使用tf-slim的inception_resnet_v2预训练模型进行图像分类

输入是jpg

代码：

#!/usr/bin/env python3# -*- coding: utf-8 -*-"""Created on Fri Sep 29 16:25:16 2017@author: wayne"""'''我们用的是tf1.2，最新的tf1.3地址是https://github.com/tensorflow/models/tree/master/research/slimhttp://geek.csdn.net/news/detail/126133如何用TensorFlow和TF-Slim实现图像分类与分割https://www.2cto.com/kf/201706/649266.html【Tensorflow】辅助工具篇——tensorflow slim(TF-Slim)介绍https://stackoverflow.com/questions/39582703/using-pre-trained-inception-resnet-v2-with-tensorflowThe Inception networks expect the input image to have color channels scaled from [-1, 1]. As seen here.You could either use the existing preprocessing, or in your example just scale the images yourself: im = 2*(im/255.0)-1.0 before feeding them to the network.Without scaling the input [0-255] is much larger than the network expects and the biases all work to very strongly predict category 918 (comic books).'''import tensorflow as tfslim = tf.contrib.slimfrom PIL import Imagefrom inception_resnet_v2 import *import numpy as npimport inception_preprocessingimport matplotlib.pyplot as pltimport imagenet  #注意需要用最新版tf中的对应文件，否则http地址是不对的tf.reset_default_graph()checkpoint_file = 'inception_resnet_v2_2016_08_30.ckpt'image = tf.image.decode_jpeg(tf.read_file('dog.jpeg'), channels=3) #['dog.jpg', 'panda.jpg']image_size = inception_resnet_v2.default_image_size #  299'''这个函数做了裁剪，缩放和归一化等'''processed_image = inception_preprocessing.preprocess_image(image,                                                         image_size,                                                         image_size,                                                        is_training=False,)processed_images  = tf.expand_dims(processed_image, 0)'''Creates the Inception Resnet V2 model.'''arg_scope = inception_resnet_v2_arg_scope()with slim.arg_scope(arg_scope):  logits, end_points = inception_resnet_v2(processed_images, is_training=False)   probabilities = tf.nn.softmax(logits)saver = tf.train.Saver()with tf.Session() as sess:    saver.restore(sess, checkpoint_file)    #predict_values, logit_values = sess.run([end_points['Predictions'], logits])    image2, network_inputs, probabilities2 = sess.run([image,                                                       processed_images,                                                       probabilities])    print(network_inputs.shape)    print(probabilities2.shape)    probabilities2 = probabilities2[0,:]    sorted_inds = [i[0] for i in sorted(enumerate(-probabilities2),                                        key=lambda x:x[1])]    # 显示下载的图片plt.figure()plt.imshow(image2)#.astype(np.uint8))plt.suptitle("Original image", fontsize=14, fontweight='bold')plt.axis('off')plt.show()# 显示最终传入网络模型的图片plt.imshow(network_inputs[0,:,:,:])plt.suptitle("Resized, Cropped and Mean-Centered inputs to network",             fontsize=14, fontweight='bold')plt.axis('off')plt.show()names = imagenet.create_readable_names_for_imagenet_labels()for i in range(5):    index = sorted_inds[i]    print(index)    # 打印top5的预测类别和相应的概率值。    print('Probability %0.2f => [%s]' % (probabilities2[index], names[index+1]))'''https://github.com/tensorflow/models/blob/master/research/slim/train_image_classifier.py'''def _get_variables_to_train():    """Returns a list of variables to train.    Returns:      A list of variables to train by the optimizer.    """    trainable_scopes = 'InceptionResnetV2/Logits,InceptionResnetV2/AuxLogits'    if trainable_scopes is None:      return tf.trainable_variables()    else:      scopes = [scope.strip() for scope in trainable_scopes.split(',')]    variables_to_train = []    for scope in scopes:      variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)      variables_to_train.extend(variables)    return variables_to_train'''一些关于inception_resnet_v2变量的测试，在理解模型代码和迁移学习中很有用'''exx = tf.trainable_variables()print(type(exx))print(exx[0])print(exx[-1])print(exx[-2])print(exx[-3])print(exx[-4])print(exx[-5])print(exx[-6])print(exx[-7])print(exx[-8])print(exx[-9])print(exx[-10])print('###############################################################')variables_to_train = _get_variables_to_train()print(variables_to_train)print('###############################################################')exclude = ['InceptionResnetV2/Logits', 'InceptionResnetV2/AuxLogits']variables_to_restore = slim.get_variables_to_restore(exclude = exclude)print(variables_to_restore[0])print(variables_to_restore[-1])print('###############################################################')exclude = ['InceptionResnetV2/Logits']variables_to_restore = slim.get_variables_to_restore(exclude = exclude)print(variables_to_restore[0])print(variables_to_restore[-1])

