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yolov5-qat

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utils/aws/resume.py 0 → 100644
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# Resume all interrupted trainings in yolov5/ dir including DDP trainings
# Usage: $ python utils/aws/resume.py
import os
import sys
from pathlib import Path
import torch
import yaml
FILE = Path(__file__).resolve()
ROOT = FILE.parents[2] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
port = 0 # --master_port
path = Path("").resolve()
for last in path.rglob("*/**/last.pt"):
ckpt = torch.load(last)
if ckpt["optimizer"] is None:
continue
# Load opt.yaml
with open(last.parent.parent / "opt.yaml", errors="ignore") as f:
opt = yaml.safe_load(f)
# Get device count
d = opt["device"].split(",") # devices
nd = len(d) # number of devices
ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel
if ddp: # multi-GPU
port += 1
cmd = f"python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}"
else: # single-GPU
cmd = f"python train.py --resume {last}"
cmd += " > /dev/null 2>&1 &" # redirect output to dev/null and run in daemon thread
print(cmd)
os.system(cmd)
utils/aws/userdata.sh 0 → 100644
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#!/bin/bash
# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html
# This script will run only once on first instance start (for a re-start script see mime.sh)
# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir
# Use >300 GB SSD
cd home/ubuntu
if [ ! -d yolov5 ]; then
echo "Running first-time script." # install dependencies, download COCO, pull Docker
git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5
cd yolov5
bash data/scripts/get_coco.sh && echo "COCO done." &
sudo docker pull ultralytics/yolov5:latest && echo "Docker done." &
python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." &
wait && echo "All tasks done." # finish background tasks
else
echo "Running re-start script." # resume interrupted runs
i=0
list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour'
while IFS= read -r id; do
((i++))
echo "restarting container $i: $id"
sudo docker start $id
# sudo docker exec -it $id python train.py --resume # single-GPU
sudo docker exec -d $id python utils/aws/resume.py # multi-scenario
done <<<"$list"
fi
utils/callbacks.py 0 → 100644
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""Callback utils."""
import threading
class Callbacks:
"""Handles all registered callbacks for YOLOv5 Hooks."""
def __init__(self):
"""Initializes a Callbacks object to manage registered YOLOv5 training event hooks."""
self._callbacks = {
"on_pretrain_routine_start": [],
"on_pretrain_routine_end": [],
"on_train_start": [],
"on_train_epoch_start": [],
"on_train_batch_start": [],
"optimizer_step": [],
"on_before_zero_grad": [],
"on_train_batch_end": [],
"on_train_epoch_end": [],
"on_val_start": [],
"on_val_batch_start": [],
"on_val_image_end": [],
"on_val_batch_end": [],
"on_val_end": [],
"on_fit_epoch_end": [], # fit = train + val
"on_model_save": [],
"on_train_end": [],
"on_params_update": [],
"teardown": [],
}
self.stop_training = False # set True to interrupt training
def register_action(self, hook, name="", callback=None):
"""
Register a new action to a callback hook.
Args:
hook: The callback hook name to register the action to
name: The name of the action for later reference
callback: The callback to fire
"""
assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
assert callable(callback), f"callback '{callback}' is not callable"
self._callbacks[hook].append({"name": name, "callback": callback})
def get_registered_actions(self, hook=None):
"""
Returns all the registered actions by callback hook.
Args:
hook: The name of the hook to check, defaults to all
"""
return self._callbacks[hook] if hook else self._callbacks
def run(self, hook, *args, thread=False, **kwargs):
"""
Loop through the registered actions and fire all callbacks on main thread.
Args:
hook: The name of the hook to check, defaults to all
args: Arguments to receive from YOLOv5
thread: (boolean) Run callbacks in daemon thread
kwargs: Keyword Arguments to receive from YOLOv5
"""
assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
for logger in self._callbacks[hook]:
if thread:
threading.Thread(target=logger["callback"], args=args, kwargs=kwargs, daemon=True).start()
else:
logger["callback"](*args, **kwargs)
utils/dataloaders.py 0 → 100644
View file @ 7a650e36
# YOLOv5 🚀 by Ultralytics, AGPL-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://docs.ultralytics.com/yolov5/tutorials/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))
WORLD_SIZE = int(os.getenv("WORLD_SIZE", 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):
"""Generates a single SHA256 hash for a list of file or directory paths by combining their sizes and paths."""
size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes
h = hashlib.sha256(str(size).encode()) # hash sizes
h.update("".join(paths).encode()) # hash paths
return h.hexdigest() # return hash
def exif_size(img):
"""Returns corrected PIL image size (width, height) considering EXIF orientation."""
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):
"""
Sets the seed for a dataloader worker to ensure reproducibility, based on PyTorch's randomness notes.
See 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)
# Inherit from DistributedSampler and override iterator
# https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py
class SmartDistributedSampler(distributed.DistributedSampler):
def __iter__(self):
"""Yields indices for distributed data sampling, shuffled deterministically based on epoch and seed."""
g = torch.Generator()
g.manual_seed(self.seed + self.epoch)
# determine the the eventual size (n) of self.indices (DDP indices)
n = int((len(self.dataset) - self.rank - 1) / self.num_replicas) + 1 # num_replicas == WORLD_SIZE
idx = torch.randperm(n, generator=g)
if not self.shuffle:
idx = idx.sort()[0]
idx = idx.tolist()
if self.drop_last:
idx = idx[: self.num_samples]
else:
padding_size = self.num_samples - len(idx)
if padding_size <= len(idx):
idx += idx[:padding_size]
else:
idx += (idx * math.ceil(padding_size / len(idx)))[:padding_size]
return iter(idx)
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,
rank=rank,
)
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 SmartDistributedSampler(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):
"""Initializes an InfiniteDataLoader that reuses workers with standard DataLoader syntax, augmenting with a
repeating sampler.
"""
super().__init__(*args, **kwargs)
object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler))
self.iterator = super().__iter__()
def __len__(self):
"""Returns the length of the batch sampler's sampler in the InfiniteDataLoader."""
return len(self.batch_sampler.sampler)
def __iter__(self):
"""Yields batches of data indefinitely in a loop by resetting the sampler when exhausted."""
for _ in range(len(self)):
yield next(self.iterator)
class _RepeatSampler:
"""
Sampler that repeats forever.
Args:
sampler (Sampler)
"""
def __init__(self, sampler):
"""Initializes a perpetual sampler wrapping a provided `Sampler` instance for endless data iteration."""
self.sampler = sampler
def __iter__(self):
"""Returns an infinite iterator over the dataset by repeatedly yielding from the given sampler."""
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):
"""
Initializes a screenshot dataloader for YOLOv5 with specified source region, image size, stride, auto, and
transforms.
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):
"""Iterates over itself, enabling use in loops and iterable contexts."""
return self
def __next__(self):
"""Captures and returns the next screen frame as a BGR numpy array, cropping to only the first three channels
from BGRA.
"""
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):
"""Initializes YOLOv5 loader for images/videos, supporting glob patterns, directories, and lists of paths."""
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):
"""Initializes iterator by resetting count and returns the iterator object itself."""
self.count = 0
return self
def __next__(self):
"""Advances to the next file in the dataset, raising StopIteration if at the end."""
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):
"""Initializes a new video capture object with path, frame count adjusted by stride, and orientation
metadata.
"""
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):
"""Rotates a cv2 image based on its orientation; supports 0, 90, and 180 degrees rotations."""
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):
"""Returns the number of files in the dataset."""
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):
"""Initializes a stream loader for processing video streams with YOLOv5, supporting various sources including
YouTube.
"""
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/LNwODJXcvt4'
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):
"""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):
# 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="",
rank=-1,
seed=0,
):
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
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)
utils/docker/Dockerfile 0 → 100644
View file @ 7a650e36
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch
FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime
# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
# Install linux packages
ENV DEBIAN_FRONTEND noninteractive
RUN apt update
RUN TZ=Etc/UTC apt install -y tzdata
RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg
# RUN alias python=python3
# Security updates
# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796
RUN apt upgrade --no-install-recommends -y openssl
# Create working directory
RUN rm -rf /usr/src/app && mkdir -p /usr/src/app
WORKDIR /usr/src/app
# Copy contents
COPY . /usr/src/app
# Install pip packages
COPY requirements.txt .
RUN python3 -m pip install --upgrade pip wheel
RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \
coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0'
# tensorflow tensorflowjs \
# Set environment variables
ENV OMP_NUM_THREADS=1
# Cleanup
ENV DEBIAN_FRONTEND teletype
# Usage Examples -------------------------------------------------------------------------------------------------------
# Build and Push
# t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t
# Pull and Run
# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
# Pull and Run with local directory access
# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
# Kill all
# sudo docker kill $(sudo docker ps -q)
# Kill all image-based
# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)
# DockerHub tag update
# t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew
# Clean up
# sudo docker system prune -a --volumes
# Update Ubuntu drivers
# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
# DDP test
# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
# GCP VM from Image
# docker.io/ultralytics/yolov5:latest
utils/docker/Dockerfile-arm64 0 → 100644
View file @ 7a650e36
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi
# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
FROM arm64v8/ubuntu:22.10
# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
# Install linux packages
ENV DEBIAN_FRONTEND noninteractive
RUN apt update
RUN TZ=Etc/UTC apt install -y tzdata
RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev
# RUN alias python=python3
# Install pip packages
COPY requirements.txt .
RUN python3 -m pip install --upgrade pip wheel
RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \
coremltools onnx onnxruntime
# tensorflow-aarch64 tensorflowjs \
# Create working directory
RUN mkdir -p /usr/src/app
WORKDIR /usr/src/app
# Copy contents
COPY . /usr/src/app
ENV DEBIAN_FRONTEND teletype
# Usage Examples -------------------------------------------------------------------------------------------------------
# Build and Push
# t=ultralytics/yolov5:latest-arm64 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t
# Pull and Run
# t=ultralytics/yolov5:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
utils/docker/Dockerfile-cpu 0 → 100644
View file @ 7a650e36
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments
# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
FROM ubuntu:23.10
# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
# Install linux packages
# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
RUN apt update \
&& apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
# RUN alias python=python3
# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error
RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED
# Install pip packages
COPY requirements.txt .
RUN python3 -m pip install --upgrade pip wheel
RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \
coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' \
# tensorflow tensorflowjs \
--extra-index-url https://download.pytorch.org/whl/cpu
# Create working directory
RUN mkdir -p /usr/src/app
WORKDIR /usr/src/app
# Copy contents
COPY . /usr/src/app
# Usage Examples -------------------------------------------------------------------------------------------------------
# Build and Push
# t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t
# Pull and Run
# t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
utils/downloads.py 0 → 100644
View file @ 7a650e36
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""Download utils."""
import logging
import subprocess
import urllib
from pathlib import Path
import requests
import torch
def is_url(url, check=True):
"""Determines if a string is a URL and optionally checks its existence online, returning a boolean."""
try:
url = str(url)
result = urllib.parse.urlparse(url)
assert all([result.scheme, result.netloc]) # check if is url
return (urllib.request.urlopen(url).getcode() == 200) if check else True # check if exists online
except (AssertionError, urllib.request.HTTPError):
return False
def gsutil_getsize(url=""):
"""
Returns the size in bytes of a file at a Google Cloud Storage URL using `gsutil du`.
