我要评论一、yolov8环境搭建
这篇文章将跳过基础的深度学习环境的搭建，如果没有完成的可以看我的这篇博客：超详细||深度学习环境搭建记录cuda+anaconda+pytorch+pycharm-csdn博客
1. 在github上下载源码：
github - ultralytics/ultralytics: new - yolov8 🚀 in pytorch > onnx > openvino > coreml > tflite
2. 安装ultralytics（yolov8改名为ultralytics）
这里有两种方式安装ultralytics
- 直接使用cli
pip install ultralytics
- 使用requirements.txt安装，这种方法是在上面下载的源码处安装，方便对yolov8进行改进
cd ultralytics
pip install -r requirements.txt
3. 安装wandb
pip install wandb登录自己的wandb账号
wandb login二、开始训练
1. 构建数据集
数据集要严格按照下面的目录格式，image的格式为jpg，label的格式为txt，对应的image和label的名字要一致
dataset
└─images
└─train
└─val
└─labels
└─train
└─val2. 创建一个dataset.yaml文件
更换自己的image train和image val的地址，labels地址不用，它会自动索引
将classes改为自己的类别，从0开始
path: ../datasets/coco128 # dataset root dir
train: images/train2017 # train images (relative to 'path') 128 images
val: images/train2017 # val images (relative to 'path') 128 images
test: # test images (optional)
# classes
names:
0: person
1: bicycle
2: car
3: motorcycle
4: airplane
5: bus
6: train
7: truck
8: boat3. 新建一个train.py，修改相关参数，运行即可开始训练
from ultralytics import yolo
if __name__ == '__main__':
# load a model
model = yolo(r'\ultralytics\detection\yolov8n\yolov8n.yaml') # 不使用预训练权重训练
# model = yolo(r'yolov8p.yaml').load("yolov8n.pt") # 使用预训练权重训练
# trainparameters ----------------------------------------------------------------------------------------------
model.train(
data=r'\ultralytics\detection\dataset\appledata.yaml',
epochs= 30 , # (int) number of epochs to train for
patience= 50 , # (int) epochs to wait for no observable improvement for early stopping of training
batch= 8 , # (int) number of images per batch (-1 for autobatch)
imgsz= 320 , # (int) size of input images as integer or w,h
save= true , # (bool) save train checkpoints and predict results
save_period= -1, # (int) save checkpoint every x epochs (disabled if < 1)
cache= false , # (bool) true/ram, disk or false. use cache for data loading
device= 0 , # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
workers= 16 , # (int) number of worker threads for data loading (per rank if ddp)
project= 'result', # (str, optional) project name
name= 'yolov8n' ,# (str, optional) experiment name, results saved to 'project/name' directory
exist_ok= false , # (bool) whether to overwrite existing experiment
pretrained= false , # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
optimizer= 'sgd', # (str) optimizer to use, choices=[sgd, adam, adamax, adamw, nadam, radam, rmsprop, auto]
verbose= true ,# (bool) whether to print verbose output
seed= 0 , # (int) random seed for reproducibility
deterministic= true , # (bool) whether to enable deterministic mode
single_cls= true , # (bool) train multi-class data as single-class
rect= false ,# (bool) rectangular training if mode='train' or rectangular validation if mode='val'
cos_lr= false , # (bool) use cosine learning rate scheduler
close_mosaic= 0, # (int) disable mosaic augmentation for final epochs
resume= false , # (bool) resume training from last checkpoint
amp= false, # (bool) automatic mixed precision (amp) training, choices=[true, false], true runs amp check
fraction= 1.0 , # (float) dataset fraction to train on (default is 1.0, all images in train set)
profile= false, # (bool) profile onnx and tensorrt speeds during training for loggers
# segmentation
overlap_mask= true , # (bool) masks should overlap during training (segment train only)
mask_ratio= 4, # (int) mask downsample ratio (segment train only)
# classification
dropout= 0.0, # (float) use dropout regularization (classify train only)
# hyperparameters ----------------------------------------------------------------------------------------------
lr0=0.01, # (float) initial learning rate (i.e. sgd=1e-2, adam=1e-3)
lrf=0.01, # (float) final learning rate (lr0 * lrf)
momentum=0.937, # (float) sgd momentum/adam beta1
weight_decay=0.0005, # (float) optimizer weight decay 5e-4
warmup_epochs=3.0, # (float) warmup epochs (fractions ok)
warmup_momentum=0.8, # (float) warmup initial momentum
warmup_bias_lr=0.1, # (float) warmup initial bias lr
box=7.5, # (float) box loss gain
cls=0.5, # (float) cls loss gain (scale with pixels)
dfl=1.5, # (float) dfl loss gain
