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.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 __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 __init__(self, log_dir, resume=False, fine_tune=False):
init_seeds(0)
if fine_tune:
self.__prepare_fine_tune()
self.fp_16 = cfg.FP16
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 = "/content/drive/MyDrive/YOLO/weights/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="/content/drive/MyDrive/YOLO/weights/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 fine_tune: self.__load_best_weights()
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()
def __init__(self,
weight_path=None,
resume: bool = False,
gpu_id: int = 0,
accumulate: bool = True,
fp_16: bool = False):
# PYTHON HASH SEED
init_seeds(0)
# device
self.fp_16: bool = fp_16
self.device: torch.device = gpu.select_device(gpu_id)
self.start_epoch: int = 0
self.best_mAP: float = 0.0 # not sure why this is necessary...
self.accumulate: bool = accumulate
self.weight_path: Path = weight_path
self.multi_scale_train: bool = cfg.TRAIN["MULTI_SCALE_TRAIN"]
# Show attention modification?
self.showatt = cfg.TRAIN["showatt"]
# Multi-scale training status
if self.multi_scale_train:
print("Using multi scales training")
else:
print(f"train img size is {cfg.TRAIN['TRAIN_IMG_SIZE']}")
# Build Dataset using helper function.
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)
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"],
lr_min=cfg.TRAIN["LR_END"],
warmup=cfg.TRAIN["WARMUP_EPOCHS"] * len(train_dataloader),
)
# Training
for epoch in range(start_epoch, epochs):
yolov4.train()