Note
This tutorial can be used interactively with Google Colab! You can also click here to run the Jupyter notebook locally.
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()
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 10.878 seconds)