pose=12.0, # (float) pose loss gain
kobj=1.0, # (float) keypoint obj loss gain
label_smoothing=0.0, # (float) label smoothing (fraction)
nbs=64, # (int) nominal batch size
hsv_h=0.015, # (float) image hsv-hue augmentation (fraction)
hsv_s=0.7, # (float) image hsv-saturation augmentation (fraction)
hsv_v=0.4, # (float) image hsv-value augmentation (fraction)
degrees=0.0, # (float) image rotation (+/- deg)
translate=0.1, # (float) image translation (+/- fraction)
scale=0.5, # (float) image scale (+/- gain)
shear=0.0, # (float) image shear (+/- deg)
perspective=0.0, # (float) image perspective (+/- fraction), range 0-0.001
flipud=0.0, # (float) image flip up-down (probability)
fliplr=0.5, # (float) image flip left-right (probability)
mosaic=1.0, # (float) image mosaic (probability)
mixup=0.0, # (float) image mixup (probability)
copy_paste=0.0, # (float) segment copy-paste (probability)
)
三、测试与验证
1. 新建一个test.py, 这个可以打印网路信息，参数量以及flops，还有每一层网络的信息
from ultralytics import yolo
if __name__ == '__main__':
# load a model
model = yolo(r'\ultralytics\detection\yolov8n\yolov8n.yaml') # build a new model from yaml
model.info()2. 新建一个val.py，这个可以打印模型在验证集上的结果，如map，推理速度等
from ultralytics import yolo
if __name__ == '__main__':
# load a model
model = yolo(r'\ultralytics\detection\yolov8n\result\yolov8n4\weights\best.pt') # build a new model from yaml
# validate the model
model.val(
val=true, # (bool) validate/test during training
data=r'\ultralytics\detection\dataset\appledata.yaml',
split='val', # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
batch=1, # 测试速度时一般设置为 1 ，设置越大速度越快。 (int) number of images per batch (-1 for autobatch)
imgsz=320, # (int) size of input images as integer or w,h
device=0, # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
workers=8, # (int) number of worker threads for data loading (per rank if ddp)
save_json=false, # (bool) save results to json file
save_hybrid=false, # (bool) save hybrid version of labels (labels + additional predictions)
conf=0.001, # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
iou=0.7, # (float) intersection over union (iou) threshold for nms
project='val', # (str, optional) project name
name='', # (str, optional) experiment name, results saved to 'project/name' directory
max_det=300, # (int) maximum number of detections per image
half=true, # (bool) use half precision (fp16)
dnn=false, # (bool) use opencv dnn for onnx inference
plots=true, # (bool) save plots during train/val
)3. 新建一个predict.py，这个可以根据训练好的权重文件进行推理，权重文件格式支持pt，onnx等，支持图片，视频，摄像头等进行推理
from ultralytics import yolo
if __name__ == '__main__':
# load a model
model = yolo(r'\deploy\yolov8n.pt') # pretrained yolov8n model
model.predict(
source=r'\deploy\output_video.mp4',
save=false, # save predict results
imgsz=320, # (int) size of input images as integer or w,h
conf=0.25, # object confidence threshold for detection (default 0.25 predict, 0.001 val)
iou=0.7, # # intersection over union (iou) threshold for nms
show=true, # show results if possible
project='', # (str, optional) project name
name='', # (str, optional) experiment name, results saved to 'project/name' directory
save_txt=false, # save results as .txt file
save_conf=true, # save results with confidence scores
save_crop=false, # save cropped images with results
show_labels=true, # show object labels in plots
show_conf=true, # show object confidence scores in plots
vid_stride=1, # video frame-rate stride
line_width=1, # bounding box thickness (pixels)
visualize=false, # visualize model features
augment=false, # apply image augmentation to prediction sources
agnostic_nms=false, # class-agnostic nms
retina_masks=false, # use high-resolution segmentation masks
boxes=true, # show boxes in segmentation predictions
)四、onnx模型部署
1. 新建一个export.py，将pt文件转化为onnx文件
from ultralytics import yolo
# load a model
model = yolo('/ultralytics/weight file/yolov8n.pt') # load a custom trained model
# export the model
model.export(format='onnx')2. 将onnx文件添加到之前提到的predict.py中，进行推理。
3. 如果想在推理的视频中添加fps信息，请把ultralytics/engine/predictor.py替换为下面的代码。
# ultralytics yolo 🚀, agpl-3.0 license
"""
run prediction on images, videos, directories, globs, youtube, webcam, streams, etc.
