35f2c9bdf2
- cmake fixed, no longer needs numpy headers. - models can be loaded from an external interepreter.
47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
import numpy as np
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import torch
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def calc_n_params(model):
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return sum(p.nelement() for p in model.parameters())
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class TorchWrapper(torch.nn.Module):
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def __init__(self, model,n_descriptors,n_elements,n_params=None):
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super().__init__()
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self.model = model
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self.model.to(self.dtype)
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if n_params is None:
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n_params = calc_n_params(model)
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self.n_params = n_params
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self.n_descriptors = n_descriptors
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self.n_elements = n_elements
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def __call__(self, elems, bispectrum, beta, energy):
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bispectrum = torch.from_numpy(bispectrum).to(self.dtype).requires_grad_(True)
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elems = torch.from_numpy(elems).to(torch.long) - 1
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with torch.autograd.enable_grad():
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energy_nn = self.model(bispectrum, elems)
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if energy_nn.ndim > 1:
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energy_nn = energy_nn.flatten()
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beta_nn = torch.autograd.grad(energy_nn.sum(), bispectrum)[0]
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beta[:] = beta_nn.detach().cpu().numpy().astype(np.float64)
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energy[:] = energy_nn.detach().cpu().numpy().astype(np.float64)
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class TorchWrapper32(TorchWrapper):
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dtype = torch.float32
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class TorchWrapper64(TorchWrapper):
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dtype = torch.float64
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class IgnoreElems(torch.nn.Module):
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def __init__(self,subnet):
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super().__init__()
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self.subnet = subnet
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def forward(self,bispectrum,elems):
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return self.subnet(bispectrum)
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