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trainHBB.py
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trainHBB.py
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import logging
import argparse
import torch.optim as optim
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
import dataloadR.datasets as data
import utils.gpu as gpu
from utils import cosine_lr_scheduler
from utils.log import Logger
from modelR.lodet_hbb import LODet
from modelR.loss.loss_hbb import Loss
from evalR.evaluator import *
from evalR.coco_eval import COCOEvaluator
from torch.cuda.amp import autocast as autocast
class Trainer(object):
def __init__(self, weight_path, resume, gpu_id):
init_seeds(0)
self.prune=0
self.sr=True
self.device = gpu.select_device(gpu_id)
print(self.device)
self.start_epoch = 0
self.best_mAP = 0.
self.epochs = cfg.TRAIN["EPOCHS"]
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.Construct_Dataset(anno_file_type="train", img_size=cfg.TRAIN["TRAIN_IMG_SIZE"])
self.train_dataloader = DataLoader(self.train_dataset,
batch_size=cfg.TRAIN["BATCH_SIZE"],
num_workers=cfg.TRAIN["NUMBER_WORKERS"],
shuffle=True,
pin_memory=True)
net_model = LODet()
if torch.cuda.device_count() >1: ## multi GPUs
print("Let's use", torch.cuda.device_count(), "GPUs!")
net_model = torch.nn.DataParallel(net_model)
self.model = net_model.to(self.device)
elif torch.cuda.device_count() ==1:
self.model = net_model.to(self.device) ## Single GPU
#self.optimizer = optim.SGD(self.model.parameters(), lr=cfg.TRAIN["LR_INIT"], momentum=cfg.TRAIN["MOMENTUM"], weight_decay=cfg.TRAIN["WEIGHT_DECAY"])
self.optimizer = optim.Adam(self.model.parameters(), lr=cfg.TRAIN["LR_INIT"])
self.criterion = Loss(anchors=cfg.MODEL["ANCHORS"], strides=cfg.MODEL["STRIDES"],
iou_threshold_loss=cfg.TRAIN["IOU_THRESHOLD_LOSS"])
if resume:
self.__load_model_weights(weight_path)
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))
def __load_model_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.model.load_state_dict(chkpt['model'])#, False
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.model.state_dict(),
'optimizer': self.optimizer.state_dict()}
torch.save(chkpt, last_weight,_use_new_zipfile_serialization=False)
if self.best_mAP == mAP:
torch.save(chkpt['model'], best_weight,_use_new_zipfile_serialization=False)
if epoch > 0 and epoch % 5 == 0:
torch.save(chkpt, os.path.join(os.path.split(self.weight_path)[0], 'backup_epoch%g.pt'%epoch))
#
del chkpt
def __save_model_weights1(self, epoch, mAP):
if mAP > self.best_mAP:
self.best_mAP = mAP
best_weight = os.path.join(os.path.split(self.weight_path)[0], "best1.pt")
last_weight = os.path.join(os.path.split(self.weight_path)[0], "last1.pt")
chkpt = {'epoch': epoch,
'best_mAP': self.best_mAP,
'model': self.model.state_dict(),
'optimizer': self.optimizer.state_dict()}
torch.save(chkpt, last_weight,_use_new_zipfile_serialization=False)
torch.save(chkpt['model'], best_weight, _use_new_zipfile_serialization=False)
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(self.model)
logger.info(" Training start! Img size:{:d}, Batchsize:{:d}, Number of workers:{:d}".format(
cfg.TRAIN["TRAIN_IMG_SIZE"], cfg.TRAIN["BATCH_SIZE"], cfg.TRAIN["NUMBER_WORKERS"]))
logger.info(" Train datasets number is : {}".format(len(self.train_dataset)))
for epoch in range(self.start_epoch, self.epochs):
start = time.time()
self.model.train()
'''
##################################################################################
sr_flag = get_sr_flag(epoch, self.sr)
if self.prune == 1:
CBL_idx, _, prune_idx, shortcut_idx, _ = parse_module_defs2(self.model) ############
if self.sr:
