fix(onnx): type check problems (#3257)

Signed-off-by: Jiyong Huang <[email protected]>
lf-edge · Sep 29, 2024 · 345d7ac · 345d7ac
1 parent b560b08
commit 345d7ac
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diff --git a/docs/directory.json b/docs/directory.json
@@ -157,6 +157,10 @@
               "title": "使用 tensorflow_lite 原生插件调用 AI 模型",
               "path": "guide/ai/tensorflow_lite"
             },
+            {
+              "title": "使用 onnx 原生插件调用 AI 模型",
+              "path": "guide/ai/onnx"
+            },
             {
               "title": "使用外部函数调用 tensorflow_lite AI 模型",
               "path": "guide/ai/tensorflow_lite_external_function_tutorial"
@@ -929,6 +933,10 @@
               "title": "Running AI Model with TensorFlow Lite Function Plugin",
               "path": "guide/ai/tensorflow_lite"
             },
+            {
+              "title": "Running AI Model with Onnx Function Plugin",
+              "path": "guide/ai/onnx"
+            },
             {
               "title": "Running tensorflow_lite AI Model with External Function",
               "path": "guide/ai/tensorflow_lite_external_function_tutorial"

diff --git a/docs/en_US/guide/ai/onnx.md b/docs/en_US/guide/ai/onnx.md
@@ -10,7 +10,7 @@ By integrating eKuiper and ONNX, users can simply upload pre-built ONNX models a
 
 ## Prerequisites
 
-### 模型下载
+### Download Models
 
 To run the ONNX interpreter, a trained model is needed. This tutorial will not cover training or model specifics; you can learn how to do this by checking the [ONNX tutorials](https://github.com/onnx/tutorials#converting-to-onnx-format).
 We can either train a new model or choose an existing one.
@@ -36,9 +36,8 @@ Note that the model input data format must be a float array, so the data type mu
 
 ```shell
 POST /streams 
-Host: 192.168.116.128:9081
 Content-Type: application/json
-Content-Length: 109
+
 {
   "sql": "CREATE STREAM onnxPubImg (data array(float)) WITH (DATASOURCE=\"onnxPubImg\", FORMAT=\"json\")"
 }
@@ -65,47 +64,67 @@ Rest API rule creation to call the model:
         {
             "log": {},
             "mqtt": {
-                "server": "127.0.0.1:1883",
+                "server": "tcp://127.0.0.1:1883",
                 "topic": "demoresult"
             }
         }
-    ],
-    "type": "string"
+    ]
 }
 ```
 
 ### Verifying Results
 
 The results are shown in the image below, indicating the predicted probabilities of different digits in the input image.
 
-![result query](../../resources/mqttx_mnist.png)
+![result query](../../../resources/mqttx_mnist.png)
 
 You can use a program like the one below to send images located in the ONNX directory.
 
 ```go
-func TestSum(t *testing.T) {
-    const TOPIC = "sum_diff_pub"
+func TestPic(t *testing.T) {
+const TOPIC = "onnxPubImg"
 
+images := []string{
+"img.png",
+// 其他你需要的图像
+}
     opts := mqtt.NewClientOptions().AddBroker("tcp://localhost:1883")
     client := mqtt.NewClient(opts)
     if token := client.Connect(); token.Wait() && token.Error() != nil {
         panic(token.Error())
     }
-    payloadF32 := []float32{0.2, 0.3, 0.6, 0.9}
-    payloadUnMarshal := MqttPayLoadFloat32Slice{
-        Data: payloadF32,
-    }
-    payload, err := json.Marshal(payloadUnMarshal)
-    if err != nil {
-        fmt.Println(err)
-        return
-    }
+for _, image := range images {
+fmt.Println("Publishing " + image)
+inputImage, err := NewProcessedImage(image, false)
 
-    if token := client.Publish(TOPIC, 2, true, payload); token.Wait() && token.Error() != nil {
-        fmt.Println(token.Error())
-    } else {
-        fmt.Println("Published ")
-    }
+if err != nil {
+fmt.Println(err)
+continue
+}
+// payload, err := os.ReadFile(image)
+payloadF32 := inputImage.GetNetworkInput()
+
+data := make([]any, len(payloadF32))
+for i := 0; i < len(data); i++ {
+data[i] = payloadF32[i]
+}
+payloadUnMarshal := MqttPayLoadFloat32Slice{
+Data: payloadF32,
+}
+payload, err := json.Marshal(payloadUnMarshal)
+if err != nil {
+fmt.Println(err)
+continue
+} else {
+fmt.Println(string(payload))
+}
+if token := client.Publish(TOPIC, 2, true, payload); token.Wait() && token.Error() != nil {
+fmt.Println(token.Error())
+} else {
+fmt.Println("Published " + image)
+}
+time.Sleep(1 * time.Second)
+}
     client.Disconnect(0)
 }
 ```
@@ -129,10 +148,7 @@ The following image shows using the Rest API to call the model.
 
 ```shell
 POST /rules 
-Host: 192.168.116.128:9081
-User-Agent: Apifox/1.0.0 (https://apifox.com)
 Content-Type: application/json
-Content-Length: 319
 
 {
     "id": "ruleSum",
@@ -141,12 +157,11 @@ Content-Length: 319
         {
             "log": {},
             "mqtt": {
-                "server": "127.0.0.1:1883",
+                "server": "tcp://127.0.0.1:1883",
                 "topic": "demoresult"
             }
         }
-    ],
-    "type": "string"
+    ]
 }
 ```
 
