, 2.160710573196411, 12.683871269226074, 16.918319702148438, 0.9933038353919983], step: 1600, lr: 9.905443942579728e-05, reference_loss: 35.090450286865234
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+ 2023-05-21 05:26:59,032 44k INFO Saving model and optimizer state at iteration 77 to ./logs/44k/G_1600.pth
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+ 2023-05-21 05:27:01,667 44k INFO Saving model and optimizer state at iteration 77 to ./logs/44k/D_1600.pth
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+ 2023-05-21 05:27:25,363 44k INFO ====> Epoch: 77, cost 49.11 s
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+ 2023-05-21 05:27:57,887 44k INFO ====> Epoch: 78, cost 32.52 s
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+ 2023-05-21 05:28:29,962 44k INFO ====> Epoch: 79, cost 32.08 s
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+ 2023-05-21 05:29:02,076 44k INFO ====> Epoch: 80, cost 32.11 s
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+ 2023-05-21 05:29:34,715 44k INFO ====> Epoch: 81, cost 32.64 s
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+ 2023-05-21 05:30:07,051 44k INFO ====> Epoch: 82, cost 32.34 s
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+ 2023-05-21 05:30:39,190 44k INFO ====> Epoch: 83, cost 32.14 s
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+ 2023-05-21 05:31:11,160 44k INFO ====> Epoch: 84, cost 31.97 s
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+ 2023-05-21 05:31:43,221 44k INFO ====> Epoch: 85, cost 32.06 s
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+ 2023-05-21 05:32:07,280 44k INFO Train Epoch: 86 [67%]
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+ 2023-05-21 05:32:07,286 44k INFO Losses: [2.2493386268615723, 2.731492042541504, 13.587553024291992, 15.12157154083252, 0.6404907703399658], step: 1800, lr: 9.894305888331732e-05, reference_loss: 34.330448150634766
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+ 2023-05-21 05:32:15,872 44k INFO ====> Epoch: 86, cost 32.65 s
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+ 2023-05-21 05:32:48,137 44k INFO ====> Epoch: 87, cost 32.27 s
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+ 2023-05-21 05:33:20,556 44k INFO ====> Epoch: 88, cost 32.42 s
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+ 2023-05-21 05:33:52,810 44k INFO ====> Epoch: 89, cost 32.25 s
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+ 2023-05-21 05:34:25,058 44k INFO ====> Epoch: 90, cost 32.25 s
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+ 2023-05-21 05:34:57,181 44k INFO ====> Epoch: 91, cost 32.12 s
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+ 2023-05-21 05:35:29,385 44k INFO ====> Epoch: 92, cost 32.20 s
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+ 2023-05-21 05:36:01,609 44k INFO ====> Epoch: 93, cost 32.22 s
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+ 2023-05-21 05:36:34,026 44k INFO ====> Epoch: 94, cost 32.42 s
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+ 2023-05-21 05:37:06,407 44k INFO ====> Epoch: 95, cost 32.38 s
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+ 2023-05-21 05:37:20,710 44k INFO Train Epoch: 96 [19%]
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+ 2023-05-21 05:37:20,711 44k INFO Losses: [2.2139039039611816, 2.295823097229004, 9.69484806060791, 17.4899845123291, 0.738105058670044], step: 2000, lr: 9.881944960586671e-05, reference_loss: 32.43266296386719
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+ 2023-05-21 05:37:39,559 44k INFO ====> Epoch: 96, cost 33.15 s
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+ 2023-05-21 05:38:12,464 44k INFO ====> Epoch: 97, cost 32.91 s
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+ 2023-05-21 05:38:44,917 44k INFO ====> Epoch: 98, cost 32.45 s
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+ 2023-05-21 05:39:17,468 44k INFO ====> Epoch: 99, cost 32.55 s
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+ 2023-05-21 05:39:50,088 44k INFO ====> Epoch: 100, cost 32.62 s
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+ 2023-05-21 05:40:22,585 44k INFO ====> Epoch: 101, cost 32.50 s
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+ 2023-05-21 05:40:55,431 44k INFO ====> Epoch: 102, cost 32.85 s
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+ 2023-05-21 05:41:28,821 44k INFO ====> Epoch: 103, cost 33.39 s
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+ 2023-05-21 05:42:02,738 44k INFO ====> Epoch: 104, cost 33.92 s
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+ 2023-05-21 05:42:30,808 44k INFO Train Epoch: 105 [71%]
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+ 2023-05-21 05:42:30,809 44k INFO Losses: [2.2192392349243164, 2.7417397499084473, 13.63097858428955, 18.681087493896484, 0.5394753217697144], step: 2200, lr: 9.870833329479095e-05, reference_loss: 37.81251907348633
