def test_load_annotations(self):
dataset_class = DATASETS.get(self.DATASET_TYPE)
# Test default behavior
dataset = dataset_class(**self.DEFAULT_ARGS)
self.assertEqual(len(dataset), 2)
self.assertEqual(dataset.CLASSES, dataset_class.CLASSES)
data_info = dataset[0]
np.testing.assert_equal(data_info['img_prefix'], self.data_prefix)
np.testing.assert_equal(data_info['img_info'], {'filename': '2.txt'})
np.testing.assert_equal(data_info['gt_label'], 3 - 1)
# Test with test_mode=True
cfg = {**self.DEFAULT_ARGS, 'test_mode': True}
dataset = dataset_class(**cfg)
self.assertEqual(len(dataset), 1)
data_info = dataset[0]
np.testing.assert_equal(data_info['img_prefix'], self.data_prefix)
np.testing.assert_equal(data_info['img_info'], {'filename': '1.txt'})
np.testing.assert_equal(data_info['gt_label'], 2 - 1)
# Test if the numbers of line are not match
cfg = {
**self.DEFAULT_ARGS, 'train_test_split_file':
self.train_test_split_file2
}
with self.assertRaisesRegex(AssertionError, 'should have same length'):
dataset_class(**cfg)
def test_initialize(self):
dataset_class = DATASETS.get(self.DATASET_TYPE)
with patch.object(dataset_class, 'load_annotations'):
# Test default behavior
cfg = {**self.DEFAULT_ARGS, 'classes': None, 'ann_file': None}
dataset = dataset_class(**cfg)
self.assertEqual(dataset.CLASSES, dataset_class.CLASSES)
self.assertFalse(dataset.test_mode)
self.assertIsNone(dataset.ann_file)
# Test setting classes as a tuple
cfg = {**self.DEFAULT_ARGS, 'classes': ('bus', 'car')}
dataset = dataset_class(**cfg)
self.assertEqual(dataset.CLASSES, ('bus', 'car'))
# Test setting classes as a tuple
cfg = {**self.DEFAULT_ARGS, 'classes': ['bus', 'car']}
dataset = dataset_class(**cfg)
self.assertEqual(dataset.CLASSES, ['bus', 'car'])
# Test setting classes through a file
classes_file = osp.join(ASSETS_ROOT, 'classes.txt')
cfg = {**self.DEFAULT_ARGS, 'classes': classes_file}
dataset = dataset_class(**cfg)
self.assertEqual(dataset.CLASSES, ['bus', 'car'])
self.assertEqual(dataset.class_to_idx, {'bus': 0, 'car': 1})
# Test invalid classes
cfg = {**self.DEFAULT_ARGS, 'classes': dict(classes=1)}
with self.assertRaisesRegex(ValueError, "type <class 'dict'>"):
dataset_class(**cfg)
def test_datasets_override_default(dataset_name):
dataset_class = DATASETS.get(dataset_name)
dataset_class.load_annotations = MagicMock()
# Test default behavior
dataset = dataset_class(data_prefix='', pipeline=[])
assert dataset.data_prefix == ''
assert not dataset.test_mode
assert dataset.ann_file is None
def test_load_annotations(self):
dataset_class = DATASETS.get(self.DATASET_TYPE)
with patch.object(dataset_class, 'download'):
# Test default behavior
dataset = dataset_class(**self.DEFAULT_ARGS)
self.assertEqual(len(dataset), 1)
data_info = dataset[0]
np.testing.assert_equal(data_info['img'], self.fake_img)
np.testing.assert_equal(data_info['gt_label'], self.fake_label)
