| from unittest import TestCase |
|
|
| import numpy as np |
| import os.path as osp |
|
|
| from datumaro.components.project import Project, Dataset |
| from datumaro.components.extractor import (DatasetItem, Label, |
| LabelCategories, AnnotationType |
| ) |
| from datumaro.plugins.imagenet_txt_format import ImagenetTxtConverter, ImagenetTxtImporter |
| from datumaro.util.test_utils import TestDir, compare_datasets |
|
|
|
|
| class ImagenetTxtFormatTest(TestCase): |
| def test_can_save_and_load(self): |
| source_dataset = Dataset.from_iterable([ |
| DatasetItem(id='1', subset='train', |
| annotations=[Label(0)] |
| ), |
| DatasetItem(id='2', subset='train', |
| annotations=[Label(0)] |
| ), |
| DatasetItem(id='3', subset='train', image=np.zeros((8, 8, 3)), |
| annotations=[Label(0)] |
| ), |
| DatasetItem(id='4', subset='train', |
| annotations=[Label(1)] |
| ), |
| DatasetItem(id='5', subset='train', image=np.zeros((4, 8, 3)), |
| annotations=[Label(1)] |
| ), |
| DatasetItem(id='6', subset='train', |
| annotations=[Label(5)] |
| ), |
| ], categories={ |
| AnnotationType.label: LabelCategories.from_iterable( |
| 'label_' + str(label) for label in range(10)), |
| }) |
|
|
| with TestDir() as test_dir: |
| ImagenetTxtConverter.convert(source_dataset, test_dir, |
| save_images=True) |
|
|
| parsed_dataset = ImagenetTxtImporter()(test_dir).make_dataset() |
|
|
| compare_datasets(self, source_dataset, parsed_dataset, |
| require_images=True) |
|
|
| def test_can_save_and_load_with_multiple_labels(self): |
| source_dataset = Dataset.from_iterable([ |
| DatasetItem(id='1', subset='train', |
| annotations=[Label(1), Label(3)] |
| ), |
| DatasetItem(id='2', subset='train', image=np.zeros((8, 6, 3)), |
| annotations=[Label(0)] |
| ), |
| DatasetItem(id='3', subset='train', image=np.zeros((2, 8, 3)), |
| ), |
| ], categories={ |
| AnnotationType.label: LabelCategories.from_iterable( |
| 'label_' + str(label) for label in range(10)), |
| }) |
|
|
| with TestDir() as test_dir: |
| ImagenetTxtConverter.convert(source_dataset, test_dir, |
| save_images=True) |
|
|
| parsed_dataset = ImagenetTxtImporter()(test_dir).make_dataset() |
|
|
| compare_datasets(self, source_dataset, parsed_dataset, |
| require_images=True) |
|
|
| def test_can_save_dataset_with_no_subsets(self): |
| source_dataset = Dataset.from_iterable([ |
| DatasetItem(id='a/b/c', image=np.zeros((8, 4, 3)), |
| annotations=[Label(1)] |
| ), |
| ], categories={ |
| AnnotationType.label: LabelCategories.from_iterable( |
| 'label_' + str(label) for label in range(10)), |
| }) |
|
|
| with TestDir() as test_dir: |
| ImagenetTxtConverter.convert(source_dataset, test_dir, |
| save_images=True) |
|
|
| parsed_dataset = ImagenetTxtImporter()(test_dir).make_dataset() |
|
|
| compare_datasets(self, source_dataset, parsed_dataset, |
| require_images=True) |
|
|
| DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'imagenet_txt_dataset') |
|
|
| class ImagenetTxtImporterTest(TestCase): |
| def test_can_import(self): |
| expected_dataset = Dataset.from_iterable([ |
| DatasetItem(id='1', subset='train', image=np.zeros((8, 6, 3)), |
| annotations=[Label(0)] |
| ), |
| DatasetItem(id='2', subset='train', image=np.zeros((2, 8, 3)), |
| annotations=[Label(5)] |
| ), |
| DatasetItem(id='3', subset='train', |
| annotations=[Label(3)] |
| ), |
| DatasetItem(id='4', subset='train', |
| annotations=[Label(5)] |
| ), |
| ], categories={ |
| AnnotationType.label: LabelCategories.from_iterable( |
| 'label_%s' % label for label in range(10)), |
| }) |
|
|
| dataset = Project.import_from(DUMMY_DATASET_DIR, 'imagenet_txt') \ |
| .make_dataset() |
|
|
| compare_datasets(self, expected_dataset, dataset, require_images=True) |
|
|
| def test_can_detect_imagenet(self): |
| self.assertTrue(ImagenetTxtImporter.detect(DUMMY_DATASET_DIR)) |
|
|
