Python源码示例：tensorflow.contrib.slim.python.slim.data.dataset.Dataset()
示例1
def _create_tfrecord_dataset(tmpdir):
if not gfile.Exists(tmpdir):
gfile.MakeDirs(tmpdir)
data_sources = test_utils.create_tfrecord_files(tmpdir, num_files=1)
keys_to_features = {
'image/encoded':
parsing_ops.FixedLenFeature(
shape=(), dtype=dtypes.string, default_value=''),
'image/format':
parsing_ops.FixedLenFeature(
shape=(), dtype=dtypes.string, default_value='jpeg'),
'image/class/label':
parsing_ops.FixedLenFeature(
shape=[1],
dtype=dtypes.int64,
default_value=array_ops.zeros(
[1], dtype=dtypes.int64))
}
items_to_handlers = {
'image': tfexample_decoder.Image(),
'label': tfexample_decoder.Tensor('image/class/label'),
}
decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
return dataset.Dataset(
data_sources=data_sources,
reader=io_ops.TFRecordReader,
decoder=decoder,
num_samples=100,
items_to_descriptions=None)
示例2
def _get_split(file_pattern, num_samples, num_views, image_size, vox_size):
"""Get dataset.Dataset for the given dataset file pattern and properties."""
# A dictionary from TF-Example keys to tf.FixedLenFeature instance.
keys_to_features = {
'image': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 3],
dtype=tf.float32, default_value=None),
'mask': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 1],
dtype=tf.float32, default_value=None),
'vox': tf.FixedLenFeature(
shape=[vox_size, vox_size, vox_size, 1],
dtype=tf.float32, default_value=None),
}
items_to_handler = {
'image': tfexample_decoder.Tensor(
'image', shape=[num_views, image_size, image_size, 3]),
'mask': tfexample_decoder.Tensor(
'mask', shape=[num_views, image_size, image_size, 1]),
'vox': tfexample_decoder.Tensor(
'vox', shape=[vox_size, vox_size, vox_size, 1])
}
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handler)
return dataset.Dataset(
data_sources=file_pattern,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=num_samples,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS)
示例3
def _get_split(file_pattern, num_samples, num_views, image_size, vox_size):
"""Get dataset.Dataset for the given dataset file pattern and properties."""
# A dictionary from TF-Example keys to tf.FixedLenFeature instance.
keys_to_features = {
'image': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 3],
dtype=tf.float32, default_value=None),
'mask': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 1],
dtype=tf.float32, default_value=None),
'vox': tf.FixedLenFeature(
shape=[vox_size, vox_size, vox_size, 1],
dtype=tf.float32, default_value=None),
}
items_to_handler = {
'image': tfexample_decoder.Tensor(
'image', shape=[num_views, image_size, image_size, 3]),
'mask': tfexample_decoder.Tensor(
'mask', shape=[num_views, image_size, image_size, 1]),
'vox': tfexample_decoder.Tensor(
'vox', shape=[vox_size, vox_size, vox_size, 1])
}
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handler)
return dataset.Dataset(
data_sources=file_pattern,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=num_samples,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS)
示例4
def _get_split(file_pattern, num_samples, num_views, image_size, vox_size):
"""Get dataset.Dataset for the given dataset file pattern and properties."""
# A dictionary from TF-Example keys to tf.FixedLenFeature instance.
keys_to_features = {
'image': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 3],
dtype=tf.float32, default_value=None),
'mask': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 1],
dtype=tf.float32, default_value=None),
'vox': tf.FixedLenFeature(
shape=[vox_size, vox_size, vox_size, 1],
dtype=tf.float32, default_value=None),
}
items_to_handler = {
'image': tfexample_decoder.Tensor(
'image', shape=[num_views, image_size, image_size, 3]),
'mask': tfexample_decoder.Tensor(
'mask', shape=[num_views, image_size, image_size, 1]),
'vox': tfexample_decoder.Tensor(
'vox', shape=[vox_size, vox_size, vox_size, 1])
}
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handler)
return dataset.Dataset(
data_sources=file_pattern,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=num_samples,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS)
示例5
def _get_split(file_pattern, num_samples, num_views, image_size, vox_size):
"""Get dataset.Dataset for the given dataset file pattern and properties."""
