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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> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
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| <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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| </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> |
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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-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul> |
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| <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> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
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| <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> |
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| <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> |
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| <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> |
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| <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> |
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| <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> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
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| </ul> |
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| <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> |
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| <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> |
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| <div class="section" id="module-mxnet.symbol.contrib"> |
| <span id="symbol-contrib"></span><h1>symbol.contrib<a class="headerlink" href="#module-mxnet.symbol.contrib" title="Permalink to this headline">¶</a></h1> |
| <p>Contrib Symbol API of MXNet.</p> |
| <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.symbol.contrib.rand_zipfian" title="mxnet.symbol.contrib.rand_zipfian"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rand_zipfian</span></code></a>(true_classes, num_sampled, …)</p></td> |
| <td><p>Draw random samples from an approximately log-uniform or Zipfian distribution.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.foreach" title="mxnet.symbol.contrib.foreach"><code class="xref py py-obj docutils literal notranslate"><span class="pre">foreach</span></code></a>(body, data, init_states[, name])</p></td> |
| <td><p>Run a for loop with user-defined computation over Symbols on dimension 0.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.while_loop" title="mxnet.symbol.contrib.while_loop"><code class="xref py py-obj docutils literal notranslate"><span class="pre">while_loop</span></code></a>(cond, func, loop_vars[, …])</p></td> |
| <td><p>Run a while loop with user-defined computation and loop condition.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.cond" title="mxnet.symbol.contrib.cond"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cond</span></code></a>(pred, then_func, else_func[, name])</p></td> |
| <td><p>Run an if-then-else using user-defined condition and computation</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.AdaptiveAvgPooling2D" title="mxnet.symbol.contrib.AdaptiveAvgPooling2D"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AdaptiveAvgPooling2D</span></code></a>([data, kernel, …])</p></td> |
| <td><p>Applies a 2D adaptive average pooling over a 4D input with the shape of (NCHW).</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.BilinearResize2D" title="mxnet.symbol.contrib.BilinearResize2D"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BilinearResize2D</span></code></a>([data, like, height, …])</p></td> |
| <td><p>Perform 2D resizing (upsampling or downsampling) for 4D input using bilinear interpolation.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.CTCLoss" title="mxnet.symbol.contrib.CTCLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CTCLoss</span></code></a>([data, label, data_lengths, …])</p></td> |
| <td><p>Connectionist Temporal Classification Loss.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.DeformablePSROIPooling" title="mxnet.symbol.contrib.DeformablePSROIPooling"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DeformablePSROIPooling</span></code></a>([data, rois, trans, …])</p></td> |
| <td><p>Performs deformable position-sensitive region-of-interest pooling on inputs.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.MultiBoxDetection" title="mxnet.symbol.contrib.MultiBoxDetection"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiBoxDetection</span></code></a>([cls_prob, loc_pred, …])</p></td> |
| <td><p>Convert multibox detection predictions.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.MultiBoxPrior" title="mxnet.symbol.contrib.MultiBoxPrior"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiBoxPrior</span></code></a>([data, sizes, ratios, clip, …])</p></td> |
| <td><p>Generate prior(anchor) boxes from data, sizes and ratios.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.MultiBoxTarget" title="mxnet.symbol.contrib.MultiBoxTarget"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiBoxTarget</span></code></a>([anchor, label, cls_pred, …])</p></td> |
| <td><p>Compute Multibox training targets</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.MultiProposal" title="mxnet.symbol.contrib.MultiProposal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiProposal</span></code></a>([cls_prob, bbox_pred, …])</p></td> |
| <td><p>Generate region proposals via RPN</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.PSROIPooling" title="mxnet.symbol.contrib.PSROIPooling"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PSROIPooling</span></code></a>([data, rois, spatial_scale, …])</p></td> |
| <td><p>Performs region-of-interest pooling on inputs.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.Proposal" title="mxnet.symbol.contrib.Proposal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Proposal</span></code></a>([cls_prob, bbox_pred, im_info, …])</p></td> |
| <td><p>Generate region proposals via RPN</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.ROIAlign" title="mxnet.symbol.contrib.ROIAlign"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ROIAlign</span></code></a>([data, rois, pooled_size, …])</p></td> |
| <td><p>This operator takes a 4D feature map as an input array and region proposals as <cite>rois</cite>, then align the feature map over sub-regions of input and produces a fixed-sized output array.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.RROIAlign" title="mxnet.symbol.contrib.RROIAlign"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RROIAlign</span></code></a>([data, rois, pooled_size, …])</p></td> |
| <td><p>Performs Rotated ROI Align on the input array.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.SyncBatchNorm" title="mxnet.symbol.contrib.SyncBatchNorm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SyncBatchNorm</span></code></a>([data, gamma, beta, …])</p></td> |
| <td><p>Batch normalization.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.allclose" title="mxnet.symbol.contrib.allclose"><code class="xref py py-obj docutils literal notranslate"><span class="pre">allclose</span></code></a>([a, b, rtol, atol, equal_nan, …])</p></td> |
| <td><p>This operators implements the numpy.allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.arange_like" title="mxnet.symbol.contrib.arange_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arange_like</span></code></a>([data, start, step, repeat, …])</p></td> |
| <td><p>Return an array with evenly spaced values.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.backward_gradientmultiplier" title="mxnet.symbol.contrib.backward_gradientmultiplier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">backward_gradientmultiplier</span></code></a>([data, scalar, …])</p></td> |
| <td><p><dl class="field-list simple"> |
| <dt class="field-odd">param data</dt> |
| <dd class="field-odd"><p>source input</p> |
| </dd> |
| </dl> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.backward_hawkesll" title="mxnet.symbol.contrib.backward_hawkesll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">backward_hawkesll</span></code></a>([name, attr, out])</p></td> |
| <td><p><dl class="field-list simple"> |
| <dt class="field-odd">param name</dt> |
| <dd class="field-odd"><p>Name of the resulting symbol.</p> |
| </dd> |
| </dl> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.backward_index_copy" title="mxnet.symbol.contrib.backward_index_copy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">backward_index_copy</span></code></a>([name, attr, out])</p></td> |
| <td><p><dl class="field-list simple"> |
| <dt class="field-odd">param name</dt> |
| <dd class="field-odd"><p>Name of the resulting symbol.</p> |
| </dd> |
| </dl> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.backward_quadratic" title="mxnet.symbol.contrib.backward_quadratic"><code class="xref py py-obj docutils literal notranslate"><span class="pre">backward_quadratic</span></code></a>([name, attr, out])</p></td> |
| <td><p><dl class="field-list simple"> |
| <dt class="field-odd">param name</dt> |
| <dd class="field-odd"><p>Name of the resulting symbol.</p> |
| </dd> |
| </dl> |
| </p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.bipartite_matching" title="mxnet.symbol.contrib.bipartite_matching"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bipartite_matching</span></code></a>([data, is_ascend, …])</p></td> |
| <td><p>Compute bipartite matching.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.boolean_mask" title="mxnet.symbol.contrib.boolean_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">boolean_mask</span></code></a>([data, index, axis, name, …])</p></td> |
| <td><p>Given an n-d NDArray data, and a 1-d NDArray index, the operator produces an un-predeterminable shaped n-d NDArray out, which stands for the rows in x where the corresonding element in index is non-zero.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.box_decode" title="mxnet.symbol.contrib.box_decode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">box_decode</span></code></a>([data, anchors, std0, std1, …])</p></td> |
| <td><p>Decode bounding boxes training target with normalized center offsets.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.box_encode" title="mxnet.symbol.contrib.box_encode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">box_encode</span></code></a>([samples, matches, anchors, …])</p></td> |
| <td><p>Encode bounding boxes training target with normalized center offsets.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.box_iou" title="mxnet.symbol.contrib.box_iou"><code class="xref py py-obj docutils literal notranslate"><span class="pre">box_iou</span></code></a>([lhs, rhs, format, name, attr, out])</p></td> |
| <td><p>Bounding box overlap of two arrays.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.box_nms" title="mxnet.symbol.contrib.box_nms"><code class="xref py py-obj docutils literal notranslate"><span class="pre">box_nms</span></code></a>([data, overlap_thresh, …])</p></td> |
| <td><p>Apply non-maximum suppression to input.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.box_non_maximum_suppression" title="mxnet.symbol.contrib.box_non_maximum_suppression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">box_non_maximum_suppression</span></code></a>([data, …])</p></td> |
| <td><p>Apply non-maximum suppression to input.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.calibrate_entropy" title="mxnet.symbol.contrib.calibrate_entropy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calibrate_entropy</span></code></a>([hist, hist_edges, …])</p></td> |
| <td><p>Provide calibrated min/max for input histogram.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.count_sketch" title="mxnet.symbol.contrib.count_sketch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">count_sketch</span></code></a>([data, h, s, out_dim, …])</p></td> |
| <td><p>Apply CountSketch to input: map a d-dimension data to k-dimension data”</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.ctc_loss" title="mxnet.symbol.contrib.ctc_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ctc_loss</span></code></a>([data, label, data_lengths, …])</p></td> |
| <td><p>Connectionist Temporal Classification Loss.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.dequantize" title="mxnet.symbol.contrib.dequantize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dequantize</span></code></a>([data, min_range, max_range, …])</p></td> |
| <td><p>Dequantize the input tensor into a float tensor.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.dgl_adjacency" title="mxnet.symbol.contrib.dgl_adjacency"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dgl_adjacency</span></code></a>([data, name, attr, out])</p></td> |
| <td><p>This operator converts a CSR matrix whose values are edge Ids to an adjacency matrix whose values are ones.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.dgl_csr_neighbor_non_uniform_sample" title="mxnet.symbol.contrib.dgl_csr_neighbor_non_uniform_sample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dgl_csr_neighbor_non_uniform_sample</span></code></a>(…)</p></td> |
| <td><p>This operator samples sub-graph from a csr graph via an non-uniform probability.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.dgl_csr_neighbor_uniform_sample" title="mxnet.symbol.contrib.dgl_csr_neighbor_uniform_sample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dgl_csr_neighbor_uniform_sample</span></code></a>(…)</p></td> |
| <td><p>This operator samples sub-graphs from a csr graph via an uniform probability.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.dgl_graph_compact" title="mxnet.symbol.contrib.dgl_graph_compact"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dgl_graph_compact</span></code></a>(*graph_data, **kwargs)</p></td> |
| <td><p>This operator compacts a CSR matrix generated by dgl_csr_neighbor_uniform_sample and dgl_csr_neighbor_non_uniform_sample.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.dgl_subgraph" title="mxnet.symbol.contrib.dgl_subgraph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dgl_subgraph</span></code></a>(*data, **kwargs)</p></td> |
| <td><p>This operator constructs an induced subgraph for a given set of vertices from a graph.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.div_sqrt_dim" title="mxnet.symbol.contrib.div_sqrt_dim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">div_sqrt_dim</span></code></a>([data, name, attr, out])</p></td> |
| <td><p>Rescale the input by the square root of the channel dimension.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.dynamic_reshape" title="mxnet.symbol.contrib.dynamic_reshape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dynamic_reshape</span></code></a>([data, shape, name, attr, out])</p></td> |
| <td><p>Experimental support for reshape operator with dynamic shape.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.edge_id" title="mxnet.symbol.contrib.edge_id"><code class="xref py py-obj docutils literal notranslate"><span class="pre">edge_id</span></code></a>([data, u, v, name, attr, out])</p></td> |
| <td><p>This operator implements the edge_id function for a graph stored in a CSR matrix (the value of the CSR stores the edge Id of the graph).</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.fft" title="mxnet.symbol.contrib.fft"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fft</span></code></a>([data, compute_size, name, attr, out])</p></td> |
| <td><p>Apply 1D FFT to input”</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.getnnz" title="mxnet.symbol.contrib.getnnz"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getnnz</span></code></a>([data, axis, name, attr, out])</p></td> |
| <td><p>Number of stored values for a sparse tensor, including explicit zeros.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.gradientmultiplier" title="mxnet.symbol.contrib.gradientmultiplier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradientmultiplier</span></code></a>([data, scalar, is_int, …])</p></td> |
| <td><p>This operator implements the gradient multiplier function.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.group_adagrad_update" title="mxnet.symbol.contrib.group_adagrad_update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">group_adagrad_update</span></code></a>([weight, grad, …])</p></td> |
| <td><p>Update function for Group AdaGrad optimizer.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.hawkesll" title="mxnet.symbol.contrib.hawkesll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hawkesll</span></code></a>([lda, alpha, beta, state, lags, …])</p></td> |
| <td><p>Computes the log likelihood of a univariate Hawkes process.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.index_array" title="mxnet.symbol.contrib.index_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">index_array</span></code></a>([data, axes, name, attr, out])</p></td> |
| <td><p>Returns an array of indexes of the input array.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.index_copy" title="mxnet.symbol.contrib.index_copy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">index_copy</span></code></a>([old_tensor, index_vector, …])</p></td> |
| <td><p>Copies the elements of a <cite>new_tensor</cite> into the <cite>old_tensor</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.interleaved_matmul_encdec_qk" title="mxnet.symbol.contrib.interleaved_matmul_encdec_qk"><code class="xref py py-obj docutils literal notranslate"><span class="pre">interleaved_matmul_encdec_qk</span></code></a>([queries, …])</p></td> |
| <td><p>Compute the matrix multiplication between the projections of queries and keys in multihead attention use as encoder-decoder.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.interleaved_matmul_encdec_valatt" title="mxnet.symbol.contrib.interleaved_matmul_encdec_valatt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">interleaved_matmul_encdec_valatt</span></code></a>([…])</p></td> |
| <td><p>Compute the matrix multiplication between the projections of values and the attention weights in multihead attention use as encoder-decoder.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.interleaved_matmul_selfatt_qk" title="mxnet.symbol.contrib.interleaved_matmul_selfatt_qk"><code class="xref py py-obj docutils literal notranslate"><span class="pre">interleaved_matmul_selfatt_qk</span></code></a>([…])</p></td> |
| <td><p>Compute the matrix multiplication between the projections of queries and keys in multihead attention use as self attention.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.interleaved_matmul_selfatt_valatt" title="mxnet.symbol.contrib.interleaved_matmul_selfatt_valatt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">interleaved_matmul_selfatt_valatt</span></code></a>([…])</p></td> |
| <td><p>Compute the matrix multiplication between the projections of values and the attention weights in multihead attention use as self attention.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.intgemm_fully_connected" title="mxnet.symbol.contrib.intgemm_fully_connected"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intgemm_fully_connected</span></code></a>([data, weight, …])</p></td> |
| <td><p>Multiply matrices using 8-bit integers.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.intgemm_maxabsolute" title="mxnet.symbol.contrib.intgemm_maxabsolute"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intgemm_maxabsolute</span></code></a>([data, name, attr, out])</p></td> |
| <td><p>Compute the maximum absolute value in a tensor of float32 fast on a CPU.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.intgemm_prepare_data" title="mxnet.symbol.contrib.intgemm_prepare_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intgemm_prepare_data</span></code></a>([data, maxabs, name, …])</p></td> |
| <td><p>This operator converts quantizes float32 to int8 while also banning -128.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.intgemm_prepare_weight" title="mxnet.symbol.contrib.intgemm_prepare_weight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intgemm_prepare_weight</span></code></a>([weight, maxabs, …])</p></td> |
| <td><p>This operator converts a weight matrix in column-major format to intgemm’s internal fast representation of weight matrices.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.intgemm_take_weight" title="mxnet.symbol.contrib.intgemm_take_weight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intgemm_take_weight</span></code></a>([weight, indices, name, …])</p></td> |
| <td><p>Index a weight matrix stored in intgemm’s weight format.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.mrcnn_mask_target" title="mxnet.symbol.contrib.mrcnn_mask_target"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mrcnn_mask_target</span></code></a>([rois, gt_masks, matches, …])</p></td> |
| <td><p>Generate mask targets for Mask-RCNN.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quadratic" title="mxnet.symbol.contrib.quadratic"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quadratic</span></code></a>([data, a, b, c, name, attr, out])</p></td> |
| <td><p>This operators implements the quadratic function.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantize" title="mxnet.symbol.contrib.quantize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize</span></code></a>([data, min_range, max_range, …])</p></td> |
| <td><p>Quantize a input tensor from float to <cite>out_type</cite>, with user-specified <cite>min_range</cite> and <cite>max_range</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantize_asym" title="mxnet.symbol.contrib.quantize_asym"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize_asym</span></code></a>([data, min_calib_range, …])</p></td> |
| <td><p>Quantize a input tensor from float to uint8_t.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantize_v2" title="mxnet.symbol.contrib.quantize_v2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize_v2</span></code></a>([data, out_type, …])</p></td> |
| <td><p>Quantize a input tensor from float to <cite>out_type</cite>, with user-specified <cite>min_calib_range</cite> and <cite>max_calib_range</cite> or the input range collected at runtime.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_act" title="mxnet.symbol.contrib.quantized_act"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_act</span></code></a>([data, min_data, max_data, …])</p></td> |
| <td><p>Activation operator for input and output data type of int8.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_batch_norm" title="mxnet.symbol.contrib.quantized_batch_norm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_batch_norm</span></code></a>([data, gamma, beta, …])</p></td> |
| <td><p>BatchNorm operator for input and output data type of int8.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_batch_norm_relu" title="mxnet.symbol.contrib.quantized_batch_norm_relu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_batch_norm_relu</span></code></a>([data, gamma, …])</p></td> |
| <td><p>BatchNorm with ReLU operator for input and output data type of int8.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_concat" title="mxnet.symbol.contrib.quantized_concat"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_concat</span></code></a>(*data, **kwargs)</p></td> |
| <td><p>Joins input arrays along a given axis.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_conv" title="mxnet.symbol.contrib.quantized_conv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_conv</span></code></a>([data, weight, bias, …])</p></td> |
| <td><p>Convolution operator for input, weight and bias data type of int8, and accumulates in type int32 for the output.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_elemwise_add" title="mxnet.symbol.contrib.quantized_elemwise_add"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_elemwise_add</span></code></a>([lhs, rhs, lhs_min, …])</p></td> |
| <td><p>elemwise_add operator for input dataA and input dataB data type of int8,</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_elemwise_mul" title="mxnet.symbol.contrib.quantized_elemwise_mul"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_elemwise_mul</span></code></a>([lhs, rhs, lhs_min, …])</p></td> |
| <td><p>Multiplies arguments int8 element-wise.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_embedding" title="mxnet.symbol.contrib.quantized_embedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_embedding</span></code></a>([data, weight, …])</p></td> |
| <td><p>Maps integer indices to int8 vector representations (embeddings).</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_flatten" title="mxnet.symbol.contrib.quantized_flatten"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_flatten</span></code></a>([data, min_data, …])</p></td> |
| <td><p><dl class="field-list simple"> |
| <dt class="field-odd">param data</dt> |
| <dd class="field-odd"><p>A ndarray/symbol of type <cite>float32</cite></p> |
| </dd> |
| </dl> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_fully_connected" title="mxnet.symbol.contrib.quantized_fully_connected"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_fully_connected</span></code></a>([data, weight, …])</p></td> |
| <td><p>Fully Connected operator for input, weight and bias data type of int8, and accumulates in type int32 for the output.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_npi_add" title="mxnet.symbol.contrib.quantized_npi_add"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_npi_add</span></code></a>([lhs, rhs, lhs_min, …])</p></td> |
| <td><p>elemwise_add operator for input dataA and input dataB data type of int8,</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_pooling" title="mxnet.symbol.contrib.quantized_pooling"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_pooling</span></code></a>([data, min_data, …])</p></td> |
| <td><p>Pooling operator for input and output data type of int8.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_reshape" title="mxnet.symbol.contrib.quantized_reshape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_reshape</span></code></a>([data, min_data, …])</p></td> |
| <td><p><dl class="field-list simple"> |
| <dt class="field-odd">param data</dt> |
| <dd class="field-odd"><p>Array to be reshaped.</p> |
| </dd> |
| </dl> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_rnn" title="mxnet.symbol.contrib.quantized_rnn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_rnn</span></code></a>([data, parameters, state, …])</p></td> |
| <td><p>RNN operator for input data type of uint8.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.quantized_transpose" title="mxnet.symbol.contrib.quantized_transpose"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantized_transpose</span></code></a>([data, min_data, …])</p></td> |
| <td><p><dl class="field-list simple"> |
| <dt class="field-odd">param data</dt> |
| <dd class="field-odd"><p>Array to be transposed.</p> |
| </dd> |
| </dl> |
| </p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.requantize" title="mxnet.symbol.contrib.requantize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">requantize</span></code></a>([data, min_range, max_range, …])</p></td> |
| <td><p>Given data that is quantized in int32 and the corresponding thresholds, requantize the data into int8 using min and max thresholds either calculated at runtime or from calibration.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.round_ste" title="mxnet.symbol.contrib.round_ste"><code class="xref py py-obj docutils literal notranslate"><span class="pre">round_ste</span></code></a>([data, name, attr, out])</p></td> |
| <td><p>Straight-through-estimator of <cite>round()</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.sign_ste" title="mxnet.symbol.contrib.sign_ste"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sign_ste</span></code></a>([data, name, attr, out])</p></td> |
| <td><p>Straight-through-estimator of <cite>sign()</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.sldwin_atten_context" title="mxnet.symbol.contrib.sldwin_atten_context"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sldwin_atten_context</span></code></a>([score, value, …])</p></td> |
| <td><p>Compute the context vector for sliding window attention, used in Longformer (<a class="reference external" href="https://arxiv.org/pdf/2004.05150.pdf">https://arxiv.org/pdf/2004.05150.pdf</a>).</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.sldwin_atten_mask_like" title="mxnet.symbol.contrib.sldwin_atten_mask_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sldwin_atten_mask_like</span></code></a>([score, dilation, …])</p></td> |
| <td><p>Compute the mask for the sliding window attention score, used in Longformer (<a class="reference external" href="https://arxiv.org/pdf/2004.05150.pdf">https://arxiv.org/pdf/2004.05150.pdf</a>).</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.symbol.contrib.sldwin_atten_score" title="mxnet.symbol.contrib.sldwin_atten_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sldwin_atten_score</span></code></a>([query, key, dilation, …])</p></td> |
| <td><p>Compute the sliding window attention score, which is used in Longformer (<a class="reference external" href="https://arxiv.org/pdf/2004.05150.pdf">https://arxiv.org/pdf/2004.05150.pdf</a>).</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.rand_zipfian"> |
| <code class="sig-name descname">rand_zipfian</code><span class="sig-paren">(</span><em class="sig-param">true_classes</em>, <em class="sig-param">num_sampled</em>, <em class="sig-param">range_max</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/symbol/contrib.html#rand_zipfian"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.symbol.contrib.rand_zipfian" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Draw random samples from an approximately log-uniform or Zipfian distribution.</p> |
| <p>This operation randomly samples <em>num_sampled</em> candidates the range of integers [0, range_max). |
| The elements of sampled_candidates are drawn with replacement from the base distribution.</p> |
| <p>The base distribution for this operator is an approximately log-uniform or Zipfian distribution:</p> |
| <p>P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)</p> |
| <p>This sampler is useful when the true classes approximately follow such a distribution. |
| For example, if the classes represent words in a lexicon sorted in decreasing order of frequency. If your classes are not ordered by decreasing frequency, do not use this op.</p> |
| <p>Additionaly, it also returns the number of times each of the true classes and the sampled classes is expected to occur.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>true_classes</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The target classes in 1-D.</p></li> |
| <li><p><strong>num_sampled</strong> (<em>int</em>) – The number of classes to randomly sample.</p></li> |
