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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
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| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
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| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.num_gpus.html">mxnet.npx.num_gpus</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.fully_connected.html">mxnet.npx.fully_connected</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <ul class="current"> |
| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
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| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
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| |
| <div class="section" id="module-mxnet.contrib.quantization"> |
| <span id="contrib-quantization"></span><h1>contrib.quantization<a class="headerlink" href="#module-mxnet.contrib.quantization" title="Permalink to this headline">¶</a></h1> |
| <p>Quantization module for generating quantized (INT8) models from FP32 models.</p> |
| <p><strong>Classes</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.contrib.quantization.CalibrationCollector" title="mxnet.contrib.quantization.CalibrationCollector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CalibrationCollector</span></code></a>()</p></td> |
| <td><p>Base class for all other collectors used with quantization</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Functions</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.contrib.quantization.calib_graph" title="mxnet.contrib.quantization.calib_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calib_graph</span></code></a>(qsym, arg_params, aux_params, …)</p></td> |
| <td><p>User-level API for calibrating a quantized model using a filled collector.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.contrib.quantization.quantize_graph" title="mxnet.contrib.quantization.quantize_graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize_graph</span></code></a>(sym, arg_params, aux_params)</p></td> |
| <td><p>User-level API for generating a quantized model from a FP32 model w/o calibration and a collector for naive or entropy calibration.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.contrib.quantization.quantize_model" title="mxnet.contrib.quantization.quantize_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize_model</span></code></a>(sym, arg_params, aux_params)</p></td> |
| <td><p>User-level API for generating a quantized model from a FP32 model w/ or w/o calibration.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.contrib.quantization.quantize_model_onednn" title="mxnet.contrib.quantization.quantize_model_onednn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize_model_onednn</span></code></a>(sym, arg_params, …)</p></td> |
| <td><p>User-level API for generating a fusion + quantized model from a FP32 model w/ or w/o calibration with oneDNN.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.contrib.quantization.quantize_net" title="mxnet.contrib.quantization.quantize_net"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize_net</span></code></a>(network[, quantized_dtype, …])</p></td> |
| <td><p>User-level API for Gluon users to generate a quantized SymbolBlock from a FP32 HybridBlock w/ or w/o calibration.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="class"> |
| <dt id="mxnet.contrib.quantization.CalibrationCollector"> |
| <em class="property">class </em><code class="sig-name descname">CalibrationCollector</code><a class="reference internal" href="../../../_modules/mxnet/contrib/quantization.html#CalibrationCollector"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.contrib.quantization.CalibrationCollector" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
| <p>Base class for all other collectors used with quantization</p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.contrib.quantization.CalibrationCollector.collect" title="mxnet.contrib.quantization.CalibrationCollector.collect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">collect</span></code></a>(name, op_name, arr)</p></td> |
| <td><p>Function which is registered to Block as monitor callback.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.contrib.quantization.CalibrationCollector.post_collect" title="mxnet.contrib.quantization.CalibrationCollector.post_collect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">post_collect</span></code></a>()</p></td> |
| <td><p>Function called after collecting parameters.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.contrib.quantization.CalibrationCollector.collect"> |
| <em class="property">abstract </em><code class="sig-name descname">collect</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">op_name</em>, <em class="sig-param">arr</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/contrib/quantization.html#CalibrationCollector.collect"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.contrib.quantization.CalibrationCollector.collect" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Function which is registered to Block as monitor callback. Names of layers |
| requiring calibration are stored in <cite>self.include_layers</cite> variable.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>str</em>) – Node name from which collected data comes from.</p></li> |
