def load_train_state(filename, info):
info.exclude_parameter = False
info.parameter_only = False
file_loaders = get_decorated_file_loader()
info.parameter_scope = nn.parameter.get_current_parameter_scope()
load_files(info, file_loaders, filename)
logger.info("Load training resume states: {}".format(filename))
def load_parameters(path, proto=None, needs_proto=False, extension=".nntxt"):
"""Load parameters from a file with the specified format.
Args:
path : path or file object
"""
if isinstance(path, str):
_, ext = os.path.splitext(path)
else:
ext = extension
ctx = FileHandlerContext()
if proto is None:
ctx.proto = nnabla_pb2.NNablaProtoBuf()
else:
ctx.proto = proto
ctx.needs_proto = needs_proto
# Get parameter file loaders
file_loaders = get_parameter_file_loader()
load_files(ctx, file_loaders, path, ext)
return ctx.proto
def load(filenames, prepare_data_iterator=True, batch_size=None, exclude_parameter=False, parameter_only=False, extension=".nntxt", context=None):
'''load
Load network information from files.
Args:
filenames (list): file-like object or List of filenames.
extension: if filenames is file-like object, extension is one of ".nntxt", ".prototxt", ".protobuf", ".h5", ".nnp".
Returns:
dict: Network information.
'''
class Info:
pass
info = Info()
info.prepare_data_iterator = prepare_data_iterator
info.batch_size = batch_size
info.exclude_parameter = exclude_parameter
info.parameter_only = parameter_only
info.proto = nnabla_pb2.NNablaProtoBuf()
# first stage file loaders
file_loaders = get_initial_file_loader()
# using global parameter scope, keep consistency with legacy implementation.
# To avoid to surprise previous developers, but it is better using
# stand-alone OrderedDict() instance.
info.parameter_scope = nn.parameter.get_current_parameter_scope()
load_files(info, file_loaders, filenames, extension)
default_context = None
if context:
if context == 'cpu':
import nnabla_ext.cpu
default_context = nnabla_ext.cpu.context()
else:
cs = context.split(':')
if cs[0] == 'cudnn':
if len(cs) == 1:
devid = 0
else:
devid = int(cs[1])
import nnabla_ext.cudnn
default_context = nnabla_ext.cudnn.context(device_id=devid)
if default_context is None:
logger.warn('Invalid context [{}]'.format(context))
elif info.proto.HasField('global_config'):
info.global_config = _global_config(proto)
info.global_config.default_context = default_context
if default_context is None:
if info.proto.HasField('global_config'):
info.global_config = _global_config(info.proto)
default_context = info.global_config.default_context
if 'cuda' in default_context.backend:
import nnabla_ext.cudnn
elif 'cuda:float' in default_context.backend:
try:
import nnabla_ext.cudnn
except:
pass
else:
import nnabla_ext.cpu
default_context = nnabla_ext.cpu.context()
info.global_config = _global_config(
None, default_context=default_context)
default_context = _check_context(default_context)
logger.log(99, 'Using context "{}"'.format(default_context))
comm = current_communicator()
if comm:
default_context.device_id = str(comm.local_rank)
if info.proto.HasField('training_config'):
info.training_config = _training_config(info.proto)
info.default_context = default_context
info.datasets = _datasets(
info.proto, prepare_data_iterator if prepare_data_iterator is not None else info.training_config.max_epoch > 0)
info.renamed_variables = {}
info.networks = _networks(info, nn.graph_def.ProtoGraph.from_proto(info.proto, param_scope=info.parameter_scope,
rng=numpy.random.RandomState(0)))
info.optimizers = _optimizers(info)
info.monitors = _monitors(info)
info.executors = _executors(info)
return info