| <li><p><strong>range_max</strong> (<em>int</em>) – The number of possible classes.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>samples</strong> (<em>Symbol</em>) – The sampled candidate classes in 1-D <cite>int64</cite> dtype.</p></li> |
| <li><p><strong>expected_count_true</strong> (<em>Symbol</em>) – The expected count for true classes in 1-D <cite>float64</cite> dtype.</p></li> |
| <li><p><strong>expected_count_sample</strong> (<em>Symbol</em>) – The expected count for sampled candidates in 1-D <cite>float64</cite> dtype.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">true_cls</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s1">'true_cls'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">samples</span><span class="p">,</span> <span class="n">exp_count_true</span><span class="p">,</span> <span class="n">exp_count_sample</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">rand_zipfian</span><span class="p">(</span><span class="n">true_cls</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">samples</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">true_cls</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">]))[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">array([1, 3, 3, 3])</span> |
| <span class="gp">>>> </span><span class="n">exp_count_true</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">true_cls</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">]))[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">array([0.12453879])</span> |
| <span class="gp">>>> </span><span class="n">exp_count_sample</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">true_cls</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">3</span><span class="p">]))[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">array([0.22629439, 0.12453879, 0.12453879, 0.12453879])</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.foreach"> |
| <code class="sig-name descname">foreach</code><span class="sig-paren">(</span><em class="sig-param">body</em>, <em class="sig-param">data</em>, <em class="sig-param">init_states</em>, <em class="sig-param">name='foreach'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/symbol/contrib.html#foreach"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.symbol.contrib.foreach" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Run a for loop with user-defined computation over Symbols on dimension 0.</p> |
| <p>This operator simulates a for loop and body has the computation for an iteration |
| of the for loop. It runs the computation in body on each slice from the input |
| NDArrays.</p> |
| <p>body takes two arguments as input and outputs a tuple of two elements, |
| as illustrated below:</p> |
| <p>out, states = body(data1, states)</p> |
| <p>data1 can be either a symbol or a list of symbols. If data is a symbol, |
| data1 is a symbol. Otherwise, data1 is a list of symbols and has the same |
| size as data. states is a list of symbols and have the same size as init_states. |
| Similarly, out can be either a symbol or a list of symbols, which are concatenated |
| as the first output of foreach; states from the last execution of body |
| are the second output of foreach.</p> |
| <p>foreach can output only output data or states. If a user only wants states, |
| the body function can return ([], states). Similarly, if a user only wants |
| output data, the body function can return (out, []).</p> |
| <p>The computation done by this operator is equivalent to the pseudo code below |
| when the input data is NDArray:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">states</span> <span class="o">=</span> <span class="n">init_states</span> |
| <span class="n">outs</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="n">out</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="n">body</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span> |
| <span class="n">outs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">out</span><span class="p">)</span> |
| <span class="n">outs</span> <span class="o">=</span> <span class="n">stack</span><span class="p">(</span><span class="o">*</span><span class="n">outs</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>body</strong> (<em>a Python function.</em>) – Define computation in an iteration.</p></li> |
| <li><p><strong>data</strong> (<em>a symbol</em><em> or </em><em>a list of symbols.</em>) – The input data.</p></li> |
| <li><p><strong>init_states</strong> (<em>a Symbol</em><em> or </em><em>nested lists of symbols.</em>) – The initial values of the loop states.</p></li> |
| <li><p><strong>name</strong> (<em>string.</em>) – The name of the operator.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>a Symbol or nested lists of Symbols.</em>) – The output data concatenated from the output of all iterations.</p></li> |
| <li><p><strong>states</strong> (<em>a Symbol or nested lists of Symbols.</em>) – The loop states in the last iteration.</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">step</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">data</span><span class="p">,</span> <span class="n">states</span><span class="p">:</span> <span class="p">(</span><span class="n">data</span> <span class="o">+</span> <span class="n">states</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="n">states</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mi">2</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'data'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">states</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'state'</span><span class="p">)]</span> |
| <span class="gp">>>> </span><span class="n">outs</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">foreach</span><span class="p">(</span><span class="n">step</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">states</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.while_loop"> |
| <code class="sig-name descname">while_loop</code><span class="sig-paren">(</span><em class="sig-param">cond</em>, <em class="sig-param">func</em>, <em class="sig-param">loop_vars</em>, <em class="sig-param">max_iterations=None</em>, <em class="sig-param">name='while_loop'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/symbol/contrib.html#while_loop"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.symbol.contrib.while_loop" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Run a while loop with user-defined computation and loop condition.</p> |
| <p>This operator simulates a while loop which iterately does customized computation |
| as long as the condition is satisfied.</p> |
| <p><cite>loop_vars</cite> is a Symbol or nested lists of Symbols on which the computation uses.</p> |
| <p><cite>cond</cite> is a user-defined function, used as the loop condition. |
| It consumes <cite>loop_vars</cite>, and produces a scalar MXNet symbol, |
| indicating the termination of the loop. |
| The loop ends when <cite>cond</cite> returns false (zero). |
| The <cite>cond</cite> is variadic, and its signature should be |
| <cite>cond(*loop_vars) => Symbol</cite>.</p> |
| <p><cite>func</cite> is a user-defined function, used as the loop body. |
| It also consumes <cite>loop_vars</cite>, and produces <cite>step_output</cite> and <cite>new_loop_vars</cite> at each step. |
| In each step, <cite>step_output</cite> should contain the same number elements. |
| Through all steps, the i-th element of <cite>step_output</cite> should have the same shape and dtype. |
| Also, <cite>new_loop_vars</cite> should contain the same number of elements as <cite>loop_vars</cite>, |
| and the corresponding element should have the same shape and dtype. |
| The <cite>func</cite> is variadic, and its signature should be |
| <cite>func(*loop_vars) => |
| (Symbol or nested List[Symbol] step_output, Symbol or nested List[Symbol] new_loop_vars)</cite>.</p> |
| <p><cite>max_iterations</cite> is a scalar that defines the maximum number of iterations allowed.</p> |
| <p>This function returns two lists. |
| The first list has the length of <cite>|step_output|</cite>, |
| in which the i-th element are all i-th elements of |
| <cite>step_output</cite> from all steps, stacked along axis 0. |
| The second list has the length of <cite>|loop_vars|</cite>, |
| which represents final states of loop variables.</p> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>For now, the axis 0 of all Symbols in the first list are <cite>max_iterations</cite>, |
| due to lack of dynamic shape inference.</p> |
| </div> |
| <div class="admonition warning"> |
| <p class="admonition-title">Warning</p> |
| <p>Even if <cite>cond</cite> is never satisfied, |
| while_loop returns a list of outputs with inferred dtype and shape. |
| This is different from the Symbol version, |
| where in this case <cite>step_outputs</cite> are assumed as an empty list.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>cond</strong> (<em>a Python function.</em>) – The loop condition.</p></li> |
| <li><p><strong>func</strong> (<em>a Python function.</em>) – The loop body.</p></li> |
| <li><p><strong>loop_vars</strong> (<em>a Symbol</em><em> or </em><em>nested lists of Symbol.</em>) – The initial values of the loop variables.</p></li> |
| <li><p><strong>max_iterations</strong> (<em>a python int.</em>) – Maximum number of iterations.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul class="simple"> |
| <li><p><strong>outputs</strong> (<em>a Symbol or nested lists of Symbols</em>) – stacked output from each step</p></li> |
| <li><p><strong>states</strong> (<em>a Symbol or nested lists of Symbols</em>) – final state</p></li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">cond</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">i</span><span class="p">,</span> <span class="n">s</span><span class="p">:</span> <span class="n">i</span> <span class="o"><=</span> <span class="mi">5</span> |
| <span class="gp">>>> </span><span class="n">func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">i</span><span class="p">,</span> <span class="n">s</span><span class="p">:</span> <span class="p">([</span><span class="n">i</span> <span class="o">+</span> <span class="n">s</span><span class="p">],</span> <span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">s</span> <span class="o">+</span> <span class="n">i</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">loop_vars</span> <span class="o">=</span> <span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'i'</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'s'</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">outputs</span><span class="p">,</span> <span class="n">states</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">while_loop</span><span class="p">(</span><span class="n">cond</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">loop_vars</span><span class="p">,</span> <span class="n">max_iterations</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.cond"> |
| <code class="sig-name descname">cond</code><span class="sig-paren">(</span><em class="sig-param">pred</em>, <em class="sig-param">then_func</em>, <em class="sig-param">else_func</em>, <em class="sig-param">name='cond'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/mxnet/symbol/contrib.html#cond"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.symbol.contrib.cond" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Run an if-then-else using user-defined condition and computation</p> |
| <p>This operator simulates a if-like branch which chooses to do one of |
| the two customized computations according to the specified condition.</p> |
| <p><cite>pred</cite> is a scalar MXNet Symbol, |
| indicating which branch of computation should be used.</p> |
| <p><cite>then_func</cite> is a user-defined function, used as computation of the then branch. |
| It produces <cite>outputs</cite>, which is a list of Symbols. |
| The signature of <cite>then_func</cite> should be |
| <cite>then_func() => nested List[Symbol]</cite>.</p> |
| <p><cite>else_func</cite> is a user-defined function, used as computation of the else branch. |
| It produces <cite>outputs</cite>, which is a list of Symbols. |
| The signature of <cite>else_func</cite> should be |
| <cite>else_func() => nested List[Symbol]</cite>.</p> |
| <p>The <cite>outputs</cite> produces by <cite>then_func</cite> and <cite>else_func</cite> should have the same number |
| of elements, all of which should be in the same shape, of the same dtype and stype.</p> |
| <p>This function returns a list of symbols, representing the computation result.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>pred</strong> (<em>a MXNet Symbol representing a scalar.</em>) – The branch condition.</p></li> |
| <li><p><strong>then_func</strong> (<em>a Python function.</em>) – The computation to be executed if <cite>pred</cite> is true.</p></li> |
| <li><p><strong>else_func</strong> (<em>a Python function.</em>) – The computation to be executed if <cite>pred</cite> is false.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>outputs</strong></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>a Symbol or nested lists of Symbols, representing the result of computation.</p> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'a'</span><span class="p">),</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'b'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">pred</span> <span class="o">=</span> <span class="n">a</span> <span class="o">*</span> <span class="n">b</span> <span class="o"><</span> <span class="mi">5</span> |
| <span class="gp">>>> </span><span class="n">then_func</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="p">(</span><span class="n">a</span> <span class="o">+</span> <span class="mi">5</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">b</span> <span class="o">+</span> <span class="mi">5</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">else_func</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="p">(</span><span class="n">a</span> <span class="o">-</span> <span class="mi">5</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">b</span> <span class="o">-</span> <span class="mi">5</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">outputs</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">cond</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">then_func</span><span class="p">,</span> <span class="n">else_func</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.AdaptiveAvgPooling2D"> |
| <code class="sig-name descname">AdaptiveAvgPooling2D</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">kernel=_Null</em>, <em class="sig-param">pool_type=_Null</em>, <em class="sig-param">global_pool=_Null</em>, <em class="sig-param">cudnn_off=_Null</em>, <em class="sig-param">pooling_convention=_Null</em>, <em class="sig-param">stride=_Null</em>, <em class="sig-param">pad=_Null</em>, <em class="sig-param">p_value=_Null</em>, <em class="sig-param">count_include_pad=_Null</em>, <em class="sig-param">layout=_Null</em>, <em class="sig-param">output_size=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.AdaptiveAvgPooling2D" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Applies a 2D adaptive average pooling over a 4D input with the shape of (NCHW). |
| The pooling kernel and stride sizes are automatically chosen for desired output sizes.</p> |
| <ul class="simple"> |
| <li><p>If a single integer is provided for output_size, the output size is (N x C x output_size x output_size) for any input (NCHW).</p></li> |
| <li><p>If a tuple of integers (height, width) are provided for output_size, the output size is (N x C x height x width) for any input (NCHW).</p></li> |
| </ul> |
| <p>Defined in /work/mxnet/src/operator/contrib/adaptive_avg_pooling.cc:L308</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data</p></li> |
| <li><p><strong>kernel</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>]</em>) – Pooling kernel size: (y, x) or (d, y, x)</p></li> |
| <li><p><strong>pool_type</strong> (<em>{'avg'</em><em>, </em><em>'lp'</em><em>, </em><em>'max'</em><em>, </em><em>'sum'}</em><em>,</em><em>optional</em><em>, </em><em>default='max'</em>) – Pooling type to be applied.</p></li> |
| <li><p><strong>global_pool</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Ignore kernel size, do global pooling based on current input feature map.</p></li> |
| <li><p><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Turn off cudnn pooling and use MXNet pooling operator.</p></li> |
| <li><p><strong>pooling_convention</strong> (<em>{'full'</em><em>, </em><em>'same'</em><em>, </em><em>'valid'}</em><em>,</em><em>optional</em><em>, </em><em>default='valid'</em>) – Pooling convention to be applied.</p></li> |
| <li><p><strong>stride</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>]</em>) – Stride: for pooling (y, x) or (d, y, x). Defaults to 1 for each dimension.</p></li> |
| <li><p><strong>pad</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>]</em>) – Pad for pooling: (y, x) or (d, y, x). Defaults to no padding.</p></li> |
| <li><p><strong>p_value</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – Value of p for Lp pooling, can be 1 or 2, required for Lp Pooling.</p></li> |
| <li><p><strong>count_include_pad</strong> (<em>boolean</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Only used for AvgPool, specify whether to count padding elements for averagecalculation. For example, with a 5*5 kernel on a 3*3 corner of a image,the sum of the 9 valid elements will be divided by 25 if this is set to true,or it will be divided by 9 if this is set to false. Defaults to true.</p></li> |
| <li><p><strong>layout</strong> (<em>{None</em><em>, </em><em>'NCDHW'</em><em>, </em><em>'NCHW'</em><em>, </em><em>'NCW'</em><em>, </em><em>'NDHWC'</em><em>, </em><em>'NHWC'</em><em>, </em><em>'NWC'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Set layout for input and output. Empty for |
| default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.</p></li> |
| <li><p><strong>output_size</strong> (<em>Shape</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Only used for Adaptive Pooling. int (output size) or a tuple of int for output (height, width).</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.BilinearResize2D"> |
| <code class="sig-name descname">BilinearResize2D</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">like=None</em>, <em class="sig-param">height=_Null</em>, <em class="sig-param">width=_Null</em>, <em class="sig-param">scale_height=_Null</em>, <em class="sig-param">scale_width=_Null</em>, <em class="sig-param">mode=_Null</em>, <em class="sig-param">align_corners=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.BilinearResize2D" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Perform 2D resizing (upsampling or downsampling) for 4D input using bilinear interpolation.</p> |
| <p>Expected input is a 4 dimensional NDArray (NCHW) and the output |
| with the shape of (N x C x height x width). |
| The key idea of bilinear interpolation is to perform linear interpolation |
| first in one direction, and then again in the other direction. See the wikipedia of |
| <a class="reference external" href="https://en.wikipedia.org/wiki/Bilinear_interpolation">Bilinear interpolation</a> |
| for more details.</p> |
| <p>Defined in /work/mxnet/src/operator/contrib/bilinear_resize.cc:L211</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data</p></li> |
| <li><p><strong>like</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Resize data to it’s shape</p></li> |
| <li><p><strong>height</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – output height (required, but ignored if scale_height is defined or mode is not “size”)</p></li> |
| <li><p><strong>width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – output width (required, but ignored if scale_width is defined or mode is not “size”)</p></li> |
| <li><p><strong>scale_height</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – sampling scale of the height (optional, used in modes “scale” and “odd_scale”)</p></li> |
| <li><p><strong>scale_width</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – sampling scale of the width (optional, used in modes “scale” and “odd_scale”)</p></li> |
| <li><p><strong>mode</strong> (<em>{'like'</em><em>, </em><em>'odd_scale'</em><em>, </em><em>'size'</em><em>, </em><em>'to_even_down'</em><em>, </em><em>'to_even_up'</em><em>, </em><em>'to_odd_down'</em><em>, </em><em>'to_odd_up'}</em><em>,</em><em>optional</em><em>, </em><em>default='size'</em>) – resizing mode. “simple” - output height equals parameter “height” if “scale_height” parameter is not defined or input height multiplied by “scale_height” otherwise. Same for width;”odd_scale” - if original height or width is odd, then result height is calculated like result_h = (original_h - 1) * scale + 1; for scale > 1 the result shape would be like if we did deconvolution with kernel = (1, 1) and stride = (height_scale, width_scale); and for scale < 1 shape would be like we did convolution with kernel = (1, 1) and stride = (int(1 / height_scale), int( 1/ width_scale);”like” - resize first input to the height and width of second input; “to_even_down” - resize input to nearest lower even height and width (if original height is odd then result height = original height - 1);”to_even_up” - resize input to nearest bigger even height and width (if original height is odd then result height = original height + 1);”to_odd_down” - resize input to nearest odd height and width (if original height is odd then result height = original height - 1);”to_odd_up” - resize input to nearest odd height and width (if original height is odd then result height = original height + 1);</p></li> |
| <li><p><strong>align_corners</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – With align_corners = True, the interpolating doesn’t proportionally align theoutput and input pixels, and thus the output values can depend on the input size.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.CTCLoss"> |
| <code class="sig-name descname">CTCLoss</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">label=None</em>, <em class="sig-param">data_lengths=None</em>, <em class="sig-param">label_lengths=None</em>, <em class="sig-param">use_data_lengths=_Null</em>, <em class="sig-param">use_label_lengths=_Null</em>, <em class="sig-param">blank_label=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.CTCLoss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Connectionist Temporal Classification Loss.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>The existing alias <code class="docutils literal notranslate"><span class="pre">contrib_CTCLoss</span></code> is deprecated.</p> |
| </div> |
| <p>The shapes of the inputs and outputs:</p> |
| <ul class="simple"> |
| <li><p><strong>data</strong>: <cite>(sequence_length, batch_size, alphabet_size)</cite></p></li> |
| <li><p><strong>label</strong>: <cite>(batch_size, label_sequence_length)</cite></p></li> |
| <li><p><strong>out</strong>: <cite>(batch_size)</cite></p></li> |
| </ul> |
| <p>The <cite>data</cite> tensor consists of sequences of activation vectors (without applying softmax), |
| with i-th channel in the last dimension corresponding to i-th label |
| for i between 0 and alphabet_size-1 (i.e always 0-indexed). |
| Alphabet size should include one additional value reserved for blank label. |
| When <cite>blank_label</cite> is <code class="docutils literal notranslate"><span class="pre">"first"</span></code>, the <code class="docutils literal notranslate"><span class="pre">0</span></code>-th channel is be reserved for |
| activation of blank label, or otherwise if it is “last”, <code class="docutils literal notranslate"><span class="pre">(alphabet_size-1)</span></code>-th channel should be |
| reserved for blank label.</p> |
| <p><code class="docutils literal notranslate"><span class="pre">label</span></code> is an index matrix of integers. When <cite>blank_label</cite> is <code class="docutils literal notranslate"><span class="pre">"first"</span></code>, |
| the value 0 is then reserved for blank label, and should not be passed in this matrix. Otherwise, |
| when <cite>blank_label</cite> is <code class="docutils literal notranslate"><span class="pre">"last"</span></code>, the value <cite>(alphabet_size-1)</cite> is reserved for blank label.</p> |
| <p>If a sequence of labels is shorter than <em>label_sequence_length</em>, use the special |
| padding value at the end of the sequence to conform it to the correct |
| length. The padding value is <cite>0</cite> when <cite>blank_label</cite> is <code class="docutils literal notranslate"><span class="pre">"first"</span></code>, and <cite>-1</cite> otherwise.</p> |
| <p>For example, suppose the vocabulary is <cite>[a, b, c]</cite>, and in one batch we have three sequences |
| ‘ba’, ‘cbb’, and ‘abac’. When <cite>blank_label</cite> is <code class="docutils literal notranslate"><span class="pre">"first"</span></code>, we can index the labels as |
| <cite>{‘a’: 1, ‘b’: 2, ‘c’: 3}</cite>, and we reserve the 0-th channel for blank label in data tensor. |
| The resulting <cite>label</cite> tensor should be padded to be:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>When <cite>blank_label</cite> is <code class="docutils literal notranslate"><span class="pre">"last"</span></code>, we can index the labels as |
| <cite>{‘a’: 0, ‘b’: 1, ‘c’: 2}</cite>, and we reserve the channel index 3 for blank label in data tensor. |
| The resulting <cite>label</cite> tensor should be padded to be:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p><code class="docutils literal notranslate"><span class="pre">out</span></code> is a list of CTC loss values, one per example in the batch.</p> |
| <p>See <em>Connectionist Temporal Classification: Labelling Unsegmented |
| Sequence Data with Recurrent Neural Networks</em>, A. Graves <em>et al</em>. for more |
| information on the definition and the algorithm.</p> |
| <p>Defined in /work/mxnet/src/operator/nn/ctc_loss.cc:L104</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input ndarray</p></li> |
| <li><p><strong>label</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Ground-truth labels for the loss.</p></li> |
| <li><p><strong>data_lengths</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Lengths of data for each of the samples. Only required when use_data_lengths is true.</p></li> |
| <li><p><strong>label_lengths</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Lengths of labels for each of the samples. Only required when use_label_lengths is true.</p></li> |
| <li><p><strong>use_data_lengths</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether the data lenghts are decided by <cite>data_lengths</cite>. If false, the lengths are equal to the max sequence length.</p></li> |
| <li><p><strong>use_label_lengths</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether the label lenghts are decided by <cite>label_lengths</cite>, or derived from <cite>padding_mask</cite>. If false, the lengths are derived from the first occurrence of the value of <cite>padding_mask</cite>. The value of <cite>padding_mask</cite> is <code class="docutils literal notranslate"><span class="pre">0</span></code> when first CTC label is reserved for blank, and <code class="docutils literal notranslate"><span class="pre">-1</span></code> when last label is reserved for blank. See <cite>blank_label</cite>.</p></li> |
| <li><p><strong>blank_label</strong> (<em>{'first'</em><em>, </em><em>'last'}</em><em>,</em><em>optional</em><em>, </em><em>default='first'</em>) – Set the label that is reserved for blank label.If “first”, 0-th label is reserved, and label values for tokens in the vocabulary are between <code class="docutils literal notranslate"><span class="pre">1</span></code> and <code class="docutils literal notranslate"><span class="pre">alphabet_size-1</span></code>, and the padding mask is <code class="docutils literal notranslate"><span class="pre">-1</span></code>. If “last”, last label value <code class="docutils literal notranslate"><span class="pre">alphabet_size-1</span></code> is reserved for blank label instead, and label values for tokens in the vocabulary are between <code class="docutils literal notranslate"><span class="pre">0</span></code> and <code class="docutils literal notranslate"><span class="pre">alphabet_size-2</span></code>, and the padding mask is <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.DeformablePSROIPooling"> |
| <code class="sig-name descname">DeformablePSROIPooling</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">rois=None</em>, <em class="sig-param">trans=None</em>, <em class="sig-param">spatial_scale=_Null</em>, <em class="sig-param">output_dim=_Null</em>, <em class="sig-param">group_size=_Null</em>, <em class="sig-param">pooled_size=_Null</em>, <em class="sig-param">part_size=_Null</em>, <em class="sig-param">sample_per_part=_Null</em>, <em class="sig-param">trans_std=_Null</em>, <em class="sig-param">no_trans=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.DeformablePSROIPooling" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Performs deformable position-sensitive region-of-interest pooling on inputs. |
| The DeformablePSROIPooling operation is described in <a class="reference external" href="https://arxiv.org/abs/1703.06211">https://arxiv.org/abs/1703.06211</a> .batch_size will change to the number of region bounding boxes after DeformablePSROIPooling</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the pooling operator, a 4D Feature maps</p></li> |
| <li><p><strong>rois</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bounding box coordinates, a 2D array of [[batch_index, x1, y1, x2, y2]]. (x1, y1) and (x2, y2) are top left and down right corners of designated region of interest. batch_index indicates the index of corresponding image in the input data</p></li> |
| <li><p><strong>trans</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – transition parameter</p></li> |
| <li><p><strong>spatial_scale</strong> (<em>float</em><em>, </em><em>required</em>) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers</p></li> |
| <li><p><strong>output_dim</strong> (<em>long</em><em>, </em><em>required</em>) – fix output dim</p></li> |
| <li><p><strong>group_size</strong> (<em>long</em><em>, </em><em>required</em>) – fix group size</p></li> |
| <li><p><strong>pooled_size</strong> (<em>long</em><em>, </em><em>required</em>) – fix pooled size</p></li> |
| <li><p><strong>part_size</strong> (<em>long</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – fix part size</p></li> |
| <li><p><strong>sample_per_part</strong> (<em>long</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – fix samples per part</p></li> |
| <li><p><strong>trans_std</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – fix transition std</p></li> |
| <li><p><strong>no_trans</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to disable trans parameter.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.MultiBoxDetection"> |
