Move initialization log message to base class

anomalib/models/cflow/lightning_model.py

@@ -17,7 +17,6 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
-import logging
 from typing import List, Tuple, Union
 
 import einops
@@ -32,8 +31,6 @@
 from anomalib.models.cflow.utils import get_logp, positional_encoding_2d
 from anomalib.models.components import AnomalyModule
 
-logger = logging.getLogger(__name__)
-
 __all__ = ["Cflow", "CflowLightning"]
 
 
@@ -54,7 +51,6 @@ def __init__(
         permute_soft: bool = False,
     ):
         super().__init__()
-        logger.info("Initializing Cflow Lightning model.")
 
         self.model: CflowModel = CflowModel(
             input_size=input_size,

anomalib/models/components/base/anomaly_module.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
+import logging
 from abc import ABC
 from typing import Any, List, Optional
 
@@ -28,6 +29,8 @@
     MinMax,
 )
 
+logger = logging.getLogger(__name__)
+
 
 class AnomalyModule(pl.LightningModule, ABC):
     """AnomalyModule to train, validate, predict and test images.
@@ -37,6 +40,8 @@ class AnomalyModule(pl.LightningModule, ABC):
 
     def __init__(self):
         super().__init__()
+        logger.info("Initializing %s model.", self.__class__.__name__)
+
         self.save_hyperparameters()
         self.model: nn.Module
         self.loss: Tensor

anomalib/models/dfkde/lightning_model.py

@@ -53,9 +53,7 @@ def __init__(
         threshold_steepness: float = 0.05,
         threshold_offset: int = 12,
     ):
-
         super().__init__()
-        logger.info("Initializing DFKDE Lightning model.")
 
         self.model = DfkdeModel(
             backbone=backbone,

anomalib/models/dfm/lightning_model.py

@@ -53,7 +53,6 @@ def __init__(
         score_type: str = "fre",
     ):
         super().__init__()
-        logger.info("Initializing DFKDE Lightning model.")
 
         self.model: DFMModel = DFMModel(
             backbone=backbone,

anomalib/models/draem/lightning_model.py

@@ -6,7 +6,6 @@
 # Copyright (C) 2022 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import logging
 from typing import Optional, Union
 
 import torch
@@ -19,8 +18,6 @@
 from anomalib.models.draem.torch_model import DraemModel
 from anomalib.models.draem.utils import Augmenter
 
-logger = logging.getLogger(__name__)
-
 __all__ = ["Draem", "DraemLightning"]
 
 

anomalib/models/fastflow/lightning_model.py

@@ -4,7 +4,6 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-import logging
 from typing import Tuple, Union
 
 import torch
@@ -16,8 +15,6 @@
 from anomalib.models.components import AnomalyModule
 from anomalib.models.fastflow.torch_model import FastflowLoss, FastflowModel
 
-logger = logging.getLogger(__name__)
-
 
 @MODEL_REGISTRY
 class Fastflow(AnomalyModule):
@@ -40,7 +37,6 @@ def __init__(
         hidden_ratio: float = 1.0,
     ):
         super().__init__()
-        logger.info("Initializing Fastflow Lightning model.")
 
         self.model = FastflowModel(
             input_size=input_size,

anomalib/models/ganomaly/lightning_model.py

@@ -62,9 +62,7 @@ def __init__(
         wcon: int = 50,
         wenc: int = 1,
     ):
-
         super().__init__()
-        logger.info("Initializing Ganomaly Lightning model.")
 
         self.model: GanomalyModel = GanomalyModel(
             input_size=input_size,

anomalib/models/padim/lightning_model.py

@@ -50,7 +50,6 @@ def __init__(
         backbone: str,
     ):
         super().__init__()
-        logger.info("Initializing Padim Lightning model.")
 
         self.layers = layers
         self.model: PadimModel = PadimModel(

anomalib/models/patchcore/lightning_model.py

@@ -52,9 +52,7 @@ def __init__(
         coreset_sampling_ratio: float = 0.1,
         num_neighbors: int = 9,
     ) -> None:
-
         super().__init__()
-        logger.info("Initializing Patchcore Lightning model.")
 
         self.model: PatchcoreModel = PatchcoreModel(
             input_size=input_size,

anomalib/models/reverse_distillation/lightning_model.py

@@ -17,7 +17,6 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
-import logging
 from typing import Dict, List, Tuple, Union
 
 from omegaconf import DictConfig, ListConfig
@@ -30,8 +29,6 @@
 from .loss import ReverseDistillationLoss
 from .torch_model import ReverseDistillationModel
 
-logger = logging.getLogger(__name__)
-
 
 @MODEL_REGISTRY
 class ReverseDistillation(AnomalyModule):
@@ -45,7 +42,6 @@ class ReverseDistillation(AnomalyModule):
 
     def __init__(self, input_size: Tuple[int, int], backbone: str, layers: List[str], anomaly_map_mode: str):
         super().__init__()
-        logger.info("Initializing Reverse Distillation Lightning model.")
         self.model = ReverseDistillationModel(
             backbone=backbone, layers=layers, input_size=input_size, anomaly_map_mode=anomaly_map_mode
         )

anomalib/models/stfpm/lightning_model.py

@@ -17,7 +17,6 @@
 # See the License for the specific language governing permissions
 # and limitations under the License.
 
-import logging
 from typing import List, Tuple, Union
 
 import torch
@@ -29,8 +28,6 @@
 from anomalib.models.components import AnomalyModule
 from anomalib.models.stfpm.torch_model import STFPMModel
 
-logger = logging.getLogger(__name__)
-
 __all__ = ["StfpmLightning"]
 
 
@@ -50,9 +47,7 @@ def __init__(
         backbone: str,
         layers: List[str],
     ):
-
         super().__init__()
-        logger.info("Initializing Stfpm Lightning model.")
 
         self.model = STFPMModel(
             input_size=input_size,