Commit
•
c0d8e31
1
Parent(s):
f159298
Upload 5 files
Browse files- config.json +16 -0
- finish_training.log +249 -0
- finish_training.py +127 -0
- sorter.py +191 -0
- training.log +935 -0
config.json
ADDED
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{
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"directory": "F:\\DATASET\\v1",
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"output_directory": "F:\\DATASET\\v1\\Spectrograms",
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"augment": true,
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"noise_snr": 10,
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"pitch_steps": 2,
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"time_stretch_rate": 1.25,
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"sample_rate": 44100,
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"n_fft": 2048,
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"hop_length": 256,
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"n_mels": 128,
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"max_frames": 500,
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"batch_size": 256,
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"min_duration": 0.1,
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"patience": 20
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}
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finish_training.log
ADDED
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2024-05-16 22:00:46,650 - root - INFO - Initializing SpectrogramDataset...
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2024-05-16 22:00:47,165 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
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2024-05-16 22:00:47,171 - root - INFO - SpectrogramDataset initialized successfully
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2024-05-16 22:00:47,377 - root - ERROR - An error occurred: name 'nn' is not defined
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2024-05-16 22:01:22,777 - root - INFO - Initializing SpectrogramDataset...
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2024-05-16 22:01:23,295 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
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2024-05-16 22:01:23,300 - root - INFO - SpectrogramDataset initialized successfully
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2024-05-16 22:01:42,720 - root - INFO - Epoch 1:
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Training Loss: 1.5260, Training Accuracy: 0.3385, Validation Loss: 1.6393, Validation Accuracy: 0.2624
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2024-05-16 22:01:42,722 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:02:00,523 - root - INFO - Epoch 2:
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Training Loss: 1.2230, Training Accuracy: 0.5228, Validation Loss: 1.5649, Validation Accuracy: 0.3120
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2024-05-16 22:02:00,524 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:02:18,855 - root - INFO - Epoch 3:
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Training Loss: 1.0560, Training Accuracy: 0.5853, Validation Loss: 1.5721, Validation Accuracy: 0.2886
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2024-05-16 22:02:18,856 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:02:37,163 - root - INFO - Epoch 4:
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Training Loss: 0.9178, Training Accuracy: 0.6221, Validation Loss: 1.5178, Validation Accuracy: 0.3644
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2024-05-16 22:02:37,165 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:06:42,042 - root - INFO - Initializing SpectrogramDataset...
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2024-05-16 22:06:42,551 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
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2024-05-16 22:06:42,556 - root - INFO - SpectrogramDataset initialized successfully
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2024-05-16 22:06:42,755 - root - ERROR - An error occurred: name 'nn' is not defined
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2024-05-16 22:07:15,641 - root - INFO - Initializing SpectrogramDataset...
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2024-05-16 22:07:16,161 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
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2024-05-16 22:07:16,166 - root - INFO - SpectrogramDataset initialized successfully
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2024-05-16 22:07:16,465 - root - INFO - Loaded the best model from previous training.
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2024-05-16 22:07:34,862 - root - INFO - Epoch 1:
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Training Loss: 0.5570, Training Accuracy: 0.7951, Validation Loss: 1.7239, Validation Accuracy: 0.5539
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2024-05-16 22:07:34,863 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:07:51,864 - root - INFO - Epoch 2:
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Training Loss: 0.5908, Training Accuracy: 0.7851, Validation Loss: 0.7348, Validation Accuracy: 0.7318
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2024-05-16 22:07:51,865 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:08:09,242 - root - INFO - Epoch 3:
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Training Loss: 0.5201, Training Accuracy: 0.8201, Validation Loss: 0.7574, Validation Accuracy: 0.7114
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2024-05-16 22:08:09,244 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:08:25,962 - root - INFO - Epoch 4:
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Training Loss: 0.4979, Training Accuracy: 0.8176, Validation Loss: 0.7149, Validation Accuracy: 0.7143
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2024-05-16 22:08:25,963 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:08:43,336 - root - INFO - Epoch 5:
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Training Loss: 0.4966, Training Accuracy: 0.8189, Validation Loss: 0.6777, Validation Accuracy: 0.7522
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2024-05-16 22:08:43,337 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:09:00,375 - root - INFO - Epoch 6:
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Training Loss: 0.4906, Training Accuracy: 0.8276, Validation Loss: 0.6189, Validation Accuracy: 0.7551
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2024-05-16 22:09:00,376 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:09:17,365 - root - INFO - Epoch 7:
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Training Loss: 0.4680, Training Accuracy: 0.8289, Validation Loss: 0.5583, Validation Accuracy: 0.7784
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2024-05-16 22:09:17,367 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:09:34,205 - root - INFO - Epoch 8:
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Training Loss: 0.4521, Training Accuracy: 0.8326, Validation Loss: 0.6058, Validation Accuracy: 0.7755
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2024-05-16 22:09:34,207 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:09:51,165 - root - INFO - Epoch 9:
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Training Loss: 0.4068, Training Accuracy: 0.8501, Validation Loss: 0.4922, Validation Accuracy: 0.8163
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2024-05-16 22:09:51,167 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:10:08,186 - root - INFO - Epoch 10:
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Training Loss: 0.4259, Training Accuracy: 0.8463, Validation Loss: 0.5306, Validation Accuracy: 0.8076
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2024-05-16 22:10:08,187 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:10:08,260 - root - INFO - Model saved to checkpoint_epoch_10.pth
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2024-05-16 22:10:25,419 - root - INFO - Epoch 11:
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Training Loss: 0.4122, Training Accuracy: 0.8451, Validation Loss: 0.5452, Validation Accuracy: 0.8105
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2024-05-16 22:10:25,421 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:10:42,549 - root - INFO - Epoch 12:
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Training Loss: 0.4157, Training Accuracy: 0.8432, Validation Loss: 0.6177, Validation Accuracy: 0.7930
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2024-05-16 22:10:42,550 - root - INFO - Current learning rate: 0.00014687223021475341
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2024-05-16 22:10:59,620 - root - INFO - Epoch 13:
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Training Loss: 0.3727, Training Accuracy: 0.8595, Validation Loss: 0.7197, Validation Accuracy: 0.7405
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2024-05-16 22:10:59,621 - root - INFO - Current learning rate: 1.4687223021475341e-05
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2024-05-16 22:11:17,022 - root - INFO - Epoch 14:
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Training Loss: 0.4019, Training Accuracy: 0.8638, Validation Loss: 0.5073, Validation Accuracy: 0.8076
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2024-05-16 22:11:17,023 - root - INFO - Current learning rate: 1.4687223021475341e-05
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2024-05-16 22:11:34,626 - root - INFO - Epoch 15:
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Training Loss: 0.3273, Training Accuracy: 0.8832, Validation Loss: 0.4066, Validation Accuracy: 0.8601
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2024-05-16 22:11:34,628 - root - INFO - Current learning rate: 1.4687223021475341e-05
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2024-05-16 22:11:51,725 - root - INFO - Epoch 16:
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Training Loss: 0.3409, Training Accuracy: 0.8713, Validation Loss: 0.4711, Validation Accuracy: 0.8280
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2024-05-16 22:11:51,726 - root - INFO - Current learning rate: 1.4687223021475341e-05
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2024-05-16 22:12:09,010 - root - INFO - Epoch 17:
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Training Loss: 0.3207, Training Accuracy: 0.8826, Validation Loss: 0.4586, Validation Accuracy: 0.8338
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2024-05-16 22:12:09,011 - root - INFO - Current learning rate: 1.4687223021475341e-05
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2024-05-16 22:12:25,474 - root - INFO - Epoch 18:
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Training Loss: 0.3405, Training Accuracy: 0.8738, Validation Loss: 0.4560, Validation Accuracy: 0.8222
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2024-05-16 22:12:25,476 - root - INFO - Current learning rate: 1.4687223021475341e-05
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2024-05-16 22:12:42,848 - root - INFO - Epoch 19:
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Training Loss: 0.3721, Training Accuracy: 0.8657, Validation Loss: 0.4278, Validation Accuracy: 0.8426
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2024-05-16 22:12:42,849 - root - INFO - Current learning rate: 1.4687223021475343e-06
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2024-05-16 22:13:00,038 - root - INFO - Epoch 20:
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Training Loss: 0.3588, Training Accuracy: 0.8701, Validation Loss: 0.4247, Validation Accuracy: 0.8397
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2024-05-16 22:13:00,039 - root - INFO - Current learning rate: 1.4687223021475343e-06
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2024-05-16 22:13:00,114 - root - INFO - Model saved to checkpoint_epoch_20.pth
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2024-05-16 22:13:17,127 - root - INFO - Epoch 21:
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Training Loss: 0.3153, Training Accuracy: 0.8745, Validation Loss: 0.4080, Validation Accuracy: 0.8571
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2024-05-16 22:13:17,129 - root - INFO - Current learning rate: 1.4687223021475343e-06
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2024-05-16 22:13:33,743 - root - INFO - Epoch 22:
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Training Loss: 0.3187, Training Accuracy: 0.8876, Validation Loss: 0.4765, Validation Accuracy: 0.8251
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2024-05-16 22:13:33,744 - root - INFO - Current learning rate: 1.4687223021475343e-06
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2024-05-16 22:13:50,886 - root - INFO - Epoch 23:
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Training Loss: 0.3215, Training Accuracy: 0.8795, Validation Loss: 0.4566, Validation Accuracy: 0.8251
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2024-05-16 22:13:50,888 - root - INFO - Current learning rate: 1.4687223021475343e-07
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2024-05-16 22:14:08,209 - root - INFO - Epoch 24:
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Training Loss: 0.3030, Training Accuracy: 0.8938, Validation Loss: 0.4290, Validation Accuracy: 0.8222
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2024-05-16 22:14:08,211 - root - INFO - Current learning rate: 1.4687223021475343e-07
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2024-05-16 22:14:25,025 - root - INFO - Epoch 25:
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Training Loss: 0.3203, Training Accuracy: 0.8869, Validation Loss: 0.4327, Validation Accuracy: 0.8484
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2024-05-16 22:14:25,027 - root - INFO - Current learning rate: 1.4687223021475343e-07
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2024-05-16 22:14:42,254 - root - INFO - Epoch 26:
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Training Loss: 0.3120, Training Accuracy: 0.8938, Validation Loss: 0.4477, Validation Accuracy: 0.8280
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2024-05-16 22:14:42,255 - root - INFO - Current learning rate: 1.4687223021475343e-07
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2024-05-16 22:14:59,624 - root - INFO - Epoch 27:
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Training Loss: 0.3136, Training Accuracy: 0.8913, Validation Loss: 0.4614, Validation Accuracy: 0.8309
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2024-05-16 22:14:59,626 - root - INFO - Current learning rate: 1.4687223021475344e-08
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2024-05-16 22:15:16,671 - root - INFO - Epoch 28:
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Training Loss: 0.3044, Training Accuracy: 0.8938, Validation Loss: 0.4706, Validation Accuracy: 0.8251
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2024-05-16 22:15:16,673 - root - INFO - Current learning rate: 1.4687223021475344e-08
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2024-05-16 22:15:33,835 - root - INFO - Epoch 29:
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Training Loss: 0.3022, Training Accuracy: 0.8938, Validation Loss: 0.4032, Validation Accuracy: 0.8455
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2024-05-16 22:15:33,836 - root - INFO - Current learning rate: 1.4687223021475344e-08
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2024-05-16 22:15:51,218 - root - INFO - Epoch 30:
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Training Loss: 0.2987, Training Accuracy: 0.8982, Validation Loss: 0.4105, Validation Accuracy: 0.8426
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2024-05-16 22:15:51,219 - root - INFO - Current learning rate: 1.4687223021475344e-08
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2024-05-16 22:15:51,293 - root - INFO - Model saved to checkpoint_epoch_30.pth
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2024-05-16 22:16:07,971 - root - INFO - Epoch 31:
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Training Loss: 0.3077, Training Accuracy: 0.8869, Validation Loss: 0.4213, Validation Accuracy: 0.8251
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2024-05-16 22:16:07,973 - root - INFO - Current learning rate: 1.4687223021475344e-08
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2024-05-16 22:16:24,561 - root - INFO - Epoch 32:
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Training Loss: 0.3193, Training Accuracy: 0.8838, Validation Loss: 0.4009, Validation Accuracy: 0.8455
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2024-05-16 22:16:24,563 - root - INFO - Current learning rate: 1.4687223021475344e-08
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2024-05-16 22:16:42,053 - root - INFO - Epoch 33:
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Training Loss: 0.3314, Training Accuracy: 0.8869, Validation Loss: 0.4344, Validation Accuracy: 0.8397
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2024-05-16 22:16:42,055 - root - INFO - Current learning rate: 1.4687223021475344e-08
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2024-05-16 22:16:59,057 - root - INFO - Epoch 34:
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Training Loss: 0.2837, Training Accuracy: 0.9001, Validation Loss: 0.4218, Validation Accuracy: 0.8280
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2024-05-16 22:16:59,059 - root - INFO - Current learning rate: 1.4687223021475344e-08
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2024-05-16 22:17:16,041 - root - INFO - Epoch 35:
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Training Loss: 0.3607, Training Accuracy: 0.8701, Validation Loss: 0.4585, Validation Accuracy: 0.8134
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2024-05-16 22:17:16,042 - root - INFO - Current learning rate: 1.4687223021475344e-08
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2024-05-16 22:17:33,182 - root - INFO - Epoch 36:
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Training Loss: 0.2987, Training Accuracy: 0.8919, Validation Loss: 0.4383, Validation Accuracy: 0.8251
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178 |