| <li><p><strong>op_name</strong> (<em>str</em>) – Operator name from which collected data comes from. Single operator |
| can have multiple input/ouput nodes - each should have different name.</p></li> |
| <li><p><strong>arr</strong> (<a class="reference internal" href="../../legacy/ndarray/ndarray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray"><em>NDArray</em></a>) – NDArray containing data of monitored node.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.contrib.quantization.CalibrationCollector.post_collect"> |
| <code class="sig-name descname">post_collect</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/contrib/quantization.html#CalibrationCollector.post_collect"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.contrib.quantization.CalibrationCollector.post_collect" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Function called after collecting parameters. Returns dictionary of min and max values |
| for each calibrated layer. If not overriden, returns content of <cite>self.min_max_dict</cite>.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.contrib.quantization.calib_graph"> |
| <code class="sig-name descname">calib_graph</code><span class="sig-paren">(</span><em class="sig-param">qsym</em>, <em class="sig-param">arg_params</em>, <em class="sig-param">aux_params</em>, <em class="sig-param">collector</em>, <em class="sig-param">calib_mode='entropy'</em>, <em class="sig-param">logger=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/contrib/quantization.html#calib_graph"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.contrib.quantization.calib_graph" title="Permalink to this definition">¶</a></dt> |
| <dd><p>User-level API for calibrating a quantized model using a filled collector. |
| The backend quantized operators are only enabled for Linux systems. Please do not run |
| inference using the quantized models on Windows for now.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>qsym</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Defines the structure of a neural network for INT8 data types.</p></li> |
| <li><p><strong>arg_params</strong> (<em>dict</em>) – Dictionary of name to <cite>NDArray</cite>.</p></li> |
| <li><p><strong>aux_params</strong> (<em>dict</em>) – Dictionary of name to <cite>NDArray</cite>.</p></li> |
| <li><p><strong>collector</strong> (<em>function</em>) – layer collector for naive or entropy calibration.</p></li> |
| <li><p><strong>calib_mode</strong> (<em>str</em>) – If calib_mode=’none’, no calibration will be used and the thresholds for |
| requantization after the corresponding layers will be calculated at runtime by |
| calling min and max operators. The quantized models generated in this |
| mode are normally 10-20% slower than those with calibrations during inference. |
| If calib_mode=’naive’, the min and max values of the layer outputs from a calibration |
| dataset will be directly taken as the thresholds for quantization. |
| If calib_mode=’entropy’ (default mode), the thresholds for quantization will be |
| derived such that the KL divergence between the distributions of FP32 layer outputs and |
| quantized layer outputs is minimized based upon the calibration dataset.</p></li> |
| <li><p><strong>quantized_dtype</strong> (<em>str</em>) – The quantized destination type for input data. Currently support ‘int8’ |
| , ‘uint8’ and ‘auto’. ‘auto’ means automatically select output type according to calibration result. |
| Default value is ‘int8’.</p></li> |
| <li><p><strong>logger</strong> (<em>Object</em>) – A logging object for printing information during the process of quantization.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>quantized_model</strong> – A tuple of calibrated symbol, quantized arg_params, aux_params.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>tuple</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.contrib.quantization.quantize_graph"> |
| <code class="sig-name descname">quantize_graph</code><span class="sig-paren">(</span><em class="sig-param">sym</em>, <em class="sig-param">arg_params</em>, <em class="sig-param">aux_params</em>, <em class="sig-param">device=cpu(0)</em>, <em class="sig-param">excluded_sym_names=None</em>, <em class="sig-param">excluded_op_names=None</em>, <em class="sig-param">calib_mode='entropy'</em>, <em class="sig-param">quantized_dtype='int8'</em>, <em class="sig-param">quantize_mode='full'</em>, <em class="sig-param">quantize_granularity='tensor-wise'</em>, <em class="sig-param">LayerOutputCollector=None</em>, <em class="sig-param">logger=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/contrib/quantization.html#quantize_graph"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.contrib.quantization.quantize_graph" title="Permalink to this definition">¶</a></dt> |
| <dd><p>User-level API for generating a quantized model from a FP32 model w/o calibration |
| and a collector for naive or entropy calibration. |
| The backend quantized operators are only enabled for Linux systems. Please do not run |
| inference using the quantized models on Windows for now.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>sym</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Defines the structure of a neural network for FP32 data types.</p></li> |
