Update anomalib version and requirements

CITATION.cff

@@ -29,7 +29,7 @@ authors:
     family-names: Genc
     email: utku.genc@intel.com
     affiliation: Intel
-version: 0.2.4
+version: 0.2.5
 doi: https://doi.org/10.48550/arXiv.2202.08341
 date-released: 2022-02-18
 references:

anomalib/__init__.py

@@ -14,4 +14,4 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
-__version__ = "0.2.4"
+__version__ = "0.2.5"

anomalib/data/btech.py

@@ -38,6 +38,7 @@
 from torch.utils.data import DataLoader
 from torch.utils.data.dataset import Dataset
 from torchvision.datasets.folder import VisionDataset
+from tqdm import tqdm
 
 from anomalib.data.inference import InferenceDataset
 from anomalib.data.utils import DownloadProgressBar, read_image
@@ -94,7 +95,9 @@ def make_btech_dataset(
     Returns:
         DataFrame: an output dataframe containing samples for the requested split (ie., train or test)
     """
-    samples_list = [(str(path),) + filename.parts[-3:] for filename in path.glob("**/*.bmp")]
+    samples_list = [
+        (str(path),) + filename.parts[-3:] for filename in path.glob("**/*") if filename.suffix in (".bmp", ".png")
+    ]
     if len(samples_list) == 0:
         raise RuntimeError(f"Found 0 images in {path}")
 
@@ -107,7 +110,7 @@ def make_btech_dataset(
         + "/ground_truth/"
         + samples.label
         + "/"
-        + samples.image_path.str.rstrip("bmp").str.rstrip(".")
+        + samples.image_path.str.rstrip("png").str.rstrip(".")
         + ".png"
     )
 
@@ -337,10 +340,9 @@ def prepare_data(self) -> None:
         if (self.root / self.category).is_dir():
             logging.info("Found the dataset.")
         else:
-            self.root.mkdir(parents=True, exist_ok=True)
-            zip_filename = self.root / "btad.zip"
+            zip_filename = self.root.parent / "btad.zip"
 
-            logging.info("Downloading the dataset.")
+            logging.info("Downloading the BTech dataset.")
             with DownloadProgressBar(unit="B", unit_scale=True, miniters=1, desc="BTech") as progress_bar:
                 urlretrieve(
                     url="https://avires.dimi.uniud.it/papers/btad/btad.zip",
@@ -350,10 +352,25 @@ def prepare_data(self) -> None:
 
             logging.info("Extracting the dataset.")
             with zipfile.ZipFile(zip_filename, "r") as zip_file:
-                zip_file.extractall(self.root)
+                zip_file.extractall(self.root.parent)
 
             logging.info("Renaming the dataset directory")
-            shutil.move(src=str(self.root / "BTech_Dataset_transformed"), dst=str(self.root / "BTech"))
+            shutil.move(src=str(self.root.parent / "BTech_Dataset_transformed"), dst=str(self.root))
+
+            # NOTE: Each BTech category has different image extension as follows
+            #       | Category | Image | Mask |
+            #       |----------|-------|------|
+            #       | 01       | bmp   | png  |
+            #       | 02       | png   | png  |
+            #       | 03       | bmp   | bmp  |
+            # To avoid any conflict, the following script converts all the extensions to png.
+            # This solution works fine, but it's also possible to properly ready the bmp and
+            # png filenames from categories in `make_btech_dataset` function.
+            logging.info("Convert the bmp formats to png to have consistent image extensions")
+            for filename in tqdm(self.root.glob("**/*.bmp"), desc="Converting bmp to png"):
+                image = cv2.imread(str(filename))
+                cv2.imwrite(str(filename.with_suffix(".png")), image)
+                filename.unlink()
 
             logging.info("Cleaning the tar file")
             zip_filename.unlink()

anomalib/models/dfkde/model.py

@@ -104,7 +104,7 @@ def __init__(self, hparams: Union[DictConfig, ListConfig]):
         self.embeddings: List[Tensor] = []
 
     @staticmethod
-    def configure_optimizers():
+    def configure_optimizers():  # pylint: disable=arguments-differ
         """DFKDE doesn't require optimization, therefore returns no optimizers."""
         return None
 

anomalib/models/dfm/model.py

@@ -38,7 +38,7 @@ def __init__(self, hparams: Union[DictConfig, ListConfig]):
         self.embeddings: List[Tensor] = []
 
