ML engine serving seems to not be working as intended - tensorflow

Question

While using the following code and doing a gcloud ml-engine local predict I get:
InvalidArgumentError (see above for traceback): You must feed a value
for placeholder tensor 'Placeholder' with dtype string and shape [?]
[[Node: Placeholder = Placeholderdtype=DT_STRING, shape=[?], _device="/job:localhost/replica:0/task:0/device:CPU:0"]] (Error code: 2)
tf_files_path = './tf'
# os.makedirs(tf_files_path) # temp dir
estimator =\
tf.keras.estimator.model_to_estimator(keras_model_path="model_data/yolo.h5",
model_dir=tf_files_path)
#up_one_dir(os.path.join(tf_files_path, 'keras'))
def serving_input_receiver_fn():
def prepare_image(image_str_tensor):
image = tf.image.decode_jpeg(image_str_tensor,
channels=3)
image = tf.divide(image, 255)
image = tf.image.convert_image_dtype(image, tf.float32)
return image
# Ensure model is batchable
# https://stackoverflow.com/questions/52303403/
input_ph = tf.placeholder(tf.string, shape=[None])
images_tensor = tf.map_fn(
prepare_image, input_ph, back_prop=False, dtype=tf.float32)
return tf.estimator.export.ServingInputReceiver(
{model.input_names[0]: images_tensor},
{'image_bytes': input_ph})
export_path = './export'
estimator.export_savedmodel(
export_path,
serving_input_receiver_fn=serving_input_receiver_fn)
The json I am sending to the ml engine looks like this:
{"image_bytes": {"b64": "/9j/4AAQSkZJRgABAQAAAQABAAD/2w..."}}
When not doing a local prediction, but sending it to ML engine itself, I get:
ERROR: (gcloud.ml-engine.predict) HTTP request failed. Response: {
"error": {
"code": 500,
"message": "Internal error encountered.",
"status": "INTERNAL"
}
}
The saved_model_cli gives:
saved_model_cli show --all --dir export/1547848897/
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:
signature_def['serving_default']:
The given SavedModel SignatureDef contains the following input(s):
inputs['image_bytes'] tensor_info:
dtype: DT_STRING
shape: (-1)
name: Placeholder:0
The given SavedModel SignatureDef contains the following output(s):
outputs['conv2d_59'] tensor_info:
dtype: DT_FLOAT
shape: (-1, -1, -1, 255)
name: conv2d_59/BiasAdd:0
outputs['conv2d_67'] tensor_info:
dtype: DT_FLOAT
shape: (-1, -1, -1, 255)
name: conv2d_67/BiasAdd:0
outputs['conv2d_75'] tensor_info:
dtype: DT_FLOAT
shape: (-1, -1, -1, 255)
name: conv2d_75/BiasAdd:0
Method name is: tensorflow/serving/predict
Does anyone see what is going wrong here?

Answer 1

The issue has been resolved. The output of the model appeared to be too big for ml-engine to send it back and it didn't capture it in a more relevant exception than 500 internal error. We added some post-processing steps in the model and it works fine now.
For the gcloud ml-engine local predict command that is returning an error, it seems to be a bug. As the model works on ml-engine now, but still does return this error with local prediction.