douwantech / gpt-sovits-train
- Public
- 181 runs
-
L40S
Prediction
douwantech/gpt-sovits-train:0dcb11219f474e580e033c889114f75cf8e8002d5c50801b8096ac6ac525e325ID9pa655bmdxrgj0cg6avt20kf9mStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- audio_or_video_url
{ "audio_or_video_url": "https://general-api.oss-cn-hangzhou.aliyuncs.com/static/2.mp4" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run douwantech/gpt-sovits-train using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "douwantech/gpt-sovits-train:0dcb11219f474e580e033c889114f75cf8e8002d5c50801b8096ac6ac525e325", { input: { audio_or_video_url: "https://general-api.oss-cn-hangzhou.aliyuncs.com/static/2.mp4" } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run douwantech/gpt-sovits-train using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "douwantech/gpt-sovits-train:0dcb11219f474e580e033c889114f75cf8e8002d5c50801b8096ac6ac525e325", input={ "audio_or_video_url": "https://general-api.oss-cn-hangzhou.aliyuncs.com/static/2.mp4" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run douwantech/gpt-sovits-train using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "douwantech/gpt-sovits-train:0dcb11219f474e580e033c889114f75cf8e8002d5c50801b8096ac6ac525e325", "input": { "audio_or_video_url": "https://general-api.oss-cn-hangzhou.aliyuncs.com/static/2.mp4" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
audio_url
Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%00:00:000Stream Type LIVERemaining Time -00:00:0001x- Chapters
- descriptions off, selected
- captions settings, opens captions settings dialog
- captions off, selected
This is a modal window.
Beginning of dialog window. Escape will cancel and close the window.
End of dialog window.
{ "completed_at": "2024-06-19T23:03:01.062813Z", "created_at": "2024-06-19T22:56:42.095000Z", "data_removed": false, "error": null, "id": "9pa655bmdxrgj0cg6avt20kf9m", "input": { "audio_or_video_url": "https://general-api.oss-cn-hangzhou.aliyuncs.com/static/2.mp4" }, "logs": "Copied file to input/351b84a7-8622-4c51-a803-ae848d62d158/origin.mp3\n执行完毕,请检查输出文件\n2024-06-19 22:59:23,923 - modelscope - INFO - PyTorch version 2.0.1+cu118 Found.\n2024-06-19 22:59:23,924 - modelscope - INFO - Loading ast index from /root/.cache/modelscope/ast_indexer\n2024-06-19 22:59:23,924 - modelscope - INFO - No valid ast index found from /root/.cache/modelscope/ast_indexer, generating ast index from prebuilt!\n2024-06-19 22:59:23,996 - modelscope - INFO - Loading done! Current index file version is 1.10.0, with md5 b51edac0938f2bdbb763b5eb2eac94ee and a total number of 946 components indexed\n2024-06-19 22:59:29,620 - modelscope - WARNING - Model revision not specified, use revision: v1.0.2\nDownloading: 0%| | 0.00/1.45k [00:00<?, ?B/s]\nDownloading: 100%|██████████| 1.45k/1.45k [00:00<00:00, 144kB/s]\nDownloading: 0%| | 0.00/903 [00:00<?, ?B/s]\nDownloading: 100%|██████████| 903/903 [00:00<00:00, 108kB/s]\nDownloading: 0%| | 0.00/177k [00:00<?, ?B/s]\nDownloading: 100%|██████████| 177k/177k [00:00<00:00, 1.16MB/s]\nDownloading: 100%|██████████| 177k/177k [00:00<00:00, 1.16MB/s]\nDownloading: 0%| | 0.00/88.2k [00:00<?, ?B/s]\nDownloading: 100%|██████████| 88.2k/88.2k [00:00<00:00, 873kB/s]\nDownloading: 100%|██████████| 88.2k/88.2k [00:00<00:00, 870kB/s]\nDownloading: 0%| | 0.00/55.3M [00:00<?, ?B/s]\nDownloading: 29%|██▉ | 16.0M/55.3M [00:00<00:01, 29.8MB/s]\nDownloading: 87%|████████▋ | 48.0M/55.3M [00:00<00:00, 84.0MB/s]\nDownloading: 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cfg.\n2024-06-19 22:59:41,013 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.\n2024-06-19 22:59:41,013 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/speech_frcrn_ans_cirm_16k'}. trying to build by task and model information.\n2024-06-19 22:59:41,013 - modelscope - WARNING - No preprocessor key ('speech_frcrn_ans_cirm_16k', 'acoustic-noise-suppression') found in PREPROCESSOR_MAP, skip building preprocessor.\n0%| | 0/1 [00:00<?, ?it/s]inputs:(1, 254096)\npadding: 26096\ninputs after padding:(1, 280192)\n100%|██████████| 1/1 [00:01<00:00, 1.37s/it]\n100%|██████████| 1/1 [00:01<00:00, 1.37s/it]\nPlease install rotary_embedding_torch by:\npip install -U rotary_embedding_torch\nPlease install rotary_embedding_torch by:\npip install -U rotary_embedding_torch\nPlease install rotary_embedding_torch by:\npip install -U rotary_embedding_torch\nPlease install rotary_embedding_torch by:\npip install -U rotary_embedding_torch\ntables:\n----------- ** dataset_classes ** --------------\n| class name | class location |\n| AudioDataset | funasr/datasets/audio_datasets/datasets.py:7 |\n----------- ** index_ds_classes ** --------------\n| class name | class location |\n| IndexDSJsonl | funasr/datasets/audio_datasets/index_ds.py:9 |\n----------- ** batch_sampler_classes ** --------------\n| class name | class location |\n| BatchSampler | funasr/datasets/audio_datasets/samplers.py:7 |\n----------- ** frontend_classes ** --------------\n| class name | class location |\n| WavFrontend | funasr/frontends/wav_frontend.py:78 |\n| WavFrontendOnline | funasr/frontends/wav_frontend.py:216 |\n----------- ** encoder_classes ** --------------\n| class name | class location |\n| BranchformerEncoder | funasr/models/branchformer/encoder.py:294 |\n| ConformerChunkEncoder | funasr/models/bat/conformer_chunk_encoder.py:315 |\n| ConformerEncoder | funasr/models/conformer/encoder.py:286 |\n| DFSMN | funasr/models/fsmn_vad_streaming/encoder.py:232 |\n| EBranchformerEncoder | funasr/models/e_branchformer/encoder.py:177 |\n| FSMN | funasr/models/fsmn_vad_streaming/encoder.py:161 |\n| SANMEncoder | funasr/models/sanm/encoder.py:161 |\n| SANMEncoderChunkOpt | funasr/models/scama/encoder.py:162 |\n| SANMVadEncoder | funasr/models/ct_transformer_streaming/encoder.py:148 |\n| TransformerEncoder | funasr/models/transformer/encoder.py:139 |\n----------- ** predictor_classes ** --------------\n| class name | class location |\n| CifPredictor | funasr/models/paraformer/cif_predictor.py:15 |\n| CifPredictorV2 | funasr/models/paraformer/cif_predictor.py:141 |\n| CifPredictorV3 | funasr/models/bicif_paraformer/cif_predictor.py:95 |\n----------- ** model_classes ** --------------\n| class name | class location |\n| BiCifParaformer | funasr/models/bicif_paraformer/model.py:37 |\n| Branchformer | funasr/models/branchformer/model.py:6 |\n| CAMPPlus | funasr/models/campplus/model.py:30 |\n| CTTransformer | funasr/models/ct_transformer/model.py:30 |\n| CTTransformerStreaming | funasr/models/ct_transformer_streaming/model.py:27 |\n| Conformer | funasr/models/conformer/model.py:8 |\n| ContextualParaformer | funasr/models/contextual_paraformer/model.py:43 |\n| EBranchformer | funasr/models/e_branchformer/model.py:6 |\n| Emotion2vec | funasr/models/emotion2vec/model.py:34 |\n| FsmnVADStreaming | funasr/models/fsmn_vad_streaming/model.py:267 |\n| MonotonicAligner | funasr/models/monotonic_aligner/model.py:24 |\n| Paraformer | funasr/models/paraformer/model.py:26 |\n| ParaformerStreaming | funasr/models/paraformer_streaming/model.py:37 |\n| SANM | funasr/models/sanm/model.py:13 |\n| SCAMA | funasr/models/scama/model.py:38 |\n| SeacoParaformer | funasr/models/seaco_paraformer/model.py:45 |\n| Transformer | funasr/models/transformer/model.py:20 |\n| UniASR | funasr/models/uniasr/model.py:26 |\n----------- ** decoder_classes ** --------------\n| class name | class location |\n| ContextualParaformerDecoder | funasr/models/contextual_paraformer/decoder.py:103 |\n| DynamicConvolution2DTransformerDecoder | funasr/models/transformer/decoder.py:588 |\n| DynamicConvolutionTransformerDecoder | funasr/models/transformer/decoder.py:527 |\n| FsmnDecoder | funasr/models/sanm/decoder.py:198 |\n| FsmnDecoderSCAMAOpt | funasr/models/scama/decoder.py:197 |\n| LightweightConvolution2DTransformerDecoder | funasr/models/transformer/decoder.py:465 |\n| LightweightConvolutionTransformerDecoder | funasr/models/transformer/decoder.py:404 |\n| ParaformerSANDecoder | funasr/models/paraformer/decoder.py:529 |\n| ParaformerSANMDecoder | funasr/models/paraformer/decoder.py:204 |\n| TransformerDecoder | funasr/models/transformer/decoder.py:355 |\n----------- ** normalize_classes ** --------------\n| class name | class location |\n| GlobalMVN | funasr/models/normalize/global_mvn.py:11 |\n| UtteranceMVN | funasr/models/normalize/utterance_mvn.py:8 |\n----------- ** specaug_classes ** --------------\n| class name | class location |\n| SpecAug | funasr/models/specaug/specaug.py:14 |\n| SpecAugLFR | funasr/models/specaug/specaug.py:104 |\n----------- ** tokenizer_classes ** --------------\n| class name | class location |\n| CharTokenizer | funasr/tokenizer/char_tokenizer.py:10 |\n2024-06-19 22:59:47,351 - modelscope - INFO - PyTorch version 2.0.1+cu118 Found.\n2024-06-19 22:59:47,352 - modelscope - INFO - Loading ast index from /root/.cache/modelscope/ast_indexer\n2024-06-19 22:59:47,398 - modelscope - INFO - Loading done! 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1/1 [00:00<00:00, 2.63it/s]\nASR 任务完成->标注文件路径: /src/output/351b84a7-8622-4c51-a803-ae848d62d158/asr_opt/denoise_opt.list\n\"/root/.pyenv/versions/3.9.19/bin/python\" GPT_SoVITS/prepare_datasets/1-get-text.py\n\"/root/.pyenv/versions/3.9.19/bin/python\" GPT_SoVITS/prepare_datasets/1-get-text.py\n('进度:1a-ing', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True})\nIMPORTANT: You are using gradio version 3.38.0, however version 4.29.0 is available, please upgrade.\n--------\nBuilding prefix dict from the default dictionary ...\nDumping model to file cache /src/TEMP/jieba.cache\nLoading model cost 1.245 seconds.\nPrefix dict has been built succesfully.\n('进度:1a-done', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True})\n\"/root/.pyenv/versions/3.9.19/bin/python\" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py\n\"/root/.pyenv/versions/3.9.19/bin/python\" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py\n('进度:1a-done, 1b-ing', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True})\n('进度:1a1b-done', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True})\n\"/root/.pyenv/versions/3.9.19/bin/python\" GPT_SoVITS/prepare_datasets/3-get-semantic.py\n\"/root/.pyenv/versions/3.9.19/bin/python\" GPT_SoVITS/prepare_datasets/3-get-semantic.py\n('进度:1a1b-done, 1cing', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True})\n<All keys matched successfully>\n<All keys matched successfully>\n('进度:all-done', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True})\n('一键三连进程结束', {'__type__': 'update', 'visible': True}, {'__type__': 'update', 'visible': False})\n('SoVITS训练开始:\"/root/.pyenv/versions/3.9.19/bin/python\" GPT_SoVITS/s2_train.py --config \"/src/TEMP/tmp_s2.json\"', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True})\n\"/root/.pyenv/versions/3.9.19/bin/python\" GPT_SoVITS/s2_train.py --config \"/src/TEMP/tmp_s2.json\"\nIMPORTANT: You are using gradio version 3.38.0, however version 4.29.0 is available, please upgrade.\n--------\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:{'train': {'log_interval': 100, 'eval_interval': 500, 'seed': 1234, 'epochs': 8, 'learning_rate': 0.0001, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 11, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 20480, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0, 'text_low_lr_rate': 0.4, 'pretrained_s2G': 'GPT_SoVITS/pretrained_models/s2G488k.pth', 'pretrained_s2D': 'GPT_SoVITS/pretrained_models/s2D488k.pth', 'if_save_latest': True, 'if_save_every_weights': True, 'save_every_epoch': 4, 'gpu_numbers': '0-1'}, 'data': {'max_wav_value': 32768.0, 'sampling_rate': 32000, 'filter_length': 2048, 'hop_length': 640, 'win_length': 2048, 'n_mel_channels': 128, 'mel_fmin': 0.0, 'mel_fmax': None, 'add_blank': True, 'n_speakers': 300, 'cleaned_text': True, 'exp_dir': 'logs/351b84a7-8622-4c51-a803-ae848d62d158'}, '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': [10, 8, 2, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 8, 2, 2], 'n_layers_q': 3, 'use_spectral_norm': False, 'gin_channels': 512, 'semantic_frame_rate': '25hz', 'freeze_quantizer': True}, 's2_ckpt_dir': 'logs/351b84a7-8622-4c51-a803-ae848d62d158', 'content_module': 'cnhubert', 'save_weight_dir': 'SoVITS_weights', 'name': '351b84a7-8622-4c51-a803-ae848d62d158', 'pretrain': None, 'resume_step': None}\nINFO:torch.distributed.distributed_c10d:Added key: store_based_barrier_key:1 to store for rank: 0\nINFO:torch.distributed.distributed_c10d:Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 1 nodes.\nphoneme_data_len: 1\nwav_data_len: 100\n0%| | 0/100 [00:00<?, ?it/s]\n100%|██████████| 100/100 [00:00<00:00, 73921.47it/s]\nskipped_phone: 0 , skipped_dur: 0\ntotal left: 100\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:loaded pretrained GPT_SoVITS/pretrained_models/s2G488k.pth\n<All keys matched successfully>\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:loaded pretrained GPT_SoVITS/pretrained_models/s2D488k.pth\n<All keys matched successfully>\n/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\nwarnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n0%| | 0/10 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\nNote: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.)\nreturn _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\nNote: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.)\nreturn _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\nNote: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.)\nreturn _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\nNote: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.)\nreturn _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\nNote: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.)\nreturn _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\nNote: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.)\nreturn _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.\nNote: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.)\nreturn _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at ../aten/src/ATen/EmptyTensor.cpp:31.)\nreturn _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]\n[W reducer.cpp:1300] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())\n/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/autograd/__init__.py:200: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.\ngrad.sizes() = [1, 9, 96], strides() = [152736, 96, 1]\nbucket_view.sizes() = [1, 9, 96], strides() = [864, 96, 1] (Triggered internally at ../torch/csrc/distributed/c10d/reducer.cpp:323.)\nVariable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:Train Epoch: 1 [0%]\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:[2.389455556869507, 2.1822030544281006, 5.724943161010742, 21.071016311645508, 0.0, 2.827393054962158, 0, 9.99875e-05]\n10%|█ | 1/10 [00:09<01:27, 9.74s/it]\n20%|██ | 2/10 [00:10<00:34, 4.25s/it]\n30%|███ | 3/10 [00:10<00:17, 2.48s/it]\n40%|████ | 4/10 [00:10<00:09, 1.65s/it]\n50%|█████ | 5/10 [00:11<00:05, 1.20s/it]\n60%|██████ | 6/10 [00:11<00:03, 1.09it/s]\n70%|███████ | 7/10 [00:12<00:02, 1.35it/s]\n80%|████████ | 8/10 [00:12<00:01, 1.55it/s]\n90%|█████████ | 9/10 [00:12<00:00, 1.77it/s]\n100%|██████████| 10/10 [00:13<00:00, 1.97it/s]\n100%|██████████| 10/10 [00:13<00:00, 1.33s/it]\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 1\n0%| | 0/10 [00:00<?, ?it/s]\n10%|█ | 1/10 [00:02<00:20, 2.29s/it]\n20%|██ | 2/10 [00:02<00:09, 1.20s/it]\n30%|███ | 3/10 [00:03<00:05, 1.18it/s]\n40%|████ | 4/10 [00:03<00:04, 1.48it/s]\n50%|█████ | 5/10 [00:03<00:02, 1.73it/s]\n60%|██████ | 6/10 [00:04<00:02, 1.93it/s]\n70%|███████ | 7/10 [00:04<00:01, 2.08it/s]\n80%|████████ | 8/10 [00:05<00:00, 2.19it/s]\n90%|█████████ | 9/10 [00:05<00:00, 2.27it/s]\n100%|██████████| 10/10 [00:05<00:00, 2.33it/s]\n100%|██████████| 10/10 [00:05<00:00, 1.67it/s]\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 2\n0%| | 0/10 [00:00<?, ?it/s]\n10%|█ | 1/10 [00:02<00:21, 2.34s/it]\n20%|██ | 2/10 [00:02<00:09, 1.23s/it]\n30%|███ | 3/10 [00:03<00:06, 1.14it/s]\n40%|████ | 4/10 [00:03<00:04, 1.45it/s]\n50%|█████ | 5/10 [00:04<00:02, 1.71it/s]\n60%|██████ | 6/10 [00:04<00:02, 1.90it/s]\n70%|███████ | 7/10 [00:04<00:01, 2.05it/s]\n80%|████████ | 8/10 [00:05<00:00, 2.17it/s]\n90%|█████████ | 9/10 [00:05<00:00, 2.25it/s]\n100%|██████████| 10/10 [00:06<00:00, 2.32it/s]\n100%|██████████| 10/10 [00:06<00:00, 1.64it/s]\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 3\n0%| | 0/10 [00:00<?, ?it/s]\n10%|█ | 1/10 [00:02<00:19, 2.21s/it]\n20%|██ | 2/10 [00:02<00:09, 1.17s/it]\n30%|███ | 3/10 [00:03<00:05, 1.18it/s]\n40%|████ | 4/10 [00:03<00:04, 1.47it/s]\n50%|█████ | 5/10 [00:03<00:02, 1.73it/s]\n60%|██████ | 6/10 [00:04<00:02, 1.93it/s]\n70%|███████ | 7/10 [00:04<00:01, 2.08it/s]\n80%|████████ | 8/10 [00:05<00:00, 2.19it/s]\n90%|█████████ | 9/10 [00:05<00:00, 2.27it/s]\n100%|██████████| 10/10 [00:05<00:00, 2.34it/s]\n100%|██████████| 10/10 [00:05<00:00, 1.68it/s]\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:Saving model and optimizer state at iteration 4 to logs/351b84a7-8622-4c51-a803-ae848d62d158/logs_s2/G_233333333333.pth\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:Saving model and optimizer state at iteration 4 to logs/351b84a7-8622-4c51-a803-ae848d62d158/logs_s2/D_233333333333.pth\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:saving ckpt 351b84a7-8622-4c51-a803-ae848d62d158_e4:Success.