How to optimize YOLOv11 implementation in MATLAB that is significantly slower than its PyTorch counterpart?

I've implemented YOLOv11 in MATLAB with Deep Learning Toolbox, but I'm experiencing significant performance issues compared to the PyTorch implementation. The MATLAB version is running much slower during training despite having the same architecture (296 layers with 425 connections).

Environment:

• MATLAB Version: R2024b
• GPU: NVIDIA RTX 3090 Ti 24GB
• CUDA Version: 11.7
• Operating System: Windows 10

Problem Description:

1. I've created a full implementation of YOLOv11 in MATLAB by adapting the network architecture from the original PyTorch model
2. Using identical batch size and training parameters on the same hardware, the MATLAB implementation is significantly slower - training takes several times longer per epoch compared to the PyTorch version (approximately 5-8x slower)
3. I suspect bottlenecks might be in my custom layer implementations, particularly in reshape and transpose operations

Code Examples: I've implemented several custom layers to handle operations that don't have direct equivalents in MATLAB, for example:

matlab

classdef ReshapeTransposeLayer < nnet.layer.Layer

methods

function layer = ReshapeTransposeLayer(name)

layer.Name = name;

layer.Description = 'Reshape and Transpose layer for YOLO';

layer.NumInputs = 3;

layer.NumOutputs = 3;

layer.InputNames = {'in1', 'in2', 'in3'};

layer.OutputNames = {'x_model_23_dfl_Trans', 'x_model_23_Sigmoid_o', 'x_model_23_Sigmoid_oNumDims'};

end

function [x_model_23_dfl_Trans, x_model_23_Sigmoid_o, x_model_23_Sigmoid_oNumDims] = predict(layer, X1, X2, X3)

try

% Calculate grid sizes

grid_size_1 = 80 * 80; % 6400

grid_size_2 = 40 * 40; % 1600

grid_size_3 = 20 * 20; % 400

total_grid_size = grid_size_1 + grid_size_2 + grid_size_3; % 8400

% Process X1 (80x80 grid)

x1_data = extractdata(X1);

% Get batch size

batchSize = size(x1_data, ndims(x1_data));

if ndims(x1_data) < 4

batchSize = 1;

end

% Process each batch

dfl_final_all = [];

cls_preds_all = [];

for b = 1:batchSize

% [More processing code with reshape operations]

end

catch e

fprintf('Error in ReshapeTransposeLayer: %s\n', e.message);

rethrow(e);

end

end

end

end

Specific Questions:

1. How can I efficiently profile and identify performance bottlenecks in my MATLAB deep learning model?
2. Are there more efficient ways to implement complex reshape and transpose operations for YOLO-type models in MATLAB?
3. What strategies could help reduce the performance gap between MATLAB and PyTorch implementations?
4. Is there a recommended approach for batch processing in custom layers that avoids explicit loops?

Answer 1

[Yair Altman]

There are numerous ways to speedup Matlab code, and your general question is too vague for specific fixes. The best recommendation is to run the built-in Matlab Profiler tool ("Run & Time" in the Editor, or the profile command in the console) to identify the specific bottelenecks in your code. Once you identify the main bottlenecks, resolve them, rerun the code to ensure that it's still valid/correct, and then repeat the profiling cycle until you're satisfied.

Additional details: https://www.mathworks.com/help/matlab/matlab_prog/profiling-for-improving-performance.html

[XiangYu]

Good, thanks. I'll give it a try.