def testComputeAreaFeatures1D(self):
features = tf.constant([[[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]],
[[1.1, 2.1], [3.1, 4.1], [5.1, 6.1], [7.1, 8.1],
[9.1, 10.1]]],
dtype=tf.float32)
area_mean, area_std, area_sum, area_height, area_widths = (
area_attention.compute_area_features(features, max_area_width=3,
epsilon=0.))
with self.test_session() as session:
session.run(tf.global_variables_initializer())
res1, res2, res3, res4, res5 = session.run([area_mean, area_std, area_sum,
area_height, area_widths])
self.assertAllClose(((((1, 2), (3, 4), (5, 6), (7, 8), (9, 10),
(2, 3), (4, 5), (6, 7), (8, 9),
(3, 4), (5, 6), (7, 8)),
((1.1, 2.1), (3.1, 4.1), (5.1, 6.1), (7.1, 8.1),
(9.1, 10.1),
(2.1, 3.1), (4.1, 5.1), (6.1, 7.1), (8.1, 9.1),
(3.1, 4.1), (5.1, 6.1), (7.1, 8.1)))),
res1,
msg="mean_1d")
expected_std = np.array([[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[1, 1], [1, 1], [1, 1], [1, 1],
[1.63299, 1.63299], [1.63299, 1.63299],
[1.63299, 1.63299]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[1, 1], [1, 1], [1, 1], [1, 1],
[1.63299, 1.63299], [1.63299, 1.63299],
[1.63299, 1.63299]]])
self.assertAllClose(expected_std, res2, atol=1e-2, msg="std_1d")
self.assertAllClose([[[1, 2], [3, 4], [5, 6], [7, 8], [9, 10],
[4, 6], [8, 10], [12, 14], [16, 18],
[9, 12], [15, 18], [21, 24]],
[[1.1, 2.1], [3.1, 4.1], [5.1, 6.1], [7.1, 8.1],
[9.1, 10.1],
[4.2, 6.2], [8.2, 10.2], [12.2, 14.2], [16.2, 18.2],
[9.3, 12.3], [15.3, 18.3], [21.3, 24.3]]],
res3,
msg="sum_1d")
self.assertAllEqual([[[1], [1], [1], [1], [1],
[1], [1], [1], [1],
[1], [1], [1]],
[[1], [1], [1], [1], [1],
[1], [1], [1], [1],
[1], [1], [1]]],
res4,
msg="height_1d")
self.assertAllEqual([[[1], [1], [1], [1], [1],
[2], [2], [2], [2],
[3], [3], [3]],
[[1], [1], [1], [1], [1],
[2], [2], [2], [2],
[3], [3], [3]]],
res5,
msg="width_1d")
def _area_key_value_fn(keys, values):
"""Custom fn for computing area keys and values."""
tf.logging.info("max_area_width=%d, area_key_mode=%s, area_value_mode=%s",
hparams.get("max_area_width", 1),
hparams.get("area_key_mode", "none"),
hparams.get("area_value_mode", "none"))
keys = area_attention.compute_area_key(
keys, max_area_width=hparams.get("max_area_width", 1),
mode=hparams.get("area_key_mode", "none"), name="decoder_encoder",
training=(hparams.mode == tf.estimator.ModeKeys.TRAIN))
if hparams.get("area_value_mode", "none") == "sum":
_, _, values, _, _ = area_attention.compute_area_features(
values, max_area_width=hparams.get("max_area_width", 1))
elif hparams.get("area_value_mode", "none") == "mean":
values, _, _, _, _ = area_attention.compute_area_features(
values, max_area_width=hparams.get("max_area_width", 1))
else:
raise ValueError(
"Unsupported area_value_mode: %s" % hparams.get(
"area_value_mode", "none"))
return keys, values
def testComputeAreaFeatures2D(self):
features = tf.constant(
[[[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]],
[[1.1, 2.1], [3.1, 4.1], [5.1, 6.1], [7.1, 8.1], [9.1, 10.1],
[11.1, 12.1]]],
dtype=tf.float32)
area_mean, area_std, area_sum, area_height, area_widths = (
area_attention.compute_area_features(features,
max_area_width=3,
max_area_height=2,
height=2,
epsilon=0.))
with self.test_session() as session:
session.run(tf.global_variables_initializer())
res1, _, res3, res4, res5 = session.run(
[area_mean, area_std, area_sum, area_height, area_widths])
expected_means = [[[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12],
[2, 3], [4, 5], [8, 9], [10, 11], [3, 4], [9, 10],
[4, 5], [6, 7], [8, 9], [5, 6], [7, 8], [6, 7]],
[[1.1, 2.1], [3.1, 4.1], [5.1, 6.1], [7.1, 8.1],
[9.1, 10.1], [11.1, 12.1], [2.1, 3.1], [4.1, 5.1],
[8.1, 9.1], [10.1, 11.1], [3.1, 4.1], [9.1, 10.1],
[4.1, 5.1], [6.1, 7.1], [8.1, 9.1], [5.1, 6.1],
[7.1, 8.1], [6.1, 7.1]]]
self.assertAllClose(expected_means, res1, msg="mean_1d")
expected_heights = [
[
[1],
[1],
[1],
[1],
[1],
[1],
# 1x2
[1],
[1],
[1],
[1],
# 1x3
[1],
[1],
# 2x1
[2],
[2],
[2],
# 2x2
[2],
[2],
# 2x3
[2]
],
[
[1],
[1],
[1],
[1],
[1],
[1],
# 1x2
[1],
[1],
[1],
[1],
# 1x3
[1],
[1],
# 2x1
[2],
[2],
[2],
# 2x2
[2],
[2],
# 2x3
[2]
]
]
self.assertAllEqual(expected_heights, res4, msg="height_1d")
expected_widths = [
[
[1],
[1],
[1],
[1],
[1],
[1],
# 1x2
[2],
[2],
[2],
[2],
# 1x3
[3],
[3],
# 2x1
[1],
[1],
[1],
# 2x2
[2],
[2],
# 2x3
[3]
],
[
[1],
[1],
[1],
[1],
[1],
[1],
# 1x2
[2],
[2],
[2],
[2],
# 1x3
[3],
[3],
# 2x1
[1],
[1],
[1],
# 2x2
[2],
[2],
# 2x3
[3]
]
]
self.assertAllEqual(expected_widths, res5, msg="width_1d")
sizes = np.multiply(np.array(expected_heights),
np.array(expected_widths))
expected_sums = np.multiply(np.array(expected_means), sizes)
self.assertAllClose(expected_sums, res3, msg="sum_1d")