Correlation module

this is a custom C++/Cuda implementation of Correlation module, used e.g. in FlowNetC

This tutorial was used as a basis for implementation.

• Build and Install C++ and CUDA extensions by executing python setup.py install,
• Benchmark C++ vs. CUDA by running python benchmark.py {cpu, cuda},
• Run gradient checks on the code by running python grad_check.py --backend {cpu, cuda}.

Requirements

This module is expected to compile for Pytorch 0.4.1, on Python > 3.5 and Python 2.7.

Installation

this module is available on pip

pip install spatial-correlation-sampler

Benchmark

• default parameters are from benchmark.py, FlowNetC parameters are same as use in FlowNetC with a batch size of 4, described in this paper, implemented here and here.
• Feel free to file an issue to add entries to this with your hardware !

CUDA Benchmark

• See here for a benchmark script working with NVIDIA's code, and Pytorch 0.3.
• Benchmark are launched with environment variable CUDA_LAUNCH_BLOCKING set to 1.
• Only float32 is benchmarked.

CPU Benchmark

• No other implementation is avalaible on CPU.