def _save_parameters(tmp_nnp_file, train_config, nntxt_str):
base = os.path.dirname(tmp_nnp_file)
base = os.path.join(base, 'results')
version_filename = base + '_version.txt'
with open(version_filename, 'w') as file:
file.write('{}\n'.format(nnp_version()))
# This is for testing, start=>
nntxt_filename = base + '_network.nntxt'
with open(nntxt_filename, "w") as file:
file.write(nntxt_str)
# <= End.
param_filename = base + '_param.h5'
nn.parameter.save_parameters(param_filename)
opti_filenames = save_optimizer_states(base, '.h5', train_config)
with zipfile.ZipFile(tmp_nnp_file, 'w') as nnp:
nnp.write(version_filename, 'nnp_version.txt')
nnp.write(nntxt_filename, "network.nntxt")
nnp.write(param_filename, 'parameter.h5')
for f in opti_filenames:
nnp.write(f, f[len(base) + 1:])
os.unlink(version_filename)
os.unlink(param_filename)
for f in opti_filenames:
os.unlink(f)
logger.info("{} is saved.".format(tmp_nnp_file))
def _save_parameters(args, suffix, epoch, train_config, force=False):
global _save_parameter_info
if suffix not in _save_parameter_info:
_save_parameter_info[suffix] = {}
_save_parameter_info[suffix]['epoch'] = 0
_save_parameter_info[suffix]['time'] = 0
current_time = time.time()
timediff = current_time - _save_parameter_info[suffix]['time']
epochdiff = epoch - _save_parameter_info[suffix]['epoch']
globname = os.path.join(args.outdir, 'results_{}_*.nnp'.format(suffix))
exists = glob.glob(globname)
base = os.path.join(args.outdir, 'results_{}_{}'.format(suffix, epoch))
base_candidate = callback.result_base(base, suffix, args.outdir)
if base_candidate is None:
if suffix is None or suffix == 'best':
base = os.path.join(args.outdir, 'results')
else:
base = base_candidate
filename = base + '.nnp'
if force or (not os.path.exists(filename) and
(timediff > 180.0 or epochdiff > 10)):
# Remove existing nnp before saving new file.
for exist in exists:
os.unlink(exist)
version_filename = base + '_version.txt'
with open(version_filename, 'w') as file:
file.write('{}\n'.format(nnp_version()))
param_filename = base + '_param.h5'
save_parameters(param_filename)
need_save_opti = train_config.optimizers and epoch % _OPTIMIZER_CHECKPOINT_INTERVAL == 0
if need_save_opti:
opti_filenames = save_optimizer_states(base, '.h5', train_config)
with zipfile.ZipFile(filename, 'w') as nnp:
nnp.write(version_filename, 'nnp_version.txt')
nnp.write(_save_parameter_info['config'],
os.path.basename(_save_parameter_info['config']))
nnp.write(param_filename, 'parameter.h5')
if need_save_opti:
for f in opti_filenames:
nnp.write(f, f[len(base) + 1:])
os.unlink(version_filename)
os.unlink(param_filename)
if need_save_opti:
for f in opti_filenames:
os.unlink(f)
_save_parameter_info[suffix]['epoch'] = epoch
_save_parameter_info[suffix]['time'] = current_time
callback.save_train_snapshot()