#! /usr/bin/env python
# coding=utf-8

import os
import time
import shutil
import numpy as np
import tensorflow as tf
import core.utils as utils
from tqdm import tqdm
from core.dataset import Dataset
from core.yolov3 import YOLOV3
from core.config import cfg

class YoloTrain(object):
    def __init__(self):  # 从config文件获取到一些变量
        self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE
        self.classes = utils.read_class_names(cfg.YOLO.CLASSES)
        self.num_classes = len(self.classes)
        self.learn_rate_init = cfg.TRAIN.LEARN_RATE_INIT
        self.learn_rate_end = cfg.TRAIN.LEARN_RATE_END
        self.first_stage_epochs = cfg.TRAIN.FISRT_STAGE_EPOCHS
        self.second_stage_epochs = cfg.TRAIN.SECOND_STAGE_EPOCHS
        self.warmup_periods = cfg.TRAIN.WARMUP_EPOCHS
        self.initial_weight = cfg.TRAIN.INITIAL_WEIGHT
        self.time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))
        self.moving_ave_decay = cfg.YOLO.MOVING_AVE_DECAY
        self.max_bbox_per_scale = 150
        self.train_logdir = "./data/log/train"  # 日志保存地址
        self.trainset = Dataset('train')
        self.testset = Dataset('test')
        self.steps_per_period = len(self.trainset)
        self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))

        with tf.name_scope('define_input'): # 定义输入层
            self.input_data = tf.placeholder(dtype=tf.float32, name='input_data')
            self.label_sbbox = tf.placeholder(dtype=tf.float32, name='label_sbbox')
            self.label_mbbox = tf.placeholder(dtype=tf.float32, name='label_mbbox')
            self.label_lbbox = tf.placeholder(dtype=tf.float32, name='label_lbbox')
            self.true_sbboxes = tf.placeholder(dtype=tf.float32, name='sbboxes')
            self.true_mbboxes = tf.placeholder(dtype=tf.float32, name='mbboxes')
            self.true_lbboxes = tf.placeholder(dtype=tf.float32, name='lbboxes')
            self.trainable = tf.placeholder(dtype=tf.bool, name='training')

        with tf.name_scope("define_loss"): # 定义损失函数
            self.model = YOLOV3(self.input_data, self.trainable)
            self.net_var = tf.global_variables()
            self.giou_loss, self.conf_loss, self.prob_loss = self.model.compute_loss(
                self.label_sbbox, self.label_mbbox, self.label_lbbox,
                self.true_sbboxes, self.true_mbboxes, self.true_lbboxes)
            self.loss = self.giou_loss + self.conf_loss + self.prob_loss

        with tf.name_scope('learn_rate'): # 定义学习率
            self.global_step = tf.Variable(1.0, dtype=tf.float64, trainable=False, name='global_step')
            warmup_steps = tf.constant(self.warmup_periods * self.steps_per_period,
                                       dtype=tf.float64, name='warmup_steps')
            train_steps = tf.constant((self.first_stage_epochs + self.second_stage_epochs) * self.steps_per_period,
                                      dtype=tf.float64, name='train_steps')
            self.learn_rate = tf.cond(
                pred=self.global_step < warmup_steps,
                true_fn=lambda: self.global_step / warmup_steps * self.learn_rate_init,
                false_fn=lambda: self.learn_rate_end + 0.5 * (self.learn_rate_init - self.learn_rate_end) *
                                 (1 + tf.cos(
                                     (self.global_step - warmup_steps) / (train_steps - warmup_steps) * np.pi))
            )
            global_step_update = tf.assign_add(self.global_step, 1.0)

        '''
        warmup_steps作用：   
        神经网络在刚开始训练的过程中容易出现loss=NaN的情况，为了尽量避免这个情况，因此初始的学习率设置得很低
        但是这又使得训练速度变慢了。因此，采用逐渐增大的学习率，从而达到既可以尽量避免出现nan，又可以等训练过程稳定了再增大训练速度的目的。
        '''

        with tf.name_scope("define_weight_decay"):  # 指数平滑，可以让算法在最后不那么震荡，结果更有鲁棒性
            moving_ave = tf.train.ExponentialMovingAverage(self.moving_ave_decay).apply(tf.trainable_variables())