+
2024-05-16 22:17:33,184 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
179 |
+
2024-05-16 22:17:50,259 - root - INFO - Epoch 37:
|
180 |
+
Training Loss: 0.3294, Training Accuracy: 0.8682, Validation Loss: 0.4253, Validation Accuracy: 0.8309
|
181 |
+
|
182 |
+
2024-05-16 22:17:50,260 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
183 |
+
2024-05-16 22:18:06,981 - root - INFO - Epoch 38:
|
184 |
+
Training Loss: 0.2821, Training Accuracy: 0.9019, Validation Loss: 0.4611, Validation Accuracy: 0.8251
|
185 |
+
|
186 |
+
2024-05-16 22:18:06,982 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
187 |
+
2024-05-16 22:18:23,998 - root - INFO - Epoch 39:
|
188 |
+
Training Loss: 0.3158, Training Accuracy: 0.8869, Validation Loss: 0.4367, Validation Accuracy: 0.8280
|
189 |
+
|
190 |
+
2024-05-16 22:18:24,000 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
191 |
+
2024-05-16 22:18:41,011 - root - INFO - Epoch 40:
|
192 |
+
Training Loss: 0.3254, Training Accuracy: 0.8888, Validation Loss: 0.3913, Validation Accuracy: 0.8513
|
193 |
+
|
194 |
+
2024-05-16 22:18:41,012 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
195 |
+
2024-05-16 22:18:41,155 - root - INFO - Model saved to checkpoint_epoch_40.pth
|
196 |
+
2024-05-16 22:18:58,103 - root - INFO - Epoch 41:
|
197 |
+
Training Loss: 0.3082, Training Accuracy: 0.8857, Validation Loss: 0.3953, Validation Accuracy: 0.8542
|
198 |
+
|
199 |
+
2024-05-16 22:18:58,105 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
200 |
+
2024-05-16 22:19:14,938 - root - INFO - Epoch 42:
|
201 |
+
Training Loss: 0.2816, Training Accuracy: 0.9007, Validation Loss: 0.4112, Validation Accuracy: 0.8484
|
202 |
+
|
203 |
+
2024-05-16 22:19:14,939 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
204 |
+
2024-05-16 22:19:32,077 - root - INFO - Epoch 43:
|
205 |
+
Training Loss: 0.3457, Training Accuracy: 0.8788, Validation Loss: 0.4241, Validation Accuracy: 0.8455
|
206 |
+
|
207 |
+
2024-05-16 22:19:32,078 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
208 |
+
2024-05-16 22:19:49,354 - root - INFO - Epoch 44:
|
209 |
+
Training Loss: 0.3149, Training Accuracy: 0.8826, Validation Loss: 0.4160, Validation Accuracy: 0.8542
|
210 |
+
|
211 |
+
2024-05-16 22:19:49,356 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
212 |
+
2024-05-16 22:20:06,691 - root - INFO - Epoch 45:
|
213 |
+
Training Loss: 0.3011, Training Accuracy: 0.8894, Validation Loss: 0.4293, Validation Accuracy: 0.8484
|
214 |
+
|
215 |
+
2024-05-16 22:20:06,692 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
216 |
+
2024-05-16 22:20:23,367 - root - INFO - Epoch 46:
|
217 |
+
Training Loss: 0.3327, Training Accuracy: 0.8795, Validation Loss: 0.3971, Validation Accuracy: 0.8630
|
218 |
+
|
219 |
+
2024-05-16 22:20:23,369 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
220 |
+
2024-05-16 22:20:40,496 - root - INFO - Epoch 47:
|
221 |
+
Training Loss: 0.3180, Training Accuracy: 0.8894, Validation Loss: 0.4143, Validation Accuracy: 0.8513
|
222 |
+
|
223 |
+
2024-05-16 22:20:40,497 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
224 |
+
2024-05-16 22:20:58,098 - root - INFO - Epoch 48:
|
225 |
+
Training Loss: 0.2915, Training Accuracy: 0.8969, Validation Loss: 0.4355, Validation Accuracy: 0.8455
|
226 |
+
|
227 |
+
2024-05-16 22:20:58,100 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
228 |
+
2024-05-16 22:21:15,492 - root - INFO - Epoch 49:
|
229 |
+
Training Loss: 0.3160, Training Accuracy: 0.8882, Validation Loss: 0.4601, Validation Accuracy: 0.8163
|
230 |
+
|
231 |
+
2024-05-16 22:21:15,494 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
232 |
+
2024-05-16 22:21:32,535 - root - INFO - Epoch 50:
|
233 |
+
Training Loss: 0.3085, Training Accuracy: 0.8832, Validation Loss: 0.4587, Validation Accuracy: 0.8105
|
234 |
+
|
235 |
+
2024-05-16 22:21:32,537 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
236 |
+
2024-05-16 22:21:32,613 - root - INFO - Model saved to checkpoint_epoch_50.pth
|
237 |
+
2024-05-16 22:21:33,771 - root - INFO - precision recall f1-score support
|
238 |
+
|
239 |
+
808 0.88 0.88 0.88 43
|
240 |
+
Clap 0.62 0.78 0.69 27
|
241 |
+
Closed Hat 0.89 0.86 0.87 63
|
242 |
+
Kick 0.94 0.93 0.94 120
|
243 |
+
Open Hat 0.76 0.87 0.81 15
|
244 |
+
Snare 0.84 0.78 0.81 76
|
245 |
+
|
246 |
+
accuracy 0.86 344
|
247 |
+
macro avg 0.82 0.85 0.83 344
|
248 |
+
weighted avg 0.87 0.86 0.86 344
|
249 |
+
|
finish_training.py
ADDED
@@ -0,0 +1,127 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
|
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|
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|
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|
|
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|
|
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|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
import torch.optim as optim
|
4 |
+
import logging
|
5 |
+
import argparse
|
6 |
+
import json
|
7 |
+
from datetime import datetime
|
8 |
+
from torch.utils.data import DataLoader, WeightedRandomSampler, random_split, RandomSampler, SequentialSampler
|
9 |
+
from prepare_data import SpectrogramDataset, collate_fn
|
10 |
+
from train_model import (
|
11 |
+
AudioResNet,
|
12 |
+
train_one_epoch,
|
13 |
+
validate_one_epoch,
|
14 |
+
evaluate_model,
|
15 |
+
plot_confusion_matrix,
|
16 |
+
device
|
17 |
+
)
|
18 |
+
from sklearn.metrics import classification_report, confusion_matrix
|
19 |
+
import numpy as np
|
20 |
+
import os
|
21 |
+
|
22 |
+
# Configure logging
|
23 |
+
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
|
24 |
+
logger = logging.getLogger()
|
25 |
+
fh = logging.FileHandler('finish_training.log')
|
26 |
+
fh.setLevel(logging.INFO)
|
27 |
+
ch = logging.StreamHandler()
|
28 |
+
ch.setLevel(logging.INFO)
|
29 |
+
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
30 |
+
fh.setFormatter(formatter)
|
31 |
+
ch.setFormatter(formatter)
|
32 |
+
logger.addHandler(fh)
|
33 |
+
logger.addHandler(ch)
|
34 |
+
|
35 |
+
def parse_args():
|
36 |
+
parser = argparse.ArgumentParser(description='Train Sample Classifier Model')
|
37 |
+
parser.add_argument('--config', type=str, required=True, help='Path to the config file')
|
38 |
+
return parser.parse_args()
|
39 |
+
|
40 |
+
def load_config(config_path):
|
41 |
+
if not os.path.exists(config_path):
|
42 |
+
raise FileNotFoundError(f"Config file not found: {config_path}")
|
43 |
+
with open(config_path, 'r') as f:
|
44 |
+
config = json.load(f)
|
45 |
+
return config
|
46 |
+
|
47 |
+
def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, device, patience=10, max_epochs=50):
|
48 |
+
best_loss = float('inf')
|
49 |
+
patience_counter = 0
|
50 |
+
|
51 |
+
for epoch in range(max_epochs):
|
52 |
+
train_loss, train_accuracy = train_one_epoch(model, train_loader, criterion, optimizer, device)
|
53 |
+
val_loss, val_accuracy = validate_one_epoch(model, val_loader, criterion, device)
|
54 |
+
|
55 |
+
log_message = (f'Epoch {epoch + 1}:\n'
|
56 |
+
f'Training Loss: {train_loss:.4f}, Training Accuracy: {train_accuracy:.4f}, '
|
57 |
+
f'Validation Loss: {val_loss:.4f}, Validation Accuracy: {val_accuracy:.4f}\n')
|
58 |
+
logging.info(log_message)
|
59 |
+
|
60 |
+
scheduler.step(val_loss)
|
61 |
+
current_lr = optimizer.param_groups[0]['lr']
|
62 |
+
logging.info(f'Current learning rate: {current_lr}')
|
63 |
+
|
64 |
+
if val_loss < best_loss:
|
65 |
+
best_loss = val_loss
|
66 |
+
patience_counter = 0
|
67 |
+
torch.save(model.state_dict(), 'best_model.pth')
|
68 |
+
else:
|
69 |
+
patience_counter += 1
|
70 |
+
|
71 |
+
if patience_counter >= patience:
|
72 |
+
logging.info('Early stopping triggered')
|
73 |
+
break
|
74 |
+
|
75 |
+
if (epoch + 1) % 10 == 0:
|
76 |
+
checkpoint_path = f'checkpoint_epoch_{epoch + 1}.pth'
|
77 |
+
torch.save(model.state_dict(), checkpoint_path)
|
78 |
+
logging.info(f'Model saved to {checkpoint_path}')
|
79 |
+
|
80 |
+
def main():
|
81 |
+
try:
|
82 |
+
args = parse_args()
|
83 |
+
config = load_config(args.config)
|
84 |
+
|
85 |
+
dataset = SpectrogramDataset(config, config['directory'], process_new=True)
|
86 |
+
if len(dataset) == 0:
|
87 |
+
raise ValueError("The dataset is empty. Please check the data loading process.")
|
88 |
+
num_classes = len(dataset.label_to_index)
|
89 |
+
class_names = list(dataset.label_to_index.keys())
|
90 |
+
|
91 |
+
train_size = int(0.7 * len(dataset))
|
92 |
+
val_size = int(0.15 * len(dataset))
|
93 |
+
test_size = len(dataset) - train_size - val_size
|
94 |
+
train_dataset, val_dataset, test_dataset = random_split(dataset, [train_size, val_size, test_size])
|
95 |
+
|
96 |
+
train_labels = [dataset.labels[i] for i in train_dataset.indices]
|
97 |
+
class_counts = np.bincount(train_labels)
|
98 |
+
class_weights = 1. / class_counts
|
99 |
+
sample_weights = class_weights[train_labels]
|
100 |
+
sampler = WeightedRandomSampler(sample_weights, len(sample_weights))
|
101 |
+
|
102 |
+
train_loader = DataLoader(train_dataset, batch_size=config['batch_size'], collate_fn=collate_fn, sampler=sampler)
|
103 |
+
val_loader = DataLoader(val_dataset, batch_size=config['batch_size'], collate_fn=collate_fn, sampler=RandomSampler(val_dataset))
|
104 |
+
test_loader = DataLoader(test_dataset, batch_size=config['batch_size'], collate_fn=collate_fn, sampler=SequentialSampler(test_dataset))
|
105 |
+
|
106 |
+
# Load best hyperparameters
|
107 |
+
best_params = {'learning_rate': 0.00014687223021475341, 'weight_decay': 2.970399818935859e-05, 'dropout_rate': 0.36508234143710705}
|
108 |
+
|
109 |
+
model = AudioResNet(num_classes=num_classes, dropout_rate=best_params['dropout_rate']).to(device)
|
110 |
+
criterion = nn.NLLLoss()
|
111 |
+
optimizer = optim.Adam(model.parameters(), lr=best_params['learning_rate'], weight_decay=best_params['weight_decay'])
|
112 |
+
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=3)
|
113 |
+
|
114 |
+
# Load the previously saved best model
|
115 |
+
if os.path.exists('checkpoint_epoch_50.pth'):
|
116 |
+
model.load_state_dict(torch.load('checkpoint_epoch_50.pth'))
|
117 |
+
logging.info("Loaded the best model from previous training.")
|
118 |
+
|
119 |
+
train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, device, patience=config['patience'], max_epochs=50)
|
120 |
+
|
121 |
+
model.load_state_dict(torch.load('checkpoint_epoch_50.pth'))
|
122 |
+
evaluate_model(model, test_loader, device, class_names)
|
123 |
+
except Exception as e:
|
124 |
+
logging.error(f"An error occurred: {e}")
|
125 |
+
|
126 |
+
if __name__ == '__main__':
|
127 |
+
main()
|
sorter.py
ADDED
@@ -0,0 +1,191 @@
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import torch
|
3 |
+
import torch.nn as nn
|
4 |
+
import torch.nn.functional as F
|
5 |
+
import tkinter as tk
|
6 |
+
from tkinter import ttk, filedialog, messagebox
|
7 |
+
import shutil
|
8 |
+
from pathlib import Path
|
9 |
+
import numpy as np
|
10 |
+
import librosa
|
11 |
+
import torchaudio
|
12 |
+
from torchvision import transforms
|
13 |
+
from tqdm import tqdm
|
14 |
+
|
15 |
+
class ResidualBlock(nn.Module):
|
16 |
+
def __init__(self, in_channels, out_channels, stride=1):
|
17 |
+
super(ResidualBlock, self).__init__()
|
18 |
+
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
|
19 |
+
self.bn1 = nn.BatchNorm2d(out_channels)
|
20 |
+
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
|
21 |
+
self.bn2 = nn.BatchNorm2d(out_channels)
|
22 |
+
if stride != 1 or in_channels != out_channels:
|
23 |
+
self.shortcut = nn.Sequential(
|
24 |
+
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
|
25 |
+
nn.BatchNorm2d(out_channels)
|
26 |
+
)
|
27 |
+
else:
|
28 |
+
self.shortcut = nn.Identity()
|
29 |
+
|
30 |
+
def forward(self, x):
|
31 |
+
out = F.relu(self.bn1(self.conv1(x)))
|
32 |
+
out = self.bn2(self.conv2(out))
|
33 |
+
out += self.shortcut(x)
|
34 |
+
out = F.relu(out)
|
35 |
+
return out
|
36 |
+
|
37 |
+
class AudioResNet(nn.Module):
|
38 |
+
def __init__(self, num_classes=6, dropout_rate=0.5):
|
39 |
+
super(AudioResNet, self).__init__()
|
40 |
+
self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
|
41 |
+
self.bn1 = nn.BatchNorm2d(64)
|
42 |
+
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
43 |
+
self.layer1 = self._make_layer(64, 64, num_blocks=2, stride=1)
|
44 |
+
self.layer2 = self._make_layer(64, 128, num_blocks=2, stride=2)
|
45 |
+
self.layer3 = self._make_layer(128, 256, num_blocks=2, stride=2)
|
46 |
+
self.layer4 = self._make_layer(256, 512, num_blocks=2, stride=2)
|
47 |
+
|
48 |
+
self.dropout = nn.Dropout(dropout_rate)
|
49 |
+
self.gap = nn.AdaptiveAvgPool2d((1, 1)) # Global Average Pooling
|
50 |
+
self.fc1 = nn.Linear(512, 1024)
|
51 |
+
self.fc2 = nn.Linear(1024, num_classes)
|
52 |
+
|
53 |
+
def _make_layer(self, in_channels, out_channels, num_blocks, stride):
|
54 |
+
layers = []
|
55 |
+
for i in range(num_blocks):
|
56 |
+
layers.append(ResidualBlock(in_channels if i == 0 else out_channels, out_channels, stride if i == 0 else 1))
|
57 |
+
return nn.Sequential(*layers)
|
58 |
+
|
59 |
+
def forward(self, x):
|
60 |
+
x = F.relu(self.bn1(self.conv1(x)))
|
61 |
+
x = self.maxpool(x)
|
62 |
+
x = self.layer1(x)
|
63 |
+
x = self.layer2(x)
|
64 |
+
x = self.layer3(x)
|
65 |
+
x = self.layer4(x)
|
66 |
+
|
67 |
+
x = self.gap(x) # Apply Global Average Pooling
|
68 |
+
x = x.view(x.size(0), -1)
|
69 |
+
|
70 |
+
x = F.relu(self.fc1(x))
|
71 |
+
x = self.dropout(x)
|
72 |
+
x = self.fc2(x)
|
73 |
+
return F.log_softmax(x, dim=1)
|
74 |
+
|
75 |
+
def load_model(model_path='checkpoint_epoch_50.pth', num_classes=6, dropout_rate=0.5):
|
76 |
+
model = AudioResNet(num_classes=num_classes, dropout_rate=dropout_rate)
|
77 |
+
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
|
78 |
+
model.eval()
|
79 |
+
return model
|
80 |
+
|
81 |
+
def validate_audio(y, sr, target_sr=44100, min_duration=0.1):
|
82 |
+
if sr != target_sr:
|
83 |
+
y = librosa.resample(y, orig_sr=sr, target_sr=target_sr)
|
84 |
+
if len(y) < min_duration * target_sr:
|
85 |
+
pad_length = int(min_duration * target_sr - len(y))
|
86 |
+
y = np.pad(y, (0, pad_length), mode='constant')
|
87 |
+
return y, target_sr
|
88 |
+
|
89 |
+
def strip_silence(y, sr, top_db=20, pad_duration=0.1):
|
90 |
+
y_trimmed, _ = librosa.effects.trim(y, top_db=top_db)
|
91 |
+
pad_length = int(pad_duration * sr)
|
92 |
+
y_padded = np.pad(y_trimmed, pad_length, mode='constant')
|
93 |
+
return y_padded
|
94 |
+
|
95 |
+
def audio_to_spectrogram(file_path, n_fft=2048, hop_length=256, n_mels=128, target_sr=44100, min_duration=0.1):
|
96 |
+
try:
|
97 |
+
y, sr = librosa.load(file_path, sr=None)
|
98 |
+
y, sr = validate_audio(y, sr, target_sr, min_duration)
|
99 |
+
y = strip_silence(y, sr)
|
100 |
+
except Exception as e:
|
101 |
+
print(f"Error reading {file_path}: {e}")
|
102 |
+
return None
|
103 |
+
|
104 |
+
y = librosa.util.normalize(y)
|
105 |
+
S = librosa.feature.melspectrogram(y=y, sr=sr, n_fft=n_fft, hop_length=hop_length, n_mels=n_mels)
|
106 |
+
S_dB = librosa.power_to_db(S, ref=np.max)
|
107 |
+
return S_dB
|
108 |
+
|
109 |
+
def classify_file(model, file_path, spectrogram_save_path):
|
110 |
+
spectrogram = audio_to_spectrogram(file_path)
|
111 |
+
if spectrogram is None:
|
112 |
+
return None, None
|
113 |
+
os.makedirs(os.path.dirname(spectrogram_save_path), exist_ok=True)
|
114 |
+
np.save(spectrogram_save_path, spectrogram)
|
115 |
+
spectrogram = torch.tensor(spectrogram, dtype=torch.float32).unsqueeze(0).unsqueeze(0)
|
116 |
+
with torch.no_grad():
|
117 |
+
output = model(spectrogram)
|
118 |
+
probabilities = torch.exp(output)
|
119 |
+
confidence, predicted = torch.max(probabilities, 1)
|
120 |
+
return confidence.item(), predicted.item()
|
121 |
+
|
122 |
+
def sort_files(model, input_folder, output_folder, confidence_threshold=0.9, progress_callback=None):
|
123 |
+
spectrogram_folder = os.path.join(output_folder, "Spectrograms")
|
124 |
+
if not os.path.exists(output_folder):
|
125 |
+
os.makedirs(output_folder)
|
126 |
+
|
127 |
+
files = list(Path(input_folder).rglob('*.wav'))
|
128 |
+
total_files = len(files)
|
129 |
+
|
130 |
+
for idx, file in enumerate(files):
|
131 |
+
spectrogram_save_path = os.path.join(spectrogram_folder, os.path.relpath(file, input_folder)) + '.npy'
|
132 |
+
confidence, label = classify_file(model, file, spectrogram_save_path)
|
133 |
+
if confidence is not None and confidence >= confidence_threshold:
|
134 |
+
label_folder = os.path.join(output_folder, str(label))
|
135 |
+
if not os.path.exists(label_folder):
|
136 |
+
os.makedirs(label_folder)
|
137 |
+
shutil.copy(file, label_folder)
|
138 |
+
if progress_callback:
|
139 |
+
progress_callback(idx + 1, total_files)
|
140 |
+
|
141 |
+
class Application(tk.Frame):
|
142 |
+
def __init__(self, master=None):
|
143 |
+
super().__init__(master)
|
144 |
+
self.master = master
|
145 |
+
self.pack()
|
146 |
+
self.create_widgets()
|
147 |
+
|
148 |
+
def create_widgets(self):
|
149 |
+
self.label = tk.Label(self, text="Select Folder:")
|
150 |
+
self.label.pack()
|
151 |
+
|
152 |
+
self.entry = tk.Entry(self, width=50)
|
153 |
+
self.entry.pack()
|
154 |
+
|
155 |
+
self.browse_button = tk.Button(self, text="Browse", command=self.browse_folder)
|
156 |
+
self.browse_button.pack()
|
157 |
+
|
158 |
+
self.progress = tk.IntVar()
|
159 |
+
self.progress_bar = ttk.Progressbar(self, orient="horizontal", length=400, mode="determinate", variable=self.progress)
|
160 |
+
self.progress_bar.pack()
|
161 |
+
|
162 |
+
self.sort_button = tk.Button(self, text="Sort Files", command=self.sort_files)
|
163 |
+
self.sort_button.pack()
|
164 |
+
|
165 |
+
self.quit = tk.Button(self, text="Quit", fg="red", command=self.master.destroy)
|
166 |
+
self.quit.pack()
|
167 |
+
|
168 |
+
def browse_folder(self):
|
169 |
+
folder_selected = filedialog.askdirectory()
|
170 |
+
self.entry.delete(0, tk.END)
|
171 |
+
self.entry.insert(0, folder_selected)
|
172 |
+
|
173 |
+
def update_progress(self, current, total):
|
174 |
+
self.progress.set(int(current / total * 100))
|
175 |
+
self.progress_bar.update()
|
176 |
+
|
177 |
+
def sort_files(self):
|
178 |
+
input_folder = self.entry.get()
|
179 |
+
output_folder = os.path.join(input_folder, "Sorted")
|
180 |
+
model_path = "0Shot1Shot2ShotV0.1.pth"
|
181 |
+
model = load_model(model_path)
|
182 |
+
try:
|
183 |
+
sort_files(model, input_folder, output_folder, progress_callback=self.update_progress)
|
184 |
+
messagebox.showinfo("Success", "Files sorted successfully!")
