numpy - Using gcloud ml serving for large images

Question

I have a trained net in tensorflow that i wish to use in gcloud ml-engine serving for prediction.

Predict gcloud ml serving should accept numpy array float32 type images with size of 320x240x3 and return 2 tiny matrices as an output.

Does anyone knows how should i create the input layers that would accept this kind of input type?

I have tried multiple ways, for example using base64 encoded json files, but casting the string into float type produces an error in which it's not supported:

"error": "Prediction failed: Exception during model execution: LocalError(code=StatusCode.UNIMPLEMENTED, details="Cast string to float is not supported
 [[Node: ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _output_shapes=[[-1,320,240,3]], _device="/job:localhost/replica:0/task:0/cpu:0"](ParseExample/ParseExample)]]")"

This is an example of creating the json file (after saving the numpy array above as jpeg):

python -c 'import base64, sys, json; img = base64.b64encode(open(sys.argv[1], "rb").read()); print json.dumps({"images": {"b64": img}})' example_img.jpg &> request.json

And the tensorflow commands attempting to handle the input:

raw_str_input = tf.placeholder(tf.string, name='source')
feature_configs = {
                'image': tf.FixedLenFeature(
                    shape=[], dtype=tf.string),
            }
tf_example = tf.parse_example(raw_str_input, feature_configs)
input = tf.identity(tf.to_float(tf_example['image/encoded']), name='input')

the above is an example of one of the tests done, also tried multiple attempts of different tensorflow commands to handle the input but none of them worked...

Answer 1

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