|
- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """eval."""
- import argparse
- import numpy as np
-
- import mindspore.common.dtype as mstype
- from mindspore import Tensor
- from mindspore import context
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from src.network import Network
-
- parser = argparse.ArgumentParser(description='MD Simulation')
- parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
- parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
- args_opt = parser.parse_args()
-
- context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
-
- if __name__ == '__main__':
- # get input data
- r = np.load(args_opt.dataset_path)
- d_coord, d_nlist, avg, std, atype, nlist = r['d_coord'], r['d_nlist'], r['avg'], r['std'], r['atype'], r['nlist']
- batch_size = 1
- atype_tensor = Tensor(atype)
- avg_tensor = Tensor(avg)
- std_tensor = Tensor(std)
- nlist_tensor = Tensor(nlist)
- d_coord_tensor = Tensor(np.reshape(d_coord, (1, -1, 3)))
- d_nlist_tensor = Tensor(d_nlist)
- frames = []
- for i in range(batch_size):
- frames.append(i * 1536)
- frames = Tensor(frames)
- # evaluation
- net = Network()
- param_dict = load_checkpoint(args_opt.checkpoint_path)
- load_param_into_net(net, param_dict)
- net.to_float(mstype.float32)
- energy, atom_ener, _ = \
- net(d_coord_tensor, d_nlist_tensor, frames, avg_tensor, std_tensor, atype_tensor, nlist_tensor)
- print('energy:', energy)
- print('atom_energy:', atom_ener)
|
