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This commit is contained in:
v-zeliu1
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data/__init__.py
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data/__init__.py
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from .build import build_loader
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data/build.py
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data/build.py
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# --------------------------------------------------------
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# Swin Transformer
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# Copyright (c) 2021 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Ze Liu
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# --------------------------------------------------------
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import os
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import torch
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import numpy as np
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import torch.distributed as dist
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from torchvision import datasets, transforms
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from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.data import Mixup
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from timm.data import create_transform
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from timm.data.transforms import _pil_interp
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from .cached_image_folder import CachedImageFolder
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from .samplers import SubsetRandomSampler
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def build_loader(config):
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config.defrost()
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dataset_train, config.MODEL.NUM_CLASSES = build_dataset(is_train=True, config=config)
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config.freeze()
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print(f"local rank {config.LOCAL_RANK} / global rank {dist.get_rank()} successfully build train dataset")
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dataset_val, _ = build_dataset(is_train=False, config=config)
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print(f"local rank {config.LOCAL_RANK} / global rank {dist.get_rank()} successfully build val dataset")
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num_tasks = dist.get_world_size()
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global_rank = dist.get_rank()
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if config.DATA.ZIP_MODE and config.DATA.CACHE_MODE == 'part':
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indices = np.arange(dist.get_rank(), len(dataset_train), dist.get_world_size())
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sampler_train = SubsetRandomSampler(indices)
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else:
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sampler_train = torch.utils.data.DistributedSampler(
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dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
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)
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indices = np.arange(dist.get_rank(), len(dataset_val), dist.get_world_size())
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sampler_val = SubsetRandomSampler(indices)
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data_loader_train = torch.utils.data.DataLoader(
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dataset_train, sampler=sampler_train,
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batch_size=config.DATA.BATCH_SIZE,
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num_workers=config.DATA.NUM_WORKERS,
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pin_memory=config.DATA.PIN_MEMORY,
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drop_last=True,
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)
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data_loader_val = torch.utils.data.DataLoader(
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dataset_val, sampler=sampler_val,
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batch_size=config.DATA.BATCH_SIZE,
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shuffle=False,
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num_workers=config.DATA.NUM_WORKERS,
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pin_memory=config.DATA.PIN_MEMORY,
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drop_last=False
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)
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# setup mixup / cutmix
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mixup_fn = None
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mixup_active = config.AUG.MIXUP > 0 or config.AUG.CUTMIX > 0. or config.AUG.CUTMIX_MINMAX is not None
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if mixup_active:
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mixup_fn = Mixup(
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mixup_alpha=config.AUG.MIXUP, cutmix_alpha=config.AUG.CUTMIX, cutmix_minmax=config.AUG.CUTMIX_MINMAX,
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prob=config.AUG.MIXUP_PROB, switch_prob=config.AUG.MIXUP_SWITCH_PROB, mode=config.AUG.MIXUP_MODE,
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label_smoothing=config.MODEL.LABEL_SMOOTHING, num_classes=config.MODEL.NUM_CLASSES)
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return dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn
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def build_dataset(is_train, config):
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transform = build_transform(is_train, config)
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if config.DATA.DATASET == 'imagenet':
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prefix = 'train' if is_train else 'val'
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if config.DATA.ZIP_MODE:
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ann_file = prefix + "_map.txt"
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prefix = prefix + ".zip@/"
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dataset = CachedImageFolder(config.DATA.DATA_PATH, ann_file, prefix, transform,
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cache_mode=config.DATA.CACHE_MODE if is_train else 'part')
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else:
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root = os.path.join(config.DATA.DATA_PATH, prefix)
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dataset = datasets.ImageFolder(root, transform=transform)
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nb_classes = 1000
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else:
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raise NotImplementedError("We only support ImageNet Now.")
