class lightenYOLOv4(pl.LightningModule):
def __init__(self, weight_path, resume, exp_name, accumulate=None):
# precision=16 for fp16
super().__init__()
self.model = Build_Model(weight_path=weight_path, resume=resume)
self.criterion = YoloV4Loss(
anchors=cfg.MODEL["ANCHORS"],
strides=cfg.MODEL["STRIDES"],
iou_threshold_loss=cfg.TRAIN["IOU_THRESHOLD_LOSS"])
self.evaluator = Evaluator(self.model,
showatt=False,
exp_name=exp_name)
self.evaluator.clear_predict_file()
# how you want your model to do inference/predictions
def forward(self, img):
p, p_d = self.model(img)
return p, p_d
"""
def training_epoch_end(self,outputs):
# the function is called after every epoch is completed
# calculating average loss
avg_loss = 0
avg_loss = torch.stack([x['loss'] for x in outputs]).mean()
# creating log dictionary
result = pl.TrainResult()
result.log('val/loss_epoch', avg_loss)
return result
"""
# the train loop INDEPENDENT of forward.
def training_step(self, batch, batch_idx):
img, label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes, _ = batch
p, p_d = self(img)
loss, loss_ciou, loss_conf, loss_cls = self.criterion(
p, p_d, label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes,
lbboxes)
result = pl.TrainResult(minimize=loss)
#result = pl.TrainResult(loss)
#result.log('train_loss_ciou', loss_ciou)
#result.log('train_loss_conf', loss_conf)
#result.log('train_loss_cls', loss_cls)
result.log('train_loss', loss, on_step=True, on_epoch=True)
return result
'''
#https://www.learnopencv.com/tensorboard-with-pytorch-lightning/
logs={"train_loss": loss,
"train_loss_ciou":loss_ciou,
"train_loss_conf":loss_conf,
"train_loss_cls":loss_cls,}
batch_dictionary={
#REQUIRED: It ie required for us to return "loss"
"loss": loss,
#optional for batch logging purposes
"log": logs,
}
return batch_dictionary
'''
def validation_epoch_end(self, outputs):
APs = self.evaluator.calc_APs()
self.evaluator.clear_predict_file()
mAP = 0
for i in APs:
mAP += APs[i]
mAP = mAP / self.model.getNC()
result = pl.EvalResult()
result.log('val/mAP_epoch', torch.Tensor([mAP]).cuda())
#trainer.logger_connector.logged_metrics
return result
def validation_step(self, batch, batch_idx):
img_batch, label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes, img_name = batch
for idx, img in tqdm(zip(img_name, img_batch)):
# CHW -> HWC
img = img.cpu().numpy().transpose(1, 2, 0)
bboxes_prd = self.evaluator.get_bbox(img,
multi_test=False,
flip_test=False)
self.evaluator.store_bbox(idx, bboxes_prd)
'''
loss, loss_ciou, loss_conf, loss_cls = self.criterion(p, p_d, label_sbbox, label_mbbox,
label_lbbox, sbboxes, mbboxes, lbboxes)
self.log('val_loss_ciou', loss_ciou)
self.log('val_loss_conf', loss_conf)
self.log('val_loss_cls', loss_cls)
self.log('val_loss', loss)
'''
return 1
def test_step(self, batch, batch_idx):
img, label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes = batch
p, p_d = self(img)
loss, loss_ciou, loss_conf, loss_cls = self.criterion(
p, p_d, label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes,
lbboxes)
#, loss_ciou, loss_conf, loss_cls
return loss
def configure_optimizers(self):
optimizer = optim.SGD(self.model.parameters(),
lr=cfg.TRAIN["LR_INIT"],
momentum=cfg.TRAIN["MOMENTUM"],
weight_decay=cfg.TRAIN["WEIGHT_DECAY"])
return optimizer
class Trainer(object):
def __init__(self, weight_path, resume, gpu_id, accumulate):
init_seeds(0)
self.device = gpu.select_device(gpu_id)
self.start_epoch = 0
self.best_mAP = 0.
