✨ [Add] General config for global settings
Browse files- examples/example_train.py +5 -7
- yolo/config/config.py +16 -3
- yolo/config/hyper/default.yaml +12 -3
- yolo/tools/log_helper.py +42 -13
- yolo/tools/trainer.py +4 -3
- yolo/utils/dataloader.py +1 -1
examples/example_train.py
CHANGED
@@ -9,8 +9,7 @@ project_root = Path(__file__).resolve().parent.parent
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sys.path.append(str(project_root))
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from yolo.config.config import Config
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from yolo.
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from yolo.tools.log_helper import custom_logger
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from yolo.tools.trainer import Trainer
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from yolo.utils.dataloader import get_dataloader
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from yolo.utils.get_dataset import prepare_dataset
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@@ -18,18 +17,17 @@ from yolo.utils.get_dataset import prepare_dataset
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@hydra.main(config_path="../yolo/config", config_name="config", version_base=None)
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def main(cfg: Config):
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if cfg.download.auto:
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prepare_dataset(cfg.download)
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dataloader = get_dataloader(cfg)
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model = get_model(cfg)
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# TODO: get_device or rank, for DDP mode
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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-
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trainer
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trainer.train(dataloader, 10)
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if __name__ == "__main__":
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custom_logger()
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main()
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sys.path.append(str(project_root))
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from yolo.config.config import Config
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from yolo.tools.log_helper import custom_logger, get_valid_folder
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from yolo.tools.trainer import Trainer
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from yolo.utils.dataloader import get_dataloader
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from yolo.utils.get_dataset import prepare_dataset
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@hydra.main(config_path="../yolo/config", config_name="config", version_base=None)
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def main(cfg: Config):
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custom_logger()
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save_path = get_valid_folder(cfg.hyper.general, cfg.name)
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if cfg.download.auto:
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prepare_dataset(cfg.download)
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dataloader = get_dataloader(cfg)
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# TODO: get_device or rank, for DDP mode
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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trainer = Trainer(cfg, save_path, device)
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trainer.train(dataloader, cfg.hyper.train.epoch)
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if __name__ == "__main__":
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main()
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yolo/config/config.py
CHANGED
@@ -25,11 +25,10 @@ class Download:
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@dataclass
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class DataLoaderConfig:
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batch_size: int
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shuffle: bool
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num_workers: int
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pin_memory: bool
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image_size: List[int]
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class_num: int
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@dataclass
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@@ -85,8 +84,22 @@ class TrainConfig:
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loss: LossConfig
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@dataclass
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class HyperConfig:
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data: DataLoaderConfig
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train: TrainConfig
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@dataclass
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class DataLoaderConfig:
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batch_size: int
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class_num: int
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image_size: List[int]
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shuffle: bool
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pin_memory: bool
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@dataclass
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loss: LossConfig
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@dataclass
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class GeneralConfig:
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out_path: str
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task: str
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device: Union[str, int, List[int]]
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cpu_num: int
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use_wandb: bool
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lucky_number: 10
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exist_ok: bool
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resume_train: bool
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use_TensorBoard: bool
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@dataclass
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class HyperConfig:
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general: GeneralConfig
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data: DataLoaderConfig
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train: TrainConfig
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yolo/config/hyper/default.yaml
CHANGED
@@ -1,10 +1,19 @@
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data:
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batch_size: 16
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shuffle: True
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num_workers: 16
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pin_memory: True
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class_num: 80
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image_size: [640, 640]
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train:
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epoch: 10
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optimizer:
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general:
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out_path: runs
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task: train
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deivce: [0]
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cpu_num: 16
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use_wandb: False
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lucky_number: 10
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exist_ok: True
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resume_train: False
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use_TensorBoard: False
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data:
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batch_size: 16
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class_num: 80
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image_size: [640, 640]
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shuffle: True
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pin_memory: True
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train:
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epoch: 10
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optimizer:
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yolo/tools/log_helper.py
CHANGED
@@ -11,6 +11,7 @@ Example:
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custom_logger()
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"""
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import sys
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from typing import Dict, List
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@@ -22,19 +23,20 @@ from rich.progress import BarColumn, Progress, TextColumn, TimeRemainingColumn
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from rich.table import Table
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from torch import Tensor
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from yolo.config.config import Config, YOLOLayer
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def custom_logger():
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logger.remove()
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logger.add(
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sys.stderr,
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-
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)
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class CustomProgress:
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def __init__(self, cfg: Config, use_wandb: bool = False):
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self.progress = Progress(
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TextColumn("[progress.description]{task.description}"),
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BarColumn(bar_width=None),
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@@ -44,18 +46,19 @@ class CustomProgress:
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self.use_wandb = use_wandb
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if self.use_wandb:
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wandb.errors.term._log = custom_wandb_log
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self.wandb = wandb.init(
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def start_train(self, num_epochs: int):
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self.task_epoch = self.progress.add_task("[cyan]Epochs [white]| Loss | Box | DFL | BCE |", total=num_epochs)
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def
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self.
