CHGNetCalculator.calculate add kwarg task: PredTask = "efsm"
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def test_trainer_composition_model(tmp_path: Path) -> None:
for param in chgnet.composition_model.parameters():
assert param.requires_grad is False
trainer = Trainer(
model=chgnet,
targets="efsm",
optimizer="Adam",
criterion="MSE",
learning_rate=1e-2,
epochs=5,
)
initial_weights = chgnet.composition_model.state_dict()["fc.weight"].clone()
> trainer.train(
train_loader, val_loader, save_dir=tmp_path, train_composition_model=True
)
tests/test_trainer.py:106:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
chgnet/trainer/trainer.py:305: in train
train_mae = self._train(train_loader, epoch, wandb_log_freq)
chgnet/trainer/trainer.py:400: in _train
combined_loss = self.criterion(targets, prediction)
../../.venv/py312/lib/python3.12/site-packages/torch/nn/modules/module.py:1553: in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
../../.venv/py312/lib/python3.12/site-packages/torch/nn/modules/module.py:1562: in _call_impl
return forward_call(*args, **kwargs)
chgnet/trainer/trainer.py:861: in forward
if mag_target is not None and not np.isnan(mag_target).any():
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = tensor([0.9620, 0.0657], device='mps:0'), dtype = None
def __array__(self, dtype=None):
if has_torch_function_unary(self):
return handle_torch_function(Tensor.__array__, (self,), self, dtype=dtype)
if dtype is None:
> return self.numpy()
E TypeError: can't convert mps:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
../../.venv/py312/lib/python3.12/site-packages/torch/_tensor.py:1083: TypeError
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reason: need a way to disable magmom predictions. noticed that frozen phonon method computes restorative forces for needless atom displacements if bad magmoms from CHGNet break the structure's symmetry. changes include a unit test for the new
taskkeyword