Returns 0 if the command fails or output is empty.
"""
output = subprocess.check_output(["gsutil", "du", url], shell=True, encoding="utf-8")
return int(output.split()[0]) if output else 0
def url_getsize(url="https://ultralytics.com/images/bus.jpg"):
"""Returns the size in bytes of a downloadable file at a given URL; defaults to -1 if not found."""
response = requests.head(url, allow_redirects=True)
return int(response.headers.get("content-length", -1))
def curl_download(url, filename, *, silent: bool = False) -> bool:
"""Download a file from a url to a filename using curl."""
silent_option = "sS" if silent else "" # silent
proc = subprocess.run(
[
"curl",
"-#",
f"-{silent_option}L",
url,
"--output",
filename,
"--retry",
"9",
"-C",
"-",
]
)
return proc.returncode == 0
def safe_download(file, url, url2=None, min_bytes=1e0, error_msg=""):
"""
Downloads a file from a URL (or alternate URL) to a specified path if file is above a minimum size.
Removes incomplete downloads.
"""
from utils.general import LOGGER
file = Path(file)
assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
try: # url1
LOGGER.info(f"Downloading {url} to {file}...")
torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO)
assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check
except Exception as e: # url2
if file.exists():
file.unlink() # remove partial downloads
LOGGER.info(f"ERROR: {e}\nRe-attempting {url2 or url} to {file}...")
# curl download, retry and resume on fail
curl_download(url2 or url, file)
finally:
if not file.exists() or file.stat().st_size < min_bytes: # check
if file.exists():
file.unlink() # remove partial downloads
LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}")
LOGGER.info("")
def attempt_download(file, repo="ultralytics/yolov5", release="v7.0"):
"""Downloads a file from GitHub release assets or via direct URL if not found locally, supporting backup
versions.
"""
from utils.general import LOGGER
def github_assets(repository, version="latest"):
# Return GitHub repo tag (i.e. 'v7.0') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...])
if version != "latest":
version = f"tags/{version}" # i.e. tags/v7.0
response = requests.get(f"https://api.github.com/repos/{repository}/releases/{version}").json() # github api
return response["tag_name"], [x["name"] for x in response["assets"]] # tag, assets
file = Path(str(file).strip().replace("'", ""))
if not file.exists():
# URL specified
name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc.
if str(file).startswith(("http:/", "https:/")): # download
url = str(file).replace(":/", "://") # Pathlib turns :// -> :/
file = name.split("?")[0] # parse authentication https://url.com/file.txt?auth...
if Path(file).is_file():
LOGGER.info(f"Found {url} locally at {file}") # file already exists
else:
safe_download(file=file, url=url, min_bytes=1e5)
return file
# GitHub assets
assets = [f"yolov5{size}{suffix}.pt" for size in "nsmlx" for suffix in ("", "6", "-cls", "-seg")] # default
try:
tag, assets = github_assets(repo, release)
except Exception:
try:
tag, assets = github_assets(repo) # latest release
except Exception:
try:
tag = subprocess.check_output("git tag", shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
except Exception:
tag = release
if name in assets:
file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required)
safe_download(
file,
url=f"https://github.com/{repo}/releases/download/{tag}/{name}",
min_bytes=1e5,
error_msg=f"{file} missing, try downloading from https://github.com/{repo}/releases/{tag}",
)
return str(file)
utils/flask_rest_api/README.md 0 → 100644
View file @ 7a650e36
# Flask REST API
[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
## Requirements
[Flask](https://palletsprojects.com/p/flask/) is required. Install with:
```shell
$ pip install Flask
```
## Run
After Flask installation run:
```shell
$ python3 restapi.py --port 5000
```
Then use [curl](https://curl.se/) to perform a request:
```shell
$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'
```
The model inference results are returned as a JSON response:
```json
[
{
"class": 0,
"confidence": 0.8900438547,
"height": 0.9318675399,
"name": "person",
"width": 0.3264600933,
"xcenter": 0.7438579798,
"ycenter": 0.5207948685
},
{
"class": 0,
"confidence": 0.8440024257,
"height": 0.7155083418,
"name": "person",
"width": 0.6546785235,
"xcenter": 0.427829951,
"ycenter": 0.6334488392
},
{
"class": 27,
"confidence": 0.3771208823,
"height": 0.3902671337,
"name": "tie",
"width": 0.0696444362,
"xcenter": 0.3675483763,
"ycenter": 0.7991207838
},
{
"class": 27,
"confidence": 0.3527112305,
"height": 0.1540903747,
"name": "tie",
"width": 0.0336618312,
"xcenter": 0.7814827561,
"ycenter": 0.5065554976
}
]
```
An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py`
utils/flask_rest_api/example_request.py 0 → 100644
View file @ 7a650e36
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""Perform test request."""
import pprint
import requests
DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
IMAGE = "zidane.jpg"
# Read image
with open(IMAGE, "rb") as f:
image_data = f.read()
response = requests.post(DETECTION_URL, files={"image": image_data}).json()
pprint.pprint(response)
utils/flask_rest_api/restapi.py 0 → 100644
View file @ 7a650e36
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""Run a Flask REST API exposing one or more YOLOv5s models."""
import argparse
import io
import torch
from flask import Flask, request
from PIL import Image
app = Flask(__name__)
models = {}
DETECTION_URL = "/v1/object-detection/<model>"
@app.route(DETECTION_URL, methods=["POST"])
def predict(model):
"""Predict and return object detections in JSON format given an image and model name via a Flask REST API POST
request.
"""
if request.method != "POST":
return
if request.files.get("image"):
# Method 1
# with request.files["image"] as f:
# im = Image.open(io.BytesIO(f.read()))
# Method 2
im_file = request.files["image"]
im_bytes = im_file.read()
im = Image.open(io.BytesIO(im_bytes))
if model in models:
results = models[model](im, size=640) # reduce size=320 for faster inference
return results.pandas().xyxy[0].to_json(orient="records")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model")
parser.add_argument("--port", default=5000, type=int, help="port number")
parser.add_argument("--model", nargs="+", default=["yolov5s"], help="model(s) to run, i.e. --model yolov5n yolov5s")
opt = parser.parse_args()
for m in opt.model:
models[m] = torch.hub.load("ultralytics/yolov5", m, force_reload=True, skip_validation=True)
app.run(host="0.0.0.0", port=opt.port) # debug=True causes Restarting with stat
utils/general.py 0 → 100644
View file @ 7a650e36
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""General utils."""
import contextlib
import glob
import inspect
import logging
import logging.config
import math
import os
import platform
import random
import re
import signal
import subprocess
import sys
import time
import urllib
from copy import deepcopy
from datetime import datetime
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from subprocess import check_output
from tarfile import is_tarfile
from typing import Optional
from zipfile import ZipFile, is_zipfile
import cv2
import numpy as np
import pandas as pd
import pkg_resources as pkg
import torch
import torchvision
import yaml
# Import 'ultralytics' package or install if missing
try:
import ultralytics
assert hasattr(ultralytics, "__version__") # verify package is not directory
except (ImportError, AssertionError):
os.system("pip install -U ultralytics")
import ultralytics
from ultralytics.utils.checks import check_requirements
from utils import TryExcept, emojis
from utils.downloads import curl_download, gsutil_getsize
from utils.metrics import box_iou, fitness
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1] # YOLOv5 root directory
RANK = int(os.getenv("RANK", -1))
# Settings
NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads
DATASETS_DIR = Path(os.getenv("YOLOv5_DATASETS_DIR", ROOT.parent / "datasets")) # global datasets directory
AUTOINSTALL = str(os.getenv("YOLOv5_AUTOINSTALL", True)).lower() == "true" # global auto-install mode
VERBOSE = str(os.getenv("YOLOv5_VERBOSE", True)).lower() == "true" # global verbose mode
TQDM_BAR_FORMAT = "{l_bar}{bar:10}{r_bar}" # tqdm bar format
FONT = "Arial.ttf" # https://ultralytics.com/assets/Arial.ttf
torch.set_printoptions(linewidth=320, precision=5, profile="long")
np.set_printoptions(linewidth=320, formatter={"float_kind": "{:11.5g}".format}) # format short g, %precision=5
pd.options.display.max_columns = 10
cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
os.environ["NUMEXPR_MAX_THREADS"] = str(NUM_THREADS) # NumExpr max threads
os.environ["OMP_NUM_THREADS"] = "1" if platform.system() == "darwin" else str(NUM_THREADS) # OpenMP (PyTorch and SciPy)
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" # suppress verbose TF compiler warnings in Colab
def is_ascii(s=""):
"""Checks if input string `s` contains only ASCII characters; returns `True` if so, otherwise `False`."""
s = str(s) # convert list, tuple, None, etc. to str
return len(s.encode().decode("ascii", "ignore")) == len(s)
def is_chinese(s="人工智能"):
"""Determines if a string `s` contains any Chinese characters; returns `True` if so, otherwise `False`."""
return bool(re.search("[\u4e00-\u9fff]", str(s)))
def is_colab():
"""Checks if the current environment is a Google Colab instance; returns `True` for Colab, otherwise `False`."""
return "google.colab" in sys.modules
def is_jupyter():
"""
Check if the current script is running inside a Jupyter Notebook. Verified on Colab, Jupyterlab, Kaggle, Paperspace.
Returns:
bool: True if running inside a Jupyter Notebook, False otherwise.
"""
with contextlib.suppress(Exception):
from IPython import get_ipython
return get_ipython() is not None
return False
def is_kaggle():
"""Checks if the current environment is a Kaggle Notebook by validating environment variables."""
return os.environ.get("PWD") == "/kaggle/working" and os.environ.get("KAGGLE_URL_BASE") == "https://www.kaggle.com"
def is_docker() -> bool:
"""Check if the process runs inside a docker container."""
if Path("/.dockerenv").exists():
return True
try: # check if docker is in control groups
with open("/proc/self/cgroup") as file:
return any("docker" in line for line in file)
except OSError:
return False
def is_writeable(dir, test=False):
"""Checks if a directory is writable, optionally testing by creating a temporary file if `test=True`."""
if not test:
return os.access(dir, os.W_OK) # possible issues on Windows
file = Path(dir) / "tmp.txt"
try:
with open(file, "w"): # open file with write permissions
pass
file.unlink() # remove file
return True
except OSError:
return False
LOGGING_NAME = "yolov5"
def set_logging(name=LOGGING_NAME, verbose=True):
"""Configures logging with specified verbosity; `name` sets the logger's name, `verbose` controls logging level."""
rank = int(os.getenv("RANK", -1)) # rank in world for Multi-GPU trainings
level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR
logging.config.dictConfig(
{
"version": 1,
"disable_existing_loggers": False,
"formatters": {name: {"format": "%(message)s"}},
"handlers": {
name: {
"class": "logging.StreamHandler",
"formatter": name,
"level": level,
}
},
"loggers": {
name: {
"level": level,
"handlers": [name],
"propagate": False,
}
},
}
)
set_logging(LOGGING_NAME) # run before defining LOGGER
LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.)
if platform.system() == "Windows":
for fn in LOGGER.info, LOGGER.warning:
setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x))) # emoji safe logging
def user_config_dir(dir="Ultralytics", env_var="YOLOV5_CONFIG_DIR"):
"""Returns user configuration directory path, preferring environment variable `YOLOV5_CONFIG_DIR` if set, else OS-
specific.
