docs: update tutorial notebook
This commit is contained in:
Jensun Ravichandran
parent
dd696ea1e0
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e21e6c7e02
@ -2,223 +2,252 @@
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"cells": [
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{
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"cell_type": "markdown",
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"id": "7ac5eff0",
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"metadata": {},
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"source": [
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"# A short tutorial for the `prototorch.models` plugin"
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "beb83780",
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"metadata": {},
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"source": [
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"## Introduction"
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "43b74278",
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"metadata": {},
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"source": [
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"This is a short tutorial for the [models](https://github.com/si-cim/prototorch_models) plugin of the [ProtoTorch](https://github.com/si-cim/prototorch) framework.\n",
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"\n",
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"[ProtoTorch](https://github.com/si-cim/prototorch) provides [torch.nn](https://pytorch.org/docs/stable/nn.html) modules and utilities to implement prototype-based models. However, it is up to the user to put these modules together into models and handle the training of these models. Expert machine-learning practioners and researchers sometimes prefer this level of control. However, this leads to a lot of boilerplate code that is essentially same across many projects. Needless to say, this is a source of a lot of frustration. [PyTorch-Lightning](https://pytorch-lightning.readthedocs.io/en/latest/) is a framework that helps avoid a lot of this frustration by handling the boilerplate code for you so you don't have to reinvent the wheel every time you need to implement a new model.\n",
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"\n",
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"With the [prototorch.models](https://github.com/si-cim/prototorch_models) plugin, we've gone one step further and pre-packaged commonly used prototype-models like GMLVQ as [Lightning-Modules](https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.core.lightning.html?highlight=lightning%20module#pytorch_lightning.core.lightning.LightningModule). With only a few lines to code, it is now possible to build and train prototype-models. It quite simply cannot get any simpler than this."
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "4e5d1fad",
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"metadata": {},
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"source": [
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"## Basics"
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "1244b66b",
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"metadata": {},
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"source": [
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"First things first. When working with the models plugin, you'll probably need `torch`, `prototorch` and `pytorch_lightning`. So, we recommend that you import all three like so:"
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dcb88e8a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import prototorch as pt\n",
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"import pytorch_lightning as pl\n",
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"import torch"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "1adbe2f8",
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"metadata": {},
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"source": [
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"### Building Models"
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "96663ab1",
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"metadata": {},
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"source": [
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"Let's start by building a `GLVQ` model. It is one of the simplest models to build. The only requirements are a prototype distribution and an initializer."
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "819ba756",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = pt.models.GLVQ(\n",
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" hparams=dict(distribution=[1, 1, 1]),\n",
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" prototypes_initializer=pt.initializers.ZerosCompInitializer(2),\n",
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")"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1b37e97c",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(model)"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "d2c86903",
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"metadata": {},
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"source": [
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"The key `distribution` in the `hparams` argument describes the prototype distribution. If it is a Python [list](https://docs.python.org/3/tutorial/datastructures.html), it is assumed that there are as many entries in this list as there are classes, and the number at each location of this list describes the number of prototypes to be used for that particular class. So, `[1, 1, 1]` implies that we have three classes with one prototype per class. If it is a Python [tuple](https://docs.python.org/3/tutorial/datastructures.html), a shorthand of `(num_classes, prototypes_per_class)` is assumed. If it is a Python [dictionary](https://docs.python.org/3/tutorial/datastructures.html), the key-value pairs describe the class label and the number of prototypes for that class respectively. So, `{0: 2, 1: 2, 2: 2}` implies that we have three classes with labels `{1, 2, 3}`, each equipped with two prototypes. If however, the dictionary contains the keys `\"num_classes\"` and `\"per_class\"`, they are parsed to use their values as one might expect.\n",
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"\n",
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"The `prototypes_initializer` argument describes how the prototypes are meant to be initialized. This argument has to be an instantiated object of some kind of [AbstractComponentsInitializer](https://github.com/si-cim/prototorch/blob/dev/prototorch/components/initializers.py#L18). If this is a [ShapeAwareCompInitializer](https://github.com/si-cim/prototorch/blob/dev/prototorch/components/initializers.py#L41), this only requires a `shape` arugment that describes the shape of the prototypes. So, `pt.initializers.ZerosCompInitializer(3)` creates 3d-vector prototypes all initialized to zeros."
