Python 对TensorFlow服务的RaggedSensor请求失败

我创建了一个使用粗糙传感器的TensorFlow模型。当调用

Model.predict

时，Model运行良好，我得到了预期的结果

input = tf.ragged.constant([[[-0.9984272718429565, -0.9422321319580078, -0.27657580375671387, -3.185823678970337, -0.6360141634941101, -1.6579184532165527, -1.9000954627990723, -0.49169546365737915, -0.6758883595466614, -0.6677696704864502, -0.532067060470581], 
                                [-0.9984272718429565, -0.9421600103378296, 2.2048349380493164, -1.273996114730835, -0.6360141634941101, -1.5917999744415283, 0.6147914528846741, -0.49169546365737915, -0.6673409938812256, -0.6583622694015503, -0.5273991227149963], 
                                [-0.9984272718429565, -0.942145586013794, 2.48842453956604, -1.6836735010147095, -0.6360141634941101, -1.5785763263702393, -1.900200605392456, -0.49169546365737915, -0.6656315326690674, -0.6583622694015503, -0.5273991227149963], 
]])
model.predict(input)

>> array([[0.5138151 , 0.3277698 , 0.26122513]], dtype=float32)

我已将该模型部署到TensorFlow服务服务器，并使用以下代码调用：

import json
import requests
headers = {"content-type": "application/json"}
data = json.dumps({"instances":[
    [-1.3523329846758267, ... more data ],
    [-1.3523329846758267, ... more data ],
    [-1.3523329846758267, ... more data ],
    [-1.3523329846758267, ... more data ,
    [-1.3523329846758267, ... more data ],
    [-1.3523329846758267, ... more data ],
    [-1.3523329846758267, ... more data ],
    [-1.3523329846758267, ... more data })
json_response = requests.post('http://localhost:8501/v1/models/fashion_model:predict', data=data, headers=headers)
predictions = json.loads(json_response.text)

但是我得到了以下错误：

"instances is a plain list, but expecting list of objects as multiple input tensors required as per tensorinfo_map"

我的型号说明：

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['__saved_model_init_op']:
  The given SavedModel SignatureDef contains the following input(s):
  The given SavedModel SignatureDef contains the following output(s):
    outputs['__saved_model_init_op'] tensor_info:
        dtype: DT_INVALID
        shape: unknown_rank
        name: NoOp
  Method name is: 

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['args_0'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 11)
        name: serving_default_args_0:0
    inputs['args_0_1'] tensor_info:
        dtype: DT_INT64
        shape: (-1)
        name: serving_default_args_0_1:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['dense_2'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 3)
        name: StatefulPartitionedCall:0
  Method name is: tensorflow/serving/predict
WARNING: Logging before flag parsing goes to stderr.
W0124 09:33:16.365564 140189730998144 deprecation.py:506] From /usr/local/lib/python2.7/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling __init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

Defined Functions:
  Function Name: '__call__'
    Option #1
      Callable with:
        Argument #1
          DType: RaggedTensorSpec
          Value: RaggedTensorSpec(TensorShape([None, None, 11]), tf.float32, 1, tf.int64)
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          DType: RaggedTensorSpec
          Value: RaggedTensorSpec(TensorShape([None, None, 11]), tf.float32, 1, tf.int64)
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None

  Function Name: '_default_save_signature'
    Option #1
      Callable with:
        Argument #1
          DType: RaggedTensorSpec
          Value: RaggedTensorSpec(TensorShape([None, None, 11]), tf.float32, 1, tf.int64)

  Function Name: 'call_and_return_all_conditional_losses'
    Option #1
      Callable with:
        Argument #1
          DType: RaggedTensorSpec
          Value: RaggedTensorSpec(TensorShape([None, None, 11]), tf.float32, 1, tf.int64)
        Argument #2
          DType: bool
          Value: True
        Argument #3
          DType: NoneType
          Value: None
    Option #2
      Callable with:
        Argument #1
          DType: RaggedTensorSpec
          Value: RaggedTensorSpec(TensorShape([None, None, 11]), tf.float32, 1, tf.int64)
        Argument #2
          DType: bool
          Value: False
        Argument #3
          DType: NoneType
          Value: None

我错过了什么

更新： 在检查了

saved\u model\u cli

output之后，我怀疑应该将请求作为如下对象发送，但我不确定输入

{
  "instances": [
    {
      "args_0": nested-list ?,
      "args_0_1": ???
    }
  ]
}

