tensorflow pb to tflite 精度下降详解

脚本专栏 2024/11/24 佚名

3 2 1

之前希望在手机端使用深度模型做OCR，于是尝试在手机端部署tensorflow模型，用于图像分类。

思路主要是想使用tflite部署到安卓端，但是在使用tflite的时候发现模型的精度大幅度下降，已经不能支持业务需求了，最后就把OCR模型调用写在服务端了，但是精度下降的原因目前也没有找到，现在这里记录一下。

工作思路：

1.训练图像分类模型；2.模型固化成pb；3.由pb转成tflite文件；

但是使用python 的tf interpreter 调用tflite文件就已经出现精度下降的问题，android端部署也是一样。

1.网络结构

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
 
import tensorflow as tf
slim = tf.contrib.slim
 
def ttnet(images, num_classes=10, is_training=False,
   dropout_keep_prob=0.5,
   prediction_fn=slim.softmax,
   scope='TtNet'):
 end_points = {}
 
 with tf.variable_scope(scope, 'TtNet', [images, num_classes]):
 net = slim.conv2d(images, 32, [3, 3], scope='conv1')
 # net = slim.conv2d(images, 64, [3, 3], scope='conv1_2')
 net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
 net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='bn1')
 # net = slim.conv2d(net, 128, [3, 3], scope='conv2_1')
 net = slim.conv2d(net, 64, [3, 3], scope='conv2')
 net = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
 net = slim.conv2d(net, 128, [3, 3], scope='conv3')
 net = slim.max_pool2d(net, [2, 2], 2, scope='pool3')
 net = slim.conv2d(net, 256, [3, 3], scope='conv4')
 net = slim.max_pool2d(net, [2, 2], 2, scope='pool4')
 net = slim.batch_norm(net, activation_fn=tf.nn.relu, scope='bn2')
 # net = slim.conv2d(net, 512, [3, 3], scope='conv5')
 # net = slim.max_pool2d(net, [2, 2], 2, scope='pool5')
 net = slim.flatten(net)
 end_points['Flatten'] = net
 
 # net = slim.fully_connected(net, 1024, scope='fc3')
 net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
      scope='dropout3')
 logits = slim.fully_connected(net, num_classes, activation_fn=None,
         scope='fc4') 
 end_points['Logits'] = logits
 end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
 
 return logits, end_points
ttnet.default_image_size = 28
 
def ttnet_arg_scope(weight_decay=0.0):
 with slim.arg_scope(
  [slim.conv2d, slim.fully_connected],
  weights_regularizer=slim.l2_regularizer(weight_decay),
  weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
  activation_fn=tf.nn.relu) as sc:
 return sc

基于slim，由于是一个比较简单的分类问题，网络结构也很简单，几个卷积加池化。

测试效果是很棒的。真实样本测试集能达到99%+的准确率。

2.模型固化，生成pb文件

#coding:utf-8
 
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from nets import nets_factory
import cv2
import os
import numpy as np
from datasets import dataset_factory
from preprocessing import preprocessing_factory
from tensorflow.python.platform import gfile
slim = tf.contrib.slim
#todo
#support arbitray image size and num_class
 
tf.app.flags.DEFINE_string(
 'checkpoint_path', '/tmp/tfmodel/',
 'The directory where the model was written to or an absolute path to a '
 'checkpoint file.')
 
tf.app.flags.DEFINE_string(
 'model_name', 'inception_v3', 'The name of the architecture to evaluate.')
tf.app.flags.DEFINE_string(
 'preprocessing_name', None, 'The name of the preprocessing to use. If left '
 'as `None`, then the model_name flag is used.')
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer(
 'eval_image_size', None, 'Eval image size')
tf.app.flags.DEFINE_integer(
 'eval_image_height', None, 'Eval image height')
tf.app.flags.DEFINE_integer(
 'eval_image_width', None, 'Eval image width')
tf.app.flags.DEFINE_string(
 'export_path', './ttnet_1.0_37_32.pb', 'the export path of the pd file')
FLAGS = tf.app.flags.FLAGS
NUM_CLASSES = 37
 
def main(_):
 network_fn = nets_factory.get_network_fn(
  FLAGS.model_name,
  num_classes=NUM_CLASSES,
  is_training=False)
 # pre_image = tf.placeholder(tf.float32, [None, None, 3], name='input_data')
 # preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
 # image_preprocessing_fn = preprocessing_factory.get_preprocessing(
 #  preprocessing_name,
 #  is_training=False)
 # image = image_preprocessing_fn(pre_image, FLAGS.eval_image_height, FLAGS.eval_image_width)
 # images2 = tf.expand_dims(image, 0)
 images2 = tf.placeholder(tf.float32, (None,32, 32, 3),name='input_data')
 logits, endpoints = network_fn(images2)
 with tf.Session() as sess:
 output = tf.identity(endpoints['Predictions'],name="output_data")
 with gfile.GFile(FLAGS.export_path, 'wb') as f:
  f.write(sess.graph_def.SerializeToString())
 
if __name__ == '__main__':
 tf.app.run()

