Feature: add option global_pool='max' to VisionTransformer

timm/models/vision_transformer.py

@@ -400,7 +400,7 @@ def __init__(
             patch_size: Union[int, Tuple[int, int]] = 16,
             in_chans: int = 3,
             num_classes: int = 1000,
-            global_pool: Literal['', 'avg', 'token', 'map'] = 'token',
+            global_pool: Literal['', 'avg', 'max', 'token', 'map'] = 'token',
             embed_dim: int = 768,
             depth: int = 12,
             num_heads: int = 12,
@@ -459,10 +459,10 @@ def __init__(
             block_fn: Transformer block layer.
         """
         super().__init__()
-        assert global_pool in ('', 'avg', 'token', 'map')
+        assert global_pool in ('', 'avg', 'max', 'token', 'map')
         assert class_token or global_pool != 'token'
         assert pos_embed in ('', 'none', 'learn')
-        use_fc_norm = global_pool == 'avg' if fc_norm is None else fc_norm
+        use_fc_norm = global_pool in ['avg', 'max'] if fc_norm is None else fc_norm
         norm_layer = get_norm_layer(norm_layer) or partial(nn.LayerNorm, eps=1e-6)
         act_layer = get_act_layer(act_layer) or nn.GELU
 
@@ -761,6 +761,8 @@ def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tenso
             x = self.attn_pool(x)
         elif self.global_pool == 'avg':
             x = x[:, self.num_prefix_tokens:].mean(dim=1)
+        elif self.global_pool == 'max':
+            x, _ = torch.max(x[:, self.num_prefix_tokens:], dim=1)
         elif self.global_pool:
             x = x[:, 0]  # class token
         x = self.fc_norm(x)