From 99ddd7c142a4ec97cb8bd14b204651790b3cf4ee Mon Sep 17 00:00:00 2001
From: Jordan Gong <jordan.gong@protonmail.com>
Date: Mon, 8 Feb 2021 18:11:25 +0800
Subject: Code refactoring, modifications and new features

1. Decode features outside of auto-encoder
2. Turn off HPM 1x1 conv by default
3. Change canonical feature map size from `feature_channels * 8 x 4 x 2` to `feature_channels * 2 x 16 x 8`
4. Use mean of canonical embeddings instead of mean of static features
5. Calculate static and dynamic loss separately
6. Calculate mean of parts in triplet loss instead of sum of parts
7. Add switch to log disentangled images
8. Change default configuration
---
 models/hpm.py | 20 ++++++++------------
 1 file changed, 8 insertions(+), 12 deletions(-)

(limited to 'models/hpm.py')

diff --git a/models/hpm.py b/models/hpm.py
index 66503e3..9879cfb 100644
--- a/models/hpm.py
+++ b/models/hpm.py
@@ -9,14 +9,16 @@ 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 = 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.use_avg_pool = use_avg_pool
         self.use_max_pool = use_max_pool
@@ -29,6 +31,7 @@ class HorizontalPyramidMatching(nn.Module):
         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)
@@ -37,23 +40,16 @@ class HorizontalPyramidMatching(nn.Module):
         return pyramid
 
     def forward(self, x):
-        # Flatten canonical features in all batches
-        t, n, c, h, w = x.size()
-        x = x.view(t * n, c, h, w)
-
+        n, c, h, w = x.size()
         feature = []
-        for pyramid_index, pyramid in enumerate(self.pyramids):
-            h_per_hpp = h // self.scales[pyramid_index]
+        for scale, pyramid in zip(self.scales, self.pyramids):
+            h_per_hpp = h // scale
             for hpp_index, hpp in enumerate(pyramid):
                 h_filter = torch.arange(hpp_index * h_per_hpp,
                                         (hpp_index + 1) * h_per_hpp)
                 x_slice = x[:, :, h_filter, :]
                 x_slice = hpp(x_slice)
-                x_slice = x_slice.view(t * n, -1)
+                x_slice = x_slice.view(n, -1)
                 feature.append(x_slice)
         x = torch.stack(feature)
-
-        # Unfold frames to original batch
-        p, _, c = x.size()
-        x = x.view(p, t, n, c)
         return x
-- 
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