# A dictionary from TF-Example keys to tf.FixedLenFeature instance.
keys_to_features = {
'image': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 3],
dtype=tf.float32, default_value=None),
'mask': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 1],
dtype=tf.float32, default_value=None),
'vox': tf.FixedLenFeature(
shape=[vox_size, vox_size, vox_size, 1],
dtype=tf.float32, default_value=None),
}
items_to_handler = {
'image': tfexample_decoder.Tensor(
'image', shape=[num_views, image_size, image_size, 3]),
'mask': tfexample_decoder.Tensor(
'mask', shape=[num_views, image_size, image_size, 1]),
'vox': tfexample_decoder.Tensor(
'vox', shape=[vox_size, vox_size, vox_size, 1])
}
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handler)
return dataset.Dataset(
data_sources=file_pattern,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=num_samples,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS)
示例6
def _get_split(file_pattern, num_samples, num_views, image_size, vox_size):
"""Get dataset.Dataset for the given dataset file pattern and properties."""
# A dictionary from TF-Example keys to tf.FixedLenFeature instance.
keys_to_features = {
'image': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 3],
dtype=tf.float32, default_value=None),
'mask': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 1],
dtype=tf.float32, default_value=None),
'vox': tf.FixedLenFeature(
shape=[vox_size, vox_size, vox_size, 1],
dtype=tf.float32, default_value=None),
}
items_to_handler = {
'image': tfexample_decoder.Tensor(
'image', shape=[num_views, image_size, image_size, 3]),
'mask': tfexample_decoder.Tensor(
'mask', shape=[num_views, image_size, image_size, 1]),
'vox': tfexample_decoder.Tensor(
'vox', shape=[vox_size, vox_size, vox_size, 1])
}
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handler)
return dataset.Dataset(
data_sources=file_pattern,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=num_samples,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS)
示例7
def _get_split(file_pattern, num_samples, num_views, image_size, vox_size):
"""Get dataset.Dataset for the given dataset file pattern and properties."""
# A dictionary from TF-Example keys to tf.FixedLenFeature instance.
keys_to_features = {
'image': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 3],
dtype=tf.float32, default_value=None),
'mask': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 1],
dtype=tf.float32, default_value=None),
'vox': tf.FixedLenFeature(
shape=[vox_size, vox_size, vox_size, 1],
dtype=tf.float32, default_value=None),
}
items_to_handler = {
'image': tfexample_decoder.Tensor(
'image', shape=[num_views, image_size, image_size, 3]),
'mask': tfexample_decoder.Tensor(
'mask', shape=[num_views, image_size, image_size, 1]),
'vox': tfexample_decoder.Tensor(
'vox', shape=[vox_size, vox_size, vox_size, 1])
}
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handler)
return dataset.Dataset(
data_sources=file_pattern,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=num_samples,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS)
示例8
def _get_split(file_pattern, num_samples, num_views, image_size, vox_size):
"""Get dataset.Dataset for the given dataset file pattern and properties."""
# A dictionary from TF-Example keys to tf.FixedLenFeature instance.
keys_to_features = {
'image': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 3],
dtype=tf.float32, default_value=None),
'mask': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 1],
dtype=tf.float32, default_value=None),
'vox': tf.FixedLenFeature(
shape=[vox_size, vox_size, vox_size, 1],
dtype=tf.float32, default_value=None),
}
items_to_handler = {
'image': tfexample_decoder.Tensor(
'image', shape=[num_views, image_size, image_size, 3]),
'mask': tfexample_decoder.Tensor(
'mask', shape=[num_views, image_size, image_size, 1]),
'vox': tfexample_decoder.Tensor(
'vox', shape=[vox_size, vox_size, vox_size, 1])
}
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handler)
return dataset.Dataset(
data_sources=file_pattern,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=num_samples,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS)
示例9
def _get_split(file_pattern, num_samples, num_views, image_size, vox_size):
"""Get dataset.Dataset for the given dataset file pattern and properties."""