        # 指定需要恢复的参数。层等信息, 位置提前，减少模型体积。
        with tf.name_scope('loader_and_saver'):
            variables_to_restore = [v for v in self.net_var if
                                    v.name.split('/')[0] not in ['conv_sbbox', 'conv_mbbox', 'conv_lbbox']]
            self.loader = tf.train.Saver(variables_to_restore)
            self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=1)

        with tf.name_scope("define_first_stage_train"): # 第一阶段训练，只训练指定层
            self.first_stage_trainable_var_list = []
            for var in tf.trainable_variables():
                var_name = var.op.name
                var_name_mess = str(var_name).split('/')
                if var_name_mess[0] in ['conv_sbbox', 'conv_mbbox', 'conv_lbbox']:
                    self.first_stage_trainable_var_list.append(var)

            first_stage_optimizer = tf.train.AdamOptimizer(self.learn_rate).minimize(self.loss,
                                                                                     var_list=self.first_stage_trainable_var_list)
            with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
                with tf.control_dependencies([first_stage_optimizer, global_step_update]):
                    with tf.control_dependencies([moving_ave]):
                        self.train_op_with_frozen_variables = tf.no_op()

        with tf.name_scope("define_second_stage_train"):    # 第二阶段训练，释放所有层
            second_stage_trainable_var_list = tf.trainable_variables()
            second_stage_optimizer = tf.train.AdamOptimizer(self.learn_rate).minimize(self.loss,
                                                                                      var_list=second_stage_trainable_var_list)

            with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
                with tf.control_dependencies([second_stage_optimizer, global_step_update]):
                    with tf.control_dependencies([moving_ave]):
                        self.train_op_with_all_variables = tf.no_op()

        with tf.name_scope('summary'):
            tf.summary.scalar("learn_rate", self.learn_rate)
            tf.summary.scalar("giou_loss", self.giou_loss)
            tf.summary.scalar("conf_loss", self.conf_loss)
            tf.summary.scalar("prob_loss", self.prob_loss)
            tf.summary.scalar("total_loss", self.loss)

            logdir = "./data/log/"  #  日志保存地址
            if os.path.exists(logdir): shutil.rmtree(logdir)
            os.mkdir(logdir)
            self.write_op = tf.summary.merge_all()
            self.summary_writer = tf.summary.FileWriter(logdir, graph=self.sess.graph)

    def train(self):
        self.sess.run(tf.global_variables_initializer())
        try:
            print('=> Restoring weights from: %s ... ' % self.initial_weight)
            self.loader.restore(self.sess, self.initial_weight)
        except:
            print('=> %s does not exist !!!' % self.initial_weight)
            print('=> Now it starts to train YOLOV3 from scratch ...')
            self.first_stage_epochs = 0

        # 阶段学习率
        for epoch in range(1, 1 + self.first_stage_epochs + self.second_stage_epochs):
            if epoch <= self.first_stage_epochs:
                train_op = self.train_op_with_frozen_variables
            else:
                train_op = self.train_op_with_all_variables

            # tqdm is a visualization tool that displays an Iterable object in a progree bar
            pbar = tqdm(self.trainset)
            train_epoch_loss, test_epoch_loss = [], []

            for train_data in pbar:
                _, summary, train_step_loss, global_step_val = self.sess.run(
                    [train_op, self.write_op, self.loss, self.global_step], feed_dict={
                        self.input_data: train_data[0],
                        self.label_sbbox: train_data[1],
                        self.label_mbbox: train_data[2],
                        self.label_lbbox: train_data[3],
                        self.true_sbboxes: train_data[4],
                        self.true_mbboxes: train_data[5],
                        self.true_lbboxes: train_data[6],
                        self.trainable: True,
                    })

                train_epoch_loss.append(train_step_loss)
                self.summary_writer.add_summary(summary, global_step_val)
                pbar.set_description("train loss: %.2f" % train_step_loss)

            for test_data in self.testset:
                test_step_loss = self.sess.run(self.loss, feed_dict={
                    self.input_data: test_data[0],
                    self.label_sbbox: test_data[1],
                    self.label_mbbox: test_data[2],
                    self.label_lbbox: test_data[3],
                    self.true_sbboxes: test_data[4],
                    self.true_mbboxes: test_data[5],
                    self.true_lbboxes: test_data[6],
                    self.trainable: False,
                })

                test_epoch_loss.append(test_step_loss)

            train_epoch_loss, test_epoch_loss = np.mean(train_epoch_loss), np.mean(test_epoch_loss)
            ckpt_file = "./checkpoint/yolov3_train_loss=%.4f.ckpt" % train_epoch_loss
            log_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
            print("=> Epoch: %2d Time: %s Train loss: %.2f Test loss: %.2f Saving %s ..."
                  % (epoch, log_time, train_epoch_loss, test_epoch_loss, ckpt_file))
            self.saver.save(self.sess, ckpt_file, global_step=epoch)    


if __name__ == '__main__': YoloTrain().train()

numpy.mean