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train_MuDet.py
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train_MuDet.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 utils.gpu as gpu
from utils import cosine_lr_scheduler
from utils.log import Logger
import dataloadR.datasets_siames as data
from modelR.MuDet import MuDet
from modelR.loss.loss import Loss
from evalR.evaluator_mudet import *
from torch.cuda import amp
import torch.backends.cudnn as cudnn
from copy import deepcopy
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
class Trainer(object):
def __init__(self, weight_path, resume, gpu_id):
init_seeds(0)
self.__num_class = cfg.DATA["NUM"]
self.device = gpu.select_device(gpu_id)
print(self.device)
self.cuda = self.device.type != 'cpu'
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_name=cfg.DATASET_NAME, 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 = MuDet(weight_path=self.weight_path)
self.model = net_model.to(self.device) ## Single GPU
torch.backends.cudnn.benchmark = True
# Optimizer
g0, g1, g2 = [], [], [] # optimizer parameter groups
for v in self.model.modules():
if hasattr(v, 'bias') and isinstance(v.bias, torch.nn.Parameter): # bias
g2.append(v.bias)
if isinstance(v, torch.nn.BatchNorm2d): # weight (no decay)
g0.append(v.weight)
elif hasattr(v, 'weight') and isinstance(v.weight, torch.nn.Parameter): # weight (with decay)
g1.append(v.weight)
self.optimizer = optim.SGD(g0, lr=cfg.TRAIN["LR_INIT"], momentum=cfg.TRAIN["MOMENTUM"], nesterov=True)
self.optimizer.add_param_group({'params': g1, 'weight_decay': cfg.TRAIN["WEIGHT_DECAY"]}) # add g1 with weight_decay
self.optimizer.add_param_group({'params': g2}) # add g2 (biases)
del g0, g1, g2
self.criterion = Loss()
self.__load_model_weights(self.weight_path, resume)
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))
self.scaler = amp.GradScaler(enabled=self.cuda)
def __load_model_weights(self, weight_path, resume):
if resume:
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'])
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
else:
self.model.load_darknet_weights(weight_path) ## Single GPU
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)
if self.best_mAP == mAP:
torch.save(chkpt['model'], best_weight)
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 train(self):
global writer
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()
mloss = torch.zeros(10)
for i, (imgs_rgb, imgs_dsm, label_sbbox, label_mbbox, label_lbbox) in enumerate(self.train_dataloader):
self.scheduler.step(len(self.train_dataloader)*epoch + i)
imgs_rgb = imgs_rgb.to(self.device)
imgs_dsm = imgs_dsm.to(self.device)
with amp.autocast(enabled=self.cuda):
p, p_d = self.model(imgs_rgb, imgs_dsm)
label_sbbox = label_sbbox.to(self.device)
label_mbbox = label_mbbox.to(self.device)
label_lbbox = label_lbbox.to(self.device)
loss, loss_fg, loss_bg, loss_pos, loss_neg, loss_iou, loss_cls, loss_s, loss_r, loss_l = self.criterion(p, p_d, label_sbbox, label_mbbox, label_lbbox, epoch, i)
self.scaler.scale(loss).backward()
self.scaler.step(self.optimizer)
self.scaler.update()
self.optimizer.zero_grad()
loss_items = 10 * torch.tensor([loss_fg, loss_bg, loss_pos, loss_neg, loss_iou, loss_cls, loss_s, loss_r, loss_l, loss])
mloss = (mloss * i + loss_items) / (i + 1)
mAP = 0
if i % 50 == 0:
logger.info(
"Epoch:[{:3}/{}] Batch:[{:3}/{}] Img_size:[{:3}] Loss:{:.4f} "
"Loss_fg:{:.4f} | Loss_bg:{:.4f} | Loss_pos:{:.4f} | Loss_neg:{:.4f} "
"| Loss_iou:{:.4f} | Loss_cls:{:.4f} | Loss_s:{:.4f} | Loss_r:{:.4f} | "
"Loss_l:{:.4f} | LR:{:g}".format(
epoch, self.epochs, i, len(self.train_dataloader) - 1, self.train_dataset.img_size,
mloss[9], mloss[0], mloss[1], mloss[2], mloss[3],
mloss[4], mloss[5], mloss[6], mloss[7], mloss[8 ],
self.optimizer.param_groups[0]['lr']
))
writer.add_scalar('loss_fg', mloss[0], len(self.train_dataloader)
* (cfg.TRAIN["BATCH_SIZE"]) * epoch + i)
writer.add_scalar('loss_bg', mloss[1], len(self.train_dataloader)
* (cfg.TRAIN["BATCH_SIZE"]) * epoch + i)
writer.add_scalar('loss_pos', mloss[2], len(self.train_dataloader)
* (cfg.TRAIN["BATCH_SIZE"]) * epoch + i)
writer.add_scalar('loss_neg', mloss[3], len(self.train_dataloader)
* (cfg.TRAIN["BATCH_SIZE"]) * epoch + i)
writer.add_scalar('loss_iou', mloss[4], len(self.train_dataloader)
* (cfg.TRAIN["BATCH_SIZE"]) * epoch + i)
writer.add_scalar('loss_cls', mloss[5], len(self.train_dataloader)
* (cfg.TRAIN["BATCH_SIZE"]) * epoch + i)
writer.add_scalar('loss_s', mloss[6], len(self.train_dataloader)
* (cfg.TRAIN["BATCH_SIZE"]) * epoch + i)
writer.add_scalar('loss_r', mloss[7], len(self.train_dataloader)
* (cfg.TRAIN["BATCH_SIZE"]) * epoch + i)
writer.add_scalar('loss_l', mloss[8], len(self.train_dataloader)
* (cfg.TRAIN["BATCH_SIZE"]) * epoch + i)
writer.add_scalar('train_loss', mloss[9], 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 >= 20 and epoch % 1 == 0 and cfg.TRAIN["EVAL_TYPE"] == 'VOC':
logger.info("===== Validate =====".format(epoch, self.epochs))
with torch.no_grad():
start = time.time()
APs, r, p, inference_time = Evaluator(deepcopy(self.model)).APs_voc()#deepcopy(self.model).cuda()
end = time.time()
logger.info("Test cost time:{:.4f}s".format(end - start))
for i in APs:
print("{} --> AP : {}".format(i, APs[i]))
mAP += APs[i]
mAP = mAP / self.__num_class
logger.info('mAP:{}'.format(mAP))
logger.info("inference time: {:.2f} ms".format(inference_time))
writer.add_scalar('test/VOCmAP', mAP)
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))
os.environ['KMP_DUPLICATE_LIB_OK']='True'
if __name__ == "__main__":
global logger, writer
parser = argparse.ArgumentParser()
parser.add_argument('--weight_path', type=str, default='weight/darknet53.weights', help='weight file path')
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='GGHLs').get_log()
Trainer(weight_path=opt.weight_path, resume=opt.resume, gpu_id=opt.gpu_id).train()