| <code class="sig-name descname">MultiBoxDetection</code><span class="sig-paren">(</span><em class="sig-param">cls_prob=None</em>, <em class="sig-param">loc_pred=None</em>, <em class="sig-param">anchor=None</em>, <em class="sig-param">clip=_Null</em>, <em class="sig-param">threshold=_Null</em>, <em class="sig-param">background_id=_Null</em>, <em class="sig-param">nms_threshold=_Null</em>, <em class="sig-param">force_suppress=_Null</em>, <em class="sig-param">variances=_Null</em>, <em class="sig-param">nms_topk=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.MultiBoxDetection" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Convert multibox detection predictions.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>cls_prob</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Class probabilities.</p></li> |
| <li><p><strong>loc_pred</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Location regression predictions.</p></li> |
| <li><p><strong>anchor</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Multibox prior anchor boxes</p></li> |
| <li><p><strong>clip</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Clip out-of-boundary boxes.</p></li> |
| <li><p><strong>threshold</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.00999999978</em>) – Threshold to be a positive prediction.</p></li> |
| <li><p><strong>background_id</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Background id.</p></li> |
| <li><p><strong>nms_threshold</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.5</em>) – Non-maximum suppression threshold.</p></li> |
| <li><p><strong>force_suppress</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Suppress all detections regardless of class_id.</p></li> |
| <li><p><strong>variances</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>0.1</em><em>,</em><em>0.1</em><em>,</em><em>0.2</em><em>,</em><em>0.2</em><em>]</em>) – Variances to be decoded from box regression output.</p></li> |
| <li><p><strong>nms_topk</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Keep maximum top k detections before nms, -1 for no limit.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.MultiBoxPrior"> |
| <code class="sig-name descname">MultiBoxPrior</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">sizes=_Null</em>, <em class="sig-param">ratios=_Null</em>, <em class="sig-param">clip=_Null</em>, <em class="sig-param">steps=_Null</em>, <em class="sig-param">offsets=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.MultiBoxPrior" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Generate prior(anchor) boxes from data, sizes and ratios.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data.</p></li> |
| <li><p><strong>sizes</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>1</em><em>]</em>) – List of sizes of generated MultiBoxPriores.</p></li> |
| <li><p><strong>ratios</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>1</em><em>]</em>) – List of aspect ratios of generated MultiBoxPriores.</p></li> |
| <li><p><strong>clip</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to clip out-of-boundary boxes.</p></li> |
| <li><p><strong>steps</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>-1</em><em>,</em><em>-1</em><em>]</em>) – Priorbox step across y and x, -1 for auto calculation.</p></li> |
| <li><p><strong>offsets</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>0.5</em><em>,</em><em>0.5</em><em>]</em>) – Priorbox center offsets, y and x respectively</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.MultiBoxTarget"> |
| <code class="sig-name descname">MultiBoxTarget</code><span class="sig-paren">(</span><em class="sig-param">anchor=None</em>, <em class="sig-param">label=None</em>, <em class="sig-param">cls_pred=None</em>, <em class="sig-param">overlap_threshold=_Null</em>, <em class="sig-param">ignore_label=_Null</em>, <em class="sig-param">negative_mining_ratio=_Null</em>, <em class="sig-param">negative_mining_thresh=_Null</em>, <em class="sig-param">minimum_negative_samples=_Null</em>, <em class="sig-param">variances=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.MultiBoxTarget" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute Multibox training targets</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>anchor</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Generated anchor boxes.</p></li> |
| <li><p><strong>label</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Object detection labels.</p></li> |
| <li><p><strong>cls_pred</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Class predictions.</p></li> |
| <li><p><strong>overlap_threshold</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.5</em>) – Anchor-GT overlap threshold to be regarded as a positive match.</p></li> |
| <li><p><strong>ignore_label</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Label for ignored anchors.</p></li> |
| <li><p><strong>negative_mining_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Max negative to positive samples ratio, use -1 to disable mining</p></li> |
| <li><p><strong>negative_mining_thresh</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.5</em>) – Threshold used for negative mining.</p></li> |
| <li><p><strong>minimum_negative_samples</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Minimum number of negative samples.</p></li> |
| <li><p><strong>variances</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>0.1</em><em>,</em><em>0.1</em><em>,</em><em>0.2</em><em>,</em><em>0.2</em><em>]</em>) – Variances to be encoded in box regression target.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.MultiProposal"> |
| <code class="sig-name descname">MultiProposal</code><span class="sig-paren">(</span><em class="sig-param">cls_prob=None</em>, <em class="sig-param">bbox_pred=None</em>, <em class="sig-param">im_info=None</em>, <em class="sig-param">rpn_pre_nms_top_n=_Null</em>, <em class="sig-param">rpn_post_nms_top_n=_Null</em>, <em class="sig-param">threshold=_Null</em>, <em class="sig-param">rpn_min_size=_Null</em>, <em class="sig-param">scales=_Null</em>, <em class="sig-param">ratios=_Null</em>, <em class="sig-param">feature_stride=_Null</em>, <em class="sig-param">output_score=_Null</em>, <em class="sig-param">iou_loss=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.MultiProposal" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Generate region proposals via RPN</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>cls_prob</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Score of how likely proposal is object.</p></li> |
| <li><p><strong>bbox_pred</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – BBox Predicted deltas from anchors for proposals</p></li> |
| <li><p><strong>im_info</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Image size and scale.</p></li> |
| <li><p><strong>rpn_pre_nms_top_n</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='6000'</em>) – Number of top scoring boxes to keep before applying NMS to RPN proposals</p></li> |
| <li><p><strong>rpn_post_nms_top_n</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='300'</em>) – Number of top scoring boxes to keep after applying NMS to RPN proposals</p></li> |
| <li><p><strong>threshold</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.699999988</em>) – NMS value, below which to suppress.</p></li> |
| <li><p><strong>rpn_min_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='16'</em>) – Minimum height or width in proposal</p></li> |
| <li><p><strong>scales</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>4</em><em>,</em><em>8</em><em>,</em><em>16</em><em>,</em><em>32</em><em>]</em>) – Used to generate anchor windows by enumerating scales</p></li> |
| <li><p><strong>ratios</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>0.5</em><em>,</em><em>1</em><em>,</em><em>2</em><em>]</em>) – Used to generate anchor windows by enumerating ratios</p></li> |
| <li><p><strong>feature_stride</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='16'</em>) – The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer.</p></li> |
| <li><p><strong>output_score</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add score to outputs</p></li> |
| <li><p><strong>iou_loss</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Usage of IoU Loss</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.PSROIPooling"> |
| <code class="sig-name descname">PSROIPooling</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">rois=None</em>, <em class="sig-param">spatial_scale=_Null</em>, <em class="sig-param">output_dim=_Null</em>, <em class="sig-param">pooled_size=_Null</em>, <em class="sig-param">group_size=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.PSROIPooling" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Performs region-of-interest pooling on inputs. Resize bounding box coordinates by spatial_scale and crop input feature maps accordingly. The cropped feature maps are pooled by max pooling to a fixed size output indicated by pooled_size. batch_size will change to the number of region bounding boxes after PSROIPooling</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the pooling operator, a 4D Feature maps</p></li> |
| <li><p><strong>rois</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bounding box coordinates, a 2D array of [[batch_index, x1, y1, x2, y2]]. (x1, y1) and (x2, y2) are top left and down right corners of designated region of interest. batch_index indicates the index of corresponding image in the input data</p></li> |
| <li><p><strong>spatial_scale</strong> (<em>float</em><em>, </em><em>required</em>) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers</p></li> |
| <li><p><strong>output_dim</strong> (<em>int</em><em>, </em><em>required</em>) – fix output dim</p></li> |
| <li><p><strong>pooled_size</strong> (<em>int</em><em>, </em><em>required</em>) – fix pooled size</p></li> |
| <li><p><strong>group_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – fix group size</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.Proposal"> |
| <code class="sig-name descname">Proposal</code><span class="sig-paren">(</span><em class="sig-param">cls_prob=None</em>, <em class="sig-param">bbox_pred=None</em>, <em class="sig-param">im_info=None</em>, <em class="sig-param">rpn_pre_nms_top_n=_Null</em>, <em class="sig-param">rpn_post_nms_top_n=_Null</em>, <em class="sig-param">threshold=_Null</em>, <em class="sig-param">rpn_min_size=_Null</em>, <em class="sig-param">scales=_Null</em>, <em class="sig-param">ratios=_Null</em>, <em class="sig-param">feature_stride=_Null</em>, <em class="sig-param">output_score=_Null</em>, <em class="sig-param">iou_loss=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.Proposal" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Generate region proposals via RPN</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>cls_prob</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Score of how likely proposal is object.</p></li> |
| <li><p><strong>bbox_pred</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – BBox Predicted deltas from anchors for proposals</p></li> |
| <li><p><strong>im_info</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Image size and scale.</p></li> |
| <li><p><strong>rpn_pre_nms_top_n</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='6000'</em>) – Number of top scoring boxes to keep before applying NMS to RPN proposals</p></li> |
| <li><p><strong>rpn_post_nms_top_n</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='300'</em>) – Number of top scoring boxes to keep after applying NMS to RPN proposals</p></li> |
| <li><p><strong>threshold</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.699999988</em>) – NMS value, below which to suppress.</p></li> |
| <li><p><strong>rpn_min_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='16'</em>) – Minimum height or width in proposal</p></li> |
| <li><p><strong>scales</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>4</em><em>,</em><em>8</em><em>,</em><em>16</em><em>,</em><em>32</em><em>]</em>) – Used to generate anchor windows by enumerating scales</p></li> |
| <li><p><strong>ratios</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>0.5</em><em>,</em><em>1</em><em>,</em><em>2</em><em>]</em>) – Used to generate anchor windows by enumerating ratios</p></li> |
| <li><p><strong>feature_stride</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='16'</em>) – The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer.</p></li> |
| <li><p><strong>output_score</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add score to outputs</p></li> |
| <li><p><strong>iou_loss</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Usage of IoU Loss</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.ROIAlign"> |
| <code class="sig-name descname">ROIAlign</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">rois=None</em>, <em class="sig-param">pooled_size=_Null</em>, <em class="sig-param">spatial_scale=_Null</em>, <em class="sig-param">sample_ratio=_Null</em>, <em class="sig-param">position_sensitive=_Null</em>, <em class="sig-param">aligned=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.ROIAlign" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This operator takes a 4D feature map as an input array and region proposals as <cite>rois</cite>, |
| then align the feature map over sub-regions of input and produces a fixed-sized output array. |
| This operator is typically used in Faster R-CNN & Mask R-CNN networks. If roi batchid is less |
| than 0, it will be ignored, and the corresponding output will be set to 0.</p> |
| <p>Different from ROI pooling, ROI Align removes the harsh quantization, properly aligning |
| the extracted features with the input. RoIAlign computes the value of each sampling point |
| by bilinear interpolation from the nearby grid points on the feature map. No quantization is |
| performed on any coordinates involved in the RoI, its bins, or the sampling points. |
| Bilinear interpolation is used to compute the exact values of the |
| input features at four regularly sampled locations in each RoI bin. |
| Then the feature map can be aggregated by avgpooling.</p> |
| <p class="rubric">References</p> |
| <p>He, Kaiming, et al. “Mask R-CNN.” ICCV, 2017</p> |
| <p>Defined in /work/mxnet/src/operator/contrib/roi_align.cc:L551</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the pooling operator, a 4D Feature maps</p></li> |
| <li><p><strong>rois</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bounding box coordinates, a 2D array, if batchid is less than 0, it will be ignored.</p></li> |
| <li><p><strong>pooled_size</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>required</em>) – ROI Align output roi feature map height and width: (h, w)</p></li> |
| <li><p><strong>spatial_scale</strong> (<em>float</em><em>, </em><em>required</em>) – Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal of total stride in convolutional layers</p></li> |
| <li><p><strong>sample_ratio</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Optional sampling ratio of ROI align, using adaptive size by default.</p></li> |
| <li><p><strong>position_sensitive</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to perform position-sensitive RoI pooling. PSRoIPooling is first proposaled by R-FCN and it can reduce the input channels by ph*pw times, where (ph, pw) is the pooled_size</p></li> |
| <li><p><strong>aligned</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Center-aligned ROIAlign introduced in Detectron2. To enable, set aligned to True.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.RROIAlign"> |
| <code class="sig-name descname">RROIAlign</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">rois=None</em>, <em class="sig-param">pooled_size=_Null</em>, <em class="sig-param">spatial_scale=_Null</em>, <em class="sig-param">sampling_ratio=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.RROIAlign" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Performs Rotated ROI Align on the input array.</p> |
| <p>This operator takes a 4D feature map as an input array and region proposals as <cite>rois</cite>, |
| then align the feature map over sub-regions of input and produces a fixed-sized output array.</p> |
| <p>Different from ROI Align, RROI Align uses rotated rois, which is suitable for text detection. |
| RRoIAlign computes the value of each sampling point by bilinear interpolation from the nearby |
| grid points on the rotated feature map. No quantization is performed on any coordinates |
| involved in the RoI, its bins, or the sampling points. Bilinear interpolation is used to |
| compute the exact values of the input features at four regularly sampled locations in |
| each RoI bin. Then the feature map can be aggregated by avgpooling.</p> |
| <p class="rubric">References</p> |
| <p>Ma, Jianqi, et al. “Arbitrary-Oriented Scene Text Detection via Rotation Proposals.” |
| IEEE Transactions on Multimedia, 2018.</p> |
| <p>Defined in /work/mxnet/src/operator/contrib/rroi_align.cc:L300</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the pooling operator, a 4D Feature maps</p></li> |
| <li><p><strong>rois</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bounding box coordinates, a 2D array</p></li> |
| <li><p><strong>pooled_size</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>required</em>) – RROI align output shape (h,w)</p></li> |
| <li><p><strong>spatial_scale</strong> (<em>float</em><em>, </em><em>required</em>) – Ratio of input feature map height (or width) to raw image height (or width). Equals the reciprocal of total stride in convolutional layers</p></li> |
| <li><p><strong>sampling_ratio</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Optional sampling ratio of RROI align, using adaptive size by default.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.SyncBatchNorm"> |
| <code class="sig-name descname">SyncBatchNorm</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">gamma=None</em>, <em class="sig-param">beta=None</em>, <em class="sig-param">moving_mean=None</em>, <em class="sig-param">moving_var=None</em>, <em class="sig-param">eps=_Null</em>, <em class="sig-param">momentum=_Null</em>, <em class="sig-param">fix_gamma=_Null</em>, <em class="sig-param">use_global_stats=_Null</em>, <em class="sig-param">output_mean_var=_Null</em>, <em class="sig-param">ndev=_Null</em>, <em class="sig-param">key=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.SyncBatchNorm" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Batch normalization.</p> |
| <p>Normalizes a data batch by mean and variance, and applies a scale <code class="docutils literal notranslate"><span class="pre">gamma</span></code> as |
| well as offset <code class="docutils literal notranslate"><span class="pre">beta</span></code>. |
| Standard BN <a class="footnote-reference brackets" href="#id3" id="id1">1</a> implementation only normalize the data within each device. |
| SyncBN normalizes the input within the whole mini-batch. |
| We follow the sync-onece implmentation described in the paper <a class="footnote-reference brackets" href="#id4" id="id2">2</a>.</p> |
| <p>Assume the input has more than one dimension and we normalize along axis 1. |
| We first compute the mean and variance along this axis:</p> |
| <div class="math notranslate nohighlight"> |
| \[\begin{split}data\_mean[i] = mean(data[:,i,:,...]) \\ |
| data\_var[i] = var(data[:,i,:,...])\end{split}\]</div> |
| <p>Then compute the normalized output, which has the same shape as input, as following:</p> |
| <div class="math notranslate nohighlight"> |
| \[out[:,i,:,...] = \frac{data[:,i,:,...] - data\_mean[i]}{\sqrt{data\_var[i]+\epsilon}} * gamma[i] + beta[i]\]</div> |
| <p>Both <em>mean</em> and <em>var</em> returns a scalar by treating the input as a vector.</p> |
| <p>Assume the input has size <em>k</em> on axis 1, then both <code class="docutils literal notranslate"><span class="pre">gamma</span></code> and <code class="docutils literal notranslate"><span class="pre">beta</span></code> |
| have shape <em>(k,)</em>. If <code class="docutils literal notranslate"><span class="pre">output_mean_var</span></code> is set to be true, then outputs both <code class="docutils literal notranslate"><span class="pre">data_mean</span></code> and |
| <code class="docutils literal notranslate"><span class="pre">data_var</span></code> as well, which are needed for the backward pass.</p> |
| <p>Besides the inputs and the outputs, this operator accepts two auxiliary |
| states, <code class="docutils literal notranslate"><span class="pre">moving_mean</span></code> and <code class="docutils literal notranslate"><span class="pre">moving_var</span></code>, which are <em>k</em>-length |
| vectors. They are global statistics for the whole dataset, which are updated |
| by:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">moving_mean</span> <span class="o">=</span> <span class="n">moving_mean</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_mean</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span> |
| <span class="n">moving_var</span> <span class="o">=</span> <span class="n">moving_var</span> <span class="o">*</span> <span class="n">momentum</span> <span class="o">+</span> <span class="n">data_var</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">momentum</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>If <code class="docutils literal notranslate"><span class="pre">use_global_stats</span></code> is set to be true, then <code class="docutils literal notranslate"><span class="pre">moving_mean</span></code> and |
| <code class="docutils literal notranslate"><span class="pre">moving_var</span></code> are used instead of <code class="docutils literal notranslate"><span class="pre">data_mean</span></code> and <code class="docutils literal notranslate"><span class="pre">data_var</span></code> to compute |
| the output. It is often used during inference.</p> |
| <p>Both <code class="docutils literal notranslate"><span class="pre">gamma</span></code> and <code class="docutils literal notranslate"><span class="pre">beta</span></code> are learnable parameters. But if <code class="docutils literal notranslate"><span class="pre">fix_gamma</span></code> is true, |
| then set <code class="docutils literal notranslate"><span class="pre">gamma</span></code> to 1 and its gradient to 0.</p> |
| <dl> |
| <dt>Reference:</dt><dd><dl class="footnote brackets"> |
| <dt class="label" id="id3"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt> |
| <dd><p>Ioffe, Sergey, and Christian Szegedy. “Batch normalization: Accelerating deep network training by reducing internal covariate shift.” <em>ICML 2015</em></p> |
| </dd> |
| <dt class="label" id="id4"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt> |
| <dd><p>Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, and Amit Agrawal. “Context Encoding for Semantic Segmentation.” <em>CVPR 2018</em></p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p>Defined in /work/mxnet/src/operator/contrib/sync_batch_norm.cc:L97</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to batch normalization</p></li> |
| <li><p><strong>gamma</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – gamma array</p></li> |
| <li><p><strong>beta</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – beta array</p></li> |
| <li><p><strong>moving_mean</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – running mean of input</p></li> |
| <li><p><strong>moving_var</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – running variance of input</p></li> |
| <li><p><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.00100000005</em>) – Epsilon to prevent div 0</p></li> |
| <li><p><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.899999976</em>) – Momentum for moving average</p></li> |
| <li><p><strong>fix_gamma</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Fix gamma while training</p></li> |
| <li><p><strong>use_global_stats</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether use global moving statistics instead of local batch-norm. This will force change batch-norm into a scale shift operator.</p></li> |
| <li><p><strong>output_mean_var</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Output All,normal mean and var</p></li> |
| <li><p><strong>ndev</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The count of GPU devices</p></li> |
| <li><p><strong>key</strong> (<em>string</em><em>, </em><em>required</em>) – Hash key for synchronization, please set the same hash key for same layer, Block.prefix is typically used as in <code class="xref py py-class docutils literal notranslate"><span class="pre">gluon.nn.contrib.SyncBatchNorm</span></code>.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.allclose"> |
| <code class="sig-name descname">allclose</code><span class="sig-paren">(</span><em class="sig-param">a=None</em>, <em class="sig-param">b=None</em>, <em class="sig-param">rtol=_Null</em>, <em class="sig-param">atol=_Null</em>, <em class="sig-param">equal_nan=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.allclose" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This operators implements the numpy.allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)</p> |
| <div class="math notranslate nohighlight"> |
| \[f(x) = |a-b|<=atol+rtol|b|\]</div> |
| <p>where |
| <span class="math notranslate nohighlight">\(a, b\)</span> are the input tensors of equal types an shapes |
| <span class="math notranslate nohighlight">\(atol, rtol\)</span> the values of absolute and relative tolerance (by default, rtol=1e-05, atol=1e-08)</p> |
| <p>Examples:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1e10</span><span class="p">,</span> <span class="mf">1e-7</span><span class="p">],</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.00001e10</span><span class="p">,</span> <span class="mf">1e-8</span><span class="p">]</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">allclose</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="kc">False</span> |
| |
| <span class="n">a</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1e10</span><span class="p">,</span> <span class="mf">1e-8</span><span class="p">],</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.00001e10</span><span class="p">,</span> <span class="mf">1e-9</span><span class="p">]</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">allclose</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="kc">True</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/allclose_op.cc:L56</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>a</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array a</p></li> |
| <li><p><strong>b</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input array b</p></li> |
| <li><p><strong>rtol</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=9.99999975e-06</em>) – Relative tolerance.</p></li> |
| <li><p><strong>atol</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=9.99999994e-09</em>) – Absolute tolerance.</p></li> |
| <li><p><strong>equal_nan</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Whether to compare NaN’s as equal. If True, NaN’s in A will be considered equal to NaN’s in B in the output array.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.arange_like"> |
| <code class="sig-name descname">arange_like</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">start=_Null</em>, <em class="sig-param">step=_Null</em>, <em class="sig-param">repeat=_Null</em>, <em class="sig-param">ctx=_Null</em>, <em class="sig-param">axis=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.arange_like" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Return an array with evenly spaced values. If axis is not given, the output will |
| have the same shape as the input array. Otherwise, the output will be a 1-D array with size of |
| the specified axis in input shape.</p> |
| <p>Examples:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.14883883</span> <span class="mf">0.7772398</span> <span class="mf">0.94865847</span> <span class="mf">0.7225052</span> <span class="p">]</span> |
| <span class="p">[</span><span class="mf">0.23729339</span> <span class="mf">0.6112595</span> <span class="mf">0.66538996</span> <span class="mf">0.5132841</span> <span class="p">]</span> |
| <span class="p">[</span><span class="mf">0.30822644</span> <span class="mf">0.9912457</span> <span class="mf">0.15502319</span> <span class="mf">0.7043658</span> <span class="p">]]</span> |
| <span class="o"><</span><span class="n">NDArray</span> <span class="mi">3</span><span class="n">x4</span> <span class="nd">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span> |
| |
| <span class="n">out</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">arange_like</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| |
| <span class="p">[[</span> <span class="mf">0.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span><span class="p">]</span> |
| <span class="p">[</span> <span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span> <span class="mf">7.</span><span class="p">]</span> |
| <span class="p">[</span> <span class="mf">8.</span> <span class="mf">9.</span> <span class="mf">10.</span> <span class="mf">11.</span><span class="p">]]</span> |
| <span class="o"><</span><span class="n">NDArray</span> <span class="mi">3</span><span class="n">x4</span> <span class="nd">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span> |
| |
| <span class="n">out</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">arange_like</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> |
| |
| <span class="p">[</span><span class="mf">0.</span> <span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span><span class="p">]</span> |
| <span class="o"><</span><span class="n">NDArray</span> <span class="mi">4</span> <span class="nd">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</p></li> |
| <li><p><strong>start</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Start of interval. The interval includes this value. The default start value is 0.</p></li> |
| <li><p><strong>step</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Spacing between values.</p></li> |
| <li><p><strong>repeat</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The repeating time of all elements. E.g repeat=3, the element a will be repeated three times –> a, a, a.</p></li> |
| <li><p><strong>ctx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Context of output, in format [cpu|gpu|cpu_pinned](n).Only used for imperative calls.</p></li> |
| <li><p><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – Arange elements according to the size of a certain axis of input array. The negative numbers are interpreted counting from the backward. If not provided, will arange elements according to the input shape.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.backward_gradientmultiplier"> |
| <code class="sig-name descname">backward_gradientmultiplier</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">scalar=_Null</em>, <em class="sig-param">is_int=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.backward_gradientmultiplier" title="Permalink to this definition">¶</a></dt> |
| <dd><dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – source input</p></li> |
| <li><p><strong>scalar</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scalar input value</p></li> |
| <li><p><strong>is_int</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Indicate whether scalar input is int type</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.backward_hawkesll"> |
