go-mxnet-predictor

go-mxnet-predictor is go binding for mxnet c_predict_api. It's as raw as original C api, wish further development for higher level APIs. Feel free to join us :)

Part 1. Steps to build your own linux dev environment

Dockerfile offered for building mxnet and go env. You could skip this part by using Docker

1.1 Install mxnet prerequisites and go

• for mxnet prerequisites check here
• for go installation check here

1.2 Get mxnet and build

mkdir /root/MXNet/
cd /root/MXNet/ && git clone https://github.com/dmlc/mxnet.git --recursive
cd /root/MXNet/mxnet && make -j2
ln -s /root/MXNet/mxnet/lib/libmxnet.so /usr/lib/libmxnet.so

Part 2. Steps to build and run flower example

2.1 Get go-mxnet-predictor and do some configuration

	go get github.com/anthonynsimon/bild
    go get -u -v github.com/songtianyi/go-mxnet-predictor
    cd $GOPATH/src/github.com/songtianyi/go-mxnet-predictor	
	sed -i "/prefix=/c prefix=\/root\/MXNet\/mxnet" travis/mxnet.pc
	cp travis/mxnet.pc /usr/lib/pkgconfig/
	pkg-config --libs mxnet

2.2 Build flowers example

	go build examples/flowers/predict.go

2.3 Download example files

To run this example, you need to download model files, mean.bin and input image. Then put them in correct path. These files are shared in dropbox.

• 102flowers-0260.params
• 102flowers-symbol.json
• flowertest.jpg
• mean.bin

2.4 Run example

	./predict

Part 3. Steps to do inference with go-mxnet-predictor

3.1 Load pre-trained model, mean image and create go predictor

	// load model
	symbol, err := ioutil.ReadFile("/data/102flowers-symbol.json")
	if err != nil {
		panic(err)
	}
	params, err := ioutil.ReadFile("/data/102flowers-0260.params")
	if err != nil {
		panic(err)
	}

	// load mean image from file
    nd, err := mxnet.CreateNDListFromFile("/data/mean.bin")
    if err != nil {
        panic(err)
    }
    // free ndarray list operator before exit
    defer nd.Free()

	// create Predictor
	p, err := mxnet.CreatePredictor(symbol, params, mxnet.Device{mxnet.CPU_DEVICE, 0}, []mxnet.InputNode{{Key: "data", Shape: []uint32{1, 3, 299, 299}}})
	if err != nil {
		panic(err)
	}
	defer p.Free()
	// see more details in examples/flowers/predict.go

3.2 Load input data and do preprocess

	// load test image for predction
	img, err := imgio.Open("/data/flowertest.jpg")
	if err != nil {
		panic(err)
	}
	// preprocess
	resized := transform.Resize(img, 299, 299, transform.Linear)
	res, err := utils.CvtImageTo1DArray(resized, item.Data)
	if err != nil {
		panic(err)
	}

3.3 Set input data to preditor

	// set input
	if err := p.SetInput("data", res); err != nil {
		panic(err)
	}

3.4 Do prediction

	// do predict
	if err := p.Forward(); err != nil {
		panic(err)
	}

3.5 Get result

	// get predict result
	data, err := p.GetOutput(0)
	if err != nil {
		panic(err)
	}
	// see more details in examples/flowers/predict.go