【tensorflow-slim】使用tensroflow-slim训练自己的图像分类数据集+冻成pb文件+预测（本文针对场景分类，手把手详细教学！）froot的博客-# the number of shards per dataset split. numsha

0. 前提

本文针对场景识别，其中采用的模型是mobilenet_v2，如有需要，可替换成其他tf-slim支持模型；

1. 准备工作

（1）tf-slim源码

git clone 官方代码仓：https://github.com/tensorflow/models 

（2）准备完成自己的场景数据集

我的数据集结构如下：

2. 将数据集转成tfrecord格式

操作均在slim文件夹下
（1）在datasets文件夹下，将download_and_convert_flowers.py文件，复制一份重新命名为：download_and_convert_place6.py，打开该文件进行修改，一共修改4处代码如下：

from __future__ import absolute_import from __future__ import division from __future__ import print_function  import math import os import random import sys  import tensorflow as tf  from datasets import dataset_utils  # The URL where the Flowers data can be downloaded. _DATA_URL = 'https://download.tensorflow.org/example_images/flower_photos.tgz' # The number of images in the validation set. _NUM_VALIDATION = 350 # Seed for repeatability. _RANDOM_SEED = 0 # The number of shards per dataset split. _NUM_SHARDS = 5 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 _get_filenames_and_classes(dataset_dir): """Returns a list of filenames and inferred class names.    Args:     dataset_dir: A directory containing a set of subdirectories representing       class names. Each subdirectory should contain PNG or JPG encoded images.    Returns:     A list of image file paths, relative to `dataset_dir` and the list of     subdirectories, representing class names.   """ #改为自己的数据集   flower_root = os.path.join(dataset_dir, 'place_photos')   directories = []   class_names = [] for filename in os.listdir(flower_root):     path = os.path.join(flower_root, filename) if os.path.isdir(path):       directories.append(path)       class_names.append(filename)    photo_filenames = [] for directory in directories: for filename in os.listdir(directory):       path = os.path.join(directory, filename)       photo_filenames.append(path) return photo_filenames, sorted(class_names) def _get_dataset_filename(dataset_dir, split_name, shard_id): #修改为place6   output_filename = 'place6_%s_%05d-of-%05d.tfrecord' % (       split_name, shard_id, _NUM_SHARDS) return os.path.join(dataset_dir, output_filename) def _convert_dataset(split_name, filenames, class_names_to_ids, dataset_dir): """Converts the given filenames to a TFRecord dataset.    Args:     split_name: The name of the dataset, either 'train' or 'validation'.     filenames: A list of absolute paths to png or jpg images.     class_names_to_ids: A dictionary from class names (strings) to ids       (integers).     dataset_dir: The directory where the converted datasets are stored.   """ assert split_name in ['train', 'validation']    num_per_shard = int(math.ceil(len(filenames) / float(_NUM_SHARDS))) with tf.Graph().as_default():     image_reader = ImageReader() with tf.Session('') as sess: for shard_id in range(_NUM_SHARDS):         output_filename = _get_dataset_filename(             dataset_dir, split_name, shard_id) with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:           start_ndx = shard_id * num_per_shard           end_ndx = min((shard_id+1) * num_per_shard, len(filenames)) for i in range(start_ndx, end_ndx):             sys.stdout.write('r>> Converting image %d/%d shard %d' % (                 i+1, len(filenames), shard_id))             sys.stdout.flush() # Read the filename:             image_data = tf.gfile.GFile(filenames[i], 'rb').read()             height, width = image_reader.read_image_dims(sess, image_data)              class_name = os.path.basename(os.path.dirname(filenames[i]))             class_id = class_names_to_ids[class_name]              example = dataset_utils.image_to_tfexample(                 image_data, b'jpg', height, width, class_id)             tfrecord_writer.write(example.SerializeToString())    sys.stdout.write('n')   sys.stdout.flush() def _clean_up_temporary_files(dataset_dir): """Removes temporary files used to create the dataset.    Args:     dataset_dir: The directory where the temporary files are stored.   """   filename = _DATA_URL.split('/')[-1]   filepath = os.path.join(dataset_dir, filename)   tf.gfile.Remove(filepath)    tmp_dir = os.path.join(dataset_dir, 'flower_photos')   tf.gfile.DeleteRecursively(tmp_dir) def _dataset_exists(dataset_dir): for split_name in ['train', 'validation']: for shard_id in range(_NUM_SHARDS):       output_filename = _get_dataset_filename(           dataset_dir, split_name, shard_id) if not tf.gfile.Exists(output_filename): return False return True def run(dataset_dir): """Runs the download and conversion operation.    Args:     dataset_dir: The dataset directory where the dataset is stored.   """ if not tf.gfile.Exists(dataset_dir):     tf.gfile.MakeDirs(dataset_dir) if _dataset_exists(dataset_dir): print('Dataset files already exist. Exiting without re-creating them.') return #无需下载，此行注释 #dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)   photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)   class_names_to_ids = dict(zip(class_names, range(len(class_names)))) # Divide into train and test:   random.seed(_RANDOM_SEED)   random.shuffle(photo_filenames)   training_filenames = photo_filenames[_NUM_VALIDATION:]   validation_filenames = photo_filenames[:_NUM_VALIDATION] # First, convert the training and validation sets.   _convert_dataset('train', training_filenames, class_names_to_ids,                    dataset_dir)   _convert_dataset('validation', validation_filenames, class_names_to_ids,                    dataset_dir) # Finally, write the labels file:   labels_to_class_names = dict(zip(range(len(class_names)), class_names))   dataset_utils.write_label_file(labels_to_class_names, dataset_dir) #此行注释，避免删除原来照片 #_clean_up_temporary_files(dataset_dir) print('nFinished converting the Flowers dataset!') 

