def test_float_faithful_to_large_values(self):
dts = ["float16", "float32", "float64", "float128"]
values = [
[0.01, 0.1, 1.0, 10.0**1, 10.0**2], # float16
[0.01, 0.1, 1.0, 10.0**1, 10.0**2, 10.0**4, 10.0**6], # float32
[0.01, 0.1, 1.0, 10.0**1, 10.0**2, 10.0**6, 10.0**10], # float64
[0.01, 0.1, 1.0, 10.0**1, 10.0**2, 10.0**7, 10.0**11], # float128
]
for dt, values_i in zip(dts, values):
for value in values_i:
with self.subTest(dtype=dt, value=value):
dt = np.dtype(dt)
minv, center, maxv = -value, 0.0, value
atol = 1e-4 if dt.name == "float16" else 1e-8
arr = np.array([[minv, center, maxv]], dtype=dt)
arr_flipped = fliplib.fliplr(arr)
expected = np.array([[maxv, center, minv]], dtype=dt)
assert arr_flipped.dtype.name == dt.name
assert arr_flipped.shape == (1, 3)
assert np.allclose(arr_flipped,
expected,
rtol=0,
atol=atol)
def test_bool_faithful(self):
arr = np.array([[False, False, True]], dtype=bool)
arr_flipped = fliplib.fliplr(arr)
expected = np.array([[True, False, False]], dtype=bool)
assert arr_flipped.dtype.name == "bool"
assert arr_flipped.shape == (1, 3)
assert np.array_equal(arr_flipped, expected)
def flip(img, point):
img = aug.fliplr(img)
length = len(point)
idxs = np.arange(0, length, 2)
point[idxs] = -point[idxs]
return img, point
def test__fliplr_cv2_called_mocked(self, mock_cv2, mock_sliced):
for dtype in ["uint8", "uint16", "int8", "int16"]:
mock_cv2.reset_mock()
mock_sliced.reset_mock()
arr = np.zeros((1, 1), dtype=dtype)
_ = fliplib.fliplr(arr)
mock_cv2.assert_called_once_with(arr)
assert mock_sliced.call_count == 0
def test__fliplr_sliced_called_mocked(self, mock_cv2, mock_sliced):
for dtype in ["bool", "uint32", "uint64", "int32", "int64",
"float16", "float32", "float64", "float128"]:
mock_cv2.reset_mock()
mock_sliced.reset_mock()
arr = np.zeros((1, 1), dtype=dtype)
_ = fliplib.fliplr(arr)
assert mock_cv2.call_count == 0
mock_sliced.assert_called_once_with(arr)
def test_float_faithful_to_min_max(self):
dts = ["float16", "float32", "float64", "float128"]
for dt in dts:
with self.subTest(dtype=dt):
dt = np.dtype(dt)
minv, center, maxv = iadt.get_value_range_of_dtype(dt)
center = int(center)
atol = 1e-4 if dt.name == "float16" else 1e-8
arr = np.array([[minv, center, maxv]], dtype=dt)
arr_flipped = fliplib.fliplr(arr)
expected = np.array([[maxv, center, minv]], dtype=dt)
assert arr_flipped.dtype.name == dt.name
assert arr_flipped.shape == (1, 3)
assert np.allclose(arr_flipped, expected, rtol=0, atol=atol)
def test_uint_int_faithful(self):
dts = ["uint8", "uint16", "uint32", "uint64",
"int8", "int16", "int32", "int64"]
for dt in dts:
with self.subTest(dtype=dt):
dt = np.dtype(dt)
minv, center, maxv = iadt.get_value_range_of_dtype(dt)
center = int(center)
arr = np.array([[minv, center, maxv]], dtype=dt)
arr_flipped = fliplib.fliplr(arr)
expected = np.array([[maxv, center, minv]], dtype=dt)
assert arr_flipped.dtype.name == dt.name
assert arr_flipped.shape == (1, 3)
assert np.array_equal(arr_flipped, expected)
def transform(tile, rot, lr):
t = Rot90(rot).augment_image(tile)
if lr:
t = fliplr(t)
return t
def _augment_batch_(self, batch, random_state, parents, hooks):
for i, images in enumerate(batch.images):
batch.images[i] = fliplr(batch.images[i])
return batch