diff options
Diffstat (limited to 'models/model.py')
| -rw-r--r-- | models/model.py | 37 |
1 files changed, 16 insertions, 21 deletions
diff --git a/models/model.py b/models/model.py index ee07615..70c43b3 100644 --- a/models/model.py +++ b/models/model.py @@ -181,8 +181,9 @@ class Model: # Training start start_time = datetime.now() running_loss = torch.zeros(5, device=self.device) - print(f"{'Iter':^5} {'Loss':^6} {'Xrecon':^8} {'PoseSim':^8}", - f"{'CanoCons':^8} {'BATripH':^8} {'BATripP':^8} {'LRs':^19}") + print(f"{'Time':^8} {'Iter':^5} {'Loss':^6}", + f"{'Xrecon':^8} {'CanoCons':^8} {'PoseSim':^8}", + f"{'BATripH':^8} {'BATripP':^8} {'LRs':^19}") for (batch_c1, batch_c2) in dataloader: self.curr_iter += 1 # Zero the parameter gradients @@ -201,8 +202,8 @@ class Model: # Write losses to TensorBoard self.writer.add_scalar('Loss/all', loss, self.curr_iter) self.writer.add_scalars('Loss/details', dict(zip([ - 'Cross reconstruction loss', 'Pose similarity loss', - 'Canonical consistency loss', 'Batch All triplet loss (HPM)', + 'Cross reconstruction loss', 'Canonical consistency loss', + 'Pose similarity loss', 'Batch All triplet loss (HPM)', 'Batch All triplet loss (PartNet)' ], losses)), self.curr_iter) @@ -217,25 +218,25 @@ class Model: ) # Write disentangled images if self.image_log_on: - (appearance_image, canonical_image, pose_image) = images + i_a, i_c, i_p = images self.writer.add_images( - 'Canonical image', canonical_image, self.curr_iter + 'Canonical image', i_c, self.curr_iter ) - for i in range(self.pr * self.k): + for (i, (o, a, p)) in enumerate(zip(x_c1, i_a, i_p)): self.writer.add_images( - f'Original image/batch {i}', x_c1[i], self.curr_iter + f'Original image/batch {i}', o, self.curr_iter ) self.writer.add_images( - f'Appearance image/batch {i}', - appearance_image[:, i, :, :, :], - self.curr_iter + f'Appearance image/batch {i}', a, self.curr_iter ) self.writer.add_images( - f'Pose image/batch {i}', - pose_image[:, i, :, :, :], - self.curr_iter + f'Pose image/batch {i}', p, self.curr_iter ) - print(f'{self.curr_iter:5d} {running_loss.sum() / 100:6.3f}', + time_used = datetime.now() - start_time + 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} {:f} {:f} {:f} {:f}'.format(*running_loss / 100), '{:.3e} {:.3e}'.format(lrs[0], lrs[1])) running_loss.zero_() @@ -250,12 +251,6 @@ class Model: 'optim_state_dict': self.optimizer.state_dict(), 'loss': loss, }, self._checkpoint_name) - print(datetime.now() - start_time, 'used') - start_time = datetime.now() - print( - f"{'Iter':^5} {'Loss':^6} {'Xrecon':^8} {'PoseSim':^8}", - f"{'CanoCons':^8} {'BATripH':^8} {'BATripP':^8} {'LRs':^19}" - ) if self.curr_iter == self.total_iter: self.writer.close() |