"""
env = os.getenv(env_var)
if env:
path = Path(env) # use environment variable
else:
cfg = {"Windows": "AppData/Roaming", "Linux": ".config", "Darwin": "Library/Application Support"} # 3 OS dirs
path = Path.home() / cfg.get(platform.system(), "") # OS-specific config dir
path = (path if is_writeable(path) else Path("/tmp")) / dir # GCP and AWS lambda fix, only /tmp is writeable
path.mkdir(exist_ok=True) # make if required
return path
CONFIG_DIR = user_config_dir() # Ultralytics settings dir
class Profile(contextlib.ContextDecorator):
# YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager
def __init__(self, t=0.0, device: torch.device = None):
"""Initializes a profiling context for YOLOv5 with optional timing threshold and device specification."""
self.t = t
self.device = device
self.cuda = bool(device and str(device).startswith("cuda"))
def __enter__(self):
"""Initializes timing at the start of a profiling context block for performance measurement."""
self.start = self.time()
return self
def __exit__(self, type, value, traceback):
"""Concludes timing, updating duration for profiling upon exiting a context block."""
self.dt = self.time() - self.start # delta-time
self.t += self.dt # accumulate dt
def time(self):
"""Measures and returns the current time, synchronizing CUDA operations if `cuda` is True."""
if self.cuda:
torch.cuda.synchronize(self.device)
return time.time()
class Timeout(contextlib.ContextDecorator):
# YOLOv5 Timeout class. Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager
def __init__(self, seconds, *, timeout_msg="", suppress_timeout_errors=True):
"""Initializes a timeout context/decorator with defined seconds, optional message, and error suppression."""
self.seconds = int(seconds)
self.timeout_message = timeout_msg
self.suppress = bool(suppress_timeout_errors)
def _timeout_handler(self, signum, frame):
"""Raises a TimeoutError with a custom message when a timeout event occurs."""
raise TimeoutError(self.timeout_message)
def __enter__(self):
"""Initializes timeout mechanism on non-Windows platforms, starting a countdown to raise TimeoutError."""
if platform.system() != "Windows": # not supported on Windows
signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM
signal.alarm(self.seconds) # start countdown for SIGALRM to be raised
def __exit__(self, exc_type, exc_val, exc_tb):
"""Disables active alarm on non-Windows systems and optionally suppresses TimeoutError if set."""
if platform.system() != "Windows":
signal.alarm(0) # Cancel SIGALRM if it's scheduled
if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError
return True
class WorkingDirectory(contextlib.ContextDecorator):
# Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager
def __init__(self, new_dir):
"""Initializes a context manager/decorator to temporarily change the working directory."""
self.dir = new_dir # new dir
self.cwd = Path.cwd().resolve() # current dir
def __enter__(self):
"""Temporarily changes the working directory within a 'with' statement context."""
os.chdir(self.dir)
def __exit__(self, exc_type, exc_val, exc_tb):
"""Restores the original working directory upon exiting a 'with' statement context."""
os.chdir(self.cwd)
def methods(instance):
"""Returns list of method names for a class/instance excluding dunder methods."""
return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")]
def print_args(args: Optional[dict] = None, show_file=True, show_func=False):
"""Logs the arguments of the calling function, with options to include the filename and function name."""
x = inspect.currentframe().f_back # previous frame
file, _, func, _, _ = inspect.getframeinfo(x)
if args is None: # get args automatically
args, _, _, frm = inspect.getargvalues(x)
args = {k: v for k, v in frm.items() if k in args}
try:
file = Path(file).resolve().relative_to(ROOT).with_suffix("")
except ValueError:
file = Path(file).stem
s = (f"{file}: " if show_file else "") + (f"{func}: " if show_func else "")
LOGGER.info(colorstr(s) + ", ".join(f"{k}={v}" for k, v in args.items()))
def init_seeds(seed=0, deterministic=False):
"""
Initializes RNG seeds and sets deterministic options if specified.
See https://pytorch.org/docs/stable/notes/randomness.html
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe
# torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287
if deterministic and check_version(torch.__version__, "1.12.0"): # https://github.com/ultralytics/yolov5/pull/8213
torch.use_deterministic_algorithms(True)
torch.backends.cudnn.deterministic = True
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
os.environ["PYTHONHASHSEED"] = str(seed)
def intersect_dicts(da, db, exclude=()):
"""Returns intersection of `da` and `db` dicts with match_boxesing keys and shapes, excluding `exclude` keys; uses `da`
values.
"""
return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape}
def get_default_args(func):
"""Returns a dict of `func` default arguments by inspecting its signature."""
signature = inspect.signature(func)
return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty}
def get_latest_run(search_dir="."):
"""Returns the path to the most recent 'last.pt' file in /runs to resume from, searches in `search_dir`."""
last_list = glob.glob(f"{search_dir}/**/last*.pt", recursive=True)
return max(last_list, key=os.path.getctime) if last_list else ""
def file_age(path=__file__):
"""Calculates and returns the age of a file in days based on its last modification time."""
dt = datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime) # delta
return dt.days # + dt.seconds / 86400 # fractional days
def file_date(path=__file__):
"""Returns a human-readable file modification date in 'YYYY-M-D' format, given a file path."""
t = datetime.fromtimestamp(Path(path).stat().st_mtime)
return f"{t.year}-{t.month}-{t.day}"
def file_size(path):
"""Returns file or directory size in megabytes (MB) for a given path, where directories are recursively summed."""
mb = 1 << 20 # bytes to MiB (1024 ** 2)
path = Path(path)
if path.is_file():
return path.stat().st_size / mb
elif path.is_dir():
return sum(f.stat().st_size for f in path.glob("**/*") if f.is_file()) / mb
else:
return 0.0
def check_online():
"""Checks internet connectivity by attempting to create a connection to "1.1.1.1" on port 443, retries once if the
first attempt fails.
"""
import socket
def run_once():
# Check once
try:
socket.create_connection(("1.1.1.1", 443), 5) # check host accessibility
return True
except OSError:
return False
return run_once() or run_once() # check twice to increase robustness to intermittent connectivity issues
def git_describe(path=ROOT):
"""
Returns a human-readable git description of the repository at `path`, or an empty string on failure.
Example output is 'fv5.0-5-g3e25f1e'. See https://git-scm.com/docs/git-describe.
"""
try:
assert (Path(path) / ".git").is_dir()
return check_output(f"git -C {path} describe --tags --long --always", shell=True).decode()[:-1]
except Exception:
return ""
@TryExcept()
@WorkingDirectory(ROOT)
def check_git_status(repo="ultralytics/yolov5", branch="master"):
"""Checks if YOLOv5 code is up-to-date with the repository, advising 'git pull' if behind; errors return informative
messages.
"""
url = f"https://github.com/{repo}"
msg = f", for updates see {url}"
s = colorstr("github: ") # string
assert Path(".git").exists(), s + "skipping check (not a git repository)" + msg
assert check_online(), s + "skipping check (offline)" + msg
splits = re.split(pattern=r"\s", string=check_output("git remote -v", shell=True).decode())
matches = [repo in s for s in splits]
if any(matches):
remote = splits[matches.index(True) - 1]
else:
remote = "ultralytics"
check_output(f"git remote add {remote} {url}", shell=True)
check_output(f"git fetch {remote}", shell=True, timeout=5) # git fetch
local_branch = check_output("git rev-parse --abbrev-ref HEAD", shell=True).decode().strip() # checked out
n = int(check_output(f"git rev-list {local_branch}..{remote}/{branch} --count", shell=True)) # commits behind
if n > 0:
pull = "git pull" if remote == "origin" else f"git pull {remote} {branch}"
s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use '{pull}' or 'git clone {url}' to update."
else:
s += f"up to date with {url} ✅"
LOGGER.info(s)
@WorkingDirectory(ROOT)
def check_git_info(path="."):
"""Checks YOLOv5 git info, returning a dict with remote URL, branch name, and commit hash."""
check_requirements("gitpython")
import git
try:
repo = git.Repo(path)
remote = repo.remotes.origin.url.replace(".git", "") # i.e. 'https://github.com/ultralytics/yolov5'
commit = repo.head.commit.hexsha # i.e. '3134699c73af83aac2a481435550b968d5792c0d'
try:
branch = repo.active_branch.name # i.e. 'main'
except TypeError: # not on any branch
branch = None # i.e. 'detached HEAD' state
return {"remote": remote, "branch": branch, "commit": commit}
except git.exc.InvalidGitRepositoryError: # path is not a git dir
return {"remote": None, "branch": None, "commit": None}
def check_python(minimum="3.8.0"):
"""Checks if current Python version meets the minimum required version, exits if not."""
check_version(platform.python_version(), minimum, name="Python ", hard=True)
def check_version(current="0.0.0", minimum="0.0.0", name="version ", pinned=False, hard=False, verbose=False):
"""Checks if the current version meets the minimum required version, exits or warns based on parameters."""
current, minimum = (pkg.parse_version(x) for x in (current, minimum))
result = (current == minimum) if pinned else (current >= minimum) # bool
s = f"WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed" # string
if hard:
assert result, emojis(s) # assert min requirements met
if verbose and not result:
LOGGER.warning(s)
return result
def check_img_size(imgsz, s=32, floor=0):
"""Adjusts image size to be divisible by stride `s`, supports int or list/tuple input, returns adjusted size."""
if isinstance(imgsz, int): # integer i.e. img_size=640
new_size = max(make_divisible(imgsz, int(s)), floor)
else: # list i.e. img_size=[640, 480]
imgsz = list(imgsz) # convert to list if tuple
new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
if new_size != imgsz:
LOGGER.warning(f"WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}")
return new_size
def check_imshow(warn=False):
"""Checks environment support for image display; warns on failure if `warn=True`."""
try:
assert not is_jupyter()
assert not is_docker()
cv2.imshow("test", np.zeros((1, 1, 3)))
cv2.waitKey(1)
cv2.destroyAllWindows()
cv2.waitKey(1)
return True
except Exception as e:
if warn:
LOGGER.warning(f"WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}")
return False
def check_suffix(file="yolov5s.pt", suffix=(".pt",), msg=""):
"""Validates if a file or files have an acceptable suffix, raising an error if not."""
if file and suffix:
if isinstance(suffix, str):
suffix = [suffix]
for f in file if isinstance(file, (list, tuple)) else [file]:
s = Path(f).suffix.lower() # file suffix
if len(s):
assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}"
def check_yaml(file, suffix=(".yaml", ".yml")):
"""Searches/downloads a YAML file, verifies its suffix (.yaml or .yml), and returns the file path."""
return check_file(file, suffix)
def check_file(file, suffix=""):
"""Searches/downloads a file, checks its suffix (if provided), and returns the file path."""
check_suffix(file, suffix) # optional
file = str(file) # convert to str()
if os.path.isfile(file) or not file: # exists
return file
elif file.startswith(("http:/", "https:/")): # download
url = file # warning: Pathlib turns :// -> :/
file = Path(urllib.parse.unquote(file).split("?")[0]).name # '%2F' to '/', split https://url.com/file.txt?auth
if os.path.isfile(file):
LOGGER.info(f"Found {url} locally at {file}") # file already exists
else:
LOGGER.info(f"Downloading {url} to {file}...")
torch.hub.download_url_to_file(url, file)
assert Path(file).exists() and Path(file).stat().st_size > 0, f"File download failed: {url}" # check
return file
elif file.startswith("clearml://"): # ClearML Dataset ID
assert (
"clearml" in sys.modules
), "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'."
return file
else: # search
files = []
for d in "data", "models", "utils": # search directories
files.extend(glob.glob(str(ROOT / d / "**" / file), recursive=True)) # find file
assert len(files), f"File not found: {file}" # assert file was found
assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique
return files[0] # return file
def check_font(font=FONT, progress=False):
"""Ensures specified font exists or downloads it from Ultralytics assets, optionally displaying progress."""
font = Path(font)
file = CONFIG_DIR / font.name
if not font.exists() and not file.exists():
url = f"https://ultralytics.com/assets/{font.name}"
LOGGER.info(f"Downloading {url} to {file}...")
torch.hub.download_url_to_file(url, str(file), progress=progress)
def check_dataset(data, autodownload=True):
"""Validates and/or auto-downloads a dataset, returning its configuration as a dictionary."""
