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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license | |
""" | |
Dataloaders and dataset utils | |
""" | |
import contextlib | |
import glob | |
import hashlib | |
import json | |
import math | |
import os | |
import random | |
import shutil | |
import time | |
from itertools import repeat | |
from multiprocessing.pool import Pool, ThreadPool | |
from pathlib import Path | |
from threading import Thread | |
from urllib.parse import urlparse | |
import numpy as np | |
import psutil | |
import torch | |
import torch.nn.functional as F | |
import torchvision | |
import yaml | |
from PIL import ExifTags, Image, ImageOps | |
from torch.utils.data import DataLoader, Dataset, dataloader, distributed | |
from tqdm import tqdm | |
from utils.augmentations import ( | |
Albumentations, | |
augment_hsv, | |
classify_albumentations, | |
classify_transforms, | |
copy_paste, | |
letterbox, | |
mixup, | |
random_perspective, | |
) | |
from utils.general import ( | |
DATASETS_DIR, | |
LOGGER, | |
NUM_THREADS, | |
TQDM_BAR_FORMAT, | |
check_dataset, | |
check_requirements, | |
check_yaml, | |
clean_str, | |
cv2, | |
is_colab, | |
is_kaggle, | |
segments2boxes, | |
unzip_file, | |
xyn2xy, | |
xywh2xyxy, | |
xywhn2xyxy, | |
xyxy2xywhn, | |
) | |
from utils.torch_utils import torch_distributed_zero_first | |
# Parameters | |
HELP_URL = "See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data" | |
IMG_FORMATS = ( | |
"bmp", | |
"dng", | |
"jpeg", | |
"jpg", | |
"mpo", | |
"png", | |
"tif", | |
"tiff", | |
"webp", | |
"pfm", | |
) # include image suffixes | |
VID_FORMATS = ( | |
"asf", | |
"avi", | |
"gif", | |
"m4v", | |
"mkv", | |
"mov", | |
"mp4", | |
"mpeg", | |
"mpg", | |
"ts", | |
"wmv", | |
) # include video suffixes | |
LOCAL_RANK = int( | |
os.getenv("LOCAL_RANK", -1) | |
) # https://pytorch.org/docs/stable/elastic/run.html | |
RANK = int(os.getenv("RANK", -1)) | |
PIN_MEMORY = ( | |
str(os.getenv("PIN_MEMORY", True)).lower() == "true" | |
) # global pin_memory for dataloaders | |
# Get orientation exif tag | |
for orientation in ExifTags.TAGS.keys(): | |
if ExifTags.TAGS[orientation] == "Orientation": | |
break | |
def get_hash(paths): | |
# Returns a single hash value of a list of paths (files or dirs) | |
size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes | |
h = hashlib.md5(str(size).encode()) # hash sizes | |
h.update("".join(paths).encode()) # hash paths | |
return h.hexdigest() # return hash | |
def exif_size(img): | |
# Returns exif-corrected PIL size | |
s = img.size # (width, height) | |
with contextlib.suppress(Exception): | |
rotation = dict(img._getexif().items())[orientation] | |
if rotation in [6, 8]: # rotation 270 or 90 | |
s = (s[1], s[0]) | |
return s | |
def exif_transpose(image): | |
""" | |
Transpose a PIL image accordingly if it has an EXIF Orientation tag. | |
Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose() | |
:param image: The image to transpose. | |
:return: An image. | |
""" | |
exif = image.getexif() | |
orientation = exif.get(0x0112, 1) # default 1 | |
if orientation > 1: | |
method = { | |
2: Image.FLIP_LEFT_RIGHT, | |
3: Image.ROTATE_180, | |
4: Image.FLIP_TOP_BOTTOM, | |
5: Image.TRANSPOSE, | |
6: Image.ROTATE_270, | |
7: Image.TRANSVERSE, | |
8: Image.ROTATE_90, | |
}.get(orientation) | |
if method is not None: | |
image = image.transpose(method) | |
del exif[0x0112] | |
image.info["exif"] = exif.tobytes() | |
return image | |
def seed_worker(worker_id): | |
# Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader | |
worker_seed = torch.initial_seed() % 2**32 | |
np.random.seed(worker_seed) | |
random.seed(worker_seed) | |
def create_dataloader( | |
path, | |
imgsz, | |
batch_size, | |
stride, | |
single_cls=False, | |
hyp=None, | |
augment=False, | |
cache=False, | |
pad=0.0, | |
rect=False, | |
rank=-1, | |
workers=8, | |
image_weights=False, | |
quad=False, | |
prefix="", | |
shuffle=False, | |
seed=0, | |
): | |
if rect and shuffle: | |
LOGGER.warning( | |
"WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False" | |
) | |
shuffle = False | |
with torch_distributed_zero_first( | |
rank | |
): # init dataset *.cache only once if DDP | |
dataset = LoadImagesAndLabels( | |
path, | |
imgsz, | |
