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opencv/modules/dnn/tutorials/tutorial_dnn_halide.markdown
Alexander Alekhin 32d1eb36df dnn: fix links
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How to enable Halide backend for improve efficiency

Introduction

This tutorial guidelines how to run your models in OpenCV deep learning module using Halide language backend. Halide is an open-source project that let us write image processing algorithms in well-readable format, schedule computations according to specific device and evaluate it with a quite good efficiency.

An official website of the Halide project: http://halide-lang.org/.

Efficiency comparison

Measured on Intel® Core™ i7-6700K CPU @ 4.00GHz x 8.

Single image forward pass (in milliseconds):

Architecture MKL backend Halide backend Speed Up ratio
AlexNet 16.55 22.38 x0.73
ResNet-50 63.69 73.91 x0.86
SqueezeNet v1.1 10.11 8.21 x1.23
Inception-5h 35.38 37.06 x0.95
ENet @ 3x512x256 82.26 41.21 x1.99

Scheduling directives might be found @ opencv_extra/testdata/dnn.

Requirements

LLVM compiler

@note LLVM compilation might take a long time.

  • Download LLVM source code from http://releases.llvm.org/4.0.0/llvm-4.0.0.src.tar.xz. Unpack it. Let llvm_root is a root directory of source code.

  • Create directory llvm_root/tools/clang

  • Download Clang with the same version as LLVM. In our case it will be from http://releases.llvm.org/4.0.0/cfe-4.0.0.src.tar.xz. Unpack it into llvm_root/tools/clang. Note that it should be a root for Clang source code.

  • Build LLVM on Linux @code cd llvm_root mkdir build && cd build cmake -DLLVM_ENABLE_TERMINFO=OFF -DLLVM_TARGETS_TO_BUILD="X86" -DLLVM_ENABLE_ASSERTIONS=ON -DCMAKE_BUILD_TYPE=Release .. make -j4 @endcode

  • Build LLVM on Windows (Developer Command Prompt) @code mkdir \path-to-llvm-build\ && cd \path-to-llvm-build\ cmake.exe -DLLVM_ENABLE_TERMINFO=OFF -DLLVM_TARGETS_TO_BUILD=X86 -DLLVM_ENABLE_ASSERTIONS=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=\path-to-llvm-install\ -G "Visual Studio 14 Win64" \path-to-llvm-src\ MSBuild.exe /m:4 /t:Build /p:Configuration=Release .\INSTALL.vcxproj @endcode

@note \\path-to-llvm-build\\ and \\path-to-llvm-install\\ are different directories.

Halide language.

  • Download source code from GitHub repository, https://github.com/halide/Halide or using git. The root directory will be a halide_root. @code git clone https://github.com/halide/Halide.git @endcode

  • Build Halide on Linux @code cd halide_root mkdir build && cd build cmake -DLLVM_DIR=llvm_root/build/lib/cmake/llvm -DCMAKE_BUILD_TYPE=Release -DLLVM_VERSION=40 -DWITH_TESTS=OFF -DWITH_APPS=OFF -DWITH_TUTORIALS=OFF .. make -j4 @endcode

  • Build Halide on Windows (Developer Command Prompt) @code cd halide_root mkdir build && cd build cmake.exe -DLLVM_DIR=\path-to-llvm-install\lib\cmake\llvm -DLLVM_VERSION=40 -DWITH_TESTS=OFF -DWITH_APPS=OFF -DWITH_TUTORIALS=OFF -DCMAKE_BUILD_TYPE=Release -G "Visual Studio 14 Win64" .. MSBuild.exe /m:4 /t:Build /p:Configuration=Release .\ALL_BUILD.vcxproj @endcode

Build OpenCV with Halide backend

When you build OpenCV add the following configuration flags:

  • WITH_HALIDE - enable Halide linkage

  • HALIDE_ROOT_DIR - path to Halide build directory

Sample

@include dnn/squeezenet_halide.cpp

Explanation

Download Caffe model from SqueezeNet repository: train_val.prototxt and squeezenet_v1.1.caffemodel.

Also you need file with names of ILSVRC2012 classes: synset_words.txt.

Put these files into working dir of this program example.

-# Read and initialize network using path to .prototxt and .caffemodel files @snippet dnn/squeezenet_halide.cpp Read and initialize network

-# Check that network was read successfully @snippet dnn/squeezenet_halide.cpp Check that network was read successfully

-# Read input image and convert to the 4-dimensional blob, acceptable by SqueezeNet v1.1 @snippet dnn/squeezenet_halide.cpp Prepare blob

-# Pass the blob to the network @snippet dnn/squeezenet_halide.cpp Set input blob

-# Enable Halide backend for layers where it is implemented @snippet dnn/squeezenet_halide.cpp Enable Halide backend

-# Make forward pass @snippet dnn/squeezenet_halide.cpp Make forward pass Remember that the first forward pass after initialization require quite more time that the next ones. It's because of runtime compilation of Halide pipelines at the first invocation.

-# Determine the best class @snippet dnn/squeezenet_halide.cpp Determine the best class

-# Print results @snippet dnn/squeezenet_halide.cpp Print results For our image we get:

Best class: #812 'space shuttle'

Probability: 97.9812%