def test_feature_detection_labelling_concise():
"""Test `feature_detection_labelling` function in `generator` module by considering a concise
labelling, *i.e.* all labels are represented into the array:
* as a preliminary verification, check if passing string labels raises an AttributeError
exception
* test if output shape is first input shape (batch size) + an additional dimension given by the
`label_ids` length
* test if both representation provides the same information (native array on the first hand and
its one-hot version on the second hand)
"""
a = np.array([[[[10, 10, 200], [10, 10, 200], [10, 10, 200]],
[[200, 200, 200], [200, 200, 200], [10, 10, 200]],
[[200, 200, 200], [200, 200, 200], [200, 200, 200]]],
[[[10, 200, 10], [10, 200, 10], [10, 10, 200]],
[[200, 10, 10], [10, 200, 10], [10, 10, 200]],
[[10, 200, 10], [200, 10, 10], [10, 10, 200]]]])
labels = np.unique(a.reshape(-1, 3), axis=0).tolist()
wrong_config = [{
'id': '0',
'color': [10, 10, 200],
'is_evaluate': True
}, {
'id': '1',
'color': [200, 10, 10],
'is_evaluate': True
}, {
'id': '2',
'color': [10, 200, 10],
'is_evaluate': True
}, {
'id': '3',
'color': [200, 200, 200],
'is_evaluate': True
}]
with pytest.raises(ValueError):
b = generator.feature_detection_labelling(a, wrong_config)
config = [{
'id': 0,
'color': [10, 10, 200],
'is_evaluate': True
}, {
'id': 1,
'color': [200, 10, 10],
'is_evaluate': True
}, {
'id': 2,
'color': [10, 200, 10],
'is_evaluate': True
}, {
'id': 3,
'color': [200, 200, 200],
'is_evaluate': True
}]
b = generator.feature_detection_labelling(a, config)
assert b.shape == (a.shape[0], len(labels))
assert b.tolist() == [[True, False, False, True],
[True, True, True, False]]
def test_feature_detection_labelling_concise():
"""Test `feature_detection_labelling` function in `generator` module by considering a concise
labelling, *i.e.* all labels are represented into the array:
* as a preliminary verification, check if passing string labels raises an AttributeError
exception
* test if output shape is first input shape (batch size) + an additional dimension given by the
`label_ids` length
* test if both representation provides the same information (native array on the first hand and
its one-hot version on the second hand)
"""
a = np.array([[[0, 0, 0, 2], [3, 3, 0, 2], [3, 3, 3, 0]],
[[2, 2, 0, 0], [1, 2, 0, 0], [2, 1, 0, 0]]])
labels = np.unique(a).tolist()
MIN, MAX = np.amin(a), np.amax(a)
with pytest.raises(ValueError):
b = generator.feature_detection_labelling(a, ['0', '1', '2', '3'])
b = generator.feature_detection_labelling(a, labels)
assert b.shape == (a.shape[0], len(labels))
assert b.tolist() == [[True, False, True, True], [True, True, True, False]]
def test_feature_detection_labelling_sparse():
"""Test `feature_detection_labelling` function in `generator` module by considering a sparse
labelling, *i.e.* the array contains unknown values (to mimic the non-evaluated label
situations):
* as a preliminary verification, check if passing string labels raises an AttributeError
exception
* test if label length is different from the list of values in the array
* test if output shape is first input shape (batch size) + an additional dimension given by the
`label_ids` length
* test if both representation provides the same information (native array on the first hand and
its one-hot version on the second hand)
"""
a = np.array([[[0, 0, 0, 1, 1], [3, 3, 0, 1, 1], [3, 3, 3, 0, 0],
[3, 3, 3, 0, 0]],
[[1, 1, 2, 1, 2], [3, 2, 2, 1, 3], [1, 1, 1, 2, 1],
[1, 1, 2, 3, 2]]])
labels = np.unique(a).tolist()[:-1]
with pytest.raises(ValueError):
b = generator.feature_detection_labelling(a, ['0', '1', '2'])
b = generator.feature_detection_labelling(a, labels)
assert len(labels) != np.amax(a) - np.amin(a) + 1
assert b.tolist() == [[True, True, False], [False, True, True]]
assert b.shape == (a.shape[0], len(labels))
def test_feature_detection_labelling_sparse():
"""Test `feature_detection_labelling` function in `generator` module by
considering a sparse labelling, *i.e.* the array contains unknown values
(to mimic the non-evaluated label situations):
* as a preliminary verification, check if passing string labels raises an
AttributeError exception
* test if label length is different from the list of values in the array
* test if output shape is first input shape (batch size) + an additional
dimension given by the `label_ids` length
* test if both representation provides the same information (native array
on the first hand and its one-hot version on the second hand)
"""
a = np.array([
[
[[10, 10, 200], [10, 10, 200], [10, 10, 200], [200, 10, 10]],
[
[200, 200, 200],
[200, 200, 200],
[10, 10, 200],
[200, 10, 10],
],
[
[200, 200, 200],
[200, 200, 200],
[200, 200, 200],
[10, 10, 200],
],
[
[200, 200, 200],
[200, 200, 200],
[200, 200, 200],
[10, 10, 200],
],
],
[
[[200, 10, 10], [200, 10, 10], [10, 200, 10], [200, 10, 10]],
[[200, 200, 200], [10, 200, 10], [10, 200, 10], [10, 200, 10]],
[[200, 10, 10], [200, 10, 10], [200, 10, 10], [200, 200, 200]],
[[200, 10, 10], [200, 10, 10], [10, 200, 10], [200, 200, 200]],
],
])
labels = np.unique(a.reshape(-1, 3), axis=0).tolist()[:-1]
wrong_config = [
{
"id": "0",
"color": [10, 10, 200],
"is_evaluate": True
},
{
"id": "1",
"color": [200, 10, 10],
"is_evaluate": True
},
{
"id": "2",
"color": [10, 200, 10],
"is_evaluate": True
},
]
with pytest.raises(ValueError):
b = generator.feature_detection_labelling(a, wrong_config)
config = [
{
"id": 0,
"color": [10, 10, 200],
"is_evaluate": True
},
{
"id": 1,
"color": [200, 10, 10],
"is_evaluate": True
},
{
"id": 2,
"color": [10, 200, 10],
"is_evaluate": True
},
]
b = generator.feature_detection_labelling(a, config)
assert len(labels) != np.amax(a) - np.amin(a) + 1
assert b.tolist() == [[True, True, False], [False, True, True]]
assert b.shape == (a.shape[0], len(labels))
