Python源码示例：input.get()
示例1
def get_inputs(dataset_dir, dataset_name, split_name, batch_size, image_size,
is_training):
"""Loads the given dataset and split."""
del image_size # Unused
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 50
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
return _get_data_from_provider(inputs, batch_size, split_name)
示例2
def get_inputs(dataset_dir, dataset_name, split_name, batch_size, image_size,
is_training):
"""Loads the given dataset and split."""
del image_size # Unused
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 50
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
return _get_data_from_provider(inputs, batch_size, split_name)
示例3
def get_inputs(dataset_dir, dataset_name, split_name, batch_size, image_size,
is_training):
"""Loads the given dataset and split."""
del image_size # Unused
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 50
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
return _get_data_from_provider(inputs, batch_size, split_name)
示例4
def get_inputs(dataset_dir, dataset_name, split_name, batch_size, image_size,
is_training):
"""Loads the given dataset and split."""
del image_size # Unused
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 50
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
return _get_data_from_provider(inputs, batch_size, split_name)
示例5
def get_inputs(dataset_dir, dataset_name, split_name, batch_size, image_size,
is_training):
"""Loads the given dataset and split."""
del image_size # Unused
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 50
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
return _get_data_from_provider(inputs, batch_size, split_name)
示例6
def get_inputs(dataset_dir, dataset_name, split_name, batch_size, image_size,
is_training):
"""Loads the given dataset and split."""
del image_size # Unused
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 50
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
return _get_data_from_provider(inputs, batch_size, split_name)
示例7
def get_inputs(dataset_dir, dataset_name, split_name, batch_size, image_size,
is_training):
"""Loads the given dataset and split."""
del image_size # Unused
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 50
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
return _get_data_from_provider(inputs, batch_size, split_name)
示例8
def get_inputs(dataset_dir, dataset_name, split_name, batch_size, image_size,
is_training):
"""Loads the given dataset and split."""
del image_size # Unused
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 50
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
return _get_data_from_provider(inputs, batch_size, split_name)
示例9
def get_model_fn(params, is_training, reuse=False):
return deeprotator_factory.get(params, is_training, reuse)
示例10
def get_inputs(self,
dataset_dir,
dataset_name,
split_name,
batch_size,
image_size,
vox_size,
is_training=True):
"""Loads data for a specified dataset and split."""
del image_size, vox_size
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 64
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
images, voxels = tf.train.batch(
[inputs['image'], inputs['voxel']],
batch_size=batch_size,
num_threads=8,
capacity=8 * batch_size,
name='batching_queues/%s/%s' % (dataset_name, split_name))
outputs = dict()
outputs['images'] = images
outputs['voxels'] = voxels
outputs['num_samples'] = inputs['num_samples']
return outputs
示例11
def get_model_fn(params, is_training, reuse=False):
return deeprotator_factory.get(params, is_training, reuse)
示例12
def get_inputs(self,
dataset_dir,
dataset_name,
split_name,
batch_size,
image_size,
vox_size,
is_training=True):
"""Loads data for a specified dataset and split."""
del image_size, vox_size
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 64
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
images, voxels = tf.train.batch(
[inputs['image'], inputs['voxel']],
batch_size=batch_size,
num_threads=8,
capacity=8 * batch_size,
name='batching_queues/%s/%s' % (dataset_name, split_name))
outputs = dict()
outputs['images'] = images
outputs['voxels'] = voxels
outputs['num_samples'] = inputs['num_samples']
return outputs
示例13
def get_model_fn(params, is_training, reuse=False):
return deeprotator_factory.get(params, is_training, reuse)
示例14
def get_inputs(self,
dataset_dir,
dataset_name,
split_name,
batch_size,
image_size,
vox_size,
is_training=True):
"""Loads data for a specified dataset and split."""