self.accumulate = accumulate
self.epochs = cfg.TRAIN["EPOCHS"]
self.weight_path = weight_path
self.multi_scale_train = cfg.TRAIN["MULTI_SCALE_TRAIN"]
self.train_dataset = data.Build_Dataset(
anno_file_type="train", img_size=cfg.TRAIN["TRAIN_IMG_SIZE"])
print('train img size is {}'.format(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)
self.yolov4 = Build_Model().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.__load_model_weights(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))
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.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
else:
self.yolov4.load_darknet_weights(weight_path)
# 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.yolov3.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 __save_model_weights(self, epoch, loss):
weight = os.path.join(
os.path.split(self.weight_path)[0],
"epoch{}, loss{}.pt".format(epoch, loss))
chkpt = {
'epoch': epoch,
'model': self.yolov4.state_dict(),
'optimizer': self.optimizer.state_dict()
}
torch.save(chkpt['model'], weight)
def train(self):
global writer # 创建一个SummaryWriter实例
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)))
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) * 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_giou, loss_conf, loss_cls = self.criterion(
p, p_d, label_sbbox, label_mbbox, label_lbbox, sbboxes,
mbboxes, lbboxes)
with amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_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_giou, 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_giou:{:.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('train_loss', mloss[3], 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(5, 15)) * 32 # for imgsize 320
# self.train_dataset.img_size = random.choice(range(12, 22)) * 32 # for imgsize 544
self.train_dataset.img_size = random.choice(range(10,
20)) * 32
if epoch >= 0 and cfg.TRAIN["DATA_TYPE"] == 'VOC':
self.__save_model_weights(epoch, mloss[3])
print('save weights done')
# mAP = 0
# if epoch >= 0:
# logger.info("===== Validate =====".format(epoch, self.epochs))
# with torch.no_grad():
# APs = Evaluator(self.yolov3,epoch,showatt=False).APs_voc()
# for i in APs:
# print("{} --> mAP : {}".format(i, APs[i]))
# mAP += APs[i]
# mAP = mAP / self.train_dataset.num_classes
# writer.add_scalar('mAP', mAP, epoch)
# logger.info(" ===test mAP:{:.3f}".format(mAP))
# writer.add_scalar('mAP', mAP, epoch)
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)
else:
assert print('dataset must be VOC or COCO')
end = time.time()
logger.info(" ===cost time:{:.4f}s".format(end - start))
logger.info(
"=====Training Finished. best_test_mAP:{:.3f}%====".format(
self.best_mAP))
class Trainer(object):
def __init__(self,
weight_path=None,
resume=False,
gpu_id=0,
accumulate=1,
fp_16=False):
init_seeds(0)
self.fp_16 = fp_16
self.device = gpu.select_device(gpu_id)
self.start_epoch = 0
self.best_mAP = 0.0
self.accumulate = accumulate
self.weight_path = weight_path
self.multi_scale_train = cfg.TRAIN["MULTI_SCALE_TRAIN"]
self.showatt = cfg.TRAIN["showatt"]
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.eval_epoch = (30 if cfg.MODEL_TYPE["TYPE"] == "YOLOv4" else 50)
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,
showatt=self.showatt).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)))
def is_valid_number(x):
return not (math.isnan(x) or math.isinf(x) or x > 1e4)
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 is_valid_number(loss.item()):
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) * epoch + i,
)
writer.add_scalar(
"loss_conf",
mloss[1],
len(self.train_dataloader) * epoch + i,
)
writer.add_scalar(
"loss_cls",
mloss[2],
len(self.train_dataloader) * epoch + i,
)
writer.add_scalar(
"train_loss",
mloss[3],
len(self.train_dataloader) * 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 = 0.0
if epoch >= self.eval_epoch:
logger.info("===== Validate =====".format(
epoch, self.epochs))
logger.info("val img size is {}".format(
cfg.VAL["TEST_IMG_SIZE"]))
with torch.no_grad():
APs, inference_time = Evaluator(
self.yolov4, showatt=self.showatt).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))
class Trainer(object):
_resume = False
_fine_tune = False
def __init__(self, log_dir, resume=False, fine_tune=False):
init_seeds(0)
self._fine_tune = fine_tune
self._resume = resume
if self._fine_tune:
self.__prepare_fine_tune()
self.device = gpu.select_device()
self.start_epoch = 0
self.best_mAP = 0.
