[RELAY][DOCS] Port from_mxnet tutorial to relay (apache#2608)

* check in

* update build and run

tutorials/relay/from_mxnet.py

@@ -0,0 +1,86 @@
+"""
+.. _tutorial-from-mxnet:
+
+Compile MXNet Models
+====================
+**Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_, `Eddie Yan <https://github.com/eqy>`_
+
+This article is an introductory tutorial to deploy mxnet models with Relay.
+
+For us to begin with, mxnet module is required to be installed.
+
+A quick solution is
+
+.. code-block:: bash
+
+    pip install mxnet --user
+
+or please refer to offical installation guide.
+https://mxnet.incubator.apache.org/versions/master/install/index.html
+"""
+# some standard imports
+import mxnet as mx
+from tvm import relay
+import tvm
+import numpy as np
+
+######################################################################
+# Download Resnet18 model from Gluon Model Zoo
+# ---------------------------------------------
+# In this section, we download a pretrained imagenet model and classify an image.
+from mxnet.gluon.model_zoo.vision import get_model
+from mxnet.gluon.utils import download
+from PIL import Image
+from matplotlib import pyplot as plt
+block = get_model('resnet18_v1', pretrained=True)
+
+img_name = 'cat.png'
+synset_url = ''.join(['https://gist.githubusercontent.com/zhreshold/',
+                      '4d0b62f3d01426887599d4f7ede23ee5/raw/',
+                      '596b27d23537e5a1b5751d2b0481ef172f58b539/',
+                      'imagenet1000_clsid_to_human.txt'])
+synset_name = 'synset.txt'
+download('https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true', img_name)
+download(synset_url, synset_name)
+with open(synset_name) as f:
+    synset = eval(f.read())
+image = Image.open(img_name).resize((224, 224))
+plt.imshow(image)
+plt.show()
+
+def transform_image(image):
+    image = np.array(image) - np.array([123., 117., 104.])
+    image /= np.array([58.395, 57.12, 57.375])
+    image = image.transpose((2, 0, 1))
+    image = image[np.newaxis, :]
+    return image
+
+x = transform_image(image)
+print('x', x.shape)
+
+######################################################################
+# Compile the Graph
+# -----------------
+# Now we would like to port the Gluon model to a portable computational graph.
+# It's as easy as several lines.
+# We support MXNet static graph(symbol) and HybridBlock in mxnet.gluon
+input_shape = (1, 3, 224, 224)
+dtype = 'float32'
+net, params = relay.frontend.from_mxnet(block, shape={'data': input_shape}, dtype=dtype)
+# we want a probability so add a softmax operator
+net = relay.Function(net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs)
+
+######################################################################
+# now compile the graph
+target = 'cuda'
+shape_dict = {'data': x.shape}
+with relay.build_config(opt_level=3):
+    intrp = relay.build_module.create_executor('graph', net, tvm.gpu(0), target) 
+
+######################################################################
+# Execute the portable graph on TVM
+# ---------------------------------
+# Now, we would like to reproduce the same forward computation using TVM.
+tvm_output = intrp.evaluate(net)(tvm.nd.array(x.astype(dtype)), **params)
+top1 = np.argmax(tvm_output.asnumpy()[0])
+print('TVM prediction top-1:', top1, synset[top1])