I've created a TensorFlow model that uses RaggedTensors. Model works fine and when calling model.predict
and I get the expected results.
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)
I've deployed the model to a TensorFlow serving server and using the following code to invoke:
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)
But then I get the following error:
"instances is a plain list, but expecting list of objects as multiple input tensors required as per tensorinfo_map"
My model description:
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
What am I missing?
Update:
After inspecting saved_model_cli
output, I suspect I should send the request as an object like below, but I'm not sure about the inputs...
{
"instances": [
{
"args_0": nested-list ?,
"args_0_1": ???
}
]
}
Update2
A Colab to test this scenario, a link to download the model is included in the Colab.
Update 3:
As suggested by @Niteya Shah I've called the API with:
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 ?
}
})
And got the results (Finally!):
{'outputs': [[0.466771603, 0.455221593, 0.581544757]]}
Then called the model with the same data like so:
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)
And got different results:
array([[0.4817084 , 0.3649785 , 0.01603118]], dtype=float32)
I guess I'm still not there.
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