[MXNET-696] Fix undefined name errors (#12137)

* Fix undefined name error in neural style example

* Fix import exception error

* Fix undefined name in AUCMetric

* Fix undefined name in a3c example

example/deep-embedded-clustering/model.py

@@ -22,7 +22,7 @@
 import numpy as np
 try:
     import cPickle as pickle
-except ModuleNotFoundError:
+except ImportError:
     import pickle
 
 
@@ -75,4 +75,4 @@ def load(self, fname):
                     self.args[key][:] = v
 
     def setup(self, *args, **kwargs):
-        raise NotImplementedError("must override this")
+        raise NotImplementedError("must override this")

example/neural-style/end_to_end/model_vgg19.py

@@ -90,6 +90,7 @@ def get_executor_with_style(style, content, input_size, ctx):
                         arg_dict=arg_dict)
 
 def get_executor_content(content, input_size, ctx):
+    out = mx.sym.Group([content])
     arg_shapes, output_shapes, aux_shapes = content.infer_shape(data=(1, 3, input_size[0], input_size[1]))
     arg_names = out.list_arguments()
     arg_dict = dict(zip(arg_names, [mx.nd.zeros(shape, ctx=ctx) for shape in arg_shapes]))

example/reinforcement-learning/a3c/a3c.py

@@ -203,7 +203,7 @@ def test():
         mx.gpu(int(i)) for i in args.gpus.split(',')]
 
     # module
-    dataiter = robo_data.RobosimsDataIter('scenes', args.batch_size, args.input_length, web_viz=True)
+    dataiter = rl_data.GymDataIter('scenes', args.batch_size, args.input_length, web_viz=True)
     print(dataiter.provide_data)
     net = sym.get_symbol_thor(dataiter.act_dim)
     module = mx.mod.Module(net, data_names=[d[0] for d in dataiter.provide_data], label_names=('policy_label', 'value_label'), context=devs)

example/sparse/factorization_machine/metric.py

@@ -107,7 +107,9 @@ def update(self, labels, preds):
         label_sum = label_weight.sum()
         if label_sum == 0 or label_sum == label_weight.size:
             raise Exception("AUC with one class is undefined")
-
+
+        label_one_num = np.count_nonzero(label_weight)
+        label_zero_num = len(label_weight) - label_one_num
         total_area = label_zero_num * label_one_num
         height = 0
         width = 0