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@ethanwharris
ethanwharris committed May 8, 2021
1 parent edee7ce commit f98318c
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19 changes: 1 addition & 18 deletions flash_notebooks/custom_task_tutorial.ipynb
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143 changes: 13 additions & 130 deletions flash_notebooks/generic_task.ipynb
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},
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"GPU available: True, used: False\n",
"TPU available: False, using: 0 TPU cores\n",
"/home/edgar/software/lightning-flash/.venv/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: GPU available but not used. Set the gpus flag in your trainer `Trainer(gpus=1)` or script `--gpus=1`.\n",
" warnings.warn(*args, **kwargs)\n"
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"trainer = pl.Trainer(\n",
" max_epochs=10,\n",
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"\n",
" | Name | Type | Params\n",
"---------------------------------------\n",
"0 | model | Sequential | 101 K \n",
"1 | metrics | ModuleDict | 0 \n",
"---------------------------------------\n",
"101 K Trainable params\n",
"0 Non-trainable params\n",
"101 K Total params\n",
"0.407 Total estimated model params size (MB)\n",
"Epoch 0: 14%|█▎ | 35/256 [00:00<00:01, 217.40it/s, loss=2.4, v_num=26, val_accuracy=0.000, val_cross_entropy=2.280, train_accuracy_step=0.000, train_cross_entropy_step=2.130] /home/edgar/software/lightning-flash/.venv/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: The dataloader, val dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
" warnings.warn(*args, **kwargs)\n",
"/home/edgar/software/lightning-flash/.venv/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
" warnings.warn(*args, **kwargs)\n",
"Epoch 0: 50%|█████ | 128/256 [00:00<00:00, 203.95it/s, loss=1.68, v_num=26, val_accuracy=0.000, val_cross_entropy=2.280, train_accuracy_step=0.000, train_cross_entropy_step=4.150]\n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Epoch 0: 52%|█████▏ | 132/256 [00:00<00:00, 202.98it/s, loss=1.68, v_num=26, val_accuracy=0.000, val_cross_entropy=2.280, train_accuracy_step=0.000, train_cross_entropy_step=4.150]\n",
"Epoch 0: 74%|███████▍ | 189/256 [00:00<00:00, 251.55it/s, loss=1.68, v_num=26, val_accuracy=0.000, val_cross_entropy=2.280, train_accuracy_step=0.000, train_cross_entropy_step=4.150]\n",
"Epoch 0: 100%|██████████| 256/256 [00:00<00:00, 297.31it/s, loss=1.68, v_num=26, val_accuracy=0.359, val_cross_entropy=1.690, train_accuracy_step=0.000, train_cross_entropy_step=2.510, train_accuracy_epoch=0.344, train_cross_entropy_epoch=2.050]\n",
"Epoch 1: 50%|█████ | 128/256 [00:00<00:00, 182.66it/s, loss=0.809, v_num=26, val_accuracy=0.359, val_cross_entropy=1.690, train_accuracy_step=1.000, train_cross_entropy_step=0.851, train_accuracy_epoch=0.344, train_cross_entropy_epoch=2.050] \n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Epoch 1: 75%|███████▌ | 193/256 [00:00<00:00, 241.14it/s, loss=0.809, v_num=26, val_accuracy=0.359, val_cross_entropy=1.690, train_accuracy_step=1.000, train_cross_entropy_step=0.851, train_accuracy_epoch=0.344, train_cross_entropy_epoch=2.050]\n",
"Epoch 1: 100%|██████████| 256/256 [00:00<00:00, 288.24it/s, loss=0.809, v_num=26, val_accuracy=0.484, val_cross_entropy=1.680, train_accuracy_step=0.000, train_cross_entropy_step=2.670, train_accuracy_epoch=0.633, train_cross_entropy_epoch=1.090]\n",
"Epoch 2: 50%|█████ | 128/256 [00:00<00:00, 194.30it/s, loss=0.458, v_num=26, val_accuracy=0.484, val_cross_entropy=1.680, train_accuracy_step=1.000, train_cross_entropy_step=0.294, train_accuracy_epoch=0.633, train_cross_entropy_epoch=1.090] \n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Epoch 2: 51%|█████ | 130/256 [00:00<00:00, 188.73it/s, loss=0.458, v_num=26, val_accuracy=0.484, val_cross_entropy=1.680, train_accuracy_step=1.000, train_cross_entropy_step=0.294, train_accuracy_epoch=0.633, train_cross_entropy_epoch=1.090]\n",
