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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/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</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/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</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/image-augmentation.html">Image Augmentation</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</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/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
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| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </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/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</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/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/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</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/image-augmentation.html">Image Augmentation</a></li> |
| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</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> |
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| <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> |
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| <div class="section" id="module-mxnet.util"> |
| <span id="mxnet-util"></span><h1>mxnet.util<a class="headerlink" href="#module-mxnet.util" title="Permalink to this headline">¶</a></h1> |
| <p>general utility functions</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.util.get_cuda_compute_capability" title="mxnet.util.get_cuda_compute_capability"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_cuda_compute_capability</span></code></a>(ctx)</p></td> |
| <td><p>Returns the cuda compute capability of the input <cite>ctx</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.getenv" title="mxnet.util.getenv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getenv</span></code></a>(name)</p></td> |
| <td><p>Get the setting of an environment variable from the C Runtime.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.is_np_array" title="mxnet.util.is_np_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_np_array</span></code></a>()</p></td> |
| <td><p>Checks whether the NumPy-array semantics is currently turned on.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.is_np_shape" title="mxnet.util.is_np_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_np_shape</span></code></a>()</p></td> |
| <td><p>Checks whether the NumPy shape semantics is currently turned on.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.np_array" title="mxnet.util.np_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_array</span></code></a>([active])</p></td> |
| <td><p>Returns an activated/deactivated NumPy-array scope to be used in ‘with’ statement and captures code that needs the NumPy-array semantics.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.np_shape" title="mxnet.util.np_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_shape</span></code></a>([active])</p></td> |
| <td><p>Returns an activated/deactivated NumPy shape scope to be used in ‘with’ statement and captures code that needs the NumPy shape semantics, i.e.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.np_ufunc_legal_option" title="mxnet.util.np_ufunc_legal_option"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_ufunc_legal_option</span></code></a>(key, value)</p></td> |
| <td><p>Checking if ufunc arguments are legal inputs</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.reset_np" title="mxnet.util.reset_np"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_np</span></code></a>()</p></td> |
| <td><p>Deactivate NumPy shape and array semantics at the same time.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.set_module" title="mxnet.util.set_module"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_module</span></code></a>(module)</p></td> |
| <td><p>Decorator for overriding __module__ on a function or class.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.set_np" title="mxnet.util.set_np"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_np</span></code></a>([shape, array])</p></td> |
| <td><p>Setting NumPy shape and array semantics at the same time.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.set_np_shape" title="mxnet.util.set_np_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_np_shape</span></code></a>(active)</p></td> |
| <td><p>Turns on/off NumPy shape semantics, in which <cite>()</cite> represents the shape of scalar tensors, and tuples with <cite>0</cite> elements, for example, <cite>(0,)</cite>, <cite>(1, 0, 2)</cite>, represent the shapes of zero-size tensors.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.setenv" title="mxnet.util.setenv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setenv</span></code></a>(name, value)</p></td> |
| <td><p>Set an environment variable in the C Runtime.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.use_np" title="mxnet.util.use_np"><code class="xref py py-obj docutils literal notranslate"><span class="pre">use_np</span></code></a>(func)</p></td> |
