我已经成功将TensorflowSharp与Faster RCNN一起使用了一段时间；但是，我最近训练了一个Retinanet模型，验证了它可以在python中工作，并创建了一个冻结的pb文件供Tensorflow使用。对于FRCNN，TensorflowSharp GitHub存储库中有一个示例，展示了如何运行/获取此模型。对于Retinanet，我尝试修改代码，但似乎无济于事。我已经尝试了Retinanet的模型摘要，但是对我来说应该使用什么并不明显。
对于FRCNN，图形以以下方式运行：
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的模型摘要中可以明显看出，输入（“ image_tensor”）和输出（“ detection_boxes”，“ detection_scores”，“ detection_classes”和“ num_detections”）是什么。对于Retinanet，它们并不相同（我已经尝试过），而且我不知道它们应该是什么。上面代码的“获取”部分导致崩溃，我猜是因为我没有正确知道节点名称。
我不会在此处粘贴整个Retinanet摘要，但这是前几个节点：
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]
__________________________________________________________________________________________________
这是最后几个节点：
__________________________________________________________________________________________________
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
对于解决此问题的“提取”部分的任何帮助，将不胜感激。
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文件中打印操作名称。当对FRCNN .pb文件执行此操作时，它清楚地给出了输出节点名称，如下所示（仅从python函数的输出中发布最后几行）。
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文件执行相同的操作，则不清楚输出是什么。这是python函数的最后几行。
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，这是我使用的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)
希望这会有所帮助。
答案 0 :(得分：0)
我不确定您面临的问题是什么；您可以从TF Serving输出中获取输出，实际上在视网膜Ipython / Jupyter笔记本中，他们也提到了输出格式
查询保存模型将给出
""" 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.
"""