Fix a few small things.

Author: rwightman

timm/models/helpers.py

@@ -185,7 +185,7 @@ def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filter_fn=Non
         state_dict = filter_fn(state_dict)
 
     input_convs = cfg.get('first_conv', None)
-    if input_convs is not None:
+    if input_convs is not None and in_chans != 3:
         if isinstance(input_convs, str):
             input_convs = (input_convs,)
         for input_conv_name in input_convs:

timm/models/inception_v3.py

@@ -32,12 +32,12 @@ def _cfg(url='', **kwargs):
     # my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)
     'tf_inception_v3': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_inception_v3-e0069de4.pth',
-        num_classes=1001, has_aux=False),
+        num_classes=1000, has_aux=False, label_offset=1),
     # my port of Tensorflow adversarially trained Inception V3 from
     # http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz
     'adv_inception_v3': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/adv_inception_v3-9e27bd63.pth',
-        num_classes=1001, has_aux=False),
+        num_classes=1000, has_aux=False, label_offset=1),
     # from gluon pretrained models, best performing in terms of accuracy/loss metrics
     # https://gluon-cv.mxnet.io/model_zoo/classification.html
     'gluon_inception_v3': _cfg(

validate.py

@@ -284,7 +284,7 @@ def main():
         if args.model == 'all':
             # validate all models in a list of names with pretrained checkpoints
             args.pretrained = True
-            model_names = list_models(pretrained=True)
+            model_names = list_models(pretrained=True, exclude_filters=['*in21k'])
             model_cfgs = [(n, '') for n in model_names]
         elif not is_model(args.model):
             # model name doesn't exist, try as wildcard filter