fix test_model_default_cfgs

Author: Yusuke Uchida

tests/test_models.py

@@ -69,6 +69,7 @@ def test_model_backward(model_name, batch_size):
 
 @pytest.mark.timeout(120)
 @pytest.mark.parametrize('model_name', list_models())
+#@pytest.mark.parametrize('model_name', ["xception41"])
 @pytest.mark.parametrize('batch_size', [1])
 def test_model_default_cfgs(model_name, batch_size):
     """Run a single forward pass with each model"""
@@ -106,8 +107,8 @@ def test_model_default_cfgs(model_name, batch_size):
             assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
 
     # check classifier and first convolution names match those in default_cfg
-    assert any([k.startswith(classifier) for k in state_dict.keys()]), f'{classifier} not in model params'
-    assert any([k.startswith(first_conv) for k in state_dict.keys()]), f'{first_conv} not in model params'
+    assert classifier + ".weight" in state_dict.keys(), f'{classifier} not in model params'
+    assert first_conv + ".weight" in state_dict.keys(), f'{first_conv} not in model params'
 
 
 if 'GITHUB_ACTIONS' not in os.environ:

timm/models/gluon_resnet.py

@@ -28,22 +28,32 @@ def _cfg(url='', **kwargs):
     'gluon_resnet50_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1b-0ebe02e2.pth'),
     'gluon_resnet101_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1b-3b017079.pth'),
     'gluon_resnet152_v1b': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1b-c1edb0dd.pth'),
-    'gluon_resnet50_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1c-48092f55.pth'),
-    'gluon_resnet101_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1c-1f26822a.pth'),
-    'gluon_resnet152_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1c-a3bb0b98.pth'),
-    'gluon_resnet50_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth'),
-    'gluon_resnet101_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth'),
-    'gluon_resnet152_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth'),
-    'gluon_resnet50_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth'),
-    'gluon_resnet101_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth'),
-    'gluon_resnet152_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth'),
+    'gluon_resnet50_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1c-48092f55.pth',
+                               first_conv='conv1.0'),
+    'gluon_resnet101_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1c-1f26822a.pth',
+                                first_conv='conv1.0'),
+    'gluon_resnet152_v1c': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1c-a3bb0b98.pth',
+                                first_conv='conv1.0'),
+    'gluon_resnet50_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth',
+                               first_conv='conv1.0'),
+    'gluon_resnet101_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth',
+                                first_conv='conv1.0'),
+    'gluon_resnet152_v1d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth',
+                                first_conv='conv1.0'),
+    'gluon_resnet50_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth',
+                               first_conv='conv1.0'),
+    'gluon_resnet101_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth',
+                                first_conv='conv1.0'),
+    'gluon_resnet152_v1s': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth',
+                                first_conv='conv1.0'),
     'gluon_resnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext50_32x4d-e6a097c1.pth'),
     'gluon_resnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_32x4d-b253c8c4.pth'),
     'gluon_resnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_64x4d-f9a8e184.pth'),
     'gluon_seresnext50_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext50_32x4d-90cf2d6e.pth'),
     'gluon_seresnext101_32x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_32x4d-cf52900d.pth'),
     'gluon_seresnext101_64x4d': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_64x4d-f9926f93.pth'),
-    'gluon_senet154': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_senet154-70a1a3c0.pth'),
+    'gluon_senet154': _cfg(url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_senet154-70a1a3c0.pth',
+                           first_conv='conv1.0'),
 }
 
 

timm/models/inception_v3.py

@@ -19,7 +19,7 @@ def _cfg(url='', **kwargs):
         'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
         'crop_pct': 0.875, 'interpolation': 'bicubic',
         'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
-        'first_conv': 'Conv2d_1a_3x3', 'classifier': 'fc',
+        'first_conv': 'Conv2d_1a_3x3.conv', 'classifier': 'fc',
         **kwargs
     }
 

timm/models/resnest.py

@@ -22,7 +22,7 @@ def _cfg(url='', **kwargs):
         'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
         'crop_pct': 0.875, 'interpolation': 'bilinear',
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
-        'first_conv': 'conv1', 'classifier': 'fc',
+        'first_conv': 'conv1.0', 'classifier': 'fc',
         **kwargs
     }
 