\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 4\n0%| | 0/10 [00:00<?, ?it/s]\n10%|█ | 1/10 [00:02<00:20, 2.33s/it]\n20%|██ | 2/10 [00:02<00:09, 1.22s/it]\n30%|███ | 3/10 [00:03<00:06, 1.16it/s]\n40%|████ | 4/10 [00:03<00:04, 1.46it/s]\n50%|█████ | 5/10 [00:04<00:02, 1.71it/s]\n60%|██████ | 6/10 [00:04<00:02, 1.77it/s]\n70%|███████ | 7/10 [00:04<00:01, 1.95it/s]\n80%|████████ | 8/10 [00:05<00:00, 2.09it/s]\n90%|█████████ | 9/10 [00:05<00:00, 2.20it/s]\n100%|██████████| 10/10 [00:06<00:00, 2.27it/s]\n100%|██████████| 10/10 [00:06<00:00, 1.62it/s]\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 5\n0%| | 0/10 [00:00<?, ?it/s]\n10%|█ | 1/10 [00:02<00:21, 2.39s/it]\n20%|██ | 2/10 [00:02<00:09, 1.24s/it]\n30%|███ | 3/10 [00:03<00:06, 1.15it/s]\n40%|████ | 4/10 [00:03<00:04, 1.46it/s]\n50%|█████ | 5/10 [00:04<00:02, 1.71it/s]\n60%|██████ | 6/10 [00:04<00:02, 1.91it/s]\n70%|███████ | 7/10 [00:04<00:01, 2.06it/s]\n80%|████████ | 8/10 [00:05<00:00, 2.18it/s]\n90%|█████████ | 9/10 [00:05<00:00, 2.26it/s]\n100%|██████████| 10/10 [00:06<00:00, 2.32it/s]\n100%|██████████| 10/10 [00:06<00:00, 1.64it/s]\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 6\n0%| | 0/10 [00:00<?, ?it/s]\n10%|█ | 1/10 [00:02<00:20, 2.32s/it]\n20%|██ | 2/10 [00:02<00:09, 1.20s/it]\n30%|███ | 3/10 [00:03<00:06, 1.16it/s]\n40%|████ | 4/10 [00:03<00:04, 1.46it/s]\n50%|█████ | 5/10 [00:04<00:02, 1.71it/s]\n60%|██████ | 6/10 [00:04<00:02, 1.91it/s]\n70%|███████ | 7/10 [00:04<00:01, 2.06it/s]\n80%|████████ | 8/10 [00:05<00:00, 2.18it/s]\n90%|█████████ | 9/10 [00:05<00:00, 2.26it/s]\n100%|██████████| 10/10 [00:06<00:00, 2.32it/s]\n100%|██████████| 10/10 [00:06<00:00, 1.66it/s]\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 7\n0%| | 0/10 [00:00<?, ?it/s]\n10%|█ | 1/10 [00:02<00:21, 2.34s/it]\n20%|██ | 2/10 [00:02<00:09, 1.22s/it]\n30%|███ | 3/10 [00:03<00:06, 1.15it/s]\n40%|████ | 4/10 [00:03<00:04, 1.45it/s]\n50%|█████ | 5/10 [00:04<00:02, 1.70it/s]\n60%|██████ | 6/10 [00:04<00:02, 1.90it/s]\n70%|███████ | 7/10 [00:04<00:01, 2.04it/s]\n80%|████████ | 8/10 [00:05<00:00, 2.16it/s]\n90%|█████████ | 9/10 [00:05<00:00, 2.24it/s]\n100%|██████████| 10/10 [00:06<00:00, 2.31it/s]\n100%|██████████| 10/10 [00:06<00:00, 1.64it/s]\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:Saving model and optimizer state at iteration 8 to logs/351b84a7-8622-4c51-a803-ae848d62d158/logs_s2/G_233333333333.pth\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:Saving model and optimizer state at iteration 8 to logs/351b84a7-8622-4c51-a803-ae848d62d158/logs_s2/D_233333333333.pth\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:saving ckpt 351b84a7-8622-4c51-a803-ae848d62d158_e8:Success.\nINFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 8\n('SoVITS训练完成', {'__type__': 'update', 'visible': True}, {'__type__': 'update', 'visible': False})\n('GPT训练开始:\"/root/.pyenv/versions/3.9.19/bin/python\" GPT_SoVITS/s1_train.py --config_file \"/src/TEMP/tmp_s1.yaml\" ', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True})\n\"/root/.pyenv/versions/3.9.19/bin/python\" GPT_SoVITS/s1_train.py --config_file \"/src/TEMP/tmp_s1.yaml\"\nIMPORTANT: You are using gradio version 3.38.0, however version 4.29.0 is available, please upgrade.\n--------\nSeed set to 1234\nUsing 16bit Automatic Mixed Precision (AMP)\nGPU available: True (cuda), used: True\nTPU available: False, using: 0 TPU cores\nHPU available: False, using: 0 HPUs\n<All keys matched successfully>\nckpt_path: None\n[rank: 0] Seed set to 1234\nInitializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1\n----------------------------------------------------------------------------------------------------\ndistributed_backend=nccl\nAll distributed processes registered. Starting with 1 processes\n----------------------------------------------------------------------------------------------------\nMissing logger folder: logs/351b84a7-8622-4c51-a803-ae848d62d158/logs_s1/logs_s1\nsemantic_data_len: 1\nphoneme_data_len: 1\nitem_name semantic_audio\n0 origin.mp3_0000000000_0000508480.wav 520 721 1005 578 283 919 290 96 142 545 8 17 7...\ndataset.__len__(): 100\nLOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n| Name | Type | Params | Mode\n-------------------------------------------------------\n0 | model | Text2SemanticDecoder | 77.5 M | train\n-------------------------------------------------------\n77.5 M Trainable params\n0 Non-trainable params\n77.5 M Total params\n309.975 Total estimated model params size (MB)\n/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (10) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\nTraining: | | 0/? [00:00<?, ?it/s]\nTraining: 0%| | 0/10 [00:00<?, ?it/s]\nEpoch 0: 0%| | 0/10 [00:00<?, ?it/s]\nEpoch 0: 10%|█ | 1/10 [00:00<00:03, 2.34it/s]\nEpoch 0: 10%|█ | 1/10 [00:00<00:03, 2.34it/s, v_num=0, total_loss_step=2.2e+4, lr_step=1e-5, top_3_acc_step=0.132]\nEpoch 0: 20%|██ | 2/10 [00:00<00:02, 3.33it/s, v_num=0, total_loss_step=2.2e+4, lr_step=1e-5, top_3_acc_step=0.132]\nEpoch 0: 20%|██ | 2/10 [00:00<00:02, 3.33it/s, v_num=0, total_loss_step=2.19e+4, lr_step=1e-5, top_3_acc_step=0.126]\nEpoch 0: 30%|███ | 3/10 [00:00<00:01, 3.91it/s, v_num=0, total_loss_step=2.19e+4, lr_step=1e-5, top_3_acc_step=0.126]\nEpoch 0: 30%|███ | 3/10 [00:00<00:01, 3.91it/s, v_num=0, total_loss_step=2.19e+4, lr_step=1e-5, top_3_acc_step=0.130]\nEpoch 0: 40%|████ | 4/10 [00:00<00:01, 4.29it/s, v_num=0, total_loss_step=2.19e+4, lr_step=1e-5, top_3_acc_step=0.130]\nEpoch 0: 40%|████ | 4/10 [00:00<00:01, 4.29it/s, v_num=0, total_loss_step=2.2e+4, lr_step=1e-5, top_3_acc_step=0.131]\nEpoch 0: 50%|█████ | 5/10 [00:01<00:01, 4.52it/s, v_num=0, total_loss_step=2.2e+4, lr_step=1e-5, top_3_acc_step=0.131]\nEpoch 0: 50%|█████ | 5/10 [00:01<00:01, 4.51it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.010, top_3_acc_step=0.128]\nEpoch 0: 60%|██████ | 6/10 [00:01<00:00, 4.70it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.010, top_3_acc_step=0.128]\nEpoch 0: 60%|██████ | 6/10 [00:01<00:00, 4.70it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.010, top_3_acc_step=0.127]\nEpoch 0: 70%|███████ | 7/10 [00:01<00:00, 4.85it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.010, top_3_acc_step=0.127]\nEpoch 0: 70%|███████ | 7/10 [00:01<00:00, 4.85it/s, v_num=0, total_loss_step=21934.0, lr_step=0.010, top_3_acc_step=0.130]\nEpoch 0: 80%|████████ | 8/10 [00:01<00:00, 4.96it/s, v_num=0, total_loss_step=21934.0, lr_step=0.010, top_3_acc_step=0.130]\nEpoch 0: 80%|████████ | 8/10 [00:01<00:00, 4.96it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.010, top_3_acc_step=0.129]\nEpoch 0: 90%|█████████ | 9/10 [00:01<00:00, 5.04it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.010, top_3_acc_step=0.129]\nEpoch 0: 90%|█████████ | 9/10 [00:01<00:00, 5.04it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131]\nEpoch 0: 100%|██████████| 10/10 [00:01<00:00, 5.39it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131]\nEpoch 0: 100%|██████████| 10/10 [00:01<00:00, 5.39it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.144]\nEpoch 0: 100%|██████████| 10/10 [00:01<00:00, 5.38it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.144, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 0: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.144, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.144, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 10%|█ | 1/10 [00:00<00:01, 5.41it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.144, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 10%|█ | 1/10 [00:00<00:01, 5.39it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 20%|██ | 2/10 [00:00<00:01, 5.61it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 20%|██ | 2/10 [00:00<00:01, 5.60it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 30%|███ | 3/10 [00:00<00:01, 5.73it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 30%|███ | 3/10 [00:00<00:01, 5.73it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.134, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 40%|████ | 4/10 [00:00<00:01, 5.80it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.134, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 40%|████ | 4/10 [00:00<00:01, 5.79it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 50%|█████ | 5/10 [00:00<00:00, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 50%|█████ | 5/10 [00:00<00:00, 5.77it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 60%|██████ | 6/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 60%|██████ | 6/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 70%|███████ | 7/10 [00:01<00:00, 5.84it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 70%|███████ | 7/10 [00:01<00:00, 5.84it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 80%|████████ | 8/10 [00:01<00:00, 5.86it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 80%|████████ | 8/10 [00:01<00:00, 5.86it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.126, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 90%|█████████ | 9/10 [00:01<00:00, 5.85it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.126, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 90%|█████████ | 9/10 [00:01<00:00, 5.85it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 100%|██████████| 10/10 [00:01<00:00, 6.31it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 100%|██████████| 10/10 [00:01<00:00, 6.30it/s, v_num=0, total_loss_step=1.98e+3, lr_step=0.002, top_3_acc_step=0.142, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129]\nEpoch 1: 100%|██████████| 10/10 [00:01<00:00, 6.30it/s, v_num=0, total_loss_step=1.98e+3, lr_step=0.002, top_3_acc_step=0.142, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 1: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.98e+3, lr_step=0.002, top_3_acc_step=0.142, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.98e+3, lr_step=0.002, top_3_acc_step=0.142, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 10%|█ | 1/10 [00:00<00:01, 5.31it/s, v_num=0, total_loss_step=1.98e+3, lr_step=0.002, top_3_acc_step=0.142, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 10%|█ | 1/10 [00:00<00:01, 5.30it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.126, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 20%|██ | 2/10 [00:00<00:01, 5.58it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.126, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 20%|██ | 2/10 [00:00<00:01, 5.57it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 30%|███ | 3/10 [00:00<00:01, 5.69it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 40%|████ | 4/10 [00:00<00:01, 5.77it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.125, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 50%|█████ | 5/10 [00:00<00:00, 5.77it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.125, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 50%|█████ | 5/10 [00:00<00:00, 5.77it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 60%|██████ | 6/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 60%|██████ | 6/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.135, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 70%|███████ | 7/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.135, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 70%|███████ | 7/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.133, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 80%|████████ | 8/10 [00:01<00:00, 5.83it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.133, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 80%|████████ | 8/10 [00:01<00:00, 5.83it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 90%|█████████ | 9/10 [00:01<00:00, 5.83it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 90%|█████████ | 9/10 [00:01<00:00, 5.83it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 100%|██████████| 10/10 [00:01<00:00, 6.28it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 100%|██████████| 10/10 [00:01<00:00, 6.27it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.139, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 100%|██████████| 10/10 [00:01<00:00, 6.27it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.139, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 2: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.139, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.139, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 10%|█ | 1/10 [00:00<00:01, 5.41it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.139, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 10%|█ | 1/10 [00:00<00:01, 5.39it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 20%|██ | 2/10 [00:00<00:01, 5.57it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 20%|██ | 2/10 [00:00<00:01, 5.56it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.125, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 30%|███ | 3/10 [00:00<00:01, 5.67it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.125, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 30%|███ | 3/10 [00:00<00:01, 5.66it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 40%|████ | 4/10 [00:00<00:01, 5.73it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 40%|████ | 4/10 [00:00<00:01, 5.72it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 50%|█████ | 5/10 [00:00<00:00, 5.74it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 50%|█████ | 5/10 [00:00<00:00, 5.73it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.132, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 60%|██████ | 6/10 [00:01<00:00, 5.75it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.132, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 60%|██████ | 6/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 70%|███████ | 7/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 70%|███████ | 7/10 [00:01<00:00, 5.77it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.132, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 80%|████████ | 8/10 [00:01<00:00, 5.80it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.132, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 80%|████████ | 8/10 [00:01<00:00, 5.80it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.127, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 90%|█████████ | 9/10 [00:01<00:00, 5.80it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.127, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 90%|█████████ | 9/10 [00:01<00:00, 5.80it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 100%|██████████| 10/10 [00:01<00:00, 6.25it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 100%|██████████| 10/10 [00:01<00:00, 6.25it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.122, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 100%|██████████| 10/10 [00:01<00:00, 6.24it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.122, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 3: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.122, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.122, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 10%|█ | 1/10 [00:00<00:01, 5.23it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.122, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 10%|█ | 1/10 [00:00<00:01, 5.22it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 20%|██ | 2/10 [00:00<00:01, 5.58it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 20%|██ | 2/10 [00:00<00:01, 5.58it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.138, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 30%|███ | 3/10 [00:00<00:01, 5.71it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.138, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 30%|███ | 3/10 [00:00<00:01, 5.71it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.132, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 40%|████ | 4/10 [00:00<00:01, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.132, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 40%|████ | 4/10 [00:00<00:01, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.133, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 50%|█████ | 5/10 [00:00<00:00, 5.74it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.133, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 50%|█████ | 5/10 [00:00<00:00, 5.73it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 60%|██████ | 6/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 60%|██████ | 6/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.126, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 70%|███████ | 7/10 [00:01<00:00, 5.82it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.126, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 70%|███████ | 7/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 80%|████████ | 8/10 [00:01<00:00, 5.84it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 80%|████████ | 8/10 [00:01<00:00, 5.83it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.127, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 90%|█████████ | 9/10 [00:01<00:00, 5.84it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.127, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 90%|█████████ | 9/10 [00:01<00:00, 5.84it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 100%|██████████| 10/10 [00:01<00:00, 6.29it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 100%|██████████| 10/10 [00:01<00:00, 6.29it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129]\nEpoch 4: 100%|██████████| 10/10 [00:01<00:00, 6.28it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 4: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 10%|█ | 1/10 [00:00<00:01, 5.30it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 10%|█ | 1/10 [00:00<00:01, 5.28it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 20%|██ | 2/10 [00:00<00:01, 5.60it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 20%|██ | 2/10 [00:00<00:01, 5.59it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.127, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.127, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 40%|████ | 4/10 [00:00<00:01, 5.77it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 40%|████ | 4/10 [00:00<00:01, 5.77it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.134, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 50%|█████ | 5/10 [00:00<00:00, 5.50it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.134, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 50%|█████ | 5/10 [00:00<00:00, 5.49it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 60%|██████ | 6/10 [00:01<00:00, 5.57it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 60%|██████ | 6/10 [00:01<00:00, 5.57it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 70%|███████ | 7/10 [00:01<00:00, 5.60it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 70%|███████ | 7/10 [00:01<00:00, 5.60it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.135, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 80%|████████ | 8/10 [00:01<00:00, 5.65it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.135, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 80%|████████ | 8/10 [00:01<00:00, 5.65it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.133, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 90%|█████████ | 9/10 [00:01<00:00, 5.60it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.133, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 90%|█████████ | 9/10 [00:01<00:00, 5.60it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.134, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 100%|██████████| 10/10 [00:01<00:00, 6.03it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.134, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 100%|██████████| 10/10 [00:01<00:00, 6.03it/s, v_num=0, total_loss_step=1.9e+3, lr_step=0.002, top_3_acc_step=0.157, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131]\nEpoch 5: 100%|██████████| 10/10 [00:01<00:00, 6.02it/s, v_num=0, total_loss_step=1.9e+3, lr_step=0.002, top_3_acc_step=0.157, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 5: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.9e+3, lr_step=0.002, top_3_acc_step=0.157, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.9e+3, lr_step=0.002, top_3_acc_step=0.157, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 10%|█ | 1/10 [00:00<00:01, 5.23it/s, v_num=0, total_loss_step=1.9e+3, lr_step=0.002, top_3_acc_step=0.157, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 10%|█ | 1/10 [00:00<00:01, 5.22it/s, v_num=0, total_loss_step=2.08e+4, lr_step=0.002, top_3_acc_step=0.159, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 20%|██ | 2/10 [00:00<00:01, 5.57it/s, v_num=0, total_loss_step=2.08e+4, lr_step=0.002, top_3_acc_step=0.159, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 20%|██ | 2/10 [00:00<00:01, 5.56it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.155, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.155, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 30%|███ | 3/10 [00:00<00:01, 5.69it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.158, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.158, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.160, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 50%|█████ | 5/10 [00:00<00:00, 5.63it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.160, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 50%|█████ | 5/10 [00:00<00:00, 5.63it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.160, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 60%|██████ | 6/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.160, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 60%|██████ | 6/10 [00:01<00:00, 5.68it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.189, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 70%|███████ | 7/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.189, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 70%|███████ | 7/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.186, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 80%|████████ | 8/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.186, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 80%|████████ | 8/10 [00:01<00:00, 5.75it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.179, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 90%|█████████ | 9/10 [00:01<00:00, 5.67it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.179, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 90%|█████████ | 9/10 [00:01<00:00, 5.67it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.185, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 100%|██████████| 10/10 [00:01<00:00, 6.10it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.185, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 100%|██████████| 10/10 [00:01<00:00, 6.10it/s, v_num=0, total_loss_step=1.75e+3, lr_step=0.002, top_3_acc_step=0.195, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132]\nEpoch 6: 100%|██████████| 10/10 [00:01<00:00, 6.09it/s, v_num=0, total_loss_step=1.75e+3, lr_step=0.002, top_3_acc_step=0.195, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 6: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.75e+3, lr_step=0.002, top_3_acc_step=0.195, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.75e+3, lr_step=0.002, top_3_acc_step=0.195, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 10%|█ | 1/10 [00:00<00:01, 5.27it/s, v_num=0, total_loss_step=1.75e+3, lr_step=0.002, top_3_acc_step=0.195, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 10%|█ | 1/10 [00:00<00:01, 5.26it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.216, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 20%|██ | 2/10 [00:00<00:01, 5.60it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.216, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 20%|██ | 2/10 [00:00<00:01, 5.59it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.221, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 30%|███ | 3/10 [00:00<00:01, 5.72it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.221, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 30%|███ | 3/10 [00:00<00:01, 5.71it/s, v_num=0, total_loss_step=1.91e+4, lr_step=0.002, top_3_acc_step=0.221, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 40%|████ | 4/10 [00:00<00:01, 5.78it/s, v_num=0, total_loss_step=1.91e+4, lr_step=0.002, top_3_acc_step=0.221, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 40%|████ | 4/10 [00:00<00:01, 5.77it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.217, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 50%|█████ | 5/10 [00:00<00:00, 5.63it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.217, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 50%|█████ | 5/10 [00:00<00:00, 5.62it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.225, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 60%|██████ | 6/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.225, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 60%|██████ | 6/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.260, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 70%|███████ | 7/10 [00:01<00:00, 5.73it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.260, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 70%|███████ | 7/10 [00:01<00:00, 5.73it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.263, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 80%|████████ | 8/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.263, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 80%|████████ | 8/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.255, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 90%|█████████ | 9/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.255, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 90%|█████████ | 9/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.256, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 100%|██████████| 10/10 [00:01<00:00, 6.14it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.256, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 100%|██████████| 10/10 [00:01<00:00, 6.13it/s, v_num=0, total_loss_step=1.56e+3, lr_step=0.002, top_3_acc_step=0.324, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170]\nEpoch 7: 100%|██████████| 10/10 [00:01<00:00, 6.13it/s, v_num=0, total_loss_step=1.56e+3, lr_step=0.002, top_3_acc_step=0.324, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 7: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.56e+3, lr_step=0.002, top_3_acc_step=0.324, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.56e+3, lr_step=0.002, top_3_acc_step=0.324, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 10%|█ | 1/10 [00:00<00:01, 5.31it/s, v_num=0, total_loss_step=1.56e+3, lr_step=0.002, top_3_acc_step=0.324, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 10%|█ | 1/10 [00:00<00:01, 5.29it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.303, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 20%|██ | 2/10 [00:00<00:01, 5.59it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.303, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 20%|██ | 2/10 [00:00<00:01, 5.59it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.317, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 30%|███ | 3/10 [00:00<00:01, 5.72it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.317, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 30%|███ | 3/10 [00:00<00:01, 5.72it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.314, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 40%|████ | 4/10 [00:00<00:01, 5.79it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.314, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 40%|████ | 4/10 [00:00<00:01, 5.78it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.310, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 50%|█████ | 5/10 [00:00<00:00, 5.64it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.310, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 50%|█████ | 5/10 [00:00<00:00, 5.64it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.313, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 60%|██████ | 6/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.313, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 60%|██████ | 6/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.376, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 70%|███████ | 7/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.376, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 70%|███████ | 7/10 [00:01<00:00, 5.73it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.374, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 80%|████████ | 8/10 [00:01<00:00, 5.77it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.374, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 80%|████████ | 8/10 [00:01<00:00, 5.77it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.383, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 90%|█████████ | 9/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.383, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 90%|█████████ | 9/10 [00:01<00:00, 5.68it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.373, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 100%|██████████| 10/10 [00:01<00:00, 6.12it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.373, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 100%|██████████| 10/10 [00:01<00:00, 6.12it/s, v_num=0, total_loss_step=1.34e+3, lr_step=0.002, top_3_acc_step=0.461, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238]\nEpoch 8: 100%|██████████| 10/10 [00:01<00:00, 6.11it/s, v_num=0, total_loss_step=1.34e+3, lr_step=0.002, top_3_acc_step=0.461, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 8: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.34e+3, lr_step=0.002, top_3_acc_step=0.461, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.34e+3, lr_step=0.002, top_3_acc_step=0.461, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 10%|█ | 1/10 [00:00<00:01, 5.24it/s, v_num=0, total_loss_step=1.34e+3, lr_step=0.002, top_3_acc_step=0.461, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 10%|█ | 1/10 [00:00<00:01, 5.22it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.444, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 20%|██ | 2/10 [00:00<00:01, 5.58it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.444, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 20%|██ | 2/10 [00:00<00:01, 5.57it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.448, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.448, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 30%|███ | 3/10 [00:00<00:01, 5.69it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.442, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.442, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.442, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 50%|█████ | 5/10 [00:00<00:00, 5.57it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.442, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 50%|█████ | 5/10 [00:00<00:00, 5.57it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.448, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 60%|██████ | 6/10 [00:01<00:00, 5.65it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.448, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 60%|██████ | 6/10 [00:01<00:00, 5.64it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.510, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 70%|███████ | 7/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.510, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 70%|███████ | 7/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.519, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 80%|████████ | 8/10 [00:01<00:00, 5.73it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.519, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 80%|████████ | 8/10 [00:01<00:00, 5.73it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.516, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 90%|█████████ | 9/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.516, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 90%|█████████ | 9/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.514, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 100%|██████████| 10/10 [00:01<00:00, 6.19it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.514, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 100%|██████████| 10/10 [00:01<00:00, 6.19it/s, v_num=0, total_loss_step=1.21e+3, lr_step=0.002, top_3_acc_step=0.501, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341]\nEpoch 9: 100%|██████████| 10/10 [00:01<00:00, 6.18it/s, v_num=0, total_loss_step=1.21e+3, lr_step=0.002, top_3_acc_step=0.501, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 9: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.21e+3, lr_step=0.002, top_3_acc_step=0.501, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.21e+3, lr_step=0.002, top_3_acc_step=0.501, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 10%|█ | 1/10 [00:00<00:01, 5.27it/s, v_num=0, total_loss_step=1.21e+3, lr_step=0.002, top_3_acc_step=0.501, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 10%|█ | 1/10 [00:00<00:01, 5.25it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.509, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 20%|██ | 2/10 [00:00<00:01, 