usage - sources:
$ yolo mode=predict model=yolov8n.pt source=0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
list.txt # list of images
list.streams # list of streams
'path/*.jpg' # glob
'https://youtu.be/zgi9g1ksqhc' # youtube
'rtsp://example.com/media.mp4' # rtsp, rtmp, http stream
usage - formats:
$ yolo mode=predict model=yolov8n.pt # pytorch
yolov8n.torchscript # torchscript
yolov8n.onnx # onnx runtime or opencv dnn with dnn=true
yolov8n_openvino_model # openvino
yolov8n.engine # tensorrt
yolov8n.mlmodel # coreml (macos-only)
yolov8n_saved_model # tensorflow savedmodel
yolov8n.pb # tensorflow graphdef
yolov8n.tflite # tensorflow lite
yolov8n_edgetpu.tflite # tensorflow edge tpu
yolov8n_paddle_model # paddlepaddle
"""
import platform
from pathlib import path
import cv2
import numpy as np
import torch
from ultralytics.cfg import get_cfg
from ultralytics.data import load_inference_source
from ultralytics.data.augment import letterbox, classify_transforms
from ultralytics.nn.autobackend import autobackend
from ultralytics.utils import default_cfg, logger, macos, settings, windows, callbacks, colorstr, ops
from ultralytics.utils.checks import check_imgsz, check_imshow
from ultralytics.utils.files import increment_path
from ultralytics.utils.torch_utils import select_device, smart_inference_mode
stream_warning = """
warning ⚠️ stream/video/webcam/dir predict source will accumulate results in ram unless `stream=true` is passed,
causing potential out-of-memory errors for large sources or long-running streams/videos.
usage:
results = model(source=..., stream=true) # generator of results objects
for r in results:
boxes = r.boxes # boxes object for bbox outputs
masks = r.masks # masks object for segment masks outputs
probs = r.probs # class probabilities for classification outputs
"""
class basepredictor:
"""
basepredictor
a base class for creating predictors.
attributes:
args (simplenamespace): configuration for the predictor.
save_dir (path): directory to save results.
done_warmup (bool): whether the predictor has finished setup.
model (nn.module): model used for prediction.
data (dict): data configuration.
device (torch.device): device used for prediction.
dataset (dataset): dataset used for prediction.
vid_path (str): path to video file.
vid_writer (cv2.videowriter): video writer for saving video output.
data_path (str): path to data.
"""
def __init__(self, cfg=default_cfg, overrides=none, _callbacks=none):
"""
initializes the basepredictor class.
args:
cfg (str, optional): path to a configuration file. defaults to default_cfg.
overrides (dict, optional): configuration overrides. defaults to none.
"""
self.args = get_cfg(cfg, overrides)
self.save_dir = self.get_save_dir()
if self.args.conf is none:
self.args.conf = 0.25 # default conf=0.25
self.done_warmup = false
if self.args.show:
self.args.show = check_imshow(warn=true)
# usable if setup is done
self.model = none
self.data = self.args.data # data_dict
self.imgsz = none
self.device = none
self.dataset = none
self.vid_path, self.vid_writer = none, none
self.plotted_img = none
self.data_path = none
self.source_type = none
self.batch = none
self.results = none
self.transforms = none
self.callbacks = _callbacks or callbacks.get_default_callbacks()
self.txt_path = none
callbacks.add_integration_callbacks(self)
def get_save_dir(self):
project = self.args.project or path(settings['runs_dir']) / self.args.task
name = self.args.name or f'{self.args.mode}'
return increment_path(path(project) / name, exist_ok=self.args.exist_ok)
def preprocess(self, im):
"""prepares input image before inference.
args:
im (torch.tensor | list(np.ndarray)): bchw for tensor, [(hwc) x b] for list.
"""
not_tensor = not isinstance(im, torch.tensor)
if not_tensor:
im = np.stack(self.pre_transform(im))
im = im[..., ::-1].transpose((0, 3, 1, 2)) # bgr to rgb, bhwc to bchw, (n, 3, h, w)
im = np.ascontiguousarray(im) # contiguous
im = torch.from_numpy(im)
img = im.to(self.device)
img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
if not_tensor:
img /= 255 # 0 - 255 to 0.0 - 1.0
return img
def inference(self, im, *args, **kwargs):
visualize = increment_path(self.save_dir / path(self.batch[0][0]).stem,
mkdir=true) if self.args.visualize and (not self.source_type.tensor) else false
return self.model(im, augment=self.args.augment, visualize=visualize)
def pre_transform(self, im):
"""pre-transform input image before inference.
args:
im (list(np.ndarray)): (n, 3, h, w) for tensor, [(h, w, 3) x n] for list.
return: a list of transformed imgs.
