参考 https://github.com/tensorflow/models/tree/master/slim

使用TensorFlow-Slim进行图像分类

准备

安装TensorFlow

参考 https://www.tensorflow.org/install/

如在Ubuntu下安装TensorFlow with GPU support, python 2.7版本

wget https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl
pip install tensorflow_gpu-1.2.0-cp27-none-linux_x86_64.whl

下载TF-slim图像模型库

cd $WORKSPACE
git clone https://github.com/tensorflow/models/

准备数据

有不少公开数据集，这里以官网提供的Flowers为例。

官网提供了下载和转换数据的代码，为了理解代码并能使用自己的数据，这里参考官方提供的代码进行修改。

cd $WORKSPACE/data
wget http://download.tensorflow.org/example_images/flower_photos.tgz
tar zxf flower_photos.tgz

数据集文件夹结构如下：

flower_photos
├── daisy
│  ├── 100080576_f52e8ee070_n.jpg
│  └── ...
├── dandelion
├── LICENSE.txt
├── roses
├── sunflowers
└── tulips

由于实际情况中我们自己的数据集并不一定把图片按类别放在不同的文件夹里，故我们生成list.txt来表示图片路径与标签的关系。

Python代码：

import os

class_names_to_ids = {'daisy': 0, 'dandelion': 1, 'roses': 2, 'sunflowers': 3, 'tulips': 4}
data_dir = 'flower_photos/'
output_path = 'list.txt'

fd = open(output_path, 'w')
for class_name in class_names_to_ids.keys():
  images_list = os.listdir(data_dir + class_name)
  for image_name in images_list:
    fd.write('{}/{} {}\n'.format(class_name, image_name, class_names_to_ids[class_name]))

fd.close()

为了方便后期查看label标签，也可以定义labels.txt：

daisy
dandelion
roses
sunflowers
tulips

随机生成训练集与验证集：

Python代码：

import random

_NUM_VALIDATION = 350
_RANDOM_SEED = 0
list_path = 'list.txt'
train_list_path = 'list_train.txt'
val_list_path = 'list_val.txt'

fd = open(list_path)
lines = fd.readlines()
fd.close()
random.seed(_RANDOM_SEED)
random.shuffle(lines)

fd = open(train_list_path, 'w')
for line in lines[_NUM_VALIDATION:]:
  fd.write(line)

fd.close()
fd = open(val_list_path, 'w')
for line in lines[:_NUM_VALIDATION]:
  fd.write(line)

fd.close()

生成TFRecord数据：

Python代码：

import sys
sys.path.insert(0, '../models/slim/')
from datasets import dataset_utils
import math
import os
import tensorflow as tf

def convert_dataset(list_path, data_dir, output_dir, _NUM_SHARDS=5):
  fd = open(list_path)
  lines = [line.split() for line in fd]
  fd.close()
  num_per_shard = int(math.ceil(len(lines) / float(_NUM_SHARDS)))
  with tf.Graph().as_default():
    decode_jpeg_data = tf.placeholder(dtype=tf.string)
    decode_jpeg = tf.image.decode_jpeg(decode_jpeg_data, channels=3)
    with tf.Session('') as sess:
      for shard_id in range(_NUM_SHARDS):
        output_path = os.path.join(output_dir,
          'data_{:05}-of-{:05}.tfrecord'.format(shard_id, _NUM_SHARDS))
        tfrecord_writer = tf.python_io.TFRecordWriter(output_path)
        start_ndx = shard_id * num_per_shard
        end_ndx = min((shard_id + 1) * num_per_shard, len(lines))
        for i in range(start_ndx, end_ndx):
          sys.stdout.write('\r>> Converting image {}/{} shard {}'.format(
            i + 1, len(lines), shard_id))
          sys.stdout.flush()
          image_data = tf.gfile.FastGFile(os.path.join(data_dir, lines[i][0]), 'rb').read()
          image = sess.run(decode_jpeg, feed_dict={decode_jpeg_data: image_data})
          height, width = image.shape[0], image.shape[1]
          example = dataset_utils.image_to_tfexample(
            image_data, b'jpg', height, width, int(lines[i][1]))
          tfrecord_writer.write(example.SerializeToString())
        tfrecord_writer.close()
  sys.stdout.write('\n')
  sys.stdout.flush()

os.system('mkdir -p train')
convert_dataset('list_train.txt', 'flower_photos', 'train/')
os.system('mkdir -p val')
convert_dataset('list_val.txt', 'flower_photos', 'val/')