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| <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-model-on-adreno-tvmc-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/613e0775d8981e544e6ca9602a975916/deploy_model_on_adreno_tvmc.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> |
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| <div class="sphx-glr-example-title section" id="deploy-the-pretrained-model-on-adreno-with-tvmc-interface"> |
| <span id="tutorial-deploy-model-on-adreno-tvmc"></span><span id="sphx-glr-how-to-deploy-models-deploy-model-on-adreno-tvmc-py"></span><h1>Deploy the Pretrained Model on Adreno™ with tvmc Interface<a class="headerlink" href="#deploy-the-pretrained-model-on-adreno-with-tvmc-interface" title="Permalink to this headline">¶</a></h1> |
| <p><strong>Author</strong>: Siva Rama Krishna</p> |
| <p>This article is a step-by-step tutorial to deploy pretrained Keras resnet50 model on Adreno™.</p> |
| <p>Besides that, you should have TVM built for Android. |
| See the following instructions on how to build it and setup RPC environment.</p> |
| <p><a class="reference external" href="https://tvm.apache.org/docs/how_to/deploy/adreno.html">Deploy to Adreno GPU</a></p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span> |
| <span class="kn">import</span> <span class="nn">tvm</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">tvm</span> <span class="kn">import</span> <span class="n">relay</span> |
| <span class="kn">from</span> <span class="nn">tvm.driver</span> <span class="kn">import</span> <span class="n">tvmc</span> |
| <span class="kn">from</span> <span class="nn">tvm.driver.tvmc.model</span> <span class="kn">import</span> <span class="n">TVMCPackage</span> |
| <span class="kn">from</span> <span class="nn">tvm.contrib</span> <span class="kn">import</span> <span class="n">utils</span> |
| </pre></div> |
| </div> |
| <div class="section" id="configuration"> |
| <h2>Configuration<a class="headerlink" href="#configuration" title="Permalink to this headline">¶</a></h2> |
| <p>Specify Adreno target before compiling to generate texture |
| leveraging kernels and get all the benefits of textures |
| Note: This generated example running on our x86 server for demonstration. |
| If running it on the Android device, we need to |
| specify its instruction set. Set <code class="code docutils literal notranslate"><span class="pre">local_demo</span></code> to False if you want |
| to run this tutorial with a real device over rpc.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a> <span class="o">=</span> <span class="kc">True</span> |
| |
| <span class="c1"># by default on CPU target will execute.</span> |
| <span class="c1"># select 'llvm', 'opencl' and 'opencl -device=adreno'</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="c1"># Change target configuration.</span> |
| <span class="c1"># Run `adb shell cat /proc/cpuinfo` to find the arch.</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">arch</span></a> <span class="o">=</span> <span class="s2">"arm64"</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_host</span></a> <span class="o">=</span> <span class="s2">"llvm -mtriple=</span><span class="si">%s</span><span class="s2">-linux-android"</span> <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">arch</span></a> |
| |
| <span class="c1"># Auto tuning is compute and time taking task, hence disabling for default run. Please enable it if required.</span> |
| <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">is_tuning</span></a> <span class="o">=</span> <span class="kc">False</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">tune_log</span></a> <span class="o">=</span> <span class="s2">"adreno-resnet50.log"</span> |
| |
| <span class="c1"># To enable OpenCLML accelerated operator library.</span> |
| <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">enable_clml</span></a> <span class="o">=</span> <span class="kc">False</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">cross_compiler</span></a> <span class="o">=</span> <span class="p">(</span> |
| <a href="https://docs.python.org/3/library/os.html#os.getenv" title="os.getenv" class="sphx-glr-backref-module-os sphx-glr-backref-type-py-function"><span class="n">os</span><span class="o">.</span><span class="n">getenv</span></a><span class="p">(</span><span class="s2">"ANDROID_NDK_HOME"</span><span class="p">,</span> <span class="s2">""</span><span class="p">)</span> |
| <span class="o">+</span> <span class="s2">"/toolchains/llvm/prebuilt/linux-x86_64/bin/aarch64-linux-android28-clang"</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="make-a-keras-resnet50-model"> |
| <h2>Make a Keras Resnet50 Model<a class="headerlink" href="#make-a-keras-resnet50-model" title="Permalink to this headline">¶</a></h2> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tensorflow.keras.applications.resnet50</span> <span class="kn">import</span> <span class="n">ResNet50</span> |
| |
| <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.TempDirectory" title="tvm.contrib.utils.TempDirectory" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmp_path</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">utils</span><span class="o">.</span><span class="n">tempdir</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">model_file_name</span></a> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.TempDirectory.relpath" title="tvm.contrib.utils.TempDirectory.relpath" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-method"><span class="n">tmp_path</span><span class="o">.</span><span class="n">relpath</span></a><span class="p">(</span><span class="s2">"resnet50.h5"</span><span class="p">)</span> |
| |
| <span class="n">model</span> <span class="o">=</span> <span class="n">ResNet50</span><span class="p">(</span><span class="n">include_top</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="s2">"imagenet"</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</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="mi">3</span><span class="p">),</span> <span class="n">classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span> |