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "45806052",
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"metadata": {},
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"source": [
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"### Data"
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "9d62c4c6",
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"metadata": {},
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"source": [
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"The preferred way to working with data in `torch` is to use the [Dataset and Dataloader API](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html). There a few pre-packaged datasets available under `prototorch.datasets`. See [here](https://prototorch.readthedocs.io/en/latest/api.html#module-prototorch.datasets) for a full list of available datasets."
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "504df02c",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_ds = pt.datasets.Iris(dims=[0, 2])"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3b8e7756",
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"metadata": {},
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"outputs": [],
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"source": [
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"type(train_ds)"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bce43afa",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_ds.data.shape, train_ds.targets.shape"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "26a83328",
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"metadata": {},
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"source": [
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"Once we have such a dataset, we could wrap it in a `Dataloader` to load the data in batches, and possibly apply some transformations on the fly."
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "67b80fbe",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_loader = torch.utils.data.DataLoader(train_ds, batch_size=2)"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c1185f31",
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"metadata": {},
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"outputs": [],
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"source": [
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"type(train_loader)"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9b5a8963",
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"metadata": {},
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"outputs": [],
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"source": [
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"x_batch, y_batch = next(iter(train_loader))\n",
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"print(f\"{x_batch=}, {y_batch=}\")"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "dd492ee2",
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"metadata": {},
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"source": [
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"This perhaps seems like a lot of work for a small dataset that fits completely in memory. However, this comes in very handy when dealing with huge datasets that can only be processed in batches."
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "5176b055",
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"metadata": {},
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"source": [
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"### Training"
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "46a7a506",
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"metadata": {},
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"source": [
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"If you're familiar with other deep learning frameworks, you might perhaps expect a `.fit(...)` or `.train(...)` method. However, in PyTorch-Lightning, this is done slightly differently. We first create a trainer and then pass the model and the Dataloader to `trainer.fit(...)` instead. So, it is more functional in style than object-oriented."
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "279e75b7",
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"metadata": {},
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"outputs": [],
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"source": [
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"trainer = pl.Trainer(max_epochs=2, weights_summary=None)"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e496b492",
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"metadata": {},
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"outputs": [],
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"source": [
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"trainer.fit(model, train_loader)"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "497fbff6",
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"metadata": {},
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"source": [
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"### From data to a trained model - a very minimal example"
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],
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"metadata": {}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ab069c5d",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_ds = pt.datasets.Iris(dims=[0, 2])\n",
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"train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)\n",
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@ -230,49 +259,189 @@
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"\n",
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"trainer = pl.Trainer(max_epochs=50, weights_summary=None)\n",
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"trainer.fit(model, train_loader)"
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],
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"outputs": [],
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"metadata": {}
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]
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},
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{
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"cell_type": "markdown",
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"id": "30c71a93",
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"metadata": {},
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"source": [
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"## Advanced"
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],
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"metadata": {}
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"### Saving/Loading trained models"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f74ed2c1",
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"metadata": {},
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"source": [
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"### Initializing prototypes with a subset of a dataset (along with transformations)"
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],
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"metadata": {}
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"Pytorch Lightning can automatically checkpoint the model during various stages of training, but it also possible to manually save a checkpoint after training."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3156658d",
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"metadata": {},
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"outputs": [],
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"source": [
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"ckpt_path = \"./checkpoints/glvq_iris.ckpt\"\n",
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"trainer.save_checkpoint(ckpt_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c1c34055",