|
185 |
+
except Exception as e:
|
186 |
+
messagebox.showerror("Error", str(e))
|
187 |
+
|
188 |
+
root = tk.Tk()
|
189 |
+
app = Application(master=root)
|
190 |
+
app.master.title("Sample Sorter")
|
191 |
+
app.mainloop()
|
training.log
ADDED
@@ -0,0 +1,935 @@
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1 |
+
2024-05-16 15:08:45,991 - root - ERROR - An error occurred: name 'os' is not defined
|
2 |
+
2024-05-16 15:09:09,391 - root - INFO - Initializing SpectrogramDataset...
|
3 |
+
2024-05-16 15:09:09,929 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
4 |
+
2024-05-16 15:09:09,934 - root - INFO - SpectrogramDataset initialized successfully
|
5 |
+
2024-05-16 15:09:09,934 - root - ERROR - An error occurred: name 'np' is not defined
|
6 |
+
2024-05-16 15:09:43,969 - root - INFO - Initializing SpectrogramDataset...
|
7 |
+
2024-05-16 15:09:44,476 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
8 |
+
2024-05-16 15:09:44,481 - root - INFO - SpectrogramDataset initialized successfully
|
9 |
+
2024-05-16 15:09:44,490 - root - ERROR - An error occurred: name 'RandomSampler' is not defined
|
10 |
+
2024-05-16 15:10:48,213 - root - INFO - Initializing SpectrogramDataset...
|
11 |
+
2024-05-16 15:10:48,734 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
12 |
+
2024-05-16 15:10:48,739 - root - INFO - SpectrogramDataset initialized successfully
|
13 |
+
2024-05-16 15:10:48,741 - root - ERROR - An error occurred: name 'SequentialSampler' is not defined
|
14 |
+
2024-05-16 15:11:11,518 - root - INFO - Initializing SpectrogramDataset...
|
15 |
+
2024-05-16 15:11:12,025 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
16 |
+
2024-05-16 15:11:12,031 - root - INFO - SpectrogramDataset initialized successfully
|
17 |
+
2024-05-16 15:11:12,337 - root - ERROR - An error occurred: list index out of range
|
18 |
+
2024-05-16 15:19:51,771 - root - INFO - Initializing SpectrogramDataset...
|
19 |
+
2024-05-16 15:19:52,293 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
20 |
+
2024-05-16 15:19:52,298 - root - INFO - SpectrogramDataset initialized successfully
|
21 |
+
2024-05-16 15:19:52,569 - root - ERROR - An error occurred: list index out of range
|
22 |
+
2024-05-16 15:25:26,353 - root - INFO - Initializing SpectrogramDataset...
|
23 |
+
2024-05-16 15:25:26,884 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
24 |
+
2024-05-16 15:25:26,890 - root - INFO - SpectrogramDataset initialized successfully
|
25 |
+
2024-05-16 15:25:26,892 - root - INFO - Dataset length: 2288
|
26 |
+
2024-05-16 15:25:26,893 - root - INFO - Train dataset length: 1601
|
27 |
+
2024-05-16 15:25:26,894 - root - INFO - Validation dataset length: 343
|
28 |
+
2024-05-16 15:25:26,894 - root - INFO - Test dataset length: 344
|
29 |
+
2024-05-16 15:25:30,518 - root - INFO - Train dataset verification passed
|
30 |
+
2024-05-16 15:25:31,162 - root - INFO - Validation dataset verification passed
|
31 |
+
2024-05-16 15:25:32,096 - root - INFO - Test dataset verification passed
|
32 |
+
2024-05-16 15:25:32,406 - root - ERROR - An error occurred: list index out of range
|
33 |
+
2024-05-16 15:31:30,879 - root - INFO - Initializing SpectrogramDataset...
|
34 |
+
2024-05-16 15:31:31,394 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
35 |
+
2024-05-16 15:31:31,400 - root - INFO - SpectrogramDataset initialized successfully
|
36 |
+
2024-05-16 15:31:31,402 - root - ERROR - An error occurred: name 'verify_dataset_and_loader' is not defined
|
37 |
+
2024-05-16 15:35:01,395 - root - INFO - Initializing SpectrogramDataset...
|
38 |
+
2024-05-16 15:35:01,929 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
39 |
+
2024-05-16 15:35:01,934 - root - INFO - SpectrogramDataset initialized successfully
|
40 |
+
2024-05-16 15:35:01,937 - root - INFO - Dataset length: 2288
|
41 |
+
2024-05-16 15:35:01,938 - root - INFO - Train dataset length: 1601
|
42 |
+
2024-05-16 15:35:01,939 - root - INFO - Validation dataset length: 343
|
43 |
+
2024-05-16 15:35:01,939 - root - INFO - Test dataset length: 344
|
44 |
+
2024-05-16 15:35:05,709 - root - INFO - Train dataset verification passed
|
45 |
+
2024-05-16 15:35:06,364 - root - INFO - Validation dataset verification passed
|
46 |
+
2024-05-16 15:35:07,138 - root - INFO - Test dataset verification passed
|
47 |
+
2024-05-16 15:35:07,141 - root - INFO - Train sampler indices: [1715, 212, 1663, 1641, 874, 2133, 230, 1692, 1653, 1717]... (total: 2288)
|
48 |
+
2024-05-16 15:35:07,142 - root - ERROR - Train sampler index out of range: 2287 >= 1601
|
49 |
+
2024-05-16 15:35:07,143 - root - INFO - Validation sampler indices: [230, 218, 4, 229, 290, 64, 207, 188, 115, 212]... (total: 343)
|
50 |
+
2024-05-16 15:35:07,143 - root - INFO - Validation sampler indices within range.
|
51 |
+
2024-05-16 15:35:07,144 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
52 |
+
2024-05-16 15:35:07,145 - root - INFO - Test sampler indices within range.
|
53 |
+
2024-05-16 15:35:07,435 - root - ERROR - An error occurred: list index out of range
|
54 |
+
2024-05-16 15:38:08,245 - root - INFO - Initializing SpectrogramDataset...
|
55 |
+
2024-05-16 15:38:08,784 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
56 |
+
2024-05-16 15:38:08,789 - root - INFO - SpectrogramDataset initialized successfully
|
57 |
+
2024-05-16 15:38:08,791 - root - ERROR - An error occurred: 'SpectrogramDataset' object has no attribute 'indices'
|
58 |
+
2024-05-16 15:39:41,986 - root - INFO - Initializing SpectrogramDataset...
|
59 |
+
2024-05-16 15:39:42,584 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
60 |
+
2024-05-16 15:39:42,588 - root - INFO - SpectrogramDataset initialized successfully
|
61 |
+
2024-05-16 15:39:42,591 - root - INFO - Dataset length: 2288
|
62 |
+
2024-05-16 15:39:42,592 - root - INFO - Train dataset length: 1601
|
63 |
+
2024-05-16 15:39:42,592 - root - INFO - Validation dataset length: 343
|
64 |
+
2024-05-16 15:39:42,593 - root - INFO - Test dataset length: 344
|
65 |
+
2024-05-16 15:39:46,571 - root - INFO - Train dataset verification passed
|
66 |
+
2024-05-16 15:39:47,363 - root - INFO - Validation dataset verification passed
|
67 |
+
2024-05-16 15:39:48,276 - root - INFO - Test dataset verification passed
|
68 |
+
2024-05-16 15:39:48,278 - root - INFO - Train sampler indices: [57, 621, 222, 1233, 1204, 758, 1312, 357, 1105, 1504]... (total: 1601)
|
69 |
+
2024-05-16 15:39:48,279 - root - INFO - Train sampler indices within range.
|
70 |
+
2024-05-16 15:39:48,280 - root - INFO - Validation sampler indices: [208, 54, 29, 193, 121, 156, 339, 189, 301, 166]... (total: 343)
|
71 |
+
2024-05-16 15:39:48,281 - root - INFO - Validation sampler indices within range.
|
72 |
+
2024-05-16 15:39:48,282 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
73 |
+
2024-05-16 15:39:48,283 - root - INFO - Test sampler indices within range.
|
74 |
+
2024-05-16 15:39:50,020 - root - ERROR - An error occurred: mat1 and mat2 shapes cannot be multiplied (256x32768 and 8192x1024)
|
75 |
+
2024-05-16 15:41:07,216 - root - INFO - Initializing SpectrogramDataset...
|
76 |
+
2024-05-16 15:41:07,794 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
77 |
+
2024-05-16 15:41:07,799 - root - INFO - SpectrogramDataset initialized successfully
|
78 |
+
2024-05-16 15:41:07,802 - root - INFO - Dataset length: 2288
|
79 |
+
2024-05-16 15:41:07,802 - root - INFO - Train dataset length: 1601
|
80 |
+
2024-05-16 15:41:07,803 - root - INFO - Validation dataset length: 343
|
81 |
+
2024-05-16 15:41:07,804 - root - INFO - Test dataset length: 344
|
82 |
+
2024-05-16 15:41:11,881 - root - INFO - Train dataset verification passed
|
83 |
+
2024-05-16 15:41:12,832 - root - INFO - Validation dataset verification passed
|
84 |
+
2024-05-16 15:41:13,661 - root - INFO - Test dataset verification passed
|
85 |
+
2024-05-16 15:41:13,663 - root - INFO - Train sampler indices: [1180, 1565, 821, 15, 559, 111, 128, 1387, 1325, 1509]... (total: 1601)
|
86 |
+
2024-05-16 15:41:13,664 - root - INFO - Train sampler indices within range.
|
87 |
+
2024-05-16 15:41:13,665 - root - INFO - Validation sampler indices: [252, 171, 14, 36, 193, 141, 57, 38, 219, 65]... (total: 343)
|
88 |
+
2024-05-16 15:41:13,665 - root - INFO - Validation sampler indices within range.
|
89 |
+
2024-05-16 15:41:13,666 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
90 |
+
2024-05-16 15:41:13,667 - root - INFO - Test sampler indices within range.
|
91 |
+
2024-05-16 15:41:15,654 - root - ERROR - An error occurred: mat1 and mat2 shapes cannot be multiplied (256x32768 and 8192x1024)
|
92 |
+
2024-05-16 15:43:06,699 - root - INFO - Initializing SpectrogramDataset...
|
93 |
+
2024-05-16 15:43:07,225 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
94 |
+
2024-05-16 15:43:07,230 - root - INFO - SpectrogramDataset initialized successfully
|
95 |
+
2024-05-16 15:43:07,232 - root - INFO - Dataset length: 2288
|
96 |
+
2024-05-16 15:43:07,233 - root - INFO - Train dataset length: 1601
|
97 |
+
2024-05-16 15:43:07,234 - root - INFO - Validation dataset length: 343
|
98 |
+
2024-05-16 15:43:07,234 - root - INFO - Test dataset length: 344
|
99 |
+
2024-05-16 15:43:10,655 - root - INFO - Train dataset verification passed
|
100 |
+
2024-05-16 15:43:11,554 - root - INFO - Validation dataset verification passed
|
101 |
+
2024-05-16 15:43:12,261 - root - INFO - Test dataset verification passed
|
102 |
+
2024-05-16 15:43:12,264 - root - INFO - Train sampler indices: [976, 895, 296, 857, 1419, 1508, 1235, 341, 1009, 78]... (total: 1601)
|
103 |
+
2024-05-16 15:43:12,265 - root - INFO - Train sampler indices within range.
|
104 |
+
2024-05-16 15:43:12,267 - root - INFO - Validation sampler indices: [142, 152, 107, 169, 147, 12, 161, 15, 61, 141]... (total: 343)
|
105 |
+
2024-05-16 15:43:12,267 - root - INFO - Validation sampler indices within range.
|
106 |
+
2024-05-16 15:43:12,268 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
107 |
+
2024-05-16 15:43:12,269 - root - INFO - Test sampler indices within range.
|
108 |
+
2024-05-16 15:43:12,828 - root - ERROR - An error occurred: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same
|
109 |
+
2024-05-16 15:46:38,338 - root - INFO - Initializing SpectrogramDataset...