def test_datasets_override_default(dataset_name):
dataset_class = DATASETS.get(dataset_name)
dataset_class.load_annotations = MagicMock()
original_classes = dataset_class.CLASSES
# Test setting classes as a tuple
dataset = dataset_class(data_prefix='',
pipeline=[],
classes=('bus', 'car'),
test_mode=True)
assert dataset.CLASSES != original_classes
assert dataset.CLASSES == ('bus', 'car')
# Test setting classes as a list
dataset = dataset_class(data_prefix='',
pipeline=[],
classes=['bus', 'car'],
test_mode=True)
assert dataset.CLASSES != original_classes
assert dataset.CLASSES == ['bus', 'car']
# Test setting classes through a file
tmp_file = tempfile.NamedTemporaryFile()
with open(tmp_file.name, 'w') as f:
f.write('bus\ncar\n')
dataset = dataset_class(data_prefix='',
pipeline=[],
classes=tmp_file.name,
test_mode=True)
tmp_file.close()
assert dataset.CLASSES != original_classes
assert dataset.CLASSES == ['bus', 'car']
# Test overriding not a subset
dataset = dataset_class(data_prefix='',
pipeline=[],
classes=['foo'],
test_mode=True)
assert dataset.CLASSES != original_classes
assert dataset.CLASSES == ['foo']
# Test default behavior
dataset = dataset_class(data_prefix='', pipeline=[])
assert dataset.data_prefix == ''
assert not dataset.test_mode
assert dataset.ann_file is None
assert dataset.CLASSES == original_classes
def test_get_cat_ids(self):
dataset_class = DATASETS.get(self.DATASET_TYPE)
fake_ann = [
dict(img_prefix='',
img_info=dict(),
gt_label=np.array(0, dtype=np.int64))
]
with patch.object(dataset_class, 'load_annotations') as mock_load:
mock_load.return_value = fake_ann
dataset = dataset_class(**self.DEFAULT_ARGS)
cat_ids = dataset.get_cat_ids(0)
self.assertIsInstance(cat_ids, list)
self.assertEqual(len(cat_ids), 1)
self.assertIsInstance(cat_ids[0], int)
def test_load_annotations(self):
dataset_class = DATASETS.get(self.DATASET_TYPE)
# Test with serialize_data=False
cfg = {**self.DEFAULT_ARGS, 'serialize_data': False}
dataset = dataset_class(**cfg)
self.assertEqual(len(dataset.data_infos), 3)
self.assertEqual(len(dataset), 3)
self.assertEqual(
dataset[0], {
'img_prefix': ASSETS_ROOT,
'img_info': {
'filename': 'a/1.JPG'
},
'gt_label': np.array(0)
})
self.assertEqual(
dataset[2], {
'img_prefix': ASSETS_ROOT,
'img_info': {
'filename': 'b/subb/2.jpeg'
},
'gt_label': np.array(1)
})
# Test with serialize_data=True
cfg = {**self.DEFAULT_ARGS, 'serialize_data': True}
dataset = dataset_class(**cfg)
self.assertEqual(len(dataset.data_infos), 0) # data_infos is clear.
self.assertEqual(len(dataset), 3)
self.assertEqual(
dataset[0], {
'img_prefix': ASSETS_ROOT,
'img_info': {
'filename': 'a/1.JPG'
},
'gt_label': np.array(0)
})
self.assertEqual(
dataset[2], {
'img_prefix': ASSETS_ROOT,
'img_info': {
'filename': 'b/subb/2.jpeg'
},
'gt_label': np.array(1)
})
def test_initialize(self):
super().test_initialize()
dataset_class = DATASETS.get(self.DATASET_TYPE)
