# Using robustness as a general training library (Part 1: Getting started)¶

In the other walkthroughs, we’ve demonstrated how to use robustness as a command line tool for training and evaluating models, and how to use it as a library for input manipulation. Here, we’ll demonstrate how robustness can be used a general library for experimenting with neural network training. We’ve found the library has saved us a tremendous amount of time both writing boilerplate code and making custom modifications to the training process (one of the primary goals of the library is to make such modifications simple).

This walkthrough will be split into two parts: in the first part (this one), we’ll show how to get started with the robustness library, and go through the process of making a main.py file for training neural networks. In the second part, we’ll show how to customize the training process through custom loss functions, architectures, datasets, logging, and more.

You can follow along using the source of robustness.main

You can also download a Jupyter notebook containing code from this walkthrough and the next!

## Step 1: Imports¶

Our goal in this tutorial will be to make a python file that works nearly identically to the robustness Command-line tool. That is, a user will be able to call python main.py [--arg value ...] to train a standard or robust model. We’ll start by importing the necessary modules from the package:

from robustness.datasets import DATASETS
from robustness.model_utils import make_and_restore_model
from robustness.train import train_model
from robustness.defaults import check_and_fill_args
from robustness.tools import constants, helpers
from robustness import defaults


To make life easier, we use cox (a super lightweight python logging library) for logging:

from cox import utils
from cox import store


Finally, we’ll also need the following external imports:

import torch as ch
from argparse import ArgumentParser
import os


## Step 2: Dealing with arguments¶

In this first step, we’ll set up an args object which has all the parameters we need to train our model. In Step 2.1 we’ll show how to use argparse to accept user input for specifying parameters via command line; in Step 2.2 we show how to sanity-check the args object and fill in reasonable defaults.

### Step 2.1: Setting up command-line args¶

The first real step in making our main file is setting up an argparse.ArgumentParser object to parse command-line arguments for us. (If you are not familiar with the python argparses module, we recommend looking there first). Note that if you’re not interested in accepting command-line input for arguments via argparse, you can skip to Step 2.2.

The robustness package provides the robustness.defaults module to make dealing with arguments less painful. In particular, the properties robustness.defaults.TRAINING_ARGS, robustness.defaults.PGD_ARGS, and robustness.defaults.MODEL_LOADER_ARGS, contain all of the arguments (along with default values) needed for training models:

• TRAINING_ARGS has all of the model training arguments, like learning rate, momentum, weight decay, learning rate schedule, etc.
• PGD_ARGS has all of the arguments needed only for adversarial training, like number of PGD steps, perturbation budget, type of norm constraint, etc.
• MODEL_LOADER_ARGS has all of the arguments for instantiating the model and data loaders: dataset, path to dataset, batch size, number of workers, etc.

You can take a look at the documentation of robustness.defaults to learn more about how these attributes are set up and see exactly which arguments they contain and with what defaults, as well as which arguments are required. The important thing is that the robustness package provides the function robustness.defaults.add_args_to_parser() which takes in an arguments object like the above, and an argparse parser, and adds the arguments to the parser:

parser = ArgumentParser()
# Note that we can add whatever extra arguments we want to the parser here
args = parser.parse_args()


Important note: Even though the arguments objects do specify defaults for the arguments, these defaults are not given to the parser directly. More on this in Step 2.2.

If you don’t want to use argparse and already know the values you want to use for the parameters, you can look at the robustness.defaults documentation, and hard-code the desired arguments as follows:

# Hard-coded base parameters
train_kwargs = {
'out_dir': "train_out",
'constraint': '2',
'eps': 0.5,
'attack_lr': 1.5,
'attack_steps': 20
}

# utils.Parameters is just an object wrapper for dicts implementing
# getattr and settattr
train_args = utils.Parameters(train_kwargs)


### Step 2.2: Sanity checks and defaults¶

We generally found the ArgumentParser defaults to be too restrictive for our applications, and we also wanted to be able to fill in argument defaults even when we were not using ArgumentParser. Thus, we fill in default arguments separately via the robustness.defaults.check_and_fill_args() function. This function takes in the args Namespace object (basically anything exposing setattr and getattr functions), the same ARGS attributes discussed above, and a dataset class (used for filling in per-dataset defaults). The function fills in the defaults it has, and then throws an error if a required argument is missing:

assert args.dataset is not None, "Must provide a dataset"
ds_class = DATASETS[args.dataset]

args = check_and_fill_args(args, defaults.TRAINING_ARGS, ds_class)
args = check_and_fill_args(args, defaults.PGD_ARGS, ds_class)


Note that the check_and_fill_args() function is totally independent of argparse, and can be used even when you don’t want to support command-line arguments. It’s a really useful way of sanity checking the args object to make sure that there aren’t any missing or misspecified arguments.

## Step 3: Creating the model, dataset, and loader¶

The next step is to create the model, dataset, and data loader. We start by creating the dataset and loaders as follows:

# Load up the dataset
data_path = os.path.expandvars(args.data)
dataset = DATASETS[args.dataset](data_path)

args.batch_size, data_aug=bool(args.data_aug))

# Prefetches data to improve performance


We can now create the model by using the robustness.model_utils.make_and_restore_model() function. This function is used for both creating new models, or (if a resume_path if passed) loading previously saved models.

model, _ = make_and_restore_model(arch=args.arch, dataset=dataset)


## Step 4: Training the model¶

Finally, we create a cox Store for saving the results of the training, and call robustness.train.train_model() to begin training:

# Create the cox store, and save the arguments in a table
store = store.Store(args.out_dir, args.exp_name)
args_dict = args.as_dict() if isinstance(args, utils.Parameters) else vars(args)
schema = store.schema_from_dict(args_dict)