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| # YOLOv5 🚀 by Ultralytics, GPL-3.0 license | |
| """ | |
| Dataloaders and dataset utils | |
| """ | |
| import contextlib | |
| import glob | |
| import hashlib | |
| import json | |
| import math | |
| import os | |
| import random | |
| import shutil | |
| import time | |
| from itertools import repeat | |
| from multiprocessing.pool import Pool, ThreadPool | |
| from pathlib import Path | |
| from threading import Thread | |
| from urllib.parse import urlparse | |
| import numpy as np | |
| import psutil | |
| import torch | |
| import torch.nn.functional as F | |
| import torchvision | |
| import yaml | |
| from PIL import ExifTags, Image, ImageOps | |
| from torch.utils.data import DataLoader, Dataset, dataloader, distributed | |
| from tqdm import tqdm | |
| from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste, | |
| cutout, letterbox, mixup, random_perspective) | |
| from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, check_dataset, check_requirements, check_yaml, clean_str, | |
| colorstr, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy, xywh2xyxy, | |
| xywhn2xyxy, xyxy2xywhn) | |
| from utils.torch_utils import torch_distributed_zero_first | |
| # Parameters | |
| HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' | |
| IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes | |
| VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes | |
| BAR_FORMAT = '{l_bar}{bar:10}{r_bar}{bar:-10b}' # tqdm bar format | |
| LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html | |
| RANK = int(os.getenv('RANK', -1)) | |
| PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true' # global pin_memory for dataloaders | |
| # Get orientation exif tag | |
| for orientation in ExifTags.TAGS.keys(): | |
| if ExifTags.TAGS[orientation] == 'Orientation': | |
| break | |
| def get_hash(paths): | |
| # Returns a single hash value of a list of paths (files or dirs) | |
| size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes | |
| h = hashlib.md5(str(size).encode()) # hash sizes | |
| h.update(''.join(paths).encode()) # hash paths | |
| return h.hexdigest() # return hash | |
| def exif_size(img): | |
| # Returns exif-corrected PIL size | |
| s = img.size # (width, height) | |
| with contextlib.suppress(Exception): | |
| rotation = dict(img._getexif().items())[orientation] | |
| if rotation in [6, 8]: # rotation 270 or 90 | |
| s = (s[1], s[0]) | |
| return s | |
| def exif_transpose(image): | |
| """ | |
| Transpose a PIL image accordingly if it has an EXIF Orientation tag. | |
| Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose() | |
| :param image: The image to transpose. | |
| :return: An image. | |
| """ | |
| exif = image.getexif() | |
| orientation = exif.get(0x0112, 1) # default 1 | |
| if orientation > 1: | |
| method = { | |
| 2: Image.FLIP_LEFT_RIGHT, | |
| 3: Image.ROTATE_180, | |
| 4: Image.FLIP_TOP_BOTTOM, | |
| 5: Image.TRANSPOSE, | |
| 6: Image.ROTATE_270, | |
| 7: Image.TRANSVERSE, | |
| 8: Image.ROTATE_90}.get(orientation) | |
| if method is not None: | |
| image = image.transpose(method) | |
| del exif[0x0112] | |
| image.info["exif"] = exif.tobytes() | |
| return image | |
| def seed_worker(worker_id): | |
| # Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader | |
| worker_seed = torch.initial_seed() % 2 ** 32 | |
| np.random.seed(worker_seed) | |
| random.seed(worker_seed) | |
| def create_dataloader(path, | |
| imgsz, | |
| batch_size, | |
| stride, | |
| single_cls=False, | |
| hyp=None, | |
| augment=False, | |
| cache=False, | |
| pad=0.0, | |
| rect=False, | |
| rank=-1, | |
| workers=8, | |
| image_weights=False, | |
| quad=False, | |
| prefix='', | |
| shuffle=False): | |
| if rect and shuffle: | |
| LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') | |
| shuffle = False | |
| with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP | |
| dataset = LoadImagesAndLabels( | |
| path, | |
| imgsz, | |
| batch_size, | |
| augment=augment, # augmentation | |
| hyp=hyp, # hyperparameters | |
| rect=rect, # rectangular batches | |
| cache_images=cache, | |
| single_cls=single_cls, | |
| stride=int(stride), | |
| pad=pad, | |
| image_weights=image_weights, | |
| prefix=prefix) | |
| batch_size = min(batch_size, len(dataset)) | |
| nd = torch.cuda.device_count() # number of CUDA devices | |
| nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers | |
| sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) | |
| loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates | |
| generator = torch.Generator() | |
| generator.manual_seed(6148914691236517205 + RANK) | |
| return loader(dataset, | |
| batch_size=batch_size, | |
| shuffle=shuffle and sampler is None, | |
| num_workers=nw, | |
| sampler=sampler, | |
| pin_memory=PIN_MEMORY, | |
| collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn, | |
| worker_init_fn=seed_worker, | |
| generator=generator), dataset | |
| class InfiniteDataLoader(dataloader.DataLoader): | |
| """ Dataloader that reuses workers | |
| Uses same syntax as vanilla DataLoader | |
| """ | |
| def __init__(self, *args, **kwargs): | |
| super().__init__(*args, **kwargs) | |
| object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) | |
| self.iterator = super().__iter__() | |
