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change to a onnx model removing omz_converter and omz_downloader #3509

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b7b727c
change to a onnx model removing omz_converter and omz_downloader
przepeck Jul 15, 2025
1b361a1
changes in README correcting info about model
przepeck Jul 17, 2025
824d3bc
deleted binary input option from demo and docs
przepeck Jul 18, 2025
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20 changes: 6 additions & 14 deletions demos/image_classification/go/README.md
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Expand Up @@ -11,25 +11,17 @@ cd model_server/demos/image_classification/go

## Get the model

To run end to end flow and get correct results, please download `resnet-50-tf` model and convert it to IR format by following [instructions available on the OpenVINO Model Zoo page](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/resnet-50-tf/README.md)
To run end to end flow and get correct results, please download `resnet-50` model in IR format.

Place converted model files (XML and BIN) under the following path: `<PATH_TO_MODELS>/resnet-50-tf/1`
Place downloaded model files (XML and BIN) under the following path: `<PATH_TO_MODELS>/1`

Where `PATH_TO_MODELS` is the path to the directory with models on the host filesystem.

For example:
```bash
mkdir models
docker run -u $(id -u):$(id -g) -v ${PWD}/models:/models openvino/ubuntu20_dev:2024.6.0 omz_downloader --name resnet-50-tf --output_dir /models
docker run -u $(id -u):$(id -g) -v ${PWD}/models:/models:rw openvino/ubuntu20_dev:2024.6.0 omz_converter --name resnet-50-tf --download_dir /models --output_dir /models --precisions FP32
mv ${PWD}/models/public/resnet-50-tf/FP32 ${PWD}/models/public/resnet-50-tf/1

tree models/public/resnet-50-tf
models/public/resnet-50-tf
├── 1
│   ├── resnet-50-tf.bin
│   └── resnet-50-tf.xml
└── resnet_v1-50.pb
mkdir -p model/1
wget -P model/1 https://github.com/onnx/models/raw/refs/heads/main/validated/vision/classification/resnet/model/resnet50-v1-12.onnx

```

## Build Go client docker image
Expand All @@ -49,7 +41,7 @@ docker build . -t ovmsclient
Before running the client launch OVMS with prepared ResNet model. You can do that with a command similar to:

```bash
docker run -d --rm -p 9000:9000 -v ${PWD}/models/public/resnet-50-tf:/models/resnet openvino/model_server:latest --model_name resnet --model_path /models/resnet --port 9000
docker run -d --rm -p 9000:9000 -v ${PWD}/model:/models openvino/model_server:latest --model_name resnet --model_path /models --port 9000 --layout NHWC:NCHW
```

**Note** Layout for downloaded resnet model is NHWC. It ensures that the model will accept binary input generated by the client. See [binary inputs](../../../docs/binary_input.md) doc if you want to learn more about this feature.
Expand Down
173 changes: 105 additions & 68 deletions demos/image_classification/go/resnet_predict.go
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,8 @@
package main

import (
"bytes"
"encoding/binary"
"context"
"flag"
"fmt"
Expand All @@ -28,25 +30,93 @@ import (
"path/filepath"
framework "tensorflow/core/framework"
pb "tensorflow_serving"

"math"
google_protobuf "github.com/golang/protobuf/ptypes/wrappers"
"github.com/nfnt/resize"
"gocv.io/x/gocv"
"google.golang.org/grpc"
)

// Target model specification
const MODEL_NAME string = "resnet"
const INPUT_NAME string = "data"
const OUTPUT_NAME string = "resnetv17_dense0_fwd"
const IMG_RESIZE_SIZE uint = 224

// Convert slice of 4 bytes to float32 (assumes Little Endian)
func readFloat32(fourBytes []byte) float32 {
buf := bytes.NewBuffer(fourBytes)
var retval float32
binary.Read(buf, binary.LittleEndian, &retval)
return retval
}

//change logits array to softmax values
func makeSoftmaxArr(x []float32) []float32 {
max := x[0]
for _, v := range x {
if v > max {
max = v
}
}

var expSum float32 = 0.0
exps := make([]float32, len(x))
for i, v := range x {
exps[i] = float32(math.Exp(float64(v - max)))
expSum += exps[i]
}

for i := range exps {
exps[i] /= expSum
}
return exps
}

func printPredictionFromResponse(responseProto *framework.TensorProto){
responseContent := responseProto.GetTensorContent()
// Get details about output shape
outputShape := responseProto.GetTensorShape()
dim := outputShape.GetDim()
classesNum := dim[1].GetSize()

//Convert response to array of float32
outArr := make([]float32, int(classesNum))
for i := 0; i < int(classesNum); i++ {
outArr[i] = readFloat32(responseContent[i*4:i*4+4])
}
softmaxArr := makeSoftmaxArr(outArr)
//Get max value and index
maxVal := softmaxArr[0]
maxLoc := 0
for i := 0; i < int(classesNum); i++ {
if maxVal < softmaxArr[i] {
maxVal = softmaxArr[i]
maxLoc = i
}
}

// Get label of the class with the highest confidence
var label string
if classesNum == 1000 {
label = labels[maxLoc]
} else if classesNum == 1001 {
label = labels[maxLoc-1]
} else {
fmt.Printf("Unexpected class number in the output")
return
}

fmt.Printf("Predicted class: %s\nClassification confidence: %f%%\n", label, maxVal*100)
}

func run_binary_input(servingAddress string, imgPath string) {
// Read the image in binary form
imgBytes, err := ioutil.ReadFile(imgPath)
if err != nil {
log.Fatalln(err)
}