     @staticmethod
-    def configure_optimizers() -> None:
+    def configure_optimizers() -> None:  # pylint: disable=arguments-differ
         """DFM doesn't require optimization, therefore returns no optimizers."""
         return None
 

anomalib/models/padim/model.py

@@ -298,7 +298,7 @@ def __init__(self, hparams: Union[DictConfig, ListConfig]):
         self.embeddings: List[Tensor] = []
 
     @staticmethod
-    def configure_optimizers():
+    def configure_optimizers():  # pylint: disable=arguments-differ
         """PADIM doesn't require optimization, therefore returns no optimizers."""
         return None
 

anomalib/utils/callbacks/__init__.py

@@ -25,13 +25,11 @@
 from .cdf_normalization import CdfNormalizationCallback
 from .min_max_normalization import MinMaxNormalizationCallback
 from .model_loader import LoadModelCallback
-from .openvino import OpenVINOCallback
 from .save_to_csv import SaveToCSVCallback
 from .timer import TimerCallback
 from .visualizer_callback import VisualizerCallback
 
 __all__ = [
-    "OpenVINOCallback",
     "LoadModelCallback",
     "TimerCallback",
     "VisualizerCallback",
@@ -97,6 +95,10 @@ def get_callbacks(config: Union[ListConfig, DictConfig]) -> List[Callback]:
                 )
             )
         if "openvino" in config.optimization and config.optimization.openvino.apply:
+            from .openvino import (  # pylint: disable=import-outside-toplevel
+                OpenVINOCallback,
+            )
+
             callbacks.append(
                 OpenVINOCallback(
                     input_size=config.model.input_size,

requirements/base.txt

@@ -7,9 +7,11 @@ kornia==0.5.6
 lxml==4.6.5
 matplotlib==3.4.3
 networkx~=2.5
-nncf@ git+https://github.com/openvinotoolkit/nncf@37a830a412e60ec2fd2d84d7f00e2524e5f62777#egg=nncf
 numpy~=1.19.5
 omegaconf==2.1.1
+opencv-python>=4.5.3.56
+opencv-contrib-python==4.5.5.62
+pandas~=1.1.5
 pillow==9.0.0
 pytorch-lightning==1.5.9
 torch==1.8.1

requirements/openvino.txt

@@ -1,6 +1,6 @@
 defusedxml==0.7.1
 requests==2.26.0
 networkx~=2.5
+nncf@ git+https://github.com/openvinotoolkit/nncf@37a830a412e60ec2fd2d84d7f00e2524e5f62777#egg=nncf
 onnx==1.10.1
-opencv-python==4.5.3.56
 openvino-dev==2021.4.2

setup.py

@@ -86,15 +86,15 @@ def get_required_packages(requirement_files: List[str]) -> List[str]:
 setup(
     name="anomalib",
     # TODO: https://github.com/openvinotoolkit/anomalib/issues/36
-    version="0.2.4",
+    version="0.2.5",
     author="Intel OpenVINO",
     author_email="help@openvino.intel.com",
     description="anomalib - Anomaly Detection Library",
     url="",
     license="Copyright (c) Intel - All Rights Reserved. "
     'Licensed under the Apache License, Version 2.0 (the "License")'
     "See LICENSE file for more details.",
-    python_requires=">=3.8",
+    python_requires=">=3.7",
     packages=find_packages("."),
     install_requires=INSTALL_REQUIRES,
     extras_require=EXTRAS_REQUIRE,

tests/pre_merge/utils/callbacks/openvino_callback/test_openvino.py

@@ -5,7 +5,7 @@
 from pytorch_lightning.callbacks.early_stopping import EarlyStopping
 
 from anomalib.config import get_configurable_parameters
-from anomalib.utils.callbacks import OpenVINOCallback
+from anomalib.utils.callbacks.openvino import OpenVINOCallback
 from tests.pre_merge.utils.callbacks.openvino_callback.dummy_lightning_model import (
     DummyLightningModule,
     FakeDataModule,

tox.ini

@@ -106,6 +106,7 @@ ignore = E203,W503
 extension-pkg-whitelist = cv2
 ignored-modules = cv2
 disable = duplicate-code,
+          arguments-differ,
           fixme,
           import-error,
           no-self-use,