batch_size, | |
augment=augment, # augmentation | |
hyp=hyp, # hyperparameters | |
rect=rect, # rectangular batches | |
cache_images=cache, | |
single_cls=single_cls, | |
stride=int(stride), | |
pad=pad, | |
image_weights=image_weights, | |
prefix=prefix, | |
) | |
batch_size = min(batch_size, len(dataset)) | |
nd = torch.cuda.device_count() # number of CUDA devices | |
nw = min( | |
[ | |
os.cpu_count() // max(nd, 1), | |
batch_size if batch_size > 1 else 0, | |
workers, | |
] | |
) # number of workers | |
sampler = ( | |
None | |
if rank == -1 | |
else distributed.DistributedSampler(dataset, shuffle=shuffle) | |
) | |
loader = ( | |
DataLoader if image_weights else InfiniteDataLoader | |
) # only DataLoader allows for attribute updates | |
generator = torch.Generator() | |
generator.manual_seed(6148914691236517205 + seed + RANK) | |
return ( | |
loader( | |
dataset, | |
batch_size=batch_size, | |
shuffle=shuffle and sampler is None, | |
num_workers=nw, | |
sampler=sampler, | |
pin_memory=PIN_MEMORY, | |
collate_fn=LoadImagesAndLabels.collate_fn4 | |
if quad | |
else LoadImagesAndLabels.collate_fn, | |
worker_init_fn=seed_worker, | |
generator=generator, | |
), | |
dataset, | |
) | |
class InfiniteDataLoader(dataloader.DataLoader): | |
"""Dataloader that reuses workers | |
Uses same syntax as vanilla DataLoader | |
""" | |
def __init__(self, *args, **kwargs): | |
super().__init__(*args, **kwargs) | |
object.__setattr__( | |
self, "batch_sampler", _RepeatSampler(self.batch_sampler) | |
) | |
self.iterator = super().__iter__() | |
def __len__(self): | |
return len(self.batch_sampler.sampler) | |
def __iter__(self): | |
for _ in range(len(self)): | |
yield next(self.iterator) | |
class _RepeatSampler: | |
"""Sampler that repeats forever | |
Args: | |
sampler (Sampler) | |
""" | |
def __init__(self, sampler): | |
self.sampler = sampler | |
def __iter__(self): | |
while True: | |
yield from iter(self.sampler) | |
class LoadScreenshots: | |
# YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"` | |
def __init__( | |
self, source, img_size=640, stride=32, auto=True, transforms=None | |
): | |
# source = [screen_number left top width height] (pixels) | |
check_requirements("mss") | |
import mss | |
source, *params = source.split() | |
self.screen, left, top, width, height = ( | |
0, | |
None, | |
None, | |
None, | |
None, | |
) # default to full screen 0 | |
if len(params) == 1: | |
self.screen = int(params[0]) | |
elif len(params) == 4: | |
left, top, width, height = (int(x) for x in params) | |
elif len(params) == 5: | |
self.screen, left, top, width, height = (int(x) for x in params) | |
self.img_size = img_size | |
self.stride = stride | |
self.transforms = transforms | |
self.auto = auto | |
self.mode = "stream" | |
self.frame = 0 | |
self.sct = mss.mss() | |
# Parse monitor shape | |
monitor = self.sct.monitors[self.screen] | |
self.top = monitor["top"] if top is None else (monitor["top"] + top) | |
self.left = ( | |
monitor["left"] if left is None else (monitor["left"] + left) | |
) | |
self.width = width or monitor["width"] | |
self.height = height or monitor["height"] | |
self.monitor = { | |
"left": self.left, | |
"top": self.top, | |
"width": self.width, | |
"height": self.height, | |
} | |
def __iter__(self): | |
return self | |
def __next__(self): | |
# mss screen capture: get raw pixels from the screen as np array | |
im0 = np.array(self.sct.grab(self.monitor))[ | |
:, :, :3 | |
] # [:, :, :3] BGRA to BGR | |
s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: " | |
if self.transforms: | |
im = self.transforms(im0) # transforms | |
else: | |
im = letterbox( | |
im0, self.img_size, stride=self.stride, auto=self.auto | |
)[ | |
0 | |
] # padded resize | |
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB | |
im = np.ascontiguousarray(im) # contiguous | |
self.frame += 1 | |
return ( | |
str(self.screen), | |
im, | |
im0, | |
None, | |
s, | |
) # screen, img, original img, im0s, s | |
class LoadImages: | |
# YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4` | |
def __init__( | |
self, | |
path, | |
img_size=640, | |
stride=32, | |
auto=True, | |
transforms=None, | |
vid_stride=1, | |
): | |
if ( | |
isinstance(path, str) and Path(path).suffix == ".txt" | |
