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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.
80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
"""Dummy model that is used to test teh visualizer callback."""
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# Copyright (C) 2023-2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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from pathlib import Path
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from typing import Any
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import torch
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from torch import nn
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from anomalib import LearningType
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from anomalib.data import ImageBatch, InferenceBatch
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from anomalib.models.components import AnomalibModule
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from anomalib.post_processing import PostProcessor
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class _DummyModel(nn.Module): ...
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class DummyPostProcessor(PostProcessor):
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"""Dummy post-processor for testing."""
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@staticmethod
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def forward(batch: InferenceBatch) -> InferenceBatch:
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"""Dummy forward method."""
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return batch
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class DummyModule(AnomalibModule):
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"""A dummy model which calls visualizer callback on fake images and masks.
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TODO(ashwinvaidya17): Remove this when the DummyModels have been refactored.
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"""
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def __init__(self, dataset_path: Path, **kwargs) -> None:
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"""Initializes the dummy model."""
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super().__init__(**kwargs)
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self.model = _DummyModel()
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self.task = "segmentation"
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self.mode = "full"
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self.dataset_path = dataset_path
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def validation_step(self, batch: ImageBatch, *args, **kwargs) -> ImageBatch:
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"""Only used to avoid NotImplementedError."""
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del batch
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return self.test_step(*args, **kwargs)
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def test_step(self, *_, **__) -> ImageBatch:
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"""Only used to trigger on_test_epoch_end."""
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self.log(name="loss", value=0.0, prog_bar=True)
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return ImageBatch(
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image_path=[Path(self.dataset_path / "mvtecad" / "dummy" / "train" / "good" / "000.png")],
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image=torch.rand((1, 3, 100, 100)).to(self.device),
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gt_mask=torch.zeros((1, 100, 100)).to(self.device),
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anomaly_map=torch.ones((1, 100, 100)).to(self.device),
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pred_score=torch.Tensor([1.0]),
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gt_label=torch.Tensor([0]).int().to(self.device),
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pred_label=torch.Tensor([0]).int().to(self.device),
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pred_mask=torch.zeros((1, 100, 100)).to(self.device),
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)
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def configure_optimizers(self) -> None:
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"""Optimization is not required."""
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@property
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def trainer_arguments(self) -> dict[str, Any]:
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"""Does not require anything specific."""
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return {}
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@property
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def learning_type(self) -> LearningType:
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"""Returns the learning type."""
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return LearningType.ZERO_SHOT
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@staticmethod
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def configure_post_processor() -> PostProcessor:
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"""Returns a dummy post-processor."""
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return DummyPostProcessor()
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