677 lines
27 KiB
Python
677 lines
27 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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import json
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from collections import defaultdict
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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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from PIL import Image
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from torch.utils.data import ConcatDataset
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from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr
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from ultralytics.utils.ops import resample_segments
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from ultralytics.utils.torch_utils import TORCHVISION_0_18
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from .augment import (
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Compose,
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Format,
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Instances,
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LetterBox,
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RandomLoadText,
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classify_augmentations,
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classify_transforms,
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v8_transforms,
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)
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from .base import BaseDataset
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from .utils import (
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HELP_URL,
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LOGGER,
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get_hash,
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img2label_paths,
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load_dataset_cache_file,
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save_dataset_cache_file,
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verify_image,
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verify_image_label,
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)
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# Ultralytics dataset *.cache version, >= 1.0.0 for YOLOv8
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DATASET_CACHE_VERSION = "1.0.3"
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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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This class supports loading data for object detection, segmentation, pose estimation, and oriented bounding box
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(OBB) tasks using the YOLO format.
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Attributes:
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use_segments (bool): Indicates if segmentation masks should be used.
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use_keypoints (bool): Indicates if keypoints should be used for pose estimation.
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use_obb (bool): Indicates if oriented bounding boxes should be used.
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data (dict): Dataset configuration dictionary.
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Methods:
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cache_labels: Cache dataset labels, check images and read shapes.
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get_labels: Returns dictionary of labels for YOLO training.
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build_transforms: Builds and appends transforms to the list.
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close_mosaic: Sets mosaic, copy_paste and mixup options to 0.0 and builds transformations.
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update_labels_info: Updates label format for different tasks.
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collate_fn: Collates data samples into batches.
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Examples:
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>>> dataset = YOLODataset(img_path="path/to/images", data={"names": {0: "person"}}, task="detect")
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>>> dataset.get_labels()
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"""
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def __init__(self, *args, data=None, task="detect", **kwargs):
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"""
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Initialize the YOLODataset.
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Args:
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data (dict, optional): Dataset configuration dictionary.
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task (str): Task type, one of 'detect', 'segment', 'pose', or 'obb'.
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*args (Any): Additional positional arguments for the parent class.
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**kwargs (Any): Additional keyword arguments for the parent class.
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"""
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self.use_segments = task == "segment"
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self.use_keypoints = task == "pose"
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self.use_obb = task == "obb"
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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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"""
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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.
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Returns:
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(dict): Dictionary containing cached labels and related information.
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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(
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"'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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)
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with ThreadPool(NUM_THREADS) as pool:
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results = pool.imap(
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func=verify_image_label,
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iterable=zip(
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self.im_files,
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self.label_files,
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repeat(self.prefix),
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repeat(self.use_keypoints),
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repeat(len(self.data["names"])),
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repeat(nkpt),
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repeat(ndim),
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repeat(self.single_cls),
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),
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)
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pbar = TQDM(results, desc=desc, total=total)
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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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{
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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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}
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)
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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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save_dataset_cache_file(self.prefix, path, x, DATASET_CACHE_VERSION)
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return x
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def get_labels(self):
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"""
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Returns dictionary of labels for YOLO training.
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This method loads labels from disk or cache, verifies their integrity, and prepares them for training.
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Returns:
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(List[dict]): List of label dictionaries, each containing information about an image and its annotations.
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"""
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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 = load_dataset_cache_file(cache_path), True # attempt to load a *.cache file
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assert cache["version"] == DATASET_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) # display results
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if cache["msgs"]:
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LOGGER.info("\n".join(cache["msgs"])) # display warnings
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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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if not labels:
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LOGGER.warning(f"WARNING ⚠️ No images found in {cache_path}, training may not work correctly. {HELP_URL}")
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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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)
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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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LOGGER.warning(f"WARNING ⚠️ No labels found in {cache_path}, training may not work correctly. {HELP_URL}")
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return labels
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def build_transforms(self, hyp=None):
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"""
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Builds and appends transforms to the list.
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Args:
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hyp (dict, optional): Hyperparameters for transforms.