): # *.txt file with img/vid/dir on each line | |
path = Path(path).read_text().rsplit() | |
files = [] | |
for p in sorted(path) if isinstance(path, (list, tuple)) else [path]: | |
p = str(Path(p).resolve()) | |
if "*" in p: | |
files.extend(sorted(glob.glob(p, recursive=True))) # glob | |
elif os.path.isdir(p): | |
files.extend(sorted(glob.glob(os.path.join(p, "*.*")))) # dir | |
elif os.path.isfile(p): | |
files.append(p) # files | |
else: | |
raise FileNotFoundError(f"{p} does not exist") | |
images = [x for x in files if x.split(".")[-1].lower() in IMG_FORMATS] | |
videos = [x for x in files if x.split(".")[-1].lower() in VID_FORMATS] | |
ni, nv = len(images), len(videos) | |
self.img_size = img_size | |
self.stride = stride | |
self.files = images + videos | |
self.nf = ni + nv # number of files | |
self.video_flag = [False] * ni + [True] * nv | |
self.mode = "image" | |
self.auto = auto | |
self.transforms = transforms # optional | |
self.vid_stride = vid_stride # video frame-rate stride | |
if any(videos): | |
self._new_video(videos[0]) # new video | |
else: | |
self.cap = None | |
assert self.nf > 0, ( | |
f"No images or videos found in {p}. " | |
f"Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}" | |
) | |
def __iter__(self): | |
self.count = 0 | |
return self | |
def __next__(self): | |
if self.count == self.nf: | |
raise StopIteration | |
path = self.files[self.count] | |
if self.video_flag[self.count]: | |
# Read video | |
self.mode = "video" | |
for _ in range(self.vid_stride): | |
self.cap.grab() | |
ret_val, im0 = self.cap.retrieve() | |
while not ret_val: | |
self.count += 1 | |
self.cap.release() | |
if self.count == self.nf: # last video | |
raise StopIteration | |
path = self.files[self.count] | |
self._new_video(path) | |
ret_val, im0 = self.cap.read() | |
self.frame += 1 | |
# im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False | |
s = f"video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: " | |
else: | |
# Read image | |
self.count += 1 | |
im0 = cv2.imread(path) # BGR | |
assert im0 is not None, f"Image Not Found {path}" | |
s = f"image {self.count}/{self.nf} {path}: " | |
if self.transforms: | |
im = self.transforms(im0) # transforms | |
else: | |
im = letterbox( | |
im0, self.img_size, stride=self.stride, auto=self.auto | |
)[ | |
0 | |
] # padded resize | |
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB | |
im = np.ascontiguousarray(im) # contiguous | |
return path, im, im0, self.cap, s | |
def _new_video(self, path): | |
# Create a new video capture object | |
self.frame = 0 | |
self.cap = cv2.VideoCapture(path) | |
self.frames = int( | |
self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride | |
) | |
self.orientation = int( | |
self.cap.get(cv2.CAP_PROP_ORIENTATION_META) | |
) # rotation degrees | |
# self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493 | |
def _cv2_rotate(self, im): | |
# Rotate a cv2 video manually | |
if self.orientation == 0: | |
return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE) | |
elif self.orientation == 180: | |
return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE) | |
elif self.orientation == 90: | |
return cv2.rotate(im, cv2.ROTATE_180) | |
return im | |
def __len__(self): | |
return self.nf # number of files | |
class LoadStreams: | |
# YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams` | |
def __init__( | |
self, | |
sources="file.streams", | |
img_size=640, | |
stride=32, | |
auto=True, | |
transforms=None, | |
vid_stride=1, | |
): | |
torch.backends.cudnn.benchmark = ( | |
True # faster for fixed-size inference | |
) | |
self.mode = "stream" | |
self.img_size = img_size | |
self.stride = stride | |
self.vid_stride = vid_stride # video frame-rate stride | |
sources = ( | |
Path(sources).read_text().rsplit() | |
if os.path.isfile(sources) | |
else [sources] | |
) | |
n = len(sources) | |
self.sources = [ | |
clean_str(x) for x in sources | |
] # clean source names for later | |
self.imgs, self.fps, self.frames, self.threads = ( | |
[None] * n, | |
[0] * n, | |
[0] * n, | |
[None] * n, | |
) | |
for i, s in enumerate(sources): # index, source | |
# Start thread to read frames from video stream | |
st = f"{i + 1}/{n}: {s}... " | |
if urlparse(s).hostname in ( | |
"www.youtube.com", | |
"youtube.com", | |