"Epoch 2: 100%|██████████| 256/256 [00:00<00:00, 294.59it/s, loss=0.458, v_num=26, val_accuracy=0.508, val_cross_entropy=1.850, train_accuracy_step=1.000, train_cross_entropy_step=0.649, train_accuracy_epoch=0.789, train_cross_entropy_epoch=0.549]\n",
"Epoch 3: 50%|█████ | 128/256 [00:00<00:00, 200.81it/s, loss=0.358, v_num=26, val_accuracy=0.508, val_cross_entropy=1.850, train_accuracy_step=1.000, train_cross_entropy_step=0.0079, train_accuracy_epoch=0.789, train_cross_entropy_epoch=0.549] \n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Epoch 3: 56%|█████▋ | 144/256 [00:00<00:00, 215.08it/s, loss=0.358, v_num=26, val_accuracy=0.508, val_cross_entropy=1.850, train_accuracy_step=1.000, train_cross_entropy_step=0.0079, train_accuracy_epoch=0.789, train_cross_entropy_epoch=0.549]\n",
"Epoch 3: 100%|██████████| 256/256 [00:00<00:00, 309.92it/s, loss=0.358, v_num=26, val_accuracy=0.602, val_cross_entropy=1.670, train_accuracy_step=0.000, train_cross_entropy_step=0.726, train_accuracy_epoch=0.875, train_cross_entropy_epoch=0.473] \n",
"Epoch 4: 50%|█████ | 128/256 [00:00<00:00, 209.98it/s, loss=0.166, v_num=26, val_accuracy=0.602, val_cross_entropy=1.670, train_accuracy_step=1.000, train_cross_entropy_step=0.105, train_accuracy_epoch=0.875, train_cross_entropy_epoch=0.473] \n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Epoch 4: 57%|█████▋ | 146/256 [00:00<00:00, 225.27it/s, loss=0.166, v_num=26, val_accuracy=0.602, val_cross_entropy=1.670, train_accuracy_step=1.000, train_cross_entropy_step=0.105, train_accuracy_epoch=0.875, train_cross_entropy_epoch=0.473]\n",
"Epoch 4: 100%|██████████| 256/256 [00:00<00:00, 317.78it/s, loss=0.166, v_num=26, val_accuracy=0.641, val_cross_entropy=1.500, train_accuracy_step=1.000, train_cross_entropy_step=0.00293, train_accuracy_epoch=0.938, train_cross_entropy_epoch=0.210]\n",
"Epoch 5: 50%|█████ | 128/256 [00:00<00:00, 185.66it/s, loss=0.0797, v_num=26, val_accuracy=0.641, val_cross_entropy=1.500, train_accuracy_step=1.000, train_cross_entropy_step=0.0154, train_accuracy_epoch=0.938, train_cross_entropy_epoch=0.210]\n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Epoch 5: 57%|█████▋ | 146/256 [00:00<00:00, 199.74it/s, loss=0.0797, v_num=26, val_accuracy=0.641, val_cross_entropy=1.500, train_accuracy_step=1.000, train_cross_entropy_step=0.0154, train_accuracy_epoch=0.938, train_cross_entropy_epoch=0.210]\n",
"Epoch 5: 100%|██████████| 256/256 [00:00<00:00, 284.90it/s, loss=0.0797, v_num=26, val_accuracy=0.586, val_cross_entropy=2.010, train_accuracy_step=1.000, train_cross_entropy_step=0.00443, train_accuracy_epoch=0.953, train_cross_entropy_epoch=0.119]\n",
"Epoch 6: 50%|█████ | 128/256 [00:00<00:00, 178.55it/s, loss=0.0912, v_num=26, val_accuracy=0.586, val_cross_entropy=2.010, train_accuracy_step=1.000, train_cross_entropy_step=0.0729, train_accuracy_epoch=0.953, train_cross_entropy_epoch=0.119]\n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Epoch 6: 57%|█████▋ | 146/256 [00:00<00:00, 191.48it/s, loss=0.0912, v_num=26, val_accuracy=0.586, val_cross_entropy=2.010, train_accuracy_step=1.000, train_cross_entropy_step=0.0729, train_accuracy_epoch=0.953, train_cross_entropy_epoch=0.119]\n",
"Epoch 6: 100%|██████████| 256/256 [00:00<00:00, 274.04it/s, loss=0.0912, v_num=26, val_accuracy=0.594, val_cross_entropy=2.020, train_accuracy_step=1.000, train_cross_entropy_step=0.129, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.146] \n",
"Epoch 7: 50%|█████ | 128/256 [00:00<00:00, 169.42it/s, loss=0.106, v_num=26, val_accuracy=0.594, val_cross_entropy=2.020, train_accuracy_step=1.000, train_cross_entropy_step=0.051, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.146] \n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Epoch 7: 57%|█████▋ | 146/256 [00:00<00:00, 182.27it/s, loss=0.106, v_num=26, val_accuracy=0.594, val_cross_entropy=2.020, train_accuracy_step=1.000, train_cross_entropy_step=0.051, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.146]\n",
