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| <div class="sphx-glr-download-link-note admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This tutorial can be used interactively with Google Colab! You can also click |
| <a class="reference internal" href="#sphx-glr-download-how-to-work-with-microtvm-micro-pytorch-py"><span class="std std-ref">here</span></a> to run the Jupyter notebook locally.</p> |
| <a class="reference external image-reference" href="https://colab.research.google.com/github/apache/tvm-site/blob/asf-site/docs/_downloads/09df7d9b9c90a2a1bdd570520693fd9f/micro_pytorch.ipynb"><img alt="https://raw.githubusercontent.com/tlc-pack/web-data/main/images/utilities/colab_button.svg" class="align-center" src="https://raw.githubusercontent.com/tlc-pack/web-data/main/images/utilities/colab_button.svg" width="300px" /></a> |
| </div> |
| <div class="sphx-glr-example-title section" id="microtvm-pytorch-tutorial"> |
| <span id="tutorial-micro-pytorch"></span><span id="sphx-glr-how-to-work-with-microtvm-micro-pytorch-py"></span><h1>4. microTVM PyTorch Tutorial<a class="headerlink" href="#microtvm-pytorch-tutorial" title="Permalink to this headline">¶</a></h1> |
| <p><strong>Authors</strong>: |
| <a class="reference external" href="https://github.com/mehrdadh">Mehrdad Hessar</a></p> |
| <p>This tutorial is showcasing microTVM host-driven AoT compilation with |
| a PyTorch model. This tutorial can be executed on a x86 CPU using C runtime (CRT).</p> |
| <p><strong>Note:</strong> This tutorial only runs on x86 CPU using CRT and does not run on Zephyr |
| since the model would not fit on our current supported Zephyr boards.</p> |
| <div class="section" id="install-microtvm-python-dependencies"> |
| <h2>Install microTVM Python dependencies<a class="headerlink" href="#install-microtvm-python-dependencies" title="Permalink to this headline">¶</a></h2> |
| <p>TVM does not include a package for Python serial communication, so |
| we must install one before using microTVM. We will also need TFLite |
| to load models.</p> |
| <blockquote> |
| <div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>%%shell |
| pip<span class="w"> </span>install<span class="w"> </span><span class="nv">pyserial</span><span class="o">==</span><span class="m">3</span>.5<span class="w"> </span><span class="nv">tflite</span><span class="o">==</span><span class="m">2</span>.1 |
| </pre></div> |
| </div> |
| </div></blockquote> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pathlib</span> |
| <span class="kn">import</span> <span class="nn">torch</span> |
| <span class="kn">import</span> <span class="nn">torchvision</span> |
| <span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">transforms</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| <span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span> |
| |
| <span class="kn">import</span> <span class="nn">tvm</span> |
| <span class="kn">from</span> <span class="nn">tvm</span> <span class="kn">import</span> <span class="n">relay</span> |
| <span class="kn">from</span> <span class="nn">tvm.contrib.download</span> <span class="kn">import</span> <span class="n">download_testdata</span> |
| <span class="kn">from</span> <span class="nn">tvm.relay.backend</span> <span class="kn">import</span> <span class="n">Executor</span> |
| <span class="kn">import</span> <span class="nn">tvm.micro.testing</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="load-a-pre-trained-pytorch-model"> |
| <h2>Load a pre-trained PyTorch model<a class="headerlink" href="#load-a-pre-trained-pytorch-model" title="Permalink to this headline">¶</a></h2> |
| <p>To begin with, load pre-trained MobileNetV2 from torchvision. Then, |
| download a cat image and preprocess it to use as the model input.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">mobilenet_v2</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="s2">"DEFAULT"</span><span class="p">,</span> <span class="n">quantize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> |
| |
| <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_shape</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">]</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_data</span></a> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_shape</span></a><span class="p">)</span> |
| <span class="n">scripted_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_data</span></a><span class="p">)</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> |
| |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_url</span></a> <span class="o">=</span> <span class="s2">"https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_path</span></a> <span class="o">=</span> <span class="n">download_testdata</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_url</span></a><span class="p">,</span> <span class="s2">"cat.png"</span><span class="p">,</span> <span class="n">module</span><span class="o">=</span><span class="s2">"data"</span><span class="p">)</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">img_path</span></a><span class="p">)</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span> |
| |
| <span class="c1"># Preprocess the image and convert to tensor</span> |
| <span class="n">my_preprocess</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span> |
| <span class="p">[</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span> <span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]),</span> |
| <span class="p">]</span> |
| <span class="p">)</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">my_preprocess</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> |
| |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_name</span></a> <span class="o">=</span> <span class="s2">"input0"</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">shape_list</span></a> <span class="o">=</span> <span class="p">[(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_name</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_shape</span></a><span class="p">)]</span> |
