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Merge PR #156: Refactor anomaly algorithms and add Hailo/KV260 backends
Merge the validated modular algorithm structure, canonical imports, common inference backends, Hailo-8/KV260 deployment support, and test coverage.
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‎anomavision/__init__.py‎

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Provides functions for performing anomaly detection in images.
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"""
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from .algorithm.common.feature_extraction import ResnetEmbeddingsExtractor
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from .algorithm.padim import Padim
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from .algorithm.patchcore import PatchCore
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from .datasets.dataset import AnodetDataset
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from .datasets.mvtec_dataset import MVTecDataset
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from .feature_extraction import ResnetEmbeddingsExtractor
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from .padim import Padim
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from .patchcore import PatchCore
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from .sampling_methods.kcenter_greedy import kCenterGreedy
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from .test import optimal_threshold, visualize_eval_data, visualize_eval_pair
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from .utils import get_logger # Export for users

‎anomavision/algorithm/__init__.py‎

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"""Anomaly detection algorithm implementations."""

‎anomavision/algorithm/common/__init__.py‎

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"""Shared algorithm components used by anomaly detection implementations."""
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from .feature_extraction import ResnetEmbeddingsExtractor, concatenate_layers
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from .mahalanobis import MahalanobisDistance
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__all__ = [
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"MahalanobisDistance",
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"ResnetEmbeddingsExtractor",
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"concatenate_layers",
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]

anomavision/feature_extraction.py renamed to anomavision/algorithm/common/feature_extraction.py

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anomavision/mahalanobis.py renamed to anomavision/algorithm/common/mahalanobis.py

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‎anomavision/algorithm/padim/__init__.py‎

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"""PaDiM anomaly detection algorithms."""
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from .padim import Padim
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from .padim_lite import PadimLite, load_padim_lite
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__all__ = ["Padim", "PadimLite", "load_padim_lite"]

anomavision/padim.py renamed to anomavision/algorithm/padim/padim.py

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import torch
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import torch.nn.functional as F
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from .feature_extraction import ResnetEmbeddingsExtractor
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from .mahalanobis import MahalanobisDistance
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from .utils import pytorch_cov, split_tensor_and_run_function
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from ...utils import pytorch_cov, split_tensor_and_run_function
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from ..common.feature_extraction import ResnetEmbeddingsExtractor
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from ..common.mahalanobis import MahalanobisDistance
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BACKBONE_FEATURE_SIZES = {
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"resnet18": OrderedDict([(0, [64]), (1, [128]), (2, [256]), (3, [512])]),

anomavision/padim_lite.py renamed to anomavision/algorithm/padim/padim_lite.py

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import torch
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import torch.nn.functional as F
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from .feature_extraction import ResnetEmbeddingsExtractor
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from .mahalanobis import MahalanobisDistance
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from ..common.feature_extraction import ResnetEmbeddingsExtractor
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from ..common.mahalanobis import MahalanobisDistance
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class PadimLite(torch.nn.Module):

‎anomavision/algorithm/patchcore/__init__.py‎

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"""Lightweight PatchCore anomaly detection algorithms."""
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from ..common.feature_extraction import ResnetEmbeddingsExtractor
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from .patchcore import PatchCore, build_patchcore_from_stats
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__all__ = [
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"PatchCore",
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"ResnetEmbeddingsExtractor",
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"build_patchcore_from_stats",
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]

anomavision/patchcore.py renamed to anomavision/algorithm/patchcore/patchcore.py

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"""Lightweight PatchCore anomaly detection.
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This module provides a bounded-memory PatchCore implementation that follows the
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public design of :mod:`anomavision.padim`: fit on a normal-image DataLoader, predict
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public design of :mod:`anomavision.algorithm.padim`: fit on a normal-image DataLoader, predict
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image scores and spatial maps, and save a compact deployment artifact.
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"""
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import torch
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import torch.nn.functional as F
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from .feature_extraction import ResnetEmbeddingsExtractor
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from ..common.feature_extraction import ResnetEmbeddingsExtractor
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class PatchCore(torch.nn.Module):
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patch distance; the pixel map is the patch-distance grid upsampled to the input
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resolution.
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The public methods intentionally mirror :class:`anomavision.padim.Padim`, so the
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The public methods intentionally mirror :class:`anomavision.algorithm.padim.Padim`, so the
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model can be selected by the existing CLI training, inference, evaluation, and
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export workflows.
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