# Ultralytics YOLO 🚀, AGPL-3.0 license from itertools import repeat from multiprocessing.pool import ThreadPool from pathlib import Path import cv2 import numpy as np import torch import torchvision from tqdm import tqdm from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM_BAR_FORMAT, is_dir_writeable from .augment import Compose, Format, Instances, LetterBox, classify_albumentations, classify_transforms, v8_transforms from .base import BaseDataset from .utils import HELP_URL, LOGGER, get_hash, img2label_paths, verify_image_label class YOLODataset(BaseDataset): """ Dataset class for loading object detection and/or segmentation labels in YOLO format. Args: data (dict, optional): A dataset YAML dictionary. Defaults to None. use_segments (bool, optional): If True, segmentation masks are used as labels. Defaults to False. use_keypoints (bool, optional): If True, keypoints are used as labels. Defaults to False. Returns: (torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model. """ cache_version = '1.0.2' # dataset labels *.cache version, >= 1.0.0 for YOLOv8 def __init__(self, *args, data=None, use_segments=False, use_keypoints=False, **kwargs): self.use_segments = use_segments self.use_keypoints = use_keypoints self.data = data assert not (self.use_segments and self.use_keypoints), 'Can not use both segments and keypoints.' super().__init__(*args, **kwargs) def cache_labels(self, path=Path('./labels.cache')): """Cache dataset labels, check images and read shapes. Args: path (Path): path where to save the cache file (default: Path('./labels.cache')). Returns: (dict): labels. """ x = {'labels': []} nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages desc = f'{self.prefix}Scanning {path.parent / path.stem}...' total = len(self.im_files) nkpt, ndim = self.data.get('kpt_shape', (0, 0)) if self.use_keypoints and (nkpt <= 0 or ndim not in (2, 3)): raise ValueError("'kpt_shape' in data.yaml missing or incorrect. Should be a list with [number of " "keypoints, number of dims (2 for x,y or 3 for x,y,visible)], i.e. 'kpt_shape: [17, 3]'") with ThreadPool(NUM_THREADS) as pool: results = pool.imap(func=verify_image_label, iterable=zip(self.im_files, self.label_files, repeat(self.prefix), repeat(self.use_keypoints), repeat(len(self.data['names'])), repeat(nkpt), repeat(ndim))) pbar = tqdm(results, desc=desc, total=total, bar_format=TQDM_BAR_FORMAT) for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in pbar: nm += nm_f nf += nf_f ne += ne_f nc += nc_f if im_file: x['labels'].append( dict( im_file=im_file, shape=shape, cls=lb[:, 0:1], # n, 1 bboxes=lb[:, 1:], # n, 4 segments=segments, keypoints=keypoint, normalized=True, bbox_format='xywh')) if msg: msgs.append(msg) pbar.desc = f'{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt' pbar.close() if msgs: LOGGER.info('\n'.join(msgs)) if nf == 0: LOGGER.warning(f'{self.prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}') x['hash'] = get_hash(self.label_files + self.im_files) x['results'] = nf, nm, ne, nc, len(self.im_files) x['msgs'] = msgs # warnings x['version'] = self.cache_version # cache version if is_dir_writeable(path.parent): if path.exists(): path.unlink() # remove *.cache file if exists np.save(str(path), x) # save cache for next time path.with_suffix('.cache.npy').rename(path) # remove .npy suffix LOGGER.info(f'{self.prefix}New cache created: {path}') else: LOGGER.warning(f'{self.prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable, cache not saved.') return x def get_labels(self): """Returns dictionary of labels for YOLO training.""" self.label_files = img2label_paths(self.im_files) cache_path = Path(self.label_files[0]).parent.with_suffix('.cache') try: import gc gc.disable() # reduce pickle load time https://github.com/ultralytics/ultralytics/pull/1585 cache, exists = np.load(str(cache_path), allow_pickle=True).item(), True # load dict gc.enable() assert cache['version'] == self.cache_version # matches current version assert cache['hash'] == get_hash(self.label_files + self.im_files) # identical hash except (FileNotFoundError, AssertionError, AttributeError): cache, exists = self.cache_labels(cache_path), False # run cache ops # Display cache nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupt, total if exists and LOCAL_RANK in (-1, 0): d = f'Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt' tqdm(None, desc=self.prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results if cache['msgs']: LOGGER.info('\n'.join(cache['msgs'])) # display warnings if nf == 0: # number of labels found raise FileNotFoundError(f'{self.prefix}No labels found in {cache_path}, can not start training. {HELP_URL}') # Read cache [cache.pop(k) for k in ('hash', 'version', 'msgs')] # remove items labels = cache['labels'] assert len(labels), f'No valid labels found, please check your dataset. {HELP_URL}' self.im_files = [lb['im_file'] for lb in labels] # update im_files # Check if the dataset is all boxes or all segments lengths = ((len(lb['cls']), len(lb['bboxes']), len(lb['segments'])) for lb in labels) len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths)) if len_segments and len_boxes != len_segments: LOGGER.warning( f'WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = {len_segments}, ' f'len(boxes) = {len_boxes}. To