3.生成tflite文件

import tensorflow as tf
 
graph_def_file = "/datastore1/Colonist_Lord/Colonist_Lord/workspace/models/model_files/passport_model_with_tflite/ocr_frozen.pb"
input_arrays = ["input_data"]
output_arrays = ["output_data"]
 
converter = tf.lite.TFLiteConverter.from_frozen_graph(
 graph_def_file, input_arrays, output_arrays)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)

使用pb文件进行测试，效果正常；使用tflite文件进行测试，精度下降严重。下面附上pb与tflite测试代码。

pb测试代码

with tf.gfile.GFile(graph_filename, "rb") as f:
 graph_def = tf.GraphDef()
 graph_def.ParseFromString(f.read())
 
with tf.Graph().as_default() as graph:
 tf.import_graph_def(graph_def)
 input_node = graph.get_tensor_by_name('import/input_data:0')
 output_node = graph.get_tensor_by_name('import/output_data:0')
 with tf.Session() as sess:
  for image_file in image_files:
   abs_path = os.path.join(image_folder, image_file)
   img = cv2.imread(abs_path).astype(np.float32)
   img = cv2.resize(img, (int(input_node.shape[1]), int(input_node.shape[2])))
   output_data = sess.run(output_node, feed_dict={input_node: [img]})
   index = np.argmax(output_data)
   label = dict_laebl[index]
   dst_floder = os.path.join(result_folder, label)
   if not os.path.exists(dst_floder):
    os.mkdir(dst_floder)
   cv2.imwrite(os.path.join(dst_floder, image_file), img)
   count += 1

tflite测试代码


model_path = "converted_model.tflite" #"/datastore1/Colonist_Lord/Colonist_Lord/data/passport_char/ocr.tflite"
interpreter = tf.contrib.lite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()
 
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
for image_file in image_files:
 abs_path = os.path.join(image_folder,image_file)
 img = cv2.imread(abs_path).astype(np.float32)
 img = cv2.resize(img, tuple(input_details[0]['shape'][1:3]))
 # input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
 interpreter.set_tensor(input_details[0]['index'], [img])
 
 interpreter.invoke()
 output_data = interpreter.get_tensor(output_details[0]['index'])
 index = np.argmax(output_data)
 label = dict_laebl[index]
 dst_floder = os.path.join(result_folder,label)
 if not os.path.exists(dst_floder):
  os.mkdir(dst_floder)
 cv2.imwrite(os.path.join(dst_floder,image_file),img)
 count+=1

最后也算是绕过这个问题解决了业务需求，后面有空的话，还是会花时间研究一下这个问题。

如果有哪个大佬知道原因，希望不吝赐教。

补充知识：.pb 转tflite代码，使用量化，减小体积，converter.post_training_quantize = True

import tensorflow as tf

path = "/home/python/Downloads/a.pb" # pb文件位置和文件名
inputs = ["input_images"] # 模型文件的输入节点名称
classes = ['feature_fusion/Conv_7/Sigmoid','feature_fusion/concat_3'] # 模型文件的输出节点名称
# converter = tf.contrib.lite.TocoConverter.from_frozen_graph(path, inputs, classes, input_shapes={'input_images':[1, 320, 320, 3]})
converter = tf.lite.TFLiteConverter.from_frozen_graph(path, inputs, classes,
              input_shapes={'input_images': [1, 320, 320, 3]})
converter.post_training_quantize = True
tflite_model = converter.convert()
open("/home/python/Downloads/aNew.tflite", "wb").write(tflite_model)

以上这篇tensorflow pb to tflite 精度下降详解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。

tensorflow,tflite,精度下降

标签：

tensorflow,tflite,精度下降

免责声明：本站文章均来自网站采集或用户投稿，网站不提供任何软件下载或自行开发的软件！如有用户或公司发现本站内容信息存在侵权行为，请邮件告知！ 858582#qq.com

评论“tensorflow pb to tflite 精度下降详解”

tensorflow pb to tflite 精度下降详解

暂无“tensorflow pb to tflite 精度下降详解”评论...

tensorflow pb to tflite 精度下降详解

tensorflow,tflite,精度下降

Pytorch转onnx、torchscript方式

使用pandas库对csv文件进行筛选保存

评论“tensorflow pb to tflite 精度下降详解”

《魔兽世界》大逃杀！60人新游玩模式《强袭风暴》3月21日上线

更新动态

友情链接