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Description:

pytorchinfluencefunctions 0.1.1

Influence Functions for PyTorch
This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper:
Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang.
The reference implementation can be found here: link.

Why Use Influence Functions?
Requirements
Installation
Usage
Background and Documentation

config

Misc parameters
Calculation parameters

s_test

Modes of computation
Output variables

Influences
Harmful
Helpful

Roadmap

v0.2
v0.3
v0.4

Why Use Influence Functions?
Influence functions help you to debug the results of your deep learning model
in terms of the dataset. When testing for a single test image, you can then
calculate which training images had the largest result on the classification
outcome. Thus, you can easily find mislabeled images in your dataset, or
compress your dataset slightly to the most influential images important for
your individual test dataset. That can increase prediction accuracy, reduce
training time, and reduce memory requirements. For more details please see
the original paper linked here.
Influence functions can of course also be used for data other than images,
as long as you have a supervised learning problem.
Requirements

Python 3.6 or later
PyTorch 1.0 or later
NumPy 1.12 or later

To run the tests, further requirements are:

torchvision 0.3 or later
PIL

Installation
You can either install this package directly through pip:
pip3 install --user pytorch-influence-functions

Or you can clone the repo and import it as a package after it's in your PATH.
Usage
Calculating the influence of the individual samples of your training dataset
on the final predictions is straight forward.
The most barebones way of getting the code to run is like this:
import pytorch_influence_functions as ptif

# Supplied by the user:
model = get_my_model()
trainloader, testloader = get_my_dataloaders()

ptif.init_logging()
config = ptif.get_default_config()

influences, harmful, helpful = ptif.calc_img_wise(config, model, trainloader, testloader)

# do someting with influences/harmful/helpful

Here, config contains default values for the influence function calculation
which can of course be changed. For details and examples, look here.
Background and Documentation
The precision of the output can be adjusted by using more iterations and/or
more recursions when approximating the influence.
config
config is a dict which contains the parameters used to calculate the
influences. You can get the default config by calling ptif.get_default_config().
I recommend you to change the following parameters to your liking. The list
below is divided into parameters affecting the calculation and parameters
affecting everything else.
Misc parameters

save_pth: Default None, folder where to save s_test and grad_z files
if saving is desired
outdir: folder name to which the result json files are written
log_filename: Default None, if set the output will be logged to this
file in addition to stdout.

Calculation parameters

seed: Default = 42, random seed for numpy, random, pytorch
gpu: Default = -1, -1 for calculation on the CPU otherwise GPU id
calc_method: Default = img_wise, choose between the two calculation methods
outlined here.
DataLoader object for the desired dataset

train_loader and test_loader

test_sample_start_per_class: Default = False, per class index from where to
start to calculate the influence function. If False, it will start from 0.
This is useful if you want to calculate the influence function of a whole
test dataset and manually split the calculation up over multiple threads/
machines/gpus. Then, you can start at various points in the dataset.
test_sample_num: Default = False, number of samples per class
starting from the test_sample_start_per_class to calculate the influence
function for. E.g. if your dataset has 10 classes and you set this value to
1, then the influence functions will be calculated for 10 * 1 test
samples, one per class. If False, calculates the influence for all images.

s_test

recursion_depth: Default = 5000, recursion depth for the s_test calculation.
Greater recursion depth improves precision.
r: Default = 1, number of s_test calculations to take the average of.
Greater r averaging improves precision.
Combined, the original paper suggests that recursion_depth * r should equal
the training dataset size, thus the above values of r = 10 and
recursion_depth = 5000 are valid for CIFAR-10 with a training dataset size
of 50000 items.
damp: Default = 0.01, damping factor during s_test calculation.
scale: Default = 25, scaling factor during s_test calculation.

Modes of computation
This packages offers two modes of computation to calculate the influence
functions. The first mode is called calc_img_wise, during which the two
values s_test and grad_z for each training image are computed on the fly
when calculating the influence of that single image. The algorithm moves then
on to the next image. The second mode is called calc_all_grad_then_test and
calculates the grad_z values for all images first and saves them to disk.
Then, it'll calculate all s_test values and save those to disk. Subsequently,
the algorithm will then calculate the influence functions for all images by
reading both values from disk and calculating the influence base on them. This
can take significant amounts of disk space (100s of GBs) but with a fast SSD
can speed up the calculation significantly as no duplicate calculations take
place. This is the case because grad_z has to be calculated twice, once for
the first approximation in s_test and once to combine with the s_test
vector to calculate the influence. Most importantnly however, s_test is only
dependent on the test sample(s). While one grad_z is used to estimate the
initial value of the Hessian during the s_test calculation, this is
insignificant. grad_z on the other hand is only dependent on the training
sample. Thus, in the calc_img_wise mode, we throw away all grad_z
calculations even if we could reuse them for all subsequent s_test
calculations, which could potentially be 10s of thousands. However, as stated
above, keeping the grad_zs only makes sense if they can be loaded faster/
kept in RAM than calculating them on-the-fly.
TL;DR: The recommended way is using calc_img_wise unless you have a crazy
fast SSD, lots of free storage space, and want to calculate the influences on
the prediction outcomes of an entire dataset or even >1000 test samples.
Output variables
Visualised, the output can look like this:

The test image on the top left is test image for which the influences were
calculated. To get the correct test outcome of ship, the Helpful images from
the training dataset were the most helpful, whereas the Harmful images were the
most harmful. Here, we used CIFAR-10 as dataset. The model was ResNet-110. The
numbers above the images show the actual influence value which was calculated.
The next figure shows the same but for a different model, DenseNet-100/12.
Thus, we can see that different models learn more from different images.

Influences
Is a dict/json containting the influences calculated of all training data
samples for each test data sample. The dict structure looks similiar to this:
{
"0": {
"label": 3,
"num_in_dataset": 0,
"time_calc_influence_s": 129.6417362689972,
"influence": [
-0.00016939856868702918,
4.3426321099104825e-06,
-9.501376189291477e-05,
...
],
"harmful": [
31527,
5110,
47217,
...
],
"helpful": [
5287,
22736,
3598,
...
]
},
"1": {
"label": 8,
"num_in_dataset": 1,
"time_calc_influence_s": 121.8709237575531,
"influence": [
3.993639438704122e-06,
3.454859779594699e-06,
-3.5805194329441292e-06,
...

Harmful
Harmful is a list of numbers, which are the IDs of the training data samples
ordered by harmfulness. If the influence function is calculated for multiple
test images, the harmfulness is ordered by average harmfullness to the
prediction outcome of the processed test samples.
Helpful
Helpful is a list of numbers, which are the IDs of the training data samples
ordered by helpfulness. If the influence function is calculated for multiple
test images, the helpfulness is ordered by average helpfulness to the
prediction outcome of the processed test samples.
Roadmap
v0.2

makes variable names etc. dataset independent
remove all dataset name checks from the code
ability to disable shell output eg for display_progress from the config
add proper result plotting support
add a dataloader for training on the most influential samples only
add some visualisation of the outcome
add recreation of some graphs of the original paper to verify
implementation

v0.3

make the config a class, so that it can readjust itself, for example
when the r and recursion_depth values can be lowered without big impact
check killing data augmentation!?
in calc_influence_function.py in load_s_test, load_grad_z don't
hard code the filenames

v0.4

integrate myPy type annotations (static type checking)
Use multiprocessing to calc the influence
use r"doc" docstrings like pytorch