Refactor

anomalib/data/mvtec.py

@@ -39,9 +39,9 @@
 from torch.utils.data.dataset import Dataset
 from torchvision.datasets.folder import VisionDataset
 
-from anomalib.data.transforms import PreProcessor
-from anomalib.data.utils import read_image
-from anomalib.utils.download_progress_bar import DownloadProgressBar
+from anomalib.data.utils.download_progress_bar import DownloadProgressBar
+from anomalib.pre_processing import PreProcessor
+from anomalib.utils.image import read_image
 
 logger = logging.getLogger(name="Dataset: MVTec")
 logger.setLevel(logging.DEBUG)

tests/config/__init__.py → anomalib/data/utils/__init__.py

@@ -1,4 +1,4 @@
-"""Tests for configuration getters/setters."""
+"""Helper utilities for data."""
 
 # Copyright (C) 2020 Intel Corporation
 #

anomalib/deploy/__init__.py

@@ -1,4 +1,4 @@
-"""Utilities for inference and deployment."""
+"""Functions for Inference and model deployment."""
 
 # Copyright (C) 2020 Intel Corporation
 #

anomalib/deploy/inferencers/base.py

@@ -22,11 +22,13 @@
 from omegaconf import DictConfig, OmegaConf
 from torch import Tensor
 
-from anomalib.data.utils import read_image
-from anomalib.utils.normalization.cdf import normalize as normalize_cdf
-from anomalib.utils.normalization.cdf import standardize
-from anomalib.utils.normalization.min_max import normalize as normalize_min_max
-from anomalib.utils.post_process import superimpose_anomaly_map
+from anomalib.post_processing.normalization.cdf import normalize as normalize_cdf
+from anomalib.post_processing.normalization.cdf import standardize
+from anomalib.post_processing.normalization.min_max import (
+    normalize as normalize_min_max,
+)
+from anomalib.post_processing.post_process import superimpose_anomaly_map
+from anomalib.utils.image import read_image
 
 
 class Inferencer(ABC):

anomalib/deploy/inferencers/openvino.py

@@ -22,7 +22,7 @@
 from omegaconf import DictConfig, ListConfig
 from openvino.inference_engine import IECore  # pylint: disable=no-name-in-module
 
-from anomalib.data.transforms.pre_process import PreProcessor
+from anomalib.pre_processing.pre_process import PreProcessor
 
 from .base import Inferencer
 

anomalib/deploy/inferencers/torch.py

@@ -23,10 +23,10 @@
 from omegaconf import DictConfig, ListConfig
 from torch import Tensor
 
-from anomalib.core.model import AnomalyModule
-from anomalib.data.transforms.pre_process import PreProcessor
 from anomalib.deploy.optimize import get_model_metadata
 from anomalib.models import get_model
+from anomalib.models.components.anomaly_models import AnomalyModule
+from anomalib.pre_processing.pre_process import PreProcessor
 
 from .base import Inferencer
 

anomalib/deploy/optimize.py

@@ -24,7 +24,7 @@
 import torch
 from torch import Tensor
 
-from anomalib.core.model.anomaly_module import AnomalyModule
+from anomalib.models.components.anomaly_models import AnomalyModule
 
 
 def get_model_metadata(model: AnomalyModule) -> Dict[str, Tensor]:

anomalib/models/__init__.py

@@ -21,7 +21,7 @@
 from omegaconf import DictConfig, ListConfig
 from torch import load
 
-from anomalib.core.model import AnomalyModule
+from anomalib.models.components.anomaly_models import AnomalyModule
 
 
 def get_model(config: Union[DictConfig, ListConfig]) -> AnomalyModule:

anomalib/models/cflow/model.py

@@ -28,9 +28,9 @@
 from pytorch_lightning.callbacks import EarlyStopping
 from torch import Tensor, nn, optim
 
-from anomalib.core.model import AnomalyModule
-from anomalib.core.model.feature_extractor import FeatureExtractor
 from anomalib.models.cflow.backbone import cflow_head, positional_encoding_2d
+from anomalib.models.components.anomaly_models import AnomalyModule
+from anomalib.models.components.feature_extractors import FeatureExtractor
 
 __all__ = ["AnomalyMapGenerator", "CflowModel", "CflowLightning"]
 

anomalib/core/__init__.py → anomalib/models/components/__init__.py

@@ -1,4 +1,4 @@
-"""This module holds common components such as callbacks, custom modules and utils."""
+"""Components used within the models."""
 
