## caffe2op-conv

Crate for implementing mathematical operators for
Digital Signal Processing and Machine Learning
computations. This crate defines a convolutional
operator for image and signal processing that
performs a 2D convolution on images or signals
with 2D filters. The crate provides the
mathematical analysis and implementation details
to perform convolution operation with stride,
padding, and dilation on tensors. The crate
includes algorithms to calculate the output size
and pre-compute the memory layout for tensor
storage.

**Note: This crate is currently being translated from C++ to Rust, and some function bodies may still be in the process of translation.**

The crate includes implementations for the
following operations:

- Convolutional operator
- Transposed convolutional operator
- Convolutional transpose unpool operator
- Convolutional transpose mobile operator

The crate provides a range of data structures to
represent convolutional neural networks, including
tensors, indices, and pairs. It also includes
a cache for storing and retrieving algorithms for
convolutional operations, and a range of functions
for copying, shuffling, and manipulating tensors.

The crate provides support for a range of
algorithms for convolutional operations, including
correlation, convolutions, and tensor cores. It
also provides support for tensor cores to speed up
convolutions on Nvidia GPUs.

The crate includes a range of utility functions
for memory management, debugging, and performance
analysis, including logging performance
statistics, calculating memory requirements, and
checking compatibility with other libraries.

Overall, this crate provides a comprehensive set
of tools for performing convolutional operations
in DSP and ML applications, with a focus on
performance, ease of use, and mathematical
correctness.