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1382 lines
59 KiB
Python
1382 lines
59 KiB
Python
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
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"""Dataloaders and dataset utils."""
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import contextlib
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import glob
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import hashlib
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import json
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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 Pool, 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 numpy as np
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import psutil
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import torch
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import torch.nn.functional as F
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import torchvision
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import yaml
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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 utils.augmentations import (
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Albumentations,
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augment_hsv,
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classify_albumentations,
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classify_transforms,
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copy_paste,
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letterbox,
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mixup,
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random_perspective,
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)
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from utils.general import (
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DATASETS_DIR,
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LOGGER,
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NUM_THREADS,
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TQDM_BAR_FORMAT,
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check_dataset,
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check_requirements,
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check_yaml,
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clean_str,
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cv2,
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is_colab,
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is_kaggle,
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segments2boxes,
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unzip_file,
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xyn2xy,
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xywh2xyxy,
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xywhn2xyxy,
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xyxy2xywhn,
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)
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from utils.torch_utils import torch_distributed_zero_first
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# Parameters
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HELP_URL = "See https://docs.ultralytics.com/yolov5/tutorials/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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WORLD_SIZE = int(os.getenv("WORLD_SIZE", 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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"""Generates a single SHA256 hash for a list of file or directory paths by combining their sizes and paths."""
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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 corrected PIL image size (width, height) considering EXIF orientation."""
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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,
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}.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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"""
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Sets the seed for a dataloader worker to ensure reproducibility, based on PyTorch's randomness notes.
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See https://pytorch.org/docs/stable/notes/randomness.html#dataloader.
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"""
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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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# Inherit from DistributedSampler and override iterator
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# https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py
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class SmartDistributedSampler(distributed.DistributedSampler):
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"""A distributed sampler ensuring deterministic shuffling and balanced data distribution across GPUs."""
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def __iter__(self):
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"""Yields indices for distributed data sampling, shuffled deterministically based on epoch and seed."""
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g = torch.Generator()
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g.manual_seed(self.seed + self.epoch)
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# determine the eventual size (n) of self.indices (DDP indices)
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n = int((len(self.dataset) - self.rank - 1) / self.num_replicas) + 1 # num_replicas == WORLD_SIZE
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idx = torch.randperm(n, generator=g)
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if not self.shuffle:
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idx = idx.sort()[0]
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idx = idx.tolist()
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if self.drop_last:
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idx = idx[: self.num_samples]
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else:
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padding_size = self.num_samples - len(idx)
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if padding_size <= len(idx):
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idx += idx[:padding_size]
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else:
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idx += (idx * math.ceil(padding_size / len(idx)))[:padding_size]
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return iter(idx)
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def create_dataloader(
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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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quad=False,
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prefix="",
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shuffle=False,
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seed=0,
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):
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"""Creates and returns a configured DataLoader instance for loading and processing image datasets."""
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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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prefix=prefix,
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rank=rank,
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)
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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 SmartDistributedSampler(dataset, shuffle=shuffle)
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loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for 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(
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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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drop_last=quad,
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pin_memory=PIN_MEMORY,
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collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
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worker_init_fn=seed_worker,
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generator=generator,
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), dataset
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class InfiniteDataLoader(dataloader.DataLoader):
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"""
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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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"""Initializes an InfiniteDataLoader that reuses workers with standard DataLoader syntax, augmenting with a
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repeating sampler.
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"""
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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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"""Returns the length of the batch sampler's sampler in the InfiniteDataLoader."""
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return len(self.batch_sampler.sampler)
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def __iter__(self):
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"""Yields batches of data indefinitely in a loop by resetting the sampler when exhausted."""
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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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"""
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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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"""Initializes a perpetual sampler wrapping a provided `Sampler` instance for endless data iteration."""
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self.sampler = sampler
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def __iter__(self):
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"""Returns an infinite iterator over the dataset by repeatedly yielding from the given sampler."""
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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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"""Loads and processes screenshots for YOLOv5 detection from specified screen regions using mss."""
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def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
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"""
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Initializes a screenshot dataloader for YOLOv5 with specified source region, image size, stride, auto, and
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transforms.
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Source = [screen_number left top width height] (pixels)
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"""
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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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"""Iterates over itself, enabling use in loops and iterable contexts."""
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return self
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def __next__(self):
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"""Captures and returns the next screen frame as a BGR numpy array, cropping to only the first three channels
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from BGRA.
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"""
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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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"""Initializes YOLOv5 loader for images/videos, supporting glob patterns, directories, and lists of paths."""
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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, (
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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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)
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def __iter__(self):
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"""Initializes iterator by resetting count and returns the iterator object itself."""
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self.count = 0
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return self
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def __next__(self):
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"""Advances to the next file in the dataset, raising StopIteration if at the end."""
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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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"""Initializes a new video capture object with path, frame count adjusted by stride, and orientation
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metadata.
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"""
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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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"""Rotates a cv2 image based on its orientation; supports 0, 90, and 180 degrees rotations."""
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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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"""Returns the number of files in the dataset."""
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return self.nf # number of files
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class LoadStreams:
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"""Loads and processes video streams for YOLOv5, supporting various sources including YouTube and IP cameras."""
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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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"""Initializes a stream loader for processing video streams with YOLOv5, supporting various sources including
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YouTube.
