Compile MXNet Models

Author: Joshua Z. Zhang, Kazutaka Morita

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

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 tvm.contrib.download import download_testdata
from mxnet.gluon.model_zoo.vision import get_model
from PIL import Image
from matplotlib import pyplot as plt
block = get_model('resnet18_v1', pretrained=True)
img_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true'
img_name = 'cat.png'
synset_url = ''.join(['https://gist.githubusercontent.com/zhreshold/',
                      '4d0b62f3d01426887599d4f7ede23ee5/raw/',
                      '596b27d23537e5a1b5751d2b0481ef172f58b539/',
                      'imagenet1000_clsid_to_human.txt'])
synset_name = 'imagenet1000_clsid_to_human.txt'
img_path = download_testdata(img_url, 'cat.png', module='data')
synset_path = download_testdata(synset_url, synset_name, module='data')
with open(synset_path) as f:
    synset = eval(f.read())
image = Image.open(img_path).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)
../../_images/sphx_glr_from_mxnet_001.png

Out:

File /workspace/.tvm_test_data/data/cat.png exists, skip.
File /workspace/.tvm_test_data/data/imagenet1000_clsid_to_human.txt exists, skip.
x (1, 3, 224, 224)

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}
mod, params = relay.frontend.from_mxnet(block, shape_dict)
## we want a probability so add a softmax operator
func = mod["main"]
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])

Out:

TVM prediction top-1: 282 tiger cat

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
mod, 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

Total running time of the script: ( 0 minutes 14.690 seconds)

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