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def start_batch(self, num_batches):
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self.batch_task = self.progress.add_task("[green]Batches", total=num_batches)
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def one_batch(self, loss_dict: Dict[str, Tensor]):
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@@ -69,15 +72,19 @@ class CustomProgress:
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self.progress.update(self.batch_task, advance=1, description=f"[green]Batches [white]{loss_str}")
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def
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self.progress.remove_task(self.batch_task)
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def custom_wandb_log(string="", level=int, newline=True, repeat=True, prefix=True, silent=False):
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if silent:
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return
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for line in string.split("\n"):
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logger.opt(raw=not newline).info("🌐 " + line)
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def log_model(model: List[YOLOLayer]):
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@@ -99,3 +106,25 @@ def log_model(model: List[YOLOLayer]):
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channels = "-"
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table.add_row(str(idx), layer.layer_type, layer.tags, f"{layer_param:,}", channels)
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console.print(table)
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custom_logger()
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"""
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import os
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import sys
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from typing import Dict, List
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from rich.table import Table
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from torch import Tensor
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from yolo.config.config import Config, GeneralConfig, YOLOLayer
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def custom_logger():
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logger.remove()
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logger.add(
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sys.stderr,
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colorize=True,
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format="<fg #003385>[{time:MM/DD HH:mm:ss}]</> <level>{level: ^8}</level>| <level>{message}</level>",
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)
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class CustomProgress:
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def __init__(self, cfg: Config, save_path: str, use_wandb: bool = False):
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self.progress = Progress(
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TextColumn("[progress.description]{task.description}"),
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BarColumn(bar_width=None),
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self.use_wandb = use_wandb
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if self.use_wandb:
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wandb.errors.term._log = custom_wandb_log
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self.wandb = wandb.init(
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project="YOLO", resume="allow", mode="online", dir=save_path, id=None, name=cfg.name
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)
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def start_train(self, num_epochs: int):
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self.task_epoch = self.progress.add_task("[cyan]Epochs [white]| Loss | Box | DFL | BCE |", total=num_epochs)
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def start_one_epoch(self, num_batches, optimizer, epoch_idx):
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if self.use_wandb:
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lr_values = [params["lr"] for params in optimizer.param_groups]
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lr_names = ["bias", "norm", "conv"]
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for lr_name, lr_value in zip(lr_names, lr_values):
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self.wandb.log({f"Learning Rate/{lr_name}": lr_value}, step=epoch_idx)
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self.batch_task = self.progress.add_task("[green]Batches", total=num_batches)
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def one_batch(self, loss_dict: Dict[str, Tensor]):
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self.progress.update(self.batch_task, advance=1, description=f"[green]Batches [white]{loss_str}")
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def finish_one_epoch(self):
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self.progress.remove_task(self.batch_task)
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self.progress.update(self.task_epoch, advance=1)
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def finish_train(self):
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self.wandb.finish()
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def custom_wandb_log(string="", level=int, newline=True, repeat=True, prefix=True, silent=False):
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if silent:
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return
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for line in string.split("\n"):
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logger.opt(raw=not newline, colors=True).info("🌐 " + line)
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def log_model(model: List[YOLOLayer]):
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channels = "-"
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table.add_row(str(idx), layer.layer_type, layer.tags, f"{layer_param:,}", channels)
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console.print(table)