"youtu.be", | |
): # if source is YouTube video | |
# YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc' | |
check_requirements(("pafy", "youtube_dl==2020.12.2")) | |
import pafy | |
s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL | |
s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam | |
if s == 0: | |
assert ( | |
not is_colab() | |
), "--source 0 webcam unsupported on Colab. Rerun command in a local environment." | |
assert ( | |
not is_kaggle() | |
), "--source 0 webcam unsupported on Kaggle. Rerun command in a local environment." | |
cap = cv2.VideoCapture(s) | |
assert cap.isOpened(), f"{st}Failed to open {s}" | |
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan | |
self.frames[i] = max( | |
int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0 | |
) or float( | |
"inf" | |
) # infinite stream fallback | |
self.fps[i] = ( | |
max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 | |
) # 30 FPS fallback | |
_, self.imgs[i] = cap.read() # guarantee first frame | |
self.threads[i] = Thread( | |
target=self.update, args=([i, cap, s]), daemon=True | |
) | |
LOGGER.info( | |
f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)" | |
) | |
self.threads[i].start() | |
LOGGER.info("") # newline | |
# check for common shapes | |
s = np.stack( | |
[ | |
letterbox(x, img_size, stride=stride, auto=auto)[0].shape | |
for x in self.imgs | |
] | |
) | |
self.rect = ( | |
np.unique(s, axis=0).shape[0] == 1 | |
) # rect inference if all shapes equal | |
self.auto = auto and self.rect | |
self.transforms = transforms # optional | |
if not self.rect: | |
LOGGER.warning( | |
"WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams." | |
) | |
def update(self, i, cap, stream): | |
# Read stream `i` frames in daemon thread | |
n, f = 0, self.frames[i] # frame number, frame array | |
while cap.isOpened() and n < f: | |
n += 1 | |
cap.grab() # .read() = .grab() followed by .retrieve() | |
if n % self.vid_stride == 0: | |
success, im = cap.retrieve() | |
if success: | |
self.imgs[i] = im | |
else: | |
LOGGER.warning( | |
"WARNING ⚠️ Video stream unresponsive, please check your IP camera connection." | |
) | |
self.imgs[i] = np.zeros_like(self.imgs[i]) | |
cap.open(stream) # re-open stream if signal was lost | |
time.sleep(0.0) # wait time | |
def __iter__(self): | |
self.count = -1 | |
return self | |
def __next__(self): | |
self.count += 1 | |
if not all(x.is_alive() for x in self.threads) or cv2.waitKey( | |
1 | |
) == ord( | |
"q" | |
): # q to quit | |
cv2.destroyAllWindows() | |
raise StopIteration | |
im0 = self.imgs.copy() | |
if self.transforms: | |
im = np.stack([self.transforms(x) for x in im0]) # transforms | |
else: | |
im = np.stack( | |
[ | |
letterbox( | |
x, self.img_size, stride=self.stride, auto=self.auto | |
)[0] | |
for x in im0 | |
] | |
) # resize | |
im = im[..., ::-1].transpose( | |
(0, 3, 1, 2) | |
) # BGR to RGB, BHWC to BCHW | |
im = np.ascontiguousarray(im) # contiguous | |
return self.sources, im, im0, None, "" | |
def __len__(self): | |
return len( | |
self.sources | |
) # 1E12 frames = 32 streams at 30 FPS for 30 years | |
def img2label_paths(img_paths): | |
# Define label paths as a function of image paths | |
sa, sb = ( | |
f"{os.sep}images{os.sep}", | |
f"{os.sep}labels{os.sep}", | |
) # /images/, /labels/ substrings | |
return [ | |
sb.join(x.rsplit(sa, 1)).rsplit(".", 1)[0] + ".txt" for x in img_paths | |
] | |
class LoadImagesAndLabels(Dataset): | |
# YOLOv5 train_loader/val_loader, loads images and labels for training and validation | |
cache_version = 0.6 # dataset labels *.cache version | |
rand_interp_methods = [ | |
cv2.INTER_NEAREST, | |
cv2.INTER_LINEAR, | |
cv2.INTER_CUBIC, | |
cv2.INTER_AREA, | |
cv2.INTER_LANCZOS4, | |
] | |
def __init__( | |
self, | |
path, | |
img_size=640, | |
batch_size=16, | |
augment=False, | |
hyp=None, | |
rect=False, | |
image_weights=False, | |
cache_images=False, | |
single_cls=False, | |
stride=32, | |
pad=0.0, | |
min_items=0, | |
prefix="", | |
): | |
self.img_size = img_size | |
self.augment = augment | |
self.hyp = hyp | |
self.image_weights = image_weights | |
self.rect = False if image_weights else rect | |
self.mosaic = ( | |
self.augment and not self.rect | |
) # load 4 images at a time into a mosaic (only during training) | |