// Target model specification
const MODEL_NAME string = "resnet"
const INPUT_NAME string = "map/TensorArrayStack/TensorArrayGatherV3"
const OUTPUT_NAME string = "softmax_tensor:0"

// Create Predict Request to OVMS
predictRequest := &pb.PredictRequest{
ModelSpec: &pb.ModelSpec{
Expand Down Expand Up @@ -96,32 +166,8 @@ func run_binary_input(servingAddress string, imgPath string) {
if !ok {
log.Fatalf("Expected output: %s does not exist in the response", OUTPUT_NAME)
}
responseContent := responseProto.GetTensorContent()

// Get details about output shape
outputShape := responseProto.GetTensorShape()
dim := outputShape.GetDim()
classesNum := dim[1].GetSize()

// Convert bytes to matrix
outMat, err := gocv.NewMatFromBytes(1, int(classesNum), gocv.MatTypeCV32FC1, responseContent)
outMat = outMat.Reshape(1, 1)

// Find maximum value along with its index in the output
_, maxVal, _, maxLoc := gocv.MinMaxLoc(outMat)

// Get label of the class with the highest confidence
var label string
if classesNum == 1000 {
label = labels[maxLoc.X]
} else if classesNum == 1001 {
label = labels[maxLoc.X-1]
} else {
fmt.Printf("Unexpected class number in the output")
return
}

fmt.Printf("Predicted class: %s\nClassification confidence: %f%%\n", label, maxVal*100)

printPredictionFromResponse(responseProto)
}

func run_with_conversion(servingAddress string, imgPath string) {
Expand All @@ -140,7 +186,7 @@ func run_with_conversion(servingAddress string, imgPath string) {
}

// Resize image to match ResNet input
resizedImg := resize.Resize(224, 224, decodedImg, resize.Lanczos3)
resizedImg := resize.Resize(IMG_RESIZE_SIZE, IMG_RESIZE_SIZE, decodedImg, resize.Lanczos3)

// Convert image to gocv.Mat type (HWC layout)
imgMat, err := gocv.ImageToMatRGB(resizedImg)
Expand All @@ -149,18 +195,33 @@ func run_with_conversion(servingAddress string, imgPath string) {
os.Exit(1)
}

newMat := gocv.NewMat()
floatMat := gocv.NewMat()
// Convert type so each value is represented by float32
// as in Mat generated by ImageToMatRGB values are represented with 8 bit precision
imgMat.ConvertTo(&newMat, gocv.MatTypeCV32FC2)

// Having right layout and precision, convert Mat to []bytes
imgBytes := newMat.ToBytes()

// Target model specification
const MODEL_NAME string = "resnet"
const INPUT_NAME string = "map/TensorArrayStack/TensorArrayGatherV3"
const OUTPUT_NAME string = "softmax_tensor:0"
imgMat.ConvertTo(&floatMat, gocv.MatTypeCV32FC2)

// Split channels
channels := gocv.Split(floatMat)

var means = [3]float32{123.675, 116.28, 103.53}
var scales = [3]float32{58.395, 57.12, 57.375}
for i := 0; i < 3; i++ {
// Subtract mean
meanScalar := gocv.NewScalar(float64(means[i]), 0, 0, 0)
meanMat := gocv.NewMatWithSizeFromScalar(meanScalar, channels[i].Rows(), channels[i].Cols(), gocv.MatTypeCV32F)
gocv.Subtract(channels[i], meanMat, &channels[i])
// Divide by scale
scaleScalar :=gocv.NewScalar(float64(scales[i]), 0, 0, 0)
scaleMat := gocv.NewMatWithSizeFromScalar(scaleScalar, channels[i].Rows(), channels[i].Cols(), gocv.MatTypeCV32F)
gocv.Divide(channels[i], scaleMat, &channels[i])
}
// Merge channels back in BGR format
normalizedMat := gocv.NewMat()
defer normalizedMat.Close()
gocv.Merge([]gocv.Mat{channels[2], channels[1], channels[0]}, &normalizedMat)

// Having right layout and precision, convert Mat to []byte
imgBytes := normalizedMat.ToBytes()

// Create Predict Request to OVMS
predictRequest := &pb.PredictRequest{
Expand Down Expand Up @@ -219,32 +280,8 @@ func run_with_conversion(servingAddress string, imgPath string) {
if !ok {
log.Fatalf("Expected output: %s does not exist in the response", OUTPUT_NAME)
}
responseContent := responseProto.GetTensorContent()

// Get details about output shape
outputShape := responseProto.GetTensorShape()
dim := outputShape.GetDim()
classesNum := dim[1].GetSize()

// Convert bytes to matrix
outMat, err := gocv.NewMatFromBytes(1, int(classesNum), gocv.MatTypeCV32FC1, responseContent)
outMat = outMat.Reshape(1, 1)

// Find maximum value along with its index in the output
_, maxVal, _, maxLoc := gocv.MinMaxLoc(outMat)

// Get label of the class with the highest confidence
var label string
if classesNum == 1000 {
label = labels[maxLoc.X]
} else if classesNum == 1001 {
label = labels[maxLoc.X-1]
} else {
fmt.Printf("Unexpected class number in the output")
return
}

fmt.Printf("Predicted class: %s\nClassification confidence: %f%%\n", label, maxVal*100)
printPredictionFromResponse(responseProto)
}

func main() {
Expand Down