| <td><p>A convenience decorator for wrapping user provided functions and classes in the scope of both NumPy-shape and NumPy-array semantics, which means that (1) empty tuples <cite>()</cite> and tuples with zeros, such as <cite>(0, 1)</cite>, <cite>(1, 0, 2)</cite>, will be treated as scalar tensors’ shapes and zero-size tensors’ shapes in shape inference functions of operators, instead of as unknown in legacy mode; (2) ndarrays of type <cite>mxnet.numpy.ndarray</cite> should be created instead of <cite>mx.nd.NDArray</cite>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.use_np_array" title="mxnet.util.use_np_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">use_np_array</span></code></a>(func)</p></td> |
| <td><p>A decorator wrapping Gluon <cite>Block`s and all its methods, properties, and static functions with the semantics of NumPy-array, which means that where ndarrays are created, `mxnet.numpy.ndarray`s should be created, instead of legacy ndarrays of type `mx.nd.NDArray</cite>.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.use_np_shape" title="mxnet.util.use_np_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">use_np_shape</span></code></a>(func)</p></td> |
| <td><p>A decorator wrapping a function or class with activated NumPy-shape semantics.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.util.wrap_np_binary_func" title="mxnet.util.wrap_np_binary_func"><code class="xref py py-obj docutils literal notranslate"><span class="pre">wrap_np_binary_func</span></code></a>(func)</p></td> |
| <td><p>A convenience decorator for wrapping numpy-compatible binary ufuncs to provide uniform error handling.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.util.wrap_np_unary_func" title="mxnet.util.wrap_np_unary_func"><code class="xref py py-obj docutils literal notranslate"><span class="pre">wrap_np_unary_func</span></code></a>(func)</p></td> |
| <td><p>A convenience decorator for wrapping numpy-compatible unary ufuncs to provide uniform error handling.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="function"> |
| <dt id="mxnet.util.get_cuda_compute_capability"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">get_cuda_compute_capability</code><span class="sig-paren">(</span><em class="sig-param">ctx</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#get_cuda_compute_capability"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.get_cuda_compute_capability" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the cuda compute capability of the input <cite>ctx</cite>.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>ctx</strong> (<a class="reference internal" href="../context/index.html#mxnet.context.Context" title="mxnet.context.Context"><em>Context</em></a>) – GPU context whose corresponding cuda compute capability is to be retrieved.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>cuda_compute_capability</strong> – CUDA compute capability. For example, it returns 70 for CUDA arch equal to <cite>sm_70</cite>.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>int</p> |
| </dd> |
| </dl> |
| <p class="rubric">References</p> |
| <p><a class="reference external" href="https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549#file-cuda_check-py">https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549#file-cuda_check-py</a></p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.getenv"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">getenv</code><span class="sig-paren">(</span><em class="sig-param">name</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#getenv"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.getenv" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get the setting of an environment variable from the C Runtime.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>name</strong> (<em>string type</em>) – The environment variable name</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>value</strong> – The value of the environment variable, or None if not set</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>string</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.is_np_array"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">is_np_array</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#is_np_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.is_np_array" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Checks whether the NumPy-array semantics is currently turned on. |
| This is currently used in Gluon for checking whether an array of type <cite>mxnet.numpy.ndarray</cite> |
| or <cite>mx.nd.NDArray</cite> should be created. For example, at the time when a parameter |
| is created in a <cite>Block</cite>, an <cite>mxnet.numpy.ndarray</cite> is created if this returns true; else |
| an <cite>mx.nd.NDArray</cite> is created.</p> |
| <p>Normally, users are not recommended to use this API directly unless you known exactly |
| what is going on under the hood.</p> |
| <p>Please note that this is designed as an infrastructure for the incoming |
| MXNet-NumPy operators. Legacy operators registered in the modules |
| <cite>mx.nd</cite> and <cite>mx.sym</cite> are not guaranteed to behave like their counterparts |
