diff options
Diffstat (limited to 'models')
| -rw-r--r-- | models/model.py | 24 |
1 files changed, 17 insertions, 7 deletions
diff --git a/models/model.py b/models/model.py index 6b799ad..cccb6c4 100644 --- a/models/model.py +++ b/models/model.py @@ -141,6 +141,7 @@ class Model: # Prepare for model, optimizer and scheduler model_hp = self.hp.get('model', {}) optim_hp: dict = self.hp.get('optimizer', {}).copy() + start_iter = optim_hp.pop('start_iter', 0) ae_optim_hp = optim_hp.pop('auto_encoder', {}) pn_optim_hp = optim_hp.pop('part_net', {}) hpm_optim_hp = optim_hp.pop('hpm', {}) @@ -151,9 +152,6 @@ class Model: self.rgb_pn = self.rgb_pn.to(self.device) self.optimizer = optim.Adam([ {'params': self.rgb_pn.ae.parameters(), **ae_optim_hp}, - {'params': self.rgb_pn.pn.parameters(), **pn_optim_hp}, - {'params': self.rgb_pn.hpm.parameters(), **hpm_optim_hp}, - {'params': self.rgb_pn.fc_mat, **fc_optim_hp}, ], **optim_hp) self.scheduler = optim.lr_scheduler.StepLR(self.optimizer, **sched_hp) self.writer = SummaryWriter(self._log_name) @@ -173,8 +171,18 @@ class Model: start_time = datetime.now() running_loss = torch.zeros(4).to(self.device) print(f"{'Iter':^5} {'Loss':^6} {'Xrecon':^8} {'PoseSim':^8}", - f"{'CanoCons':^8} {'BATrip':^8} {'LR':^9}") + f"{'CanoCons':^8} {'BATrip':^8} LR(s)") for (batch_c1, batch_c2) in dataloader: + if self.curr_iter == start_iter: + self.optimizer.add_param_group( + {'params': self.rgb_pn.pn.parameters(), **pn_optim_hp} + ) + self.optimizer.add_param_group( + {'params': self.rgb_pn.hpm.parameters(), **hpm_optim_hp} + ) + self.optimizer.add_param_group( + {'params': self.rgb_pn.fc_mat, **fc_optim_hp} + ) self.curr_iter += 1 # Zero the parameter gradients self.optimizer.zero_grad() @@ -186,8 +194,6 @@ class Model: loss = losses.sum() loss.backward() self.optimizer.step() - # Step scheduler - self.scheduler.step() # Statistics and checkpoint running_loss += losses.detach() @@ -199,11 +205,15 @@ class Model: ], losses)), self.curr_iter) if self.curr_iter % 100 == 0: + lrs = self.scheduler.get_last_lr() print(f'{self.curr_iter:5d} {running_loss.sum() / 100:6.3f}', '{:f} {:f} {:f} {:f}'.format(*running_loss / 100), - f'{self.scheduler.get_last_lr()[0]:.3e}') + ' '.join(('{:.3e}'.format(lr) for lr in lrs))) running_loss.zero_() + # Step scheduler + self.scheduler.step() + if self.curr_iter % 1000 == 0: torch.save({ 'iter': self.curr_iter, |