|
110 |
+
2024-05-16 15:46:38,853 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
111 |
+
2024-05-16 15:46:38,905 - root - INFO - SpectrogramDataset initialized successfully
|
112 |
+
2024-05-16 15:46:38,909 - root - INFO - Dataset length: 2288
|
113 |
+
2024-05-16 15:46:38,910 - root - INFO - Train dataset length: 1601
|
114 |
+
2024-05-16 15:46:38,912 - root - INFO - Validation dataset length: 343
|
115 |
+
2024-05-16 15:46:38,913 - root - INFO - Test dataset length: 344
|
116 |
+
2024-05-16 15:46:42,470 - root - INFO - Train dataset verification passed
|
117 |
+
2024-05-16 15:46:43,177 - root - INFO - Validation dataset verification passed
|
118 |
+
2024-05-16 15:46:43,924 - root - INFO - Test dataset verification passed
|
119 |
+
2024-05-16 15:46:43,927 - root - INFO - Train sampler indices: [772, 408, 1328, 1382, 558, 1027, 1482, 1487, 198, 69]... (total: 1601)
|
120 |
+
2024-05-16 15:46:43,928 - root - INFO - Train sampler indices within range.
|
121 |
+
2024-05-16 15:46:43,929 - root - INFO - Validation sampler indices: [13, 20, 65, 277, 248, 107, 263, 31, 179, 159]... (total: 343)
|
122 |
+
2024-05-16 15:46:43,929 - root - INFO - Validation sampler indices within range.
|
123 |
+
2024-05-16 15:46:43,930 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
124 |
+
2024-05-16 15:46:43,931 - root - INFO - Test sampler indices within range.
|
125 |
+
2024-05-16 15:46:44,414 - root - ERROR - An error occurred: 'AudioResNet' object has no attribute 'fc1'
|
126 |
+
2024-05-16 15:50:57,787 - root - INFO - Initializing SpectrogramDataset...
|
127 |
+
2024-05-16 15:50:58,328 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
128 |
+
2024-05-16 15:50:58,333 - root - INFO - SpectrogramDataset initialized successfully
|
129 |
+
2024-05-16 15:50:58,335 - root - INFO - Dataset length: 2288
|
130 |
+
2024-05-16 15:50:58,336 - root - INFO - Train dataset length: 1601
|
131 |
+
2024-05-16 15:50:58,337 - root - INFO - Validation dataset length: 343
|
132 |
+
2024-05-16 15:50:58,337 - root - INFO - Test dataset length: 344
|
133 |
+
2024-05-16 15:51:01,977 - root - INFO - Train dataset verification passed
|
134 |
+
2024-05-16 15:51:02,979 - root - INFO - Validation dataset verification passed
|
135 |
+
2024-05-16 15:51:03,681 - root - INFO - Test dataset verification passed
|
136 |
+
2024-05-16 15:51:03,684 - root - INFO - Train sampler indices: [857, 128, 1276, 675, 178, 746, 1561, 1386, 1350, 1563]... (total: 1601)
|
137 |
+
2024-05-16 15:51:03,685 - root - INFO - Train sampler indices within range.
|
138 |
+
2024-05-16 15:51:03,686 - root - INFO - Validation sampler indices: [183, 83, 79, 230, 84, 20, 227, 30, 273, 196]... (total: 343)
|
139 |
+
2024-05-16 15:51:03,686 - root - INFO - Validation sampler indices within range.
|
140 |
+
2024-05-16 15:51:03,687 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
141 |
+
2024-05-16 15:51:03,688 - root - INFO - Test sampler indices within range.
|
142 |
+
2024-05-16 15:51:05,540 - root - ERROR - An error occurred: mat1 and mat2 shapes cannot be multiplied (256x32768 and 8192x1024)
|
143 |
+
2024-05-16 15:52:16,452 - root - INFO - Initializing SpectrogramDataset...
|
144 |
+
2024-05-16 15:52:16,965 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
145 |
+
2024-05-16 15:52:16,971 - root - INFO - SpectrogramDataset initialized successfully
|
146 |
+
2024-05-16 15:52:16,973 - root - INFO - Dataset length: 2288
|
147 |
+
2024-05-16 15:52:16,974 - root - INFO - Train dataset length: 1601
|
148 |
+
2024-05-16 15:52:16,975 - root - INFO - Validation dataset length: 343
|
149 |
+
2024-05-16 15:52:16,976 - root - INFO - Test dataset length: 344
|
150 |
+
2024-05-16 15:52:20,456 - root - INFO - Train dataset verification passed
|
151 |
+
2024-05-16 15:52:21,253 - root - INFO - Validation dataset verification passed
|
152 |
+
2024-05-16 15:52:21,949 - root - INFO - Test dataset verification passed
|
153 |
+
2024-05-16 15:52:21,952 - root - INFO - Train sampler indices: [299, 79, 986, 896, 1037, 1500, 744, 719, 643, 617]... (total: 1601)
|
154 |
+
2024-05-16 15:52:21,953 - root - INFO - Train sampler indices within range.
|
155 |
+
2024-05-16 15:52:21,954 - root - INFO - Validation sampler indices: [306, 310, 294, 202, 235, 302, 263, 115, 269, 131]... (total: 343)
|
156 |
+
2024-05-16 15:52:21,954 - root - INFO - Validation sampler indices within range.
|
157 |
+
2024-05-16 15:52:21,955 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
158 |
+
2024-05-16 15:52:21,956 - root - INFO - Test sampler indices within range.
|
159 |
+
2024-05-16 15:52:23,868 - root - ERROR - An error occurred: mat1 and mat2 shapes cannot be multiplied (256x32768 and 8192x1024)
|
160 |
+
2024-05-16 15:53:57,879 - root - INFO - Initializing SpectrogramDataset...
|
161 |
+
2024-05-16 15:53:58,421 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
162 |
+
2024-05-16 15:53:58,426 - root - INFO - SpectrogramDataset initialized successfully
|
163 |
+
2024-05-16 15:53:58,428 - root - INFO - Dataset length: 2288
|
164 |
+
2024-05-16 15:53:58,429 - root - INFO - Train dataset length: 1601
|
165 |
+
2024-05-16 15:53:58,430 - root - INFO - Validation dataset length: 343
|
166 |
+
2024-05-16 15:53:58,431 - root - INFO - Test dataset length: 344
|
167 |
+
2024-05-16 15:54:01,990 - root - INFO - Train dataset verification passed
|
168 |
+
2024-05-16 15:54:02,647 - root - INFO - Validation dataset verification passed
|
169 |
+
2024-05-16 15:54:03,543 - root - INFO - Test dataset verification passed
|
170 |
+
2024-05-16 15:54:03,546 - root - INFO - Train sampler indices: [1546, 728, 897, 1176, 902, 685, 927, 191, 553, 1434]... (total: 1601)
|
171 |
+
2024-05-16 15:54:03,546 - root - INFO - Train sampler indices within range.
|
172 |
+
2024-05-16 15:54:03,548 - root - INFO - Validation sampler indices: [293, 119, 112, 212, 257, 185, 237, 77, 285, 248]... (total: 343)
|
173 |
+
2024-05-16 15:54:03,548 - root - INFO - Validation sampler indices within range.
|
174 |
+
2024-05-16 15:54:03,549 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
175 |
+
2024-05-16 15:54:03,550 - root - INFO - Test sampler indices within range.
|
176 |
+
2024-05-16 15:54:05,500 - root - ERROR - An error occurred: mat1 and mat2 shapes cannot be multiplied (256x32768 and 8192x1024)
|
177 |
+
2024-05-16 16:40:54,571 - root - INFO - Initializing SpectrogramDataset...
|
178 |
+
2024-05-16 16:40:55,090 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
179 |
+
2024-05-16 16:40:55,095 - root - INFO - SpectrogramDataset initialized successfully
|
180 |
+
2024-05-16 16:40:55,098 - root - INFO - Dataset length: 2288
|
181 |
+
2024-05-16 16:40:55,099 - root - INFO - Train dataset length: 1601
|
182 |
+
2024-05-16 16:40:55,099 - root - INFO - Validation dataset length: 343
|
183 |
+
2024-05-16 16:40:55,100 - root - INFO - Test dataset length: 344
|
184 |
+
2024-05-16 16:40:58,581 - root - INFO - Train dataset verification passed
|
185 |
+
2024-05-16 16:40:59,386 - root - INFO - Validation dataset verification passed
|
186 |
+
2024-05-16 16:41:00,025 - root - INFO - Test dataset verification passed
|
187 |
+
2024-05-16 16:41:00,028 - root - INFO - Train sampler indices: [1258, 418, 585, 1154, 76, 11, 847, 845, 612, 483]... (total: 1601)
|
188 |
+
2024-05-16 16:41:00,028 - root - INFO - Train sampler indices within range.
|
189 |
+
2024-05-16 16:41:00,029 - root - INFO - Validation sampler indices: [185, 112, 269, 159, 203, 219, 94, 240, 34, 252]... (total: 343)
|
190 |
+
2024-05-16 16:41:00,030 - root - INFO - Validation sampler indices within range.
|
191 |
+
2024-05-16 16:41:00,030 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
192 |
+
2024-05-16 16:41:00,031 - root - INFO - Test sampler indices within range.
|
193 |
+
2024-05-16 16:41:01,972 - root - ERROR - An error occurred: mat1 and mat2 shapes cannot be multiplied (256x32768 and 8192x1024)
|
194 |
+
2024-05-16 16:42:14,104 - root - INFO - Initializing SpectrogramDataset...
|
195 |
+
2024-05-16 16:42:14,609 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
196 |
+
2024-05-16 16:42:14,615 - root - INFO - SpectrogramDataset initialized successfully
|
197 |
+
2024-05-16 16:42:14,618 - root - INFO - Dataset length: 2288
|
198 |
+
2024-05-16 16:42:14,619 - root - INFO - Train dataset length: 1601
|
199 |
+
2024-05-16 16:42:14,619 - root - INFO - Validation dataset length: 343
|
200 |
+
2024-05-16 16:42:14,620 - root - INFO - Test dataset length: 344
|
201 |
+
2024-05-16 16:42:18,231 - root - INFO - Train dataset verification passed
|
202 |
+
2024-05-16 16:42:18,857 - root - INFO - Validation dataset verification passed
|
203 |
+
2024-05-16 16:42:19,612 - root - INFO - Test dataset verification passed
|
204 |
+
2024-05-16 16:42:19,614 - root - INFO - Train sampler indices: [1079, 1334, 1520, 1425, 1063, 958, 1569, 283, 389, 1485]... (total: 1601)
|
205 |
+
2024-05-16 16:42:19,615 - root - INFO - Train sampler indices within range.
|
206 |
+
2024-05-16 16:42:19,616 - root - INFO - Validation sampler indices: [80, 280, 119, 79, 32, 196, 221, 326, 128, 141]... (total: 343)
|
207 |
+
2024-05-16 16:42:19,616 - root - INFO - Validation sampler indices within range.
|
208 |
+
2024-05-16 16:42:19,617 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
209 |
+
2024-05-16 16:42:19,618 - root - INFO - Test sampler indices within range.
|
210 |
+
2024-05-16 16:42:21,472 - root - ERROR - An error occurred: mat1 and mat2 shapes cannot be multiplied (256x32768 and 8192x1024)
|
211 |
+
2024-05-16 16:45:00,618 - root - INFO - Initializing SpectrogramDataset...
|
212 |
+
2024-05-16 16:45:01,133 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
213 |
+
2024-05-16 16:45:01,138 - root - INFO - SpectrogramDataset initialized successfully
|
214 |
+
2024-05-16 16:45:01,141 - root - INFO - Dataset length: 2288
|
215 |
+
2024-05-16 16:45:01,142 - root - INFO - Train dataset length: 1601
|
216 |
+
2024-05-16 16:45:01,143 - root - INFO - Validation dataset length: 343
|
217 |
+
2024-05-16 16:45:01,144 - root - INFO - Test dataset length: 344
|
218 |
+
2024-05-16 16:45:04,845 - root - INFO - Train dataset verification passed
|
219 |
+
2024-05-16 16:45:05,730 - root - INFO - Validation dataset verification passed
|
220 |
+
2024-05-16 16:45:06,456 - root - INFO - Test dataset verification passed
|
221 |
+
2024-05-16 16:45:06,459 - root - INFO - Train sampler indices: [1162, 1145, 1130, 1430, 470, 1266, 178, 1590, 1313, 1475]... (total: 1601)
|
222 |
+
2024-05-16 16:45:06,459 - root - INFO - Train sampler indices within range.
|
223 |
+
2024-05-16 16:45:06,461 - root - INFO - Validation sampler indices: [310, 213, 333, 315, 25, 224, 304, 210, 136, 134]... (total: 343)
|
224 |
+
2024-05-16 16:45:06,461 - root - INFO - Validation sampler indices within range.
|
225 |
+
2024-05-16 16:45:06,462 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
226 |
+
2024-05-16 16:45:06,462 - root - INFO - Test sampler indices within range.
|
227 |
+
2024-05-16 16:45:08,309 - root - ERROR - An error occurred: mat1 and mat2 shapes cannot be multiplied (256x32768 and 8192x1024)
|
228 |
+
2024-05-16 16:48:33,071 - root - INFO - Initializing SpectrogramDataset...
|
229 |
+
2024-05-16 16:48:33,582 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
230 |
+
2024-05-16 16:48:33,587 - root - INFO - SpectrogramDataset initialized successfully
|
231 |
+
2024-05-16 16:48:33,590 - root - INFO - Dataset length: 2288
|
232 |
+
2024-05-16 16:48:33,591 - root - INFO - Train dataset length: 1601
|
233 |
+
2024-05-16 16:48:33,591 - root - INFO - Validation dataset length: 343
|
234 |
+
2024-05-16 16:48:33,592 - root - INFO - Test dataset length: 344
|
235 |
+
2024-05-16 16:48:37,170 - root - INFO - Train dataset verification passed
|
236 |
+
2024-05-16 16:48:38,002 - root - INFO - Validation dataset verification passed
|
237 |
+
2024-05-16 16:48:38,710 - root - INFO - Test dataset verification passed
|
238 |
+
2024-05-16 16:48:38,713 - root - INFO - Train sampler indices: [937, 1119, 318, 526, 1492, 643, 231, 300, 479, 1239]... (total: 1601)
|
239 |
+
2024-05-16 16:48:38,713 - root - INFO - Train sampler indices within range.
|
240 |
+
2024-05-16 16:48:38,714 - root - INFO - Validation sampler indices: [240, 43, 198, 107, 57, 126, 341, 52, 128, 172]... (total: 343)
|
241 |
+
2024-05-16 16:48:38,715 - root - INFO - Validation sampler indices within range.
|
242 |
+
2024-05-16 16:48:38,716 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
243 |
+
2024-05-16 16:48:38,716 - root - INFO - Test sampler indices within range.
|
244 |
+
2024-05-16 16:53:13,787 - root - INFO - Initializing SpectrogramDataset...
|
245 |
+
2024-05-16 16:53:14,367 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
246 |
+
2024-05-16 16:53:14,373 - root - INFO - SpectrogramDataset initialized successfully
|
247 |
+
2024-05-16 16:53:14,376 - root - INFO - Dataset length: 2288
|
248 |
+
2024-05-16 16:53:14,377 - root - INFO - Train dataset length: 1601
|
249 |
+
2024-05-16 16:53:14,378 - root - INFO - Validation dataset length: 343
|
250 |
+
2024-05-16 16:53:14,378 - root - INFO - Test dataset length: 344
|
251 |
+
2024-05-16 16:53:18,114 - root - INFO - Train dataset verification passed
|
252 |
+
2024-05-16 16:53:18,700 - root - INFO - Validation dataset verification passed
|
253 |
+
2024-05-16 16:53:19,364 - root - INFO - Test dataset verification passed
|
254 |
+
2024-05-16 16:53:19,367 - root - INFO - Train sampler indices: [417, 305, 1305, 1228, 557, 285, 665, 354, 630, 7]... (total: 1601)
|
255 |
+
2024-05-16 16:53:19,367 - root - INFO - Train sampler indices within range.
|
256 |
+
2024-05-16 16:53:19,368 - root - INFO - Validation sampler indices: [132, 250, 298, 27, 275, 336, 224, 12, 267, 110]... (total: 343)
|
257 |
+
2024-05-16 16:53:19,369 - root - INFO - Validation sampler indices within range.
|
258 |
+
2024-05-16 16:53:19,369 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
259 |
+
2024-05-16 16:53:19,370 - root - INFO - Test sampler indices within range.