| in NumPy within this semantics.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p></p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>A bool value indicating whether the NumPy-array semantics is currently on.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.is_np_shape"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">is_np_shape</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#is_np_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.is_np_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Checks whether the NumPy shape semantics is currently turned on. |
| In NumPy shape semantics, <cite>()</cite> represents the shape of scalar tensors, |
| and tuples with <cite>0</cite> elements, for example, <cite>(0,)</cite>, <cite>(1, 0, 2)</cite>, represent |
| the shapes of zero-size tensors. This is turned off by default for keeping |
| backward compatibility.</p> |
| <p>In the NumPy shape semantics, <cite>-1</cite> indicates an unknown size. For example, |
| <cite>(-1, 2, 2)</cite> means that the size of the first dimension is unknown. Its size |
| may be inferred during shape inference.</p> |
| <p>Please note that this is designed as an infrastructure for the incoming |
| MXNet-NumPy operators. Legacy operators registered in the modules |
| <cite>mx.nd</cite> and <cite>mx.sym</cite> are not guaranteed to behave like their counterparts |
| in NumPy within this semantics.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p></p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>A bool value indicating whether the NumPy shape semantics is currently on.</p> |
| </dd> |
| </dl> |
| <p class="rubric">Example</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="gp">>>> </span><span class="n">prev_state</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">set_np_shape</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">prev_state</span><span class="p">)</span> |
| <span class="go">False</span> |
| <span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">is_np_shape</span><span class="p">())</span> |
| <span class="go">True</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.np_array"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">np_array</code><span class="sig-paren">(</span><em class="sig-param">active=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#np_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.np_array" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns an activated/deactivated NumPy-array scope to be used in ‘with’ statement |
| and captures code that needs the NumPy-array semantics.</p> |
| <p>Currently, this is used in Gluon to enforce array creation in <cite>Block`s as type |
| `mxnet.numpy.ndarray</cite>, instead of <cite>mx.nd.NDArray</cite>.</p> |
| <p>It is recommended to use the decorator <cite>use_np_array</cite> to decorate the classes |
| that need this semantics, instead of using this function in a <cite>with</cite> statement |
| unless you know exactly what has been scoped by this semantics.</p> |
| <p>Please note that this is designed as an infrastructure for the incoming |
| MXNet-NumPy operators. Legacy operators registered in the modules |
| <cite>mx.nd</cite> and <cite>mx.sym</cite> are not guaranteed to behave like their counterparts |
| in NumPy even within this scope.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>active</strong> (<em>bool</em>) – Indicates whether to activate NumPy-array semantics.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>A scope object for wrapping the code w/ or w/o NumPy-shape semantics.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>_NumpyShapeScope</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.np_shape"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">np_shape</code><span class="sig-paren">(</span><em class="sig-param">active=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#np_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.np_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns an activated/deactivated NumPy shape scope to be used in ‘with’ statement |
| and captures code that needs the NumPy shape semantics, i.e. support of scalar and |
| zero-size tensors.</p> |
| <p>Please note that this is designed as an infrastructure for the incoming |
| MXNet-NumPy operators. Legacy operators registered in the modules |
| <cite>mx.nd</cite> and <cite>mx.sym</cite> are not guaranteed to behave like their counterparts |
| in NumPy even within this scope.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>active</strong> (<em>bool</em>) – Indicates whether to activate NumPy-shape semantics.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><ul> |
| <li><p><em>_NumpyShapeScope</em> – A scope object for wrapping the code w/ or w/o NumPy-shape semantics.</p></li> |
| <li><p><em>Example::</em> –</p> |
| <dl> |
| <dt>with mx.np_shape(active=True):</dt><dd><p># A scalar tensor’s shape is <cite>()</cite>, whose <cite>ndim</cite> is <cite>0</cite>. |
| scalar = mx.nd.ones(shape=()) |
| assert scalar.shape == ()</p> |
| <p># If NumPy shape semantics is enabled, 0 in a shape means that |
| # dimension contains zero elements. |
| data = mx.sym.var(“data”, shape=(0, 2, 3)) |
