caffe2op-channelstats

Description for caffe2op-channelstats Rust crate

The caffe2op-channelstats crate defines a mathematical operator used in DSP and machine learning computations. The main purpose of this crate is to compute the mean and standard deviation of a set of input channels for a given tensor.

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

ChannelStatsOp

The ChannelStatsOp is the main operator provided by this crate. It takes as input a tensor and returns its mean and standard deviation across a specified set of channels. The operator can be applied in two different ways: ComputeChannelStatsNCHW and ComputeChannelStatsNHWC.

ComputeChannelStatsNCHW computes the channel-wise statistics of a tensor with dimensions [batch_size, channels, height, width], while ComputeChannelStatsNHWC computes the channel-wise statistics of a tensor with dimensions [batch_size, height, width, channels].

Mathematical Analysis

Given a tensor X with dimensions [batch_size, channels, height, width], the mean and standard deviation for the ith channel can be computed as follows:

mean_i = sum(X[:,i,:,:]) / (batch_size * height * width)
std_i = sqrt(sum((X[:,i,:,:] - mean_i)**2) / (batch_size * height * width))

Similarly, for a tensor X with dimensions [batch_size, height, width, channels], the mean and standard deviation for the ith channel can be computed as follows:

mean_i = sum(X[:,:,:,i]) / (batch_size * height * width)
std_i = sqrt(sum((X[:,:,:,i] - mean_i)**2) / (batch_size * height * width))

The ChannelStatsOp applies these computations channel-wise to produce the mean and standard deviation for the specified set of channels.

Usage

The ChannelStatsOp can be used in conjunction with other operators in deep learning frameworks such as Caffe2 and PyTorch to perform normalization of input data. By computing the channel-wise statistics of a tensor, it is possible to subtract the mean and divide by the standard deviation, which can help to improve the performance of deep neural networks.

This crate provides both NCHW and NHWC implementations of the operator to ensure compatibility with different deep learning frameworks.

Example

use caffe2op_channelstats::{ChannelStatsOp, ComputeChannelStatsNCHW};

let input_tensor = // some tensor with dimensions [batch_size, channels, height, width]
let num_channels = // number of channels to compute statistics for
let op = ChannelStatsOp::new(ComputeChannelStatsNCHW(num_channels));
let (mean, std) = op.forward(&[input_tensor]);