def testMakeParseExampleSpec(self):
image_column = hub.image_embedding_column("image", self.spec)
parsing_spec = tf_v1.feature_column.make_parse_example_spec(
[image_column])
self.assertEqual(
parsing_spec,
{"image": tf_v1.FixedLenFeature([1, 2, 3], dtype=tf.float32)})
def testMakeParseExampleSpec(self):
text_column = hub.text_embedding_column("text",
self.spec,
trainable=False)
parsing_spec = tf_v1.feature_column.make_parse_example_spec(
[text_column])
self.assertEqual(parsing_spec,
{"text": tf_v1.FixedLenFeature([1], dtype=tf.string)})
def parse_example_spec(self):
"""Returns a `tf.Example` parsing spec as dict."""
if self.image_size:
height, width = self.image_size
else:
height, width = image_util.get_expected_image_size(
self.module_spec)
input_shape = [height, width, 3]
return {self.key: tf_v1.FixedLenFeature(input_shape, tf.float32)}
def testImageSizeManuallySpecified(self):
spec = hub.create_module_spec(create_image_module_fn([None, None]))
image_column = hub.image_embedding_column("image",
spec,
image_size=[229, 229])
parsing_spec = tf_v1.feature_column.make_parse_example_spec(
[image_column])
self.assertEqual(
parsing_spec,
{"image": tf_v1.FixedLenFeature([229, 229, 3], dtype=tf.float32)})
def parse_example_spec(self):
"""Returns a `tf.Example` parsing spec as dict."""
return {self.key: tf_v1.FixedLenFeature([1], tf.string)}
