1、下载slim模型包：
cd /Users/javalong/Download
git clone https://github.com/tensorflow/models/
2、 数据可以是slim提供的数据集或者是自己采集的图片
2.1、下载slim提供的数据集flowers
2.1.1、设置下载目录命令：
DATA_DIR=/Users/javalong/Desktop/Test/output/flowers
2.1.2、进入到slim模型目录命令：
cd /Users/javalong/Downloads/models-master/slim
2.1.3、下载数据集命令：
python3 download_and_convert_data.py \
--dataset_name=flowers \
--dataset_dir="${DATA_DIR}"
2.1.4、查看目录下的文件命令：
ls ${DATA_DIR}
得到：
flowers_train-00000-of-00005.tfrecord
...
flowers_train-00004-of-00005.tfrecord
flowers_validation-00000-of-00005.tfrecord
...
flowers_validation-00004-of-00005.tfrecord
labels.txt
2.2、我们可以看到下载slim提供的数据文件是tfrecord格式，所以我们要训练自己采集的图片，第一步先将图片转换成tfrecord格式。
2.2.1、将图片转换成TFRecord文件，需要安装的软件
pip3 install Pillow
pip3 install matplotlib
2.2.2、在/Users/javalong/Downloads/models-master/slim下创建一个fu_img_to_tfrecord.py文件。
如图：
![a665c93bb752afbd2964b75354a376c14c42eea6]()
2.2.3、fu_img_to_tfrecord.py的内容为：
import os
import os.path
import tensorflow as tf
from PIL import Image
import matplotlib.pyplot as plt
import sys
import pprint
pp = pprint.PrettyPrinter(indent = 2)
data_dir=sys.argv[1]
train_dir=sys.argv[2]
classes=[]
for dir in os.listdir(data_dir):
path = os.path.join(data_dir, dir)
if os.path.isdir(path):
classes.append(dir)
train= tf.python_io.TFRecordWriter(train_dir+"/iss_train.tfrecord")
test= tf.python_io.TFRecordWriter(train_dir+"/iss_test.tfrecord")
def int64_feature(values):
if not isinstance(values, (tuple, list)):
values = [values]
return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
def bytes_feature(values):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))
def image_to_tfexample(image_data, image_format, height, width, class_id):
return tf.train.Example(features=tf.train.Features(feature={
'image/encoded': bytes_feature(image_data),
'image/format': bytes_feature(image_format),
'image/class/label': int64_feature(class_id),
'image/height': int64_feature(height),
'image/width': int64_feature(width),
}))
def get_extension(path):
return os.path.splitext(path)[1]
class ImageReader(object):
"""Helper class that provides TensorFlow image coding utilities."""
def __init__(self):
# Initializes function that decodes RGB JPEG data.
self._decode_jpeg_data = tf.placeholder(dtype=tf.string)
self._decode_jpeg = tf.image.decode_jpeg(self._decode_jpeg_data, channels=3)
def read_image_dims(self, sess, image_data):
image = self.decode_jpeg(sess, image_data)
return image.shape[0], image.shape[1]
def decode_jpeg(self, sess, image_data):
image = sess.run(self._decode_jpeg,
feed_dict={self._decode_jpeg_data: image_data})
assert len(image.shape) == 3
assert image.shape[2] == 3
return image
def write_label_file(labels_to_class_names, dataset_dir,
filename='lables.txt'):
"""Writes a file with the list of class names.
Args:
labels_to_class_names: A map of (integer) labels to class names.
dataset_dir: The directory in which the labels file should be written.
filename: The filename where the class names are written.
