robust-deep-learning 0.0.1

The Robust Deep Learning Library

THIS IS A EARLY DRAFT VERSION. PLEASE, DO NOT USE IT.

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The Robust Deep Learning Library

Train your model from scratch or fine-tune a pretraneind model to produce deep neural networks with improved robustness and uncertainty estimations.

• Model Independent: Use models from timm library or whatever you want.
• Hyperparameter-Free: Do not train many times! Use hyperparameter-free "You Only Train Once" (YOTO) losses.
• Large-Scale Models and Datasets: Train on ImageNet or any other large-scale dataset.
• Media Independent: Most cases work for anytype of media (e.g., image, text, audio, and others).
• Standard Interface: Use the same API to train models with improved robustness using diferent losses.
• State-of-the-art: SOTA results for Out-of-Distribution Detection and Uncertainty Estimation.
• Efficient Inferences: The trained models are as efficient as the ones trained using the cross-entropy losss.
• No Need for Additional Data: The losses used in this library do not requere collecting additional data.
• Temperature Calibration: Calculate the Uncertainty Estimation and update the temperature of last layer.
• Threshold Computation: Compute the threshold for deciding regarding out-of-distribution examples.
• Scores Computation: Compute the scores opting from a set of many diferent types available.
• Detect Out-of-Distrbution: Detect out-of-distribution examples using the computed scores.
• Scalability: Our entropic losses perform better and better as the size of the dataset and models increase.

Some code reused from deep_Mahalanobis_detector and odin-pytorch.

Installation

pip install robust-deep-learning

Usage

# Import the robust deep learning library
import robust-deep-learning as rdl

########################################################################################
########################################################################################
# Training or Fine-tuning the Robust Deep Neural Network
########################################################################################
########################################################################################


##############
# Custom model
##############

# Create from a model definition file. 
# For example, you imported a class "Model" froma model definition file
model = Model()
# You may load a checkpoint now if you intent to fine-tuning rather training from scratch
# To fine-tuning, load a checkpoint from a pretrained model 

# Chance the classifier last layer of your model.
# if your classification last layer is called "classifier".
# For example, you have something like
# "model.classifier = nn.Linear(num_features, num_classes)",
# then add the following line 
model.classifier = rdl.DisMaxLossFirstPart(num_features, num_classes)

# Replace the Cross Entropy Loss
criterion = rdl.DisMaxLossSecondPart(model.classifier, add_on="FPR")

############
# timm model
############

# For use a model from timm lib, use "create_model" functionality
# It is possible to start for a pretrained model and fine-tune using the new loss
model = timm.create_model('resnet18', pretrained=False)

# If you are using timm models and the last layer is called "fc", do the following instead:
model.fc = rdl.DisMaxLossFirstPart(model.get_classifier().in_features, num_classes)

# If you are using timm models and the last layer is called "fc", do the following instead:
criterion = rdl.DisMaxLossSecondPart(model.fc, add_on="FPR")

###############
# training loop
###############

for epoch in epochs:

    # training loop
    for inputs, targets in in_data_train_loader:

        # In the training loop, add the line below for preprocessing before forwarding.
        inputs, targets = criterion.preprocess(inputs, targets) 

        # The three bellow lines are usually find in the training loop!
        # These lines should not be chanced!
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()

########################################################################################
########################################################################################
# Uncertainty Estimation
########################################################################################
########################################################################################

#############
# calibrating
#############

# In the training loop, add the line of code below for preprocessing before forwarding.
results = rdl.get_outputs_labels_and_metrics(model, in_data_val_loader)

# In the training loop, add the line of code below for preprocessing before forwarding.
probabilities = torch.nn.Softmax(dim=1)(results["outputs"])
print(probabilities)
print(results["acc"], results["ece"], results["nll"])

# In the training loop, add the line of code below for preprocessing before forwarding.
rdl.calibrate_temperature(model.classifier, model, in_data_val_loader, optimize="ECE")

# print the new temperature after calibration
print(model.classifier.temperature)

######################
# verifing calibration
######################

# In the training loop, add the line of code below for preprocessing before forwarding.
results = rdl.get_outputs_labels_and_metrics(model, in_data_val_loader)

