Compile PaddlePaddle Models

Author: Ziyuan Ma

This article is an introductory tutorial to deploy PaddlePaddle models with Relay. To begin, we’ll install PaddlePaddle>=2.1.3:

pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple

For more details, refer to the official install instructions at: https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html

import tarfile
import paddle
import numpy as np
import tvm
from tvm import relay
from tvm.contrib.download import download_testdata

Load pretrained ResNet50 model

We load a pretrained ResNet50 provided by PaddlePaddle.

url = "https://bj.bcebos.com/x2paddle/models/paddle_resnet50.tar"
model_path = download_testdata(url, "paddle_resnet50.tar", module="model")

with tarfile.open(model_path) as tar:
    names = tar.getnames()
    for name in names:
        tar.extract(name, "./")

model = paddle.jit.load("./paddle_resnet50/model")

Load a test image

A single cat dominates the examples!

from PIL import Image
import paddle.vision.transforms as T


transforms = T.Compose(
    [
        T.Resize((256, 256)),
        T.CenterCrop(224),
        T.ToTensor(),
        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ]
)

img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
img_path = download_testdata(img_url, "cat.png", module="data")
img = Image.open(img_path).resize((224, 224))

img = transforms(img)
img = np.expand_dims(img, axis=0)

Compile the model with relay

target = "llvm"
shape_dict = {"inputs": img.shape}
mod, params = relay.frontend.from_paddle(model, shape_dict)

with tvm.transform.PassContext(opt_level=3):
    executor = relay.build_module.create_executor(
        "graph", mod, tvm.cpu(0), target, params
    ).evaluate()

Execute on TVM

dtype = "float32"
tvm_output = executor(tvm.nd.array(img.astype(dtype))).numpy()

Look up synset name

Look up prediction top 1 index in 1000 class synset.

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 = f.readlines()

top1 = np.argmax(tvm_output[0])
print(f"TVM prediction top-1 id: {top1}, class name: {synset[top1]}")
TVM prediction top-1 id: 282, class name:  282: 'tiger cat',

Total running time of the script: ( 1 minutes 4.693 seconds)

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