print('shortcut sparse training')
elif self.prune == 0:
CBL_idx, _, prune_idx = parse_module_defs(self.model) ############ model.cfg -> idx
if self.sr:
print('normal sparse training ')
print(prune_idx)#[1, 3, 7, 10, 14, 17, 20, 23, 26, 29, 32, 35, 39, 42, 45, 48, 51, 54, 57, 60, 64, 67, 70, 73, 76, 77, 78, 79, 80, 81, 88, 89, 90, 91, 92, 93, 100, 101, 102, 103, 104, 105]
###################################################################################
'''
mloss = torch.zeros(4)
mAP = 0
self.__save_model_weights1(epoch, mAP)
for i, (imgs, label_sbbox, label_mbbox, label_lbbox,
sbboxes, mbboxes, lbboxes) in enumerate(self.train_dataloader):
self.scheduler.step(len(self.train_dataloader)*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.model(imgs)
loss, loss_iou, loss_conf, loss_cls = self.criterion(p, p_d, label_sbbox, label_mbbox,
label_lbbox, sbboxes, mbboxes, lbboxes)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
'''
########################
idx2mask = None
# if opt.sr and opt.prune==1 and epoch > opt.epochs * 0.5:
# idx2mask = get_mask2(model, prune_idx, 0.85)
##self.model.module_list = self.model.module.module_list
BNOptimizer.updateBN(sr_flag, self.model, 0.001, prune_idx, epoch, idx2mask) ###########实际剪枝更新的部分
###################################################
'''
loss_items = torch.tensor([loss_iou, loss_conf, loss_cls, loss])
mloss = (mloss * i + loss_items) / (i + 1)
if i % 50 == 0:
logger.info(
" Epoch:[{:3}/{}] Batch:[{:3}/{}] Img_size:[{:3}] Loss:{:.4f} "
"Loss_IoU:{:.4f} | Loss_Conf:{:.4f} | Loss_Cls:{:.4f} LR:{:g}".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_iou', 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)
if self.multi_scale_train and (i+1) % 10 == 0:
self.train_dataset.img_size = random.choice(range(
cfg.TRAIN["MULTI_TRAIN_RANGE"][0], cfg.TRAIN["MULTI_TRAIN_RANGE"][1],
cfg.TRAIN["MULTI_TRAIN_RANGE"][2])) * 32
if epoch >= 60 and epoch % 5 == 0 and cfg.TRAIN["EVAL_TYPE"] == 'VOC':
logger.info("===== Validate =====".format(epoch, self.epochs))
with torch.no_grad():
APs, inference_time = Evaluator(self.model).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)
elif epoch >= 60 and epoch % 5 == 0 and cfg.TRAIN["EVAL_TYPE"] == 'COCO':
logger.info("===== Validate =====".format(epoch, self.epochs))
with torch.no_grad():
evaluator = COCOEvaluator(data_dir=cfg.DATA_PATH,
img_size=cfg.TEST["TEST_IMG_SIZE"],
confthre=cfg.TEST["CONF_THRESH"],
nmsthre=cfg.TEST["NMS_THRESH"])
ap50_95, ap50, inference_time = evaluator.evaluate(self.model)
mAP = ap50
logger.info('ap50_95:{} | ap50:{}'.format(ap50_95, ap50))
logger.info("inference time: {:.2f} ms".format(inference_time))
writer.add_scalar('val/COCOAP50', ap50, epoch)
writer.add_scalar('val/COCOAP50_95', ap50_95, epoch)
self.__save_model_weights(epoch, mAP)
logger.info('Save weights Done')
logger.info("mAP: {:.3f}".format(mAP))
end = time.time()
logger.info("Inference time: {:.4f}s".format(end - start))
logger.info("Training finished. Best_mAP: {:.3f}%".format(self.best_mAP))
if __name__ == "__main__":
global logger, writer
parser = argparse.ArgumentParser()
parser.add_argument('--weight_path', type=str, default='weight/mobilenetv2_1.0-0c6065bc.pth',
help='weight file path') #default=None
parser.add_argument('--resume', action='store_true',default=False, help='resume training flag')
parser.add_argument('--gpu_id', type=int, default=0, help='gpu id')
parser.add_argument('--log_path', type=str, default='log/', help='log path')
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='NPMMRDet').get_log()
Trainer(weight_path=opt.weight_path, resume=opt.resume, gpu_id=opt.gpu_id).train()