Add mambaout builder support, pretrained weight remap

timm/models/mambaout.py

@@ -5,17 +5,16 @@ timm (https://github.com/rwightman/pytorch-image-models),
 MetaFormer (https://github.com/sail-sg/metaformer),
 InceptionNeXt (https://github.com/sail-sg/inceptionnext)
 """
-from functools import partial
 from typing import Optional
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
+
-from timm.models.layers import trunc_normal_, DropPath, LayerNorm
-from .vision_transformer import LayerScale
-from ._manipulate import checkpoint_seq
-from timm.models.registry import register_model
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from timm.layers import trunc_normal_, DropPath, LayerNorm, LayerScale
+from ._builder import build_model_with_cfg
+from ._manipulate import checkpoint_seq
+from ._registry import register_model
 
 
 class Stem(nn.Module):
@@ -275,6 +274,7 @@ class MambaOut(nn.Module):
             act_layer=nn.GELU,
             conv_ratio=1.0,
             kernel_size=7,
+            stem_mid_norm=True,
             ls_init_value=None,
             drop_path_rate=0.,
             drop_rate=0.,
@@ -293,7 +293,13 @@ class MambaOut(nn.Module):
         num_stage = len(depths)
         self.num_stage = num_stage
 
-        self.stem = Stem(in_chans, dims[0], act_layer=act_layer, norm_layer=norm_layer)
+        self.stem = Stem(
+            in_chans,
+            dims[0],
+            mid_norm=stem_mid_norm,
+            act_layer=act_layer,
+            norm_layer=norm_layer,
+        )
         prev_dim = dims[0]
         dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
         self.stages = nn.ModuleList()
@@ -338,7 +344,7 @@ class MambaOut(nn.Module):
             x = s(x)
         return x
 
-    def forward_head(self, x):
+    def forward_head(self, x, pre_logits: bool = False):
         x = x.mean((1, 2))
         x = self.norm(x)
         x = self.head(x)
@@ -350,6 +356,21 @@ class MambaOut(nn.Module):
         return x
 
 
+def checkpoint_filter_fn(state_dict, model):
+    if 'model' in state_dict:
+        state_dict = state_dict['model']
+
+    import re
+    out_dict = {}
+    for k, v in state_dict.items():
+        k = k.replace('downsample_layers.0.', 'stem.')
+        k = re.sub(r'stages.([0-9]+).([0-9]+)', r'stages.\1.blocks.\2', k)
+        k = re.sub(r'downsample_layers.([0-9]+)', r'stages.\1.downsample', k)
+        out_dict[k] = v
+
+    return out_dict
+
+
 def _cfg(url='', **kwargs):
     return {
         'url': url,
@@ -376,105 +397,63 @@ default_cfgs = {
 }
 
 
+def _create_mambaout(variant, pretrained=False, **kwargs):
+    model = build_model_with_cfg(
+        MambaOut, variant, pretrained,
+        pretrained_filter_fn=checkpoint_filter_fn,
+        feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
+        **kwargs,
+    )
+    return model
+
+
 # a series of MambaOut models
 @register_model
 def mambaout_femto(pretrained=False, **kwargs):
-    model = MambaOut(
+    model_args = dict(depths=(3, 3, 9, 3), dims=(48, 96, 192, 288))
-        depths=[3, 3, 9, 3],
+    return _create_mambaout('mambaout_femto', pretrained=pretrained, **dict(model_args, **kwargs))
-        dims=[48, 96, 192, 288],
-        **kwargs)
-    model.default_cfg = default_cfgs['mambaout_femto']
-    if pretrained:
-        state_dict = torch.hub.load_state_dict_from_url(
-            url=model.default_cfg['url'], map_location="cpu", check_hash=True)
-        model.load_state_dict(state_dict)
-    return model
-
 
