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The 3-Tier API Architecture

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@codewithdark-git codewithdark-git released this 07 Dec 16:53
· 26 commits to main since this release
v2.0.0
36e35b8

Release Notes v2.0.0

🚀 Major Release: The 3-Tier API Architecture

Version 2.0.0 represents a complete paradigm shift for torchvision-customizer. We have moved away from the monolithic CustomCNN class to a flexible, component-based 3-tier architecture that offers unprecedented control over model design.

✨ Key Features

1. Tier 1: Component Registry 🗂️

The new registry system serves as the single source of truth for all building blocks.

  • Unified Discovery: registry.list('block') to find all available components.
  • Easy Access: registry.get('residual') returns the class directly.
  • Extensible: Easily register your own components with @registry.register.

2. Tier 2: Architecture Recipes 📜

A declarative way to define models using human-readable strings. Perfect for config-driven experiments.

  • String Definitions: "residual(64) x 2 | downsample"
  • Serialization: Recipes can be saved/loaded from YAML/JSON.
  • Safety: Automated parsing ensures valid arguments.

3. Tier 3: Model Composer 🎼

A fluent, pythonic API for building models programmatically.

  • Operator Overloading: Use >> for sequential connection, | for branching, and * for repetition.
  • Context Awareness: Stage and Head automatically infer input channels.
  • Mix & Match: Combine different patterns (e.g., 'residual+se') in a single stage.

4. Parametric Templates 🏗️

We have re-implemented standard architectures from scratch to be fully parametric and customizable. No pre-trained weights, just pure architectural flexibility.

  • ResNet: 18, 34, 50, 101, 152 (Basic & Bottleneck)
  • VGG: 11, 13, 16, 19
  • MobileNet: V1, V2, V3 (Small/Large)
  • DenseNet: 121, 169, 201, 264
  • EfficientNet: B0-B7

💥 Breaking Changes

  • Removed CustomCNN: The legacy CustomCNN class and the old torchvision_customizer.models module have been removed. Please migrate to Recipe or Compose.
  • Python Version: Minimum Python version lowered to 3.9 (previously 3.13) for better compatibility.

🛠️ Improvements

  • Model Introspection: Added model.explain() for a beautiful, ASCII-art summary of your custom architecture.
  • Windows Support: Fixed character encoding issues in model visualization.
  • Strict Typing: Comprehensive type hints across the entire codebase.
  • Documentation: Completely rewritten documentation to reflect the new API tiers.

📦 Quick Example

Before (v1.x):

model = CustomCNN(layers=50, architecture='resnet') # Limited customization

After (v2.0):

# Declarative
recipe = Recipe(stem="conv(64)", stages=["residual(64) x 3"], head="linear(10)")
model = build_recipe(recipe)

# OR Fluent
model = Stem(64) >> Stage(64, blocks=3, pattern='residual') >> Head(10)

🔗 Contributors

Contributors

codewithdark-git
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