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"\n",
"metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n",
"del metrics[\"step\"]\n",
"metrics.set_index(\"epoch\", inplace=True)\n",
"display(metrics.dropna(axis=1, how=\"all\").head())\n",
"sn.relplot(data=metrics, kind=\"line\")"
]
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"cell_type": "markdown",
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"source": [
"### Bonus: Use [Stochastic Weight Averaging](https://arxiv.org/abs/1803.05407) to get a boost on performance\n",
"\n",
"Use SWA from torch.optim to get a quick performance boost. Also shows a couple of cool features from Lightning:\n",
"- Use `training_epoch_end` to run code after the end of every epoch\n",
"- Use a pretrained model directly with this wrapper for SWA"
]
},
{
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"source": [
"class SWAResnet(LitResnet):\n",
" def __init__(self, trained_model, lr=0.01):\n",
" super().__init__()\n",
"\n",
" self.save_hyperparameters(\"lr\")\n",
" self.model = trained_model\n",
" self.swa_model = AveragedModel(self.model)\n",
"\n",
" def forward(self, x):\n",
" out = self.swa_model(x)\n",
" return F.log_softmax(out, dim=1)\n",
"\n",
" def training_epoch_end(self, training_step_outputs):\n",
" self.swa_model.update_parameters(self.model)\n",
"\n",
" def validation_step(self, batch, batch_idx, stage=None):\n",
" x, y = batch\n",
" logits = F.log_softmax(self.model(x), dim=1)\n",
" loss = F.nll_loss(logits, y)\n",
" preds = torch.argmax(logits, dim=1)\n",
" acc = accuracy(preds, y)\n",
"\n",
" self.log(\"val_loss\", loss, prog_bar=True)\n",
" self.log(\"val_acc\", acc, prog_bar=True)\n",
"\n",
" def configure_optimizers(self):\n",
" optimizer = torch.optim.SGD(self.model.parameters(), lr=self.hparams.lr, momentum=0.9, weight_decay=5e-4)\n",
" return optimizer\n",
"\n",
" def on_train_end(self):\n",
" update_bn(self.trainer.datamodule.train_dataloader(), self.swa_model, device=self.device)"
]
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" | Name | Type | Params\n",
"--------------------------------------------\n",
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"1 | swa_model | AveragedModel | 11.2 M\n",
"--------------------------------------------\n",
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"swa_model = SWAResnet(model.model, lr=0.01)\n",
"swa_model.datamodule = cifar10_dm\n",
"\n",
"swa_trainer = Trainer(\n",
" max_epochs=20,\n",
" accelerator=\"auto\",\n",
" devices=1 if torch.cuda.is_available() else None, # limiting got iPython runs\n",
" callbacks=[TQDMProgressBar(refresh_rate=20)],\n",
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" NaN \n",
" 0.004934 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" \n",
" \n",
" 0.0 \n",
" NaN \n",
" 1.724640 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" \n",
" \n",
" NaN \n",
" 0.006107 \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" NaN \n",
" \n",
" \n",
"
\n",
"
"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
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"needs_background": "light"
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],
"source": [
"\n",
"metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n",
"del metrics[\"step\"]\n",
"metrics.set_index(\"epoch\", inplace=True)\n",
"display(metrics.dropna(axis=1, how=\"all\").head())\n",
"sn.relplot(data=metrics, kind=\"line\")"
]
},
{
"cell_type": "markdown",
"id": "3ab2d27e",
"metadata": {
"papermill": {
"duration": 0.654577,
"end_time": "2022-04-28T12:54:53.274782",
"exception": false,
"start_time": "2022-04-28T12:54:52.620205",
"status": "completed"
},
"tags": []
},
"source": [
"## Congratulations - Time to Join the Community!\n",
"\n",
"Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning\n",
"movement, you can do so in the following ways!\n",
"\n",
"### Star [Lightning](https://github.com/PyTorchLightning/pytorch-lightning) on GitHub\n",
"The easiest way to help our community is just by starring the GitHub repos! This helps raise awareness of the cool\n",
"tools we're building.\n",
"\n",
"### Join our [Slack](https://www.pytorchlightning.ai/community)!\n",
"The best way to keep up to date on the latest advancements is to join our community! Make sure to introduce yourself\n",
"and share your interests in `#general` channel\n",
"\n",
"\n",
"### Contributions !\n",
"The best way to contribute to our community is to become a code contributor! At any time you can go to\n",
"[Lightning](https://github.com/PyTorchLightning/pytorch-lightning) or [Bolt](https://github.com/PyTorchLightning/lightning-bolts)\n",
"GitHub Issues page and filter for \"good first issue\".\n",
"\n",
"* [Lightning good first issue](https://github.com/PyTorchLightning/pytorch-lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n",
"* [Bolt good first issue](https://github.com/PyTorchLightning/lightning-bolts/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n",
"* You can also contribute your own notebooks with useful examples !\n",
"\n",
"### Great thanks from the entire Pytorch Lightning Team for your interest !\n",
"\n",
"[{height=\"60px\" width=\"240px\"}](https://pytorchlightning.ai)"
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
".. customcarditem::\n",
" :header: PyTorch Lightning CIFAR10 ~94% Baseline Tutorial\n",
" :card_description: Train a Resnet to 94% accuracy on Cifar10!\n",
" :tags: Image,GPU/TPU,Lightning-Examples"
]
}
],
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"exception": null,
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