def test_generalized_dice():
shape = (8, 32, 32, 32, 16)
x = np.zeros(shape)
y = np.zeros(shape)
assert_array_equal(losses.generalized_dice(x, y), np.zeros(shape[0]))
shape = (8, 32, 32, 32, 16)
x = np.ones(shape)
y = np.ones(shape)
assert_array_equal(losses.generalized_dice(x, y), np.zeros(shape[0]))
shape = (8, 32, 32, 32, 16)
x = np.ones(shape)
y = np.zeros(shape)
# Why aren't the losses exactly one? Could it be the propogation of floating
# point inaccuracies when summing?
assert_allclose(losses.generalized_dice(x, y),
np.ones(shape[0]),
atol=1e-03)
assert_allclose(
losses.GeneralizedDice(axis=(1, 2, 3))(x, y),
losses.generalized_dice(x, y))
x = np.ones((4, 32, 32, 32, 1), dtype=np.float64)
y = x.copy()
x[:2, :10, 10:] = 0
y[:2, :3, 20:] = 0
y[3:, 10:] = 0
# Dice is similar to generalized Dice for one class. The weight factor
# makes the generalized form slightly different from Dice.
gd = losses.generalized_dice(x, y, axis=(1, 2, 3)).numpy()
dd = losses.dice(x, y, axis=(1, 2, 3, 4)).numpy()
assert_allclose(gd, dd, rtol=1e-02) # is this close enough?
def test_dice():
x = np.zeros(4)
y = np.zeros(4)
out = losses.dice(x, y, axis=None).numpy()
assert_allclose(out, 0)
x = np.ones(4)
y = np.ones(4)
out = losses.dice(x, y, axis=None).numpy()
assert_allclose(out, 0)
x = [0., 0., 1., 1.]
y = [1., 1., 1., 1.]
out = losses.dice(x, y, axis=None).numpy()
ref = scipy.spatial.distance.dice(x, y)
assert_allclose(out, ref)
x = [0., 0., 1., 1.]
y = [1., 1., 0., 0.]
out = losses.dice(x, y, axis=None).numpy()
ref = scipy.spatial.distance.dice(x, y)
assert_allclose(out, ref)
assert_allclose(out, 1)
x = np.ones((4, 32, 32, 32, 1), dtype=np.float32)
y = x.copy()
x[:2, :10, 10:] = 0
y[:2, :3, 20:] = 0
y[3:, 10:] = 0
dices = np.empty(x.shape[0])
for i in range(x.shape[0]):
dices[i] = scipy.spatial.distance.dice(x[i].flatten(), y[i].flatten())
assert_allclose(losses.dice(x, y, axis=(1, 2, 3, 4)), dices, rtol=1e-05)
assert_allclose(losses.Dice(axis=(1, 2, 3, 4))(x, y),
dices.mean(),
rtol=1e-05)
assert_allclose(losses.Dice(axis=(1, 2, 3, 4))(y, x),
dices.mean(),
rtol=1e-05)
def test_dice():
with tf.Session() as sess:
shape = (2, 4, 4)
dtype = np.float32
labels = np.zeros(shape=shape, dtype=dtype)
predictions = np.zeros(shape=shape, dtype=dtype)
labels[0, :2, :2] = 1
labels[1, 2:, 2:] = 1
predictions[0, 1:3, 1:3] = 1
predictions[1, :, 2:] = 1
ll = sess.run(
losses.dice(labels=labels,
predictions=predictions,
axis=(1, 2),
reduction='none'))
assert np.allclose(ll[0], 0.75)
assert np.allclose(ll[1], 0.3333333333)
assert np.allclose(
sess.run(
losses.dice(labels=labels,
predictions=predictions,
axis=(1, 2))), 0.5416667)
zeros = np.zeros((2, 2), dtype=np.float32)
ones = np.ones((2, 2), dtype=np.float32)
# Perfect (all zero).
ll = sess.run(losses.dice(labels=zeros, predictions=zeros, axis=(1, )))
assert np.allclose(ll, 0)
# Perfect (all one).
ll = sess.run(losses.dice(labels=ones, predictions=ones, axis=(1, )))
assert np.allclose(ll, 0)
# All wrong.
ll = sess.run(losses.dice(labels=zeros, predictions=ones, axis=(1, )))
assert np.allclose(ll, 1)