5.57it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.509, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 20%|██ | 2/10 [00:00<00:01, 5.56it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.512, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.512, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.511, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.511, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.506, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 50%|█████ | 5/10 [00:00<00:00, 5.72it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.506, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 50%|█████ | 5/10 [00:00<00:00, 5.72it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.513, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 60%|██████ | 6/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.513, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 60%|██████ | 6/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.508, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 70%|███████ | 7/10 [00:01<00:00, 5.79it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.508, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 70%|███████ | 7/10 [00:01<00:00, 5.79it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.505, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 80%|████████ | 8/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.505, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 80%|████████ | 8/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.513, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 90%|█████████ | 9/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.513, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 90%|█████████ | 9/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.520, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 100%|██████████| 10/10 [00:01<00:00, 6.19it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.520, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 100%|██████████| 10/10 [00:01<00:00, 6.18it/s, v_num=0, total_loss_step=1.08e+3, lr_step=0.002, top_3_acc_step=0.567, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476]\nEpoch 10: 100%|██████████| 10/10 [00:01<00:00, 6.18it/s, v_num=0, total_loss_step=1.08e+3, lr_step=0.002, top_3_acc_step=0.567, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 10: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.08e+3, lr_step=0.002, top_3_acc_step=0.567, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.08e+3, lr_step=0.002, top_3_acc_step=0.567, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 10%|█ | 1/10 [00:00<00:01, 5.43it/s, v_num=0, total_loss_step=1.08e+3, lr_step=0.002, top_3_acc_step=0.567, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 10%|█ | 1/10 [00:00<00:01, 5.42it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.587, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 20%|██ | 2/10 [00:00<00:01, 5.62it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.587, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 20%|██ | 2/10 [00:00<00:01, 5.61it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.592, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 30%|███ | 3/10 [00:00<00:01, 5.73it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.592, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 30%|███ | 3/10 [00:00<00:01, 5.73it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.578, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 40%|████ | 4/10 [00:00<00:01, 5.80it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.578, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 40%|████ | 4/10 [00:00<00:01, 5.79it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.578, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 50%|█████ | 5/10 [00:00<00:00, 5.65it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.578, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 50%|█████ | 5/10 [00:00<00:00, 5.65it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.589, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 60%|██████ | 6/10 [00:01<00:00, 5.71it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.589, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 60%|██████ | 6/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.661, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 70%|███████ | 7/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.661, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 70%|███████ | 7/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.658, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 80%|████████ | 8/10 [00:01<00:00, 5.77it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.658, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 80%|████████ | 8/10 [00:01<00:00, 5.77it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.668, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 90%|█████████ | 9/10 [00:01<00:00, 5.68it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.668, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 90%|█████████ | 9/10 [00:01<00:00, 5.68it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.665, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 100%|██████████| 10/10 [00:01<00:00, 6.12it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.665, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 100%|██████████| 10/10 [00:01<00:00, 6.12it/s, v_num=0, total_loss_step=783.0, lr_step=0.002, top_3_acc_step=0.752, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511]\nEpoch 11: 100%|██████████| 10/10 [00:01<00:00, 6.12it/s, v_num=0, total_loss_step=783.0, lr_step=0.002, top_3_acc_step=0.752, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 11: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=783.0, lr_step=0.002, top_3_acc_step=0.752, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=783.0, lr_step=0.002, top_3_acc_step=0.752, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 10%|█ | 1/10 [00:00<00:01, 5.47it/s, v_num=0, total_loss_step=783.0, lr_step=0.002, top_3_acc_step=0.752, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 10%|█ | 1/10 [00:00<00:01, 5.45it/s, v_num=0, total_loss_step=8.83e+3, lr_step=0.002, top_3_acc_step=0.730, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 20%|██ | 2/10 [00:00<00:01, 5.69it/s, v_num=0, total_loss_step=8.83e+3, lr_step=0.002, top_3_acc_step=0.730, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 20%|██ | 2/10 [00:00<00:01, 5.68it/s, v_num=0, total_loss_step=8.75e+3, lr_step=0.002, top_3_acc_step=0.739, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 30%|███ | 3/10 [00:00<00:01, 5.75it/s, v_num=0, total_loss_step=8.75e+3, lr_step=0.002, top_3_acc_step=0.739, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 30%|███ | 3/10 [00:00<00:01, 5.75it/s, v_num=0, total_loss_step=8.78e+3, lr_step=0.002, top_3_acc_step=0.732, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 40%|████ | 4/10 [00:00<00:01, 5.81it/s, v_num=0, total_loss_step=8.78e+3, lr_step=0.002, top_3_acc_step=0.732, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 40%|████ | 4/10 [00:00<00:01, 5.80it/s, v_num=0, total_loss_step=8.71e+3, lr_step=0.002, top_3_acc_step=0.734, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 50%|█████ | 5/10 [00:00<00:00, 5.68it/s, v_num=0, total_loss_step=8.71e+3, lr_step=0.002, top_3_acc_step=0.734, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 50%|█████ | 5/10 [00:00<00:00, 5.68it/s, v_num=0, total_loss_step=8.76e+3, lr_step=0.002, top_3_acc_step=0.736, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 60%|██████ | 6/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=8.76e+3, lr_step=0.002, top_3_acc_step=0.736, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 60%|██████ | 6/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=7.45e+3, lr_step=0.002, top_3_acc_step=0.797, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 70%|███████ | 7/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=7.45e+3, lr_step=0.002, top_3_acc_step=0.797, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 70%|███████ | 7/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=7.4e+3, lr_step=0.002, top_3_acc_step=0.794, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 80%|████████ | 8/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=7.4e+3, lr_step=0.002, top_3_acc_step=0.794, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 80%|████████ | 8/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=7.47e+3, lr_step=0.002, top_3_acc_step=0.792, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 90%|█████████ | 9/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=7.47e+3, lr_step=0.002, top_3_acc_step=0.792, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 90%|█████████ | 9/10 [00:01<00:00, 5.71it/s, v_num=0, total_loss_step=7.4e+3, lr_step=0.002, top_3_acc_step=0.798, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 100%|██████████| 10/10 [00:01<00:00, 6.15it/s, v_num=0, total_loss_step=7.4e+3, lr_step=0.002, top_3_acc_step=0.798, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 100%|██████████| 10/10 [00:01<00:00, 6.15it/s, v_num=0, total_loss_step=570.0, lr_step=0.002, top_3_acc_step=0.843, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621]\nEpoch 12: 100%|██████████| 10/10 [00:01<00:00, 6.14it/s, v_num=0, total_loss_step=570.0, lr_step=0.002, top_3_acc_step=0.843, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 12: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=570.0, lr_step=0.002, top_3_acc_step=0.843, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=570.0, lr_step=0.002, top_3_acc_step=0.843, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 10%|█ | 1/10 [00:00<00:01, 5.31it/s, v_num=0, total_loss_step=570.0, lr_step=0.002, top_3_acc_step=0.843, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 10%|█ | 1/10 [00:00<00:01, 5.29it/s, v_num=0, total_loss_step=6.31e+3, lr_step=0.002, top_3_acc_step=0.851, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 20%|██ | 2/10 [00:00<00:01, 5.62it/s, v_num=0, total_loss_step=6.31e+3, lr_step=0.002, top_3_acc_step=0.851, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 20%|██ | 2/10 [00:00<00:01, 5.61it/s, v_num=0, total_loss_step=6.27e+3, lr_step=0.002, top_3_acc_step=0.857, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=6.27e+3, lr_step=0.002, top_3_acc_step=0.857, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 30%|███ | 3/10 [00:00<00:01, 5.69it/s, v_num=0, total_loss_step=6.28e+3, lr_step=0.002, top_3_acc_step=0.856, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=6.28e+3, lr_step=0.002, top_3_acc_step=0.856, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=6.24e+3, lr_step=0.002, top_3_acc_step=0.856, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 50%|█████ | 5/10 [00:00<00:00, 5.64it/s, v_num=0, total_loss_step=6.24e+3, lr_step=0.002, top_3_acc_step=0.856, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 50%|█████ | 5/10 [00:00<00:00, 5.64it/s, v_num=0, total_loss_step=6.29e+3, lr_step=0.002, top_3_acc_step=0.854, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 60%|██████ | 6/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=6.29e+3, lr_step=0.002, top_3_acc_step=0.854, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 60%|██████ | 6/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=5.24e+3, lr_step=0.002, top_3_acc_step=0.901, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 70%|███████ | 7/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=5.24e+3, lr_step=0.002, top_3_acc_step=0.901, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 70%|███████ | 7/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=5.18e+3, lr_step=0.002, top_3_acc_step=0.910, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 80%|████████ | 8/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=5.18e+3, lr_step=0.002, top_3_acc_step=0.910, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 80%|████████ | 8/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=5.23e+3, lr_step=0.002, top_3_acc_step=0.904, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 90%|█████████ | 9/10 [00:01<00:00, 5.66it/s, v_num=0, total_loss_step=5.23e+3, lr_step=0.002, top_3_acc_step=0.904, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 90%|█████████ | 9/10 [00:01<00:00, 5.66it/s, v_num=0, total_loss_step=5.26e+3, lr_step=0.002, top_3_acc_step=0.899, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 100%|██████████| 10/10 [00:01<00:00, 6.09it/s, v_num=0, total_loss_step=5.26e+3, lr_step=0.002, top_3_acc_step=0.899, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 100%|██████████| 10/10 [00:01<00:00, 6.09it/s, v_num=0, total_loss_step=395.0, lr_step=0.002, top_3_acc_step=0.929, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762]\nEpoch 13: 100%|██████████| 10/10 [00:01<00:00, 6.09it/s, v_num=0, total_loss_step=395.0, lr_step=0.002, top_3_acc_step=0.929, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 13: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=395.0, lr_step=0.002, top_3_acc_step=0.929, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=395.0, lr_step=0.002, top_3_acc_step=0.929, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 10%|█ | 1/10 [00:00<00:01, 5.43it/s, v_num=0, total_loss_step=395.0, lr_step=0.002, top_3_acc_step=0.929, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 10%|█ | 1/10 [00:00<00:01, 5.41it/s, v_num=0, total_loss_step=4.31e+3, lr_step=0.002, top_3_acc_step=0.943, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 20%|██ | 2/10 [00:00<00:01, 5.65it/s, v_num=0, total_loss_step=4.31e+3, lr_step=0.002, top_3_acc_step=0.943, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 20%|██ | 2/10 [00:00<00:01, 5.64it/s, v_num=0, total_loss_step=4.3e+3, lr_step=0.002, top_3_acc_step=0.943, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 30%|███ | 3/10 [00:00<00:01, 5.73it/s, v_num=0, total_loss_step=4.3e+3, lr_step=0.002, top_3_acc_step=0.943, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 30%|███ | 3/10 [00:00<00:01, 5.72it/s, v_num=0, total_loss_step=4.28e+3, lr_step=0.002, top_3_acc_step=0.945, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 40%|████ | 4/10 [00:00<00:01, 5.77it/s, v_num=0, total_loss_step=4.28e+3, lr_step=0.002, top_3_acc_step=0.945, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=4.32e+3, lr_step=0.002, top_3_acc_step=0.938, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 50%|█████ | 5/10 [00:00<00:00, 5.62it/s, v_num=0, total_loss_step=4.32e+3, lr_step=0.002, top_3_acc_step=0.938, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 50%|█████ | 5/10 [00:00<00:00, 5.61it/s, v_num=0, total_loss_step=4.31e+3, lr_step=0.002, top_3_acc_step=0.944, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 60%|██████ | 6/10 [00:01<00:00, 5.68it/s, v_num=0, total_loss_step=4.31e+3, lr_step=0.002, top_3_acc_step=0.944, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 60%|██████ | 6/10 [00:01<00:00, 5.68it/s, v_num=0, total_loss_step=3.49e+3, lr_step=0.002, top_3_acc_step=0.965, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 70%|███████ | 7/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=3.49e+3, lr_step=0.002, top_3_acc_step=0.965, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 70%|███████ | 7/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=3.44e+3, lr_step=0.002, top_3_acc_step=0.967, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 80%|████████ | 8/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=3.44e+3, lr_step=0.002, top_3_acc_step=0.967, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 80%|████████ | 8/10 [00:01<00:00, 5.75it/s, v_num=0, total_loss_step=3.41e+3, lr_step=0.002, top_3_acc_step=0.971, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 90%|█████████ | 9/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=3.41e+3, lr_step=0.002, top_3_acc_step=0.971, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 90%|█████████ | 9/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=3.44e+3, lr_step=0.002, top_3_acc_step=0.971, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 100%|██████████| 10/10 [00:01<00:00, 6.13it/s, v_num=0, total_loss_step=3.44e+3, lr_step=0.002, top_3_acc_step=0.971, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 100%|██████████| 10/10 [00:01<00:00, 6.13it/s, v_num=0, total_loss_step=261.0, lr_step=0.002, top_3_acc_step=0.980, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877]\nEpoch 14: 100%|██████████| 10/10 [00:01<00:00, 6.12it/s, v_num=0, total_loss_step=261.0, lr_step=0.002, top_3_acc_step=0.980, total_loss_epoch=3.89e+3, lr_epoch=0.002, top_3_acc_epoch=0.954]`Trainer.fit` stopped: `max_epochs=15` reached.\nEpoch 14: 100%|██████████| 10/10 [00:03<00:00, 2.80it/s, v_num=0, total_loss_step=261.0, lr_step=0.002, top_3_acc_step=0.980, total_loss_epoch=3.89e+3, lr_epoch=0.002, top_3_acc_epoch=0.954]\n('GPT训练完成', {'__type__': 'update', 'visible': True}, {'__type__': 'update', 'visible': False})\nFiles in the zip archive:\ninput.wav\nlog.txt\nasr_opt/denoise_opt.list\ndenoise_opt/origin.mp3_0000000000_0000508480.wav\n351b84a7-8622-4c51-a803-ae848d62d158_e8_s80.pth\n351b84a7-8622-4c51-a803-ae848d62d158-e15.ckpt\nCreated zip file: 351b84a7-8622-4c51-a803-ae848d62d158.zip", "metrics": { "predict_time": 224.418920217, "total_time": 378.967813 }, "output": { "zip_url": "https://replicate.delivery/pbxt/ga1uOrFf6fikm0dCby3ywnEMOI5MSIWiiaBUOTC2U6XgWRATA/351b84a7-8622-4c51-a803-ae848d62d158.zip", "audio_url": "https://replicate.delivery/pbxt/FXc7LfgsbdWpekCGC3gwAemvhvBMStSI1zbh61TUpG0ItiAmA/origin.mp3" }, "started_at": "2024-06-19T22:59:16.643893Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/9pa655bmdxrgj0cg6avt20kf9m", "cancel": "https://api.replicate.com/v1/predictions/9pa655bmdxrgj0cg6avt20kf9m/cancel" }, "version": "0dcb11219f474e580e033c889114f75cf8e8002d5c50801b8096ac6ac525e325" }
