def run(self, model, training_set, epoch):
if self.collect_stats_frequency > 0 and epoch % self.collect_stats_frequency == 0:
validation_set = next(self.validation_sets)
other_values = {"lr": self.get_lr()}
ma.CollectStatsFromModel(
model=model,
epoch=epoch,
training_set=training_set,
validation_set=validation_set,
writer=self._writer,
other_values=other_values,
logger=self.logger,
sample_size=self.collect_stats_params["sample_size"]).run()
self.lr_scheduler.step(epoch=epoch)
lr_reached_min = (self.get_lr() < self.lr_params["min"])
if lr_reached_min or self.epochs == epoch \
or (self.save_frequency > 0 and (epoch % self.save_frequency == 0)):
model.save(self._model_path(epoch))
if self._writer and (epoch % self.WRITER_CACHE_EPOCHS == 0):
self._reset_writer()
return not lr_reached_min
def run(self, model, training_set, epoch):
if self.collect_stats_frequency > 0 and epoch % self.collect_stats_frequency == 0:
validation_set = next(self.validation_sets)
other_values = {"lr": self.get_lr()}
stats = ma.CollectStatsFromModel(
model=model,
epoch=epoch,
training_set=training_set,
validation_set=validation_set,
writer=self._writer,
other_values=other_values,
logger=self.logger,
sample_size=self.collect_stats_params["sample_size"],
to_mol_func=uc.get_mol_func(
self.collect_stats_params["smiles_type"])).run()
self._metric_epochs.append(stats["nll_plot/jsd_joined"])
if isinstance(self.lr_scheduler,
torch.optim.lr_scheduler.ReduceLROnPlateau):
metric = np.mean(
self._metric_epochs[-self.lr_params["average_steps"]:])
self.lr_scheduler.step(metric, epoch=epoch)
else:
self.lr_scheduler.step(epoch=epoch)
lr_reached_min = (self.get_lr() < self.lr_params["min"])
if lr_reached_min or self.epochs == epoch \
or (self.save_frequency > 0 and (epoch % self.save_frequency == 0)):
model.save(self._model_path(epoch))
if self._writer and (epoch % self.WRITER_CACHE_EPOCHS == 0):
self._reset_writer()
return not lr_reached_min
def main():
"""Main function."""
args = parse_args()
model = mm.Model.load_from_file(args.model_path, mode="sampling")
training_set = list(uc.read_smi_file(args.training_set_path))
validation_set = list(uc.read_smi_file(args.validation_set_path))
writer = tbx.SummaryWriter(log_dir=args.log_path)
ma.CollectStatsFromModel(model,
args.epoch,
training_set,
validation_set,
writer,
sample_size=args.sample_size,
with_weights=args.with_weights,
to_mol_func=uc.get_mol_func(args.smiles_type),
logger=LOG).run()
writer.close()
def main():
"""Main function."""
args = parse_args()
model = mm.DecoratorModel.load_from_file(args.model_path, mode="sampling")
training_set = list(uc.read_csv_file(args.training_set_path, num_fields=2))
validation_set = list(
uc.read_csv_file(args.validation_set_path, num_fields=2))
writer = tbx.SummaryWriter(log_dir=args.log_path)
ma.CollectStatsFromModel(model,
args.epoch,
training_set,
validation_set,
writer,
sample_size=args.sample_size,
decoration_type=args.decoration_type,
with_weights=args.with_weights,
logger=LOG).run()
writer.close()