README.md

DeepInspect: Testing DNN Image Classifier for Confusion & Bias Errors (ICSE'20)

See the ICSE'20 paper Testing DNN Image Classifier for Confusion & Bias Errors for more details.

There are two directories: (1) Reproduce paper results and (2) DeepInspect. DeepInspect is the implementation of the tool that analyzes the target model and dataset under test and outputs potential confusion and bias errors. The scripts in Reproduce paper results directory evaluate DeepInspect, i.e., they analyze the tool's output and report precision/recall, false positive and true positive of our predictions.

1. Reproduce paper results

Prerequisite

Python 3, numpy, scipy, matplotlib, sklearn, pandas

Reproducing results in Table 3 and Figure 6 in paper:

cd reproduce
python3 confusion_bugs.py

Reproducing results in Table 4 and Figure 10 in paper:

cd reproduce
python3 bias_bugs_estimate_ab_and_acd.py
python3 bias_bugs_generate_results.py

2. DeepInspect

Generating neuron coverage and computing probability matrix for large dataset may take hours. It is recommended to run the CIFAR-10 script first to get a general idea of DeepInspect, set up the environment and get familiar with the whole workflow.

Note1: neuron coverage for each dataset are only required to compute once, so you can comment out the function call (with name like get_coverage) in the main function in each script. The neuron coverage is saved into a pickle file with name like "globalcoverage*.pickle". Since it is cumulatively saved to this pickle file, if you need to re-call the get_coverage function again, please remove this pickle file before calling it again.

Note2: we only use 90% of test data's predictions to predict confusion and bias bugs, that is why we use sample_10 as parameter in scripts to intentionally keep 10% of data for other use in future. You can always set sample_10 to empty list to leverage all the test data's predictions.

2.1. Inspect pre-trained COCO model (model from paper)

Prerequisite

Python 2.7, numpy-1.16, tqdm-4.41, torch-1.3.1, scikit-learn-0.20.4, matplotlib-2.2.4

Download COCO 2014 dataset

COCO dataset structure

cocodataset  
├── annotations  
│   ├── instances_train2014.json             
│   ├── instances_val2014.json  
├── train2014                    
│   ├── COCO_train2014_000000291797.jpg      
│   ├── ...     
├── val2014                   
│   ├── COCO_val2014_000000581929.jpg               
│   ├── ...                    

Run deepinspect on pre-trained COCO model

cd deepinspect/coco/
python2 coco_deepinspect.py

Copy the generated csv files to override the files in data/coco/ folder and run the code in Section 1.

2.2. Inspect pre-trained COCO gender(COCO dataset with man/woman label) model (model from paper)

Prerequisite

Python 2.7, numpy-1.16, tqdm-4.41, torch-1.3.1, scikit-learn-0.20.4, matplotlib-2.2.4

COCO gender dataset structure

Same as COCO dataset structure

Run deepinspect on pre-trained COCO gender model

cd deepinspect/coco_gender/
python2 coco_gender_deepinspect.py

Copy the generated csv files to override the files in data/coco_gender/ folder and run the code in Section 1.

2.3. Inspect robust CIFAR-10 models (models from paper1, paper2)

Prerequisite

Python 2.7, numpy-1.16, tqdm-4.41, torch-1.3.1, torchsummary

Run deepinspect on three robust training CIFAR-10 models

cd deepinspect/robust_cifar10/
python2 cifar10_small_deepinspect.py
python2 cifar10_large_deepinspect.py
python2 cifar10_resnet_deepinspect.py

Copy the generated csv files to override the files in data/robust_cifar10_{small/large/resnet/}/ folder and run the code in Section 1.

2.4. Inspect CIFAR-100 model (model from repo)

Prerequisite

Python 2.7, numpy-1.16, tqdm-4.41, torch-1.3.1, jupyter

Run deepinspect on CIFAR-100 model

cd deepinspect/cifar100/
python2 cifar100_deepinspect.py

Copy the generated csv files to override the files in data/cifar100/ folder and run the code in Section 1.

2.5. Inspect pre-trained ImageNet model for ILSVRC2012 dataset(model from torchvision)

Prerequisite

Python 2.7, numpy-1.16, tqdm-4.41, torch, opencv-python, torchvision, cPickle, Pillow

Run deepinspect on ImageNet model

cd deepinspect/imagenet/
python2 imagenet_coverage.py
python2 Imagenet_deepinspect.py

Copy the generated csv files to override the files in data/imagenet/ folder and run the code in Section 1.

2.6. Inspect pre-trained baseline_crf ResNet model for imSitu dataset(model from paper)

Prerequisite

Python 2.7, numpy-1.16, tqdm-4.41, torch **0.3.1**, opencv-python, torchvision **0.2**, pickle

Run deepinspect on imSitu model

Install the original models from imSitu
Copy folder baseline_models to deepinspect/imsitu/ Download dataset to folder resized_256/.

cd deepinspect/imsitu/
python2 baseline_crf_deepinspect.py resized_256/adjusting_1.jpg # test environment, this should output predictions without any error.

python2 baseline_crf_deepinspect.py

Copy the generated csv files to override the files in data/imsitu/ folder and run the code in Section 1.