|
260 |
+
2024-05-16 16:54:30,006 - root - ERROR - An error occurred: mat1 and mat2 shapes cannot be multiplied (65x28672 and 32768x1024)
|
261 |
+
2024-05-16 16:58:46,198 - root - INFO - Initializing SpectrogramDataset...
|
262 |
+
2024-05-16 16:58:46,715 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
263 |
+
2024-05-16 16:58:46,720 - root - INFO - SpectrogramDataset initialized successfully
|
264 |
+
2024-05-16 16:58:46,723 - root - INFO - Dataset length: 2288
|
265 |
+
2024-05-16 16:58:46,723 - root - INFO - Train dataset length: 1601
|
266 |
+
2024-05-16 16:58:46,724 - root - INFO - Validation dataset length: 343
|
267 |
+
2024-05-16 16:58:46,725 - root - INFO - Test dataset length: 344
|
268 |
+
2024-05-16 16:58:50,330 - root - INFO - Train dataset verification passed
|
269 |
+
2024-05-16 16:58:51,110 - root - INFO - Validation dataset verification passed
|
270 |
+
2024-05-16 16:58:51,943 - root - INFO - Test dataset verification passed
|
271 |
+
2024-05-16 16:58:51,945 - root - INFO - Train sampler indices: [963, 1570, 599, 1093, 923, 1455, 61, 711, 76, 753]... (total: 1601)
|
272 |
+
2024-05-16 16:58:51,946 - root - INFO - Train sampler indices within range.
|
273 |
+
2024-05-16 16:58:51,947 - root - INFO - Validation sampler indices: [93, 4, 216, 142, 203, 314, 263, 222, 326, 199]... (total: 343)
|
274 |
+
2024-05-16 16:58:51,948 - root - INFO - Validation sampler indices within range.
|
275 |
+
2024-05-16 16:58:51,948 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
276 |
+
2024-05-16 16:58:51,949 - root - INFO - Test sampler indices within range.
|
277 |
+
2024-05-16 17:01:19,991 - root - INFO - Initializing SpectrogramDataset...
|
278 |
+
2024-05-16 17:01:20,507 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
279 |
+
2024-05-16 17:01:20,512 - root - INFO - SpectrogramDataset initialized successfully
|
280 |
+
2024-05-16 17:01:20,515 - root - INFO - Dataset length: 2288
|
281 |
+
2024-05-16 17:01:20,516 - root - INFO - Train dataset length: 1601
|
282 |
+
2024-05-16 17:01:20,516 - root - INFO - Validation dataset length: 343
|
283 |
+
2024-05-16 17:01:20,517 - root - INFO - Test dataset length: 344
|
284 |
+
2024-05-16 17:01:23,995 - root - INFO - Train dataset verification passed
|
285 |
+
2024-05-16 17:01:24,677 - root - INFO - Validation dataset verification passed
|
286 |
+
2024-05-16 17:01:25,476 - root - INFO - Test dataset verification passed
|
287 |
+
2024-05-16 17:01:25,479 - root - INFO - Train sampler indices: [1387, 1163, 1160, 689, 400, 288, 1388, 1466, 1248, 921]... (total: 1601)
|
288 |
+
2024-05-16 17:01:25,479 - root - INFO - Train sampler indices within range.
|
289 |
+
2024-05-16 17:01:25,480 - root - INFO - Validation sampler indices: [18, 46, 244, 12, 240, 334, 280, 73, 322, 177]... (total: 343)
|
290 |
+
2024-05-16 17:01:25,481 - root - INFO - Validation sampler indices within range.
|
291 |
+
2024-05-16 17:01:25,481 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
292 |
+
2024-05-16 17:01:25,482 - root - INFO - Test sampler indices within range.
|
293 |
+
2024-05-16 17:09:53,291 - root - INFO - Initializing SpectrogramDataset...
|
294 |
+
2024-05-16 17:09:53,807 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
295 |
+
2024-05-16 17:09:53,965 - root - INFO - SpectrogramDataset initialized successfully
|
296 |
+
2024-05-16 17:09:53,968 - root - INFO - Dataset length: 2288
|
297 |
+
2024-05-16 17:09:53,969 - root - INFO - Train dataset length: 1601
|
298 |
+
2024-05-16 17:09:53,970 - root - INFO - Validation dataset length: 343
|
299 |
+
2024-05-16 17:09:53,971 - root - INFO - Test dataset length: 344
|
300 |
+
2024-05-16 17:09:57,660 - root - INFO - Train dataset verification passed
|
301 |
+
2024-05-16 17:09:58,343 - root - INFO - Validation dataset verification passed
|
302 |
+
2024-05-16 17:09:59,001 - root - INFO - Test dataset verification passed
|
303 |
+
2024-05-16 17:09:59,004 - root - INFO - Train sampler indices: [1020, 1164, 378, 1445, 1077, 545, 635, 390, 489, 52]... (total: 1601)
|
304 |
+
2024-05-16 17:09:59,004 - root - INFO - Train sampler indices within range.
|
305 |
+
2024-05-16 17:09:59,006 - root - INFO - Validation sampler indices: [317, 276, 168, 294, 80, 262, 68, 152, 337, 193]... (total: 343)
|
306 |
+
2024-05-16 17:09:59,006 - root - INFO - Validation sampler indices within range.
|
307 |
+
2024-05-16 17:09:59,007 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
308 |
+
2024-05-16 17:09:59,007 - root - INFO - Test sampler indices within range.
|
309 |
+
2024-05-16 17:14:42,976 - root - INFO - Initializing SpectrogramDataset...
|
310 |
+
2024-05-16 17:14:43,536 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
311 |
+
2024-05-16 17:14:43,541 - root - INFO - SpectrogramDataset initialized successfully
|
312 |
+
2024-05-16 17:14:43,544 - root - INFO - Dataset length: 2288
|
313 |
+
2024-05-16 17:14:43,544 - root - INFO - Train dataset length: 1601
|
314 |
+
2024-05-16 17:14:43,545 - root - INFO - Validation dataset length: 343
|
315 |
+
2024-05-16 17:14:43,546 - root - INFO - Test dataset length: 344
|
316 |
+
2024-05-16 17:14:47,402 - root - INFO - Train dataset verification passed
|
317 |
+
2024-05-16 17:14:48,119 - root - INFO - Validation dataset verification passed
|
318 |
+
2024-05-16 17:14:48,836 - root - INFO - Test dataset verification passed
|
319 |
+
2024-05-16 17:14:48,839 - root - INFO - Train sampler indices: [51, 468, 1020, 401, 1069, 884, 566, 1287, 1280, 514]... (total: 1601)
|
320 |
+
2024-05-16 17:14:48,839 - root - INFO - Train sampler indices within range.
|
321 |
+
2024-05-16 17:14:48,840 - root - INFO - Validation sampler indices: [40, 77, 104, 254, 113, 284, 119, 195, 133, 4]... (total: 343)
|
322 |
+
2024-05-16 17:14:48,841 - root - INFO - Validation sampler indices within range.
|
323 |
+
2024-05-16 17:14:48,841 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
324 |
+
2024-05-16 17:14:48,842 - root - INFO - Test sampler indices within range.
|
325 |
+
2024-05-16 17:16:34,292 - root - INFO - Initializing SpectrogramDataset...
|
326 |
+
2024-05-16 17:16:34,820 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
327 |
+
2024-05-16 17:16:34,825 - root - INFO - SpectrogramDataset initialized successfully
|
328 |
+
2024-05-16 17:16:34,828 - root - INFO - Dataset length: 2288
|
329 |
+
2024-05-16 17:16:34,829 - root - INFO - Train dataset length: 1601
|
330 |
+
2024-05-16 17:16:34,830 - root - INFO - Validation dataset length: 343
|
331 |
+
2024-05-16 17:16:34,831 - root - INFO - Test dataset length: 344
|
332 |
+
2024-05-16 17:16:38,483 - root - INFO - Train dataset verification passed
|
333 |
+
2024-05-16 17:16:39,261 - root - INFO - Validation dataset verification passed
|
334 |
+
2024-05-16 17:16:40,178 - root - INFO - Test dataset verification passed
|
335 |
+
2024-05-16 17:16:40,182 - root - INFO - Train sampler indices: [161, 1459, 1297, 1364, 621, 261, 361, 1526, 159, 676]... (total: 1601)
|
336 |
+
2024-05-16 17:16:40,182 - root - INFO - Train sampler indices within range.
|
337 |
+
2024-05-16 17:16:40,183 - root - INFO - Validation sampler indices: [108, 159, 16, 2, 255, 169, 322, 176, 257, 319]... (total: 343)
|
338 |
+
2024-05-16 17:16:40,184 - root - INFO - Validation sampler indices within range.
|
339 |
+
2024-05-16 17:16:40,185 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
340 |
+
2024-05-16 17:16:40,185 - root - INFO - Test sampler indices within range.
|
341 |
+
2024-05-16 17:22:38,962 - root - INFO - Initializing SpectrogramDataset...
|
342 |
+
2024-05-16 17:22:39,476 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
343 |
+
2024-05-16 17:22:39,482 - root - INFO - SpectrogramDataset initialized successfully
|
344 |
+
2024-05-16 17:22:39,484 - root - INFO - Dataset length: 2288
|
345 |
+
2024-05-16 17:22:39,485 - root - INFO - Train dataset length: 1601
|
346 |
+
2024-05-16 17:22:39,486 - root - INFO - Validation dataset length: 343
|
347 |
+
2024-05-16 17:22:39,486 - root - INFO - Test dataset length: 344
|
348 |
+
2024-05-16 17:22:43,533 - root - INFO - Train dataset verification passed
|
349 |
+
2024-05-16 17:22:44,327 - root - INFO - Validation dataset verification passed
|
350 |
+
2024-05-16 17:22:45,030 - root - INFO - Test dataset verification passed
|
351 |
+
2024-05-16 17:22:45,033 - root - INFO - Train sampler indices: [118, 829, 811, 1417, 665, 623, 127, 1084, 1138, 1530]... (total: 1601)
|
352 |
+
2024-05-16 17:22:45,033 - root - INFO - Train sampler indices within range.
|
353 |
+
2024-05-16 17:22:45,035 - root - INFO - Validation sampler indices: [143, 9, 118, 218, 205, 141, 320, 39, 336, 33]... (total: 343)
|
354 |
+
2024-05-16 17:22:45,035 - root - INFO - Validation sampler indices within range.
|
355 |
+
2024-05-16 17:22:45,036 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
356 |
+
2024-05-16 17:22:45,036 - root - INFO - Test sampler indices within range.
|
357 |
+
2024-05-16 17:25:24,529 - root - INFO - Initializing SpectrogramDataset...
|
358 |
+
2024-05-16 17:25:25,119 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
359 |
+
2024-05-16 17:25:25,125 - root - INFO - SpectrogramDataset initialized successfully
|
360 |
+
2024-05-16 17:25:25,128 - root - INFO - Dataset length: 2288
|
361 |
+
2024-05-16 17:25:25,129 - root - INFO - Train dataset length: 1601
|
362 |
+
2024-05-16 17:25:25,130 - root - INFO - Validation dataset length: 343
|
363 |
+
2024-05-16 17:25:25,131 - root - INFO - Test dataset length: 344
|
364 |
+
2024-05-16 17:25:28,714 - root - INFO - Train dataset verification passed
|
365 |
+
2024-05-16 17:25:29,462 - root - INFO - Validation dataset verification passed
|
366 |
+
2024-05-16 17:25:30,173 - root - INFO - Test dataset verification passed
|
367 |
+
2024-05-16 17:25:30,176 - root - INFO - Train sampler indices: [902, 936, 524, 793, 626, 1586, 205, 1589, 421, 1497]... (total: 1601)
|
368 |
+
2024-05-16 17:25:30,176 - root - INFO - Train sampler indices within range.
|
369 |
+
2024-05-16 17:25:30,177 - root - INFO - Validation sampler indices: [231, 277, 199, 19, 226, 308, 339, 291, 36, 148]... (total: 343)
|
370 |
+
2024-05-16 17:25:30,178 - root - INFO - Validation sampler indices within range.
|
371 |
+
2024-05-16 17:25:30,178 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
372 |
+
2024-05-16 17:25:30,179 - root - INFO - Test sampler indices within range.
|
373 |
+
2024-05-16 17:27:12,712 - root - INFO - Initializing SpectrogramDataset...
|
374 |
+
2024-05-16 17:27:13,220 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
375 |
+
2024-05-16 17:27:13,226 - root - INFO - SpectrogramDataset initialized successfully
|
376 |
+
2024-05-16 17:27:13,229 - root - INFO - Dataset length: 2288
|
377 |
+
2024-05-16 17:27:13,230 - root - INFO - Train dataset length: 1601
|
378 |
+
2024-05-16 17:27:13,231 - root - INFO - Validation dataset length: 343
|
379 |
+
2024-05-16 17:27:13,231 - root - INFO - Test dataset length: 344
|
380 |
+
2024-05-16 17:27:16,751 - root - INFO - Train dataset verification passed
|
381 |
+
2024-05-16 17:27:17,505 - root - INFO - Validation dataset verification passed
|
382 |
+
2024-05-16 17:27:18,216 - root - INFO - Test dataset verification passed
|
383 |
+
2024-05-16 17:27:18,219 - root - INFO - Train sampler indices: [554, 203, 939, 1461, 214, 1459, 1, 114, 1008, 835]... (total: 1601)
|
384 |
+
2024-05-16 17:27:18,219 - root - INFO - Train sampler indices within range.
|
385 |
+
2024-05-16 17:27:18,221 - root - INFO - Validation sampler indices: [134, 260, 160, 205, 259, 177, 241, 41, 48, 58]... (total: 343)
|
386 |
+
2024-05-16 17:27:18,221 - root - INFO - Validation sampler indices within range.
|
387 |
+
2024-05-16 17:27:18,222 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
388 |
+
2024-05-16 17:27:18,222 - root - INFO - Test sampler indices within range.
|
389 |
+
2024-05-16 17:32:36,459 - root - INFO - Initializing SpectrogramDataset...
|
390 |
+
2024-05-16 17:32:37,002 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
391 |
+
2024-05-16 17:32:37,008 - root - INFO - SpectrogramDataset initialized successfully
|
392 |
+
2024-05-16 17:32:37,011 - root - INFO - Dataset length: 2288
|
393 |
+
2024-05-16 17:32:37,012 - root - INFO - Train dataset length: 1601
|
394 |
+
2024-05-16 17:32:37,013 - root - INFO - Validation dataset length: 343
|
395 |
+
2024-05-16 17:32:37,014 - root - INFO - Test dataset length: 344
|
396 |
+
2024-05-16 17:32:40,988 - root - INFO - Train dataset verification passed
|
397 |
+
2024-05-16 17:32:41,669 - root - INFO - Validation dataset verification passed
|
398 |
+
2024-05-16 17:32:42,237 - root - INFO - Test dataset verification passed
|
399 |
+
2024-05-16 17:32:42,239 - root - INFO - Train sampler indices: [1107, 648, 26, 1289, 277, 753, 139, 1574, 579, 566]... (total: 1601)
|
400 |
+
2024-05-16 17:32:42,240 - root - INFO - Train sampler indices within range.
|
401 |
+
2024-05-16 17:32:42,241 - root - INFO - Validation sampler indices: [324, 93, 175, 231, 205, 84, 1, 66, 140, 120]... (total: 343)
|
402 |
+
2024-05-16 17:32:42,242 - root - INFO - Validation sampler indices within range.
|
403 |
+
2024-05-16 17:32:42,242 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
404 |
+
2024-05-16 17:32:42,243 - root - INFO - Test sampler indices within range.
|
405 |
+
2024-05-16 17:35:48,077 - root - INFO - Initializing SpectrogramDataset...
|
406 |
+
2024-05-16 17:35:48,610 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
407 |
+
2024-05-16 17:35:48,614 - root - INFO - SpectrogramDataset initialized successfully
|
408 |
+
2024-05-16 17:35:48,617 - root - INFO - Dataset length: 2288
|
409 |
+
2024-05-16 17:35:48,618 - root - INFO - Train dataset length: 1601
|
410 |
+
2024-05-16 17:35:48,619 - root - INFO - Validation dataset length: 343
|
411 |
+
2024-05-16 17:35:48,619 - root - INFO - Test dataset length: 344
|
412 |
+
2024-05-16 17:35:52,394 - root - INFO - Train dataset verification passed
|
413 |
+
2024-05-16 17:35:53,318 - root - INFO - Validation dataset verification passed
|
414 |
+
2024-05-16 17:35:54,123 - root - INFO - Test dataset verification passed
|
415 |
+
2024-05-16 17:35:54,127 - root - INFO - Train sampler indices: [927, 1138, 180, 622, 0, 824, 702, 784, 273, 1378]... (total: 1601)
|
416 |
+
2024-05-16 17:35:54,127 - root - INFO - Train sampler indices within range.
