Add console logger (#241)

* ☑︎ Check if openvino is in `config.yaml` file.

* 🗑 Remove `update_device_config` from `get_config`

* ➕ `devices` and 🗑 `gpus` from test configs

* ➕ `devices` and 🗑 `gpus` from model configs.

* 🗑 `terminate_on_nan` from trainer configs.

* ➕ Added new Trainer config params and 🗑 deprecated ones.

* 🗑 callback

* 🗑 device and set progress bar to true

* 🛠  Fix tests

* ➕ Added console logger to `anomalib.utils.logger`

* 🏷  Rename `get_logger` to `get_experiment_logger`

* ➕ Added logger information to BTech dataset.

* ➕ Added logger information to Folder dataset.

* ➕ Added logger information to Mvtec AD dataset.

* Fix typos in data loggers.

* ✏️ Add logging info to lightning models.

* ✏️ Add logging info to callbacks.

* Added suppress warning as argument to train.py

* Update Padim lightning module

* 🗑️ Remove print statements from patchcore torch model.

* ✏️ a typo in model load checkpoint

* 🔁 Replace prints with logger.info

* Updated console

* Update train

* 🔧 Update logger name in mvtec

* 🏷 Renamed get_logger to get_experiment_logger

* ➕ Added console logger to train.py

* 🛠  Modify data loggers

* 🛠  Modify lightning model loggers

* 🛠  Modify torch model loggers

* 🛠  Modify callback loggers

* 🗑  Removed console.py annd ➕  add get_console_logger to __init__

* 🛠  Modify train entrypoint

* 🏷  Renamed get_console_logger to configure_logger

* 🔧 Made the changes to use logging.getLogger instead

anomalib/data/btech.py

@@ -48,8 +48,7 @@
 )
 from anomalib.pre_processing import PreProcessor
 
-logger = logging.getLogger(name="Dataset: BTech")
-logger.setLevel(logging.DEBUG)
+logger = logging.getLogger(__name__)
 
 
 def make_btech_dataset(
@@ -349,23 +348,23 @@ def __init__(
     def prepare_data(self) -> None:
         """Download the dataset if not available."""
         if (self.root / self.category).is_dir():
-            logging.info("Found the dataset.")
+            logger.info("Found the dataset.")
         else:
             zip_filename = self.root.parent / "btad.zip"
 
-            logging.info("Downloading the BTech dataset.")
+            logger.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",
                     filename=zip_filename,
                     reporthook=progress_bar.update_to,
                 )  # nosec
 
-            logging.info("Extracting the dataset.")
+            logger.info("Extracting the dataset.")
             with zipfile.ZipFile(zip_filename, "r") as zip_file:
                 zip_file.extractall(self.root.parent)
 
-            logging.info("Renaming the dataset directory")
+            logger.info("Renaming the dataset directory")
             shutil.move(src=str(self.root.parent / "BTech_Dataset_transformed"), dst=str(self.root))
 
             # NOTE: Each BTech category has different image extension as follows
@@ -377,13 +376,13 @@ def prepare_data(self) -> None:
             # 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")
+            logger.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")
+            logger.info("Cleaning the tar file")
             zip_filename.unlink()
 
     def setup(self, stage: Optional[str] = None) -> None:
@@ -396,6 +395,7 @@ def setup(self, stage: Optional[str] = None) -> None:
           stage: Optional[str]:  Train/Val/Test stages. (Default value = None)
 
         """
+        logger.info("Setting up train, validation, test and prediction datasets.")
         if stage in (None, "fit"):
             self.train_data = BTech(
                 root=self.root,

anomalib/data/folder.py

@@ -39,8 +39,7 @@
 )
 from anomalib.pre_processing import PreProcessor
 
-logger = logging.getLogger(name="Dataset: Folder Dataset")
-logger.setLevel(logging.DEBUG)
+logger = logging.getLogger(__name__)
 
 
 def _check_and_convert_path(path: Union[str, Path]) -> Path:
@@ -459,6 +458,7 @@ def setup(self, stage: Optional[str] = None) -> None:
           stage: Optional[str]:  Train/Val/Test stages. (Default value = None)
 
         """
+        logger.info("Setting up train, validation, test and prediction datasets.")
         if stage in (None, "fit"):
             self.train_data = FolderDataset(
                 normal_dir=self.normal_dir,

anomalib/data/mvtec.py

@@ -64,8 +64,7 @@
 )
 from anomalib.pre_processing import PreProcessor
 
-logger = logging.getLogger(name="Dataset: MVTec AD")
-logger.setLevel(logging.DEBUG)
+logger = logging.getLogger(__name__)
 
 
 def make_mvtec_dataset(
@@ -372,24 +371,24 @@ def __init__(
     def prepare_data(self) -> None:
         """Download the dataset if not available."""
         if (self.root / self.category).is_dir():
-            logging.info("Found the dataset.")
+            logger.info("Found the dataset.")
         else:
             self.root.mkdir(parents=True, exist_ok=True)
             dataset_name = "mvtec_anomaly_detection.tar.xz"
 