| <li><p><strong>device</strong> (<a class="reference internal" href="../../device/index.html#mxnet.device.Device" title="mxnet.device.Device"><em>Device</em></a>) – Defines the device that users want to run forward propagation on the calibration |
| dataset for collecting layer output statistics. Currently, only supports single device.</p></li> |
| <li><p><strong>arg_params</strong> (<em>dict</em>) – Dictionary of name to <cite>NDArray</cite>.</p></li> |
| <li><p><strong>aux_params</strong> (<em>dict</em>) – Dictionary of name to <cite>NDArray</cite>.</p></li> |
| <li><p><strong>excluded_sym_names</strong> (<em>list of strings</em>) – A list of strings representing the names of the symbols that users want to excluding |
| from being quantized.</p></li> |
| <li><p><strong>excluded_op_names</strong> (<em>list of strings</em>) – A list of strings representing the names of the operators that users want to excluding</p></li> |
| <li><p><strong>calib_mode</strong> (<em>str</em>) – If calib_mode=’none’, no calibration will be used and the thresholds for |
| requantization after the corresponding layers will be calculated at runtime by |
| calling min and max operators. The quantized models generated in this |
| mode are normally 10-20% slower than those with calibrations during inference. |
| If calib_mode=’naive’, the min and max values of the layer outputs from a calibration |
| dataset will be directly taken as the thresholds for quantization. |
| If calib_mode=’entropy’ (default mode), the thresholds for quantization will be |
| derived such that the KL divergence between the distributions of FP32 layer outputs and |
| quantized layer outputs is minimized based upon the calibration dataset.</p></li> |
| <li><p><strong>quantized_dtype</strong> (<em>str</em>) – The quantized destination type for input data. Currently support ‘int8’ |
| , ‘uint8’ and ‘auto’. ‘auto’ means automatically select output type according to calibration result. |
| Default value is ‘int8’.</p></li> |
| <li><p><strong>quantize_mode</strong> (<em>str</em>) – The mode that quantization pass to apply. Support ‘full’ and ‘smart’. |
| ‘full’ means quantize all operator if possible. |
| ‘smart’ means quantization pass will smartly choice which operator should be quantized.</p></li> |
| <li><p><strong>quantize_granularity</strong> (<em>str</em>) – The granularity of quantization, currently supports ‘tensor-wise’ and ‘channel-wise’ |
| quantization. The default value is ‘tensor-wise’.</p></li> |
| <li><p><strong>LayerOutputCollector</strong> (<em>subclass of CalibrationCollector</em>) – For custom calibration method usage. |
| Passed object’s include_layers attribute will be feed with names of layers which needs calibration</p></li> |
| <li><p><strong>logger</strong> (<em>Object</em>) – A logging object for printing information during the process of quantization.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>quantized_model</strong> – A tuple of quantized symbol, quantized arg_params, aux_params and collector.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>tuple</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.contrib.quantization.quantize_model"> |
| <code class="sig-name descname">quantize_model</code><span class="sig-paren">(</span><em class="sig-param">sym</em>, <em class="sig-param">arg_params</em>, <em class="sig-param">aux_params</em>, <em class="sig-param">data_names=('data'</em>, <em class="sig-param">)</em>, <em class="sig-param">device=cpu(0)</em>, <em class="sig-param">excluded_sym_names=None</em>, <em class="sig-param">excluded_op_names=None</em>, <em class="sig-param">calib_mode='entropy'</em>, <em class="sig-param">calib_data=None</em>, <em class="sig-param">num_calib_batches=None</em>, <em class="sig-param">quantized_dtype='int8'</em>, <em class="sig-param">quantize_mode='smart'</em>, <em class="sig-param">quantize_granularity='tensor-wise'</em>, <em class="sig-param">logger=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/contrib/quantization.html#quantize_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.contrib.quantization.quantize_model" title="Permalink to this definition">¶</a></dt> |
| <dd><p>User-level API for generating a quantized model from a FP32 model w/ or w/o calibration. |
| The backend quantized operators are only enabled for Linux systems. Please do not run |
| inference using the quantized models on Windows for now. |
| The quantization implementation adopts the TensorFlow’s approach: |
| <a class="reference external" href="https://www.tensorflow.org/lite/performance/post_training_quantization">https://www.tensorflow.org/lite/performance/post_training_quantization</a>. |
| The calibration implementation borrows the idea of Nvidia’s 8-bit Inference with TensorRT: |
| <a class="reference external" href="http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf">http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf</a> |
| and adapts the method to MXNet.</p> |
| <dl class="field-list simple" id="quantize-model-params"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>sym</strong> (<a class="reference internal" href="../../legacy/symbol/symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Defines the structure of a neural network for FP32 data types.</p></li> |
| <li><p><strong>arg_params</strong> (<em>dict</em>) – Dictionary of name to <cite>NDArray</cite>.</p></li> |
