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:///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/datawget http://download.tensorflow.org/example_images/flower_photos.tgztar 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 osclass_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：

daisydandelionrosessunflowerstulips

随机生成训练集与验证集：

Python代码：

import random_NUM_VALIDATION = 350_RANDOM_SEED = 0list_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 syssys.path.insert(0, '../models/slim/')from datasets import dataset_utilsimport mathimport osimport tensorflow as tfdef 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/')

得到的文件夹结构如下：

data├── flower_photos├── labels.txt├── list_train.txt├── list.txt├── list_val.txt├── train│ ├── data_00000-of-00005.tfrecord│ ├── ...│ └── data_00004-of-00005.tfrecord└── val ├── data_00000-of-00005.tfrecord ├── ... └── data_00004-of-00005.tfrecord

（可选）下载模型

官方提供了不少预训练模型，这里以Inception-ResNet-v2以例。

cd $WORKSPACE/checkpointswget http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gztar zxf inception_resnet_v2_2016_08_30.tar.gz

训练

读入数据

官方提供了读入Flowers数据集的代码models/slim/datasets/flowers.py，同样这里也是参考并修改成能读入上面定义的通用数据集。

把下面代码写入models/slim/datasets/dataset_classification.py。

import osimport tensorflow as tfslim = tf.contrib.slimdef get_dataset(dataset_dir, num_samples, num_classes, labels_to_names_path=None, file_pattern='*.tfrecord'): file_pattern = os.path.join(dataset_dir, file_pattern) keys_to_features = { 'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''), 'image/format': tf.FixedLenFeature((), tf.string, default_value='png'), 'image/class/label': tf.FixedLenFeature( [], tf.int64, default_value=tf.zeros([], dtype=tf.int64)), } items_to_handlers = { 'image': slim.tfexample_decoder.Image(), 'label': slim.tfexample_decoder.Tensor('image/class/label'), } decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers) items_to_descriptions = { 'image': 'A color image of varying size.', 'label': 'A single integer between 0 and ' + str(num_classes - 1), } labels_to_names = None if labels_to_names_path is not None: fd = open(labels_to_names_path) labels_to_names = {i : line.strip() for i, line in enumerate(fd)} fd.close() return slim.dataset.Dataset( data_sources=file_pattern, reader=tf.TFRecordReader, decoder=decoder, num_samples=num_samples, items_to_descriptions=items_to_descriptions, num_classes=num_classes, labels_to_names=labels_to_names)

构建模型

官方提供了许多模型在models/slim/nets/。

如需要自定义模型，则参考官方提供的模型并放在对应的文件夹即可。

开始训练

官方提供了训练脚本，如果使用官方的数据读入和处理，可使用以下方式开始训练。

cd $WORKSPACE/models/slimCUDA_VISIBLE_DEVICES="0" python train_image_classifier.py \ --train_dir=train_logs \ --dataset_name=flowers \ --dataset_split_name=train \ --dataset_dir=../../data/flowers \ --model_name=inception_resnet_v2 \ --checkpoint_path=../../checkpoints/inception_resnet_v2_2016_08_30.ckpt \ --checkpoint_exclude_scopes=InceptionResnetV2/Logits,InceptionResnetV2/AuxLogits \ --trainable_scopes=InceptionResnetV2/Logits,InceptionResnetV2/AuxLogits \ --max_number_of_steps=1000 \ --batch_size=32 \ --learning_rate=0.01 \ --learning_rate_decay_type=fixed \ --save_interval_secs=60 \ --save_summaries_secs=60 \ --log_every_n_steps=10 \ --optimizer=rmsprop \ --weight_decay=0.00004

不fine-tune把--checkpoint_path, --checkpoint_exclude_scopes和--trainable_scopes删掉。

fine-tune所有层把--checkpoint_exclude_scopes和--trainable_scopes删掉。

如果只使用CPU则加上--clone_on_cpu=True。

其它参数可删掉用默认值或自行修改。

使用自己的数据则需要修改models/slim/train_image_classifier.py：

把

from datasets import dataset_factory

修改为

from datasets import dataset_classification

把

dataset = dataset_factory.get_dataset( FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)

修改为

dataset = dataset_classification.get_dataset( FLAGS.dataset_dir, FLAGS.num_samples, FLAGS.num_classes, FLAGS.labels_to_names_path)

在

tf.app.flags.DEFINE_string( 'dataset_dir', None, 'The directory where the dataset files are stored.')

后加入

tf.app.flags.DEFINE_integer( 'num_samples', 3320, 'Number of samples.')tf.app.flags.DEFINE_integer( 'num_classes', 5, 'Number of classes.')tf.app.flags.DEFINE_string( 'labels_to_names_path', None, 'Label names file path.')

