def validate(self, split="val", epoch=None):
print("### Evaluating on {}.".format(split))
self.model.eval()
meter = Meter(split=split)
loader = self.val_loader if split == "val" else self.test_loader
for i, batch in enumerate(loader):
# Forward.
out, metrics = self._forward(batch)
loss = self._compute_loss(out, batch)
# Update meter.
meter_update_dict = {"loss": loss.item()}
meter_update_dict.update(metrics)
meter.update(meter_update_dict)
# Make plots.
if self.logger is not None and epoch is not None:
log_dict = meter.get_scalar_dict()
log_dict.update({"epoch": epoch + 1})
self.logger.log(
log_dict,
step=(epoch + 1) * len(self.train_loader),
split=split,
)
print(meter)
def validate_relaxation(self, split="val", epoch=None):
print("### Evaluating ML-relaxation")
self.model.eval()
metrics = {}
meter = Meter(split=split)
mae_energy, mae_structure = relax_eval(
trainer=self,
traj_dir=self.config["task"]["relaxation_dir"],
metric=self.config["task"]["metric"],
steps=self.config["task"].get("relaxation_steps", 300),
fmax=self.config["task"].get("relaxation_fmax", 0.01),
results_dir=self.config["cmd"]["results_dir"],
)
metrics["relaxed_energy/{}".format(
self.config["task"]["metric"])] = mae_energy
metrics["relaxed_structure/{}".format(
self.config["task"]["metric"])] = mae_structure
meter.update(metrics)
# Make plots.
if self.logger is not None and epoch is not None:
log_dict = meter.get_scalar_dict()
log_dict.update({"epoch": epoch + 1})
self.logger.log(
log_dict,
step=(epoch + 1) * len(self.train_loader),
split=split,
)
print(meter)
return mae_energy, mae_structure
def validate(self, split="val", epoch=None):
print("### Evaluating on {}.".format(split))
self.model.eval()
meter = Meter(split=split)
loader = self.val_loader if split == "val" else self.test_loader
for i, batch in enumerate(loader):
batch = batch.to(self.device)
# Forward.
out, metrics = self._forward(batch)
loss = self._compute_loss(out, batch)
# Update meter.
meter_update_dict = {"loss": loss.item()}
meter_update_dict.update(metrics)
meter.update(meter_update_dict)
# Make plots.
if self.logger is not None and epoch is not None:
log_dict = meter.get_scalar_dict()
log_dict.update({"epoch": epoch + 1})
self.logger.log(
log_dict,
step=(epoch + 1) * len(self.train_loader),
split=split,
)
print(meter)
return (
float(meter.loss.global_avg),
float(meter.meters[self.config["task"]["labels"][0] + "/" +
self.config["task"]["metric"]].global_avg),
)