usage/callbacks/

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usage/callbacks/

Explore Ultralytics callbacks for training, validation, exporting, and prediction. Learn how to use and customize them for your ML models.

https://docs.ultralytics.com/usage/callbacks/

[D3RK0]

Hi, in the documentation above, I see that predictor.batch is used, which I assume refers to the current batch of the predictor. Is there a similar trainer.batch for a trainer? I need it in order to apply data augmentation during the training of my YOLOv8 classifier.

[glenn-jocher]

@D3RK0 to apply data augmentation during training, you can use the on_train_batch_start callback to modify the data before each training batch is processed.

[D3RK0]

@glenn-jocher Many thanks for your quick response!

I am dealing with following problem though:

Problem

When I do trainer.batch, I get the following error:

Traceback (most recent call last):
  File "C:\Program Files\JetBrains\PyCharm Community Edition 2022.3.3\plugins\python-ce\helpers\pydev\pydevd.py", line 1534, in _exec
    pydev_imports.execfile(file, globals, locals)  # execute the script
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Program Files\JetBrains\PyCharm Community Edition 2022.3.3\plugins\python-ce\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "C:\Users\derk_\Documents\Universiteit\IRP\Research_Project\main.py", line 397, in <module>
    main()
  File "C:\Users\derk_\Documents\Universiteit\IRP\Research_Project\main.py", line 377, in main
    model.train(mode="classify",
  File "C:\Users\derk_\anaconda3\envs\cobra\Lib\site-packages\ultralytics\engine\model.py", line 802, in train
    self.trainer.train()
  File "C:\Users\derk_\anaconda3\envs\cobra\Lib\site-packages\ultralytics\engine\trainer.py", line 207, in train
    self._do_train(world_size)
  File "C:\Users\derk_\anaconda3\envs\cobra\Lib\site-packages\ultralytics\engine\trainer.py", line 368, in _do_train
    self.run_callbacks("on_train_batch_start")
  File "C:\Users\derk_\anaconda3\envs\cobra\Lib\site-packages\ultralytics\engine\trainer.py", line 168, in run_callbacks
    callback(self)
  File "C:\Users\derk_\Documents\Universiteit\IRP\Research_Project\main.py", line 185, in batch_augmentation_callback
    batch = trainer.batch  # Access the current batch
            ^^^^^^^^^^^^^
AttributeError: 'ClassificationTrainer' object has no attribute 'batch'

My code

model = YOLO("yolov8m-cls.pt")

model.add_callback("on_train_batch_start", batch_augmentation_callback)

model.train(mode="classify",
            data=data_file_dir,
            epochs=args.n_epochs)

# Where batch_augmentation_callback is my callback function
def batch_augmentation_callback(trainer):
    batch = trainer.batch                                        # This code line causes the error reported above
    images, labels = batch['img'], batch['label']
    # More code below to create data augmentation

Content of trainer object that is passed into batch_augmentation_callback (my callback function)

