resnet_train_val_onset.py

#coding=utf-8
# https://blog.csdn.net/guyuealian/article/details/81560537

import tensorflow as tf 
import numpy as np 
import pdb
import os
from datetime import datetime
import slim.nets.resnet_v2 as resnet
from create_tf_record import *
import tensorflow.contrib.slim as slim


labels_nums = 4  # 类别个数
batch_size = 32  #
resize_height = 224  # 指定存储图片高度
resize_width = 224  # 指定存储图片宽度
depths = 3
data_shape = [batch_size, resize_height, resize_width, depths]

# 定义input_images为图片数据
input_images = tf.placeholder(dtype=tf.float32, shape=[None, resize_height, resize_width, depths], name='input')
# 定义input_labels为labels数据
# input_labels = tf.placeholder(dtype=tf.int32, shape=[None], name='label')
input_labels = tf.placeholder(dtype=tf.int32, shape=[None, labels_nums], name='label')

# 定义dropout的概率
keep_prob = tf.placeholder(tf.float32,name='keep_prob')
is_training = tf.placeholder(tf.bool, name='is_training')

def net_evaluation(sess,loss,accuracy,val_images_batch,val_labels_batch,val_nums):
    val_max_steps = int(val_nums / batch_size)
    val_losses = []
    val_accs = []
    for _ in range(val_max_steps):
        val_x, val_y = sess.run([val_images_batch, val_labels_batch])
        # print('labels:',val_y)
        # val_loss = sess.run(loss, feed_dict={x: val_x, y: val_y, keep_prob: 1.0})
        # val_acc = sess.run(accuracy,feed_dict={x: val_x, y: val_y, keep_prob: 1.0})
        val_loss,val_acc = sess.run([loss,accuracy], feed_dict={input_images: val_x, input_labels: val_y, keep_prob:1.0, is_training: False})
        val_losses.append(val_loss)
        val_accs.append(val_acc)
    mean_loss = np.array(val_losses, dtype=np.float32).mean()
    mean_acc = np.array(val_accs, dtype=np.float32).mean()
    return mean_loss, mean_acc

def evaluation_detail(sess,score,classIds,val_images_batch,val_labels_batch,val_nums):
    val_max_steps = int(val_nums / batch_size)
    allClasses = []
    allYs = []
    allScores = []
    for _ in range(val_max_steps):
        val_x, val_y = sess.run([val_images_batch, val_labels_batch])
        val_score,val_classIds = sess.run([score,classIds], feed_dict={input_images: val_x, input_labels: val_y, keep_prob:1.0, is_training: False})
        val_y = sess.run(tf.argmax(val_y, 1))
        allClasses = np.hstack((allClasses,val_classIds))
        allYs = np.hstack((allYs,val_y))
        allScores.append(val_score)
    print(array_diff(allClasses,allYs))
    return allScores,allClasses,allYs

def array_diff(allClasses,allYs):
    diffs = []
    types = ['A','B','C','D']
    for i in range(len(allClasses)):
        if allClasses[i] != allYs[i]:
            diffs.append(types[int(allYs[i])] + ' predict to ' + types[int(allClasses[i])])
    return diffs


def step_train(train_op,loss,accuracy,score,classIds,
               train_images_batch,train_labels_batch,train_nums,train_log_step,
               val_images_batch,val_labels_batch,val_nums,val_log_step,
               snapshot_prefix,snapshot):
    '''
    循环迭代训练过程
    :param train_op: 训练op
    :param loss:     loss函数
    :param accuracy: 准确率函数
    :param train_images_batch: 训练images数据
    :param train_labels_batch: 训练labels数据
    :param train_nums:         总训练数据
    :param train_log_step:   训练log显示间隔
    :param val_images_batch: 验证images数据
    :param val_labels_batch: 验证labels数据
    :param val_nums:         总验证数据
    :param val_log_step:     验证log显示间隔
    :param snapshot_prefix: 模型保存的路径
    :param snapshot:        模型保存间隔
    :return: None
    '''
    saver = tf.train.Saver()
    max_acc = 0.0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        sess.run(tf.local_variables_initializer())
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)
        for i in range(max_steps + 1):
            batch_input_images, batch_input_labels = sess.run([train_images_batch, train_labels_batch])
            _, train_loss = sess.run([train_op, loss], feed_dict={input_images: batch_input_images,
                                                                  input_labels: batch_input_labels,
                                                                  keep_prob: 0.55, is_training: True})
            # train测试(这里仅测试训练集的一个batch)
            if i % train_log_step == 0:
                train_acc = sess.run(accuracy, feed_dict={input_images: batch_input_images,
                                                          input_labels: batch_input_labels,
                                                          keep_prob: 1.0, is_training: False})
                print("%s: Step [%d]  train Loss : %f, training accuracy :  %g" % (
                datetime.now(), i, train_loss, train_acc))

            # val测试(测试全部val数据)
            if i % val_log_step == 0:
                mean_loss, mean_acc = net_evaluation(sess, loss, accuracy, val_images_batch, val_labels_batch, val_nums)
                print("%s: Step [%d]  val Loss : %f, valuation accuracy :  %g" % (datetime.now(), i, mean_loss, mean_acc))
                if train_acc > 0.8 and mean_acc > 0.3:
                    val_score, val_classIds, val_y = evaluation_detail(sess,score, classIds, val_images_batch, val_labels_batch,val_nums)

