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train.py
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train.py
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import logging
import utils.gpu as gpu
from model.build_model import Build_Model
from model.loss.yolo_loss import YoloV4Loss
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
import utils.datasets as data
import time
import random
import argparse
from eval.evaluator import *
from utils.tools import *
from tensorboardX import SummaryWriter
import config.yolov4_config as cfg
from utils import cosine_lr_scheduler
from utils.log import Logger
from apex import amp
from eval_coco import *
from eval.cocoapi_evaluator import COCOAPIEvaluator
def detection_collate(batch):
targets = []
imgs = []
for sample in batch:
imgs.append(sample[0])
targets.append(sample[1])
return torch.stack(imgs,0),targets
class Trainer(object):
def __init__(self, weight_path, resume, gpu_id, accumulate, fp_16):
init_seeds(0)
self.fp_16 = fp_16
self.device = gpu.select_device(gpu_id)
self.start_epoch = 0
self.best_mAP = 0.
self.accumulate = accumulate
self.weight_path = weight_path
self.multi_scale_train = cfg.TRAIN["MULTI_SCALE_TRAIN"]
if self.multi_scale_train:print('Using multi scales training')
else:print('train img size is {}'.format(cfg.TRAIN["TRAIN_IMG_SIZE"]))
self.train_dataset = data.Build_Dataset(anno_file_type="train", img_size=cfg.TRAIN["TRAIN_IMG_SIZE"])
self.epochs = cfg.TRAIN["YOLO_EPOCHS"] if cfg.MODEL_TYPE["TYPE"] == 'YOLOv4' else cfg.TRAIN["Mobilenet_YOLO_EPOCHS"]
self.train_dataloader = DataLoader(self.train_dataset,
batch_size=cfg.TRAIN["BATCH_SIZE"],
num_workers=cfg.TRAIN["NUMBER_WORKERS"],
shuffle=True, pin_memory=True
)
self.yolov4 = Build_Model(weight_path=weight_path, resume=resume).to(self.device)
self.optimizer = optim.SGD(self.yolov4.parameters(), lr=cfg.TRAIN["LR_INIT"],
momentum=cfg.TRAIN["MOMENTUM"], weight_decay=cfg.TRAIN["WEIGHT_DECAY"])
self.criterion = YoloV4Loss(anchors=cfg.MODEL["ANCHORS"], strides=cfg.MODEL["STRIDES"],
iou_threshold_loss=cfg.TRAIN["IOU_THRESHOLD_LOSS"])
self.scheduler = cosine_lr_scheduler.CosineDecayLR(self.optimizer,
T_max=self.epochs*len(self.train_dataloader),
lr_init=cfg.TRAIN["LR_INIT"],
lr_min=cfg.TRAIN["LR_END"],
warmup=cfg.TRAIN["WARMUP_EPOCHS"]*len(self.train_dataloader))
if resume: self.__load_resume_weights(weight_path)
def __load_resume_weights(self, weight_path):
last_weight = os.path.join(os.path.split(weight_path)[0], "last.pt")
chkpt = torch.load(last_weight, map_location=self.device)
self.yolov4.load_state_dict(chkpt['model'])
self.start_epoch = chkpt['epoch'] + 1
if chkpt['optimizer'] is not None:
self.optimizer.load_state_dict(chkpt['optimizer'])
self.best_mAP = chkpt['best_mAP']
del chkpt
def __save_model_weights(self, epoch, mAP):
if mAP > self.best_mAP:
self.best_mAP = mAP
best_weight = os.path.join(os.path.split(self.weight_path)[0], "best.pt")
last_weight = os.path.join(os.path.split(self.weight_path)[0], "last.pt")
chkpt = {'epoch': epoch,
'best_mAP': self.best_mAP,
'model': self.yolov4.state_dict(),
'optimizer': self.optimizer.state_dict()}
torch.save(chkpt, last_weight)
if self.best_mAP == mAP:
torch.save(chkpt['model'], best_weight)
if epoch > 0 and epoch % 10 == 0:
torch.save(chkpt, os.path.join(os.path.split(self.weight_path)[0], 'backup_epoch%g.pt'%epoch))
del chkpt
def train(self):
global writer
logger.info("Training start,img size is: {:d},batchsize is: {:d},work number is {:d}".format(cfg.TRAIN["TRAIN_IMG_SIZE"],cfg.TRAIN["BATCH_SIZE"],cfg.TRAIN["NUMBER_WORKERS"]))
logger.info(self.yolov4)
logger.info("Train datasets number is : {}".format(len(self.train_dataset)))
if self.fp_16: self.yolov4, self.optimizer = amp.initialize(self.yolov4, self.optimizer, opt_level='O1', verbosity=0)
logger.info(" ======= start training ====== ")
for epoch in range(self.start_epoch, self.epochs):
start = time.time()
self.yolov4.train()
mloss = torch.zeros(4)
logger.info("===Epoch:[{}/{}]===".format(epoch, self.epochs))
for i, (imgs, label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes) in enumerate(self.train_dataloader):
