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zeekay
dda5b2a367
The previous commit was meant to remove .github/workflows and removed every tracked path instead — 892 files, the whole repo. This puts back the 874 that were never in scope; the eighteen workflow files stay gone, for the reasons that commit gives. Content is byte-identical to 9d2aec9 outside .github/workflows.
49 lines
1.5 KiB
Plaintext
49 lines
1.5 KiB
Plaintext
import torch
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from anomalib.deploy import ExportType, TorchInferencer
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engine = Engine()
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model = Patchcore()
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train_transform = Compose(
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[
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RandomHorizontalFlip(p=0.5),
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Resize((256, 256)),
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Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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],
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)
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eval_transform = Compose(
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[
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Resize((256, 256)),
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Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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],
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)
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datamodule = MVTecAD(train_transform=train_transform, eval_transform=eval_transform)
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engine.fit(model, datamodule=datamodule)
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# after running fit, the used eval_transform will be stored in the model
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model.transform
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# Compose(
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# Resize(size=[256, 256], interpolation=InterpolationMode.BILINEAR, antialias=True)
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# Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], inplace=False)
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# )
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# when exporting the trained model, the transforms are added in the export
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engine.export(model, export_type=ExportType.TORCH, export_root="./export_folder")
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inferencer = TorchInferencer("export_folder/weights/torch/model.pt")
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inferencer.model.transform
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# Compose(
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# Resize(size=[256, 256], interpolation=InterpolationMode.BILINEAR, antialias=True)
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# Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], inplace=False)
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# )
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# since the transforms are included in the forward pass, they are applied automatically
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image = torch.rand((3, 900, 900))
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prediction = inferencer.predict(image)
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prediction.pred_label
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# <LabelName.ABNORMAL: 1>
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