File size: 3,872 Bytes
d21cb06 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | 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_format import ImagenetConverter
from datumaro.plugins.imagenet_format import ImagenetImporter
from datumaro.util.test_utils import TestDir, compare_datasets
class ImagenetFormatTest(TestCase):
def test_can_save_and_load(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1',
image=np.ones((8, 8, 3)),
annotations=[Label(0)]
),
DatasetItem(id='2',
image=np.ones((10, 10, 3)),
annotations=[Label(1)]
),
DatasetItem(id='3',
image=np.ones((10, 10, 3)),
annotations=[Label(0)]
),
DatasetItem(id='4',
image=np.ones((8, 8, 3)),
annotations=[Label(2)]
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(3)),
})
with TestDir() as test_dir:
ImagenetConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = ImagenetImporter()(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',
image=np.ones((8, 8, 3)),
annotations=[Label(0), Label(1)]
),
DatasetItem(id='2',
image=np.ones((10, 10, 3)),
annotations=[Label(0), Label(1)]
),
DatasetItem(id='3',
image=np.ones((10, 10, 3)),
annotations=[Label(0), Label(2)]
),
DatasetItem(id='4',
image=np.ones((8, 8, 3)),
annotations=[Label(2), Label(4)]
),
DatasetItem(id='5',
image=np.ones((10, 10, 3)),
annotations=[Label(3), Label(4)]
),
DatasetItem(id='6',
image=np.ones((10, 10, 3)),
),
DatasetItem(id='7',
image=np.ones((8, 8, 3))
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(5)),
})
with TestDir() as test_dir:
ImagenetConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = ImagenetImporter()(test_dir).make_dataset()
compare_datasets(self, source_dataset, parsed_dataset,
require_images=True)
DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'imagenet_dataset')
class ImagenetImporterTest(TestCase):
def test_can_import(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id='1',
image=np.ones((8, 8, 3)),
annotations=[Label(0), Label(1)]
),
DatasetItem(id='2',
image=np.ones((10, 10, 3)),
annotations=[Label(0)]
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(2)),
})
dataset = Project.import_from(DUMMY_DATASET_DIR, 'imagenet').make_dataset()
compare_datasets(self, expected_dataset, dataset, require_images=True)
def test_can_detect_imagenet(self):
self.assertTrue(ImagenetImporter.detect(DUMMY_DATASET_DIR))
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