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Returns:
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(Compose): Composed transforms.
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"""
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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(
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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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return_obb=self.use_obb,
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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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bgr=hyp.bgr if self.augment else 0.0, # only affect training.
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)
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)
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return transforms
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def close_mosaic(self, hyp):
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"""
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Sets mosaic, copy_paste and mixup options to 0.0 and builds transformations.
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Args:
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hyp (dict): Hyperparameters for transforms.
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"""
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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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"""
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Custom your label format here.
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Args:
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label (dict): Label dictionary containing bboxes, segments, keypoints, etc.
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Returns:
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(dict): Updated label dictionary with instances.
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Note:
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cls is not with bboxes now, classification and semantic segmentation need an independent cls label
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Can also support classification and semantic segmentation by adding or removing dict keys there.
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"""
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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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# NOTE: do NOT resample oriented boxes
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segment_resamples = 100 if self.use_obb else 1000
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if len(segments) > 0:
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# make sure segments interpolate correctly if original length is greater than segment_resamples
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max_len = max(len(s) for s in segments)
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segment_resamples = (max_len + 1) if segment_resamples < max_len else segment_resamples
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# list[np.array(segment_resamples, 2)] * num_samples
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segments = np.stack(resample_segments(segments, n=segment_resamples), axis=0)
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else:
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segments = np.zeros((0, segment_resamples, 2), dtype=np.float32)
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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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"""
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Collates data samples into batches.
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Args:
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batch (List[dict]): List of dictionaries containing sample data.
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Returns:
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(dict): Collated batch with stacked tensors.
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"""
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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", "segments", "obb"}:
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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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class YOLOMultiModalDataset(YOLODataset):
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"""
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Dataset class for loading object detection and/or segmentation labels in YOLO format with multi-modal support.
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This class extends YOLODataset to add text information for multi-modal model training, enabling models to
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process both image and text data.
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Methods:
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update_labels_info: Adds text information for multi-modal model training.
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build_transforms: Enhances data transformations with text augmentation.
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Examples:
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>>> dataset = YOLOMultiModalDataset(img_path="path/to/images", data={"names": {0: "person"}}, task="detect")
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>>> batch = next(iter(dataset))
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>>> print(batch.keys()) # Should include 'texts'
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"""
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def __init__(self, *args, data=None, task="detect", **kwargs):
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"""
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Initialize a YOLOMultiModalDataset.
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Args:
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data (dict, optional): Dataset configuration dictionary.
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task (str): Task type, one of 'detect', 'segment', 'pose', or 'obb'.
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*args (Any): Additional positional arguments for the parent class.
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**kwargs (Any): Additional keyword arguments for the parent class.
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"""
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super().__init__(*args, data=data, task=task, **kwargs)
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def update_labels_info(self, label):
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"""
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Add texts information for multi-modal model training.
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Args:
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label (dict): Label dictionary containing bboxes, segments, keypoints, etc.
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Returns:
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(dict): Updated label dictionary with instances and texts.
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"""
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labels = super().update_labels_info(label)
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# NOTE: some categories are concatenated with its synonyms by `/`.
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labels["texts"] = [v.split("/") for _, v in self.data["names"].items()]
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return labels
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def build_transforms(self, hyp=None):
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"""
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Enhances data transformations with optional text augmentation for multi-modal training.
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Args:
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hyp (dict, optional): Hyperparameters for transforms.
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Returns:
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(Compose): Composed transforms including text augmentation if applicable.
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"""
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transforms = super().build_transforms(hyp)
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if self.augment:
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# NOTE: hard-coded the args for now.
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transforms.insert(-1, RandomLoadText(max_samples=min(self.data["nc"], 80), padding=True))
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return transforms
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class GroundingDataset(YOLODataset):
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"""
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Handles object detection tasks by loading annotations from a specified JSON file, supporting YOLO format.
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This dataset is designed for grounding tasks where annotations are provided in a JSON file rather than
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the standard YOLO format text files.
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Attributes:
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json_file (str): Path to the JSON file containing annotations.
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Methods:
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get_img_files: Returns empty list as image files are read in get_labels.