# Download (optional)
extract_dir = ""
if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
download(data, dir=f"{DATASETS_DIR}/{Path(data).stem}", unzip=True, delete=False, curl=False, threads=1)
data = next((DATASETS_DIR / Path(data).stem).rglob("*.yaml"))
extract_dir, autodownload = data.parent, False
# Read yaml (optional)
if isinstance(data, (str, Path)):
data = yaml_load(data) # dictionary
# Checks
for k in "train", "val", "names":
assert k in data, emojis(f"data.yaml '{k}:' field missing ❌")
if isinstance(data["names"], (list, tuple)): # old array format
data["names"] = dict(enumerate(data["names"])) # convert to dict
assert all(isinstance(k, int) for k in data["names"].keys()), "data.yaml names keys must be integers, i.e. 2: car"
data["nc"] = len(data["names"])
# Resolve paths
path = Path(extract_dir or data.get("path") or "") # optional 'path' default to '.'
if not path.is_absolute():
path = (ROOT / path).resolve()
data["path"] = path # download scripts
for k in "train", "val", "test":
if data.get(k): # prepend path
if isinstance(data[k], str):
x = (path / data[k]).resolve()
if not x.exists() and data[k].startswith("../"):
x = (path / data[k][3:]).resolve()
data[k] = str(x)
else:
data[k] = [str((path / x).resolve()) for x in data[k]]
# Parse yaml
train, val, test, s = (data.get(x) for x in ("train", "val", "test", "download"))
if val:
val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path
if not all(x.exists() for x in val):
LOGGER.info("\nDataset not found ⚠️, missing paths %s" % [str(x) for x in val if not x.exists()])
if not s or not autodownload:
raise Exception("Dataset not found ❌")
t = time.time()
if s.startswith("http") and s.endswith(".zip"): # URL
f = Path(s).name # filename
LOGGER.info(f"Downloading {s} to {f}...")
torch.hub.download_url_to_file(s, f)
Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root
unzip_file(f, path=DATASETS_DIR) # unzip
Path(f).unlink() # remove zip
r = None # success
elif s.startswith("bash "): # bash script
LOGGER.info(f"Running {s} ...")
r = subprocess.run(s, shell=True)
else: # python script
r = exec(s, {"yaml": data}) # return None
dt = f"({round(time.time() - t, 1)}s)"
s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f"failure {dt} ❌"
LOGGER.info(f"Dataset download {s}")
check_font("Arial.ttf" if is_ascii(data["names"]) else "Arial.Unicode.ttf", progress=True) # download fonts
return data # dictionary
def check_amp(model):
"""Checks PyTorch AMP functionality for a model, returns True if AMP operates correctly, otherwise False."""
from models.common import AutoShape, DetectMultiBackend
def amp_allclose(model, im):
# All close FP32 vs AMP results
m = AutoShape(model, verbose=False) # model
a = m(im).xywhn[0] # FP32 inference
m.amp = True
b = m(im).xywhn[0] # AMP inference
return a.shape == b.shape and torch.allclose(a, b, atol=0.1) # close to 10% absolute tolerance
prefix = colorstr("AMP: ")
device = next(model.parameters()).device # get model device
if device.type in ("cpu", "mps"):
return False # AMP only used on CUDA devices
f = ROOT / "data" / "images" / "bus.jpg" # image to check
im = f if f.exists() else "https://ultralytics.com/images/bus.jpg" if check_online() else np.ones((640, 640, 3))
try:
assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend("yolov5n.pt", device), im)
LOGGER.info(f"{prefix}checks passed ✅")
return True
except Exception:
help_url = "https://github.com/ultralytics/yolov5/issues/7908"
LOGGER.warning(f"{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}")
return False
def yaml_load(file="data.yaml"):
"""Safely loads and returns the contents of a YAML file specified by `file` argument."""
with open(file, errors="ignore") as f:
return yaml.safe_load(f)
def yaml_save(file="data.yaml", data={}):
"""Safely saves `data` to a YAML file specified by `file`, converting `Path` objects to strings; `data` is a
dictionary.
"""
with open(file, "w") as f:
yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False)
def unzip_file(file, path=None, exclude=(".DS_Store", "__MACOSX")):
"""Unzips `file` to `path` (default: file's parent), excluding filenames containing any in `exclude` (`.DS_Store`,
`__MACOSX`).
"""
if path is None:
path = Path(file).parent # default path
with ZipFile(file) as zipObj:
for f in zipObj.namelist(): # list all archived filenames in the zip
if all(x not in f for x in exclude):
zipObj.extract(f, path=path)
def url2file(url):
"""
Converts a URL string to a valid filename by stripping protocol, domain, and any query parameters.
Example https://url.com/file.txt?auth -> file.txt
"""
url = str(Path(url)).replace(":/", "://") # Pathlib turns :// -> :/
return Path(urllib.parse.unquote(url)).name.split("?")[0] # '%2F' to '/', split https://url.com/file.txt?auth
def download(url, dir=".", unzip=True, delete=True, curl=False, threads=1, retry=3):
"""Downloads and optionally unzips files concurrently, supporting retries and curl fallback."""
def download_one(url, dir):
# Download 1 file
success = True
if os.path.isfile(url):
f = Path(url) # filename
else: # does not exist
f = dir / Path(url).name
LOGGER.info(f"Downloading {url} to {f}...")
for i in range(retry + 1):
if curl:
success = curl_download(url, f, silent=(threads > 1))
else:
torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download
success = f.is_file()
if success:
break
elif i < retry:
LOGGER.warning(f"⚠️ Download failure, retrying {i + 1}/{retry} {url}...")
else:
LOGGER.warning(f"❌ Failed to download {url}...")
if unzip and success and (f.suffix == ".gz" or is_zipfile(f) or is_tarfile(f)):
LOGGER.info(f"Unzipping {f}...")
if is_zipfile(f):
unzip_file(f, dir) # unzip
elif is_tarfile(f):
subprocess.run(["tar", "xf", f, "--directory", f.parent], check=True) # unzip
elif f.suffix == ".gz":
subprocess.run(["tar", "xfz", f, "--directory", f.parent], check=True) # unzip
if delete:
f.unlink() # remove zip
dir = Path(dir)
dir.mkdir(parents=True, exist_ok=True) # make directory
if threads > 1:
pool = ThreadPool(threads)
pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multithreaded
pool.close()
pool.join()
else:
for u in [url] if isinstance(url, (str, Path)) else url:
download_one(u, dir)
def make_divisible(x, divisor):
"""Adjusts `x` to be divisible by `divisor`, returning the nearest greater or equal value."""
if isinstance(divisor, torch.Tensor):
divisor = int(divisor.max()) # to int
return math.ceil(x / divisor) * divisor
def clean_str(s):
"""Cleans a string by replacing special characters with underscore, e.g., `clean_str('#example!')` returns
'_example_'.
"""
return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
def one_cycle(y1=0.0, y2=1.0, steps=100):
"""
Generates a lambda for a sinusoidal ramp from y1 to y2 over 'steps'.
See https://arxiv.org/pdf/1812.01187.pdf for details.
"""
return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
def colorstr(*input):
"""
Colors a string using ANSI escape codes, e.g., colorstr('blue', 'hello world').
See https://en.wikipedia.org/wiki/ANSI_escape_code.
"""
*args, string = input if len(input) > 1 else ("blue", "bold", input[0]) # color arguments, string
colors = {
"black": "\033[30m", # basic colors
"red": "\033[31m",
"green": "\033[32m",
"yellow": "\033[33m",
"blue": "\033[34m",
"magenta": "\033[35m",
"cyan": "\033[36m",
"white": "\033[37m",
"bright_black": "\033[90m", # bright colors
"bright_red": "\033[91m",
"bright_green": "\033[92m",
"bright_yellow": "\033[93m",
"bright_blue": "\033[94m",
"bright_magenta": "\033[95m",
"bright_cyan": "\033[96m",
"bright_white": "\033[97m",
"end": "\033[0m", # misc
"bold": "\033[1m",
"underline": "\033[4m",
}
return "".join(colors[x] for x in args) + f"{string}" + colors["end"]
def labels_to_class_weights(labels, nc=80):
"""Calculates class weights from labels to handle class imbalance in training; input shape: (n, 5)."""
if labels[0] is None: # no labels loaded
return torch.Tensor()
labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO
classes = labels[:, 0].astype(int) # labels = [class xywh]
weights = np.bincount(classes, minlength=nc) # occurrences per class
# Prepend gridpoint count (for uCE training)
# gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image
# weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start
weights[weights == 0] = 1 # replace empty bins with 1
weights = 1 / weights # number of targets per class
weights /= weights.sum() # normalize
return torch.from_numpy(weights).float()
def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
"""Calculates image weights from labels using class weights for weighted sampling."""
# Usage: index = random.choices(range(n), weights=image_weights, k=1) # weighted image sample
class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels])
return (class_weights.reshape(1, nc) * class_counts).sum(1)
def coco80_to_coco91_class():
"""
Converts COCO 80-class index to COCO 91-class index used in the paper.
Reference: https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
"""
# a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
# b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
# x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
# x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
return [
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
27,
28,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59,
60,
61,
62,
63,
64,
65,
67,
70,
72,
73,
74,
75,
76,
77,
78,
79,
80,
81,
82,
84,
85,
86,
87,
88,
89,
90,
]
def xyxy2xywh(x):
"""Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right."""
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center
y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center
y[..., 2] = x[..., 2] - x[..., 0] # width
y[..., 3] = x[..., 3] - x[..., 1] # height
return y
def xywh2xyxy(x):
"""Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right."""
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x
y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y
y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x
y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y
return y
def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
"""Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right."""
