fix inputvar problems with py3, and lmdb bug

README.md

@@ -33,13 +33,13 @@ Describe your training task with three components:
 	+ Print some variables of interest
 	+ Run inference on a test dataset
 	+ Run some operations once a while
-	+ Send the accuracy to your phone
+	+ Send loss to your phone
 
 With the above components defined, tensorpack trainer will run the training iterations for you.
 Multi-GPU training is off-the-shelf by simply switching the trainer.
 You can also define your own trainer for non-standard training (e.g. GAN).
 
-The components are designed to be independent. You can use only Model or DataFlow in your project.
+The components are designed to be independent. You can use Model or DataFlow in other projects as well.
 
 ## Dependencies:
 

examples/GAN/Image2Image.py

@@ -20,8 +20,9 @@ from GAN import GANTrainer, RandomZData, build_GAN_losses
 To train:
     ./Image2Image.py --data /path/to/datadir --mode {AtoB,BtoA}
     # datadir should contain 512x256 images formed by A and B
+    # training visualization will appear be in tensorboard
 
-To visualize:
+To visualize on test set:
     ./Image2Image.py --sample --data /path/to/test/datadir --mode {AtoB,BtoA} --load pretrained.model
 """
 

tensorpack/dataflow/format.py

@@ -128,7 +128,7 @@ class LMDBDataDecoder(LMDBData):
 class LMDBDataPoint(LMDBDataDecoder):
     """ Read a LMDB file where each value is a serialized datapoint"""
     def __init__(self, lmdb_path, shuffle=True):
-        super(SimpleLMDBLoader, self).__init__(
+        super(LMDBDataPoint, self).__init__(
                 lmdb_path, decoder=lambda k, v: loads(v), shuffle=shuffle)
 
 class CaffeLMDB(LMDBDataDecoder):

tensorpack/models/batch_norm.py

@@ -149,7 +149,7 @@ def BatchNormV2(x, use_local_stat=None, decay=0.9, epsilon=1e-5):
 
     if use_local_stat:
         xn, batch_mean, batch_var = tf.nn.fused_batch_norm(x, gamma, beta,
-                epsilon=epsilon, is_training=ctx.is_training)
+                epsilon=epsilon, is_training=True)
         if ctx.is_training:
             # maintain EMA if training
             update_op1 = moving_averages.assign_moving_average(

tensorpack/models/model_desc.py

@@ -17,12 +17,13 @@ from ..tfutils.tower import get_current_tower_context
 
 __all__ = ['ModelDesc', 'InputVar', 'ModelFromMetaGraph' ]
 
-_InputVar = namedtuple('InputVar', ['type', 'shape', 'name', 'sparse'])
-class InputVar(_InputVar):
+#_InputVar = namedtuple('InputVar', ['type', 'shape', 'name', 'sparse'])
+class InputVar(object):
     def __init__(self, type, shape, name, sparse=False):
-        super(InputVar, self).__init__(type, shape, name, sparse)
-    def __new__(cls, type, shape, name, sparse=False):
-        return super(InputVar, cls).__new__(cls, type, shape, name, sparse)
+        self.type = type
+        self.shape = shape
+        self.name = name
+        self.sparse = sparse
     def dumps(self):
         return pickle.dumps(self)
     @staticmethod

tensorpack/utils/loadcaffe.py

@@ -97,7 +97,7 @@ class CaffeLayerProcessor(object):
 
 def load_caffe(model_desc, model_file):
     """
-    return a dict of params
+    :return: a dict of params
     """
     with change_env('GLOG_minloglevel', '2'):
         import caffe