Merge branch 'master' into python3.8

# Conflicts:
#	models/model.py
#	models/rgb_part_net.py

1 files changed, 18 insertions, 3 deletions

diff --git a/models/model.py b/models/model.py
index 2b7e401..a42a5c6 100644
--- a/models/model.py
+++ b/models/model.py
@@ -18,6 +18,7 @@ from utils.configuration import DataloaderConfiguration, \
     SystemConfiguration
 from utils.dataset import CASIAB, ClipConditions, ClipViews, ClipClasses
 from utils.sampler import TripletSampler
+from utils.triplet_loss import JointBatchAllTripletLoss
 
 
 class Model:
@@ -67,6 +68,7 @@ class Model:
         self._dataset_sig: str = 'undefined'
 
         self.rgb_pn: Optional[RGBPartNet] = None
+        self.ba_triplet_loss: Optional[JointBatchAllTripletLoss] = None
         self.optimizer: Optional[optim.Adam] = None
         self.scheduler: Optional[optim.lr_scheduler.StepLR] = None
         self.writer: Optional[SummaryWriter] = None
@@ -140,7 +142,8 @@ class Model:
         dataset = self._parse_dataset_config(dataset_config)
         dataloader = self._parse_dataloader_config(dataset, dataloader_config)
         # Prepare for model, optimizer and scheduler
-        model_hp = self.hp.get('model', {})
+        model_hp: Dict = self.hp.get('model', {}).copy()
+        triplet_margins = model_hp.pop('triplet_margins', (0.2, 0.2))
         optim_hp: Dict = self.hp.get('optimizer', {}).copy()
         start_iter = optim_hp.pop('start_iter', 0)
         ae_optim_hp = optim_hp.pop('auto_encoder', {})
@@ -150,8 +153,12 @@ class Model:
         sched_hp = self.hp.get('scheduler', {})
         self.rgb_pn = RGBPartNet(self.in_channels, self.in_size, **model_hp,
                                  image_log_on=self.image_log_on)
+        self.ba_triplet_loss = JointBatchAllTripletLoss(
+            self.rgb_pn.hpm_num_parts, triplet_margins
+        )
         # Try to accelerate computation using CUDA or others
         self.rgb_pn = self.rgb_pn.to(self.device)
+        self.ba_triplet_loss = self.ba_triplet_loss.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},
@@ -193,10 +200,18 @@ class Model:
             # forward + backward + optimize
             x_c1 = batch_c1['clip'].to(self.device)
             x_c2 = batch_c2['clip'].to(self.device)
+            feature, ae_losses, images = self.rgb_pn(x_c1, x_c2)
             y = batch_c1['label'].to(self.device)
             # Duplicate labels for each part
-            y = y.unsqueeze(1).repeat(1, self.rgb_pn.num_total_parts)
-            losses, images = self.rgb_pn(x_c1, x_c2, y)
+            y = y.repeat(self.rgb_pn.num_total_parts, 1)
+            triplet_loss = self.ba_triplet_loss(feature, y)
+            losses = torch.cat((
+                ae_losses,
+                torch.stack((
+                    triplet_loss[:self.rgb_pn.hpm_num_parts].mean(),
+                    triplet_loss[self.rgb_pn.hpm_num_parts:].mean()
+                ))
+            ))
             loss = losses.sum()
             loss.backward()
             self.optimizer.step()