Add initial weights for my first 3 naflexvit_base models

timm/models/naflexvit.py

@@ -532,7 +532,11 @@ def _apply_learned_pos_embed(
             pos_embed_flat = self.pos_embed.reshape(1, orig_h * orig_w, -1)
         else:
             # Resize if needed - directly using F.interpolate
-            _interp_size = to_2tuple(max(grid_size)) if self.pos_embed_ar_preserving else grid_size
+            if self.pos_embed_ar_preserving:
+                L = max(grid_size)
+                _interp_size = L, L
+            else:
+                _interp_size = grid_size
             pos_embed_flat = F.interpolate(
                 self.pos_embed.permute(0, 3, 1, 2).float(),  # B,C,H,W
                 size=_interp_size,
@@ -968,7 +972,7 @@ def __init__(
             cfg: Model configuration. If None, uses default NaFlexVitCfg.
             in_chans: Number of input image channels.
             num_classes: Number of classification classes.
-            img_size: Input image size for backwards compatibility.
+            img_size: Input image size (for backwards compatibility with classic vit).
             **kwargs: Additional config parameters to override cfg values.
         """
         super().__init__()
@@ -1523,9 +1527,9 @@ def _cfg(url: str = '', **kwargs) -> Dict[str, Any]:
     return {
         'url': url,
         'num_classes': 1000,
-        'input_size': (3, 256, 256),
+        'input_size': (3, 384, 384),
         'pool_size': None,
-        'crop_pct': 0.95,
+        'crop_pct': 1.0,
         'interpolation': 'bicubic',
         'mean': IMAGENET_INCEPTION_MEAN,
         'std': IMAGENET_INCEPTION_STD,
@@ -1537,11 +1541,19 @@ def _cfg(url: str = '', **kwargs) -> Dict[str, Any]:
 
 
 default_cfgs = generate_default_cfgs({
-    'naflexvit_base_patch16_gap': _cfg(),
-    'naflexvit_base_patch16_map': _cfg(),
-
-    'naflexvit_base_patch16_siglip': _cfg(),
-    'naflexvit_so400m_patch16_siglip': _cfg(),
+    'naflexvit_base_patch16_gap.e300_s576_in1k': _cfg(
+        hf_hub_id='timm/',
+    ),
+    'naflexvit_base_patch16_par_gap.e300_s576_in1k': _cfg(
+        hf_hub_id='timm/',
+    ),
+    'naflexvit_base_patch16_parfac_gap.e300_s576_in1k': _cfg(
+        hf_hub_id='timm/',
+    ),
+    'naflexvit_base_patch16_map.untrained': _cfg(),
+
+    'naflexvit_base_patch16_siglip.untrained': _cfg(),
+    'naflexvit_so400m_patch16_siglip.untrained': _cfg(),
 })
 
 
@@ -1623,6 +1635,45 @@ def naflexvit_base_patch16_gap(pretrained: bool = False, **kwargs) -> NaFlexVit:
     return model
 
 
+@register_model
+def naflexvit_base_patch16_par_gap(pretrained: bool = False, **kwargs) -> NaFlexVit:
+    """ViT-Base with NaFlex functionality, aspect preserving pos embed, global average pooling.
+    """
+    cfg = NaFlexVitCfg(
+        patch_size=16,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        init_values=1e-5,
+        pos_embed_ar_preserving=True,
+        global_pool='avg',
+        reg_tokens=4,
+        fc_norm=True,
+    )
+    model = _create_naflexvit('naflexvit_base_patch16_par_gap', pretrained=pretrained, cfg=cfg, **kwargs)
+    return model
+
+
+@register_model
+def naflexvit_base_patch16_parfac_gap(pretrained: bool = False, **kwargs) -> NaFlexVit:
+    """ViT-Base with NaFlex functionality, aspect preserving & factorized pos embed, global average pooling.
+    """
+    cfg = NaFlexVitCfg(
+        patch_size=16,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        init_values=1e-5,
+        pos_embed_ar_preserving=True,
+        pos_embed='factorized',
+        global_pool='avg',
+        reg_tokens=4,
+        fc_norm=True,
+    )
+    model = _create_naflexvit('naflexvit_base_patch16_parfac_gap', pretrained=pretrained, cfg=cfg, **kwargs)
+    return model
+
+
 @register_model
 def naflexvit_base_patch16_map(pretrained: bool = False, **kwargs) -> NaFlexVit:
     """ViT-Base with NaFlex functionality and MAP attention pooling.