 # Copyright (C) 2020 Intel Corporation
 #

anomalib/core/model/__init__.py → anomalib/models/components/anomaly_models/__init__.py

@@ -1,4 +1,4 @@
-"""Anomalib Core Model Entities."""
+"""Anomalib library for research and benchmarking."""
 
 # Copyright (C) 2020 Intel Corporation
 #

anomalib/core/model/anomaly_module.py → anomalib/models/components/anomaly_models/anomaly_module.py

@@ -22,7 +22,7 @@
 from torch import Tensor, nn
 from torchmetrics import F1, MetricCollection
 
-from anomalib.core.metrics import (
+from anomalib.utils.metrics import (
     AUROC,
     AdaptiveThreshold,
     AnomalyScoreDistribution,

anomalib/models/components/dimensionality_reduction/__init__.py

@@ -0,0 +1,15 @@
+"""Algorithms for decomposition and dimensionality reduction."""
+
+# Copyright (C) 2020 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions
+# and limitations under the License.

anomalib/core/model/pca.py → anomalib/models/components/dimensionality_reduction/pca.py

@@ -19,7 +19,7 @@
 import torch
 from torch import Tensor
 
-from anomalib.core.model.dynamic_module import DynamicBufferModule
+from anomalib.models.components.anomaly_models.dynamic_module import DynamicBufferModule
 
 
 class PCA(DynamicBufferModule):

anomalib/models/components/feature_extractors/__init__.py

@@ -0,0 +1,19 @@
+"""Feature extractors."""
+
+# Copyright (C) 2020 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions
+# and limitations under the License.
+
+from .feature_extractor import FeatureExtractor
+
+__all__ = ["FeatureExtractor"]

anomalib/models/components/neighbors/__init__.py

@@ -0,0 +1,15 @@
+"""Functions for calculating nearest neighbors."""
+
+# Copyright (C) 2020 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions
+# and limitations under the License.

anomalib/models/components/neighbors/nearest_neighbors.py

@@ -0,0 +1,62 @@
+"""This module comprises PatchCore Sampling Methods for the embedding.
+
+- Nearest Neighbours
+"""
+
+# Copyright (C) 2020 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions
+# and limitations under the License.
+
+from typing import Tuple
+
+import torch
+from torch import Tensor
+
+from anomalib.models.components.anomaly_models.dynamic_module import DynamicBufferModule
+
+
+class NearestNeighbors(DynamicBufferModule):
+    """Nearest Neighbours using brute force method and euclidean norm.
+
+    Args:
+        n_neighbors (int): Number of neighbors to look at
+    """
+
+    def __init__(self, n_neighbors: int):
+        super().__init__()
+        self.n_neighbors = n_neighbors
+
+        self.register_buffer("_fit_x", Tensor())
+        self._fit_x: Tensor
+
+    def fit(self, train_features: Tensor):
+        """Saves the train features for NN search later.
+
+        Args:
+            train_features (Tensor): Training data
+        """
+        self._fit_x = train_features
+
+    def kneighbors(self, test_features: Tensor) -> Tuple[Tensor, Tensor]:
+        """Return k-nearest neighbors.
+
+        It is calculated based on bruteforce method.
+
+        Args:
+            test_features (Tensor): test data
+
+        Returns:
+            Tuple[Tensor, Tensor]: distances, indices
+        """
+        distances = torch.cdist(test_features, self._fit_x, p=2.0)  # euclidean norm
+        return distances.topk(k=self.n_neighbors, largest=False, dim=1)

anomalib/models/components/sampling/__init__.py

@@ -0,0 +1,19 @@
+"""Sampling methods."""
+
+# Copyright (C) 2020 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions
+# and limitations under the License.
+
+from .k_center_greedy import KCenterGreedy
+
+__all__ = ["KCenterGreedy"]

anomalib/core/model/k_center_greedy.py → anomalib/models/components/sampling/k_center_greedy.py