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x
y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y
y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x
y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y
return y
def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
"""Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right."""
if clip:
clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center
y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center
y[..., 2] = (x[..., 2] - x[..., 0]) / w # width
y[..., 3] = (x[..., 3] - x[..., 1]) / h # height
return y
def xyn2xy(x, w=640, h=640, padw=0, padh=0):
"""Convert normalized segments into pixel segments, shape (n,2)."""
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = w * x[..., 0] + padw # top left x
y[..., 1] = h * x[..., 1] + padh # top left y
return y
def segment2box(segment, width=640, height=640):
"""Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)."""
x, y = segment.T # segment xy
inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
(
x,
y,
) = x[inside], y[inside]
return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy
def segments2boxes(segments):
"""Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)."""
boxes = []
for s in segments:
x, y = s.T # segment xy
boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy
return xyxy2xywh(np.array(boxes)) # cls, xywh
def resample_segments(segments, n=1000):
"""Resamples an (n,2) segment to a fixed number of points for consistent representation."""
for i, s in enumerate(segments):
s = np.concatenate((s, s[0:1, :]), axis=0)
x = np.linspace(0, len(s) - 1, n)
xp = np.arange(len(s))
segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy
return segments
def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
"""Rescales (xyxy) bounding boxes from img1_shape to img0_shape, optionally using provided `ratio_pad`."""
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
boxes[..., [0, 2]] -= pad[0] # x padding
boxes[..., [1, 3]] -= pad[1] # y padding
boxes[..., :4] /= gain
clip_boxes(boxes, img0_shape)
return boxes
def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False):
"""Rescales segment coordinates from img1_shape to img0_shape, optionally normalizing them with custom padding."""
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
segments[:, 0] -= pad[0] # x padding
segments[:, 1] -= pad[1] # y padding
segments /= gain
clip_segments(segments, img0_shape)
if normalize:
segments[:, 0] /= img0_shape[1] # width
segments[:, 1] /= img0_shape[0] # height
return segments
def clip_boxes(boxes, shape):
"""Clips bounding box coordinates (xyxy) to fit within the specified image shape (height, width)."""
if isinstance(boxes, torch.Tensor): # faster individually
boxes[..., 0].clamp_(0, shape[1]) # x1
boxes[..., 1].clamp_(0, shape[0]) # y1
boxes[..., 2].clamp_(0, shape[1]) # x2
boxes[..., 3].clamp_(0, shape[0]) # y2
else: # np.array (faster grouped)
boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2
boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2
def clip_segments(segments, shape):
"""Clips segment coordinates (xy1, xy2, ...) to an image's boundaries given its shape (height, width)."""
if isinstance(segments, torch.Tensor): # faster individually
segments[:, 0].clamp_(0, shape[1]) # x
segments[:, 1].clamp_(0, shape[0]) # y
else: # np.array (faster grouped)
segments[:, 0] = segments[:, 0].clip(0, shape[1]) # x
segments[:, 1] = segments[:, 1].clip(0, shape[0]) # y
def non_max_suppression(
prediction,
conf_thres=0.25,
iou_thres=0.45,
classes=None,
agnostic=False,
multi_label=False,
labels=(),
max_det=300,
nm=0, # number of masks
):
"""
Non-Maximum Suppression (NMS) on inference results to reject overlapping detections.
Returns:
list of detections, on (n,6) tensor per image [xyxy, conf, cls]
"""
# Checks
assert 0 <= conf_thres <= 1, f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0"
assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0"
if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out)
prediction = prediction[0] # select only inference output
device = prediction.device
mps = "mps" in device.type # Apple MPS
if mps: # MPS not fully supported yet, convert tensors to CPU before NMS
prediction = prediction.cpu()
bs = prediction.shape[0] # batch size
nc = prediction.shape[2] - nm - 5 # number of classes
xc = prediction[..., 4] > conf_thres # candidates
# Settings
# min_wh = 2 # (pixels) minimum box width and height
max_wh = 7680 # (pixels) maximum box width and height
max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
time_limit = 0.5 + 0.05 * bs # seconds to quit after
redundant = True # require redundant detections
multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
merge = False # use merge-NMS
t = time.time()
mi = 5 + nc # mask start index
output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs
for xi, x in enumerate(prediction): # image index, image inference
# Apply constraints
# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
x = x[xc[xi]] # confidence
# Cat apriori labels if autolabelling
if labels and len(labels[xi]):
lb = labels[xi]
v = torch.zeros((len(lb), nc + nm + 5), device=x.device)
v[:, :4] = lb[:, 1:5] # box
v[:, 4] = 1.0 # conf
v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls
x = torch.cat((x, v), 0)
# If none remain process next image
if not x.shape[0]:
continue
# Compute conf
x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
# Box/Mask
box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2)
mask = x[:, mi:] # zero columns if no masks
# Detections matrix nx6 (xyxy, conf, cls)
if multi_label:
i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T
x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)
else: # best class only
conf, j = x[:, 5:mi].max(1, keepdim=True)
x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]
# Filter by class
if classes is not None:
x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
# Apply finite constraint
# if not torch.isfinite(x).all():
# x = x[torch.isfinite(x).all(1)]
# Check shape
n = x.shape[0] # number of boxes
if not n: # no boxes
continue
x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes
# Batched NMS
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
i = i[:max_det] # limit detections
if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean)
# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
weights = iou * scores[None] # box weights
x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
if redundant:
i = i[iou.sum(1) > 1] # require redundancy
output[xi] = x[i]
if mps:
output[xi] = output[xi].to(device)
if (time.time() - t) > time_limit:
LOGGER.warning(f"WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded")
break # time limit exceeded
return output
def strip_optimizer(f="best.pt", s=""):
"""
Strips optimizer and optionally saves checkpoint to finalize training; arguments are file path 'f' and save path
's'.
Example: from utils.general import *; strip_optimizer()
"""
x = torch.load(f, map_location=torch.device("cpu"))
if x.get("ema"):
x["model"] = x["ema"] # replace model with ema
for k in "optimizer", "best_fitness", "ema", "updates": # keys
x[k] = None
x["epoch"] = -1
x["model"].half() # to FP16
for p in x["model"].parameters():
p.requires_grad = False
torch.save(x, s or f)
mb = os.path.getsize(s or f) / 1e6 # filesize
LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")
def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr("evolve: ")):
"""Logs evolution results and saves to CSV and YAML in `save_dir`, optionally syncs with `bucket`."""
evolve_csv = save_dir / "evolve.csv"
evolve_yaml = save_dir / "hyp_evolve.yaml"
keys = tuple(keys) + tuple(hyp.keys()) # [results + hyps]
keys = tuple(x.strip() for x in keys)
vals = results + tuple(hyp.values())
n = len(keys)
# Download (optional)
if bucket:
url = f"gs://{bucket}/evolve.csv"
if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0):
subprocess.run(["gsutil", "cp", f"{url}", f"{save_dir}"]) # download evolve.csv if larger than local
# Log to evolve.csv
s = "" if evolve_csv.exists() else (("%20s," * n % keys).rstrip(",") + "\n") # add header
with open(evolve_csv, "a") as f:
f.write(s + ("%20.5g," * n % vals).rstrip(",") + "\n")
# Save yaml
with open(evolve_yaml, "w") as f:
data = pd.read_csv(evolve_csv, skipinitialspace=True)
data = data.rename(columns=lambda x: x.strip()) # strip keys
i = np.argmax(fitness(data.values[:, :4])) #
generations = len(data)
f.write(
"# YOLOv5 Hyperparameter Evolution Results\n"
+ f"# Best generation: {i}\n"
+ f"# Last generation: {generations - 1}\n"
+ "# "
+ ", ".join(f"{x.strip():>20s}" for x in keys[:7])
+ "\n"
+ "# "
+ ", ".join(f"{x:>20.5g}" for x in data.values[i, :7])
+ "\n\n"
)
yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False)
# Print to screen
LOGGER.info(
prefix
+ f"{generations} generations finished, current result:\n"
+ prefix
+ ", ".join(f"{x.strip():>20s}" for x in keys)
+ "\n"
+ prefix
+ ", ".join(f"{x:20.5g}" for x in vals)
+ "\n\n"
)
if bucket:
subprocess.run(["gsutil", "cp", f"{evolve_csv}", f"{evolve_yaml}", f"gs://{bucket}"]) # upload
def apply_classifier(x, model, img, im0):
"""Applies second-stage classifier to YOLO outputs, filtering detections by class match."""
# Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval()
im0 = [im0] if isinstance(im0, np.ndarray) else im0
for i, d in enumerate(x): # per image
if d is not None and len(d):
d = d.clone()
# Reshape and pad cutouts
b = xyxy2xywh(d[:, :4]) # boxes
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square
b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad
d[:, :4] = xywh2xyxy(b).long()
# Rescale boxes from img_size to im0 size
scale_boxes(img.shape[2:], d[:, :4], im0[i].shape)
# Classes
pred_cls1 = d[:, 5].long()
ims = []
for a in d:
cutout = im0[i][int(a[1]) : int(a[3]), int(a[0]) : int(a[2])]
im = cv2.resize(cutout, (224, 224)) # BGR
im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32
im /= 255 # 0 - 255 to 0.0 - 1.0
ims.append(im)
pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction
x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections
return x
def increment_path(path, exist_ok=False, sep="", mkdir=False):
"""
Generates an incremented file or directory path if it exists, with optional mkdir; args: path, exist_ok=False,
sep="", mkdir=False.
Example: runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc
"""
path = Path(path) # os-agnostic
if path.exists() and not exist_ok:
path, suffix = (path.with_suffix(""), path.suffix) if path.is_file() else (path, "")
# Method 1
for n in range(2, 9999):
p = f"{path}{sep}{n}{suffix}" # increment path
if not os.path.exists(p): #
break
path = Path(p)
# Method 2 (deprecated)
# dirs = glob.glob(f"{path}{sep}*") # similar paths
# matches = [re.search(rf"{path.stem}{sep}(\d+)", d) for d in dirs]
# i = [int(m.groups()[0]) for m in matches if m] # indices
# n = max(i) + 1 if i else 2 # increment number
# path = Path(f"{path}{sep}{n}{suffix}") # increment path
if mkdir:
path.mkdir(parents=True, exist_ok=True) # make directory
return path
# OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------------
imshow_ = cv2.imshow # copy to avoid recursion errors
def imread(filename, flags=cv2.IMREAD_COLOR):
"""Reads an image from a file and returns it as a numpy array, using OpenCV's imdecode to support multilanguage
paths.