# The multi_label option is not implemented not.
cfg = {**self.DEFAULT_ARGS, 'multi_label': True}
with self.assertRaisesRegex(NotImplementedError, 'not supported'):
dataset_class(**cfg)
# Warn about ann_file
cfg = {**self.DEFAULT_ARGS, 'ann_file': None}
with self.assertWarnsRegex(UserWarning, 'specify the `ann_file`'):
dataset_class(**cfg)
# Warn about classes
cfg = {**self.DEFAULT_ARGS, 'classes': None}
with self.assertWarnsRegex(UserWarning, 'specify the `classes`'):
dataset_class(**cfg)
def setUpClass(cls) -> None:
super().setUpClass()
tmpdir = tempfile.TemporaryDirectory()
cls.tmpdir = tmpdir
data_prefix = tmpdir.name
cls.DEFAULT_ARGS = dict(data_prefix=data_prefix, pipeline=[])
dataset_class = DATASETS.get(cls.DATASET_TYPE)
base_folder = osp.join(data_prefix, dataset_class.base_folder)
os.mkdir(base_folder)
cls.fake_imgs = np.random.randint(0,
255,
size=(6, 3 * 32 * 32),
dtype=np.uint8)
cls.fake_labels = np.random.randint(0, 10, size=(6, ))
cls.fake_classes = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
batch1 = dict(data=cls.fake_imgs[:2],
labels=cls.fake_labels[:2].tolist())
with open(osp.join(base_folder, 'data_batch_1'), 'wb') as f:
f.write(pickle.dumps(batch1))
batch2 = dict(data=cls.fake_imgs[2:4],
labels=cls.fake_labels[2:4].tolist())
with open(osp.join(base_folder, 'data_batch_2'), 'wb') as f:
f.write(pickle.dumps(batch2))
test_batch = dict(data=cls.fake_imgs[4:],
labels=cls.fake_labels[4:].tolist())
with open(osp.join(base_folder, 'test_batch'), 'wb') as f:
f.write(pickle.dumps(test_batch))
meta = {dataset_class.meta['key']: cls.fake_classes}
meta_filename = dataset_class.meta['filename']
with open(osp.join(base_folder, meta_filename), 'wb') as f:
f.write(pickle.dumps(meta))
dataset_class.train_list = [['data_batch_1', None],
['data_batch_2', None]]
dataset_class.test_list = [['test_batch', None]]
dataset_class.meta['md5'] = None
def setUpClass(cls) -> None:
super().setUpClass()
tmpdir = tempfile.TemporaryDirectory()
cls.tmpdir = tmpdir
data_prefix = tmpdir.name
cls.DEFAULT_ARGS = dict(data_prefix=data_prefix, pipeline=[])
dataset_class = DATASETS.get(cls.DATASET_TYPE)
def rm_suffix(s):
return s[:s.rfind('.')]
train_image_file = osp.join(
data_prefix,
rm_suffix(dataset_class.resources['train_image_file'][0]))
train_label_file = osp.join(
data_prefix,
rm_suffix(dataset_class.resources['train_label_file'][0]))
test_image_file = osp.join(
data_prefix,
rm_suffix(dataset_class.resources['test_image_file'][0]))
test_label_file = osp.join(
data_prefix,
rm_suffix(dataset_class.resources['test_label_file'][0]))
cls.fake_img = np.random.randint(0, 255, size=(28, 28), dtype=np.uint8)
cls.fake_label = np.random.randint(0, 10, size=(1, ), dtype=np.uint8)
for file in [train_image_file, test_image_file]:
magic = b'\x00\x00\x08\x03' # num_dims = 3, type = uint8
head = b'\x00\x00\x00\x01' + b'\x00\x00\x00\x1c' * 2 # (1, 28, 28)
data = magic + head + cls.fake_img.flatten().tobytes()
with open(file, 'wb') as f:
f.write(data)
for file in [train_label_file, test_label_file]:
magic = b'\x00\x00\x08\x01' # num_dims = 3, type = uint8
head = b'\x00\x00\x00\x01' # (1, )
data = magic + head + cls.fake_label.tobytes()
with open(file, 'wb') as f:
f.write(data)
def test_evaluate(self):
