.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_examples_apps_lightning_model.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_examples_apps_lightning_model.py:


Tiny ImageNet Model
====================

This is a toy model for doing regression on the tiny imagenet dataset. It's used
by the apps in the same folder.


.. code-block:: default


    import os.path
    import subprocess
    from typing import List, Optional, Tuple

    import fsspec
    import pytorch_lightning as pl
    import torch
    import torch.jit
    from torch.nn import functional as F
    from torchmetrics.classification import MulticlassAccuracy
    from torchvision.models.resnet import BasicBlock, ResNet


    class TinyImageNetModel(pl.LightningModule):
        """
        An very simple linear model for the tiny image net dataset.
        """

        def __init__(
            self, layer_sizes: Optional[List[int]] = None, lr: Optional[float] = None
        ) -> None:
            super().__init__()

            if not layer_sizes:
                layer_sizes = [1, 1, 1, 1]

            self.lr: float = lr or 0.001

            # We use the torchvision resnet model with some small tweaks to match
            # TinyImageNet.
            m = ResNet(BasicBlock, layer_sizes, num_classes=200)
            m.avgpool = torch.nn.AdaptiveAvgPool2d(1)
            self.model: ResNet = m

            self.train_acc = MulticlassAccuracy(num_classes=m.fc.out_features)
            self.val_acc = MulticlassAccuracy(num_classes=m.fc.out_features)

        # pyre-fixme[14]
        def forward(self, x: torch.Tensor) -> torch.Tensor:
            return self.model(x)

        # pyre-fixme[14]
        def training_step(
            self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int
        ) -> torch.Tensor:
            return self._step("train", self.train_acc, batch, batch_idx)

        # pyre-fixme[14]
        def validation_step(
            self, val_batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int
        ) -> torch.Tensor:
            return self._step("val", self.val_acc, val_batch, batch_idx)

        def _step(
            self,
            step_name: str,
            acc_metric: MulticlassAccuracy,
            batch: Tuple[torch.Tensor, torch.Tensor],
            batch_idx: int,
        ) -> torch.Tensor:
            x, y = batch
            y_pred = self(x)
            loss = F.cross_entropy(y_pred, y)
            self.log(f"{step_name}_loss", loss)
            acc_metric(y_pred, y)
            self.log(f"{step_name}_acc", acc_metric.compute())
            return loss

        # pyre-fixme[3]: TODO(aivanou): Figure out why oss pyre can identify type but fb cannot.
        def configure_optimizers(self):
            return torch.optim.AdamW(self.parameters(), lr=self.lr)


    def export_inference_model(
        model: TinyImageNetModel, out_path: str, tmpdir: str
    ) -> None:
        """
        export_inference_model uses TorchScript JIT to serialize the
        TinyImageNetModel into a standalone file that can be used during inference.
        TorchServe can also handle interpreted models with just the model.py file if
        your model can't be JITed.
        """

        print("exporting inference model")
        jit_path = os.path.join(tmpdir, "model_jit.pt")
        jitted = torch.jit.script(model)
        print(f"saving JIT model to {jit_path}")
        torch.jit.save(jitted, jit_path)

        model_name = "tiny_image_net"

        mar_path = os.path.join(tmpdir, f"{model_name}.mar")
        print(f"creating model archive at {mar_path}")
        subprocess.run(
            [
                "torch-model-archiver",
                "--model-name",
                "tiny_image_net",
                "--handler",
                "torchx/examples/apps/lightning/handler/handler.py",
                "--version",
                "1",
                "--serialized-file",
                jit_path,
                "--export-path",
                tmpdir,
            ],
            check=True,
        )

        remote_path = os.path.join(out_path, "model.mar")
        print(f"uploading to {remote_path}")
        fs, _, rpaths = fsspec.get_fs_token_paths(remote_path)
        assert len(rpaths) == 1, "must have single path"
        fs.put(mar_path, rpaths[0])


    # sphinx_gallery_thumbnail_path = '_static/img/gallery-lib.png'


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_examples_apps_lightning_model.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: model.py <model.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: model.ipynb <model.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_