Here we provide the implementation of Fully Convolutional Data Description (FCDD), an explainable approach to deep one-class classification. The implementation is based on PyTorch 1.9.1 and Python 3.8. The code is tested on Linux only. There is a windows branch where we have fixed some errors to make the code Windows compatible. However, there are no guarantees that it will work as expected.
Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away. Because this transformation is highly non-linear, finding interpretations poses a significant challenge. In this paper we present an explainable deep one-class classification method, Fully Convolutional Data Description (FCDD), where the mapped samples are themselves also an explanation heatmap. FCDD yields competitive detection performance and provides reasonable explanations on common anomaly detection benchmarks with CIFAR-10 and ImageNet. On MVTec-AD, a recent manufacturing dataset offering ground-truth anomaly maps, FCDD sets a new state of the art in the unsupervised setting. Our method can incorporate ground-truth anomaly maps during training and using even a few of these (โผ5) improves performance significantly. Finally, using FCDDโs explanations we demonstrate the vulnerability of deep one-class classification models to spurious image features such as image watermarks. The following image shows some of the FCDD explanation heatmaps for test samples of MVTec-AD:
A PDF of our ICLR 2021 paper is available at: https://openreview.net/forum?id=A5VV3UyIQz.
If you use our work, please also cite the paper:
@inproceedings{
liznerski2021explainable,
title={Explainable Deep One-Class Classification},
author={Philipp Liznerski and Lukas Ruff and Robert A. Vandermeulen and Billy Joe Franks and Marius Kloft and Klaus-Robert M{\"u}ller},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=A5VV3UyIQz}
}
It is recommended to use a virtual environment to install FCDD.
Assuming you're in the python directory, install FCDD via pip:
virtualenv -p python3 venv
source venv/bin/activate
pip install .
Log data Log data -- i.e. heatmaps, metrics, snapshots, etc. -- is stored on the disc in a certain log directory.
The default log directory is located at ../../data/results/fcdd_TIMESTAMP.
Thus, log data is being saved in the data directory in the root folder of this repository if the code is run from python/fcdd.
The same holds for the data directory, where the datasets are downloaded to.
You can change both, the log directory and the data directory, by altering the respective arguments (logdir and datadir).
Virtual Environment If you have used a virtual environment to install FCDD, make sure to activate it.
Train Scripts We recommend training FCDD by starting one of the runners in python/fcdd/runners from the python/fcdd directory.
They train several (randomly initialized) separate FCDD models for each class, where in each the respective class is considered nominal.
With EMNIST OE:
python runners/run_fmnist.py --noise-mode emnist
With CIFAR-100 OE:
python runners/run_fmnist.py
For full OE:
python runners/run_cifar10.py
For limited OE, change the respective parameter, e.g. for using 8 OE samples:
python runners/run_cifar10.py --oe-limit 8
For full OE:
python runners/run_imagenet.py
Please note that you have to manually download ImageNet1k and ImageNet22k and place them in the correct folders.
Let dsdir be your specified dataset directory (per default ../../data/datasets/).
ImageNet1k needs to be in dsdir/imagenet/, containing the devkit, train, and val split in form of a tar file each -- with names ILSVRC2012_devkit_t12.tar.gz, ILSVRC2012_img_train.tar, and ILSVRC2012_img_val.tar.
These are the default names expected by the PyTorch loaders. You can download ImageNet1k on the official website: http://image-net.org/download. Note that you have to register beforehand.
ImageNet22k needs to be in dsdir/imagenet22k/fall11_whole_extracted/, containing all the extracted class directories with pictures, e.g. the folder n12267677 having pictures of acorns.
Decompressing the downloaded archive should automatically yield this structure.
ImageNet22k, i.e. the full release fall 11, can also be downloaded on the official website: http://image-net.org/download.
Using confetti noise:
python runners/run_mvtec.py
Using a semi-supervised setup with one true anomaly per defection:
python runners/run_mvtec.py --supervise-mode noise --noise-mode mvtec_gt --oe-limit 1
python runners/run_pascalvoc.py
Let again dsdir be your specified dataset directory (per default ../../data/datasets/).
Place your training data in dsdir/custom/train/classX/ and your test data in dsdir/custom/test/classX/, with classX being one of the class folders (they can have arbitrary names, but need to be consistent for training and testing).
For a one-vs-rest setup (as used for Cifar-10, etc.), place the corresponding images directly in the class folders and run:
python runners/run_custom.py -ovr
Otherwise, each class requires a separate set of nominal and anomalous test samples.
Place the corresponding images in dsdir/custom/test/classX/normal/, dsdir/custom/test/classX/anomalous/, dsdir/custom/train/classX/normal/ and run:
python runners/run_custom.py
If you have some training anomalies in dsdir/custom/train/classX/anomalous/, you can use them in a semi-supervised setting with
python runners/run_custom.py --supervise-mode other
In general, you can adapt most training parameters using the program's arguments (see python runners/run_custom.py --help).
