Tensorflowsharp和Retinanet --如何确定在运行图时获取什么？

Question

我已经成功地将TensorflowSharp与更快的RCNN一起使用了一段时间；然而，我最近训练了Retinanet模型，验证了它在python中的工作，并创建了一个冻结的pb文件供Tensorflow使用。对于FRCNN，在TensorflowSharp GitHub回购中有一个示例展示了如何运行/获取该模型。对于Retinanet，我试着修改代码，但似乎没有任何效果。我有一个关于Retinanet的模型摘要，我已经尝试过了，但是对于我来说还不清楚应该使用什么。

对于FRCNN，图形是这样运行的：

    var runner = m_session.GetRunner();

    runner
        .AddInput(m_graph["image_tensor"][0], tensor)
        .Fetch(
        m_graph["detection_boxes"][0],
        m_graph["detection_scores"][0],
        m_graph["detection_classes"][0],
        m_graph["num_detections"][0]);

       var output = runner.Run();

        var boxes = (float[,,])output[0].GetValue(jagged: false);
        var scores = (float[,])output[1].GetValue(jagged: false);
        var classes = (float[,])output[2].GetValue(jagged: false);
        var num = (float[])output[3].GetValue(jagged: false);

从FRCNN的模型总结来看，很明显输入("image_tensor")和输出("detection_boxes“、"detection_scores”、"detection_classes“和"num_detections")是什么。对于Retinanet来说，它们是不一样的(我试过了)，我不知道它们应该是什么。上面代码的"Fetch“部分会导致崩溃，我猜这是因为我没有正确地使用节点名称。

我不会在这里粘贴整个Retinanet摘要，但下面是前几个节点：

Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, None, None, 3 0                                            
__________________________________________________________________________________________________
padding_conv1 (ZeroPadding2D)   (None, None, None, 3 0           input_1[0][0]                    
__________________________________________________________________________________________________
conv1 (Conv2D)                  (None, None, None, 6 9408        padding_conv1[0][0]              
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization)   (None, None, None, 6 256         conv1[0][0]                      
__________________________________________________________________________________________________
conv1_relu (Activation)         (None, None, None, 6 0           bn_conv1[0][0]                   
__________________________________________________________________________________________________

下面是最后几个节点：

__________________________________________________________________________________________________
anchors_0 (Anchors)             (None, None, 4)      0           P3[0][0]                         
__________________________________________________________________________________________________
anchors_1 (Anchors)             (None, None, 4)      0           P4[0][0]                         
__________________________________________________________________________________________________
anchors_2 (Anchors)             (None, None, 4)      0           P5[0][0]                         
__________________________________________________________________________________________________
anchors_3 (Anchors)             (None, None, 4)      0           P6[0][0]                         
__________________________________________________________________________________________________
anchors_4 (Anchors)             (None, None, 4)      0           P7[0][0]                         
__________________________________________________________________________________________________
regression_submodel (Model)     (None, None, 4)      2443300     P3[0][0]                         
                                                                 P4[0][0]                         
                                                                 P5[0][0]                         
                                                                 P6[0][0]                         
                                                                 P7[0][0]                         
__________________________________________________________________________________________________
anchors (Concatenate)           (None, None, 4)      0           anchors_0[0][0]                  
                                                                 anchors_1[0][0]                  
                                                                 anchors_2[0][0]                  
                                                                 anchors_3[0][0]                  
                                                                 anchors_4[0][0]                  
__________________________________________________________________________________________________
regression (Concatenate)        (None, None, 4)      0           regression_submodel[1][0]        
                                                                 regression_submodel[2][0]        
                                                                 regression_submodel[3][0]        
                                                                 regression_submodel[4][0]        
                                                                 regression_submodel[5][0]        
__________________________________________________________________________________________________
boxes (RegressBoxes)            (None, None, 4)      0           anchors[0][0]                    
                                                                 regression[0][0]                 
__________________________________________________________________________________________________
classification_submodel (Model) (None, None, 1)      2381065     P3[0][0]                         
                                                                 P4[0][0]                         
                                                                 P5[0][0]                         
                                                                 P6[0][0]                         
                                                                 P7[0][0]                         
__________________________________________________________________________________________________
clipped_boxes (ClipBoxes)       (None, None, 4)      0           input_1[0][0]                    
                                                                 boxes[0][0]                      
__________________________________________________________________________________________________
classification (Concatenate)    (None, None, 1)      0           classification_submodel[1][0]    
                                                                 classification_submodel[2][0]    
                                                                 classification_submodel[3][0]    
                                                                 classification_submodel[4][0]    
                                                                 classification_submodel[5][0]    
__________________________________________________________________________________________________
filtered_detections (FilterDete [(None, 300, 4), (No 0           clipped_boxes[0][0]              
                                                                 classification[0][0]             
==================================================================================================
Total params: 36,382,957
Trainable params: 36,276,717
Non-trainable params: 106,240

