本文整理汇总了Python中tensorflow.contrib.slim.python.slim.data.dataset_data_provider.DatasetDataProvider方法的典型用法代码示例。如果您正苦于以下问题：Python dataset_data_provider.DatasetDataProvider方法的具体用法？Python dataset_data_provider.DatasetDataProvider怎么用？Python dataset_data_provider.DatasetDataProvider使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.contrib.slim.python.slim.data.dataset_data_provider的用法示例。
在下文中一共展示了dataset_data_provider.DatasetDataProvider方法的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: testTFRecordDataset
# 需要导入模块: from tensorflow.contrib.slim.python.slim.data import dataset_data_provider [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.data.dataset_data_provider import DatasetDataProvider [as 别名]
def testTFRecordDataset(self):
dataset_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(),
'tfrecord_dataset'))
height = 300
width = 280
with self.test_session():
provider = dataset_data_provider.DatasetDataProvider(
_create_tfrecord_dataset(dataset_dir))
image, label = provider.get(['image', 'label'])
image = _resize_image(image, height, width)
with session.Session('') as sess:
with queues.QueueRunners(sess):
image, label = sess.run([image, label])
self.assertListEqual([height, width, 3], list(image.shape))
self.assertListEqual([1], list(label.shape)) 示例2: testTFRecordSeparateGetDataset
# 需要导入模块: from tensorflow.contrib.slim.python.slim.data import dataset_data_provider [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.data.dataset_data_provider import DatasetDataProvider [as 别名]
def testTFRecordSeparateGetDataset(self):
dataset_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(),
'tfrecord_separate_get'))
height = 300
width = 280
with self.test_session():
provider = dataset_data_provider.DatasetDataProvider(
_create_tfrecord_dataset(dataset_dir))
[image] = provider.get(['image'])
[label] = provider.get(['label'])
image = _resize_image(image, height, width)
with session.Session('') as sess:
with queues.QueueRunners(sess):
image, label = sess.run([image, label])
self.assertListEqual([height, width, 3], list(image.shape))
self.assertListEqual([1], list(label.shape)) 示例3: get
# 需要导入模块: from tensorflow.contrib.slim.python.slim.data import dataset_data_provider [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.data.dataset_data_provider import DatasetDataProvider [as 别名]
def get(dataset_dir,
dataset_name,
split_name,
shuffle=True,
num_readers=1,
common_queue_capacity=64,
common_queue_min=50):
"""Provides input data for a specified dataset and split."""
dataset_to_kwargs = {
'shapenet_chair': {
'file_pattern': '03001627_%s.tfrecords' % split_name,
'num_views': 24,
'image_size': 64,
'vox_size': 32,
}, 'shapenet_all': {
'file_pattern': '*_%s.tfrecords' % split_name,
'num_views': 24,
'image_size': 64,
'vox_size': 32,
},
}
split_sizes = {
'shapenet_chair': {
'train': 4744,
'val': 678,
'test': 1356,
},
'shapenet_all': {
'train': 30643,
'val': 4378,
'test': 8762,
}
}
kwargs = dataset_to_kwargs[dataset_name]
kwargs['file_pattern'] = os.path.join(dataset_dir, kwargs['file_pattern'])
kwargs['num_samples'] = split_sizes[dataset_name][split_name]
dataset_split = _get_split(**kwargs)
data_provider = dataset_data_provider.DatasetDataProvider(
dataset_split,
num_readers=num_readers,
common_queue_capacity=common_queue_capacity,
common_queue_min=common_queue_min,
shuffle=shuffle)
inputs = {
'num_samples': dataset_split.num_samples,
}
[image, mask, vox] = data_provider.get(['image', 'mask', 'vox'])
inputs['image'] = image
inputs['mask'] = mask
inputs['voxel'] = vox
return inputs