self.accumulate = cfg.TRAIN.ACCUMULATE
self.log_dir = log_dir
self.weight_path = "yolov4.weights"
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_Train_Dataset(anno_file=cfg.TRAIN.ANNO_FILE, anno_file_type="train", img_size=cfg.TRAIN.TRAIN_IMG_SIZE)
self.epochs = cfg.TRAIN.YOLO_EPOCHS if cfg.MODEL.MODEL_TYPE == 'YOLOv4' else cfg.TRAIN.Mobilenet_YOLO_EPOCHS
self.train_dataloader = DataLoader(self.train_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE//cfg.TRAIN.ACCUMULATE,
num_workers=cfg.TRAIN.NUMBER_WORKERS,
shuffle=True, pin_memory=True)
self.yolov4 = Build_Model(weight_path="yolov4.weights", resume=resume)
self.yolov4 = self.yolov4.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()
if self._fine_tune: self.__load_best_weights()
def __prepare_fine_tune(self):
cfg.defrost()
cfg.TRAIN.ANNO_FILE = cfg.FINE_TUNE.ANNO_FILE
cfg.TRAIN.YOLO_EPOCHS = cfg.FINE_TUNE.YOLO_EPOCHS
cfg.TRAIN.LR_INIT = cfg.FINE_TUNE.LR_INIT
cfg.TRAIN.LR_END = cfg.FINE_TUNE.LR_END
cfg.TRAIN.WARMUP_EPOCHS = cfg.FINE_TUNE.WARMUP_EPOCHS
cfg.freeze()
def __load_best_weights(self):
best_weight = os.path.join(log_dir,"checkpoints", "best.pt")
last_weight = os.path.join(log_dir,"checkpoints", "last.pt")
shutil.copy2(best_weight,
best_weight.replace("best.pt","best_before_fine_tune.pt"))
shutil.copy2(last_weight,
last_weight.replace("last.pt","last_before_fine_tune.pt"))
last_chkpt = torch.load(last_weight, map_location=self.device)
best_chkpt = torch.load(best_weight, map_location=self.device)
self.yolov4.load_state_dict(best_chkpt)
self.best_mAP = 0
del last_chkpt, best_chkpt
def __load_resume_weights(self):
last_weight = os.path.join(log_dir,"checkpoints", "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(log_dir,"checkpoints", "best.pt")
last_weight = os.path.join(log_dir,"checkpoints", "last.pt")
chkpt = {'epoch': epoch,
'best_mAP': self.best_mAP,
'model': self.yolov4.module.state_dict() if torch.cuda.device_count()>1 else 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 self._fine_tune and epoch % 5 == 0:
torch.save(chkpt['model'], os.path.join(log_dir,"checkpoints", 'backup_fine_tune_epoch_{:02d}.pt'.format(epoch)))
del chkpt
def train(self):
global writer
logger.info("Training start,img size is: {:d},batchsize is: {:d}, subdivision: {:d}, worker number is {:d}".format(cfg.TRAIN.TRAIN_IMG_SIZE, cfg.TRAIN.BATCH_SIZE, cfg.TRAIN.ACCUMULATE, cfg.TRAIN.NUMBER_WORKERS))
logger.info(self.yolov4)
n_train = len(self.train_dataset)
n_step = n_train // (cfg.TRAIN.BATCH_SIZE//cfg.TRAIN.ACCUMULATE) + 1
logger.info("Train datasets number is : {}".format(n_train))
evaluator = COCOAPIEvaluator(cfg=cfg,
img_size=cfg.VAL.TEST_IMG_SIZE,
confthre=cfg.VAL.CONF_THRESH,
nmsthre=cfg.VAL.NMS_THRESH)
if torch.cuda.device_count() > 1: self.yolov4 = torch.nn.DataParallel(self.yolov4)
logger.info("\n=============== start training ===============")
for epoch in range(self.start_epoch, self.epochs):
start = time.time()
self.yolov4.train()
with tqdm(total=n_train, unit="imgs", desc=f'Epoch {epoch}/{self.epochs}', ncols=30) as pbar:
for i, (imgs, label_sbbox, label_mbbox, label_lbbox, sbboxes, mbboxes, lbboxes) in enumerate(self.train_dataloader):
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)
loss.backward()
# Accumulate gradient for x batches before optimizing
if i % self.accumulate == 0:
self.scheduler.step(n_step*epoch + i)
self.optimizer.step()
self.optimizer.zero_grad()
# Print batch results
if i % (5*self.accumulate) == 0:
logger.info("{:3}: total_loss:{:.4f} | loss_ciou:{:.4f} | loss_conf:{:.4f} | loss_cls:{:.4f} | lr:{:.6f}".format(
self.train_dataset.img_size, loss, loss_ciou, loss_conf, loss_cls, self.optimizer.param_groups[0]['lr']
))
writer.add_scalar('train/loss_ciou', loss_ciou, n_step * epoch + i)
writer.add_scalar('train/loss_conf', loss_conf, n_step * epoch + i)
writer.add_scalar('train/loss_cls', loss_cls, n_step * epoch + i)
writer.add_scalar('train/train_loss', loss, n_step * epoch + i)
writer.add_scalar('train/lr', self.optimizer.param_groups[0]['lr'], n_step * epoch + i)
# multi-sclae training (320-608 pixels) every 10 batches
if self.multi_scale_train and (i+1) % (5*self.accumulate) == 0:
self.train_dataset.img_size = random.choice(range(10, 20)) * 32
pbar.update(imgs.shape[0])
mAP = 0.