| <span class="n">relay_mod</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a> <span class="o">=</span> <a href="../../reference/api/python/relay/frontend.html#tvm.relay.frontend.from_pytorch" title="tvm.relay.frontend.from_pytorch" class="sphx-glr-backref-module-tvm-relay-frontend sphx-glr-backref-type-py-function"><span class="n">relay</span><span class="o">.</span><span class="n">frontend</span><span class="o">.</span><span class="n">from_pytorch</span></a><span class="p">(</span><span class="n">scripted_model</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">shape_list</span></a><span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/ao/quantization/utils.py:310: UserWarning: must run observer before calling calculate_qparams. Returning default values. |
| warnings.warn( |
| Downloading: "https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth" to /workspace/.cache/torch/hub/checkpoints/mobilenet_v2_qnnpack_37f702c5.pth |
| |
| 0%| | 0.00/3.42M [00:00<?, ?B/s] |
| 100%|##########| 3.42M/3.42M [00:00<00:00, 74.1MB/s] |
| /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/_utils.py:314: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() |
| device=storage.device, |
| /workspace/python/tvm/relay/frontend/pytorch_utils.py:47: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. |
| return LooseVersion(torch_ver) > ver |
| /venv/apache-tvm-py3.8/lib/python3.8/site-packages/setuptools/_distutils/version.py:346: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. |
| other = LooseVersion(other) |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="define-target-runtime-and-executor"> |
| <h2>Define Target, Runtime and Executor<a class="headerlink" href="#define-target-runtime-and-executor" title="Permalink to this headline">¶</a></h2> |
| <p>In this tutorial we use AOT host-driven executor. To compile the model |
| for an emulated embedded environment on an x86 machine we use C runtime (CRT) |
| and we use <cite>host</cite> micro target. Using this setup, TVM compiles the model |
| for C runtime which can run on a x86 CPU machine with the same flow that |
| would run on a physical microcontroller. |
| CRT Uses the main() from <cite>src/runtime/crt/host/main.cc</cite> |
| To use physical hardware, replace <cite>board</cite> with another physical micro target, e.g. <cite>nrf5340dk_nrf5340_cpuapp</cite> |
| or <cite>mps2_an521</cite> and change the platform type to Zephyr. |
| See more target examples in <a class="reference internal" href="micro_train.html#tutorial-micro-train-arduino"><span class="std std-ref">Training Vision Models for microTVM on Arduino</span></a> |
| and <a class="reference internal" href="micro_tflite.html#tutorial-micro-tflite"><span class="std std-ref">microTVM TFLite Tutorial</span></a>.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">micro</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">get_target</span><span class="p">(</span><span class="n">platform</span><span class="o">=</span><span class="s2">"crt"</span><span class="p">,</span> <span class="n">board</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> |
| |
| <span class="c1"># Use the C runtime (crt) and enable static linking by setting system-lib to True</span> |
| <span class="n">runtime</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">relay</span><span class="o">.</span><span class="n">backend</span><span class="o">.</span><span class="n">Runtime</span><span class="p">(</span><span class="s2">"crt"</span><span class="p">,</span> <span class="p">{</span><span class="s2">"system-lib"</span><span class="p">:</span> <span class="kc">True</span><span class="p">})</span> |
| |
| <span class="c1"># Use the AOT executor rather than graph or vm executors. Don't use unpacked API or C calling style.</span> |
| <span class="n">executor</span> <span class="o">=</span> <span class="n">Executor</span><span class="p">(</span><span class="s2">"aot"</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="compile-the-model"> |
| <h2>Compile the model<a class="headerlink" href="#compile-the-model" title="Permalink to this headline">¶</a></h2> |
| <p>Now, we compile the model for the target:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <a href="../../reference/api/python/ir.html#tvm.transform.PassContext" title="tvm.transform.PassContext" class="sphx-glr-backref-module-tvm-transform sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tvm</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">PassContext</span></a><span class="p">(</span> |
| <span class="n">opt_level</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> |
| <span class="n">config</span><span class="o">=</span><span class="p">{</span><span class="s2">"tir.disable_vectorize"</span><span class="p">:</span> <span class="kc">True</span><span class="p">},</span> |
| <span class="p">):</span> |
| <span class="n">module</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">relay</span><span class="o">.</span><span class="n">build</span><span class="p">(</span> |
| <span class="n">relay_mod</span><span class="p">,</span> <a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="o">=</span><a href="../../reference/api/python/target.html#tvm.target.Target" title="tvm.target.Target" class="sphx-glr-backref-module-tvm-target sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target</span></a><span class="p">,</span> <span class="n">runtime</span><span class="o">=</span><span class="n">runtime</span><span class="p">,</span> <span class="n">executor</span><span class="o">=</span><span class="n">executor</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">params</span></a> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="create-a-microtvm-project"> |
| <h2>Create a microTVM project<a class="headerlink" href="#create-a-microtvm-project" title="Permalink to this headline">¶</a></h2> |
| <p>Now that we have the compiled model as an IRModule, we need to create a firmware project |