"""
return cv2.imdecode(np.fromfile(filename, np.uint8), flags)
def imwrite(filename, img):
"""Writes an image to a file, returns True on success and False on failure, supports multilanguage paths."""
try:
cv2.imencode(Path(filename).suffix, img)[1].tofile(filename)
return True
except Exception:
return False
def imshow(path, im):
"""Displays an image using Unicode path, requires encoded path and image matrix as input."""
imshow_(path.encode("unicode_escape").decode(), im)
if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename:
cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow # redefine
# Variables ------------------------------------------------------------------------------------------------------------
utils/google_app_engine/Dockerfile 0 → 100644
View file @ 7a650e36
FROM gcr.io/google-appengine/python
# Create a virtualenv for dependencies. This isolates these packages from
# system-level packages.
# Use -p python3 or -p python3.7 to select python version. Default is version 2.
RUN virtualenv /env -p python3
# Setting these environment variables are the same as running
# source /env/bin/activate.
ENV VIRTUAL_ENV /env
ENV PATH /env/bin:$PATH
RUN apt-get update && apt-get install -y python-opencv
# Copy the application's requirements.txt and run pip to install all
# dependencies into the virtualenv.
ADD requirements.txt /app/requirements.txt
RUN pip install -r /app/requirements.txt
# Add the application source code.
ADD . /app
# Run a WSGI server to serve the application. gunicorn must be declared as
# a dependency in requirements.txt.
CMD gunicorn -b :$PORT main:app
utils/google_app_engine/additional_requirements.txt 0 → 100644
View file @ 7a650e36
# add these requirements in your app on top of the existing ones
pip==23.3
Flask==2.3.2
gunicorn==19.10.0
werkzeug>=3.0.1 # not directly required, pinned by Snyk to avoid a vulnerability
utils/google_app_engine/app.yaml 0 → 100644
View file @ 7a650e36
runtime: custom
env: flex
service: yolov5app
liveness_check:
initial_delay_sec: 600
manual_scaling:
instances: 1
resources:
cpu: 1
memory_gb: 4
disk_size_gb: 20
utils/loggers/__init__.py 0 → 100644
View file @ 7a650e36
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""Logging utils."""
import json
import os
import warnings
from pathlib import Path
import pkg_resources as pkg
import torch
from utils.general import LOGGER, colorstr, cv2
from utils.loggers.clearml.clearml_utils import ClearmlLogger
from utils.loggers.wandb.wandb_utils import WandbLogger
from utils.plots import plot_images, plot_labels, plot_results
from utils.torch_utils import de_parallel
LOGGERS = ("csv", "tb", "wandb", "clearml", "comet") # *.csv, TensorBoard, Weights & Biases, ClearML
RANK = int(os.getenv("RANK", -1))
try:
from torch.utils.tensorboard import SummaryWriter
except ImportError:
SummaryWriter = lambda *args: None # None = SummaryWriter(str)
try:
import wandb
assert hasattr(wandb, "__version__") # verify package import not local dir
if pkg.parse_version(wandb.__version__) >= pkg.parse_version("0.12.2") and RANK in {0, -1}:
try:
wandb_login_success = wandb.login(timeout=30)
except wandb.errors.UsageError: # known non-TTY terminal issue
wandb_login_success = False
if not wandb_login_success:
wandb = None
except (ImportError, AssertionError):
wandb = None
try:
import clearml
assert hasattr(clearml, "__version__") # verify package import not local dir
except (ImportError, AssertionError):
clearml = None
try:
if RANK in {0, -1}:
import comet_ml
assert hasattr(comet_ml, "__version__") # verify package import not local dir
from utils.loggers.comet import CometLogger
else:
comet_ml = None
except (ImportError, AssertionError):
comet_ml = None
def _json_default(value):
"""
Format `value` for JSON serialization (e.g. unwrap tensors).
Fall back to strings.
"""
if isinstance(value, torch.Tensor):
try:
value = value.item()
except ValueError: # "only one element tensors can be converted to Python scalars"
pass
return value if isinstance(value, float) else str(value)
class Loggers:
# YOLOv5 Loggers class
def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
"""Initializes loggers for YOLOv5 training and validation metrics, paths, and options."""
self.save_dir = save_dir
self.weights = weights
self.opt = opt
self.hyp = hyp
self.plots = not opt.noplots # plot results
self.logger = logger # for printing results to console
self.include = include
self.keys = [
"train/box_loss",
"train/obj_loss",
"train/cls_loss", # train loss
"metrics/precision",
"metrics/recall",
"metrics/mAP_0.5",
"metrics/mAP_0.5:0.95", # metrics
"val/box_loss",
"val/obj_loss",
"val/cls_loss", # val loss
"x/lr0",
"x/lr1",
"x/lr2",
] # params
self.best_keys = ["best/epoch", "best/precision", "best/recall", "best/mAP_0.5", "best/mAP_0.5:0.95"]
for k in LOGGERS:
setattr(self, k, None) # init empty logger dictionary
self.csv = True # always log to csv
self.ndjson_console = "ndjson_console" in self.include # log ndjson to console
self.ndjson_file = "ndjson_file" in self.include # log ndjson to file
# Messages
if not comet_ml:
prefix = colorstr("Comet: ")
s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet"
self.logger.info(s)
# TensorBoard
s = self.save_dir
if "tb" in self.include and not self.opt.evolve:
prefix = colorstr("TensorBoard: ")
self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/")
self.tb = SummaryWriter(str(s))
# W&B
if wandb and "wandb" in self.include:
self.opt.hyp = self.hyp # add hyperparameters
self.wandb = WandbLogger(self.opt)
else:
self.wandb = None
# ClearML
if clearml and "clearml" in self.include:
try:
self.clearml = ClearmlLogger(self.opt, self.hyp)
except Exception:
self.clearml = None
prefix = colorstr("ClearML: ")
LOGGER.warning(
f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging."
f" See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme"
)
else:
self.clearml = None
# Comet
if comet_ml and "comet" in self.include:
if isinstance(self.opt.resume, str) and self.opt.resume.startswith("comet://"):
run_id = self.opt.resume.split("/")[-1]
self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id)
else:
self.comet_logger = CometLogger(self.opt, self.hyp)
else:
self.comet_logger = None
@property
def remote_dataset(self):
"""Fetches dataset dictionary from remote logging services like ClearML, Weights & Biases, or Comet ML."""
data_dict = None
if self.clearml:
data_dict = self.clearml.data_dict
if self.wandb:
data_dict = self.wandb.data_dict
if self.comet_logger:
data_dict = self.comet_logger.data_dict
return data_dict
def on_train_start(self):
"""Initializes the training process for Comet ML logger if it's configured."""
if self.comet_logger:
self.comet_logger.on_train_start()
def on_pretrain_routine_start(self):
"""Invokes pre-training routine start hook for Comet ML logger if available."""
if self.comet_logger:
self.comet_logger.on_pretrain_routine_start()
def on_pretrain_routine_end(self, labels, names):
"""Callback that runs at the end of pre-training routine, logging label plots if enabled."""
if self.plots:
plot_labels(labels, names, self.save_dir)
paths = self.save_dir.glob("*labels*.jpg") # training labels
if self.wandb:
self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
if self.comet_logger:
self.comet_logger.on_pretrain_routine_end(paths)
if self.clearml:
for path in paths:
self.clearml.log_plot(title=path.stem, plot_path=path)
def on_train_batch_end(self, model, ni, imgs, targets, paths, vals):
"""Logs training batch end events, plots images, and updates external loggers with batch-end data."""
log_dict = dict(zip(self.keys[:3], vals))
# Callback runs on train batch end
# ni: number integrated batches (since train start)
if self.plots:
if ni < 3:
f = self.save_dir / f"train_batch{ni}.jpg" # filename
plot_images(imgs, targets, paths, f)
if ni == 0 and self.tb and not self.opt.sync_bn:
log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz))
if ni == 10 and (self.wandb or self.clearml):
files = sorted(self.save_dir.glob("train*.jpg"))
if self.wandb:
self.wandb.log({"Mosaics": [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
if self.clearml:
self.clearml.log_debug_samples(files, title="Mosaics")
if self.comet_logger:
self.comet_logger.on_train_batch_end(log_dict, step=ni)
def on_train_epoch_end(self, epoch):
"""Callback that updates the current epoch in Weights & Biases at the end of a training epoch."""
if self.wandb:
self.wandb.current_epoch = epoch + 1
if self.comet_logger:
self.comet_logger.on_train_epoch_end(epoch)
def on_val_start(self):
"""Callback that signals the start of a validation phase to the Comet logger."""
if self.comet_logger:
self.comet_logger.on_val_start()
def on_val_image_end(self, pred, predn, path, names, im):
"""Callback that logs a validation image and its predictions to WandB or ClearML."""
if self.wandb:
self.wandb.val_one_image(pred, predn, path, names, im)
if self.clearml:
self.clearml.log_image_with_boxes(path, pred, names, im)
def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out):
"""Logs validation batch results to Comet ML during training at the end of each validation batch."""
if self.comet_logger:
self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out)
def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):
"""Logs validation results to WandB or ClearML at the end of the validation process."""
if self.wandb or self.clearml:
files = sorted(self.save_dir.glob("val*.jpg"))
if self.wandb:
self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
if self.clearml:
self.clearml.log_debug_samples(files, title="Validation")
if self.comet_logger:
self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
"""Callback that logs metrics and saves them to CSV or NDJSON at the end of each fit (train+val) epoch."""
x = dict(zip(self.keys, vals))
if self.csv:
file = self.save_dir / "results.csv"
n = len(x) + 1 # number of cols
s = "" if file.exists() else (("%20s," * n % tuple(["epoch"] + self.keys)).rstrip(",") + "\n") # add header
with open(file, "a") as f:
f.write(s + ("%20.5g," * n % tuple([epoch] + vals)).rstrip(",") + "\n")
if self.ndjson_console or self.ndjson_file:
json_data = json.dumps(dict(epoch=epoch, **x), default=_json_default)
if self.ndjson_console:
print(json_data)
if self.ndjson_file:
file = self.save_dir / "results.ndjson"
with open(file, "a") as f:
print(json_data, file=f)
if self.tb:
for k, v in x.items():
self.tb.add_scalar(k, v, epoch)
elif self.clearml: # log to ClearML if TensorBoard not used
self.clearml.log_scalars(x, epoch)
if self.wandb:
if best_fitness == fi:
best_results = [epoch] + vals[3:7]
for i, name in enumerate(self.best_keys):
self.wandb.wandb_run.summary[name] = best_results[i] # log best results in the summary
self.wandb.log(x)
self.wandb.end_epoch()
if self.clearml:
self.clearml.current_epoch_logged_images = set() # reset epoch image limit
self.clearml.current_epoch += 1
if self.comet_logger:
self.comet_logger.on_fit_epoch_end(x, epoch=epoch)
def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
"""Callback that handles model saving events, logging to Weights & Biases or ClearML if enabled."""
if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1:
if self.wandb:
self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
if self.clearml:
self.clearml.task.update_output_model(
model_path=str(last), model_name="Latest Model", auto_delete_file=False
)
if self.comet_logger:
self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi)
def on_train_end(self, last, best, epoch, results):
"""Callback that runs at the end of training to save plots and log results."""
if self.plots:
plot_results(file=self.save_dir / "results.csv") # save results.png
files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))]
files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter
self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}")
if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles
for f in files:
self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC")
if self.wandb:
self.wandb.log(dict(zip(self.keys[3:10], results)))
self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]})
# Calling wandb.log. TODO: Refactor this into WandbLogger.log_model
if not self.opt.evolve:
wandb.log_artifact(
str(best if best.exists() else last),
type="model",
name=f"run_{self.wandb.wandb_run.id}_model",
aliases=["latest", "best", "stripped"],
)
self.wandb.finish_run()
if self.clearml and not self.opt.evolve:
self.clearml.log_summary(dict(zip(self.keys[3:10], results)))
[self.clearml.log_plot(title=f.stem, plot_path=f) for f in files]
self.clearml.log_model(
str(best if best.exists() else last), "Best Model" if best.exists() else "Last Model", epoch
)
if self.comet_logger:
final_results = dict(zip(self.keys[3:10], results))
self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results)
def on_params_update(self, params: dict):
"""Updates experiment hyperparameters or configurations in WandB, Comet, or ClearML."""
if self.wandb:
self.wandb.wandb_run.config.update(params, allow_val_change=True)
if self.comet_logger:
self.comet_logger.on_params_update(params)
if self.clearml:
self.clearml.task.connect(params)
class GenericLogger:
"""
YOLOv5 General purpose logger for non-task specific logging
Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...)
