Deploy the Pretrained Model on Jetson Nano (apache#11037)

* Create deploy_model_on_nano.py

add deploy_model_on_nano.py

* Update deploy_model_on_nano.py

* fix doc build bug

* Update deploy_model_on_nano.py

* fix ci error

* Update deploy_model_on_nano.py

gallery/how_to/deploy_models/deploy_model_on_nano.py

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+# 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.
+"""
+.. _tutorial-deploy-model-on-nano:
+
+Deploy the Pretrained Model on Jetson Nano
+===========================================
+**Author**: `BBuf <https://github.com/BBuf>`_
+
+This is an example of using Relay to compile a ResNet model and deploy
+it on Jetson Nano.
+"""
+
+# sphinx_gallery_start_ignore
+from tvm import testing
+
+testing.utils.install_request_hook(depth=3)
+# sphinx_gallery_end_ignore
+
+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:
+#
+# 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.
+#
+# .. code-block:: bash
+#
+#   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 :code:`~/.bashrc` file. We can edit :code:`~/.bashrc`
+# using :code:`vi ~/.bashrc` and add the line below (Assuming your TVM
+# directory is in :code:`~/tvm`):
+#
+# .. code-block:: bash
+#
+#   export PYTHONPATH=$PYTHONPATH:~/tvm/python
+#
+# To update the environment variables, execute :code:`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).
+#
+#   .. code-block:: bash
+#
+#     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.
+#
+#    .. code-block:: bash
+#
+#      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
+# `MXNet Gluon model zoo <https://mxnet.apache.org/api/python/gluon/model_zoo.html>`_.
+# You can found more details about this part at tutorial :ref:`tutorial-from-mxnet`.
+
+from mxnet.gluon.model_zoo.vision import get_model
+from PIL import Image
+import numpy as np
+
+# one line to get the model
+block = get_model("resnet18_v1", pretrained=True)
+
+######################################################################
+# 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.
+
+# We support MXNet static graph(symbol) and HybridBlock in mxnet.gluon
+shape_dict = {"data": x.shape}
+mod, params = relay.frontend.from_mxnet(block, shape_dict)
+# 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 :py:func:`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 :code:`net` and :code:`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 :code:`llvm`. If running it on the Jetson Nano, we need to
+# set it as :code:`nvidia/jetson-nano`. Set :code:`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)
+
+######################################################################
+# 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("data", 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]))