（2）在slim文件夹下打开download_and_convert_data.py文件，添加如下两处代码：

from datasets import download_and_convert_place6 

 elif FLAGS.dataset_name == 'place6':     download_and_convert_place6.run(FLAGS.dataset_dir) 

（3）最后运行如下命令：

python download_and_convert_data.py  --dataset_name=place6  --dataset_dir=dataset的位置 

完成后可以看到如下终端输出:
数据集被分为 16848 （训练集）和 350（验证集）；

3. 模型训练过程

（1）将slim/datasets/flowers.py复制一份，并且命名为place6.py，将其中的3处内容修改如下：
可以知道，实际训练时的训练集设置为16198张图像，验证集设置为1000张图像；

_FILE_PATTERN = 'place6_%s_*.tfrecord'  SPLITS_TO_SIZES = {'train': 16198, 'validation': 1000}  _NUM_CLASSES = 7  _ITEMS_TO_DESCRIPTIONS = { 'image': 'A color image of varying size.', 'label': 'A single integer between 0 and 6', } 

（2）修改slim/datasets/dataset_factory.py内容，一共修改2处：

from datasets import place6 

datasets_map = { 'cifar10': cifar10, 'flowers': flowers, 'imagenet': imagenet, 'mnist': mnist, 'visualwakewords': visualwakewords, 'place6':place6, } 

通过以下命令训练：

python3 train_image_classifier.py   --train_dir=/*/research/slim/mobilenet_v2_place6/training   --dataset_dir=/*/slim/intel_placedata/place6   --dataset_name=place6   --dataset_split_name=train   --model_name=mobilenet_v2   --max_number_of_steps=40000   --learning_rate=0.001   --save_interval_secs=60   --save_summaries_secs=60   --log_every_n_steps=10   --optimizer=adam  

训练时的终端输出如下：

通过tensorboard查看训练情况：

~/*/slim/mobilenet_v2_place6$ tensorboard --logdir=training 

4. 评价（evaluation）训练结果

同样是在slim目录下，输入以下命令：

python3 eval_image_classifier.py    --checkpoint_path=/*/slim/mobilenet_v2_place6/training    --eval_dir=/*/slim/mobilenet_v2_place6/eval    --dataset_name=place6    --dataset_split_name=validation    --dataset_dir=/*/slim/intel_placedata/place6    --model_name=mobilenet_v2 

5. 最终冻成pb文件（使用tensorflow模块功能导出）

两种方式：

(1)只用如下这个单步骤，会有问题，对于冻好的.pb文件会缺少第一个输入层，导致使用该.pb文件时报错说shape问题；

报错信息为： （1,224,224,3）的shape和（32,224,224,3）的shape不一致； 

python3 -m tensorflow.python.tools.freeze_graph    --input_graph /*/slim/mobilenet_v2_place6/training1/graph.pbtxt    --input_checkpoint /*/slim/mobilenet_v2_place6/training1/model.ckpt-50000    --input_binary false    --output_graph /*/slim/mobilenet_v2_place6/model/mobilenet_v2place6_frozen.pb    --output_node_names MobilenetV2/Predictions/Reshape_1 

