def load_symbol_def(input_model_name, input_symbol, input_names: str = '', nd_prefix_name: str = '',
pretrained_model_name: str = '', legacy_mxnet_model: bool = False):
if not nd_prefix_name and not pretrained_model_name:
# model name always has extension 'param'
try:
model_name, iteration_number = parse_input_model(input_model_name)
except ValueError as err:
raise Error(
'Input model name {} is not in an expected format, cannot extract iteration number. ' +
refer_to_faq_msg(48),
input_model_name)
if input_names:
model_params = load_params(input_model_name, data_names=input_names.split(','))
else:
model_params = load_params(input_model_name)
elif nd_prefix_name and pretrained_model_name and input_symbol:
model_name, iteration_number = parse_input_model(pretrained_model_name)
model_name = '-'.join(input_symbol.split('-')[:-1])
model_params = build_params_file(nd_prefix_name, pretrained_model_name, input_names)
else:
raise Error(
"Arguments --nd_prefix_name, --pretrained_model_name and --input_symbol should be provided. Please provide all or do not use any. " +
refer_to_faq_msg(81))
model_nodes = load_symbol_nodes(model_name, input_symbol, legacy_mxnet_model)
return model_nodes, model_params, model_name, iteration_number
def build_params_file(nd_prefix_name: str = '',
pretrained_model: str = '',
input_names: str = ''):
path_wo_ext = '.'.join(pretrained_model.split('.')[:-1])
pretrained_model_name_w_iter = path_wo_ext.split(os.sep)[-1]
pretrained_model_name = '-'.join(path_wo_ext.split('-')[:-1])
iteration_number = int(pretrained_model_name_w_iter.split('-')[-1])
files_dir = os.path.dirname(pretrained_model)
if input_names:
model_params = load_params(pretrained_model,
data_names=input_names.split(','))
else:
model_params = load_params(pretrained_model)
pretrained_params = mx.nd.load(
pretrained_model) if pretrained_model_name else None
nd_args = mx.nd.load(os.path.join(
files_dir, '%s_args.nd' % nd_prefix_name)) if nd_prefix_name else None
nd_auxs = mx.nd.load(os.path.join(
files_dir, '%s_auxs.nd' % nd_prefix_name)) if nd_prefix_name else None
nd_args = add_pretrained_model(pretrained_params, nd_args,
pretrained_model_name, iteration_number,
input_names)
model_params._arg_params = nd_args
model_params._aux_params = nd_auxs
model_params._param_names = list(nd_args.keys())
model_params._aux_names = list(nd_auxs.keys())
return model_params
def test_load_symbol_nodes_from_args_nd(self, mock_nd_load):
mock_nd_load.return_value = {'conv0_weight': mx.nd.array([1, 2], dtype='float32'),
'conv1_weight': mx.nd.array([2, 3], dtype='float32')}
model_params = load_params("args_model.nd", data_names=('data1', 'data2'))
self.assertTrue('conv0_weight' in model_params._param_names)
self.assertTrue('conv1_weight' in model_params._param_names)
self.assertEqual([1., 2.], model_params._arg_params['conv0_weight'].asnumpy().tolist())
self.assertEqual([2., 3.], model_params._arg_params['conv1_weight'].asnumpy().tolist())
def test_load_symbol_nodes_from_auxs_nd(self, mock_nd_load):
mock_nd_load.return_value = {
'bn_data_mean': mx.nd.array([5, 6], dtype='float32')
}
model_params = load_params("auxs_model.nd")
self.assertTrue('bn_data_mean' in model_params._aux_names)
self.assertEqual(
[5., 6.],
model_params._aux_params['bn_data_mean'].asnumpy().tolist())
def test_load_symbol_nodes_from_params(self, mock_nd_load):
mock_nd_load.return_value = {'arg:conv0_weight': mx.nd.array([1, 2], dtype='float32'),
'arg:conv1_weight': mx.nd.array([2, 3], dtype='float32'),
'aux:bn_data_mean': mx.nd.array([5, 6], dtype='float32')}
model_params = load_params("model.params")
self.assertTrue('conv0_weight' in model_params._param_names)
self.assertTrue('conv1_weight' in model_params._param_names)
self.assertTrue('bn_data_mean' in model_params._aux_names)
self.assertEqual([1., 2.], model_params._arg_params['conv0_weight'].asnumpy().tolist())
self.assertEqual([2., 3.], model_params._arg_params['conv1_weight'].asnumpy().tolist())
self.assertEqual([5., 6.], model_params._aux_params['bn_data_mean'].asnumpy().tolist())
