def test_compare_preprocess_with_improved_gan(self, nrows, ncols):
"""Compares the image resizing function with that of openai/improved-gan."""
if tf.executing_eagerly():
# Eval is not supported when eager execution is enabled.
return
test_image = []
for j in range(nrows):
test_image.append([[(i // 2 + j) % 256] * 3 for i in range(ncols)])
test_image = np.array(test_image, dtype=np.uint8)
improved_image = _transform(test_image, npx=128, is_crop=True, resize_w=128)
dummy_record = {
'image': tf.constant(test_image, dtype=tf.uint8),
'label': [4],
}
process_fn = data_provider._preprocess_dataset_record_fn(image_size=128)
processed_record = process_fn(dummy_record)
with self.session():
# There's a relatively large gap between the two results, mainly because
# of PIL's sampling strategy. E.g.,
# """
# test_image = np.array([[0], [255]], dtype=np.uint8)
# image_lib.fromarray(test_image).resize([2, 2], image_lib.BILINEAR)
# """
# results with [[0, 0], [127, 127]] (instead of [[0, 0], [255, 255]]).
self.assertLess(
tf.norm(tensor=improved_image - processed_record[0],
ord=np.inf).eval(), 4. / 256.)
def test_preprocess_dataset_record_shapes(self):
dummy_record = {
'image': tf.zeros([123, 456, 3], dtype=tf.uint8),
'label': tf.constant([4]),
}
process_fn = data_provider._preprocess_dataset_record_fn(image_size=128)
processed_record = process_fn(dummy_record)
processed_record[0].shape.assert_is_compatible_with([128, 128, 3])
processed_record[1].shape.assert_is_compatible_with([1])
def test_preprocess_dataset_record_centering(self):
"""Checks that `_preprocess_dataset_record` correctly crops image."""
center_size = 4
padding_size = 5
dummy_record = {
'image':
tf.concat([
tf.zeros([center_size, padding_size, 3], dtype=tf.uint8),
255 * tf.ones([center_size, center_size, 3], dtype=tf.uint8),
tf.zeros([center_size, padding_size, 3], dtype=tf.uint8)
],
axis=1),
'label':
tf.constant([4]),
}
image_size = 7
process_fn = data_provider._preprocess_dataset_record_fn(
image_size=image_size)
processed_record = process_fn(dummy_record)
processed_record[0].shape.assert_is_compatible_with(
[image_size, image_size, 3])
# Test that output is all-ones (ignore the boundary in the check).
self.assertAllEqual(processed_record[0][1:-1, 1:-1, :],
tf.ones([image_size - 2, image_size - 2, 3]))
