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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""
.. _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 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)
######################################################################
# 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])
######################################################################
# 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
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