训练时执行以下命令即可：

cd $WORKSPACE/models/slimpython train_image_classifier.py \ --train_dir=train_logs \ --dataset_dir=../../data/train \ --num_samples=3320 \ --num_classes=5 \ --labels_to_names_path=../../data/labels.txt \ --model_name=inception_resnet_v2 \ --checkpoint_path=../../checkpoints/inception_resnet_v2_2016_08_30.ckpt \ --checkpoint_exclude_scopes=InceptionResnetV2/Logits,InceptionResnetV2/AuxLogits \ --trainable_scopes=InceptionResnetV2/Logits,InceptionResnetV2/AuxLogits

可视化log

可一边训练一边可视化训练的log，可看到Loss趋势。

tensorboard --logdir train_logs/

验证

官方提供了验证脚本。

python eval_image_classifier.py \ --checkpoint_path=train_logs \ --eval_dir=eval_logs \ --dataset_name=flowers \ --dataset_split_name=validation \ --dataset_dir=../../data/flowers \ --model_name=inception_resnet_v2

同样，如果是使用自己的数据集，则需要修改models/slim/eval_image_classifier.py：

把

from datasets import dataset_factory

修改为

from datasets import dataset_classification

把

dataset = dataset_factory.get_dataset( FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)

修改为

dataset = dataset_classification.get_dataset( FLAGS.dataset_dir, FLAGS.num_samples, FLAGS.num_classes, FLAGS.labels_to_names_path)

在

tf.app.flags.DEFINE_string( 'dataset_dir', None, 'The directory where the dataset files are stored.')

后加入

tf.app.flags.DEFINE_integer( 'num_samples', 350, 'Number of samples.')tf.app.flags.DEFINE_integer( 'num_classes', 5, 'Number of classes.')tf.app.flags.DEFINE_string( 'labels_to_names_path', None, 'Label names file path.')

验证时执行以下命令即可：

python eval_image_classifier.py \ --checkpoint_path=train_logs \ --eval_dir=eval_logs \ --dataset_dir=../../data/val \ --num_samples=350 \ --num_classes=5 \ --model_name=inception_resnet_v2

可以一边训练一边验证，，注意使用其它的GPU或合理分配显存。

同样也可以可视化log，如果已经在可视化训练的log则建议使用其它端口，如：

tensorboard --logdir eval_logs/ --port 6007

测试

参考models/slim/eval_image_classifier.py，可编写读取图片用模型进行推导的脚本models/slim/test_image_classifier.py

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport osimport mathimport tensorflow as tffrom nets import nets_factoryfrom preprocessing import preprocessing_factoryslim = tf.contrib.slimtf.app.flags.DEFINE_string( 'master', '', 'The address of the TensorFlow master to use.')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( 'test_path', '', 'Test image path.')tf.app.flags.DEFINE_integer( 'num_classes', 5, 'Number of classes.')tf.app.flags.DEFINE_integer( 'labels_offset', 0, 'An offset for the labels in the dataset. This flag is primarily used to ' 'evaluate the VGG and ResNet architectures which do not use a background ' 'class for the ImageNet dataset.')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.')tf.app.flags.DEFINE_integer( 'test_image_size', None, 'Eval image size')FLAGS = tf.app.flags.FLAGSdef main(_): if not FLAGS.test_list: raise ValueError('You must supply the test list with --test_list') tf.logging.set_verbosity(tf.logging.INFO) with tf.Graph().as_default(): tf_global_step = slim.get_or_create_global_step() #################### # Select the model # #################### network_fn = nets_factory.get_network_fn( FLAGS.model_name, num_classes=(FLAGS.num_classes - FLAGS.labels_offset), is_training=False) ##################################### # Select the preprocessing function # ##################################### preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name image_preprocessing_fn = preprocessing_factory.get_preprocessing( preprocessing_name, is_training=False) test_image_size = FLAGS.test_image_size or network_fn.default_image_size if tf.gfile.IsDirectory(FLAGS.checkpoint_path): checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path) else: checkpoint_path = FLAGS.checkpoint_path tf.Graph().as_default() with tf.Session() as sess: image = open(FLAGS.test_path, 'rb').read() image = tf.image.decode_jpeg(image, channels=3) processed_image = image_preprocessing_fn(image, test_image_size, test_image_size) processed_images = tf.expand_dims(processed_image, 0) logits, _ = network_fn(processed_images) predictions = tf.argmax(logits, 1) saver = tf.train.Saver() saver.restore(sess, checkpoint_path) np_image, network_input, predictions = sess.run([image, processed_image, predictions]) print('{} {}'.format(FLAGS.test_path, predictions[0]))if __name__ == '__main__': tf.app.run()

测试时执行以下命令即可：

python test_image_classifier.py \ --checkpoint_path=train_logs/ \ --test_path=../../data/flower_photos/tulips/6948239566_0ac0a124ee_n.jpg \ --num_classes=5 \ --model_name=inception_resnet_v2

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。