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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_tiny import YOLOV3Tiny
from core.yolov3 import YOLOV3
from core.yolov4 import YOLOV4
from core.yolov5 import YOLOV5
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()
class YoloTrain(object):
def __init__(self, net_type):
self.net_type = net_type
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.ckpt_path = cfg.TRAIN.CKPT_PATH
if not os.path.exists(self.ckpt_path):
os.makedirs(self.ckpt_path)
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.log_path = ('log/%s' % net_type)
if os.path.exists(self.log_path):
shutil.rmtree(self.log_path)
#os.removedirs(self.log_path)
os.makedirs(self.log_path)
self.trainset = Dataset('train', self.net_type)
self.testset = Dataset('test', self.net_type)
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('input'):
if net_type == 'tiny':
self.input_data = tf.placeholder(dtype=tf.float32, name='input_data')
self.label_mbbox = tf.placeholder(dtype=tf.float32, name='label_mbbox')
self.label_lbbox = tf.placeholder(dtype=tf.float32, name='label_lbbox')
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')
else:
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'):
if self.net_type == 'tiny':
self.model = YOLOV3Tiny(self.input_data, self.trainable)
self.net_var = tf.global_variables()
self.iou_loss, self.conf_loss, self.prob_loss = self.model.compute_loss(self.label_mbbox, self.label_lbbox,
self.true_mbboxes, self.true_lbboxes)
self.loss = self.iou_loss + self.conf_loss + self.prob_loss
elif self.net_type == 'yolov3':
self.model = YOLOV3(self.input_data, self.trainable)
self.net_var = tf.global_variables()
self.iou_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.iou_loss + self.conf_loss + self.prob_loss
elif self.net_type == 'yolov4' or self.net_type == 'yolov5':
iou_use = 1 # (0, 1, 2) ==> (giou_loss, diou_loss, ciou_loss)
focal_use = False # (False, True) ==> (normal, focal_loss)
label_smoothing = 0
if self.net_type == 'yolov4':
self.model = YOLOV4(self.input_data, self.trainable)
else:
self.model = YOLOV5(self.input_data, self.trainable)
self.net_var = tf.global_variables()
self.iou_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,
iou_use, focal_use, label_smoothing)
self.loss = self.iou_loss + self.conf_loss + self.prob_loss
# self.loss = tf.Print(self.loss, [self.iou_loss, self.conf_loss, self.prob_loss], message='loss: ')
else:
print('self.net_type=%s error' % self.net_type)
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 net_type == 'tiny':
bboxes = ['conv_mbbox', 'conv_lbbox']
else:
bboxes = ['conv_sbbox', 'conv_mbbox', 'conv_lbbox']
if var_name_mess[0] in bboxes:
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=1000)
with tf.name_scope('summary'):
tf.summary.scalar('learn_rate', self.learn_rate)
tf.summary.scalar('iou_loss', self.iou_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.log_path, 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 YOLO-%s from scratch ...' % self.net_type)
self.first_stage_epochs = 0
saving = 0.0
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:
if net_type == 'tiny':
_, 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_mbbox: train_data[1], self.label_lbbox: train_data[2],
self.true_mbboxes: train_data[3], self.true_lbboxes: train_data[4],
self.trainable: True,})
else:
_, 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:
if net_type == 'tiny':
test_step_loss = self.sess.run(self.loss,
feed_dict={self.input_data: test_data[0],
self.label_mbbox: test_data[1], self.label_lbbox: test_data[2],
self.true_mbboxes: test_data[3], self.true_lbboxes: test_data[4],
self.trainable: False,})
else:
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)
train_epoch_loss = np.mean(train_epoch_loss)
ckpt_file = os.path.join(self.ckpt_path, 'social_%s_test-loss=%.4f.ckpt' % (self.net_type, test_epoch_loss))
log_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
if saving == 0.0:
saving = train_epoch_loss
print('=> Epoch: %2d Time: %s Train loss: %.2f' % (epoch, log_time, train_epoch_loss))
elif saving > train_epoch_loss:
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)
saving = train_epoch_loss
else:
print('=> Epoch: %2d Time: %s Train loss: %.2f' % (epoch, log_time, train_epoch_loss))
if __name__ == '__main__':
"""
argv = sys.argv
if len(argv) < 3:
print('usage: python train.py gpu_id net_type(yolov5/yolov4/yolov3/tiny)')
sys.exit()
"""
gpu_id = 0 #argv[1]
net_type = 'yolov3' #argv[2]
print('train gpu_id=%s, net_type=%s' % (gpu_id, net_type))
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
YoloTrain(net_type).train()