Arguments
opt: Run arguments
console_logger: Console logger
include: loggers to include
"""
def __init__(self, opt, console_logger, include=("tb", "wandb", "clearml")):
"""Initializes a generic logger with optional TensorBoard, W&B, and ClearML support."""
self.save_dir = Path(opt.save_dir)
self.include = include
self.console_logger = console_logger
self.csv = self.save_dir / "results.csv" # CSV logger
if "tb" in self.include:
prefix = colorstr("TensorBoard: ")
self.console_logger.info(
f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/"
)
self.tb = SummaryWriter(str(self.save_dir))
if wandb and "wandb" in self.include:
self.wandb = wandb.init(
project=web_project_name(str(opt.project)), name=None if opt.name == "exp" else opt.name, config=opt
)
else:
self.wandb = None
if clearml and "clearml" in self.include:
try:
# Hyp is not available in classification mode
hyp = {} if "hyp" not in opt else opt.hyp
self.clearml = ClearmlLogger(opt, hyp)
except Exception:
self.clearml = None
prefix = colorstr("ClearML: ")
LOGGER.warning(
f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging."
f" See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme"
)
else:
self.clearml = None
def log_metrics(self, metrics, epoch):
"""Logs metrics to CSV, TensorBoard, W&B, and ClearML; `metrics` is a dict, `epoch` is an int."""
if self.csv:
keys, vals = list(metrics.keys()), list(metrics.values())
n = len(metrics) + 1 # number of cols
s = "" if self.csv.exists() else (("%23s," * n % tuple(["epoch"] + keys)).rstrip(",") + "\n") # header
with open(self.csv, "a") as f:
f.write(s + ("%23.5g," * n % tuple([epoch] + vals)).rstrip(",") + "\n")
if self.tb:
for k, v in metrics.items():
self.tb.add_scalar(k, v, epoch)
if self.wandb:
self.wandb.log(metrics, step=epoch)
if self.clearml:
self.clearml.log_scalars(metrics, epoch)
def log_images(self, files, name="Images", epoch=0):
"""Logs images to all loggers with optional naming and epoch specification."""
files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path
files = [f for f in files if f.exists()] # filter by exists
if self.tb:
for f in files:
self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC")
if self.wandb:
self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch)
if self.clearml:
if name == "Results":
[self.clearml.log_plot(f.stem, f) for f in files]
else:
self.clearml.log_debug_samples(files, title=name)
def log_graph(self, model, imgsz=(640, 640)):
"""Logs model graph to all configured loggers with specified input image size."""
if self.tb:
log_tensorboard_graph(self.tb, model, imgsz)
def log_model(self, model_path, epoch=0, metadata=None):
"""Logs the model to all configured loggers with optional epoch and metadata."""
if metadata is None:
metadata = {}
# Log model to all loggers
if self.wandb:
art = wandb.Artifact(name=f"run_{wandb.run.id}_model", type="model", metadata=metadata)
art.add_file(str(model_path))
wandb.log_artifact(art)
if self.clearml:
self.clearml.log_model(model_path=model_path, model_name=model_path.stem)
def update_params(self, params):
"""Updates logged parameters in WandB and/or ClearML if enabled."""
if self.wandb:
wandb.run.config.update(params, allow_val_change=True)
if self.clearml:
self.clearml.task.connect(params)
def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
"""Logs the model graph to TensorBoard with specified image size and model."""
try:
p = next(model.parameters()) # for device, type
imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand
im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image (WARNING: must be zeros, not empty)
with warnings.catch_warnings():
warnings.simplefilter("ignore") # suppress jit trace warning
tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), [])
except Exception as e:
LOGGER.warning(f"WARNING ⚠️ TensorBoard graph visualization failure {e}")
def web_project_name(project):
"""Converts a local project name to a standardized web project name with optional suffixes."""
if not project.startswith("runs/train"):
return project
suffix = "-Classify" if project.endswith("-cls") else "-Segment" if project.endswith("-seg") else ""
return f"YOLOv5{suffix}"
utils/loggers/clearml/README.md 0 → 100644
View file @ 7a650e36
# ClearML Integration
<img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_dark.png#gh-light-mode-only" alt="Clear|ML"><img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_light.png#gh-dark-mode-only" alt="Clear|ML">
## About ClearML
[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️.
🔨 Track every YOLOv5 training run in the <b>experiment manager</b>
🔧 Version and easily access your custom training data with the integrated ClearML <b>Data Versioning Tool</b>
🔦 <b>Remotely train and monitor</b> your YOLOv5 training runs using ClearML Agent
🔬 Get the very best mAP using ClearML <b>Hyperparameter Optimization</b>
🔭 Turn your newly trained <b>YOLOv5 model into an API</b> with just a few commands using ClearML Serving
<br />
And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline!
<br />
<br />
![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif)
<br />
<br />
## 🦾 Setting Things Up
To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one:
Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go!
1. Install the `clearml` python package:
```bash
pip install clearml
```
2. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
```bash
clearml-init
```
That's it! You're done 😎
<br />
## 🚀 Training YOLOv5 With ClearML
To enable ClearML experiment tracking, simply install the ClearML pip package.
```bash
pip install clearml>=1.2.0
```
This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager.
If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`. PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name!
```bash
python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
```
or with custom project and task name:
```bash
python train.py --project my_project --name my_training --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
```
This will capture:
- Source code + uncommitted changes
- Installed packages
- (Hyper)parameters
- Model files (use `--save-period n` to save a checkpoint every n epochs)
- Console output
- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...)
- General info such as machine details, runtime, creation date etc.
- All produced plots such as label correlogram and confusion matrix
- Images with bounding boxes per epoch
- Mosaic per epoch
- Validation images per epoch
- ...
That's a lot right? 🤯 Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them!
There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works!
<br />
## 🔗 Dataset Version Management
Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment!
![ClearML Dataset Interface](https://github.com/thepycoder/clearml_screenshots/raw/main/clearml_data.gif)
### Prepare Your Dataset
The YOLOv5 repository supports a number of different datasets by using yaml files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the yaml or with the scripts provided by yolov5, you get this folder structure:
```
..
|_ yolov5
|_ datasets
|_ coco128
|_ images
|_ labels
|_ LICENSE
|_ README.txt
```
But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure.
Next, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. This yaml files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example yamls.
Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`.
```
..
|_ yolov5
|_ datasets
|_ coco128
|_ images
|_ labels
|_ coco128.yaml # <---- HERE!
|_ LICENSE
|_ README.txt
```
### Upload Your Dataset
To get this dataset into ClearML as a versioned dataset, go to the dataset root folder and run the following command:
```bash
cd coco128
clearml-data sync --project YOLOv5 --name coco128 --folder .
```
The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other:
```bash
# Optionally add --parent <parent_dataset_id> if you want to base
# this version on another dataset version, so no duplicate files are uploaded!
clearml-data create --name coco128 --project YOLOv5
clearml-data add --files .
clearml-data close
```
### Run Training Using A ClearML Dataset
Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models!
```bash
python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache
```
<br />
## 👀 Hyperparameter Optimization
Now that we have our experiments and data versioned, it's time to take a look at what we can build on top!
Using the code information, installed packages and environment details, the experiment itself is now **completely reproducible**. In fact, ClearML allows you to clone an experiment and even change its parameters. We can then just rerun it with these new parameters automatically, this is basically what HPO does!
To **run hyperparameter optimization locally**, we've included a pre-made script for you. Just make sure a training task has been run at least once, so it is in the ClearML experiment manager, we will essentially clone it and change its hyperparameters.
You'll need to fill in the ID of this `template task` in the script found at `utils/loggers/clearml/hpo.py` and then just run it :) You can change `task.execute_locally()` to `task.execute()` to put it in a ClearML queue and have a remote agent work on it instead.
```bash
# To use optuna, install it first, otherwise you can change the optimizer to just be RandomSearch
pip install optuna
python utils/loggers/clearml/hpo.py
```
![HPO](https://github.com/thepycoder/clearml_screenshots/raw/main/hpo.png)
## 🤯 Remote Execution (advanced)
Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs. This is where the ClearML Agent comes into play. Check out what the agent can do here:
- [YouTube video](https://youtu.be/MX3BrXnaULs)
- [Documentation](https://clear.ml/docs/latest/docs/clearml_agent)
In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager.
You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running:
```bash
clearml-agent daemon --queue <queues_to_listen_to> [--docker]
```
### Cloning, Editing And Enqueuing
With our agent running, we can give it some work. Remember from the HPO section that we can clone a task and edit the hyperparameters? We can do that from the interface too!
🪄 Clone the experiment by right-clicking it
🎯 Edit the hyperparameters to what you wish them to be
⏳ Enqueue the task to any of the queues by right-clicking it
![Enqueue a task from the UI](https://github.com/thepycoder/clearml_screenshots/raw/main/enqueue.gif)
### Executing A Task Remotely
Now you can clone a task like we explained above, or simply mark your current script by adding `task.execute_remotely()` and on execution it will be put into a queue, for the agent to start working on!
To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the clearml logger has been instantiated:
```python
# ...
# Loggers
data_dict = None
if RANK in {-1, 0}:
loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance
if loggers.clearml:
loggers.clearml.task.execute_remotely(queue="my_queue") # <------ ADD THIS LINE
# Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML
data_dict = loggers.clearml.data_dict
# ...
```
When running the training script after this change, python will run the script up until that line, after which it will package the code and send it to the queue instead!
### Autoscaling workers
ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue. Once the tasks are processed, the autoscaler will automatically shut down the remote machines, and you stop paying!
Check out the autoscalers getting started video below.
[![Watch the video](https://img.youtube.com/vi/j4XVMAaUt3E/0.jpg)](https://youtu.be/j4XVMAaUt3E)
utils/loggers/clearml/clearml_utils.py 0 → 100644
View file @ 7a650e36
"""Main Logger class for ClearML experiment tracking."""
import glob
import re
from pathlib import Path
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import yaml
from ultralytics.utils.plotting import Annotator, colors
try:
import clearml
from clearml import Dataset, Task
assert hasattr(clearml, "__version__") # verify package import not local dir
except (ImportError, AssertionError):
clearml = None
def construct_dataset(clearml_info_string):
"""Load in a clearml dataset and fill the internal data_dict with its contents."""
dataset_id = clearml_info_string.replace("clearml://", "")
dataset = Dataset.get(dataset_id=dataset_id)
dataset_root_path = Path(dataset.get_local_copy())
# We'll search for the yaml file definition in the dataset
yaml_filenames = list(glob.glob(str(dataset_root_path / "*.yaml")) + glob.glob(str(dataset_root_path / "*.yml")))
if len(yaml_filenames) > 1:
raise ValueError(
"More than one yaml file was found in the dataset root, cannot determine which one contains "
"the dataset definition this way."