| <span class="n">model</span><span class="o">.</span><span class="n">save</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">model_file_name</span></a><span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5 |
| |
| 8192/102967424 [..............................] - ETA: 0s |
| 7208960/102967424 [=>............................] - ETA: 0s |
| 12632064/102967424 [==>...........................] - ETA: 0s |
| 17702912/102967424 [====>.........................] - ETA: 0s |
| 22740992/102967424 [=====>........................] - ETA: 0s |
| 25157632/102967424 [======>.......................] - ETA: 0s |
| 33546240/102967424 [========>.....................] - ETA: 0s |
| 40517632/102967424 [==========>...................] - ETA: 0s |
| 42770432/102967424 [===========>..................] - ETA: 0s |
| 50323456/102967424 [=============>................] - ETA: 0s |
| 55844864/102967424 [===============>..............] - ETA: 0s |
| 60841984/102967424 [================>.............] - ETA: 0s |
| 65814528/102967424 [==================>...........] - ETA: 0s |
| 70828032/102967424 [===================>..........] - ETA: 0s |
| 75489280/102967424 [====================>.........] - ETA: 0s |
| 82010112/102967424 [======================>.......] - ETA: 0s |
| 87105536/102967424 [========================>.....] - ETA: 0s |
| 92045312/102967424 [=========================>....] - ETA: 0s |
| 95789056/102967424 [==========================>...] - ETA: 0s |
| 102121472/102967424 [============================>.] - ETA: 0s |
| 102967424/102967424 [==============================] - 1s 0us/step |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="load-model"> |
| <h2>Load Model<a class="headerlink" href="#load-model" title="Permalink to this headline">¶</a></h2> |
| <p>Convert a model from any framework to a tvm relay module. |
| tvmc.load supports models from any framework (like tensorflow saves_model, onnx, tflite ..etc) and auto detects the filetype.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tvmc_model</span> <span class="o">=</span> <span class="n">tvmc</span><span class="o">.</span><span class="n">load</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">model_file_name</span></a><span class="p">)</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="n">tvmc_model</span><span class="o">.</span><span class="n">mod</span><span class="p">)</span> |
| |
| <span class="c1"># tvmc_model consists of tvmc_mode.mod which is relay module and tvmc_model.params which parms of the module.</span> |
| </pre></div> |
| </div> |
| <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>def @main(%input_2: Tensor[(1, 224, 224, 3), float32], %v_param_1: Tensor[(7, 7, 3, 64), float32], %v_param_2: Tensor[(64), float32], %v_param_3: Tensor[(64), float32], %v_param_4: Tensor[(64), float32], %v_param_5: Tensor[(64), float32], %v_param_6: Tensor[(64), float32], %v_param_19: Tensor[(1, 1, 64, 256), float32], %v_param_20: Tensor[(256), float32], %v_param_23: Tensor[(256), float32], %v_param_24: Tensor[(256), float32], %v_param_25: Tensor[(256), float32], %v_param_26: Tensor[(256), float32], %v_param_7: Tensor[(1, 1, 64, 64), float32], %v_param_8: Tensor[(64), float32], %v_param_9: Tensor[(64), float32], %v_param_10: Tensor[(64), float32], %v_param_11: Tensor[(64), float32], %v_param_12: Tensor[(64), float32], %v_param_13: Tensor[(3, 3, 64, 64), float32], %v_param_14: Tensor[(64), float32], %v_param_15: Tensor[(64), float32], %v_param_16: Tensor[(64), float32], %v_param_17: Tensor[(64), float32], %v_param_18: Tensor[(64), float32], %v_param_21: Tensor[(1, 1, 64, 256), float32], %v_param_22: Tensor[(256), float32], %v_param_27: Tensor[(256), float32], %v_param_28: Tensor[(256), float32], %v_param_29: Tensor[(256), float32], %v_param_30: Tensor[(256), float32], %v_param_31: Tensor[(1, 1, 256, 64), float32], %v_param_32: Tensor[(64), float32], %v_param_33: Tensor[(64), float32], %v_param_34: Tensor[(64), float32], %v_param_35: Tensor[(64), float32], %v_param_36: Tensor[(64), float32], %v_param_37: Tensor[(3, 3, 64, 64), float32], %v_param_38: Tensor[(64), float32], %v_param_39: Tensor[(64), float32], %v_param_40: Tensor[(64), float32], %v_param_41: Tensor[(64), float32], %v_param_42: Tensor[(64), float32], %v_param_43: Tensor[(1, 1, 64, 256), float32], %v_param_44: Tensor[(256), float32], %v_param_45: Tensor[(256), float32], %v_param_46: Tensor[(256), float32], %v_param_47: Tensor[(256), float32], %v_param_48: Tensor[(256), float32], %v_param_49: Tensor[(1, 1, 256, 64), float32], %v_param_50: Tensor[(64), float32], %v_param_51: Tensor[(64), float32], %v_param_52: Tensor[(64), float32], %v_param_53: Tensor[(64), float32], %v_param_54: Tensor[(64), float32], %v_param_55: Tensor[(3, 3, 64, 64), float32], %v_param_56: Tensor[(64), float32], %v_param_57: Tensor[(64), float32], %v_param_58: Tensor[(64), float32], %v_param_59: Tensor[(64), float32], %v_param_60: Tensor[(64), float32], %v_param_61: Tensor[(1, 1, 64, 256), float32], %v_param_62: Tensor[(256), float32], %v_param_63: Tensor[(256), float32], %v_param_64: Tensor[(256), float32], %v_param_65: Tensor[(256), float32], %v_param_66: Tensor[(256), float32], %v_param_79: Tensor[(1, 1, 256, 512), float32], %v_param_80: Tensor[(512), float32], %v_param_83: Tensor[(512), float32], %v_param_84: Tensor[(512), float32], %v_param_85: Tensor[(512), float32], %v_param_86: Tensor[(512), float32], %v_param_67: Tensor[(1, 1, 256, 128), float32], %v_param_68: Tensor[(128), float32], %v_param_69: Tensor[(128), float32], %v_param_70: Tensor[(128), float32], %v_param_71: Tensor[(128), float32], %v_param_72: Tensor[(128), float32], %v_param_73: Tensor[(3, 3, 128, 128), float32], %v_param_74: Tensor[(128), float32], %v_param_75: Tensor[(128), float32], %v_param_76: Tensor[(128), float32], %v_param_77: Tensor[(128), float32], %v_param_78: Tensor[(128), float32], %v_param_81: Tensor[(1, 1, 128, 512), float32], %v_param_82: Tensor[(512), float32], %v_param_87: Tensor[(512), float32], %v_param_88: Tensor[(512), float32], %v_param_89: Tensor[(512), float32], %v_param_90: Tensor[(512), float32], %v_param_91: Tensor[(1, 1, 512, 128), float32], %v_param_92: Tensor[(128), float32], %v_param_93: Tensor[(128), float32], %v_param_94: Tensor[(128), float32], %v_param_95: Tensor[(128), float32], %v_param_96: Tensor[(128), float32], %v_param_97: Tensor[(3, 3, 128, 128), float32], %v_param_98: Tensor[(128), float32], %v_param_99: Tensor[(128), float32], %v_param_100: Tensor[(128), float32], %v_param_101: Tensor[(128), float32], %v_param_102: Tensor[(128), float32], %v_param_103: Tensor[(1, 1, 128, 512), float32], %v_param_104: Tensor[(512), float32], %v_param_105: Tensor[(512), float32], %v_param_106: Tensor[(512), float32], %v_param_107: Tensor[(512), float32], %v_param_108: Tensor[(512), float32], %v_param_109: Tensor[(1, 1, 512, 128), float32], %v_param_110: Tensor[(128), float32], %v_param_111: Tensor[(128), float32], %v_param_112: Tensor[(128), float32], %v_param_113: Tensor[(128), float32], %v_param_114: Tensor[(128), float32], %v_param_115: Tensor[(3, 3, 128, 128), float32], %v_param_116: Tensor[(128), float32], %v_param_117: Tensor[(128), float32], %v_param_118: Tensor[(128), float32], %v_param_119: Tensor[(128), float32], %v_param_120: Tensor[(128), float32], %v_param_121: Tensor[(1, 1, 128, 512), float32], %v_param_122: Tensor[(512), float32], %v_param_123: Tensor[(512), float32], %v_param_124: Tensor[(512), float32], %v_param_125: Tensor[(512), float32], %v_param_126: Tensor[(512), float32], %v_param_127: Tensor[(1, 1, 512, 128), float32], %v_param_128: Tensor[(128), float32], %v_param_129: Tensor[(128), float32], %v_param_130: Tensor[(128), float32], %v_param_131: Tensor[(128), float32], %v_param_132: Tensor[(128), float32], %v_param_133: Tensor[(3, 3, 128, 128), float32], %v_param_134: Tensor[(128), float32], %v_param_135: Tensor[(128), float32], %v_param_136: Tensor[(128), float32], %v_param_137: Tensor[(128), float32], %v_param_138: Tensor[(128), float32], %v_param_139: Tensor[(1, 1, 128, 512), float32], %v_param_140: Tensor[(512), float32], %v_param_141: Tensor[(512), float32], %v_param_142: Tensor[(512), float32], %v_param_143: Tensor[(512), float32], %v_param_144: Tensor[(512), float32], %v_param_157: Tensor[(1, 1, 512, 1024), float32], %v_param_158: Tensor[(1024), float32], %v_param_161: Tensor[(1024), float32], %v_param_162: Tensor[(1024), float32], %v_param_163: Tensor[(1024), float32], %v_param_164: Tensor[(1024), float32], %v_param_145: Tensor[(1, 1, 512, 256), float32], %v_param_146: Tensor[(256), float32], %v_param_147: Tensor[(256), float32], %v_param_148: Tensor[(256), float32], %v_param_149: Tensor[(256), float32], %v_param_150: Tensor[(256), float32], %v_param_151: Tensor[(3, 3, 256, 256), float32], %v_param_152: Tensor[(256), float32], %v_param_153: Tensor[(256), float32], %v_param_154: Tensor[(256), float32], %v_param_155: Tensor[(256), float32], %v_param_156: Tensor[(256), float32], %v_param_159: Tensor[(1, 1, 256, 1024), float32], %v_param_160: Tensor[(1024), float32], %v_param_165: Tensor[(1024), float32], %v_param_166: Tensor[(1024), float32], %v_param_167: Tensor[(1024), float32], %v_param_168: Tensor[(1024), float32], %v_param_169: Tensor[(1, 1, 1024, 256), float32], %v_param_170: Tensor[(256), float32], %v_param_171: Tensor[(256), float32], %v_param_172: Tensor[(256), float32], %v_param_173: Tensor[(256), float32], %v_param_174: Tensor[(256), float32], %v_param_175: Tensor[(3, 3, 256, 256), float32], %v_param_176: Tensor[(256), float32], %v_param_177: Tensor[(256), float32], %v_param_178: Tensor[(256), float32], %v_param_179: Tensor[(256), float32], %v_param_180: Tensor[(256), float32], %v_param_181: Tensor[(1, 1, 256, 1024), float32], %v_param_182: Tensor[(1024), float32], %v_param_183: Tensor[(1024), float32], %v_param_184: Tensor[(1024), float32], %v_param_185: Tensor[(1024), float32], %v_param_186: Tensor[(1024), float32], %v_param_187: Tensor[(1, 1, 1024, 256), float32], %v_param_188: Tensor[(256), float32], %v_param_189: Tensor[(256), float32], %v_param_190: Tensor[(256), float32], %v_param_191: Tensor[(256), float32], %v_param_192: Tensor[(256), float32], %v_param_193: Tensor[(3, 3, 256, 256), float32], %v_param_194: Tensor[(256), float32], %v_param_195: Tensor[(256), float32], %v_param_196: Tensor[(256), float32], %v_param_197: Tensor[(256), float32], %v_param_198: Tensor[(256), float32], %v_param_199: Tensor[(1, 1, 256, 1024), float32], %v_param_200: Tensor[(1024), float32], %v_param_201: Tensor[(1024), float32], %v_param_202: Tensor[(1024), float32], %v_param_203: Tensor[(1024), float32], %v_param_204: Tensor[(1024), float32], %v_param_205: Tensor[(1, 1, 1024, 256), float32], %v_param_206: Tensor[(256), float32], %v_param_207: Tensor[(256), float32], %v_param_208: Tensor[(256), float32], %v_param_209: Tensor[(256), float32], %v_param_210: Tensor[(256), float32], %v_param_211: Tensor[(3, 3, 256, 256), float32], %v_param_212: Tensor[(256), float32], %v_param_213: Tensor[(256), float32], %v_param_214: Tensor[(256), float32], %v_param_215: Tensor[(256), float32], %v_param_216: Tensor[(256), float32], %v_param_217: Tensor[(1, 1, 256, 1024), float32], %v_param_218: Tensor[(1024), float32], %v_param_219: Tensor[(1024), float32], %v_param_220: Tensor[(1024), float32], %v_param_221: Tensor[(1024), float32], %v_param_222: Tensor[(1024), float32], %v_param_223: Tensor[(1, 1, 1024, 256), float32], %v_param_224: Tensor[(256), float32], %v_param_225: Tensor[(256), float32], %v_param_226: Tensor[(256), float32], %v_param_227: Tensor[(256), float32], %v_param_228: Tensor[(256), float32], %v_param_229: Tensor[(3, 3, 256, 256), float32], %v_param_230: Tensor[(256), float32], %v_param_231: Tensor[(256), float32], %v_param_232: Tensor[(256), float32], %v_param_233: Tensor[(256), float32], %v_param_234: Tensor[(256), float32], %v_param_235: Tensor[(1, 1, 256, 1024), float32], %v_param_236: Tensor[(1024), float32], %v_param_237: Tensor[(1024), float32], %v_param_238: Tensor[(1024), float32], %v_param_239: Tensor[(1024), float32], %v_param_240: Tensor[(1024), float32], %v_param_241: Tensor[(1, 1, 1024, 256), float32], %v_param_242: Tensor[(256), float32], %v_param_243: Tensor[(256), float32], %v_param_244: Tensor[(256), float32], %v_param_245: Tensor[(256), float32], %v_param_246: Tensor[(256), float32], %v_param_247: Tensor[(3, 3, 256, 256), float32], %v_param_248: Tensor[(256), float32], %v_param_249: Tensor[(256), float32], %v_param_250: Tensor[(256), float32], %v_param_251: Tensor[(256), float32], %v_param_252: Tensor[(256), float32], %v_param_253: Tensor[(1, 1, 256, 1024), float32], %v_param_254: Tensor[(1024), float32], %v_param_255: Tensor[(1024), float32], %v_param_256: Tensor[(1024), float32], %v_param_257: Tensor[(1024), float32], %v_param_258: Tensor[(1024), float32], %v_param_271: Tensor[(1, 1, 1024, 2048), float32], %v_param_272: Tensor[(2048), float32], %v_param_275: Tensor[(2048), float32], %v_param_276: Tensor[(2048), float32], %v_param_277: Tensor[(2048), float32], %v_param_278: Tensor[(2048), float32], %v_param_259: Tensor[(1, 1, 1024, 512), float32], %v_param_260: Tensor[(512), float32], %v_param_261: Tensor[(512), float32], %v_param_262: Tensor[(512), float32], %v_param_263: Tensor[(512), float32], %v_param_264: Tensor[(512), float32], %v_param_265: Tensor[(3, 3, 512, 512), float32], %v_param_266: Tensor[(512), float32], %v_param_267: Tensor[(512), float32], %v_param_268: Tensor[(512), float32], %v_param_269: Tensor[(512), float32], %v_param_270: Tensor[(512), float32], %v_param_273: Tensor[(1, 1, 512, 2048), float32], %v_param_274: Tensor[(2048), float32], %v_param_279: Tensor[(2048), float32], %v_param_280: Tensor[(2048), float32], %v_param_281: Tensor[(2048), float32], %v_param_282: Tensor[(2048), float32], %v_param_283: Tensor[(1, 1, 2048, 512), float32], %v_param_284: Tensor[(512), float32], %v_param_285: Tensor[(512), float32], %v_param_286: Tensor[(512), float32], %v_param_287: Tensor[(512), float32], %v_param_288: Tensor[(512), float32], %v_param_289: Tensor[(3, 3, 512, 512), float32], %v_param_290: Tensor[(512), float32], %v_param_291: Tensor[(512), float32], %v_param_292: Tensor[(512), float32], %v_param_293: Tensor[(512), float32], %v_param_294: Tensor[(512), float32], %v_param_295: Tensor[(1, 1, 512, 2048), float32], %v_param_296: Tensor[(2048), float32], %v_param_297: Tensor[(2048), float32], %v_param_298: Tensor[(2048), float32], %v_param_299: Tensor[(2048), float32], %v_param_300: Tensor[(2048), float32], %v_param_301: Tensor[(1, 1, 2048, 512), float32], %v_param_302: Tensor[(512), float32], %v_param_303: Tensor[(512), float32], %v_param_304: Tensor[(512), float32], %v_param_305: Tensor[(512), float32], %v_param_306: Tensor[(512), float32], %v_param_307: Tensor[(3, 3, 512, 512), float32], %v_param_308: Tensor[(512), float32], %v_param_309: Tensor[(512), float32], %v_param_310: Tensor[(512), float32], %v_param_311: Tensor[(512), float32], %v_param_312: Tensor[(512), float32], %v_param_313: Tensor[(1, 1, 512, 2048), float32], %v_param_314: Tensor[(2048), float32], %v_param_315: Tensor[(2048), float32], %v_param_316: Tensor[(2048), float32], %v_param_317: Tensor[(2048), float32], %v_param_318: Tensor[(2048), float32], %v_param_319: Tensor[(1000, 2048), float32], %v_param_320: Tensor[(1000), float32]) { |
| %0 = nn.pad(%input_2, 0, pad_width=[[0, 0], [3, 3], [3, 3], [0, 0]]); |
| %1 = nn.conv2d(%0, %v_param_1, strides=[2, 2], padding=[0, 0, 0, 0], channels=64, kernel_size=[7, 7], data_layout="NHWC", kernel_layout="HWIO"); |
| %2 = nn.bias_add(%1, %v_param_2, axis=-1); |
| %3 = nn.batch_norm(%2, %v_param_3, %v_param_4, %v_param_5, %v_param_6, axis=3, epsilon=1.001e-05f); |
| %4 = %3.0; |
| %5 = nn.relu(%4); |
| %6 = nn.pad(%5, 0, pad_width=[[0, 0], [1, 1], [1, 1], [0, 0]]); |
| %7 = nn.max_pool2d(%6, pool_size=[3, 3], strides=[2, 2], padding=[0, 0, 0, 0], layout="NHWC"); |
| %8 = nn.conv2d(%7, %v_param_19, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %9 = nn.bias_add(%8, %v_param_20, axis=-1); |
| %10 = nn.batch_norm(%9, %v_param_23, %v_param_24, %v_param_25, %v_param_26, axis=3, epsilon=1.001e-05f); |
| %11 = nn.conv2d(%7, %v_param_7, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %12 = nn.bias_add(%11, %v_param_8, axis=-1); |
| %13 = nn.batch_norm(%12, %v_param_9, %v_param_10, %v_param_11, %v_param_12, axis=3, epsilon=1.001e-05f); |
| %14 = %13.0; |
| %15 = nn.relu(%14); |
| %16 = nn.conv2d(%15, %v_param_13, padding=[1i64, 1i64, 1i64, 1i64], channels=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %17 = nn.bias_add(%16, %v_param_14, axis=-1); |
| %18 = nn.batch_norm(%17, %v_param_15, %v_param_16, %v_param_17, %v_param_18, axis=3, epsilon=1.001e-05f); |
| %19 = %18.0; |
| %20 = nn.relu(%19); |
| %21 = nn.conv2d(%20, %v_param_21, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %22 = nn.bias_add(%21, %v_param_22, axis=-1); |
| %23 = nn.batch_norm(%22, %v_param_27, %v_param_28, %v_param_29, %v_param_30, axis=3, epsilon=1.001e-05f); |
| %24 = %10.0; |
| %25 = %23.0; |
| %26 = add(%24, %25); |
| %27 = nn.relu(%26); |
| %28 = nn.conv2d(%27, %v_param_31, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %29 = nn.bias_add(%28, %v_param_32, axis=-1); |
| %30 = nn.batch_norm(%29, %v_param_33, %v_param_34, %v_param_35, %v_param_36, axis=3, epsilon=1.001e-05f); |
| %31 = %30.0; |
| %32 = nn.relu(%31); |
| %33 = nn.conv2d(%32, %v_param_37, padding=[1i64, 1i64, 1i64, 1i64], channels=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %34 = nn.bias_add(%33, %v_param_38, axis=-1); |
| %35 = nn.batch_norm(%34, %v_param_39, %v_param_40, %v_param_41, %v_param_42, axis=3, epsilon=1.001e-05f); |
| %36 = %35.0; |
| %37 = nn.relu(%36); |
| %38 = nn.conv2d(%37, %v_param_43, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %39 = nn.bias_add(%38, %v_param_44, axis=-1); |
| %40 = nn.batch_norm(%39, %v_param_45, %v_param_46, %v_param_47, %v_param_48, axis=3, epsilon=1.001e-05f); |
| %41 = %40.0; |
| %42 = add(%27, %41); |
| %43 = nn.relu(%42); |
| %44 = nn.conv2d(%43, %v_param_49, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %45 = nn.bias_add(%44, %v_param_50, axis=-1); |