"""
same_shapes = all(x.shape == im[0].shape for x in im)
auto = same_shapes and self.model.pt
return [letterbox(self.imgsz, auto=auto, stride=self.model.stride)(image=x) for x in im]
def write_results(self, idx, results, batch):
"""write inference results to a file or directory."""
p, im, _ = batch
log_string = ''
if len(im.shape) == 3:
im = im[none] # expand for batch dim
if self.source_type.webcam or self.source_type.from_img or self.source_type.tensor: # batch_size >= 1
log_string += f'{idx}: '
frame = self.dataset.count
else:
frame = getattr(self.dataset, 'frame', 0)
self.data_path = p
self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
log_string += '%gx%g ' % im.shape[2:] # print string
result = results[idx]
log_string += result.verbose()
if self.args.save or self.args.show: # add bbox to image
plot_args = {
'line_width': self.args.line_width,
'boxes': self.args.boxes,
'conf': self.args.show_conf,
'labels': self.args.show_labels}
if not self.args.retina_masks:
plot_args['im_gpu'] = im[idx]
self.plotted_img = result.plot(**plot_args)
# write
if self.args.save_txt:
result.save_txt(f'{self.txt_path}.txt', save_conf=self.args.save_conf)
if self.args.save_crop:
result.save_crop(save_dir=self.save_dir / 'crops',
file_name=self.data_path.stem + ('' if self.dataset.mode == 'image' else f'_{frame}'))
return log_string
def postprocess(self, preds, img, orig_imgs):
"""post-processes predictions for an image and returns them."""
return preds
def __call__(self, source=none, model=none, stream=false, *args, **kwargs):
"""performs inference on an image or stream."""
self.stream = stream
if stream:
return self.stream_inference(source, model, *args, **kwargs)
else:
return list(self.stream_inference(source, model, *args, **kwargs)) # merge list of result into one
def predict_cli(self, source=none, model=none):
"""method used for cli prediction. it uses always generator as outputs as not required by cli mode."""
gen = self.stream_inference(source, model)
for _ in gen: # running cli inference without accumulating any outputs (do not modify)
pass
def setup_source(self, source):
"""sets up source and inference mode."""
self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2) # check image size
self.transforms = getattr(self.model.model, 'transforms', classify_transforms(
self.imgsz[0])) if self.args.task == 'classify' else none
self.dataset = load_inference_source(source=source, imgsz=self.imgsz, vid_stride=self.args.vid_stride)
self.source_type = self.dataset.source_type
if not getattr(self, 'stream', true) and (self.dataset.mode == 'stream' or # streams
len(self.dataset) > 1000 or # images
any(getattr(self.dataset, 'video_flag', [false]))): # videos
logger.warning(stream_warning)
self.vid_path, self.vid_writer = [none] * self.dataset.bs, [none] * self.dataset.bs
@smart_inference_mode()
def stream_inference(self, source=none, model=none, *args, **kwargs):
"""streams real-time inference on camera feed and saves results to file."""
