Deploy the Pretrained Model on Jetson Nano

Author: BBuf

This is an example of using Relay to compile a ResNet model and deploy it on Jetson Nano.

import tvm
from tvm import te
import tvm.relay as relay
from tvm import rpc
from tvm.contrib import utils, graph_executor as runtime
from tvm.contrib.download import download_testdata

Build TVM Runtime on Jetson Nano

The first step is to build the 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. Jetson Nano. 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.

git clone --recursive https://github.com/apache/tvm tvm
cd tvm
mkdir build
cp cmake/config.cmake build
cd build
cmake ..
make runtime -j4

Note

If we want to use Jetson Nano’s GPU for inference, we need to enable the CUDA option in config.cmake, that is, set(USE_CUDA ON)

After building runtime successfully, we need to set environment varibles in ~/.bashrc file. We can edit ~/.bashrc using vi ~/.bashrc and add the line below (Assuming your TVM directory is in ~/tvm):

export PYTHONPATH=$PYTHONPATH:~/tvm/python

To update the environment variables, execute source ~/.bashrc.

Set Up RPC Server on Device

To start an RPC server, run the following command on your remote device (Which is Jetson Nano in our example).

python -m tvm.exec.rpc_server --host 0.0.0.0 --port=9091

If you see the line below, it means the RPC server started successfully on your device.

INFO:RPCServer:bind to 0.0.0.0:9091

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 torchvision

import torch
import torchvision
from PIL import Image
import numpy as np

# one line to get the model
model_name = "resnet18"
model = getattr(torchvision.models, model_name)(pretrained=True)
model = model.eval()

# We grab the TorchScripted model via tracing
input_shape = [1, 3, 224, 224]
input_data = torch.randn(input_shape)
scripted_model = torch.jit.trace(model, input_data).eval()

/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
  warnings.warn(
/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.
  warnings.warn(msg)

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/main/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))


def transform_image(image):
    image = np.array(image) - np.array([123.0, 117.0, 104.0])
    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 = "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())

Now we would like to port the Gluon model to a portable computational graph. It’s as easy as several lines.

input_name = "input0"
shape_list = [(input_name, x.shape)]
mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
# 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)

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 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 net and 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 llvm. If running it on the Jetson Nano, we need to set it as nvidia/jetson-nano. Set local_demo to False if you want to run this tutorial with a real device.

local_demo = True

if local_demo:
    target = tvm.target.Target("llvm")
else:
    target = tvm.target.Target("nvidia/jetson-nano")
    assert target.kind.name == "cuda"
    assert target.attrs["arch"] == "sm_53"
    assert target.attrs["shared_memory_per_block"] == 49152
    assert target.attrs["max_threads_per_block"] == 1024
    assert target.attrs["thread_warp_size"] == 32
    assert target.attrs["registers_per_block"] == 32768

with tvm.transform.PassContext(opt_level=3):
    lib = 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 = utils.tempdir()
lib_fname = tmp.relpath("net.tar")
lib.export_library(lib_fname)

/workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function)
  warnings.warn(

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 = "192.168.1.11"
    port = 9091
    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
if local_demo:
    dev = remote.cpu(0)
else:
    dev = remote.cuda(0)

module = runtime.GraphModule(rlib["default"](dev))
# set input data
module.set_input(input_name, tvm.nd.array(x.astype("float32")))
# run
module.run()
# get output
out = module.get_output(0)
# get top1 result
top1 = np.argmax(out.numpy())
print("TVM prediction top-1: {}".format(synset[top1]))

TVM prediction top-1: tabby, tabby cat