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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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from itertools import repeat |
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from multiprocessing.pool import ThreadPool |
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from pathlib import Path |
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import cv2 |
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import numpy as np |
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import torch |
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import torchvision |
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from tqdm import tqdm |
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from ..utils import LOCAL_RANK, NUM_THREADS, TQDM_BAR_FORMAT, is_dir_writeable |
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from .augment import Compose, Format, Instances, LetterBox, classify_albumentations, classify_transforms, v8_transforms |
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from .base import BaseDataset |
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from .utils import HELP_URL, LOGGER, get_hash, img2label_paths, verify_image_label |
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class YOLODataset(BaseDataset): |
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""" |
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Dataset class for loading object detection and/or segmentation labels in YOLO format. |
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Args: |
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data (dict, optional): A dataset YAML dictionary. Defaults to None. |
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use_segments (bool, optional): If True, segmentation masks are used as labels. Defaults to False. |
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use_keypoints (bool, optional): If True, keypoints are used as labels. Defaults to False. |
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Returns: |
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(torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model. |
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""" |
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cache_version = '1.0.2' # dataset labels *.cache version, >= 1.0.0 for YOLOv8 |
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rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4] |
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def __init__(self, *args, data=None, use_segments=False, use_keypoints=False, **kwargs): |
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self.use_segments = use_segments |
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self.use_keypoints = use_keypoints |
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self.data = data |
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assert not (self.use_segments and self.use_keypoints), 'Can not use both segments and keypoints.' |
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super().__init__(*args, **kwargs) |
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def cache_labels(self, path=Path('./labels.cache')): |
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"""Cache dataset labels, check images and read shapes. |
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Args: |
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path (Path): path where to save the cache file (default: Path('./labels.cache')). |
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Returns: |
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(dict): labels. |
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""" |
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x = {'labels': []} |
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nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages |
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desc = f'{self.prefix}Scanning {path.parent / path.stem}...' |
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total = len(self.im_files) |
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nkpt, ndim = self.data.get('kpt_shape', (0, 0)) |
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if self.use_keypoints and (nkpt <= 0 or ndim not in (2, 3)): |
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raise ValueError("'kpt_shape' in data.yaml missing or incorrect. Should be a list with [number of " |
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"keypoints, number of dims (2 for x,y or 3 for x,y,visible)], i.e. 'kpt_shape: [17, 3]'") |
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with ThreadPool(NUM_THREADS) as pool: |
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results = pool.imap(func=verify_image_label, |
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iterable=zip(self.im_files, self.label_files, repeat(self.prefix), |
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repeat(self.use_keypoints), repeat(len(self.data['names'])), repeat(nkpt), |
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repeat(ndim))) |
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pbar = tqdm(results, desc=desc, total=total, bar_format=TQDM_BAR_FORMAT) |
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for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in pbar: |
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nm += nm_f |
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nf += nf_f |
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ne += ne_f |
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nc += nc_f |
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if im_file: |
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x['labels'].append( |
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dict( |
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im_file=im_file, |
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shape=shape, |
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cls=lb[:, 0:1], # n, 1 |
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bboxes=lb[:, 1:], # n, 4 |
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segments=segments, |
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keypoints=keypoint, |
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normalized=True, |
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bbox_format='xywh')) |
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if msg: |
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msgs.append(msg) |
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pbar.desc = f'{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt' |
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pbar.close() |
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if msgs: |
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LOGGER.info('\n'.join(msgs)) |
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if nf == 0: |
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LOGGER.warning(f'{self.prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}') |
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x['hash'] = get_hash(self.label_files + self.im_files) |
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x['results'] = nf, nm, ne, nc, len(self.im_files) |
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x['msgs'] = msgs # warnings |
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x['version'] = self.cache_version # cache version |
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if is_dir_writeable(path.parent): |
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if path.exists(): |
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path.unlink() # remove *.cache file if exists |
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np.save(str(path), x) # save cache for next time |