Generated inCopied file to input/351b84a7-8622-4c51-a803-ae848d62d158/origin.mp3 执行完毕,请检查输出文件 2024-06-19 22:59:23,923 - modelscope - INFO - PyTorch version 2.0.1+cu118 Found. 2024-06-19 22:59:23,924 - modelscope - INFO - Loading ast index from /root/.cache/modelscope/ast_indexer 2024-06-19 22:59:23,924 - modelscope - INFO - No valid ast index found from /root/.cache/modelscope/ast_indexer, generating ast index from prebuilt! 2024-06-19 22:59:23,996 - modelscope - INFO - Loading done! Current index file version is 1.10.0, with md5 b51edac0938f2bdbb763b5eb2eac94ee and a total number of 946 components indexed 2024-06-19 22:59:29,620 - modelscope - WARNING - Model revision not specified, use revision: v1.0.2 Downloading: 0%| | 0.00/1.45k [00:00<?, ?B/s] Downloading: 100%|██████████| 1.45k/1.45k [00:00<00:00, 144kB/s] Downloading: 0%| | 0.00/903 [00:00<?, ?B/s] Downloading: 100%|██████████| 903/903 [00:00<00:00, 108kB/s] Downloading: 0%| | 0.00/177k [00:00<?, ?B/s] Downloading: 100%|██████████| 177k/177k [00:00<00:00, 1.16MB/s] Downloading: 100%|██████████| 177k/177k [00:00<00:00, 1.16MB/s] Downloading: 0%| | 0.00/88.2k [00:00<?, ?B/s] Downloading: 100%|██████████| 88.2k/88.2k [00:00<00:00, 873kB/s] Downloading: 100%|██████████| 88.2k/88.2k [00:00<00:00, 870kB/s] Downloading: 0%| | 0.00/55.3M [00:00<?, ?B/s] Downloading: 29%|██▉ | 16.0M/55.3M [00:00<00:01, 29.8MB/s] Downloading: 87%|████████▋ | 48.0M/55.3M [00:00<00:00, 84.0MB/s] Downloading: 100%|██████████| 55.3M/55.3M [00:00<00:00, 78.4MB/s] Downloading: 0%| | 0.00/12.8k [00:00<?, ?B/s] Downloading: 100%|██████████| 12.8k/12.8k [00:00<00:00, 8.86MB/s] Downloading: 0%| | 0.00/75.0k [00:00<?, ?B/s] Downloading: 100%|██████████| 75.0k/75.0k [00:00<00:00, 738kB/s] Downloading: 100%|██████████| 75.0k/75.0k [00:00<00:00, 736kB/s] Downloading: 0%| | 0.00/152k [00:00<?, ?B/s] Downloading: 100%|██████████| 152k/152k [00:00<00:00, 991kB/s] Downloading: 100%|██████████| 152k/152k [00:00<00:00, 989kB/s] 2024-06-19 22:59:40,492 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/speech_frcrn_ans_cirm_16k 2024-06-19 22:59:40,492 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/speech_frcrn_ans_cirm_16k. 2024-06-19 22:59:40,494 - modelscope - INFO - initialize model from /root/.cache/modelscope/hub/damo/speech_frcrn_ans_cirm_16k 2024-06-19 22:59:41,013 - modelscope - WARNING - No preprocessor field found in cfg. 2024-06-19 22:59:41,013 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file. 2024-06-19 22:59:41,013 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/speech_frcrn_ans_cirm_16k'}. trying to build by task and model information. 2024-06-19 22:59:41,013 - modelscope - WARNING - No preprocessor key ('speech_frcrn_ans_cirm_16k', 'acoustic-noise-suppression') found in PREPROCESSOR_MAP, skip building preprocessor. 0%| | 0/1 [00:00<?, ?it/s]inputs:(1, 254096) padding: 26096 inputs after padding:(1, 280192) 100%|██████████| 1/1 [00:01<00:00, 1.37s/it] 100%|██████████| 1/1 [00:01<00:00, 1.37s/it] Please install rotary_embedding_torch by: pip install -U rotary_embedding_torch Please install rotary_embedding_torch by: pip install -U rotary_embedding_torch Please install rotary_embedding_torch by: pip install -U rotary_embedding_torch Please install rotary_embedding_torch by: pip install -U rotary_embedding_torch tables: ----------- ** dataset_classes ** -------------- | class name | class location | | AudioDataset | funasr/datasets/audio_datasets/datasets.py:7 | ----------- ** index_ds_classes ** -------------- | class name | class location | | IndexDSJsonl | funasr/datasets/audio_datasets/index_ds.py:9 | ----------- ** batch_sampler_classes ** -------------- | class name | class location | | BatchSampler | funasr/datasets/audio_datasets/samplers.py:7 | ----------- ** frontend_classes ** -------------- | class name | class location | | WavFrontend | funasr/frontends/wav_frontend.py:78 | | WavFrontendOnline | funasr/frontends/wav_frontend.py:216 | ----------- ** encoder_classes ** -------------- | class name | class location | | BranchformerEncoder | funasr/models/branchformer/encoder.py:294 | | ConformerChunkEncoder | funasr/models/bat/conformer_chunk_encoder.py:315 | | ConformerEncoder | funasr/models/conformer/encoder.py:286 | | DFSMN | funasr/models/fsmn_vad_streaming/encoder.py:232 | | EBranchformerEncoder | funasr/models/e_branchformer/encoder.py:177 | | FSMN | funasr/models/fsmn_vad_streaming/encoder.py:161 | | SANMEncoder | funasr/models/sanm/encoder.py:161 | | SANMEncoderChunkOpt | funasr/models/scama/encoder.py:162 | | SANMVadEncoder | funasr/models/ct_transformer_streaming/encoder.py:148 | | TransformerEncoder | funasr/models/transformer/encoder.py:139 | ----------- ** predictor_classes ** -------------- | class name | class location | | CifPredictor | funasr/models/paraformer/cif_predictor.py:15 | | CifPredictorV2 | funasr/models/paraformer/cif_predictor.py:141 | | CifPredictorV3 | funasr/models/bicif_paraformer/cif_predictor.py:95 | ----------- ** model_classes ** -------------- | class name | class location | | BiCifParaformer | funasr/models/bicif_paraformer/model.py:37 | | Branchformer | funasr/models/branchformer/model.py:6 | | CAMPPlus | funasr/models/campplus/model.py:30 | | CTTransformer | funasr/models/ct_transformer/model.py:30 | | CTTransformerStreaming | funasr/models/ct_transformer_streaming/model.py:27 | | Conformer | funasr/models/conformer/model.py:8 | | ContextualParaformer | funasr/models/contextual_paraformer/model.py:43 | | EBranchformer | funasr/models/e_branchformer/model.py:6 | | Emotion2vec | funasr/models/emotion2vec/model.py:34 | | FsmnVADStreaming | funasr/models/fsmn_vad_streaming/model.py:267 | | MonotonicAligner | funasr/models/monotonic_aligner/model.py:24 | | Paraformer | funasr/models/paraformer/model.py:26 | | ParaformerStreaming | funasr/models/paraformer_streaming/model.py:37 | | SANM | funasr/models/sanm/model.py:13 | | SCAMA | funasr/models/scama/model.py:38 | | SeacoParaformer | funasr/models/seaco_paraformer/model.py:45 | | Transformer | funasr/models/transformer/model.py:20 | | UniASR | funasr/models/uniasr/model.py:26 | ----------- ** decoder_classes ** -------------- | class name | class location | | ContextualParaformerDecoder | funasr/models/contextual_paraformer/decoder.py:103 | | DynamicConvolution2DTransformerDecoder | funasr/models/transformer/decoder.py:588 | | DynamicConvolutionTransformerDecoder | funasr/models/transformer/decoder.py:527 | | FsmnDecoder | funasr/models/sanm/decoder.py:198 | | FsmnDecoderSCAMAOpt | funasr/models/scama/decoder.py:197 | | LightweightConvolution2DTransformerDecoder | funasr/models/transformer/decoder.py:465 | | LightweightConvolutionTransformerDecoder | funasr/models/transformer/decoder.py:404 | | ParaformerSANDecoder | funasr/models/paraformer/decoder.py:529 | | ParaformerSANMDecoder | funasr/models/paraformer/decoder.py:204 | | TransformerDecoder | funasr/models/transformer/decoder.py:355 | ----------- ** normalize_classes ** -------------- | class name | class location | | GlobalMVN | funasr/models/normalize/global_mvn.py:11 | | UtteranceMVN | funasr/models/normalize/utterance_mvn.py:8 | ----------- ** specaug_classes ** -------------- | class name | class location | | SpecAug | funasr/models/specaug/specaug.py:14 | | SpecAugLFR | funasr/models/specaug/specaug.py:104 | ----------- ** tokenizer_classes ** -------------- | class name | class location | | CharTokenizer | funasr/tokenizer/char_tokenizer.py:10 | 2024-06-19 22:59:47,351 - modelscope - INFO - PyTorch version 2.0.1+cu118 Found. 2024-06-19 22:59:47,352 - modelscope - INFO - Loading ast index from /root/.cache/modelscope/ast_indexer 2024-06-19 22:59:47,398 - modelscope - INFO - Loading done! 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Dumping model to file cache /src/TEMP/jieba.cache Loading model cost 1.245 seconds. Prefix dict has been built succesfully. ('进度:1a-done', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True}) "/root/.pyenv/versions/3.9.19/bin/python" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py "/root/.pyenv/versions/3.9.19/bin/python" GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py ('进度:1a-done, 1b-ing', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True}) ('进度:1a1b-done', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True}) "/root/.pyenv/versions/3.9.19/bin/python" GPT_SoVITS/prepare_datasets/3-get-semantic.py "/root/.pyenv/versions/3.9.19/bin/python" GPT_SoVITS/prepare_datasets/3-get-semantic.py ('进度:1a1b-done, 1cing', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True}) <All keys matched successfully> <All keys matched successfully> ('进度:all-done', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True}) ('一键三连进程结束', {'__type__': 'update', 'visible': True}, {'__type__': 'update', 'visible': False}) ('SoVITS训练开始:"/root/.pyenv/versions/3.9.19/bin/python" GPT_SoVITS/s2_train.py --config "/src/TEMP/tmp_s2.json"', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True}) "/root/.pyenv/versions/3.9.19/bin/python" GPT_SoVITS/s2_train.py --config "/src/TEMP/tmp_s2.json" IMPORTANT: You are using gradio version 3.38.0, however version 4.29.0 is available, please upgrade. -------- INFO:351b84a7-8622-4c51-a803-ae848d62d158:{'train': {'log_interval': 100, 'eval_interval': 500, 'seed': 1234, 'epochs': 8, 'learning_rate': 0.0001, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 11, 'fp16_run': True, 'lr_decay': 0.999875, 'segment_size': 20480, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0, 'text_low_lr_rate': 0.4, 'pretrained_s2G': 'GPT_SoVITS/pretrained_models/s2G488k.pth', 'pretrained_s2D': 'GPT_SoVITS/pretrained_models/s2D488k.pth', 'if_save_latest': True, 'if_save_every_weights': True, 'save_every_epoch': 4, 'gpu_numbers': '0-1'}, 'data': {'max_wav_value': 32768.0, 'sampling_rate': 32000, 'filter_length': 2048, 'hop_length': 640, 'win_length': 2048, 'n_mel_channels': 128, 'mel_fmin': 0.0, 'mel_fmax': None, 'add_blank': True, 'n_speakers': 300, 'cleaned_text': True, 'exp_dir': 'logs/351b84a7-8622-4c51-a803-ae848d62d158'}, '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': [10, 8, 2, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 8, 2, 2], 'n_layers_q': 3, 'use_spectral_norm': False, 'gin_channels': 512, 'semantic_frame_rate': '25hz', 'freeze_quantizer': True}, 's2_ckpt_dir': 'logs/351b84a7-8622-4c51-a803-ae848d62d158', 'content_module': 'cnhubert', 'save_weight_dir': 'SoVITS_weights', 'name': '351b84a7-8622-4c51-a803-ae848d62d158', 'pretrain': None, 'resume_step': None} INFO:torch.distributed.distributed_c10d:Added key: store_based_barrier_key:1 to store for rank: 0 INFO:torch.distributed.distributed_c10d:Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 1 nodes. phoneme_data_len: 1 wav_data_len: 100 0%| | 0/100 [00:00<?, ?it/s] 100%|██████████| 100/100 [00:00<00:00, 73921.47it/s] skipped_phone: 0 , skipped_dur: 0 total left: 100 INFO:351b84a7-8622-4c51-a803-ae848d62d158:loaded pretrained GPT_SoVITS/pretrained_models/s2G488k.pth <All keys matched successfully> INFO:351b84a7-8622-4c51-a803-ae848d62d158:loaded pretrained GPT_SoVITS/pretrained_models/s2D488k.pth <All keys matched successfully> /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. " 0%| | 0/10 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error. Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error. Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error. Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error. Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error. Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error. Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error. Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at ../aten/src/ATen/native/SpectralOps.cpp:862.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/functional.py:641: UserWarning: ComplexHalf support is experimental and many operators don't support it yet. (Triggered internally at ../aten/src/ATen/EmptyTensor.cpp:31.) return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined] [W reducer.cpp:1300] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator()) /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torch/autograd/__init__.py:200: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance. grad.sizes() = [1, 9, 96], strides() = [152736, 96, 1] bucket_view.sizes() = [1, 9, 96], strides() = [864, 96, 1] (Triggered internally at ../torch/csrc/distributed/c10d/reducer.cpp:323.) Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass INFO:351b84a7-8622-4c51-a803-ae848d62d158:Train Epoch: 1 [0%] INFO:351b84a7-8622-4c51-a803-ae848d62d158:[2.389455556869507, 2.1822030544281006, 5.724943161010742, 21.071016311645508, 0.0, 2.827393054962158, 0, 9.99875e-05] 10%|█ | 1/10 [00:09<01:27, 9.74s/it] 20%|██ | 2/10 [00:10<00:34, 4.25s/it] 30%|███ | 3/10 [00:10<00:17, 2.48s/it] 40%|████ | 4/10 [00:10<00:09, 1.65s/it] 50%|█████ | 5/10 [00:11<00:05, 1.20s/it] 60%|██████ | 6/10 [00:11<00:03, 1.09it/s] 70%|███████ | 7/10 [00:12<00:02, 1.35it/s] 80%|████████ | 8/10 [00:12<00:01, 1.55it/s] 90%|█████████ | 9/10 [00:12<00:00, 1.77it/s] 100%|██████████| 10/10 [00:13<00:00, 1.97it/s] 100%|██████████| 10/10 [00:13<00:00, 1.33s/it] INFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 1 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:02<00:20, 2.29s/it] 20%|██ | 2/10 [00:02<00:09, 1.20s/it] 30%|███ | 3/10 [00:03<00:05, 1.18it/s] 40%|████ | 4/10 [00:03<00:04, 1.48it/s] 50%|█████ | 5/10 [00:03<00:02, 1.73it/s] 60%|██████ | 6/10 [00:04<00:02, 1.93it/s] 70%|███████ | 7/10 [00:04<00:01, 2.08it/s] 80%|████████ | 8/10 [00:05<00:00, 2.19it/s] 90%|█████████ | 9/10 [00:05<00:00, 2.27it/s] 100%|██████████| 10/10 [00:05<00:00, 2.33it/s] 100%|██████████| 10/10 [00:05<00:00, 1.67it/s] INFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 2 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:02<00:21, 2.34s/it] 20%|██ | 2/10 [00:02<00:09, 1.23s/it] 30%|███ | 3/10 [00:03<00:06, 1.14it/s] 40%|████ | 4/10 [00:03<00:04, 1.45it/s] 50%|█████ | 5/10 [00:04<00:02, 1.71it/s] 60%|██████ | 6/10 [00:04<00:02, 1.90it/s] 70%|███████ | 7/10 [00:04<00:01, 2.05it/s] 80%|████████ | 8/10 [00:05<00:00, 2.17it/s] 90%|█████████ | 9/10 [00:05<00:00, 2.25it/s] 100%|██████████| 10/10 [00:06<00:00, 2.32it/s] 100%|██████████| 10/10 [00:06<00:00, 1.64it/s] INFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 3 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:02<00:19, 2.21s/it] 20%|██ | 2/10 [00:02<00:09, 1.17s/it] 30%|███ | 3/10 [00:03<00:05, 1.18it/s] 40%|████ | 4/10 [00:03<00:04, 1.47it/s] 50%|█████ | 5/10 [00:03<00:02, 1.73it/s] 60%|██████ | 6/10 [00:04<00:02, 1.93it/s] 70%|███████ | 7/10 [00:04<00:01, 2.08it/s] 80%|████████ | 8/10 [00:05<00:00, 2.19it/s] 90%|█████████ | 9/10 [00:05<00:00, 2.27it/s] 100%|██████████| 10/10 [00:05<00:00, 2.34it/s] 100%|██████████| 10/10 [00:05<00:00, 1.68it/s] INFO:351b84a7-8622-4c51-a803-ae848d62d158:Saving model and optimizer state at iteration 4 to logs/351b84a7-8622-4c51-a803-ae848d62d158/logs_s2/G_233333333333.pth INFO:351b84a7-8622-4c51-a803-ae848d62d158:Saving model and optimizer state at iteration 4 to logs/351b84a7-8622-4c51-a803-ae848d62d158/logs_s2/D_233333333333.pth INFO:351b84a7-8622-4c51-a803-ae848d62d158:saving ckpt 351b84a7-8622-4c51-a803-ae848d62d158_e4:Success. INFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 4 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:02<00:20, 2.33s/it] 20%|██ | 2/10 [00:02<00:09, 1.22s/it] 30%|███ | 3/10 [00:03<00:06, 1.16it/s] 40%|████ | 4/10 [00:03<00:04, 1.46it/s] 50%|█████ | 5/10 [00:04<00:02, 1.71it/s] 60%|██████ | 6/10 [00:04<00:02, 1.77it/s] 70%|███████ | 7/10 [00:04<00:01, 1.95it/s] 80%|████████ | 8/10 [00:05<00:00, 2.09it/s] 90%|█████████ | 9/10 [00:05<00:00, 2.20it/s] 100%|██████████| 10/10 [00:06<00:00, 2.27it/s] 100%|██████████| 10/10 [00:06<00:00, 1.62it/s] INFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 5 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:02<00:21, 2.39s/it] 20%|██ | 2/10 [00:02<00:09, 1.24s/it] 30%|███ | 3/10 [00:03<00:06, 1.15it/s] 40%|████ | 4/10 [00:03<00:04, 1.46it/s] 50%|█████ | 5/10 [00:04<00:02, 1.71it/s] 