Minor fix: Update folder dataset + notebooks link

README.md

@@ -41,6 +41,13 @@ ___
 
 To get an overview of all the devices where `anomalib` as been tested thoroughly, look at the [Supported Hardware](https://openvinotoolkit.github.io/anomalib/#supported-hardware) section in the documentation.
 
+### Jupyter Notebooks
+
+For getting started with a Jupyter Notebook, please refer to the [Notebooks](./notebooks) folder of this repository. Additionally, you can refer to a few created by the community:
+
+- [Google Colab](https://colab.research.google.com/drive/1K4a4z2iZGBNhWdmt9Aqdld7kTAxBfAmi?usp=sharing) by @bth5
+- [Kaggle](https://www.kaggle.com/code/ipythonx/mvtec-ad-anomaly-detection-with-anomalib-library) by @innat
+
 ### PyPI Install
 
 You can get started with `anomalib` by just using pip.
@@ -110,18 +117,20 @@ dataset:
   name: <name-of-the-dataset>
   format: folder
   path: <path/to/folder/dataset>
-  normal: normal # name of the folder containing normal images.
-  abnormal: abnormal # name of the folder containing abnormal images.
+  normal_dir: normal # name of the folder containing normal images.
+  abnormal_dir: abnormal # name of the folder containing abnormal images.
+  normal_test_dir: null # name of the folder containing normal test images.
   task: segmentation # classification or segmentation
   mask: <path/to/mask/annotations> #optional
   extensions: null
   split_ratio: 0.2  # ratio of the normal images that will be used to create a test split
-  seed: 0
   image_size: 256
   train_batch_size: 32
   test_batch_size: 32
   num_workers: 8
-  transform_config: null
+  transform_config:
+    train: null
+    val: null
   create_validation_set: true
   tiling:
     apply: false

anomalib/data/__init__.py

@@ -76,7 +76,7 @@ def get_datamodule(config: Union[DictConfig, ListConfig]) -> LightningDataModule
             mask_dir=config.dataset.mask,
             extensions=config.dataset.extensions,
             split_ratio=config.dataset.split_ratio,
-            seed=config.dataset.seed,
+            seed=config.project.seed,
             image_size=(config.dataset.image_size[0], config.dataset.image_size[1]),
             train_batch_size=config.dataset.train_batch_size,
             test_batch_size=config.dataset.test_batch_size,

requirements/base.txt

@@ -5,9 +5,9 @@ omegaconf>=2.1.1
 opencv-python>=4.5.3.56
 pandas>=1.1.0
 pytorch-lightning>=1.6.0
-torchmetrics>=0.8.0
+torchmetrics==0.8.0
 torchvision>=0.9.1
 torchtext>=0.9.1
-wandb==0.12.9
+wandb==0.12.17
 matplotlib>=3.4.3
 gradio>=2.9.4

tests/pre_merge/utils/loggers/test_get_logger.py

@@ -14,6 +14,11 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
+from unittest.mock import patch
+
+patch("pytorch_lightning.utilities.imports._package_available", False)
+patch("pytorch_lightning.loggers.wandb.WandbLogger")
+
 import pytest
 from omegaconf import OmegaConf
 from pytorch_lightning.loggers import CSVLogger
@@ -37,36 +42,38 @@ def test_get_experiment_logger():
         }
     )
 
-    # get no logger
-    logger = get_experiment_logger(config=config)
-    assert isinstance(logger, bool)
-    config.project.logger = False
-    logger = get_experiment_logger(config=config)
-    assert isinstance(logger, bool)
+    with patch("pytorch_lightning.loggers.wandb.wandb"):
 
-    # get tensorboard
-    config.project.logger = "tensorboard"
-    logger = get_experiment_logger(config=config)
-    assert isinstance(logger[0], AnomalibTensorBoardLogger)
+        # get no logger
+        logger = get_experiment_logger(config=config)
+        assert isinstance(logger, bool)
+        config.project.logger = False
+        logger = get_experiment_logger(config=config)
+        assert isinstance(logger, bool)
 
-    # get wandb logger
-    config.project.logger = "wandb"
-    logger = get_experiment_logger(config=config)
-    assert isinstance(logger[0], AnomalibWandbLogger)
+        # get tensorboard
+        config.project.logger = "tensorboard"
+        logger = get_experiment_logger(config=config)
+        assert isinstance(logger[0], AnomalibTensorBoardLogger)
 
-    # get csv logger.
-    config.project.logger = "csv"
-    logger = get_experiment_logger(config=config)
-    assert isinstance(logger[0], CSVLogger)
+        # get wandb logger
+        config.project.logger = "wandb"
+        logger = get_experiment_logger(config=config)
+        assert isinstance(logger[0], AnomalibWandbLogger)
 
-    # get multiple loggers
-    config.project.logger = ["tensorboard", "wandb", "csv"]
-    logger = get_experiment_logger(config=config)
-    assert isinstance(logger[0], AnomalibTensorBoardLogger)
-    assert isinstance(logger[1], AnomalibWandbLogger)
-    assert isinstance(logger[2], CSVLogger)
+        # get csv logger.
+        config.project.logger = "csv"
+        logger = get_experiment_logger(config=config)
+        assert isinstance(logger[0], CSVLogger)
 
-    # raise unknown
-    with pytest.raises(UnknownLogger):
-        config.project.logger = "randomlogger"
+        # get multiple loggers
+        config.project.logger = ["tensorboard", "wandb", "csv"]
         logger = get_experiment_logger(config=config)
+        assert isinstance(logger[0], AnomalibTensorBoardLogger)
+        assert isinstance(logger[1], AnomalibWandbLogger)
+        assert isinstance(logger[2], CSVLogger)
+
+        # raise unknown
+        with pytest.raises(UnknownLogger):
+            config.project.logger = "randomlogger"
+            logger = get_experiment_logger(config=config)