| to use the compiled model with microTVM. To do this, we use Project API.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">template_project_path</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="pathlib.Path" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class"><span class="n">pathlib</span><span class="o">.</span><span class="n">Path</span></a><span class="p">(</span><a href="../../reference/api/python/micro.html#tvm.micro.get_microtvm_template_projects" title="tvm.micro.get_microtvm_template_projects" class="sphx-glr-backref-module-tvm-micro sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">micro</span><span class="o">.</span><span class="n">get_microtvm_template_projects</span></a><span class="p">(</span><span class="s2">"crt"</span><span class="p">))</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">project_options</span></a> <span class="o">=</span> <span class="p">{</span><span class="s2">"verbose"</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span> <span class="s2">"workspace_size_bytes"</span><span class="p">:</span> <span class="mi">6</span> <span class="o">*</span> <span class="mi">1024</span> <span class="o">*</span> <span class="mi">1024</span><span class="p">}</span> |
| |
| <a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_dir</span></a> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.tempdir" title="tvm.contrib.utils.tempdir" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">tempdir</span></a><span class="p">()</span> <span class="o">/</span> <span class="s2">"project"</span> |
| <a href="../../reference/api/python/micro.html#tvm.micro.GeneratedProject" title="tvm.micro.GeneratedProject" class="sphx-glr-backref-module-tvm-micro sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">project</span></a> <span class="o">=</span> <a href="../../reference/api/python/micro.html#tvm.micro.generate_project" title="tvm.micro.generate_project" class="sphx-glr-backref-module-tvm-micro sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">micro</span><span class="o">.</span><span class="n">generate_project</span></a><span class="p">(</span> |
| <span class="nb">str</span><span class="p">(</span><a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">template_project_path</span></a><span class="p">),</span> |
| <span class="n">module</span><span class="p">,</span> |
| <a href="https://docs.python.org/3/library/pathlib.html#pathlib.PosixPath" title="pathlib.PosixPath" class="sphx-glr-backref-module-pathlib sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">temp_dir</span></a><span class="p">,</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">project_options</span></a><span class="p">,</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="build-flash-and-execute-the-model"> |
| <h2>Build, flash and execute the model<a class="headerlink" href="#build-flash-and-execute-the-model" title="Permalink to this headline">¶</a></h2> |
| <p>Next, we build the microTVM project and flash it. Flash step is specific to |
| physical microcontroller and it is skipped if it is simulating a microcontroller |
| via the host <cite>main.cc`</cite> or if a Zephyr emulated board is selected as the target.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../reference/api/python/micro.html#tvm.micro.GeneratedProject" title="tvm.micro.GeneratedProject" class="sphx-glr-backref-module-tvm-micro sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">project</span></a><span class="o">.</span><span class="n">build</span><span class="p">()</span> |
| <a href="../../reference/api/python/micro.html#tvm.micro.GeneratedProject" title="tvm.micro.GeneratedProject" class="sphx-glr-backref-module-tvm-micro sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">project</span></a><span class="o">.</span><span class="n">flash</span><span class="p">()</span> |
| |
| <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_data</span></a> <span class="o">=</span> <span class="p">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_name</span></a><span class="p">:</span> <a href="../../reference/api/python/ndarray.html#tvm.nd.array" title="tvm.nd.array" class="sphx-glr-backref-module-tvm-nd sphx-glr-backref-type-py-function"><span class="n">tvm</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span></a><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">))}</span> |
| <span class="k">with</span> <a href="../../reference/api/python/micro.html#tvm.micro.Session" title="tvm.micro.Session" class="sphx-glr-backref-module-tvm-micro sphx-glr-backref-type-py-class"><span class="n">tvm</span><span class="o">.</span><span class="n">micro</span><span class="o">.</span><span class="n">Session</span></a><span class="p">(</span><a href="../../reference/api/python/micro.html#tvm.micro.GeneratedProject" title="tvm.micro.GeneratedProject" class="sphx-glr-backref-module-tvm-micro sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">project</span></a><span class="o">.</span><span class="n">transport</span><span class="p">())</span> <span class="k">as</span> <a href="../../reference/api/python/micro.html#tvm.micro.Session" title="tvm.micro.Session" class="sphx-glr-backref-module-tvm-micro sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">session</span></a><span class="p">:</span> |
| <span class="n">aot_executor</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">executor</span><span class="o">.</span><span class="n">aot_executor</span><span class="o">.</span><span class="n">AotModule</span><span class="p">(</span><a href="../../reference/api/python/micro.html#tvm.micro.Session" title="tvm.micro.Session" class="sphx-glr-backref-module-tvm-micro sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">session</span></a><span class="o">.</span><span class="n">create_aot_executor</span><span class="p">())</span> |
| <span class="n">aot_executor</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="o">**</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_data</span></a><span class="p">)</span> |
| <span class="n">aot_executor</span><span class="o">.</span><span class="n">run</span><span class="p">()</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">aot_executor</span><span class="o">.</span><span class="n">get_output</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="look-up-synset-name"> |
