I need some help in understanding for setting up the config files for each model

[kumarsyamala]

Hi

Can someone help me understand how to set up config files for each model and what each section indicates or importance's of it.

Right now, I am facing an error for multiple models while I am trying to run the anomalib, the error coming from the config files.

This is the error which I am facing while running the anomalib model and the config file.

C:\Users\Rama.Reddy\Downloads\defect\anomalib\src\anomalib\config\config.py:238: UserWarning: The seed value is now fixed to 0. Up to v0.3.7, the seed was not fixed when the seed value was set to 0. If you want to use the random seed, please select `None` for the seed value (`null` in the YAML file) or remove the `seed` key from the YAML file.
  warn(
C:\Users\Rama.Reddy\Downloads\defect\anomalib\src\anomalib\config\config.py:275: UserWarning: config.project.unique_dir is set to False. This does not ensure that your results will be written in an empty directory and you may overwrite files.
  warn(
Global seed set to 0
2023-06-08 19:39:25,282 - anomalib.data - INFO - Loading the datamodule
2023-06-08 19:39:25,284 - anomalib.data.utils.transform - INFO - No config file has been provided. Using default transforms.
2023-06-08 19:39:25,288 - anomalib.data.utils.transform - INFO - No config file has been provided. Using default transforms.
2023-06-08 19:39:25,288 - anomalib.models - INFO - Loading the model.
2023-06-08 19:39:25,288 - anomalib.models.components.base.anomaly_module - INFO - Initializing PatchcoreLightning model.
c:\Users\Rama.Reddy\Anaconda3\envs\hand\lib\site-packages\torchmetrics\utilities\prints.py:36: UserWarning: Metric `PrecisionRecallCurve` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.
  warnings.warn(*args, **kwargs)
2023-06-08 19:39:25,337 - anomalib.models.components.feature_extractors.timm - WARNING - FeatureExtractor is deprecated. Use TimmFeatureExtractor instead. Both FeatureExtractor and TimmFeatureExtractor will be removed in a future release.
2023-06-08 19:39:31,168 - timm.models.helpers - INFO - Loading pretrained weights from url (https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/wide_resnet50_racm-8234f177.pth)
2023-06-08 19:39:32,075 - anomalib.utils.loggers - INFO - Loading the experiment logger(s)
2023-06-08 19:39:32,075 - anomalib.utils.callbacks - INFO - Loading the callbacks
C:\Users\Rama.Reddy\Downloads\defect\anomalib\src\anomalib\utils\callbacks\__init__.py:142: UserWarning: Export option: None not found. Defaulting to no model export
  warnings.warn(f"Export option: {config.optimization.export_mode} not found. Defaulting to no model export")
2023-06-08 19:39:32,630 - pytorch_lightning.utilities.rank_zero - INFO - GPU available: False, used: False
2023-06-08 19:39:32,630 - pytorch_lightning.utilities.rank_zero - INFO - TPU available: False, using: 0 TPU cores
2023-06-08 19:39:32,630 - pytorch_lightning.utilities.rank_zero - INFO - IPU available: False, using: 0 IPUs
2023-06-08 19:39:32,630 - pytorch_lightning.utilities.rank_zero - INFO - HPU available: False, using: 0 HPUs
2023-06-08 19:39:32,632 - pytorch_lightning.utilities.rank_zero - INFO - `Trainer(limit_train_batches=1.0)` was configured so 100% of the batches per epoch will be used..
2023-06-08 19:39:32,632 - pytorch_lightning.utilities.rank_zero - INFO - `Trainer(limit_val_batches=1.0)` was configured so 100% of the batches will be used..
2023-06-08 19:39:32,632 - pytorch_lightning.utilities.rank_zero - INFO - `Trainer(limit_test_batches=1.0)` was configured so 100% of the batches will be used..
2023-06-08 19:39:32,633 - pytorch_lightning.utilities.rank_zero - INFO - `Trainer(limit_predict_batches=1.0)` was configured so 100% of the batches will be used..
2023-06-08 19:39:32,633 - pytorch_lightning.utilities.rank_zero - INFO - `Trainer(val_check_interval=1.0)` was configured so validation will run at the end of the training epoch..
2023-06-08 19:39:32,633 - anomalib - INFO - Training the model.
2023-06-08 19:39:33,173 - anomalib.data.base.datamodule - INFO - No normal test images found. Sampling from training set using a split ratio of 0.20
2023-06-08 19:39:33,394 - anomalib.utils.callbacks.metrics_configuration - WARNING - Cannot perform pixel-level evaluation when task type is classification. Ignoring the following pixel-level metrics: ['F1Score', 'AUROC']
c:\Users\Rama.Reddy\Anaconda3\envs\hand\lib\site-packages\torchmetrics\utilities\prints.py:36: UserWarning: Metric `ROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.
  warnings.warn(*args, **kwargs)
C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\core\optimizer.py:183: UserWarning: `LightningModule.configure_optimizers` returned `None`, this fit will run with no optimizer
