本文整理汇总了Python中deeplab.common.IMAGE_NAME属性的典型用法代码示例。如果您正苦于以下问题：Python common.IMAGE_NAME属性的具体用法？Python common.IMAGE_NAME怎么用？Python common.IMAGE_NAME使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类deeplab.common的用法示例。
在下文中一共展示了common.IMAGE_NAME属性的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _get_data
# 需要导入模块: from deeplab import common [as 别名]
# 或者: from deeplab.common import IMAGE_NAME [as 别名]
def _get_data(data_provider, dataset_split):
"""Gets data from data provider.
Args:
data_provider: An object of slim.data_provider.
dataset_split: Dataset split.
Returns:
image: Image Tensor.
label: Label Tensor storing segmentation annotations.
image_name: Image name.
height: Image height.
width: Image width.
Raises:
ValueError: Failed to find label.
"""
if common.LABELS_CLASS not in data_provider.list_items():
raise ValueError('Failed to find labels.')
image, height, width = data_provider.get(
[common.IMAGE, common.HEIGHT, common.WIDTH])
# Some datasets do not contain image_name.
if common.IMAGE_NAME in data_provider.list_items():
image_name, = data_provider.get([common.IMAGE_NAME])
else:
image_name = tf.constant('')
label = None
if dataset_split != common.TEST_SET:
label, = data_provider.get([common.LABELS_CLASS])
return image, label, image_name, height, width 示例2: testPascalVocSegTestData
# 需要导入模块: from deeplab import common [as 别名]
# 或者: from deeplab.common import IMAGE_NAME [as 别名]
def testPascalVocSegTestData(self):
dataset = data_generator.Dataset(
dataset_name='pascal_voc_seg',
split_name='val',
dataset_dir=
'deeplab/testing/pascal_voc_seg',
batch_size=1,
crop_size=[3, 3], # Use small size for testing.
min_resize_value=3,
max_resize_value=3,
resize_factor=None,
min_scale_factor=0.01,
max_scale_factor=2.0,
scale_factor_step_size=0.25,
is_training=False,
model_variant='mobilenet_v2')
self.assertAllEqual(dataset.num_of_classes, 21)
self.assertAllEqual(dataset.ignore_label, 255)
num_of_images = 3
with self.test_session() as sess:
iterator = dataset.get_one_shot_iterator()
for i in range(num_of_images):
batch = iterator.get_next()
batch, = sess.run([batch])
image_attributes = _get_attributes_of_image(i)
self.assertAllEqual(batch[common.IMAGE][0], image_attributes.image)
self.assertAllEqual(batch[common.LABEL][0], image_attributes.label)
self.assertEqual(batch[common.HEIGHT][0], image_attributes.height)
self.assertEqual(batch[common.WIDTH][0], image_attributes.width)
self.assertEqual(batch[common.IMAGE_NAME][0],
image_attributes.image_name)
# All data have been read.
with self.assertRaisesRegexp(tf.errors.OutOfRangeError, ''):
sess.run([iterator.get_next()]) 示例3: testPascalVocSegTestData
# 需要导入模块: from deeplab import common [as 别名]
# 或者: from deeplab.common import IMAGE_NAME [as 别名]
def testPascalVocSegTestData(self):
dataset = data_generator.Dataset(
dataset_name='pascal_voc_seg',
split_name='val',
dataset_dir=
'deeplab/testing/pascal_voc_seg',
batch_size=1,
crop_size=[3, 3], # Use small size for testing.
min_resize_value=3,
max_resize_value=3,
resize_factor=None,
min_scale_factor=0.01,
max_scale_factor=2.0,
scale_factor_step_size=0.25,
is_training=False,
model_variant='mobilenet_v2')
self.assertAllEqual(dataset.num_of_classes, 21)
self.assertAllEqual(dataset.ignore_label, 255)
num_of_images = 3
with self.test_session() as sess:
iterator = dataset.get_one_shot_iterator()
for i in range(num_of_images):
batch = iterator.get_next()
batch, = sess.run([batch])
image_attributes = _get_attributes_of_image(i)
self.assertEqual(batch[common.HEIGHT][0], image_attributes.height)
self.assertEqual(batch[common.WIDTH][0], image_attributes.width)
self.assertEqual(batch[common.IMAGE_NAME][0],
image_attributes.image_name.encode())
# All data have been read.
with self.assertRaisesRegexp(tf.errors.OutOfRangeError, ''):
sess.run([iterator.get_next()])