Fix rotary embed version of attn pool. Bit of cleanup/naming

Author: rwightman

timm/layers/attention_pool2d.py

@@ -42,7 +42,7 @@ def __init__(
             qkv_bias: bool = True,
             qkv_separate: bool = False,
             pool_type: str = 'token',
-            avg_token: bool = True,
+            class_token: bool = False,
             drop_rate: float = 0.,
     ):
         super().__init__()
@@ -63,6 +63,11 @@ def __init__(
         self.scale = self.head_dim ** -0.5
         self.fused_attn = use_fused_attn()
 
+        if class_token:
+            self.cls_token = nn.Parameter(torch.zeros(1, embed_dim))
+        else:
+            self.cls_token = None
+
         if qkv_separate:
             self.q = nn.Linear(in_features, embed_dim, bias=qkv_bias)
             self.k = nn.Linear(in_features, embed_dim, bias=qkv_bias)
@@ -109,7 +114,10 @@ def forward(self, x, pre_logits: bool = False):
         B, _, H, W = x.shape
         N = H * W
         x = x.flatten(2).transpose(1, 2)
-        x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
+        if self.cls_token is None:
+            x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
+        else:
+            x = torch.cat([self.cls_token.expand(x.shape[0], -1, -1), x], dim=1)
         if self.qkv is None:
             q = self.q(x).reshape(B, N + 1, self.num_heads, self.head_dim).transpose(1, 2)
             k = self.k(x).reshape(B, N + 1, self.num_heads, self.head_dim).transpose(1, 2)
@@ -130,7 +138,6 @@ def forward(self, x, pre_logits: bool = False):
             attn = attn.softmax(dim=-1)
             x = attn @ v
         x = x.transpose(1, 2).reshape(B, N + 1, -1)
-        x = x[:, 0]
         x = self.drop(x)
         if pre_logits:
             x = self._pool(x, H, W)
@@ -162,7 +169,7 @@ def __init__(
             qkv_bias: bool = True,
             qkv_separate: bool = False,
             pool_type: str = 'token',
-            learned_token: bool = False,
+            class_token: bool = False,
             drop_rate: float = 0.,
     ):
         super().__init__()
@@ -184,10 +191,10 @@ def __init__(
         self.scale = self.head_dim ** -0.5
         self.fused_attn = use_fused_attn()
 
-        if learned_token:
-            self.token = nn.Parameter(torch.zeros(1, embed_dim))
+        if class_token:
+            self.cls_token = nn.Parameter(torch.zeros(1, embed_dim))
         else:
-            self.token = None
+            self.cls_token = None
 
         if qkv_separate:
             self.q = nn.Linear(in_features, embed_dim, bias=qkv_bias)
@@ -239,10 +246,10 @@ def forward(self, x, pre_logits: bool = False):
         B, _, H, W = x.shape
         N = H * W
         x = x.flatten(2).transpose(1, 2)
-        if self.token is not None:
-            x = torch.cat([self.token.expand(x.shape[0], -1, -1), x], dim=1)
-        else:
+        if self.cls_token is None:
             x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
+        else:
+            x = torch.cat([self.cls_token.expand(x.shape[0], -1, -1), x], dim=1)
         pos_embed = resample_abs_pos_embed(self.pos_embed.unsqueeze(0), (H, W), num_prefix_tokens=1)
         x = x + pos_embed
 

timm/models/byobnet.py

@@ -1945,7 +1945,6 @@ def _init_weights(module, name='', zero_init_last=False):
         downsample='avg',
         aa_layer='avg',
         head_type='attn_abs',
-        #head_hidden_size=512,
     ),
 
     resnet50x4_clip=ByoModelCfg(
@@ -1962,7 +1961,6 @@ def _init_weights(module, name='', zero_init_last=False):
         downsample='avg',
         aa_layer='avg',
         head_type='attn_abs',
-        #head_hidden_size=640,
     ),
 
     resnet50x16_clip=ByoModelCfg(
@@ -1979,7 +1977,6 @@ def _init_weights(module, name='', zero_init_last=False):
         downsample='avg',
         aa_layer='avg',
         head_type='attn_abs',
-        #head_hidden_size=768,
     ),
 
     resnet50x64_clip=ByoModelCfg(
@@ -1996,7 +1993,6 @@ def _init_weights(module, name='', zero_init_last=False):
         downsample='avg',
         aa_layer='avg',
         head_type='attn_abs',
-        #head_hidden_size=1024,
     ),
 
     resnet50_mlp=ByoModelCfg(