self.mosaic_border = [-img_size // 2, -img_size // 2] | |
self.stride = stride | |
self.path = path | |
self.albumentations = ( | |
Albumentations(size=img_size) if augment else None | |
) | |
try: | |
f = [] # image files | |
for p in path if isinstance(path, list) else [path]: | |
p = Path(p) # os-agnostic | |
if p.is_dir(): # dir | |
f += glob.glob(str(p / "**" / "*.*"), recursive=True) | |
# f = list(p.rglob('*.*')) # pathlib | |
elif p.is_file(): # file | |
with open(p) as t: | |
t = t.read().strip().splitlines() | |
parent = str(p.parent) + os.sep | |
f += [ | |
x.replace("./", parent, 1) | |
if x.startswith("./") | |
else x | |
for x in t | |
] # to global path | |
# f += [p.parent / x.lstrip(os.sep) for x in t] # to global path (pathlib) | |
else: | |
raise FileNotFoundError(f"{prefix}{p} does not exist") | |
self.im_files = sorted( | |
x.replace("/", os.sep) | |
for x in f | |
if x.split(".")[-1].lower() in IMG_FORMATS | |
) | |
# self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib | |
assert self.im_files, f"{prefix}No images found" | |
except Exception as e: | |
raise Exception( | |
f"{prefix}Error loading data from {path}: {e}\n{HELP_URL}" | |
) from e | |
# Check cache | |
self.label_files = img2label_paths(self.im_files) # labels | |
cache_path = ( | |
p if p.is_file() else Path(self.label_files[0]).parent | |
).with_suffix(".cache") | |
try: | |
cache, exists = ( | |
np.load(cache_path, allow_pickle=True).item(), | |
True, | |
) # load dict | |
assert ( | |
cache["version"] == self.cache_version | |
) # matches current version | |
assert cache["hash"] == get_hash( | |
self.label_files + self.im_files | |
) # identical hash | |
except Exception: | |
cache, exists = ( | |
self.cache_labels(cache_path, prefix), | |
False, | |
) # run cache ops | |
# Display cache | |
nf, nm, ne, nc, n = cache.pop( | |
"results" | |
) # found, missing, empty, corrupt, total | |
if exists and LOCAL_RANK in {-1, 0}: | |
d = f"Scanning {cache_path}... {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[j] | |
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(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 | |
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): | |
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 | |
def collate_fn(batch): | |
im, label, path, shapes = zip(*batch) # transposed | |
for i, lb in enumerate(label): | |
lb[:, 0] = i # add target image index for build_targets() | |
return torch.stack(im, 0), torch.cat(label, 0), path, shapes | |
def collate_fn4(batch): | |
im, label, path, shapes = zip(*batch) # transposed | |
n = len(shapes) // 4 | |
im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] | |
ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]]) | |
wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]]) | |
s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]]) # scale | |
for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW | |
i *= 4 | |
if random.random() < 0.5: | |
im1 = F.interpolate( | |
im[i].unsqueeze(0).float(), | |
scale_factor=2.0, | |
mode="bilinear", | |
align_corners=False, | |
)[0].type(im[i].type()) | |
lb = label[i] | |
else: | |
im1 = torch.cat( | |
( | |
torch.cat((im[i], im[i + 1]), 1), | |
torch.cat((im[i + 2], im[i + 3]), 1), | |
), | |
2, | |
) | |
lb = ( | |
torch.cat( | |
( | |
label[i], | |
label[i + 1] + ho, | |
label[i + 2] + wo, | |
label[i + 3] + ho + wo, | |
), | |
0, | |
) | |
* s | |
) | |
im4.append(im1) | |
label4.append(lb) | |
for i, lb in enumerate(label4): | |
lb[:, 0] = i # add target image index for build_targets() | |
return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4 | |
# Ancillary functions -------------------------------------------------------------------------------------------------- | |
def flatten_recursive(path=DATASETS_DIR / "coco128"): | |
# Flatten a recursive directory by bringing all files to top level | |
new_path = Path(f"{str(path)}_flat") | |
if os.path.exists(new_path): | |
shutil.rmtree(new_path) # delete output folder | |
os.makedirs(new_path) # make new output folder | |
for file in tqdm(glob.glob(f"{str(Path(path))}/**/*.*", recursive=True)): | |
shutil.copyfile(file, new_path / Path(file).name) | |
def extract_boxes( | |
