def build_nasnet_large(images,
num_classes,
is_training=True,
final_endpoint=None,
config=None,
current_step=None):
"""Build NASNet Large model for the ImageNet Dataset."""
hparams = (large_imagenet_config()
if config is None else copy.deepcopy(config))
_update_hparams(hparams, is_training)
if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
tf.logging.info(
'A GPU is available on the machine, consider using NCHW '
'data format for increased speed on GPU.')
if hparams.data_format == 'NCHW':
images = tf.transpose(images, [0, 3, 1, 2])
# Calculate the total number of cells in the network
# Add 2 for the reduction cells
total_num_cells = hparams.num_cells + 2
# If ImageNet, then add an additional two for the stem cells
total_num_cells += 2
normal_cell = nasnet_utils.NasNetANormalCell(hparams.num_conv_filters,
hparams.drop_path_keep_prob,
total_num_cells,
hparams.total_training_steps)
reduction_cell = nasnet_utils.NasNetAReductionCell(
hparams.num_conv_filters, hparams.drop_path_keep_prob, total_num_cells,
hparams.total_training_steps)
with arg_scope([slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
is_training=is_training):
with arg_scope([
slim.avg_pool2d, slim.max_pool2d, slim.conv2d, slim.batch_norm,
slim.separable_conv2d, nasnet_utils.factorized_reduction,
nasnet_utils.global_avg_pool, nasnet_utils.get_channel_index,
nasnet_utils.get_channel_dim
],
data_format=hparams.data_format):
return _build_nasnet_base(images,
normal_cell=normal_cell,
reduction_cell=reduction_cell,
num_classes=num_classes,
hparams=hparams,
is_training=is_training,
stem_type='imagenet',
final_endpoint=final_endpoint,
current_step=current_step)
def build_nasnet_large(images, is_training=True, final_endpoint=None):
"""Build NASNet Large model for the ImageNet Dataset."""
hparams = _large_imagenet_config(is_training=is_training)
if hparams.data_format == 'NCHW':
images = tf.transpose(images, [0, 3, 1, 2])
# Calculate the total number of cells in the network
# Add 2 for the reduction cells
total_num_cells = hparams.num_cells + 2
# If ImageNet, then add an additional two for the stem cells
total_num_cells += 2
normal_cell = nasnet_utils.NasNetANormalCell(hparams.num_conv_filters,
hparams.drop_path_keep_prob,
total_num_cells,
hparams.total_training_steps)
reduction_cell = nasnet_utils.NasNetAReductionCell(
hparams.num_conv_filters, hparams.drop_path_keep_prob, total_num_cells,
hparams.total_training_steps)
with arg_scope([slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
is_training=is_training):
with arg_scope([
slim.avg_pool2d, slim.max_pool2d, slim.conv2d, slim.batch_norm,
slim.separable_conv2d, nasnet_utils.factorized_reduction,
nasnet_utils.global_avg_pool, nasnet_utils.get_channel_index,
nasnet_utils.get_channel_dim
],
data_format=hparams.data_format):
netout, end_points = build_nasnet_base_my(
images,
normal_cell=normal_cell,
reduction_cell=reduction_cell,
hparams=hparams,
final_endpoint=final_endpoint)
# dicts = end_points.keys()
# for n in range(len(dicts)):
# print '%s:' % dicts[n], end_points[dicts[n]].shape.as_list()
return netout, end_points, dict({
4: end_points['Cell_17'],
3: end_points['Cell_11'],
2: end_points['Cell_5'],
1: end_points['Stem_4']
})
def build_nasnet_cifar(
images, num_classes, is_training=True):
"""Build NASNet model for the Cifar Dataset."""
hparams = _cifar_config(is_training=is_training)
if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
tf.logging.info('A GPU is available on the machine, consider using NCHW '
'data format for increased speed on GPU.')
if hparams.data_format == 'NCHW':
images = tf.transpose(images, [0, 3, 1, 2])
# Calculate the total number of cells in the network
# Add 2 for the reduction cells
total_num_cells = hparams.num_cells + 2
normal_cell = nasnet_utils.NasNetANormalCell(
hparams.num_conv_filters, hparams.drop_path_keep_prob,
total_num_cells, hparams.total_training_steps)
reduction_cell = nasnet_utils.NasNetAReductionCell(
hparams.num_conv_filters, hparams.drop_path_keep_prob,
total_num_cells, hparams.total_training_steps)
with arg_scope([slim.dropout, nasnet_utils.drop_path, slim.batch_norm],
is_training=is_training):
with arg_scope([slim.avg_pool2d,
slim.max_pool2d,
slim.conv2d,
slim.batch_norm,
slim.separable_conv2d,
nasnet_utils.factorized_reduction,
nasnet_utils.global_avg_pool,
nasnet_utils.get_channel_index,
nasnet_utils.get_channel_dim],
data_format=hparams.data_format):
return _build_nasnet_base(images,
normal_cell=normal_cell,
reduction_cell=reduction_cell,
num_classes=num_classes,
hparams=hparams,
is_training=is_training,
stem_type='cifar')
