PyTorch Model Recovery

소개

Provides a structured framework for recovering deep learning models when original source code is missing, corrupted, or incomplete. This skill guides users through a systematic four-phase process—weight analysis, architecture reconstruction, minimal verification testing, and resource-aware execution—to ensure recovered models are accurate and functional. It is particularly valuable for inferring complex architectures like Transformers or CNNs from state dictionaries and managing model verification within CPU-constrained environments where performance estimation is critical.

주요 기능

• Strict architecture verification protocols using state dictionary matching
• Systematic inference of model parameters from weight tensor shapes and naming patterns
• 16 GitHub stars
• Resource-aware benchmarking for CPU-only environments to prevent execution timeouts
• Aggressive memory optimization and progress monitoring strategies for recovery tasks
• Pre-defined architecture mapping patterns for Transformers and CNNs

사용 사례

• Performing model forensics to understand the structure of unknown PyTorch artifacts
• Rebuilding a functional model when the original source code is lost but the checkpoint file remains
• Debugging architectural mismatches between saved state dictionaries and updated model definitions