  rank_zero_warn(
2023-06-08 19:39:33,493 - pytorch_lightning.callbacks.model_summary - INFO - 
  | Name                  | Type                     | Params
-------------------------------------------------------------------
0 | image_threshold       | AnomalyScoreThreshold    | 0     
1 | pixel_threshold       | AnomalyScoreThreshold    | 0     
2 | model                 | PatchcoreModel           | 24.9 M
3 | image_metrics         | AnomalibMetricCollection | 0     
4 | pixel_metrics         | AnomalibMetricCollection | 0     
5 | normalization_metrics | MinMax                   | 0     
-------------------------------------------------------------------
24.9 M    Trainable params
0         Non-trainable params
24.9 M    Total params
99.450    Total estimated model params size (MB)
C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py:138: UserWarning: `training_step` returned `None`. If this was on purpose, ignore this warning...
  self.warning_cache.warn("`training_step` returned `None`. If this was on purpose, ignore this warning...")
2023-06-08 19:46:44,888 - anomalib.models.patchcore.lightning_model - INFO - Aggregating the embedding extracted from the training set.
2023-06-08 19:46:45,099 - anomalib.models.patchcore.lightning_model - INFO - Applying core-set subsampling to get the embedding.
Traceback (most recent call last):
  File "tools/train.py", line 81, in <module>
    train(args)
  File "tools/train.py", line 66, in train
    trainer.fit(model=model, datamodule=datamodule)
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\trainer\trainer.py", line 608, in fit
    call._call_and_handle_interrupt(
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\trainer\call.py", line 38, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\trainer\trainer.py", line 650, in _fit_impl
    self._run(model, ckpt_path=self.ckpt_path)
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\trainer\trainer.py", line 1112, in _run
    results = self._run_stage()
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\trainer\trainer.py", line 1191, in _run_stage
    self._run_train()
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\trainer\trainer.py", line 1214, in _run_train
    self.fit_loop.run()
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\loops\loop.py", line 199, in run
    self.advance(*args, **kwargs)
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\loops\fit_loop.py", line 267, in advance
    self._outputs = self.epoch_loop.run(self._data_fetcher)
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\loops\loop.py", line 200, in run
    self.on_advance_end()
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\loops\epoch\training_epoch_loop.py", line 250, in on_advance_end
    self._run_validation()
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\loops\epoch\training_epoch_loop.py", line 308, in _run_validation
    self.val_loop.run()
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\loops\loop.py", line 206, in run
    output = self.on_run_end()
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\loops\dataloader\evaluation_loop.py", line 180, in on_run_end
    self._evaluation_epoch_end(self._outputs)
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\loops\dataloader\evaluation_loop.py", line 288, in _evaluation_epoch_end
    self.trainer._call_lightning_module_hook(hook_name, output_or_outputs)
  File "C:\Users\Rama.Reddy\AppData\Roaming\Python\Python38\site-packages\pytorch_lightning\trainer\trainer.py", line 1356, in _call_lightning_module_hook
    output = fn(*args, **kwargs)
  File "C:\Users\Rama.Reddy\Downloads\defect\anomalib\src\anomalib\models\components\base\anomaly_module.py", line 146, in validation_epoch_end
    self._collect_outputs(self.image_metrics, self.pixel_metrics, outputs)
  File "C:\Users\Rama.Reddy\Downloads\defect\anomalib\src\anomalib\models\components\base\anomaly_module.py", line 179, in _collect_outputs
    image_metric.update(output["pred_scores"], output["label"].int())
  File "C:\Users\Rama.Reddy\Downloads\defect\anomalib\src\anomalib\utils\metrics\collection.py", line 26, in update
    super().update(*args, **kwargs)
  File "c:\Users\Rama.Reddy\Anaconda3\envs\hand\lib\site-packages\torchmetrics\collections.py", line 156, in update
    self._merge_compute_groups()
  File "c:\Users\Rama.Reddy\Anaconda3\envs\hand\lib\site-packages\torchmetrics\collections.py", line 171, in _merge_compute_groups
    metric1 = getattr(self, cg_members1[0])
  File "c:\Users\Rama.Reddy\Anaconda3\envs\hand\lib\site-packages\torch\nn\modules\module.py", line 1269, in __getattr__
    raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'AnomalibMetricCollection' object has no attribute 'image_F1Score'

