MambaOut weights on hub, configs finalized

timm/models/mambaout.py

@@ -15,7 +15,7 @@
 from timm.layers import trunc_normal_, DropPath, LayerNorm, LayerScale, ClNormMlpClassifierHead, get_act_layer
 from ._builder import build_model_with_cfg
 from ._manipulate import checkpoint_seq
-from ._registry import register_model
+from ._registry import register_model, generate_default_cfgs
 
 
 class Stem(nn.Module):
@@ -435,6 +435,8 @@ def forward(self, x):
 def checkpoint_filter_fn(state_dict, model):
     if 'model' in state_dict:
         state_dict = state_dict['model']
+    if 'stem.conv1.weight' in state_dict:
+        return state_dict
 
     import re
     out_dict = {}
@@ -458,30 +460,52 @@ def checkpoint_filter_fn(state_dict, model):
 def _cfg(url='', **kwargs):
     return {
         'url': url,
-        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
-        'crop_pct': 1.0, 'interpolation': 'bicubic',
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'test_input_size': (3, 288, 288),
+        'pool_size': (7, 7), 'crop_pct': 1.0, 'interpolation': 'bicubic',
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
         'first_conv': 'stem.conv1', 'classifier': 'head.fc',
         **kwargs
     }
 
 
-default_cfgs = {
-    'mambaout_femto': _cfg(
-        url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_femto.pth'),
-    'mambaout_kobe': _cfg(
-        url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_kobe.pth'),
-    'mambaout_tiny': _cfg(
-        url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_tiny.pth'),
-    'mambaout_small': _cfg(
-        url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_small.pth'),
-    'mambaout_base': _cfg(
-        url='https://github.com/yuweihao/MambaOut/releases/download/model/mambaout_base.pth'),
-    'mambaout_small_rw': _cfg(),
-    'mambaout_base_slim_rw': _cfg(),
-    'mambaout_base_plus_rw': _cfg(),
-    'test_mambaout': _cfg(input_size=(3, 160, 160), pool_size=(5, 5)),
-}
+default_cfgs = generate_default_cfgs({
+    # original weights
+    'mambaout_femto.in1k': _cfg(
+        hf_hub_id='timm/'),
+    'mambaout_kobe.in1k': _cfg(
+        hf_hub_id='timm/'),
+    'mambaout_tiny.in1k': _cfg(
+        hf_hub_id='timm/'),
+    'mambaout_small.in1k': _cfg(
+        hf_hub_id='timm/'),
+    'mambaout_base.in1k': _cfg(
+        hf_hub_id='timm/'),
+
+    # timm experiments below
+    'mambaout_small_rw.sw_e450_in1k': _cfg(
+        hf_hub_id='timm/',
+    ),
+    'mambaout_base_short_rw.sw_e500_in1k': _cfg(
+        hf_hub_id='timm/',
+        crop_pct=0.95, test_crop_pct=1.0,
+    ),
+    'mambaout_base_tall_rw.sw_e500_in1k': _cfg(
+        hf_hub_id='timm/',
+        crop_pct=0.95, test_crop_pct=1.0,
+    ),
+    'mambaout_base_wide_rw.sw_e500_in1k': _cfg(
+        hf_hub_id='timm/',
+        crop_pct=0.95, test_crop_pct=1.0,
+    ),
+    'mambaout_base_plus_rw.sw_e150_in12k_ft_in1k': _cfg(
+        hf_hub_id='timm/',
+    ),
+    'mambaout_base_plus_rw.sw_e150_in12k': _cfg(
+        hf_hub_id='timm/',
+        num_classes=11821,
+    ),
+    'test_mambaout': _cfg(input_size=(3, 160, 160), test_input_size=(3, 192, 192), pool_size=(5, 5)),
+})
 
 
 def _create_mambaout(variant, pretrained=False, **kwargs):
@@ -538,9 +562,24 @@ def mambaout_small_rw(pretrained=False, **kwargs):
 
 
 @register_model
-def mambaout_base_slim_rw(pretrained=False, **kwargs):
+def mambaout_base_short_rw(pretrained=False, **kwargs):
     model_args = dict(
-        depths=(3, 4, 27, 3),
+        depths=(3, 3, 25, 3),
+        dims=(128, 256, 512, 768),
+        expansion_ratio=3.0,
+        conv_ratio=1.25,
+        stem_mid_norm=False,
+        downsample='conv_nf',
+        ls_init_value=1e-6,
+        head_fn='norm_mlp',
+    )
+    return _create_mambaout('mambaout_base_short_rw', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+@register_model
+def mambaout_base_tall_rw(pretrained=False, **kwargs):
+    model_args = dict(
+        depths=(3, 4, 30, 3),
         dims=(128, 256, 512, 768),
         expansion_ratio=2.5,
         conv_ratio=1.25,
@@ -549,11 +588,11 @@ def mambaout_base_slim_rw(pretrained=False, **kwargs):
         ls_init_value=1e-6,
         head_fn='norm_mlp',
     )
-    return _create_mambaout('mambaout_base_slim_rw', pretrained=pretrained, **dict(model_args, **kwargs))
+    return _create_mambaout('mambaout_base_tall_rw', pretrained=pretrained, **dict(model_args, **kwargs))
 
 
 @register_model
-def mambaout_base_plus_rw(pretrained=False, **kwargs):
+def mambaout_base_wide_rw(pretrained=False, **kwargs):
     model_args = dict(
         depths=(3, 4, 27, 3),
         dims=(128, 256, 512, 768),
@@ -565,6 +604,22 @@ def mambaout_base_plus_rw(pretrained=False, **kwargs):
         act_layer='silu',
         head_fn='norm_mlp',
     )
+    return _create_mambaout('mambaout_base_wide_rw', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+@register_model
+def mambaout_base_plus_rw(pretrained=False, **kwargs):
+    model_args = dict(
+        depths=(3, 4, 30, 3),
+        dims=(128, 256, 512, 768),
+        expansion_ratio=3.0,
+        conv_ratio=1.5,
+        stem_mid_norm=False,
+        downsample='conv_nf',
+        ls_init_value=1e-6,
+        act_layer='silu',
+        head_fn='norm_mlp',
+    )
     return _create_mambaout('mambaout_base_plus_rw', pretrained=pretrained, **dict(model_args, **kwargs))