import glob
import os.path
import numpy as np
import tensorflow as tf
from tensorflow.python.platform import gfile
import tensorflow.contrib.slim as slim

# 加载通过TensorFlow-Slim定义好的inception_v3模型。
import tensorflow.contrib.slim.python.slim.nets.inception_v3 as inception_v3

# 处理好之后的数据文件。
INPUT_DATA = '../../datasets/flower_processed_data.npy'
# 保存训练好的模型的路径。
TRAIN_FILE = 'train_dir/model'
# 谷歌提供的训练好的模型文件地址。因为GitHub无法保存大于100M的文件,所以
# 在运行时需要先自行从Google下载inception_v3.ckpt文件。
CKPT_FILE = '../../datasets/inception_v3.ckpt'

# 定义训练中使用的参数。
LEARNING_RATE = 0.0001
STEPS = 300
BATCH = 32
N_CLASSES = 5

# 不需要从谷歌训练好的模型中加载的参数。
CHECKPOINT_EXCLUDE_SCOPES = 'InceptionV3/Logits,InceptionV3/AuxLogits'
# 需要训练的网络层参数明层,在fine-tuning的过程中就是最后的全联接层。
TRAINABLE_SCOPES='InceptionV3/Logits,InceptionV3/AuxLogit'
def get_tuned_variables():
    exclusions = [scope.strip() for scope in CHECKPOINT_EXCLUDE_SCOPES.split(',')]

    variables_to_restore = []
    # 枚举inception-v3模型中所有的参数,然后判断是否需要从加载列表中移除。
    for var in slim.get_model_variables():
        excluded = False
        for exclusion in exclusions:
            if var.op.name.startswith(exclusion):
                excluded = True
                break
        if not excluded:
            variables_to_restore.append(var)
    return variables_to_restore
def get_trainable_variables():    
    scopes = [scope.strip() for scope in TRAINABLE_SCOPES.split(',')]
    variables_to_train = []
    
    # 枚举所有需要训练的参数前缀,并通过这些前缀找到所有需要训练的参数。
    for scope in scopes:
        variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)
        variables_to_train.extend(variables)
    return variables_to_train
def main():
    # 加载预处理好的数据。
    processed_data = np.load(INPUT_DATA)
    training_images = processed_data[0]
    n_training_example = len(training_images)
    training_labels = processed_data[1]
    
    validation_images = processed_data[2]
    validation_labels = processed_data[3]
    
    testing_images = processed_data[4]
    testing_labels = processed_data[5]
    print("%d training examples, %d validation examples and %d testing examples." % (
        n_training_example, len(validation_labels), len(testing_labels)))

    # 定义inception-v3的输入,images为输入图片,labels为每一张图片对应的标签。
    images = tf.placeholder(tf.float32, [None, 299, 299, 3], name='input_images')
    labels = tf.placeholder(tf.int64, [None], name='labels')
    
    # 定义inception-v3模型。因为谷歌给出的只有模型参数取值,所以这里
    # 需要在这个代码中定义inception-v3的模型结构。虽然理论上需要区分训练和
    # 测试中使用到的模型,也就是说在测试时应该使用is_training=False,但是
    # 因为预先训练好的inception-v3模型中使用的batch normalization参数与
    # 新的数据会有出入,所以这里直接使用同一个模型来做测试。
    with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
        logits, _ = inception_v3.inception_v3(
            images, num_classes=N_CLASSES, is_training=True)
    
    trainable_variables = get_trainable_variables()
    # 定义损失函数和训练过程。
    tf.losses.softmax_cross_entropy(
        tf.one_hot(labels, N_CLASSES), logits, weights=1.0)
    total_loss = tf.losses.get_total_loss()
    train_step = tf.train.RMSPropOptimizer(LEARNING_RATE).minimize(total_loss)
    
    # 计算正确率。
    with tf.name_scope('evaluation'):
        correct_prediction = tf.equal(tf.argmax(logits, 1), labels)
        evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
                
    # 定义加载Google训练好的Inception-v3模型的Saver。
    load_fn = slim.assign_from_checkpoint_fn(
      CKPT_FILE,
      get_tuned_variables(),
      ignore_missing_vars=True)
    
    # 定义保存新模型的Saver。
    saver = tf.train.Saver()
    
    with tf.Session() as sess:
        # 初始化没有加载进来的变量。
        init = tf.global_variables_initializer()
        sess.run(init)
        
        # 加载谷歌已经训练好的模型。
        print('Loading tuned variables from %s' % CKPT_FILE)
        load_fn(sess)
            
        start = 0
        end = BATCH
        for i in range(STEPS):            
            _, loss = sess.run([train_step, total_loss], feed_dict={
                images: training_images[start:end], 
                labels: training_labels[start:end]})

            if i % 30 == 0 or i + 1 == STEPS:
                saver.save(sess, TRAIN_FILE, global_step=i)
                
                validation_accuracy = sess.run(evaluation_step, feed_dict={
                    images: validation_images, labels: validation_labels})
                print('Step %d: Training loss is %.1f Validation accuracy = %.1f%%' % (
                    i, loss, validation_accuracy * 100.0))
                            
            start = end
            if start == n_training_example:
                start = 0
            
            end = start + BATCH
            if end > n_training_example: 
                end = n_training_example
            
        # 在最后的测试数据上测试正确率。
        test_accuracy = sess.run(evaluation_step, feed_dict={
            images: testing_images, labels: testing_labels})
        print('Final test accuracy = %.1f%%' % (test_accuracy * 100))