add PaddlePaddle tutorial

docs/conf.py

@@ -257,6 +257,7 @@ def git_describe_version(original_version):
         "from_coreml.py",
         "from_darknet.py",
         "from_caffe2.py",
+        "from_paddle.py",
     ],
     "language": [
         "schedule_primitives.py",

tutorials/frontend/from_paddle.py

@@ -0,0 +1,114 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Compile PaddlePaddle Models
+===================
+**Author**: `Ziyuan Ma <https://github.com/ZiyuanMa/>`_
+This article is an introductory tutorial to deploy PaddlePaddle models with Relay.
+For us to begin with, PaddlePaddle>=2.3.1 is required to be installed.
+A quick solution is
+.. code-block:: bash
+    pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
+or please refer to official site.
+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
+import tvm.relay as 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"
+img_path = download_testdata(img_url, "cat.png", module="data")
+synset_path = download_testdata(synset_url, synset_name, module="data")
+with open(synset_path) as f:
+    synset = eval(f.read())
+
+top1 = np.argmax(tvm_output[0])
+print(f"TVM prediction top-1 id: {top1}, class name: {synset[top1]}")