| def __len__(self): | |
| return len(self.batch_sampler.sampler) | |
| def __iter__(self): | |
| for _ in range(len(self)): | |
| yield next(self.iterator) | |
| class _RepeatSampler: | |
| """ Sampler that repeats forever | |
| Args: | |
| sampler (Sampler) | |
| """ | |
| def __init__(self, sampler): | |
| self.sampler = sampler | |
| def __iter__(self): | |
| while True: | |
| yield from iter(self.sampler) | |
| class LoadScreenshots: | |
| # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"` | |
| def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None): | |
| # source = [screen_number left top width height] (pixels) | |
| check_requirements('mss') | |
| import mss | |
| source, *params = source.split() | |
| self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0 | |
| if len(params) == 1: | |
| self.screen = int(params[0]) | |
| elif len(params) == 4: | |
| left, top, width, height = (int(x) for x in params) | |
| elif len(params) == 5: | |
| self.screen, left, top, width, height = (int(x) for x in params) | |
| self.img_size = img_size | |
| self.stride = stride | |
| self.transforms = transforms | |
| self.auto = auto | |
| self.mode = 'stream' | |
| self.frame = 0 | |
| self.sct = mss.mss() | |
| # Parse monitor shape | |
| monitor = self.sct.monitors[self.screen] | |
| self.top = monitor["top"] if top is None else (monitor["top"] + top) | |
| self.left = monitor["left"] if left is None else (monitor["left"] + left) | |
| self.width = width or monitor["width"] | |
| self.height = height or monitor["height"] | |
| self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height} | |
| def __iter__(self): | |
| return self | |
| def __next__(self): | |
| # mss screen capture: get raw pixels from the screen as np array | |
| im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR | |
| s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: " | |
| if self.transforms: | |
| im = self.transforms(im0) # transforms | |
| else: | |
| im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize | |
| im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB | |
| im = np.ascontiguousarray(im) # contiguous | |
| self.frame += 1 | |
| return str(self.screen), im, im0, None, s # screen, img, original img, im0s, s | |
| class LoadImages: | |
| # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4` | |
| def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): | |
| files = [] | |
| for p in sorted(path) if isinstance(path, (list, tuple)) else [path]: | |
| p = str(Path(p).resolve()) | |
| if '*' in p: | |
| files.extend(sorted(glob.glob(p, recursive=True))) # glob | |
| elif os.path.isdir(p): | |
| files.extend(sorted(glob.glob(os.path.join(p, '*.*')))) # dir | |
| elif os.path.isfile(p): | |
| files.append(p) # files | |
| else: | |
| raise FileNotFoundError(f'{p} does not exist') | |
| images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS] | |
| videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS] | |
| ni, nv = len(images), len(videos) | |
| self.img_size = img_size | |
| self.stride = stride | |
| self.files = images + videos | |
| self.nf = ni + nv # number of files | |
| self.video_flag = [False] * ni + [True] * nv | |
| self.mode = 'image' | |
| self.auto = auto | |
| self.transforms = transforms # optional | |
| self.vid_stride = vid_stride # video frame-rate stride | |
| if any(videos): | |
| self._new_video(videos[0]) # new video | |
| else: | |
| self.cap = None | |
| assert self.nf > 0, f'No images or videos found in {p}. ' \ | |
| f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}' | |
| def __iter__(self): | |
| self.count = 0 | |
| return self | |
| def __next__(self): | |
| if self.count == self.nf: | |
| raise StopIteration | |
| path = self.files[self.count] | |
| if self.video_flag[self.count]: | |
| # Read video | |
| self.mode = 'video' | |
| for _ in range(self.vid_stride): | |
| self.cap.grab() | |
| ret_val, im0 = self.cap.retrieve() | |
| while not ret_val: | |
| self.count += 1 | |
| self.cap.release() | |
| if self.count == self.nf: # last video | |
| raise StopIteration | |
| path = self.files[self.count] | |
| self._new_video(path) | |
| ret_val, im0 = self.cap.read() | |
| self.frame += 1 | |
| # im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False | |
| s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: ' | |
| else: | |
| # Read image | |
| self.count += 1 | |
| im0 = cv2.imread(path) # BGR | |
| assert im0 is not None, f'Image Not Found {path}' | |
| s = f'image {self.count}/{self.nf} {path}: ' | |
| if self.transforms: | |
| im = self.transforms(im0) # transforms | |
| else: | |
| im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize | |
| im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB | |
| im = np.ascontiguousarray(im) # contiguous | |
| return path, im, im0, self.cap, s | |
| def _new_video(self, path): | |
| # Create a new video capture object | |
| self.frame = 0 | |
| self.cap = cv2.VideoCapture(path) | |
| self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride) | |
| self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees | |
| # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493 | |
| def _cv2_rotate(self, im): | |
| # Rotate a cv2 video manually | |
| if self.orientation == 0: | |
