Deploy the Pretrained Model on Android

Author: Tomohiro Kato

This is an example of using Relay to compile a keras model and deploy it on Android device.

import os
import numpy as np
from PIL import Image
import keras
from keras.applications.mobilenet_v2 import MobileNetV2
import tvm
import tvm.relay as relay
from tvm import rpc
from tvm.contrib import util, ndk, graph_runtime as runtime
from tvm.contrib.download import download_testdata

Setup Environment

Since there are many required packages for Android, it is recommended to use the official Docker Image.

First, to build and run Docker Image, we can run the following command.

git clone --recursive https://github.com/dmlc/tvm
cd tvm
docker build -t tvm.demo_android -f docker/Dockerfile.demo_android ./docker
docker run --pid=host -h tvm -v $PWD:/workspace \
       -w /workspace -p 9190:9190 --name tvm -it tvm.demo_android bash

You are now inside the container. The cloned TVM directory is mounted on /workspace. At this time, mount the 9190 port used by RPC described later.

Note

Please execute the following steps in the container. We can execute docker exec -it tvm bash to open a new terminal in the container.

Next we build the TVM.

mkdir build
cd build
cmake -DUSE_LLVM=llvm-config-6.0 \
      -DUSE_RPC=ON \
      -DUSE_SORT=ON \
      -DUSE_VULKAN=ON \
      -DUSE_GRAPH_RUNTIME=ON \
      ..
make -j10

After building TVM successfully, Please set PYTHONPATH.

echo 'export PYTHONPATH=/workspace/python:/workspacem/topi/python:/workspace/nnvm/python/:/workspace/vta/python:${PYTHONPATH}' >> ~/.bashrc
source ~/.bashrc

Start RPC Tracker

TVM uses RPC session to communicate with Android device.

To start an RPC tracker, run this command in the container. The tracker is required during the whole tuning process, so we need to open a new terminal for this command:

python -m tvm.exec.rpc_tracker --host=0.0.0.0 --port=9190

The expected output is

INFO:RPCTracker:bind to 0.0.0.0:9190

Register Android device to RPC Tracker

Now we can register our Android device to the tracker.

Follow this readme page to install TVM RPC APK on the android device.

Here is an example of config.mk. I enabled OpenCL and Vulkan.

APP_ABI = arm64-v8a

APP_PLATFORM = android-24

# whether enable OpenCL during compile
USE_OPENCL = 1

# whether to enable Vulkan during compile
USE_VULKAN = 1

ifeq ($(USE_VULKAN), 1)
  # Statically linking vulkan requires API Level 24 or higher
  APP_PLATFORM = android-24
endif

# the additional include headers you want to add, e.g., SDK_PATH/adrenosdk/Development/Inc
ADD_C_INCLUDES += /work/adrenosdk-linux-5_0/Development/Inc
# download from https://github.com/KhronosGroup/OpenCL-Headers
ADD_C_INCLUDES += /workspace/3rdparty/OpenCL-Headers/

# the additional link libs you want to add, e.g., ANDROID_LIB_PATH/libOpenCL.so
ADD_LDLIBS = /workspace/pull-from-android-device/libOpenCL.so

Note

At this time, don’t forget to create a standalone toolchain .

for example

/opt/android-sdk-linux/ndk-bundle/build/tools/make-standalone-toolchain.sh \
   --platform=android-24 --use-llvm --arch=arm64 --install-dir=/opt/android-toolchain-arm64
export TVM_NDK_CC=/opt/android-toolchain-arm64/bin/aarch64-linux-android-g++

Next, start the Android application and enter the IP address and port of RPC Tracker. Then you have already registered your device.

After registering devices, we can confirm it by querying rpc_tracker

python -m tvm.exec.query_rpc_tracker --host=0.0.0.0 --port=9190

For example, if we have 1 Android device. the output can be

Queue Status
----------------------------------
key          total  free  pending
----------------------------------
android      1      1     0
----------------------------------

To confirm that you can communicate with Android, we can run following test script. If you use OpenCL and Vulkan, please set test_opencl and test_vulkan in the script.

export TVM_TRACKER_HOST=0.0.0.0
export TVM_TRACKER_PORT=9190
cd /workspace/apps/android_rpc
python tests/android_rpc_test.py

Load pretrained keras model

We load a pretrained MobileNetV2(alpha=0.5) classification model provided by keras.

keras.backend.clear_session()  # Destroys the current TF graph and creates a new one.
weights_url = ''.join(['https://github.com/JonathanCMitchell/',
                       'mobilenet_v2_keras/releases/download/v1.1/',
                       'mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.5_224.h5'])
weights_file = 'mobilenet_v2_weights.h5'
weights_path = download_testdata(weights_url, weights_file, module='keras')
keras_mobilenet_v2 = MobileNetV2(alpha=0.5, include_top=True, weights=None,
                                input_shape=(224, 224, 3), classes=1000)
keras_mobilenet_v2.load_weights(weights_path)

Out:

File /workspace/.tvm_test_data/keras/mobilenet_v2_weights.h5 exists, skip.

