def testDiscretizedMixLogisticLoss(self):
batch = 2
height = 4
width = 4
channels = 3
num_mixtures = 5
logits = tf.concat( # assign all probability mass to first component
[tf.ones([batch, height, width, 1]) * 1e8,
tf.zeros([batch, height, width, num_mixtures - 1])],
axis=-1)
locs = tf.random_uniform([batch, height, width, num_mixtures * 3],
minval=-.9, maxval=.9)
log_scales = tf.random_uniform([batch, height, width, num_mixtures * 3],
minval=-1., maxval=1.)
coeffs = tf.atanh(tf.zeros([batch, height, width, num_mixtures * 3]))
pred = tf.concat([logits, locs, log_scales, coeffs], axis=-1)
# Test labels that don't satisfy edge cases where 8-bit value is 0 or 255.
labels = tf.random_uniform([batch, height, width, channels],
minval=-.9, maxval=.9)
locs_0 = locs[..., :3]
log_scales_0 = log_scales[..., :3]
centered_labels = labels - locs_0
inv_stdv = tf.exp(-log_scales_0)
plus_in = inv_stdv * (centered_labels + 1. / 255.)
min_in = inv_stdv * (centered_labels - 1. / 255.)
cdf_plus = tf.nn.sigmoid(plus_in)
cdf_min = tf.nn.sigmoid(min_in)
expected_loss = -tf.reduce_sum(tf.log(cdf_plus - cdf_min), axis=-1)
actual_loss = common_layers.discretized_mix_logistic_loss(
pred=pred, labels=labels)
actual_loss_val, expected_loss_val = self.evaluate(
[actual_loss, expected_loss])
self.assertAllClose(actual_loss_val, expected_loss_val, rtol=1e-5)
def testDiscretizedMixLogisticLoss(self):
batch = 2
height = 4
width = 4
channels = 3
num_mixtures = 5
logits = tf.concat( # assign all probability mass to first component
[tf.ones([batch, height, width, 1]) * 1e8,
tf.zeros([batch, height, width, num_mixtures - 1])],
axis=-1)
locs = tf.random_uniform([batch, height, width, num_mixtures * 3],
minval=-.9, maxval=.9)
log_scales = tf.random_uniform([batch, height, width, num_mixtures * 3],
minval=-1., maxval=1.)
coeffs = tf.atanh(tf.zeros([batch, height, width, num_mixtures * 3]))
pred = tf.concat([logits, locs, log_scales, coeffs], axis=-1)
# Test labels that don't satisfy edge cases where 8-bit value is 0 or 255.
labels = tf.random_uniform([batch, height, width, channels],
minval=-.9, maxval=.9)
locs_0 = locs[..., :3]
log_scales_0 = log_scales[..., :3]
centered_labels = labels - locs_0
inv_stdv = tf.exp(-log_scales_0)
plus_in = inv_stdv * (centered_labels + 1. / 255.)
min_in = inv_stdv * (centered_labels - 1. / 255.)
cdf_plus = tf.nn.sigmoid(plus_in)
cdf_min = tf.nn.sigmoid(min_in)
expected_loss = -tf.reduce_sum(tf.log(cdf_plus - cdf_min), axis=-1)
actual_loss = common_layers.discretized_mix_logistic_loss(
pred=pred, labels=labels)
actual_loss_val, expected_loss_val = self.evaluate(
[actual_loss, expected_loss])
self.assertAllClose(actual_loss_val, expected_loss_val, rtol=1e-5)
def testDmlLoss(self, batch, height, width, num_mixtures, reduce_sum):
channels = 3
pred = tf.random_normal([batch, height, width, num_mixtures * 10])
labels = tf.random_uniform([batch, height, width, channels],
minval=0, maxval=256, dtype=tf.int32)
actual_loss_num, actual_loss_den = common_layers.dml_loss(
pred=pred, labels=labels, reduce_sum=reduce_sum)
actual_loss = actual_loss_num / actual_loss_den
real_labels = common_layers.convert_rgb_to_symmetric_real(labels)
expected_loss = common_layers.discretized_mix_logistic_loss(
pred=pred, labels=real_labels) / channels
if reduce_sum:
expected_loss = tf.reduce_mean(expected_loss)
actual_loss_val, expected_loss_val = self.evaluate(
[actual_loss, expected_loss])
self.assertAllClose(actual_loss_val, expected_loss_val)
def testDmlLoss(self, batch, height, width, num_mixtures, reduce_sum):
channels = 3
pred = tf.random_normal([batch, height, width, num_mixtures * 10])
labels = tf.random_uniform([batch, height, width, channels],
minval=0, maxval=256, dtype=tf.int32)
actual_loss_num, actual_loss_den = common_layers.dml_loss(
pred=pred, labels=labels, reduce_sum=reduce_sum)
actual_loss = actual_loss_num / actual_loss_den
real_labels = common_layers.convert_rgb_to_symmetric_real(labels)
expected_loss = common_layers.discretized_mix_logistic_loss(
pred=pred, labels=real_labels) / channels
if reduce_sum:
expected_loss = tf.reduce_mean(expected_loss)
actual_loss_val, expected_loss_val = self.evaluate(
[actual_loss, expected_loss])
self.assertAllClose(actual_loss_val, expected_loss_val)