| <h2>Look up synset name<a class="headerlink" href="#look-up-synset-name" title="Permalink to this headline">¶</a></h2> |
| <p>Look up prediction top 1 index in 1000 class synset.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset_url</span></a> <span class="o">=</span> <span class="p">(</span> |
| <span class="s2">"https://raw.githubusercontent.com/Cadene/"</span> |
| <span class="s2">"pretrained-models.pytorch/master/data/"</span> |
| <span class="s2">"imagenet_synsets.txt"</span> |
| <span class="p">)</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset_name</span></a> <span class="o">=</span> <span class="s2">"imagenet_synsets.txt"</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset_path</span></a> <span class="o">=</span> <span class="n">download_testdata</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset_url</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset_name</span></a><span class="p">,</span> <span class="n">module</span><span class="o">=</span><span class="s2">"data"</span><span class="p">)</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synset_path</span></a><span class="p">)</span> <span class="k">as</span> <a href="https://docs.python.org/3/library/io.html#io.TextIOWrapper" title="io.TextIOWrapper" class="sphx-glr-backref-module-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">:</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synsets</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/io.html#io.TextIOWrapper" title="io.TextIOWrapper" class="sphx-glr-backref-module-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="o">.</span><span class="n">readlines</span><span class="p">()</span> |
| |
| <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synsets</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synsets</span></a><span class="p">]</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">" "</span><span class="p">)</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">synsets</span></a><span class="p">]</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">key_to_classname</span></a> <span class="o">=</span> <span class="p">{</span><span class="n">spl</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span> <span class="s2">" "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">spl</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="k">for</span> <span class="n">spl</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">}</span> |
| |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_url</span></a> <span class="o">=</span> <span class="p">(</span> |
| <span class="s2">"https://raw.githubusercontent.com/Cadene/"</span> |
| <span class="s2">"pretrained-models.pytorch/master/data/"</span> |
| <span class="s2">"imagenet_classes.txt"</span> |
| <span class="p">)</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_path</span></a> <span class="o">=</span> <span class="n">download_testdata</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_url</span></a><span class="p">,</span> <span class="s2">"imagenet_classes.txt"</span><span class="p">,</span> <span class="n">module</span><span class="o">=</span><span class="s2">"data"</span><span class="p">)</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_path</span></a><span class="p">)</span> <span class="k">as</span> <a href="https://docs.python.org/3/library/io.html#io.TextIOWrapper" title="io.TextIOWrapper" class="sphx-glr-backref-module-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="p">:</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_id_to_key</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/io.html#io.TextIOWrapper" title="io.TextIOWrapper" class="sphx-glr-backref-module-io sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">f</span></a><span class="o">.</span><span class="n">readlines</span><span class="p">()</span> |
| |
| <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_id_to_key</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_id_to_key</span></a><span class="p">]</span> |
| |
| <span class="c1"># Get top-1 result for TVM</span> |
| <span class="n">top1_tvm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">result</span><span class="p">)</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tvm_class_key</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_id_to_key</span></a><span class="p">[</span><span class="n">top1_tvm</span><span class="p">]</span> |
| |
| <span class="c1"># Convert input to PyTorch variable and get PyTorch result for comparison</span> |
| <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span> |
| <span class="n">torch_img</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">torch_img</span><span class="p">)</span> |
| |
| <span class="c1"># Get top-1 result for PyTorch</span> |
| <span class="n">top1_torch</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch_class_key</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_id_to_key</span></a><span class="p">[</span><span class="n">top1_torch</span><span class="p">]</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Relay top-1 id: </span><span class="si">{}</span><span class="s2">, class name: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">top1_tvm</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">key_to_classname</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tvm_class_key</span></a><span class="p">]))</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Torch top-1 id: </span><span class="si">{}</span><span class="s2">, class name: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">top1_torch</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">key_to_classname</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch_class_key</span></a><span class="p">]))</span> |
| </pre></div> |
| </div> |
| <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Relay top-1 id: 282, class name: tiger cat |
| Torch top-1 id: 282, class name: tiger cat |
| </pre></div> |
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