|
|
|
|
|
import subprocess
|
|
|
|
|
|
import time
|
|
|
|
|
|
from pathlib import Path
|
|
|
|
|
|
|
|
|
|
|
|
import hydra
|
|
|
|
|
|
import torch
|
|
|
|
|
|
import torch.hub as hub
|
|
|
|
|
|
import torchvision
|
|
|
|
|
|
import torchvision.transforms as T
|
|
|
|
|
|
from omegaconf import DictConfig, OmegaConf
|
|
|
|
|
|
|
|
|
|
|
|
from ultralytics.yolo import BaseTrainer, utils, v8
|
|
|
|
|
|
from ultralytics.yolo.data import build_classification_dataloader
|
|
|
|
|
|
from ultralytics.yolo.engine.trainer import CONFIG_PATH_ABS, DEFAULT_CONFIG
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# BaseTrainer python usage
|
|
|
|
|
|
class Trainer(BaseTrainer):
|
|
|
|
|
|
|
|
|
|
|
|
def get_dataset(self):
|
|
|
|
|
|
# temporary solution. Replace with new ultralytics.yolo.ClassificationDataset module
|
|
|
|
|
|
data = Path("datasets") / self.data
|
|
|
|
|
|
with utils.torch_distributed_zero_first(utils.LOCAL_RANK), utils.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/{self.data}.zip'
|
|
|
|
|
|
utils.download(url, dir=data_dir.parent)
|
|
|
|
|
|
# TODO: add colorstr
|
|
|
|
|
|
s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {'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, batch_size=None, rank=0):
|
|
|
|
|
|
return build_classification_dataloader(path=dataset, batch_size=self.train.batch_size, rank=rank)
|
|
|
|
|
|
|
|
|
|
|
|
def get_model(self):
|
|
|
|
|
|
# temp. minimal. only supports torchvision models
|
|
|
|
|
|
if self.model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0
|
|
|
|
|
|
model = torchvision.models.__dict__[self.model](weights='IMAGENET1K_V1' if self.train.pretrained else None)
|
|
|
|
|
|
else:
|
|
|
|
|
|
raise ModuleNotFoundError(f'--model {self.model} not found.')
|
|
|
|
|
|
for m in model.modules():
|
|
|
|
|
|
if not self.train.pretrained and hasattr(m, 'reset_parameters'):
|
|
|
|
|
|
m.reset_parameters()
|
|
|
|
|
|
for p in model.parameters():
|
|
|
|
|
|
p.requires_grad = True # for training
|
|
|
|
|
|
|
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@hydra.main(version_base=None, config_path=CONFIG_PATH_ABS, config_name=str(DEFAULT_CONFIG).split(".")[0])
|
|
|
|
|
|
def train(cfg):
|
|
|
|
|
|
model = "squeezenet1_0"
|
|
|
|
|
|
dataset = "imagenette160" # or yolo.ClassificationDataset("mnist")
|
|
|
|
|
|
criterion = torch.nn.CrossEntropyLoss() # yolo.Loss object
|
|
|
|
|
|
trainer = Trainer(model, dataset, criterion, config=cfg)
|
|
|
|
|
|
trainer.run()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
|
|
"""
|
|
|
|
|
|
CLI usage:
|
|
|
|
|
|
python ../path/to/train.py train.epochs=10 train.project="name" hyps.lr0=0.1
|
|
|
|
|
|
|
|
|
|
|
|
TODO:
|
|
|
|
|
|
Direct cli support, i.e, yolov8 classify_train train.epochs 10
|
|
|
|
|
|
"""
|
|
|
|
|
|
train()
|