dataset:
  name: demo-anomaly
  format: folder
  path: C:\Users\Rama.Reddy\Downloads\defect\defect_anamoly\grid
  normal_dir: train/good # name of the folder containing normal images.
  abnormal_dir: test/not_good # name of the folder containing abnormal images.
  normal_test_dir: null # name of the folder containing normal test images.
  task: classification # classification or segmentation
  mask: null #optional
  extensions: null
  split_ratio: 0.2 # ratio of the normal images that will be used to create a test split
  image_size: 256
  train_batch_size: 32
  test_batch_size: 32
  eval_batch_size: 32
  num_workers: 8
  center_crop: 224 # dimensions to which images are center-cropped after resizing (optional)
  normalization: imagenet # data distribution to which the images will be normalized: [none, imagenet]
  test_split_mode: from_dir # options: [from_dir, synthetic]
  test_split_ratio: 0.2 # fraction of train images held out testing (usage depends on test_split_mode)
  val_split_mode: same_as_test # options: [same_as_test, from_test, synthetic]
  val_split_ratio: 0.5 # fraction of train/test images held out for validation (usage depends on val_split_mode)
  transform_config:
    train: null
    val: null
  create_validation_set: true
  tiling:
    apply: false
    tile_size: null
    stride: null
    remove_border_count: 0
    use_random_tiling: False
    random_tile_count: 16

model:
  name: patchcore
  backbone: wide_resnet50_2
  pre_trained: true
  layers:
    - layer2
    - layer3
  coreset_sampling_ratio: 0.1
  num_neighbors: 9
  early_stopping:
    patience: 5
    metric: image_F1Score
    mode: max
  normalization_method: min_max # options: [null, min_max, cdf]

metrics:
  image:
    - F1Score
    - AUROC
  pixel:
    - F1Score
    - AUROC
  threshold:
    method: adaptive #options: [adaptive, manual]
    manual_image: null
    manual_pixel: null

visualization:
  show_images: False # show images on the screen
  save_images: True # save images to the file system
  log_images: True # log images to the available loggers (if any)
  image_save_path: null # path to which images will be saved
  mode: full # options: ["full", "simple"]

project:
  seed: 0
  path: ./results

logging:
  logger: [] # options: [comet, tensorboard, wandb, csv] or combinations.
  log_graph: false # Logs the model graph to respective logger.

optimization:
  export_mode: null # options: onnx, openvino

# PL Trainer Args. Don't add extra parameter here.
trainer:
  enable_checkpointing: true
  default_root_dir: null
  gradient_clip_val: 0
  gradient_clip_algorithm: norm
  num_nodes: 1
  devices: 1
  enable_progress_bar: true
  overfit_batches: 0.0
  track_grad_norm: -1
  check_val_every_n_epoch: 1 # Don't validate before extracting features.
  fast_dev_run: false
  accumulate_grad_batches: 1
  max_epochs: 1
  min_epochs: null
  max_steps: -1
  min_steps: null
  max_time: null
  limit_train_batches: 1.0
  limit_val_batches: 1.0
  limit_test_batches: 1.0
  limit_predict_batches: 1.0
  val_check_interval: 1.0 # Don't validate before extracting features.
  log_every_n_steps: 50
  accelerator: auto # <"cpu", "gpu", "tpu", "ipu", "hpu", "auto">
  strategy: null
  sync_batchnorm: false
  precision: 32
  enable_model_summary: true
  num_sanity_val_steps: 0
  profiler: null
  benchmark: false
  deterministic: false
  reload_dataloaders_every_n_epochs: 0
  auto_lr_find: false
  replace_sampler_ddp: true
  detect_anomaly: false
  auto_scale_batch_size: false
  plugins: null
  move_metrics_to_cpu: false
  multiple_trainloader_mode: max_size_cycle

[samet-akcay]

I'll try to reproduce this