| <code class="sig-name descname">backward_hawkesll</code><span class="sig-paren">(</span><em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.backward_hawkesll" title="Permalink to this definition">¶</a></dt> |
| <dd><dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.backward_index_copy"> |
| <code class="sig-name descname">backward_index_copy</code><span class="sig-paren">(</span><em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.backward_index_copy" title="Permalink to this definition">¶</a></dt> |
| <dd><dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.backward_quadratic"> |
| <code class="sig-name descname">backward_quadratic</code><span class="sig-paren">(</span><em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.backward_quadratic" title="Permalink to this definition">¶</a></dt> |
| <dd><dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.bipartite_matching"> |
| <code class="sig-name descname">bipartite_matching</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">is_ascend=_Null</em>, <em class="sig-param">threshold=_Null</em>, <em class="sig-param">topk=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.bipartite_matching" title="Permalink to this definition">¶</a></dt> |
| <dd><dl> |
| <dt>Compute bipartite matching.</dt><dd><p>The matching is performed on score matrix with shape [B, N, M] |
| - B: batch_size |
| - N: number of rows to match |
| - M: number of columns as reference to be matched against.</p> |
| <p>Returns: |
| x : matched column indices. -1 indicating non-matched elements in rows. |
| y : matched row indices.</p> |
| <p>Note:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Zero</span> <span class="n">gradients</span> <span class="n">are</span> <span class="n">back</span><span class="o">-</span><span class="n">propagated</span> <span class="ow">in</span> <span class="n">this</span> <span class="n">op</span> <span class="k">for</span> <span class="n">now</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">]]</span> |
| <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">bipartite_matching</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">1e-12</span><span class="p">,</span> <span class="n">is_ascend</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| </dd> |
| </dl> |
| <p>Defined in /work/mxnet/src/operator/contrib/bounding_box.cc:L183</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</p></li> |
| <li><p><strong>is_ascend</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Use ascend order for scores instead of descending. Please set threshold accordingly.</p></li> |
| <li><p><strong>threshold</strong> (<em>float</em><em>, </em><em>required</em>) – Ignore matching when score < thresh, if is_ascend=false, or ignore score > thresh, if is_ascend=true.</p></li> |
| <li><p><strong>topk</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Limit the number of matches to topk, set -1 for no limit</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.boolean_mask"> |
| <code class="sig-name descname">boolean_mask</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">index=None</em>, <em class="sig-param">axis=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.boolean_mask" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Given an n-d NDArray data, and a 1-d NDArray index, |
| the operator produces an un-predeterminable shaped n-d NDArray out, |
| which stands for the rows in x where the corresonding element in index is non-zero.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">]])</span> |
| <span class="gp">>>> </span><span class="n">index</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">out</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">boolean_mask</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">index</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">out</span> |
| </pre></div> |
| </div> |
| <p>[[4. 5. 6.]] |
| <NDArray 1x3 @cpu(0)></p> |
| <p>Defined in /work/mxnet/src/operator/contrib/boolean_mask.cc:L213</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Data</p></li> |
| <li><p><strong>index</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Mask</p></li> |
| <li><p><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – An integer that represents the axis in NDArray to mask from.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.box_decode"> |
| <code class="sig-name descname">box_decode</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">anchors=None</em>, <em class="sig-param">std0=_Null</em>, <em class="sig-param">std1=_Null</em>, <em class="sig-param">std2=_Null</em>, <em class="sig-param">std3=_Null</em>, <em class="sig-param">clip=_Null</em>, <em class="sig-param">format=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.box_decode" title="Permalink to this definition">¶</a></dt> |
| <dd><dl class="simple"> |
| <dt>Decode bounding boxes training target with normalized center offsets.</dt><dd><p>Input bounding boxes are using corner type: <code class="docutils literal notranslate"><span class="pre">x_{min},</span> <span class="pre">y_{min},</span> <span class="pre">x_{max},</span> <span class="pre">y_{max}</span></code> |
| or center type: <code class="docutils literal notranslate"><span class="pre">x,</span> <span class="pre">y,</span> <span class="pre">width,</span> <span class="pre">height</span></code>.) array</p> |
| </dd> |
| </dl> |
| <p>Defined in /work/mxnet/src/operator/contrib/bounding_box.cc:L241</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – (B, N, 4) predicted bbox offset</p></li> |
| <li><p><strong>anchors</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – (1, N, 4) encoded in corner or center</p></li> |
| <li><p><strong>std0</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – value to be divided from the 1st encoded values</p></li> |
| <li><p><strong>std1</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – value to be divided from the 2nd encoded values</p></li> |
| <li><p><strong>std2</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – value to be divided from the 3rd encoded values</p></li> |
| <li><p><strong>std3</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – value to be divided from the 4th encoded values</p></li> |
| <li><p><strong>clip</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – If larger than 0, bounding box target will be clipped to this value.</p></li> |
| <li><p><strong>format</strong> (<em>{'center'</em><em>, </em><em>'corner'}</em><em>,</em><em>optional</em><em>, </em><em>default='center'</em>) – The box encoding type. |
| “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height].</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.box_encode"> |
| <code class="sig-name descname">box_encode</code><span class="sig-paren">(</span><em class="sig-param">samples=None</em>, <em class="sig-param">matches=None</em>, <em class="sig-param">anchors=None</em>, <em class="sig-param">refs=None</em>, <em class="sig-param">means=None</em>, <em class="sig-param">stds=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.box_encode" title="Permalink to this definition">¶</a></dt> |
| <dd><dl class="simple"> |
| <dt>Encode bounding boxes training target with normalized center offsets.</dt><dd><p>Input bounding boxes are using corner type: <cite>x_{min}, y_{min}, x_{max}, y_{max}</cite>.) array</p> |
| </dd> |
| </dl> |
| <p>Defined in /work/mxnet/src/operator/contrib/bounding_box.cc:L212</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>samples</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – (B, N) value +1 (positive), -1 (negative), 0 (ignore)</p></li> |
| <li><p><strong>matches</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – (B, N) value range [0, M)</p></li> |
| <li><p><strong>anchors</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – (B, N, 4) encoded in corner</p></li> |
| <li><p><strong>refs</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – (B, M, 4) encoded in corner</p></li> |
| <li><p><strong>means</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – (4,) Mean value to be subtracted from encoded values</p></li> |
| <li><p><strong>stds</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – (4,) Std value to be divided from encoded values</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.box_iou"> |
| <code class="sig-name descname">box_iou</code><span class="sig-paren">(</span><em class="sig-param">lhs=None</em>, <em class="sig-param">rhs=None</em>, <em class="sig-param">format=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.box_iou" title="Permalink to this definition">¶</a></dt> |
| <dd><dl> |
| <dt>Bounding box overlap of two arrays.</dt><dd><p>The overlap is defined as Intersection-over-Union, aka, IOU. |
| - lhs: (a_1, a_2, …, a_n, 4) array |
| - rhs: (b_1, b_2, …, b_n, 4) array |
| - output: (a_1, a_2, …, a_n, b_1, b_2, …, b_n) array</p> |
| <p>Note:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Zero</span> <span class="n">gradients</span> <span class="n">are</span> <span class="n">back</span><span class="o">-</span><span class="n">propagated</span> <span class="ow">in</span> <span class="n">this</span> <span class="n">op</span> <span class="k">for</span> <span class="n">now</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">]]</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">]]</span> |
| <span class="n">box_iou</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s1">'corner'</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.1428</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.1428</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| </dd> |
| </dl> |
| <p>Defined in /work/mxnet/src/operator/contrib/bounding_box.cc:L136</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>lhs</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The first input</p></li> |
| <li><p><strong>rhs</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The second input</p></li> |
| <li><p><strong>format</strong> (<em>{'center'</em><em>, </em><em>'corner'}</em><em>,</em><em>optional</em><em>, </em><em>default='corner'</em>) – The box encoding type. |
| “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height].</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.box_nms"> |
| <code class="sig-name descname">box_nms</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">overlap_thresh=_Null</em>, <em class="sig-param">valid_thresh=_Null</em>, <em class="sig-param">topk=_Null</em>, <em class="sig-param">coord_start=_Null</em>, <em class="sig-param">score_index=_Null</em>, <em class="sig-param">id_index=_Null</em>, <em class="sig-param">background_id=_Null</em>, <em class="sig-param">force_suppress=_Null</em>, <em class="sig-param">in_format=_Null</em>, <em class="sig-param">out_format=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.box_nms" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Apply non-maximum suppression to input.</p> |
| <p>The output will be sorted in descending order according to <code class="docutils literal notranslate"><span class="pre">score</span></code>. Boxes with |
| overlaps larger than <code class="docutils literal notranslate"><span class="pre">overlap_thresh</span></code>, smaller scores and background boxes |
| will be removed and filled with -1, the corresponding position will be recorded |
| for backward propogation.</p> |
| <p>During back-propagation, the gradient will be copied to the original |
| position according to the input index. For positions that have been suppressed, |
| the in_grad will be assigned 0. |
| In summary, gradients are sticked to its boxes, will either be moved or discarded |
| according to its original index in input.</p> |
| <p>Input requirements:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mf">1.</span> <span class="n">Input</span> <span class="n">tensor</span> <span class="n">have</span> <span class="n">at</span> <span class="n">least</span> <span class="mi">2</span> <span class="n">dimensions</span><span class="p">,</span> <span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">),</span> <span class="nb">any</span> <span class="n">higher</span> <span class="n">dims</span> <span class="n">will</span> <span class="n">be</span> <span class="n">regarded</span> |
| <span class="k">as</span> <span class="n">batch</span><span class="p">,</span> <span class="n">e</span><span class="o">.</span><span class="n">g</span><span class="o">.</span> <span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">a</span><span class="o">*</span><span class="n">b</span><span class="o">*</span><span class="n">c</span><span class="o">*</span><span class="n">d</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> |
| <span class="mf">2.</span> <span class="n">n</span> <span class="ow">is</span> <span class="n">the</span> <span class="n">number</span> <span class="n">of</span> <span class="n">boxes</span> <span class="ow">in</span> <span class="n">each</span> <span class="n">batch</span> |
| <span class="mf">3.</span> <span class="n">k</span> <span class="ow">is</span> <span class="n">the</span> <span class="n">width</span> <span class="n">of</span> <span class="n">each</span> <span class="n">box</span> <span class="n">item</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| <p>By default, a box is [id, score, xmin, ymin, xmax, ymax, …], |
| additional elements are allowed.</p> |
| <ul> |
| <li><p><code class="docutils literal notranslate"><span class="pre">id_index</span></code>: optional, use -1 to ignore, useful if <code class="docutils literal notranslate"><span class="pre">force_suppress=False</span></code>, which means |
| we will skip highly overlapped boxes if one is <code class="docutils literal notranslate"><span class="pre">apple</span></code> while the other is <code class="docutils literal notranslate"><span class="pre">car</span></code>.</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">background_id</span></code>: optional, default=-1, class id for background boxes, useful |
| when <code class="docutils literal notranslate"><span class="pre">id_index</span> <span class="pre">>=</span> <span class="pre">0</span></code> which means boxes with background id will be filtered before nms.</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">coord_start</span></code>: required, default=2, the starting index of the 4 coordinates. |
| Two formats are supported:</p> |
| <blockquote> |
| <div><ul class="simple"> |
| <li><p><code class="docutils literal notranslate"><span class="pre">corner</span></code>: [xmin, ymin, xmax, ymax]</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">center</span></code>: [x, y, width, height]</p></li> |
| </ul> |
| </div></blockquote> |
| </li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">score_index</span></code>: required, default=1, box score/confidence. |
| When two boxes overlap IOU > <code class="docutils literal notranslate"><span class="pre">overlap_thresh</span></code>, the one with smaller score will be suppressed.</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">in_format</span></code> and <code class="docutils literal notranslate"><span class="pre">out_format</span></code>: default=’corner’, specify in/out box formats.</p></li> |
| </ul> |
| <p>Examples:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.14</span><span class="p">,</span> <span class="mf">0.14</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">]]</span> |
| <span class="n">box_nms</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">overlap_thresh</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">coord_start</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">score_index</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">id_index</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="n">force_suppress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">in_format</span><span class="o">=</span><span class="s1">'corner'</span><span class="p">,</span> <span class="n">out_typ</span><span class="o">=</span><span class="s1">'corner'</span><span class="p">)</span> <span class="o">=</span> |
| <span class="p">[[</span><span class="mi">2</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span> |
| <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]]</span> |
| <span class="n">out_grad</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span> |
| <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">]]</span> |
| <span class="c1"># exe.backward</span> |
| <span class="n">in_grad</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/bounding_box.cc:L93</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</p></li> |
| <li><p><strong>overlap_thresh</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.5</em>) – Overlapping(IoU) threshold to suppress object with smaller score.</p></li> |
| <li><p><strong>valid_thresh</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Filter input boxes to those whose scores greater than valid_thresh.</p></li> |
| <li><p><strong>topk</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Apply nms to topk boxes with descending scores, -1 to no restriction.</p></li> |
| <li><p><strong>coord_start</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='2'</em>) – Start index of the consecutive 4 coordinates.</p></li> |
| <li><p><strong>score_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Index of the scores/confidence of boxes.</p></li> |
| <li><p><strong>id_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Optional, index of the class categories, -1 to disable.</p></li> |
| <li><p><strong>background_id</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Optional, id of the background class which will be ignored in nms.</p></li> |
| <li><p><strong>force_suppress</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Optional, if set false and id_index is provided, nms will only apply to boxes belongs to the same category</p></li> |
| <li><p><strong>in_format</strong> (<em>{'center'</em><em>, </em><em>'corner'}</em><em>,</em><em>optional</em><em>, </em><em>default='corner'</em>) – The input box encoding type. |
| “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height].</p></li> |
| <li><p><strong>out_format</strong> (<em>{'center'</em><em>, </em><em>'corner'}</em><em>,</em><em>optional</em><em>, </em><em>default='corner'</em>) – The output box encoding type. |
| “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height].</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.box_non_maximum_suppression"> |
| <code class="sig-name descname">box_non_maximum_suppression</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">overlap_thresh=_Null</em>, <em class="sig-param">valid_thresh=_Null</em>, <em class="sig-param">topk=_Null</em>, <em class="sig-param">coord_start=_Null</em>, <em class="sig-param">score_index=_Null</em>, <em class="sig-param">id_index=_Null</em>, <em class="sig-param">background_id=_Null</em>, <em class="sig-param">force_suppress=_Null</em>, <em class="sig-param">in_format=_Null</em>, <em class="sig-param">out_format=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.box_non_maximum_suppression" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Apply non-maximum suppression to input.</p> |
| <p>The output will be sorted in descending order according to <code class="docutils literal notranslate"><span class="pre">score</span></code>. Boxes with |
| overlaps larger than <code class="docutils literal notranslate"><span class="pre">overlap_thresh</span></code>, smaller scores and background boxes |
| will be removed and filled with -1, the corresponding position will be recorded |
| for backward propogation.</p> |
| <p>During back-propagation, the gradient will be copied to the original |
| position according to the input index. For positions that have been suppressed, |
| the in_grad will be assigned 0. |
| In summary, gradients are sticked to its boxes, will either be moved or discarded |
| according to its original index in input.</p> |
| <p>Input requirements:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mf">1.</span> <span class="n">Input</span> <span class="n">tensor</span> <span class="n">have</span> <span class="n">at</span> <span class="n">least</span> <span class="mi">2</span> <span class="n">dimensions</span><span class="p">,</span> <span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">),</span> <span class="nb">any</span> <span class="n">higher</span> <span class="n">dims</span> <span class="n">will</span> <span class="n">be</span> <span class="n">regarded</span> |
| <span class="k">as</span> <span class="n">batch</span><span class="p">,</span> <span class="n">e</span><span class="o">.</span><span class="n">g</span><span class="o">.</span> <span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">a</span><span class="o">*</span><span class="n">b</span><span class="o">*</span><span class="n">c</span><span class="o">*</span><span class="n">d</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> |
| <span class="mf">2.</span> <span class="n">n</span> <span class="ow">is</span> <span class="n">the</span> <span class="n">number</span> <span class="n">of</span> <span class="n">boxes</span> <span class="ow">in</span> <span class="n">each</span> <span class="n">batch</span> |
| <span class="mf">3.</span> <span class="n">k</span> <span class="ow">is</span> <span class="n">the</span> <span class="n">width</span> <span class="n">of</span> <span class="n">each</span> <span class="n">box</span> <span class="n">item</span><span class="o">.</span> |
| </pre></div> |
| </div> |
| <p>By default, a box is [id, score, xmin, ymin, xmax, ymax, …], |
| additional elements are allowed.</p> |
| <ul> |
| <li><p><code class="docutils literal notranslate"><span class="pre">id_index</span></code>: optional, use -1 to ignore, useful if <code class="docutils literal notranslate"><span class="pre">force_suppress=False</span></code>, which means |
| we will skip highly overlapped boxes if one is <code class="docutils literal notranslate"><span class="pre">apple</span></code> while the other is <code class="docutils literal notranslate"><span class="pre">car</span></code>.</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">background_id</span></code>: optional, default=-1, class id for background boxes, useful |
| when <code class="docutils literal notranslate"><span class="pre">id_index</span> <span class="pre">>=</span> <span class="pre">0</span></code> which means boxes with background id will be filtered before nms.</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">coord_start</span></code>: required, default=2, the starting index of the 4 coordinates. |
| Two formats are supported:</p> |
| <blockquote> |
| <div><ul class="simple"> |
| <li><p><code class="docutils literal notranslate"><span class="pre">corner</span></code>: [xmin, ymin, xmax, ymax]</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">center</span></code>: [x, y, width, height]</p></li> |
| </ul> |
| </div></blockquote> |
| </li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">score_index</span></code>: required, default=1, box score/confidence. |
| When two boxes overlap IOU > <code class="docutils literal notranslate"><span class="pre">overlap_thresh</span></code>, the one with smaller score will be suppressed.</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">in_format</span></code> and <code class="docutils literal notranslate"><span class="pre">out_format</span></code>: default=’corner’, specify in/out box formats.</p></li> |
| </ul> |
| <p>Examples:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.14</span><span class="p">,</span> <span class="mf">0.14</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">]]</span> |
| <span class="n">box_nms</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">overlap_thresh</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">coord_start</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">score_index</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">id_index</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="n">force_suppress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">in_format</span><span class="o">=</span><span class="s1">'corner'</span><span class="p">,</span> <span class="n">out_typ</span><span class="o">=</span><span class="s1">'corner'</span><span class="p">)</span> <span class="o">=</span> |
| <span class="p">[[</span><span class="mi">2</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span> |
| <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]]</span> |
| <span class="n">out_grad</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span> |
| <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">]]</span> |
| <span class="c1"># exe.backward</span> |
| <span class="n">in_grad</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/bounding_box.cc:L93</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input</p></li> |
| <li><p><strong>overlap_thresh</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.5</em>) – Overlapping(IoU) threshold to suppress object with smaller score.</p></li> |
| <li><p><strong>valid_thresh</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Filter input boxes to those whose scores greater than valid_thresh.</p></li> |
| <li><p><strong>topk</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Apply nms to topk boxes with descending scores, -1 to no restriction.</p></li> |
| <li><p><strong>coord_start</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='2'</em>) – Start index of the consecutive 4 coordinates.</p></li> |
| <li><p><strong>score_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Index of the scores/confidence of boxes.</p></li> |
| <li><p><strong>id_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Optional, index of the class categories, -1 to disable.</p></li> |
| <li><p><strong>background_id</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Optional, id of the background class which will be ignored in nms.</p></li> |
| <li><p><strong>force_suppress</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Optional, if set false and id_index is provided, nms will only apply to boxes belongs to the same category</p></li> |
| <li><p><strong>in_format</strong> (<em>{'center'</em><em>, </em><em>'corner'}</em><em>,</em><em>optional</em><em>, </em><em>default='corner'</em>) – The input box encoding type. |
| “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height].</p></li> |
| <li><p><strong>out_format</strong> (<em>{'center'</em><em>, </em><em>'corner'}</em><em>,</em><em>optional</em><em>, </em><em>default='corner'</em>) – The output box encoding type. |
| “corner” means boxes are encoded as [xmin, ymin, xmax, ymax], “center” means boxes are encodes as [x, y, width, height].</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.calibrate_entropy"> |
| <code class="sig-name descname">calibrate_entropy</code><span class="sig-paren">(</span><em class="sig-param">hist=None</em>, <em class="sig-param">hist_edges=None</em>, <em class="sig-param">num_quantized_bins=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.calibrate_entropy" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Provide calibrated min/max for input histogram.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propagation. DO NOT use it in training.</p> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/quantization/calibrate.cc:L207</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>hist</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – A ndarray/symbol of type <cite>float32</cite></p></li> |
| <li><p><strong>hist_edges</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – A ndarray/symbol of type <cite>float32</cite></p></li> |
| <li><p><strong>num_quantized_bins</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='255'</em>) – The number of quantized bins.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.count_sketch"> |
| <code class="sig-name descname">count_sketch</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">h=None</em>, <em class="sig-param">s=None</em>, <em class="sig-param">out_dim=_Null</em>, <em class="sig-param">processing_batch_size=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.count_sketch" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Apply CountSketch to input: map a d-dimension data to k-dimension data”</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p><cite>count_sketch</cite> is only available on GPU.</p> |
| </div> |
| <p>Assume input data has shape (N, d), sign hash table s has shape (N, d), |
| index hash table h has shape (N, d) and mapping dimension out_dim = k, |
| each element in s is either +1 or -1, each element in h is random integer from 0 to k-1. |
| Then the operator computs:</p> |
| <div class="math notranslate nohighlight"> |
| \[out[h[i]] += data[i] * s[i]\]</div> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">out_dim</span> <span class="o">=</span> <span class="mi">5</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.2</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mf">3.4</span><span class="p">],[</span><span class="mf">3.2</span><span class="p">,</span> <span class="mf">5.7</span><span class="p">,</span> <span class="mf">6.6</span><span class="p">]]</span> |
| <span class="n">h</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">count_sketch</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">h</span><span class="o">=</span><span class="n">h</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="n">s</span><span class="p">,</span> <span class="n">out_dim</span> <span class="o">=</span> <span class="mi">5</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">1.2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">3.4</span><span class="p">],</span> |
| <span class="p">[</span><span class="mf">3.2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mf">5.7</span><span class="p">,</span> <span class="mf">6.6</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/count_sketch.cc:L67</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the CountSketchOp.</p></li> |
| <li><p><strong>h</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The index vector</p></li> |
| <li><p><strong>s</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The sign vector</p></li> |
| <li><p><strong>out_dim</strong> (<em>int</em><em>, </em><em>required</em>) – The output dimension.</p></li> |
| <li><p><strong>processing_batch_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='32'</em>) – How many sketch vectors to process at one time.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.ctc_loss"> |
| <code class="sig-name descname">ctc_loss</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">label=None</em>, <em class="sig-param">data_lengths=None</em>, <em class="sig-param">label_lengths=None</em>, <em class="sig-param">use_data_lengths=_Null</em>, <em class="sig-param">use_label_lengths=_Null</em>, <em class="sig-param">blank_label=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.ctc_loss" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Connectionist Temporal Classification Loss.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>The existing alias <code class="docutils literal notranslate"><span class="pre">contrib_CTCLoss</span></code> is deprecated.</p> |
| </div> |
| <p>The shapes of the inputs and outputs:</p> |