| %46 = nn.batch_norm(%45, %v_param_51, %v_param_52, %v_param_53, %v_param_54, axis=3, epsilon=1.001e-05f); |
| %47 = %46.0; |
| %48 = nn.relu(%47); |
| %49 = nn.conv2d(%48, %v_param_55, padding=[1i64, 1i64, 1i64, 1i64], channels=64, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %50 = nn.bias_add(%49, %v_param_56, axis=-1); |
| %51 = nn.batch_norm(%50, %v_param_57, %v_param_58, %v_param_59, %v_param_60, axis=3, epsilon=1.001e-05f); |
| %52 = %51.0; |
| %53 = nn.relu(%52); |
| %54 = nn.conv2d(%53, %v_param_61, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %55 = nn.bias_add(%54, %v_param_62, axis=-1); |
| %56 = nn.batch_norm(%55, %v_param_63, %v_param_64, %v_param_65, %v_param_66, axis=3, epsilon=1.001e-05f); |
| %57 = %56.0; |
| %58 = add(%43, %57); |
| %59 = nn.relu(%58); |
| %60 = nn.conv2d(%59, %v_param_79, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %61 = nn.bias_add(%60, %v_param_80, axis=-1); |
| %62 = nn.batch_norm(%61, %v_param_83, %v_param_84, %v_param_85, %v_param_86, axis=3, epsilon=1.001e-05f); |
| %63 = nn.conv2d(%59, %v_param_67, strides=[2, 2], padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %64 = nn.bias_add(%63, %v_param_68, axis=-1); |
| %65 = nn.batch_norm(%64, %v_param_69, %v_param_70, %v_param_71, %v_param_72, axis=3, epsilon=1.001e-05f); |
| %66 = %65.0; |
| %67 = nn.relu(%66); |
| %68 = nn.conv2d(%67, %v_param_73, padding=[1i64, 1i64, 1i64, 1i64], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %69 = nn.bias_add(%68, %v_param_74, axis=-1); |
| %70 = nn.batch_norm(%69, %v_param_75, %v_param_76, %v_param_77, %v_param_78, axis=3, epsilon=1.001e-05f); |
| %71 = %70.0; |
| %72 = nn.relu(%71); |
| %73 = nn.conv2d(%72, %v_param_81, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %74 = nn.bias_add(%73, %v_param_82, axis=-1); |
| %75 = nn.batch_norm(%74, %v_param_87, %v_param_88, %v_param_89, %v_param_90, axis=3, epsilon=1.001e-05f); |
| %76 = %62.0; |
| %77 = %75.0; |
| %78 = add(%76, %77); |
| %79 = nn.relu(%78); |
| %80 = nn.conv2d(%79, %v_param_91, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %81 = nn.bias_add(%80, %v_param_92, axis=-1); |
| %82 = nn.batch_norm(%81, %v_param_93, %v_param_94, %v_param_95, %v_param_96, axis=3, epsilon=1.001e-05f); |
| %83 = %82.0; |
| %84 = nn.relu(%83); |
| %85 = nn.conv2d(%84, %v_param_97, padding=[1i64, 1i64, 1i64, 1i64], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %86 = nn.bias_add(%85, %v_param_98, axis=-1); |
| %87 = nn.batch_norm(%86, %v_param_99, %v_param_100, %v_param_101, %v_param_102, axis=3, epsilon=1.001e-05f); |
| %88 = %87.0; |
| %89 = nn.relu(%88); |
| %90 = nn.conv2d(%89, %v_param_103, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %91 = nn.bias_add(%90, %v_param_104, axis=-1); |
| %92 = nn.batch_norm(%91, %v_param_105, %v_param_106, %v_param_107, %v_param_108, axis=3, epsilon=1.001e-05f); |
| %93 = %92.0; |
| %94 = add(%79, %93); |
| %95 = nn.relu(%94); |
| %96 = nn.conv2d(%95, %v_param_109, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %97 = nn.bias_add(%96, %v_param_110, axis=-1); |
| %98 = nn.batch_norm(%97, %v_param_111, %v_param_112, %v_param_113, %v_param_114, axis=3, epsilon=1.001e-05f); |
| %99 = %98.0; |
| %100 = nn.relu(%99); |
| %101 = nn.conv2d(%100, %v_param_115, padding=[1i64, 1i64, 1i64, 1i64], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %102 = nn.bias_add(%101, %v_param_116, axis=-1); |
| %103 = nn.batch_norm(%102, %v_param_117, %v_param_118, %v_param_119, %v_param_120, axis=3, epsilon=1.001e-05f); |
| %104 = %103.0; |
| %105 = nn.relu(%104); |
| %106 = nn.conv2d(%105, %v_param_121, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %107 = nn.bias_add(%106, %v_param_122, axis=-1); |
| %108 = nn.batch_norm(%107, %v_param_123, %v_param_124, %v_param_125, %v_param_126, axis=3, epsilon=1.001e-05f); |
| %109 = %108.0; |
| %110 = add(%95, %109); |
| %111 = nn.relu(%110); |
| %112 = nn.conv2d(%111, %v_param_127, padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %113 = nn.bias_add(%112, %v_param_128, axis=-1); |
| %114 = nn.batch_norm(%113, %v_param_129, %v_param_130, %v_param_131, %v_param_132, axis=3, epsilon=1.001e-05f); |
| %115 = %114.0; |
| %116 = nn.relu(%115); |
| %117 = nn.conv2d(%116, %v_param_133, padding=[1i64, 1i64, 1i64, 1i64], channels=128, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %118 = nn.bias_add(%117, %v_param_134, axis=-1); |
| %119 = nn.batch_norm(%118, %v_param_135, %v_param_136, %v_param_137, %v_param_138, axis=3, epsilon=1.001e-05f); |
| %120 = %119.0; |
| %121 = nn.relu(%120); |
| %122 = nn.conv2d(%121, %v_param_139, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %123 = nn.bias_add(%122, %v_param_140, axis=-1); |
| %124 = nn.batch_norm(%123, %v_param_141, %v_param_142, %v_param_143, %v_param_144, axis=3, epsilon=1.001e-05f); |
| %125 = %124.0; |
| %126 = add(%111, %125); |
| %127 = nn.relu(%126); |
| %128 = nn.conv2d(%127, %v_param_157, strides=[2, 2], padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %129 = nn.bias_add(%128, %v_param_158, axis=-1); |
| %130 = nn.batch_norm(%129, %v_param_161, %v_param_162, %v_param_163, %v_param_164, axis=3, epsilon=1.001e-05f); |
| %131 = nn.conv2d(%127, %v_param_145, strides=[2, 2], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %132 = nn.bias_add(%131, %v_param_146, axis=-1); |
| %133 = nn.batch_norm(%132, %v_param_147, %v_param_148, %v_param_149, %v_param_150, axis=3, epsilon=1.001e-05f); |
| %134 = %133.0; |
| %135 = nn.relu(%134); |
| %136 = nn.conv2d(%135, %v_param_151, padding=[1i64, 1i64, 1i64, 1i64], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %137 = nn.bias_add(%136, %v_param_152, axis=-1); |
| %138 = nn.batch_norm(%137, %v_param_153, %v_param_154, %v_param_155, %v_param_156, axis=3, epsilon=1.001e-05f); |
| %139 = %138.0; |
| %140 = nn.relu(%139); |
| %141 = nn.conv2d(%140, %v_param_159, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %142 = nn.bias_add(%141, %v_param_160, axis=-1); |
| %143 = nn.batch_norm(%142, %v_param_165, %v_param_166, %v_param_167, %v_param_168, axis=3, epsilon=1.001e-05f); |
| %144 = %130.0; |