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"metadata": {},
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"outputs": [],
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"source": [
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"loaded_model = pt.models.GLVQ.load_from_checkpoint(ckpt_path, strict=False)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bbbb08e9",
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"metadata": {},
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"source": [
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"### Visualizing decision boundaries in 2D"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "53ca52dc",
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"metadata": {},
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"outputs": [],
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"source": [
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"pt.models.VisGLVQ2D(data=train_ds).visualize(loaded_model)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8373531f",
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"metadata": {},
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"source": [
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"### Saving/Loading trained weights"
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]
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},
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{
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"cell_type": "markdown",
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"id": "937bc458",
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"metadata": {},
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"source": [
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"In most cases, the checkpointing workflow is sufficient. In some cases however, one might want to only save the trained weights from the model. The disadvantage of this method is that the model has be re-created using compatible initialization parameters before the weights could be loaded."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1f2035af",
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"metadata": {},
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"outputs": [],
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"source": [
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"ckpt_path = \"./checkpoints/glvq_iris_weights.pth\"\n",
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"torch.save(model.state_dict(), ckpt_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1206021a",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = pt.models.GLVQ(\n",
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" dict(distribution=(3, 2)),\n",
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" prototypes_initializer=pt.initializers.ZerosCompInitializer(2),\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9f2a4beb",
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"metadata": {},
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"outputs": [],
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"source": [
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"pt.models.VisGLVQ2D(data=train_ds, title=\"Before loading the weights\").visualize(model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "528d2fc2",
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"metadata": {},
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"outputs": [],
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"source": [
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"torch.load(ckpt_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ec817e6b",
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"metadata": {},
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"outputs": [],
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"source": [
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"model.load_state_dict(torch.load(ckpt_path), strict=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a208eab7",
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"metadata": {},
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"outputs": [],
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"source": [
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"pt.models.VisGLVQ2D(data=train_ds, title=\"After loading the weights\").visualize(model)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f8de748f",
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"metadata": {},
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"source": [
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"## Advanced"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1f6a33a5",
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"metadata": {},
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"source": [
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"### Initializing prototypes with a subset of a dataset (along with transformations)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "946ce341",
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"metadata": {},
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"outputs": [],
|
||||
"source": [
|
||||
"import prototorch as pt\n",
|
||||
"import pytorch_lightning as pl\n",
|
||||
"import torch\n",
|
||||
"from torchvision import transforms\n",
|
||||
"from torchvision.datasets import MNIST"
|
||||
],
|
||||
"outputs": [],
|
||||
"metadata": {}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "510d9bd4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from matplotlib import pyplot as plt"
|
||||
],
|
||||
"outputs": [],
|
||||
"metadata": {}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ea7c1228",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_ds = MNIST(\n",
|
||||
" \"~/datasets\",\n",
|
||||
@ -284,59 +453,64 @@
|
||||
" transforms.ToTensor(),\n",
|
||||
" ]),\n",
|
||||
")"
|
||||
],
|
||||
"outputs": [],
|
||||
"metadata": {}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1b9eaf5c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"s = int(0.05 * len(train_ds))\n",
|
||||
"init_ds, rest_ds = torch.utils.data.random_split(train_ds, [s, len(train_ds) - s])"
|
||||
],
|
||||
"outputs": [],
|
||||
"metadata": {}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8c32c9f2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"init_ds"
|
||||
],
|
||||
"outputs": [],
|
||||
"metadata": {}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "68a9a8b9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = pt.models.ImageGLVQ(\n",
|
||||
" dict(distribution=(10, 5)),\n",
|
||||
" prototypes_initializer=pt.initializers.SMCI(init_ds),\n",
|
||||
")"
|
||||
],
|
||||
"outputs": [],
|
||||
"metadata": {}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6f23df86",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.imshow(model.get_prototype_grid(num_columns=10))"
|
||||
],
|
||||
"outputs": [],
|
||||
"metadata": {}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4fa69f92",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## FAQs"
|
||||
],
|
||||
"metadata": {}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "fa20f9ac",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### How do I Retrieve the prototypes and their respective labels from the model?\n",
|
||||
"\n",
|
||||
@ -351,11 +525,12 @@
|
||||
"```python\n",
|
||||
">>> model.prototype_labels\n",
|
||||
"```"
|
||||
],
|
||||
"metadata": {}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ba8215bf",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### How do I make inferences/predictions/recall with my trained model?\n",
|
||||
"\n",
|
||||
@ -370,13 +545,12 @@
|
||||
"```python\n",
|
||||
">>> y_pred = model(torch.Tensor(x_train)) # returns probabilities\n",
|
||||
"```"
|
||||
],
|
||||
"metadata": {}
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -390,7 +564,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.4"
|
||||
"version": "3.9.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user