Fix the mobilenet bug. Support Mobilenet training now.

Author: Ruotian Luo

README.md

@@ -24,7 +24,7 @@ With Resnet101 (last ``conv4``):
   - Train on COCO 2014 trainval35k and test on minival (800k/1190k), **35.1**(from scratch) **35.4**(converted) （**35.4** for tf-faster-rcnn).
 
 More Results:
-  - Train Mobilenet (1.0, 224) on COCO 2014 trainval35k and test on minival (900k/1190k), **21.9**(converted) （**21.8** for tf-faster-rcnn).
+  - Train Mobilenet (1.0, 224) on COCO 2014 trainval35k and test on minival (900k/1190k), **21.4**(from scratch), **21.9**(converted) （**21.8** for tf-faster-rcnn).
   - Train Resnet50 on COCO 2014 trainval35k and test on minival (900k/1190k), **32.4**(converted) (**32.4** for tf-faster-rcnn).
   - Train Resnet152 on COCO 2014 trainval35k and test on minival (900k/1190k), **36.7**(converted) (**36.1** for tf-faster-rcnn).
 

lib/nets/mobilenet_v1.py

@@ -241,6 +241,22 @@ def l2_regularizer(m, wd, regu_depth):
     self._layers['head'] = nn.Sequential(*list(self.mobilenet.children())[:12])
     self._layers['tail'] = nn.Sequential(*list(self.mobilenet.children())[12:])
 
+  def train(self, mode=True):
+    # Override train so that the training mode is set as we want
+    nn.Module.train(self, mode)
+    if mode:
+      # Set fixed blocks to be in eval mode (not really doing anything)
+      for m in list(self.mobilenet.children())[:cfg.MOBILENET.FIXED_LAYERS]:
+        m.eval()
+
+      # Set batchnorm always in eval mode during training
+      def set_bn_eval(m):
+        classname = m.__class__.__name__
+        if classname.find('BatchNorm') != -1:
+          m.eval()
+
+      self.mobilenet.apply(set_bn_eval)
+
   def load_pretrained_cnn(self, state_dict):
     print('Warning: No available pretrained model yet')
     self.mobilenet.load_state_dict({k: state_dict['features.'+k] for k in list(self.mobilenet.state_dict())})