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Awesome GitHub RepositoriesRuntime Shape Tracking
Capturing actual tensor dimensions during a forward pass using execution hooks.
Distinct from Tensor Shape Inferences: Distinct from Tensor Shape Inferences: focuses on recording runtime shapes via hooks rather than compile-time or static inference.
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Awesome Runtime Shape Tracking GitHub Repositories
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- sksq96/pytorch-summary
sksq96/pytorch-summary
4,054عرض على GitHubpytorch-summary is a collection of utilities for PyTorch neural networks designed to generate model summaries, calculate memory requirements, and visualize layer-by-layer tensor shapes. It functions as a reporting tool that provides detailed breakdowns of network layers and output shapes to assist with model debugging and inspection. The project provides specialized capabilities for estimating the total memory usage of forward and backward passes based on input dimensions and parameter counts. It generates human-readable visualizations of model structures to verify architectural designs and i
Intercepts PyTorch forward passes using hooks to record the exact shape of tensors at every layer.
Pythondeep-learningkeraspytorch