)
elif not yaml_filenames:
raise ValueError(
"No yaml definition found in dataset root path, check that there is a correct yaml file "
"inside the dataset root path."
)
with open(yaml_filenames[0]) as f:
dataset_definition = yaml.safe_load(f)
assert set(
dataset_definition.keys()
).issuperset(
{"train", "test", "val", "nc", "names"}
), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')"
data_dict = {}
data_dict["train"] = (
str((dataset_root_path / dataset_definition["train"]).resolve()) if dataset_definition["train"] else None
)
data_dict["test"] = (
str((dataset_root_path / dataset_definition["test"]).resolve()) if dataset_definition["test"] else None
)
data_dict["val"] = (
str((dataset_root_path / dataset_definition["val"]).resolve()) if dataset_definition["val"] else None
)
data_dict["nc"] = dataset_definition["nc"]
data_dict["names"] = dataset_definition["names"]
return data_dict
class ClearmlLogger:
"""
Log training runs, datasets, models, and predictions to ClearML.
This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, this information
includes hyperparameters, system configuration and metrics, model metrics, code information and basic data metrics
and analyses.
By providing additional command line arguments to train.py, datasets, models and predictions can also be logged.
"""
def __init__(self, opt, hyp):
"""
- Initialize ClearML Task, this object will capture the experiment
- Upload dataset version to ClearML Data if opt.upload_dataset is True
arguments:
opt (namespace) -- Commandline arguments for this run
hyp (dict) -- Hyperparameters for this run
"""
self.current_epoch = 0
# Keep tracked of amount of logged images to enforce a limit
self.current_epoch_logged_images = set()
# Maximum number of images to log to clearML per epoch
self.max_imgs_to_log_per_epoch = 16
# Get the interval of epochs when bounding box images should be logged
# Only for detection task though!
if "bbox_interval" in opt:
self.bbox_interval = opt.bbox_interval
self.clearml = clearml
self.task = None
self.data_dict = None
if self.clearml:
self.task = Task.init(
project_name="YOLOv5" if str(opt.project).startswith("runs/") else opt.project,
task_name=opt.name if opt.name != "exp" else "Training",
tags=["YOLOv5"],
output_uri=True,
reuse_last_task_id=opt.exist_ok,
auto_connect_frameworks={"pytorch": False, "matplotlib": False},
# We disconnect pytorch auto-detection, because we added manual model save points in the code
)
# ClearML's hooks will already grab all general parameters
# Only the hyperparameters coming from the yaml config file
# will have to be added manually!
self.task.connect(hyp, name="Hyperparameters")
self.task.connect(opt, name="Args")
# Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent
self.task.set_base_docker(
"ultralytics/yolov5:latest",
docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"',
docker_setup_bash_script="pip install clearml",
)
# Get ClearML Dataset Version if requested
if opt.data.startswith("clearml://"):
# data_dict should have the following keys:
# names, nc (number of classes), test, train, val (all three relative paths to ../datasets)
self.data_dict = construct_dataset(opt.data)
# Set data to data_dict because wandb will crash without this information and opt is the best way
# to give it to them
opt.data = self.data_dict
def log_scalars(self, metrics, epoch):
"""
Log scalars/metrics to ClearML.
arguments:
metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...}
epoch (int) iteration number for the current set of metrics
"""
for k, v in metrics.items():
title, series = k.split("/")
self.task.get_logger().report_scalar(title, series, v, epoch)
def log_model(self, model_path, model_name, epoch=0):
"""
Log model weights to ClearML.
arguments:
model_path (PosixPath or str) Path to the model weights
model_name (str) Name of the model visible in ClearML
epoch (int) Iteration / epoch of the model weights
"""
self.task.update_output_model(
model_path=str(model_path), name=model_name, iteration=epoch, auto_delete_file=False
)
def log_summary(self, metrics):
"""
Log final metrics to a summary table.
arguments:
metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...}
"""
for k, v in metrics.items():
self.task.get_logger().report_single_value(k, v)
def log_plot(self, title, plot_path):
"""
Log image as plot in the plot section of ClearML.
arguments:
title (str) Title of the plot
plot_path (PosixPath or str) Path to the saved image file
"""
img = mpimg.imread(plot_path)
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect="auto", xticks=[], yticks=[]) # no ticks
ax.imshow(img)
self.task.get_logger().report_matplotlib_figure(title, "", figure=fig, report_interactive=False)
def log_debug_samples(self, files, title="Debug Samples"):
"""
Log files (images) as debug samples in the ClearML task.
arguments:
files (List(PosixPath)) a list of file paths in PosixPath format
title (str) A title that groups together images with the same values
"""
for f in files:
if f.exists():
it = re.search(r"_batch(\d+)", f.name)
iteration = int(it.groups()[0]) if it else 0
self.task.get_logger().report_image(
title=title, series=f.name.replace(f"_batch{iteration}", ""), local_path=str(f), iteration=iteration
)
def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25):
"""
Draw the bounding boxes on a single image and report the result as a ClearML debug sample.
arguments:
image_path (PosixPath) the path the original image file
boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
class_names (dict): dict containing mapping of class int to class name
image (Tensor): A torch tensor containing the actual image data
"""
if (
len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch
and self.current_epoch >= 0
and (self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images)
):
im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2))
annotator = Annotator(im=im, pil=True)
for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])):
color = colors(i)
class_name = class_names[int(class_nr)]
confidence_percentage = round(float(conf) * 100, 2)
label = f"{class_name}: {confidence_percentage}%"
if conf > conf_threshold:
annotator.rectangle(box.cpu().numpy(), outline=color)
annotator.box_label(box.cpu().numpy(), label=label, color=color)
annotated_image = annotator.result()
self.task.get_logger().report_image(
title="Bounding Boxes", series=image_path.name, iteration=self.current_epoch, image=annotated_image
)
self.current_epoch_logged_images.add(image_path)
utils/loggers/clearml/hpo.py 0 → 100644
View file @ 7a650e36
from clearml import Task
# Connecting ClearML with the current process,
# from here on everything is logged automatically
from clearml.automation import HyperParameterOptimizer, UniformParameterRange
from clearml.automation.optuna import OptimizerOptuna
task = Task.init(
project_name="Hyper-Parameter Optimization",
task_name="YOLOv5",
task_type=Task.TaskTypes.optimizer,
reuse_last_task_id=False,
)
# Example use case:
optimizer = HyperParameterOptimizer(
# This is the experiment we want to optimize
base_task_id="<your_template_task_id>",
# here we define the hyper-parameters to optimize
# Notice: The parameter name should exactly match what you see in the UI: <section_name>/<parameter>
# For Example, here we see in the base experiment a section Named: "General"
# under it a parameter named "batch_size", this becomes "General/batch_size"
# If you have `argparse` for example, then arguments will appear under the "Args" section,
# and you should instead pass "Args/batch_size"
hyper_parameters=[
UniformParameterRange("Hyperparameters/lr0", min_value=1e-5, max_value=1e-1),
UniformParameterRange("Hyperparameters/lrf", min_value=0.01, max_value=1.0),
UniformParameterRange("Hyperparameters/momentum", min_value=0.6, max_value=0.98),
UniformParameterRange("Hyperparameters/weight_decay", min_value=0.0, max_value=0.001),
UniformParameterRange("Hyperparameters/warmup_epochs", min_value=0.0, max_value=5.0),
UniformParameterRange("Hyperparameters/warmup_momentum", min_value=0.0, max_value=0.95),
UniformParameterRange("Hyperparameters/warmup_bias_lr", min_value=0.0, max_value=0.2),
UniformParameterRange("Hyperparameters/box", min_value=0.02, max_value=0.2),
UniformParameterRange("Hyperparameters/cls", min_value=0.2, max_value=4.0),
UniformParameterRange("Hyperparameters/cls_pw", min_value=0.5, max_value=2.0),
UniformParameterRange("Hyperparameters/obj", min_value=0.2, max_value=4.0),
UniformParameterRange("Hyperparameters/obj_pw", min_value=0.5, max_value=2.0),
UniformParameterRange("Hyperparameters/iou_t", min_value=0.1, max_value=0.7),
UniformParameterRange("Hyperparameters/anchor_t", min_value=2.0, max_value=8.0),
UniformParameterRange("Hyperparameters/fl_gamma", min_value=0.0, max_value=4.0),
UniformParameterRange("Hyperparameters/hsv_h", min_value=0.0, max_value=0.1),
UniformParameterRange("Hyperparameters/hsv_s", min_value=0.0, max_value=0.9),
UniformParameterRange("Hyperparameters/hsv_v", min_value=0.0, max_value=0.9),
UniformParameterRange("Hyperparameters/degrees", min_value=0.0, max_value=45.0),
UniformParameterRange("Hyperparameters/translate", min_value=0.0, max_value=0.9),
UniformParameterRange("Hyperparameters/scale", min_value=0.0, max_value=0.9),
UniformParameterRange("Hyperparameters/shear", min_value=0.0, max_value=10.0),
UniformParameterRange("Hyperparameters/perspective", min_value=0.0, max_value=0.001),
UniformParameterRange("Hyperparameters/flipud", min_value=0.0, max_value=1.0),
UniformParameterRange("Hyperparameters/fliplr", min_value=0.0, max_value=1.0),
UniformParameterRange("Hyperparameters/mosaic", min_value=0.0, max_value=1.0),
UniformParameterRange("Hyperparameters/mixup", min_value=0.0, max_value=1.0),
UniformParameterRange("Hyperparameters/copy_paste", min_value=0.0, max_value=1.0),
],
# this is the objective metric we want to maximize/minimize
objective_metric_title="metrics",
objective_metric_series="mAP_0.5",
# now we decide if we want to maximize it or minimize it (accuracy we maximize)
objective_metric_sign="max",
# let us limit the number of concurrent experiments,
# this in turn will make sure we do dont bombard the scheduler with experiments.
# if we have an auto-scaler connected, this, by proxy, will limit the number of machine
max_number_of_concurrent_tasks=1,
# this is the optimizer class (actually doing the optimization)
# Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)
optimizer_class=OptimizerOptuna,
# If specified only the top K performing Tasks will be kept, the others will be automatically archived
save_top_k_tasks_only=5, # 5,
compute_time_limit=None,
total_max_jobs=20,
min_iteration_per_job=None,
max_iteration_per_job=None,
)
# report every 10 seconds, this is way too often, but we are testing here
optimizer.set_report_period(10 / 60)
# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent
# an_optimizer.start_locally(job_complete_callback=job_complete_callback)
# set the time limit for the optimization process (2 hours)
optimizer.set_time_limit(in_minutes=120.0)
# Start the optimization process in the local environment
optimizer.start_locally()
# wait until process is done (notice we are controlling the optimization process in the background)
optimizer.wait()
# make sure background optimization stopped
optimizer.stop()
print("We are done, good bye")
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