Add pre_train as a configurable parameter

anomalib/models/cflow/config.yaml

@@ -18,6 +18,7 @@ dataset:
 model:
   name: cflow
   backbone: wide_resnet50_2
+  pre_trained: true
   layers:
     - layer2
     - layer3

anomalib/models/cflow/lightning_model.py

@@ -43,6 +43,7 @@ def __init__(
         input_size: Tuple[int, int],
         backbone: str,
         layers: List[str],
+        pre_trained: bool = True,
         fiber_batch_size: int = 64,
         decoder: str = "freia-cflow",
         condition_vector: int = 128,
@@ -56,6 +57,7 @@ def __init__(
         self.model: CflowModel = CflowModel(
             input_size=input_size,
             backbone=backbone,
+            pre_trained=pre_trained,
             layers=layers,
             fiber_batch_size=fiber_batch_size,
             decoder=decoder,

anomalib/models/cflow/torch_model.py

@@ -34,6 +34,7 @@ def __init__(
         input_size: Tuple[int, int],
         backbone: str,
         layers: List[str],
+        pre_trained: bool = True,
         fiber_batch_size: int = 64,
         decoder: str = "freia-cflow",
         condition_vector: int = 128,
@@ -49,7 +50,7 @@ def __init__(
         self.dec_arch = decoder
         self.pool_layers = layers
 
-        self.encoder = FeatureExtractor(backbone=self.backbone(pretrained=True), layers=self.pool_layers)
+        self.encoder = FeatureExtractor(backbone=self.backbone(pretrained=pre_trained), layers=self.pool_layers)
         self.pool_dims = self.encoder.out_dims
         self.decoders = nn.ModuleList(
             [

anomalib/models/dfkde/config.yaml

@@ -16,6 +16,7 @@ dataset:
 model:
   name: dfkde
   backbone: resnet18
+  pre_trained: true
   max_training_points: 40000
   confidence_threshold: 0.5
   pre_processing: scale

anomalib/models/dfkde/lightning_model.py

@@ -34,6 +34,7 @@ class Dfkde(AnomalyModule):
 
     Args:
         backbone (str): Pre-trained model backbone.
+        pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
         max_training_points (int, optional): Number of training points to fit the KDE model.
             Defaults to 40000.
         pre_processing (str, optional): Preprocess features before passing to KDE.
@@ -47,6 +48,7 @@ class Dfkde(AnomalyModule):
     def __init__(
         self,
         backbone: str,
+        pre_trained: bool = True,
         max_training_points: int = 40000,
         pre_processing: str = "scale",
         n_components: int = 16,
@@ -57,6 +59,7 @@ def __init__(
 
         self.model = DfkdeModel(
             backbone=backbone,
+            pre_trained=pre_trained,
             n_comps=n_components,
             pre_processing=pre_processing,
             filter_count=max_training_points,

anomalib/models/dfkde/torch_model.py

@@ -32,6 +32,7 @@ class DfkdeModel(nn.Module):
 
     Args:
         backbone (str): Pre-trained model backbone.
+        pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
         n_comps (int, optional): Number of PCA components. Defaults to 16.
         pre_processing (str, optional): Preprocess features before passing to KDE.
             Options are between `norm` and `scale`. Defaults to "scale".
@@ -43,6 +44,7 @@ class DfkdeModel(nn.Module):
     def __init__(
         self,
         backbone: str,
+        pre_trained: bool = True,
         n_comps: int = 16,
         pre_processing: str = "scale",
         filter_count: int = 40000,
@@ -57,7 +59,7 @@ def __init__(
         self.threshold_offset = threshold_offset
 