-            logging.info("Downloading the dataset.")
+            logger.info("Downloading the Mvtec AD dataset.")
             with DownloadProgressBar(unit="B", unit_scale=True, miniters=1, desc="MVTec AD") as progress_bar:
                 urlretrieve(
                     url=f"ftp://guest:GU.205dldo@ftp.softronics.ch/mvtec_anomaly_detection/{dataset_name}",
                     filename=self.root / dataset_name,
                     reporthook=progress_bar.update_to,
                 )
 
-            logging.info("Extracting the dataset.")
+            logger.info("Extracting the dataset.")
             with tarfile.open(self.root / dataset_name) as tar_file:
                 tar_file.extractall(self.root)
 
-            logging.info("Cleaning the tar file")
+            logger.info("Cleaning the tar file")
             (self.root / dataset_name).unlink()
 
     def setup(self, stage: Optional[str] = None) -> None:
@@ -399,6 +398,7 @@ def setup(self, stage: Optional[str] = None) -> None:
           stage: Optional[str]:  Train/Val/Test stages. (Default value = None)
 
         """
+        logger.info("Setting up train, validation, test and prediction datasets.")
         if stage in (None, "fit"):
             self.train_data = MVTec(
                 root=self.root,

anomalib/models/cflow/lightning_model.py

@@ -17,6 +17,8 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
+import logging
+
 import einops
 import torch
 import torch.nn.functional as F
@@ -27,6 +29,8 @@
 from anomalib.models.cflow.utils import get_logp, positional_encoding_2d
 from anomalib.models.components import AnomalyModule
 
+logger = logging.getLogger(__name__)
+
 __all__ = ["CflowLightning"]
 
 
@@ -35,6 +39,7 @@ class CflowLightning(AnomalyModule):
 
     def __init__(self, hparams):
         super().__init__(hparams)
+        logger.info("Initializing Cflow Lightning model.")
 
         self.model: CflowModel = CflowModel(hparams)
         self.loss_val = 0

anomalib/models/cflow/utils.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
+import logging
 import math
 
 import numpy as np
@@ -23,6 +24,8 @@
 from anomalib.models.components.freia.framework import SequenceINN
 from anomalib.models.components.freia.modules import AllInOneBlock
 
+logger = logging.getLogger(__name__)
+
 
 def get_logp(dim_feature_vector: int, p_u: torch.Tensor, logdet_j: torch.Tensor) -> torch.Tensor:
     """Returns the log likelihood estimation.
@@ -108,7 +111,7 @@ def cflow_head(
         SequenceINN: decoder network block
     """
     coder = SequenceINN(n_features)
-    print("CNF coder:", n_features)
+    logger.info("CNF coder: %d", n_features)
     for _ in range(coupling_blocks):
         coder.append(
             AllInOneBlock,

anomalib/models/dfkde/lightning_model.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
+import logging
 from typing import List, Union
 
 from omegaconf.dictconfig import DictConfig
@@ -24,6 +25,8 @@
 
 from .torch_model import DfkdeModel
 
+logger = logging.getLogger(__name__)
+
 
 class DfkdeLightning(AnomalyModule):
     """DFKDE: Deep Feature Kernel Density Estimation.
@@ -34,6 +37,7 @@ class DfkdeLightning(AnomalyModule):
 
     def __init__(self, hparams: Union[DictConfig, ListConfig]):
         super().__init__(hparams)
+        logger.info("Initializing DFKDE Lightning model.")
         threshold_steepness = 0.05
         threshold_offset = 12
 
@@ -75,6 +79,7 @@ def on_validation_start(self) -> None:
         # NOTE: Previous anomalib versions fit Gaussian at the end of the epoch.
         #   This is not possible anymore with PyTorch Lightning v1.4.0 since validation
         #   is run within train epoch.
+        logger.info("Fitting a KDE model to the embedding collected from the training set.")
         self.model.fit(self.embeddings)
 
     def validation_step(self, batch, _):  # pylint: disable=arguments-differ

anomalib/models/dfkde/torch_model.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
+import logging
 import random
 from typing import List, Optional, Tuple
 
@@ -23,6 +24,8 @@
 
 from anomalib.models.components import PCA, FeatureExtractor, GaussianKDE
 
+logger = logging.getLogger(__name__)
+
 
 class DfkdeModel(nn.Module):
     """Normality Model for the DFKDE algorithm.
@@ -88,7 +91,7 @@ def fit(self, embeddings: List[Tensor]) -> bool:
         _embeddings = torch.vstack(embeddings)
 
         if _embeddings.shape[0] < self.n_components:
-            print("Not enough features to commit. Not making a model.")
+            logger.info("Not enough features to commit. Not making a model.")
             return False
 