| <ul class="simple"> |
| <li><p><strong>data</strong>: <cite>(sequence_length, batch_size, alphabet_size)</cite></p></li> |
| <li><p><strong>label</strong>: <cite>(batch_size, label_sequence_length)</cite></p></li> |
| <li><p><strong>out</strong>: <cite>(batch_size)</cite></p></li> |
| </ul> |
| <p>The <cite>data</cite> tensor consists of sequences of activation vectors (without applying softmax), |
| with i-th channel in the last dimension corresponding to i-th label |
| for i between 0 and alphabet_size-1 (i.e always 0-indexed). |
| Alphabet size should include one additional value reserved for blank label. |
| When <cite>blank_label</cite> is <code class="docutils literal notranslate"><span class="pre">"first"</span></code>, the <code class="docutils literal notranslate"><span class="pre">0</span></code>-th channel is be reserved for |
| activation of blank label, or otherwise if it is “last”, <code class="docutils literal notranslate"><span class="pre">(alphabet_size-1)</span></code>-th channel should be |
| reserved for blank label.</p> |
| <p><code class="docutils literal notranslate"><span class="pre">label</span></code> is an index matrix of integers. When <cite>blank_label</cite> is <code class="docutils literal notranslate"><span class="pre">"first"</span></code>, |
| the value 0 is then reserved for blank label, and should not be passed in this matrix. Otherwise, |
| when <cite>blank_label</cite> is <code class="docutils literal notranslate"><span class="pre">"last"</span></code>, the value <cite>(alphabet_size-1)</cite> is reserved for blank label.</p> |
| <p>If a sequence of labels is shorter than <em>label_sequence_length</em>, use the special |
| padding value at the end of the sequence to conform it to the correct |
| length. The padding value is <cite>0</cite> when <cite>blank_label</cite> is <code class="docutils literal notranslate"><span class="pre">"first"</span></code>, and <cite>-1</cite> otherwise.</p> |
| <p>For example, suppose the vocabulary is <cite>[a, b, c]</cite>, and in one batch we have three sequences |
| ‘ba’, ‘cbb’, and ‘abac’. When <cite>blank_label</cite> is <code class="docutils literal notranslate"><span class="pre">"first"</span></code>, we can index the labels as |
| <cite>{‘a’: 1, ‘b’: 2, ‘c’: 3}</cite>, and we reserve the 0-th channel for blank label in data tensor. |
| The resulting <cite>label</cite> tensor should be padded to be:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>When <cite>blank_label</cite> is <code class="docutils literal notranslate"><span class="pre">"last"</span></code>, we can index the labels as |
| <cite>{‘a’: 0, ‘b’: 1, ‘c’: 2}</cite>, and we reserve the channel index 3 for blank label in data tensor. |
| The resulting <cite>label</cite> tensor should be padded to be:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p><code class="docutils literal notranslate"><span class="pre">out</span></code> is a list of CTC loss values, one per example in the batch.</p> |
| <p>See <em>Connectionist Temporal Classification: Labelling Unsegmented |
| Sequence Data with Recurrent Neural Networks</em>, A. Graves <em>et al</em>. for more |
| information on the definition and the algorithm.</p> |
| <p>Defined in /work/mxnet/src/operator/nn/ctc_loss.cc:L104</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input ndarray</p></li> |
| <li><p><strong>label</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Ground-truth labels for the loss.</p></li> |
| <li><p><strong>data_lengths</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Lengths of data for each of the samples. Only required when use_data_lengths is true.</p></li> |
| <li><p><strong>label_lengths</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Lengths of labels for each of the samples. Only required when use_label_lengths is true.</p></li> |
| <li><p><strong>use_data_lengths</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether the data lenghts are decided by <cite>data_lengths</cite>. If false, the lengths are equal to the max sequence length.</p></li> |
| <li><p><strong>use_label_lengths</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether the label lenghts are decided by <cite>label_lengths</cite>, or derived from <cite>padding_mask</cite>. If false, the lengths are derived from the first occurrence of the value of <cite>padding_mask</cite>. The value of <cite>padding_mask</cite> is <code class="docutils literal notranslate"><span class="pre">0</span></code> when first CTC label is reserved for blank, and <code class="docutils literal notranslate"><span class="pre">-1</span></code> when last label is reserved for blank. See <cite>blank_label</cite>.</p></li> |
| <li><p><strong>blank_label</strong> (<em>{'first'</em><em>, </em><em>'last'}</em><em>,</em><em>optional</em><em>, </em><em>default='first'</em>) – Set the label that is reserved for blank label.If “first”, 0-th label is reserved, and label values for tokens in the vocabulary are between <code class="docutils literal notranslate"><span class="pre">1</span></code> and <code class="docutils literal notranslate"><span class="pre">alphabet_size-1</span></code>, and the padding mask is <code class="docutils literal notranslate"><span class="pre">-1</span></code>. If “last”, last label value <code class="docutils literal notranslate"><span class="pre">alphabet_size-1</span></code> is reserved for blank label instead, and label values for tokens in the vocabulary are between <code class="docutils literal notranslate"><span class="pre">0</span></code> and <code class="docutils literal notranslate"><span class="pre">alphabet_size-2</span></code>, and the padding mask is <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.dequantize"> |
| <code class="sig-name descname">dequantize</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">min_range=None</em>, <em class="sig-param">max_range=None</em>, <em class="sig-param">out_type=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.dequantize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Dequantize the input tensor into a float tensor. |
| min_range and max_range are scalar floats that specify the range for |
| the output data.</p> |
| <p>When input data type is <cite>uint8</cite>, the output is calculated using the following equation:</p> |
| <p><cite>out[i] = in[i] * (max_range - min_range) / 255.0</cite>,</p> |
| <p>When input data type is <cite>int8</cite>, the output is calculate using the following equation |
| by keep zero centered for the quantized value:</p> |
| <p><cite>out[i] = in[i] * MaxAbs(min_range, max_range) / 127.0</cite>,</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propogation. DO NOT use it in training.</p> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/quantization/dequantize.cc:L81</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – A ndarray/symbol of type <cite>uint8</cite></p></li> |
| <li><p><strong>min_range</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The minimum scalar value possibly produced for the input in float32</p></li> |
| <li><p><strong>max_range</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The maximum scalar value possibly produced for the input in float32</p></li> |
| <li><p><strong>out_type</strong> (<em>{'float32'}</em><em>,</em><em>optional</em><em>, </em><em>default='float32'</em>) – Output data type.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.dgl_adjacency"> |
| <code class="sig-name descname">dgl_adjacency</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.dgl_adjacency" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This operator converts a CSR matrix whose values are edge Ids |
| to an adjacency matrix whose values are ones. The output CSR matrix always has |
| the data value of float32.</p> |
| <p class="rubric">Example</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span> <span class="p">],</span> |
| <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span> <span class="p">],</span> |
| <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span> <span class="p">]]</span> |
| <span class="n">dgl_adjacency</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> |
| <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span> <span class="p">],</span> |
| <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span> <span class="p">],</span> |
| <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span> <span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/dgl_graph.cc:L1419</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input ndarray</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.dgl_csr_neighbor_non_uniform_sample"> |
| <code class="sig-name descname">dgl_csr_neighbor_non_uniform_sample</code><span class="sig-paren">(</span><em class="sig-param">*seed_arrays</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.dgl_csr_neighbor_non_uniform_sample" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This operator samples sub-graph from a csr graph via an |
| non-uniform probability. The operator is designed for DGL.</p> |
| <p>The operator outputs four sets of NDArrays to represent the sampled results |
| (the number of NDArrays in each set is the same as the number of seed NDArrays): |
| 1) a set of 1D NDArrays containing the sampled vertices, 2) a set of CSRNDArrays representing |
| the sampled edges, 3) a set of 1D NDArrays with the probability that vertices are sampled, |
| 4) a set of 1D NDArrays indicating the layer where a vertex is sampled. |
| The first set of 1D NDArrays have a length of max_num_vertices+1. The last element in an NDArray |
| indicate the acutal number of vertices in a subgraph. The third and fourth set of NDArrays have a length |
| of max_num_vertices, and the valid number of vertices is the same as the ones in the first set.</p> |
| <p class="rubric">Example</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> |
| <span class="n">prob</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> |
| <span class="n">data_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="mi">13</span><span class="p">,</span><span class="mi">14</span><span class="p">,</span><span class="mi">15</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">17</span><span class="p">,</span><span class="mi">18</span><span class="p">,</span><span class="mi">19</span><span class="p">,</span><span class="mi">20</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="n">indices_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="n">indptr_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">20</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">((</span><span class="n">data_np</span><span class="p">,</span> <span class="n">indices_np</span><span class="p">,</span> <span class="n">indptr_np</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">)</span> |
| <span class="n">seed</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">dgl_csr_neighbor_non_uniform_sample</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">prob</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="n">num_args</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">num_hops</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_neighbor</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">max_num_vertices</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span> |
| |
| <span class="n">out</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="p">[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span><span class="p">]</span> |
| <span class="o"><</span><span class="n">NDArray</span> <span class="mi">6</span> <span class="nd">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span> |
| |
| <span class="n">out</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">array</span><span class="p">([[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">13</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">18</span><span class="p">,</span> <span class="mi">19</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span> |
| |
| <span class="n">out</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> |
| <span class="p">[</span><span class="mf">0.9</span> <span class="mf">0.8</span> <span class="mf">0.2</span> <span class="mf">0.4</span> <span class="mf">0.1</span><span class="p">]</span> |
| <span class="o"><</span><span class="n">NDArray</span> <span class="mi">5</span> <span class="nd">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span> |
| |
| <span class="n">out</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> |
| <span class="p">[</span><span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">]</span> |
| <span class="o"><</span><span class="n">NDArray</span> <span class="mi">5</span> <span class="nd">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/dgl_graph.cc:L886 |
| This function support variable length of positional input.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>csr_matrix</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – csr matrix</p></li> |
| <li><p><strong>probability</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – probability vector</p></li> |
| <li><p><strong>seed_arrays</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em>]</em>) – seed vertices</p></li> |
| <li><p><strong>num_hops</strong> (<em>long</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Number of hops.</p></li> |
| <li><p><strong>num_neighbor</strong> (<em>long</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – Number of neighbor.</p></li> |
| <li><p><strong>max_num_vertices</strong> (<em>long</em><em>, </em><em>optional</em><em>, </em><em>default=100</em>) – Max number of vertices.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.dgl_csr_neighbor_uniform_sample"> |
| <code class="sig-name descname">dgl_csr_neighbor_uniform_sample</code><span class="sig-paren">(</span><em class="sig-param">*seed_arrays</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.dgl_csr_neighbor_uniform_sample" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This operator samples sub-graphs from a csr graph via an |
| uniform probability. The operator is designed for DGL.</p> |
| <p>The operator outputs three sets of NDArrays to represent the sampled results |
| (the number of NDArrays in each set is the same as the number of seed NDArrays): |
| 1) a set of 1D NDArrays containing the sampled vertices, 2) a set of CSRNDArrays representing |
| the sampled edges, 3) a set of 1D NDArrays indicating the layer where a vertex is sampled. |
| The first set of 1D NDArrays have a length of max_num_vertices+1. The last element in an NDArray |
| indicate the acutal number of vertices in a subgraph. The third set of NDArrays have a length |
| of max_num_vertices, and the valid number of vertices is the same as the ones in the first set.</p> |
| <p class="rubric">Example</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> |
| <span class="n">data_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="mi">13</span><span class="p">,</span><span class="mi">14</span><span class="p">,</span><span class="mi">15</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">17</span><span class="p">,</span><span class="mi">18</span><span class="p">,</span><span class="mi">19</span><span class="p">,</span><span class="mi">20</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="n">indices_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="n">indptr_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">20</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">((</span><span class="n">data_np</span><span class="p">,</span> <span class="n">indices_np</span><span class="p">,</span> <span class="n">indptr_np</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">)</span> |
| <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">seed</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">dgl_csr_neighbor_uniform_sample</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="n">num_args</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">num_hops</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_neighbor</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">max_num_vertices</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span> |
| |
| <span class="n">out</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="p">[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="mi">5</span><span class="p">]</span> |
| <span class="o"><</span><span class="n">NDArray</span> <span class="mi">6</span> <span class="nd">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span> |
| |
| <span class="n">out</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">array</span><span class="p">([[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">13</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">17</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">19</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span> |
| |
| <span class="n">out</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> |
| <span class="p">[</span><span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">]</span> |
| <span class="o"><</span><span class="n">NDArray</span> <span class="mi">5</span> <span class="nd">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/dgl_graph.cc:L777 |
| This function support variable length of positional input.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>csr_matrix</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – csr matrix</p></li> |
| <li><p><strong>seed_arrays</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em>]</em>) – seed vertices</p></li> |
| <li><p><strong>num_hops</strong> (<em>long</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Number of hops.</p></li> |
| <li><p><strong>num_neighbor</strong> (<em>long</em><em>, </em><em>optional</em><em>, </em><em>default=2</em>) – Number of neighbor.</p></li> |
| <li><p><strong>max_num_vertices</strong> (<em>long</em><em>, </em><em>optional</em><em>, </em><em>default=100</em>) – Max number of vertices.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.dgl_graph_compact"> |
| <code class="sig-name descname">dgl_graph_compact</code><span class="sig-paren">(</span><em class="sig-param">*graph_data</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.dgl_graph_compact" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This operator compacts a CSR matrix generated by |
| dgl_csr_neighbor_uniform_sample and dgl_csr_neighbor_non_uniform_sample. |
| The CSR matrices generated by these two operators may have many empty |
| rows at the end and many empty columns. This operator removes these |
| empty rows and empty columns.</p> |
| <p class="rubric">Example</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> |
| <span class="n">data_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="mi">13</span><span class="p">,</span><span class="mi">14</span><span class="p">,</span><span class="mi">15</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">17</span><span class="p">,</span><span class="mi">18</span><span class="p">,</span><span class="mi">19</span><span class="p">,</span><span class="mi">20</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="n">indices_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="n">indptr_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">20</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">((</span><span class="n">data_np</span><span class="p">,</span> <span class="n">indices_np</span><span class="p">,</span> <span class="n">indptr_np</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">)</span> |
| <span class="n">seed</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">dgl_csr_neighbor_uniform_sample</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="n">num_args</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">num_hops</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> |
| <span class="n">num_neighbor</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">max_num_vertices</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span> |
| <span class="n">subg_v</span> <span class="o">=</span> <span class="n">out</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">subg</span> <span class="o">=</span> <span class="n">out</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">compact</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">dgl_graph_compact</span><span class="p">(</span><span class="n">subg</span><span class="p">,</span> <span class="n">subg_v</span><span class="p">,</span> |
| <span class="n">graph_sizes</span><span class="o">=</span><span class="p">(</span><span class="n">subg_v</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">return_mapping</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| |
| <span class="n">compact</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/dgl_graph.cc:L1608 |
| This function support variable length of positional input.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>graph_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em>]</em>) – Input graphs and input vertex Ids.</p></li> |
| <li><p><strong>return_mapping</strong> (<em>boolean</em><em>, </em><em>required</em>) – Return mapping of vid and eid between the subgraph and the parent graph.</p></li> |
| <li><p><strong>graph_sizes</strong> (<em>tuple of <long></em><em>, </em><em>required</em>) – the number of vertices in each graph.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.dgl_subgraph"> |
| <code class="sig-name descname">dgl_subgraph</code><span class="sig-paren">(</span><em class="sig-param">*data</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.dgl_subgraph" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This operator constructs an induced subgraph for |
| a given set of vertices from a graph. The operator accepts multiple |
| sets of vertices as input. For each set of vertices, it returns a pair |
| of CSR matrices if return_mapping is True: the first matrix contains edges |
| with new edge Ids, the second matrix contains edges with the original |
| edge Ids.</p> |
| <p class="rubric">Example</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span><span class="o">=</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span> |
| <span class="n">v</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> |
| <span class="n">dgl_subgraph</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">return_mapping</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">=</span> |
| <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">]],</span> |
| <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> |
| <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/dgl_graph.cc:L1154 |
| This function support variable length of positional input.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>graph</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input graph where we sample vertices.</p></li> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em>]</em>) – The input arrays that include data arrays and states.</p></li> |
| <li><p><strong>return_mapping</strong> (<em>boolean</em><em>, </em><em>required</em>) – Return mapping of vid and eid between the subgraph and the parent graph.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.div_sqrt_dim"> |
| <code class="sig-name descname">div_sqrt_dim</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.div_sqrt_dim" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Rescale the input by the square root of the channel dimension.</p> |
| <blockquote> |
| <div><p>out = data / sqrt(data.shape[-1])</p> |
| </div></blockquote> |
| <p>Defined in /work/mxnet/src/operator/contrib/transformer.cc:L881</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.dynamic_reshape"> |
| <code class="sig-name descname">dynamic_reshape</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">shape=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.dynamic_reshape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Experimental support for reshape operator with dynamic shape.</p> |
| <p>Accepts 2 inputs - data and shape. |
| The output returns data in the new shape.</p> |
| <p>Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}. The significance of each is explained below: |
| - <code class="docutils literal notranslate"><span class="pre">0</span></code> copy this dimension from the input to the output shape. Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span> |
| <span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <ul> |
| <li><p><code class="docutils literal notranslate"><span class="pre">-1</span></code> infers the dimension of the output shape by using the remainder of the input dimensions |
| keeping the size of the new array same as that of the input array. |
| At most one dimension of shape can be -1. Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span> |
| <span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">)</span> |
| <span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">24</span><span class="p">,)</span> |
| </pre></div> |
| </div> |
| </li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">-2</span></code> copy all/remainder of the input dimensions to the output shape. Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span> |
| <span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span> |
| <span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">-3</span></code> use the product of two consecutive dimensions of the input shape as the output dimension. Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span> |
| <span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">3</span><span class="p">),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">20</span><span class="p">)</span> |
| <span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">3</span><span class="p">),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span> |
| <span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">-4</span></code> split one dimension of the input into two dimensions passed subsequent to -4 in shape (can contain -1). Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span> |
| <span class="o">-</span> <span class="nb">input</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">),</span> <span class="n">output</span> <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </li> |
| </ul> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,(</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">)))</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">dynamic_reshape</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">,</span> <span class="n">shape</span> <span class="o">=</span> <span class="n">shape</span><span class="p">)</span> |
| <span class="o">//</span> <span class="n">out</span> <span class="n">will</span> <span class="n">be</span> <span class="n">of</span> <span class="n">shape</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">75</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/dynamic_shape_ops.cc:L120</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Data</p></li> |
| <li><p><strong>shape</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Shape</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.edge_id"> |
| <code class="sig-name descname">edge_id</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">u=None</em>, <em class="sig-param">v=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.edge_id" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This operator implements the edge_id function for a graph |
| stored in a CSR matrix (the value of the CSR stores the edge Id of the graph). |
| output[i] = input[u[i], v[i]] if there is an edge between u[i] and v[i]], |
| otherwise output[i] will be -1. Both u and v should be 1D vectors.</p> |
| <p class="rubric">Example</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span> <span class="p">],</span> |
| <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span> <span class="p">],</span> |
| <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span> <span class="p">]]</span> |
| <span class="n">u</span> <span class="o">=</span> <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span> <span class="p">]</span> |
| <span class="n">v</span> <span class="o">=</span> <span class="p">[</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span> <span class="p">]</span> |
| <span class="n">edge_id</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="o">=</span> <span class="p">[</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span> <span class="p">]</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>The storage type of <code class="docutils literal notranslate"><span class="pre">edge_id</span></code> output depends on storage types of inputs</dt><dd><ul class="simple"> |
| <li><p>edge_id(csr, default, default) = default</p></li> |
| <li><p>default and rsp inputs are not supported</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p>Defined in /work/mxnet/src/operator/contrib/dgl_graph.cc:L1347</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input ndarray</p></li> |
| <li><p><strong>u</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – u ndarray</p></li> |
| <li><p><strong>v</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – v ndarray</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.fft"> |
| <code class="sig-name descname">fft</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">compute_size=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.fft" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Apply 1D FFT to input”</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p><cite>fft</cite> is only available on GPU.</p> |
| </div> |
| <p>Currently accept 2 input data shapes: (N, d) or (N1, N2, N3, d), data can only be real numbers. |
| The output data has shape: (N, 2*d) or (N1, N2, N3, 2*d). The format is: [real0, imag0, real1, imag1, …].</p> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,(</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="n">ctx</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)))</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/fft.cc:L56</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data to the FFTOp.</p></li> |
| <li><p><strong>compute_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='128'</em>) – Maximum size of sub-batch to be forwarded at one time</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.getnnz"> |
| <code class="sig-name descname">getnnz</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">axis=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.getnnz" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Number of stored values for a sparse tensor, including explicit zeros.</p> |
| <p>This operator only supports CSR matrix on CPU.</p> |
| <p>Defined in /work/mxnet/src/operator/contrib/nnz.cc:L177</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input</p></li> |
| <li><p><strong>axis</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – Select between the number of values across the whole matrix, in each column, or in each row.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.gradientmultiplier"> |
| <code class="sig-name descname">gradientmultiplier</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">scalar=_Null</em>, <em class="sig-param">is_int=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.gradientmultiplier" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This operator implements the gradient multiplier function. |