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get_labels: Loads annotations from a JSON file and prepares them for training.
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build_transforms: Configures augmentations for training with optional text loading.
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Examples:
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>>> dataset = GroundingDataset(img_path="path/to/images", json_file="annotations.json", task="detect")
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>>> len(dataset) # Number of valid images with annotations
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"""
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def __init__(self, *args, task="detect", json_file, **kwargs):
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"""
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Initialize a GroundingDataset for object detection.
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Args:
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json_file (str): Path to the JSON file containing annotations.
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task (str): Must be 'detect' for GroundingDataset.
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*args (Any): Additional positional arguments for the parent class.
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**kwargs (Any): Additional keyword arguments for the parent class.
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"""
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assert task == "detect", "`GroundingDataset` only support `detect` task for now!"
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self.json_file = json_file
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super().__init__(*args, task=task, data={}, **kwargs)
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def get_img_files(self, img_path):
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"""
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The image files would be read in `get_labels` function, return empty list here.
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Args:
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img_path (str): Path to the directory containing images.
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Returns:
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(List): Empty list as image files are read in get_labels.
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"""
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return []
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def get_labels(self):
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"""
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Loads annotations from a JSON file, filters, and normalizes bounding boxes for each image.
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Returns:
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(List[dict]): List of label dictionaries, each containing information about an image and its annotations.
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"""
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labels = []
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LOGGER.info("Loading annotation file...")
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with open(self.json_file) as f:
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annotations = json.load(f)
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images = {f"{x['id']:d}": x for x in annotations["images"]}
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img_to_anns = defaultdict(list)
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for ann in annotations["annotations"]:
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img_to_anns[ann["image_id"]].append(ann)
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for img_id, anns in TQDM(img_to_anns.items(), desc=f"Reading annotations {self.json_file}"):
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img = images[f"{img_id:d}"]
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h, w, f = img["height"], img["width"], img["file_name"]
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im_file = Path(self.img_path) / f
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if not im_file.exists():
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continue
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self.im_files.append(str(im_file))
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bboxes = []
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cat2id = {}
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texts = []
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for ann in anns:
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if ann["iscrowd"]:
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continue
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box = np.array(ann["bbox"], dtype=np.float32)
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box[:2] += box[2:] / 2
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box[[0, 2]] /= float(w)
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box[[1, 3]] /= float(h)
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if box[2] <= 0 or box[3] <= 0:
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continue
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caption = img["caption"]
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cat_name = " ".join([caption[t[0] : t[1]] for t in ann["tokens_positive"]])
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if cat_name not in cat2id:
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cat2id[cat_name] = len(cat2id)
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texts.append([cat_name])
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cls = cat2id[cat_name] # class
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box = [cls] + box.tolist()
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if box not in bboxes:
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bboxes.append(box)
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lb = np.array(bboxes, dtype=np.float32) if len(bboxes) else np.zeros((0, 5), dtype=np.float32)
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labels.append(
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{
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"im_file": im_file,
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"shape": (h, w),
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"cls": lb[:, 0:1], # n, 1
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"bboxes": lb[:, 1:], # n, 4
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"normalized": True,
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"bbox_format": "xywh",
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"texts": texts,
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}
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)
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return labels
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def build_transforms(self, hyp=None):
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"""
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Configures augmentations for training with optional text loading.
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Args:
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hyp (dict, optional): Hyperparameters for transforms.
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|
Returns:
|
|
(Compose): Composed transforms including text augmentation if applicable.
|
|
"""
|
|
transforms = super().build_transforms(hyp)
|
|
if self.augment:
|
|
# NOTE: hard-coded the args for now.
|
|
transforms.insert(-1, RandomLoadText(max_samples=80, padding=True))
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|
return transforms
|
|
|
|
|
|
class YOLOConcatDataset(ConcatDataset):
|
|
"""
|
|
Dataset as a concatenation of multiple datasets.
|
|
|
|
This class is useful to assemble different existing datasets for YOLO training, ensuring they use the same
|
|
collation function.
