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