60%|██████ | 6/10 [00:04<00:02, 1.91it/s] 70%|███████ | 7/10 [00:04<00:01, 2.06it/s] 80%|████████ | 8/10 [00:05<00:00, 2.18it/s] 90%|█████████ | 9/10 [00:05<00:00, 2.26it/s] 100%|██████████| 10/10 [00:06<00:00, 2.32it/s] 100%|██████████| 10/10 [00:06<00:00, 1.64it/s] INFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 6 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:02<00:20, 2.32s/it] 20%|██ | 2/10 [00:02<00:09, 1.20s/it] 30%|███ | 3/10 [00:03<00:06, 1.16it/s] 40%|████ | 4/10 [00:03<00:04, 1.46it/s] 50%|█████ | 5/10 [00:04<00:02, 1.71it/s] 60%|██████ | 6/10 [00:04<00:02, 1.91it/s] 70%|███████ | 7/10 [00:04<00:01, 2.06it/s] 80%|████████ | 8/10 [00:05<00:00, 2.18it/s] 90%|█████████ | 9/10 [00:05<00:00, 2.26it/s] 100%|██████████| 10/10 [00:06<00:00, 2.32it/s] 100%|██████████| 10/10 [00:06<00:00, 1.66it/s] INFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 7 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:02<00:21, 2.34s/it] 20%|██ | 2/10 [00:02<00:09, 1.22s/it] 30%|███ | 3/10 [00:03<00:06, 1.15it/s] 40%|████ | 4/10 [00:03<00:04, 1.45it/s] 50%|█████ | 5/10 [00:04<00:02, 1.70it/s] 60%|██████ | 6/10 [00:04<00:02, 1.90it/s] 70%|███████ | 7/10 [00:04<00:01, 2.04it/s] 80%|████████ | 8/10 [00:05<00:00, 2.16it/s] 90%|█████████ | 9/10 [00:05<00:00, 2.24it/s] 100%|██████████| 10/10 [00:06<00:00, 2.31it/s] 100%|██████████| 10/10 [00:06<00:00, 1.64it/s] INFO:351b84a7-8622-4c51-a803-ae848d62d158:Saving model and optimizer state at iteration 8 to logs/351b84a7-8622-4c51-a803-ae848d62d158/logs_s2/G_233333333333.pth INFO:351b84a7-8622-4c51-a803-ae848d62d158:Saving model and optimizer state at iteration 8 to logs/351b84a7-8622-4c51-a803-ae848d62d158/logs_s2/D_233333333333.pth INFO:351b84a7-8622-4c51-a803-ae848d62d158:saving ckpt 351b84a7-8622-4c51-a803-ae848d62d158_e8:Success. INFO:351b84a7-8622-4c51-a803-ae848d62d158:====> Epoch: 8 ('SoVITS训练完成', {'__type__': 'update', 'visible': True}, {'__type__': 'update', 'visible': False}) ('GPT训练开始:"/root/.pyenv/versions/3.9.19/bin/python" GPT_SoVITS/s1_train.py --config_file "/src/TEMP/tmp_s1.yaml" ', {'__type__': 'update', 'visible': False}, {'__type__': 'update', 'visible': True}) "/root/.pyenv/versions/3.9.19/bin/python" GPT_SoVITS/s1_train.py --config_file "/src/TEMP/tmp_s1.yaml" IMPORTANT: You are using gradio version 3.38.0, however version 4.29.0 is available, please upgrade. -------- Seed set to 1234 Using 16bit Automatic Mixed Precision (AMP) GPU available: True (cuda), used: True TPU available: False, using: 0 TPU cores HPU available: False, using: 0 HPUs <All keys matched successfully> ckpt_path: None [rank: 0] Seed set to 1234 Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1 ---------------------------------------------------------------------------------------------------- distributed_backend=nccl All distributed processes registered. Starting with 1 processes ---------------------------------------------------------------------------------------------------- Missing logger folder: logs/351b84a7-8622-4c51-a803-ae848d62d158/logs_s1/logs_s1 semantic_data_len: 1 phoneme_data_len: 1 item_name semantic_audio 0 origin.mp3_0000000000_0000508480.wav 520 721 1005 578 283 919 290 96 142 545 8 17 7... dataset.__len__(): 100 LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1] | Name | Type | Params | Mode ------------------------------------------------------- 0 | model | Text2SemanticDecoder | 77.5 M | train ------------------------------------------------------- 77.5 M Trainable params 0 Non-trainable params 77.5 M Total params 309.975 Total estimated model params size (MB) /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/pytorch_lightning/loops/fit_loop.py:298: The number of training batches (10) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch. Training: | | 0/? [00:00<?, ?it/s] Training: 0%| | 0/10 [00:00<?, ?it/s] Epoch 0: 0%| | 0/10 [00:00<?, ?it/s] Epoch 0: 10%|█ | 1/10 [00:00<00:03, 2.34it/s] Epoch 0: 10%|█ | 1/10 [00:00<00:03, 2.34it/s, v_num=0, total_loss_step=2.2e+4, lr_step=1e-5, top_3_acc_step=0.132] Epoch 0: 20%|██ | 2/10 [00:00<00:02, 3.33it/s, v_num=0, total_loss_step=2.2e+4, lr_step=1e-5, top_3_acc_step=0.132] Epoch 0: 20%|██ | 2/10 [00:00<00:02, 3.33it/s, v_num=0, total_loss_step=2.19e+4, lr_step=1e-5, top_3_acc_step=0.126] Epoch 0: 30%|███ | 3/10 [00:00<00:01, 3.91it/s, v_num=0, total_loss_step=2.19e+4, lr_step=1e-5, top_3_acc_step=0.126] Epoch 0: 30%|███ | 3/10 [00:00<00:01, 3.91it/s, v_num=0, total_loss_step=2.19e+4, lr_step=1e-5, top_3_acc_step=0.130] Epoch 0: 40%|████ | 4/10 [00:00<00:01, 4.29it/s, v_num=0, total_loss_step=2.19e+4, lr_step=1e-5, top_3_acc_step=0.130] Epoch 0: 40%|████ | 4/10 [00:00<00:01, 4.29it/s, v_num=0, total_loss_step=2.2e+4, lr_step=1e-5, top_3_acc_step=0.131] Epoch 0: 50%|█████ | 5/10 [00:01<00:01, 4.52it/s, v_num=0, total_loss_step=2.2e+4, lr_step=1e-5, top_3_acc_step=0.131] Epoch 0: 50%|█████ | 5/10 [00:01<00:01, 4.51it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.010, top_3_acc_step=0.128] Epoch 0: 60%|██████ | 6/10 [00:01<00:00, 4.70it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.010, top_3_acc_step=0.128] Epoch 0: 60%|██████ | 6/10 [00:01<00:00, 4.70it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.010, top_3_acc_step=0.127] Epoch 0: 70%|███████ | 7/10 [00:01<00:00, 4.85it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.010, top_3_acc_step=0.127] Epoch 0: 70%|███████ | 7/10 [00:01<00:00, 4.85it/s, v_num=0, total_loss_step=21934.0, lr_step=0.010, top_3_acc_step=0.130] Epoch 0: 80%|████████ | 8/10 [00:01<00:00, 4.96it/s, v_num=0, total_loss_step=21934.0, lr_step=0.010, top_3_acc_step=0.130] Epoch 0: 80%|████████ | 8/10 [00:01<00:00, 4.96it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.010, top_3_acc_step=0.129] Epoch 0: 90%|█████████ | 9/10 [00:01<00:00, 5.04it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.010, top_3_acc_step=0.129] Epoch 0: 90%|█████████ | 9/10 [00:01<00:00, 5.04it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131] Epoch 0: 100%|██████████| 10/10 [00:01<00:00, 5.39it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131] Epoch 0: 100%|██████████| 10/10 [00:01<00:00, 5.39it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.144] Epoch 0: 100%|██████████| 10/10 [00:01<00:00, 5.38it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.144, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 0: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.144, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.144, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 10%|█ | 1/10 [00:00<00:01, 5.41it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.144, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 10%|█ | 1/10 [00:00<00:01, 5.39it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 20%|██ | 2/10 [00:00<00:01, 5.61it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 20%|██ | 2/10 [00:00<00:01, 5.60it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 30%|███ | 3/10 [00:00<00:01, 5.73it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 30%|███ | 3/10 [00:00<00:01, 5.73it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.134, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 40%|████ | 4/10 [00:00<00:01, 5.80it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.134, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 40%|████ | 4/10 [00:00<00:01, 5.79it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 50%|█████ | 5/10 [00:00<00:00, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 50%|█████ | 5/10 [00:00<00:00, 5.77it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 60%|██████ | 6/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 60%|██████ | 6/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 70%|███████ | 7/10 [00:01<00:00, 5.84it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 70%|███████ | 7/10 [00:01<00:00, 5.84it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 80%|████████ | 8/10 [00:01<00:00, 5.86it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 80%|████████ | 8/10 [00:01<00:00, 5.86it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.126, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 90%|█████████ | 9/10 [00:01<00:00, 5.85it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.126, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 90%|█████████ | 9/10 [00:01<00:00, 5.85it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 100%|██████████| 10/10 [00:01<00:00, 6.31it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 100%|██████████| 10/10 [00:01<00:00, 6.30it/s, v_num=0, total_loss_step=1.98e+3, lr_step=0.002, top_3_acc_step=0.142, total_loss_epoch=2.17e+4, lr_epoch=0.00464, top_3_acc_epoch=0.129] Epoch 1: 100%|██████████| 10/10 [00:01<00:00, 6.30it/s, v_num=0, total_loss_step=1.98e+3, lr_step=0.002, top_3_acc_step=0.142, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 1: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.98e+3, lr_step=0.002, top_3_acc_step=0.142, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.98e+3, lr_step=0.002, top_3_acc_step=0.142, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 10%|█ | 1/10 [00:00<00:01, 5.31it/s, v_num=0, total_loss_step=1.98e+3, lr_step=0.002, top_3_acc_step=0.142, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 10%|█ | 1/10 [00:00<00:01, 5.30it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.126, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 20%|██ | 2/10 [00:00<00:01, 5.58it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.126, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 20%|██ | 2/10 [00:00<00:01, 5.57it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 30%|███ | 3/10 [00:00<00:01, 5.69it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 40%|████ | 4/10 [00:00<00:01, 5.77it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.125, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 50%|█████ | 5/10 [00:00<00:00, 5.77it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.125, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 50%|█████ | 5/10 [00:00<00:00, 5.77it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 60%|██████ | 6/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 60%|██████ | 6/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.135, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 70%|███████ | 7/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.135, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 70%|███████ | 7/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.133, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 80%|████████ | 8/10 [00:01<00:00, 5.83it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.133, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 80%|████████ | 8/10 [00:01<00:00, 5.83it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 90%|█████████ | 9/10 [00:01<00:00, 5.83it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 90%|█████████ | 9/10 [00:01<00:00, 5.83it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 100%|██████████| 10/10 [00:01<00:00, 6.28it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 100%|██████████| 10/10 [00:01<00:00, 6.27it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.139, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 100%|██████████| 10/10 [00:01<00:00, 6.27it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.139, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 2: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.139, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.139, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 10%|█ | 1/10 [00:00<00:01, 5.41it/s, v_num=0, total_loss_step=1.99e+3, lr_step=0.002, top_3_acc_step=0.139, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 10%|█ | 1/10 [00:00<00:01, 5.39it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 20%|██ | 2/10 [00:00<00:01, 5.57it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 20%|██ | 2/10 [00:00<00:01, 5.56it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.125, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 30%|███ | 3/10 [00:00<00:01, 5.67it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.125, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 30%|███ | 3/10 [00:00<00:01, 5.66it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 40%|████ | 4/10 [00:00<00:01, 5.73it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 40%|████ | 4/10 [00:00<00:01, 5.72it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 50%|█████ | 5/10 [00:00<00:00, 5.74it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 50%|█████ | 5/10 [00:00<00:00, 5.73it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.132, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 60%|██████ | 6/10 [00:01<00:00, 5.75it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.132, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 60%|██████ | 6/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 70%|███████ | 7/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 70%|███████ | 7/10 [00:01<00:00, 5.77it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.132, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 80%|████████ | 8/10 [00:01<00:00, 5.80it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.132, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 80%|████████ | 8/10 [00:01<00:00, 5.80it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.127, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 90%|█████████ | 9/10 [00:01<00:00, 5.80it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.127, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 90%|█████████ | 9/10 [00:01<00:00, 5.80it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 100%|██████████| 10/10 [00:01<00:00, 6.25it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 100%|██████████| 10/10 [00:01<00:00, 6.25it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.122, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 100%|██████████| 10/10 [00:01<00:00, 6.24it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.122, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 3: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.122, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.122, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 10%|█ | 1/10 [00:00<00:01, 5.23it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.122, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 10%|█ | 1/10 [00:00<00:01, 5.22it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 20%|██ | 2/10 [00:00<00:01, 5.58it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.129, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 20%|██ | 2/10 [00:00<00:01, 5.58it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.138, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 30%|███ | 3/10 [00:00<00:01, 5.71it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.138, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 30%|███ | 3/10 [00:00<00:01, 5.71it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.132, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 40%|████ | 4/10 [00:00<00:01, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.132, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 40%|████ | 4/10 [00:00<00:01, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.133, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 50%|█████ | 5/10 [00:00<00:00, 5.74it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.133, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 50%|█████ | 5/10 [00:00<00:00, 5.73it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 60%|██████ | 6/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 60%|██████ | 6/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.126, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 70%|███████ | 7/10 [00:01<00:00, 5.82it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.126, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 70%|███████ | 7/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 80%|████████ | 8/10 [00:01<00:00, 5.84it/s, v_num=0, total_loss_step=2.2e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 80%|████████ | 8/10 [00:01<00:00, 5.83it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.127, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 90%|█████████ | 9/10 [00:01<00:00, 5.84it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.127, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 90%|█████████ | 9/10 [00:01<00:00, 5.84it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 100%|██████████| 10/10 [00:01<00:00, 6.29it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 100%|██████████| 10/10 [00:01<00:00, 6.29it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.129] Epoch 4: 100%|██████████| 10/10 [00:01<00:00, 6.28it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 4: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 10%|█ | 1/10 [00:00<00:01, 5.30it/s, v_num=0, total_loss_step=2e+3, lr_step=0.002, top_3_acc_step=0.124, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 