如何理解tensorflow.js对象检测张量输出？

问题描述

我的动机是构建一个自定义的异物检测Web应用程序。我从model zoo下载了一个tf2预训练的SSD resnet1010模型。我的想法是，如果该实现可行，我将使用自己的数据训练模型。我运行$saved_model_cli show --dir saved_model --tag_set serve --signature_def serving_default来找出输入和输出节点。

The given SavedModel SignatureDef contains the following input(s):
  inputs['input_tensor'] tensor_info:
      dtype: DT_UINT8
      shape: (1,-1,3)
      name: serving_default_input_tensor:0
The given SavedModel SignatureDef contains the following output(s):
  outputs['detection_anchor_indices'] tensor_info:
      dtype: DT_FLOAT
      shape: (1,100)
      name: StatefulPartitionedCall:0
  outputs['detection_Boxes'] tensor_info:
      dtype: DT_FLOAT
      shape: (1,100,4)
      name: StatefulPartitionedCall:1
  outputs['detection_classes'] tensor_info:
      dtype: DT_FLOAT
      shape: (1,100)
      name: StatefulPartitionedCall:2
  outputs['detection_multiclass_scores'] tensor_info:
      dtype: DT_FLOAT
      shape: (1,91)
      name: StatefulPartitionedCall:3
  outputs['detection_scores'] tensor_info:
      dtype: DT_FLOAT
      shape: (1,100)
      name: StatefulPartitionedCall:4
  outputs['num_detections'] tensor_info:
      dtype: DT_FLOAT
      shape: (1)
      name: StatefulPartitionedCall:5
  outputs['raw_detection_Boxes'] tensor_info:
      dtype: DT_FLOAT
      shape: (1,51150,4)
      name: StatefulPartitionedCall:6
  outputs['raw_detection_scores'] tensor_info:
      dtype: DT_FLOAT
      shape: (1,91)
      name: StatefulPartitionedCall:7
Method name is: tensorflow/serving/predict

然后我通过运行将模型转换为tensorflowjs模型

tensorflowjs_converter --input_format=tf_saved_model --output_node_names='detection_anchor_indices,detection_Boxes,detection_classes,detection_multiclass_scores,detection_scores,num_detections,raw_detection_Boxes,raw_detection_scores' --saved_model_tags=serve --output_format=tfjs_graph_model saved_model js_model

这是我的JavaScript代码（在vue方法中）

    loadTfModel: async function(){
        try {
            this.model = await tf.loadGraphModel(this.MODEL_URL);
        } catch(error) {
            console.log(error);
        }

   },predictImg: async function() {
        const imgData = document.getElementById('img');
        let tf_img = tf.browser.fromPixels(imgData);
        tf_img = tf_img.expandDims(0);
        const predictions = await this.model.executeAsync(tf_img);
        const data = []
        for (let i = 0; i < predictions.length; i++){
            data.push(predictions[i].dataSync());
        }
        console.log(data);
    }

输出看起来像这样：

我的问题是数组中的这八个项目是否对应于八个已定义的输出节点？如何理解这些数据？以及如何将其转换为人类可读的格式（例如python）？
更新1： 我已经尝试过answer，并编辑了我的预测方法：

predictImg: async function() {
        const imgData = document.getElementById('img');
        let tf_img = tf.browser.fromPixels(imgData);
        tf_img = tf_img.expandDims(0);
        const predictions = await this.model.executeAsync(tf_img,['detection_classes']).then(predictions => {
            const data = predictions.dataSync()
            console.log('Predictions: ',data);
        })

    }

我最终得到"Error: The output 'detection_classes' is not found in the graph"。我将不胜感激。

解决方法

在 { "dialogAction": { "type": "Close","fulfillmentState": "Fulfilled","message": { "contentType": "PlainText","content": "Thanks,your pizza has been ordered." },"responseCard": { "version": integer-value,"contentType": "application/vnd.amazonaws.card.generic","genericAttachments": [ { "title":"card-title","subTitle":"card-sub-title","imageUrl":"URL of the image to be shown","attachmentLinkUrl":"URL of the attachment to be associated with the card","buttons":[ { "text":"button-text","value":"Value sent to server on button click" } ] } ] } } }中指定的输出节点可能存在错误。另外，这里this.model.executeAsync(tf_img,['detection_classes'])不需要使用await。使用await this.model.executeAsync(tf_img,['detection_classes'])或使用await。

获取then的另一种方法是索引输出数组：

detection_classes