@@ -167,35 +182,18 @@ The results are shown in the image below, with the inference returning:
 ]
 ```
 
-![result query](../../resources/mqttx_sum_and_difference.png)
+![result query](../../../resources/mqttx_sum_and_difference.png)
 
-You can use a program like the one below to send test data.
+Send test data like below through MQTT client.
 
-```go
-func TestSum(t *testing.T) {
-    const TOPIC = "sum_diff_pub"
-
-    opts := mqtt.NewClientOptions().AddBroker("tcp://localhost:1883")
-    client := mqtt.NewClient(opts)
-    if token := client.Connect(); token.Wait() && token.Error() != nil {
-        panic(token.Error())
-    }
-    payloadF32 := []float32{0.2, 0.3, 0.6, 0.9}
-    payloadUnMarshal := MqttPayLoadFloat32Slice{
-        Data: payloadF32,
-    }
-    payload, err := json.Marshal(payloadUnMarshal)
-    if err != nil {
-        fmt.Println(err)
-        return
-    }
-
-    if token := client.Publish(TOPIC, 2, true, payload); token.Wait() && token.Error() != nil {
-        fmt.Println(token.Error())
-    } else {
-        fmt.Println("Published ")
-    }
-    client.Disconnect(0)
+```json
+{
+  "data": [
+    0.2,
+    0.3,
+    0.6,
+    0.9
+  ]
 }
 ```
 

diff --git a/docs/zh_CN/resources/mqttx_mnist.png → docs/resources/mqttx_mnist.png b/docs/zh_CN/resources/mqttx_mnist.png → docs/resources/mqttx_mnist.png
diff --git a/...CN/resources/mqttx_sum_and_difference.png → docs/resources/mqttx_sum_and_difference.png b/...CN/resources/mqttx_sum_and_difference.png → docs/resources/mqttx_sum_and_difference.png
diff --git a/docs/zh_CN/guide/ai/onnx.md b/docs/zh_CN/guide/ai/onnx.md
@@ -33,9 +33,8 @@
 
 ```shell
 POST /streams 
-Host: 192.168.116.128:9081
 Content-Type: application/json
-Content-Length: 109
+
 {
   "sql": "CREATE STREAM onnxPubImg (data array(float)) WITH (DATASOURCE=\"onnxPubImg\", FORMAT=\"json\")"
 }
@@ -62,20 +61,19 @@ Rest API 创建规则以调用模型：
         {
             "log": {},
             "mqtt": {
-                "server": "127.0.0.1:1883",
+                "server": "tcp://127.0.0.1:1883",
                 "topic": "demoresult"
             }
         }
-    ],
-    "type": "string"
+    ]
 }
 ```
 
 ### 验证结果
 
 结果如下图所示，输入图片之后，推导出图片中不同数字的输出可能性。
 
-![结果查询](../../resources/mqttx_mnist.png)
+![结果查询](../../../resources/mqttx_mnist.png)
 
 你可以使用类似如下程序的方式来发送图片，图片位于ONNX目录下。
 
@@ -146,10 +144,6 @@ func TestPic(t *testing.T) {
 
 ```shell
 POST /rules 
-Host: 192.168.116.128:9081
-User-Agent: Apifox/1.0.0 (https://apifox.com)
-Content-Type: application/json
-Content-Length: 319
 
 {
     "id": "ruleSum",
@@ -158,12 +152,11 @@ Content-Length: 319
         {
             "log": {},
             "mqtt": {
-                "server": "127.0.0.1:1883",
+                "server": "tcp://127.0.0.1:1883",
                 "topic": "demoresult"
             }
         }
-    ],
-    "type": "string"
+    ]
 }
 ```
 
