A python script that loads a tensorflow model into memory without holding the global tensor flow graph hostage

CustomVisionCategorizer.py

import tensorflow as tf
import numpy as np


# The category name and probability percentage
class CategoryScore:
    def __init__(self, category, probability: float):
        self.category = category
        self.probability = probability


# The categorizer handles running tensorflow models
class Categorizer:
    def __init__(self, model_file_path: str, map: []):
        self.map = map

        self.graph = tf.Graph()
        self.graph.as_default()
        self.graph_def = self.graph.as_graph_def()

        with tf.gfile.GFile(model_file_path, 'rb') as f:
            self.graph_def.ParseFromString(f.read())
            tf.import_graph_def(self.graph_def, name='')

        output_layer = 'loss:0'
        self.input_node = 'Placeholder:0'

        self.sess = tf.Session()
        self.prob_tensor = self.sess.graph.get_tensor_by_name(output_layer)

        tf.reset_default_graph()

    def score(self, image):
        predictions, = self.sess.run(self.prob_tensor, {self.input_node: [image]})

        label_index = 0
        scores = []

        for p in predictions:
            category_score = CategoryScore(self.map[label_index],np.float64(np.round(p, 8)))
            scores.append(category_score)
            label_index += 1

        return scores