forked from apache/tvm
-
Notifications
You must be signed in to change notification settings - Fork 30
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[Doc] Relay tutorial - Deploy the Pretrained Model on Raspberry Pi (a…
- Loading branch information
Showing
1 changed file
with
207 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,207 @@ | ||
""" | ||
.. _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. | ||
""" | ||
|
||
import tvm | ||
import tvm.relay as relay | ||
from tvm import rpc | ||
from tvm.contrib import util, graph_runtime as runtime | ||
|
||
###################################################################### | ||
# .. _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`. | ||
|
||
###################################################################### | ||
# 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 | ||
# | ||
|
||
###################################################################### | ||
# 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`. | ||
|
||
from mxnet.gluon.model_zoo.vision import get_model | ||
from mxnet.gluon.utils import download | ||
from PIL import Image | ||
import numpy as np | ||
|
||
# one line to get the model | ||
block = get_model('resnet18_v1', pretrained=True) | ||
|
||
###################################################################### | ||
# 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)) | ||
|
||
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) | ||
|
||
###################################################################### | ||
# 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()) | ||
|
||
###################################################################### | ||
# 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) | ||
|
||
###################################################################### | ||
# Here are some basic data workload configurations. | ||
batch_size = 1 | ||
num_classes = 1000 | ||
image_shape = (3, 224, 224) | ||
data_shape = (batch_size,) + image_shape | ||
|
||
###################################################################### | ||
# 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. | ||
|
||
###################################################################### | ||
# 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. | ||
|
||
local_demo = True | ||
|
||
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') | ||
|
||
with relay.build_config(opt_level=3): | ||
graph, lib, params = relay.build(func, target, params=params) | ||
|
||
# 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. | ||
|
||
# Save the library at local temporary directory. | ||
tmp = util.tempdir() | ||
lib_fname = tmp.relpath('net.tar') | ||
lib.export_library(lib_fname) | ||
|
||
###################################################################### | ||
# Deploy the Model Remotely by RPC | ||
# -------------------------------- | ||
# With RPC, you can deploy the model remotely from your host machine | ||
# to the remote device. | ||
|
||
# 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) | ||
|
||
# upload the library to remote device and load it | ||
remote.upload(lib_fname) | ||
rlib = remote.load_module('net.tar') | ||
|
||
# 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])) |