Per default, it chooses some parameters that are assumed general-purpose, such as the imagenet-pre-trained CNN for 224x224 images and imagenet22k outlier exposure.
To, for example, use confetti noise instead of outlier exposure, set --supervise-mode to malformed_normal and --noise-mode to confetti.
To run the baseline experiments -- i.e. HSC with heatmaps based on gradients and AE with reconstruction error heatmaps -- little has to be done. Only a few parameters have to be adjusted, most importantly the objective and the network. For instance, in the case of Cifar-10 and HSC:
python runners/run_cifar10.py --objective hsc -n CNN32 --blur-heatmaps
Similarily for the AE baseline:
python runners/run_cifar10.py --objective ae -n AE32 --supervise-mode unsupervised --blur-heatmaps
Alternatively, one can use the add_exp_to_base script:
The script is located at python/fcdd/runners/add_exp_to_base.py.
It takes a log directory of a previous experiment as input and automatically changes the arguments to
run baseline experiments with the same basic configuration.
This script has also the advantage of automatically aggregating heatmaps of the given old experiment and baseline experiments to create combined pictures as seen in the paper.
For instance, let ../../data/results/fcdd_20200801120000_my_awesome_experiment be a log directory of an old experiment.
Then, to produce both baselines, run:
python runners/add_exp_to_base.py ../../data/results/fcdd_20200801120000_my_awesome_experiment
This will create log directories for each baseline by appending a suffix to the given one, i.e. in this case
../../data/results/fcdd_20200801120000_my_awesome_experiment_HSC and ../../data/results/fcdd_20200801120000_my_awesome_experiment_AE.
Note that the previous experiment needs to be an FCDD experiment and not a baseline itself.
Also, note that the baseline experiments use the same data directory parameter as found in the old experiment configuration file.
That is, if add_exp_to_base.py is not started from the same directory (e.g. python/fcdd), it will not find the datasets
and attempt to download it to another one.
A runner saves the achieved scores, metrics, plots, snapshots, and heatmaps in a given log directory. Each log directory contains a separate subdirectory for each class that is trained to be nominal. These subdirectories are named "normal_x", where x is the class number. The class subdirectories again contain a subdirectory for each random seed. These are named "it_x" for x being the iteration number (random seed). Inside the seed subdirectories all actual log data can be found. Additionally, summarized plots will be created for the class subdirectories and the root log directory. For instance, a plot containing ROC curves for each class (averaged over all seeds) can be found in the root log directory.
Visualization for 2 classes and 2 random seeds:
./log_directory
./log_directory/normal_0
./log_directory/normal_0/it_0
./log_directory/normal_0/it_1
./log_directory/normal_1
./log_directory/normal_1/it_0
./log_directory/normal_1/it_1
...
Note that the leaf nodes, i.e. the iteration subdirectories, contain completely separate training results and have no impact on each other.
The actual log data consists of:
- config.txt: A file containing all the arguments of the training.
- ds_preview.png: A preview of the dataset, i.e. some random nominal and anomalous samples from the training set. Includes augmentation and data preprocessing. Also shows corresponding ground-truth maps, if such are available.
- err.pdf: A plot of the loss over time.
- err_anomalous.pdf: A plot of the loss for anomalous samples only.
- err_normal.pdf: A plot of the loss for nominal samples only.
- heatmaps_paper_x_lbly.png: An image of test heatmaps, test inputs, and ground-truth maps. One image for each x-y combination. x is the normalization used, either local (each heatmap is normalized w.r.t. itself only) or semi_global (each heatmap is normalized w.r.t. to all heatmaps in the image). y is the label, i.e. either 1 or 0 (per default 1 is for anomalies).
- heatmaps_global.png: An image of the first test heatmaps and inputs found in the dataset. The first row shows nominal samples, the second-row anomalous samples. The third row shows the ten most nominal rated nominal samples on the left and the ten most anomalous rated nominal samples on the right. The fourth row shows the ten most nominal rated anomalies on the left and the ten most anomalous rated anomalies on the right. Note that changing the nominal label to 1 flips this.
- train_heatmaps_global.png: Like above, but for training samples.
- history.json: A file containing metrics in text form.
- log.txt: A file containing some logged text lines, like the average AUC value achieved and the duration of the training.
- print.log: A file that contains all text that has been printed on the console using the Logger.
- roc.json: ROC values saved as text (not very readable for humans).
- roc_curve.pdf: ROC for detection performance.
- gtmap_roc_curve.pdf: ROC for explanation performance. Only for MVTec-AD (or datasets with ground-truth maps).
