Merge branch 'master' into data_parallel

# Conflicts:
#	models/model.py

1 files changed, 3 insertions, 17 deletions

diff --git a/models/rgb_part_net.py b/models/rgb_part_net.py
index 67acac3..408bca0 100644
--- a/models/rgb_part_net.py
+++ b/models/rgb_part_net.py
@@ -4,7 +4,6 @@ import torch.nn as nn
 from models.auto_encoder import AutoEncoder
 from models.hpm import HorizontalPyramidMatching
 from models.part_net import PartNet
-from utils.triplet_loss import BatchAllTripletLoss
 
 
 class RGBPartNet(nn.Module):
@@ -25,7 +24,6 @@ class RGBPartNet(nn.Module):
             tfa_squeeze_ratio: int = 4,
             tfa_num_parts: int = 16,
             embedding_dims: int = 256,
-            triplet_margins: tuple[float, float] = (0.2, 0.2),
             image_log_on: bool = False
     ):
         super().__init__()
@@ -50,17 +48,13 @@ class RGBPartNet(nn.Module):
                                out_channels, embedding_dims)
         self.fc_mat = nn.Parameter(empty_fc)
 
-        (hpm_margin, pn_margin) = triplet_margins
-        self.hpm_ba_trip = BatchAllTripletLoss(hpm_margin)
-        self.pn_ba_trip = BatchAllTripletLoss(pn_margin)
-
     def fc(self, x):
         return x @ self.fc_mat
 
-    def forward(self, x_c1, x_c2=None, y=None):
+    def forward(self, x_c1, x_c2=None):
         # Step 1: Disentanglement
         # n, t, c, h, w
-        ((x_c, x_p), losses, images) = self._disentangle(x_c1, x_c2)
+        ((x_c, x_p), ae_losses, images) = self._disentangle(x_c1, x_c2)
 
         # Step 2.a: Static Gait Feature Aggregation & HPM
         # n, c, h, w
@@ -77,15 +71,7 @@ class RGBPartNet(nn.Module):
         x = self.fc(x)
 
         if self.training:
-            y = y.T
-            hpm_ba_trip = self.hpm_ba_trip(
-                x[:self.hpm_num_parts], y[:self.hpm_num_parts]
-            )
-            pn_ba_trip = self.pn_ba_trip(
-                x[self.hpm_num_parts:], y[self.hpm_num_parts:]
-            )
-            losses = torch.stack((*losses, hpm_ba_trip, pn_ba_trip))
-            return losses, images
+            return x, ae_losses, images
         else:
             return x.unsqueeze(1).view(-1)