def sampler_summaries(x_repeated_depth, g_sampler, args):
with tf.variable_scope('sampler_metrics'):
# mean and var metrics for sampler
g_sampler = tf.reshape(
g_sampler, x_repeated_depth.shape) # reattach shape info
sampler_gan.summarize_moments(g_sampler, 'depth', args)
sampler_gan.summarize_moments(
g_sampler - tf.reduce_mean(g_sampler), 'depth_normalized',
args)
# ground truth example
ic = tf.expand_dims(x_repeated_depth[0], axis=0)
ic = hem.rescale(ic, (-1, 1), (0, 1))
ic = hem.colorize(ic)
tf.summary.image('real_depth', ic)
# mean and min l2 loss from sampler
sample_l2_loss = tf.reduce_mean(tf.square(x_repeated_depth -
g_sampler),
axis=[1, 2, 3])
mean_l2_loss = tf.reduce_mean(sample_l2_loss)
min_l2_loss = tf.reduce_min(sample_l2_loss)
tf.summary.scalar('mean sample l2', mean_l2_loss)
tf.summary.scalar('min sample l2', min_l2_loss)
sample_rmse_loss = tf.reduce_mean(tf.sqrt(
tf.square(x_repeated_depth - g_sampler)),
axis=[1, 2, 3])
mean_rmse_loss = tf.reduce_mean(sample_rmse_loss)
min_rmse_loss = tf.reduce_min(sample_rmse_loss)
tf.summary.scalar('mean sample rmse', mean_rmse_loss)
tf.summary.scalar('min sample rmse', min_rmse_loss)
def summarize_moments(x, name, args):
mean, var = tf.nn.moments(x, axes=[0])
tf.summary.scalar(name + '/mean', tf.reduce_mean(mean))
tf.summary.scalar(name + '/var', tf.reduce_mean(var))
tf.summary.histogram(name + '/mean', mean)
tf.summary.histogram(name + '/var', var)
var = tf.expand_dims(var, axis=0)
var = hem.colorize(var)
tf.summary.image(name + '/var', var)
def montage(x, height=0, width=0, colorize=False, num_examples=-1, name=None):
"""Generates a m x n image montage from the given tensor.
If m or n is 0, attempts to infer the input shape at run time. Note
that this may not always be possible.
Args:
x: Tensor, images to combine in montage.
m: Integer, number of rows in montage grid.
n: Integer, number of columns in montage grid.
name: String, name of this summary.
Returns:
Summary image node containing the montage.
"""
with tf.name_scope(name, 'montage', [x]) as scope:
# restrict montage to specific size
if num_examples == -1:
num_examples = x.get_shape()[0]
x = x[0:num_examples]
# colorize if asked and using a single-channel. otherwise make sure to convert to NHWC
if colorize and x.get_shape()[1] == 1:
x = hem.colorize(x)
else:
if len(x.shape) == 4:
x = tf.transpose(x, [0, 2, 3, 1])
# figure out w/h dynamically if necessary
if height == 0 or width == 0:
height, width = factorization(x.get_shape()[0].value)
# create montage
images = tf.split(x, width, axis=0)
images = tf.concat(images, axis=1)
images = tf.unstack(images, height, axis=0)
images = tf.concat(images, axis=1)
# if this is a b/w images, add a third dimension
if len(images.shape) < 3:
images = tf.expand_dims(images, axis=2)
# add a first dimension for expected TB format
images = tf.expand_dims(images, axis=0)
y = tf.summary.image(scope, images)
return y