# A dictionary from TF-Example keys to tf.FixedLenFeature instance.
keys_to_features = {
'image': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 3],
dtype=tf.float32, default_value=None),
'mask': tf.FixedLenFeature(
shape=[num_views, image_size, image_size, 1],
dtype=tf.float32, default_value=None),
'vox': tf.FixedLenFeature(
shape=[vox_size, vox_size, vox_size, 1],
dtype=tf.float32, default_value=None),
}
items_to_handler = {
'image': tfexample_decoder.Tensor(
'image', shape=[num_views, image_size, image_size, 3]),
'mask': tfexample_decoder.Tensor(
'mask', shape=[num_views, image_size, image_size, 1]),
'vox': tfexample_decoder.Tensor(
'vox', shape=[vox_size, vox_size, vox_size, 1])
}
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handler)
return dataset.Dataset(
data_sources=file_pattern,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=num_samples,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS)
示例10
def _create_tfrecord_dataset(tmpdir):
if not gfile.Exists(tmpdir):
gfile.MakeDirs(tmpdir)
data_sources = test_utils.create_tfrecord_files(tmpdir, num_files=1)
keys_to_features = {
'image/encoded':
parsing_ops.FixedLenFeature(
shape=(), dtype=dtypes.string, default_value=''),
'image/format':
parsing_ops.FixedLenFeature(
shape=(), dtype=dtypes.string, default_value='jpeg'),
'image/class/label':
parsing_ops.FixedLenFeature(
shape=[1],
dtype=dtypes.int64,
default_value=array_ops.zeros(
[1], dtype=dtypes.int64))
}
items_to_handlers = {
'image': tfexample_decoder.Image(),
'label': tfexample_decoder.Tensor('image/class/label'),
}
decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
return dataset.Dataset(
data_sources=data_sources,
reader=io_ops.TFRecordReader,
decoder=decoder,
num_samples=100,
items_to_descriptions=None)
示例11
def get_split(split_name, dataset_dir=None):
"""Gets a dataset tuple with instructions for reading cifar100.
Args:
split_name: A train/test split name.
dataset_dir: The base directory of the dataset sources.
Returns:
A `Dataset` namedtuple. Image tensors are integers in [0, 255].
Raises:
ValueError: if `split_name` is not a valid train/test split.
"""
if split_name not in _SPLITS_TO_SIZES:
raise ValueError('split name %s was not recognized.' % split_name)
file_pattern = os.path.join(dataset_dir, _FILE_PATTERN % split_name)
keys_to_features = {
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string, default_value=''),
'image/class/fine_label': tf.FixedLenFeature(
[1], tf.int64, default_value=tf.zeros([1], dtype=tf.int64)),
'image/class/coarse_label': tf.FixedLenFeature(
[1], tf.int64, default_value=tf.zeros([1], dtype=tf.int64)),
}
items_to_handlers = {
'image': tfexample_decoder.Image(shape=[32, 32, 3]),
'fine_label': tfexample_decoder.Tensor('image/class/fine_label'),
'coarse_label': tfexample_decoder.Tensor('image/class/coarse_label'),
}
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handlers)
return dataset.Dataset(
data_sources=file_pattern,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=_SPLITS_TO_SIZES[split_name],
num_classes=_NUM_CLASSES,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS)
示例12
def get_split(split_name, dataset_dir=None):
"""Gets a dataset tuple with instructions for reading cifar10.
Args:
split_name: A train/test split name.
dataset_dir: The base directory of the dataset sources.
Returns:
A `Dataset` namedtuple. Image tensors are integers in [0, 255].
Raises:
ValueError: if `split_name` is not a valid train/test split.
"""
if split_name not in _SPLITS_TO_SIZES:
raise ValueError('split name %s was not recognized.' % split_name)
if dataset_dir is None:
dataset_dir = _DATASET_DIR
file_pattern = os.path.join(dataset_dir, _FILE_PATTERN % split_name)
keys_to_features = {
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string, default_value=''),
'image/class/label': tf.FixedLenFeature(
[1], tf.int64, default_value=tf.zeros([1], dtype=tf.int64)),
}
items_to_handlers = {
'image': tfexample_decoder.Image(shape=[32, 32, 3]),
'label': tfexample_decoder.Tensor('image/class/label'),
}
decoder = tfexample_decoder.TFExampleDecoder(
keys_to_features, items_to_handlers)
return dataset.Dataset(
data_sources=file_pattern,
reader=tf.TFRecordReader,
decoder=decoder,
num_samples=_SPLITS_TO_SIZES[split_name],
num_classes=_NUM_CLASSES,
items_to_descriptions=_ITEMS_TO_DESCRIPTIONS)
