33 lines
1.2 KiB
Python
33 lines
1.2 KiB
Python
import sys
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import numpy as np
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import torch
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# torch.nn.modules useful for defining a MLIAPPY model.
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from lammps.mliap.pytorch import TorchWrapper, IgnoreElems
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# Read coefficients
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coeffs = np.genfromtxt("linear_ACE_coeff.acecoeff",skip_header=4)
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# If using the "linear_ACE_pot.yace" instead of just the clebsch-gordan coefficients in "linear_ACE_ccs.yace",
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# uncomment below
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#B_coeffs = np.genfromtxt("linear_ACE_coeff.acecoeff",skip_header=4)
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#coeffs = np.append(np.zeros(1),np.ones(len(B_coeffs)-1))
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# Write coefficients to a pytorch linear model
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bias = coeffs[0]
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weights = coeffs[1:]
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lin = torch.nn.Linear(weights.shape[0],1)
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lin.to(torch.float64)
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with torch.autograd.no_grad():
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lin.weight.set_(torch.from_numpy(weights).unsqueeze(0))
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lin.bias.set_(torch.as_tensor(bias,dtype=torch.float64).unsqueeze(0))
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# Wrap the pytorch model for usage with mliappy coupling.
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model = IgnoreElems(lin) # The linear module does not use the types.
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n_descriptors = lin.weight.shape[1]
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print ('ndescriptors',n_descriptors)
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n_elements = 1
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linked_model = TorchWrapper(model,n_descriptors=n_descriptors,n_elements=n_elements)
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torch.save(linked_model,"Ta_ACE.mliap.pytorch.model.pt")
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