Devel update #30
Devel update #30
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If so, throw the following error: ``` -- PyTorch CXX11 ABI: 0 CMake Error at CMakeLists.txt:162 (message): PyTorch CXX11 ABI mismatch TensorFlow: 0 != 1 ``` Signed-off-by: Jinzhe Zeng <[email protected]>
Fix #3120. One can disable building the TensorFlow backend during `pip install` by setting `DP_ENABLE_TENSORFLOW=0`. --------- Signed-off-by: Jinzhe Zeng <[email protected]>
…g net. (#3199) - add dp model format (backend independent definition) for the fitting - refactor torch support, compatible with dp model format - fix mlp issue: the idt should only be used when a skip connection is available. - add tools `to_numpy_array` and `to_torch_tensor`. --------- Co-authored-by: Han Wang <[email protected]>
Co-authored-by: Han Wang <[email protected]>
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| # if atype_tebd is not None: | ||
| # inputs = torch.concat([inputs, atype_tebd], dim=-1) |
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| if nap > 0: | ||
| iap = rng.normal(size=(self.nf, self.nloc, nap - 1)) | ||
| with self.assertRaises(ValueError) as context: | ||
| ret0 = ifn0(dd[0], atype, fparam=ifp, aparam=iap) |
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Variable defined multiple times Warning test
| self.assertEqual(onk.dtype, npp) | ||
| with self.assertRaises(ValueError) as ee: | ||
| foo = foo.astype(np.int32) | ||
| bar = to_torch_tensor(foo) |
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Variable defined multiple times Warning test
| def test_new_old( | ||
| self, | ||
| ): | ||
| rng = np.random.default_rng() |
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Unused local variable Note test
| ) | ||
| atype = torch.tensor(self.atype_ext[:, :nloc], dtype=int, device=env.DEVICE) | ||
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| od = 1 |
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| @classmethod | ||
| def deserialize(cls, data: dict) -> "InvarFitting": | ||
| data = copy.deepcopy(data) | ||
| variables = data.pop("@variables") |
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| def deserialize(cls, data: dict) -> "InvarFitting": | ||
| data = copy.deepcopy(data) | ||
| variables = data.pop("@variables") | ||
| nets = data.pop("nets") |
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| @classmethod | ||
| def deserialize(cls, data: dict) -> "InvarFitting": | ||
| data = copy.deepcopy(data) | ||
| variables = data.pop("@variables") |
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Modification of parameter with default Error
| def deserialize(cls, data: dict) -> "InvarFitting": | ||
| data = copy.deepcopy(data) | ||
| variables = data.pop("@variables") | ||
| nets = data.pop("nets") |
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Modification of parameter with default Error
| if use_aparam_as_mask: | ||
| raise NotImplementedError("use_aparam_as_mask is not implemented") | ||
| if use_aparam_as_mask: | ||
| raise NotImplementedError("use_aparam_as_mask is not implemented") |
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