Mambaout tweaks

timm/models/mambaout.py

@@ -12,7 +12,7 @@
 from torch import nn
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
-from timm.layers import trunc_normal_, DropPath, LayerNorm, LayerScale, ClNormMlpClassifierHead
+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
@@ -318,10 +318,12 @@ def __init__(
         super().__init__()
         self.num_classes = num_classes
         self.drop_rate = drop_rate
+        self.output_fmt = 'NHWC'
         if not isinstance(depths, (list, tuple)):
             depths = [depths]  # it means the model has only one stage
         if not isinstance(dims, (list, tuple)):
             dims = [dims]
+        act_layer = get_act_layer(act_layer)
 
         num_stage = len(depths)
         self.num_stage = num_stage
@@ -456,7 +458,7 @@ def checkpoint_filter_fn(state_dict, model):
 def _cfg(url='', **kwargs):
     return {
         'url': url,
-        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
         'crop_pct': 1.0, 'interpolation': 'bicubic',
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'classifier': 'head.fc',
         **kwargs
@@ -477,6 +479,7 @@ def _cfg(url='', **kwargs):
     '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)),
 }
 
 
@@ -554,9 +557,26 @@ def mambaout_base_plus_rw(pretrained=False, **kwargs):
         depths=(3, 4, 27, 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))
+
+
+@register_model
+def test_mambaout(pretrained=False, **kwargs):
+    model_args = dict(
+        depths=(1, 1, 3, 1),
+        dims=(16, 32, 48, 64),
+        expansion_ratio=3,
+        stem_mid_norm=False,
+        downsample='conv_nf',
+        ls_init_value=1e-4,
+        act_layer='silu',
+        head_fn='norm_mlp',
+    )
+    return _create_mambaout('test_mambaout', pretrained=pretrained, **dict(model_args, **kwargs))