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f614769 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | import torch
from fairchem.core import pretrained_mlip, FAIRChemCalculator
from fairchem.core.datasets import data_list_collater
from types import MethodType
from ase.stress import full_3x3_to_voigt_6_stress, voigt_6_to_full_3x3_stress
import os
# set hf token here
os.environ["HF_TOKEN"] = ""
def load_pretrained_uma(model_name="uma-s-1p1", device="cpu", task_name="omat"):
predictor = pretrained_mlip.get_predict_unit(model_name, device=device)#, cache_dir="/userhome/home/aymaheshwari/hf-cache")
calc = FAIRChemCalculator(predictor, task_name=task_name)
# Use predictor as the model-like object
uma_model = predictor
def forward(self, atoms):
# Convert atoms to data object using the calculator's a2g converter
data_object = calc.a2g(atoms)
batch = data_list_collater([data_object], otf_graph=True)
# Get predictions directly from the model
output = uma_model.predict(batch)
results = {
"energy": output["energy"],
"forces": output["forces"],
"stress": torch.tensor(full_3x3_to_voigt_6_stress(output["stress"].reshape(3,3).detach().cpu().numpy())), # as voigt notation),,
}
return results
uma_model.forward = MethodType(forward, uma_model)
return uma_model |