| """ |
| .. _tutorial-deploy-model-on-rasp: |
| |
| Deploy the Pretrained Model on Raspberry Pi |
| =========================================== |
| **Author**: `Ziheng Jiang <https://ziheng.org/>`_ |
| |
| This is an example of using NNVM to compile a ResNet model and deploy |
| it on Raspberry Pi. |
| """ |
| |
| import tvm |
| import nnvm.compiler |
| import nnvm.testing |
| from tvm import rpc |
| from tvm.contrib import util, graph_runtime as runtime |
| |
| ###################################################################### |
| # .. _build-tvm-runtime-on-device: |
| # |
| # Build TVM Runtime on Device |
| # --------------------------- |
| # |
| # The first step is to build 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. Raspberry Pi. 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/dmlc/tvm |
| # cd tvm |
| # make runtime -j4 |
| # |
| # 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 Raspberry Pi in our example). |
| # |
| # .. code-block:: bash |
| # |
| # python -m tvm.exec.rpc_server --host 0.0.0.0 --port=9090 |
| # |
| # If you see the line below, it means the RPC server started |
| # successfully on your device. |
| # |
| # .. code-block:: bash |
| # |
| # INFO:root:RPCServer: bind to 0.0.0.0:9090 |
| # |
| |
| ###################################################################### |
| # 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.incubator.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 mxnet.gluon.utils import download |
| 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_name = 'cat.png' |
| download('https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true', img_name) |
| image = Image.open(img_name).resize((224, 224)) |
| |
| def transform_image(image): |
| image = np.array(image) - np.array([123., 117., 104.]) |
| 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 = 'synset.txt' |
| download(synset_url, synset_name) |
| with open(synset_name) 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 |
| net, params = nnvm.frontend.from_mxnet(block) |
| # we want a probability so add a softmax operator |
| net = nnvm.sym.softmax(net) |
| |
| ###################################################################### |
| # 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 :any:`nnvm.compiler.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 |
| # NNVM 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 Raspberry Pi, we need to |
| # specify its instruction set. 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.create('llvm') |
| else: |
| target = tvm.target.arm_cpu('rasp3b') |
| # The above line is a simple form of |
| # target = tvm.target.create('llvm -device=arm_cpu -model=bcm2837 -target=armv7l-linux-gnueabihf -mattr=+neon') |
| |
| with nnvm.compiler.build_config(opt_level=3): |
| graph, lib, params = nnvm.compiler.build( |
| net, target, shape={"data": data_shape}, params=params) |
| |
| # After `nnvm.compiler.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 = util.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 = '10.77.1.162' |
| port = 9090 |
| 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 |
| ctx = remote.cpu(0) |
| module = runtime.create(graph, rlib, ctx) |
| # set parameter (upload params to the remote device. This may take a while) |
| module.set_input(**params) |
| # 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.asnumpy()) |
| print('TVM prediction top-1: {}'.format(synset[top1])) |