# In the training loop, add the line of code below for preprocessing before forwarding.
probabilities = torch.nn.Softmax(dim=1)(results["outputs"])
print(probabilities)
print(results["acc"], results["ece"],= results["nll"])

#######################################################################################
#######################################################################################
# Out-of-Distribution Detection
#######################################################################################
#######################################################################################

########################
# estimation performance
########################

# Define a score type. Typically the best for the loss you are using.
score_type = "MMLES"

# Evaluate the out-of-distrbution detection performance. Do all scores!
ood_metrics = rdl.get_ood_metrics(model, in_data_val_loader, out_data_loader, score_type, fpr=0.05)

# In the training loop, add the line of code below for preprocessing before forwarding.
results = rdl.get_outputs_labels_and_metrics(model, in_data_val_loader)
in_data_scores = rdl.get_scores(results["outputs"], score_type)

# In the training loop, add the line of code below for preprocessing before forwarding.
results = rdl.get_outputs_labels_and_metrics(model, out_data_loader)
out_data_scores = rdl.get_scores(results["outputs"], score_type)

#results = rdl.get_ood_detection_metrics(model, score, test_loader, out_test_loader)
ood_metrics = rdl.get_ood_metrics_from_scores(in_data_scores, out_data_scores, fpr=0.05)

###########
# detecting
###########

# In the training loop, add the line of code below for preprocessing before forwarding.
#thresholds = rdl.get_thresholds(results["outputs"], score_types="MMLES")
thresholds = rdl.get_thresholds(model, in_data_val_loader, score_type)

# In the training loop, add the line of code below for preprocessing before forwarding.
#thresholds = rdl.get_thresholds(results["outputs"], score_types="MMLES")
thresholds = rdl.get_thresholds_from_scores(in_data_scores) # guarder 

# scores and threshold calculated above should have used the same score_types
# the 5 means 10 percentile! See other values...
# next(iter(data_loader))
ood_detections = rdl.get_ood_detections(model, inputs, thresholds, fpr="0.05")

Examples

Please, move to the data directory and run all the prepare data bash scripts:

# Download and prepare out-of-distrbution data for CIFAR10 and CIFAR100 datasets.
./prepare-cifar.sh
# Download and prepare out-of-distrbution data for ImageNet.
./prepare-imagenet.sh

For an example training on CIFAR using a model definition file, please see:

# Run the CIFAR dataset example.
python -m robust_deep_learning.examples.cifar

For an example training on ImageNet using timm library model, please see:

# Run the ImageNet dataset example.
python -m robust_deep_learning.examples.imagenet

Losses and Scores

The following losses are implemented:

• Isotropy Maximization Loss (arXiv)
• Isotropy Maximization Loss (conference version)
• Isotropy Maximization Loss (journal version)
• Enhanced Isotropy Maximization Loss (arXiv)
• Distinction Maximization Loss (arXiv)

The following scores are implemented:

• Features
• Features
• Features
• Features
• Features
• Features

Questions and Answers

• Should I use augumentation? Use models from timm library or whatever you want.
• Should I use inference-based approaches? Use hyperparameter-free "You Only Train Once" (YOTO) losses.

Results

Dataset: ImageNet

Model	Loss	Score	Classification (ACC)	OOD Detection [ImageNet-O] (AUROC)
ResNet18	SoftMax (Baseline)	MPS	69.9	52.4
ResNet18	DisMax	MMLES	69.6	75.8

Experiments

Train and evaluate the classification, uncertainty estimation, and out-of-distribution detection performances:

./run_cifar100_densenetbc100.sh*
./run_cifar100_resnet34.sh*
./run_cifar100_wideresnet2810.sh*
./run_cifar10_densenetbc100.sh*
./run_cifar10_resnet34.sh*
./run_cifar10_wideresnet2810.sh*
./run_imagenet1k_resnet18.sh*

Print the experiment results:

./analize.sh

Citing

BibTeX

Please, cite our papers if you use our losses in your work:

@article{macedo2022distinction,
    title={Distinction Maximization Loss:
    Efficiently Improving Out-of-Distribution Detection
    and Uncertainty Estimation by Replacing the Loss and Calibrating}, 
    author={David Macêdo and Cleber Zanchettin and Teresa Ludermir},
    year={2022},
    eprint={2205.05874},
    archivePrefix={arXiv},
    primaryClass={cs.LG}}