Correct feature dims after disentanglement and HPM backbone removal

1. Features used in HPM is decoded canonical embedding without transpose convolution
2. Decode pose embedding to image for Part Net
3. Backbone seems to be redundant, we can use feature map given by auto-decoder

1 files changed, 5 insertions, 17 deletions

diff --git a/models/hpm.py b/models/hpm.py
index 5553094..66503e3 100644
--- a/models/hpm.py
+++ b/models/hpm.py
@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-from torchvision.models import resnet50
 
 from models.layers import HorizontalPyramidPooling
 
@@ -8,12 +7,11 @@ from models.layers import HorizontalPyramidPooling
 class HorizontalPyramidMatching(nn.Module):
     def __init__(
             self,
-            in_channels: int = 3,
+            in_channels: int,
             out_channels: int = 128,
-            scales: tuple[int, ...] = (1, 2, 4, 8),
+            scales: tuple[int, ...] = (1, 2, 4),
             use_avg_pool: bool = True,
             use_max_pool: bool = True,
-            use_backbone: bool = False,
             **kwargs
     ):
         super().__init__()
@@ -22,11 +20,6 @@ class HorizontalPyramidMatching(nn.Module):
         self.scales = scales
         self.use_avg_pool = use_avg_pool
         self.use_max_pool = use_max_pool
-        self.use_backbone = use_backbone
-
-        if self.use_backbone:
-            self.backbone = resnet50(pretrained=True)
-            self.in_channels = self.backbone.layer4[-1].conv1.in_channels
 
         self.pyramids = nn.ModuleList([
             self._make_pyramid(scale, **kwargs) for scale in self.scales
@@ -44,15 +37,10 @@ class HorizontalPyramidMatching(nn.Module):
         return pyramid
 
     def forward(self, x):
-        # Flatten frames in all batches
+        # Flatten canonical features in all batches
         t, n, c, h, w = x.size()
-        x = x.view(-1, c, h, w)
-
-        if self.use_backbone:
-            # FIXME Inconsistent dimensions
-            x = self.backbone(x)
+        x = x.view(t * n, c, h, w)
 
-        t_n, _, h, _ = x.size()
         feature = []
         for pyramid_index, pyramid in enumerate(self.pyramids):
             h_per_hpp = h // self.scales[pyramid_index]
@@ -61,7 +49,7 @@ class HorizontalPyramidMatching(nn.Module):
                                         (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(t * n, -1)
                 feature.append(x_slice)
         x = torch.stack(feature)