self.scheduler.step(len(self.train_dataloader)/(cfg.TRAIN["BATCH_SIZE"])*epoch + i)
imgs = imgs.to(self.device)
label_sbbox = label_sbbox.to(self.device)
label_mbbox = label_mbbox.to(self.device)
label_lbbox = label_lbbox.to(self.device)
sbboxes = sbboxes.to(self.device)
mbboxes = mbboxes.to(self.device)
lbboxes = lbboxes.to(self.device)
p, p_d = self.yolov4(imgs)
loss, loss_ciou, loss_conf, loss_cls = self.criterion(p, p_d, label_sbbox, label_mbbox,
label_lbbox, sbboxes, mbboxes, lbboxes)
if self.fp_16:
with amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Accumulate gradient for x batches before optimizing
if i % self.accumulate == 0:
self.optimizer.step()
self.optimizer.zero_grad()
# Update running mean of tracked metrics
loss_items = torch.tensor([loss_ciou, loss_conf, loss_cls, loss])
mloss = (mloss * i + loss_items) / (i + 1)
# Print batch results
if i % 10 == 0:
logger.info(" === Epoch:[{:3}/{}],step:[{:3}/{}],img_size:[{:3}],total_loss:{:.4f}|loss_ciou:{:.4f}|loss_conf:{:.4f}|loss_cls:{:.4f}|lr:{:.4f}".format(
epoch, self.epochs,i, len(self.train_dataloader) - 1, self.train_dataset.img_size,mloss[3], mloss[0], mloss[1],mloss[2],self.optimizer.param_groups[0]['lr']
))
writer.add_scalar('loss_ciou', mloss[0],
len(self.train_dataloader) / (cfg.TRAIN["BATCH_SIZE"]) * epoch + i)
writer.add_scalar('loss_conf', mloss[1],
len(self.train_dataloader) / (cfg.TRAIN["BATCH_SIZE"]) * epoch + i)
writer.add_scalar('loss_cls', mloss[2],
len(self.train_dataloader) / (cfg.TRAIN["BATCH_SIZE"]) * epoch + i)
writer.add_scalar('train_loss', mloss[3],
len(self.train_dataloader) / (cfg.TRAIN["BATCH_SIZE"]) * epoch + i)
# multi-sclae training (320-608 pixels) every 10 batches
if self.multi_scale_train and (i+1) % 10 == 0:
self.train_dataset.img_size = random.choice(range(10, 20)) * 32
if cfg.TRAIN["DATA_TYPE"] == 'VOC' or cfg.TRAIN["DATA_TYPE"] == 'Customer':
mAP = 0.
if epoch >= 0:
logger.info("===== Validate =====".format(epoch, self.epochs))
with torch.no_grad():
APs, inference_time = Evaluator(self.yolov4, showatt=False).APs_voc()
for i in APs:
logger.info("{} --> mAP : {}".format(i, APs[i]))
mAP += APs[i]
mAP = mAP / self.train_dataset.num_classes
logger.info("mAP : {}".format(mAP))
logger.info("inference time: {:.2f} ms".format(inference_time))
writer.add_scalar('mAP', mAP, epoch)
self.__save_model_weights(epoch, mAP)
logger.info('save weights done')
logger.info(" ===test mAP:{:.3f}".format(mAP))
elif epoch >= 0 and cfg.TRAIN["DATA_TYPE"] == 'COCO':
evaluator = COCOAPIEvaluator(model_type='YOLOv4',
data_dir=cfg.DATA_PATH,
img_size=cfg.VAL["TEST_IMG_SIZE"],
confthre=0.08,
nmsthre=cfg.VAL["NMS_THRESH"])
ap50_95, ap50 = evaluator.evaluate(self.yolov4)
logger.info('ap50_95:{}|ap50:{}'.format(ap50_95, ap50))
writer.add_scalar('val/COCOAP50', ap50, epoch)
writer.add_scalar('val/COCOAP50_95', ap50_95, epoch)
self.__save_model_weights(epoch, ap50)
print('save weights done')
end = time.time()
logger.info(" ===cost time:{:.4f}s".format(end - start))
logger.info("=====Training Finished. best_test_mAP:{:.3f}%====".format(self.best_mAP))
if __name__ == "__main__":
global logger, writer
parser = argparse.ArgumentParser()
parser.add_argument('--weight_path', type=str, default='weight/mobilenetv3.pth', help='weight file path')#weight/darknet53_448.weights
parser.add_argument('--resume', action='store_true',default=False, help='resume training flag')
parser.add_argument('--gpu_id', type=int, default=-1, help='whither use GPU(eg:0,1,2,3,4,5,6,7,8) or CPU(-1)')
parser.add_argument('--log_path', type=str, default='log/', help='log path')
parser.add_argument('--accumulate', type=int, default=2, help='batches to accumulate before optimizing')
parser.add_argument('--fp_16', type=bool, default=False, help='whither to use fp16 precision')
opt = parser.parse_args()
writer = SummaryWriter(logdir=opt.log_path + '/event')
logger = Logger(log_file_name=opt.log_path + '/log.txt', log_level=logging.DEBUG, logger_name='YOLOv4').get_log()
Trainer(weight_path=opt.weight_path,
resume=opt.resume,
gpu_id=opt.gpu_id,
accumulate=opt.accumulate,
fp_16=opt.fp_16).train()