def test_label_has_shape_defined(self):
serialized = 'fake'
decoder = fsns.get_split('train', dataset_dir()).decoder
[label_tf] = decoder.decode(serialized, ['label'])
self.assertEqual(label_tf.get_shape().dims[0], 37)
def test_label_has_shape_defined(self):
serialized = 'fake'
decoder = fsns.get_split('train', dataset_dir()).decoder
[label_tf] = decoder.decode(serialized, ['label'])
self.assertEqual(label_tf.get_shape().dims[0], 37)
def test_decodes_example_proto(self):
expected_label = range(37)
expected_image, encoded = unittest_utils.create_random_image(
'PNG', shape=(150, 600, 3))
serialized = unittest_utils.create_serialized_example({
'image/encoded': [encoded],
'image/format': ['PNG'],
'image/class':
expected_label,
'image/unpadded_class':
range(10),
'image/text': ['Raw text'],
'image/orig_width': [150],
'image/width': [600]
})
decoder = fsns.get_split('train', dataset_dir()).decoder
with self.test_session() as sess:
data_tuple = collections.namedtuple('DecodedData',
decoder.list_items())
data = sess.run(data_tuple(*decoder.decode(serialized)))
self.assertAllEqual(expected_image, data.image)
self.assertAllEqual(expected_label, data.label)
self.assertEqual(['Raw text'], data.text)
self.assertEqual([1], data.num_of_views)
def test_decodes_example_proto(self):
expected_label = range(37)
expected_image, encoded = unittest_utils.create_random_image(
'PNG', shape=(150, 600, 3))
serialized = unittest_utils.create_serialized_example({
'image/encoded': [encoded],
'image/format': ['PNG'],
'image/class':
expected_label,
'image/unpadded_class':
range(10),
'image/text': ['Raw text'],
'image/orig_width': [150],
'image/width': [600]
})
decoder = fsns.get_split('train', dataset_dir()).decoder
with self.test_session() as sess:
data_tuple = collections.namedtuple('DecodedData', decoder.list_items())
data = sess.run(data_tuple(*decoder.decode(serialized)))
self.assertAllEqual(expected_image, data.image)
self.assertAllEqual(expected_label, data.label)
self.assertEqual(['Raw text'], data.text)
self.assertEqual([1], data.num_of_views)
def get_split(split_name, dataset_dir=None, config=None):
if not dataset_dir:
dataset_dir = DEFAULT_DATASET_DIR
if not config:
config = DEFAULT_CONFIG
return fsns.get_split(split_name, dataset_dir, config)
def test_dataset_tuple_has_all_extra_attributes(self):
dataset = fsns.get_split('train', dataset_dir())
self.assertTrue(dataset.charset)
self.assertTrue(dataset.num_char_classes)
self.assertTrue(dataset.num_of_views)
self.assertTrue(dataset.max_sequence_length)
self.assertTrue(dataset.null_code)
def test_dataset_tuple_has_all_extra_attributes(self):
dataset = fsns.get_split('train', dataset_dir())
self.assertTrue(dataset.charset)
self.assertTrue(dataset.num_char_classes)
self.assertTrue(dataset.num_of_views)
self.assertTrue(dataset.max_sequence_length)
self.assertTrue(dataset.null_code)
def get_test_split():
config = fsns.DEFAULT_CONFIG.copy()
config['splits'] = {'test': {'size': 50, 'pattern': 'fsns-00000-of-00001'}}
return fsns.get_split('test', dataset_dir(), config)
def get_test_split():
config = fsns.DEFAULT_CONFIG.copy()
config['splits'] = {'test': {'size': 50, 'pattern': 'fsns-00000-of-00001'}}
return fsns.get_split('test', dataset_dir(), config)