dataset_class = DATASETS.get(self.DATASET_TYPE)
fake_ann = [
dict(gt_label=np.array([1, 1, 0, -1], dtype=np.int8)),
dict(gt_label=np.array([1, 1, 0, -1], dtype=np.int8)),
dict(gt_label=np.array([0, -1, 1, -1], dtype=np.int8)),
dict(gt_label=np.array([0, 1, 0, -1], dtype=np.int8)),
dict(gt_label=np.array([0, 1, 0, -1], dtype=np.int8)),
]
with patch.object(dataset_class, 'load_annotations') as mock_load:
mock_load.return_value = fake_ann
dataset = dataset_class(**self.DEFAULT_ARGS)
fake_results = np.array([
[0.9, 0.8, 0.3, 0.2],
[0.1, 0.2, 0.2, 0.1],
[0.7, 0.5, 0.9, 0.3],
[0.8, 0.1, 0.1, 0.2],
[0.8, 0.1, 0.1, 0.2],
])
# the metric must be valid for the dataset
with self.assertRaisesRegex(ValueError,
"{'unknown'} is not supported"):
dataset.evaluate(fake_results, metric='unknown')
# only one metric
eval_results = dataset.evaluate(fake_results, metric='mAP')
self.assertEqual(eval_results.keys(), {'mAP'})
self.assertAlmostEqual(eval_results['mAP'], 67.5, places=4)
# multiple metrics
eval_results = dataset.evaluate(fake_results,
metric=['mAP', 'CR', 'OF1'])
self.assertEqual(eval_results.keys(), {'mAP', 'CR', 'OF1'})
self.assertAlmostEqual(eval_results['mAP'], 67.50, places=2)
self.assertAlmostEqual(eval_results['CR'], 43.75, places=2)
self.assertAlmostEqual(eval_results['OF1'], 42.86, places=2)
def test_load_annotations(self):
dataset_class = DATASETS.get(self.DATASET_TYPE)
# Test default behavior
dataset = dataset_class(**self.DEFAULT_ARGS)
self.assertEqual(len(dataset), 4)
self.assertEqual(dataset.CLASSES, self.fake_classes)
data_info = dataset[0]
fake_img = self.fake_imgs[0].reshape(3, 32, 32).transpose(1, 2, 0)
np.testing.assert_equal(data_info['img'], fake_img)
np.testing.assert_equal(data_info['gt_label'], self.fake_labels[0])
# Test with test_mode=True
cfg = {**self.DEFAULT_ARGS, 'test_mode': True}
dataset = dataset_class(**cfg)
self.assertEqual(len(dataset), 2)
data_info = dataset[0]
fake_img = self.fake_imgs[4].reshape(3, 32, 32).transpose(1, 2, 0)
np.testing.assert_equal(data_info['img'], fake_img)
np.testing.assert_equal(data_info['gt_label'], self.fake_labels[4])
def test_load_annotations(self):
dataset_class = DATASETS.get(self.DATASET_TYPE)
# test classes number
cfg = {
**self.DEFAULT_ARGS,
'data_prefix': ASSETS_ROOT,
'ann_file': None,
}
with self.assertRaisesRegex(
AssertionError, r"\(2\) doesn't match .* classes \(1000\)"):
dataset_class(**cfg)
# test override classes
cfg = {
**self.DEFAULT_ARGS,
'data_prefix': ASSETS_ROOT,
'classes': ['cat', 'dog'],
'ann_file': None,
}
dataset = dataset_class(**cfg)
self.assertEqual(len(dataset), 3)
self.assertEqual(dataset.CLASSES, ['cat', 'dog'])
def test_evaluate(self):
dataset_class = DATASETS.get(self.DATASET_TYPE)
fake_ann = [
dict(gt_label=np.array(0, dtype=np.int64)),
dict(gt_label=np.array(0, dtype=np.int64)),
dict(gt_label=np.array(1, dtype=np.int64)),
dict(gt_label=np.array(2, dtype=np.int64)),
dict(gt_label=np.array(1, dtype=np.int64)),
dict(gt_label=np.array(0, dtype=np.int64)),
]
with patch.object(dataset_class, 'load_annotations') as mock_load:
mock_load.return_value = fake_ann
dataset = dataset_class(**self.DEFAULT_ARGS)
fake_results = np.array([
[0.7, 0.0, 0.3],
[0.5, 0.2, 0.3],
[0.4, 0.5, 0.1],
[0.0, 0.0, 1.0],
[0.0, 0.0, 1.0],
[0.0, 0.0, 1.0],
])
