def evaluate(model,
loader,
loss_fn,
device,
return_results=True,
loss_is_normalized=True,
submodel=None,
**kwargs):
"""Evaluate the current state of the model using a given dataloader
"""
model.eval()
model.to(device)
eval_loss = 0.0
n_eval = 0
all_results = []
all_batches = []
for batch in loader:
# append batch_size
batch = batch_to(batch, device)
vsize = batch['nxyz'].size(0)
n_eval += vsize
# e.g. if the result is a sum of results from two models, and you just
# want the prediction of one of those models
if submodel is not None:
results = getattr(model, submodel)(batch)
else:
results = model(batch, **kwargs)
eval_batch_loss = loss_fn(batch, results).data.cpu().numpy()
if loss_is_normalized:
eval_loss += eval_batch_loss * vsize
else:
eval_loss += eval_batch_loss
all_results.append(batch_detach(results))
all_batches.append(batch_detach(batch))
# del results
# del batch
# weighted average over batches
if loss_is_normalized:
eval_loss /= n_eval
if not return_results:
return {}, {}, eval_loss
else:
# this step can be slow,
all_results = concatenate_dict(*all_results)
all_batches = concatenate_dict(*all_batches)
return all_results, all_batches, eval_loss
def test_inexistent_list_lists(self):
a = {'a': [[[1, 2]], [[3, 4]]], 'b': [5, 6]}
b = {'b': [7, 8]}
ab = concatenate_dict(a, b)
expected = {'a': [[[1, 2]], [[3, 4]], None, None], 'b': [5, 6, 7, 8]}
self.assertEqual(ab, expected)
def test_concatenate(self):
dict_1 = self.dataset[0]
dict_2 = self.dataset[1:3]
concat_dict = concatenate_dict(dict_1, dict_2)
print(concat_dict['energy'])
print(concat_dict['smiles'])
def test_tensors(self):
d1 = {'a': torch.tensor([1.])}
d2 = {'a': torch.tensor([2., 3.])}
dcat = concatenate_dict(d1, d2)
expected = {
'a': [
torch.tensor(1.),
torch.tensor(2.),
torch.tensor(3.),
]
}
self.assertEqual(dcat, expected)
def evaluate(model, loader, device, track, **kwargs):
"""
Evaluate a model on a dataset.
Args:
model (nff.nn.models): original NFF model loaded
loader (torch.utils.data.DataLoader): data loader
device (Union[str, int]): device on which you run the model
Returns:
all_results (dict): dictionary of results
all_batches (dict): dictionary of ground truth
"""
model.eval()
model.to(device)
all_results = []
all_batches = []
iter_func = get_iter_func(track)
for batch in iter_func(loader):
batch = batch_to(batch, device)
results = fps_and_pred(model, batch, **kwargs)
all_results.append(batch_detach(results))
# don't overload memory with unnecessary keys
reduced_batch = {
key: val
for key, val in batch.items() if key not in
['bond_idx', 'ji_idx', 'kj_idx', 'nbr_list', 'bonded_nbr_list']
}
all_batches.append(batch_detach(reduced_batch))
all_results = concatenate_dict(*all_results)
all_batches = concatenate_dict(*all_batches)
return all_results, all_batches
def test_concat_tensors(self):
t = {
'a': torch.tensor(1),
'b': [torch.tensor([2, 3])],
'c': torch.tensor([[1, 0], [0, 1]]),
}
tt = {
'a': [torch.tensor(1)] * 2,
'b': [torch.tensor([2, 3])] * 2,
'c': [torch.tensor([[1, 0], [0, 1]])] * 2,
}
concat = concatenate_dict(t, t)
for key, val in concat.items():
for i, j in zip(val, tt[key]):
self.assertTrue((i == j).all().item())
def setUp(self):
self.quartz = {
"nxyz":
np.array(
[[14.0, -1.19984241582007, 2.07818802527655, 4.59909615202747],
[14.0, 1.31404847917993, 2.27599872954824, 2.7594569553608],
[14.0, 2.39968483164015, 0.0, 0.919817758694137],
[8.0, -1.06646793438585, 3.24694318819338, 0.20609293956337],
[8.0, 0.235189576572621, 1.80712683722845, 3.8853713328967],
[8.0, 0.831278357813231, 3.65430348422777, 2.04573213623004],
[8.0, 3.34516925281323, 0.699883270597028, 5.31282465043663],
[8.0, 1.44742296061415, 1.10724356663142, 1.6335462571033],
[8.0, 2.74908047157262, 2.54705991759635, 3.47318545376996]]),
"lattice":
np.array([[5.02778179, 0.0, 3.07862843796742e-16],
[-2.513890895, 4.3541867548248, 3.07862843796742e-16],
[0.0, 0.0, 5.51891759]])
}
self.qtz_dataset = Dataset(concatenate_dict(*[self.quartz] * 3))
def test_concat_list_lists(self):
dd = concatenate_dict(self.dict_d, self.dict_d)
self.assertEqual(dd, self.dict_dd)
def test_concat_single_dict_lists(self):
a = concatenate_dict(self.dict_a_list)
self.assertEqual(a, self.dict_a_list)
def test_concat_2(self):
ac = concatenate_dict(self.dict_a, self.dict_c)
self.assertEqual(ac, self.dict_ac)
def test_concat_1(self):
ab = concatenate_dict(self.dict_a, self.dict_b)
self.assertEqual(ab, self.dict_ab)
