Microsoft/MMdnn

Features

• Model Intermediate Representations - Uses a standardized intermediate representation to decouple model structures from specific deep learning framework implementations.
• Model Migrators - Migrates pre-trained neural network architectures and weights between different frameworks like TensorFlow, PyTorch, and Keras.
• Architecture Code Generation - Generates native Python reconstruction scripts to rebuild and fine-tune neural network structures in target frameworks.
• Cross-Framework Model Conversion - Translates neural network architectures and weights between different machine learning frameworks.
• Deep Learning Model Converters - Translates neural network architectures and weights between different frameworks like TensorFlow, PyTorch, and Keras.
• Model-to-Code Generators - Generates executable native Python source code from model representations to enable rebuilding and fine-tuning in target frameworks.
• Model Binary Assembly - Assembles binary model files by combining architecture definitions and weight data for target environments.
• Model Reconstruction Tools - Provides utilities to assemble functional deployable models by combining generated source code with corresponding weight files.
• Native Framework Reconstructions - Synthesizes original framework-specific model binaries by combining generated architecture code and binary weight data.
• Mobile Model Deployment - Converts deep learning models into optimized formats like CoreML for execution on mobile devices.
• Mobile Model Format Converters - Transforms deep learning models into mobile-compatible formats like CoreML and TensorFlow Lite.
• IR to Model Code Translation - Translates standardized intermediate representations into executable framework-specific Python code.
• Model IR Translations - Translates CoreML checkpoint files into a standardized intermediate representation of architecture and weight files.
• Intermediate Representations - Parses model files into a framework-neutral intermediate representation to facilitate visualization and conversion.
• Model Architecture Code Generators - Generates native framework architecture and weight files from intermediate model representations via Python code snippets.
• CoreML Model Generation - Transforms neural network architectures and weights from other frameworks into the CoreML format for Apple platform deployment.
• Inference Model Deployment - Converts models from high-level frameworks into optimized compute graphs for hardware execution and deployment.
• Inference Verification - Runs image inference tests on converted models to validate that the output results match the original model.
• Computational Graph Visualizers - Inspects model meta files and graph structures to identify node names and network topology.
• Model Architecture Visualizations - Renders the structural layout and layer connectivity of deep learning models graphically for inspection.
• Model Checkpoints - Generates native framework checkpoint files from converted code and weights for direct model loading.
• Model Inference Execution - Provides capabilities to run inference on converted model code to generate predictions or intermediate layer outputs.
• Model Integrity Validators - Includes tools for executing inference tests on transformed models to verify that output consistency is preserved.
• Retraining Snippets - Generates code snippets during the conversion process to simplify the fine-tuning or inference of migrated models.
• Decoupled Weight-Architecture Management - Separates neural network structural definitions from binary weight data to allow independent transformation and reconstruction.
• Neural Network Visualizations - Generates graphical representations of neural network architectures and topologies to diagnose network structures.
• Graph Structure Inspection - Analyzes and renders the internal graph representations of models to inspect network nodes and connections.
• Weight Serialization - Packages model architecture and weight data into binary files for efficient execution on target hardware.
• Conversion Verifications - Runs a converted model against sample data to confirm the migration preserved original behavior.
• Keras - Transforms pre-trained Keras model files into compatible network code and weight files.
• Conversion Validations - Runs inference tests on converted models to verify that the migration preserved the original behavior and accuracy.
• Darknet Converters - Translates neural network architectures and weights from the Darknet framework into standardized intermediate formats.
• Model Serialization - Packages model architectures and weights into portable, framework-neutral binary formats for consistent deployment.
• Pluggable Framework Converters - Employs a modular system of converters to map layers and operations from source frameworks to target equivalents.
• Model Conversion - Facilitates interoperability between different deep learning frameworks.

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