[TUTORIAL] use resnet v2 (#51)

* use resnet v2

* fix

tutorials/from_mxnet.py

@@ -28,7 +28,7 @@
 from mxnet.gluon.utils import download
 import Image
 from matplotlib import pyplot as plt
-block = get_model('resnet18_v1', pretrained=True)
+block = get_model('resnet18_v2', pretrained=True)
 img_name = 'cat.jpg'
 synset_url = ''.join(['https://gist.githubusercontent.com/zhreshold/',
                       '4d0b62f3d01426887599d4f7ede23ee5/raw/',
@@ -85,7 +85,7 @@ def transform_image(image):
 m.run()
 # get outputs
 tvm_output = m.get_output(0, tvm.nd.empty((1000,), dtype))
-top1 = np.argmax(tvm_output)
+top1 = np.argmax(tvm_output.asnumpy())
 print('TVM prediction top-1:', top1, synset[top1])
 
 ######################################################################
@@ -103,12 +103,12 @@ def block2symbol(block):
     return sym, args, auxs
 mx_sym, args, auxs = block2symbol(block)
 # usually we would save/load it as checkpoint
-mx.model.save_checkpoint('resnet18_v1', 0, mx_sym, args, auxs)
-# there are 'resnet18_v1-0000.params' and 'resnet18_v1-symbol.json' on disk
+mx.model.save_checkpoint('resnet18_v2', 0, mx_sym, args, auxs)
+# there are 'resnet18_v2-0000.params' and 'resnet18_v2-symbol.json' on disk
 
 ######################################################################
 # for a normal mxnet model, we start from here
-mx_sym, args, auxs = mx.model.load_checkpoint('resnet18_v1', 0)
+mx_sym, args, auxs = mx.model.load_checkpoint('resnet18_v2', 0)
 # now we use the same API to get NNVM compatible symbol
 nnvm_sym, nnvm_params = nnvm.frontend.from_mxnet(mx_sym, args, auxs)
 # repeat the same steps to run this model using TVM