- This repo provides train/inference procedure of MNIST model known as Hello World of Deep Learning on Julia runtime. These techniques are based on Chainer and PyCall.jl.
- NEW: Add feature to convert ResNet50/Chainer -> ResNet50/Flux.jl
- This package is not registered as official julia package, so called 野良(nora), which means we should specify repository url:
- Note that Package
Gomah.jldepends onPyCall.jl. So before installing, We recommend set environment variable in Julia.
$ julia
julia> ENV["PYTHON"] = Sys.which("python3")
pkg> add https://github.com/terasakisatoshi/Gomah.jl.git
julia> using GomahPyCall.jlprovides interface between Python and Julia.- This means you can construct training script of Chainer on Julia environment.
- If you are familiar with some Deep learnig framework, checkout our
src/mnist.jl. - We provide an example of training MNIST classifier.
$ julia
julia> using Gomah
julia> train()
epoch main/loss main/accuracy validation/main/loss validation/main/accuracy elapsed_time
1 0.56446 0.840621 0.28946 0.914727 3.49408
2 0.248238 0.927429 0.212789 0.937381 5.90154
3 0.189395 0.945183 0.175694 0.948423 8.43833
4 0.152917 0.95592 0.145494 0.957898 10.832
5 0.128008 0.963307 0.128785 0.962386 13.2714
6 0.108765 0.968306 0.121768 0.961222 15.6906
7 0.0948146 0.97286 0.103434 0.969201 18.361
8 0.0854993 0.974762 0.10229 0.96818 20.7444
9 0.0746463 0.977655 0.0916977 0.972739 23.1146
10 0.0663983 0.980598 0.0889726 0.972573 25.5239
julia> predict()
accuracy for test set = 97.31 [%]
- We found the structure (shape) of parameter i.e. weight of Chainer is similar that of Flux.
- The structure of weight of Convolution of Chainer is
NCHW. On the other hand, Conv of Flux.jl has weight its shape isWHCN, whereNis batchsize,H(resp.W) is height (resp. width) of kernel andCis num of channel. - We provided script to convert ResNet50 of Chainer to that of Flux.jl
- Here is a example of How to use converted model. What you have to do is ...
- Install chainer and chainercv
- Install Flux.jl, PyCall, Gomah.jl
- Prepare sample RGB image. e.g.
pineapple.png - Run the following the script.
using Gomah
using Gomah: L, np, reversedims
using Flux
using PyCall
using Test
using BenchmarkTools
py"""
import chainer
import chainercv
import numpy as np
num = 50
PyResNet = chainercv.links.model.resnet.ResNet
resnet = PyResNet(num, pretrained_model = "imagenet")
img=chainercv.utils.read_image("pineapple.png",dtype=np.float32,alpha="ignore")
img=chainercv.transforms.resize(img,(224,224))
_imagenet_mean = np.array(
[123.15163084, 115.90288257, 103.0626238],
dtype=np.float32
)[:, np.newaxis, np.newaxis]
img=img-_imagenet_mean
img=np.expand_dims(img,axis=0)
resnet.pick=resnet.layer_names
with chainer.using_config('train', False):
pyret=resnet(img)
result=np.squeeze(pyret[-1].array)
chidx=int(np.argmax(result))
chprob=100*float(result[chidx])
print(chidx)
print(chprob)
"""
@testset "regression" begin
num = 50
myres = ResNet(num)
Flux.testmode!.(myres.layers)
img = reversedims(py"img")
@show size(img), typeof(img)
ret, name2data = myres(img)
for (i,name) in enumerate(myres.layer_names)
pyr = reversedims(py"pyret[$i-1].array")
flr = name2data[name]
@show name, size(flr)
@test size(pyr) == size(flr)
@show maximum(abs.(pyr .- flr))
end
flidx = argmax(ret)
flprob = 100ret[argmax(ret)]
@show flidx,flprob
@test Int(py"chidx") == flidx[1]-1
@show Float32(py"chprob") - flprob
end
@testset "benchmark" begin
num=50
img = reversedims(py"img")
myres = ResNet(num)
chainmodel = Chain(myres.layers...)
Flux.testmode!(chainmodel)
@time chainmodel(img)
@time chainmodel(img)
@time chainmodel(img)
@time chainmodel(img)
@time chainmodel(img)
@time chainmodel(img)
end- My favorite thing ごまちゃん
- Inspired by Menoh
- Gomah.jl will be promoted as DNN inference library (in the future)
- Script for training MNIST is based on this notebook.
- See also Julia で Chainer を動かすぞ💪
- Construct Chain is based on Flux.jl
- Basic implementation of ResNet is taken from ChainerCV project.