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+ 2023-05-21 05:42:38,700 44k INFO ====> Epoch: 105, cost 35.96 s
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+ 2023-05-21 05:43:13,054 44k INFO ====> Epoch: 106, cost 34.35 s
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+ 2023-05-21 05:43:47,619 44k INFO ====> Epoch: 107, cost 34.56 s
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+ 2023-05-21 05:44:24,249 44k INFO ====> Epoch: 108, cost 36.63 s
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+ 2023-05-21 05:44:59,453 44k INFO ====> Epoch: 109, cost 35.20 s
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+ 2023-05-21 05:45:32,699 44k INFO ====> Epoch: 110, cost 33.25 s
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+ 2023-05-21 05:46:05,362 44k INFO ====> Epoch: 111, cost 32.66 s
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+ 2023-05-21 05:46:37,904 44k INFO ====> Epoch: 112, cost 32.54 s
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+ 2023-05-21 05:47:10,343 44k INFO ====> Epoch: 113, cost 32.44 s
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+ 2023-05-21 05:47:43,227 44k INFO ====> Epoch: 114, cost 32.88 s
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+ 2023-05-21 05:47:59,052 44k INFO Train Epoch: 115 [24%]
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+ 2023-05-21 05:47:59,057 44k INFO Losses: [2.2330069541931152, 2.7372024059295654, 8.495346069335938, 17.723203659057617, 0.7708501219749451], step: 2400, lr: 9.858501725933955e-05, reference_loss: 31.95960807800293
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+ 2023-05-21 05:48:08,136 44k INFO Saving model and optimizer state at iteration 115 to ./logs/44k/G_2400.pth
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+ 2023-05-21 05:48:12,093 44k INFO Saving model and optimizer state at iteration 115 to ./logs/44k/D_2400.pth
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+ 2023-05-21 05:48:33,640 44k INFO ====> Epoch: 115, cost 50.41 s
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+ 2023-05-21 05:49:06,586 44k INFO ====> Epoch: 116, cost 32.95 s
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+ 2023-05-21 05:49:39,074 44k INFO ====> Epoch: 117, cost 32.49 s
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+ 2023-05-21 05:50:12,016 44k INFO ====> Epoch: 118, cost 32.94 s
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+ 2023-05-21 05:50:44,838 44k INFO ====> Epoch: 119, cost 32.82 s
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+ 2023-05-21 05:51:17,762 44k INFO ====> Epoch: 120, cost 32.92 s
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+ 2023-05-21 05:51:50,661 44k INFO ====> Epoch: 121, cost 32.90 s
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+ 2023-05-21 05:52:23,470 44k INFO ====> Epoch: 122, cost 32.81 s
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+ 2023-05-21 05:52:56,124 44k INFO ====> Epoch: 123, cost 32.65 s
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+ 2023-05-21 05:53:23,266 44k INFO Train Epoch: 124 [76%]
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+ 2023-05-21 05:53:23,267 44k INFO Losses: [2.200976848602295, 2.4772040843963623, 16.151382446289062, 17.823810577392578, 0.8057739734649658], step: 2600, lr: 9.847416455282387e-05, reference_loss: 39.45914840698242
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+ 2023-05-21 05:53:30,688 44k INFO ====> Epoch: 124, cost 34.56 s
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+ 2023-05-21 05:54:04,912 44k INFO ====> Epoch: 125, cost 34.22 s
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+ 2023-05-21 05:54:39,924 44k INFO ====> Epoch: 126, cost 35.01 s
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+ 2023-05-21 05:55:15,121 44k INFO ====> Epoch: 127, cost 35.20 s
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+ 2023-05-21 05:55:50,216 44k INFO ====> Epoch: 128, cost 35.10 s
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+ 2023-05-21 05:56:25,353 44k INFO ====> Epoch: 129, cost 35.14 s
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+ 2023-05-21 05:57:01,674 44k INFO ====> Epoch: 130, cost 36.32 s
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+ 2023-05-21 05:57:36,466 44k INFO ====> Epoch: 131, cost 34.79 s
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+ 2023-05-21 05:58:09,526 44k INFO ====> Epoch: 132, cost 33.06 s
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+ 2023-05-21 05:58:42,501 44k INFO ====> Epoch: 133, cost 32.97 s
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+ 2023-05-21 05:58:59,391 44k INFO Train Epoch: 134 [29%]
