Merge pull request #1867 from tensorflow/fix-reversions

Fix reversions plus a few improvements

slim/README.md

@@ -213,7 +213,7 @@ Model | TF-Slim File | Checkpoint | Top-1 Accuracy| Top-5 Accuracy |
 ^ ResNet V2 models use Inception pre-processing and input image size of 299 (use
 `--preprocessing_name inception --eval_image_size 299` when using
 `eval_image_classifier.py`). Performance numbers for ResNet V2 models are
-reported on ImageNet valdiation set.
+reported on the ImageNet validation set.
 
 All 16 MobileNet Models reported in the [MobileNet Paper](https://arxiv.org/abs/1704.04861) can be found [here](https://github.com/tensorflow/models/tree/master/slim/nets/mobilenet_v1.md).
 

slim/nets/nets_factory.py

@@ -100,10 +100,10 @@ def get_network_fn(name, num_classes, weight_decay=0.0, is_training=False):
   """
   if name not in networks_map:
     raise ValueError('Name of network unknown %s' % name)
-  arg_scope = arg_scopes_map[name](weight_decay=weight_decay)
   func = networks_map[name]
   @functools.wraps(func)
   def network_fn(images):
+    arg_scope = arg_scopes_map[name](weight_decay=weight_decay)
     with slim.arg_scope(arg_scope):
       return func(images, num_classes, is_training=is_training)
   if hasattr(func, 'default_image_size'):

slim/preprocessing/preprocessing_factory.py

@@ -57,6 +57,11 @@ def get_preprocessing(name, is_training=False):
       'resnet_v1_50': vgg_preprocessing,
       'resnet_v1_101': vgg_preprocessing,
       'resnet_v1_152': vgg_preprocessing,
+      'resnet_v1_200': vgg_preprocessing,
+      'resnet_v2_50': vgg_preprocessing,
+      'resnet_v2_101': vgg_preprocessing,
+      'resnet_v2_152': vgg_preprocessing,
+      'resnet_v2_200': vgg_preprocessing,
       'vgg': vgg_preprocessing,
       'vgg_a': vgg_preprocessing,
       'vgg_16': vgg_preprocessing,