@@ -11,7 +11,9 @@
 import torch.nn.functional as F
 from torch import Tensor
 
-from anomalib.core.model.random_projection import SparseRandomProjection
+from anomalib.models.components.dimensionality_reduction.random_projection import (
+    SparseRandomProjection,
+)
 
 
 class KCenterGreedy:

anomalib/models/components/stats/__init__.py

@@ -0,0 +1,15 @@
+"""Statistical functions."""
+
+# Copyright (C) 2020 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions
+# and limitations under the License.

anomalib/core/model/kde.py → anomalib/models/components/stats/kde.py

@@ -20,7 +20,7 @@
 import torch
 from torch import Tensor
 
-from anomalib.core.model.dynamic_module import DynamicBufferModule
+from anomalib.models.components.anomaly_models.dynamic_module import DynamicBufferModule
 
 
 class GaussianKDE(DynamicBufferModule):

anomalib/models/dfkde/model.py

@@ -21,8 +21,10 @@
 from omegaconf.dictconfig import DictConfig
 from omegaconf.listconfig import ListConfig
 
-from anomalib.core.model import AnomalyModule
-from anomalib.core.model.feature_extractor import FeatureExtractor
+from anomalib.models.components.anomaly_models import AnomalyModule
+from anomalib.models.components.feature_extractors.feature_extractor import (
+    FeatureExtractor,
+)
 from anomalib.models.dfkde.normality_model import NormalityModel
 
 

anomalib/models/dfkde/normality_model.py

@@ -20,8 +20,8 @@
 import torch
 from torch import Tensor, nn
 
-from anomalib.core.model.kde import GaussianKDE
-from anomalib.core.model.pca import PCA
+from anomalib.models.components.dimensionality_reduction.pca import PCA
+from anomalib.models.components.stats.kde import GaussianKDE
 
 
 class NormalityModel(nn.Module):

anomalib/models/dfm/dfm_model.py

@@ -19,8 +19,8 @@
 import torch
 from torch import Tensor, nn
 
-from anomalib.core.model.dynamic_module import DynamicBufferModule
-from anomalib.core.model.pca import PCA
+from anomalib.models.components.anomaly_models.dynamic_module import DynamicBufferModule
+from anomalib.models.components.dimensionality_reduction.pca import PCA
 
 
 class SingleClassGaussian(DynamicBufferModule):

anomalib/models/dfm/model.py

@@ -21,8 +21,10 @@
 from omegaconf import DictConfig, ListConfig
 from torch import Tensor
 
-from anomalib.core.model import AnomalyModule
-from anomalib.core.model.feature_extractor import FeatureExtractor
+from anomalib.models.components.anomaly_models import AnomalyModule
+from anomalib.models.components.feature_extractors.feature_extractor import (
+    FeatureExtractor,
+)
 from anomalib.models.dfm.dfm_model import DFMModel
 
 

anomalib/models/padim/model.py

@@ -27,10 +27,12 @@
 from omegaconf import DictConfig, ListConfig
 from torch import Tensor, nn
 
-from anomalib.core.model import AnomalyModule
-from anomalib.core.model.feature_extractor import FeatureExtractor
-from anomalib.core.model.multi_variate_gaussian import MultiVariateGaussian
-from anomalib.data.tiler import Tiler
+from anomalib.models.components.anomaly_models import AnomalyModule
+from anomalib.models.components.feature_extractors.feature_extractor import (
+    FeatureExtractor,
+)
+from anomalib.models.components.stats.multi_variate_gaussian import MultiVariateGaussian
+from anomalib.pre_processing.tiler import Tiler
 
 __all__ = ["PadimLightning"]