         # if max training points is non-zero and smaller than number of staged features, select random subset

anomalib/models/dfm/config.yaml

@@ -79,7 +79,6 @@ trainer:
   overfit_batches: 0.0
   plugins: null
   precision: 32
-
   profiler: null
   reload_dataloaders_every_n_epochs: 0
   replace_sampler_ddp: true

anomalib/models/dfm/lightning_model.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
+import logging
 from typing import List, Union
 
 import torch
@@ -24,12 +25,15 @@
 
 from .torch_model import DFMModel
 
+logger = logging.getLogger(__name__)
+
 
 class DfmLightning(AnomalyModule):
     """DFM: Deep Featured Kernel Density Estimation."""
 
     def __init__(self, hparams: Union[DictConfig, ListConfig]):
         super().__init__(hparams)
+        logger.info("Initializing DFKDE Lightning model.")
 
         self.model: DFMModel = DFMModel(
             backbone=hparams.model.backbone,
@@ -66,11 +70,14 @@ def training_step(self, batch, _):  # pylint: disable=arguments-differ
         self.embeddings.append(embedding)
 
     def on_validation_start(self) -> None:
-        """Fit a KDE Model to the embedding collected from the training set."""
+        """Fit a PCA transformation and a Gaussian model to dataset."""
         # NOTE: Previous anomalib versions fit Gaussian at the end of the epoch.
         #   This is not possible anymore with PyTorch Lightning v1.4.0 since validation
         #   is run within train epoch.
+        logger.info("Aggregating the embedding extracted from the training set.")
         embeddings = torch.vstack(self.embeddings)
+
+        logger.info("Fitting a PCA and a Gaussian model to dataset.")
         self.model.fit(embeddings)
 
     def validation_step(self, batch, _):  # pylint: disable=arguments-differ

anomalib/models/ganomaly/config.yaml

@@ -97,7 +97,6 @@ trainer:
   overfit_batches: 0.0
   plugins: null
   precision: 32
-
   profiler: null
   reload_dataloaders_every_n_epochs: 0
   replace_sampler_ddp: true

anomalib/models/ganomaly/lightning_model.py

@@ -17,6 +17,7 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
+import logging
 from typing import Dict, List, Union
 
 import torch
@@ -29,6 +30,8 @@
 
 from .torch_model import GanomalyModel
 
+logger = logging.getLogger(__name__)
+
 
 class GanomalyLightning(AnomalyModule):
     """PL Lightning Module for the GANomaly Algorithm.
@@ -39,6 +42,7 @@ class GanomalyLightning(AnomalyModule):
 
     def __init__(self, hparams: Union[DictConfig, ListConfig]):
         super().__init__(hparams)
+        logger.info("Initializing Ganomaly Lightning model.")
 
         self.model: GanomalyModel = GanomalyModel(
             input_size=hparams.model.input_size,
@@ -140,6 +144,7 @@ def validation_step(self, batch, _) -> Dict[str, Tensor]:  # type: ignore # pyli
 
     def validation_epoch_end(self, outputs):
         """Normalize outputs based on min/max values."""
+        logger.info("Normalizing validation outputs based on min/max values.")
         for prediction in outputs:
             prediction["pred_scores"] = self._normalize(prediction["pred_scores"])
         super().validation_epoch_end(outputs)
@@ -159,6 +164,7 @@ def test_step(self, batch, _):
 
     def test_epoch_end(self, outputs):
         """Normalize outputs based on min/max values."""
+        logger.info("Normalizing test outputs based on min/max values.")
         for prediction in outputs:
             prediction["pred_scores"] = self._normalize(prediction["pred_scores"])
         super().test_epoch_end(outputs)

anomalib/models/padim/config.yaml

@@ -92,7 +92,6 @@ trainer:
   profiler: null
   reload_dataloaders_every_n_epochs: 0
   replace_sampler_ddp: true
-
   sync_batchnorm: false
   tpu_cores: null
   track_grad_norm: -1

anomalib/models/padim/lightning_model.py

@@ -17,6 +17,7 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
+import logging
 from typing import List, Union
 
 import torch
@@ -26,6 +27,8 @@
 from anomalib.models.components import AnomalyModule
 from anomalib.models.padim.torch_model import PadimModel
 
+logger = logging.getLogger(__name__)
+
 __all__ = ["PadimLightning"]
 
 
@@ -38,6 +41,8 @@ class PadimLightning(AnomalyModule):
 
     def __init__(self, hparams: Union[DictConfig, ListConfig]):
         super().__init__(hparams)
+        logger.info("Initializing Padim Lightning model.")
+
         self.layers = hparams.model.layers
         self.model: PadimModel = PadimModel(
             layers=hparams.model.layers,
@@ -80,7 +85,10 @@ def on_validation_start(self) -> None:
         # NOTE: Previous anomalib versions fit Gaussian at the end of the epoch.
         #   This is not possible anymore with PyTorch Lightning v1.4.0 since validation
         #   is run within train epoch.
+        logger.info("Aggregating the embedding extracted from the training set.")
         embeddings = torch.vstack(self.embeddings)
+
+        logger.info("Fitting a Gaussian to the embedding collected from the training set.")
         self.stats = self.model.gaussian.fit(embeddings)
 
     def validation_step(self, batch, _):  # pylint: disable=arguments-differ