Added STNet-prepended TransCrowd GAP

model/stn.py

@@ -1,4 +1,6 @@
 # stole from pytorch tutorial
+# "Great artists steal" -- they say,
+# but thieves also steal so you know :P
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -36,7 +38,7 @@ class STNet(nn.Module):
 
         # (3.2) Regressor for the 3 * 2 affine matrix
         self.fc_loc = nn.Sequential(
-            # XXX: Should
+            # XXX: Dimensionality reduction across channels or not?
             nn.Linear(np.prod(self._loc_net_out_shape), 32),
             nn.ReLU(True),
             nn.Linear(32, 3 * 2)

model/transcrowd_gap.py

@@ -20,7 +20,9 @@ from timm.models.vision_transformer import VisionTransformer, _cfg
 from timm.models.registry import register_model
 from timm.models.layers import trunc_normal_
 
-class VisionTransformerGAPwithFeatureMap(VisionTransformer):
+from .stn import STNet
+
+class VisionTransformerGAP(VisionTransformer):
     # [XXX] It might be a bad idea to use vision transformer for small datasets.
     # ref: ViT paper -- "transformers lack some of the inductive biases inherent
     # to CNNs, such as translation equivariance and locality".
@@ -93,3 +95,13 @@ class VisionTransformerGAPwithFeatureMap(VisionTransformer):
                                             # Resized to ???
         x = self.output1(x)                 # Regression head
         return x
+
+
+class STNet_VisionTransformerGAP(VisionTransformerGAP):
+    def __init__(self, img_shape: torch.Size, *args, **kwargs):
+        super(STNet_VisionTransformerGAP, self).__init__(*args, **kwargs)
+        self.stnet = STNet(img_shape)
+
+    def forward(self, x):
+        x = self.stnet(x)
+        return super(STNet_VisionTransformerGAP, self).forward(x)