Tensorflow Serving returns "Tensor name: probability has no shape information"

For given exported model:

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

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['input'] tensor_info:
        dtype: DT_STRING
        shape: unknown_rank
        name: input_image_as_bytes:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['output'] tensor_info:
        dtype: DT_STRING
        shape: unknown_rank
        name: prediction:0
    outputs['probability'] tensor_info:
        dtype: DT_DOUBLE
        shape: unknown_rank
        name: probability:0
  Method name is: tensorflow/serving/predict

Sending a following request to the serving REST API:

curl -X POST \
  http://localhost:9001/v1/models/testmodel:predict \
  -H 'cache-control: no-cache' \
  -H 'content-type: application/json' \
  -d '{
  "signature_name": "serving_default",
  "instances": [
     { "b64": "/9j/4AAk=" }
   ]
}'

returns an error:

{
    "error": "Tensor name: probability has no shape information "
}

is it something that needs to be adjusted in the export function in aocr?

[MariusMez]

Hi,

Since months I’m stuck on the same problem using the curl command provided as exemple. But today I tried to understand why this error { "error": "Tensor name: probability has no shape information " } occur with the documentation here: https://www.tensorflow.org/serving/api_rest and I found an answer using this curl command instead:

curl -X POST --output - \
  http://yourserver:9001/v1/models/aocr:predict \
  -H 'cache-control: no-cache' \
  -H 'content-type: application/json' \
  -d '{
  "signature_name": "serving_default",
  "inputs": 
     {
     	"input": { "b64": "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" }
     }
}'

Yeah! I’ve the correct output:

{
    "outputs": {
        "probability": 0.999405,
        "output": "TTXJHC"
    }
}

I dont’ know why we have to specify the input tensors in column format instead of using the row format.

Specifying input tensors in row format.

This format is similar to PredictRequest proto of gRPC API and the CMLE predict API. Use this format if all named input tensors have the same 0-th dimension. If they don’t, use the columnar format described later below.

"instances": [ { "b64": "/9j/4AAk==" } ] –> { "error": "Tensor name: probability has no shape information " }

Specifying input tensors in column format.

Use this format to specify your input tensors, if individual named inputs do not have the same 0-th dimension or you want a more compact representation. This format is similar to the inputs field of the gRPC Predict request.

"inputs": { "input": { "b64": "/9j/4AAk==" } } –> Working

[emedvedev]

Oh, that’s interesting. I’ll try to get to it during the weekend.