🐞 Error in attribute 'update_called'

### Describe the bug

I'm trying to run the GANomaly model using my own dataset. I followed the basic instructions to validate the operation of the model and used the code provided in the respective documentation to verify that the execution goes as expected. However, the following error occurs:

`AttributeError: 'F1AdaptiveThreshold' object has no attribute 'update_called'. Did you mean: '_update_called'?`

### Dataset

Folder

### Model

GANomaly

### Steps to reproduce the behavior

Steps:
1. Install
git clone https://github.com/openvinotoolkit/anomalib.git
cd anomalib
pip install -e .

2. Code
`
from pathlib import Path
from anomalib.data import Folder
from anomalib.models import Ganomaly
from anomalib.engine import Engine
from lightning.pytorch import Trainer
import torch

def main():
    root_path = Path("D:/AI/anomalib/dataset")

    datamodule = Folder(
        name="mydataset",
        root=root_path,
        normal_dir=root_path / "train/class_x",
        abnormal_dir=root_path / "val/class_y",
        normal_test_dir=root_path / "val/class_x",
    )

    model = Ganomaly()
    engine = Engine()
    engine.fit(model, datamodule=datamodule)
    predictions = engine.predict(model, datamodule=datamodule)

if __name__ == "__main__":
    main()
`

3. Error
AttributeError: 'F1AdaptiveThreshold' object has no attribute 'update_called'. Did you mean: '_update_called'?





### OS information

OS information:
- OS: Windows 10
- Python version: 3.11.9pip
- Anomalib version: 2.0.0
- PyTorch version: 2.7.1
- CUDA/cuDNN version: 11.8
- GPU models and configuration: RTX 4000 ada
- Any other relevant information: I'm using a custom dataset


### Expected behavior

What I hope is that the network would be trained and visualize the results.

### Screenshots

_No response_

### Pip/GitHub

pip

### What version/branch did you use?

main

### Configuration YAML

```yaml
pre_processor: true
post_processor: true
evaluator: true
visualizer: true
batch_size: 32
n_features: 64
latent_vec_size: 100
extra_layers: 0
add_final_conv_layer: true
wadv: 1
wcon: 50
wenc: 1
lr: 0.0002
beta1: 0.5
beta2: 0.999
```

### Logs

```shell
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\loops\utilities.py:73: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.
You are using a CUDA device ('NVIDIA RTX A4000') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

  | Name               | Type              | Params | Mode 
-----------------------------------------------------------------
0 | pre_processor      | PreProcessor      | 0      | train
1 | post_processor     | PostProcessor     | 0      | train
2 | evaluator          | Evaluator         | 0      | train
3 | model              | GanomalyModel     | 188 M  | train
4 | generator_loss     | GeneratorLoss     | 0      | train
5 | discriminator_loss | DiscriminatorLoss | 0      | train
-----------------------------------------------------------------
188 M     Trainable params
0         Non-trainable params
188 M     Total params
754.758   Total estimated model params size (MB)
112       Modules in train mode
0         Modules in eval mode
D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\trainer\connectors\data_connector.py:420: Consider setting `persistent_workers=True` in 'train_dataloader' to speed up the dataloader worker initialization.
Epoch 0:   0%|          | 0/71 [00:00<?, ?it/s] D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\trainer\connectors\data_connector.py:420: Consider setting `persistent_workers=True` in 'val_dataloader' to speed up the dataloader worker initialization.
D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\core\module.py:512: You called `self.log('generator_loss', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`
D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\utilities\data.py:79: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 3. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\core\module.py:512: You called `self.log('discriminator_loss', ..., logger=True)` but have no logger configured. You can enable one by doing `Trainer(logger=ALogger(...))`
Epoch 0: 100%|██████████| 71/71 [00:24<00:00,  2.89it/s, generator_loss_step=59.30, discriminator_loss_step=3.92e-6] 
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Validation DataLoader 0: 100%|██████████| 88/88 [00:04<00:00, 18.69it/s]Traceback (most recent call last):
  File "D:\AI\anomalib\main.py", line 25, in <module>
    main()
  File "D:\AI\anomalib\main.py", line 21, in main
    engine.fit(model, datamodule=datamodule)
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\anomalib\engine\engine.py", line 416, in fit
    self.trainer.fit(model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\trainer\trainer.py", line 561, in fit
    call._call_and_handle_interrupt(
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\trainer\call.py", line 48, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\trainer\trainer.py", line 599, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\trainer\trainer.py", line 1012, in _run
    results = self._run_stage()
              ^^^^^^^^^^^^^^^^^
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\trainer\trainer.py", line 1056, in _run_stage
    self.fit_loop.run()
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\loops\fit_loop.py", line 216, in run
    self.advance()
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\loops\fit_loop.py", line 455, in advance
    self.epoch_loop.run(self._data_fetcher)
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\loops\training_epoch_loop.py", line 153, in run
    self.on_advance_end(data_fetcher)
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\loops\training_epoch_loop.py", line 394, in on_advance_end
    self.val_loop.run()
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\loops\utilities.py", line 179, in _decorator
    return loop_run(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\loops\evaluation_loop.py", line 152, in run
    return self.on_run_end()
           ^^^^^^^^^^^^^^^^^
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\loops\evaluation_loop.py", line 295, in on_run_end
    self._on_evaluation_epoch_end()
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\loops\evaluation_loop.py", line 374, in _on_evaluation_epoch_end
    call._call_callback_hooks(trainer, hook_name)
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\lightning\pytorch\trainer\call.py", line 227, in _call_callback_hooks
    fn(trainer, trainer.lightning_module, *args, **kwargs)
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\anomalib\post_processing\post_processor.py", line 138, in on_validation_epoch_end
    if self._image_threshold_metric.update_called:
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\AI\anomalib\.venvAlib\Lib\site-packages\torch\nn\modules\module.py", line 1940, in __getattr__
    raise AttributeError(
AttributeError: 'F1AdaptiveThreshold' object has no attribute 'update_called'. Did you mean: '_update_called'?
Epoch 0: 100%|██████████| 71/71 [01:31<00:00,  0.77it/s, generator_loss_step=59.30, discriminator_loss_step=3.92e-6]
```

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- [x] I agree to follow this project's Code of Conduct

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Uh oh!

🐞 Error in attribute 'update_called' #2862

Describe the bug

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🐞 Error in attribute 'update_called' #2862

Description

Describe the bug

Dataset

Model

Steps to reproduce the behavior

OS information

Expected behavior

Screenshots

Pip/GitHub

What version/branch did you use?

Configuration YAML

Logs

Code of Conduct

Metadata

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

Issue actions