| return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE) | |
| elif self.orientation == 180: | |
| return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE) | |
| elif self.orientation == 90: | |
| return cv2.rotate(im, cv2.ROTATE_180) | |
| return im | |
| def __len__(self): | |
| return self.nf # number of files | |
| class LoadStreams: | |
| # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams` | |
| def __init__(self, sources='streams.txt', img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): | |
| torch.backends.cudnn.benchmark = True # faster for fixed-size inference | |
| self.mode = 'stream' | |
| self.img_size = img_size | |
| self.stride = stride | |
| self.vid_stride = vid_stride # video frame-rate stride | |
| sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources] | |
| n = len(sources) | |
| self.sources = [clean_str(x) for x in sources] # clean source names for later | |
| self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n | |
| for i, s in enumerate(sources): # index, source | |
| # Start thread to read frames from video stream | |
| st = f'{i + 1}/{n}: {s}... ' | |
| if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'): # if source is YouTube video | |
| # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc' | |
| check_requirements(('pafy', 'youtube_dl==2020.12.2')) | |
| import pafy | |
| s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL | |
| s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam | |
| if s == 0: | |
| assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.' | |
| assert not is_kaggle(), '--source 0 webcam unsupported on Kaggle. Rerun command in a local environment.' | |
| cap = cv2.VideoCapture(s) | |
| assert cap.isOpened(), f'{st}Failed to open {s}' | |
| w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
| h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
| fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan | |
| self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback | |
| self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback | |
| _, self.imgs[i] = cap.read() # guarantee first frame | |
| self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True) | |
| LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)") | |
| self.threads[i].start() | |
| LOGGER.info('') # newline | |
| # check for common shapes | |
| s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs]) | |
| self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal | |
| self.auto = auto and self.rect | |
| self.transforms = transforms # optional | |
| if not self.rect: | |
| LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.') | |
| def update(self, i, cap, stream): | |
| # Read stream `i` frames in daemon thread | |
| n, f = 0, self.frames[i] # frame number, frame array | |
| while cap.isOpened() and n < f: | |
| n += 1 | |
| cap.grab() # .read() = .grab() followed by .retrieve() | |
| if n % self.vid_stride == 0: | |
| success, im = cap.retrieve() | |
| if success: | |
| self.imgs[i] = im | |
| else: | |
| LOGGER.warning('WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.') | |
| self.imgs[i] = np.zeros_like(self.imgs[i]) | |
| cap.open(stream) # re-open stream if signal was lost | |
| time.sleep(0.0) # wait time | |
| def __iter__(self): | |
| self.count = -1 | |
| return self | |
| def __next__(self): | |
| self.count += 1 | |
| if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'): # q to quit | |
| cv2.destroyAllWindows() | |
| raise StopIteration | |
| im0 = self.imgs.copy() | |
| if self.transforms: | |
| im = np.stack([self.transforms(x) for x in im0]) # transforms | |
| else: | |
| im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0]) # resize | |
| im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW | |
| im = np.ascontiguousarray(im) # contiguous | |
| return self.sources, im, im0, None, '' | |
| def __len__(self): | |
| return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years | |
| def img2label_paths(img_paths): | |
| # Define label paths as a function of image paths | |
| sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}' # /images/, /labels/ substrings | |
| return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths] | |
| class LoadImagesAndLabels(Dataset): | |
| # YOLOv5 train_loader/val_loader, loads images and labels for training and validation | |
| cache_version = 0.6 # dataset labels *.cache version | |
| rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4] | |
| def __init__(self, | |
| path, | |
| img_size=640, | |
| batch_size=16, | |
| augment=False, | |
| hyp=None, | |
| rect=False, | |
| image_weights=False, | |
| cache_images=False, | |
| single_cls=False, | |
| stride=32, | |
| pad=0.0, | |
| min_items=0, | |
| prefix=''): | |
| self.img_size = img_size | |
| self.augment = augment | |
| self.hyp = hyp | |
| self.image_weights = image_weights | |
| self.rect = False if image_weights else rect | |
| self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) | |
| self.mosaic_border = [-img_size // 2, -img_size // 2] | |
| self.stride = stride | |
| self.path = path | |
| self.albumentations = Albumentations(size=img_size) if augment else None | |
| try: | |
| f = [] # image files | |
| for p in path if isinstance(path, list) else [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, 1) if x.startswith('./') else x for x in t] # to global path | |