| %145 = %143.0; |
| %146 = add(%144, %145); |
| %147 = nn.relu(%146); |
| %148 = nn.conv2d(%147, %v_param_169, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %149 = nn.bias_add(%148, %v_param_170, axis=-1); |
| %150 = nn.batch_norm(%149, %v_param_171, %v_param_172, %v_param_173, %v_param_174, axis=3, epsilon=1.001e-05f); |
| %151 = %150.0; |
| %152 = nn.relu(%151); |
| %153 = nn.conv2d(%152, %v_param_175, padding=[1i64, 1i64, 1i64, 1i64], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %154 = nn.bias_add(%153, %v_param_176, axis=-1); |
| %155 = nn.batch_norm(%154, %v_param_177, %v_param_178, %v_param_179, %v_param_180, axis=3, epsilon=1.001e-05f); |
| %156 = %155.0; |
| %157 = nn.relu(%156); |
| %158 = nn.conv2d(%157, %v_param_181, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %159 = nn.bias_add(%158, %v_param_182, axis=-1); |
| %160 = nn.batch_norm(%159, %v_param_183, %v_param_184, %v_param_185, %v_param_186, axis=3, epsilon=1.001e-05f); |
| %161 = %160.0; |
| %162 = add(%147, %161); |
| %163 = nn.relu(%162); |
| %164 = nn.conv2d(%163, %v_param_187, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %165 = nn.bias_add(%164, %v_param_188, axis=-1); |
| %166 = nn.batch_norm(%165, %v_param_189, %v_param_190, %v_param_191, %v_param_192, axis=3, epsilon=1.001e-05f); |
| %167 = %166.0; |
| %168 = nn.relu(%167); |
| %169 = nn.conv2d(%168, %v_param_193, padding=[1i64, 1i64, 1i64, 1i64], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %170 = nn.bias_add(%169, %v_param_194, axis=-1); |
| %171 = nn.batch_norm(%170, %v_param_195, %v_param_196, %v_param_197, %v_param_198, axis=3, epsilon=1.001e-05f); |
| %172 = %171.0; |
| %173 = nn.relu(%172); |
| %174 = nn.conv2d(%173, %v_param_199, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %175 = nn.bias_add(%174, %v_param_200, axis=-1); |
| %176 = nn.batch_norm(%175, %v_param_201, %v_param_202, %v_param_203, %v_param_204, axis=3, epsilon=1.001e-05f); |
| %177 = %176.0; |
| %178 = add(%163, %177); |
| %179 = nn.relu(%178); |
| %180 = nn.conv2d(%179, %v_param_205, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %181 = nn.bias_add(%180, %v_param_206, axis=-1); |
| %182 = nn.batch_norm(%181, %v_param_207, %v_param_208, %v_param_209, %v_param_210, axis=3, epsilon=1.001e-05f); |
| %183 = %182.0; |
| %184 = nn.relu(%183); |
| %185 = nn.conv2d(%184, %v_param_211, padding=[1i64, 1i64, 1i64, 1i64], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %186 = nn.bias_add(%185, %v_param_212, axis=-1); |
| %187 = nn.batch_norm(%186, %v_param_213, %v_param_214, %v_param_215, %v_param_216, axis=3, epsilon=1.001e-05f); |
| %188 = %187.0; |
| %189 = nn.relu(%188); |
| %190 = nn.conv2d(%189, %v_param_217, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %191 = nn.bias_add(%190, %v_param_218, axis=-1); |
| %192 = nn.batch_norm(%191, %v_param_219, %v_param_220, %v_param_221, %v_param_222, axis=3, epsilon=1.001e-05f); |
| %193 = %192.0; |
| %194 = add(%179, %193); |
| %195 = nn.relu(%194); |
| %196 = nn.conv2d(%195, %v_param_223, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %197 = nn.bias_add(%196, %v_param_224, axis=-1); |
| %198 = nn.batch_norm(%197, %v_param_225, %v_param_226, %v_param_227, %v_param_228, axis=3, epsilon=1.001e-05f); |
| %199 = %198.0; |
| %200 = nn.relu(%199); |
| %201 = nn.conv2d(%200, %v_param_229, padding=[1i64, 1i64, 1i64, 1i64], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %202 = nn.bias_add(%201, %v_param_230, axis=-1); |
| %203 = nn.batch_norm(%202, %v_param_231, %v_param_232, %v_param_233, %v_param_234, axis=3, epsilon=1.001e-05f); |
| %204 = %203.0; |
| %205 = nn.relu(%204); |
| %206 = nn.conv2d(%205, %v_param_235, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %207 = nn.bias_add(%206, %v_param_236, axis=-1); |
| %208 = nn.batch_norm(%207, %v_param_237, %v_param_238, %v_param_239, %v_param_240, axis=3, epsilon=1.001e-05f); |
| %209 = %208.0; |
| %210 = add(%195, %209); |
| %211 = nn.relu(%210); |
| %212 = nn.conv2d(%211, %v_param_241, padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %213 = nn.bias_add(%212, %v_param_242, axis=-1); |
| %214 = nn.batch_norm(%213, %v_param_243, %v_param_244, %v_param_245, %v_param_246, axis=3, epsilon=1.001e-05f); |
| %215 = %214.0; |
| %216 = nn.relu(%215); |
| %217 = nn.conv2d(%216, %v_param_247, padding=[1i64, 1i64, 1i64, 1i64], channels=256, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %218 = nn.bias_add(%217, %v_param_248, axis=-1); |
| %219 = nn.batch_norm(%218, %v_param_249, %v_param_250, %v_param_251, %v_param_252, axis=3, epsilon=1.001e-05f); |
| %220 = %219.0; |
| %221 = nn.relu(%220); |
| %222 = nn.conv2d(%221, %v_param_253, padding=[0, 0, 0, 0], channels=1024, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %223 = nn.bias_add(%222, %v_param_254, axis=-1); |
| %224 = nn.batch_norm(%223, %v_param_255, %v_param_256, %v_param_257, %v_param_258, axis=3, epsilon=1.001e-05f); |
| %225 = %224.0; |
| %226 = add(%211, %225); |
| %227 = nn.relu(%226); |
| %228 = nn.conv2d(%227, %v_param_271, strides=[2, 2], padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %229 = nn.bias_add(%228, %v_param_272, axis=-1); |
| %230 = nn.batch_norm(%229, %v_param_275, %v_param_276, %v_param_277, %v_param_278, axis=3, epsilon=1.001e-05f); |
| %231 = nn.conv2d(%227, %v_param_259, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %232 = nn.bias_add(%231, %v_param_260, axis=-1); |
| %233 = nn.batch_norm(%232, %v_param_261, %v_param_262, %v_param_263, %v_param_264, axis=3, epsilon=1.001e-05f); |
| %234 = %233.0; |
| %235 = nn.relu(%234); |
| %236 = nn.conv2d(%235, %v_param_265, padding=[1i64, 1i64, 1i64, 1i64], channels=512, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %237 = nn.bias_add(%236, %v_param_266, axis=-1); |
| %238 = nn.batch_norm(%237, %v_param_267, %v_param_268, %v_param_269, %v_param_270, axis=3, epsilon=1.001e-05f); |
| %239 = %238.0; |
| %240 = nn.relu(%239); |
| %241 = nn.conv2d(%240, %v_param_273, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %242 = nn.bias_add(%241, %v_param_274, axis=-1); |
