深度学习pretrain之数据预处理

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我是靠谱客的博主寒冷水池，最近开发中收集的这篇文章主要介绍深度学习pretrain之数据预处理，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

采用迁移学习，我们必须严格按照预训练模型初始训练时的方式归一化，而且不同模型的归一化方式不同，VGG和ResNet是减去训练集均值，Xception和InceptionV3是线性缩放到[-1,1]；

网络默认输入为：

VGG和ResNet是224*224*3

Xception和InceptionV3是299*299*3

tensorflow数据预处理

tensorflow使用slim库对数据进行预处理，各模型和预处理方式对应如下：

https://github.com/tensorflow/models/blob/master/research/slim/preprocessing/preprocessing_factory.py

preprocessing_fn_map = {
      'cifarnet': cifarnet_preprocessing,
      'inception': inception_preprocessing,
      'inception_v1': inception_preprocessing,
      'inception_v2': inception_preprocessing,
      'inception_v3': inception_preprocessing,
      'inception_v4': inception_preprocessing,
      'inception_resnet_v2': inception_preprocessing,
      'lenet': lenet_preprocessing,
      'mobilenet_v1': inception_preprocessing,
      'mobilenet_v2': inception_preprocessing,
      'mobilenet_v2_035': inception_preprocessing,
      'mobilenet_v2_140': inception_preprocessing,
      'nasnet_mobile': inception_preprocessing,
      'nasnet_large': inception_preprocessing,
      'pnasnet_mobile': inception_preprocessing,
      'pnasnet_large': inception_preprocessing,
      'resnet_v1_50': vgg_preprocessing,
      'resnet_v1_101': vgg_preprocessing,
      'resnet_v1_152': vgg_preprocessing,
      'resnet_v1_200': vgg_preprocessing,
      'resnet_v2_50': vgg_preprocessing,
      'resnet_v2_101': vgg_preprocessing,
      'resnet_v2_152': vgg_preprocessing,
      'resnet_v2_200': vgg_preprocessing,
      'vgg': vgg_preprocessing,
      'vgg_a': vgg_preprocessing,
      'vgg_16': vgg_preprocessing,
      'vgg_19': vgg_preprocessing,
  }

代码实例：

import numpy as np
import tensorflow as tf
from preprocessing import vgg_preprocessing, inception_preprocessing
from tensorflow.contrib import slim
import sys
sys.path.append("D:\人工智能学习资料\github\models\research\slim")
from nets.resnet_v1 import *
from nets.resnet_v2 import *
n_images = 50000
batch_size = 100
n_top = 1

model = "resnet_v2_50"

with tf.Graph().as_default():
    with slim.arg_scope(resnet_arg_scope()):
        variables_to_restore = slim.get_model_variables()
        print(variables_to_restore)
        input_string = tf.placeholder(tf.string)
        input_images = tf.read_file(input_string)
        input_images = tf.image.decode_jpeg(input_images, channels=3)
        input_images = tf.cast(input_images, tf.float32)
        if model == "resnet_v1_50":
            processed_images = vgg_preprocessing.preprocess_image(input_images, 224, 224, is_training=False)
            processed_images = tf.expand_dims(processed_images, 0)
            logits, _ = resnet_v1_50(processed_images,
                                     num_classes=1000,
                                     is_training=False)
        elif model == "resnet_v2_50":
            processed_images = vgg_preprocessing.preprocess_image(input_images, 224, 224, is_training=False)
            processed_images = tf.expand_dims(processed_images, 0)
            logits, _ = resnet_v2_50(processed_images,
                                     num_classes=1001,
                                     is_training=False)

        probabilities = tf.nn.softmax(logits)

        init_fn = slim.assign_from_checkpoint_fn(checkpoint_file, slim.get_model_variables(model))

        config = tf.ConfigProto()
        config.gpu_options.allow_growth = True
        with tf.Session(config=config) as sess:
            init_fn(sess)
            labels_sort = []
            success_search = 0
            import time
            start_time = time.time()
            for b in range(int(np.ceil(n_images / np.float(batch_size)))):
                start = b * batch_size + 1
                stop = np.minimum(n_images + 1, start + batch_size)
                nx = []
                for i in range(start, stop, 1):
                    img_path = imges_path + "ILSVRC2012_val_%08d.JPEG" % (i)
                    x = imread(img_path).astype(float)
                    check_time = time.time()
                    pred = sess.run(probabilities, feed_dict={input_string: img_path})
                    x_gen = sess.run(processed_images, feed_dict={input_string: img_path})
                    if i == start:
                        preds = pred
                    else:
                        preds = np.concatenate([preds, pred], axis=0)
                print(np.max(x_gen), np.min(x_gen))
                labels = np.argsort(preds, axis=1)[:, ::-1]

                labels_ntop = labels[:, :n_top]
                for idx in range(start, stop, 1):
                    if PIE_TRUTH[idx - 1] in labels_ntop[idx - start, :]:
                        success_search += 1
                print("Process %d images on %d query, suceess %d images, %0.2f take time %s" % 
                      (stop, n_images, success_search, 100 * success_search / np.float(stop), check_time - start_time))

Kares数据预处理

kares中数据预处理相对比较简单，使用各个模型封装好的preprocess_input即可

实例：
from keras.applications.resnet50 import ResNet50,preprocess_input,decode_predictions
from keras.preprocessing import image

def path_to_tensor(img_path):
    # 用PIL加载RGB图像为PIL.Image.Image类型
    img = image.load_img(img_path, target_size=(224, 224))
    # 将PIL.Image.Image类型转化为格式为(224, 224, 3)的3维张量
    x = image.img_to_array(img)
    # 将3维张量转化为格式为(1, 224, 224, 3)的4维张量并返回
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    return x

image_path = 'kera_data/check/cat/cat.0.jpg'
image = path_to_tensor(image_path)
preds = ResNet_model.predict(img)
print(preds[0][0:20])
print(np.array(decode_predictions(preds,top=10)[0])[:,0])