| # f += [p.parent / x.lstrip(os.sep) for x in t] # to global path (pathlib) | |
| else: | |
| raise FileNotFoundError(f'{prefix}{p} does not exist') | |
| self.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 self.im_files, f'{prefix}No images found' | |
| except Exception as e: | |
| raise Exception(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}') from e | |
| # Check cache | |
| self.label_files = img2label_paths(self.im_files) # labels | |
| cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') | |
| try: | |
| cache, exists = np.load(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, prefix), 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}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupt" | |
| tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=BAR_FORMAT) # display cache results | |
| if cache['msgs']: | |
| LOGGER.info('\n'.join(cache['msgs'])) # display warnings | |
| assert nf > 0 or not augment, f'{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, shapes, self.segments = zip(*cache.values()) | |
| nl = len(np.concatenate(labels, 0)) # number of labels | |
| assert nl > 0 or not augment, f'{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}' | |
| self.labels = list(labels) | |
| self.shapes = np.array(shapes) | |
| self.im_files = list(cache.keys()) # update | |
| self.label_files = img2label_paths(cache.keys()) # update | |
| # Filter images | |
| if min_items: | |
| include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int) | |
| LOGGER.info(f'{prefix}{n - len(include)}/{n} images filtered from dataset') | |
| self.im_files = [self.im_files[i] for i in include] | |
| self.label_files = [self.label_files[i] for i in include] | |
| self.labels = [self.labels[i] for i in include] | |
| self.segments = [self.segments[i] for i in include] | |
| self.shapes = self.shapes[include] # wh | |
| # Create indices | |
| n = len(self.shapes) # number of images | |
| bi = np.floor(np.arange(n) / batch_size).astype(int) # batch index | |
| nb = bi[-1] + 1 # number of batches | |
| self.batch = bi # batch index of image | |
| self.n = n | |
| self.indices = range(n) | |
| # Update labels | |
| include_class = [] # filter labels to include only these classes (optional) | |
| include_class_array = np.array(include_class).reshape(1, -1) | |
| for i, (label, segment) in enumerate(zip(self.labels, self.segments)): | |
| if include_class: | |
| j = (label[:, 0:1] == include_class_array).any(1) | |
| self.labels[i] = label[j] | |
| if segment: | |
| self.segments[i] = segment[j] | |
| if single_cls: # single-class training, merge all classes into 0 | |
| self.labels[i][:, 0] = 0 | |
| if segment: | |
| self.segments[i][:, 0] = 0 | |
| # Rectangular Training | |
| if self.rect: | |
| # Sort by aspect ratio | |
| s = self.shapes # wh | |
| ar = s[:, 1] / s[:, 0] # aspect ratio | |
| irect = ar.argsort() | |
| self.im_files = [self.im_files[i] for i in irect] | |
| self.label_files = [self.label_files[i] for i in irect] | |
| self.labels = [self.labels[i] for i in irect] | |
| self.segments = [self.segments[i] for i in irect] | |
| self.shapes = s[irect] # wh | |
| 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) * img_size / stride + pad).astype(int) * stride | |
| # Cache images into RAM/disk for faster training | |
| if cache_images == 'ram' and not self.check_cache_ram(prefix=prefix): | |
| cache_images = False | |
| self.ims = [None] * n | |
| self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files] | |
| if cache_images: | |
| b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes | |
| self.im_hw0, self.im_hw = [None] * n, [None] * n | |
| fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image | |
| results = ThreadPool(NUM_THREADS).imap(fcn, range(n)) | |
| pbar = tqdm(enumerate(results), total=n, bar_format=BAR_FORMAT, disable=LOCAL_RANK > 0) | |
| for i, x in pbar: | |
| if cache_images == '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'{prefix}Caching images ({b / gb:.1f}GB {cache_images})' | |
| pbar.close() | |
| def check_cache_ram(self, safety_margin=0.1, prefix=''): | |
| # Check image caching requirements vs available memory | |
| b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes | |
| n = min(self.n, 30) # extrapolate from 30 random images | |
| for _ in range(n): | |
| im = cv2.imread(random.choice(self.im_files)) # sample image | |
| ratio = self.img_size / max(im.shape[0], im.shape[1]) # max(h, w) # ratio | |
| b += im.nbytes * ratio ** 2 | |
| mem_required = b * self.n / n # GB required to cache dataset into RAM | |
| mem = psutil.virtual_memory() | |
| cache = mem_required * (1 + safety_margin) < mem.available # to cache or not to cache, that is the question | |
| if not cache: | |
| LOGGER.info(f"{prefix}{mem_required / gb:.1f}GB RAM required, " | |
| f"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, " | |
| f"{'caching images ✅' if cache else 'not caching images ⚠️'}") | |
| return cache | |
| def cache_labels(self, path=Path('./labels.cache'), prefix=''): | |
| # Cache dataset labels, check images and read shapes | |
| x = {} # dict | |
| nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages | |
| desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels..." | |