| %243 = nn.batch_norm(%242, %v_param_279, %v_param_280, %v_param_281, %v_param_282, axis=3, epsilon=1.001e-05f); |
| %244 = %230.0; |
| %245 = %243.0; |
| %246 = add(%244, %245); |
| %247 = nn.relu(%246); |
| %248 = nn.conv2d(%247, %v_param_283, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %249 = nn.bias_add(%248, %v_param_284, axis=-1); |
| %250 = nn.batch_norm(%249, %v_param_285, %v_param_286, %v_param_287, %v_param_288, axis=3, epsilon=1.001e-05f); |
| %251 = %250.0; |
| %252 = nn.relu(%251); |
| %253 = nn.conv2d(%252, %v_param_289, padding=[1i64, 1i64, 1i64, 1i64], channels=512, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %254 = nn.bias_add(%253, %v_param_290, axis=-1); |
| %255 = nn.batch_norm(%254, %v_param_291, %v_param_292, %v_param_293, %v_param_294, axis=3, epsilon=1.001e-05f); |
| %256 = %255.0; |
| %257 = nn.relu(%256); |
| %258 = nn.conv2d(%257, %v_param_295, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %259 = nn.bias_add(%258, %v_param_296, axis=-1); |
| %260 = nn.batch_norm(%259, %v_param_297, %v_param_298, %v_param_299, %v_param_300, axis=3, epsilon=1.001e-05f); |
| %261 = %260.0; |
| %262 = add(%247, %261); |
| %263 = nn.relu(%262); |
| %264 = nn.conv2d(%263, %v_param_301, padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %265 = nn.bias_add(%264, %v_param_302, axis=-1); |
| %266 = nn.batch_norm(%265, %v_param_303, %v_param_304, %v_param_305, %v_param_306, axis=3, epsilon=1.001e-05f); |
| %267 = %266.0; |
| %268 = nn.relu(%267); |
| %269 = nn.conv2d(%268, %v_param_307, padding=[1i64, 1i64, 1i64, 1i64], channels=512, kernel_size=[3, 3], data_layout="NHWC", kernel_layout="HWIO"); |
| %270 = nn.bias_add(%269, %v_param_308, axis=-1); |
| %271 = nn.batch_norm(%270, %v_param_309, %v_param_310, %v_param_311, %v_param_312, axis=3, epsilon=1.001e-05f); |
| %272 = %271.0; |
| %273 = nn.relu(%272); |
| %274 = nn.conv2d(%273, %v_param_313, padding=[0, 0, 0, 0], channels=2048, kernel_size=[1, 1], data_layout="NHWC", kernel_layout="HWIO"); |
| %275 = nn.bias_add(%274, %v_param_314, axis=-1); |
| %276 = nn.batch_norm(%275, %v_param_315, %v_param_316, %v_param_317, %v_param_318, axis=3, epsilon=1.001e-05f); |
| %277 = %276.0; |
| %278 = add(%263, %277); |
| %279 = nn.relu(%278); |
| %280 = nn.global_avg_pool2d(%279, layout="NHWC"); |
| %281 = nn.batch_flatten(%280); |
| %282 = nn.dense(%281, %v_param_319, units=1000); |
| %283 = nn.bias_add(%282, %v_param_320); |
| nn.softmax(%283) |
| } |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="autotuning"> |
| <h2>AutoTuning<a class="headerlink" href="#autotuning" title="Permalink to this headline">¶</a></h2> |
| <p>Now, the below api can be used for autotuning the model for any target. |
| Tuning required RPC setup and please refer to |
| <a class="reference external" href="https://tvm.apache.org/docs/how_to/deploy/adreno.html">Deploy to Adreno GPU</a></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">rpc_tracker_host</span></a> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"TVM_TRACKER_HOST"</span><span class="p">,</span> <span class="s2">"127.0.0.1"</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">rpc_tracker_port</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"TVM_TRACKER_PORT"</span><span class="p">,</span> <span class="mi">9190</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">rpc_key</span></a> <span class="o">=</span> <span class="s2">"android"</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">rpc_tracker</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">rpc_tracker_host</span></a> <span class="o">+</span> <span class="s2">":"</span> <span class="o">+</span> <span class="nb">str</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">rpc_tracker_port</span></a><span class="p">)</span> |
| |
| <span class="c1"># Auto tuning is compute intensive and time taking task.</span> |
| <span class="c1"># It is set to False in above configuration as this script runs in x86 for demonstration.</span> |
| <span class="c1"># Please to set :code:`is_tuning` to True to enable auto tuning.</span> |
| |
| <span class="c1"># Also, :code:`test_target` is set to :code:`llvm` as this example to make compatible for x86 demonstration.</span> |
| <span class="c1"># Please change it to :code:`opencl` or :code:`opencl -device=adreno` for RPC target in configuration above.</span> |
| |
| <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">is_tuning</span></a><span class="p">:</span> |
| <span class="n">tvmc</span><span class="o">.</span><span class="n">tune</span><span class="p">(</span> |
| <span class="n">tvmc_model</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> |
| <span class="n">tuning_records</span><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">tune_log</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">target_host</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_host</span></a><span class="p">,</span> |
| <span class="n">hostname</span><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">rpc_tracker_host</span></a><span class="p">,</span> |
| <span class="n">port</span><span class="o">=</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">rpc_tracker_port</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">rpc_key</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">rpc_key</span></a><span class="p">,</span> |
| <span class="n">tuner</span><span class="o">=</span><span class="s2">"xgb"</span><span class="p">,</span> |
| <span class="n">repeat</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> |
| <span class="n">trials</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> |
| <span class="n">early_stopping</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="compilation"> |
| <h2>Compilation<a class="headerlink" href="#compilation" title="Permalink to this headline">¶</a></h2> |
| <p>Compilation to produce tvm artifacts</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># This generated example running on our x86 server for demonstration.</span> |
| <span class="c1"># To deply and tun on real target over RPC please set :code:`local_demo` to False in above configuration sestion.</span> |