if self.args.verbose:
logger.info('')
# setup model
if not self.model:
self.setup_model(model)
# setup source every time predict is called
self.setup_source(source if source is not none else self.args.source)
# check if save_dir/ label file exists
if self.args.save or self.args.save_txt:
(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=true, exist_ok=true)
# warmup model
if not self.done_warmup:
self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.dataset.bs, 3, *self.imgsz))
self.done_warmup = true
self.seen, self.windows, self.batch, self.profilers = 0, [], none, (ops.profile(), ops.profile(), ops.profile())
self.run_callbacks('on_predict_start')
for batch in self.dataset:
self.run_callbacks('on_predict_batch_start')
self.batch = batch
path, im0s, vid_cap, s = batch
# preprocess
with self.profilers[0]:
im = self.preprocess(im0s)
# inference
with self.profilers[1]:
preds = self.inference(im, *args, **kwargs)
# postprocess
with self.profilers[2]:
self.results = self.postprocess(preds, im, im0s)
self.run_callbacks('on_predict_postprocess_end')
# visualize, save, write results
n = len(im0s)
for i in range(n):
self.seen += 1
self.results[i].speed = {
'preprocess': self.profilers[0].dt * 1e3 / n,
'inference': self.profilers[1].dt * 1e3 / n,
'postprocess': self.profilers[2].dt * 1e3 / n}
p, im0 = path[i], none if self.source_type.tensor else im0s[i].copy()
p = path(p)
if self.args.verbose or self.args.save or self.args.save_txt or self.args.show:
s += self.write_results(i, self.results, (p, im, im0))
if self.args.save or self.args.save_txt:
self.results[i].save_dir = self.save_dir.__str__()
if self.args.show and self.plotted_img is not none:
self.show(p)
if self.args.save and self.plotted_img is not none:
self.save_preds(vid_cap, i, str(self.save_dir / p.name))
self.run_callbacks('on_predict_batch_end')
yield from self.results
# print time (inference-not only)
if self.args.verbose:
logger.info(f'{s}{(self.profilers[0].dt+self.profilers[1].dt+self.profilers[2].dt) * 1e3:.2f}ms')
# release assets
if isinstance(self.vid_writer[-1], cv2.videowriter):
self.vid_writer[-1].release() # release final video writer
# print results
if self.args.verbose and self.seen:
t = tuple(x.t / self.seen * 1e3 for x in self.profilers) # speeds per image
logger.info(f'speed: %.2fms preprocess, %.2fms inference, %.2fms postprocess per image at shape '
f'{(1, 3, *im.shape[2:])}' % t)
if self.args.save or self.args.save_txt or self.args.save_crop:
nl = len(list(self.save_dir.glob('labels/*.txt'))) # number of labels
s = f"\n{nl} label{'s' * (nl > 1)} saved to {self.save_dir / 'labels'}" if self.args.save_txt else ''
logger.info(f"results saved to {colorstr('bold', self.save_dir)}{s}")
self.run_callbacks('on_predict_end')
def setup_model(self, model, verbose=true):
"""initialize yolo model with given parameters and set it to evaluation mode."""
self.model = autobackend(model or self.args.model,
device=select_device(self.args.device, verbose=verbose),
dnn=self.args.dnn,
data=self.args.data,
fp16=self.args.half,
fuse=true,
verbose=verbose)
self.device = self.model.device # update device
self.args.half = self.model.fp16 # update half
self.model.eval()
def show(self, p):
"""display an image in a window using opencv imshow()."""
im0 = self.plotted_img
#--------------------后添加-----------------------------
str_fps = "fps: %.2f" % (1. / (self.profilers[0].dt + self.profilers[1].dt + self.profilers[2].dt))
cv2.puttext(im0, str_fps, (50, 50), cv2.font_hershey_complex, 1, (0, 255, 0), 3)
# --------------------后添加-----------------------------
if platform.system() == 'linux' and p not in self.windows:
self.windows.append(p)
cv2.namedwindow(str(p), cv2.window_normal | cv2.window_keepratio) # allow window resize (linux)
cv2.resizewindow(str(p), im0.shape[1], im0.shape[0])
cv2.imshow(str(p), im0)
cv2.waitkey(500 if self.batch[3].startswith('image') else 1) # 1 millisecond
def save_preds(self, vid_cap, idx, save_path):
"""save video predictions as mp4 at specified path."""
im0 = self.plotted_img
# save imgs
if self.dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if self.vid_path[idx] != save_path: # new video
self.vid_path[idx] = save_path
if isinstance(self.vid_writer[idx], cv2.videowriter):
self.vid_writer[idx].release() # release previous video writer
if vid_cap: # video
fps = int(vid_cap.get(cv2.cap_prop_fps)) # integer required, floats produce error in mp4 codec
w = int(vid_cap.get(cv2.cap_prop_frame_width))
h = int(vid_cap.get(cv2.cap_prop_frame_height))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
suffix = '.mp4' if macos else '.avi' if windows else '.avi'
fourcc = 'avc1' if macos else 'wmv2' if windows else 'mjpg'
save_path = str(path(save_path).with_suffix(suffix))
self.vid_writer[idx] = cv2.videowriter(save_path, cv2.videowriter_fourcc(*fourcc), fps, (w, h))
self.vid_writer[idx].write(im0)
def run_callbacks(self, event: str):
"""runs all registered callbacks for a specific event."""
for callback in self.callbacks.get(event, []):
callback(self)
def add_callback(self, event: str, func):
"""
add callback
"""
self.callbacks[event].append(func)
发表评论