| with Pool(NUM_THREADS) as pool: | |
| pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))), | |
| desc=desc, | |
| total=len(self.im_files), | |
| bar_format=BAR_FORMAT) | |
| for im_file, lb, shape, segments, 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[im_file] = [lb, shape, segments] | |
| if msg: | |
| msgs.append(msg) | |
| pbar.desc = f"{desc}{nf} found, {nm} missing, {ne} empty, {nc} corrupt" | |
| pbar.close() | |
| if msgs: | |
| LOGGER.info('\n'.join(msgs)) | |
| if nf == 0: | |
| LOGGER.warning(f'{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'{prefix}New cache created: {path}') | |
| except Exception as e: | |
| LOGGER.warning(f'{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}') # not writeable | |
| return x | |
| def __len__(self): | |
| return len(self.im_files) | |
| # def __iter__(self): | |
| # self.count = -1 | |
| # print('ran dataset iter') | |
| # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) | |
| # return self | |
| def __getitem__(self, index): | |
| index = self.indices[index] # linear, shuffled, or image_weights | |
| hyp = self.hyp | |
| mosaic = self.mosaic and random.random() < hyp['mosaic'] | |
| if mosaic: | |
| # Load mosaic | |
| img, labels = self.load_mosaic(index) | |
| shapes = None | |
| # MixUp augmentation | |
| if random.random() < hyp['mixup']: | |
| img, labels = mixup(img, labels, *self.load_mosaic(random.randint(0, self.n - 1))) | |
| else: | |
| # Load image | |
| img, (h0, w0), (h, w) = self.load_image(index) | |
| # Letterbox | |
| shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape | |
| img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) | |
| shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling | |
| labels = self.labels[index].copy() | |
| if labels.size: # normalized xywh to pixel xyxy format | |
| labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) | |
| if self.augment: | |
| img, labels = random_perspective(img, | |
| labels, | |
| degrees=hyp['degrees'], | |
| translate=hyp['translate'], | |
| scale=hyp['scale'], | |
| shear=hyp['shear'], | |
| perspective=hyp['perspective']) | |
| nl = len(labels) # number of labels | |
| if nl: | |
| labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1E-3) | |
| if self.augment: | |
| # Albumentations | |
| img, labels = self.albumentations(img, labels) | |
| nl = len(labels) # update after albumentations | |
| # HSV color-space | |
| augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) | |
| # Flip up-down | |
| if random.random() < hyp['flipud']: | |
| img = np.flipud(img) | |
| if nl: | |
| labels[:, 2] = 1 - labels[:, 2] | |
| # Flip left-right | |
| if random.random() < hyp['fliplr']: | |
| img = np.fliplr(img) | |
| if nl: | |
| labels[:, 1] = 1 - labels[:, 1] | |
| # Cutouts | |
| # labels = cutout(img, labels, p=0.5) | |
| # nl = len(labels) # update after cutout | |
| labels_out = torch.zeros((nl, 6)) | |
| if nl: | |
| labels_out[:, 1:] = torch.from_numpy(labels) | |
| # Convert | |
| img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB | |
| img = np.ascontiguousarray(img) | |
| return torch.from_numpy(img), labels_out, self.im_files[index], shapes | |
| def load_image(self, i): | |
| # Loads 1 image from dataset index 'i', returns (im, original hw, 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 | |
| assert im is not None, f'Image Not Found {f}' | |
| h0, w0 = im.shape[:2] # orig hw | |
| r = self.img_size / max(h0, w0) # ratio | |
| if r != 1: # if sizes are not equal | |
| interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA | |
| im = cv2.resize(im, (int(w0 * r), int(h0 * r)), interpolation=interp) | |
| return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized | |
| return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized | |
| 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])) | |
| def load_mosaic(self, index): | |
| # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic | |
| labels4, segments4 = [], [] | |
| s = self.img_size | |
| yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y | |
| indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices | |
| random.shuffle(indices) | |
| for i, index in enumerate(indices): | |
| # Load image | |
| img, _, (h, w) = self.load_image(index) | |
| # place img in img4 | |
| if i == 0: # top left | |
| img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles | |
| x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) | |
| x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) | |
| elif i == 1: # top right | |
| x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc | |
| x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h | |
| elif i == 2: # bottom left | |
| x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) | |
| x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) | |
| elif i == 3: # bottom right | |
| x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) | |
| x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) | |
| img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] | |
| padw = x1a - x1b | |
| padh = y1a - y1b | |
| # Labels | |
| labels, segments = self.labels[index].copy(), self.segments[index].copy() | |
| if labels.size: | |
| labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format | |
