diff options
Diffstat (limited to 'models/model.py')
| -rw-r--r-- | models/model.py | 23 |
1 files changed, 18 insertions, 5 deletions
diff --git a/models/model.py b/models/model.py index eae15e3..199e371 100644 --- a/models/model.py +++ b/models/model.py @@ -179,7 +179,7 @@ class Model: # Training start start_time = datetime.now() running_loss = torch.zeros(5, device=self.device) - print(f"{'Time':^8} {'Iter':^5} {'Loss':^6}", + print(f"{'Time':^8} {'Iter':^5} {'Loss':^5}", f"{'Xrecon':^8} {'CanoCons':^8} {'PoseSim':^8}", f"{'BATripH':^8} {'BATripP':^8} {'LRs':^19}") for (batch_c1, batch_c2) in dataloader: @@ -187,10 +187,21 @@ class Model: # Zero the parameter gradients self.optimizer.zero_grad() # forward + backward + optimize + # Feed data twice in order to reduce memory usage x_c1 = batch_c1['clip'].to(self.device) - x_c2 = batch_c2['clip'].to(self.device) y = batch_c1['label'].to(self.device) - losses, images = self.rgb_pn(x_c1, x_c2, y) + # Duplicate labels for each part + y = y.unsqueeze(1).repeat(1, self.rgb_pn.num_total_parts) + # Feed condition 1 clips first + losses, images = self.rgb_pn(x_c1, y) + (xrecon_loss, hpm_ba_trip, pn_ba_trip) = losses + x_c2 = batch_c2['clip'].to(self.device) + # Then feed condition 2 clips + cano_cons_loss, pose_sim_loss = self.rgb_pn(x_c2, is_c1=False) + losses = torch.stack(( + xrecon_loss, cano_cons_loss, pose_sim_loss, + hpm_ba_trip, pn_ba_trip + )) loss = losses.sum() loss.backward() self.optimizer.step() @@ -220,7 +231,9 @@ class Model: self.writer.add_images( 'Canonical image', i_c, self.curr_iter ) - for (i, (o, a, p)) in enumerate(zip(x_c1, i_a, i_p)): + for (i, (o, a, p)) in enumerate(zip( + batch_c1['clip'], i_a, i_p + )): self.writer.add_images( f'Original image/batch {i}', o, self.curr_iter ) @@ -234,7 +247,7 @@ class Model: remaining_minute, second = divmod(time_used.seconds, 60) hour, minute = divmod(remaining_minute, 60) print(f'{hour:02}:{minute:02}:{second:02}', - f'{self.curr_iter:5d} {running_loss.sum() / 100:6.3f}', + f'{self.curr_iter:5d} {running_loss.sum() / 100:5.3f}', '{:f} {:f} {:f} {:f} {:f}'.format(*running_loss / 100), '{:.3e} {:.3e}'.format(lrs[0], lrs[1])) running_loss.zero_() |