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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-deploy-models-deploy-object-detection-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/399e1d7889ca66b69d51655784827503/deploy_object_detection_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="compile-pytorch-object-detection-models"> |
| <span id="sphx-glr-how-to-deploy-models-deploy-object-detection-pytorch-py"></span><h1>Compile PyTorch Object Detection Models<a class="headerlink" href="#compile-pytorch-object-detection-models" title="Permalink to this headline">¶</a></h1> |
| <p>This article is an introductory tutorial to deploy PyTorch object |
| detection models with Relay VM.</p> |
| <p>For us to begin with, PyTorch should be installed. |
| TorchVision is also required since we will be using it as our model zoo.</p> |
| <p>A quick solution is to install via pip</p> |
| <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>torch |
| pip<span class="w"> </span>install<span class="w"> </span>torchvision |
| </pre></div> |
| </div> |
| <p>or please refer to official site |
| <a class="reference external" href="https://pytorch.org/get-started/locally/">https://pytorch.org/get-started/locally/</a></p> |
| <p>PyTorch versions should be backwards compatible but should be used |
| with the proper TorchVision version.</p> |
| <p>Currently, TVM supports PyTorch 1.7 and 1.4. Other versions may |
| be unstable.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></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</span> <span class="kn">import</span> <span class="n">relay</span> |
| <span class="kn">from</span> <span class="nn">tvm.runtime.vm</span> <span class="kn">import</span> <span class="n">VirtualMachine</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">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| <span class="kn">import</span> <span class="nn">cv2</span> |
| |
| <span class="c1"># PyTorch imports</span> |
| <span class="kn">import</span> <span class="nn">torch</span> |
| <span class="kn">import</span> <span class="nn">torchvision</span> |
| </pre></div> |
| </div> |
| <div class="section" id="load-pre-trained-maskrcnn-from-torchvision-and-do-tracing"> |
| <h2>Load pre-trained maskrcnn from torchvision and do tracing<a class="headerlink" href="#load-pre-trained-maskrcnn-from-torchvision-and-do-tracing" title="Permalink to this headline">¶</a></h2> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_size</span></a> <span class="o">=</span> <span class="mi">300</span> |
| |
| <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" 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> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_size</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_size</span></a><span class="p">)</span> |
| |
| |
| <span class="k">def</span> <span class="nf">do_trace</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inp</span><span class="p">):</span> |
| <span class="n">model_trace</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> <span class="n">inp</span><span class="p">)</span> |
| <span class="n">model_trace</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> |
| <span class="k">return</span> <span class="n">model_trace</span> |
| |
| |
| <span class="k">def</span> <span class="nf">dict_to_tuple</span><span class="p">(</span><span class="n">out_dict</span><span class="p">):</span> |
| <span class="k">if</span> <span class="s2">"masks"</span> <span class="ow">in</span> <span class="n">out_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span> |
| <span class="k">return</span> <span class="n">out_dict</span><span class="p">[</span><span class="s2">"boxes"</span><span class="p">],</span> <span class="n">out_dict</span><span class="p">[</span><span class="s2">"scores"</span><span class="p">],</span> <span class="n">out_dict</span><span class="p">[</span><span class="s2">"labels"</span><span class="p">],</span> <span class="n">out_dict</span><span class="p">[</span><span class="s2">"masks"</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">out_dict</span><span class="p">[</span><span class="s2">"boxes"</span><span class="p">],</span> <span class="n">out_dict</span><span class="p">[</span><span class="s2">"scores"</span><span class="p">],</span> <span class="n">out_dict</span><span class="p">[</span><span class="s2">"labels"</span><span class="p">]</span> |
| |
| |
| <span class="k">class</span> <span class="nc">TraceWrapper</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span> |
| |
| <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inp</span><span class="p">):</span> |
| <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">out</span></a> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">inp</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">dict_to_tuple</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">out</span></a><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| |
| |
| <span class="n">model_func</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">detection</span><span class="o">.</span><span class="n">maskrcnn_resnet50_fpn</span> |
| <span class="n">model</span> <span class="o">=</span> <span class="n">TraceWrapper</span><span class="p">(</span><span class="n">model_func</span><span class="p">(</span><span class="n">pretrained</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">eval</span><span class="p">()</span> |
| <span class="n">inp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">250.0</span><span class="p">,</span> <span class="n">size</span><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> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_size</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_size</span></a><span class="p">)))</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> |
| <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">out</span></a> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inp</span><span class="p">)</span> |
| <span class="n">script_module</span> <span class="o">=</span> <span class="n">do_trace</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inp</span><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/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead. |
| warnings.warn( |
| /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MaskRCNN_ResNet50_FPN_Weights.COCO_V1`. You can also use `weights=MaskRCNN_ResNet50_FPN_Weights.DEFAULT` to get the most up-to-date weights. |
| warnings.warn(msg) |
| Downloading: "https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth" to /workspace/.cache/torch/hub/checkpoints/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth |
| |
| 0%| | 0.00/170M [00:00<?, ?B/s] |
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| 100%|##########| 170M/170M [00:04<00:00, 39.3MB/s] |
| /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/nn/functional.py:3912: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). |
| (torch.floor((input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32)).float())) |
| /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:157: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). |
| boxes_x = torch.min(boxes_x, torch.tensor(width, dtype=boxes.dtype, device=boxes.device)) |