| segments = [xyn2xy(x, w, h, padw, padh) for x in segments] | |
| labels4.append(labels) | |
| segments4.extend(segments) | |
| # Concat/clip labels | |
| labels4 = np.concatenate(labels4, 0) | |
| for x in (labels4[:, 1:], *segments4): | |
| np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() | |
| # img4, labels4 = replicate(img4, labels4) # replicate | |
| # Augment | |
| img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste']) | |
| img4, labels4 = random_perspective(img4, | |
| labels4, | |
| segments4, | |
| degrees=self.hyp['degrees'], | |
| translate=self.hyp['translate'], | |
| scale=self.hyp['scale'], | |
| shear=self.hyp['shear'], | |
| perspective=self.hyp['perspective'], | |
| border=self.mosaic_border) # border to remove | |
| return img4, labels4 | |
| def load_mosaic9(self, index): | |
| # YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic | |
| labels9, segments9 = [], [] | |
| s = self.img_size | |
| indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices | |
| random.shuffle(indices) | |
| hp, wp = -1, -1 # height, width previous | |
| for i, index in enumerate(indices): | |
| # Load image | |
| img, _, (h, w) = self.load_image(index) | |
| # place img in img9 | |
| if i == 0: # center | |
| img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles | |
| h0, w0 = h, w | |
| c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates | |
| elif i == 1: # top | |
| c = s, s - h, s + w, s | |
| elif i == 2: # top right | |
| c = s + wp, s - h, s + wp + w, s | |
| elif i == 3: # right | |
| c = s + w0, s, s + w0 + w, s + h | |
| elif i == 4: # bottom right | |
| c = s + w0, s + hp, s + w0 + w, s + hp + h | |
| elif i == 5: # bottom | |
| c = s + w0 - w, s + h0, s + w0, s + h0 + h | |
| elif i == 6: # bottom left | |
| c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h | |
| elif i == 7: # left | |
| c = s - w, s + h0 - h, s, s + h0 | |
| elif i == 8: # top left | |
| c = s - w, s + h0 - hp - h, s, s + h0 - hp | |
| padx, pady = c[:2] | |
| x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords | |
| # Labels | |
| labels, segments = self.labels[index].copy(), self.segments[index].copy() | |
| if labels.size: | |
| labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format | |
| segments = [xyn2xy(x, w, h, padx, pady) for x in segments] | |
| labels9.append(labels) | |
| segments9.extend(segments) | |
| # Image | |
| img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] | |
| hp, wp = h, w # height, width previous | |
| # Offset | |
| yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border) # mosaic center x, y | |
| img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] | |
| # Concat/clip labels | |
| labels9 = np.concatenate(labels9, 0) | |
| labels9[:, [1, 3]] -= xc | |
| labels9[:, [2, 4]] -= yc | |
| c = np.array([xc, yc]) # centers | |
| segments9 = [x - c for x in segments9] | |
| for x in (labels9[:, 1:], *segments9): | |
| np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() | |
| # img9, labels9 = replicate(img9, labels9) # replicate | |
| # Augment | |
| img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp['copy_paste']) | |
| img9, labels9 = random_perspective(img9, | |
| labels9, | |
| segments9, | |
| degrees=self.hyp['degrees'], | |
| translate=self.hyp['translate'], | |
| scale=self.hyp['scale'], | |
| shear=self.hyp['shear'], | |
| perspective=self.hyp['perspective'], | |
| border=self.mosaic_border) # border to remove | |
| return img9, labels9 | |
| def collate_fn(batch): | |
| im, label, path, shapes = zip(*batch) # transposed | |
| for i, lb in enumerate(label): | |
| lb[:, 0] = i # add target image index for build_targets() | |
| return torch.stack(im, 0), torch.cat(label, 0), path, shapes | |
| def collate_fn4(batch): | |
| im, label, path, shapes = zip(*batch) # transposed | |
| n = len(shapes) // 4 | |
| im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] | |
| ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]]) | |
| wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]]) | |
| s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]]) # scale | |
| for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW | |
| i *= 4 | |
| if random.random() < 0.5: | |
| im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear', | |
| align_corners=False)[0].type(im[i].type()) | |
| lb = label[i] | |
| else: | |
| im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2) | |
| lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s | |
| im4.append(im1) | |
| label4.append(lb) | |
| for i, lb in enumerate(label4): | |
| lb[:, 0] = i # add target image index for build_targets() | |
| return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4 | |
| # Ancillary functions -------------------------------------------------------------------------------------------------- | |
| def flatten_recursive(path=DATASETS_DIR / 'coco128'): | |
| # Flatten a recursive directory by bringing all files to top level | |
| new_path = Path(f'{str(path)}_flat') | |
| if os.path.exists(new_path): | |
| shutil.rmtree(new_path) # delete output folder | |
| os.makedirs(new_path) # make new output folder | |
| for file in tqdm(glob.glob(f'{str(Path(path))}/**/*.*', recursive=True)): | |
| shutil.copyfile(file, new_path / Path(file).name) | |