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"""
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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/LNwODJXcvt4'
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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}"
|
|
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):
|
|
"""Reads frames from stream `i`, updating imgs array; handles stream reopening on signal loss."""
|
|
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):
|
|
"""Resets and returns the iterator for iterating over video frames or images in a dataset."""
|
|
self.count = -1
|
|
return self
|
|
|
|
def __next__(self):
|
|
"""Iterates over video frames or images, halting on thread stop or 'q' key press, raising `StopIteration` when
|
|
done.
|
|
"""
|
|
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):
|
|
"""Returns the number of sources in the dataset, supporting up to 32 streams at 30 FPS over 30 years."""
|
|
return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years
|
|
|
|
|
|
def img2label_paths(img_paths):
|
|
"""Generates label file paths from corresponding image file paths by replacing `/images/` with `/labels/` and
|
|
extension with `.txt`.
|
|
"""
|
|
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):
|
|
"""Loads images and their corresponding labels for training and validation in YOLOv5."""
|
|
|
|
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="",
|
|
rank=-1,
|
|
seed=0,
|
|
):
|
|
"""Initializes the YOLOv5 dataset loader, handling images and their labels, caching, and preprocessing."""
|
|
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}... {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 = np.arange(n)
|
|
if rank > -1: # DDP indices (see: SmartDistributedSampler)
|
|
# force each rank (i.e. GPU process) to sample the same subset of data on every epoch
|
|
self.indices = self.indices[np.random.RandomState(seed=seed).permutation(n) % WORLD_SIZE == RANK]
|
|
|
|
# Update labels
|
|
include_class = [] # filter labels to include only these classes (optional)
|
|
self.segments = list(self.segments)
|
|
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[idx] for idx, elem in enumerate(j) if elem]
|
|
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
|
|
results = ThreadPool(NUM_THREADS).imap(lambda i: (i, fcn(i)), self.indices)
|
|
pbar = tqdm(results, total=len(self.indices), 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 * WORLD_SIZE
|
|
pbar.desc = f"{prefix}Caching images ({b / gb:.1f}GB {cache_images})"
|
|
pbar.close()
|
|
|
|
def check_cache_ram(self, safety_margin=0.1, prefix=""):
|
|
"""Checks if available RAM is sufficient for caching images, adjusting for a safety margin."""
|
|
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=""):
|
|
"""Caches dataset labels, verifies images, reads shapes, and tracks dataset integrity."""
|
|
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}..."
|
|
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=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
|
|
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):
|
|
"""Returns the number of images in the dataset."""
|
|
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):
|
|
"""Fetches the dataset item at the given index, considering linear, shuffled, or weighted sampling."""
|
|
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.choice(self.indices)))
|
|
|
|
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 an image by index, returning the image, its original dimensions, and resized dimensions.
|
|
|
|
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 to disk as an *.npy file for quicker loading, identified by index `i`."""
|
|
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):
|
|
"""Loads a 4-image mosaic for YOLOv5, combining 1 selected and 3 random images, with labels and segments."""
|
|
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):
|
|
"""Loads 1 image + 8 random images into a 9-image mosaic for augmented YOLOv5 training, returning labels and
|
|
segments.
|
|
"""
|
|
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):
|
|
"""Batches images, labels, paths, and shapes, assigning unique indices to targets in merged label tensor."""
|
|
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
|
|
|
|
@staticmethod
|
|
def collate_fn4(batch):
|
|
"""Bundles a batch's data by quartering the number of shapes and paths, preparing it for model input."""
|
|
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"):
|
|
"""Flattens a directory by copying all files from subdirectories to a new top-level directory, preserving
|
|
filenames.
|
|
"""
|
|
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"):
|
|
"""
|
|
Converts a detection dataset to a classification dataset, creating a directory for each class and extracting
|
|
bounding boxes.
|
|
|
|
Example: from utils.dataloaders import *; extract_boxes()
|
|
"""
|
|
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 / "classification") / 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):
|
|
"""Verifies a single image-label pair, ensuring image format, size, and legal label values."""
|
|
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):
|
|
"""Initializes HUBDatasetStats with optional auto-download for datasets, given a path to dataset YAML or ZIP
|
|
file.
|
|
"""
|
|
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
|
|
|
|
@staticmethod
|
|
def _find_yaml(dir):
|
|
"""Finds and returns the path to a single '.yaml' file in the specified directory, preferring files that match
|
|
the directory name.
|
|
"""
|
|
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):
|
|
"""Unzips a .zip file at 'path', returning success status, unzipped directory, and path to YAML file within."""
|
|
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):
|
|
"""Resizes and saves an image at reduced quality for web/app viewing, supporting both PIL and OpenCV."""
|
|
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):
|
|
"""Generates dataset JSON for Ultralytics HUB, optionally saves or prints it; save=bool, verbose=bool."""
|
|
|
|
def _round(labels):
|
|
"""Rounds class labels to integers and coordinates to 4 decimal places for improved label accuracy."""
|
|
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):
|
|
"""Compresses images for Ultralytics HUB across 'train', 'val', 'test' splits and saves to specified
|
|
directory.
|
|
"""
|
|
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):
|
|
"""Initializes YOLOv5 Classification Dataset with optional caching, augmentations, and transforms for image
|
|
classification.
|
|
"""
|
|
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):
|
|
"""Fetches and transforms an image sample by index, supporting RAM/disk caching and Augmentations."""
|
|
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
|
|
"""Creates a DataLoader for image classification, supporting caching, augmentation, and distributed training."""
|
|
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)
|