timm/models/resnet.py

@@ -42,13 +42,15 @@ def _cfg(url='', **kwargs):
         interpolation='bicubic'),
     'resnet26d': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26d-69e92c46.pth',
-        interpolation='bicubic'),
+        interpolation='bicubic',
+        first_conv='conv1.0'),
     'resnet50': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50_ram-a26f946b.pth',
         interpolation='bicubic'),
     'resnet50d': _cfg(
         url='',
-        interpolation='bicubic'),
+        interpolation='bicubic',
+        first_conv='conv1.0'),
     'resnet101': _cfg(url='https://download.pytorch.org/models/resnet101-5d3b4d8f.pth'),
     'resnet152': _cfg(url='https://download.pytorch.org/models/resnet152-b121ed2d.pth'),
     'tv_resnet34': _cfg(url='https://download.pytorch.org/models/resnet34-333f7ec4.pth'),
@@ -62,7 +64,8 @@ def _cfg(url='', **kwargs):
         interpolation='bicubic'),
     'resnext50d_32x4d': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50d_32x4d-103e99f8.pth',
-        interpolation='bicubic'),
+        interpolation='bicubic',
+        first_conv='conv1.0'),
     'resnext101_32x4d': _cfg(url=''),
     'resnext101_32x8d': _cfg(url='https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth'),
     'resnext101_64x4d': _cfg(url=''),
@@ -118,7 +121,8 @@ def _cfg(url='', **kwargs):
         interpolation='bicubic'),
     'seresnet50tn': _cfg(
         url='',
-        interpolation='bicubic'),
+        interpolation='bicubic',
+        first_conv='conv1.0'),
     'seresnet101': _cfg(
         url='',
         interpolation='bicubic'),
@@ -132,13 +136,16 @@ def _cfg(url='', **kwargs):
         interpolation='bicubic'),
     'seresnext26d_32x4d': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26d_32x4d-80fa48a3.pth',
-        interpolation='bicubic'),
+        interpolation='bicubic',
+        first_conv='conv1.0'),
     'seresnext26t_32x4d': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26t_32x4d-361bc1c4.pth',
-        interpolation='bicubic'),
+        interpolation='bicubic',
+        first_conv='conv1.0'),
     'seresnext26tn_32x4d': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26tn_32x4d-569cb627.pth',
-        interpolation='bicubic'),
+        interpolation='bicubic',
+        first_conv='conv1.0'),
     'seresnext50_32x4d': _cfg(
         interpolation='bicubic'),
     'seresnext101_32x4d': _cfg(
@@ -149,7 +156,8 @@ def _cfg(url='', **kwargs):
         interpolation='bicubic'),
     'senet154': _cfg(
         url='',
-        interpolation='bicubic'),
+        interpolation='bicubic',
+        first_conv='conv1.0'),
 
     # Efficient Channel Attention ResNets
     'ecaresnet18': _cfg(),
@@ -159,21 +167,26 @@ def _cfg(url='', **kwargs):
         interpolation='bicubic'),
     'ecaresnet50d': _cfg(
         url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet50D_833caf58.pth',
-        interpolation='bicubic'),
+        interpolation='bicubic',
+        first_conv='conv1.0'),
     'ecaresnet50d_pruned': _cfg(
         url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45899/outputs/ECAResNet50D_P_9c67f710.pth',
-        interpolation='bicubic'),
+        interpolation='bicubic',
+        first_conv='conv1.0'),
     'ecaresnet101d': _cfg(
         url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45402/outputs/ECAResNet101D_281c5844.pth',
-        interpolation='bicubic'),
+        interpolation='bicubic',
+        first_conv='conv1.0'),
     'ecaresnet101d_pruned': _cfg(
         url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.pth',
-        interpolation='bicubic'),
+        interpolation='bicubic',
+        first_conv='conv1.0'),
 
     # Efficient Channel Attention ResNeXts
     'ecaresnext26tn_32x4d': _cfg(
         url='',
-        interpolation='bicubic'),
+        interpolation='bicubic',
+        first_conv='conv1.0'),
     'ecaresnext50_32x4d': _cfg(
         url='',
         interpolation='bicubic'),

timm/models/selecsls.py

@@ -29,7 +29,7 @@ def _cfg(url='', **kwargs):
         'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (4, 4),
         'crop_pct': 0.875, 'interpolation': 'bilinear',
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
-        'first_conv': 'stem', 'classifier': 'fc',
+        'first_conv': 'stem.0', 'classifier': 'fc',
         **kwargs
     }
 

timm/models/sknet.py

@@ -36,7 +36,8 @@ def _cfg(url='', **kwargs):
     'skresnet34': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnet34_ra-bdc0ccde.pth'),
     'skresnet50': _cfg(),
-    'skresnet50d': _cfg(),
+    'skresnet50d': _cfg(
+        first_conv='conv1.0'),
     'skresnext50_32x4d': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/skresnext50_ra-f40e40bf.pth'),
 }

timm/models/tresnet.py

@@ -25,7 +25,7 @@ def _cfg(url='', **kwargs):
         'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
         'crop_pct': 0.875, 'interpolation': 'bilinear',
         'mean': (0, 0, 0), 'std': (1, 1, 1),
-        'first_conv': 'body.conv1', 'classifier': 'head.fc',
+        'first_conv': 'body.conv1.0', 'classifier': 'head.fc',
         **kwargs
     }
 

timm/models/xception_aligned.py

@@ -25,7 +25,7 @@ def _cfg(url='', **kwargs):
         'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (10, 10),
         'crop_pct': 0.903, 'interpolation': 'bicubic',
         'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
-        'first_conv': 'stem.0', 'classifier': 'head.fc',
+        'first_conv': 'stem.0.conv', 'classifier': 'head.fc',
         **kwargs
     }