| def extract_boxes(path=DATASETS_DIR / 'coco128'): # from utils.dataloaders import *; extract_boxes() | |
| # Convert detection dataset into classification dataset, with one directory per class | |
| path = Path(path) # images dir | |
| shutil.rmtree(path / 'classification') if (path / 'classification').is_dir() else None # remove existing | |
| files = list(path.rglob('*.*')) | |
| n = len(files) # number of files | |
| for im_file in tqdm(files, total=n): | |
| if im_file.suffix[1:] in IMG_FORMATS: | |
| # image | |
| im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB | |
| h, w = im.shape[:2] | |
| # labels | |
| lb_file = Path(img2label_paths([str(im_file)])[0]) | |
| if Path(lb_file).exists(): | |
| with open(lb_file) as f: | |
| lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels | |
| for j, x in enumerate(lb): | |
| c = int(x[0]) # class | |
| f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg' # new filename | |
| if not f.parent.is_dir(): | |
| f.parent.mkdir(parents=True) | |
| b = x[1:] * [w, h, w, h] # box | |
| # b[2:] = b[2:].max() # rectangle to square | |
| b[2:] = b[2:] * 1.2 + 3 # pad | |
| b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int) | |
| b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image | |
| b[[1, 3]] = np.clip(b[[1, 3]], 0, h) | |
| assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}' | |
| def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False): | |
| """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files | |
| Usage: from utils.dataloaders import *; autosplit() | |
| Arguments | |
| path: Path to images directory | |
| weights: Train, val, test weights (list, tuple) | |
| annotated_only: Only use images with an annotated txt file | |
| """ | |
| path = Path(path) # images dir | |
| files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS) # image files only | |
| n = len(files) # number of files | |
| random.seed(0) # for reproducibility | |
| indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split | |
| txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files | |
| for x in txt: | |
| if (path.parent / x).exists(): | |
| (path.parent / x).unlink() # remove existing | |
| print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only) | |
| for i, img in tqdm(zip(indices, files), total=n): | |
| if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label | |
| with open(path.parent / txt[i], 'a') as f: | |
| f.write(f'./{img.relative_to(path.parent).as_posix()}' + '\n') # add image to txt file | |
| def verify_image_label(args): | |
| # Verify one image-label pair | |
| im_file, lb_file, prefix = args | |
| nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', [] # number (missing, found, empty, corrupt), message, segments | |
| try: | |
| # verify images | |
| im = Image.open(im_file) | |
| im.verify() # PIL verify | |
| shape = exif_size(im) # image size | |
| assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels' | |
| assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}' | |
| if im.format.lower() in ('jpg', 'jpeg'): | |
| with open(im_file, 'rb') as f: | |
| f.seek(-2, 2) | |
| if f.read() != b'\xff\xd9': # corrupt JPEG | |
| ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100) | |
| msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved' | |
| # verify labels | |
| if os.path.isfile(lb_file): | |
| nf = 1 # label found | |
| with open(lb_file) as f: | |
| lb = [x.split() for x in f.read().strip().splitlines() if len(x)] | |
| if any(len(x) > 6 for x in lb): # is segment | |
| classes = np.array([x[0] for x in lb], dtype=np.float32) | |
| segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...) | |
| lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) | |
| lb = np.array(lb, dtype=np.float32) | |
| nl = len(lb) | |
| if nl: | |
| assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected' | |
| assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}' | |
| assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}' | |
| _, i = np.unique(lb, axis=0, return_index=True) | |
| if len(i) < nl: # duplicate row check | |
| lb = lb[i] # remove duplicates | |
| if segments: | |
| segments = [segments[x] for x in i] | |
| msg = f'{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed' | |
| else: | |
| ne = 1 # label empty | |
| lb = np.zeros((0, 5), dtype=np.float32) | |
| else: | |
| nm = 1 # label missing | |
| lb = np.zeros((0, 5), dtype=np.float32) | |
| return im_file, lb, shape, segments, nm, nf, ne, nc, msg | |
| except Exception as e: | |
| nc = 1 | |
| msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}' | |
| return [None, None, None, None, nm, nf, ne, nc, msg] | |
| class HUBDatasetStats(): | |
| """ Class for generating HUB dataset JSON and `-hub` dataset directory | |
| Arguments | |
| path: Path to data.yaml or data.zip (with data.yaml inside data.zip) | |
| autodownload: Attempt to download dataset if not found locally | |
| Usage | |
| from utils.dataloaders import HUBDatasetStats | |
| stats = HUBDatasetStats('coco128.yaml', autodownload=True) # usage 1 | |
| stats = HUBDatasetStats('path/to/coco128.zip') # usage 2 | |
| stats.get_json(save=False) | |
| stats.process_images() | |
| """ | |
| def __init__(self, path='coco128.yaml', autodownload=False): | |
| # Initialize class | |