|
417 |
+
2024-05-16 17:35:54,129 - root - INFO - Validation sampler indices: [192, 283, 183, 187, 227, 32, 199, 268, 174, 259]... (total: 343)
|
418 |
+
2024-05-16 17:35:54,130 - root - INFO - Validation sampler indices within range.
|
419 |
+
2024-05-16 17:35:54,131 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
420 |
+
2024-05-16 17:35:54,132 - root - INFO - Test sampler indices within range.
|
421 |
+
2024-05-16 17:36:27,073 - root - INFO - Initializing SpectrogramDataset...
|
422 |
+
2024-05-16 17:36:27,582 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
423 |
+
2024-05-16 17:36:27,587 - root - INFO - SpectrogramDataset initialized successfully
|
424 |
+
2024-05-16 17:36:27,590 - root - INFO - Dataset length: 2288
|
425 |
+
2024-05-16 17:36:27,591 - root - INFO - Train dataset length: 1601
|
426 |
+
2024-05-16 17:36:27,592 - root - INFO - Validation dataset length: 343
|
427 |
+
2024-05-16 17:36:27,593 - root - INFO - Test dataset length: 344
|
428 |
+
2024-05-16 17:36:31,059 - root - INFO - Train dataset verification passed
|
429 |
+
2024-05-16 17:36:31,803 - root - INFO - Validation dataset verification passed
|
430 |
+
2024-05-16 17:36:32,610 - root - INFO - Test dataset verification passed
|
431 |
+
2024-05-16 17:36:32,613 - root - INFO - Train sampler indices: [837, 328, 1195, 909, 1489, 664, 151, 1415, 267, 484]... (total: 1601)
|
432 |
+
2024-05-16 17:36:32,613 - root - INFO - Train sampler indices within range.
|
433 |
+
2024-05-16 17:36:32,615 - root - INFO - Validation sampler indices: [123, 143, 321, 66, 222, 160, 292, 324, 64, 103]... (total: 343)
|
434 |
+
2024-05-16 17:36:32,615 - root - INFO - Validation sampler indices within range.
|
435 |
+
2024-05-16 17:36:32,616 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
436 |
+
2024-05-16 17:36:32,617 - root - INFO - Test sampler indices within range.
|
437 |
+
2024-05-16 17:38:25,497 - root - INFO - Initializing SpectrogramDataset...
|
438 |
+
2024-05-16 17:38:26,055 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
439 |
+
2024-05-16 17:38:26,060 - root - INFO - SpectrogramDataset initialized successfully
|
440 |
+
2024-05-16 17:38:26,063 - root - INFO - Dataset length: 2288
|
441 |
+
2024-05-16 17:38:26,064 - root - INFO - Train dataset length: 1601
|
442 |
+
2024-05-16 17:38:26,064 - root - INFO - Validation dataset length: 343
|
443 |
+
2024-05-16 17:38:26,065 - root - INFO - Test dataset length: 344
|
444 |
+
2024-05-16 17:38:29,643 - root - INFO - Train dataset verification passed
|
445 |
+
2024-05-16 17:38:30,352 - root - INFO - Validation dataset verification passed
|
446 |
+
2024-05-16 17:38:31,124 - root - INFO - Test dataset verification passed
|
447 |
+
2024-05-16 17:38:31,127 - root - INFO - Train sampler indices: [175, 1137, 485, 1569, 1512, 540, 916, 448, 467, 1365]... (total: 1601)
|
448 |
+
2024-05-16 17:38:31,127 - root - INFO - Train sampler indices within range.
|
449 |
+
2024-05-16 17:38:31,129 - root - INFO - Validation sampler indices: [79, 304, 232, 145, 290, 57, 301, 45, 261, 97]... (total: 343)
|
450 |
+
2024-05-16 17:38:31,129 - root - INFO - Validation sampler indices within range.
|
451 |
+
2024-05-16 17:38:31,130 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
452 |
+
2024-05-16 17:38:31,130 - root - INFO - Test sampler indices within range.
|
453 |
+
2024-05-16 17:42:09,758 - root - INFO - Initializing SpectrogramDataset...
|
454 |
+
2024-05-16 17:42:10,265 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
455 |
+
2024-05-16 17:42:10,270 - root - INFO - SpectrogramDataset initialized successfully
|
456 |
+
2024-05-16 17:42:10,273 - root - INFO - Dataset length: 2288
|
457 |
+
2024-05-16 17:42:10,274 - root - INFO - Train dataset length: 1601
|
458 |
+
2024-05-16 17:42:10,274 - root - INFO - Validation dataset length: 343
|
459 |
+
2024-05-16 17:42:10,275 - root - INFO - Test dataset length: 344
|
460 |
+
2024-05-16 17:42:13,857 - root - INFO - Train dataset verification passed
|
461 |
+
2024-05-16 17:42:14,445 - root - INFO - Validation dataset verification passed
|
462 |
+
2024-05-16 17:42:15,193 - root - INFO - Test dataset verification passed
|
463 |
+
2024-05-16 17:42:15,195 - root - INFO - Train sampler indices: [480, 1221, 1063, 1056, 567, 1140, 877, 909, 686, 1147]... (total: 1601)
|
464 |
+
2024-05-16 17:42:15,195 - root - INFO - Train sampler indices within range.
|
465 |
+
2024-05-16 17:42:15,196 - root - INFO - Validation sampler indices: [12, 273, 22, 283, 14, 231, 4, 316, 229, 232]... (total: 343)
|
466 |
+
2024-05-16 17:42:15,197 - root - INFO - Validation sampler indices within range.
|
467 |
+
2024-05-16 17:42:15,198 - root - INFO - Test sampler indices: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]... (total: 344)
|
468 |
+
2024-05-16 17:42:15,198 - root - INFO - Test sampler indices within range.
|
469 |
+
2024-05-16 19:59:59,273 - root - INFO - Epoch 1:
|
470 |
+
Training Loss: 1.5577, Training Accuracy: 0.3242, Validation Loss: 1.6561, Validation Accuracy: 0.1603
|
471 |
+
|
472 |
+
2024-05-16 19:59:59,275 - root - INFO - Current learning rate: 0.00014687223021475341
|
473 |
+
2024-05-16 20:00:16,791 - root - INFO - Epoch 2:
|
474 |
+
Training Loss: 1.1934, Training Accuracy: 0.5572, Validation Loss: 1.6995, Validation Accuracy: 0.1545
|
475 |
+
|
476 |
+
2024-05-16 20:00:16,792 - root - INFO - Current learning rate: 0.00014687223021475341
|
477 |
+
2024-05-16 20:00:33,986 - root - INFO - Epoch 3:
|
478 |
+
Training Loss: 1.0130, Training Accuracy: 0.6140, Validation Loss: 1.6373, Validation Accuracy: 0.1983
|
479 |
+
|
480 |
+
2024-05-16 20:00:33,987 - root - INFO - Current learning rate: 0.00014687223021475341
|
481 |
+
2024-05-16 20:00:51,043 - root - INFO - Epoch 4:
|
482 |
+
Training Loss: 0.9106, Training Accuracy: 0.6452, Validation Loss: 1.6587, Validation Accuracy: 0.3586
|
483 |
+
|
484 |
+
2024-05-16 20:00:51,044 - root - INFO - Current learning rate: 0.00014687223021475341
|
485 |
+
2024-05-16 20:01:07,976 - root - INFO - Epoch 5:
|
486 |
+
Training Loss: 0.8757, Training Accuracy: 0.6377, Validation Loss: 0.9697, Validation Accuracy: 0.6501
|
487 |
+
|
488 |
+
2024-05-16 20:01:07,977 - root - INFO - Current learning rate: 0.00014687223021475341
|
489 |
+
2024-05-16 20:01:25,453 - root - INFO - Epoch 6:
|
490 |
+
Training Loss: 0.7976, Training Accuracy: 0.6677, Validation Loss: 0.8183, Validation Accuracy: 0.6618
|
491 |
+
|
492 |
+
2024-05-16 20:01:25,454 - root - INFO - Current learning rate: 0.00014687223021475341
|
493 |
+
2024-05-16 20:01:42,979 - root - INFO - Epoch 7:
|
494 |
+
Training Loss: 0.7621, Training Accuracy: 0.7021, Validation Loss: 0.8188, Validation Accuracy: 0.6822
|
495 |
+
|
496 |
+
2024-05-16 20:01:42,980 - root - INFO - Current learning rate: 0.00014687223021475341
|
497 |
+
2024-05-16 20:02:00,123 - root - INFO - Epoch 8:
|
498 |
+
Training Loss: 0.7261, Training Accuracy: 0.7164, Validation Loss: 0.8117, Validation Accuracy: 0.6939
|
499 |
+
|
500 |
+
2024-05-16 20:02:00,124 - root - INFO - Current learning rate: 0.00014687223021475341
|
501 |
+
2024-05-16 20:02:17,571 - root - INFO - Epoch 9:
|
502 |
+
Training Loss: 0.6669, Training Accuracy: 0.7439, Validation Loss: 1.3692, Validation Accuracy: 0.4111
|
503 |
+
|
504 |
+
2024-05-16 20:02:17,572 - root - INFO - Current learning rate: 0.00014687223021475341
|
505 |
+
2024-05-16 20:02:35,083 - root - INFO - Epoch 10:
|
506 |
+
Training Loss: 0.6629, Training Accuracy: 0.7383, Validation Loss: 1.0841, Validation Accuracy: 0.5656
|
507 |
+
|
508 |
+
2024-05-16 20:02:35,084 - root - INFO - Current learning rate: 0.00014687223021475341
|
509 |
+
2024-05-16 20:02:35,156 - root - INFO - Model saved to checkpoint_epoch_10.pth
|
510 |
+
2024-05-16 20:02:52,706 - root - INFO - Epoch 11:
|
511 |
+
Training Loss: 0.6576, Training Accuracy: 0.7639, Validation Loss: 1.1429, Validation Accuracy: 0.4985
|
512 |
+
|
513 |
+
2024-05-16 20:02:52,706 - root - INFO - Current learning rate: 0.00014687223021475341
|
514 |
+
2024-05-16 20:03:09,804 - root - INFO - Epoch 12:
|
515 |
+
Training Loss: 0.6069, Training Accuracy: 0.7758, Validation Loss: 0.6139, Validation Accuracy: 0.7580
|
516 |
+
|
517 |
+
2024-05-16 20:03:09,805 - root - INFO - Current learning rate: 0.00014687223021475341
|
518 |
+
2024-05-16 20:03:27,549 - root - INFO - Epoch 13:
|
519 |
+
Training Loss: 0.6503, Training Accuracy: 0.7676, Validation Loss: 0.7179, Validation Accuracy: 0.7405
|
520 |
+
|
521 |
+
2024-05-16 20:03:27,550 - root - INFO - Current learning rate: 0.00014687223021475341
|
522 |
+
2024-05-16 20:03:45,089 - root - INFO - Epoch 14:
|
523 |
+
Training Loss: 0.6080, Training Accuracy: 0.7739, Validation Loss: 0.8486, Validation Accuracy: 0.6735
|
524 |
+
|
525 |
+
2024-05-16 20:03:45,090 - root - INFO - Current learning rate: 0.00014687223021475341
|
526 |
+
2024-05-16 20:04:02,210 - root - INFO - Epoch 15:
|
527 |
+
Training Loss: 0.5748, Training Accuracy: 0.7901, Validation Loss: 0.5868, Validation Accuracy: 0.7813
|
528 |
+
|
529 |
+
2024-05-16 20:04:02,211 - root - INFO - Current learning rate: 0.00014687223021475341
|
530 |
+
2024-05-16 20:04:20,031 - root - INFO - Epoch 16:
|
531 |
+
Training Loss: 0.5869, Training Accuracy: 0.7914, Validation Loss: 0.6960, Validation Accuracy: 0.7551
|
532 |
+
|
533 |
+
2024-05-16 20:04:20,033 - root - INFO - Current learning rate: 0.00014687223021475341
|
534 |
+
2024-05-16 20:04:37,050 - root - INFO - Epoch 17:
|
535 |
+
Training Loss: 0.5558, Training Accuracy: 0.8082, Validation Loss: 0.8577, Validation Accuracy: 0.6297
|
536 |
+
|
537 |
+
2024-05-16 20:04:37,051 - root - INFO - Current learning rate: 0.00014687223021475341
|
538 |
+
2024-05-16 20:04:54,387 - root - INFO - Epoch 18:
|
539 |
+
Training Loss: 0.4989, Training Accuracy: 0.8126, Validation Loss: 0.7182, Validation Accuracy: 0.7230
|
540 |
+
|
541 |
+
2024-05-16 20:04:54,388 - root - INFO - Current learning rate: 0.00014687223021475341
|
542 |
+
2024-05-16 20:05:11,701 - root - INFO - Epoch 19:
|
543 |
+
Training Loss: 0.4998, Training Accuracy: 0.8239, Validation Loss: 1.1774, Validation Accuracy: 0.5073
|
544 |
+
|
545 |
+
2024-05-16 20:05:11,702 - root - INFO - Current learning rate: 1.4687223021475341e-05
|
546 |
+
2024-05-16 20:05:28,909 - root - INFO - Epoch 20:
|
547 |
+
Training Loss: 0.4958, Training Accuracy: 0.8245, Validation Loss: 0.6923, Validation Accuracy: 0.7201
|
548 |
+
|
549 |
+
2024-05-16 20:05:28,910 - root - INFO - Current learning rate: 1.4687223021475341e-05
|
550 |
+
2024-05-16 20:05:29,023 - root - INFO - Model saved to checkpoint_epoch_20.pth
|
551 |
+
2024-05-16 20:05:46,100 - root - INFO - Epoch 21:
|
552 |
+
Training Loss: 0.4622, Training Accuracy: 0.8332, Validation Loss: 0.5315, Validation Accuracy: 0.8017
|
553 |
+
|
554 |
+
2024-05-16 20:05:46,101 - root - INFO - Current learning rate: 1.4687223021475341e-05
|
555 |
+
2024-05-16 20:06:03,727 - root - INFO - Epoch 22:
|
556 |
+
Training Loss: 0.4357, Training Accuracy: 0.8463, Validation Loss: 0.4926, Validation Accuracy: 0.8163
