401 lines
14 KiB
Plaintext
401 lines
14 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "01-mnist-hello-world.ipynb",
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"provenance": [],
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"collapsed_sections": [],
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"authorship_tag": "ABX9TyOtAKVa5POQ6Xg3UcTQqXDJ",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/01-mnist-hello-world.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "i7XbLCXGkll9",
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"colab_type": "text"
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},
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"source": [
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"# Introduction to Pytorch Lightning ⚡\n",
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"\n",
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"In this notebook, we'll go over the basics of lightning by preparing models to train on the [MNIST Handwritten Digits dataset](https://en.wikipedia.org/wiki/MNIST_database).\n",
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"\n",
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"---\n",
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" - Give us a ⭐ [on Github](https://www.github.com/PytorchLightning/pytorch-lightning/)\n",
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" - Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/latest/)\n",
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" - Join us [on Slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-f6bl2l0l-JYMK3tbAgAmGRrlNr00f1A)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "2LODD6w9ixlT",
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"colab_type": "text"
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},
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"source": [
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"### Setup \n",
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"Lightning is easy to install. Simply ```pip install pytorch-lightning```"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "zK7-Gg69kMnG",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"! pip install pytorch-lightning --quiet"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "w4_TYnt_keJi",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"import os\n",
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"\n",
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"import torch\n",
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"from torch import nn\n",
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"from torch.nn import functional as F\n",
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"from torch.utils.data import DataLoader, random_split\n",
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"from torchvision.datasets import MNIST\n",
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"from torchvision import transforms\n",
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"import pytorch_lightning as pl\n",
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"from pytorch_lightning.metrics.functional import accuracy"
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],
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"execution_count": 2,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "EHpyMPKFkVbZ",
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"colab_type": "text"
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},
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"source": [
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"## Simplest example\n",
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"\n",
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"Here's the simplest most minimal example with just a training loop (no validation, no testing).\n",
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"\n",
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"**Keep in Mind** - A `LightningModule` *is* a PyTorch `nn.Module` - it just has a few more helpful features."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "V7ELesz1kVQo",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"class MNISTModel(pl.LightningModule):\n",
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"\n",
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" def __init__(self):\n",
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" super(MNISTModel, self).__init__()\n",
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" self.l1 = torch.nn.Linear(28 * 28, 10)\n",
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"\n",
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" def forward(self, x):\n",
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" return torch.relu(self.l1(x.view(x.size(0), -1)))\n",
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"\n",
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" def training_step(self, batch, batch_nb):\n",
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" x, y = batch\n",
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" loss = F.cross_entropy(self(x), y)\n",
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" return loss\n",
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"\n",
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" def configure_optimizers(self):\n",
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" return torch.optim.Adam(self.parameters(), lr=0.02)"
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],
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"execution_count": 3,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "hIrtHg-Dv8TJ",
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"colab_type": "text"
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},
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"source": [
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"By using the `Trainer` you automatically get:\n",
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"1. Tensorboard logging\n",
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"2. Model checkpointing\n",
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"3. Training and validation loop\n",
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"4. early-stopping"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "4Dk6Ykv8lI7X",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"# Init our model\n",
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"mnist_model = MNISTModel()\n",
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"\n",
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"# Init DataLoader from MNIST Dataset\n",
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"train_ds = MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor())\n",
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"train_loader = DataLoader(train_ds, batch_size=32)\n",
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"\n",
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"# Initialize a trainer\n",
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"trainer = pl.Trainer(gpus=1, max_epochs=3, progress_bar_refresh_rate=20)\n",
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"\n",
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"# Train the model ⚡\n",
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"trainer.fit(mnist_model, train_loader)"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "KNpOoBeIjscS",
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"colab_type": "text"
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},
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"source": [
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"## A more complete MNIST Lightning Module Example\n",
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"\n",
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"That wasn't so hard was it?\n",
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"\n",
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"Now that we've got our feet wet, let's dive in a bit deeper and write a more complete `LightningModule` for MNIST...\n",
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"\n",
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"This time, we'll bake in all the dataset specific pieces directly in the `LightningModule`. This way, we can avoid writing extra code at the beginning of our script every time we want to run it.\n",
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"\n",
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"---\n",
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"\n",
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"### Note what the following built-in functions are doing:\n",
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"\n",
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"1. [prepare_data()](https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.core.lightning.html#pytorch_lightning.core.lightning.LightningModule.prepare_data) 💾\n",
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" - This is where we can download the dataset. We point to our desired dataset and ask torchvision's `MNIST` dataset class to download if the dataset isn't found there.\n",
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" - **Note we do not make any state assignments in this function** (i.e. `self.something = ...`)\n",
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"\n",
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"2. [setup(stage)](https://pytorch-lightning.readthedocs.io/en/latest/lightning-module.html#setup) ⚙️\n",
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" - Loads in data from file and prepares PyTorch tensor datasets for each split (train, val, test). \n",
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" - Setup expects a 'stage' arg which is used to separate logic for 'fit' and 'test'.\n",
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" - If you don't mind loading all your datasets at once, you can set up a condition to allow for both 'fit' related setup and 'test' related setup to run whenever `None` is passed to `stage` (or ignore it altogether and exclude any conditionals).\n",
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" - **Note this runs across all GPUs and it *is* safe to make state assignments here**\n",
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"\n",
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"3. [x_dataloader()](https://pytorch-lightning.readthedocs.io/en/latest/lightning-module.html#data-hooks) ♻️\n",