| zipped, data_dir, yaml_path = self._unzip(Path(path)) | |
| try: | |
| with open(check_yaml(yaml_path), errors='ignore') as f: | |
| data = yaml.safe_load(f) # data dict | |
| if zipped: | |
| data['path'] = data_dir | |
| except Exception as e: | |
| raise Exception("error/HUB/dataset_stats/yaml_load") from e | |
| check_dataset(data, autodownload) # download dataset if missing | |
| self.hub_dir = Path(data['path'] + '-hub') | |
| self.im_dir = self.hub_dir / 'images' | |
| self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images | |
| self.stats = {'nc': data['nc'], 'names': list(data['names'].values())} # statistics dictionary | |
| self.data = data | |
| def _find_yaml(dir): | |
| # Return data.yaml file | |
| files = list(dir.glob('*.yaml')) or list(dir.rglob('*.yaml')) # try root level first and then recursive | |
| assert files, f'No *.yaml file found in {dir}' | |
| if len(files) > 1: | |
| files = [f for f in files if f.stem == dir.stem] # prefer *.yaml files that match dir name | |
| assert files, f'Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed' | |
| assert len(files) == 1, f'Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}' | |
| return files[0] | |
| def _unzip(self, path): | |
| # Unzip data.zip | |
| if not str(path).endswith('.zip'): # path is data.yaml | |
| return False, None, path | |
| assert Path(path).is_file(), f'Error unzipping {path}, file not found' | |
| unzip_file(path, path=path.parent) | |
| dir = path.with_suffix('') # dataset directory == zip name | |
| assert dir.is_dir(), f'Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/' | |
| return True, str(dir), self._find_yaml(dir) # zipped, data_dir, yaml_path | |
| def _hub_ops(self, f, max_dim=1920): | |
| # HUB ops for 1 image 'f': resize and save at reduced quality in /dataset-hub for web/app viewing | |
| f_new = self.im_dir / Path(f).name # dataset-hub image filename | |
| try: # use PIL | |
| im = Image.open(f) | |
| r = max_dim / max(im.height, im.width) # ratio | |
| if r < 1.0: # image too large | |
| im = im.resize((int(im.width * r), int(im.height * r))) | |
| im.save(f_new, 'JPEG', quality=50, optimize=True) # save | |
| except Exception as e: # use OpenCV | |
| LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}') | |
| im = cv2.imread(f) | |
| im_height, im_width = im.shape[:2] | |
| r = max_dim / max(im_height, im_width) # ratio | |
| if r < 1.0: # image too large | |
| im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA) | |
| cv2.imwrite(str(f_new), im) | |
| def get_json(self, save=False, verbose=False): | |
| # Return dataset JSON for Ultralytics HUB | |
| def _round(labels): | |
| # Update labels to integer class and 6 decimal place floats | |
| return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels] | |
| for split in 'train', 'val', 'test': | |
| if self.data.get(split) is None: | |
| self.stats[split] = None # i.e. no test set | |
| continue | |
| dataset = LoadImagesAndLabels(self.data[split]) # load dataset | |
| x = np.array([ | |
| np.bincount(label[:, 0].astype(int), minlength=self.data['nc']) | |
| for label in tqdm(dataset.labels, total=dataset.n, desc='Statistics')]) # shape(128x80) | |
| self.stats[split] = { | |
| 'instance_stats': { | |
| 'total': int(x.sum()), | |
| 'per_class': x.sum(0).tolist()}, | |
| 'image_stats': { | |
| 'total': dataset.n, | |
| 'unlabelled': int(np.all(x == 0, 1).sum()), | |
| 'per_class': (x > 0).sum(0).tolist()}, | |
| 'labels': [{ | |
| str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)]} | |
| # Save, print and return | |
| if save: | |
| stats_path = self.hub_dir / 'stats.json' | |
| print(f'Saving {stats_path.resolve()}...') | |
| with open(stats_path, 'w') as f: | |
| json.dump(self.stats, f) # save stats.json | |
| if verbose: | |
| print(json.dumps(self.stats, indent=2, sort_keys=False)) | |
| return self.stats | |
| def process_images(self): | |
| # Compress images for Ultralytics HUB | |
| for split in 'train', 'val', 'test': | |
| if self.data.get(split) is None: | |
| continue | |
| dataset = LoadImagesAndLabels(self.data[split]) # load dataset | |
| desc = f'{split} images' | |
| for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc): | |
| pass | |
| print(f'Done. All images saved to {self.im_dir}') | |
| return self.im_dir | |
| # 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 sample, j | |
| def create_classification_dataloader(path, | |
| imgsz=224, | |
| batch_size=16, | |
| augment=True, | |
| cache=False, | |
| rank=-1, | |
| workers=8, | |
| shuffle=True): | |
| # Returns Dataloader object to be used with YOLOv5 Classifier | |
| with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP | |
| dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache) | |
| batch_size = min(batch_size, len(dataset)) | |
| nd = torch.cuda.device_count() | |
| nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) | |
| sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) | |
| generator = torch.Generator() | |
| generator.manual_seed(6148914691236517205 + RANK) | |
| return InfiniteDataLoader(dataset, | |
| batch_size=batch_size, | |
| shuffle=shuffle and sampler is None, | |
| num_workers=nw, | |
| sampler=sampler, | |
| pin_memory=PIN_MEMORY, | |
| worker_init_fn=seed_worker, | |
| generator=generator) # or DataLoader(persistent_workers=True) | |