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"""Test Dataset."""
import os
import numpy as np
import pytest
from anomalib.config import update_input_size_config
from anomalib.data import (
BTechDataModule,
FolderDataModule,
MVTecDataModule,
get_datamodule,
)
from anomalib.pre_processing.transforms import Denormalize, ToNumpy
from tests.helpers.config import get_test_configurable_parameters
from tests.helpers.dataset import TestDataset, get_dataset_path
@pytest.fixture(autouse=True)
def mvtec_data_module():
datamodule = MVTecDataModule(
root=get_dataset_path(dataset="MVTec"),
category="leather",
image_size=(256, 256),
train_batch_size=1,
test_batch_size=1,
num_workers=0,
)
datamodule.prepare_data()
datamodule.setup()
return datamodule
@pytest.fixture(autouse=True)
def btech_data_module():
"""Create BTech Data Module."""
datamodule = BTechDataModule(
root=get_dataset_path(dataset="BTech"),
category="01",
image_size=(256, 256),
train_batch_size=1,
test_batch_size=1,
num_workers=0,
)
datamodule.prepare_data()
datamodule.setup()
return datamodule
@pytest.fixture(autouse=True)
def folder_data_module():
"""Create Folder Data Module."""
root = get_dataset_path(dataset="bottle")
datamodule = FolderDataModule(
root=root,
normal_dir="good",
abnormal_dir="broken_large",
mask_dir=os.path.join(root, "ground_truth/broken_large"),
task="segmentation",
split_ratio=0.2,
seed=0,
image_size=(256, 256),
train_batch_size=32,
test_batch_size=32,
num_workers=8,
create_validation_set=True,
)
datamodule.setup()
return datamodule
@pytest.fixture(autouse=True)
def data_sample(mvtec_data_module):
_, data = next(enumerate(mvtec_data_module.train_dataloader()))
return data
class TestMVTecDataModule:
"""Test MVTec AD Data Module."""
def test_batch_size(self, mvtec_data_module):
"""test_mvtec_datamodule [summary]"""
_, train_data_sample = next(enumerate(mvtec_data_module.train_dataloader()))
_, val_data_sample = next(enumerate(mvtec_data_module.val_dataloader()))
assert train_data_sample["image"].shape[0] == 1
assert val_data_sample["image"].shape[0] == 1
def test_val_and_test_dataloaders_has_mask_and_gt(self, mvtec_data_module):
"""Test Validation and Test dataloaders should return filenames, image, mask and label."""
_, val_data = next(enumerate(mvtec_data_module.val_dataloader()))
_, test_data = next(enumerate(mvtec_data_module.test_dataloader()))
assert sorted(["image_path", "mask_path", "image", "label", "mask"]) == sorted(val_data.keys())
assert sorted(["image_path", "mask_path", "image", "label", "mask"]) == sorted(test_data.keys())
class TestBTechDataModule:
"""Test BTech Data Module."""
def test_batch_size(self, btech_data_module):
"""Test batch size."""
_, train_data_sample = next(enumerate(btech_data_module.train_dataloader()))
_, val_data_sample = next(enumerate(btech_data_module.val_dataloader()))
assert train_data_sample["image"].shape[0] == 1
assert val_data_sample["image"].shape[0] == 1
def test_val_and_test_dataloaders_has_mask_and_gt(self, btech_data_module):
"""Test Validation and Test dataloaders should return filenames, image, mask and label."""
_, val_data = next(enumerate(btech_data_module.val_dataloader()))
_, test_data = next(enumerate(btech_data_module.test_dataloader()))
assert sorted(["image_path", "mask_path", "image", "label", "mask"]) == sorted(val_data.keys())
assert sorted(["image_path", "mask_path", "image", "label", "mask"]) == sorted(test_data.keys())
class TestFolderDataModule:
"""Test Folder Data Module."""
def test_batch_size(self, folder_data_module):
"""Test batch size."""