path=DATASETS_DIR / "coco128", | |
): # from utils.dataloaders import *; extract_boxes() | |
# Convert detection dataset into classification dataset, with one directory per class | |
path = Path(path) # images dir | |
shutil.rmtree(path / "classification") if ( | |
path / "classification" | |
).is_dir() else None # remove existing | |
files = list(path.rglob("*.*")) | |
n = len(files) # number of files | |
for im_file in tqdm(files, total=n): | |
if im_file.suffix[1:] in IMG_FORMATS: | |
# image | |
im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB | |
h, w = im.shape[:2] | |
# labels | |
lb_file = Path(img2label_paths([str(im_file)])[0]) | |
if Path(lb_file).exists(): | |
with open(lb_file) as f: | |
lb = np.array( | |
[x.split() for x in f.read().strip().splitlines()], | |
dtype=np.float32, | |
) # labels | |
for j, x in enumerate(lb): | |
c = int(x[0]) # class | |
f = ( | |
(path / "classifier") | |
/ f"{c}" | |
/ f"{path.stem}_{im_file.stem}_{j}.jpg" | |
) # new filename | |
if not f.parent.is_dir(): | |
f.parent.mkdir(parents=True) | |
b = x[1:] * [w, h, w, h] # box | |
# b[2:] = b[2:].max() # rectangle to square | |
b[2:] = b[2:] * 1.2 + 3 # pad | |
b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int) | |
b[[0, 2]] = np.clip( | |
b[[0, 2]], 0, w | |
) # clip boxes outside of image | |
b[[1, 3]] = np.clip(b[[1, 3]], 0, h) | |
assert cv2.imwrite( | |
str(f), im[b[1] : b[3], b[0] : b[2]] | |
), f"box failure in {f}" | |
def autosplit( | |
path=DATASETS_DIR / "coco128/images", | |
weights=(0.9, 0.1, 0.0), | |
annotated_only=False, | |
): | |
"""Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files | |
Usage: from utils.dataloaders import *; autosplit() | |
Arguments | |
path: Path to images directory | |
weights: Train, val, test weights (list, tuple) | |
annotated_only: Only use images with an annotated txt file | |
""" | |
path = Path(path) # images dir | |
files = sorted( | |
x for x in path.rglob("*.*") if x.suffix[1:].lower() in IMG_FORMATS | |
) # image files only | |
n = len(files) # number of files | |
random.seed(0) # for reproducibility | |
indices = random.choices( | |
[0, 1, 2], weights=weights, k=n | |
) # assign each image to a split | |
txt = [ | |
"autosplit_train.txt", | |
"autosplit_val.txt", | |
"autosplit_test.txt", | |
] # 3 txt files | |
for x in txt: | |
if (path.parent / x).exists(): | |
(path.parent / x).unlink() # remove existing | |
print( | |
f"Autosplitting images from {path}" | |
+ ", using *.txt labeled images only" * annotated_only | |
) | |
for i, img in tqdm(zip(indices, files), total=n): | |
if ( | |
not annotated_only or Path(img2label_paths([str(img)])[0]).exists() | |
): # check label | |
with open(path.parent / txt[i], "a") as f: | |
f.write( | |
f"./{img.relative_to(path.parent).as_posix()}" + "\n" | |
) # add image to txt file | |
def verify_image_label(args): | |
# Verify one image-label pair | |
im_file, lb_file, prefix = args | |
nm, nf, ne, nc, msg, segments = ( | |
0, | |
0, | |
0, | |
0, | |
"", | |
[], | |
) # number (missing, found, empty, corrupt), message, segments | |
try: | |
# verify images | |
im = Image.open(im_file) | |
im.verify() # PIL verify | |
shape = exif_size(im) # image size | |
assert (shape[0] > 9) & ( | |
shape[1] > 9 | |
), f"image size {shape} <10 pixels" | |
assert ( | |
im.format.lower() in IMG_FORMATS | |
), f"invalid image format {im.format}" | |
if im.format.lower() in ("jpg", "jpeg"): | |
with open(im_file, "rb") as f: | |
f.seek(-2, 2) | |
if f.read() != b"\xff\xd9": # corrupt JPEG | |
ImageOps.exif_transpose(Image.open(im_file)).save( | |
im_file, "JPEG", subsampling=0, quality=100 | |
) | |
msg = f"{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved" | |
# verify labels | |
if os.path.isfile(lb_file): | |
nf = 1 # label found | |
with open(lb_file) as f: | |
lb = [ | |
x.split() for x in f.read().strip().splitlines() if len(x) | |
] | |
if any(len(x) > 6 for x in lb): # is segment | |
classes = np.array([x[0] for x in lb], dtype=np.float32) | |
segments = [ | |
np.array(x[1:], dtype=np.float32).reshape(-1, 2) | |
for x in lb | |
] # (cls, xy1...) | |
lb = np.concatenate( | |
(classes.reshape(-1, 1), segments2boxes(segments)), 1 | |
) # (cls, xywh) | |
lb = np.array(lb, dtype=np.float32) | |
nl = len(lb) | |
