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[Doc] Relay tutorial - Deploy the Pretrained Model on Raspberry Pi (a…
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""" | ||
.. _tutorial-deploy-model-on-rasp: | ||
Deploy the Pretrained Model on Raspberry Pi | ||
=========================================== | ||
**Author**: `Ziheng Jiang <https://ziheng.org/>`_, \ | ||
`Hiroyuki Makino <https://makihiro.github.io/>`_ | ||
This is an example of using Relay to compile a ResNet model and deploy | ||
it on Raspberry Pi. | ||
""" | ||
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import tvm | ||
import tvm.relay as relay | ||
from tvm import rpc | ||
from tvm.contrib import util, graph_runtime as runtime | ||
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###################################################################### | ||
# .. _build-tvm-runtime-on-device: | ||
# | ||
# Build TVM Runtime on Device | ||
# --------------------------- | ||
# | ||
# The first step is to build tvm runtime on the remote device. | ||
# | ||
# .. note:: | ||
# | ||
# All instructions in both this section and next section should be | ||
# executed on the target device, e.g. Raspberry Pi. And we assume it | ||
# has Linux running. | ||
# | ||
# Since we do compilation on local machine, the remote device is only used | ||
# for running the generated code. We only need to build tvm runtime on | ||
# the remote device. | ||
# | ||
# .. code-block:: bash | ||
# | ||
# git clone --recursive https://github.com/dmlc/tvm | ||
# cd tvm | ||
# mkdir build | ||
# cp cmake/config.cmake build | ||
# cd build | ||
# cmake .. | ||
# make runtime -j4 | ||
# | ||
# After building runtime successfully, we need to set environment varibles | ||
# in :code:`~/.bashrc` file. We can edit :code:`~/.bashrc` | ||
# using :code:`vi ~/.bashrc` and add the line below (Assuming your TVM | ||
# directory is in :code:`~/tvm`): | ||
# | ||
# .. code-block:: bash | ||
# | ||
# export PYTHONPATH=$PYTHONPATH:~/tvm/python | ||
# | ||
# To update the environment variables, execute :code:`source ~/.bashrc`. | ||
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###################################################################### | ||
# Set Up RPC Server on Device | ||
# --------------------------- | ||
# To start an RPC server, run the following command on your remote device | ||
# (Which is Raspberry Pi in our example). | ||
# | ||
# .. code-block:: bash | ||
# | ||
# python -m tvm.exec.rpc_server --host 0.0.0.0 --port=9090 | ||
# | ||
# If you see the line below, it means the RPC server started | ||
# successfully on your device. | ||
# | ||
# .. code-block:: bash | ||
# | ||
# INFO:root:RPCServer: bind to 0.0.0.0:9090 | ||
# | ||
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###################################################################### | ||
# Prepare the Pre-trained Model | ||
# ----------------------------- | ||
# Back to the host machine, which should have a full TVM installed (with LLVM). | ||
# | ||
# We will use pre-trained model from | ||
# `MXNet Gluon model zoo <https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html>`_. | ||
# You can found more details about this part at tutorial :ref:`tutorial-from-mxnet`. | ||
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from mxnet.gluon.model_zoo.vision import get_model | ||
from mxnet.gluon.utils import download | ||
from PIL import Image | ||
import numpy as np | ||
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# one line to get the model | ||
block = get_model('resnet18_v1', pretrained=True) | ||
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###################################################################### | ||
# In order to test our model, here we download an image of cat and | ||
# transform its format. | ||
img_name = 'cat.png' | ||
download('https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true', img_name) | ||
image = Image.open(img_name).resize((224, 224)) | ||
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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) | ||
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###################################################################### | ||
# synset is used to transform the label from number of ImageNet class to | ||
# the word human can understand. | ||
synset_url = ''.join(['https://gist.githubusercontent.com/zhreshold/', | ||
'4d0b62f3d01426887599d4f7ede23ee5/raw/', | ||
'596b27d23537e5a1b5751d2b0481ef172f58b539/', | ||
'imagenet1000_clsid_to_human.txt']) | ||
synset_name = 'synset.txt' | ||
download(synset_url, synset_name) | ||
with open(synset_name) as f: | ||
synset = eval(f.read()) | ||
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###################################################################### | ||
# Now we would like to port the Gluon model to a portable computational graph. | ||
# It's as easy as several lines. | ||
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# 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) | ||
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###################################################################### | ||
# Here are some basic data workload configurations. | ||
batch_size = 1 | ||
num_classes = 1000 | ||
image_shape = (3, 224, 224) | ||
data_shape = (batch_size,) + image_shape | ||
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###################################################################### | ||
# Compile The Graph | ||
# ----------------- | ||
# To compile the graph, we call the :any:`relay.build` function | ||
# with the graph configuration and parameters. However, You cannot to | ||
# deploy a x86 program on a device with ARM instruction set. It means | ||
# Relay also needs to know the compilation option of target device, | ||
# apart from arguments :code:`net` and :code:`params` to specify the | ||
# deep learning workload. Actually, the option matters, different option | ||
# will lead to very different performance. | ||
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###################################################################### | ||
# If we run the example on our x86 server for demonstration, we can simply | ||
# set it as :code:`llvm`. If running it on the Raspberry Pi, we need to | ||
# specify its instruction set. Set :code:`local_demo` to False if you want | ||
# to run this tutorial with a real device. | ||
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local_demo = True | ||
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if local_demo: | ||
target = tvm.target.create('llvm') | ||
else: | ||
target = tvm.target.arm_cpu('rasp3b') | ||
# The above line is a simple form of | ||
# target = tvm.target.create('llvm -device=arm_cpu -model=bcm2837 -target=armv7l-linux-gnueabihf -mattr=+neon') | ||
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with relay.build_config(opt_level=3): | ||
graph, lib, params = relay.build(func, target, params=params) | ||
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# After `relay.build`, you will get three return values: graph, | ||
# library and the new parameter, since we do some optimization that will | ||
# change the parameters but keep the result of model as the same. | ||
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# Save the library at local temporary directory. | ||
tmp = util.tempdir() | ||
lib_fname = tmp.relpath('net.tar') | ||
lib.export_library(lib_fname) | ||
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###################################################################### | ||
# Deploy the Model Remotely by RPC | ||
# -------------------------------- | ||
# With RPC, you can deploy the model remotely from your host machine | ||
# to the remote device. | ||
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# obtain an RPC session from remote device. | ||
if local_demo: | ||
remote = rpc.LocalSession() | ||
else: | ||
# The following is my environment, change this to the IP address of your target device | ||
host = '10.77.1.162' | ||
port = 9090 | ||
remote = rpc.connect(host, port) | ||
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# upload the library to remote device and load it | ||
remote.upload(lib_fname) | ||
rlib = remote.load_module('net.tar') | ||
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# create the remote runtime module | ||
ctx = remote.cpu(0) | ||
module = runtime.create(graph, rlib, ctx) | ||
# set parameter (upload params to the remote device. This may take a while) | ||
module.set_input(**params) | ||
# set input data | ||
module.set_input('data', tvm.nd.array(x.astype('float32'))) | ||
# run | ||
module.run() | ||
# get output | ||
out = module.get_output(0) | ||
# get top1 result | ||
top1 = np.argmax(out.asnumpy()) | ||
print('TVM prediction top-1: {}'.format(synset[top1])) |