         _backbone = getattr(torchvision.models, backbone)
-        self.feature_extractor = FeatureExtractor(backbone=_backbone(pretrained=True), layers=["avgpool"]).eval()
+        self.feature_extractor = FeatureExtractor(backbone=_backbone(pretrained=pre_trained), layers=["avgpool"]).eval()
 
         self.pca_model = PCA(n_components=self.n_components)
         self.kde_model = GaussianKDE()

anomalib/models/dfm/config.yaml

@@ -16,6 +16,7 @@ dataset:
 model:
   name: dfm
   backbone: resnet18
+  pre_trained: true
   layer: layer3
   pooling_kernel_size: 4
   pca_level: 0.97

anomalib/models/dfm/lightning_model.py

@@ -36,6 +36,7 @@ class Dfm(AnomalyModule):
     Args:
         backbone (str): Backbone CNN network
         layer (str): Layer to extract features from the backbone CNN
+        pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
         pooling_kernel_size (int, optional): Kernel size to pool features extracted from the CNN.
             Defaults to 4.
         pca_level (float, optional): Ratio from which number of components for PCA are calculated.
@@ -48,6 +49,7 @@ def __init__(
         self,
         backbone: str,
         layer: str,
+        pre_trained: bool = True,
         pooling_kernel_size: int = 4,
         pca_level: float = 0.97,
         score_type: str = "fre",
@@ -56,6 +58,7 @@ def __init__(
 
         self.model: DFMModel = DFMModel(
             backbone=backbone,
+            pre_trained=pre_trained,
             layer=layer,
             pooling_kernel_size=pooling_kernel_size,
             n_comps=pca_level,

anomalib/models/dfm/torch_model.py

@@ -87,13 +87,20 @@ class DFMModel(nn.Module):
     Args:
         backbone (str): Pre-trained model backbone.
         layer (str): Layer from which to extract features.
-        pooling_kernel_size (int): Kernel size to pool features extracted from the CNN.
+        pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
+        pooling_kernel_size (int, optional): Kernel size to pool features extracted from the CNN.
         n_comps (float, optional): Ratio from which number of components for PCA are calculated. Defaults to 0.97.
         score_type (str, optional): Scoring type. Options are `fre` and `nll`. Defaults to "fre".
     """
 
     def __init__(
-        self, backbone: str, layer: str, pooling_kernel_size: int, n_comps: float = 0.97, score_type: str = "fre"
+        self,
+        backbone: str,
+        layer: str,
+        pre_trained: bool = True,
+        pooling_kernel_size: int = 4,
+        n_comps: float = 0.97,
+        score_type: str = "fre",
     ):
         super().__init__()
         self.backbone = getattr(torchvision.models, backbone)
@@ -102,7 +109,7 @@ def __init__(
         self.pca_model = PCA(n_components=self.n_components)
         self.gaussian_model = SingleClassGaussian()
         self.score_type = score_type
-        self.feature_extractor = FeatureExtractor(backbone=self.backbone(pretrained=True), layers=[layer]).eval()
+        self.feature_extractor = FeatureExtractor(backbone=self.backbone(pretrained=pre_trained), layers=[layer]).eval()
 
     def fit(self, dataset: Tensor) -> None:
         """Fit a pca transformation and a Gaussian model to dataset.

anomalib/models/fastflow/config.yaml

@@ -23,6 +23,7 @@ dataset:
 model:
   name: fastflow
   backbone: resnet18 # options: [resnet18, wide_resnet50_2, cait_m48_448, deit_base_distilled_patch16_384]
+  pre_trained: true
   flow_steps: 8 # options: [8, 8, 20, 20] - for each supported backbone
   hidden_ratio: 1.0 # options: [1.0, 1.0, 0.16, 0.16] - for each supported backbone
   conv3x3_only: True # options: [True, False, False, False] - for each supported backbone

anomalib/models/fastflow/lightning_model.py

@@ -24,7 +24,8 @@ class Fastflow(AnomalyModule):
     Args:
         input_size (Tuple[int, int]): Model input size.
         backbone (str): Backbone CNN network
-        flow_steps (int): Flow steps.
+        pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
+        flow_steps (int, optional): Flow steps.
         conv3x3_only (bool, optinoal): Use only conv3x3 in fast_flow model. Defaults to False.
         hidden_ratio (float, optional): Ratio to calculate hidden var channels. Defaults to 1.0.
     """
@@ -33,7 +34,8 @@ def __init__(
         self,
         input_size: Tuple[int, int],
         backbone: str,
-        flow_steps: int,
+        pre_trained: bool = True,
+        flow_steps: int = 8,
         conv3x3_only: bool = False,
         hidden_ratio: float = 1.0,
     ):
@@ -42,6 +44,7 @@ def __init__(
         self.model = FastflowModel(
             input_size=input_size,
             backbone=backbone,
+            pre_trained=pre_trained,
             flow_steps=flow_steps,
             conv3x3_only=conv3x3_only,
             hidden_ratio=hidden_ratio,