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return dataset, nb_classes
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def build_transform(is_train, config):
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resize_im = config.DATA.IMG_SIZE > 32
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if is_train:
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# this should always dispatch to transforms_imagenet_train
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transform = create_transform(
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input_size=config.DATA.IMG_SIZE,
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is_training=True,
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color_jitter=config.AUG.COLOR_JITTER if config.AUG.COLOR_JITTER > 0 else None,
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auto_augment=config.AUG.AUTO_AUGMENT if config.AUG.AUTO_AUGMENT != 'none' else None,
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re_prob=config.AUG.REPROB,
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re_mode=config.AUG.REMODE,
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re_count=config.AUG.RECOUNT,
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interpolation=config.DATA.INTERPOLATION,
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)
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if not resize_im:
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# replace RandomResizedCropAndInterpolation with
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# RandomCrop
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transform.transforms[0] = transforms.RandomCrop(config.DATA.IMG_SIZE, padding=4)
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return transform
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t = []
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if resize_im:
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if config.TEST.CROP:
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size = int((256 / 224) * config.DATA.IMG_SIZE)
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t.append(
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transforms.Resize(size, interpolation=_pil_interp(config.DATA.INTERPOLATION)),
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# to maintain same ratio w.r.t. 224 images
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)
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t.append(transforms.CenterCrop(config.DATA.IMG_SIZE))
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else:
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t.append(
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transforms.Resize((config.DATA.IMG_SIZE, config.DATA.IMG_SIZE),
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interpolation=_pil_interp(config.DATA.INTERPOLATION))
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)
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t.append(transforms.ToTensor())
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t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
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return transforms.Compose(t)
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251
data/cached_image_folder.py
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data/cached_image_folder.py
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# --------------------------------------------------------
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# Swin Transformer
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# Copyright (c) 2021 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Ze Liu
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# --------------------------------------------------------
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import io
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import os
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import time
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import torch.distributed as dist
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import torch.utils.data as data
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from PIL import Image
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from .zipreader import is_zip_path, ZipReader
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def has_file_allowed_extension(filename, extensions):
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"""Checks if a file is an allowed extension.
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Args:
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filename (string): path to a file
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Returns:
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bool: True if the filename ends with a known image extension
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"""
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filename_lower = filename.lower()
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return any(filename_lower.endswith(ext) for ext in extensions)
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def find_classes(dir):
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classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))]
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classes.sort()
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class_to_idx = {classes[i]: i for i in range(len(classes))}
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return classes, class_to_idx
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def make_dataset(dir, class_to_idx, extensions):
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images = []
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dir = os.path.expanduser(dir)
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for target in sorted(os.listdir(dir)):
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d = os.path.join(dir, target)
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if not os.path.isdir(d):
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continue
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for root, _, fnames in sorted(os.walk(d)):
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for fname in sorted(fnames):
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if has_file_allowed_extension(fname, extensions):
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path = os.path.join(root, fname)
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item = (path, class_to_idx[target])
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images.append(item)
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return images
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def make_dataset_with_ann(ann_file, img_prefix, extensions):
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images = []
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with open(ann_file, "r") as f:
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contents = f.readlines()
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for line_str in contents:
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path_contents = [c for c in line_str.split('\t')]
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im_file_name = path_contents[0]
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class_index = int(path_contents[1])
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assert str.lower(os.path.splitext(im_file_name)[-1]) in extensions
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item = (os.path.join(img_prefix, im_file_name), class_index)
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images.append(item)
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return images
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class DatasetFolder(data.Dataset):
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"""A generic data loader where the samples are arranged in this way: ::
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root/class_x/xxx.ext
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root/class_x/xxy.ext
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root/class_x/xxz.ext
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root/class_y/123.ext
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root/class_y/nsdf3.ext
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root/class_y/asd932_.ext
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Args:
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root (string): Root directory path.
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loader (callable): A function to load a sample given its path.
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extensions (list[string]): A list of allowed extensions.
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transform (callable, optional): A function/transform that takes in
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a sample and returns a transformed version.
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E.g, ``transforms.RandomCrop`` for images.
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target_transform (callable, optional): A function/transform that takes
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in the target and transforms it.