更新2 为了测试这个场景，Colab中包含了一个下载模型的链接

更新3:

正如@Niteya Shah所建议的，我用以下方法调用了API：

data = json.dumps({
 "inputs": {
   "args_0": [[-0.9984272718429565, -0.9422321319580078, -0.27657580375671387, -3.185823678970337, -0.6360141634941101, -1.6579184532165527, -1.9000954627990723, -0.49169546365737915, -0.6758883595466614, -0.6677696704864502, -0.532067060470581], 
              [-0.9984272718429565, -0.9421600103378296, 2.2048349380493164, -1.273996114730835, -0.6360141634941101, -1.5917999744415283, 0.6147914528846741, -0.49169546365737915, -0.6673409938812256, -0.6583622694015503, -0.5273991227149963]],
   "args_0_1": [1, 2]  #Please Check what inputs come here ?
  }
})

并且得到了结果（最终！）：

然后用相同的数据调用模型，如下所示：

import numpy as np
input = tf.ragged.constant([[
                            [-0.9984272718429565, -0.9422321319580078, -0.27657580375671387, -3.185823678970337, -0.6360141634941101, -1.6579184532165527, -1.9000954627990723, -0.49169546365737915, -0.6758883595466614, -0.6677696704864502, -0.532067060470581], 
                            [-0.9984272718429565, -0.9421600103378296, 2.2048349380493164, -1.273996114730835, -0.6360141634941101, -1.5917999744415283, 0.6147914528846741, -0.49169546365737915, -0.6673409938812256, -0.6583622694015503, -0.5273991227149963]
]])
model.predict(input)

得到了不同的结果：

array([[0.4817084 , 0.3649785 , 0.01603118]], dtype=float32)

我想我还是不在那里。

我认为您需要使用RESTAPI中推荐的列格式，而不是行格式，因为第0个输入的维度不匹配。 这意味着您将不得不使用输入而不是实例。 由于您也有多个输入，因此还必须将其作为命名输入提及

示例数据请求可能如下所示

data = json.dumps({
 "inputs": {
   "args_0": [[-0.9984272718429565, -0.9422321319580078, -0.27657580375671387, -3.185823678970337, -0.6360141634941101, -1.6579184532165527, -1.9000954627990723, -0.49169546365737915, -0.6758883595466614, -0.6677696704864502, -0.532067060470581], 
              [-0.9984272718429565, -0.9421600103378296, 2.2048349380493164, -1.273996114730835, -0.6360141634941101, -1.5917999744415283, 0.6147914528846741, -0.49169546365737915, -0.6673409938812256, -0.6583622694015503, -0.5273991227149963]],
   "args_0_1": [10, 11]  #Substitute this with the correct row partition values. 
  }
})

编辑：

我从中了解到了不规则张量，似乎第二个输入可能是行分区。我在文档中找不到关于通常使用的行分区样式的信息，所以我使用的是行长度方法。

---

幸运的是，TensorFlow ragged提供了为我们这样做的服务。使用

值

和

行分割

属性访问它们。那应该行。

谢谢你的回复，那么args_0_1到底是什么呢？我尝试了你的解决方案，但是我认为我用错了，因为我在使用

模型时得到了不同的结果。使用相同的数据预测
（请参阅我的更新）args_0_1是你模型的命名输入。如果查看签名定义，它是第二个命名输入。我认为这也是导致您输入错误的原因之一。我更新了我的答案来解决这个问题。你确定你的模型是确定性的吗？否则，这可能会解释不同的输出。模型是确定的，问题是第二个参数中给出的行分割值
data = json.dumps({
 "inputs": {
   "args_0": [[-0.9984272718429565, -0.9422321319580078, -0.27657580375671387, -3.185823678970337, -0.6360141634941101, -1.6579184532165527, -1.9000954627990723, -0.49169546365737915, -0.6758883595466614, -0.6677696704864502, -0.532067060470581], 
              [-0.9984272718429565, -0.9421600103378296, 2.2048349380493164, -1.273996114730835, -0.6360141634941101, -1.5917999744415283, 0.6147914528846741, -0.49169546365737915, -0.6673409938812256, -0.6583622694015503, -0.5273991227149963]],
   "args_0_1": [10, 11]  #Substitute this with the correct row partition values. 
  }
})