Merge branch 'master' into data_parallel

# Conflicts:
#	models/model.py

3 files changed, 106 insertions, 33 deletions

diff --git a/models/layers.py b/models/layers.py
index ef53a95..f1d72b6 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 a086e7b..2eeaf5e 100644
--- a/models/model.py
+++ b/models/model.py
@@ -18,7 +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
+from utils.triplet_loss import JointBatchTripletLoss, BatchTripletLoss
 
 
 class Model:
@@ -68,7 +68,7 @@ class Model:
         self._dataset_sig: str = 'undefined'
 
         self.rgb_pn: Optional[RGBPartNet] = None
-        self.ba_triplet_loss: Optional[JointBatchAllTripletLoss] = None
+        self.triplet_loss: Optional[JointBatchTripletLoss] = None
         self.optimizer: Optional[optim.Adam] = None
         self.scheduler: Optional[optim.lr_scheduler.StepLR] = None
         self.writer: Optional[SummaryWriter] = None
@@ -143,9 +143,10 @@ class Model:
         dataloader = self._parse_dataloader_config(dataset, dataloader_config)
         # Prepare for model, optimizer and scheduler
         model_hp: dict = self.hp.get('model', {}).copy()
-        triplet_margins = model_hp.pop('triplet_margins', (0.2, 0.2))
+        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)
         ae_optim_hp = optim_hp.pop('auto_encoder', {})
         pn_optim_hp = optim_hp.pop('part_net', {})
         hpm_optim_hp = optim_hp.pop('hpm', {})
@@ -153,28 +154,48 @@ 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
-        )
+        # Hard margins
+        if triplet_margins:
+            # Same margins
+            if triplet_margins[0] == triplet_margins[1]:
+                self.triplet_loss = BatchTripletLoss(
+                    triplet_is_hard, triplet_margins[0]
+                )
+            else:  # Different margins
+                self.triplet_loss = JointBatchTripletLoss(
+                    self.rgb_pn.hpm_num_parts,
+                    triplet_is_hard, triplet_is_mean, triplet_margins
+                )
+        else:  # Soft margins
+            self.triplet_loss = BatchTripletLoss(
+                triplet_is_hard, triplet_is_mean, None
+            )
+
+        num_pairs = (self.pr*self.k-1) * (self.pr*self.k) // 2
+        num_pos_pairs = (self.k*(self.k-1)//2) * self.pr
+
         # Try to accelerate computation using CUDA or others
         self.rgb_pn = nn.DataParallel(self.rgb_pn)
         self.rgb_pn = self.rgb_pn.to(self.device)
-        self.ba_triplet_loss = nn.DataParallel(self.ba_triplet_loss)
-        self.ba_triplet_loss = self.ba_triplet_loss.to(self.device)
+        self.triplet_loss = nn.DataParallel(self.triplet_loss)
+        self.triplet_loss = self.triplet_loss.to(self.device)
         self.optimizer = optim.Adam([
             {'params': self.rgb_pn.module.ae.parameters(), **ae_optim_hp},
             {'params': self.rgb_pn.module.pn.parameters(), **pn_optim_hp},
             {'params': self.rgb_pn.module.hpm.parameters(), **hpm_optim_hp},
             {'params': self.rgb_pn.module.fc_mat, **fc_optim_hp}
         ], **optim_hp)
-        sched_gamma = sched_hp.get('gamma', 0.9)
-        sched_step_size = sched_hp.get('step_size', 500)
+        sched_final_gamma = sched_hp.get('final_gamma', 0.001)
+        sched_start_step = sched_hp.get('start_step', 15_000)
+
+        def lr_lambda(epoch):
+            passed_step = epoch - sched_start_step
+            all_step = self.total_iter - sched_start_step
+            return sched_final_gamma ** (passed_step / all_step)
         self.scheduler = optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=[
-            lambda epoch: sched_gamma ** (epoch // sched_step_size),
-            lambda epoch: 0 if epoch < start_iter else 1,
-            lambda epoch: 0 if epoch < start_iter else 1,
-            lambda epoch: 0 if epoch < start_iter else 1,
+            lr_lambda, lr_lambda, lr_lambda, lr_lambda
         ])
+
         self.writer = SummaryWriter(self._log_name)
 
