#Установить
!pip install -U ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple

import ultralytics 
ultralytics.checks()

yolo_dataset
├── images
│   ├── train
│   │   ├── train0.jpg
│   │   └── train1.jpg
│   ├── val
│   │   ├── val0.jpg
│   │   └── val1.jpg
│   └── test
│       ├── test0.jpg
│       └── test1.jpg
└── labels
    ├── train
    │   ├── train0.txt
    │   └── train1.txt
    ├── val
    │   ├── val0.txt
    │   └── val1.txt
    └── test
        ├── test0.txt
        └── test1.txt

class_id center_x center_y bbox_width bbox_height
0 0.300926 0.617063 0.601852 0.765873
1 0.575 0.319531 0.4 0.551562

%%writefile balloon.yaml
# Ultralytics YOLO 🚀, GPL-3.0 license

path: /tf/liangyun2/torchkeras/notebooks/datasets/balloon   # dataset root dir
train: images/train  # train images (relative to 'path') 128 images
val: images/val  # val images (relative to 'path') 128 images
test:  # test images (optional)

# Classes
names:
  0: ballon

Overwriting balloon.yaml

import torch
from torch.utils.data import DataLoader
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.utils import DEFAULT_CFG,yaml_load 
from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset
from ultralytics.yolo.data import build_yolo_dataset,build_dataloader

overrides = {'task':'detect',
             'data':'balloon.yaml',
             'imgsz':640,
             'workers':4
            }
cfg = get_cfg(cfg = DEFAULT_CFG,overrides=overrides)
data_info = check_det_dataset(cfg.data)

ds_train = build_yolo_dataset(cfg,img_path=data_info['train'],batch=cfg.batch,
                              data_info = data_info,mode='train',rect=False,stride=32)

ds_val = build_yolo_dataset(cfg,img_path=data_info['val'],batch=cfg.batch,data_info = data_info,
    mode='val',rect=False,stride=32)

#dl_train = build_dataloader(ds_train,batch=cfg.batch,workers=0)
#dl_val = build_dataloader(ds_val,batch=cfg.batch,workers =0,shuffle=False)

dl_train = DataLoader(ds_train,batch_size = cfg.batch, num_workers = cfg.workers,
                      collate_fn = ds_train.collate_fn)

dl_val = DataLoader(ds_val,batch_size = cfg.batch, num_workers = cfg.workers,
                      collate_fn = ds_val.collate_fn)

for batch in dl_val:
    break 

batch.keys()

dict_keys(['im_file', 'ori_shape', 'resized_shape', 'ratio_pad', 'img', 'cls', 'bboxes', 'batch_idx'])

from ultralytics.nn.tasks import DetectionModel

model = DetectionModel(cfg = 'yolov8n.yaml', ch=3, nc=1)
#weights = torch.hub.load_state_dict_from_url('https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt')
weights = torch.load('yolov8n.pt')
model.load(weights['model'])

model.args = cfg
model.nc = data_info['nc']  # attach number of classes to model
model.names = data_info['names']

from ultralytics import YOLO 
yolo_model = YOLO('yolov8n.pt')

yolo_model.train(data='balloon.yaml',epochs=10)

for batch in dl_train:
    break 

from ultralytics.yolo.v8.detect.train import Loss 

model.cuda()
loss_fn = Loss(model)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4) 


x = batch['img'].float()/255 

preds = model.forward(x.cuda())
loss = loss_fn(preds,batch)[0]
print(loss)

tensor(74.5465, device='cuda:0', grad_fn=<MulBackward0>)

from torchkeras import KerasModel 

#Нам нужно изменить StepRunner, чтобы он адаптировался к формату набора данных Yolov8.

class StepRunner:
    def __init__(self, net, loss_fn, accelerator, stage = "train", metrics_dict = None, 
                 optimizer = None, lr_scheduler = None
                 ):
        self.net,self.loss_fn,self.metrics_dict,self.stage = net,loss_fn,metrics_dict,stage
        self.optimizer,self.lr_scheduler = optimizer,lr_scheduler
        self.accelerator = accelerator
        if self.stage=='train':
            self.net.train() 
        else:
            self.net.eval()
    
    def __call__(self, batch):
        
        features = batch['img'].float() / 255
        
        #loss
        preds = self.net(features)
        loss = self.loss_fn(preds,batch)[0]

        #backward()
        if self.optimizer is not None and self.stage=="train":
            self.accelerator.backward(loss)
            self.optimizer.step()
            if self.lr_scheduler is not None:
                self.lr_scheduler.step()
            self.optimizer.zero_grad()
            
        all_preds = self.accelerator.gather(preds)
        all_loss = self.accelerator.gather(loss).sum()
        
        #losses
        step_losses = {self.stage+"_loss":all_loss.item()}
        
        #metrics
        step_metrics = {}
        
        if self.stage=="train":
            if self.optimizer is not None:
                step_metrics['lr'] = self.optimizer.state_dict()['param_groups'][0]['lr']
            else:
                step_metrics['lr'] = 0.0
        return step_losses,step_metrics
    
KerasModel.StepRunner = StepRunner 

keras_model = KerasModel(net = model, 
                         loss_fn = loss_fn, 
                         optimizer = optimizer)

keras_model.fit(train_data=dl_train,
                val_data=dl_val,
                epochs = 200,
                ckpt_path='checkpoint.pt',
                patience=20,
                monitor='val_loss',
                mode='min',
                mixed_precision='no',
                plot= True,
                wandb = False,
                quiet = True
               )

keras_model.evaluate(dl_val)

100%|██████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.32it/s, val_loss=28.7]



{'val_loss': 28.715129852294922}

from ultralytics import YOLO 
keras_model.load_ckpt('checkpoint.pt')
save_dic = dict(model = keras_model.net, train_args =dict(cfg))
torch.save(save_dic, 'best_yolo.pt')

from ultralytics import YOLO 
best_model = YOLO(model = 'best_yolo.pt')

metrics = best_model.val(data = cfg.data )

metrics.results_dict

{'metrics/precision(B)': 0.9188790992746612,
 'metrics/recall(B)': 0.74,
 'metrics/mAP50(B)': 0.8516599658911874,
 'metrics/mAP50-95(B)': 0.7321355695315829,
 'fitness': 0.7440880091675434}

import pandas as pd 
df = pd.DataFrame()
df['metric'] = metrics.keys
for i,c in best_model.names.items():
    df[c] = metrics.class_result(i)

df 

from pathlib import Path 
root_path = './datasets/balloon/'
data_root = Path(root_path)

best_model = YOLO(model = 'best_yolo.pt')

val_imgs = [str(x) for x in (data_root/'images'/'train').rglob("*.jpg") if 'checkpoint' not in str(x)]
img_path = val_imgs[5] 

import os 
from PIL import Image 
result = best_model.predict(source = img_path,save=True)
best_model.predictor.save_dir/os.path.basename(img_path)
Image.open(best_model.predictor.save_dir/os.path.basename(img_path))

best_model.export(format='onnx')

from ultralytics.yolo.v8.detect.predict import DetectionPredictor
predictor = DetectionPredictor(
    overrides=dict(model='best_yolo.onnx'))

results = list(predictor.stream_inference(source=img_path))