A few miil weights naming tweaks to improve compat with model registry and filtering wildcards.

Author: rwightman

timm/models/mobilenetv3.py

@@ -39,10 +39,10 @@ def _cfg(url='', **kwargs):
     'mobilenetv3_large_100': _cfg(
         interpolation='bicubic',
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth'),
-    'mobilenetv3_large_100_1k_miil_78_0': _cfg(
+    'mobilenetv3_large_100_miil': _cfg(
         interpolation='bilinear', mean=(0, 0, 0), std=(1, 1, 1),
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mobilenetv3_large_100_1k_miil_78_0.pth'),
-    'mobilenetv3_large_100_in21k_miil': _cfg(
+    'mobilenetv3_large_100_miil_in21k': _cfg(
         interpolation='bilinear', mean=(0, 0, 0), std=(1, 1, 1),
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mobilenetv3_large_100_in21k_miil.pth', num_classes=11221),
     'mobilenetv3_small_075': _cfg(url=''),
@@ -374,20 +374,20 @@ def mobilenetv3_large_100(pretrained=False, **kwargs):
 
 
 @register_model
-def mobilenetv3_large_100_1k_miil(pretrained=False, **kwargs):
+def mobilenetv3_large_100_miil(pretrained=False, **kwargs):
     """ MobileNet V3
     Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
     """
-    model = _gen_mobilenet_v3('mobilenetv3_large_100_1k_miil_78_0', 1.0, pretrained=pretrained, **kwargs)
+    model = _gen_mobilenet_v3('mobilenetv3_large_100_miil', 1.0, pretrained=pretrained, **kwargs)
     return model
 
 
 @register_model
-def mobilenetv3_large_100_in21k_miil(pretrained=False, **kwargs):
+def mobilenetv3_large_100_miil_in21k(pretrained=False, **kwargs):
     """ MobileNet V3, 21k pretraining
     Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
     """
-    model = _gen_mobilenet_v3('mobilenetv3_large_100_in21k_miil', 1.0, pretrained=pretrained, **kwargs)
+    model = _gen_mobilenet_v3('mobilenetv3_large_100_miil_in21k', 1.0, pretrained=pretrained, **kwargs)
     return model
 
 

timm/models/tresnet.py

@@ -33,7 +33,7 @@ def _cfg(url='', **kwargs):
 default_cfgs = {
     'tresnet_m': _cfg(
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/tresnet_m_1k_miil_83_1.pth'),
-    'tresnet_m_in21k_miil': _cfg(
+    'tresnet_m_miil_in21k': _cfg(
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/tresnet_m_miil_in21k.pth', num_classes=11221),
     'tresnet_l': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/tresnet_l_81_5-235b486c.pth'),
@@ -266,10 +266,12 @@ def tresnet_m(pretrained=False, **kwargs):
     model_kwargs = dict(layers=[3, 4, 11, 3], **kwargs)
     return _create_tresnet('tresnet_m', pretrained=pretrained, **model_kwargs)
 
+
 @register_model
-def tresnet_m_in21k_miil(pretrained=False, **kwargs):
+def tresnet_m_miil_in21k(pretrained=False, **kwargs):
     model_kwargs = dict(layers=[3, 4, 11, 3], **kwargs)
-    return _create_tresnet('tresnet_m_in21k_miil', pretrained=pretrained, **model_kwargs)
+    return _create_tresnet('tresnet_m_miil_in21k', pretrained=pretrained, **model_kwargs)
+
 
 @register_model
 def tresnet_l(pretrained=False, **kwargs):

timm/models/vision_transformer.py

@@ -120,11 +120,11 @@ def _cfg(url='', **kwargs):
         input_size=(3, 384, 384), crop_pct=1.0, classifier=('head', 'head_dist')),
 
     # ViT ImageNet-21K-P pretraining
-    'vit_base_patch16_224_in21k_miil': _cfg(
+    'vit_base_patch16_224_miil_in21k': _cfg(
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/vit_base_patch16_224_in21k_miil.pth',
         mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
     ),
-    'vit_base_patch16_224_1k_miil': _cfg(
+    'vit_base_patch16_224_miil': _cfg(
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm'
             '/vit_base_patch16_224_1k_miil_84_4.pth',
         mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear',
@@ -699,20 +699,22 @@ def vit_deit_base_distilled_patch16_384(pretrained=False, **kwargs):
         'vit_deit_base_distilled_patch16_384', pretrained=pretrained, distilled=True, **model_kwargs)
     return model
 
+
 @register_model
-def vit_base_patch16_224_in21k_miil(pretrained=False, **kwargs):
+def vit_base_patch16_224_miil_in21k(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
     Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
     """
     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
-    model = _create_vision_transformer('vit_base_patch16_224_in21k_miil', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_base_patch16_224_miil_in21k', pretrained=pretrained, **model_kwargs)
     return model
 
+
 @register_model
-def vit_base_patch16_224_1k_miil(pretrained=False, **kwargs):
+def vit_base_patch16_224_miil(pretrained=False, **kwargs):
     """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
     Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
     """
     model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
-    model = _create_vision_transformer('vit_base_patch16_224_1k_miil', pretrained=pretrained, **model_kwargs)
+    model = _create_vision_transformer('vit_base_patch16_224_miil', pretrained=pretrained, **model_kwargs)
     return model