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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.
120 lines
3.8 KiB
Python
120 lines
3.8 KiB
Python
"""Tiling related tests for tiled ensemble."""
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# Copyright (C) 2023-2025 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import copy
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import pytest
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import torch
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from anomalib.data import AnomalibDataModule
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from anomalib.pipelines.tiled_ensemble.components.utils.helper_functions import get_ensemble_tiler
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tiler_config = {
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"tiling": {
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"image_size": 512,
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"tile_size": 256,
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"stride": 256,
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},
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}
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tiler_config_overlap = {
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"tiling": {
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"image_size": 512,
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"tile_size": 256,
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"stride": 128,
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},
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}
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class TestTiler:
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"""EnsembleTiler tests."""
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@staticmethod
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@pytest.mark.parametrize(
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("input_shape", "config", "expected_shape"),
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[
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(torch.Size([5, 3, 512, 512]), tiler_config, torch.Size([2, 2, 5, 3, 256, 256])),
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(torch.Size([5, 3, 512, 512]), tiler_config_overlap, torch.Size([3, 3, 5, 3, 256, 256])),
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(torch.Size([5, 3, 500, 500]), tiler_config, torch.Size([2, 2, 5, 3, 256, 256])),
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(torch.Size([5, 3, 500, 500]), tiler_config_overlap, torch.Size([3, 3, 5, 3, 256, 256])),
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],
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)
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def test_basic_tile_for_ensemble(input_shape: torch.Size, config: dict, expected_shape: torch.Size) -> None:
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"""Test basic tiling of data."""
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config = copy.deepcopy(config)
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config["tiling"]["image_size"] = input_shape[-1]
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tiler = get_ensemble_tiler(config["tiling"])
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images = torch.rand(size=input_shape)
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tiled = tiler.tile(images)
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assert tiled.shape == expected_shape
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@staticmethod
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@pytest.mark.parametrize(
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("input_shape", "config"),
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[
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(torch.Size([5, 3, 512, 512]), tiler_config),
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(torch.Size([5, 3, 512, 512]), tiler_config_overlap),
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(torch.Size([5, 3, 500, 500]), tiler_config),
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(torch.Size([5, 3, 500, 500]), tiler_config_overlap),
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],
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)
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def test_basic_tile_reconstruction(input_shape: torch.Size, config: dict) -> None:
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"""Test basic reconstruction of tiled data."""
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config = copy.deepcopy(config)
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config["tiling"]["image_size"] = input_shape[-1]
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tiler = get_ensemble_tiler(config["tiling"])
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images = torch.rand(size=input_shape)
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tiled = tiler.tile(images.clone())
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untiled = tiler.untile(tiled)
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assert images.shape == untiled.shape
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assert images.equal(untiled)
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@staticmethod
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@pytest.mark.parametrize(
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("input_shape", "config"),
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[
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(torch.Size([5, 3, 512, 512]), tiler_config),
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(torch.Size([5, 3, 500, 500]), tiler_config),
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],
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)
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def test_untile_different_instance(input_shape: torch.Size, config: dict) -> None:
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"""Test untiling with different Tiler instance."""
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config = copy.deepcopy(config)
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config["tiling"]["image_size"] = input_shape[-1]
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tiler_1 = get_ensemble_tiler(config["tiling"])
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tiler_2 = get_ensemble_tiler(config["tiling"])
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images = torch.rand(size=input_shape)
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tiled = tiler_1.tile(images.clone())
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untiled = tiler_2.untile(tiled)
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# untiling should work even with different instance of tiler
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assert images.shape == untiled.shape
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assert images.equal(untiled)
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class TestTileCollater:
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"""Test tile collater."""
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@staticmethod
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def test_collate_tile_shape(get_ensemble_config: dict, get_datamodule: AnomalibDataModule) -> None:
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"""Test that collate function successfully tiles the image."""
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config = get_ensemble_config
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# datamodule with tile collater
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datamodule = get_datamodule
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tile_w, tile_h = config["tiling"]["tile_size"]
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batch = next(iter(datamodule.train_dataloader()))
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assert batch.image.shape[1:] == (3, tile_w, tile_h)
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assert batch.gt_mask.shape[1:] == (tile_w, tile_h)
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