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" - `train_dataloader()`, `val_dataloader()`, and `test_dataloader()` all return PyTorch `DataLoader` instances that are created by wrapping their respective datasets that we prepared in `setup()`"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "4DNItffri95Q",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"class LitMNIST(pl.LightningModule):\n",
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" \n",
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" def __init__(self, data_dir='./', hidden_size=64, learning_rate=2e-4):\n",
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"\n",
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" super().__init__()\n",
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"\n",
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" # Set our init args as class attributes\n",
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" self.data_dir = data_dir\n",
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" self.hidden_size = hidden_size\n",
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" self.learning_rate = learning_rate\n",
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"\n",
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" # Hardcode some dataset specific attributes\n",
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" self.num_classes = 10\n",
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" self.dims = (1, 28, 28)\n",
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" channels, width, height = self.dims\n",
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" self.transform = transforms.Compose([\n",
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" transforms.ToTensor(),\n",
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" transforms.Normalize((0.1307,), (0.3081,))\n",
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" ])\n",
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"\n",
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" # Define PyTorch model\n",
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" self.model = nn.Sequential(\n",
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" nn.Flatten(),\n",
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" nn.Linear(channels * width * height, hidden_size),\n",
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" nn.ReLU(),\n",
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" nn.Dropout(0.1),\n",
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" nn.Linear(hidden_size, hidden_size),\n",
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" nn.ReLU(),\n",
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" nn.Dropout(0.1),\n",
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" nn.Linear(hidden_size, self.num_classes)\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.model(x)\n",
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" return F.log_softmax(x, dim=1)\n",
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"\n",
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" def training_step(self, batch, batch_idx):\n",
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" x, y = batch\n",
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" logits = self(x)\n",
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" loss = F.nll_loss(logits, y)\n",
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" return loss\n",
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"\n",
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" def validation_step(self, batch, batch_idx):\n",
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" x, y = batch\n",
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" logits = self(x)\n",
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" loss = F.nll_loss(logits, y)\n",
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" preds = torch.argmax(logits, dim=1)\n",
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" acc = accuracy(preds, y)\n",
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"\n",
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" # Calling self.log will surface up scalars for you in TensorBoard\n",
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" self.log('val_loss', loss, prog_bar=True)\n",
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" self.log('val_acc', acc, prog_bar=True)\n",
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" return loss\n",
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"\n",
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" def test_step(self, batch, batch_idx):\n",
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" # Here we just reuse the validation_step for testing\n",
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" return self.validation_step(batch, batch_idx)\n",
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"\n",
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" def configure_optimizers(self):\n",
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" optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
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" return optimizer\n",
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"\n",
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" ####################\n",
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" # DATA RELATED HOOKS\n",
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" ####################\n",
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"\n",
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" def prepare_data(self):\n",
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" # download\n",
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" MNIST(self.data_dir, train=True, download=True)\n",
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" MNIST(self.data_dir, train=False, download=True)\n",
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"\n",
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" def setup(self, stage=None):\n",
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"\n",
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" # Assign train/val datasets for use in dataloaders\n",
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" if stage == 'fit' or stage is None:\n",
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" mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)\n",
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" self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])\n",
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"\n",
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" # Assign test dataset for use in dataloader(s)\n",
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" if stage == 'test' or stage is None:\n",
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" self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)\n",
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"\n",
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" def train_dataloader(self):\n",
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" return DataLoader(self.mnist_train, batch_size=32)\n",
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"\n",
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" def val_dataloader(self):\n",
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" return DataLoader(self.mnist_val, batch_size=32)\n",
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"\n",
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" def test_dataloader(self):\n",
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" return DataLoader(self.mnist_test, batch_size=32)"
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],
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"execution_count": 5,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Mb0U5Rk2kLBy",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"model = LitMNIST()\n",
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"trainer = pl.Trainer(gpus=1, max_epochs=3, progress_bar_refresh_rate=20)\n",
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"trainer.fit(model)"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "nht8AvMptY6I",
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"colab_type": "text"
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},
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"source": [
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"### Testing\n",
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"\n",
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"To test a model, call `trainer.test(model)`.\n",
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"\n",
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"Or, if you've just trained a model, you can just call `trainer.test()` and Lightning will automatically test using the best saved checkpoint (conditioned on val_loss)."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "PA151FkLtprO",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"trainer.test()"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "T3-3lbbNtr5T",
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"colab_type": "text"
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},
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"source": [
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"### Bonus Tip\n",
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"\n",
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"You can keep calling `trainer.fit(model)` as many times as you'd like to continue training"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "IFBwCbLet2r6",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"trainer.fit(model)"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "8TRyS5CCt3n9",
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"colab_type": "text"
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},
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"source": [
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"In Colab, you can use the TensorBoard magic function to view the logs that Lightning has created for you!"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "wizS-QiLuAYo",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"# Start tensorboard.\n",
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"%load_ext tensorboard\n",
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"%tensorboard --logdir lightning_logs/"
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],
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"execution_count": null,
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"outputs": []
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}
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]
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}
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