[DOC] MXNet frontend tutorial (#2688)

tutorials/frontend/from_mxnet.py

+"""
+.. _tutorial-from-mxnet:
+
+Compile MXNet Models
+====================
+**Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_, \
+            `Kazutaka Morita <https://github.com/kazum>`_
+
+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
+import tvm
+import tvm.relay as relay
+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
+shape_dict = {'data': x.shape}
+func, params = relay.frontend.from_mxnet(block, shape_dict)
+## we want a probability so add a softmax operator
+func = relay.Function(func.params, relay.nn.softmax(func.body), None, func.type_params, func.attrs)
+
+######################################################################
+# now compile the graph
+target = 'cuda'
+with relay.build_config(opt_level=3):
+    graph, lib, params = relay.build(func, target, params=params)
+
+######################################################################
+# Execute the portable graph on TVM
+# ---------------------------------
+# Now, we would like to reproduce the same forward computation using TVM.
+from tvm.contrib import graph_runtime
+ctx = tvm.gpu(0)
+dtype = 'float32'
+m = graph_runtime.create(graph, lib, ctx)
+# set inputs
+m.set_input('data', tvm.nd.array(x.astype(dtype)))
+m.set_input(**params)
+# execute
+m.run()
+# get outputs
+tvm_output = m.get_output(0)
+top1 = np.argmax(tvm_output.asnumpy()[0])
+print('TVM prediction top-1:', top1, synset[top1])
+
+######################################################################
+# Use MXNet symbol with pretrained weights
+# ----------------------------------------
+# MXNet often use `arg_params` and `aux_params` to store network parameters
+# separately, here we show how to use these weights with existing API
+def block2symbol(block):
+    data = mx.sym.Variable('data')
+    sym = block(data)
+    args = {}
+    auxs = {}
+    for k, v in block.collect_params().items():
+        args[k] = mx.nd.array(v.data().asnumpy())
+    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
+
+######################################################################
+# for a normal mxnet model, we start from here
+mx_sym, args, auxs = mx.model.load_checkpoint('resnet18_v1', 0)
+# now we use the same API to get Relay computation graph
+relay_func, relay_params = relay.frontend.from_mxnet(mx_sym, shape_dict,
+                                                     arg_params=args, aux_params=auxs)
+# repeat the same steps to run this model using TVM