- snapshot.pt: Snapshot of the training state and model parameters.
- src.tar.gz: Snapshot of the complete source code at the moment of the training start time.
- tims: A directory containing raw tensors heatmaps (not readable for humans).
In general, the FCDD implementation splits in five packages: datasets, models, runners, training, and util.
dataset contains an implementation of the base class for AD datasets, actual torchvision-style dataset implementations, and artificial anomaly generation implementations.
models contains an implementation of the network base class -- including receptive field upsampling and gradient-based heatmap computation -- and implementations of all network architectures.
runners contains scripts for starting training runs, processing program arguments, and preparing all training parameters -- like creating an optimizer and network instances.
training contains an implementation for actual training and evaluation of the network.
util contains all the rest, i.e. e.g. a logger that handles all I/O interactions.
In the following we give a brief tutorial on how to modify the FCDD implementation for specific requirements.
Add a new Network Architecture
-
Create a new python script in the
modelspackage. Implement a PyTorch module that inherits thefcdd.models.bases.BaseNetor thefcdd.models.bases.ReceptiveNetclass. The latter takes track of the receptive field. Read the documentation for further details. -
Import the new network class in
fcdd.models.__init__to automatically add the network name to the available ones. Make sure that the class name is unique.
Add a new Dataset
First, you should check whether it's possible to use the custom dataset implementation. If not:
-
Create a new python script in the
datasetspackage. Implement a dataset that inherits thefcdd.datasets.bases.TorchvisionDatasetclass. Your implementation needs to process all parameters of thefcdd.datasets.bases.load_datasetfunction in its initialization. You can use the preproc parameter to switch between different data preprocessing pipelines (augmentation, etc). In the end, your implementation needs to have at least all attributes defined infcdd.datasets.bases.BaseADDatasetclass. Most importantly, the _train_set attribute and the _test_set attribute containing the corresponding torchvision-style datasets. Have a look at the already available implementations. -
Add a name for your dataset to the
fcdd.datasets.__init__.DS_CHOICESvariable. Add your dataset to the "switch-case" in thefcdd.datasets.__init__.load_datasetfunction. Add the number of available class for your dataset to thefcdd.datasets.__init__.no_classesfunction and add the class names tofcdd.datasets.__init__.str_labels.
Add a new Training Parameter
-
Add an argument to the
fcdd.runners.argparse_configs.DefaultConfigclass. -
Add this argument as a parameter to the
fcdd.training.setup.trainer_setupfunction. Also, make it return the parameter itself, or some other thing that has been created based on your new argument (e.g. an instance of some network, optimizer, etc.). -
Add whatever new is being returned by the
fcdd.training.setup.trainer_setupfunction as a parameter to thefcdd.training.super_trainer.SuperTrainerclass. -
Implement your requested novel behavior based on the new parameter in the SuperTrainer or one of the actual model trainers (FCDDTrainer, HSCTrainer, AETrainer), depending on the objective.
Add a new Runner
-
Create a new python script in the
runnerspackage. Implement a configuration that inheritsfcdd.runners.argparse_configs.DefaultConfig. You need to implement the call method for it, where you can add new arguments to the given parser or change the default values. -
Create an instance of one of the runners, e.g.
fcdd.runners.bases.ClassesRunnerorfcdd.runners.bases.SeedsRunner, and use your configuration implementation for that. For both, the SeedsRunner and ClassesRunner, you need to additionally add an argument in your configuration named it that determines the number of different random seeds. Invoke the instance's run method.
Add a new Objective / Trainer
-
Add a string identification for your new objective to the
fcdd.training.setup.OBJECTIVESvariable. -
Create a new trainer script in the
trainingpackage. Implement a trainer inheritingfcdd.training.bases.BaseADTrainer. At least implement the loss and snapshot method. -
Add the creation of an instance for your new trainer to the "switch-case" in the initialization of
fcdd.training.super_trainer.SuperTrainer.
Add a new Type of Synthetic Anomaly
-
Implement your new synthetic anomaly in
fcdd.datasets.noise. -
Add a string identification for you new function to the
fcdd.datasets.noise_modes.MODESvariable. -
Add an invocation of your new function to the "switch-case" in
fcdd.datasets.noise_modes.generate_noise.
Add a new Outlier Exposure Dataset
- Create a new python script in the
datasets/outlier_exposurepackage. Implement a torchvision-style dataset that expects at least the following parameters: size, root, and limit_var. Also, you need to at least implement a get_item method and data_loader method. Cf. one of the existing implementations of Outlier Exposure datasets, e.g.fcdd.datasets.outlier_exposure.emnist.
If you find any bugs, have questions, need help modifying FCDD, or want to get in touch in general, feel free to write us an email!
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