# evaluator = COCOAPIEvaluator(cfg=cfg,
# img_size=cfg.VAL.TEST_IMG_SIZE,
# confthre=cfg.VAL.CONF_THRESH,
# nmsthre=cfg.VAL.NMS_THRESH)
coco_stat = evaluator.evaluate(self.yolov4)
logger.info("Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = {:.04f}".format(coco_stat[0]))
logger.info("Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = {:.04f}".format(coco_stat[1]))
logger.info("Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = {:.04f}".format(coco_stat[2]))
logger.info("Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = {:.04f}".format(coco_stat[3]))
logger.info("Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = {:.04f}".format(coco_stat[4]))
logger.info("Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = {:.04f}".format(coco_stat[5]))
logger.info("Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = {:.04f}".format(coco_stat[6]))
logger.info("Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = {:.04f}".format(coco_stat[7]))
logger.info("Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = {:.04f}".format(coco_stat[8]))
logger.info("Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = {:.04f}".format(coco_stat[9]))
logger.info("Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = {:.04f}".format(coco_stat[10]))
logger.info("Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = {:.04f}".format(coco_stat[11]))
writer.add_scalar('val/mAP_50_95', coco_stat[0], epoch)
writer.add_scalar('val/mAP_50', coco_stat[1], epoch)
writer.add_scalar('val/mAP_75', coco_stat[2], epoch)
writer.add_scalar('val/mAP_small', coco_stat[3], epoch)
writer.add_scalar('val/mAP_medium', coco_stat[4], epoch)
writer.add_scalar('val/mAP_large', coco_stat[5], epoch)
writer.add_scalar('val/mAR_max_1', coco_stat[6], epoch)
writer.add_scalar('val/mAR_max_10', coco_stat[7], epoch)
writer.add_scalar('val/mAR_max_100',coco_stat[8], epoch)
writer.add_scalar('val/mAR_small', coco_stat[9], epoch)
writer.add_scalar('val/mAR_medium', coco_stat[10], epoch)
writer.add_scalar('val/mAR_large', coco_stat[11], epoch)
self.__save_model_weights(epoch, coco_stat[0])
logger.info('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))
train_dataset = BuildDataset(train_anno_path)
train_dataloader = DataLoader(
train_dataset,
batch_size=batch_size,
num_workers=0,
shuffle=True,
pin_memory=True,
)
# model
yolov4 = Build_Model(weight_path=weight_path).to(device)
optimizer = optim.SGD(
yolov4.parameters(),
lr=cfg.TRAIN["LR_INIT"],
momentum=cfg.TRAIN["MOMENTUM"],
weight_decay=cfg.TRAIN["WEIGHT_DECAY"],
)
criterion = YoloV4Loss(
anchors=cfg.MODEL["ANCHORS"],
strides=cfg.MODEL["STRIDES"],
iou_threshold_loss=cfg.TRAIN["IOU_THRESHOLD_LOSS"],
)
scheduler = cosine_lr_scheduler.CosineDecayLR(
optimizer,
T_max=epochs * len(train_dataloader),
lr_init=cfg.TRAIN["LR_INIT"],