            # 模型保存:每迭代snapshot次或者最后一次保存模型
            if (i % snapshot == 0 and i > 0) or i == max_steps:
                print('-----save:{}-{}'.format(snapshot_prefix, i))
                saver.save(sess, snapshot_prefix, global_step=i)
            # 保存val准确率最高的模型
            if mean_acc > max_acc and mean_acc > 0.7:
                max_acc = mean_acc
                path = os.path.dirname(snapshot_prefix)
                best_models = os.path.join(path, 'best_models_{}_{:.4f}.ckpt'.format(i, max_acc))
                print('------save:{}'.format(best_models))
                saver.save(sess, best_models)

        coord.request_stop()
        coord.join(threads)

def train(train_record_file,
          train_log_step,
          train_param,
          val_record_file,
          val_log_step,
          labels_nums,
          data_shape,
          snapshot,
          snapshot_prefix):
    '''
    :param train_record_file: 训练的tfrecord文件
    :param train_log_step: 显示训练过程log信息间隔
    :param train_param: train参数
    :param val_record_file: 验证的tfrecord文件
    :param val_log_step: 显示验证过程log信息间隔
    :param val_param: val参数
    :param labels_nums: labels数
    :param data_shape: 输入数据shape
    :param snapshot: 保存模型间隔
    :param snapshot_prefix: 保存模型文件的前缀名
    :return:
    '''
    [base_lr,max_steps]=train_param
    [batch_size,resize_height,resize_width,depths]=data_shape

    # 获得训练和测试的样本数
    train_nums=get_example_nums(train_record_file)
    val_nums=get_example_nums(val_record_file)
    print('train nums:%d,val nums:%d'%(train_nums,val_nums))

    regularizer = slim.l2_regularizer(0.0005)
    # 从record中读取图片和labels数据
    # train数据,训练数据一般要求打乱顺序shuffle=True
    train_images, train_labels = read_records(train_record_file, resize_height, resize_width, type='normalization')
    train_images_batch, train_labels_batch = get_batch_images(train_images, train_labels,
                                                              batch_size=batch_size, labels_nums=labels_nums,
                                                              one_hot=True, shuffle=True)
    # val数据,验证数据可以不需要打乱数据
    val_images, val_labels = read_records(val_record_file, resize_height, resize_width, type='normalization')
    val_images_batch, val_labels_batch = get_batch_images(val_images, val_labels,
                                                          batch_size=batch_size, labels_nums=labels_nums,
                                                          one_hot=True, shuffle=False)

    # Define the model:
    with slim.arg_scope(resnet.resnet_arg_scope()):
        out, end_points = resnet.resnet_v2_50(inputs=input_images, num_classes=labels_nums,  is_training=is_training)

    # Specify the loss function: tf.losses定义的loss函数都会自动添加到loss函数,不需要add_loss()了
    tf.losses.softmax_cross_entropy(onehot_labels=input_labels, logits=out)#添加交叉熵损失loss=1.6
    # slim.losses.add_loss(my_loss)
    loss = tf.losses.get_total_loss(add_regularization_losses=True)#添加正则化损失loss=2.2
    accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(out, 1), tf.argmax(input_labels, 1)), tf.float32))

    score = tf.nn.softmax(out, name='score')
    classIds = tf.argmax(out, 1)
    # Specify the optimization scheme:
    # optimizer = tf.train.GradientDescentOptimizer(learning_rate=base_lr)


    # global_step = tf.Variable(0, trainable=False)
    # learning_rate = tf.train.exponential_decay(0.05, global_step, 150, 0.9)
    #
    optimizer = tf.train.MomentumOptimizer(learning_rate=base_lr,momentum= 0.9)
    # # train_tensor = optimizer.minimize(loss, global_step)
    # train_op = slim.learning.create_train_op(loss, optimizer,global_step=global_step)


    # 在定义训练的时候, 注意到我们使用了`batch_norm`层时,需要更新每一层的`average`和`variance`参数,
    # 更新的过程不包含在正常的训练过程中, 需要我们去手动像下面这样更新
    # 通过`tf.get_collection`获得所有需要更新的`op`
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    # 使用`tensorflow`的控制流, 先执行更新算子, 再执行训练
    with tf.control_dependencies(update_ops):
        # create_train_op that ensures that when we evaluate it to get the loss,
        # the update_ops are done and the gradient updates are computed.
        # train_op = slim.learning.create_train_op(total_loss=loss,optimizer=optimizer)
        train_op = slim.learning.create_train_op(total_loss=loss, optimizer=optimizer)


    # 循环迭代过程
    step_train(train_op, loss, accuracy,score,classIds,
               train_images_batch, train_labels_batch, train_nums, train_log_step,
               val_images_batch, val_labels_batch, val_nums, val_log_step,
               snapshot_prefix, snapshot)


if __name__ == '__main__':
    # train_record_file='dataset/record/train.tfrecords'
    # val_record_file='dataset/record/val.tfrecords'

    train_record_file = './onsets/record/train.tfrecords'
    val_record_file = './onsets/record/val.tfrecords'

    train_log_step=100
    base_lr = 0.001  # 学习率
    max_steps = 10000  # 迭代次数
    train_param=[base_lr,max_steps]

    val_log_step=200
    snapshot=2000#保存文件间隔
    snapshot_prefix='./models/onsets/resnet/model.ckpt'
    train(train_record_file=train_record_file,
          train_log_step=train_log_step,
          train_param=train_param,
          val_record_file=val_record_file,
          val_log_step=val_log_step,
          labels_nums=labels_nums,
          data_shape=data_shape,
          snapshot=snapshot,
          snapshot_prefix=snapshot_prefix)