| In forward pass it acts as an identity transform. During backpropagation it |
| multiplies the gradient from the subsequent level by a scalar factor lambda and passes it to |
| the preceding layer.</p> |
| <p>Defined in /work/mxnet/src/operator/contrib/gradient_multiplier_op.cc:L77</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</p></li> |
| <li><p><strong>scalar</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Scalar input value</p></li> |
| <li><p><strong>is_int</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Indicate whether scalar input is int type</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.group_adagrad_update"> |
| <code class="sig-name descname">group_adagrad_update</code><span class="sig-paren">(</span><em class="sig-param">weight=None</em>, <em class="sig-param">grad=None</em>, <em class="sig-param">history=None</em>, <em class="sig-param">lr=_Null</em>, <em class="sig-param">rescale_grad=_Null</em>, <em class="sig-param">clip_gradient=_Null</em>, <em class="sig-param">epsilon=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.group_adagrad_update" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Update function for Group AdaGrad optimizer.</p> |
| <p>Referenced from <em>Adaptive Subgradient Methods for Online Learning and Stochastic Optimization</em>, |
| and available at <a class="reference external" href="http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf">http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf</a> but |
| uses only a single learning rate for every row of the parameter array.</p> |
| <p>Updates are applied by:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">grad</span> <span class="o">=</span> <span class="n">clip</span><span class="p">(</span><span class="n">grad</span> <span class="o">*</span> <span class="n">rescale_grad</span><span class="p">,</span> <span class="n">clip_gradient</span><span class="p">)</span> |
| <span class="n">history</span> <span class="o">+=</span> <span class="n">mean</span><span class="p">(</span><span class="n">square</span><span class="p">(</span><span class="n">grad</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">div</span> <span class="o">=</span> <span class="n">grad</span> <span class="o">/</span> <span class="p">(</span><span class="n">sqrt</span><span class="p">(</span><span class="n">history</span><span class="p">)</span> <span class="o">+</span> <span class="n">epsilon</span><span class="p">)</span> |
| <span class="n">weight</span> <span class="o">-=</span> <span class="n">div</span> <span class="o">*</span> <span class="n">lr</span> |
| </pre></div> |
| </div> |
| <p>Weights are updated lazily if the gradient is sparse.</p> |
| <p>Note that non-zero values for the weight decay option are not supported.</p> |
| <p>Defined in /work/mxnet/src/operator/contrib/optimizer_op.cc:L69</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>weight</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Weight</p></li> |
| <li><p><strong>grad</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Gradient</p></li> |
| <li><p><strong>history</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – History</p></li> |
| <li><p><strong>lr</strong> (<em>float</em><em>, </em><em>required</em>) – Learning rate</p></li> |
| <li><p><strong>rescale_grad</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Rescale gradient to grad = rescale_grad*grad.</p></li> |
| <li><p><strong>clip_gradient</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).</p></li> |
| <li><p><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=9.99999975e-06</em>) – Epsilon for numerical stability</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.hawkesll"> |
| <code class="sig-name descname">hawkesll</code><span class="sig-paren">(</span><em class="sig-param">lda=None</em>, <em class="sig-param">alpha=None</em>, <em class="sig-param">beta=None</em>, <em class="sig-param">state=None</em>, <em class="sig-param">lags=None</em>, <em class="sig-param">marks=None</em>, <em class="sig-param">valid_length=None</em>, <em class="sig-param">max_time=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.hawkesll" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Computes the log likelihood of a univariate Hawkes process.</p> |
| <p>The log likelihood is calculated on point process observations represented |
| as <em>ragged</em> matrices for <em>lags</em> (interarrival times w.r.t. the previous point), |
| and <em>marks</em> (identifiers for the process ID). Note that each mark is considered independent, |
| i.e., computes the joint likelihood of a set of Hawkes processes determined by the conditional intensity:</p> |
| <div class="math notranslate nohighlight"> |
| \[\lambda_k^*(t) = \lambda_k + \alpha_k \sum_{\{t_i < t, y_i = k\}} \beta_k \exp(-\beta_k (t - t_i))\]</div> |
| <p>where <span class="math notranslate nohighlight">\(\lambda_k\)</span> specifies the background intensity <code class="docutils literal notranslate"><span class="pre">lda</span></code>, <span class="math notranslate nohighlight">\(\alpha_k\)</span> specifies the <em>branching ratio</em> or <code class="docutils literal notranslate"><span class="pre">alpha</span></code>, and <span class="math notranslate nohighlight">\(\beta_k\)</span> the delay density parameter <code class="docutils literal notranslate"><span class="pre">beta</span></code>.</p> |
| <p><code class="docutils literal notranslate"><span class="pre">lags</span></code> and <code class="docutils literal notranslate"><span class="pre">marks</span></code> are two NDArrays of shape (N, T) and correspond to the representation of the point process observation, the first dimension corresponds to the batch index, and the second to the sequence. These are “left-aligned” <em>ragged</em> matrices (the first index of the second dimension is the beginning of every sequence. The length of each sequence is given by <code class="docutils literal notranslate"><span class="pre">valid_length</span></code>, of shape (N,) where <code class="docutils literal notranslate"><span class="pre">valid_length[i]</span></code> corresponds to the number of valid points in <code class="docutils literal notranslate"><span class="pre">lags[i,</span> <span class="pre">:]</span></code> and <code class="docutils literal notranslate"><span class="pre">marks[i,</span> <span class="pre">:]</span></code>.</p> |
| <p><code class="docutils literal notranslate"><span class="pre">max_time</span></code> is the length of the observation period of the point process. That is, specifying <code class="docutils literal notranslate"><span class="pre">max_time[i]</span> <span class="pre">=</span> <span class="pre">5</span></code> computes the likelihood of the i-th sample as observed on the time interval <span class="math notranslate nohighlight">\((0, 5]\)</span>. Naturally, the sum of all valid <code class="docutils literal notranslate"><span class="pre">lags[i,</span> <span class="pre">:valid_length[i]]</span></code> must be less than or equal to 5.</p> |
| <p>The input <code class="docutils literal notranslate"><span class="pre">state</span></code> specifies the <em>memory</em> of the Hawkes process. Invoking the memoryless property of exponential decays, we compute the <em>memory</em> as</p> |
| <div class="math notranslate nohighlight"> |
| \[s_k(t) = \sum_{t_i < t} \exp(-\beta_k (t - t_i)).\]</div> |
| <p>The <code class="docutils literal notranslate"><span class="pre">state</span></code> to be provided is <span class="math notranslate nohighlight">\(s_k(0)\)</span> and carries the added intensity due to past events before the current batch. <span class="math notranslate nohighlight">\(s_k(T)\)</span> is returned from the function where <span class="math notranslate nohighlight">\(T\)</span> is <code class="docutils literal notranslate"><span class="pre">max_time[T]</span></code>.</p> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># define the Hawkes process parameters</span> |
| <span class="n">lda</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">])</span><span class="o">.</span><span class="n">tile</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> |
| <span class="n">alpha</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">])</span> <span class="c1"># branching ratios should be < 1</span> |
| <span class="n">beta</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">])</span> |
| |
| <span class="c1"># the "data", or observations</span> |
| <span class="n">ia_times</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">]])</span> |
| <span class="n">marks</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> |
| |
| <span class="c1"># starting "state" of the process</span> |
| <span class="n">states</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">N</span><span class="p">,</span> <span class="n">K</span><span class="p">))</span> |
| |
| <span class="n">valid_length</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> <span class="c1"># number of valid points in each sequence</span> |
| <span class="n">max_time</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">N</span><span class="p">,))</span> <span class="o">*</span> <span class="mf">100.0</span> <span class="c1"># length of the observation period</span> |
| |
| <span class="n">A</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">hawkesll</span><span class="p">(</span> |
| <span class="n">lda</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">beta</span><span class="p">,</span> <span class="n">states</span><span class="p">,</span> <span class="n">ia_times</span><span class="p">,</span> <span class="n">marks</span><span class="p">,</span> <span class="n">valid_length</span><span class="p">,</span> <span class="n">max_time</span> |
| <span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>References:</p> |
| <ul class="simple"> |
| <li><p>Bacry, E., Mastromatteo, I., & Muzy, J. F. (2015). |
| Hawkes processes in finance. Market Microstructure and Liquidity |
| , 1(01), 1550005.</p></li> |
| </ul> |
| <p>Defined in /work/mxnet/src/operator/contrib/hawkes_ll.cc:L83</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>lda</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Shape (N, K) The intensity for each of the K processes, for each sample</p></li> |
| <li><p><strong>alpha</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Shape (K,) The infectivity factor (branching ratio) for each process</p></li> |
| <li><p><strong>beta</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Shape (K,) The decay parameter for each process</p></li> |
| <li><p><strong>state</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Shape (N, K) the Hawkes state for each process</p></li> |
| <li><p><strong>lags</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Shape (N, T) the interarrival times</p></li> |
| <li><p><strong>marks</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Shape (N, T) the marks (process ids)</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The number of valid points in the process</p></li> |
| <li><p><strong>max_time</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – the length of the interval where the processes were sampled</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.index_array"> |
| <code class="sig-name descname">index_array</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">axes=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.index_array" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns an array of indexes of the input array.</p> |
| <p>For an input array with shape <span class="math notranslate nohighlight">\((d_1, d_2, ..., d_n)\)</span>, <cite>index_array</cite> returns a |
| <span class="math notranslate nohighlight">\((d_1, d_2, ..., d_n, n)\)</span> array <cite>idx</cite>, where |
| <span class="math notranslate nohighlight">\(idx[i_1, i_2, ..., i_n, :] = [i_1, i_2, ..., i_n]\)</span>.</p> |
| <p>Additionally, when the parameter <cite>axes</cite> is specified, <cite>idx</cite> will be a |
| <span class="math notranslate nohighlight">\((d_1, d_2, ..., d_n, m)\)</span> array where <cite>m</cite> is the length of <cite>axes</cite>, and the following |
| equality will hold: <span class="math notranslate nohighlight">\(idx[i_1, i_2, ..., i_n, j] = i_{axes[j]}\)</span>.</p> |
| <p>Examples:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> |
| |
| <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">index_array</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">=</span> <span class="p">[[[</span><span class="mi">0</span> <span class="mi">0</span><span class="p">]</span> |
| <span class="p">[</span><span class="mi">0</span> <span class="mi">1</span><span class="p">]]</span> |
| |
| <span class="p">[[</span><span class="mi">1</span> <span class="mi">0</span><span class="p">]</span> |
| <span class="p">[</span><span class="mi">1</span> <span class="mi">1</span><span class="p">]]</span> |
| |
| <span class="p">[[</span><span class="mi">2</span> <span class="mi">0</span><span class="p">]</span> |
| <span class="p">[</span><span class="mi">2</span> <span class="mi">1</span><span class="p">]]]</span> |
| |
| <span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> |
| |
| <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">index_array</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="o">=</span> <span class="p">[[[[</span><span class="mi">0</span> <span class="mi">0</span><span class="p">]</span> |
| <span class="p">[</span><span class="mi">0</span> <span class="mi">0</span><span class="p">]]</span> |
| |
| <span class="p">[[</span><span class="mi">1</span> <span class="mi">0</span><span class="p">]</span> |
| <span class="p">[</span><span class="mi">1</span> <span class="mi">0</span><span class="p">]]]</span> |
| |
| |
| <span class="p">[[[</span><span class="mi">0</span> <span class="mi">1</span><span class="p">]</span> |
| <span class="p">[</span><span class="mi">0</span> <span class="mi">1</span><span class="p">]]</span> |
| |
| <span class="p">[[</span><span class="mi">1</span> <span class="mi">1</span><span class="p">]</span> |
| <span class="p">[</span><span class="mi">1</span> <span class="mi">1</span><span class="p">]]]</span> |
| |
| |
| <span class="p">[[[</span><span class="mi">0</span> <span class="mi">2</span><span class="p">]</span> |
| <span class="p">[</span><span class="mi">0</span> <span class="mi">2</span><span class="p">]]</span> |
| |
| <span class="p">[[</span><span class="mi">1</span> <span class="mi">2</span><span class="p">]</span> |
| <span class="p">[</span><span class="mi">1</span> <span class="mi">2</span><span class="p">]]]]</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/index_array.cc:L117</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data</p></li> |
| <li><p><strong>axes</strong> (<em>Shape</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – The axes to include in the index array. Supports negative values.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.index_copy"> |
| <code class="sig-name descname">index_copy</code><span class="sig-paren">(</span><em class="sig-param">old_tensor=None</em>, <em class="sig-param">index_vector=None</em>, <em class="sig-param">new_tensor=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.index_copy" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Copies the elements of a <cite>new_tensor</cite> into the <cite>old_tensor</cite>.</p> |
| <p>This operator copies the elements by selecting the indices in the order given in <cite>index</cite>. |
| The output will be a new tensor containing the rest elements of old tensor and |
| the copied elements of new tensor. |
| For example, if <cite>index[i] == j</cite>, then the <cite>i</cite> th row of <cite>new_tensor</cite> is copied to the |
| <cite>j</cite> th row of output.</p> |
| <p>The <cite>index</cite> must be a vector and it must have the same size with the <cite>0</cite> th dimension of |
| <cite>new_tensor</cite>. Also, the <cite>0</cite> th dimension of old_tensor must <cite>>=</cite> the <cite>0</cite> th dimension of |
| <cite>new_tensor</cite>, or an error will be raised.</p> |
| <p>Examples:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span> |
| <span class="n">t</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">],[</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">]])</span> |
| <span class="n">index</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span> |
| |
| <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">index_copy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span> |
| |
| <span class="p">[[</span><span class="mf">1.</span> <span class="mf">2.</span> <span class="mf">3.</span><span class="p">]</span> |
| <span class="p">[</span><span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span> |
| <span class="p">[</span><span class="mf">7.</span> <span class="mf">8.</span> <span class="mf">9.</span><span class="p">]</span> |
| <span class="p">[</span><span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span> |
| <span class="p">[</span><span class="mf">4.</span> <span class="mf">5.</span> <span class="mf">6.</span><span class="p">]]</span> |
| <span class="o"><</span><span class="n">NDArray</span> <span class="mi">5</span><span class="n">x3</span> <span class="nd">@cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/index_copy.cc:L206</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>old_tensor</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Old tensor</p></li> |
| <li><p><strong>index_vector</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Index vector</p></li> |
| <li><p><strong>new_tensor</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – New tensor to be copied</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.interleaved_matmul_encdec_qk"> |
| <code class="sig-name descname">interleaved_matmul_encdec_qk</code><span class="sig-paren">(</span><em class="sig-param">queries=None</em>, <em class="sig-param">keys_values=None</em>, <em class="sig-param">heads=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.interleaved_matmul_encdec_qk" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute the matrix multiplication between the projections of |
| queries and keys in multihead attention use as encoder-decoder.</p> |
| <p>the inputs must be a tensor of projections of queries following the layout: |
| (seq_length, batch_size, num_heads * head_dim)</p> |
| <p>and a tensor of interleaved projections of values and keys following the layout: |
| (seq_length, batch_size, num_heads * head_dim * 2)</p> |
| <p>the equivalent code would be:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">q_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">queries</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> |
| <span class="n">q_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">q_proj</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">q_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">div_sqrt_dim</span><span class="p">(</span><span class="n">q_proj</span><span class="p">)</span> |
| <span class="n">tmp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">keys_values</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">num_heads</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span> |
| <span class="n">k_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">tmp</span><span class="p">[:,:,:,</span><span class="mi">0</span><span class="p">,:],</span> <span class="n">axes</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> |
| <span class="n">k_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshap</span><span class="p">(</span><span class="n">k_proj</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">batch_dot</span><span class="p">(</span><span class="n">q_proj</span><span class="p">,</span> <span class="n">k_proj</span><span class="p">,</span> <span class="n">transpose_b</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/transformer.cc:L797</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>queries</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Queries</p></li> |
| <li><p><strong>keys_values</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Keys and values interleaved</p></li> |
| <li><p><strong>heads</strong> (<em>int</em><em>, </em><em>required</em>) – Set number of heads</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.interleaved_matmul_encdec_valatt"> |
| <code class="sig-name descname">interleaved_matmul_encdec_valatt</code><span class="sig-paren">(</span><em class="sig-param">keys_values=None</em>, <em class="sig-param">attention=None</em>, <em class="sig-param">heads=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.interleaved_matmul_encdec_valatt" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute the matrix multiplication between the projections of |
| values and the attention weights in multihead attention use as encoder-decoder.</p> |
| <p>the inputs must be a tensor of interleaved projections of |
| keys and values following the layout: |
| (seq_length, batch_size, num_heads * head_dim * 2)</p> |
| <p>and the attention weights following the layout: |
| (batch_size, seq_length, seq_length)</p> |
| <p>the equivalent code would be:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tmp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">queries_keys_values</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">num_heads</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span> |
| <span class="n">v_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">tmp</span><span class="p">[:,:,:,</span><span class="mi">1</span><span class="p">,:],</span> <span class="n">axes</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> |
| <span class="n">v_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">v_proj</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">batch_dot</span><span class="p">(</span><span class="n">attention</span><span class="p">,</span> <span class="n">v_proj</span><span class="p">,</span> <span class="n">transpose_b</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_heads</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/transformer.cc:L847</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>keys_values</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Keys and values interleaved</p></li> |
| <li><p><strong>attention</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Attention maps</p></li> |
| <li><p><strong>heads</strong> (<em>int</em><em>, </em><em>required</em>) – Set number of heads</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.interleaved_matmul_selfatt_qk"> |
| <code class="sig-name descname">interleaved_matmul_selfatt_qk</code><span class="sig-paren">(</span><em class="sig-param">queries_keys_values=None</em>, <em class="sig-param">heads=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.interleaved_matmul_selfatt_qk" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute the matrix multiplication between the projections of |
| queries and keys in multihead attention use as self attention.</p> |
| <p>the input must be a single tensor of interleaved projections |
| of queries, keys and values following the layout: |
| (seq_length, batch_size, num_heads * head_dim * 3)</p> |
| <p>the equivalent code would be:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tmp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">queries_keys_values</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">num_heads</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span> |
| <span class="n">q_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">tmp</span><span class="p">[:,:,:,</span><span class="mi">0</span><span class="p">,:],</span> <span class="n">axes</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> |
| <span class="n">q_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">q_proj</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">q_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">div_sqrt_dim</span><span class="p">(</span><span class="n">q_proj</span><span class="p">)</span> |
| <span class="n">k_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">tmp</span><span class="p">[:,:,:,</span><span class="mi">1</span><span class="p">,:],</span> <span class="n">axes</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> |
| <span class="n">k_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">k_proj</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">batch_dot</span><span class="p">(</span><span class="n">q_proj</span><span class="p">,</span> <span class="n">k_proj</span><span class="p">,</span> <span class="n">transpose_b</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/transformer.cc:L694</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>queries_keys_values</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Interleaved queries, keys and values</p></li> |
| <li><p><strong>heads</strong> (<em>int</em><em>, </em><em>required</em>) – Set number of heads</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.interleaved_matmul_selfatt_valatt"> |
| <code class="sig-name descname">interleaved_matmul_selfatt_valatt</code><span class="sig-paren">(</span><em class="sig-param">queries_keys_values=None</em>, <em class="sig-param">attention=None</em>, <em class="sig-param">heads=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.interleaved_matmul_selfatt_valatt" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute the matrix multiplication between the projections of |
| values and the attention weights in multihead attention use as self attention.</p> |
| <p>the inputs must be a tensor of interleaved projections |
| of queries, keys and values following the layout: |
| (seq_length, batch_size, num_heads * head_dim * 3)</p> |
| <p>and the attention weights following the layout: |
| (batch_size, seq_length, seq_length)</p> |
| <p>the equivalent code would be:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tmp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">queries_keys_values</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">num_heads</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span> |
| <span class="n">v_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">tmp</span><span class="p">[:,:,:,</span><span class="mi">2</span><span class="p">,:],</span> <span class="n">axes</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> |
| <span class="n">v_proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">v_proj</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">batch_dot</span><span class="p">(</span><span class="n">attention</span><span class="p">,</span> <span class="n">v_proj</span><span class="p">)</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_heads</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/contrib/transformer.cc:L745</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>queries_keys_values</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Queries, keys and values interleaved</p></li> |
| <li><p><strong>attention</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Attention maps</p></li> |
| <li><p><strong>heads</strong> (<em>int</em><em>, </em><em>required</em>) – Set number of heads</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.intgemm_fully_connected"> |
| <code class="sig-name descname">intgemm_fully_connected</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">scaling=None</em>, <em class="sig-param">bias=None</em>, <em class="sig-param">num_hidden=_Null</em>, <em class="sig-param">no_bias=_Null</em>, <em class="sig-param">flatten=_Null</em>, <em class="sig-param">out_type=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.intgemm_fully_connected" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Multiply matrices using 8-bit integers. data * weight.</p> |
| <p>Input tensor arguments are: data weight [scaling] [bias]</p> |
| <p>data: either float32 or prepared using intgemm_prepare_data (in which case it is int8).</p> |
| <p>weight: must be prepared using intgemm_prepare_weight.</p> |
| <p>scaling: present if and only if out_type is float32. If so this is multiplied by the result before adding bias. Typically: |
| scaling = (max passed to intgemm_prepare_weight)/127.0 if data is in float32 |
| scaling = (max_passed to intgemm_prepare_data)/127.0 * (max passed to intgemm_prepare_weight)/127.0 if data is in int8</p> |
| <p>bias: present if and only if !no_bias. This is added to the output after scaling and has the same number of columns as the output.</p> |
| <p>out_type: type of the output.</p> |
| <p>Defined in /work/mxnet/src/operator/contrib/intgemm/intgemm_fully_connected_op.cc:L284</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – First argument to multiplication. Tensor of float32 (quantized on the fly) or int8 from intgemm_prepare_data. If you use a different quantizer, be sure to ban -128. The last dimension must be a multiple of 64.</p></li> |
| <li><p><strong>weight</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Second argument to multiplication. Tensor of int8 from intgemm_prepare_weight. The last dimension must be a multiple of 64. The product of non-last dimensions must be a multiple of 8.</p></li> |
| <li><p><strong>scaling</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Scaling factor to apply if output type is float32.</p></li> |
| <li><p><strong>bias</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bias term.</p></li> |
| <li><p><strong>num_hidden</strong> (<em>int</em><em>, </em><em>required</em>) – Number of hidden nodes of the output.</p></li> |
| <li><p><strong>no_bias</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to disable bias parameter.</p></li> |
| <li><p><strong>flatten</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Whether to collapse all but the first axis of the input data tensor.</p></li> |
| <li><p><strong>out_type</strong> (<em>{'float32'</em><em>, </em><em>'int32'}</em><em>,</em><em>optional</em><em>, </em><em>default='float32'</em>) – Output data type.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.intgemm_maxabsolute"> |
| <code class="sig-name descname">intgemm_maxabsolute</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.intgemm_maxabsolute" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute the maximum absolute value in a tensor of float32 fast on a CPU. The tensor’s total size must be a multiple of 16 and aligned to a multiple of 64 bytes. |
| mxnet.nd.contrib.intgemm_maxabsolute(arr) == arr.abs().max()</p> |
| <p>Defined in /work/mxnet/src/operator/contrib/intgemm/max_absolute_op.cc:L102</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor to compute maximum absolute value of</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.intgemm_prepare_data"> |
| <code class="sig-name descname">intgemm_prepare_data</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">maxabs=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.intgemm_prepare_data" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This operator converts quantizes float32 to int8 while also banning -128.</p> |
| <p>It it suitable for preparing an data matrix for use by intgemm’s C=data * weights operation.</p> |
