def test_prepare_video(self):
# at each timestep the sum over all other dimensions of the video should stay the same
V_before = np.random.random((4, 10, 3, 20, 20))
V_after = _prepare_video(np.copy(V_before))
V_before = np.swapaxes(V_before, 0, 1)
V_before = np.reshape(V_before, newshape=(10, -1))
V_after = np.reshape(V_after, newshape=(10, -1))
np.testing.assert_array_almost_equal(np.sum(V_before, axis=1), np.sum(V_after, axis=1))
def test_numpy_vid_uint8(self):
V_input = np.random.randint(0, 256, (16, 30, 3, 28, 28)).astype(np.uint8)
V_after = _prepare_video(np.copy(V_input)) * 255
total_frame = V_input.shape[1]
V_input = np.swapaxes(V_input, 0, 1)
for f in range(total_frame):
x = np.reshape(V_input[f], newshape=(-1))
y = np.reshape(V_after[f], newshape=(-1))
np.testing.assert_array_almost_equal(np.sum(x), np.sum(y))
def test_prepare_video(self):
# At each timeframe, the sum over all other
# dimensions of the video should be the same.
shapes = [(16, 30, 3, 28, 28), (36, 30, 3, 28, 28),
(19, 29, 3, 23, 19), (3, 3, 3, 3, 3)]
for s in shapes:
V_input = np.random.random(s)
V_after = _prepare_video(np.copy(V_input))
total_frame = s[1]
V_input = np.swapaxes(V_input, 0, 1)
for f in range(total_frame):
x = np.reshape(V_input[f], newshape=(-1))
y = np.reshape(V_after[f], newshape=(-1))
np.testing.assert_array_almost_equal(np.sum(x), np.sum(y))