| <li><p><strong>aux_params</strong> (<em>dict</em>) – Dictionary of name to <cite>NDArray</cite>.</p></li> |
| <li><p><strong>data_names</strong> (<em>list of strings</em>) – Data names required for creating a Module object to run forward propagation on the |
| calibration dataset.</p></li> |
| <li><p><strong>device</strong> (<a class="reference internal" href="../../device/index.html#mxnet.device.Device" title="mxnet.device.Device"><em>Device</em></a>) – Defines the device that users want to run forward propagation on the calibration |
| dataset for collecting layer output statistics. Currently, only supports single device.</p></li> |
| <li><p><strong>excluded_sym_names</strong> (<em>list of strings</em>) – A list of strings representing the names of the symbols that users want to excluding |
| from being quantized.</p></li> |
| <li><p><strong>excluded_op_names</strong> (<em>list of strings</em>) – A list of strings representing the names of the operators that users want to excluding |
| from being quantized.</p></li> |
| <li><p><strong>calib_mode</strong> (<em>str</em>) – If calib_mode=’none’, no calibration will be used and the thresholds for |
| requantization after the corresponding layers will be calculated at runtime by |
| calling min and max operators. The quantized models generated in this |
| mode are normally 10-20% slower than those with calibrations during inference. |
| If calib_mode=’naive’, the min and max values of the layer outputs from a calibration |
| dataset will be directly taken as the thresholds for quantization. |
| If calib_mode=’entropy’ (default mode), the thresholds for quantization will be |
| derived such that the KL divergence between the distributions of FP32 layer outputs and |
| quantized layer outputs is minimized based upon the calibration dataset.</p></li> |
| <li><p><strong>calib_data</strong> (<a class="reference internal" href="../../gluon/data/index.html#mxnet.gluon.data.DataLoader" title="mxnet.gluon.data.DataLoader"><em>DataLoader</em></a>) – A DataLoader initialized by the calibration dataset.</p></li> |
| <li><p><strong>num_calib_batches</strong> (<em>int</em><em> or </em><em>None</em>) – The maximum number of batches that user would like to use for calibration. If not provided, |
| the whole calibration dataset will be used.</p></li> |
| <li><p><strong>quantized_dtype</strong> (<em>str</em>) – The quantized destination type for input data. Currently support ‘int8’, ‘uint8’ and ‘auto’. |
| ‘auto’ means automatically select output type according to calibration result. |
| Default value is ‘int8’.</p></li> |
| <li><p><strong>quantize_mode</strong> (<em>str</em>) – The mode that quantization pass to apply. Support ‘full’ and ‘smart’. |
| ‘full’ means quantize all operator if possible. |
| ‘smart’ means quantization pass will smartly choice which operator should be quantized.</p></li> |
| <li><p><strong>quantize_granularity</strong> (<em>str</em>) – The granularity of quantization, currently supports ‘tensor-wise’ and ‘channel-wise’ |
| quantization. The default value is ‘tensor-wise’.</p></li> |
| <li><p><strong>logger</strong> (<em>Object</em>) – A logging object for printing information during the process of quantization.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>quantized_model</strong> – A tuple of quantized symbol, quantized arg_params, and aux_params.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>tuple</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.contrib.quantization.quantize_model_onednn"> |
| <code class="sig-name descname">quantize_model_onednn</code><span class="sig-paren">(</span><em class="sig-param">sym</em>, <em class="sig-param">arg_params</em>, <em class="sig-param">aux_params</em>, <em class="sig-param">data_names=('data'</em>, <em class="sig-param">)</em>, <em class="sig-param">device=cpu(0)</em>, <em class="sig-param">excluded_sym_names=None</em>, <em class="sig-param">excluded_op_names=None</em>, <em class="sig-param">calib_mode='entropy'</em>, <em class="sig-param">calib_data=None</em>, <em class="sig-param">num_calib_batches=None</em>, <em class="sig-param">quantized_dtype='int8'</em>, <em class="sig-param">quantize_mode='smart'</em>, <em class="sig-param">quantize_granularity='tensor-wise'</em>, <em class="sig-param">logger=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/contrib/quantization.html#quantize_model_onednn"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.contrib.quantization.quantize_model_onednn" title="Permalink to this definition">¶</a></dt> |
| <dd><p>User-level API for generating a fusion + quantized model from a FP32 model |
| w/ or w/o calibration with oneDNN. |
| The backend quantized operators are only enabled for Linux systems. Please do not run |
| inference using the quantized models on Windows for now.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>all</strong> – <a class="reference internal" href="#quantize-model-params"><span class="std std-ref">As in quantize_model</span></a></p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>quantized_model</strong> – A tuple of quantized symbol, quantized arg_params, and aux_params.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>tuple</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.contrib.quantization.quantize_net"> |