_, train_data_sample = next(enumerate(folder_data_module.train_dataloader()))
_, val_data_sample = next(enumerate(folder_data_module.val_dataloader()))
assert train_data_sample["image"].shape[0] == 16
assert val_data_sample["image"].shape[0] == 12
def test_val_and_test_dataloaders_has_mask_and_gt(self, folder_data_module):
"""Test Validation and Test dataloaders should return filenames, image, mask and label."""
_, val_data = next(enumerate(folder_data_module.val_dataloader()))
_, test_data = next(enumerate(folder_data_module.test_dataloader()))
assert sorted(["image_path", "mask_path", "image", "label", "mask"]) == sorted(val_data.keys())
assert sorted(["image_path", "mask_path", "image", "label", "mask"]) == sorted(test_data.keys())
class TestDenormalize:
"""Test Denormalize Util."""
def test_denormalize_image_pixel_values(self, data_sample):
"""Test Denormalize denormalizes tensor into [0, 256] range."""
denormalized_sample = Denormalize().__call__(data_sample["image"].squeeze())
assert denormalized_sample.min() >= 0 and denormalized_sample.max() <= 256
def test_denormalize_return_numpy(self, data_sample):
"""Denormalize should return a numpy array."""
denormalized_sample = Denormalize()(data_sample["image"].squeeze())
assert isinstance(denormalized_sample, np.ndarray)
def test_denormalize_channel_order(self, data_sample):
"""Denormalize should return a numpy array of order [HxWxC]"""
denormalized_sample = Denormalize().__call__(data_sample["image"].squeeze())
assert len(denormalized_sample.shape) == 3 and denormalized_sample.shape[-1] == 3
def test_representation(self):
"""Test Denormalize representation should return string
Denormalize()"""
assert str(Denormalize()) == "Denormalize()"
class TestToNumpy:
"""Test ToNumpy whether it properly converts tensor into numpy array."""
def test_to_numpy_image_pixel_values(self, data_sample):
"""Test ToNumpy should return an array whose pixels in the range of [0,
256]"""
array = ToNumpy()(data_sample["image"])
assert array.min() >= 0 and array.max() <= 256
def test_to_numpy_converts_tensor_to_np_array(self, data_sample):
"""ToNumpy returns a numpy array."""
array = ToNumpy()(data_sample["image"])
assert isinstance(array, np.ndarray)
def test_to_numpy_channel_order(self, data_sample):
"""ToNumpy() should return a numpy array of order [HxWxC]"""
array = ToNumpy()(data_sample["image"])
assert len(array.shape) == 3 and array.shape[-1] == 3
def test_one_channel_images(self, data_sample):
"""One channel tensor should be converted to HxW np array."""
data = data_sample["image"][:, 0, :, :].unsqueeze(0)
array = ToNumpy()(data)
assert len(array.shape) == 2
def test_representation(self):
"""Test ToNumpy() representation should return string `ToNumpy()`"""
assert str(ToNumpy()) == "ToNumpy()"
class TestConfigToDataModule:
"""Tests that check if the dataset parameters in the config achieve the desired effect."""
@pytest.mark.parametrize(
["input_size", "effective_image_size"],
[
(512, (512, 512)),
((245, 276), (245, 276)),
((263, 134), (263, 134)),
((267, 267), (267, 267)),
],
)
@TestDataset(num_train=20, num_test=10)
def test_image_size(self, input_size, effective_image_size, category="shapes", path=None):
"""Test if the image size parameter works as expected."""
configurable_parameters = get_test_configurable_parameters(dataset_path=path, model_name="stfpm")
configurable_parameters.dataset.category = category
configurable_parameters.dataset.image_size = input_size
configurable_parameters = update_input_size_config(configurable_parameters)
data_module = get_datamodule(configurable_parameters)
data_module.setup()
assert iter(data_module.train_dataloader()).__next__()["image"].shape[-2:] == effective_image_size
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