Merge branch 'master' into python3.8

# Conflicts:
#	utils/configuration.py
#	utils/triplet_loss.py

2 files changed, 62 insertions, 16 deletions

diff --git a/models/layers.py b/models/layers.py
index ae61583..e30d0c4 100644
--- a/models/layers.py
+++ b/models/layers.py
@@ -80,7 +80,9 @@ class DCGANConvTranspose2d(BasicConvTranspose2d):
         if self.is_last_layer:
             return self.trans_conv(x)
         else:
-            return super().forward(x)
+            x = self.trans_conv(x)
+            x = self.bn(x)
+            return F.leaky_relu(x, 0.2, inplace=True)
 
 
 class BasicLinear(nn.Module):
diff --git a/models/model.py b/models/model.py
index 591808a..48dcfaf 100644
--- a/models/model.py
+++ b/models/model.py
@@ -59,6 +59,8 @@ class Model:
         self.in_size: Tuple[int, int] = (64, 48)
         self.pr: Optional[int] = None
         self.k: Optional[int] = None
+        self.num_pairs: Optional[int] = None
+        self.num_pos_pairs: Optional[int] = None
 
         self._gallery_dataset_meta: Optional[Dict[str, List]] = None
         self._probe_datasets_meta: Optional[Dict[str, Dict[str, List]]] = None
@@ -144,6 +146,7 @@ class Model:
         # Prepare for model, optimizer and scheduler
         model_hp: Dict = self.hp.get('model', {}).copy()
         triplet_is_hard = model_hp.pop('triplet_is_hard', True)
+        triplet_is_mean = model_hp.pop('triplet_is_mean', True)
         triplet_margins = model_hp.pop('triplet_margins', None)
         optim_hp: Dict = self.hp.get('optimizer', {}).copy()
         start_iter = optim_hp.pop('start_iter', 0)
@@ -163,10 +166,13 @@ class Model:
                 )
             else:  # Different margins
                 self.triplet_loss = JointBatchTripletLoss(
-                    self.rgb_pn.hpm_num_parts, triplet_is_hard, triplet_margins
+                    self.rgb_pn.hpm_num_parts,
+                    triplet_is_hard, triplet_is_mean, triplet_margins
                 )
         else:  # Soft margins
-            self.triplet_loss = BatchTripletLoss(triplet_is_hard, None)
+            self.triplet_loss = BatchTripletLoss(
+                triplet_is_hard, triplet_is_mean, None
+            )
 
         # Try to accelerate computation using CUDA or others
         self.rgb_pn = self.rgb_pn.to(self.device)
@@ -216,7 +222,7 @@ class Model:
             y = batch_c1['label'].to(self.device)
             # Duplicate labels for each part
             y = y.repeat(self.rgb_pn.num_total_parts, 1)
-            trip_loss, dist, non_zero_counts = self.triplet_loss(embedding, y)
+            trip_loss, dist, num_non_zero = self.triplet_loss(embedding, y)
             losses = torch.cat((
                 ae_losses,
                 torch.stack((
@@ -240,18 +246,36 @@ class Model:
                 'HPM': losses[3],
                 'PartNet': losses[4]
             }, self.curr_iter)
-            self.writer.add_scalars('Loss/non-zero counts', {
-                'HPM': non_zero_counts[:self.rgb_pn.hpm_num_parts].mean(),
-                'PartNet': non_zero_counts[self.rgb_pn.hpm_num_parts:].mean()
-            }, self.curr_iter)
-            self.writer.add_scalars('Embedding/distance', {
-                'HPM': dist[:self.rgb_pn.hpm_num_parts].mean(),
-                'PartNet': dist[self.rgb_pn.hpm_num_parts].mean()
-            }, self.curr_iter)
-            self.writer.add_scalars('Embedding/2-norm', {
-                'HPM': embedding[:self.rgb_pn.hpm_num_parts].norm(),
-                'PartNet': embedding[self.rgb_pn.hpm_num_parts].norm()
-            }, self.curr_iter)
+            # None-zero losses in batch
+            if num_non_zero is not None:
+                self.writer.add_scalars('Loss/non-zero counts', {
+                    'HPM': num_non_zero[:self.rgb_pn.hpm_num_parts].mean(),
+                    'PartNet': num_non_zero[self.rgb_pn.hpm_num_parts:].mean()
+                }, self.curr_iter)
+            # Embedding distance
+            mean_hpm_dist = dist[:self.rgb_pn.hpm_num_parts].mean(0)
+            self._add_ranked_scalars(
+                'Embedding/HPM distance', mean_hpm_dist,
+                self.num_pos_pairs, self.num_pairs, self.curr_iter
+            )
+            mean_pa_dist = dist[self.rgb_pn.hpm_num_parts:].mean(0)
+            self._add_ranked_scalars(
+                'Embedding/ParNet distance', mean_pa_dist,
+                self.num_pos_pairs, self.num_pairs, self.curr_iter
+            )
+            # Embedding norm
+            mean_hpm_embedding = embedding[:self.rgb_pn.hpm_num_parts].mean(0)
+            mean_hpm_norm = mean_hpm_embedding.norm(dim=-1)
+            self._add_ranked_scalars(
+                'Embedding/HPM norm', mean_hpm_norm,
+                self.k, self.pr * self.k, self.curr_iter
+            )
+            mean_pa_embedding = embedding[self.rgb_pn.hpm_num_parts:].mean(0)
+            mean_pa_norm = mean_pa_embedding.norm(dim=-1)
+            self._add_ranked_scalars(
+                'Embedding/PartNet norm', mean_pa_norm,
+                self.k, self.pr * self.k, self.curr_iter
+            )
 
             if self.curr_iter % 100 == 0:
                 lrs = self.scheduler.get_last_lr()
@@ -303,6 +327,24 @@ class Model:
                 self.writer.close()
                 break
 
+    def _add_ranked_scalars(
+            self,
+            main_tag: str,
+            metric: torch.Tensor,
+            num_pos: int,
+            num_all: int,
+            global_step: int
+    ):
+        rank = metric.argsort()
+        pos_ile = 100 - (num_pos - 1) * 100 // num_all
+        self.writer.add_scalars(main_tag, {
+            '0%-ile': metric[rank[-1]],
+            f'{100 - pos_ile}%-ile': metric[rank[-num_pos]],
+            '50%-ile': metric[rank[num_all // 2 - 1]],
+            f'{pos_ile}%-ile': metric[rank[num_pos - 1]],
+            '100%-ile': metric[rank[0]]
+        }, global_step)
+
     def predict_all(
             self,
             iters: Tuple[int],
@@ -524,6 +566,8 @@ class Model:
     ) -> DataLoader:
         config: Dict = dataloader_config.copy()
         (self.pr, self.k) = config.pop('batch_size', (8, 16))
+        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
         if self.is_train:
             triplet_sampler = TripletSampler(dataset, (self.pr, self.k))
             return DataLoader(dataset,