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on
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Running
on
T4
| import argparse | |
| from io import BytesIO | |
| import multiprocessing | |
| from functools import partial | |
| import os | |
| from PIL import Image | |
| import lmdb | |
| from tqdm import tqdm | |
| from torchvision import datasets | |
| from torchvision.transforms import functional as trans_fn | |
| def resize_and_convert(img, size, resample, quality=100): | |
| img = trans_fn.resize(img, size, resample) | |
| img = trans_fn.center_crop(img, size) | |
| buffer = BytesIO() | |
| img.save(buffer, format="jpeg", quality=quality) | |
| val = buffer.getvalue() | |
| return val | |
| def resize_multiple( | |
| img, sizes=(128, 256, 512, 1024), resample=Image.LANCZOS, quality=100 | |
| ): | |
| imgs = [] | |
| for size in sizes: | |
| imgs.append(resize_and_convert(img, size, resample, quality)) | |
| return imgs | |
| def resize_worker(img_file, sizes, resample): | |
| i, file = img_file | |
| img = Image.open(file) | |
| img = img.convert("RGB") | |
| out = resize_multiple(img, sizes=sizes, resample=resample) | |
| return i, out | |
| def prepare( | |
| env, dataset, n_worker, sizes=(128, 256, 512, 1024), resample=Image.LANCZOS | |
| ): | |
| resize_fn = partial(resize_worker, sizes=sizes, resample=resample) | |
| files = sorted(dataset.imgs, key=lambda x: x[0]) | |
| files = [(i, file) for i, (file, label) in enumerate(files)] | |
| total = 0 | |
| with multiprocessing.Pool(n_worker) as pool: | |
| for i, imgs in tqdm(pool.imap_unordered(resize_fn, files)): | |
| for size, img in zip(sizes, imgs): | |
| key = f"{size}-{str(i).zfill(5)}".encode("utf-8") | |
| with env.begin(write=True) as txn: | |
| txn.put(key, img) | |
| total += 1 | |
| with env.begin(write=True) as txn: | |
| txn.put("length".encode("utf-8"), str(total).encode("utf-8")) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser(description="Preprocess images for model training") | |
| parser.add_argument("--out", type=str, help="filename of the result lmdb dataset") | |
| parser.add_argument( | |
| "--size", | |
| type=str, | |
| default="128,256,512,1024", | |
| help="resolutions of images for the dataset", | |
| ) | |
| parser.add_argument( | |
| "--n_worker", | |
| type=int, | |
| default=8, | |
| help="number of workers for preparing dataset", | |
| ) | |
| parser.add_argument( | |
| "--resample", | |
| type=str, | |
| default="lanczos", | |
| help="resampling methods for resizing images", | |
| ) | |
| parser.add_argument("path", type=str, help="path to the image dataset") | |
| args = parser.parse_args() | |
| if not os.path.exists(args.out): | |
| os.makedirs(args.out) | |
| resample_map = {"lanczos": Image.LANCZOS, "bilinear": Image.BILINEAR} | |
| resample = resample_map[args.resample] | |
| sizes = [int(s.strip()) for s in args.size.split(",")] | |
| print(f"Make dataset of image sizes:", ", ".join(str(s) for s in sizes)) | |
| imgset = datasets.ImageFolder(args.path) | |
| with lmdb.open(args.out, map_size=1024 ** 4, readahead=False) as env: | |
| prepare(env, imgset, args.n_worker, sizes=sizes, resample=resample) | |