batrlatom

import onnx
from onnx_tf.backend import prepare
import tensorflow as tf
from tensorflow.python.client import timeline
import time

# Prepare the inputs, here we use numpy to generate some random inputs for demo purpose
import numpy as np
img = np.random.randn(1, 3, 224, 224).astype(np.float32)

batrlatom

import tensorflow as tf
import argparse
import numpy as np
import cv2

def load_graph(frozen_graph_filename):
     with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
         graph_def = tf.GraphDef()
         graph_def.ParseFromString(f.read())

batrlatom

import torch
import torch.onnx
from networks import EmbeddingNetwork

sample_batch_size = 1
channel = 3
height = 224
width = 224

def load(file_path):