anomalib/models/fastflow/torch_model.py

@@ -94,7 +94,8 @@ class FastflowModel(nn.Module):
     Args:
         input_size (Tuple[int, int]): Model input size.
         backbone (str): Backbone CNN network
-        flow_steps (int): Flow steps.
+        pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
+        flow_steps (int, optional): Flow steps.
         conv3x3_only (bool, optinoal): Use only conv3x3 in fast_flow model. Defaults to False.
         hidden_ratio (float, optional): Ratio to calculate hidden var channels. Defaults to 1.0.
 
@@ -106,7 +107,8 @@ def __init__(
         self,
         input_size: Tuple[int, int],
         backbone: str,
-        flow_steps: int,
+        pre_trained: bool = True,
+        flow_steps: int = 8,
         conv3x3_only: bool = False,
         hidden_ratio: float = 1.0,
     ) -> None:
@@ -115,13 +117,13 @@ def __init__(
         self.input_size = input_size
 
         if backbone in ["cait_m48_448", "deit_base_distilled_patch16_384"]:
-            self.feature_extractor = timm.create_model(backbone, pretrained=True)
+            self.feature_extractor = timm.create_model(backbone, pretrained=pre_trained)
             channels = [768]
             scales = [16]
         elif backbone in ["resnet18", "wide_resnet50_2"]:
             self.feature_extractor = timm.create_model(
                 backbone,
-                pretrained=True,
+                pretrained=pre_trained,
                 features_only=True,
                 out_indices=[1, 2, 3],
             )

anomalib/models/padim/config.yaml

@@ -23,6 +23,7 @@ dataset:
 model:
   name: padim
   backbone: resnet18
+  pre_trained: true
   layers:
     - layer1
     - layer2

anomalib/models/padim/lightning_model.py

@@ -41,20 +41,23 @@ class Padim(AnomalyModule):
         layers (List[str]): Layers to extract features from the backbone CNN
         input_size (Tuple[int, int]): Size of the model input.
         backbone (str): Backbone CNN network
+        pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
     """
 
     def __init__(
         self,
         layers: List[str],
         input_size: Tuple[int, int],
         backbone: str,
+        pre_trained: bool = True,
     ):
         super().__init__()
 
         self.layers = layers
         self.model: PadimModel = PadimModel(
             input_size=input_size,
             backbone=backbone,
+            pre_trained=pre_trained,
             layers=layers,
         ).eval()
 

anomalib/models/padim/torch_model.py

@@ -39,20 +39,22 @@ class PadimModel(nn.Module):
         input_size (Tuple[int, int]): Input size for the model.
         layers (List[str]): Layers used for feature extraction
         backbone (str, optional): Pre-trained model backbone. Defaults to "resnet18".
+        pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
     """
 
     def __init__(
         self,
         input_size: Tuple[int, int],
         layers: List[str],
         backbone: str = "resnet18",
+        pre_trained: bool = True,
     ):
         super().__init__()
         self.tiler: Optional[Tiler] = None
 
         self.backbone = getattr(torchvision.models, backbone)
         self.layers = layers
-        self.feature_extractor = FeatureExtractor(backbone=self.backbone(pretrained=True), layers=self.layers)
+        self.feature_extractor = FeatureExtractor(backbone=self.backbone(pretrained=pre_trained), layers=self.layers)
         self.dims = DIMS[backbone]
         # pylint: disable=not-callable
         # Since idx is randomly selected, save it with model to get same results

anomalib/models/patchcore/config.yaml

@@ -23,6 +23,7 @@ dataset:
 model:
   name: patchcore
   backbone: wide_resnet50_2
+  pre_trained: true
   layers:
     - layer2
     - layer3

