Revert cross-reconstruction loss factor and make image log steps adjustable

2 files changed, 19 insertions, 17 deletions

diff --git a/models/auto_encoder.py b/models/auto_encoder.py
index 49cb78b..96dfdb3 100644
--- a/models/auto_encoder.py
+++ b/models/auto_encoder.py
@@ -152,7 +152,7 @@ class AutoEncoder(nn.Module):
 
             return (
                 (f_a_c1_t2, f_c_c1_t2, f_p_c1_t2),
-                (xrecon_loss / 10, cano_cons_loss, pose_sim_loss * 10)
+                (xrecon_loss, cano_cons_loss, pose_sim_loss * 10)
             )
         else:  # evaluating
             return f_a_c1_t2, f_c_c1_t2, f_p_c1_t2
diff --git a/models/model.py b/models/model.py
index ceadb92..9dbf609 100644
--- a/models/model.py
+++ b/models/model.py
@@ -79,6 +79,7 @@ class Model:
         self.scheduler: Optional[optim.lr_scheduler.StepLR] = None
         self.writer: Optional[SummaryWriter] = None
         self.image_log_on = system_config.get('image_log_on', False)
+        self.image_log_steps = system_config.get('image_log_steps', 100)
         self.val_size = system_config.get('val_size', 10)
 
         self.CASIAB_GALLERY_SELECTOR = {
@@ -196,8 +197,8 @@ class Model:
                 triplet_is_hard, triplet_is_mean, None
             )
 
-        self.num_pairs = (self.pr*self.k-1) * (self.pr*self.k) // 2
-        self.num_pos_pairs = (self.k*(self.k-1)//2) * self.pr
+        self.num_pairs = (self.pr * self.k - 1) * (self.pr * self.k) // 2
+        self.num_pos_pairs = (self.k * (self.k - 1) // 2) * self.pr
 
         # Try to accelerate computation using CUDA or others
         self.rgb_pn = self.rgb_pn.to(self.device)
@@ -278,24 +279,25 @@ class Model:
                 'Train', embed_c, embed_p, hpm_result, pn_result, loss, losses
             )
 
-            if self.curr_iter % 100 == 99:
-                # Write disentangled images
-                if self.image_log_on:
-                    i_a, i_c, i_p = images
+            # Write disentangled images
+            if self.image_log_on and self.curr_iter % self.image_log_steps \
+                    == self.image_log_steps - 1:
+                i_a, i_c, i_p = images
+                self.writer.add_images(
+                    'Appearance image', i_a, self.curr_iter
+                )
+                self.writer.add_images(
+                    'Canonical image', i_c, self.curr_iter
+                )
+                for i, (o, p) in enumerate(zip(x_c1, i_p)):
                     self.writer.add_images(
-                        'Appearance image', i_a, self.curr_iter
+                        f'Original image/batch {i}', o, self.curr_iter
                     )
                     self.writer.add_images(
-                        'Canonical image', i_c, self.curr_iter
+                        f'Pose image/batch {i}', p, self.curr_iter
                     )
-                    for i, (o, p) in enumerate(zip(x_c1, i_p)):
-                        self.writer.add_images(
-                            f'Original image/batch {i}', o, self.curr_iter
-                        )
-                        self.writer.add_images(
-                            f'Pose image/batch {i}', p, self.curr_iter
-                        )
 
+            if self.curr_iter % 100 == 99:
                 # Validation
                 embed_c = self._flatten_embedding(embed_c)
                 embed_p = self._flatten_embedding(embed_p)
@@ -352,7 +354,7 @@ class Model:
     def _write_embedding(self, tag, embed, x, y):
         frame = x[:, 0, :, :, :].cpu()
         n, c, h, w = frame.size()
-        padding = torch.zeros(n, c, h, (h-w) // 2)
+        padding = torch.zeros(n, c, h, (h - w) // 2)
         padded_frame = torch.cat((padding, frame, padding), dim=-1)
         self.writer.add_embedding(
             embed,