diff --git a/tests/test_models.py b/tests/test_models.py
index f06ddd95..6489892c 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -202,13 +202,15 @@ def test_model_default_cfgs_non_std(model_name, batch_size):
         pytest.skip("Fixed input size model > limit.")
 
     input_tensor = torch.randn((batch_size, *input_size))
+    feat_dim = getattr(model, 'feature_dim', None)
 
     outputs = model.forward_features(input_tensor)
     if isinstance(outputs, (tuple, list)):
         # cannot currently verify multi-tensor output.
         pass
     else:
-        feat_dim = -1 if outputs.ndim == 3 else 1
+        if feat_dim is None:
+            feat_dim = -1 if outputs.ndim == 3 else 1
         assert outputs.shape[feat_dim] == model.num_features
 
     # test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
@@ -216,14 +218,16 @@ def test_model_default_cfgs_non_std(model_name, batch_size):
     outputs = model.forward(input_tensor)
     if isinstance(outputs,  (tuple, list)):
         outputs = outputs[0]
-    feat_dim = -1 if outputs.ndim == 3 else 1
+    if feat_dim is None:
+        feat_dim = -1 if outputs.ndim == 3 else 1
     assert outputs.shape[feat_dim] == model.num_features, 'pooled num_features != config'
 
     model = create_model(model_name, pretrained=False, num_classes=0).eval()
     outputs = model.forward(input_tensor)
     if isinstance(outputs, (tuple, list)):
         outputs = outputs[0]
-    feat_dim = -1 if outputs.ndim == 3 else 1
+    if feat_dim is None:
+        feat_dim = -1 if outputs.ndim == 3 else 1
     assert outputs.shape[feat_dim] == model.num_features
 
     # check classifier name matches default_cfg
diff --git a/timm/models/sequencer.py b/timm/models/sequencer.py
index 5fff04d1..b1ae92a4 100644
--- a/timm/models/sequencer.py
+++ b/timm/models/sequencer.py
@@ -288,6 +288,7 @@ class Sequencer2D(nn.Module):
         self.num_classes = num_classes
         self.global_pool = global_pool
         self.num_features = embed_dims[-1]  # num_features for consistency with other models
+        self.feature_dim = -1  # channel dim index for feature outputs (rank 4, NHWC)
         self.embed_dims = embed_dims
         self.stem = PatchEmbed(
             img_size=img_size, patch_size=patch_sizes[0], in_chans=in_chans,
@@ -333,7 +334,7 @@ class Sequencer2D(nn.Module):
 
     def reset_classifier(self, num_classes, global_pool=None):
         self.num_classes = num_classes
-        if self.global_pool is not None:
+        if global_pool is not None:
             assert global_pool in ('', 'avg')
             self.global_pool = global_pool
         self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()