|
557 |
+
|
558 |
+
2024-05-16 20:06:03,728 - root - INFO - Current learning rate: 1.4687223021475341e-05
|
559 |
+
2024-05-16 20:06:21,316 - root - INFO - Epoch 23:
|
560 |
+
Training Loss: 0.4551, Training Accuracy: 0.8332, Validation Loss: 0.5084, Validation Accuracy: 0.8105
|
561 |
+
|
562 |
+
2024-05-16 20:06:21,318 - root - INFO - Current learning rate: 1.4687223021475341e-05
|
563 |
+
2024-05-16 20:06:38,567 - root - INFO - Epoch 24:
|
564 |
+
Training Loss: 0.4457, Training Accuracy: 0.8326, Validation Loss: 0.4975, Validation Accuracy: 0.8309
|
565 |
+
|
566 |
+
2024-05-16 20:06:38,568 - root - INFO - Current learning rate: 1.4687223021475341e-05
|
567 |
+
2024-05-16 20:06:55,865 - root - INFO - Epoch 25:
|
568 |
+
Training Loss: 0.4442, Training Accuracy: 0.8445, Validation Loss: 0.5614, Validation Accuracy: 0.7726
|
569 |
+
|
570 |
+
2024-05-16 20:06:55,866 - root - INFO - Current learning rate: 1.4687223021475341e-05
|
571 |
+
2024-05-16 20:07:12,862 - root - INFO - Epoch 26:
|
572 |
+
Training Loss: 0.4198, Training Accuracy: 0.8482, Validation Loss: 0.5582, Validation Accuracy: 0.7872
|
573 |
+
|
574 |
+
2024-05-16 20:07:12,863 - root - INFO - Current learning rate: 1.4687223021475343e-06
|
575 |
+
2024-05-16 20:07:29,969 - root - INFO - Epoch 27:
|
576 |
+
Training Loss: 0.4044, Training Accuracy: 0.8576, Validation Loss: 0.5670, Validation Accuracy: 0.7784
|
577 |
+
|
578 |
+
2024-05-16 20:07:29,970 - root - INFO - Current learning rate: 1.4687223021475343e-06
|
579 |
+
2024-05-16 20:07:47,649 - root - INFO - Epoch 28:
|
580 |
+
Training Loss: 0.3945, Training Accuracy: 0.8638, Validation Loss: 0.5677, Validation Accuracy: 0.7930
|
581 |
+
|
582 |
+
2024-05-16 20:07:47,650 - root - INFO - Current learning rate: 1.4687223021475343e-06
|
583 |
+
2024-05-16 20:08:05,090 - root - INFO - Epoch 29:
|
584 |
+
Training Loss: 0.4371, Training Accuracy: 0.8463, Validation Loss: 0.5071, Validation Accuracy: 0.8105
|
585 |
+
|
586 |
+
2024-05-16 20:08:05,091 - root - INFO - Current learning rate: 1.4687223021475343e-06
|
587 |
+
2024-05-16 20:08:22,390 - root - INFO - Epoch 30:
|
588 |
+
Training Loss: 0.3655, Training Accuracy: 0.8713, Validation Loss: 0.5345, Validation Accuracy: 0.8105
|
589 |
+
|
590 |
+
2024-05-16 20:08:22,391 - root - INFO - Current learning rate: 1.4687223021475343e-07
|
591 |
+
2024-05-16 20:08:22,461 - root - INFO - Model saved to checkpoint_epoch_30.pth
|
592 |
+
2024-05-16 20:08:39,718 - root - INFO - Epoch 31:
|
593 |
+
Training Loss: 0.4207, Training Accuracy: 0.8376, Validation Loss: 0.4701, Validation Accuracy: 0.8426
|
594 |
+
|
595 |
+
2024-05-16 20:08:39,719 - root - INFO - Current learning rate: 1.4687223021475343e-07
|
596 |
+
2024-05-16 20:08:57,038 - root - INFO - Epoch 32:
|
597 |
+
Training Loss: 0.4016, Training Accuracy: 0.8588, Validation Loss: 0.5002, Validation Accuracy: 0.8251
|
598 |
+
|
599 |
+
2024-05-16 20:08:57,039 - root - INFO - Current learning rate: 1.4687223021475343e-07
|
600 |
+
2024-05-16 20:09:14,469 - root - INFO - Epoch 33:
|
601 |
+
Training Loss: 0.3913, Training Accuracy: 0.8507, Validation Loss: 0.5197, Validation Accuracy: 0.8192
|
602 |
+
|
603 |
+
2024-05-16 20:09:14,470 - root - INFO - Current learning rate: 1.4687223021475343e-07
|
604 |
+
2024-05-16 20:09:31,318 - root - INFO - Epoch 34:
|
605 |
+
Training Loss: 0.3930, Training Accuracy: 0.8582, Validation Loss: 0.5009, Validation Accuracy: 0.8163
|
606 |
+
|
607 |
+
2024-05-16 20:09:31,319 - root - INFO - Current learning rate: 1.4687223021475343e-07
|
608 |
+
2024-05-16 20:09:48,349 - root - INFO - Epoch 35:
|
609 |
+
Training Loss: 0.4068, Training Accuracy: 0.8582, Validation Loss: 0.5252, Validation Accuracy: 0.8017
|
610 |
+
|
611 |
+
2024-05-16 20:09:48,350 - root - INFO - Current learning rate: 1.4687223021475344e-08
|
612 |
+
2024-05-16 20:10:05,715 - root - INFO - Epoch 36:
|
613 |
+
Training Loss: 0.3837, Training Accuracy: 0.8626, Validation Loss: 0.4920, Validation Accuracy: 0.8222
|
614 |
+
|
615 |
+
2024-05-16 20:10:05,716 - root - INFO - Current learning rate: 1.4687223021475344e-08
|
616 |
+
2024-05-16 20:10:22,830 - root - INFO - Epoch 37:
|
617 |
+
Training Loss: 0.3958, Training Accuracy: 0.8551, Validation Loss: 0.5132, Validation Accuracy: 0.8017
|
618 |
+
|
619 |
+
2024-05-16 20:10:22,831 - root - INFO - Current learning rate: 1.4687223021475344e-08
|
620 |
+
2024-05-16 20:10:39,743 - root - INFO - Epoch 38:
|
621 |
+
Training Loss: 0.4118, Training Accuracy: 0.8545, Validation Loss: 0.4982, Validation Accuracy: 0.8222
|
622 |
+
|
623 |
+
2024-05-16 20:10:39,744 - root - INFO - Current learning rate: 1.4687223021475344e-08
|
624 |
+
2024-05-16 20:10:56,961 - root - INFO - Epoch 39:
|
625 |
+
Training Loss: 0.3992, Training Accuracy: 0.8501, Validation Loss: 0.5586, Validation Accuracy: 0.7813
|
626 |
+
|
627 |
+
2024-05-16 20:10:56,962 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
628 |
+
2024-05-16 20:11:14,143 - root - INFO - Epoch 40:
|
629 |
+
Training Loss: 0.4223, Training Accuracy: 0.8407, Validation Loss: 0.5121, Validation Accuracy: 0.8017
|
630 |
+
|
631 |
+
2024-05-16 20:11:14,144 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
632 |
+
2024-05-16 20:11:14,223 - root - INFO - Model saved to checkpoint_epoch_40.pth
|
633 |
+
2024-05-16 20:11:31,461 - root - INFO - Epoch 41:
|
634 |
+
Training Loss: 0.4029, Training Accuracy: 0.8570, Validation Loss: 0.4884, Validation Accuracy: 0.8280
|
635 |
+
|
636 |
+
2024-05-16 20:11:31,462 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
637 |
+
2024-05-16 20:11:48,923 - root - INFO - Epoch 42:
|
638 |
+
Training Loss: 0.3903, Training Accuracy: 0.8526, Validation Loss: 0.5349, Validation Accuracy: 0.8017
|
639 |
+
|
640 |
+
2024-05-16 20:11:48,924 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
641 |
+
2024-05-16 20:12:06,239 - root - INFO - Epoch 43:
|
642 |
+
Training Loss: 0.3991, Training Accuracy: 0.8595, Validation Loss: 0.5401, Validation Accuracy: 0.7784
|
643 |
+
|
644 |
+
2024-05-16 20:12:06,240 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
645 |
+
2024-05-16 20:12:23,515 - root - INFO - Epoch 44:
|
646 |
+
Training Loss: 0.4197, Training Accuracy: 0.8395, Validation Loss: 0.5497, Validation Accuracy: 0.7872
|
647 |
+
|
648 |
+
2024-05-16 20:12:23,516 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
649 |
+
2024-05-16 20:12:40,642 - root - INFO - Epoch 45:
|
650 |
+
Training Loss: 0.4323, Training Accuracy: 0.8438, Validation Loss: 0.5315, Validation Accuracy: 0.8076
|
651 |
+
|
652 |
+
2024-05-16 20:12:40,643 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
653 |
+
2024-05-16 20:12:58,112 - root - INFO - Epoch 46:
|
654 |
+
Training Loss: 0.3983, Training Accuracy: 0.8557, Validation Loss: 0.5449, Validation Accuracy: 0.7959
|
655 |
+
|
656 |
+
2024-05-16 20:12:58,113 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
657 |
+
2024-05-16 20:13:15,440 - root - INFO - Epoch 47:
|
658 |
+
Training Loss: 0.3883, Training Accuracy: 0.8545, Validation Loss: 0.5234, Validation Accuracy: 0.8076
|
659 |
+
|
660 |
+
2024-05-16 20:13:15,441 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
661 |
+
2024-05-16 20:13:32,209 - root - INFO - Epoch 48:
|
662 |
+
Training Loss: 0.4173, Training Accuracy: 0.8432, Validation Loss: 0.5416, Validation Accuracy: 0.7930
|
663 |
+
|
664 |
+
2024-05-16 20:13:32,210 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
665 |
+
2024-05-16 20:13:49,067 - root - INFO - Epoch 49:
|
666 |
+
Training Loss: 0.4379, Training Accuracy: 0.8389, Validation Loss: 0.5227, Validation Accuracy: 0.8076
|
667 |
+
|
668 |
+
2024-05-16 20:13:49,068 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
669 |
+
2024-05-16 20:14:06,293 - root - INFO - Epoch 50:
|
670 |
+
Training Loss: 0.4189, Training Accuracy: 0.8389, Validation Loss: 0.5142, Validation Accuracy: 0.8076
|
671 |
+
|
672 |
+
2024-05-16 20:14:06,294 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
673 |
+
2024-05-16 20:14:06,363 - root - INFO - Model saved to checkpoint_epoch_50.pth
|
674 |
+
2024-05-16 20:14:07,450 - root - INFO - precision recall f1-score support
|
675 |
+
|
676 |
+
808 0.89 0.93 0.91 44
|
677 |
+
Clap 0.51 0.79 0.62 24
|
678 |
+
Closed Hat 0.77 0.81 0.79 54
|
679 |
+
Kick 0.93 0.89 0.91 114
|
680 |
+
Open Hat 0.80 0.76 0.78 21
|
681 |
+
Snare 0.80 0.69 0.74 87
|
682 |
+
|
683 |
+
accuracy 0.82 344
|
684 |
+
macro avg 0.78 0.81 0.79 344
|
685 |
+
weighted avg 0.83 0.82 0.82 344
|
686 |
+
|
687 |
+
2024-05-16 22:00:46,650 - root - INFO - Initializing SpectrogramDataset...
|
688 |
+
2024-05-16 22:00:47,165 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
689 |
+
2024-05-16 22:00:47,171 - root - INFO - SpectrogramDataset initialized successfully
|
690 |
+
2024-05-16 22:00:47,377 - root - ERROR - An error occurred: name 'nn' is not defined
|
691 |
+
2024-05-16 22:01:22,777 - root - INFO - Initializing SpectrogramDataset...
|
692 |
+
2024-05-16 22:01:23,295 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
693 |
+
2024-05-16 22:01:23,300 - root - INFO - SpectrogramDataset initialized successfully
|
694 |
+
2024-05-16 22:01:42,720 - root - INFO - Epoch 1:
|
695 |
+
Training Loss: 1.5260, Training Accuracy: 0.3385, Validation Loss: 1.6393, Validation Accuracy: 0.2624
|
696 |
+
|
697 |
+
2024-05-16 22:01:42,722 - root - INFO - Current learning rate: 0.00014687223021475341
|
698 |
+
2024-05-16 22:02:00,523 - root - INFO - Epoch 2:
|
699 |
+
Training Loss: 1.2230, Training Accuracy: 0.5228, Validation Loss: 1.5649, Validation Accuracy: 0.3120
|
700 |
+
|
701 |
+
2024-05-16 22:02:00,524 - root - INFO - Current learning rate: 0.00014687223021475341
|
702 |
+
2024-05-16 22:02:18,855 - root - INFO - Epoch 3:
|
703 |
+
Training Loss: 1.0560, Training Accuracy: 0.5853, Validation Loss: 1.5721, Validation Accuracy: 0.2886
|
704 |
+
|
705 |
+
2024-05-16 22:02:18,856 - root - INFO - Current learning rate: 0.00014687223021475341
|
706 |
+
2024-05-16 22:02:37,163 - root - INFO - Epoch 4:
|
707 |
+
Training Loss: 0.9178, Training Accuracy: 0.6221, Validation Loss: 1.5178, Validation Accuracy: 0.3644
|
708 |
+
|
709 |
+
2024-05-16 22:02:37,165 - root - INFO - Current learning rate: 0.00014687223021475341
|
710 |
+
2024-05-16 22:06:42,042 - root - INFO - Initializing SpectrogramDataset...
|
711 |
+
2024-05-16 22:06:42,551 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
712 |
+
2024-05-16 22:06:42,556 - root - INFO - SpectrogramDataset initialized successfully
|
713 |
+
2024-05-16 22:06:42,755 - root - ERROR - An error occurred: name 'nn' is not defined
|
714 |
+
2024-05-16 22:07:15,641 - root - INFO - Initializing SpectrogramDataset...
|
715 |
+
2024-05-16 22:07:16,161 - root - INFO - Dataset object loaded from F:\DATASET\v1\Spectrograms\spectrogram_dataset.pkl
|
716 |
+
2024-05-16 22:07:16,166 - root - INFO - SpectrogramDataset initialized successfully
|
717 |
+
2024-05-16 22:07:16,465 - root - INFO - Loaded the best model from previous training.
