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# Ultralytics YOLO 🚀, GPL-3.0 license |
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""" |
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Dataloaders and dataset utils |
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""" |
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import contextlib |
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import glob |
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import hashlib |
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import math |
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import os |
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import random |
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import shutil |
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import time |
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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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from threading import Thread |
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from urllib.parse import urlparse |
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import cv2 |
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import numpy as np |
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import psutil |
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import torch |
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import torchvision |
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from PIL import ExifTags, Image, ImageOps |
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from torch.utils.data import DataLoader, Dataset, dataloader, distributed |
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from tqdm import tqdm |
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from ultralytics.yolo.utils import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, is_colab, is_dir_writeable, |
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is_kaggle) |
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from ultralytics.yolo.utils.checks import check_requirements |
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from ultralytics.yolo.utils.ops import clean_str, segments2boxes, xyn2xy, xywh2xyxy, xywhn2xyxy, xyxy2xywhn |
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from ultralytics.yolo.utils.torch_utils import torch_distributed_zero_first |
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from .v5augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste, |
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letterbox, mixup, random_perspective) |
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# Parameters |
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HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' |
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IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes |
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VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes |
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LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html |
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RANK = int(os.getenv('RANK', -1)) |
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PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true' # global pin_memory for dataloaders |
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# Get orientation exif tag |
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for orientation in ExifTags.TAGS.keys(): |
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if ExifTags.TAGS[orientation] == 'Orientation': |
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break |
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def get_hash(paths): |
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# Returns a single hash value of a list of paths (files or dirs) |
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size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes |
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h = hashlib.sha256(str(size).encode()) # hash sizes |
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h.update(''.join(paths).encode()) # hash paths |
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return h.hexdigest() # return hash |
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def exif_size(img): |
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# Returns exif-corrected PIL size |
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s = img.size # (width, height) |
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with contextlib.suppress(Exception): |
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rotation = dict(img._getexif().items())[orientation] |
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if rotation in [6, 8]: # rotation 270 or 90 |
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s = (s[1], s[0]) |
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return s |
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def exif_transpose(image): |
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""" |
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Transpose a PIL image accordingly if it has an EXIF Orientation tag. |
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Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose() |
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:param image: The image to transpose. |
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:return: An image. |
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""" |
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exif = image.getexif() |
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orientation = exif.get(0x0112, 1) # default 1 |
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if orientation > 1: |
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method = { |
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2: Image.FLIP_LEFT_RIGHT, |
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3: Image.ROTATE_180, |
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4: Image.FLIP_TOP_BOTTOM, |
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5: Image.TRANSPOSE, |
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6: Image.ROTATE_270, |
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7: Image.TRANSVERSE, |
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8: Image.ROTATE_90}.get(orientation) |
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if method is not None: |
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image = image.transpose(method) |
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del exif[0x0112] |
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image.info['exif'] = exif.tobytes() |
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return image |
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def seed_worker(worker_id): |
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# Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader |
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worker_seed = torch.initial_seed() % 2 ** 32 |
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np.random.seed(worker_seed) |
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random.seed(worker_seed) |
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def create_dataloader(path, |
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imgsz, |
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batch_size, |
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stride, |
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single_cls=False, |
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hyp=None, |
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augment=False, |
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cache=False, |
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pad=0.0, |
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rect=False, |
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rank=-1, |
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workers=8, |
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image_weights=False, |
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close_mosaic=False, |
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min_items=0, |
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prefix='', |
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shuffle=False, |
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seed=0): |
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if rect and shuffle: |
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LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') |
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shuffle = False |
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with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP |
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dataset = LoadImagesAndLabels( |
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path, |
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imgsz, |
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batch_size, |
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augment=augment, # augmentation |
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hyp=hyp, # hyperparameters |
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rect=rect, # rectangular batches |
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cache_images=cache, |
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single_cls=single_cls, |
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stride=int(stride), |
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pad=pad, |
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image_weights=image_weights, |
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min_items=min_items, |
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prefix=prefix) |
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batch_size = min(batch_size, len(dataset)) |
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nd = torch.cuda.device_count() # number of CUDA devices |
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nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers |
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sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) |
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loader = DataLoader if image_weights or close_mosaic else InfiniteDataLoader # DataLoader allows attribute updates |
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generator = torch.Generator() |
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generator.manual_seed(6148914691236517205 + seed + RANK) |
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return loader(dataset, |
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batch_size=batch_size, |
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shuffle=shuffle and sampler is None, |
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num_workers=nw, |
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sampler=sampler, |
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pin_memory=PIN_MEMORY, |
