Code refactoring

1. Separate FCs and triplet losses for HPM and PartNet
2. Remove FC-equivalent 1x1 conv layers in HPM
3. Support adjustable learning rate schedulers

1 files changed, 12 insertions, 13 deletions

diff --git a/models/hpm.py b/models/hpm.py
index 9879cfb..8186b20 100644
--- a/models/hpm.py
+++ b/models/hpm.py
@@ -9,32 +9,26 @@ class HorizontalPyramidMatching(nn.Module):
             self,
             in_channels: int,
             out_channels: int = 128,
-            use_1x1conv: bool = False,
             scales: tuple[int, ...] = (1, 2, 4),
             use_avg_pool: bool = True,
             use_max_pool: bool = False,
-            **kwargs
     ):
         super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.use_1x1conv = use_1x1conv
         self.scales = scales
+        self.num_parts = sum(scales)
         self.use_avg_pool = use_avg_pool
         self.use_max_pool = use_max_pool
 
         self.pyramids = nn.ModuleList([
-            self._make_pyramid(scale, **kwargs) for scale in self.scales
+            self._make_pyramid(scale) for scale in scales
         ])
+        self.fc_mat = nn.Parameter(
+            torch.empty(self.num_parts, in_channels, out_channels)
+        )
 
-    def _make_pyramid(self, scale: int, **kwargs):
+    def _make_pyramid(self, scale: int):
         pyramid = nn.ModuleList([
-            HorizontalPyramidPooling(self.in_channels,
-                                     self.out_channels,
-                                     use_1x1conv=self.use_1x1conv,
-                                     use_avg_pool=self.use_avg_pool,
-                                     use_max_pool=self.use_max_pool,
-                                     **kwargs)
+            HorizontalPyramidPooling(self.use_avg_pool, self.use_max_pool)
             for _ in range(scale)
         ])
         return pyramid
@@ -52,4 +46,9 @@ class HorizontalPyramidMatching(nn.Module):
                 x_slice = x_slice.view(n, -1)
                 feature.append(x_slice)
         x = torch.stack(feature)
+
+        # p, n, c
+        x = x @ self.fc_mat
+        # p, n, d
+
         return x