• trainer = {ClassificationTrainer} <ultralytics.models.yolo.classify.train.ClassificationTrainer object at 0x0000016929FA5010>
• accumulate = {int} 4
• amp = {bool} False
• args = {IterableSimpleNamespace} IterableSimpleNamespace(task='classify', mode='train', model='yolov8m-cls.pt', data='C:/Users/derk_/Documents/Universiteit/IRP...ugment='randaugment', erasing=0.4, crop_fraction=1.0, cfg=None, tracker='botsort.yaml', save_dir='C:\runs\classify\train53')
• batch_size = {int} 16
• best = {WindowsPath} WindowsPath('C:/runs/classify/train53/weights/best.pt')
• best_fitness = {NoneType} None
• callbacks = {defaultdict: 25} defaultdict(<class 'list'>, {'on_pretrain_routine_start': [<function on_pretrain_routine_start at 0x0000016929C79440>, <functi...C7A2A0>, <function on_export_start at 0x000001692A55C220>], 'on_export_end': [<function on_export_end at 0x0000016929C7A340>]})
• csv = {WindowsPath} WindowsPath('C:/runs/classify/train53/results.csv')
• data = {dict: 5} {'names': {0: '1', 1: '10', 2: '100', 3: '101', 4: '102', 5: '11', 6: '12', 7: '13', 8: '14', 9: '15', 10: '16', 11: '17', 12: '18', 13: '19', 14: '2', 15: '20', 16: '21', 17: '22', 18: '23', 19: '24', 20: '25', 21: '26', 22: '27', 23: '28', 24: '29', 25:
• device = {device} device(type='cpu')
• ema = {ModelEMA} <ultralytics.utils.torch_utils.ModelEMA object at 0x000001692AC80C90>
• epoch = {int} 0
• epoch_time = {NoneType} None
• epoch_time_start = {float} 1731355033.8569636
• epochs = {int} 1
• fitness = {NoneType} None
• hub_session = {NoneType} None
• last = {WindowsPath} WindowsPath('C:/runs/classify/train53/weights/last.pt')
• loss = {NoneType} None
• loss_names = {list: 1} ['loss']
• metrics = {dict: 3} {'metrics/accuracy_top1': 0, 'metrics/accuracy_top5': 0, 'val/loss': 0}
• model = {ClassificationModel} ClassificationModel(\n (model): Sequential(\n (0): Conv(\n (conv): Conv2d(3, 48, kernel_size=(3, 3), stride=(2, 2), padd...1)\n (drop): Dropout(p=0.0, inplace=True)\n (linear): Linear(in_features=1280, out_features=102, bias=True)\n )\n )\n)
• optimizer = {AdamW} AdamW (\nParameter Group 0\n amsgrad: False\n base_momentum: 0.85\n betas: (0.9499811807836418, 0.999)\n capturable: Fa... lr: 0.004018066447703833\n max_lr: 0.1\n max_momentum: 0.95\n maximize: False\n min_lr: 4e-07\n weight_decay: 0.0\n)
• plot_idx = {list: 6} [0, 1, 2, -3465, -3464, -3463]
• plots = {dict: 0} {}
• resume = {bool} False
• save_dir = {WindowsPath} WindowsPath('C:/runs/classify/train53')
• save_period = {int} 1
• scaler = {GradScaler} <torch.amp.grad_scaler.GradScaler object at 0x000001692921A0D0>
• scheduler = {OneCycleLR} <torch.optim.lr_scheduler.OneCycleLR object at 0x000001692ACF5210>
• start_epoch = {int} 0
• stop = {bool} False
• stopper = {EarlyStopping} <ultralytics.utils.torch_utils.EarlyStopping object at 0x000001692ACF7190>
• stride = {int} 32
• test_loader = {InfiniteDataLoader: 32} <ultralytics.data.build.InfiniteDataLoader object at 0x000001692AAD7B50>
• testset = {WindowsPath} WindowsPath('C:/Users/derk_/Documents/Universiteit/IRP/Research_Project/Oxford-Flower-dataset/jpg/val')
• tloss = {NoneType} None
• train_loader = {InfiniteDataLoader: 385} <ultralytics.data.build.InfiniteDataLoader object at 0x00000169297FFDD0>
• train_time_start = {float} 1731355033.8569636
• trainset = {WindowsPath} WindowsPath('C:/Users/derk_/Documents/Universiteit/IRP/Research_Project/Oxford-Flower-dataset/jpg/train')
• validator = {ClassificationValidator} <ultralytics.models.yolo.classify.val.ClassificationValidator object at 0x000001692AC80BD0>
• wdir = {WindowsPath} WindowsPath('C:/runs/classify/train53/weights')

Question

If it is right that trainer.batch does not exist (in this case), which other object in trainer - as listed above in the content of trainer - can I extract the current batch?

[glenn-jocher]

The trainer.batch attribute does not exist in the current implementation. Instead, you can access the current batch through the train_loader attribute of the trainer object. You can iterate over trainer.train_loader to access batches during the on_train_batch_start callback.

[D3RK0]

@glenn-jocher Many thanks for answering!

Okay, I have been able to access the batches via trainer.train_loader and subsequently apply data augmentation to each batch. However, ...

Problem

... assigning the batch back (see also the code below) to trainer.train_loader is not possible, as I get:

trainer.train_loader[i] = batch ~~~~~~~~~~~~~~~~~~~~^^^ TypeError: 'InfiniteDataLoader' object does not support item assignment

Code

def batches_augmentation_callback(trainer):
    for i, batch in enumerate(trainer.train_loader):
        images, labels = batch['img'], batch['cls']
        cutmix_or_mixup = LoadOxfordFlowerDataset.data_augmentation(n_classes_in_batch=args.n_classes)
        images, labels = cutmix_or_mixup(images, labels)
        batch = {"img": images, "cls": labels}
        trainer.train_loader[i] = batch     # BUG: trainer.train_loader object does not support item assignment

Question

How can I return the augmented batch to the trainer object if assignment does not work?

[glenn-jocher]

You cannot directly assign to trainer.train_loader. Instead, apply your augmentation within the loop and yield the modified batch back to the training process. Consider using a custom data loader or modifying the dataset directly for more complex augmentations.

[UltralyticsAssistant]

👋 Hello, thank you for your interest in Ultralytics 🚀! It seems like you're diving into Ultralytics callbacks for various stages like training, validation, exporting, and prediction. That's a great way to customize your workflows! For comprehensive details, I recommend checking out our callbacks documentation where you'll find everything you need to know about implementing and customizing these callbacks.

If you experience any issues or have specific questions, please feel free to share more details or examples, and be assured that a member of the Ultralytics team will assist you shortly 😊.

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