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https://github.com/nimarb/pytorch_influence_functions

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.
https://github.com/nimarb/pytorch_influence_functions

deep-learning influence-functions pytorch pytorch-implementation

Last synced: about 17 hours ago
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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.

Host: GitHub
URL: https://github.com/nimarb/pytorch_influence_functions
Owner: nimarb
License: other
Created: 2019-11-30T15:38:20.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2023-10-29T07:10:32.000Z (about 1 year ago)
Last Synced: 2024-10-30T08:18:51.675Z (14 days ago)
Topics: deep-learning, influence-functions, pytorch, pytorch-implementation
Language: Python
Homepage:
Size: 448 KB
Stars: 315
Watchers: 7
Forks: 71
Open Issues: 27
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

        # Influence Functions for PyTorch

This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper:

[Understanding Black-box Predictions via Influence Functions](https://arxiv.org/abs/1703.04730) by Pang Wei Koh and Percy Liang.

The reference implementation can be found here: [link](https://github.com/kohpangwei/influence-release).

- [Why Use Influence Functions?](#why-use-influence-functions)

- [Requirements](#requirements)

- [Installation](#installation)

- [Usage](#usage)

- [Background and Documentation](#background-and-documentation)

  - [config](#config)

    - [Misc parameters](#misc-parameters)

    - [Calculation parameters](#calculation-parameters)

      - [s_test](#stest)

  - [Modes of computation](#modes-of-computation)

  - [Output variables](#output-variables)

    - [Influences](#influences)

    - [Harmful](#harmful)

    - [Helpful](#helpful)

- [Roadmap](#roadmap)

  - [v0.2](#v02)

  - [v0.3](#v03)

  - [v0.4](#v04)

## 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](https://arxiv.org/abs/1703.04730).

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:

```bash

pip3 install --user pytorch-influence-functions

```

Or you can clone the repo and 

* import it as a package after it's in your `PATH`.

* install it using `python setup.py install`

* install it using `python setup.py develop` (if you want to edit the code)

## 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:

```python

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](#config).

## 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](#modes-of-computation).

* `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_z`s 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:

![influences for ship on cifar10-resnet](figs/inf_resnet_basic_110_ship_1.png)

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 for ship on cifar10-densenet](figs/inf_densenet_BC_100_12_ship_1.png)

#### 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:

```python

{

    "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

* [x] makes variable names etc. dataset independent

* [x] 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

* [x] add some visualisation of the outcome

* [ ] add recreation of some graphs of the original paper to verify

  implementation

* [ ] allow custom save name for the influence json

### 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