| <code class="sig-name descname">quantize_net</code><span class="sig-paren">(</span><em class="sig-param">network</em>, <em class="sig-param">quantized_dtype='auto'</em>, <em class="sig-param">quantize_mode='full'</em>, <em class="sig-param">quantize_granularity='tensor-wise'</em>, <em class="sig-param">exclude_layers=None</em>, <em class="sig-param">exclude_layers_match=None</em>, <em class="sig-param">exclude_operators=None</em>, <em class="sig-param">calib_data=None</em>, <em class="sig-param">data_shapes=None</em>, <em class="sig-param">calib_mode='none'</em>, <em class="sig-param">num_calib_batches=None</em>, <em class="sig-param">device=cpu(0)</em>, <em class="sig-param">LayerOutputCollector=None</em>, <em class="sig-param">logger=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/contrib/quantization.html#quantize_net"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.contrib.quantization.quantize_net" title="Permalink to this definition">¶</a></dt> |
| <dd><p>User-level API for Gluon users to generate a quantized SymbolBlock from a FP32 HybridBlock w/ or w/o calibration. |
| The backend quantized operators are only enabled for Linux systems. Please do not run |
| inference using the quantized models on Windows for now.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>network</strong> (<em>Gluon HybridBlock</em>) – Defines the structure of a neural network for FP32 data types.</p></li> |
| <li><p><strong>quantized_dtype</strong> (<em>str</em>) – The quantized destination type for input data. Currently support ‘int8’ |
| , ‘uint8’ and ‘auto’. ‘auto’ means automatically select output type according to calibration result. |
| Default value is ‘int8’.</p></li> |
| <li><p><strong>quantize_mode</strong> (<em>str</em>) – The mode that quantization pass to apply. Support ‘full’ and ‘smart’. |
| ‘full’ means quantize all operator if possible. |
| ‘smart’ means quantization pass will smartly choice which operator should be quantized.</p></li> |
| <li><p><strong>quantize_granularity</strong> (<em>str</em>) – The granularity of quantization, currently supports ‘tensor-wise’ and ‘channel-wise’ |
| quantization. The default value is ‘tensor-wise’.</p></li> |
| <li><p><strong>exclude_layers</strong> (<em>list of strings</em>) – A list of strings representing the names of the symbols that users want to excluding</p></li> |
| <li><p><strong>exclude_layers_match</strong> (<em>list of strings</em>) – A list of strings wildcard matching the names of the symbols that users want to excluding |
| from being quantized.</p></li> |
| <li><p><strong>exclude_operators</strong> (<em>list of strings</em>) – A list of strings representing the names of the operators that users want to excluding</p></li> |
| <li><p><strong>calib_data</strong> (<em>gluon.DataLoader</em>) – A iterable data loading object.</p></li> |
| <li><p><strong>data_shapes</strong> (<em>list of DataDesc</em><em> or </em><em>list of tuple</em>) – A list of data shapes. Required if calib_data is not provided. In case of tuples, |
| the names of inputs are generated.</p></li> |
| <li><p><strong>calib_mode</strong> (<em>str</em>) – If calib_mode=’none’, no calibration will be used and the thresholds for |
| requantization after the corresponding layers will be calculated at runtime by |
| calling min and max operators. The quantized models generated in this |
| mode are normally 10-20% slower than those with calibrations during inference. |
| If calib_mode=’naive’, the min and max values of the layer outputs from a calibration |
| dataset will be directly taken as the thresholds for quantization. |
| If calib_mode=’entropy’ (default mode), the thresholds for quantization will be |
| derived such that the KL divergence between the distributions of FP32 layer outputs and |
| quantized layer outputs is minimized based upon the calibration dataset. |
| If calib_mode=’custom’, the provided LayerOutputCollector will be used to determine |
| the thresholds for quantization. For more information refer to CalibrationCollector |
| documentation.</p></li> |
| <li><p><strong>num_calib_batches</strong> (<em>int</em><em> or </em><em>None</em>) – The maximum number of batches that user would like to use for calibration. If not provided, |
| the whole calibration dataset will be used.</p></li> |
| <li><p><strong>device</strong> (<a class="reference internal" href="../../device/index.html#mxnet.device.Device" title="mxnet.device.Device"><em>Device</em></a>) – Defines the device that users want to run forward propagation on the calibration |
| dataset for collecting layer output statistics. Currently, only supports single device.</p></li> |
| <li><p><strong>LayerOutputCollector</strong> (<em>subclass of CalibrationCollector</em>) – For <cite>custom</cite> calibration method usage. |
| Passed object’s include_layers attribute will be feed with names of layers which needs calibration</p></li> |
| <li><p><strong>logger</strong> (<em>Object</em>) – A logging object for printing information during the process of quantization.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>network</strong> – Defines the structure of a neural network for INT8 data types.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>Gluon SymbolBlock</p> |
| </dd> |
| </dl> |
| </dd></dl> |
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