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Attributes:
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samples (list): List of (sample path, class_index) tuples
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"""
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def __init__(self, root, loader, extensions, ann_file='', img_prefix='', transform=None, target_transform=None,
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cache_mode="no"):
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# image folder mode
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if ann_file == '':
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_, class_to_idx = find_classes(root)
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samples = make_dataset(root, class_to_idx, extensions)
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# zip mode
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else:
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samples = make_dataset_with_ann(os.path.join(root, ann_file),
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os.path.join(root, img_prefix),
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extensions)
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if len(samples) == 0:
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raise (RuntimeError("Found 0 files in subfolders of: " + root + "\n" +
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"Supported extensions are: " + ",".join(extensions)))
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self.root = root
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self.loader = loader
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self.extensions = extensions
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self.samples = samples
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self.labels = [y_1k for _, y_1k in samples]
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self.classes = list(set(self.labels))
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self.transform = transform
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self.target_transform = target_transform
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self.cache_mode = cache_mode
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if self.cache_mode != "no":
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self.init_cache()
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def init_cache(self):
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assert self.cache_mode in ["part", "full"]
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n_sample = len(self.samples)
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global_rank = dist.get_rank()
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world_size = dist.get_world_size()
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samples_bytes = [None for _ in range(n_sample)]
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start_time = time.time()
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for index in range(n_sample):
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if index % (n_sample // 10) == 0:
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t = time.time() - start_time
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print(f'global_rank {dist.get_rank()} cached {index}/{n_sample} takes {t:.2f}s per block')
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start_time = time.time()
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path, target = self.samples[index]
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if self.cache_mode == "full":
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samples_bytes[index] = (ZipReader.read(path), target)
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elif self.cache_mode == "part" and index % world_size == global_rank:
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samples_bytes[index] = (ZipReader.read(path), target)
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else:
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samples_bytes[index] = (path, target)
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self.samples = samples_bytes
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def __getitem__(self, index):
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"""
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Args:
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index (int): Index
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Returns:
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tuple: (sample, target) where target is class_index of the target class.
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"""
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path, target = self.samples[index]
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sample = self.loader(path)
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if self.transform is not None:
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sample = self.transform(sample)
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if self.target_transform is not None:
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target = self.target_transform(target)
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return sample, target
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def __len__(self):
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return len(self.samples)
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def __repr__(self):
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fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
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fmt_str += ' Number of datapoints: {}\n'.format(self.__len__())
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fmt_str += ' Root Location: {}\n'.format(self.root)
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tmp = ' Transforms (if any): '
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fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
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tmp = ' Target Transforms (if any): '
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fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
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return fmt_str
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IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']
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def pil_loader(path):
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# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
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if isinstance(path, bytes):
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img = Image.open(io.BytesIO(path))
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elif is_zip_path(path):
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data = ZipReader.read(path)
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img = Image.open(io.BytesIO(data))
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else:
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with open(path, 'rb') as f:
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img = Image.open(f)
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return img.convert('RGB')
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def accimage_loader(path):
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import accimage
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try:
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return accimage.Image(path)
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except IOError:
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# Potentially a decoding problem, fall back to PIL.Image
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return pil_loader(path)
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def default_img_loader(path):
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from torchvision import get_image_backend
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if get_image_backend() == 'accimage':
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return accimage_loader(path)
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else:
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return pil_loader(path)
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class CachedImageFolder(DatasetFolder):
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"""A generic data loader where the images are arranged in this way: ::
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root/dog/xxx.png
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root/dog/xxy.png
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root/dog/xxz.png
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root/cat/123.png
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root/cat/nsdf3.png
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root/cat/asd932_.png
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Args:
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root (string): Root directory path.
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transform (callable, optional): A function/transform that takes in an PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.
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loader (callable, optional): A function to load an image given its path.
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Attributes:
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imgs (list): List of (image path, class_index) tuples
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"""
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def __init__(self, root, ann_file='', img_prefix='', transform=None, target_transform=None,
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loader=default_img_loader, cache_mode="no"):
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super(CachedImageFolder, self).__init__(root, loader, IMG_EXTENSIONS,
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ann_file=ann_file, img_prefix=img_prefix,
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transform=transform, target_transform=target_transform,
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cache_mode=cache_mode)
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self.imgs = self.samples
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def __getitem__(self, index):
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"""
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Args:
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index (int): Index
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Returns:
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tuple: (image, target) where target is class_index of the target class.
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"""
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path, target = self.samples[index]
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image = self.loader(path)
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if self.transform is not None:
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img = self.transform(image)
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else:
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img = image
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if self.target_transform is not None:
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target = self.target_transform(target)
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return img, target
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29
data/samplers.py
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29
data/samplers.py
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# --------------------------------------------------------
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# Swin Transformer
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# Copyright (c) 2021 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Ze Liu
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# --------------------------------------------------------
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import torch
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class SubsetRandomSampler(torch.utils.data.Sampler):
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r"""Samples elements randomly from a given list of indices, without replacement.