任何帮助，以了解如何修复“获取”部分，这将是非常感谢。

编辑：

为了进一步了解这一点，我找到了一个python函数，用于从.pb文件中打印操作名。在为FRCNN .pb文件执行此操作时，它清楚地给出了输出节点的名称，如下所示(只发布python函数输出的最后几行)。

import/SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack_4/TensorArrayGatherV3
import/SecondStagePostprocessor/ToFloat_1
import/add/y
import/add
import/detection_boxes
import/detection_scores
import/detection_classes
import/num_detections

如果我对Retinanet .pb文件做同样的事情，那么输出是什么就不太明显了。下面是python函数的最后几行代码。

import/filtered_detections/map/while/NextIteration_4
import/filtered_detections/map/while/Exit_2
import/filtered_detections/map/while/Exit_3
import/filtered_detections/map/while/Exit_4
import/filtered_detections/map/TensorArrayStack/TensorArraySizeV3
import/filtered_detections/map/TensorArrayStack/range/start
import/filtered_detections/map/TensorArrayStack/range/delta
import/filtered_detections/map/TensorArrayStack/range
import/filtered_detections/map/TensorArrayStack/TensorArrayGatherV3
import/filtered_detections/map/TensorArrayStack_1/TensorArraySizeV3
import/filtered_detections/map/TensorArrayStack_1/range/start
import/filtered_detections/map/TensorArrayStack_1/range/delta
import/filtered_detections/map/TensorArrayStack_1/range
import/filtered_detections/map/TensorArrayStack_1/TensorArrayGatherV3
import/filtered_detections/map/TensorArrayStack_2/TensorArraySizeV3
import/filtered_detections/map/TensorArrayStack_2/range/start
import/filtered_detections/map/TensorArrayStack_2/range/delta
import/filtered_detections/map/TensorArrayStack_2/range
import/filtered_detections/map/TensorArrayStack_2/TensorArrayGatherV3

作为参考，下面是我使用的python函数：

def printTensors(pb_file):

    # read pb into graph_def
    with tf.gfile.GFile(pb_file, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    # import graph_def
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(graph_def)

    # print operations
    for op in graph.get_operations():
        print(op.name)

希望这能有所帮助。

Answer 1

我不太清楚你所面临的问题；你可以从TF服务输出中得到输出，实际上，在视障Ipython/木星笔记本中，他们也提到了输出格式。

查询保存模型

  """  The given SavedModel SignatureDef contains the following output(s):
    outputs['filtered_detections/map/TensorArrayStack/TensorArrayGatherV3:0'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 300, 4)
        name: filtered_detections/map/TensorArrayStack/TensorArrayGatherV3:0
    outputs['filtered_detections/map/TensorArrayStack_1/TensorArrayGatherV3:0'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 300)
        name: filtered_detections/map/TensorArrayStack_1/TensorArrayGatherV3:0
    outputs['filtered_detections/map/TensorArrayStack_2/TensorArrayGatherV3:0'] tensor_info:
        dtype: DT_INT32
        shape: (-1, 300)
        name: filtered_detections/map/TensorArrayStack_2/TensorArrayGatherV3:0
  Method name is: tensorflow/serving/predict
  ---
  From retina-net
  In general, inference of the network works as follows:
  boxes, scores, labels = model.predict_on_batch(inputs)
  Where `boxes` are shaped `(None, None, 4)` (for `(x1, y1, x2, y2)`), scores is shaped `(None, None)` (classification score) and labels is shaped `(None, None)` (label corresponding to the score). In all three outputs, the first dimension represents the shape and the second dimension indexes the list of detections.
"""