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path.with_suffix('.cache.npy').rename(path) # remove .npy suffix |
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LOGGER.info(f'{self.prefix}New cache created: {path}') |
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else: |
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LOGGER.warning(f'{self.prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable, cache not saved.') |
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return x |
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def get_labels(self): |
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"""Returns dictionary of labels for YOLO training.""" |
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self.label_files = img2label_paths(self.im_files) |
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cache_path = Path(self.label_files[0]).parent.with_suffix('.cache') |
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try: |
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import gc |
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gc.disable() # reduce pickle load time https://github.com/ultralytics/ultralytics/pull/1585 |
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cache, exists = np.load(str(cache_path), allow_pickle=True).item(), True # load dict |
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gc.enable() |
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assert cache['version'] == self.cache_version # matches current version |
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assert cache['hash'] == get_hash(self.label_files + self.im_files) # identical hash |
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except (FileNotFoundError, AssertionError, AttributeError): |
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cache, exists = self.cache_labels(cache_path), False # run cache ops |
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# Display cache |
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nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupt, total |
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if exists and LOCAL_RANK in (-1, 0): |
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d = f'Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt' |
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tqdm(None, desc=self.prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results |
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if cache['msgs']: |
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LOGGER.info('\n'.join(cache['msgs'])) # display warnings |
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if nf == 0: # number of labels found |
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raise FileNotFoundError(f'{self.prefix}No labels found in {cache_path}, can not start training. {HELP_URL}') |
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# Read cache |
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[cache.pop(k) for k in ('hash', 'version', 'msgs')] # remove items |
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labels = cache['labels'] |
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self.im_files = [lb['im_file'] for lb in labels] # update im_files |
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# Check if the dataset is all boxes or all segments |
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lengths = ((len(lb['cls']), len(lb['bboxes']), len(lb['segments'])) for lb in labels) |
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len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths)) |
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if len_segments and len_boxes != len_segments: |
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LOGGER.warning( |
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f'WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = {len_segments}, ' |
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f'len(boxes) = {len_boxes}. To resolve this only boxes will be used and all segments will be removed. ' |
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'To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.') |
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for lb in labels: |
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lb['segments'] = [] |
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if len_cls == 0: |
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raise ValueError(f'All labels empty in {cache_path}, can not start training without labels. {HELP_URL}') |
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return labels |
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# TODO: use hyp config to set all these augmentations |
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def build_transforms(self, hyp=None): |
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"""Builds and appends transforms to the list.""" |
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if self.augment: |
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hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0 |
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hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0 |
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transforms = v8_transforms(self, self.imgsz, hyp) |
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else: |
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transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), scaleup=False)]) |
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transforms.append( |
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Format(bbox_format='xywh', |
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normalize=True, |
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return_mask=self.use_segments, |
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return_keypoint=self.use_keypoints, |
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batch_idx=True, |
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mask_ratio=hyp.mask_ratio, |
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mask_overlap=hyp.overlap_mask)) |
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return transforms |
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def close_mosaic(self, hyp): |
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"""Sets mosaic, copy_paste and mixup options to 0.0 and builds transformations.""" |
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hyp.mosaic = 0.0 # set mosaic ratio=0.0 |
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hyp.copy_paste = 0.0 # keep the same behavior as previous v8 close-mosaic |
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hyp.mixup = 0.0 # keep the same behavior as previous v8 close-mosaic |
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self.transforms = self.build_transforms(hyp) |
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def update_labels_info(self, label): |
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"""custom your label format here.""" |
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# NOTE: cls is not with bboxes now, classification and semantic segmentation need an independent cls label |
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# we can make it also support classification and semantic segmentation by add or remove some dict keys there. |
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bboxes = label.pop('bboxes') |
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segments = label.pop('segments') |
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keypoints = label.pop('keypoints', None) |
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bbox_format = label.pop('bbox_format') |
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normalized = label.pop('normalized') |
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label['instances'] = Instances(bboxes, segments, keypoints, bbox_format=bbox_format, normalized=normalized) |