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collate_fn=LoadImagesAndLabels.collate_fn, |
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worker_init_fn=seed_worker, |
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generator=generator), dataset |
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class InfiniteDataLoader(dataloader.DataLoader): |
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""" Dataloader that reuses workers |
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Uses same syntax as vanilla DataLoader |
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""" |
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def __init__(self, *args, **kwargs): |
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super().__init__(*args, **kwargs) |
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object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) |
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self.iterator = super().__iter__() |
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def __len__(self): |
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return len(self.batch_sampler.sampler) |
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def __iter__(self): |
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for _ in range(len(self)): |
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yield next(self.iterator) |
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class _RepeatSampler: |
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""" Sampler that repeats forever |
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Args: |
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sampler (Sampler) |
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""" |
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def __init__(self, sampler): |
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self.sampler = sampler |
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def __iter__(self): |
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while True: |
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yield from iter(self.sampler) |
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class LoadScreenshots: |
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# YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"` |
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def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None): |
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# source = [screen_number left top width height] (pixels) |
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check_requirements('mss') |
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import mss |
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source, *params = source.split() |
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self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0 |
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if len(params) == 1: |
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self.screen = int(params[0]) |
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elif len(params) == 4: |
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left, top, width, height = (int(x) for x in params) |
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elif len(params) == 5: |
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self.screen, left, top, width, height = (int(x) for x in params) |
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self.img_size = img_size |
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self.stride = stride |
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self.transforms = transforms |
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self.auto = auto |
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self.mode = 'stream' |
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self.frame = 0 |
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self.sct = mss.mss() |
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# Parse monitor shape |
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monitor = self.sct.monitors[self.screen] |
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self.top = monitor['top'] if top is None else (monitor['top'] + top) |
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self.left = monitor['left'] if left is None else (monitor['left'] + left) |
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self.width = width or monitor['width'] |
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self.height = height or monitor['height'] |
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self.monitor = {'left': self.left, 'top': self.top, 'width': self.width, 'height': self.height} |
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def __iter__(self): |
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return self |
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def __next__(self): |
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# mss screen capture: get raw pixels from the screen as np array |
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im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR |
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s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: ' |
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if self.transforms: |
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im = self.transforms(im0) # transforms |
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else: |
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im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize |
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im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB |
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im = np.ascontiguousarray(im) # contiguous |
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self.frame += 1 |
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return str(self.screen), im, im0, None, s # screen, img, original img, im0s, s |
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class LoadImages: |
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# YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4` |
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def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): |
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if isinstance(path, str) and Path(path).suffix == '.txt': # *.txt file with img/vid/dir on each line |
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path = Path(path).read_text().rsplit() |
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files = [] |
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for p in sorted(path) if isinstance(path, (list, tuple)) else [path]: |
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p = str(Path(p).resolve()) |
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if '*' in p: |
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files.extend(sorted(glob.glob(p, recursive=True))) # glob |
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elif os.path.isdir(p): |
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files.extend(sorted(glob.glob(os.path.join(p, '*.*')))) # dir |
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elif os.path.isfile(p): |
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files.append(p) # files |
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else: |
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raise FileNotFoundError(f'{p} does not exist') |
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images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS] |
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videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS] |
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ni, nv = len(images), len(videos) |
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self.img_size = img_size |
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self.stride = stride |
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self.files = images + videos |
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self.nf = ni + nv # number of files |
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self.video_flag = [False] * ni + [True] * nv |
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self.mode = 'image' |
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self.auto = auto |
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self.transforms = transforms # optional |
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self.vid_stride = vid_stride # video frame-rate stride |
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if any(videos): |
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self._new_video(videos[0]) # new video |
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else: |
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self.cap = None |
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assert self.nf > 0, f'No images or videos found in {p}. ' \ |
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f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}' |
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def __iter__(self): |
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self.count = 0 |
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return self |
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def __next__(self): |
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if self.count == self.nf: |
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raise StopIteration |
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path = self.files[self.count] |
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if self.video_flag[self.count]: |
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# Read video |
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self.mode = 'video' |
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for _ in range(self.vid_stride): |
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self.cap.grab() |
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ret_val, im0 = self.cap.retrieve() |
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while not ret_val: |
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self.count += 1 |
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self.cap.release() |
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if self.count == self.nf: # last video |
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raise StopIteration |
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path = self.files[self.count] |
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self._new_video(path) |
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ret_val, im0 = self.cap.read() |
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self.frame += 1 |
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# im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False |
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s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: ' |
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else: |
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# Read image |
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self.count += 1 |
