I've frozen and exported a SavedModel, which takes as input a batch of videos that has the following format according to saved_model_cli:
The given SavedModel SignatureDef contains the following input(s): inputs['ims_ph'] tensor_info: dtype: DT_UINT8 shape: (1, 248, 224, 224, 3) name: Placeholder:0 inputs['samples_ph'] tensor_info: dtype: DT_FLOAT shape: (1, 173774, 2) name: Placeholder_1:0 The given SavedModel SignatureDef contains the following output(s): ... << OUTPUTS >> ...... Method name is: tensorflow/serving/predict I have a TF-Serving (HTTP/REST) server successfully running locally. From my Python client code, I have 2 populated objects of type numpy.ndarray, named ims of shape (1, 248, 224, 224, 3) -- and samples of shape (1, 173774, 2).
I am trying to run an inference against my TF model server (see client code below) but am receiving the following error: {u'error': u'JSON Parse error: Invalid value. at offset: 0'}
# I have tried the following combinations without success: data = {"instances" : [{"ims_ph": ims.tolist()}, {"samples_ph": samples.tolist()} ]} data = {"inputs" : { "ims_ph": ims, "samples_ph": samples} } r = requests.post(url="http://localhost:9000/v1/models/multisensory:predict", data=data) The TF-Serving REST docs don't seem to indicate that any extra escaping / encoding is required here for these two input tensors. As these aren't binary data, I don't think base64 encoding is the right approach either. Any pointers to a working approach here would be greatly appreciated!