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Arguments:
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indices (sequence): a sequence of indices
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"""
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def __init__(self, indices):
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self.epoch = 0
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self.indices = indices
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def __iter__(self):
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return (self.indices[i] for i in torch.randperm(len(self.indices)))
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def __len__(self):
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return len(self.indices)
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def set_epoch(self, epoch):
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self.epoch = epoch
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103
data/zipreader.py
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103
data/zipreader.py
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# --------------------------------------------------------
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# Swin Transformer
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# Copyright (c) 2021 Microsoft
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||||
# Licensed under The MIT License [see LICENSE for details]
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# Written by Ze Liu
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# --------------------------------------------------------
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import os
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import zipfile
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import io
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import numpy as np
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from PIL import Image
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from PIL import ImageFile
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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def is_zip_path(img_or_path):
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"""judge if this is a zip path"""
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return '.zip@' in img_or_path
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class ZipReader(object):
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"""A class to read zipped files"""
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zip_bank = dict()
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def __init__(self):
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super(ZipReader, self).__init__()
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@staticmethod
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def get_zipfile(path):
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zip_bank = ZipReader.zip_bank
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if path not in zip_bank:
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zfile = zipfile.ZipFile(path, 'r')
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zip_bank[path] = zfile
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return zip_bank[path]
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@staticmethod
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def split_zip_style_path(path):
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pos_at = path.index('@')
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assert pos_at != -1, "character '@' is not found from the given path '%s'" % path
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zip_path = path[0: pos_at]
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folder_path = path[pos_at + 1:]
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folder_path = str.strip(folder_path, '/')
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return zip_path, folder_path
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||||
|
||||
@staticmethod
|
||||
def list_folder(path):
|
||||
zip_path, folder_path = ZipReader.split_zip_style_path(path)
|
||||
|
||||
zfile = ZipReader.get_zipfile(zip_path)
|
||||
folder_list = []
|
||||
for file_foler_name in zfile.namelist():
|
||||
file_foler_name = str.strip(file_foler_name, '/')
|
||||
if file_foler_name.startswith(folder_path) and \
|
||||
len(os.path.splitext(file_foler_name)[-1]) == 0 and \
|
||||
file_foler_name != folder_path:
|
||||
if len(folder_path) == 0:
|
||||
folder_list.append(file_foler_name)
|
||||
else:
|
||||
folder_list.append(file_foler_name[len(folder_path) + 1:])
|
||||
|
||||
return folder_list
|
||||
|
||||
@staticmethod
|
||||
def list_files(path, extension=None):
|
||||
if extension is None:
|
||||
extension = ['.*']
|
||||
zip_path, folder_path = ZipReader.split_zip_style_path(path)
|
||||
|
||||
zfile = ZipReader.get_zipfile(zip_path)
|
||||
file_lists = []
|
||||
for file_foler_name in zfile.namelist():
|
||||
file_foler_name = str.strip(file_foler_name, '/')
|
||||
if file_foler_name.startswith(folder_path) and \
|
||||
str.lower(os.path.splitext(file_foler_name)[-1]) in extension:
|
||||
if len(folder_path) == 0:
|
||||
file_lists.append(file_foler_name)
|
||||
else:
|
||||
file_lists.append(file_foler_name[len(folder_path) + 1:])
|
||||
|
||||
return file_lists
|
||||
|
||||
@staticmethod
|
||||
def read(path):
|
||||
zip_path, path_img = ZipReader.split_zip_style_path(path)
|
||||
zfile = ZipReader.get_zipfile(zip_path)
|
||||
data = zfile.read(path_img)
|
||||
return data
|
||||
|
||||
@staticmethod
|
||||
def imread(path):
|
||||
zip_path, path_img = ZipReader.split_zip_style_path(path)
|
||||
zfile = ZipReader.get_zipfile(zip_path)
|
||||
data = zfile.read(path_img)
|
||||
try:
|
||||
im = Image.open(io.BytesIO(data))
|
||||
except:
|
||||
print("ERROR IMG LOADED: ", path_img)
|
||||
random_img = np.random.rand(224, 224, 3) * 255
|
||||
im = Image.fromarray(np.uint8(random_img))
|
||||
return im
|
||||
Reference in New Issue
Block a user