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* [Tutorial] mxnet update add from_gluon add to __init__ fix tutorial and from_gluon fix doc lint merge from_mxnet fix fix fix tutorial fix fix header * fix tutorial * fix data * fix
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""" | ||
Compiling MXNet Models with NNVM | ||
================================ | ||
**Author**: `Joshua Z. Zhang <https://zhreshold.github.io/>`_ | ||
This article is an introductory tutorial to deploy mxnet models with NNVM. | ||
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 nnvm | ||
import tvm | ||
import numpy as np | ||
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||
###################################################################### | ||
# 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 | ||
import Image | ||
from matplotlib import pyplot as plt | ||
block = get_model('resnet18_v1', pretrained=True) | ||
img_name = 'cat.jpg' | ||
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() | ||
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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 | ||
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x = transform_image(image) | ||
print('x', x.shape) | ||
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###################################################################### | ||
# 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 | ||
sym, params = nnvm.frontend.from_mxnet(block) | ||
# we want a probability so add a softmax operator | ||
sym = nnvm.sym.softmax(sym) | ||
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###################################################################### | ||
# now compile the graph | ||
import nnvm.compiler | ||
target = 'cuda' | ||
shape_dict = {'data': x.shape} | ||
graph, lib, params = nnvm.compiler.build(sym, target, shape_dict, params=params) | ||
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###################################################################### | ||
# 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, tvm.nd.empty((1000,), dtype)) | ||
top1 = np.argmax(tvm_output) | ||
print('TVM prediction top-1:', top1, synset[top1]) | ||
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###################################################################### | ||
# Use MXNet symbol with pretrained weights | ||
# ---------------------------------------- | ||
# MXNet often use `arg_prams` 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 | ||
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###################################################################### | ||
# 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 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 |