aiverify-environment-corruptions 2.2.1

Part of AI Verify image corruption toolbox. This package includes algorithms that add environmental corruptions (rain, fog and snow) to images at different severity levels, to test the robustness of machine learning models.

Algorithm - Environment Corruptions

Description

• Robustness plugin with environment corruptions

License

• Licensed under Apache Software License 2.0

Developers

• AI Verify

Installation

Each test algorithm can now be installed via pip and run individually.

pip install aiverify-environment-corruptions

Example Usage

Run the following bash script to execute the plugin

#!/bin/bash

root_path="<PATH_TO_FOLDER>/aiverify/stock-plugins/user_defined_files"

python -m aiverify_environment_corruptions \
  --data_path $root_path/data/raw_fashion_image_10 \
  --model_path $root_path/pipeline/sample_fashion_mnist_sklearn \
  --model_type CLASSIFICATION \
  --ground_truth_path $root_path/data/pickle_pandas_fashion_mnist_annotated_labels_10.sav \
  --ground_truth label \
  --file_name_label file_name \
  --set_seed 10

If the algorithm runs successfully, the results of the test will be saved in an output folder.

Including Specific Corruptions

Usage

By default, all corruption functions are applied. You can use the --corruptions flag to specify which functions to run.

--corruptions [FUNCTION_NAME ...]

Options

• all -> Runs all environment corruption functions (default)
• snow
• fog
• rain

Example: Applying only Snow and Rain corruptions

#!/bin/bash

root_path="<PATH_TO_FOLDER>/aiverify/stock-plugins/user_defined_files"

python -m aiverify_environment_corruptions \
  --data_path $root_path/data/raw_fashion_image_10 \
  --model_path $root_path/pipeline/sample_fashion_mnist_sklearn \
  --model_type CLASSIFICATION \
  --ground_truth_path $root_path/data/pickle_pandas_fashion_mnist_annotated_labels_10.sav \
  --ground_truth label \
  --file_name_label file_name \
  --set_seed 10
  --corruptions snow rain

Customizing Parameters

To fine-tune the corruption parameters, use the Environment Corruption Playground Notebook. This notebook allows you to:

✅ Visualize the effects of different corruption functions.

✅ Experiment with different parameter values.

✅ Apply custom values in the CLI using flags like:

#!/bin/bash

root_path="<PATH_TO_FOLDER>/aiverify/stock-plugins/user_defined_files"

python -m aiverify_environment_corruptions \
  --data_path $root_path/data/raw_fashion_image_10 \
  --model_path $root_path/pipeline/sample_fashion_mnist_sklearn \
  --model_type CLASSIFICATION \
  --ground_truth_path $root_path/data/pickle_pandas_fashion_mnist_annotated_labels_10.sav \
  --ground_truth label \
  --file_name_label file_name \
  --set_seed 10
  --snow_intensity 1.0 2.0 3.0

PyTorch/TensorFlow support

To use a custom PyTorch/TensorFlow model with this plugin, follow the steps below:

1. Install PyTorch/TensorFlow

   Ensure you have installed a PyTorch/TensorFlow version compatible with your model.

2. Specify Model Path

   Use the --model_path command-line argument to specify the path to a folder containing:

   • The model class definition (e.g., model.py).
   • The model weights file (e.g., model_weights.pt).

3. Implement a predict Function

   Your model class must implement a predict function. This function should:

   • Accept a batch of image file paths as input.
   • Return a batch of predictions.

   For reference, see the sample implementation in user_defined_files/pipeline/sample_fashion_mnist_pytorch.

Example Directory Structure

<model_path>/
├── model.py             # Contains the model class definition
├── model_weights.pt     # Contains the trained model weights

Example predict Function (PyTorch)

# model.py
from typing import Iterable

import numpy as np
import torch
from PIL import Image
from torchvision import transforms


class CustomModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        # Define your model architecture here
        ...

    def forward(self, x):
        # Define the forward pass
        ...

    def predict(self, image_paths: Iterable[str]) -> np.ndarray:
        transform = transforms.Compose([
            transforms.Resize((224, 224)),
            ...,
            transforms.ToTensor(),
        ])
        images = [Image.open(path).convert("RGB") for path in image_paths]
        image_tensors = torch.stack([transform(image) for image in images])

        self.eval()
        with torch.no_grad():
            predictions = self(image_tensors).argmax(dim=1).detach().cpu().numpy()
        return predictions

By following these steps, you can integrate your custom PyTorch/TensorFlow model into the corruption plugin.

Develop plugin locally

Execute the below bash script in the project root

#!/bin/bash

# setup virtual environment
python -m venv .venv
source .venv/bin/activate

# install plugin
cd aiverify/stock-plugins/aiverify.stock.image-corruption-toolbox/algorithms/environment_corruptions/
pip install .

python -m aiverify_environment_corruptions --data_path  <data_path> --model_path <model_path> --model_type CLASSIFICATION --ground_truth_path <ground_truth_path> --ground_truth <str> --file_name_label <str> --set_seed <int>

Build Plugin

cd aiverify/stock-plugins/aiverify.stock.image-corruption-toolbox/algorithms/environment_corruptions/
hatch build

Tests

Run the following steps to execute the unit and integration tests inside the tests/ folder

cd aiverify/stock-plugins/aiverify.stock.image-corruption-toolbox/algorithms/environment_corruptions/
pytest .

Run using Docker

In the aiverify root directory, run the below command to build the docker image

docker build -t aiverify-environment-corruptions -f stock-plugins/aiverify.stock.image-corruption-toolbox/algorithms/environment_corruptions/Dockerfile .

Run the below bash script to run the algorithm

#!/bin/bash
docker run \
  -v $(pwd)/stock-plugins/user_defined_files:/input \
  -v $(pwd)/stock-plugins/aiverify.stock.image-corruption-toolbox/algorithms/environment_corruptions/output:/app/aiverify/output \
  aiverify-environment-corruptions \
  --data_path /input/data/raw_fashion_image_10 \
  --model_path /input/pipeline/sample_fashion_mnist_sklearn \
  --model_type CLASSIFICATION \
  --ground_truth_path /input/data/pickle_pandas_fashion_mnist_annotated_labels_10.sav \
  --ground_truth label \
  --file_name_label file_name \
  --set_seed 10

If the algorithm runs successfully, the results of the test will be saved in an output folder in the algorithm directory.

Tests

Run the following steps to execute the unit and integration tests inside the tests/ folder

docker run \
  --entrypoint python3 \
  -w /app/aiverify/stock-plugins/aiverify.stock.image-corruption-toolbox/algorithms/environment_corruptions \
  aiverify-environment-corruptions \
  -m pytest .