fix hard-coded strides

timm/models/visformer.py

@@ -167,14 +167,14 @@ class Visformer(nn.Module):
             self.patch_embed1 = PatchEmbed(
                 img_size=img_size, patch_size=patch_size, in_chans=in_chans,
                 embed_dim=embed_dim, norm_layer=embed_norm, flatten=False)
-            img_size = [x // 16 for x in img_size]
+            img_size = [x // patch_size for x in img_size]
         else:
             if self.init_channels is None:
                 self.stem = None
                 self.patch_embed1 = PatchEmbed(
                     img_size=img_size, patch_size=patch_size // 2, in_chans=in_chans,
                     embed_dim=embed_dim // 2, norm_layer=embed_norm, flatten=False)
-                img_size = [x // 8 for x in img_size]
+                img_size = [x // (patch_size // 2) for x in img_size]
             else:
                 self.stem = nn.Sequential(
                     nn.Conv2d(in_chans, self.init_channels, 7, stride=2, padding=3, bias=False),
@@ -185,7 +185,7 @@ class Visformer(nn.Module):
                 self.patch_embed1 = PatchEmbed(
                     img_size=img_size, patch_size=patch_size // 4, in_chans=self.init_channels,
                     embed_dim=embed_dim // 2, norm_layer=embed_norm, flatten=False)
-                img_size = [x // 4 for x in img_size]
+                img_size = [x // (patch_size // 4) for x in img_size]
 
         if self.pos_embed:
             if self.vit_stem:
@@ -207,7 +207,7 @@ class Visformer(nn.Module):
             self.patch_embed2 = PatchEmbed(
                 img_size=img_size, patch_size=patch_size // 8, in_chans=embed_dim // 2,
                 embed_dim=embed_dim, norm_layer=embed_norm, flatten=False)
-            img_size = [x // 2 for x in img_size]
+            img_size = [x // (patch_size // 8) for x in img_size]
             if self.pos_embed:
                 self.pos_embed2 = nn.Parameter(torch.zeros(1, embed_dim, *img_size))
         self.stage2 = nn.ModuleList([
@@ -224,7 +224,7 @@ class Visformer(nn.Module):
             self.patch_embed3 = PatchEmbed(
                 img_size=img_size, patch_size=patch_size // 8, in_chans=embed_dim,
                 embed_dim=embed_dim * 2, norm_layer=embed_norm, flatten=False)
-            img_size = [x // 2 for x in img_size]
+            img_size = [x // (patch_size // 8) for x in img_size]
             if self.pos_embed:
                 self.pos_embed3 = nn.Parameter(torch.zeros(1, embed_dim*2, *img_size))
         self.stage3 = nn.ModuleList([