(2)使用如下两个步骤可以正常使用，第一步先导出前向传播图，第二步再利用前向传播图+cpkt文件的参数导入到前向传播图中得到最终的.pb模型，这个就可以使用：

python export_inference_graph.py    --alsologtostderr    --dataset_dir=/*/slim/intel_placedata/place6    --dataset_name=place6    --model_name=mobilenet_v2    --image_size=224    --output_file=/*/slim/mobilenet_v2_place6/model/mobilenet_v2_inf.pb 

python3 -m tensorflow.python.tools.freeze_graph    --input_graph /*/slim/mobilenet_v2_place6/model/mobilenet_v2_inf.pb    --input_checkpoint /*/slim/mobilenet_v2_place6/training1/model.ckpt-50000    --input_binary True    --output_graph /*/slim/mobilenet_v2_place6/model/mobilenet_v2place61_frozen.pb    --output_node_names MobilenetV2/Predictions/Reshape_1 

6. 利用pb文件随机预测一张图

预测代码（自己写的，较为简单）：

• (1) 使用IPython，只能做到终端输出字符串，不能实现在图上写出结果

import tensorflow as tf import numpy as np import cv2 from datasets import dataset_utils from IPython import display import pylab import PIL  image_dir='/*/slim/seg_pred/52.jpg' dataset_dir='/*/slim/intel_placedata/place6' model_dir ='/*/slim/mobilenet_v2_place6/model/mobilenet_v2place61_frozen.pb'   display.display(display.Image(image_dir))  img = np.array(PIL.Image.open(image_dir).resize((224, 224))).astype(np.float) / 128 - 1 gd = tf.GraphDef.FromString(open(model_dir, 'rb').read()) inp, predictions = tf.import_graph_def(gd,  return_elements = ['input:0','MobilenetV2/Predictions/Reshape_1:0']) with tf.Session(graph=inp.graph):   x = predictions.eval(feed_dict={inp: img.reshape(1, 224,224, 3)})  label_map = dataset_utils.read_label_file(dataset_dir) print("Top 1 Prediction: ", x.argmax(),label_map[x.argmax()], x.max()) 

终端输出：

预测正确right！

• (2) 采用opencv+PIL (个人写的代码，可以直接在图片上显示出来分类)

import tensorflow as tf import numpy as np import cv2 from datasets import dataset_utils from IPython import display import pylab import PIL from PIL import Image from PIL import ImageDraw from PIL import ImageFont import matplotlib.font_manager as fm  image_dir='/*/slim/seg_pred/52.jpg' dataset_dir='/*/slim/intel_placedata/place6' model_dir ='/*/slim/mobilenet_v2_place6/model/mobilenet_v2place61_frozen.pb' #opencv class TOD(object): def __init__(self):     self.PATH_TO_CKPT = '/*/slim/mobilenet_v2_place6/model/mobilenet_v2place61_frozen.pb'     self.NUM_CLASSES = 7     self.detection_graph = self._load_model()     self.label_map = dataset_utils.read_label_file(dataset_dir) def _load_model(self):     detection_graph = tf.Graph() with detection_graph.as_default():       od_graph_def = tf.GraphDef() with tf.gfile.GFile(self.PATH_TO_CKPT, 'rb') as fid:         serialized_graph = fid.read()         od_graph_def.ParseFromString(serialized_graph)         tf.import_graph_def(od_graph_def, name='') return detection_graph    def visualization(self,image,str):     image_pil = Image.fromarray(np.uint8(image)).convert('RGB')     draw = ImageDraw.Draw(image_pil)     font = ImageFont.truetype(fm.findfont(fm.FontProperties(family='DejaVu Sans')), 15) # 设置字体DejaVu Sans     draw.text((10, 10), str, 'red', font) # 'fuchsia'     np.copyto(image, np.array(image_pil)) return image    def detect(self,image,resized): with self.detection_graph.as_default(): with tf.Session(graph=self.detection_graph) as sess: # Expand dimensions since the model expects images to have shape: [1, None, None, 3]         image_np_expanded = np.expand_dims(resized, axis=0)         inp = self.detection_graph.get_tensor_by_name('input:0')         predictions = self.detection_graph.get_tensor_by_name('MobilenetV2/Predictions/Reshape_1:0')         x = predictions.eval(feed_dict={inp: image_np_expanded})         font1 = str(self.label_map[x.argmax()])         font2 = str(x.max())         font3 = font1 + ":" + font2         image = self.visualization(image,font3) #print("Top 1 Prediction: ", x.argmax(), self.label_map[x.argmax()], x.max())      cv2.namedWindow("detection", cv2.WINDOW_NORMAL)     cv2.imshow("detection", image)     cv2.waitKey(0) if __name__ == '__main__':   image = cv2.imread(image_dir) # dst=cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)   width = 224   height = 224   dim = (width, height) # resize image to [-1,1] Maps pixel values to the range [-1, 1]   resized = (cv2.resize(image, dim)).astype(np.float) / 128 - 1   detecotr = TOD()   detecotr.detect(image,resized)