Reference for ultralytics/data/base.py

class BaseDataset(Dataset):
    """
    Base dataset class for loading and processing image data.

    Args:
        img_path (str): Path to the folder containing images.
        imgsz (int, optional): Image size. Defaults to 640.
        cache (bool, optional): Cache images to RAM or disk during training. Defaults to False.
        augment (bool, optional): If True, data augmentation is applied. Defaults to True.
        hyp (dict, optional): Hyperparameters to apply data augmentation. Defaults to None.
        prefix (str, optional): Prefix to print in log messages. Defaults to ''.
        rect (bool, optional): If True, rectangular training is used. Defaults to False.
        batch_size (int, optional): Size of batches. Defaults to None.
        stride (int, optional): Stride. Defaults to 32.
        pad (float, optional): Padding. Defaults to 0.0.
        single_cls (bool, optional): If True, single class training is used. Defaults to False.
        classes (list): List of included classes. Default is None.
        fraction (float): Fraction of dataset to utilize. Default is 1.0 (use all data).

    Attributes:
        im_files (list): List of image file paths.
        labels (list): List of label data dictionaries.
        ni (int): Number of images in the dataset.
        ims (list): List of loaded images.
        npy_files (list): List of numpy file paths.
        transforms (callable): Image transformation function.
    """

    def __init__(self,
                 img_path,
                 imgsz=640,
                 cache=False,
                 augment=True,
                 hyp=DEFAULT_CFG,
                 prefix='',
                 rect=False,
                 batch_size=16,
                 stride=32,
                 pad=0.5,
                 single_cls=False,
                 classes=None,
                 fraction=1.0):
        super().__init__()
        self.img_path = img_path
        self.imgsz = imgsz
        self.augment = augment
        self.single_cls = single_cls
        self.prefix = prefix
        self.fraction = fraction
        self.im_files = self.get_img_files(self.img_path)
        self.labels = self.get_labels()
        self.update_labels(include_class=classes)  # single_cls and include_class
        self.ni = len(self.labels)  # number of images
        self.rect = rect
        self.batch_size = batch_size
        self.stride = stride
        self.pad = pad
        if self.rect:
            assert self.batch_size is not None
            self.set_rectangle()

        # Buffer thread for mosaic images
        self.buffer = []  # buffer size = batch size
        self.max_buffer_length = min((self.ni, self.batch_size * 8, 1000)) if self.augment else 0

        # Cache stuff
        if cache == 'ram' and not self.check_cache_ram():
            cache = False
        self.ims, self.im_hw0, self.im_hw = [None] * self.ni, [None] * self.ni, [None] * self.ni
        self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files]
        if cache:
            self.cache_images(cache)

        # Transforms
        self.transforms = self.build_transforms(hyp=hyp)

    def get_img_files(self, img_path):
        """Read image files."""
        try:
            f = []  # image files
            for p in img_path if isinstance(img_path, list) else [img_path]:
                p = Path(p)  # os-agnostic
                if p.is_dir():  # dir
                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)
                    # F = list(p.rglob('*.*'))  # pathlib
                elif p.is_file():  # file
                    with open(p) as t:
                        t = t.read().strip().splitlines()
                        parent = str(p.parent) + os.sep
                        f += [x.replace('./', parent) if x.startswith('./') else x for x in t]  # local to global path
                        # F += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)
                else:
                    raise FileNotFoundError(f'{self.prefix}{p} does not exist')
            im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS)
            # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
            assert im_files, f'{self.prefix}No images found in {img_path}'
        except Exception as e:
            raise FileNotFoundError(f'{self.prefix}Error loading data from {img_path}\n{HELP_URL}') from e
        if self.fraction < 1:
            im_files = im_files[:round(len(im_files) * self.fraction)]
        return im_files

    def update_labels(self, include_class: Optional[list]):
        """include_class, filter labels to include only these classes (optional)."""
        include_class_array = np.array(include_class).reshape(1, -1)
        for i in range(len(self.labels)):
            if include_class is not None:
                cls = self.labels[i]['cls']
                bboxes = self.labels[i]['bboxes']
                segments = self.labels[i]['segments']
                keypoints = self.labels[i]['keypoints']
                j = (cls == include_class_array).any(1)
                self.labels[i]['cls'] = cls[j]
                self.labels[i]['bboxes'] = bboxes[j]
                if segments:
                    self.labels[i]['segments'] = [segments[si] for si, idx in enumerate(j) if idx]
                if keypoints is not None:
                    self.labels[i]['keypoints'] = keypoints[j]
            if self.single_cls:
                self.labels[i]['cls'][:, 0] = 0

    def load_image(self, i, rect_mode=True):
        """Loads 1 image from dataset index 'i', returns (im, resized hw)."""
        im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
        if im is None:  # not cached in RAM
            if fn.exists():  # load npy
                im = np.load(fn)
            else:  # read image
                im = cv2.imread(f)  # BGR
                if im is None:
                    raise FileNotFoundError(f'Image Not Found {f}')
            h0, w0 = im.shape[:2]  # orig hw
            if rect_mode:  # resize long side to imgsz while maintaining aspect ratio
                r = self.imgsz / max(h0, w0)  # ratio
                if r != 1:  # if sizes are not equal
                    w, h = (min(math.ceil(w0 * r), self.imgsz), min(math.ceil(h0 * r), self.imgsz))
                    im = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
            elif not (h0 == w0 == self.imgsz):  # resize by stretching image to square imgsz
                im = cv2.resize(im, (self.imgsz, self.imgsz), interpolation=cv2.INTER_LINEAR)

