对 TensorFlow 服务的 RaggedTensor 请求失败

问题描述

我创建了一个使用 RaggedTensors 的 TensorFlow 模型。模型工作正常，当调用 model.predict 时，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,-1.5917999744415283,0.6147914528846741,-0.6673409938812256,-0.6583622694015503,-0.5273991227149963],-0.942145586013794,2.48842453956604,-1.6836735010147095,-1.5785763263702393,-1.900200605392456,-0.6656315326690674,]])
model.predict(input)

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

我已将模型部署到 TensorFlow 服务服务器并使用以下代码进行调用：

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

但是我收到以下错误：

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

我的模型描述：

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,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,tf.int64)

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

我错过了什么？

更新： 检查 saved_model_cli 输出后，我怀疑我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 用于测试此场景的 Colab，Colab 中包含下载模型的链接。

更新 3：

正如@Niteya Shah 所建议的那样，我调用了 API：

data = json.dumps({
 "inputs": {
   "args_0": [[-0.9984272718429565,-0.5273991227149963]],"args_0_1": [1,2]  #Please Check what inputs come here ?
  }
})

并得到结果（终于！）：

{'outputs': [[0.466771603,0.455221593,0.581544757]]}

然后使用相同的数据调用模型，如下所示：

import numpy as np
input = tf.ragged.constant([[
                            [-0.9984272718429565,-0.5273991227149963]
]])
model.predict(input)

得到了不同的结果：

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

我想我还没到。

解决方法

https://www.tensorflow.org/tfx/serving/api_rest#predict_api

我认为您需要使用 REST API 中推荐的列格式而不是行格式，因为您的第 0 个输入的尺寸不匹配。 这意味着您将不得不使用输入而不是实例。 由于您还有多个输入，因此您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,-1.5917999744415283,0.6147914528846741,-0.6673409938812256,-0.6583622694015503,-0.5273991227149963]],"args_0_1": [10,11]  #Substitute this with the correct row partition values. 
  }
})

编辑：

我从 here 读到 Ragged 张量，似乎第二个输入可能是行分区。我在关于通常使用什么行分区样式的文档中找不到它，所以我使用了行长度方法。 幸运的是，TensorFlow ragged 为我们提供了 methods。使用 values 和 row_splits 属性访问它们。那应该可行。