segmentation-robustness-framework 0.2.1

Segmentation Robustness Framework - a powerful toolkit for evaluating the robustness of semantic segmentation models against adversarial attacks.

Segmentation Robustness Framework Documentation

Welcome to the comprehensive documentation for the Segmentation Robustness Framework - a powerful toolkit for evaluating the robustness of semantic segmentation models against adversarial attacks.

🚀 Quick Start

Get started in minutes with our comprehensive examples:

from segmentation_robustness_framework.engine.pipeline import SegmentationRobustnessPipeline
from segmentation_robustness_framework.utils.metrics import MetricsCollection
from segmentation_robustness_framework.attacks import FGSM
from segmentation_robustness_framework.datasets import VOCSegmentation
from segmentation_robustness_framework.loaders.models.universal_loader import UniversalModelLoader
from segmentation_robustness_framework.utils import image_preprocessing
import torch

# Load model with universal loader
loader = UniversalModelLoader()
model = loader.load_model(
    model_type="torchvision",
    model_config={"name": "deeplabv3_resnet50", "num_classes": 21}
)

# Set device and move model to it (IMPORTANT: Do this before creating attacks!)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

# Setup dataset with preprocessing
preprocess, target_preprocess = image_preprocessing.get_preprocessing_fn(
    [512, 512], dataset_name="voc"
)
dataset = VOCSegmentation(
    split="val", 
    root="path/to/existing/VOCdevkit/VOC2012/",
    transform=preprocess,
    target_transform=target_preprocess
)

# Setup attack and metrics (attacks will use the same device as the model)
attack = FGSM(model, eps=2/255)

# Setup metrics
metrics_collection = MetricsCollection(num_classes=21)
metrics = [metrics_collection.mean_iou, metrics_collection.pixel_accuracy]

# Create and run pipeline
pipeline = SegmentationRobustnessPipeline(
    model=model,
    dataset=dataset,
    attacks=[attack],
    metrics=metrics,
    batch_size=4,
    device=device
)

results = pipeline.run(save=True, show=True)
pipeline.print_summary()

📚 Documentation Structure

🎯 Getting Started

• Installation Guide - Setup and installation instructions
• Quick Start Guide - Your first evaluation in 5 minutes
• Framework Concepts - Core framework concepts and architecture

📖 User Guides

• User Guide - Complete guide for using the framework
• Custom Components - Adding your own datasets, models, and attacks

🎓 Learning Path

1. Start Here: Installation Guide → Quick Start
2. Basic Usage: User Guide
3. Custom Components: Custom Components

🎯 Key Features

🔬 Comprehensive Evaluation

• Multiple Attacks: FGSM, PGD, RFGSM, TPGD, and custom attacks
• Rich Metrics: IoU, pixel accuracy, precision, recall, dice score
• Flexible Output: JSON, CSV
• Batch Processing: Efficient evaluation of large datasets

🏗️ Universal Model Support

• Torchvision Models: DeepLab, FCN, LRASPP architectures
• SMP Models: UNet, LinkNet, PSPNet, and more
• HuggingFace Models: Transformers-based segmentation models
• Custom Models: Easy integration with your own models

📊 Built-in Datasets

• VOC: Pascal VOC 2012 (21 classes)
• ADE20K: Scene parsing dataset (150 classes)
• Cityscapes: Urban scene understanding (35 classes)
  • Note: Cityscapes cannot be downloaded automatically due to required authorization. You must register and download it manually from https://www.cityscapes-dataset.com/.
• Stanford Background: Natural scene dataset (9 classes)

⚡ Easy Integration

• Registry System: Automatic discovery of custom components
• Adapter Pattern: Standardized model interfaces
• Preprocessing Pipeline: Automatic data normalization and conversion
• Error Handling: Comprehensive error messages and debugging

🚀 Quick Examples

Basic Evaluation

# Load components
from segmentation_robustness_framework import *

# Setup pipeline
pipeline = SegmentationRobustnessPipeline(
    model=load_model(),
    dataset=load_dataset(),
    attacks=[FGSM(model, eps=2/255)],
    metrics=[mean_iou, pixel_accuracy]
)

# Run evaluation
results = pipeline.run()

Custom Dataset

@register_dataset("my_dataset")
class MyDataset(Dataset):
    def __init__(self, root, transform=None):
        self.num_classes = 5
        # ... implementation
    
    def __getitem__(self, idx):
        return image, mask

Custom Attack

@register_attack("my_attack")
class MyAttack(AdversarialAttack):
    def apply(self, images, labels):
        # Implement attack logic
        return adversarial_images

Custom Model Example

# Register and use a custom adapter for your model
from segmentation_robustness_framework.adapters import CustomAdapter
from segmentation_robustness_framework.adapters.registry import register_adapter

@register_adapter("my_custom_adapter")
class MyCustomAdapter(CustomAdapter):
    pass  # Optionally override methods if your model's output format is different

# Use your custom adapter with the universal loader
loader = UniversalModelLoader()
model = loader.load_model(
    model_type="my_custom_adapter",
    model_config={
        "model_class": "path.to.MyCustomModel",
        "model_args": [21],
        "model_kwargs": {"pretrained": True}
    }
)

# Set device and create attacks
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
attacks = [FGSM(model, eps=2/255)]

pipeline = SegmentationRobustnessPipeline(
    model=model, dataset=dataset, attacks=attacks, metrics=metrics, device=device
)
results = pipeline.run()

🛠️ Installation

# Install from PyPI
pip install segmentation-robustness-framework

# Or install from source
git clone https://github.com/your-repo/segmentation-robustness-framework
cd segmentation-robustness-framework
pip install -e .

📖 Framework Architecture

The framework follows a modular architecture with clear separation of concerns:

🤝 Contributing

We welcome contributions! Please see our contributing guidelines for details on:

• Code style and standards
• Testing requirements
• Documentation guidelines
• Pull request process

📞 Support

• Documentation: Browse the guides above
• Issues: Report bugs and request features on GitHub
• Discussions: Join our community discussions

📄 License

This project is licensed under the MIT License - see the LICENSE file in the project root for details.

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Ready to evaluate your segmentation models? 🚀

Start with our Quick Start Guide and have your first evaluation running in minutes!