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Getting Started - TensorFlow

In the main export directory there is the exported model and a directory named variables that make up the Tensorflow model. There is also a file named signature.json which contains information about your Lobe project. With these, you are ready to use your model! If you want to see an example of how to use this model, there are instructions below for running a quick test script.

Example Content

signature.json is created by Lobe and contains information about the model such as label names and the image size and shape the model expects. is a simple script to quickly test your exported model. It takes a path to an image on your file system, prepares the image and returns the predicted class and confidence level.

requirements.txt is where the Python libraries and version information required to run the script are found.

Run Example

You will need Python 3.6 and the path to an image on your machine to test.

Create a virtual environment

python -m venv tf-venv

Activate the virtual environment

macOS source tf-venv/bin/activate

Windows tf-venv/Scripts/activate

Install the the dependencies for the example

python -m pip install --upgrade pip && pip install -r requirements.txt

Run the example and see the model output

python path/to/image/for/testing

# -------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# -------------------------------------------------------------
Skeleton code showing how to load and run the TensorFlow SavedModel export package from Lobe.
import argparse
import os
import json
import tensorflow as tf
from PIL import Image
import numpy as np
MODEL_DIR = os.path.join(os.path.dirname(__file__), "..") # default assume that our export is in this file's parent directory
class Model(object):
def __init__(self, model_dir=MODEL_DIR):
# make sure our exported SavedModel folder exists
model_path = os.path.realpath(model_dir)
if not os.path.exists(model_path):
raise ValueError(f"Exported model folder doesn't exist {model_dir}")
self.model_path = model_path
# load our signature json file, this shows us the model inputs and outputs
# you should open this file and take a look at the inputs/outputs to see their data types, shapes, and names
with open(os.path.join(model_path, "signature.json"), "r") as f:
self.signature = json.load(f)
self.inputs = self.signature.get("inputs")
self.outputs = self.signature.get("outputs")
# placeholder for the tensorflow session
self.session = None
def load(self):
# create a new tensorflow session
self.session = tf.compat.v1.Session(graph=tf.Graph())
# load our model into the session
tf.compat.v1.saved_model.loader.load(sess=self.session, tags=self.signature.get("tags"), export_dir=self.model_path)
def predict(self, image: Image.Image):
# load the model if we don't have a session
if self.session is None:
# get the image width and height
width, height = image.size
# center crop image (you can substitute any other method to make a square image, such as just resizing or padding edges with 0)
if width != height:
square_size = min(width, height)
left = (width - square_size) / 2
top = (height - square_size) / 2
right = (width + square_size) / 2
bottom = (height + square_size) / 2
# Crop the center of the image
image = image.crop((left, top, right, bottom))
# now the image is square, resize it to be the right shape for the model input
if "Image" not in self.inputs:
raise ValueError("Couldn't find Image in model inputs - please report issue to Lobe!")
input_width, input_height = self.inputs["Image"]["shape"][1:3]
if image.width != input_width or image.height != input_height:
image = image.resize((input_width, input_height))
# make 0-1 float instead of 0-255 int (that PIL Image loads by default)
image = np.asarray(image) / 255.0
# create the feed dictionary that is the input to the model
# first, add our image to the dictionary (comes from our signature.json file)
feed_dict = {self.inputs["Image"]["name"]: [image]}
# list the outputs we want from the model -- these come from our signature.json file
# since we are using dictionaries that could have different orders, make tuples of (key, name) to keep track for putting
# the results back together in a dictionary
fetches = [(key, output["name"]) for key, output in self.outputs.items()]
# run the model! there will be as many outputs from as you have in the fetches list
outputs =[name for _, name in fetches], feed_dict=feed_dict)
# do a bit of postprocessing
results = {}
# since we actually ran on a batch of size 1, index out the items from the returned numpy arrays
for i, (key, _) in enumerate(fetches):
val = outputs[i].tolist()[0]
if isinstance(val, bytes):
val = val.decode()
results[key] = val
return results
def cleanup(self):
# close our tensorflow session if one exists
if self.session is not None:
self.session = None
def __del__(self):
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Predict a label for an image.")
parser.add_argument("image", help="Path to your image file.")
args = parser.parse_args()
if os.path.isfile(args.image):
image =
# convert to rgb image if this isn't one
if image.mode != "RGB":
image = image.convert("RGB")
model = Model()
outputs = model.predict(image)
print(f"Predicted: {outputs}")
print(f"Couldn't find image file {args.image}")
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