eval_results = dataset.evaluate(
fake_results,
metric=['precision', 'recall', 'f1_score', 'support', 'accuracy'],
metric_options={'topk': 1})
# Test results
self.assertAlmostEqual(eval_results['precision'],
(1 + 1 + 1 / 3) / 3 * 100.0,
places=4)
self.assertAlmostEqual(eval_results['recall'],
(2 / 3 + 1 / 2 + 1) / 3 * 100.0,
places=4)
self.assertAlmostEqual(eval_results['f1_score'],
(4 / 5 + 2 / 3 + 1 / 2) / 3 * 100.0,
places=4)
self.assertEqual(eval_results['support'], 6)
self.assertAlmostEqual(eval_results['accuracy'], 4 / 6 * 100, places=4)
# test indices
eval_results_ = dataset.evaluate(
fake_results[:5],
metric=['precision', 'recall', 'f1_score', 'support', 'accuracy'],
metric_options={'topk': 1},
indices=range(5))
self.assertAlmostEqual(eval_results_['precision'],
(1 + 1 + 1 / 2) / 3 * 100.0,
places=4)
self.assertAlmostEqual(eval_results_['recall'],
(1 + 1 / 2 + 1) / 3 * 100.0,
places=4)
self.assertAlmostEqual(eval_results_['f1_score'],
(1 + 2 / 3 + 2 / 3) / 3 * 100.0,
places=4)
self.assertEqual(eval_results_['support'], 5)
self.assertAlmostEqual(eval_results_['accuracy'],
4 / 5 * 100,
places=4)
# test input as tensor
fake_results_tensor = torch.from_numpy(fake_results)
eval_results_ = dataset.evaluate(
fake_results_tensor,
metric=['precision', 'recall', 'f1_score', 'support', 'accuracy'],
metric_options={'topk': 1})
assert eval_results_ == eval_results
# test thr
eval_results = dataset.evaluate(
fake_results,
metric=['precision', 'recall', 'f1_score', 'accuracy'],
metric_options={
'thrs': 0.6,
'topk': 1
})
self.assertAlmostEqual(eval_results['precision'],
(1 + 0 + 1 / 3) / 3 * 100.0,
places=4)
self.assertAlmostEqual(eval_results['recall'],
(1 / 3 + 0 + 1) / 3 * 100.0,
places=4)
self.assertAlmostEqual(eval_results['f1_score'],
(1 / 2 + 0 + 1 / 2) / 3 * 100.0,
places=4)
self.assertAlmostEqual(eval_results['accuracy'], 2 / 6 * 100, places=4)
# thrs must be a number or tuple
with self.assertRaises(TypeError):
dataset.evaluate(
fake_results,
metric=['precision', 'recall', 'f1_score', 'accuracy'],
metric_options={
'thrs': 'thr',
'topk': 1
})
# test topk and thr as tuple
eval_results = dataset.evaluate(
fake_results,
metric=['precision', 'recall', 'f1_score', 'accuracy'],
metric_options={
'thrs': (0.5, 0.6),
'topk': (1, 2)
})
self.assertEqual(
{
'precision_thr_0.50', 'precision_thr_0.60', 'recall_thr_0.50',
'recall_thr_0.60', 'f1_score_thr_0.50', 'f1_score_thr_0.60',
'accuracy_top-1_thr_0.50', 'accuracy_top-1_thr_0.60',
'accuracy_top-2_thr_0.50', 'accuracy_top-2_thr_0.60'
}, eval_results.keys())
self.assertIsInstance(eval_results['precision_thr_0.50'], float)
self.assertIsInstance(eval_results['recall_thr_0.50'], float)
self.assertIsInstance(eval_results['f1_score_thr_0.50'], float)
self.assertIsInstance(eval_results['accuracy_top-1_thr_0.50'], float)
# test topk is tuple while thrs is number
eval_results = dataset.evaluate(fake_results,
metric='accuracy',
metric_options={
'thrs': 0.5,
'topk': (1, 2)
})
self.assertEqual({'accuracy_top-1', 'accuracy_top-2'},
eval_results.keys())
self.assertIsInstance(eval_results['accuracy_top-1'], float)
# test topk is number while thrs is tuple
eval_results = dataset.evaluate(fake_results,
metric='accuracy',
metric_options={
'thrs': (0.5, 0.6),
'topk': 1
})
