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https://github.com/songtianyi/go-mxnet-predictor
go binding for mxnet c_predict_api to do inference with pre-trained model
https://github.com/songtianyi/go-mxnet-predictor
cgo deep-learning golang inference machine-learning mxnet
Last synced: 3 months ago
JSON representation
go binding for mxnet c_predict_api to do inference with pre-trained model
- Host: GitHub
- URL: https://github.com/songtianyi/go-mxnet-predictor
- Owner: songtianyi
- License: apache-2.0
- Created: 2016-12-12T10:28:04.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2018-06-06T11:41:16.000Z (over 6 years ago)
- Last Synced: 2024-07-31T20:27:50.323Z (6 months ago)
- Topics: cgo, deep-learning, golang, inference, machine-learning, mxnet
- Language: Go
- Homepage:
- Size: 1.7 MB
- Stars: 55
- Watchers: 6
- Forks: 15
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Distributed-Deep-Learning - go-mxnet-predictor - Go binding for MXNet c_predict_api to do inference with pre-trained model. (Frameworks / **[Contributing](#contributing)** -->)
- Awesome-MXNet - go-mxnet-predictor
README
## go-mxnet-predictor
[](https://travis-ci.org/songtianyi/go-mxnet-predictor)
[](https://goreportcard.com/report/github.com/songtianyi/go-mxnet-predictor)
[](https://opensource.org/licenses/Apache-2.0)
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](https://github.com/songtianyi/docker-dev-envs/blob/master/gmp.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](http://mxnet.io/get_started/setup.html#prerequisites)
* for go installation check [here](https://golang.org/doc/install)
##### 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
```shell
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
```shell
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 and baidu storage service.
###### dropbox
* [102flowers-0260.params](https://www.dropbox.com/s/7l8zye9jpv2bywu/102flowers-0260.params?dl=0)
* [102flowers-symbol.json](https://www.dropbox.com/s/507hikz8561hwxg/102flowers-symbol.json?dl=0)
* [flowertest.jpg](https://www.dropbox.com/s/9ej43gpkcdw3q32/flowertest.jpg?dl=0)
* [mean.bin](https://www.dropbox.com/s/rg45ma97x886i53/mean.bin?dl=0)
###### pan.baidu.com
* [102flowers-0260.params](https://pan.baidu.com/s/1qYuHE5A)
* [102flowers-symbol.json](https://pan.baidu.com/s/1i5sTZY9)
* [flowertest.jpg](https://pan.baidu.com/s/1skUXirz)
* [mean.bin](https://pan.baidu.com/s/1kVlyy5x)
##### 2.4 Run example
```shell
./predict
```
##### 2.5 Python version of flower example
You might need this
[mxnet-flower-python](https://github.com/burness/mxnet-101/tree/master/day4)
## Part 3. Steps to do inference with go-mxnet-predictor
##### 3.1 Load pre-trained model, mean image and create go predictor
```go
// 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
```go
// 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
```go
// set input
if err := p.SetInput("data", res); err != nil {
panic(err)
}
```
##### 3.4 Do prediction
```go
// do predict
if err := p.Forward(); err != nil {
panic(err)
}
```
##### 3.5 Get result
```go
// get predict result
data, err := p.GetOutput(0)
if err != nil {
panic(err)
}
// see more details in examples/flowers/predict.go
```