 # Kobe Memorial Version with 24 Gated CNN blocks
 @register_model
 def mambaout_kobe(pretrained=False, **kwargs):
-    model = MambaOut(
+    model_args = dict(depths=[3, 3, 15, 3], dims=[48, 96, 192, 288])
-        depths=[3, 3, 15, 3],
+    return _create_mambaout('mambaout_kobe', pretrained=pretrained, **dict(model_args, **kwargs))
-        dims=[48, 96, 192, 288],
-        **kwargs)
-    model.default_cfg = default_cfgs['mambaout_kobe']
-    if pretrained:
-        state_dict = torch.hub.load_state_dict_from_url(
-            url=model.default_cfg['url'], map_location="cpu", check_hash=True)
-        model.load_state_dict(state_dict)
-    return model
-
 
 @register_model
 def mambaout_tiny(pretrained=False, **kwargs):
-    model = MambaOut(
+    model_args = dict(depths=[3, 3, 9, 3], dims=[96, 192, 384, 576])
-        depths=[3, 3, 9, 3],
+    return _create_mambaout('mambaout_tiny', pretrained=pretrained, **dict(model_args, **kwargs))
-        dims=[96, 192, 384, 576],
-        **kwargs)
-    model.default_cfg = default_cfgs['mambaout_tiny']
-    if pretrained:
-        state_dict = torch.hub.load_state_dict_from_url(
-            url=model.default_cfg['url'], map_location="cpu", check_hash=True)
-        model.load_state_dict(state_dict)
-    return model
 
 
 @register_model
 def mambaout_small(pretrained=False, **kwargs):
-    model = MambaOut(
+    model_args = dict(depths=[3, 4, 27, 3], dims=[96, 192, 384, 576])
-        depths=[3, 4, 27, 3],
+    return _create_mambaout('mambaout_small', pretrained=pretrained, **dict(model_args, **kwargs))
-        dims=[96, 192, 384, 576],
-        **kwargs)
-    model.default_cfg = default_cfgs['mambaout_small']
-    if pretrained:
-        state_dict = torch.hub.load_state_dict_from_url(
-            url=model.default_cfg['url'], map_location="cpu", check_hash=True)
-        model.load_state_dict(state_dict)
-    return model
 
 
 @register_model
 def mambaout_base(pretrained=False, **kwargs):
-    model = MambaOut(
+    model_args = dict(depths=[3, 4, 27, 3], dims=[128, 256, 512, 768])
-        depths=[3, 4, 27, 3],
+    return _create_mambaout('mambaout_base', pretrained=pretrained, **dict(model_args, **kwargs))
-        dims=[128, 256, 512, 768],
-        **kwargs)
-    model.default_cfg = default_cfgs['mambaout_base']
-    if pretrained:
-        state_dict = torch.hub.load_state_dict_from_url(
-            url=model.default_cfg['url'], map_location="cpu", check_hash=True)
-        model.load_state_dict(state_dict)
-    return model
 
 
 @register_model
 def mambaout_small_rw(pretrained=False, **kwargs):
-    model = MambaOut(
+    model_args = dict(
         depths=[3, 4, 27, 3],
         dims=[96, 192, 384, 576],
+        stem_mid_norm=False,
         ls_init_value=1e-6,
-        **kwargs,
     )
-    model.default_cfg = default_cfgs['mambaout_small']
+    return _create_mambaout('mambaout_small_rw', pretrained=pretrained, **dict(model_args, **kwargs))
-    if pretrained:
-        state_dict = torch.hub.load_state_dict_from_url(
-            url=model.default_cfg['url'], map_location="cpu", check_hash=True)
-        model.load_state_dict(state_dict)
-    return model
 
 
 @register_model
 def mambaout_base_rw(pretrained=False, **kwargs):
-    model = MambaOut(
+    model_args = dict(
         depths=(3, 4, 27, 3),
         dims=(128, 256, 512, 768),
+        stem_mid_norm=False,
         ls_init_value=1e-6,
-        **kwargs
     )
-    model.default_cfg = default_cfgs['mambaout_base']
+    return _create_mambaout('mambaout_base_rw', pretrained=pretrained, **dict(model_args, **kwargs))
-    if pretrained:
-        state_dict = torch.hub.load_state_dict_from_url(
-            url=model.default_cfg['url'], map_location="cpu", check_hash=True)
-        model.load_state_dict(state_dict)
-    return model