def testTintInvariant(self):
"""Tests tint invariant property."""
batch_size = 100
pred_illum_rgb = tf.constant(np.random.rand(batch_size, 3))
true_illum_rgb = tf.constant(np.random.rand(batch_size, 3))
true_scene_rgb = tf.constant(np.exp(np.random.rand(batch_size, 3)))
tint = tf.constant(np.random.rand(1, 3))
self.assertAllClose(
losses.anisotropic_reproduction_error(pred_illum_rgb,
true_illum_rgb,
true_scene_rgb),
losses.anisotropic_reproduction_error(
tf.multiply(tint, pred_illum_rgb),
tf.multiply(tint, true_illum_rgb), true_scene_rgb))
def testAgainstRefImpl(self):
batch_size = 100
pred_illum_rgb = np.random.uniform(low=sys.float_info.epsilon,
size=(batch_size, 3))
pred_illum_rgb /= np.sum(pred_illum_rgb, axis=1, keepdims=True)
true_illum_rgb = np.random.uniform(low=sys.float_info.epsilon,
size=(batch_size, 3))
true_illum_rgb /= np.sum(true_illum_rgb, axis=1, keepdims=True)
true_wb_rgb = 1.0 / true_illum_rgb
true_wb_rgb /= np.expand_dims(true_wb_rgb[:, 1], axis=1)
true_scene_rgb = np.random.uniform(low=sys.float_info.epsilon,
size=(batch_size, 3))
input_scene_rgb = true_scene_rgb / true_wb_rgb
height = 64
width = 48
rgb_stats = np.tile(input_scene_rgb[:, np.newaxis, np.newaxis, :],
reps=[1, height, width, 1])
with self.test_session() as sess:
actual_result = sess.run(
losses.anisotropic_reproduction_loss(
ops.rgb_to_uv(tf.constant(pred_illum_rgb)),
ops.rgb_to_uv(tf.constant(true_illum_rgb)),
tf.constant(rgb_stats)))
expected_result = sess.run(
losses.anisotropic_reproduction_error(
tf.constant(pred_illum_rgb), tf.constant(true_illum_rgb),
tf.constant(true_scene_rgb)))
np.testing.assert_almost_equal(actual_result, expected_result)
def testAgainstIdenticalIluminations(self):
"""Tests the case where predicted ilumminants is as same as ground truth."""
batch_size = 100
pred_illum_rgb = tf.constant(np.random.rand(batch_size, 3))
true_scene_rgb = tf.constant(np.random.rand(batch_size, 3))
# The errors should drop to zeros.
self.assertAllClose(losses.anisotropic_reproduction_error(
pred_illum_rgb, pred_illum_rgb, true_scene_rgb),
np.zeros(batch_size),
atol=1e-5)
def testNonNegativeScaleInvariant(self):
"""Tests invariant property wrt (non-negative) scale of true_scene_rgb."""
batch_size = 100
pred_illum_rgb = tf.constant(np.random.rand(batch_size, 3))
true_illum_rgb = tf.constant(np.random.rand(batch_size, 3))
true_scene_rgb = tf.constant(np.random.rand(batch_size, 3))
scale = tf.constant(np.exp(np.random.rand(batch_size, 1)))
self.assertAllClose(
losses.anisotropic_reproduction_error(pred_illum_rgb,
true_illum_rgb,
true_scene_rgb),
losses.anisotropic_reproduction_error(
pred_illum_rgb, true_illum_rgb,
tf.multiply(scale, true_scene_rgb)))
self.assertAllClose(
losses.anisotropic_reproduction_error(pred_illum_rgb,
true_illum_rgb,
true_scene_rgb),
losses.anisotropic_reproduction_error(
pred_illum_rgb, tf.multiply(scale, true_illum_rgb),
true_scene_rgb))
self.assertAllClose(
losses.anisotropic_reproduction_error(pred_illum_rgb,
true_illum_rgb,
true_scene_rgb),
losses.anisotropic_reproduction_error(
tf.multiply(scale, pred_illum_rgb), true_illum_rgb,
true_scene_rgb))
def testZeroedWeightsSingleChannel(self):
"""Tests one of the true_scene_rgb is zero."""
batch_size = 100
pred_illum_rgb = np.random.rand(batch_size, 3)
true_illum_rgb = np.random.rand(batch_size, 3)
true_scene_rgb = np.exp(np.random.rand(batch_size, 3))
for c in range(3):
zeroed_true_scene_rgb = copy.deepcopy(true_scene_rgb)
zeroed_true_scene_rgb[:, c] = 0
true_illum_rgb_adjusted = copy.deepcopy(true_illum_rgb)
true_illum_rgb_adjusted[:, c] = true_illum_rgb_adjusted[:, c] + 0.1
# The error should be invariant on the channel with zero weight.
self.assertAllClose(
losses.anisotropic_reproduction_error(
tf.constant(pred_illum_rgb), tf.constant(true_illum_rgb),
tf.constant(zeroed_true_scene_rgb)),
losses.anisotropic_reproduction_error(
tf.constant(pred_illum_rgb),
tf.constant(true_illum_rgb_adjusted),
tf.constant(zeroed_true_scene_rgb)))
def testConstantWeight(self):
"""Tests constant weights."""
batch_size = 100
pred_illum_rgb = tf.constant(np.random.rand(batch_size, 3))
true_illum_rgb = tf.constant(np.random.rand(batch_size, 3))
true_scene_rgb = tf.constant(
np.exp(np.random.rand(batch_size, 1)) *
np.ones(shape=(batch_size, 3)))
self.assertAllClose(
losses.anisotropic_reproduction_error(pred_illum_rgb,
true_illum_rgb,
true_scene_rgb),
losses.reproduction_error(pred_illum_rgb, true_illum_rgb))
def testZeroedWeightsDualChannels(self):
"""Tests two of the true_scene_rgb are zero."""
batch_size = 100
pred_illum_rgb = np.random.rand(batch_size, 3)
true_illum_rgb = np.random.rand(batch_size, 3)
true_scene_rgb = np.exp(np.random.rand(batch_size, 3))
for c in range(3):
zeroed_true_scene_rgb = np.zeros(shape=(batch_size, 3))
zeroed_true_scene_rgb[:, c] = true_scene_rgb[:, c]
# The errors should drop to zeros.
self.assertAllClose(losses.anisotropic_reproduction_error(
tf.constant(pred_illum_rgb), tf.constant(true_illum_rgb),
tf.constant(zeroed_true_scene_rgb)),
np.zeros(batch_size),
atol=1e-5)
def testAgainstRefImpl(self):
"""Tests against reference implementation."""
batch_size = 100
pred_illum_rgb = np.random.rand(batch_size, 3)
true_illum_rgb = np.random.rand(batch_size, 3)
true_scene_rgb = np.random.rand(batch_size, 3)
expected_angle = (180. / math.pi) * np.arccos(
np.clip(
np.sum(_weighted_normalized(true_illum_rgb / pred_illum_rgb,
true_scene_rgb) *
_weighted_normalized(np.ones(shape=(1, 3)),
true_scene_rgb),
axis=1), -1, 1))
self.assertAllClose(losses.anisotropic_reproduction_error(
tf.constant(pred_illum_rgb), tf.constant(true_illum_rgb),
tf.constant(true_scene_rgb)),
expected_angle,
atol=1e-5)