resolve this only boxes will be used and all segments will be removed. ' 'To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.') for lb in labels: lb['segments'] = [] if len_cls == 0: raise ValueError(f'All labels empty in {cache_path}, can not start training without labels. {HELP_URL}') return labels # TODO: use hyp config to set all these augmentations def build_transforms(self, hyp=None): """Builds and appends transforms to the list.""" self.augment = False if self.augment: hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0 hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0 transforms = v8_transforms(self, self.imgsz, hyp) else: transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), scaleup=False)]) transforms.append( Format(bbox_format='xywh', normalize=True, return_mask=self.use_segments, return_keypoint=self.use_keypoints, batch_idx=True, mask_ratio=hyp.mask_ratio, mask_overlap=hyp.overlap_mask)) return transforms def close_mosaic(self, hyp): """Sets mosaic, copy_paste and mixup options to 0.0 and builds transformations.""" hyp.mosaic = 0.0 # set mosaic ratio=0.0 hyp.copy_paste = 0.0 # keep the same behavior as previous v8 close-mosaic hyp.mixup = 0.0 # keep the same behavior as previous v8 close-mosaic self.transforms = self.build_transforms(hyp) def update_labels_info(self, label): """custom your label format here.""" # NOTE: cls is not with bboxes now, classification and semantic segmentation need an independent cls label # we can make it also support classification and semantic segmentation by add or remove some dict keys there. bboxes = label.pop('bboxes') segments = label.pop('segments') keypoints = label.pop('keypoints', None) bbox_format = label.pop('bbox_format') normalized = label.pop('normalized') label['instances'] = Instances(bboxes, segments, keypoints, bbox_format=bbox_format, normalized=normalized) return label @staticmethod def collate_fn(batch): """Collates data samples into batches.""" new_batch = {} keys = batch[0].keys() values = list(zip(*[list(b.values()) for b in batch])) for i, k in enumerate(keys): value = values[i] if k == 'img': value = torch.stack(value, 0) if k in ['masks', 'keypoints', 'bboxes', 'cls']: value = torch.cat(value, 0) new_batch[k] = value new_batch['batch_idx'] = list(new_batch['batch_idx']) for i in range(len(new_batch['batch_idx'])): new_batch['batch_idx'][i] += i # add target image index for build_targets() new_batch['batch_idx'] = torch.cat(new_batch['batch_idx'], 0) return new_batch # Classification dataloaders ------------------------------------------------------------------------------------------- class ClassificationDataset(torchvision.datasets.ImageFolder): """ YOLO Classification Dataset. Args: root (str): Dataset path. Attributes: cache_ram (bool): True if images should be cached in RAM, False otherwise. cache_disk (bool): True if images should be cached on disk, False otherwise. samples (list): List of samples containing file, index, npy, and im. torch_transforms (callable): torchvision transforms applied to the dataset. album_transforms (callable, optional): Albumentations transforms applied to the dataset if augment is True. """ def __init__(self, root, args, augment=False, cache=False): """ Initialize YOLO object with root, image size, augmentations, and cache settings. Args: root (str): Dataset path. args (Namespace): Argument parser containing dataset related settings. augment (bool, optional): True if dataset should be augmented, False otherwise. Defaults to False. cache (bool | str | optional): Cache setting, can be True, False, 'ram' or 'disk'. Defaults to False. """ super().__init__(root=root) if augment and args.fraction < 1.0: # reduce training fraction self.samples = self.samples[:round(len(self.samples) * args.fraction)] self.cache_ram = cache is True or cache == 'ram' self.cache_disk = cache == 'disk' self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples] # file, index, npy, im self.torch_transforms = classify_transforms(args.imgsz) self.album_transforms = classify_albumentations( augment=augment, size=args.imgsz, scale=(1.0 - args.scale, 1.0), # (0.08, 1.0) hflip=args.fliplr, vflip=args.flipud, hsv_h=args.hsv_h, # HSV-Hue augmentation (fraction) hsv_s=args.hsv_s, # HSV-Saturation augmentation (fraction) hsv_v=args.hsv_v, # HSV-Value augmentation (fraction) mean=(0.0, 0.0, 0.0), # IMAGENET_MEAN std=(1.0, 1.0, 1.0), # IMAGENET_STD auto_aug=False) if augment else None def __getitem__(self, i): """Returns subset of data and targets corresponding to given indices.""" f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image if self.cache_ram and im is None: im = self.samples[i][3] = cv2.imread(f) elif self.cache_disk: if not fn.exists(): # load npy np.save(fn.as_posix(), cv2.imread(f)) im = np.load(fn) else: # read image im = cv2.imread(f) # BGR if self.album_transforms: sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image'] else: sample = self.torch_transforms(im) return {'img': sample, 'cls': j} def __len__(self) -> int: return len(self.samples) # TODO: support semantic segmentation class SemanticDataset(BaseDataset): def __init__(self): """Initialize a SemanticDataset object.""" super().__init__()