if nl: | |
assert ( | |
lb.shape[1] == 5 | |
), f"labels require 5 columns, {lb.shape[1]} columns detected" | |
assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}" | |
assert ( | |
lb[:, 1:] <= 1 | |
).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}" | |
_, i = np.unique(lb, axis=0, return_index=True) | |
if len(i) < nl: # duplicate row check | |
lb = lb[i] # remove duplicates | |
if segments: | |
segments = [segments[x] for x in i] | |
msg = f"{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed" | |
else: | |
ne = 1 # label empty | |
lb = np.zeros((0, 5), dtype=np.float32) | |
else: | |
nm = 1 # label missing | |
lb = np.zeros((0, 5), dtype=np.float32) | |
return im_file, lb, shape, segments, nm, nf, ne, nc, msg | |
except Exception as e: | |
nc = 1 | |
msg = ( | |
f"{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}" | |
) | |
return [None, None, None, None, nm, nf, ne, nc, msg] | |
class HUBDatasetStats: | |
"""Class for generating HUB dataset JSON and `-hub` dataset directory | |
Arguments | |
path: Path to data.yaml or data.zip (with data.yaml inside data.zip) | |
autodownload: Attempt to download dataset if not found locally | |
Usage | |
from utils.dataloaders import HUBDatasetStats | |
stats = HUBDatasetStats('coco128.yaml', autodownload=True) # usage 1 | |
stats = HUBDatasetStats('path/to/coco128.zip') # usage 2 | |
stats.get_json(save=False) | |
stats.process_images() | |
""" | |
def __init__(self, path="coco128.yaml", autodownload=False): | |
# Initialize class | |
zipped, data_dir, yaml_path = self._unzip(Path(path)) | |
try: | |
with open(check_yaml(yaml_path), errors="ignore") as f: | |
data = yaml.safe_load(f) # data dict | |
if zipped: | |
data["path"] = data_dir | |
except Exception as e: | |
raise Exception("error/HUB/dataset_stats/yaml_load") from e | |
check_dataset(data, autodownload) # download dataset if missing | |
self.hub_dir = Path(data["path"] + "-hub") | |
self.im_dir = self.hub_dir / "images" | |
self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images | |
self.stats = { | |
"nc": data["nc"], | |
"names": list(data["names"].values()), | |
} # statistics dictionary | |
self.data = data | |
def _find_yaml(dir): | |
# Return data.yaml file | |
files = list(dir.glob("*.yaml")) or list( | |
dir.rglob("*.yaml") | |
) # try root level first and then recursive | |
assert files, f"No *.yaml file found in {dir}" | |
if len(files) > 1: | |
files = [ | |
f for f in files if f.stem == dir.stem | |
] # prefer *.yaml files that match dir name | |
assert ( | |
files | |
), f"Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed" | |
assert ( | |
len(files) == 1 | |
), f"Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}" | |
return files[0] | |
def _unzip(self, path): | |
# Unzip data.zip | |
if not str(path).endswith(".zip"): # path is data.yaml | |
return False, None, path | |
assert Path(path).is_file(), f"Error unzipping {path}, file not found" | |
unzip_file(path, path=path.parent) | |
dir = path.with_suffix("") # dataset directory == zip name | |
assert ( | |
dir.is_dir() | |
), f"Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/" | |
return ( | |
True, | |
str(dir), | |
self._find_yaml(dir), | |
) # zipped, data_dir, yaml_path | |
def _hub_ops(self, f, max_dim=1920): | |
# HUB ops for 1 image 'f': resize and save at reduced quality in /dataset-hub for web/app viewing | |
f_new = self.im_dir / Path(f).name # dataset-hub image filename | |
try: # use PIL | |
im = Image.open(f) | |
r = max_dim / max(im.height, im.width) # ratio | |
if r < 1.0: # image too large | |
im = im.resize((int(im.width * r), int(im.height * r))) | |
im.save(f_new, "JPEG", quality=50, optimize=True) # save | |
except Exception as e: # use OpenCV | |
LOGGER.info(f"WARNING ⚠️ HUB ops PIL failure {f}: {e}") | |
im = cv2.imread(f) | |
im_height, im_width = im.shape[:2] | |
r = max_dim / max(im_height, im_width) # ratio | |
if r < 1.0: # image too large | |
im = cv2.resize( | |
im, | |
(int(im_width * r), int(im_height * r)), | |
interpolation=cv2.INTER_AREA, | |
) | |
cv2.imwrite(str(f_new), im) | |
def get_json(self, save=False, verbose=False): | |
# Return dataset JSON for Ultralytics HUB | |
def _round(labels): | |
# Update labels to integer class and 6 decimal place floats | |
return [ | |
