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"""Tests for summary image op."""
import tensorflow.python.platform
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import image_ops
class SummaryImageOpTest(tf.test.TestCase):
def _AsSummary(self, s):
summ = tf.Summary()
summ.ParseFromString(s)
return summ
def testImageSummary(self):
np.random.seed(7)
with self.test_session() as sess:
for depth in 1, 3, 4:
shape = (4, 5, 7) + (depth,)
bad_color = [255, 0, 0, 255][:depth]
for positive in False, True:
# Build a mostly random image with one nan
const = np.random.randn(*shape)
const[0, 1, 2] = 0 # Make the nan entry not the max
if positive:
const = 1 + np.maximum(const, 0)
scale = 255 / const.reshape(4, -1).max(axis=1)
offset = 0
else:
scale = 127 / np.abs(const.reshape(4, -1)).max(axis=1)
offset = 128
adjusted = np.floor(scale[:, None, None, None] * const + offset)
const[0, 1, 2, depth / 2] = np.nan
# Summarize
summ = tf.image_summary("img", const)
value = sess.run(summ)
self.assertEqual([], summ.get_shape())
image_summ = self._AsSummary(value)
# Decode the first image and check consistency
image = image_ops.decode_png(
image_summ.value[0].image.encoded_image_string).eval()
self.assertAllEqual(image[1, 2], bad_color)
image[1, 2] = adjusted[0, 1, 2]
self.assertAllClose(image, adjusted[0])
# Check the rest of the proto
# Only the first 3 images are returned.
for v in image_summ.value:
v.image.ClearField("encoded_image_string")
expected = '\n'.join("""
value {
tag: "img/image/%d"
image { height: %d width: %d colorspace: %d }
}""" % ((i,) + shape[1:]) for i in xrange(3))
self.assertProtoEquals(expected, image_summ)
if __name__ == "__main__":
tf.test.main()
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