nvnmd #1707
nvnmd #1707
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Codecov Report
@@ Coverage Diff @@
## devel #1707 +/- ##
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+ Coverage 76.08% 77.10% +1.01%
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Files 96 118 +22
Lines 7927 9411 +1484
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+ Hits 6031 7256 +1225
- Misses 1896 2155 +259
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Before further review, I have the following general comments:
(1) Translate all Chinese comments into English;
(2) Format codes under deepmd/nvnmd with pep8;
(3) Add doc to each new class and methods to explain the role of them;
(4) Reduce data in the examples diretory. As an example, one data set is enough.
(5) Add unit tests for new codes;
(6) Use os.path.join to combine paths instead of /.
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I notice that all examples are removed. What is the reason?
Please follow @njzjz 's comment :
(4) Reduce data in the examples diretory. As an example, one data set is enough.
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| .Input("dv: T") | ||
| .Input("grad_v: T") | ||
| .Input("grad_dv: T") | ||
| .Attr("prec: float") |
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Here prec is float but x may be either double or float. I think this cannot improve accuracy to double when you multiply x by prec.
deepmd/nvnmd/utils/config.py
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| self.quantize_descriptor = True | ||
| self.quantize_fitting_net = True | ||
| else: | ||
| pass |
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raise RuntimeError if it's not expected behavior
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| log = logging.getLogger(__name__) | ||
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| class Encode(): |
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A lot of codes don't have the unit test, for example this class. See details of converge:
https://app.codecov.io/gh/deepmodeling/deepmd-kit/compare/1707/tree/deepmd/nvnmd
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Code quality is acceptable. ping @wanghan-iapcm for more comments |
deepmd/nvnmd/descriptor/se_a.py
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| def descrpt2r4(inputs, natoms): | ||
| """ replace :math:`r_{ji} \rightarrow r'_{ji}` |
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| """ replace :math:`r_{ji} \rightarrow r'_{ji}` | |
| r""" replace :math:`r_{ji} \rightarrow r'_{ji}` |
otherwise \r will be recognized as the line separator.
Co-authored-by: LiuGroupHNU <liujie123@HNU> Co-authored-by: MoPinghui <mopinghui1020@gmail.com> Co-authored-by: Han Wang <92130845+wanghan-iapcm@users.noreply.github.com>
Our training procedure consists of not only the continuous neural network (CNN) training, but also the quantized neural network (QNN) training which uses the results of CNN as inputs, to help users train machine-learning models that are compatible with the unique non von Neumann computer.