| ret = mx.sym.sin(data) |
| arg_shapes, out_shapes, _ = ret.infer_shape() |
| assert arg_shapes[0] == (0, 2, 3) |
| assert out_shapes[0] == (0, 2, 3)</p> |
| <p># -1 means unknown shape dimension size in the new NumPy shape definition |
| data = mx.sym.var(“data”, shape=(-1, 2, 3)) |
| ret = mx.sym.sin(data) |
| arg_shapes, out_shapes, _ = ret.infer_shape_partial() |
| assert arg_shapes[0] == (-1, 2, 3) |
| assert out_shapes[0] == (-1, 2, 3)</p> |
| <p># When a shape is completely unknown when NumPy shape semantics is on, it is |
| # represented as <cite>None</cite> in Python. |
| data = mx.sym.var(“data”) |
| ret = mx.sym.sin(data) |
| arg_shapes, out_shapes, _ = ret.infer_shape_partial() |
| assert arg_shapes[0] is None |
| assert out_shapes[0] is None</p> |
| </dd> |
| <dt>with mx.np_shape(active=False):</dt><dd><p># 0 means unknown shape dimension size in the legacy shape definition. |
| data = mx.sym.var(“data”, shape=(0, 2, 3)) |
| ret = mx.sym.sin(data) |
| arg_shapes, out_shapes, _ = ret.infer_shape_partial() |
| assert arg_shapes[0] == (0, 2, 3) |
| assert out_shapes[0] == (0, 2, 3)</p> |
| <p># When a shape is completely unknown in the legacy mode (default), its ndim is |
| # equal to 0 and it is represented as <cite>()</cite> in Python. |
| data = mx.sym.var(“data”) |
| ret = mx.sym.sin(data) |
| arg_shapes, out_shapes, _ = ret.infer_shape_partial() |
| assert arg_shapes[0] == () |
| assert out_shapes[0] == ()</p> |
| </dd> |
| </dl> |
| </li> |
| </ul> |
| </p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.np_ufunc_legal_option"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">np_ufunc_legal_option</code><span class="sig-paren">(</span><em class="sig-param">key</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#np_ufunc_legal_option"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.np_ufunc_legal_option" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Checking if ufunc arguments are legal inputs</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>key</strong> (<em>string</em>) – the key of the ufunc argument.</p></li> |
| <li><p><strong>value</strong> (<em>string</em>) – the value of the ufunc argument.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p><strong>legal</strong> – Whether or not the argument is a legal one. True when the key is one of the ufunc |
| arguments and value is an allowed value. False when the key is not one of the ufunc |
| arugments or the value is not an allowed value even when the key is a legal one.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>boolean</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.reset_np"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">reset_np</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#reset_np"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.reset_np" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Deactivate NumPy shape and array semantics at the same time.</p> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.set_module"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">set_module</code><span class="sig-paren">(</span><em class="sig-param">module</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#set_module"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.set_module" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Decorator for overriding __module__ on a function or class.</p> |
| <p>Example usage:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@set_module</span><span class="p">(</span><span class="s1">'mxnet.numpy'</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">example</span><span class="p">():</span> |
| <span class="k">pass</span> |
| |
| <span class="k">assert</span> <span class="n">example</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">==</span> <span class="s1">'numpy'</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.set_np"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">set_np</code><span class="sig-paren">(</span><em class="sig-param">shape=True</em>, <em class="sig-param">array=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#set_np"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.set_np" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Setting NumPy shape and array semantics at the same time. |
| It is required to keep NumPy shape semantics active while activating NumPy array semantics. |
| Deactivating NumPy shape semantics while NumPy array semantics is still active is not allowed. |
| It is highly recommended to set these two flags to <cite>True</cite> at the same time to fully enable |
| NumPy-like behaviors. Please refer to the Examples section for a better understanding.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>shape</strong> (<em>bool</em>) – A boolean value indicating whether the NumPy-shape semantics should be turned on or off. |
| When this flag is set to <cite>True</cite>, zero-size and zero-dim shapes are all valid shapes in |
| shape inference process, instead of treated as unknown shapes in legacy mode.</p></li> |
| <li><p><strong>array</strong> (<em>bool</em>) – A boolean value indicating whether the NumPy-array semantics should be turned on or off. |