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im0 = cv2.imread(path) # BGR |
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assert im0 is not None, f'Image Not Found {path}' |
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s = f'image {self.count}/{self.nf} {path}: ' |
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if self.transforms: |
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im = self.transforms(im0) # transforms |
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else: |
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im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize |
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im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB |
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im = np.ascontiguousarray(im) # contiguous |
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return path, im, im0, self.cap, s |
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def _new_video(self, path): |
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# Create a new video capture object |
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self.frame = 0 |
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self.cap = cv2.VideoCapture(path) |
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self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride) |
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self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees |
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# self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493 |
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def _cv2_rotate(self, im): |
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# Rotate a cv2 video manually |
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if self.orientation == 0: |
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return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE) |
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elif self.orientation == 180: |
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return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE) |
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elif self.orientation == 90: |
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return cv2.rotate(im, cv2.ROTATE_180) |
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return im |
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def __len__(self): |
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return self.nf # number of files |
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class LoadStreams: |
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# YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams` |
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def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): |
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torch.backends.cudnn.benchmark = True # faster for fixed-size inference |
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self.mode = 'stream' |
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self.img_size = img_size |
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self.stride = stride |
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self.vid_stride = vid_stride # video frame-rate stride |
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sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources] |
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n = len(sources) |
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self.sources = [clean_str(x) for x in sources] # clean source names for later |
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self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n |
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for i, s in enumerate(sources): # index, source |
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# Start thread to read frames from video stream |
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st = f'{i + 1}/{n}: {s}... ' |
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if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'): # if source is YouTube video |
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# YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc' |
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check_requirements(('pafy', 'youtube_dl==2020.12.2')) |
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import pafy |
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s = pafy.new(s).getbest(preftype='mp4').url # YouTube URL |
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s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam |
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if s == 0: |
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assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.' |
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assert not is_kaggle(), '--source 0 webcam unsupported on Kaggle. Rerun command in a local environment.' |
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cap = cv2.VideoCapture(s) |
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assert cap.isOpened(), f'{st}Failed to open {s}' |
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w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) |
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h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) |
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fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan |
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self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback |
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self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback |
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_, self.imgs[i] = cap.read() # guarantee first frame |
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self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True) |
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LOGGER.info(f'{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)') |
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self.threads[i].start() |
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LOGGER.info('') # newline |
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# check for common shapes |
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s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs]) |
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self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal |
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self.auto = auto and self.rect |
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self.transforms = transforms # optional |
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if not self.rect: |
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LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.') |
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def update(self, i, cap, stream): |
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# Read stream `i` frames in daemon thread |
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n, f = 0, self.frames[i] # frame number, frame array |
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while cap.isOpened() and n < f: |
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n += 1 |
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cap.grab() # .read() = .grab() followed by .retrieve() |
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if n % self.vid_stride == 0: |
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success, im = cap.retrieve() |
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if success: |
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self.imgs[i] = im |
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else: |
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LOGGER.warning('WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.') |
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self.imgs[i] = np.zeros_like(self.imgs[i]) |
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cap.open(stream) # re-open stream if signal was lost |
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time.sleep(0.0) # wait time |
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def __iter__(self): |
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self.count = -1 |
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return self |
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def __next__(self): |
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self.count += 1 |
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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 FileNotFoundError(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 (FileNotFoundError, AssertionError, AttributeError): |
|
|
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}... {nf} images, {nm + ne} backgrounds, {nc} corrupt' |
|
|
tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results |
|
|
if cache['msgs']: |
|
|
LOGGER.info('\n'.join(cache['msgs'])) # display warnings |
|
|
assert nf > 0 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[si] for si, idx in enumerate(j) if idx] |
|
|
if single_cls: # single-class training, merge all classes into 0 |
|
|
self.labels[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 |
|
|
with ThreadPool(NUM_THREADS) as pool: |
|
|
results = pool.imap(fcn, range(n)) |
|
|
pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_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 |
|
|
if path.exists(): |
|
|
path.unlink() # remove *.cache file if exists |
|
|
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}...' |
|
|
total = len(self.im_files) |
|
|
with ThreadPool(NUM_THREADS) as pool: |
|
|
results = pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))) |
|
|
pbar = tqdm(results, desc=desc, total=total, bar_format=TQDM_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} images, {nm + ne} backgrounds, {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 |
|
|
if is_dir_writeable(path.parent): |
|
|
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') # 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, (math.ceil(w0 * r), math.ceil(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 |
|
|
|
|
|
@staticmethod |
|
|
def collate_fn(batch): |
|
|
# YOLOv8 collate function, outputs dict |
|
|
im, label, path, shapes = zip(*batch) # transposed |
|
|
for i, lb in enumerate(label): |
|
|
lb[:, 0] = i # add target image index for build_targets() |
|
|
batch_idx, cls, bboxes = torch.cat(label, 0).split((1, 1, 4), dim=1) |
|
|
return { |
|
|
'ori_shape': tuple((x[0] if x else None) for x in shapes), |
|
|
'ratio_pad': tuple((x[1] if x else None) for x in shapes), |
|
|
'im_file': path, |
|
|
'img': torch.stack(im, 0), |
|
|
'cls': cls, |
|
|
'bboxes': bboxes, |
|
|
'batch_idx': batch_idx.view(-1)} |
|
|
|
|
|
@staticmethod |
|
|
def collate_fn_old(batch): |
|
|
# YOLOv5 original collate function |
|
|
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 |
|
|
|
|
|
|
|
|
# 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] |
|
|
|
|
|
|
|
|
# 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)
|
|
|
|
