Allow zero weight_decay in ImagenetModel

examples/FasterRCNN/data.py

@@ -271,10 +271,10 @@ def get_eval_dataflow():
 
 
 if __name__ == '__main__':
-    config.BASEDIR = '/home/wyx/data/coco'
+    config.BASEDIR = '/private/home/yuxinwu/data/coco'
     config.TRAIN_DATASET = ['train2014']
     from tensorpack.dataflow import PrintData
-    ds = get_train_dataflow()
+    ds = get_train_dataflow(add_mask=config.MODE_MASK)
     ds = PrintData(ds, 100)
     TestDataSpeed(ds, 50000).start()
     ds.reset_state()

examples/ResNet/imagenet_utils.py

@@ -156,10 +156,15 @@ class ImageNetModel(ModelDesc):
 
         logits = self.get_logits(image)
         loss = ImageNetModel.compute_loss_and_error(logits, label)
+
+        if self.weight_decay > 0:
             wd_loss = regularize_cost('.*/W', tf.contrib.layers.l2_regularizer(self.weight_decay),
                                       name='l2_regularize_loss')
             add_moving_summary(loss, wd_loss)
             self.cost = tf.add_n([loss, wd_loss], name='cost')
+        else:
+            self.cost = tf.identity(loss, name='cost')
+            add_moving_summary(self.cost)
 
     @abstractmethod
     def get_logits(self, image):