| When this flag is set to <cite>True</cite>, it enables Gluon code flow to use or generate <cite>mxnet.numpy.ndarray`s |
| instead of `mxnet.ndarray.NDArray</cite>. For example, a <cite>Block</cite> would create parameters of type |
| <cite>mxnet.numpy.ndarray</cite>.</p></li> |
| </ul> |
| </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="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| </pre></div> |
| </div> |
| <p>Creating zero-dim ndarray in legacy mode would fail at shape inference.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </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="n">shape</span><span class="o">=</span><span class="p">())</span> |
| <span class="go">mxnet.base.MXNetError: Operator _ones inferring shapes failed.</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </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="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">3</span><span class="p">))</span> |
| <span class="go">mxnet.base.MXNetError: Operator _ones inferring shapes failed.</span> |
| </pre></div> |
| </div> |
| <p>In legacy mode, Gluon layers would create parameters and outputs of type <cite>mx.nd.NDArray</cite>.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">nn</span> |
| <span class="gp">>>> </span><span class="n">dense</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">dense</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">dense</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">ones</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="mi">2</span><span class="p">)))</span> |
| <span class="go">[[0.01983214 0.07832371]</span> |
| <span class="go"> [0.01983214 0.07832371]</span> |
| <span class="go"> [0.01983214 0.07832371]]</span> |
| <span class="go"><NDArray 3x2 @cpu(0)></span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">[</span><span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">dense</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="p">()]</span> |
| <span class="go">[</span> |
| <span class="go">[[0.0068339 0.01299825]</span> |
| <span class="go"> [0.0301265 0.04819721]]</span> |
| <span class="go"><NDArray 2x2 @cpu(0)>,</span> |
| <span class="go">[0. 0.]</span> |
| <span class="go"><NDArray 2 @cpu(0)>]</span> |
| </pre></div> |
| </div> |
| <p>When the <cite>shape</cite> flag is <cite>True</cite>, both shape inferences are successful.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">np</span><span class="p">,</span> <span class="n">npx</span> |
| <span class="gp">>>> </span><span class="n">npx</span><span class="o">.</span><span class="n">set_np</span><span class="p">()</span> <span class="c1"># this is required to activate NumPy-like behaviors</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">())</span> |
| <span class="go">array(1.)</span> |
| <span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">ones</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">3</span><span class="p">))</span> |
| <span class="go">array([], shape=(2, 0, 3))</span> |
| </pre></div> |
| </div> |
| <p>When the <cite>array</cite> flag is <cite>True</cite>, Gluon layers would create parameters and outputs of type <cite>mx.np.ndarray</cite>.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">dense</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">dense</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">dense</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</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="mi">2</span><span class="p">)))</span> |
| <span class="go">array([[0.01983214, 0.07832371],</span> |
| <span class="go"> [0.01983214, 0.07832371],</span> |
| <span class="go"> [0.01983214, 0.07832371]])</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">[</span><span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">dense</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="p">()]</span> |
| <span class="go">[array([[0.0068339 , 0.01299825],</span> |
| <span class="go"> [0.0301265 , 0.04819721]]), array([0., 0.])]</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.set_np_shape"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">set_np_shape</code><span class="sig-paren">(</span><em class="sig-param">active</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#set_np_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.set_np_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Turns on/off NumPy shape semantics, in which <cite>()</cite> represents the shape of scalar tensors, |
| and tuples with <cite>0</cite> elements, for example, <cite>(0,)</cite>, <cite>(1, 0, 2)</cite>, represent the shapes |
| of zero-size tensors. This is turned off by default for keeping backward compatibility.</p> |
| <p>Please note that this is designed as an infrastructure for the incoming |
| MXNet-NumPy operators. Legacy operators registered in the modules |
| <cite>mx.nd</cite> and <cite>mx.sym</cite> are not guaranteed to behave like their counterparts |
| in NumPy within this semantics.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>active</strong> (<em>bool</em>) – Indicates whether to turn on/off NumPy shape semantics.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p></p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>A bool value indicating the previous state of NumPy shape semantics.</p> |