"""
labels_filename = os.path.join(dataset_dir, filename)
with tf.gfile.Open(labels_filename, 'w') as f:
for label in labels_to_class_names:
class_name = labels_to_class_names[label]
f.write('%d:%s\n' % (label, class_name))
lable_file=train_dir+'/lable.txt'
lable_input=open(lable_file, 'w')
info_file=train_dir+'/meta_info.txt'
test_num=0;
train_num=0;
with tf.Graph().as_default():
image_reader = ImageReader()
with tf.Session('') as sess:
for index,name in enumerate(classes):
lable_input.write('%d:%s\n' % (index, name))
class_path=data_dir+'/'+name+'/'
for num, img_name in enumerate(os.listdir(class_path)):
img_path=class_path+img_name
format=get_extension(img_name)
image_data = tf.gfile.FastGFile(img_path, 'rb').read()
height, width = image_reader.read_image_dims(sess, image_data)
example = image_to_tfexample(image_data, b'jpg', height, width, index)
if num % 5 == 0:
test_num= test_num+1
#pass
#print img_path + " " + str(index) + " " + name
test.write(example.SerializeToString())
else:
train_num=train_num+1
train.write(example.SerializeToString())
#print img_path + " " + str(index) + " " + name
train.close()
test.close()
info_input=open(info_file,'w')
info_input.write("train_num:"+str(train_num)+'\n')
info_input.write("test_num:"+str(test_num)+'\n')
info_input.close()
lable_input.close()
2.2.4、执行转换命令：
python3 /Users/javalong/Downloads/models-master/slim/fu_img_to_tfrecord.py /Users/javalong/Desktop/flowers /Users/javalong/Desktop/flower_record
注：
2.2.5、/Users/javalong/Desktop/flowers是存放采集的图片，如图：
![a9fb532a0a37402e2a2063e65e8518763175e0e4]()
2.2.6、/Users/javalong/Desktop/flower_record是生成的tfrecord格式文件存放目录。最终生成的文件如图：
![f2ff611499c9a63665c7f550c5804f95e8246afd]()
2.2.7、使用/Users/javalong/Desktop/flowers目录的子目录名作为分类文本会存储到生成的label.txt中。如图：
![e41d2c85d9b9be759fa8bca267c6ee00e0e272be]()
2.2.8、fu_img_to_tfrecord.py功能实现参考/Users/javalong/Downloads/models-master/slim/datasets/download_and_convert_flowers.py文件
3、用预训练数据集inception_v3来训练数据集flowers
3.1、设置相应的目录：
DATASET_DIR=/Users/javalong/Desktop/Test/output/flowers
CHECKPOINT_PATH=/Users/javalong/Desktop/Test/output/inception/inception_v3.ckpt
TRAIN_DIR=/Users/javalong/Desktop/Test/output/tran
3.2、训练命令：
python3 train_image_classifier.py \
--train_dir=${TRAIN_DIR} \
--dataset_dir=${DATASET_DIR} \
--dataset_name=flowers \
--dataset_split_name=train \
--model_name=inception_v3 \
--checkpoint_path=${CHECKPOINT_PATH} \
--checkpoint_exclude_scopes=InceptionV3/Logits,InceptionV3/AuxLogits \
--trainable_scopes=InceptionV3/Logits,InceptionV3/AuxLogits \
--clone_on_cpu=true
4、生成.pb文件
4.1、在/Users/javalong/Downloads/models-master/slim下创建一个bbb.py文件。
如图：
![c3fc804bf05dacac52d013da34c160cdeb47c057]()
4.2、bbb.py的内容为：
import os
import tensorflow as tf
import tensorflow.contrib.slim as slim
from nets import inception
from nets import inception_v1
from nets import inception_v3
from nets import nets_factory
from tensorflow.python.framework import graph_util
from tensorflow.python.platform import gfile
from google.protobuf import text_format
checkpoint_path = tf.train.latest_checkpoint('/Users/javalong/Desktop/Test/output/tran')
with tf.Graph().as_default() as graph:
input_tensor = tf.placeholder(tf.float32, shape=(None, 299, 299, 3), name='input_image')
with tf.Session() as sess:
# with tf.variable_scope('model') as scope:
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(input_tensor, num_classes=5, is_training=False)
saver = tf.train.Saver()
saver.restore(sess, checkpoint_path)
output_node_names = 'InceptionV3/Predictions/Reshape_1'
input_graph_def = graph.as_graph_def()
output_graph_def = graph_util.convert_variables_to_constants(sess, input_graph_def, output_node_names.split(","))
with open('/Users/javalong/Desktop/Test/output/output_graph_nodes.txt', 'w') as f:
f.write(text_format.MessageToString(output_graph_def))
output_graph = '/Users/javalong/Desktop/Test/output/inception_v3_final.pb'
with gfile.FastGFile(output_graph, 'wb') as f:
f.write(output_graph_def.SerializeToString())
5、优化模型并去掉iOS不支持的算子
查考此篇文章
