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# Ultralytics YOLO 🚀, GPL-3.0 license
from itertools import repeat
from multiprocessing.pool import Pool
from pathlib import Path
import torchvision
from tqdm import tqdm
from ..utils import NUM_THREADS, TQDM_BAR_FORMAT
from .augment import *
from .base import BaseDataset
from .utils import HELP_URL, LOCAL_RANK, get_hash, img2label_paths, verify_image_label
class YOLODataset(BaseDataset):
cache_version = 1.0 # dataset labels *.cache version, >= 1.0 for YOLOv8
rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
"""YOLO Dataset.
Args:
img_path (str): image path.
prefix (str): prefix.
"""
def __init__(
self,
img_path,
imgsz=640,
label_path=None,
cache=False,
augment=True,
hyp=None,
prefix="",
rect=False,
batch_size=None,
stride=32,
pad=0.0,
single_cls=False,
use_segments=False,
use_keypoints=False,
):
self.use_segments = use_segments
self.use_keypoints = use_keypoints
assert not (self.use_segments and self.use_keypoints), "Can not use both segments and keypoints."
super().__init__(img_path, imgsz, label_path, cache, augment, hyp, prefix, rect, batch_size, stride, pad,
single_cls)
def cache_labels(self, path=Path("./labels.cache")):
# Cache dataset labels, check images and read shapes
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}..."
with Pool(NUM_THREADS) as pool:
pbar = tqdm(
pool.imap(verify_image_label,
zip(self.im_files, self.label_files, repeat(self.prefix), repeat(self.use_keypoints))),
desc=desc,
total=len(self.im_files),
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
try:
np.save(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}")
except Exception as e:
LOGGER.warning(
f"{self.prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}") # not writeable
return x
def get_labels(self):
self.label_files = img2label_paths(self.im_files)
cache_path = Path(self.label_files[0]).parent.with_suffix(".cache")
try:
cache, exists = np.load(str(cache_path), allow_pickle=True).item(), True # load dict
assert cache["version"] == self.cache_version # matches current version
assert cache["hash"] == get_hash(self.label_files + self.im_files) # identical hash
except Exception:
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
assert nf > 0, 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"]
nl = len(np.concatenate([label["cls"] for label in labels], 0)) # number of labels
assert nl > 0, f"{self.prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}"
return labels
# TODO: use hyp config to set all these augmentations
def build_transforms(self, hyp=None):
if self.augment:
mosaic = self.augment and not self.rect
transforms = mosaic_transforms(self, self.imgsz, hyp) if mosaic else affine_transforms(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))
return transforms
def close_mosaic(self, hyp):
self.transforms = affine_transforms(self.imgsz, hyp)
self.transforms.append(
Format(bbox_format="xywh",
normalize=True,
return_mask=self.use_segments,
return_keypoint=self.use_keypoints,
batch_idx=True))
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):
# TODO: returning a dict can make thing easier and cleaner when using dataset in training
# but I don't know if this will slow down a little bit.
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):
"""
YOLOv5 Classification Dataset.
Arguments
root: Dataset path
transform: torchvision transforms, used by default
album_transform: Albumentations transforms, used if installed
"""
def __init__(self, root, augment, imgsz, cache=False):
super().__init__(root=root)
self.torch_transforms = classify_transforms(imgsz)
self.album_transforms = classify_albumentations(augment, imgsz) if augment else None
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
def __getitem__(self, i):
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):
pass
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