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return label |
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@staticmethod |
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def collate_fn(batch): |
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"""Collates data samples into batches.""" |
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new_batch = {} |
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keys = batch[0].keys() |
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values = list(zip(*[list(b.values()) for b in batch])) |
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for i, k in enumerate(keys): |
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value = values[i] |
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if k == 'img': |
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value = torch.stack(value, 0) |
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if k in ['masks', 'keypoints', 'bboxes', 'cls']: |
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value = torch.cat(value, 0) |
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new_batch[k] = value |
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new_batch['batch_idx'] = list(new_batch['batch_idx']) |
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for i in range(len(new_batch['batch_idx'])): |
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new_batch['batch_idx'][i] += i # add target image index for build_targets() |
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new_batch['batch_idx'] = torch.cat(new_batch['batch_idx'], 0) |
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return new_batch |
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# Classification dataloaders ------------------------------------------------------------------------------------------- |
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class ClassificationDataset(torchvision.datasets.ImageFolder): |
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""" |
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YOLO Classification Dataset. |
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Args: |
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root (str): Dataset path. |
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Attributes: |
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cache_ram (bool): True if images should be cached in RAM, False otherwise. |
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cache_disk (bool): True if images should be cached on disk, False otherwise. |
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samples (list): List of samples containing file, index, npy, and im. |
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torch_transforms (callable): torchvision transforms applied to the dataset. |
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album_transforms (callable, optional): Albumentations transforms applied to the dataset if augment is True. |
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""" |
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def __init__(self, root, args, augment=False, cache=False): |
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""" |
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Initialize YOLO object with root, image size, augmentations, and cache settings. |
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Args: |
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root (str): Dataset path. |
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args (Namespace): Argument parser containing dataset related settings. |
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augment (bool, optional): True if dataset should be augmented, False otherwise. Defaults to False. |
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cache (bool | str | optional): Cache setting, can be True, False, 'ram' or 'disk'. Defaults to False. |
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""" |
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super().__init__(root=root) |
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if augment and args.fraction < 1.0: # reduce training fraction |
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self.samples = self.samples[:round(len(self.samples) * args.fraction)] |
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self.cache_ram = cache is True or cache == 'ram' |
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self.cache_disk = cache == 'disk' |
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self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples] # file, index, npy, im |
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self.torch_transforms = classify_transforms(args.imgsz) |
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self.album_transforms = classify_albumentations( |
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augment=augment, |
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size=args.imgsz, |
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scale=(1.0 - args.scale, 1.0), # (0.08, 1.0) |
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hflip=args.fliplr, |
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vflip=args.flipud, |
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hsv_h=args.hsv_h, # HSV-Hue augmentation (fraction) |
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hsv_s=args.hsv_s, # HSV-Saturation augmentation (fraction) |
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hsv_v=args.hsv_v, # HSV-Value augmentation (fraction) |
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mean=(0.0, 0.0, 0.0), # IMAGENET_MEAN |
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std=(1.0, 1.0, 1.0), # IMAGENET_STD |
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auto_aug=False) if augment else None |
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def __getitem__(self, i): |
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"""Returns subset of data and targets corresponding to given indices.""" |
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f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image |
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if self.cache_ram and im is None: |
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im = self.samples[i][3] = cv2.imread(f) |
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elif self.cache_disk: |
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if not fn.exists(): # load npy |
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np.save(fn.as_posix(), cv2.imread(f)) |
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im = np.load(fn) |
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else: # read image |
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im = cv2.imread(f) # BGR |
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if self.album_transforms: |
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sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image'] |
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else: |
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sample = self.torch_transforms(im) |
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return {'img': sample, 'cls': j} |
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def __len__(self) -> int: |
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return len(self.samples) |
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# TODO: support semantic segmentation |
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class SemanticDataset(BaseDataset): |
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def __init__(self): |
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"""Initialize a SemanticDataset object.""" |
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super().__init__()
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