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。