"Epoch 7: 86%|████████▌ | 219/256 [00:00<00:00, 237.78it/s, loss=0.106, v_num=26, val_accuracy=0.594, val_cross_entropy=2.020, train_accuracy_step=1.000, train_cross_entropy_step=0.051, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.146]\n",
"Epoch 7: 100%|██████████| 256/256 [00:00<00:00, 259.69it/s, loss=0.106, v_num=26, val_accuracy=0.586, val_cross_entropy=2.420, train_accuracy_step=1.000, train_cross_entropy_step=8.46e-6, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.178]\n",
"Epoch 8: 50%|█████ | 128/256 [00:00<00:00, 170.78it/s, loss=0.068, v_num=26, val_accuracy=0.586, val_cross_entropy=2.420, train_accuracy_step=1.000, train_cross_entropy_step=0.00895, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.178]\n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Epoch 8: 57%|█████▋ | 146/256 [00:00<00:00, 183.98it/s, loss=0.068, v_num=26, val_accuracy=0.586, val_cross_entropy=2.420, train_accuracy_step=1.000, train_cross_entropy_step=0.00895, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.178]\n",
"Epoch 8: 86%|████████▌ | 219/256 [00:00<00:00, 240.22it/s, loss=0.068, v_num=26, val_accuracy=0.586, val_cross_entropy=2.420, train_accuracy_step=1.000, train_cross_entropy_step=0.00895, train_accuracy_epoch=0.945, train_cross_entropy_epoch=0.178]\n",
"Epoch 8: 100%|██████████| 256/256 [00:00<00:00, 262.77it/s, loss=0.068, v_num=26, val_accuracy=0.656, val_cross_entropy=1.890, train_accuracy_step=1.000, train_cross_entropy_step=3.76e-5, train_accuracy_epoch=0.953, train_cross_entropy_epoch=0.150]\n",
"Epoch 9: 50%|█████ | 128/256 [00:00<00:00, 195.85it/s, loss=0.0109, v_num=26, val_accuracy=0.656, val_cross_entropy=1.890, train_accuracy_step=1.000, train_cross_entropy_step=0.00395, train_accuracy_epoch=0.953, train_cross_entropy_epoch=0.150]\n",
"Validating: 0it [00:00, ?it/s]\u001b[A\n",
"Epoch 9: 57%|█████▋ | 146/256 [00:00<00:00, 208.94it/s, loss=0.0109, v_num=26, val_accuracy=0.656, val_cross_entropy=1.890, train_accuracy_step=1.000, train_cross_entropy_step=0.00395, train_accuracy_epoch=0.953, train_cross_entropy_epoch=0.150]\n",
"Epoch 9: 100%|██████████| 256/256 [00:00<00:00, 299.16it/s, loss=0.0109, v_num=26, val_accuracy=0.641, val_cross_entropy=2.290, train_accuracy_step=1.000, train_cross_entropy_step=2.74e-5, train_accuracy_epoch=0.992, train_cross_entropy_epoch=0.0366]\n",
"Epoch 9: 100%|██████████| 256/256 [00:00<00:00, 297.26it/s, loss=0.0109, v_num=26, val_accuracy=0.641, val_cross_entropy=2.290, train_accuracy_step=1.000, train_cross_entropy_step=2.74e-5, train_accuracy_epoch=0.992, train_cross_entropy_epoch=0.0366]\n"
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"trainer.fit(classifier, DataLoader(train), DataLoader(val))"
]
Expand All @@ -286,24 +200,10 @@
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"Testing: 1%| | 58/4975 [00:00<00:08, 575.00it/s]/home/edgar/software/lightning-flash/.venv/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: The dataloader, test dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
" warnings.warn(*args, **kwargs)\n",
"Testing: 100%|██████████| 4975/4975 [00:06<00:00, 768.58it/s]\n",
"--------------------------------------------------------------------------------\n",
"DATALOADER:0 TEST RESULTS\n",
"{'test_accuracy': 0.72120600938797, 'test_cross_entropy': 1.481386661529541}\n",
"--------------------------------------------------------------------------------\n"
]
}
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"outputs": [],
"source": [
"results = trainer.test(classifier, test_dataloaders=DataLoader(test))"
]
Expand Down Expand Up @@ -350,24 +250,7 @@
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19 changes: 1 addition & 18 deletions flash_notebooks/image_classification.ipynb
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]
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20 changes: 1 addition & 19 deletions flash_notebooks/tabular_classification.ipynb
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]
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