rjdp
diff --git a/‎README.md
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diff --git a/‎inference.py
Lines changed: 3 additions & 3 deletions b/‎inference.py
Lines changed: 3 additions & 3 deletions
diff --git a/‎tests/test_models.py
Lines changed: 3 additions & 1 deletion b/‎tests/test_models.py
Lines changed: 3 additions & 1 deletion
diff --git a/‎timm/models/__init__.py
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@@ -26,9 +26,9 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
 ### May 14, 2021
 * Add EfficientNet-V2 official model defs w/ ported weights from official [Tensorflow/Keras](https://github.com/google/automl/tree/master/efficientnetv2) impl.
   * 1k trained variants: `tf_efficientnetv2_s/m/l`
-  * 21k trained variants: `tf_efficientnetv2_s/m/l_21k`
-  * 21k pretrained -> 1k fine-tuned: `tf_efficientnetv2_s/m/l_21ft1k`
-  * v2 models w/ v1 scaling: `tf_efficientnet_v2_b0` through `b3`
+  * 21k trained variants: `tf_efficientnetv2_s/m/l_in21k`
+  * 21k pretrained -> 1k fine-tuned: `tf_efficientnetv2_s/m/l_in21ft1k`
+  * v2 models w/ v1 scaling: `tf_efficientnetv2_b0` through `b3`
   * Rename my prev V2 guess `efficientnet_v2s` -> `efficientnetv2_rw_s`
   * Some blank `efficientnetv2_*` models in-place for future native PyTorch training
 
 
@@ -114,13 +114,13 @@ def main():
                 _logger.info('Predict: [{0}/{1}] Time {batch_time.val:.3f} ({batch_time.avg:.3f})'.format(
                     batch_idx, len(loader), batch_time=batch_time))
 
-    topk_ids = np.concatenate(topk_ids, axis=0).squeeze()
+    topk_ids = np.concatenate(topk_ids, axis=0)
 
     with open(os.path.join(args.output_dir, './topk_ids.csv'), 'w') as out_file:
         filenames = loader.dataset.filenames(basename=True)
         for filename, label in zip(filenames, topk_ids):
-            out_file.write('{0},{1},{2},{3},{4},{5}\n'.format(
-                filename, label[0], label[1], label[2], label[3], label[4]))
+            out_file.write('{0},{1}\n'.format(
+                filename, ','.join([ str(v) for v in label])))
 
 
 if __name__ == '__main__':
 
@@ -15,7 +15,9 @@
     torch._C._jit_set_profiling_mode(False)
 
 # transformer models don't support many of the spatial / feature based model functionalities
-NON_STD_FILTERS = ['vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', 'mixer_*', 'levit*', 'visformer*']
+NON_STD_FILTERS = [
+    'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
+    'convit_*', 'levit*', 'visformer*']
 NUM_NON_STD = len(NON_STD_FILTERS)
 
 # exclude models that cause specific test failures
 
@@ -2,6 +2,7 @@
 from .byobnet import *
 from .cait import *
 from .coat import *
+from .convit import *
 from .cspnet import *
 from .densenet import *
 from .dla import *
@@ -42,6 +43,7 @@
 from .vovnet import *
 from .xception import *
 from .xception_aligned import *
+from .twins import *
 
 from .factory import create_model, split_model_name, safe_model_name
 from .helpers import load_checkpoint, resume_checkpoint, model_parameters