From 19c9d11fcf1a7cd17ffa5bdcf37a820850e439ed Mon Sep 17 00:00:00 2001
From: Fredo Guan <fredo.guan@hotmail.com>
Date: Sat, 10 Dec 2022 21:27:19 -0800
Subject: [PATCH] Update davit.py

---
 timm/models/davit.py | 55 --------------------------------------------
 1 file changed, 55 deletions(-)

diff --git a/timm/models/davit.py b/timm/models/davit.py
index ff3442d6..cc924aa4 100644
--- a/timm/models/davit.py
+++ b/timm/models/davit.py
@@ -66,61 +66,6 @@ class ConvPosEnc(nn.Module):
             feat = feat.flatten(2).transpose(1, 2)
         x = x + self.activation(feat).transpose(1, 2).view(B, C, H, W)
         return x
-        
-
-@register_notrace_module
-class PatchEmbedOld(nn.Module):
-    """ Size-agnostic implementation of 2D image to patch embedding,
-        allowing input size to be adjusted during model forward operation
-    """
-
-    def __init__(
-            self,
-            patch_size=4,
-            in_chans=3,
-            embed_dim=96,
-            overlapped=False):
-        super().__init__()
-        patch_size = to_2tuple(patch_size)
-        self.patch_size = patch_size
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        if patch_size[0] == 4:
-            self.proj = nn.Conv2d(
-                in_chans,
-                embed_dim,
-                kernel_size=(7, 7),
-                stride=patch_size,
-                padding=(3, 3))
-            self.norm = nn.LayerNorm(embed_dim)
-        if patch_size[0] == 2:
-            kernel = 3 if overlapped else 2
-            pad = 1 if overlapped else 0
-            self.proj = nn.Conv2d(
-                in_chans,
-                embed_dim,
-                kernel_size=to_2tuple(kernel),
-                stride=patch_size,
-                padding=to_2tuple(pad))
-            self.norm = nn.LayerNorm(in_chans)
-
-    
-    def forward(self, x : Tensor):
-        B, C, H, W = x.shape
-        if self.norm.normalized_shape[0] == self.in_chans:
-            x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
-
-        if W % self.patch_size[1] != 0:
-            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
-        if H % self.patch_size[0] != 0:
-            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-
-        x = self.proj(x)
-
-        if self.norm.normalized_shape[0] == self.embed_dim:
-            x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
-        return x
 
 
 class PatchEmbed(nn.Module):