Some pretrianed URL changes

* host some of Cadene's weights on github instead of .fr for speed
* add my old port of ensemble adversarial inception resnet v2
* switch to my TF port of normal inception res v2 and change FC layer back to 'classif' for compat with ens_adv

timm/models/inception_resnet_v2.py

@@ -6,16 +6,25 @@ from .helpers import load_pretrained
 from .adaptive_avgmax_pool import *
 from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
 
-_models = ['inception_resnet_v2']
+_models = ['inception_resnet_v2', 'ens_adv_inception_resnet_v2']
 __all__ = ['InceptionResnetV2'] + _models
 
 default_cfgs = {
+    # ported from http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz
     'inception_resnet_v2': {
-        'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth',
+        'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/inception_resnet_v2-940b1cd6.pth',
         'num_classes': 1001, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
         'crop_pct': 0.8975, 'interpolation': 'bicubic',
         'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
-        'first_conv': 'conv2d_1a.conv', 'classifier': 'last_linear',
+        'first_conv': 'conv2d_1a.conv', 'classifier': 'classif',
+    },
+    # ported from http://download.tensorflow.org/models/ens_adv_inception_resnet_v2_2017_08_18.tar.gz
+    'ens_adv_inception_resnet_v2': {
+        'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ens_adv_inception_resnet_v2-2592a550.pth',
+        'num_classes': 1001, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
+        'crop_pct': 0.8975, 'interpolation': 'bicubic',
+        'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
+        'first_conv': 'conv2d_1a.conv', 'classifier': 'classif',
     }
 }
 
@@ -274,19 +283,20 @@ class InceptionResnetV2(nn.Module):
         )
         self.block8 = Block8(noReLU=True)
         self.conv2d_7b = BasicConv2d(2080, self.num_features, kernel_size=1, stride=1)
-        self.last_linear = nn.Linear(self.num_features, num_classes)
+        # NOTE some variants/checkpoints for this model may have 'last_linear' as the name for the FC
+        self.classif = nn.Linear(self.num_features, num_classes)
 
     def get_classifier(self):
-        return self.last_linear
+        return self.classif
 
     def reset_classifier(self, num_classes, global_pool='avg'):
         self.global_pool = global_pool
         self.num_classes = num_classes
-        del self.last_linear
+        del self.classif
         if num_classes:
-            self.last_linear = torch.nn.Linear(self.num_features, num_classes)
+            self.classif = torch.nn.Linear(self.num_features, num_classes)
         else:
-            self.last_linear = None
+            self.classif = None
 
     def forward_features(self, x, pool=True):
         x = self.conv2d_1a(x)
@@ -314,13 +324,13 @@ class InceptionResnetV2(nn.Module):
         x = self.forward_features(x, pool=True)
         if self.drop_rate > 0:
             x = F.dropout(x, p=self.drop_rate, training=self.training)
-        x = self.last_linear(x)
+        x = self.classif(x)
         return x
 
 
 def inception_resnet_v2(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
     r"""InceptionResnetV2 model architecture from the
-    `"InceptionV4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>`_ paper.
+    `"InceptionV4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>` paper.
     """
     default_cfg = default_cfgs['inception_resnet_v2']
     model = InceptionResnetV2(num_classes=num_classes, in_chans=in_chans, **kwargs)
@@ -330,3 +340,16 @@ def inception_resnet_v2(pretrained=False, num_classes=1000, in_chans=3, **kwargs
 
     return model
 
+
+def ens_adv_inception_resnet_v2(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
+    r""" Ensemble Adversarially trained InceptionResnetV2 model architecture
+    As per https://arxiv.org/abs/1705.07204 and
+    https://github.com/tensorflow/models/tree/master/research/adv_imagenet_models.
+    """
+    default_cfg = default_cfgs['ens_adv_inception_resnet_v2']
+    model = InceptionResnetV2(num_classes=num_classes, in_chans=in_chans, **kwargs)
+    model.default_cfg = default_cfg
+    if pretrained:
+        load_pretrained(model, default_cfg, num_classes, in_chans)
+
+    return model

timm/models/inception_v4.py

@@ -11,7 +11,7 @@ __all__ = ['InceptionV4'] + _models
 
 default_cfgs = {
     'inception_v4': {
-        'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth',
+        'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/inceptionv4-8e4777a0.pth',
         'num_classes': 1001, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
         'crop_pct': 0.875, 'interpolation': 'bicubic',
         'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,

timm/models/pnasnet.py

@@ -20,7 +20,7 @@ __all__ = ['PNASNet5Large'] + _models
 
 default_cfgs = {
     'pnasnet5large': {
-        'url': 'http://data.lip6.fr/cadene/pretrainedmodels/pnasnet5large-bf079911.pth',
+        'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/pnasnet5large-bf079911.pth',
         'input_size': (3, 331, 331),
         'pool_size': (11, 11),
         'crop_pct': 0.875,

timm/models/senet.py

@@ -37,20 +37,20 @@ def _cfg(url='', **kwargs):
 default_cfgs = {
     'senet154':
         _cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth'),
-    'seresnet18':
-        _cfg(url='https://www.dropbox.com/s/3o3nd8mfhxod7rq/seresnet18-4bb0ce65.pth?dl=1',
-             interpolation='bicubic'),
-    'seresnet34':
-        _cfg(url='https://www.dropbox.com/s/q31ccy22aq0fju7/seresnet34-a4004e63.pth?dl=1'),
-    'seresnet50':
-        _cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/se_resnet50-ce0d4300.pth'),
-    'seresnet101':
-        _cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/se_resnet101-7e38fcc6.pth'),
-    'seresnet152':
-        _cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/se_resnet152-d17c99b7.pth'),
-    'seresnext26_32x4d':
-        _cfg(url='https://www.dropbox.com/s/zaeruz2bejcdhh3/seresnext26_32x4d-65ebdb501.pth?dl=1',
-             interpolation='bicubic'),
+    'seresnet18': _cfg(
+        url='https://www.dropbox.com/s/3o3nd8mfhxod7rq/seresnet18-4bb0ce65.pth?dl=1',
+        interpolation='bicubic'),
+    'seresnet34': _cfg(
+        url='https://www.dropbox.com/s/q31ccy22aq0fju7/seresnet34-a4004e63.pth?dl=1'),
+    'seresnet50': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet50-ce0d4300.pth'),
+    'seresnet101': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet101-7e38fcc6.pth'),
+    'seresnet152': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet152-d17c99b7.pth'),
+    'seresnext26_32x4d': _cfg(
+        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26_32x4d-65ebdb501.pth',
+        interpolation='bicubic'),
     'seresnext50_32x4d':
         _cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth'),
     'seresnext101_32x4d':

timm/models/xception.py

@@ -35,7 +35,7 @@ __all__ = ['Xception'] + _models
 
 default_cfgs = {
     'xception': {
-        'url': 'http://data.lip6.fr/cadene/pretrainedmodels/xception-43020ad28.pth',
+        'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/xception-43020ad28.pth',
         'input_size': (3, 299, 299),
         'crop_pct': 0.8975,
         'interpolation': 'bicubic',