[int(c), *(round(x, 4) for x in points)] | |
for c, *points in labels | |
] | |
for split in "train", "val", "test": | |
if self.data.get(split) is None: | |
self.stats[split] = None # i.e. no test set | |
continue | |
dataset = LoadImagesAndLabels(self.data[split]) # load dataset | |
x = np.array( | |
[ | |
np.bincount( | |
label[:, 0].astype(int), minlength=self.data["nc"] | |
) | |
for label in tqdm( | |
dataset.labels, total=dataset.n, desc="Statistics" | |
) | |
] | |
) # shape(128x80) | |
self.stats[split] = { | |
"instance_stats": { | |
"total": int(x.sum()), | |
"per_class": x.sum(0).tolist(), | |
}, | |
"image_stats": { | |
"total": dataset.n, | |
"unlabelled": int(np.all(x == 0, 1).sum()), | |
"per_class": (x > 0).sum(0).tolist(), | |
}, | |
"labels": [ | |
{str(Path(k).name): _round(v.tolist())} | |
for k, v in zip(dataset.im_files, dataset.labels) | |
], | |
} | |
# Save, print and return | |
if save: | |
stats_path = self.hub_dir / "stats.json" | |
print(f"Saving {stats_path.resolve()}...") | |
with open(stats_path, "w") as f: | |
json.dump(self.stats, f) # save stats.json | |
if verbose: | |
print(json.dumps(self.stats, indent=2, sort_keys=False)) | |
return self.stats | |
def process_images(self): | |
# Compress images for Ultralytics HUB | |
for split in "train", "val", "test": | |
if self.data.get(split) is None: | |
continue | |
dataset = LoadImagesAndLabels(self.data[split]) # load dataset | |
desc = f"{split} images" | |
for _ in tqdm( | |
ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), | |
total=dataset.n, | |
desc=desc, | |
): | |
pass | |
print(f"Done. All images saved to {self.im_dir}") | |
return self.im_dir | |
# Classification dataloaders ------------------------------------------------------------------------------------------- | |
class ClassificationDataset(torchvision.datasets.ImageFolder): | |
""" | |
YOLOv5 Classification Dataset. | |
Arguments | |
root: Dataset path | |
transform: torchvision transforms, used by default | |
album_transform: Albumentations transforms, used if installed | |
""" | |
def __init__(self, root, augment, imgsz, cache=False): | |
super().__init__(root=root) | |
self.torch_transforms = classify_transforms(imgsz) | |
self.album_transforms = ( | |
classify_albumentations(augment, imgsz) if augment else None | |
) | |
self.cache_ram = cache is True or cache == "ram" | |
self.cache_disk = cache == "disk" | |
self.samples = [ | |
list(x) + [Path(x[0]).with_suffix(".npy"), None] | |
for x in self.samples | |
] # file, index, npy, im | |
def __getitem__(self, i): | |
f, j, fn, im = self.samples[ | |
i | |
] # filename, index, filename.with_suffix('.npy'), image | |
if self.cache_ram and im is None: | |
im = self.samples[i][3] = cv2.imread(f) | |
elif self.cache_disk: | |
if not fn.exists(): # load npy | |
np.save(fn.as_posix(), cv2.imread(f)) | |
im = np.load(fn) | |
else: # read image | |
im = cv2.imread(f) # BGR | |
if self.album_transforms: | |
sample = self.album_transforms( | |
image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB) | |
)["image"] | |
else: | |
sample = self.torch_transforms(im) | |
return sample, j | |
def create_classification_dataloader( | |
path, | |
imgsz=224, | |
batch_size=16, | |
augment=True, | |
cache=False, | |
rank=-1, | |
workers=8, | |
shuffle=True, | |
): | |
# Returns Dataloader object to be used with YOLOv5 Classifier | |
with torch_distributed_zero_first( | |
rank | |
): # init dataset *.cache only once if DDP | |
dataset = ClassificationDataset( | |
root=path, imgsz=imgsz, augment=augment, cache=cache | |
) | |
batch_size = min(batch_size, len(dataset)) | |
nd = torch.cuda.device_count() | |
nw = min( | |
[ | |
os.cpu_count() // max(nd, 1), | |
batch_size if batch_size > 1 else 0, | |
workers, | |
] | |
) | |
sampler = ( | |
None | |
if rank == -1 | |
else distributed.DistributedSampler(dataset, shuffle=shuffle) | |
) | |
generator = torch.Generator() | |
generator.manual_seed(6148914691236517205 + RANK) | |
return InfiniteDataLoader( | |
dataset, | |
batch_size=batch_size, | |
shuffle=shuffle and sampler is None, | |
num_workers=nw, | |
sampler=sampler, | |
pin_memory=PIN_MEMORY, | |
worker_init_fn=seed_worker, | |
generator=generator, | |
) # or DataLoader(persistent_workers=True) | |