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+ 2023-05-21 05:58:59,396 44k INFO Losses: [2.4819719791412354, 2.301201105117798, 14.97828197479248, 16.09484100341797, 0.7705917358398438], step: 2800, lr: 9.835114106370493e-05, reference_loss: 36.626888275146484
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+ 2023-05-21 05:59:16,031 44k INFO ====> Epoch: 134, cost 33.53 s
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+ 2023-05-21 05:59:49,318 44k INFO ====> Epoch: 135, cost 33.29 s
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+ 2023-05-21 06:00:22,788 44k INFO ====> Epoch: 136, cost 33.47 s
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+ 2023-05-21 06:00:56,655 44k INFO ====> Epoch: 137, cost 33.87 s
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+ 2023-05-21 06:01:31,309 44k INFO ====> Epoch: 138, cost 34.65 s
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+ 2023-05-21 06:02:06,233 44k INFO ====> Epoch: 139, cost 34.92 s
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+ 2023-05-21 06:02:41,223 44k INFO ====> Epoch: 140, cost 34.99 s
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+ 2023-05-21 06:03:16,447 44k INFO ====> Epoch: 141, cost 35.22 s
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+ 2023-05-21 06:03:52,380 44k INFO ====> Epoch: 142, cost 35.93 s
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+ 2023-05-21 06:04:21,519 44k INFO Train Epoch: 143 [81%]
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+ 2023-05-21 06:04:21,524 44k INFO Losses: [1.8807615041732788, 2.6372532844543457, 14.652959823608398, 15.112489700317383, 0.5258215069770813], step: 3000, lr: 9.824055133639235e-05, reference_loss: 34.80928421020508
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+ 2023-05-21 06:04:28,625 44k INFO ====> Epoch: 143, cost 36.25 s
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+ 2023-05-21 06:05:01,690 44k INFO ====> Epoch: 144, cost 33.07 s
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+ 2023-05-21 06:05:34,433 44k INFO ====> Epoch: 145, cost 32.74 s
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+ 2023-05-21 06:06:07,368 44k INFO ====> Epoch: 146, cost 32.94 s
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+ 2023-05-21 06:06:39,959 44k INFO ====> Epoch: 147, cost 32.59 s
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+ 2023-05-21 06:07:12,854 44k INFO ====> Epoch: 148, cost 32.89 s
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+ 2023-05-21 06:07:45,489 44k INFO ====> Epoch: 149, cost 32.64 s
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+ 2023-05-21 06:08:18,144 44k INFO ====> Epoch: 150, cost 32.66 s
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+ 2023-05-21 06:08:50,747 44k INFO ====> Epoch: 151, cost 32.60 s
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+ 2023-05-21 06:09:23,202 44k INFO ====> Epoch: 152, cost 32.46 s
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+ 2023-05-21 06:09:40,817 44k INFO Train Epoch: 153 [33%]
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+ 2023-05-21 06:09:40,820 44k INFO Losses: [2.426485300064087, 2.5262508392333984, 14.231987953186035, 18.29201889038086, 0.7122567892074585], step: 3200, lr: 9.811781969958938e-05, reference_loss: 38.18899917602539
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+ 2023-05-21 06:09:50,503 44k INFO Saving model and optimizer state at iteration 153 to ./logs/44k/G_3200.pth
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+ 2023-05-21 06:09:58,273 44k INFO Saving model and optimizer state at iteration 153 to ./logs/44k/D_3200.pth
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+ 2023-05-21 06:10:00,551 44k INFO .. Free up space by deleting ckpt ./logs/44k/G_800.pth
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+ 2023-05-21 06:10:00,552 44k INFO .. Free up space by deleting ckpt ./logs/44k/D_800.pth
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+ 2023-05-21 06:10:17,278 44k INFO ====> Epoch: 153, cost 54.08 s
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+ 2023-05-21 06:10:52,325 44k INFO ====> Epoch: 154, cost 35.05 s
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+ 2023-05-21 06:11:27,070 44k INFO ====> Epoch: 155, cost 34.74 s
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+ 2023-05-21 06:12:02,019 44k INFO ====> Epoch: 156, cost 34.95 s
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+ 2023-05-21 06:12:37,932 44k INFO ====> Epoch: 157, cost 35.91 s
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+ 2023-05-21 06:13:13,223 44k INFO ====> Epoch: 158, cost 35.29 s
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+ 2023-05-21 06:13:47,471 44k INFO ====> Epoch: 159, cost 34.25 s
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+ 2023-05-21 06:14:20,359 44k INFO ====> Epoch: 160, cost 32.89 s