def test_pretrained_no_config():
config_1 = copy.deepcopy(config)
trainer = AtomsTrainer(config_1)
trainer.train()
trained_cpdir = trainer.cp_dir
e_mae_1, f_mae_1 = get_metrics(trainer)
trainer_2 = AtomsTrainer()
trainer_2.load_pretrained(trained_cpdir)
e_mae_2, f_mae_2 = get_metrics(trainer_2)
assert e_mae_1 == e_mae_2, "configless - pretrained energy metrics inconsistent!"
assert f_mae_1 == f_mae_2, "configless - pretrained force metrics inconsistent!"
def train_and_combine(args_list):
"""
method for training trainer on ensemble sets, then create neural net calc,
returns trained calc
"""
training_dataset = args_list[0]
trainer = args_list[1]
trainer.train(raw_data=training_dataset)
check_path = trainer.cp_dir
trainer = AtomsTrainer()
trainer.load_pretrained(checkpoint_path=check_path)
trainer_calc = trainer.get_calc()
return trainer_calc
def test_pretrained():
torch.set_num_threads(1)
trainer = AtomsTrainer(config)
trainer.train()
trained_cpdir = trainer.cp_dir
e_mae_1, f_mae_1 = get_metrics(trainer)
config["optim"]["epochs"] = 100
pretrained_trainer = AtomsTrainer(config)
pretrained_trainer.load_pretrained(trained_cpdir)
e_mae_2, f_mae_2 = get_metrics(pretrained_trainer)
assert e_mae_1 == e_mae_2, "Pretrained energy metrics inconsistent!"
assert f_mae_1 == f_mae_2, "Pretrained force metrics inconsistent!"
pretrained_trainer.train()
e_mae_3, f_mae_3 = get_metrics(pretrained_trainer)
assert e_mae_3 < e_mae_2, "Retrained metrics are larger!"
assert f_mae_3 < f_mae_2, "Retrained metrics are larger!"
MCSHs = MCSHs_dict[MCSHs_index]
MCSHs = {
"MCSHs": MCSHs,
"atom_gaussians": potential_files,
"cutoff": cutoff_distance
}
elements = [
'Pt', 'Al', 'V', 'Pd', 'Fe', 'Sn', 'Ge', 'Bi', 'Ir', 'Re', 'Cd', 'Cr',
'Ag', 'Hf', 'Ru', 'Ti', 'Cs', 'Os', 'N', 'As', 'O', 'S', 'Mo', 'Ta', 'Zn',
'Y', 'Mn', 'Na', 'Rh', 'Hg', 'C', 'Co', 'Nb', 'Sc', 'Sr', 'H', 'Au', 'Ga',
'Tl', 'K', 'Se', 'B', 'Pb', 'Ca', 'Cl', 'Cu', 'Zr', 'Rb', 'P', 'W', 'Tc',
'Te', 'Ni', 'Sb', 'Si', 'In'
]
trainer = AtomsTrainer()
trainer.load_pretrained(checkpoint_name, gpu2cpu=True)
trainer.config["dataset"]["save_fps"] = False
result_dirname = "./test_result_val_id/sigma{}_MCSH{}_nodes{}_layers{}_cutoff{}_numtraining{}_results".format(
sigmas_index, MCSHs_index, num_nodes, num_layers, cutoff_distance,
num_training)
test_mae = predict_data(trainer,
test_images,
set_index,
result_dirname=result_dirname,
image_type="test")
def load_trainer(checkpoint_path):
trainer = AtomsTrainer()
# loading the pretrained model
trainer.load_pretrained(checkpoint_path)
return trainer
"batch_size": batch_size,
"epochs": epochs,
"loss": "mae",
},
"dataset": {
"lmdb_path": lmdb_paths,
"val_split": val_split,
"val_split_mode": "inorder",
"cache": "partial"
},
"cmd": {
"debug": False,
"run_dir": "./",
"seed": 1,
"identifier": "test",
"verbose": True,
# Weights and Biases used for logging - an account(free) is required
"logger": False,
},
}
trainer = AtomsTrainer(config)
if os.path.isdir(checkpoint_name):
trainer.load_pretrained(checkpoint_name)
else:
print(
"**** WARNING: checkpoint not found: {} ****".format(checkpoint_name))
print("training")
trainer.train()
print("end training")
def calculate_energy(x0):
OH_bond_length = x0[0]
bond_angle = x0[1]
images = read("../data/water.traj", index=":")
images = [images[0]]
sigmas = np.logspace(np.log10(0.02), np.log10(1.0), num=5)
MCSHs = {
"MCSHs": {
"0": {"groups": [1], "sigmas": sigmas},
"1": {"groups": [1], "sigmas": sigmas},
"2": {"groups": [1, 2], "sigmas": sigmas},
"3": {"groups": [1, 2, 3], "sigmas": sigmas},
"4": {"groups": [1, 2, 3, 4], "sigmas": sigmas},
"5": {"groups": [1, 2, 3, 4, 5], "sigmas": sigmas},
# "6": {"groups": [1, 2, 3, 4, 5, 6, 7], "sigmas": sigmas},
},
"atom_gaussians": {
"H": "../MCSH_potentials/H_pseudodensity_2.g",
"O": "../MCSH_potentials/O_pseudodensity_4.g",
},
"cutoff": 8,
}
elements = ["H", "O"]
config = {
"model": {"get_forces": True, "num_layers": 3, "num_nodes": 20},
"optim": {
"device": "cpu",
"force_coefficient": 0.2,
"lr": 1e-3,
"batch_size": 8,
"epochs": 500,
},
"dataset": {
"raw_data": images,
# "val_split": 0.1,
"elements": elements,
"fp_scheme": "mcsh",
"fp_params": MCSHs,
"save_fps": True,
},
"cmd": {
"debug": False,
"run_dir": "./",
"seed": 1,
"identifier": "test",
"verbose": True,
"logger": False,
},
}
trainer = AtomsTrainer(config)
# loading the pretrained model
trainer.load_pretrained("../checkpoints/2021-03-22-14-02-20-test")
calculator = AMPtorch(trainer)
image = molecule('H2O')
image.set_distance(0, 2, OH_bond_length, fix=0)
image.set_angle(1, 0, 2, bond_angle)
image.set_cell([10, 10, 10])
image.center()
image.set_calculator(calculator)
return image.get_potential_energy()