| </dd> |
| </dl> |
| <p class="rubric">Example</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="gp">>>> </span><span class="n">prev_state</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">set_np_shape</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">prev_state</span><span class="p">)</span> |
| <span class="go">False</span> |
| <span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">is_np_shape</span><span class="p">())</span> |
| <span class="go">True</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.setenv"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">setenv</code><span class="sig-paren">(</span><em class="sig-param">name</em>, <em class="sig-param">value</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#setenv"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.setenv" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set an environment variable in the C Runtime.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>name</strong> (<em>string type</em>) – The environment variable name</p></li> |
| <li><p><strong>value</strong> (<em>string type</em>) – The desired value to set the environment value to</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.use_np"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">use_np</code><span class="sig-paren">(</span><em class="sig-param">func</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#use_np"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.use_np" title="Permalink to this definition">¶</a></dt> |
| <dd><p>A convenience decorator for wrapping user provided functions and classes in the scope of |
| both NumPy-shape and NumPy-array semantics, which means that (1) empty tuples <cite>()</cite> and tuples |
| with zeros, such as <cite>(0, 1)</cite>, <cite>(1, 0, 2)</cite>, will be treated as scalar tensors’ shapes and |
| zero-size tensors’ shapes in shape inference functions of operators, instead of as unknown |
| in legacy mode; (2) ndarrays of type <cite>mxnet.numpy.ndarray</cite> should be created instead of |
| <cite>mx.nd.NDArray</cite>.</p> |
| <dl> |
| <dt>Example::</dt><dd><p>import mxnet as mx |
| from mxnet import gluon, np</p> |
| <dl class="simple"> |
| <dt>class TestHybridBlock1(gluon.HybridBlock):</dt><dd><dl class="simple"> |
| <dt>def __init__(self):</dt><dd><p>super(TestHybridBlock1, self).__init__() |
| self.w = self.params.get(‘w’, shape=(2, 2))</p> |
| </dd> |
| <dt>def hybrid_forward(self, F, x, w):</dt><dd><p>return F.dot(x, w) + F.ones((1,))</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p>x = mx.nd.ones((2, 2)) |
| net1 = TestHybridBlock1() |
| net1.initialize() |
| out = net1.forward(x) |
| for _, v in net1.collect_params().items():</p> |
| <blockquote> |
| <div><p>assert type(v.data()) is mx.nd.NDArray</p> |
| </div></blockquote> |
| <p>assert type(out) is mx.nd.NDArray</p> |
| <p>@np.use_np |
| class TestHybridBlock2(gluon.HybridBlock):</p> |
| <blockquote> |
| <div><dl class="simple"> |
| <dt>def __init__(self):</dt><dd><p>super(TestHybridBlock2, self).__init__() |
| self.w = self.params.get(‘w’, shape=(2, 2))</p> |
| </dd> |
| <dt>def hybrid_forward(self, F, x, w):</dt><dd><p>return F.np.dot(x, w) + F.np.ones(())</p> |
| </dd> |
| </dl> |
| </div></blockquote> |
| <p>x = np.ones((2, 2)) |
| net2 = TestHybridBlock2() |
| net2.initialize() |
| out = net2.forward(x) |
| for _, v in net2.collect_params().items():</p> |
| <blockquote> |
| <div><p>print(type(v.data())) |
| assert type(v.data()) is np.ndarray</p> |
| </div></blockquote> |
| <p>assert type(out) is np.ndarray</p> |
| </dd> |
| </dl> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>func</strong> (<em>a user-provided callable function</em><em> or </em><em>class to be scoped by the</em>) – </p></li> |
| <li><p><strong>and NumPy-array semantics.</strong> (<em>NumPy-shape</em>) – </p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>A function or class wrapped in the Numpy-shape and NumPy-array scope.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../../autograd/index.html#mxnet.autograd.Function" title="mxnet.autograd.Function">Function</a> or class</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.use_np_array"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">use_np_array</code><span class="sig-paren">(</span><em class="sig-param">func</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#use_np_array"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.use_np_array" title="Permalink to this definition">¶</a></dt> |
| <dd><p>A decorator wrapping Gluon <cite>Block`s and all its methods, properties, and static functions |
| with the semantics of NumPy-array, which means that where ndarrays are created, |
| `mxnet.numpy.ndarray`s should be created, instead of legacy ndarrays of type `mx.nd.NDArray</cite>. |
| For example, at the time when a parameter is created in a <cite>Block</cite>, an <cite>mxnet.numpy.ndarray</cite> |
| is created if it’s decorated with this decorator.</p> |
| <dl> |
| <dt>Example::</dt><dd><p>import mxnet as mx |