         self.rgb_pn.train()
@@ -194,7 +215,7 @@ class Model:
         running_loss = torch.zeros(5, device=self.device)
         print(f"{'Time':^8} {'Iter':^5} {'Loss':^6}",
               f"{'Xrecon':^8} {'CanoCons':^8} {'PoseSim':^8}",
-              f"{'BATripH':^8} {'BATripP':^8} {'LRs':^19}")
+              f"{'BATripH':^8} {'BATripP':^8} {'LR':^9}")
         for (batch_c1, batch_c2) in dataloader:
             self.curr_iter += 1
             # Zero the parameter gradients
@@ -202,16 +223,16 @@ 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)
+            embedding, ae_losses, images = self.rgb_pn(x_c1, x_c2)
             y = batch_c1['label'].to(self.device)
             # Duplicate labels for each part
             y = y.repeat(self.rgb_pn.num_total_parts, 1)
-            triplet_loss = self.ba_triplet_loss(feature, y)
+            trip_loss, dist, num_non_zero = self.triplet_loss(embedding, y)
             losses = torch.cat((
                 ae_losses.mean(0),
                 torch.stack((
-                    triplet_loss[:self.rgb_pn.hpm_num_parts].mean(),
-                    triplet_loss[self.rgb_pn.hpm_num_parts:].mean()
+                    trip_loss[:self.rgb_pn.hpm_num_parts].mean(),
+                    trip_loss[self.rgb_pn.hpm_num_parts:].mean()
                 ))
             ))
             loss = losses.sum()
@@ -222,20 +243,50 @@ class Model:
             running_loss += losses.detach()
             # Write losses to TensorBoard
             self.writer.add_scalar('Loss/all', loss, self.curr_iter)
-            self.writer.add_scalars('Loss/details', dict(zip([
+            self.writer.add_scalars('Loss/disentanglement', dict(zip((
                 'Cross reconstruction loss', 'Canonical consistency loss',
-                'Pose similarity loss', 'Batch All triplet loss (HPM)',
-                'Batch All triplet loss (PartNet)'
-            ], losses)), self.curr_iter)
+                'Pose similarity loss'
+            ), ae_losses)), self.curr_iter)
+            self.writer.add_scalars('Loss/triplet loss', {
+                'HPM': losses[3],
+                'PartNet': losses[4]
+            }, 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,
+                num_pos_pairs, 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,
+                num_pos_pairs, 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()
                 # Write learning rates
                 self.writer.add_scalar(
-                    'Learning rate/Auto-encoder', lrs[0], self.curr_iter
-                )
-                self.writer.add_scalar(
-                    'Learning rate/Others', lrs[1], self.curr_iter
+                    'Learning rate', lrs[0], self.curr_iter
                 )
                 # Write disentangled images
                 if self.image_log_on:
@@ -259,7 +310,7 @@ class Model:
                 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]))
+                      f'{lrs[0]:.3e}')
                 running_loss.zero_()
 
             # Step scheduler
@@ -278,6 +329,24 @@ 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],
@@ -317,6 +386,8 @@ class Model:
 
         # Init models
         model_hp: dict = self.hp.get('model', {}).copy()
+        model_hp.pop('triplet_is_hard', True)
+        model_hp.pop('triplet_is_mean', True)
         model_hp.pop('triplet_margins', None)
         self.rgb_pn = RGBPartNet(self.in_channels, self.in_size, **model_hp)
         # Try to accelerate computation using CUDA or others
diff --git a/models/rgb_part_net.py b/models/rgb_part_net.py
index 4367c62..8a0f3a7 100644
--- a/models/rgb_part_net.py
+++ b/models/rgb_part_net.py
@@ -79,7 +79,7 @@ class RGBPartNet(nn.Module):
             ((f_a_, f_c_, f_p_), losses) = self.ae(x_c1_t2, x_c1_t1, x_c2_t2)
             # Decode features
             x_c = self._decode_cano_feature(f_c_, n, t, device)
-            x_p_ = self._decode_pose_feature(f_p_, n, t, c, h, w, device)
+            x_p_ = self._decode_pose_feature(f_p_, n, t, device)
             x_p = x_p_.view(n, t, self.pn_in_channels, self.h // 4, self.w // 4)
 
             i_a, i_c, i_p = None, None, None
@@ -98,7 +98,7 @@ class RGBPartNet(nn.Module):
         else:  # evaluating
             f_c_, f_p_ = self.ae(x_c1_t2)
             x_c = self._decode_cano_feature(f_c_, n, t, device)
-            x_p_ = self._decode_pose_feature(f_p_, n, t, c, h, w, device)
+            x_p_ = self._decode_pose_feature(f_p_, n, t, device)
             x_p = x_p_.view(n, t, self.pn_in_channels, self.h // 4, self.w // 4)
             return (x_c, x_p), None, None
 
@@ -123,7 +123,7 @@ class RGBPartNet(nn.Module):
         )
         return x_c
 
-    def _decode_pose_feature(self, f_p_, n, t, c, h, w, device):
+    def _decode_pose_feature(self, f_p_, n, t, device):
         # Decode pose features to images
         x_p_ = self.ae.decoder(
             torch.zeros((n * t, self.f_a_dim), device=device),