|
718 |
+
2024-05-16 22:07:34,862 - root - INFO - Epoch 1:
|
719 |
+
Training Loss: 0.5570, Training Accuracy: 0.7951, Validation Loss: 1.7239, Validation Accuracy: 0.5539
|
720 |
+
|
721 |
+
2024-05-16 22:07:34,863 - root - INFO - Current learning rate: 0.00014687223021475341
|
722 |
+
2024-05-16 22:07:51,864 - root - INFO - Epoch 2:
|
723 |
+
Training Loss: 0.5908, Training Accuracy: 0.7851, Validation Loss: 0.7348, Validation Accuracy: 0.7318
|
724 |
+
|
725 |
+
2024-05-16 22:07:51,865 - root - INFO - Current learning rate: 0.00014687223021475341
|
726 |
+
2024-05-16 22:08:09,242 - root - INFO - Epoch 3:
|
727 |
+
Training Loss: 0.5201, Training Accuracy: 0.8201, Validation Loss: 0.7574, Validation Accuracy: 0.7114
|
728 |
+
|
729 |
+
2024-05-16 22:08:09,244 - root - INFO - Current learning rate: 0.00014687223021475341
|
730 |
+
2024-05-16 22:08:25,962 - root - INFO - Epoch 4:
|
731 |
+
Training Loss: 0.4979, Training Accuracy: 0.8176, Validation Loss: 0.7149, Validation Accuracy: 0.7143
|
732 |
+
|
733 |
+
2024-05-16 22:08:25,963 - root - INFO - Current learning rate: 0.00014687223021475341
|
734 |
+
2024-05-16 22:08:43,336 - root - INFO - Epoch 5:
|
735 |
+
Training Loss: 0.4966, Training Accuracy: 0.8189, Validation Loss: 0.6777, Validation Accuracy: 0.7522
|
736 |
+
|
737 |
+
2024-05-16 22:08:43,337 - root - INFO - Current learning rate: 0.00014687223021475341
|
738 |
+
2024-05-16 22:09:00,375 - root - INFO - Epoch 6:
|
739 |
+
Training Loss: 0.4906, Training Accuracy: 0.8276, Validation Loss: 0.6189, Validation Accuracy: 0.7551
|
740 |
+
|
741 |
+
2024-05-16 22:09:00,376 - root - INFO - Current learning rate: 0.00014687223021475341
|
742 |
+
2024-05-16 22:09:17,365 - root - INFO - Epoch 7:
|
743 |
+
Training Loss: 0.4680, Training Accuracy: 0.8289, Validation Loss: 0.5583, Validation Accuracy: 0.7784
|
744 |
+
|
745 |
+
2024-05-16 22:09:17,367 - root - INFO - Current learning rate: 0.00014687223021475341
|
746 |
+
2024-05-16 22:09:34,205 - root - INFO - Epoch 8:
|
747 |
+
Training Loss: 0.4521, Training Accuracy: 0.8326, Validation Loss: 0.6058, Validation Accuracy: 0.7755
|
748 |
+
|
749 |
+
2024-05-16 22:09:34,207 - root - INFO - Current learning rate: 0.00014687223021475341
|
750 |
+
2024-05-16 22:09:51,165 - root - INFO - Epoch 9:
|
751 |
+
Training Loss: 0.4068, Training Accuracy: 0.8501, Validation Loss: 0.4922, Validation Accuracy: 0.8163
|
752 |
+
|
753 |
+
2024-05-16 22:09:51,167 - root - INFO - Current learning rate: 0.00014687223021475341
|
754 |
+
2024-05-16 22:10:08,186 - root - INFO - Epoch 10:
|
755 |
+
Training Loss: 0.4259, Training Accuracy: 0.8463, Validation Loss: 0.5306, Validation Accuracy: 0.8076
|
756 |
+
|
757 |
+
2024-05-16 22:10:08,187 - root - INFO - Current learning rate: 0.00014687223021475341
|
758 |
+
2024-05-16 22:10:08,260 - root - INFO - Model saved to checkpoint_epoch_10.pth
|
759 |
+
2024-05-16 22:10:25,419 - root - INFO - Epoch 11:
|
760 |
+
Training Loss: 0.4122, Training Accuracy: 0.8451, Validation Loss: 0.5452, Validation Accuracy: 0.8105
|
761 |
+
|
762 |
+
2024-05-16 22:10:25,421 - root - INFO - Current learning rate: 0.00014687223021475341
|
763 |
+
2024-05-16 22:10:42,549 - root - INFO - Epoch 12:
|
764 |
+
Training Loss: 0.4157, Training Accuracy: 0.8432, Validation Loss: 0.6177, Validation Accuracy: 0.7930
|
765 |
+
|
766 |
+
2024-05-16 22:10:42,550 - root - INFO - Current learning rate: 0.00014687223021475341
|
767 |
+
2024-05-16 22:10:59,620 - root - INFO - Epoch 13:
|
768 |
+
Training Loss: 0.3727, Training Accuracy: 0.8595, Validation Loss: 0.7197, Validation Accuracy: 0.7405
|
769 |
+
|
770 |
+
2024-05-16 22:10:59,621 - root - INFO - Current learning rate: 1.4687223021475341e-05
|
771 |
+
2024-05-16 22:11:17,022 - root - INFO - Epoch 14:
|
772 |
+
Training Loss: 0.4019, Training Accuracy: 0.8638, Validation Loss: 0.5073, Validation Accuracy: 0.8076
|
773 |
+
|
774 |
+
2024-05-16 22:11:17,023 - root - INFO - Current learning rate: 1.4687223021475341e-05
|
775 |
+
2024-05-16 22:11:34,626 - root - INFO - Epoch 15:
|
776 |
+
Training Loss: 0.3273, Training Accuracy: 0.8832, Validation Loss: 0.4066, Validation Accuracy: 0.8601
|
777 |
+
|
778 |
+
2024-05-16 22:11:34,628 - root - INFO - Current learning rate: 1.4687223021475341e-05
|
779 |
+
2024-05-16 22:11:51,725 - root - INFO - Epoch 16:
|
780 |
+
Training Loss: 0.3409, Training Accuracy: 0.8713, Validation Loss: 0.4711, Validation Accuracy: 0.8280
|
781 |
+
|
782 |
+
2024-05-16 22:11:51,726 - root - INFO - Current learning rate: 1.4687223021475341e-05
|
783 |
+
2024-05-16 22:12:09,010 - root - INFO - Epoch 17:
|
784 |
+
Training Loss: 0.3207, Training Accuracy: 0.8826, Validation Loss: 0.4586, Validation Accuracy: 0.8338
|
785 |
+
|
786 |
+
2024-05-16 22:12:09,011 - root - INFO - Current learning rate: 1.4687223021475341e-05
|
787 |
+
2024-05-16 22:12:25,474 - root - INFO - Epoch 18:
|
788 |
+
Training Loss: 0.3405, Training Accuracy: 0.8738, Validation Loss: 0.4560, Validation Accuracy: 0.8222
|
789 |
+
|
790 |
+
2024-05-16 22:12:25,476 - root - INFO - Current learning rate: 1.4687223021475341e-05
|
791 |
+
2024-05-16 22:12:42,848 - root - INFO - Epoch 19:
|
792 |
+
Training Loss: 0.3721, Training Accuracy: 0.8657, Validation Loss: 0.4278, Validation Accuracy: 0.8426
|
793 |
+
|
794 |
+
2024-05-16 22:12:42,849 - root - INFO - Current learning rate: 1.4687223021475343e-06
|
795 |
+
2024-05-16 22:13:00,038 - root - INFO - Epoch 20:
|
796 |
+
Training Loss: 0.3588, Training Accuracy: 0.8701, Validation Loss: 0.4247, Validation Accuracy: 0.8397
|
797 |
+
|
798 |
+
2024-05-16 22:13:00,039 - root - INFO - Current learning rate: 1.4687223021475343e-06
|
799 |
+
2024-05-16 22:13:00,114 - root - INFO - Model saved to checkpoint_epoch_20.pth
|
800 |
+
2024-05-16 22:13:17,127 - root - INFO - Epoch 21:
|
801 |
+
Training Loss: 0.3153, Training Accuracy: 0.8745, Validation Loss: 0.4080, Validation Accuracy: 0.8571
|
802 |
+
|
803 |
+
2024-05-16 22:13:17,129 - root - INFO - Current learning rate: 1.4687223021475343e-06
|
804 |
+
2024-05-16 22:13:33,743 - root - INFO - Epoch 22:
|
805 |
+
Training Loss: 0.3187, Training Accuracy: 0.8876, Validation Loss: 0.4765, Validation Accuracy: 0.8251
|
806 |
+
|
807 |
+
2024-05-16 22:13:33,744 - root - INFO - Current learning rate: 1.4687223021475343e-06
|
808 |
+
2024-05-16 22:13:50,886 - root - INFO - Epoch 23:
|
809 |
+
Training Loss: 0.3215, Training Accuracy: 0.8795, Validation Loss: 0.4566, Validation Accuracy: 0.8251
|
810 |
+
|
811 |
+
2024-05-16 22:13:50,888 - root - INFO - Current learning rate: 1.4687223021475343e-07
|
812 |
+
2024-05-16 22:14:08,209 - root - INFO - Epoch 24:
|
813 |
+
Training Loss: 0.3030, Training Accuracy: 0.8938, Validation Loss: 0.4290, Validation Accuracy: 0.8222
|
814 |
+
|
815 |
+
2024-05-16 22:14:08,211 - root - INFO - Current learning rate: 1.4687223021475343e-07
|
816 |
+
2024-05-16 22:14:25,025 - root - INFO - Epoch 25:
|
817 |
+
Training Loss: 0.3203, Training Accuracy: 0.8869, Validation Loss: 0.4327, Validation Accuracy: 0.8484
|
818 |
+
|
819 |
+
2024-05-16 22:14:25,027 - root - INFO - Current learning rate: 1.4687223021475343e-07
|
820 |
+
2024-05-16 22:14:42,254 - root - INFO - Epoch 26:
|
821 |
+
Training Loss: 0.3120, Training Accuracy: 0.8938, Validation Loss: 0.4477, Validation Accuracy: 0.8280
|
822 |
+
|
823 |
+
2024-05-16 22:14:42,255 - root - INFO - Current learning rate: 1.4687223021475343e-07
|
824 |
+
2024-05-16 22:14:59,624 - root - INFO - Epoch 27:
|
825 |
+
Training Loss: 0.3136, Training Accuracy: 0.8913, Validation Loss: 0.4614, Validation Accuracy: 0.8309
|
826 |
+
|
827 |
+
2024-05-16 22:14:59,626 - root - INFO - Current learning rate: 1.4687223021475344e-08
|
828 |
+
2024-05-16 22:15:16,671 - root - INFO - Epoch 28:
|
829 |
+
Training Loss: 0.3044, Training Accuracy: 0.8938, Validation Loss: 0.4706, Validation Accuracy: 0.8251
|
830 |
+
|
831 |
+
2024-05-16 22:15:16,673 - root - INFO - Current learning rate: 1.4687223021475344e-08
|
832 |
+
2024-05-16 22:15:33,835 - root - INFO - Epoch 29:
|
833 |
+
Training Loss: 0.3022, Training Accuracy: 0.8938, Validation Loss: 0.4032, Validation Accuracy: 0.8455
|
834 |
+
|
835 |
+
2024-05-16 22:15:33,836 - root - INFO - Current learning rate: 1.4687223021475344e-08
|
836 |
+
2024-05-16 22:15:51,218 - root - INFO - Epoch 30:
|
837 |
+
Training Loss: 0.2987, Training Accuracy: 0.8982, Validation Loss: 0.4105, Validation Accuracy: 0.8426
|
838 |
+
|
839 |
+
2024-05-16 22:15:51,219 - root - INFO - Current learning rate: 1.4687223021475344e-08
|
840 |
+
2024-05-16 22:15:51,293 - root - INFO - Model saved to checkpoint_epoch_30.pth
|
841 |
+
2024-05-16 22:16:07,971 - root - INFO - Epoch 31:
|
842 |
+
Training Loss: 0.3077, Training Accuracy: 0.8869, Validation Loss: 0.4213, Validation Accuracy: 0.8251
|
843 |
+
|
844 |
+
2024-05-16 22:16:07,973 - root - INFO - Current learning rate: 1.4687223021475344e-08
|
845 |
+
2024-05-16 22:16:24,561 - root - INFO - Epoch 32:
|
846 |
+
Training Loss: 0.3193, Training Accuracy: 0.8838, Validation Loss: 0.4009, Validation Accuracy: 0.8455
|
847 |
+
|
848 |
+
2024-05-16 22:16:24,563 - root - INFO - Current learning rate: 1.4687223021475344e-08
|
849 |
+
2024-05-16 22:16:42,053 - root - INFO - Epoch 33:
|
850 |
+
Training Loss: 0.3314, Training Accuracy: 0.8869, Validation Loss: 0.4344, Validation Accuracy: 0.8397
|
851 |
+
|
852 |
+
2024-05-16 22:16:42,055 - root - INFO - Current learning rate: 1.4687223021475344e-08
|
853 |
+
2024-05-16 22:16:59,057 - root - INFO - Epoch 34:
|
854 |
+
Training Loss: 0.2837, Training Accuracy: 0.9001, Validation Loss: 0.4218, Validation Accuracy: 0.8280
|
855 |
+
|
856 |
+
2024-05-16 22:16:59,059 - root - INFO - Current learning rate: 1.4687223021475344e-08
|
857 |
+
2024-05-16 22:17:16,041 - root - INFO - Epoch 35:
|
858 |
+
Training Loss: 0.3607, Training Accuracy: 0.8701, Validation Loss: 0.4585, Validation Accuracy: 0.8134
|
859 |
+
|
860 |
+
2024-05-16 22:17:16,042 - root - INFO - Current learning rate: 1.4687223021475344e-08
|
861 |
+
2024-05-16 22:17:33,182 - root - INFO - Epoch 36:
|
862 |
+
Training Loss: 0.2987, Training Accuracy: 0.8919, Validation Loss: 0.4383, Validation Accuracy: 0.8251
|
863 |
+
|
864 |
+
2024-05-16 22:17:33,184 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
865 |
+
2024-05-16 22:17:50,259 - root - INFO - Epoch 37:
|
866 |
+
Training Loss: 0.3294, Training Accuracy: 0.8682, Validation Loss: 0.4253, Validation Accuracy: 0.8309
|
867 |
+
|
868 |
+
2024-05-16 22:17:50,260 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
869 |
+
2024-05-16 22:18:06,981 - root - INFO - Epoch 38:
|
870 |
+
Training Loss: 0.2821, Training Accuracy: 0.9019, Validation Loss: 0.4611, Validation Accuracy: 0.8251
|
871 |
+
|
872 |
+
2024-05-16 22:18:06,982 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
873 |
+
2024-05-16 22:18:23,998 - root - INFO - Epoch 39:
|
874 |
+
Training Loss: 0.3158, Training Accuracy: 0.8869, Validation Loss: 0.4367, Validation Accuracy: 0.8280
|
875 |
+
|
876 |
+
2024-05-16 22:18:24,000 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
877 |
+
2024-05-16 22:18:41,011 - root - INFO - Epoch 40:
|
878 |
+
Training Loss: 0.3254, Training Accuracy: 0.8888, Validation Loss: 0.3913, Validation Accuracy: 0.8513
|
879 |
+
|
880 |
+
2024-05-16 22:18:41,012 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
881 |
+
2024-05-16 22:18:41,155 - root - INFO - Model saved to checkpoint_epoch_40.pth
|
882 |
+
2024-05-16 22:18:58,103 - root - INFO - Epoch 41:
|
883 |
+
Training Loss: 0.3082, Training Accuracy: 0.8857, Validation Loss: 0.3953, Validation Accuracy: 0.8542
|
884 |
+
|
885 |
+
2024-05-16 22:18:58,105 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
886 |
+
2024-05-16 22:19:14,938 - root - INFO - Epoch 42:
|
887 |
+
Training Loss: 0.2816, Training Accuracy: 0.9007, Validation Loss: 0.4112, Validation Accuracy: 0.8484
|
888 |
+
|
889 |
+
2024-05-16 22:19:14,939 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
890 |
+
2024-05-16 22:19:32,077 - root - INFO - Epoch 43:
|
891 |
+
Training Loss: 0.3457, Training Accuracy: 0.8788, Validation Loss: 0.4241, Validation Accuracy: 0.8455
|
892 |
+
|
893 |
+
2024-05-16 22:19:32,078 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
894 |
+
2024-05-16 22:19:49,354 - root - INFO - Epoch 44:
|
895 |
+
Training Loss: 0.3149, Training Accuracy: 0.8826, Validation Loss: 0.4160, Validation Accuracy: 0.8542
|
896 |
+
|
897 |
+
2024-05-16 22:19:49,356 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
898 |
+
2024-05-16 22:20:06,691 - root - INFO - Epoch 45:
|
899 |
+
Training Loss: 0.3011, Training Accuracy: 0.8894, Validation Loss: 0.4293, Validation Accuracy: 0.8484
|
900 |
+
|
901 |
+
2024-05-16 22:20:06,692 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
902 |
+
2024-05-16 22:20:23,367 - root - INFO - Epoch 46:
|
903 |
+
Training Loss: 0.3327, Training Accuracy: 0.8795, Validation Loss: 0.3971, Validation Accuracy: 0.8630
|
904 |
+
|
905 |
+
2024-05-16 22:20:23,369 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
906 |
+
2024-05-16 22:20:40,496 - root - INFO - Epoch 47:
|
907 |
+
Training Loss: 0.3180, Training Accuracy: 0.8894, Validation Loss: 0.4143, Validation Accuracy: 0.8513
|
908 |
+
|
909 |
+
2024-05-16 22:20:40,497 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
910 |
+
2024-05-16 22:20:58,098 - root - INFO - Epoch 48:
|
911 |
+
Training Loss: 0.2915, Training Accuracy: 0.8969, Validation Loss: 0.4355, Validation Accuracy: 0.8455
|
912 |
+
|
913 |
+
2024-05-16 22:20:58,100 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
914 |
+
2024-05-16 22:21:15,492 - root - INFO - Epoch 49:
|
915 |
+
Training Loss: 0.3160, Training Accuracy: 0.8882, Validation Loss: 0.4601, Validation Accuracy: 0.8163
|
916 |
+
|
917 |
+
2024-05-16 22:21:15,494 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
918 |
+
2024-05-16 22:21:32,535 - root - INFO - Epoch 50:
|
919 |
+
Training Loss: 0.3085, Training Accuracy: 0.8832, Validation Loss: 0.4587, Validation Accuracy: 0.8105
|
920 |
+
|
921 |
+
2024-05-16 22:21:32,537 - root - INFO - Current learning rate: 1.4687223021475344e-09
|
922 |
+
2024-05-16 22:21:32,613 - root - INFO - Model saved to checkpoint_epoch_50.pth
|
923 |
+
2024-05-16 22:21:33,771 - root - INFO - precision recall f1-score support
|
924 |
+
|
925 |
+
808 0.88 0.88 0.88 43
|
926 |
+
Clap 0.62 0.78 0.69 27
|
927 |
+
Closed Hat 0.89 0.86 0.87 63
|
928 |
+
Kick 0.94 0.93 0.94 120
|
929 |
+
Open Hat 0.76 0.87 0.81 15
|
930 |
+
Snare 0.84 0.78 0.81 76
|
931 |
+
|
932 |
+
accuracy 0.86 344
|
933 |
+
macro avg 0.82 0.85 0.83 344
|
934 |
+
weighted avg 0.87 0.86 0.86 344
|
935 |
+
|