Google CloudML serving_input_receiver_fn() b64 decode error

Question

I am sending a base64 encoded image via AJAX POST to a model stored in Google CloudML. I am getting an error telling me that my input_fn(): is failing to decode the image and transform it into jpeg.

Error:

Prediction failed: Error during model execution: 
AbortionError(code=StatusCode.INVALID_ARGUMENT,  
details="Expected image (JPEG, PNG, or GIF), got 
unknown format starting with 'u\253Z\212f\240{\370
\351z\006\332\261\356\270\377' [[{{node map/while
/DecodeJpeg}} = DecodeJpeg[_output_shapes=
[[?,?,3]], acceptable_fraction=1, channels=3, 
dct_method="", fancy_upscaling=true, ratio=1, 
try_recover_truncated=false, 
_device="/job:localhost/replica:0 /task:0
/device:CPU:0"](map/while/TensorArrayReadV3)]]")

Below is the full Serving_input_receiver_fn():

The first step I believe is to handle the incoming b64 encoded string and decode it. This is done with:

image = tensorflow.io.decode_base64(image_str_tensor)
The next step I believe is to open the bytes, but this is where I dont know how to handle the decoded b64 string with tensorflow code and need help.

With a python Flask app this can be done with:

image = Image.open(io.BytesIO(decoded))

pass the bytes through to get decoded by tf.image.decode_jpeg ????

image = tensorflow.image.decode_jpeg(image_str_tensor, channels=CHANNELS)

Full input_fn(): code

def serving_input_receiver_fn(): 
   def prepare_image(image_str_tensor): 
   image = tensorflow.io.decode_base64(image_str_tensor)
   image = tensorflow.image.decode_jpeg(image_str_tensor, channels=CHANNELS)
   image = tensorflow.expand_dims(image, 0) image = tensorflow.image.resize_bilinear(image, [HEIGHT, WIDTH], align_corners=False)
   image = tensorflow.squeeze(image, axis=[0])    
   image = tensorflow.cast(image, dtype=tensorflow.uint8) 
   return image

How do I decode my b64 string back into jpeg and then convert the jpeg to a tensor?

Can you show your image_str_tensor? AFAIK TF serving http can encode the image base64 string as JSON. — johnjohnlys, Feb 18 '19 at 06:16
Thanks for pointing out the image_str_tensor, johnjohnlys. Something that has been bothering me. It just shows up in this answer: (https://stackoverflow.com/questions/51432589/how-do-i-get-a-tensorflow-keras-model-that-takes-images-as-input-to-serve-predic) and this answer: (https://github.com/mhwilder/tf-keras-gcloud-deployment/blob/master/export_models.py) -- as you can see, I just copied from the first answer and it appears image_str_tensor just passes through without being declared. — Pysnek313, Feb 18 '19 at 11:59
I already have my image encoded and sent with json.stringify via Ajax post. It's decoding and converting back to jpeg once it gets to the serving input receiver function. — Pysnek313, Feb 18 '19 at 17:01
to send an image (binary) input as part of http payload, ml engine requires you to send it as base64 encoded. ML engine will *decode* it before sending it to your graph. So you don't need a decode in your graph. — Bhupesh, Mar 05 '19 at 02:13

score 0 · Accepted Answer · answered Aug 22 '19 at 22:47

This is a sample for processing b64 images.

HEIGHT = 224
WIDTH = 224
CHANNELS = 3
IMAGE_SHAPE = (HEIGHT, WIDTH)
version = 'v1'

def serving_input_receiver_fn():
    def prepare_image(image_str_tensor):
        image = tf.image.decode_jpeg(image_str_tensor, channels=CHANNELS)
        return image_preprocessing(image)

    input_ph = tf.placeholder(tf.string, shape=[None])
    images_tensor = tf.map_fn(
        prepare_image, input_ph, back_prop=False, dtype=tf.uint8)
    images_tensor = tf.image.convert_image_dtype(images_tensor, dtype=tf.float32)

    return tf.estimator.export.ServingInputReceiver(
        {'input': images_tensor},
        {'image_bytes': input_ph})

export_path = os.path.join('/tmp/models/json_b64', version)
if os.path.exists(export_path):  # clean up old exports with this version
    shutil.rmtree(export_path)
estimator.export_savedmodel(
    export_path,
    serving_input_receiver_fn=serving_input_receiver_fn)

Google CloudML serving_input_receiver_fn() b64 decode error

1 Answers1