| |
| <span class="c1"># OpenCLML offloading will try to accelerate supported operators by using OpenCLML proprietory operator library.</span> |
| <span class="c1"># By default :code:`enable_clml` is set to False in above configuration section.</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">enable_clml</span></a><span class="p">:</span> |
| <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a><span class="p">:</span> |
| <span class="n">tvmc_package</span> <span class="o">=</span> <span class="n">tvmc</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span> |
| <span class="n">tvmc_model</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> |
| <span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">tvmc_package</span> <span class="o">=</span> <span class="n">tvmc</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span> |
| <span class="n">tvmc_model</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#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_host</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_host</span></a><span class="p">,</span> |
| <span class="n">cross</span><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">cross_compiler</span></a><span class="p">,</span> |
| <span class="n">tuning_records</span><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">tune_log</span></a><span class="p">,</span> |
| <span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># Altrernatively, we can save the compilation output and save it as a TVMCPackage.</span> |
| <span class="c1"># This way avoids loading of compiled module without compiling again.</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="o">+</span> <span class="s2">", clml"</span> |
| <span class="n">pkg_path</span> <span class="o">=</span> <a href="../../reference/api/python/contrib.html#tvm.contrib.utils.TempDirectory.relpath" title="tvm.contrib.utils.TempDirectory.relpath" class="sphx-glr-backref-module-tvm-contrib-utils sphx-glr-backref-type-py-method"><span class="n">tmp_path</span><span class="o">.</span><span class="n">relpath</span></a><span class="p">(</span><span class="s2">"keras-resnet50.tar"</span><span class="p">)</span> |
| <span class="n">tvmc</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span> |
| <span class="n">tvmc_model</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#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_host</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_host</span></a><span class="p">,</span> |
| <span class="n">cross</span><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">cross_compiler</span></a><span class="p">,</span> |
| <span class="n">tuning_records</span><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">tune_log</span></a><span class="p">,</span> |
| <span class="n">package_path</span><span class="o">=</span><span class="n">pkg_path</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="c1"># Load the compiled package</span> |
| <span class="n">tvmc_package</span> <span class="o">=</span> <span class="n">TVMCPackage</span><span class="p">(</span><span class="n">package_path</span><span class="o">=</span><span class="n">pkg_path</span><span class="p">)</span> |
| |
| <span class="c1"># tvmc_package consists of tvmc_package.lib_path, tvmc_package.graph, tvmc_package.params</span> |
| <span class="c1"># Saved TVMPackage is nothing but tar archive with mod.so, mod.json and mod.params.</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="deploy-run"> |
| <h2>Deploy & Run<a class="headerlink" href="#deploy-run" title="Permalink to this headline">¶</a></h2> |
| <p>Deploy and run the compiled model on RPC |
| Let tvmc fill inputs using random</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Run on RPC setup</span> |
| <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">local_demo</span></a><span class="p">:</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">tvmc</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tvmc_package</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"cpu"</span><span class="p">,</span> <span class="n">fill_mode</span><span class="o">=</span><span class="s2">"random"</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">tvmc</span><span class="o">.</span><span class="n">run</span><span class="p">(</span> |
| <span class="n">tvmc_package</span><span class="p">,</span> |
| <span class="n">device</span><span class="o">=</span><span class="s2">"cl"</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">rpc_key</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">rpc_key</span></a><span class="p">,</span> |
| <span class="n">hostname</span><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">rpc_tracker_host</span></a><span class="p">,</span> |
| <span class="n">port</span><span class="o">=</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">rpc_tracker_port</span></a><span class="p">,</span> |
| <span class="n">fill_mode</span><span class="o">=</span><span class="s2">"random"</span><span class="p">,</span> |
| <span class="p">)</span> |
| |
| <span class="c1"># result is a dictionary of outputs.</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Result:"</span><span class="p">,</span> <span class="n">result</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Result: [] |
| Output Names: |
| ['output_0'] |
| </pre></div> |
| </div> |
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| <p><a class="reference download internal" download="" href="../../_downloads/f84e457773d8f292bfac905e2ed3307f/deploy_model_on_adreno_tvmc.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_model_on_adreno_tvmc.py</span></code></a></p> |
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| <p><a class="reference download internal" download="" href="../../_downloads/613e0775d8981e544e6ca9602a975916/deploy_model_on_adreno_tvmc.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">deploy_model_on_adreno_tvmc.ipynb</span></code></a></p> |
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