self.assertEqual({'accuracy_thr_0.50', 'accuracy_thr_0.60'},
eval_results.keys())
self.assertIsInstance(eval_results['accuracy_thr_0.50'], float)
# test evaluation results for classes
eval_results = dataset.evaluate(
fake_results,
metric=['precision', 'recall', 'f1_score', 'support'],
metric_options={'average_mode': 'none'})
self.assertEqual(eval_results['precision'].shape, (3, ))
self.assertEqual(eval_results['recall'].shape, (3, ))
self.assertEqual(eval_results['f1_score'].shape, (3, ))
self.assertEqual(eval_results['support'].shape, (3, ))
# the average_mode method must be valid
with self.assertRaises(ValueError):
dataset.evaluate(
fake_results,
metric=['precision', 'recall', 'f1_score', 'support'],
metric_options={'average_mode': 'micro'})
# the metric must be valid for the dataset
with self.assertRaisesRegex(ValueError,
"{'unknown'} is not supported"):
dataset.evaluate(fake_results, metric='unknown')
def test_load_annotations(self):
dataset_class = DATASETS.get(self.DATASET_TYPE)
# test load without ann_file
cfg = {
**self.DEFAULT_ARGS,
'data_prefix': ASSETS_ROOT,
'ann_file': None,
}
dataset = dataset_class(**cfg)
self.assertEqual(len(dataset), 3)
self.assertEqual(dataset.CLASSES, ['a', 'b']) # auto infer classes
self.assertEqual(
dataset.data_infos[0], {
'img_prefix': ASSETS_ROOT,
'img_info': {
'filename': 'a/1.JPG'
},
'gt_label': np.array(0)
})
self.assertEqual(
dataset.data_infos[2], {
'img_prefix': ASSETS_ROOT,
'img_info': {
'filename': 'b/subb/3.jpg'
},
'gt_label': np.array(1)
})
# test ann_file assertion
cfg = {
**self.DEFAULT_ARGS,
'data_prefix': ASSETS_ROOT,
'ann_file': ['ann_file.txt'],
}
with self.assertRaisesRegex(TypeError, 'must be a str'):
dataset_class(**cfg)
# test load with ann_file
cfg = {
**self.DEFAULT_ARGS,
'data_prefix': ASSETS_ROOT,
'ann_file': osp.join(ASSETS_ROOT, 'ann.txt'),
}
dataset = dataset_class(**cfg)
self.assertEqual(len(dataset), 3)
# custom dataset won't infer CLASSES from ann_file
self.assertEqual(dataset.CLASSES, dataset_class.CLASSES)
self.assertEqual(
dataset.data_infos[0], {
'img_prefix': ASSETS_ROOT,
'img_info': {
'filename': 'a/1.JPG'
},
'gt_label': np.array(0)
})
self.assertEqual(
dataset.data_infos[2], {
'img_prefix': ASSETS_ROOT,
'img_info': {
'filename': 'b/subb/2.jpeg'
},
'gt_label': np.array(1)
})
# test extensions filter
cfg = {
**self.DEFAULT_ARGS, 'data_prefix': ASSETS_ROOT,
'ann_file': None,
'extensions': ('.txt', )
}
with self.assertRaisesRegex(RuntimeError,
'Supported extensions are: .txt'):
dataset_class(**cfg)
cfg = {
**self.DEFAULT_ARGS, 'data_prefix': ASSETS_ROOT,
'ann_file': None,
'extensions': ('.jpeg', )
}
with self.assertWarnsRegex(UserWarning,
'Supported extensions are: .jpeg'):
dataset = dataset_class(**cfg)
self.assertEqual(len(dataset), 1)
self.assertEqual(
dataset.data_infos[0], {
'img_prefix': ASSETS_ROOT,
'img_info': {
'filename': 'b/2.jpeg'
},
'gt_label': np.array(1)
})
# test classes check
cfg = {
**self.DEFAULT_ARGS,
'data_prefix': ASSETS_ROOT,
'classes': ['apple', 'banana'],
'ann_file': None,
}
dataset = dataset_class(**cfg)
self.assertEqual(dataset.CLASSES, ['apple', 'banana'])
cfg['classes'] = ['apple', 'banana', 'dog']
with self.assertRaisesRegex(AssertionError,
r"\(2\) doesn't match .* classes \(3\)"):
dataset_class(**cfg)