            # Add to buffer if training with augmentations
            if self.augment:
                self.ims[i], self.im_hw0[i], self.im_hw[i] = im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
                self.buffer.append(i)
                if len(self.buffer) >= self.max_buffer_length:
                    j = self.buffer.pop(0)
                    self.ims[j], self.im_hw0[j], self.im_hw[j] = None, None, None

            return im, (h0, w0), im.shape[:2]

        return self.ims[i], self.im_hw0[i], self.im_hw[i]

    def cache_images(self, cache):
        """Cache images to memory or disk."""
        b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
        fcn = self.cache_images_to_disk if cache == 'disk' else self.load_image
        with ThreadPool(NUM_THREADS) as pool:
            results = pool.imap(fcn, range(self.ni))
            pbar = TQDM(enumerate(results), total=self.ni, disable=LOCAL_RANK > 0)
            for i, x in pbar:
                if cache == 'disk':
                    b += self.npy_files[i].stat().st_size
                else:  # 'ram'
                    self.ims[i], self.im_hw0[i], self.im_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
                    b += self.ims[i].nbytes
                pbar.desc = f'{self.prefix}Caching images ({b / gb:.1f}GB {cache})'
            pbar.close()

    def cache_images_to_disk(self, i):
        """Saves an image as an *.npy file for faster loading."""
        f = self.npy_files[i]
        if not f.exists():
            np.save(f.as_posix(), cv2.imread(self.im_files[i]), allow_pickle=False)

    def check_cache_ram(self, safety_margin=0.5):
        """Check image caching requirements vs available memory."""
        b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
        n = min(self.ni, 30)  # extrapolate from 30 random images
        for _ in range(n):
            im = cv2.imread(random.choice(self.im_files))  # sample image
            ratio = self.imgsz / max(im.shape[0], im.shape[1])  # max(h, w)  # ratio
            b += im.nbytes * ratio ** 2
        mem_required = b * self.ni / n * (1 + safety_margin)  # GB required to cache dataset into RAM
        mem = psutil.virtual_memory()
        cache = mem_required < mem.available  # to cache or not to cache, that is the question
        if not cache:
            LOGGER.info(f'{self.prefix}{mem_required / gb:.1f}GB RAM required to cache images '
                        f'with {int(safety_margin * 100)}% safety margin but only '
                        f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, '
                        f"{'caching images ✅' if cache else 'not caching images ⚠️'}")
        return cache

    def set_rectangle(self):
        """Sets the shape of bounding boxes for YOLO detections as rectangles."""
        bi = np.floor(np.arange(self.ni) / self.batch_size).astype(int)  # batch index
        nb = bi[-1] + 1  # number of batches

        s = np.array([x.pop('shape') for x in self.labels])  # hw
        ar = s[:, 0] / s[:, 1]  # aspect ratio
        irect = ar.argsort()
        self.im_files = [self.im_files[i] for i in irect]
        self.labels = [self.labels[i] for i in irect]
        ar = ar[irect]

        # Set training image shapes
        shapes = [[1, 1]] * nb
        for i in range(nb):
            ari = ar[bi == i]
            mini, maxi = ari.min(), ari.max()
            if maxi < 1:
                shapes[i] = [maxi, 1]
            elif mini > 1:
                shapes[i] = [1, 1 / mini]

        self.batch_shapes = np.ceil(np.array(shapes) * self.imgsz / self.stride + self.pad).astype(int) * self.stride
        self.batch = bi  # batch index of image

    def __getitem__(self, index):
        """Returns transformed label information for given index."""
        return self.transforms(self.get_image_and_label(index))

    def get_image_and_label(self, index):
        """Get and return label information from the dataset."""
        label = deepcopy(self.labels[index])  # requires deepcopy() https://github.com/ultralytics/ultralytics/pull/1948
        label.pop('shape', None)  # shape is for rect, remove it
        label['img'], label['ori_shape'], label['resized_shape'] = self.load_image(index)
        label['ratio_pad'] = (label['resized_shape'][0] / label['ori_shape'][0],
                              label['resized_shape'][1] / label['ori_shape'][1])  # for evaluation
        if self.rect:
            label['rect_shape'] = self.batch_shapes[self.batch[index]]
        return self.update_labels_info(label)

    def __len__(self):
        """Returns the length of the labels list for the dataset."""
        return len(self.labels)

    def update_labels_info(self, label):
        """custom your label format here."""
        return label

    def build_transforms(self, hyp=None):
        """Users can custom augmentations here
        like:
            if self.augment:
                # Training transforms
                return Compose([])
            else:
                # Val transforms
                return Compose([])
        """
        raise NotImplementedError

    def get_labels(self):
        """Users can custom their own format here.
        Make sure your output is a list with each element like below:
            dict(
                im_file=im_file,
                shape=shape,  # format: (height, width)
                cls=cls,
                bboxes=bboxes, # xywh
                segments=segments,  # xy
                keypoints=keypoints, # xy
                normalized=True, # or False
                bbox_format="xyxy",  # or xywh, ltwh
            )
        """
        raise NotImplementedError