del image_size, vox_size
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 64
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
images, voxels = tf.train.batch(
[inputs['image'], inputs['voxel']],
batch_size=batch_size,
num_threads=8,
capacity=8 * batch_size,
name='batching_queues/%s/%s' % (dataset_name, split_name))
outputs = dict()
outputs['images'] = images
outputs['voxels'] = voxels
outputs['num_samples'] = inputs['num_samples']
return outputs
示例15
def get_model_fn(params, is_training, reuse=False):
return deeprotator_factory.get(params, is_training, reuse)
示例16
def get_inputs(self,
dataset_dir,
dataset_name,
split_name,
batch_size,
image_size,
vox_size,
is_training=True):
"""Loads data for a specified dataset and split."""
del image_size, vox_size
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 64
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
images, voxels = tf.train.batch(
[inputs['image'], inputs['voxel']],
batch_size=batch_size,
num_threads=8,
capacity=8 * batch_size,
name='batching_queues/%s/%s' % (dataset_name, split_name))
outputs = dict()
outputs['images'] = images
outputs['voxels'] = voxels
outputs['num_samples'] = inputs['num_samples']
return outputs
示例17
def get_model_fn(params, is_training, reuse=False):
return deeprotator_factory.get(params, is_training, reuse)
示例18
def get_inputs(self,
dataset_dir,
dataset_name,
split_name,
batch_size,
image_size,
vox_size,
is_training=True):
"""Loads data for a specified dataset and split."""
del image_size, vox_size
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 64
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
images, voxels = tf.train.batch(
[inputs['image'], inputs['voxel']],
batch_size=batch_size,
num_threads=8,
capacity=8 * batch_size,
name='batching_queues/%s/%s' % (dataset_name, split_name))
outputs = dict()
outputs['images'] = images
outputs['voxels'] = voxels
outputs['num_samples'] = inputs['num_samples']
return outputs
示例19
def get_model_fn(params, is_training, reuse=False):
return deeprotator_factory.get(params, is_training, reuse)
示例20
def get_inputs(self,
dataset_dir,
dataset_name,
split_name,
batch_size,
image_size,
vox_size,
is_training=True):
"""Loads data for a specified dataset and split."""
del image_size, vox_size
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 64
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
images, voxels = tf.train.batch(
[inputs['image'], inputs['voxel']],
batch_size=batch_size,
num_threads=8,
capacity=8 * batch_size,
name='batching_queues/%s/%s' % (dataset_name, split_name))
outputs = dict()
outputs['images'] = images
outputs['voxels'] = voxels
outputs['num_samples'] = inputs['num_samples']
return outputs
示例21
def get_model_fn(params, is_training, reuse=False):
return deeprotator_factory.get(params, is_training, reuse)
示例22
def get_inputs(self,
dataset_dir,
dataset_name,
split_name,
batch_size,
image_size,
vox_size,
is_training=True):
"""Loads data for a specified dataset and split."""
del image_size, vox_size
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 64
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
images, voxels = tf.train.batch(
[inputs['image'], inputs['voxel']],
batch_size=batch_size,
num_threads=8,
capacity=8 * batch_size,
name='batching_queues/%s/%s' % (dataset_name, split_name))
outputs = dict()
outputs['images'] = images
outputs['voxels'] = voxels
outputs['num_samples'] = inputs['num_samples']
return outputs
示例23
def get_model_fn(params, is_training, reuse=False):
return deeprotator_factory.get(params, is_training, reuse)
示例24
def get_inputs(self,
dataset_dir,
dataset_name,
split_name,
batch_size,
image_size,
vox_size,
is_training=True):
"""Loads data for a specified dataset and split."""
del image_size, vox_size
with tf.variable_scope('data_loading_%s/%s' % (dataset_name, split_name)):
common_queue_min = 64
common_queue_capacity = 256
num_readers = 4
inputs = input_generator.get(
dataset_dir,
dataset_name,
split_name,
shuffle=is_training,
num_readers=num_readers,
common_queue_min=common_queue_min,
common_queue_capacity=common_queue_capacity)
images, voxels = tf.train.batch(
[inputs['image'], inputs['voxel']],
batch_size=batch_size,
num_threads=8,
capacity=8 * batch_size,
name='batching_queues/%s/%s' % (dataset_name, split_name))
outputs = dict()
outputs['images'] = images
outputs['voxels'] = voxels
outputs['num_samples'] = inputs['num_samples']
return outputs