|
|
|
|
Methods:
|
|
collate_fn: Static method that collates data samples into batches using YOLODataset's collation function.
|
|
|
|
Examples:
|
|
>>> dataset1 = YOLODataset(...)
|
|
>>> dataset2 = YOLODataset(...)
|
|
>>> combined_dataset = YOLOConcatDataset([dataset1, dataset2])
|
|
"""
|
|
|
|
@staticmethod
|
|
def collate_fn(batch):
|
|
"""
|
|
Collates data samples into batches.
|
|
|
|
Args:
|
|
batch (List[dict]): List of dictionaries containing sample data.
|
|
|
|
Returns:
|
|
(dict): Collated batch with stacked tensors.
|
|
"""
|
|
return YOLODataset.collate_fn(batch)
|
|
|
|
|
|
# TODO: support semantic segmentation
|
|
class SemanticDataset(BaseDataset):
|
|
"""Semantic Segmentation Dataset."""
|
|
|
|
def __init__(self):
|
|
"""Initialize a SemanticDataset object."""
|
|
super().__init__()
|
|
|
|
|
|
class ClassificationDataset:
|
|
"""
|
|
Extends torchvision ImageFolder to support YOLO classification tasks.
|
|
|
|
This class offers functionalities like image augmentation, caching, and verification. It's designed to efficiently
|
|
handle large datasets for training deep learning models, with optional image transformations and caching mechanisms
|
|
to speed up training.
|
|
|
|
Attributes:
|
|
cache_ram (bool): Indicates if caching in RAM is enabled.
|
|
cache_disk (bool): Indicates if caching on disk is enabled.
|
|
samples (List): A list of tuples, each containing the path to an image, its class index, path to its .npy cache
|
|
file (if caching on disk), and optionally the loaded image array (if caching in RAM).
|
|
torch_transforms (callable): PyTorch transforms to be applied to the images.
|
|
root (str): Root directory of the dataset.
|
|
prefix (str): Prefix for logging and cache filenames.
|
|
|
|
Methods:
|
|
__getitem__: Returns subset of data and targets corresponding to given indices.
|
|
__len__: Returns the total number of samples in the dataset.
|
|
verify_images: Verifies all images in dataset.
|
|
"""
|
|
|
|
def __init__(self, root, args, augment=False, prefix=""):
|
|
"""
|
|
Initialize YOLO object with root, image size, augmentations, and cache settings.
|
|
|
|
Args:
|
|
root (str): Path to the dataset directory where images are stored in a class-specific folder structure.
|
|
args (Namespace): Configuration containing dataset-related settings such as image size, augmentation
|
|
parameters, and cache settings.
|
|
augment (bool, optional): Whether to apply augmentations to the dataset.
|
|
prefix (str, optional): Prefix for logging and cache filenames, aiding in dataset identification.
|
|
"""
|
|
import torchvision # scope for faster 'import ultralytics'
|
|
|
|
# Base class assigned as attribute rather than used as base class to allow for scoping slow torchvision import
|
|
if TORCHVISION_0_18: # 'allow_empty' argument first introduced in torchvision 0.18
|
|
self.base = torchvision.datasets.ImageFolder(root=root, allow_empty=True)
|
|
else:
|
|
self.base = torchvision.datasets.ImageFolder(root=root)
|
|
self.samples = self.base.samples
|
|
self.root = self.base.root
|
|
|
|
# Initialize attributes
|
|
if augment and args.fraction < 1.0: # reduce training fraction
|
|
self.samples = self.samples[: round(len(self.samples) * args.fraction)]
|
|
self.prefix = colorstr(f"{prefix}: ") if prefix else ""
|
|
self.cache_ram = args.cache is True or str(args.cache).lower() == "ram" # cache images into RAM
|
|
if self.cache_ram:
|
|
LOGGER.warning(
|
|
"WARNING ⚠️ Classification `cache_ram` training has known memory leak in "
|
|
"https://github.com/ultralytics/ultralytics/issues/9824, setting `cache_ram=False`."
