import tensorflow as tf
from tensorflow.python.tools import optimize_for_inference_lib
from tensorflow.tools.graph_transforms import TransformGraph
pb_file = 'savedmodeldir/efficientdet-d0_frozen.pb'
graph_def = tf.compat.v1.GraphDef()
try:
    with tf.io.gfile.GFile(pb_file, 'rb') as f:
        graph_def.ParseFromString(f.read())
except:
    with tf.gfile.FastGFile(pb_file, 'rb') as f:
        graph_def.ParseFromString(f.read())
graph_def = optimize_for_inference_lib.optimize_for_inference(graph_def, ['image_arrays'], ['detections'], tf.uint8.as_datatype_enum)
graph_def = TransformGraph(graph_def, ['image_arrays'], ['detections'], ['fold_constants'])
with tf.gfile.FastGFile('efficientdet-d0_opt.pb', 'wb') as f:
   f.write(graph_def.SerializeToString())

import cv2 as cv
net = cv.dnn_DetectionModel('efficientdet-d0_opt.pb', 'efficientdet-d0_opt.pbtxt')
net.setInputSize(512, 512)
net.setInputScale(1.0 / 255)
net.setInputMean((123.675, 116.28, 103.53))
frame = cv.imread('example.jpg')
classes, confidences, boxes = net.detect(frame, confThreshold=0.5, nmsThreshold=0.4)
for classId, confidence, box in zip(classes.flatten(), confidences.flatten(), boxes):
    cv.rectangle(frame, box, color=(255, 0, 0), thickness=3)
    cv.rectangle(frame, box, color=(0, 255, 0), thickness=1)
cv.imshow('out', frame)
cv.waitKey()