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May 6, 2022 17:35
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| import wandb | |
| import timm | |
| import argparse | |
| from fastai.vision.all import * | |
| from fastai.callback.wandb import WandbCallback | |
| from torchvision import models | |
| def parse_args(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--batch_size', type=int, default=64) | |
| parser.add_argument('--epochs', type=int, default=5) | |
| parser.add_argument('--num_experiments', type=int, default=1) | |
| parser.add_argument('--learning_rate', type=float, default=0.002) | |
| parser.add_argument('--img_size', type=int, default=224) | |
| parser.add_argument('--model_name', type=str, default='resnet18') | |
| parser.add_argument('--seed', type=int, default=42) | |
| parser.add_argument('--mixup', action='store_true') | |
| parser.add_argument('--force_torchvision', action='store_true') | |
| parser.add_argument('--wandb_project', type=str, default='fine_tune_timm') | |
| return parser.parse_args() | |
| if __name__ == "__main__": | |
| args = parse_args() | |
| set_seed(args.seed) | |
| for _ in range(args.num_experiments): | |
| with wandb.init(project=args.wandb_project, group="torchvision" if args.force_torchvision else "timm", config=args): | |
| dataset_path = untar_data(URLs.PETS) | |
| files = get_image_files(dataset_path/"images") | |
| pat = re.compile('(^[a-zA-Z]+_*[a-zA-Z]+)') | |
| labels = [pat.match(f.name)[0] for f in files] | |
| dls = ImageDataLoaders.from_name_re(dataset_path, files, r'(^[a-zA-Z]+_*[a-zA-Z]+)', valid_pct=0.2, seed=42, item_tfms=Resize(224)) | |
| cbs = [MixedPrecision(), WandbCallback(log_preds=False)] | |
| if args.mixup: cbs.append(MixUp()) | |
| if args.force_torchvision: | |
| model_name = getattr(models, args.model_name) | |
| else: | |
| model_name = args.model_name | |
| learn = vision_learner(dls, | |
| model_name, | |
| metrics=[accuracy], | |
| cbs=cbs, | |
| pretrained=True) | |
| learn.fine_tune(args.epochs, args.learning_rate) |
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