输出：

INFO:tensorflow:Restoring parameters from inception_resnet_v2_2016_08_30.ckpt(1, 299, 299, 3)(1, 1001)208Probability 0.87 => [Labrador retriever]209Probability 0.03 => [Chesapeake Bay retriever]258Probability 0.00 => [Samoyed, Samoyede]853Probability 0.00 => [thatch, thatched roof]245Probability 0.00 => [French bulldog]<class 'list'><tf.Variable 'InceptionResnetV2/Conv2d_1a_3x3/weights:0' shape=(3, 3, 3, 32) dtype=float32_ref><tf.Variable 'InceptionResnetV2/Logits/Logits/biases:0' shape=(1001,) dtype=float32_ref><tf.Variable 'InceptionResnetV2/Logits/Logits/weights:0' shape=(1536, 1001) dtype=float32_ref><tf.Variable 'InceptionResnetV2/AuxLogits/Logits/biases:0' shape=(1001,) dtype=float32_ref><tf.Variable 'InceptionResnetV2/AuxLogits/Logits/weights:0' shape=(768, 1001) dtype=float32_ref><tf.Variable 'InceptionResnetV2/AuxLogits/Conv2d_2a_5x5/BatchNorm/beta:0' shape=(768,) dtype=float32_ref><tf.Variable 'InceptionResnetV2/AuxLogits/Conv2d_2a_5x5/weights:0' shape=(5, 5, 128, 768) dtype=float32_ref><tf.Variable 'InceptionResnetV2/AuxLogits/Conv2d_1b_1x1/BatchNorm/beta:0' shape=(128,) dtype=float32_ref><tf.Variable 'InceptionResnetV2/AuxLogits/Conv2d_1b_1x1/weights:0' shape=(1, 1, 1088, 128) dtype=float32_ref><tf.Variable 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/beta:0' shape=(1536,) dtype=float32_ref><tf.Variable 'InceptionResnetV2/Conv2d_7b_1x1/weights:0' shape=(1, 1, 2080, 1536) dtype=float32_ref>###############################################################[<tf.Variable 'InceptionResnetV2/Logits/Logits/weights:0' shape=(1536, 1001) dtype=float32_ref>, <tf.Variable 'InceptionResnetV2/Logits/Logits/biases:0' shape=(1001,) dtype=float32_ref>, <tf.Variable 'InceptionResnetV2/AuxLogits/Conv2d_1b_1x1/weights:0' shape=(1, 1, 1088, 128) dtype=float32_ref>, <tf.Variable 'InceptionResnetV2/AuxLogits/Conv2d_1b_1x1/BatchNorm/beta:0' shape=(128,) dtype=float32_ref>, <tf.Variable 'InceptionResnetV2/AuxLogits/Conv2d_2a_5x5/weights:0' shape=(5, 5, 128, 768) dtype=float32_ref>, <tf.Variable 'InceptionResnetV2/AuxLogits/Conv2d_2a_5x5/BatchNorm/beta:0' shape=(768,) dtype=float32_ref>, <tf.Variable 'InceptionResnetV2/AuxLogits/Logits/weights:0' shape=(768, 1001) dtype=float32_ref>, <tf.Variable 'InceptionResnetV2/AuxLogits/Logits/biases:0' shape=(1001,) dtype=float32_ref>]###############################################################<tf.Variable 'InceptionResnetV2/Conv2d_1a_3x3/weights:0' shape=(3, 3, 3, 32) dtype=float32_ref><tf.Variable 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_variance:0' shape=(1536,) dtype=float32_ref>###############################################################<tf.Variable 'InceptionResnetV2/Conv2d_1a_3x3/weights:0' shape=(3, 3, 3, 32) dtype=float32_ref><tf.Variable 'InceptionResnetV2/AuxLogits/Logits/biases:0' shape=(1001,) dtype=float32_ref>