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376 lines
16 KiB
Python
376 lines
16 KiB
Python
1 year ago
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import contextlib
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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 PIL import Image
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from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr, is_dir_writeable
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from ultralytics.utils.ops import resample_segments
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from .augment import Compose, Format, Instances, LetterBox, classify_augmentations, 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, verify_image_label
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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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Args:
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data (dict, optional): A dataset YAML dictionary. Defaults to None.
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task (str): An explicit arg to point current task, Defaults to 'detect'.
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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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def __init__(self, *args, data=None, task="detect", **kwargs):
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"""Initializes the YOLODataset with optional configurations for segments and keypoints."""
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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. Default is 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(
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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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),
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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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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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)
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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)
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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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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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"""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(
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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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)
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)
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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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"""
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Custom your label format here.
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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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# list[np.array(1000, 2)] * num_samples
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# (N, 1000, 2)
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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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"""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", "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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# 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, prefix=""):
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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.prefix = colorstr(f"{prefix}: ") if prefix else ""
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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 = self.verify_images() # filter out bad images
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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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scale = (1.0 - args.scale, 1.0) # (0.08, 1.0)
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self.torch_transforms = (
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classify_augmentations(
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size=args.imgsz,
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scale=scale,
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hflip=args.fliplr,
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vflip=args.flipud,
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erasing=args.erasing,
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auto_augment=args.auto_augment,
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hsv_h=args.hsv_h,
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hsv_s=args.hsv_s,
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hsv_v=args.hsv_v,
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)
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if augment
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else classify_transforms(size=args.imgsz, crop_fraction=args.crop_fraction)
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)
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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), allow_pickle=False)
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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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# Convert NumPy array to PIL image
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im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
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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 the total number of samples in the dataset."""
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return len(self.samples)
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def verify_images(self):
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"""Verify all images in dataset."""
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desc = f"{self.prefix}Scanning {self.root}..."
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path = Path(self.root).with_suffix(".cache") # *.cache file path
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with contextlib.suppress(FileNotFoundError, AssertionError, AttributeError):
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cache = load_dataset_cache_file(path) # 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([x[0] for x in self.samples]) # identical hash
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nf, nc, n, samples = cache.pop("results") # found, missing, empty, corrupt, total
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if LOCAL_RANK in (-1, 0):
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d = f"{desc} {nf} images, {nc} corrupt"
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TQDM(None, desc=d, total=n, initial=n)
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if cache["msgs"]:
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LOGGER.info("\n".join(cache["msgs"])) # display warnings
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return samples
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# Run scan if *.cache retrieval failed
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nf, nc, msgs, samples, x = 0, 0, [], [], {}
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with ThreadPool(NUM_THREADS) as pool:
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results = pool.imap(func=verify_image, iterable=zip(self.samples, repeat(self.prefix)))
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pbar = TQDM(results, desc=desc, total=len(self.samples))
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for sample, nf_f, nc_f, msg in pbar:
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if nf_f:
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samples.append(sample)
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if msg:
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msgs.append(msg)
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nf += nf_f
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nc += nc_f
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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)
|
||
|
|
return samples
|
||
|
|
|
||
|
|
|
||
|
|
def load_dataset_cache_file(path):
|
||
|
|
"""Load an Ultralytics *.cache dictionary from path."""
|
||
|
|
import gc
|
||
|
|
|
||
|
|
gc.disable() # reduce pickle load time https://github.com/ultralytics/ultralytics/pull/1585
|
||
|
|
cache = np.load(str(path), allow_pickle=True).item() # load dict
|
||
|
|
gc.enable()
|
||
|
|
return cache
|
||
|
|
|
||
|
|
|
||
|
|
def save_dataset_cache_file(prefix, path, x):
|
||
|
|
"""Save an Ultralytics dataset *.cache dictionary x to path."""
|
||
|
|
x["version"] = DATASET_CACHE_VERSION # add cache version
|
||
|
|
if is_dir_writeable(path.parent):
|
||
|
|
if path.exists():
|
||
|
|
path.unlink() # remove *.cache file if exists
|
||
|
|
np.save(str(path), x) # save cache for next time
|
||
|
|
path.with_suffix(".cache.npy").rename(path) # remove .npy suffix
|
||
|
|
LOGGER.info(f"{prefix}New cache created: {path}")
|
||
|
|
else:
|
||
|
|
LOGGER.warning(f"{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable, cache not saved.")
|
||
|
|
|
||
|
|
|
||
|
|
# TODO: support semantic segmentation
|
||
|
|
class SemanticDataset(BaseDataset):
|
||
|
|
"""
|
||
|
|
Semantic Segmentation Dataset.
|
||
|
|
|
||
|
|
This class is responsible for handling datasets used for semantic segmentation tasks. It inherits functionalities
|
||
|
|
from the BaseDataset class.
|
||
|
|
|
||
|
|
Note:
|
||
|
|
This class is currently a placeholder and needs to be populated with methods and attributes for supporting
|
||
|
|
semantic segmentation tasks.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self):
|
||
|
|
"""Initialize a SemanticDataset object."""
|
||
|
|
super().__init__()
|