@@ -175,35 +168,18 @@ Content-Length: 319
 [{"onnx":[[1.9999883,0.60734314]]}]
 ```
 
-![结果查询](../../resources/mqttx_sum_and_difference.png)
-
-你可以使用类似如下程序的方式来发送测试数据。
-
-```go
-func TestSum(t *testing.T) {
-    const TOPIC = "sum_diff_pub"
+![结果查询](../../../resources/mqttx_sum_and_difference.png)
 
-    opts := mqtt.NewClientOptions().AddBroker("tcp://localhost:1883")
-    client := mqtt.NewClient(opts)
-    if token := client.Connect(); token.Wait() && token.Error() != nil {
-        panic(token.Error())
-    }
-    payloadF32 := []float32{0.2, 0.3, 0.6, 0.9}
-    payloadUnMarshal := MqttPayLoadFloat32Slice{
-        Data: payloadF32,
-    }
-    payload, err := json.Marshal(payloadUnMarshal)
-    if err != nil {
-        fmt.Println(err)
-        return
-    }
+你可以使用 MQTT 客户端发送测试数据。
 
-    if token := client.Publish(TOPIC, 2, true, payload); token.Wait() && token.Error() != nil {
-        fmt.Println(token.Error())
-    } else {
-        fmt.Println("Published ")
-    }
-    client.Disconnect(0)
+```json
+{
+  "data": [
+    0.2,
+    0.3,
+    0.6,
+    0.9
+  ]
 }
 ```
 

diff --git a/extensions/functions/onnx/install.sh b/extensions/functions/onnx/install.sh
@@ -17,20 +17,16 @@
 
 dir=/usr/local/onnx
 
-# 获取操作系统和架构信息
 OS=$(uname -s)
 ARCH=$(uname -m)
 
-# 检查操作系统和架构并返回对应的库路径
-
-
 cur=$(dirname "$0")
 echo "Base path $cur"
 if [ -d "$dir" ]; then
   echo "SDK path $dir exists."
 else
   echo "Creating SDK path $dir"
-  mkdir -p $dir
+  mkdir -p $dir/lib
   echo "Created SDK path $dir"
   echo "Moving libs"
 

diff --git a/extensions/functions/onnx/onnx.go b/extensions/functions/onnx/onnx.go
@@ -1,4 +1,4 @@
-// Copyright 2021-2024 思无邪. All rights reserved.
+// Copyright 2021-2024 EMQ Technologies Co., Ltd.
 //
 // Licensed under the Apache License, Version 2.0 (the "License");
 // you may not use this file except in compliance with the License.
@@ -46,6 +46,7 @@ func (f *OnnxFunc) Validate(args []interface{}) error {
 }
 
 func (f *OnnxFunc) Exec(ctx api.FunctionContext, args []any) (any, bool) {
+	ctx.GetLogger().Debugf("onnx args %[1]T(%[1]v)", args)
 	modelName, ok := args[0].(string)
 	if !ok {
 		return fmt.Errorf("onnx function first parameter must be a string, but got %[1]T(%[1]v)", args[0]), false
@@ -58,19 +59,18 @@ func (f *OnnxFunc) Exec(ctx api.FunctionContext, args []any) (any, bool) {
 	if len(args)-1 != inputCount {
 		return fmt.Errorf("onnx function requires %d tensors but got %d", inputCount, len(args)-1), false
 	}
-
 	ctx.GetLogger().Debugf("onnx function %s with %d tensors", modelName, inputCount)
 
 	var inputTensors []ort.ArbitraryTensor
 	// Set input tensors
 	for i := 1; i < len(args); i++ {
-
 		inputInfo := interpreter.inputInfo[i-1]
 		var arg []interface{}
 		switch v := args[i].(type) {
 		case []any: // only supports one dimensional arg. Even dim 0 must be an array of 1 element
 			arg = v
-			return fmt.Errorf("onnx function parameter %d must be a bytea or array of bytea, but got %[1]T(%[1]v)", i, v), false
+		default:
+			return fmt.Errorf("onnx function parameter %d must be a bytea or array of bytea, but got %[1]T(%[1]v)", v), false
 		}
 
 		notSupportedDataLen := -1
@@ -256,10 +256,9 @@ func (f *OnnxFunc) Exec(ctx api.FunctionContext, args []any) (any, bool) {
 			modelParaLen *= inputInfo.Dimensions[j]
 		}
 		ctx.GetLogger().Debugf("receive tensor %v, require %d length", arg, modelParaLen)
-		if modelParaLen != inputTensors[i].GetShape().FlattenedSize() {
+		if modelParaLen != inputTensors[i-1].GetShape().FlattenedSize() {
 			return fmt.Errorf("onnx function input tensor %d must have %d elements but got %d", i-1, modelParaLen, len(arg)), false
 		}
-
 	}
 	// todo :optimize: avoid creating output tensor every time