|
|
)
|
|
self.cache_ram = False
|
|
self.cache_disk = str(args.cache).lower() == "disk" # cache images on hard drive as uncompressed *.npy files
|
|
self.samples = self.verify_images() # filter out bad images
|
|
self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples] # file, index, npy, im
|
|
scale = (1.0 - args.scale, 1.0) # (0.08, 1.0)
|
|
self.torch_transforms = (
|
|
classify_augmentations(
|
|
size=args.imgsz,
|
|
scale=scale,
|
|
hflip=args.fliplr,
|
|
vflip=args.flipud,
|
|
erasing=args.erasing,
|
|
auto_augment=args.auto_augment,
|
|
hsv_h=args.hsv_h,
|
|
hsv_s=args.hsv_s,
|
|
hsv_v=args.hsv_v,
|
|
)
|
|
if augment
|
|
else classify_transforms(size=args.imgsz, crop_fraction=args.crop_fraction)
|
|
)
|
|
|
|
def __getitem__(self, i):
|
|
"""
|
|
Returns subset of data and targets corresponding to given indices.
|
|
|
|
Args:
|
|
i (int): Index of the sample to retrieve.
|
|
|
|
Returns:
|
|
(dict): Dictionary containing the image and its class index.
|
|
"""
|
|
f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image
|
|
if self.cache_ram:
|
|
if im is None: # Warning: two separate if statements required here, do not combine this with previous line
|
|
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), allow_pickle=False)
|
|
im = np.load(fn)
|
|
else: # read image
|
|
im = cv2.imread(f) # BGR
|
|
# Convert NumPy array to PIL image
|
|
im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
|
|
sample = self.torch_transforms(im)
|
|
return {"img": sample, "cls": j}
|
|
|
|
def __len__(self) -> int:
|
|
"""Return the total number of samples in the dataset."""
|
|
return len(self.samples)
|
|
|
|
def verify_images(self):
|
|
"""
|
|
Verify all images in dataset.
|
|
|
|
Returns:
|
|
(List): List of valid samples after verification.
|
|
"""
|
|
desc = f"{self.prefix}Scanning {self.root}..."
|
|
path = Path(self.root).with_suffix(".cache") # *.cache file path
|
|
|
|
try:
|
|
cache = load_dataset_cache_file(path) # attempt to load a *.cache file
|
|
assert cache["version"] == DATASET_CACHE_VERSION # matches current version
|
|
assert cache["hash"] == get_hash([x[0] for x in self.samples]) # identical hash
|
|
nf, nc, n, samples = cache.pop("results") # found, missing, empty, corrupt, total
|
|
if LOCAL_RANK in {-1, 0}:
|
|
d = f"{desc} {nf} images, {nc} corrupt"
|
|
TQDM(None, desc=d, total=n, initial=n)
|
|
if cache["msgs"]:
|
|
LOGGER.info("\n".join(cache["msgs"])) # display warnings
|
|
return samples
|
|
|
|
except (FileNotFoundError, AssertionError, AttributeError):
|
|
# Run scan if *.cache retrieval failed
|
|
nf, nc, msgs, samples, x = 0, 0, [], [], {}
|
|
with ThreadPool(NUM_THREADS) as pool:
|
|
results = pool.imap(func=verify_image, iterable=zip(self.samples, repeat(self.prefix)))
|
|
pbar = TQDM(results, desc=desc, total=len(self.samples))
|
|
for sample, nf_f, nc_f, msg in pbar:
|
|
if nf_f:
|
|
samples.append(sample)
|
|
if msg:
|
|
msgs.append(msg)
|
|
nf += nf_f
|
|
nc += nc_f
|
|
pbar.desc = f"{desc} {nf} images, {nc} corrupt"
|
|
pbar.close()
|
|
if msgs:
|
|
LOGGER.info("\n".join(msgs))
|
|
x["hash"] = get_hash([x[0] for x in self.samples])
|
|
x["results"] = nf, nc, len(samples), samples
|
|
x["msgs"] = msgs # warnings
|
|
save_dataset_cache_file(self.prefix, path, x, DATASET_CACHE_VERSION)
|
|
return samples
|