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)}