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train.py
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train.py
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#! /usr/bin/env python
# coding=utf-8
import os
import time
import shutil
import numpy as np
import core.utils as utils
from tqdm import tqdm
from core.dataset import Dataset
from core.yolov3 import YOLOV3
from core.config import cfg
import tensorflow
print('tensorflow.version=', tensorflow.__version__)
if tensorflow.__version__.startswith('1.'):
import tensorflow as tf
else:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
"""TF_CPP_MIN_LOG_LEVEL 取值 0 0也是默认值，输出所有信息
TF_CPP_MIN_LOG_LEVEL 取值 1 屏蔽通知信息
TF_CPP_MIN_LOG_LEVEL 取值 2 屏蔽通知信息和警告信息
TF_CPP_MIN_LOG_LEVEL 取值 3 屏蔽通知信息、警告信息和报错信息"""
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # or any {'0', '1', '2'}
class YoloTrain(object):
def __init__(self):
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 = "log/train"
if os.path.exists(self.train_logdir):
shutil.rmtree(self.train_logdir)
os.makedirs(self.train_logdir)
self.ckpt_path = cfg.TRAIN.CKPT_PATH
if not os.path.exists(self.ckpt_path):
os.makedirs(self.ckpt_path)
self.trainset = Dataset('train')
self.testset = Dataset('test')
self.steps_per_period = len(self.trainset)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
with tf.name_scope('define_input'):
self.input_data = tf.placeholder(dtype=tf.float32, name='input_data')
self.lwir_input_data = tf.placeholder(dtype=tf.float32, name='lwir_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"):
self.model = YOLOV3(self.input_data, self.lwir_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'):
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)
with tf.name_scope("define_weight_decay"):
moving_ave = tf.train.ExponentialMovingAverage(self.moving_ave_decay).apply(tf.trainable_variables())
with tf.name_scope("define_first_stage_train"):
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"):
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('loader_and_saver'):
self.loader = tf.train.Saver(self.net_var)
self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=10000)
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)
self.write_op = tf.summary.merge_all()
self.summary_writer = tf.summary.FileWriter(self.train_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
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
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.lwir_input_data: train_data[1],
self.label_sbbox: train_data[2], self.label_mbbox: train_data[3], self.label_lbbox: train_data[4],
self.true_sbboxes: train_data[5], self.true_mbboxes: train_data[6], self.true_lbboxes: train_data[7],
self.trainable: True,})
train_epoch_loss.append(train_step_loss)
self.summary_writer.add_summary(summary, global_step_val)
pbar.set_description("train loss: %.4f" % 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.lwir_input_data: test_data[1],
self.label_sbbox: test_data[2], self.label_mbbox: test_data[3], self.label_lbbox: test_data[4],
self.true_sbboxes: test_data[5], self.true_mbboxes: test_data[6], self.true_lbboxes: test_data[7],
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 = os.path.join(self.ckpt_path, "Pedestrian_yolov3_loss=%.4f-%.4f.ckpt" % (train_epoch_loss, test_epoch_loss))
log_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
print("=> Epoch: %2d Time: %s Train loss: %.4f Test loss: %.4f 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()