| <p>The float32 values are scaled such that maxabs maps to 127. Typically maxabs = maxabsolute(A).</p> |
| <p>Defined in /work/mxnet/src/operator/contrib/intgemm/prepare_data_op.cc:L112</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Activation matrix to be prepared for multiplication.</p></li> |
| <li><p><strong>maxabs</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum absolute value to be used for scaling. (The values will be multiplied by 127.0 / maxabs.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.intgemm_prepare_weight"> |
| <code class="sig-name descname">intgemm_prepare_weight</code><span class="sig-paren">(</span><em class="sig-param">weight=None</em>, <em class="sig-param">maxabs=None</em>, <em class="sig-param">already_quantized=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.intgemm_prepare_weight" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This operator converts a weight matrix in column-major format to intgemm’s internal fast representation of weight matrices. MXNet customarily stores weight matrices in column-major (transposed) format. This operator is not meant to be fast; it is meant to be run offline to quantize a model.</p> |
| <p>In other words, it prepares weight for the operation C = data * weight^T.</p> |
| <p>If the provided weight matrix is float32, it will be quantized first. The quantization function is (int8_t)(127.0 / max * weight) where multiplier is provided as argument 1 (the weight matrix is argument 0). Then the matrix will be rearranged into the CPU-dependent format.</p> |
| <p>If the provided weight matrix is already int8, the matrix will only be rearranged into the CPU-dependent format. This way one can quantize with intgemm_prepare_data (which just quantizes), store to disk in a consistent format, then at load time convert to CPU-dependent format with intgemm_prepare_weight.</p> |
| <p>The internal representation depends on register length. So AVX512, AVX2, and SSSE3 have different formats. AVX512BW and AVX512VNNI have the same representation.</p> |
| <p>Defined in /work/mxnet/src/operator/contrib/intgemm/prepare_weight_op.cc:L152</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>weight</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Parameter matrix to be prepared for multiplication.</p></li> |
| <li><p><strong>maxabs</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum absolute value for scaling. The weights will be multipled by 127.0 / maxabs.</p></li> |
| <li><p><strong>already_quantized</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Is the weight matrix already quantized?</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.intgemm_take_weight"> |
| <code class="sig-name descname">intgemm_take_weight</code><span class="sig-paren">(</span><em class="sig-param">weight=None</em>, <em class="sig-param">indices=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.intgemm_take_weight" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Index a weight matrix stored in intgemm’s weight format. |
| The indices select the outputs of matrix multiplication, not the inner dot product dimension.</p> |
| <p>Defined in /work/mxnet/src/operator/contrib/intgemm/take_weight_op.cc:L125</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>weight</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Tensor already in intgemm weight format to select from</p></li> |
| <li><p><strong>indices</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – indices to select on the 0th dimension of weight</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.mrcnn_mask_target"> |
| <code class="sig-name descname">mrcnn_mask_target</code><span class="sig-paren">(</span><em class="sig-param">rois=None</em>, <em class="sig-param">gt_masks=None</em>, <em class="sig-param">matches=None</em>, <em class="sig-param">cls_targets=None</em>, <em class="sig-param">num_rois=_Null</em>, <em class="sig-param">num_classes=_Null</em>, <em class="sig-param">mask_size=_Null</em>, <em class="sig-param">sample_ratio=_Null</em>, <em class="sig-param">aligned=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.mrcnn_mask_target" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Generate mask targets for Mask-RCNN.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>rois</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Bounding box coordinates, a 3D array</p></li> |
| <li><p><strong>gt_masks</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input masks of full image size, a 4D array</p></li> |
| <li><p><strong>matches</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Index to a gt_mask, a 2D array</p></li> |
| <li><p><strong>cls_targets</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Value [0, num_class), excluding background class, a 2D array</p></li> |
| <li><p><strong>num_rois</strong> (<em>int</em><em>, </em><em>required</em>) – Number of sampled RoIs.</p></li> |
| <li><p><strong>num_classes</strong> (<em>int</em><em>, </em><em>required</em>) – Number of classes.</p></li> |
| <li><p><strong>mask_size</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>required</em>) – Size of the pooled masks height and width: (h, w).</p></li> |
| <li><p><strong>sample_ratio</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='2'</em>) – Sampling ratio of ROI align. Set to -1 to use adaptative size.</p></li> |
| <li><p><strong>aligned</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Center-aligned ROIAlign introduced in Detectron2. To enable, set aligned to True.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quadratic"> |
| <code class="sig-name descname">quadratic</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">a=_Null</em>, <em class="sig-param">b=_Null</em>, <em class="sig-param">c=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quadratic" title="Permalink to this definition">¶</a></dt> |
| <dd><p>This operators implements the quadratic function.</p> |
| <div class="math notranslate nohighlight"> |
| \[f(x) = ax^2+bx+c\]</div> |
| <p>where <span class="math notranslate nohighlight">\(x\)</span> is an input tensor and all operations |
| in the function are element-wise.</p> |
| <p>Example:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">quadratic</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">11</span><span class="p">],</span> <span class="p">[</span><span class="mi">18</span><span class="p">,</span> <span class="mi">27</span><span class="p">]]</span> |
| </pre></div> |
| </div> |
| <dl class="simple"> |
| <dt>The storage type of <code class="docutils literal notranslate"><span class="pre">quadratic</span></code> output depends on storage types of inputs</dt><dd><ul class="simple"> |
| <li><p>quadratic(csr, a, b, 0) = csr</p></li> |
| <li><p>quadratic(default, a, b, c) = default</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p>Defined in /work/mxnet/src/operator/contrib/quadratic_op.cc:L50</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input ndarray</p></li> |
| <li><p><strong>a</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Coefficient of the quadratic term in the quadratic function.</p></li> |
| <li><p><strong>b</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Coefficient of the linear term in the quadratic function.</p></li> |
| <li><p><strong>c</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Constant term in the quadratic function.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantize"> |
| <code class="sig-name descname">quantize</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">min_range=None</em>, <em class="sig-param">max_range=None</em>, <em class="sig-param">out_type=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Quantize a input tensor from float to <cite>out_type</cite>, |
| with user-specified <cite>min_range</cite> and <cite>max_range</cite>.</p> |
| <p>min_range and max_range are scalar floats that specify the range for |
| the input data.</p> |
| <p>When out_type is <cite>uint8</cite>, the output is calculated using the following equation:</p> |
| <p><cite>out[i] = (in[i] - min_range) * range(OUTPUT_TYPE) / (max_range - min_range) + 0.5</cite>,</p> |
| <p>where <cite>range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()</cite>.</p> |
| <p>When out_type is <cite>int8</cite>, the output is calculate using the following equation |
| by keep zero centered for the quantized value:</p> |
| <p><cite>out[i] = sign(in[i]) * min(abs(in[i] * scale + 0.5f, quantized_range)</cite>,</p> |
| <p>where |
| <cite>quantized_range = MinAbs(max(int8), min(int8))</cite> and |
| <cite>scale = quantized_range / MaxAbs(min_range, max_range).</cite></p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propagation. DO NOT use it in training.</p> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/quantization/quantize.cc:L92</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – A ndarray/symbol of type <cite>float32</cite></p></li> |
| <li><p><strong>min_range</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The minimum scalar value possibly produced for the input</p></li> |
| <li><p><strong>max_range</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The maximum scalar value possibly produced for the input</p></li> |
| <li><p><strong>out_type</strong> (<em>{'int8'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='uint8'</em>) – Output data type.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantize_asym"> |
| <code class="sig-name descname">quantize_asym</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">min_calib_range=_Null</em>, <em class="sig-param">max_calib_range=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantize_asym" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Quantize a input tensor from float to uint8_t. |
| Output <cite>scale</cite> and <cite>shift</cite> are scalar floats that specify the quantization |
| parameters for the input data. The output is calculated using the following equation:</p> |
| <p><cite>out[i] = in[i] * scale + shift + 0.5</cite>,</p> |
| <p>where <cite>scale = uint8_range / (max_range - min_range)</cite> and |
| <cite>shift = numeric_limits<T>::max - max_range * scale</cite>.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propagation. DO NOT use it in training.</p> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/quantization/quantize_asym.cc:L119</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – A ndarray/symbol of type <cite>float32</cite></p></li> |
| <li><p><strong>min_calib_range</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – The minimum scalar value in the form of float32. If present, it will be used to quantize the fp32 data.</p></li> |
| <li><p><strong>max_calib_range</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – The maximum scalar value in the form of float32. If present, it will be used to quantize the fp32 data.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantize_v2"> |
| <code class="sig-name descname">quantize_v2</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">out_type=_Null</em>, <em class="sig-param">min_calib_range=_Null</em>, <em class="sig-param">max_calib_range=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantize_v2" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Quantize a input tensor from float to <cite>out_type</cite>, |
| with user-specified <cite>min_calib_range</cite> and <cite>max_calib_range</cite> or the input range collected at runtime.</p> |
| <p>Output <cite>min_range</cite> and <cite>max_range</cite> are scalar floats that specify the range for the input data.</p> |
| <p>When out_type is <cite>uint8</cite>, the output is calculated using the following equation:</p> |
| <p><cite>out[i] = (in[i] - min_range) * range(OUTPUT_TYPE) / (max_range - min_range) + 0.5</cite>,</p> |
| <p>where <cite>range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()</cite>.</p> |
| <p>When out_type is <cite>int8</cite>, the output is calculate using the following equation |
| by keep zero centered for the quantized value:</p> |
| <p><cite>out[i] = sign(in[i]) * min(abs(in[i] * scale + 0.5f, quantized_range)</cite>,</p> |
| <p>where |
| <cite>quantized_range = MinAbs(max(int8), min(int8))</cite> and |
| <cite>scale = quantized_range / MaxAbs(min_range, max_range).</cite></p> |
| <p>When out_type is <cite>auto</cite>, the output type is automatically determined by min_calib_range if presented. |
| If min_calib_range < 0.0f, the output type will be int8, otherwise will be uint8. |
| If min_calib_range isn’t presented, the output type will be int8.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propagation. DO NOT use it in training.</p> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/quantization/quantize_v2.cc:L95</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – A ndarray/symbol of type <cite>float32</cite></p></li> |
| <li><p><strong>out_type</strong> (<em>{'auto'</em><em>, </em><em>'int8'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='int8'</em>) – Output data type. <cite>auto</cite> can be specified to automatically determine output type according to min_calib_range.</p></li> |
| <li><p><strong>min_calib_range</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – The minimum scalar value in the form of float32. If present, it will be used to quantize the fp32 data into int8 or uint8.</p></li> |
| <li><p><strong>max_calib_range</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – The maximum scalar value in the form of float32. If present, it will be used to quantize the fp32 data into int8 or uint8.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_act"> |
| <code class="sig-name descname">quantized_act</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">min_data=None</em>, <em class="sig-param">max_data=None</em>, <em class="sig-param">act_type=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_act" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Activation operator for input and output data type of int8. |
| The input and output data comes with min and max thresholds for quantizing |
| the float32 data into int8.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propogation. DO NOT use it in training. |
| This operator only supports <cite>relu</cite></p> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/quantization/quantized_activation.cc:L92</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data.</p></li> |
| <li><p><strong>min_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Minimum value of data.</p></li> |
| <li><p><strong>max_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum value of data.</p></li> |
| <li><p><strong>act_type</strong> (<em>{'log_sigmoid'</em><em>, </em><em>'mish'</em><em>, </em><em>'relu'</em><em>, </em><em>'sigmoid'</em><em>, </em><em>'softrelu'</em><em>, </em><em>'softsign'</em><em>, </em><em>'tanh'}</em><em>, </em><em>required</em>) – Activation function to be applied.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_batch_norm"> |
| <code class="sig-name descname">quantized_batch_norm</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">gamma=None</em>, <em class="sig-param">beta=None</em>, <em class="sig-param">moving_mean=None</em>, <em class="sig-param">moving_var=None</em>, <em class="sig-param">min_data=None</em>, <em class="sig-param">max_data=None</em>, <em class="sig-param">eps=_Null</em>, <em class="sig-param">momentum=_Null</em>, <em class="sig-param">fix_gamma=_Null</em>, <em class="sig-param">use_global_stats=_Null</em>, <em class="sig-param">output_mean_var=_Null</em>, <em class="sig-param">axis=_Null</em>, <em class="sig-param">cudnn_off=_Null</em>, <em class="sig-param">min_calib_range=_Null</em>, <em class="sig-param">max_calib_range=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_batch_norm" title="Permalink to this definition">¶</a></dt> |
| <dd><p>BatchNorm operator for input and output data type of int8. |
| The input and output data comes with min and max thresholds for quantizing |
| the float32 data into int8.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propogation. DO NOT use it in training.</p> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/quantization/quantized_batch_norm.cc:L96</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data.</p></li> |
| <li><p><strong>gamma</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – gamma.</p></li> |
| <li><p><strong>beta</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – beta.</p></li> |
| <li><p><strong>moving_mean</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – moving_mean.</p></li> |
| <li><p><strong>moving_var</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – moving_var.</p></li> |
| <li><p><strong>min_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Minimum value of data.</p></li> |
| <li><p><strong>max_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum value of data.</p></li> |
| <li><p><strong>eps</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=0.0010000000474974513</em>) – Epsilon to prevent div 0. Must be no less than CUDNN_BN_MIN_EPSILON defined in cudnn.h when using cudnn (usually 1e-5)</p></li> |
| <li><p><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.899999976</em>) – Momentum for moving average</p></li> |
| <li><p><strong>fix_gamma</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Fix gamma while training</p></li> |
| <li><p><strong>use_global_stats</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether use global moving statistics instead of local batch-norm. This will force change batch-norm into a scale shift operator.</p></li> |
| <li><p><strong>output_mean_var</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Output the mean and inverse std</p></li> |
| <li><p><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Specify which shape axis the channel is specified</p></li> |
| <li><p><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Do not select CUDNN operator, if available</p></li> |
| <li><p><strong>min_calib_range</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – The minimum scalar value in the form of float32 obtained through calibration. If present, it will be used to by quantized batch norm op to calculate primitive scale.Note: this calib_range is to calib bn output.</p></li> |
| <li><p><strong>max_calib_range</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – The maximum scalar value in the form of float32 obtained through calibration. If present, it will be used to by quantized batch norm op to calculate primitive scale.Note: this calib_range is to calib bn output.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_batch_norm_relu"> |
| <code class="sig-name descname">quantized_batch_norm_relu</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">gamma=None</em>, <em class="sig-param">beta=None</em>, <em class="sig-param">moving_mean=None</em>, <em class="sig-param">moving_var=None</em>, <em class="sig-param">min_data=None</em>, <em class="sig-param">max_data=None</em>, <em class="sig-param">eps=_Null</em>, <em class="sig-param">momentum=_Null</em>, <em class="sig-param">fix_gamma=_Null</em>, <em class="sig-param">use_global_stats=_Null</em>, <em class="sig-param">output_mean_var=_Null</em>, <em class="sig-param">axis=_Null</em>, <em class="sig-param">cudnn_off=_Null</em>, <em class="sig-param">min_calib_range=_Null</em>, <em class="sig-param">max_calib_range=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_batch_norm_relu" title="Permalink to this definition">¶</a></dt> |
| <dd><p>BatchNorm with ReLU operator for input and output data type of int8. |
| The input and output data comes with min and max thresholds for quantizing |
| the float32 data into int8.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propogation. DO NOT use it in training.</p> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/quantization/quantized_batch_norm_relu.cc:L96</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data.</p></li> |
| <li><p><strong>gamma</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – gamma.</p></li> |
| <li><p><strong>beta</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – beta.</p></li> |
| <li><p><strong>moving_mean</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – moving_mean.</p></li> |
| <li><p><strong>moving_var</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – moving_var.</p></li> |
| <li><p><strong>min_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Minimum value of data.</p></li> |
| <li><p><strong>max_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum value of data.</p></li> |
| <li><p><strong>eps</strong> (<em>double</em><em>, </em><em>optional</em><em>, </em><em>default=0.0010000000474974513</em>) – Epsilon to prevent div 0. Must be no less than CUDNN_BN_MIN_EPSILON defined in cudnn.h when using cudnn (usually 1e-5)</p></li> |
| <li><p><strong>momentum</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.899999976</em>) – Momentum for moving average</p></li> |
| <li><p><strong>fix_gamma</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Fix gamma while training</p></li> |
| <li><p><strong>use_global_stats</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether use global moving statistics instead of local batch-norm. This will force change batch-norm into a scale shift operator.</p></li> |
| <li><p><strong>output_mean_var</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Output the mean and inverse std</p></li> |
| <li><p><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Specify which shape axis the channel is specified</p></li> |
| <li><p><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Do not select CUDNN operator, if available</p></li> |
| <li><p><strong>min_calib_range</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – The minimum scalar value in the form of float32 obtained through calibration. If present, it will be used to by quantized batch norm op to calculate primitive scale.Note: this calib_range is to calib bn output.</p></li> |
| <li><p><strong>max_calib_range</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – The maximum scalar value in the form of float32 obtained through calibration. If present, it will be used to by quantized batch norm op to calculate primitive scale.Note: this calib_range is to calib bn output.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_concat"> |
| <code class="sig-name descname">quantized_concat</code><span class="sig-paren">(</span><em class="sig-param">*data</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_concat" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Joins input arrays along a given axis.</p> |
| <p>The dimensions of the input arrays should be the same except the axis along |
| which they will be concatenated. |
| The dimension of the output array along the concatenated axis will be equal |
| to the sum of the corresponding dimensions of the input arrays. |
| All inputs with different min/max will be rescaled by using largest [min, max] pairs. |
| If any input holds int8, then the output will be int8. Otherwise output will be uint8.</p> |
| <p>Defined in /work/mxnet/src/operator/quantization/quantized_concat.cc:L113 |
| This function support variable length of positional input.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a><em>[</em><em>]</em>) – List of arrays to concatenate</p></li> |
| <li><p><strong>dim</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – the dimension to be concated.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_conv"> |
| <code class="sig-name descname">quantized_conv</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">bias=None</em>, <em class="sig-param">min_data=None</em>, <em class="sig-param">max_data=None</em>, <em class="sig-param">min_weight=None</em>, <em class="sig-param">max_weight=None</em>, <em class="sig-param">min_bias=None</em>, <em class="sig-param">max_bias=None</em>, <em class="sig-param">kernel=_Null</em>, <em class="sig-param">stride=_Null</em>, <em class="sig-param">dilate=_Null</em>, <em class="sig-param">pad=_Null</em>, <em class="sig-param">num_filter=_Null</em>, <em class="sig-param">num_group=_Null</em>, <em class="sig-param">workspace=_Null</em>, <em class="sig-param">no_bias=_Null</em>, <em class="sig-param">cudnn_tune=_Null</em>, <em class="sig-param">cudnn_off=_Null</em>, <em class="sig-param">layout=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_conv" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Convolution operator for input, weight and bias data type of int8, |
| and accumulates in type int32 for the output. For each argument, two more arguments of type |
| float32 must be provided representing the thresholds of quantizing argument from data |
| type float32 to int8. The final outputs contain the convolution result in int32, and min |
| and max thresholds representing the threholds for quantizing the float32 output into int32.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propogation. DO NOT use it in training.</p> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/quantization/quantized_conv.cc:L185</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data.</p></li> |
| <li><p><strong>weight</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – weight.</p></li> |
| <li><p><strong>bias</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – bias.</p></li> |
| <li><p><strong>min_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Minimum value of data.</p></li> |
| <li><p><strong>max_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum value of data.</p></li> |
| <li><p><strong>min_weight</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Minimum value of weight.</p></li> |
| <li><p><strong>max_weight</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum value of weight.</p></li> |
| <li><p><strong>min_bias</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Minimum value of bias.</p></li> |
| <li><p><strong>max_bias</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum value of bias.</p></li> |
| <li><p><strong>kernel</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>required</em>) – Convolution kernel size: (w,), (h, w) or (d, h, w)</p></li> |
| <li><p><strong>stride</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>]</em>) – Convolution stride: (w,), (h, w) or (d, h, w). Defaults to 1 for each dimension.</p></li> |
| <li><p><strong>dilate</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>]</em>) – Convolution dilate: (w,), (h, w) or (d, h, w). Defaults to 1 for each dimension.</p></li> |
| <li><p><strong>pad</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>]</em>) – Zero pad for convolution: (w,), (h, w) or (d, h, w). Defaults to no padding.</p></li> |
| <li><p><strong>num_filter</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>required</em>) – Convolution filter(channel) number</p></li> |
| <li><p><strong>num_group</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Number of group partitions.</p></li> |
| <li><p><strong>workspace</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=1024</em>) – Maximum temporary workspace allowed (MB) in convolution.This parameter has two usages. When CUDNN is not used, it determines the effective batch size of the convolution kernel. When CUDNN is used, it controls the maximum temporary storage used for tuning the best CUDNN kernel when <cite>limited_workspace</cite> strategy is used.</p></li> |
| <li><p><strong>no_bias</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to disable bias parameter.</p></li> |
| <li><p><strong>cudnn_tune</strong> (<em>{None</em><em>, </em><em>'fastest'</em><em>, </em><em>'limited_workspace'</em><em>, </em><em>'off'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Whether to pick convolution algo by running performance test.</p></li> |
| <li><p><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Turn off cudnn for this layer.</p></li> |
| <li><p><strong>layout</strong> (<em>{None</em><em>, </em><em>'NCDHW'</em><em>, </em><em>'NCHW'</em><em>, </em><em>'NCW'</em><em>, </em><em>'NDHWC'</em><em>, </em><em>'NHWC'</em><em>, </em><em>'NWC'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Set layout for input, output and weight. Empty for |
| default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.NHWC and NDHWC are only supported on GPU.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_elemwise_add"> |
| <code class="sig-name descname">quantized_elemwise_add</code><span class="sig-paren">(</span><em class="sig-param">lhs=None</em>, <em class="sig-param">rhs=None</em>, <em class="sig-param">lhs_min=None</em>, <em class="sig-param">lhs_max=None</em>, <em class="sig-param">rhs_min=None</em>, <em class="sig-param">rhs_max=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_elemwise_add" title="Permalink to this definition">¶</a></dt> |
| <dd><p>elemwise_add operator for input dataA and input dataB data type of int8, |
| and accumulates in type int32 for the output. For each argument, two more arguments of type |
| float32 must be provided representing the thresholds of quantizing argument from data |
| type float32 to int8. The final outputs contain result in int32, and min |
| and max thresholds representing the threholds for quantizing the float32 output into int32.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propogation. DO NOT use it in training.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>lhs</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – first input</p></li> |
| <li><p><strong>rhs</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – second input</p></li> |
| <li><p><strong>lhs_min</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – 3rd input</p></li> |
| <li><p><strong>lhs_max</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – 4th input</p></li> |
| <li><p><strong>rhs_min</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – 5th input</p></li> |
| <li><p><strong>rhs_max</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – 6th input</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_elemwise_mul"> |
| <code class="sig-name descname">quantized_elemwise_mul</code><span class="sig-paren">(</span><em class="sig-param">lhs=None</em>, <em class="sig-param">rhs=None</em>, <em class="sig-param">lhs_min=None</em>, <em class="sig-param">lhs_max=None</em>, <em class="sig-param">rhs_min=None</em>, <em class="sig-param">rhs_max=None</em>, <em class="sig-param">min_calib_range=_Null</em>, <em class="sig-param">max_calib_range=_Null</em>, <em class="sig-param">enable_float_output=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_elemwise_mul" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Multiplies arguments int8 element-wise.</p> |
| <p>Defined in /work/mxnet/src/operator/quantization/quantized_elemwise_mul.cc:L214</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>lhs</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – first input</p></li> |
| <li><p><strong>rhs</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – second input</p></li> |
| <li><p><strong>lhs_min</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Minimum value of first input.</p></li> |
| <li><p><strong>lhs_max</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum value of first input.</p></li> |
| <li><p><strong>rhs_min</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Minimum value of second input.</p></li> |