| /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/ops/boxes.py:159: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). |
| boxes_y = torch.min(boxes_y, torch.tensor(height, dtype=boxes.dtype, device=boxes.device)) |
| /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torch/__init__.py:1209: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! |
| assert condition, message |
| /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/detection/transform.py:298: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). |
| torch.tensor(s, dtype=torch.float32, device=boxes.device) |
| /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/detection/transform.py:299: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). |
| / torch.tensor(s_orig, dtype=torch.float32, device=boxes.device) |
| /venv/apache-tvm-py3.8/lib/python3.8/site-packages/torchvision/models/detection/roi_heads.py:389: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). |
| return torch.tensor(M + 2 * padding).to(torch.float32) / torch.tensor(M).to(torch.float32) |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="download-a-test-image-and-pre-process"> |
| <h2>Download a test image and pre-process<a class="headerlink" href="#download-a-test-image-and-pre-process" title="Permalink to this headline">¶</a></h2> |
| <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">img_url</span></a> <span class="o">=</span> <span class="p">(</span> |
| <span class="s2">"https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/detection/street_small.jpg"</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="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">"test_street_small.jpg"</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">cv2</span><span class="o">.</span><span class="n">imread</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">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">resize</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_size</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_size</span></a><span class="p">))</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2RGB</span></a><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">transpose</span><span class="p">(</span><span class="n">img</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</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="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="import-the-graph-to-relay"> |
| <h2>Import the graph to Relay<a class="headerlink" href="#import-the-graph-to-relay" title="Permalink to this headline">¶</a></h2> |
| <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">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#tuple" title="builtins.tuple" 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">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">script_module</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>/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) |
| /workspace/python/tvm/relay/build_module.py:345: DeprecationWarning: Please use input parameter mod (tvm.IRModule) instead of deprecated parameter mod (tvm.relay.function.Function) |
| warnings.warn( |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="compile-with-relay-vm"> |
| <h2>Compile with Relay VM<a class="headerlink" href="#compile-with-relay-vm" title="Permalink to this headline">¶</a></h2> |
| <p>Note: Currently only CPU target is supported. For x86 target, it is |
| highly recommended to build TVM with Intel MKL and Intel OpenMP to get |
| best performance, due to the existence of large dense operator in |
| torchvision rcnn models.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Add "-libs=mkl" to get best performance on x86 target.</span> |
| <span class="c1"># For x86 machine supports AVX512, the complete target is</span> |
| <span class="c1"># "llvm -mcpu=skylake-avx512 -libs=mkl"</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">target</span></a> <span class="o">=</span> <span class="s2">"llvm"</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">disabled_pass</span><span class="o">=</span><span class="p">[</span><span class="s2">"FoldScaleAxis"</span><span class="p">]):</span> |
| <span class="n">vm_exec</span> <span class="o">=</span> <span class="n">relay</span><span class="o">.</span><span class="n">vm</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">mod</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">target</span></a><span class="o">=</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">target</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">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="inference-with-relay-vm"> |
| <h2>Inference with Relay VM<a class="headerlink" href="#inference-with-relay-vm" title="Permalink to this headline">¶</a></h2> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dev</span> <span class="o">=</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span> |
| <span class="n">vm</span> <span class="o">=</span> <span class="n">VirtualMachine</span><span class="p">(</span><span class="n">vm_exec</span><span class="p">,</span> <span class="n">dev</span><span class="p">)</span> |
| <span class="n">vm</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="s2">"main"</span><span class="p">,</span> <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> <span class="n">img</span><span class="p">})</span> |
| <span class="n">tvm_res</span> <span class="o">=</span> <span class="n">vm</span><span class="o">.</span><span class="n">run</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="get-boxes-with-score-larger-than-0-9"> |
| <h2>Get boxes with score larger than 0.9<a class="headerlink" href="#get-boxes-with-score-larger-than-0-9" title="Permalink to this headline">¶</a></h2> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">score_threshold</span></a> <span class="o">=</span> <span class="mf">0.9</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">boxes</span></a> <span class="o">=</span> <span class="n">tvm_res</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><span class="o">.</span><span class="n">tolist</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">valid_boxes</span></a> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">score</span></a> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tvm_res</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()):</span> |
| <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">score</span></a> <span class="o">></span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">score_threshold</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">valid_boxes</span></a><span class="o">.</span><span class="n">append</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">boxes</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">])</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">break</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Get </span><span class="si">{}</span><span class="s2"> valid boxes"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</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">valid_boxes</span></a><span class="p">)))</span> |
| </pre></div> |
| </div> |
| <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Get 9 valid boxes |
| </pre></div> |
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