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259 lines
12 KiB
259 lines
12 KiB
# Ultralytics YOLO 🚀, GPL-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 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, LOCAL_RANK, LOGGER, get_hash, img2label_paths, verify_image_label
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class YOLODataset(BaseDataset):
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cache_version = '1.0.1' # 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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"""
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Dataset class for loading images object detection and/or segmentation labels in YOLO format.
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Args:
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img_path (str): path to the folder containing images.
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imgsz (int): image size (default: 640).
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cache (bool): if True, a cache file of the labels is created to speed up future creation of dataset instances
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(default: False).
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augment (bool): if True, data augmentation is applied (default: True).
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hyp (dict): hyperparameters to apply data augmentation (default: None).
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prefix (str): prefix to print in log messages (default: '').
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rect (bool): if True, rectangular training is used (default: False).
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batch_size (int): size of batches (default: None).
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stride (int): stride (default: 32).
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pad (float): padding (default: 0.0).
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single_cls (bool): if True, single class training is used (default: False).
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use_segments (bool): if True, segmentation masks are used as labels (default: False).
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use_keypoints (bool): if True, keypoints are used as labels (default: False).
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names (list): class names (default: None).
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Returns:
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A PyTorch dataset object that can be used for training an object detection or segmentation model.
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"""
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def __init__(self,
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img_path,
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imgsz=640,
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cache=False,
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augment=True,
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hyp=None,
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prefix='',
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rect=False,
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batch_size=None,
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stride=32,
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pad=0.0,
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single_cls=False,
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use_segments=False,
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use_keypoints=False,
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names=None):
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self.use_segments = use_segments
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self.use_keypoints = use_keypoints
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self.names = names
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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__(img_path, imgsz, cache, augment, hyp, prefix, rect, batch_size, stride, pad, single_cls)
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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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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.names))))
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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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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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cache, exists = np.load(str(cache_path), allow_pickle=True).item(), True # load dict
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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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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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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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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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YOLOv5 Classification Dataset.
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Arguments
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root: Dataset path
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transform: torchvision transforms, used by default
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album_transform: Albumentations transforms, used if installed
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"""
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def __init__(self, root, augment, imgsz, cache=False):
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super().__init__(root=root)
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self.torch_transforms = classify_transforms(imgsz)
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self.album_transforms = classify_albumentations(augment, imgsz) if augment else None
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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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def __getitem__(self, i):
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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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pass
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