Fix a few missed model deprecations and one missed pretrained cfg

timm/models/efficientnet.py

@@ -1441,12 +1441,6 @@ def mnasnet_100(pretrained=False, **kwargs) -> EfficientNet:
     return model
 
 
-@register_model
-def mnasnet_b1(pretrained=False, **kwargs) -> EfficientNet:
-    """ MNASNet B1, depth multiplier of 1.0. """
-    return mnasnet_100(pretrained, **kwargs)
-
-
 @register_model
 def mnasnet_140(pretrained=False, **kwargs) -> EfficientNet:
     """ MNASNet B1,  depth multiplier of 1.4 """
@@ -1475,12 +1469,6 @@ def semnasnet_100(pretrained=False, **kwargs) -> EfficientNet:
     return model
 
 
-@register_model
-def mnasnet_a1(pretrained=False, **kwargs) -> EfficientNet:
-    """ MNASNet A1 (w/ SE), depth multiplier of 1.0. """
-    return semnasnet_100(pretrained, **kwargs)
-
-
 @register_model
 def semnasnet_140(pretrained=False, **kwargs) -> EfficientNet:
     """ MNASNet A1 (w/ SE), depth multiplier of 1.4. """
@@ -1590,13 +1578,6 @@ def efficientnet_b2(pretrained=False, **kwargs) -> EfficientNet:
     return model
 
 
-@register_model
-def efficientnet_b2a(pretrained=False, **kwargs) -> EfficientNet:
-    """ EfficientNet-B2 @ 288x288 w/ 1.0 test crop"""
-    # WARN this model def is deprecated, different train/test res + test crop handled by default_cfg now
-    return efficientnet_b2(pretrained=pretrained, **kwargs)
-
-
 @register_model
 def efficientnet_b3(pretrained=False, **kwargs) -> EfficientNet:
     """ EfficientNet-B3 """
@@ -1606,13 +1587,6 @@ def efficientnet_b3(pretrained=False, **kwargs) -> EfficientNet:
     return model
 
 
-@register_model
-def efficientnet_b3a(pretrained=False, **kwargs) -> EfficientNet:
-    """ EfficientNet-B3 @ 320x320 w/ 1.0 test crop-pct """
-    # WARN this model def is deprecated, different train/test res + test crop handled by default_cfg now
-    return efficientnet_b3(pretrained=pretrained, **kwargs)
-
-
 @register_model
 def efficientnet_b4(pretrained=False, **kwargs) -> EfficientNet:
     """ EfficientNet-B4 """
@@ -2361,4 +2335,8 @@ def tinynet_e(pretrained=False, **kwargs) -> EfficientNet:
     'tf_efficientnetv2_m_in21k': 'tf_efficientnetv2_m.in21k',
     'tf_efficientnetv2_l_in21k': 'tf_efficientnetv2_l.in21k',
     'tf_efficientnetv2_xl_in21k': 'tf_efficientnetv2_xl.in21k',
+    'efficientnet_b2a': 'efficientnet_b2',
+    'efficientnet_b3a': 'efficientnet_b3',
+    'mnasnet_a1': 'semnasnet_100',
+    'mnasnet_b1': 'mnasnet_100',
 })

timm/models/resnet.py

@@ -1116,6 +1116,7 @@ def _gcfg(url='', **kwargs):
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnetblur50-84f4748f.pth'),
     'resnetblur50d.untrained': _ttcfg(first_conv='conv1.0'),
     'resnetblur101d.untrained': _ttcfg(first_conv='conv1.0'),
+    'resnetaa34d.untrained': _ttcfg(first_conv='conv1.0'),
     'resnetaa50.a1h_in1k': _rcfg(
         hf_hub_id='timm/',
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnetaa50_a1h-4cf422b3.pth'),
@@ -1730,15 +1731,6 @@ def seresnext26t_32x4d(pretrained: bool = False, **kwargs) -> ResNet:
     return _create_resnet('seresnext26t_32x4d', pretrained, **dict(model_args, **kwargs))
 
 
-@register_model
-def seresnext26tn_32x4d(pretrained: bool = False, **kwargs) -> ResNet:
-    """Constructs a SE-ResNeXt-26-T model.
-    NOTE I deprecated previous 't' model defs and replaced 't' with 'tn', this was the only tn model of note
-    so keeping this def for backwards compat with any uses out there. Old 't' model is lost.
-    """
-    return seresnext26t_32x4d(pretrained=pretrained, **kwargs)
-
-
 @register_model
 def seresnext50_32x4d(pretrained: bool = False, **kwargs) -> ResNet:
     model_args = dict(
@@ -2028,4 +2020,5 @@ def resnetrs420(pretrained: bool = False, **kwargs) -> ResNet:
     'gluon_seresnext101_32x4d': 'seresnext101_32x4d.gluon_in1k',
     'gluon_seresnext101_64x4d': 'seresnext101_64x4d.gluon_in1k',
     'gluon_senet154': 'senet154.gluon_in1k',
+    'seresnext26tn_32x4d': 'seresnext26t_32x4d',
 })