In order to test our model, here we download an image of cat and transform its format.

img_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true'
img_name = 'cat.png'
img_path = download_testdata(img_url, img_name, module='data')
image = Image.open(img_path).resize((224, 224))
dtype = 'float32'

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)

Out:

File /workspace/.tvm_test_data/data/cat.png exists, skip.

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 = 'imagenet1000_clsid_to_human.txt'
synset_path = download_testdata(synset_url, synset_name, module='data')
with open(synset_path) as f:
    synset = eval(f.read())

Out:

File /workspace/.tvm_test_data/data/imagenet1000_clsid_to_human.txt exists, skip.

Compile the model with relay

If we run the example on our x86 server for demonstration, we can simply set it as llvm. If running it on the Android device, we need to specify its instruction set. Set local_demo to False if you want to run this tutorial with a real device.

local_demo = True

# by default on CPU target will execute.
# select 'cpu', 'opencl' and 'vulkan'
test_target = 'cpu'

# Change target configuration.
# Run `adb shell cat /proc/cpuinfo` to find the arch.
arch = 'arm64'
target = 'llvm -target=%s-linux-android' % arch
target_host = None

if local_demo:
    target_host = None
    target = 'llvm'
elif test_target == 'opencl':
    target_host = target
    target = 'opencl'
elif test_target == 'vulkan':
    target_host = target
    target = 'vulkan'

input_name = 'input_1'
shape_dict = {input_name: x.shape}
func, params = relay.frontend.from_keras(keras_mobilenet_v2, shape_dict)

with relay.build_config(opt_level=3):
    graph, lib, params = relay.build(func, target=target,
                                     target_host=target_host, 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.so')
fcompile = ndk.create_shared if not local_demo else None
lib.export_library(lib_fname, fcompile)

Deploy the Model Remotely by RPC

With RPC, you can deploy the model remotely from your host machine to the remote android device.

tracker_host = os.environ.get('TVM_TRACKER_HOST', '0.0.0.0')
tracker_port = int(os.environ.get('TVM_TRACKER_PORT', 9190))
key = 'android'

if local_demo:
    remote = rpc.LocalSession()
else:
    tracker = rpc.connect_tracker(tracker_host, tracker_port)
    # When running a heavy model, we should increase the `session_timeout`
    remote = tracker.request(key, priority=0,
                             session_timeout=60)

if local_demo:
    ctx = remote.cpu(0)
elif test_target == 'opencl':
    ctx = remote.cl(0)
elif test_target == 'vulkan':
    ctx = remote.vulkan(0)
else:
    ctx = remote.cpu(0)

# upload the library to remote device and load it
remote.upload(lib_fname)
rlib = remote.load_module('net.so')

# create the remote runtime module
module = runtime.create(graph, rlib, ctx)

Execute on TVM

# set parameter (upload params to the remote device. This may take a while)
module.set_input(**params)
# set input data
module.set_input(input_name, tvm.nd.array(x.astype(dtype)))
# 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]))

print('Evaluate inference time cost...')
ftimer = module.module.time_evaluator('run', ctx, number=1, repeat=10)
prof_res = np.array(ftimer().results) * 1000  # convert to millisecond
print('Mean inference time (std dev): %.2f ms (%.2f ms)' % (np.mean(prof_res),
                                                            np.std(prof_res)))

Out:

TVM prediction top-1: tiger cat
Evaluate inference time cost...
Mean inference time (std dev): 54.79 ms (15.45 ms)

Sample Output

The following is the result of ‘cpu’, ‘opencl’ and ‘vulkan’ using Adreno 530 on Snapdragon 820

Although we can run on a GPU, it is slower than CPU. To speed up, we need to write and optimize the schedule according to the GPU architecture.

# cpu
TVM prediction top-1: tiger cat
Evaluate inference time cost...
Mean inference time (std dev): 37.92 ms (19.67 ms)

# opencl
TVM prediction top-1: tiger cat
Evaluate inference time cost...
Mean inference time (std dev): 419.83 ms (7.49 ms)

# vulkan
TVM prediction top-1: tiger cat
Evaluate inference time cost...
Mean inference time (std dev): 465.80 ms (4.52 ms)

Total running time of the script: ( 1 minutes 12.160 seconds)

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