anomalib/models/patchcore/lightning_model.py

@@ -39,6 +39,7 @@ class Patchcore(AnomalyModule):
         input_size (Tuple[int, int]): Size of the model input.
         backbone (str): Backbone CNN network
         layers (List[str]): Layers to extract features from the backbone CNN
+        pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
         coreset_sampling_ratio (float, optional): Coreset sampling ratio to subsample embedding.
             Defaults to 0.1.
         num_neighbors (int, optional): Number of nearest neighbors. Defaults to 9.
@@ -49,6 +50,7 @@ def __init__(
         input_size: Tuple[int, int],
         backbone: str,
         layers: List[str],
+        pre_trained: bool = True,
         coreset_sampling_ratio: float = 0.1,
         num_neighbors: int = 9,
     ) -> None:
@@ -57,6 +59,7 @@ def __init__(
         self.model: PatchcoreModel = PatchcoreModel(
             input_size=input_size,
             backbone=backbone,
+            pre_trained=pre_trained,
             layers=layers,
             num_neighbors=num_neighbors,
         )

anomalib/models/patchcore/torch_model.py

@@ -38,6 +38,7 @@ def __init__(
         input_size: Tuple[int, int],
         layers: List[str],
         backbone: str = "wide_resnet50_2",
+        pre_trained: bool = True,
         num_neighbors: int = 9,
     ) -> None:
         super().__init__()
@@ -48,7 +49,7 @@ def __init__(
         self.input_size = input_size
         self.num_neighbors = num_neighbors
 
-        self.feature_extractor = FeatureExtractor(backbone=self.backbone(pretrained=True), layers=self.layers)
+        self.feature_extractor = FeatureExtractor(backbone=self.backbone(pretrained=pre_trained), layers=self.layers)
         self.feature_pooler = torch.nn.AvgPool2d(3, 1, 1)
         self.anomaly_map_generator = AnomalyMapGenerator(input_size=input_size)
 

anomalib/models/reverse_distillation/config.yaml

@@ -25,6 +25,7 @@ model:
   name: reverse_distillation
   lr: 0.005
   backbone: wide_resnet50_2
+  pre_trained: true
   layers:
     - layer1
     - layer2

anomalib/models/reverse_distillation/lightning_model.py

@@ -38,6 +38,7 @@ class ReverseDistillation(AnomalyModule):
         input_size (Tuple[int, int]): Size of model input
         backbone (str): Backbone of CNN network
         layers (List[str]): Layers to extract features from the backbone CNN
+        pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
     """
 
     def __init__(
@@ -49,10 +50,15 @@ def __init__(
         lr: float,
         beta1: float,
         beta2: float,
+        pre_trained: bool = True,
     ):
         super().__init__()
         self.model = ReverseDistillationModel(
-            backbone=backbone, layers=layers, input_size=input_size, anomaly_map_mode=anomaly_map_mode
+            backbone=backbone,
+            pre_trained=pre_trained,
+            layers=layers,
+            input_size=input_size,
+            anomaly_map_mode=anomaly_map_mode,
         )
         self.loss = ReverseDistillationLoss()
         # TODO: LR should be part of optimizer in config.yaml! Since reverse distillation has custom

anomalib/models/reverse_distillation/torch_model.py

@@ -36,15 +36,23 @@ class ReverseDistillationModel(nn.Module):
         input_size (Tuple[int, int]): Size of input image
         layers (List[str]): Name of layers from which the features are extracted.
         anomaly_map_mode (str): Mode used to generate anomaly map. Options are between ``multiply`` and ``add``.
+        pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
     """
 
-    def __init__(self, backbone: str, input_size: Tuple[int, int], layers: List[str], anomaly_map_mode: str):
+    def __init__(
+        self,
+        backbone: str,
+        input_size: Tuple[int, int],
+        layers: List[str],
+        anomaly_map_mode: str,
+        pre_trained: bool = True,
+    ):
         super().__init__()
         self.tiler: Optional[Tiler] = None
 
         encoder_backbone = getattr(torchvision.models, backbone)
         # TODO replace with TIMM feature extractor
-        self.encoder = FeatureExtractor(backbone=encoder_backbone(pretrained=True), layers=layers)
+        self.encoder = FeatureExtractor(backbone=encoder_backbone(pretrained=pre_trained), layers=layers)
         self.bottleneck = get_bottleneck_layer(backbone)
         self.decoder = get_decoder(backbone)