|
|
import subprocess |
|
|
import time |
|
|
from pathlib import Path |
|
|
|
|
|
import hydra |
|
|
import torch |
|
|
|
|
|
from ultralytics.yolo import v8 |
|
|
from ultralytics.yolo.data import build_classification_dataloader |
|
|
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer |
|
|
from ultralytics.yolo.utils.downloads import download |
|
|
from ultralytics.yolo.utils.files import WorkingDirectory |
|
|
from ultralytics.yolo.utils.loggers import colorstr |
|
|
from ultralytics.yolo.utils.torch_utils import LOCAL_RANK, torch_distributed_zero_first |
|
|
|
|
|
|
|
|
# BaseTrainer python usage |
|
|
class ClassificationTrainer(BaseTrainer): |
|
|
|
|
|
def get_dataset(self, dataset): |
|
|
# temporary solution. Replace with new ultralytics.yolo.ClassificationDataset module |
|
|
data = Path("datasets") / dataset |
|
|
with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(Path.cwd()): |
|
|
data_dir = data if data.is_dir() else (Path.cwd() / data) |
|
|
if not data_dir.is_dir(): |
|
|
self.console.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...') |
|
|
t = time.time() |
|
|
if str(data) == 'imagenet': |
|
|
subprocess.run(f"bash {v8.ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True) |
|
|
else: |
|
|
url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip' |
|
|
download(url, dir=data_dir.parent) |
|
|
s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n" |
|
|
self.console.info(s) |
|
|
train_set = data_dir / "train" |
|
|
test_set = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val' # data/test or data/val |
|
|
|
|
|
return train_set, test_set |
|
|
|
|
|
def get_dataloader(self, dataset_path, batch_size=None, rank=0): |
|
|
return build_classification_dataloader(path=dataset_path, |
|
|
imgsz=self.args.img_size, |
|
|
batch_size=self.args.batch_size, |
|
|
rank=rank) |
|
|
|
|
|
def preprocess_batch(self, batch): |
|
|
batch["img"] = batch["img"].to(self.device) |
|
|
batch["cls"] = batch["cls"].to(self.device) |
|
|
return batch |
|
|
|
|
|
def get_validator(self): |
|
|
return v8.classify.ClassificationValidator(self.test_loader, self.device, logger=self.console) |
|
|
|
|
|
def criterion(self, preds, batch): |
|
|
loss = torch.nn.functional.cross_entropy(preds, batch["cls"]) |
|
|
return loss, loss |
|
|
|
|
|
|
|
|
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name) |
|
|
def train(cfg): |
|
|
cfg.model = cfg.model or "resnet18" |
|
|
cfg.data = cfg.data or "imagenette160" # or yolo.ClassificationDataset("mnist") |
|
|
trainer = ClassificationTrainer(cfg) |
|
|
trainer.train() |
|
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
""" |
|
|
CLI usage: |
|
|
python ultralytics/yolo/v8/classify/train.py model=resnet18 data=imagenette160 epochs=1 img_size=224 |
|
|
|
|
|
TODO: |
|
|
Direct cli support, i.e, yolov8 classify_train args.epochs 10 |
|
|
""" |
|
|
train()
|
|
|
|