10%|█ | 1/10 [00:00<00:01, 5.28it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 20%|██ | 2/10 [00:00<00:01, 5.60it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 20%|██ | 2/10 [00:00<00:01, 5.59it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.127, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.127, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 40%|████ | 4/10 [00:00<00:01, 5.77it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.128, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 40%|████ | 4/10 [00:00<00:01, 5.77it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.134, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 50%|█████ | 5/10 [00:00<00:00, 5.50it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.134, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 50%|█████ | 5/10 [00:00<00:00, 5.49it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 60%|██████ | 6/10 [00:01<00:00, 5.57it/s, v_num=0, total_loss_step=2.19e+4, lr_step=0.002, top_3_acc_step=0.130, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 60%|██████ | 6/10 [00:01<00:00, 5.57it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 70%|███████ | 7/10 [00:01<00:00, 5.60it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.131, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 70%|███████ | 7/10 [00:01<00:00, 5.60it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.135, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 80%|████████ | 8/10 [00:01<00:00, 5.65it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.135, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 80%|████████ | 8/10 [00:01<00:00, 5.65it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.133, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 90%|█████████ | 9/10 [00:01<00:00, 5.60it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.133, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 90%|█████████ | 9/10 [00:01<00:00, 5.60it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.134, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 100%|██████████| 10/10 [00:01<00:00, 6.03it/s, v_num=0, total_loss_step=2.17e+4, lr_step=0.002, top_3_acc_step=0.134, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 100%|██████████| 10/10 [00:01<00:00, 6.03it/s, v_num=0, total_loss_step=1.9e+3, lr_step=0.002, top_3_acc_step=0.157, total_loss_epoch=2.17e+4, lr_epoch=0.002, top_3_acc_epoch=0.131] Epoch 5: 100%|██████████| 10/10 [00:01<00:00, 6.02it/s, v_num=0, total_loss_step=1.9e+3, lr_step=0.002, top_3_acc_step=0.157, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 5: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.9e+3, lr_step=0.002, top_3_acc_step=0.157, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.9e+3, lr_step=0.002, top_3_acc_step=0.157, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 10%|█ | 1/10 [00:00<00:01, 5.23it/s, v_num=0, total_loss_step=1.9e+3, lr_step=0.002, top_3_acc_step=0.157, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 10%|█ | 1/10 [00:00<00:01, 5.22it/s, v_num=0, total_loss_step=2.08e+4, lr_step=0.002, top_3_acc_step=0.159, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 20%|██ | 2/10 [00:00<00:01, 5.57it/s, v_num=0, total_loss_step=2.08e+4, lr_step=0.002, top_3_acc_step=0.159, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 20%|██ | 2/10 [00:00<00:01, 5.56it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.155, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.155, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 30%|███ | 3/10 [00:00<00:01, 5.69it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.158, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.158, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.160, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 50%|█████ | 5/10 [00:00<00:00, 5.63it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.160, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 50%|█████ | 5/10 [00:00<00:00, 5.63it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.160, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 60%|██████ | 6/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=2.09e+4, lr_step=0.002, top_3_acc_step=0.160, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 60%|██████ | 6/10 [00:01<00:00, 5.68it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.189, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 70%|███████ | 7/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.189, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 70%|███████ | 7/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.186, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 80%|████████ | 8/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.186, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 80%|████████ | 8/10 [00:01<00:00, 5.75it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.179, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 90%|█████████ | 9/10 [00:01<00:00, 5.67it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.179, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 90%|█████████ | 9/10 [00:01<00:00, 5.67it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.185, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 100%|██████████| 10/10 [00:01<00:00, 6.10it/s, v_num=0, total_loss_step=2.01e+4, lr_step=0.002, top_3_acc_step=0.185, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 100%|██████████| 10/10 [00:01<00:00, 6.10it/s, v_num=0, total_loss_step=1.75e+3, lr_step=0.002, top_3_acc_step=0.195, total_loss_epoch=2.16e+4, lr_epoch=0.002, top_3_acc_epoch=0.132] Epoch 6: 100%|██████████| 10/10 [00:01<00:00, 6.09it/s, v_num=0, total_loss_step=1.75e+3, lr_step=0.002, top_3_acc_step=0.195, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 6: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.75e+3, lr_step=0.002, top_3_acc_step=0.195, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.75e+3, lr_step=0.002, top_3_acc_step=0.195, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 10%|█ | 1/10 [00:00<00:01, 5.27it/s, v_num=0, total_loss_step=1.75e+3, lr_step=0.002, top_3_acc_step=0.195, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 10%|█ | 1/10 [00:00<00:01, 5.26it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.216, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 20%|██ | 2/10 [00:00<00:01, 5.60it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.216, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 20%|██ | 2/10 [00:00<00:01, 5.59it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.221, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 30%|███ | 3/10 [00:00<00:01, 5.72it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.221, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 30%|███ | 3/10 [00:00<00:01, 5.71it/s, v_num=0, total_loss_step=1.91e+4, lr_step=0.002, top_3_acc_step=0.221, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 40%|████ | 4/10 [00:00<00:01, 5.78it/s, v_num=0, total_loss_step=1.91e+4, lr_step=0.002, top_3_acc_step=0.221, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 40%|████ | 4/10 [00:00<00:01, 5.77it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.217, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 50%|█████ | 5/10 [00:00<00:00, 5.63it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.217, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 50%|█████ | 5/10 [00:00<00:00, 5.62it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.225, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 60%|██████ | 6/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=1.92e+4, lr_step=0.002, top_3_acc_step=0.225, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 60%|██████ | 6/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.260, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 70%|███████ | 7/10 [00:01<00:00, 5.73it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.260, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 70%|███████ | 7/10 [00:01<00:00, 5.73it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.263, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 80%|████████ | 8/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.263, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 80%|████████ | 8/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.255, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 90%|█████████ | 9/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.255, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 90%|█████████ | 9/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.256, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 100%|██████████| 10/10 [00:01<00:00, 6.14it/s, v_num=0, total_loss_step=1.82e+4, lr_step=0.002, top_3_acc_step=0.256, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 100%|██████████| 10/10 [00:01<00:00, 6.13it/s, v_num=0, total_loss_step=1.56e+3, lr_step=0.002, top_3_acc_step=0.324, total_loss_epoch=2.03e+4, lr_epoch=0.002, top_3_acc_epoch=0.170] Epoch 7: 100%|██████████| 10/10 [00:01<00:00, 6.13it/s, v_num=0, total_loss_step=1.56e+3, lr_step=0.002, top_3_acc_step=0.324, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 7: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.56e+3, lr_step=0.002, top_3_acc_step=0.324, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.56e+3, lr_step=0.002, top_3_acc_step=0.324, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 10%|█ | 1/10 [00:00<00:01, 5.31it/s, v_num=0, total_loss_step=1.56e+3, lr_step=0.002, top_3_acc_step=0.324, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 10%|█ | 1/10 [00:00<00:01, 5.29it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.303, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 20%|██ | 2/10 [00:00<00:01, 5.59it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.303, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 20%|██ | 2/10 [00:00<00:01, 5.59it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.317, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 30%|███ | 3/10 [00:00<00:01, 5.72it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.317, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 30%|███ | 3/10 [00:00<00:01, 5.72it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.314, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 40%|████ | 4/10 [00:00<00:01, 5.79it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.314, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 40%|████ | 4/10 [00:00<00:01, 5.78it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.310, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 50%|█████ | 5/10 [00:00<00:00, 5.64it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.310, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 50%|█████ | 5/10 [00:00<00:00, 5.64it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.313, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 60%|██████ | 6/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=1.71e+4, lr_step=0.002, top_3_acc_step=0.313, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 60%|██████ | 6/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.376, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 70%|███████ | 7/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.376, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 70%|███████ | 7/10 [00:01<00:00, 5.73it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.374, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 80%|████████ | 8/10 [00:01<00:00, 5.77it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.374, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 80%|████████ | 8/10 [00:01<00:00, 5.77it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.383, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 90%|█████████ | 9/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.383, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 90%|█████████ | 9/10 [00:01<00:00, 5.68it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.373, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 100%|██████████| 10/10 [00:01<00:00, 6.12it/s, v_num=0, total_loss_step=1.59e+4, lr_step=0.002, top_3_acc_step=0.373, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 100%|██████████| 10/10 [00:01<00:00, 6.12it/s, v_num=0, total_loss_step=1.34e+3, lr_step=0.002, top_3_acc_step=0.461, total_loss_epoch=1.86e+4, lr_epoch=0.002, top_3_acc_epoch=0.238] Epoch 8: 100%|██████████| 10/10 [00:01<00:00, 6.11it/s, v_num=0, total_loss_step=1.34e+3, lr_step=0.002, top_3_acc_step=0.461, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 8: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.34e+3, lr_step=0.002, top_3_acc_step=0.461, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.34e+3, lr_step=0.002, top_3_acc_step=0.461, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 10%|█ | 1/10 [00:00<00:01, 5.24it/s, v_num=0, total_loss_step=1.34e+3, lr_step=0.002, top_3_acc_step=0.461, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 10%|█ | 1/10 [00:00<00:01, 5.22it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.444, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 20%|██ | 2/10 [00:00<00:01, 5.58it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.444, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 20%|██ | 2/10 [00:00<00:01, 5.57it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.448, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.448, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 30%|███ | 3/10 [00:00<00:01, 5.69it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.442, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.442, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.442, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 50%|█████ | 5/10 [00:00<00:00, 5.57it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.442, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 50%|█████ | 5/10 [00:00<00:00, 5.57it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.448, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 60%|██████ | 6/10 [00:01<00:00, 5.65it/s, v_num=0, total_loss_step=1.46e+4, lr_step=0.002, top_3_acc_step=0.448, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 60%|██████ | 6/10 [00:01<00:00, 5.64it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.510, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 70%|███████ | 7/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.510, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 70%|███████ | 7/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.519, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 80%|████████ | 8/10 [00:01<00:00, 5.73it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.519, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 80%|████████ | 8/10 [00:01<00:00, 5.73it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.516, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 90%|█████████ | 9/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.516, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 90%|█████████ | 9/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.514, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 100%|██████████| 10/10 [00:01<00:00, 6.19it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.514, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 100%|██████████| 10/10 [00:01<00:00, 6.19it/s, v_num=0, total_loss_step=1.21e+3, lr_step=0.002, top_3_acc_step=0.501, total_loss_epoch=1.64e+4, lr_epoch=0.002, top_3_acc_epoch=0.341] Epoch 9: 100%|██████████| 10/10 [00:01<00:00, 6.18it/s, v_num=0, total_loss_step=1.21e+3, lr_step=0.002, top_3_acc_step=0.501, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 9: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.21e+3, lr_step=0.002, top_3_acc_step=0.501, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.21e+3, lr_step=0.002, top_3_acc_step=0.501, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 10%|█ | 1/10 [00:00<00:01, 5.27it/s, v_num=0, total_loss_step=1.21e+3, lr_step=0.002, top_3_acc_step=0.501, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 10%|█ | 1/10 [00:00<00:01, 5.25it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.509, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 20%|██ | 2/10 [00:00<00:01, 5.57it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.509, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 20%|██ | 2/10 [00:00<00:01, 5.56it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.512, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.512, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.511, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.511, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.506, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 50%|█████ | 5/10 [00:00<00:00, 5.72it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.506, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 50%|█████ | 5/10 [00:00<00:00, 5.72it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.513, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 60%|██████ | 6/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.513, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 60%|██████ | 6/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.508, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 70%|███████ | 7/10 [00:01<00:00, 5.79it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.508, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 70%|███████ | 7/10 [00:01<00:00, 5.79it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.505, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 80%|████████ | 8/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=1.32e+4, lr_step=0.002, top_3_acc_step=0.505, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 80%|████████ | 8/10 [00:01<00:00, 5.81it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.513, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 90%|█████████ | 9/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.513, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 90%|█████████ | 9/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.520, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 100%|██████████| 10/10 [00:01<00:00, 6.19it/s, v_num=0, total_loss_step=1.31e+4, lr_step=0.002, top_3_acc_step=0.520, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 100%|██████████| 10/10 [00:01<00:00, 6.18it/s, v_num=0, total_loss_step=1.08e+3, lr_step=0.002, top_3_acc_step=0.567, total_loss_epoch=1.38e+4, lr_epoch=0.002, top_3_acc_epoch=0.476] Epoch 10: 100%|██████████| 10/10 [00:01<00:00, 6.18it/s, v_num=0, total_loss_step=1.08e+3, lr_step=0.002, top_3_acc_step=0.567, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 10: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.08e+3, lr_step=0.002, top_3_acc_step=0.567, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=1.08e+3, lr_step=0.002, top_3_acc_step=0.567, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 10%|█ | 1/10 [00:00<00:01, 5.43it/s, v_num=0, total_loss_step=1.08e+3, lr_step=0.002, top_3_acc_step=0.567, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 10%|█ | 1/10 [00:00<00:01, 5.42it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.587, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 20%|██ | 2/10 [00:00<00:01, 5.62it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.587, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 20%|██ | 2/10 [00:00<00:01, 5.61it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.592, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 30%|███ | 3/10 [00:00<00:01, 5.73it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.592, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 30%|███ | 3/10 [00:00<00:01, 5.73it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.578, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 40%|████ | 4/10 [00:00<00:01, 5.80it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.578, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 40%|████ | 4/10 [00:00<00:01, 5.79it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.578, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 50%|█████ | 5/10 [00:00<00:00, 5.65it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.578, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 50%|█████ | 5/10 [00:00<00:00, 5.65it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.589, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 60%|██████ | 6/10 [00:01<00:00, 5.71it/s, v_num=0, total_loss_step=1.17e+4, lr_step=0.002, top_3_acc_step=0.589, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 60%|██████ | 6/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.661, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 70%|███████ | 7/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.661, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 70%|███████ | 7/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.658, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 80%|████████ | 8/10 [00:01<00:00, 5.77it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.658, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 80%|████████ | 8/10 [00:01<00:00, 5.77it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.668, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 90%|█████████ | 9/10 [00:01<00:00, 5.68it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.668, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 90%|█████████ | 9/10 [00:01<00:00, 5.68it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.665, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 100%|██████████| 10/10 [00:01<00:00, 6.12it/s, v_num=0, total_loss_step=1.02e+4, lr_step=0.002, top_3_acc_step=0.665, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 100%|██████████| 10/10 [00:01<00:00, 6.12it/s, v_num=0, total_loss_step=783.0, lr_step=0.002, top_3_acc_step=0.752, total_loss_epoch=1.31e+4, lr_epoch=0.002, top_3_acc_epoch=0.511] Epoch 11: 100%|██████████| 10/10 [00:01<00:00, 6.12it/s, v_num=0, total_loss_step=783.0, lr_step=0.002, top_3_acc_step=0.752, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 11: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=783.0, lr_step=0.002, top_3_acc_step=0.752, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=783.0, lr_step=0.002, top_3_acc_step=0.752, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 10%|█ | 1/10 [00:00<00:01, 5.47it/s, v_num=0, total_loss_step=783.0, lr_step=0.002, top_3_acc_step=0.752, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 10%|█ | 1/10 [00:00<00:01, 5.45it/s, v_num=0, total_loss_step=8.83e+3, lr_step=0.002, top_3_acc_step=0.730, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 20%|██ | 2/10 [00:00<00:01, 5.69it/s, v_num=0, total_loss_step=8.83e+3, lr_step=0.002, top_3_acc_step=0.730, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 20%|██ | 2/10 [00:00<00:01, 5.68it/s, v_num=0, total_loss_step=8.75e+3, lr_step=0.002, top_3_acc_step=0.739, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 30%|███ | 3/10 [00:00<00:01, 5.75it/s, v_num=0, total_loss_step=8.75e+3, lr_step=0.002, top_3_acc_step=0.739, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 30%|███ | 3/10 [00:00<00:01, 5.75it/s, v_num=0, total_loss_step=8.78e+3, lr_step=0.002, top_3_acc_step=0.732, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 40%|████ | 4/10 [00:00<00:01, 5.81it/s, v_num=0, total_loss_step=8.78e+3, lr_step=0.002, top_3_acc_step=0.732, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 40%|████ | 4/10 [00:00<00:01, 5.80it/s, v_num=0, total_loss_step=8.71e+3, lr_step=0.002, top_3_acc_step=0.734, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 50%|█████ | 5/10 [00:00<00:00, 5.68it/s, v_num=0, total_loss_step=8.71e+3, lr_step=0.002, top_3_acc_step=0.734, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 50%|█████ | 5/10 [00:00<00:00, 5.68it/s, v_num=0, total_loss_step=8.76e+3, lr_step=0.002, top_3_acc_step=0.736, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 60%|██████ | 6/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=8.76e+3, lr_step=0.002, top_3_acc_step=0.736, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 60%|██████ | 6/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=7.45e+3, lr_step=0.002, top_3_acc_step=0.797, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 70%|███████ | 7/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=7.45e+3, lr_step=0.002, top_3_acc_step=0.797, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 70%|███████ | 7/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=7.4e+3, lr_step=0.002, top_3_acc_step=0.794, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 80%|████████ | 8/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=7.4e+3, lr_step=0.002, top_3_acc_step=0.794, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 80%|████████ | 8/10 [00:01<00:00, 5.78it/s, v_num=0, total_loss_step=7.47e+3, lr_step=0.002, top_3_acc_step=0.792, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 90%|█████████ | 9/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=7.47e+3, lr_step=0.002, top_3_acc_step=0.792, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 90%|█████████ | 9/10 [00:01<00:00, 5.71it/s, v_num=0, total_loss_step=7.4e+3, lr_step=0.002, top_3_acc_step=0.798, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 100%|██████████| 10/10 [00:01<00:00, 6.15it/s, v_num=0, total_loss_step=7.4e+3, lr_step=0.002, top_3_acc_step=0.798, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 100%|██████████| 10/10 [00:01<00:00, 6.15it/s, v_num=0, total_loss_step=570.0, lr_step=0.002, top_3_acc_step=0.843, total_loss_epoch=1.09e+4, lr_epoch=0.002, top_3_acc_epoch=0.621] Epoch 12: 100%|██████████| 10/10 [00:01<00:00, 6.14it/s, v_num=0, total_loss_step=570.0, lr_step=0.002, top_3_acc_step=0.843, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 12: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=570.0, lr_step=0.002, top_3_acc_step=0.843, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=570.0, lr_step=0.002, top_3_acc_step=0.843, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 10%|█ | 1/10 [00:00<00:01, 5.31it/s, v_num=0, total_loss_step=570.0, lr_step=0.002, top_3_acc_step=0.843, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 10%|█ | 1/10 [00:00<00:01, 5.29it/s, v_num=0, total_loss_step=6.31e+3, lr_step=0.002, top_3_acc_step=0.851, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 20%|██ | 2/10 [00:00<00:01, 5.62it/s, v_num=0, total_loss_step=6.31e+3, lr_step=0.002, top_3_acc_step=0.851, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 20%|██ | 2/10 [00:00<00:01, 5.61it/s, v_num=0, total_loss_step=6.27e+3, lr_step=0.002, top_3_acc_step=0.857, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 30%|███ | 3/10 [00:00<00:01, 5.70it/s, v_num=0, total_loss_step=6.27e+3, lr_step=0.002, top_3_acc_step=0.857, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 30%|███ | 3/10 [00:00<00:01, 5.69it/s, v_num=0, total_loss_step=6.28e+3, lr_step=0.002, top_3_acc_step=0.856, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=6.28e+3, lr_step=0.002, top_3_acc_step=0.856, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=6.24e+3, lr_step=0.002, top_3_acc_step=0.856, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 50%|█████ | 5/10 [00:00<00:00, 5.64it/s, v_num=0, total_loss_step=6.24e+3, lr_step=0.002, top_3_acc_step=0.856, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 50%|█████ | 5/10 [00:00<00:00, 5.64it/s, v_num=0, total_loss_step=6.29e+3, lr_step=0.002, top_3_acc_step=0.854, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 60%|██████ | 6/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=6.29e+3, lr_step=0.002, top_3_acc_step=0.854, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 60%|██████ | 6/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=5.24e+3, lr_step=0.002, top_3_acc_step=0.901, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 70%|███████ | 7/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=5.24e+3, lr_step=0.002, top_3_acc_step=0.901, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 70%|███████ | 7/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=5.18e+3, lr_step=0.002, top_3_acc_step=0.910, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 80%|████████ | 8/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=5.18e+3, lr_step=0.002, top_3_acc_step=0.910, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 80%|████████ | 8/10 [00:01<00:00, 5.74it/s, v_num=0, total_loss_step=5.23e+3, lr_step=0.002, top_3_acc_step=0.904, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 90%|█████████ | 9/10 [00:01<00:00, 5.66it/s, v_num=0, total_loss_step=5.23e+3, lr_step=0.002, top_3_acc_step=0.904, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 90%|█████████ | 9/10 [00:01<00:00, 5.66it/s, v_num=0, total_loss_step=5.26e+3, lr_step=0.002, top_3_acc_step=0.899, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 100%|██████████| 10/10 [00:01<00:00, 6.09it/s, v_num=0, total_loss_step=5.26e+3, lr_step=0.002, top_3_acc_step=0.899, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 100%|██████████| 10/10 [00:01<00:00, 6.09it/s, v_num=0, total_loss_step=395.0, lr_step=0.002, top_3_acc_step=0.929, total_loss_epoch=8.1e+3, lr_epoch=0.002, top_3_acc_epoch=0.762] Epoch 13: 100%|██████████| 10/10 [00:01<00:00, 6.09it/s, v_num=0, total_loss_step=395.0, lr_step=0.002, top_3_acc_step=0.929, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 13: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=395.0, lr_step=0.002, top_3_acc_step=0.929, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 0%| | 0/10 [00:00<?, ?it/s, v_num=0, total_loss_step=395.0, lr_step=0.002, top_3_acc_step=0.929, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 10%|█ | 1/10 [00:00<00:01, 5.43it/s, v_num=0, total_loss_step=395.0, lr_step=0.002, top_3_acc_step=0.929, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 10%|█ | 1/10 [00:00<00:01, 5.41it/s, v_num=0, total_loss_step=4.31e+3, lr_step=0.002, top_3_acc_step=0.943, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 20%|██ | 2/10 [00:00<00:01, 5.65it/s, v_num=0, total_loss_step=4.31e+3, lr_step=0.002, top_3_acc_step=0.943, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 20%|██ | 2/10 [00:00<00:01, 5.64it/s, v_num=0, total_loss_step=4.3e+3, lr_step=0.002, top_3_acc_step=0.943, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 30%|███ | 3/10 [00:00<00:01, 5.73it/s, v_num=0, total_loss_step=4.3e+3, lr_step=0.002, top_3_acc_step=0.943, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 30%|███ | 3/10 [00:00<00:01, 5.72it/s, v_num=0, total_loss_step=4.28e+3, lr_step=0.002, top_3_acc_step=0.945, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 40%|████ | 4/10 [00:00<00:01, 5.77it/s, v_num=0, total_loss_step=4.28e+3, lr_step=0.002, top_3_acc_step=0.945, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 40%|████ | 4/10 [00:00<00:01, 5.76it/s, v_num=0, total_loss_step=4.32e+3, lr_step=0.002, top_3_acc_step=0.938, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 50%|█████ | 5/10 [00:00<00:00, 5.62it/s, v_num=0, total_loss_step=4.32e+3, lr_step=0.002, top_3_acc_step=0.938, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 50%|█████ | 5/10 [00:00<00:00, 5.61it/s, v_num=0, total_loss_step=4.31e+3, lr_step=0.002, top_3_acc_step=0.944, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 60%|██████ | 6/10 [00:01<00:00, 5.68it/s, v_num=0, total_loss_step=4.31e+3, lr_step=0.002, top_3_acc_step=0.944, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 60%|██████ | 6/10 [00:01<00:00, 5.68it/s, v_num=0, total_loss_step=3.49e+3, lr_step=0.002, top_3_acc_step=0.965, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 70%|███████ | 7/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=3.49e+3, lr_step=0.002, top_3_acc_step=0.965, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 70%|███████ | 7/10 [00:01<00:00, 5.72it/s, v_num=0, total_loss_step=3.44e+3, lr_step=0.002, top_3_acc_step=0.967, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 80%|████████ | 8/10 [00:01<00:00, 5.76it/s, v_num=0, total_loss_step=3.44e+3, lr_step=0.002, top_3_acc_step=0.967, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 80%|████████ | 8/10 [00:01<00:00, 5.75it/s, v_num=0, total_loss_step=3.41e+3, lr_step=0.002, top_3_acc_step=0.971, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 90%|█████████ | 9/10 [00:01<00:00, 5.70it/s, v_num=0, total_loss_step=3.41e+3, lr_step=0.002, top_3_acc_step=0.971, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 90%|█████████ | 9/10 [00:01<00:00, 5.69it/s, v_num=0, total_loss_step=3.44e+3, lr_step=0.002, top_3_acc_step=0.971, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 100%|██████████| 10/10 [00:01<00:00, 6.13it/s, v_num=0, total_loss_step=3.44e+3, lr_step=0.002, top_3_acc_step=0.971, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 100%|██████████| 10/10 [00:01<00:00, 6.13it/s, v_num=0, total_loss_step=261.0, lr_step=0.002, top_3_acc_step=0.980, total_loss_epoch=5.76e+3, lr_epoch=0.002, top_3_acc_epoch=0.877] Epoch 14: 100%|██████████| 10/10 [00:01<00:00, 6.12it/s, v_num=0, total_loss_step=261.0, lr_step=0.002, top_3_acc_step=0.980, total_loss_epoch=3.89e+3, lr_epoch=0.002, top_3_acc_epoch=0.954]`Trainer.fit` stopped: `max_epochs=15` reached. Epoch 14: 100%|██████████| 10/10 [00:03<00:00, 2.80it/s, v_num=0, total_loss_step=261.0, lr_step=0.002, top_3_acc_step=0.980, total_loss_epoch=3.89e+3, lr_epoch=0.002, top_3_acc_epoch=0.954] ('GPT训练完成', {'__type__': 'update', 'visible': True}, {'__type__': 'update', 'visible': False}) Files in the zip archive: input.wav log.txt asr_opt/denoise_opt.list denoise_opt/origin.mp3_0000000000_0000508480.wav 351b84a7-8622-4c51-a803-ae848d62d158_e8_s80.pth 351b84a7-8622-4c51-a803-ae848d62d158-e15.ckpt Created zip file: 351b84a7-8622-4c51-a803-ae848d62d158.zip
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