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def get_valid_folder(general_cfg: GeneralConfig, exp_name):
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base_path = os.path.join(general_cfg.out_path, general_cfg.task)
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save_path = os.path.join(base_path, exp_name)
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if not general_cfg.exist_ok:
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index = 1
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old_exp_name = exp_name
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while os.path.isdir(save_path):
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exp_name = f"{old_exp_name}{index}"
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save_path = os.path.join(base_path, exp_name)
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index += 1
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if index > 1:
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logger.opt(colors=True).warning(
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f"🔀 Experiment directory exists! Changed <red>{old_exp_name}</> to <green>{exp_name}</>"
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)
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os.makedirs(save_path, exist_ok=True)
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logger.opt(colors=True).info(f"📄 Created log folder: <u><fg #808080>{save_path}</></>")
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logger.add(os.path.join(save_path, "output.log"), backtrace=True, diagnose=True)
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return save_path
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yolo/tools/trainer.py
CHANGED
@@ -6,22 +6,23 @@ from torch import Tensor
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from torch.cuda.amp import GradScaler, autocast
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from yolo.config.config import Config, TrainConfig
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from yolo.model.yolo import
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from yolo.tools.log_helper import CustomProgress
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from yolo.tools.model_helper import EMA, get_optimizer, get_scheduler
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from yolo.utils.loss import get_loss_function
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class Trainer:
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def __init__(self,
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train_cfg: TrainConfig = cfg.hyper.train
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self.model = model.to(device)
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self.device = device
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self.optimizer = get_optimizer(model.parameters(), train_cfg.optimizer)
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self.scheduler = get_scheduler(self.optimizer, train_cfg.scheduler)
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self.loss_fn = get_loss_function(cfg)
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self.progress = CustomProgress(cfg, use_wandb=True)
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if getattr(train_cfg.ema, "enabled", False):
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self.ema = EMA(model, decay=train_cfg.ema.decay)
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from torch.cuda.amp import GradScaler, autocast
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from yolo.config.config import Config, TrainConfig
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from yolo.model.yolo import get_model
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from yolo.tools.log_helper import CustomProgress
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from yolo.tools.model_helper import EMA, get_optimizer, get_scheduler
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from yolo.utils.loss import get_loss_function
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class Trainer:
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def __init__(self, cfg: Config, save_path: str, device):
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train_cfg: TrainConfig = cfg.hyper.train
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model = get_model(cfg)
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self.model = model.to(device)
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self.device = device
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self.optimizer = get_optimizer(model.parameters(), train_cfg.optimizer)
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self.scheduler = get_scheduler(self.optimizer, train_cfg.scheduler)
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self.loss_fn = get_loss_function(cfg)
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self.progress = CustomProgress(cfg, save_path, use_wandb=True)
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if getattr(train_cfg.ema, "enabled", False):
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self.ema = EMA(model, decay=train_cfg.ema.decay)
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yolo/utils/dataloader.py
CHANGED
@@ -160,7 +160,7 @@ class YoloDataLoader(DataLoader):
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dataset,
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batch_size=hyper.batch_size,
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shuffle=hyper.shuffle,
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num_workers=hyper.
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pin_memory=hyper.pin_memory,
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collate_fn=self.collate_fn,
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)
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dataset,
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batch_size=hyper.batch_size,
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shuffle=hyper.shuffle,
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num_workers=config.hyper.general.cpu_num,
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pin_memory=hyper.pin_memory,
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collate_fn=self.collate_fn,
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)
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