| <li><p><strong>rhs_max</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum value of second input.</p></li> |
| <li><p><strong>min_calib_range</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – The minimum scalar value in the form of float32 obtained through calibration. If present, it will be used to requantize the int8 output data.</p></li> |
| <li><p><strong>max_calib_range</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – The maximum scalar value in the form of float32 obtained through calibration. If present, it will be used to requantize the int8 output data.</p></li> |
| <li><p><strong>enable_float_output</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to enable float32 output</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_embedding"> |
| <code class="sig-name descname">quantized_embedding</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">min_weight=None</em>, <em class="sig-param">max_weight=None</em>, <em class="sig-param">input_dim=_Null</em>, <em class="sig-param">output_dim=_Null</em>, <em class="sig-param">dtype=_Null</em>, <em class="sig-param">sparse_grad=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_embedding" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Maps integer indices to int8 vector representations (embeddings).</p> |
| <p>Defined in /work/mxnet/src/operator/quantization/quantized_indexing_op.cc:L134</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array to the embedding operator.</p></li> |
| <li><p><strong>weight</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The embedding weight matrix.</p></li> |
| <li><p><strong>min_weight</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Minimum value of data.</p></li> |
| <li><p><strong>max_weight</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum value of data.</p></li> |
| <li><p><strong>input_dim</strong> (<em>long</em><em>, </em><em>required</em>) – Vocabulary size of the input indices.</p></li> |
| <li><p><strong>output_dim</strong> (<em>long</em><em>, </em><em>required</em>) – Dimension of the embedding vectors.</p></li> |
| <li><p><strong>dtype</strong> (<em>{'bfloat16'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'int64'</em><em>, </em><em>'int8'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='float32'</em>) – Data type of weight.</p></li> |
| <li><p><strong>sparse_grad</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Compute row sparse gradient in the backward calculation. If set to True, the grad’s storage type is row_sparse.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_flatten"> |
| <code class="sig-name descname">quantized_flatten</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">min_data=None</em>, <em class="sig-param">max_data=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_flatten" title="Permalink to this definition">¶</a></dt> |
| <dd><dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – A ndarray/symbol of type <cite>float32</cite></p></li> |
| <li><p><strong>min_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The minimum scalar value possibly produced for the data</p></li> |
| <li><p><strong>max_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The maximum scalar value possibly produced for the data</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_fully_connected"> |
| <code class="sig-name descname">quantized_fully_connected</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">weight=None</em>, <em class="sig-param">bias=None</em>, <em class="sig-param">min_data=None</em>, <em class="sig-param">max_data=None</em>, <em class="sig-param">min_weight=None</em>, <em class="sig-param">max_weight=None</em>, <em class="sig-param">min_bias=None</em>, <em class="sig-param">max_bias=None</em>, <em class="sig-param">num_hidden=_Null</em>, <em class="sig-param">no_bias=_Null</em>, <em class="sig-param">flatten=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_fully_connected" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Fully Connected operator for input, weight and bias data type of int8, |
| and accumulates in type int32 for the output. For each argument, two more arguments of type |
| float32 must be provided representing the thresholds of quantizing argument from data |
| type float32 to int8. The final outputs contain the convolution result in int32, and min |
| and max thresholds representing the threholds for quantizing the float32 output into int32.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propogation. DO NOT use it in training.</p> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/quantization/quantized_fully_connected.cc:L326</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data.</p></li> |
| <li><p><strong>weight</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – weight.</p></li> |
| <li><p><strong>bias</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – bias.</p></li> |
| <li><p><strong>min_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Minimum value of data.</p></li> |
| <li><p><strong>max_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum value of data.</p></li> |
| <li><p><strong>min_weight</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Minimum value of weight.</p></li> |
| <li><p><strong>max_weight</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum value of weight.</p></li> |
| <li><p><strong>min_bias</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Minimum value of bias.</p></li> |
| <li><p><strong>max_bias</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum value of bias.</p></li> |
| <li><p><strong>num_hidden</strong> (<em>int</em><em>, </em><em>required</em>) – Number of hidden nodes of the output.</p></li> |
| <li><p><strong>no_bias</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to disable bias parameter.</p></li> |
| <li><p><strong>flatten</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Whether to collapse all but the first axis of the input data tensor.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_npi_add"> |
| <code class="sig-name descname">quantized_npi_add</code><span class="sig-paren">(</span><em class="sig-param">lhs=None</em>, <em class="sig-param">rhs=None</em>, <em class="sig-param">lhs_min=None</em>, <em class="sig-param">lhs_max=None</em>, <em class="sig-param">rhs_min=None</em>, <em class="sig-param">rhs_max=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_npi_add" title="Permalink to this definition">¶</a></dt> |
| <dd><p>elemwise_add operator for input dataA and input dataB data type of int8, |
| and accumulates in type int32 for the output. For each argument, two more arguments of type |
| float32 must be provided representing the thresholds of quantizing argument from data |
| type float32 to int8. The final outputs contain result in int32, and min |
| and max thresholds representing the threholds for quantizing the float32 output into int32.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propogation. DO NOT use it in training.</p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>lhs</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – first input</p></li> |
| <li><p><strong>rhs</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – second input</p></li> |
| <li><p><strong>lhs_min</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – 3rd input</p></li> |
| <li><p><strong>lhs_max</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – 4th input</p></li> |
| <li><p><strong>rhs_min</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – 5th input</p></li> |
| <li><p><strong>rhs_max</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – 6th input</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_pooling"> |
| <code class="sig-name descname">quantized_pooling</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">min_data=None</em>, <em class="sig-param">max_data=None</em>, <em class="sig-param">kernel=_Null</em>, <em class="sig-param">pool_type=_Null</em>, <em class="sig-param">global_pool=_Null</em>, <em class="sig-param">cudnn_off=_Null</em>, <em class="sig-param">pooling_convention=_Null</em>, <em class="sig-param">stride=_Null</em>, <em class="sig-param">pad=_Null</em>, <em class="sig-param">p_value=_Null</em>, <em class="sig-param">count_include_pad=_Null</em>, <em class="sig-param">layout=_Null</em>, <em class="sig-param">output_size=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_pooling" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Pooling operator for input and output data type of int8. |
| The input and output data comes with min and max thresholds for quantizing |
| the float32 data into int8.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propogation. DO NOT use it in training. |
| This operator only supports <cite>pool_type</cite> of <cite>avg</cite> or <cite>max</cite>.</p> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/quantization/quantized_pooling.cc:L184</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data.</p></li> |
| <li><p><strong>min_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Minimum value of data.</p></li> |
| <li><p><strong>max_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Maximum value of data.</p></li> |
| <li><p><strong>kernel</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>]</em>) – Pooling kernel size: (y, x) or (d, y, x)</p></li> |
| <li><p><strong>pool_type</strong> (<em>{'avg'</em><em>, </em><em>'lp'</em><em>, </em><em>'max'</em><em>, </em><em>'sum'}</em><em>,</em><em>optional</em><em>, </em><em>default='max'</em>) – Pooling type to be applied.</p></li> |
| <li><p><strong>global_pool</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Ignore kernel size, do global pooling based on current input feature map.</p></li> |
| <li><p><strong>cudnn_off</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Turn off cudnn pooling and use MXNet pooling operator.</p></li> |
| <li><p><strong>pooling_convention</strong> (<em>{'full'</em><em>, </em><em>'same'</em><em>, </em><em>'valid'}</em><em>,</em><em>optional</em><em>, </em><em>default='valid'</em>) – Pooling convention to be applied.</p></li> |
| <li><p><strong>stride</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>]</em>) – Stride: for pooling (y, x) or (d, y, x). Defaults to 1 for each dimension.</p></li> |
| <li><p><strong>pad</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>]</em>) – Pad for pooling: (y, x) or (d, y, x). Defaults to no padding.</p></li> |
| <li><p><strong>p_value</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – Value of p for Lp pooling, can be 1 or 2, required for Lp Pooling.</p></li> |
| <li><p><strong>count_include_pad</strong> (<em>boolean</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Only used for AvgPool, specify whether to count padding elements for averagecalculation. For example, with a 5*5 kernel on a 3*3 corner of a image,the sum of the 9 valid elements will be divided by 25 if this is set to true,or it will be divided by 9 if this is set to false. Defaults to true.</p></li> |
| <li><p><strong>layout</strong> (<em>{None</em><em>, </em><em>'NCDHW'</em><em>, </em><em>'NCHW'</em><em>, </em><em>'NCW'</em><em>, </em><em>'NDHWC'</em><em>, </em><em>'NHWC'</em><em>, </em><em>'NWC'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Set layout for input and output. Empty for |
| default layout: NCW for 1d, NCHW for 2d and NCDHW for 3d.</p></li> |
| <li><p><strong>output_size</strong> (<em>Shape</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Only used for Adaptive Pooling. int (output size) or a tuple of int for output (height, width).</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_reshape"> |
| <code class="sig-name descname">quantized_reshape</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">min_data=None</em>, <em class="sig-param">max_data=None</em>, <em class="sig-param">shape=_Null</em>, <em class="sig-param">reverse=_Null</em>, <em class="sig-param">target_shape=_Null</em>, <em class="sig-param">keep_highest=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_reshape" title="Permalink to this definition">¶</a></dt> |
| <dd><dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Array to be reshaped.</p></li> |
| <li><p><strong>min_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The minimum scalar value possibly produced for the data</p></li> |
| <li><p><strong>max_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The maximum scalar value possibly produced for the data</p></li> |
| <li><p><strong>shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>]</em>) – The target shape</p></li> |
| <li><p><strong>reverse</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – If true then the special values are inferred from right to left</p></li> |
| <li><p><strong>target_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>]</em>) – (Deprecated! Use <code class="docutils literal notranslate"><span class="pre">shape</span></code> instead.) Target new shape. One and only one dim can be 0, in which case it will be inferred from the rest of dims</p></li> |
| <li><p><strong>keep_highest</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – (Deprecated! Use <code class="docutils literal notranslate"><span class="pre">shape</span></code> instead.) Whether keep the highest dim unchanged.If set to true, then the first dim in target_shape is ignored,and always fixed as input</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_rnn"> |
| <code class="sig-name descname">quantized_rnn</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">parameters=None</em>, <em class="sig-param">state=None</em>, <em class="sig-param">state_cell=None</em>, <em class="sig-param">data_scale=None</em>, <em class="sig-param">data_shift=None</em>, <em class="sig-param">state_size=_Null</em>, <em class="sig-param">num_layers=_Null</em>, <em class="sig-param">bidirectional=_Null</em>, <em class="sig-param">mode=_Null</em>, <em class="sig-param">p=_Null</em>, <em class="sig-param">state_outputs=_Null</em>, <em class="sig-param">projection_size=_Null</em>, <em class="sig-param">lstm_state_clip_min=_Null</em>, <em class="sig-param">lstm_state_clip_max=_Null</em>, <em class="sig-param">lstm_state_clip_nan=_Null</em>, <em class="sig-param">use_sequence_length=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_rnn" title="Permalink to this definition">¶</a></dt> |
| <dd><p>RNN operator for input data type of uint8. The weight of each |
| gates is converted to int8, while bias is accumulated in type float32. |
| The hidden state and cell state are in type float32. For the input data, two more arguments |
| of type float32 must be provided representing the thresholds of quantizing argument from |
| data type float32 to uint8. The final outputs contain the recurrent result in float32. |
| It only supports quantization for Vanilla LSTM network.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propagation. DO NOT use it in training.</p> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/quantization/quantized_rnn.cc:L301</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Input data.</p></li> |
| <li><p><strong>parameters</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – weight.</p></li> |
| <li><p><strong>state</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – initial hidden state of the RNN</p></li> |
| <li><p><strong>state_cell</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – initial cell state for LSTM networks (only for LSTM)</p></li> |
| <li><p><strong>data_scale</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – quantization scale of data.</p></li> |
| <li><p><strong>data_shift</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – quantization shift of data.</p></li> |
| <li><p><strong>state_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>required</em>) – size of the state for each layer</p></li> |
| <li><p><strong>num_layers</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>required</em>) – number of stacked layers</p></li> |
| <li><p><strong>bidirectional</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – whether to use bidirectional recurrent layers</p></li> |
| <li><p><strong>mode</strong> (<em>{'gru'</em><em>, </em><em>'lstm'</em><em>, </em><em>'rnn_relu'</em><em>, </em><em>'rnn_tanh'}</em><em>, </em><em>required</em>) – the type of RNN to compute</p></li> |
| <li><p><strong>p</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – drop rate of the dropout on the outputs of each RNN layer, except the last layer.</p></li> |
| <li><p><strong>state_outputs</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to have the states as symbol outputs.</p></li> |
| <li><p><strong>projection_size</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – size of project size</p></li> |
| <li><p><strong>lstm_state_clip_min</strong> (<em>double</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Minimum clip value of LSTM states. This option must be used together with lstm_state_clip_max.</p></li> |
| <li><p><strong>lstm_state_clip_max</strong> (<em>double</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Maximum clip value of LSTM states. This option must be used together with lstm_state_clip_min.</p></li> |
| <li><p><strong>lstm_state_clip_nan</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to stop NaN from propagating in state by clipping it to min/max. If clipping range is not specified, this option is ignored.</p></li> |
| <li><p><strong>use_sequence_length</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – If set to true, this layer takes in an extra input parameter <cite>sequence_length</cite> to specify variable length sequence</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.quantized_transpose"> |
| <code class="sig-name descname">quantized_transpose</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">min_data=None</em>, <em class="sig-param">max_data=None</em>, <em class="sig-param">axes=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.quantized_transpose" title="Permalink to this definition">¶</a></dt> |
| <dd><dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – Array to be transposed.</p></li> |
| <li><p><strong>min_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The minimum scalar value possibly produced for the data</p></li> |
| <li><p><strong>max_data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The maximum scalar value possibly produced for the data</p></li> |
| <li><p><strong>axes</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>]</em>) – Target axis order. By default the axes will be inverted.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.requantize"> |
| <code class="sig-name descname">requantize</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">min_range=None</em>, <em class="sig-param">max_range=None</em>, <em class="sig-param">out_type=_Null</em>, <em class="sig-param">min_calib_range=_Null</em>, <em class="sig-param">max_calib_range=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.requantize" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Given data that is quantized in int32 and the corresponding thresholds, |
| requantize the data into int8 using min and max thresholds either calculated at runtime |
| or from calibration. It’s highly recommended to pre-calucate the min and max thresholds |
| through calibration since it is able to save the runtime of the operator and improve the |
| inference accuracy.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>This operator only supports forward propogation. DO NOT use it in training.</p> |
| </div> |
| <p>Defined in /work/mxnet/src/operator/quantization/requantize.cc:L80</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – A ndarray/symbol of type <cite>int32</cite></p></li> |
| <li><p><strong>min_range</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The original minimum scalar value in the form of float32 used for quantizing data into int32.</p></li> |
| <li><p><strong>max_range</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The original maximum scalar value in the form of float32 used for quantizing data into int32.</p></li> |
| <li><p><strong>out_type</strong> (<em>{'auto'</em><em>, </em><em>'int8'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='int8'</em>) – Output data type. <cite>auto</cite> can be specified to automatically determine output type according to min_calib_range.</p></li> |
| <li><p><strong>min_calib_range</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – The minimum scalar value in the form of float32 obtained through calibration. If present, it will be used to requantize the int32 data into int8.</p></li> |
| <li><p><strong>max_calib_range</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – The maximum scalar value in the form of float32 obtained through calibration. If present, it will be used to requantize the int32 data into int8.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.round_ste"> |
| <code class="sig-name descname">round_ste</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.round_ste" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Straight-through-estimator of <cite>round()</cite>.</p> |
| <p>In forward pass, returns element-wise rounded value to the nearest integer of the input (same as <cite>round()</cite>).</p> |
| <p>In backward pass, returns gradients of <code class="docutils literal notranslate"><span class="pre">1</span></code> everywhere (instead of <code class="docutils literal notranslate"><span class="pre">0</span></code> everywhere as in <cite>round()</cite>): |
| <span class="math notranslate nohighlight">\(\frac{d}{dx}{round\_ste(x)} = 1\)</span> vs. <span class="math notranslate nohighlight">\(\frac{d}{dx}{round(x)} = 0\)</span>. |
| This is useful for quantized training.</p> |
| <p>Reference: Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.</p> |
| <dl class="simple"> |
| <dt>Example::</dt><dd><p>x = round_ste([-1.5, 1.5, -1.9, 1.9, 2.7]) |
| x.backward() |
| x = [-2., 2., -2., 2., 3.] |
| x.grad() = [1., 1., 1., 1., 1.]</p> |
| </dd> |
| <dt>The storage type of <code class="docutils literal notranslate"><span class="pre">round_ste</span></code> output depends upon the input storage type:</dt><dd><ul class="simple"> |
| <li><p>round_ste(default) = default</p></li> |
| <li><p>round_ste(row_sparse) = row_sparse</p></li> |
| <li><p>round_ste(csr) = csr</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p>Defined in /work/mxnet/src/operator/contrib/stes_op.cc:L54</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.sign_ste"> |
| <code class="sig-name descname">sign_ste</code><span class="sig-paren">(</span><em class="sig-param">data=None</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.sign_ste" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Straight-through-estimator of <cite>sign()</cite>.</p> |
| <p>In forward pass, returns element-wise sign of the input (same as <cite>sign()</cite>).</p> |
| <p>In backward pass, returns gradients of <code class="docutils literal notranslate"><span class="pre">1</span></code> everywhere (instead of <code class="docutils literal notranslate"><span class="pre">0</span></code> everywhere as in <code class="docutils literal notranslate"><span class="pre">sign()</span></code>): |
| <span class="math notranslate nohighlight">\(\frac{d}{dx}{sign\_ste(x)} = 1\)</span> vs. <span class="math notranslate nohighlight">\(\frac{d}{dx}{sign(x)} = 0\)</span>. |
| This is useful for quantized training.</p> |
| <p>Reference: Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.</p> |
| <dl class="simple"> |
| <dt>Example::</dt><dd><p>x = sign_ste([-2, 0, 3]) |
| x.backward() |
| x = [-1., 0., 1.] |
| x.grad() = [1., 1., 1.]</p> |
| </dd> |
| <dt>The storage type of <code class="docutils literal notranslate"><span class="pre">sign_ste</span></code> output depends upon the input storage type:</dt><dd><ul class="simple"> |
| <li><p>round_ste(default) = default</p></li> |
| <li><p>round_ste(row_sparse) = row_sparse</p></li> |
| <li><p>round_ste(csr) = csr</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p>Defined in /work/mxnet/src/operator/contrib/stes_op.cc:L80</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – The input array.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.sldwin_atten_context"> |
| <code class="sig-name descname">sldwin_atten_context</code><span class="sig-paren">(</span><em class="sig-param">score=None</em>, <em class="sig-param">value=None</em>, <em class="sig-param">dilation=None</em>, <em class="sig-param">w=_Null</em>, <em class="sig-param">symmetric=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.sldwin_atten_context" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute the context vector for sliding window attention, used in |
| Longformer (<a class="reference external" href="https://arxiv.org/pdf/2004.05150.pdf">https://arxiv.org/pdf/2004.05150.pdf</a>).</p> |
| <p>In this attention pattern, |
| given a fixed window size <em>2w</em>, each token attends to <em>w</em> tokens on the left side |
| if we use causal attention (setting <em>symmetric</em> to <em>False</em>), |
| otherwise each token attends to <em>w</em> tokens on each side.</p> |
| <p>The shapes of the inputs are: |
| - <em>score</em> :</p> |
| <blockquote> |
| <div><ul class="simple"> |
| <li><p>(batch_size, seq_length, num_heads, w + w + 1) if symmetric is True,</p></li> |
| <li><p>(batch_size, seq_length, num_heads, w + 1) otherwise</p></li> |
| </ul> |
| </div></blockquote> |
| <ul class="simple"> |
| <li><p><em>value</em> : (batch_size, seq_length, num_heads, num_head_units)</p></li> |
| <li><p><em>dilation</em> : (num_heads,)</p></li> |
| </ul> |
| <p>The shape of the output is: |
| - <em>context_vec</em> : (batch_size, seq_length, num_heads, num_head_units)</p> |
| <p>Defined in /work/mxnet/src/operator/contrib/transformer.cc:L1045</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>score</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – score</p></li> |
| <li><p><strong>value</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – value</p></li> |
| <li><p><strong>dilation</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – dilation</p></li> |
| <li><p><strong>w</strong> (<em>int</em><em>, </em><em>required</em>) – The one-sided window length</p></li> |
| <li><p><strong>symmetric</strong> (<em>boolean</em><em>, </em><em>required</em>) – If false, each token will only attend to itself and the previous tokens.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.sldwin_atten_mask_like"> |
| <code class="sig-name descname">sldwin_atten_mask_like</code><span class="sig-paren">(</span><em class="sig-param">score=None</em>, <em class="sig-param">dilation=None</em>, <em class="sig-param">valid_length=None</em>, <em class="sig-param">w=_Null</em>, <em class="sig-param">symmetric=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.sldwin_atten_mask_like" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute the mask for the sliding window attention score, used in |
| Longformer (<a class="reference external" href="https://arxiv.org/pdf/2004.05150.pdf">https://arxiv.org/pdf/2004.05150.pdf</a>).</p> |
| <p>In this attention pattern, |
| given a fixed window size <em>2w</em>, each token attends to <em>w</em> tokens on the left side |
| if we use causal attention (setting <em>symmetric</em> to <em>False</em>), |
| otherwise each token attends to <em>w</em> tokens on each side.</p> |
| <p>The shapes of the inputs are: |
| - <em>score</em> :</p> |
| <blockquote> |
| <div><ul class="simple"> |
| <li><p>(batch_size, seq_length, num_heads, w + w + 1) if symmetric is True,</p></li> |
| <li><p>(batch_size, seq_length, num_heads, w + 1) otherwise.</p></li> |
| </ul> |
| </div></blockquote> |
| <ul class="simple"> |
| <li><p><em>dilation</em> : (num_heads,)</p></li> |
| <li><p><em>valid_length</em> : (batch_size,)</p></li> |
| </ul> |
| <p>The shape of the output is: |
| - <em>mask</em> : same as the shape of <em>score</em></p> |
| <p>Defined in /work/mxnet/src/operator/contrib/transformer.cc:L909</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>score</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – sliding window attention score</p></li> |
| <li><p><strong>dilation</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – dilation</p></li> |
| <li><p><strong>valid_length</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – valid length</p></li> |
| <li><p><strong>w</strong> (<em>int</em><em>, </em><em>required</em>) – The one-sided window length</p></li> |
| <li><p><strong>symmetric</strong> (<em>boolean</em><em>, </em><em>required</em>) – If false, each token will only attend to itself and the previous tokens.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.symbol.contrib.sldwin_atten_score"> |
| <code class="sig-name descname">sldwin_atten_score</code><span class="sig-paren">(</span><em class="sig-param">query=None</em>, <em class="sig-param">key=None</em>, <em class="sig-param">dilation=None</em>, <em class="sig-param">w=_Null</em>, <em class="sig-param">symmetric=_Null</em>, <em class="sig-param">name=None</em>, <em class="sig-param">attr=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.symbol.contrib.sldwin_atten_score" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute the sliding window attention score, which is used in |
| Longformer (<a class="reference external" href="https://arxiv.org/pdf/2004.05150.pdf">https://arxiv.org/pdf/2004.05150.pdf</a>). In this attention pattern, |
| given a fixed window size <em>2w</em>, each token attends to <em>w</em> tokens on the left side |
| if we use causal attention (setting <em>symmetric</em> to <em>False</em>), |
| otherwise each token attends to <em>w</em> tokens on each side.</p> |
| <p>The shapes of the inputs are: |
| - <em>query</em> : (batch_size, seq_length, num_heads, num_head_units) |
| - <em>key</em> : (batch_size, seq_length, num_heads, num_head_units) |
| - <em>dilation</em> : (num_heads,)</p> |
| <p>The shape of the output is: |
| - <em>score</em> :</p> |
| <blockquote> |
| <div><ul class="simple"> |
| <li><p>(batch_size, seq_length, num_heads, w + w + 1) if symmetric is True,</p></li> |
| <li><p>(batch_size, seq_length, num_heads, w + 1) otherwise.</p></li> |
| </ul> |
| </div></blockquote> |
| <p>Defined in /work/mxnet/src/operator/contrib/transformer.cc:L969</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>query</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – query</p></li> |
| <li><p><strong>key</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – key</p></li> |
| <li><p><strong>dilation</strong> (<a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol"><em>Symbol</em></a>) – dilation</p></li> |
| <li><p><strong>w</strong> (<em>int</em><em>, </em><em>required</em>) – The one-sided window length</p></li> |
| <li><p><strong>symmetric</strong> (<em>boolean</em><em>, </em><em>required</em>) – If false, each token will only attend to itself and the previous tokens.</p></li> |
| <li><p><strong>name</strong> (<em>string</em><em>, </em><em>optional.</em>) – Name of the resulting symbol.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result symbol.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../symbol.html#mxnet.symbol.Symbol" title="mxnet.symbol.Symbol">Symbol</a></p> |
| </dd> |
| </dl> |
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