| from mxnet import gluon, np</p> |
| <dl class="simple"> |
| <dt>class TestHybridBlock1(gluon.HybridBlock):</dt><dd><dl class="simple"> |
| <dt>def __init__(self):</dt><dd><p>super(TestHybridBlock1, self).__init__() |
| self.w = self.params.get(‘w’, shape=(2, 2))</p> |
| </dd> |
| <dt>def hybrid_forward(self, F, x, w):</dt><dd><p>return F.dot(x, w)</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p>x = mx.nd.ones((2, 2)) |
| net1 = TestHybridBlock1() |
| net1.initialize() |
| out = net1.forward(x) |
| for _, v in net1.collect_params().items():</p> |
| <blockquote> |
| <div><p>assert type(v.data()) is mx.nd.NDArray</p> |
| </div></blockquote> |
| <p>assert type(out) is mx.nd.NDArray</p> |
| <p>@np.use_np_array |
| class TestHybridBlock2(gluon.HybridBlock):</p> |
| <blockquote> |
| <div><dl class="simple"> |
| <dt>def __init__(self):</dt><dd><p>super(TestHybridBlock2, self).__init__() |
| self.w = self.params.get(‘w’, shape=(2, 2))</p> |
| </dd> |
| <dt>def hybrid_forward(self, F, x, w):</dt><dd><p>return F.np.dot(x, w)</p> |
| </dd> |
| </dl> |
| </div></blockquote> |
| <p>x = np.ones((2, 2)) |
| net2 = TestHybridBlock2() |
| net2.initialize() |
| out = net2.forward(x) |
| for _, v in net2.collect_params().items():</p> |
| <blockquote> |
| <div><p>print(type(v.data())) |
| assert type(v.data()) is np.ndarray</p> |
| </div></blockquote> |
| <p>assert type(out) is np.ndarray</p> |
| </dd> |
| </dl> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>func</strong> (<em>a user-provided callable function</em><em> or </em><em>class to be scoped by the NumPy-array semantics.</em>) – </p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>A function or class wrapped in the NumPy-array scope.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../../autograd/index.html#mxnet.autograd.Function" title="mxnet.autograd.Function">Function</a> or class</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.use_np_shape"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">use_np_shape</code><span class="sig-paren">(</span><em class="sig-param">func</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#use_np_shape"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.use_np_shape" title="Permalink to this definition">¶</a></dt> |
| <dd><p>A decorator wrapping a function or class with activated NumPy-shape semantics. |
| When <cite>func</cite> is a function, this ensures that the execution of the function is scoped with NumPy |
| shape semantics, such as the support for zero-dim and zero size tensors. When |
| <cite>func</cite> is a class, it ensures that all the methods, static functions, and properties |
| of the class are executed with the NumPy shape semantics.</p> |
| <dl> |
| <dt>Example::</dt><dd><p>import mxnet as mx |
| @mx.use_np_shape |
| def scalar_one():</p> |
| <blockquote> |
| <div><p>return mx.nd.ones(())</p> |
| </div></blockquote> |
| <p>print(scalar_one())</p> |
| <p>@np.use_np_shape |
| class ScalarTensor(object):</p> |
| <blockquote> |
| <div><dl> |
| <dt>def __init__(self, val=None):</dt><dd><dl class="simple"> |
| <dt>if val is None:</dt><dd><p>val = ScalarTensor.random().value</p> |
| </dd> |
| </dl> |
| <p>self._scalar = mx.nd.ones(()) * val</p> |
| </dd> |
| <dt>def __repr__(self):</dt><dd><p>print(“Is __repr__ in np_shape semantics? {}!”.format(str(np.is_np_shape()))) |
| return str(self._scalar.asnumpy())</p> |
| </dd> |
| </dl> |
| <p>@staticmethod |
| def random():</p> |
| <blockquote> |
| <div><p>val = mx.nd.random.uniform().asnumpy().item() |
| return ScalarTensor(val)</p> |
| </div></blockquote> |
| <p>@property |
| def value(self):</p> |
| <blockquote> |
| <div><p>print(“Is value property in np_shape semantics? {}!”.format(str(np.is_np_shape()))) |
| return self._scalar.asnumpy().item()</p> |
| </div></blockquote> |
| </div></blockquote> |
| <p>print(“Is global scope of np_shape activated? {}!”.format(str(np.is_np_shape()))) |
| scalar_tensor = ScalarTensor() |
| print(scalar_tensor)</p> |
| </dd> |
| </dl> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>func</strong> (<em>a user-provided callable function</em><em> or </em><em>class to be scoped by the NumPy-shape semantics.</em>) – </p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>A function or class wrapped in the NumPy-shape scope.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../../autograd/index.html#mxnet.autograd.Function" title="mxnet.autograd.Function">Function</a> or class</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.wrap_np_binary_func"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">wrap_np_binary_func</code><span class="sig-paren">(</span><em class="sig-param">func</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#wrap_np_binary_func"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.wrap_np_binary_func" title="Permalink to this definition">¶</a></dt> |
| <dd><p>A convenience decorator for wrapping numpy-compatible binary ufuncs to provide uniform |
| error handling.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>func</strong> (<em>a numpy-compatible binary function to be wrapped for better error handling.</em>) – </p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>A function wrapped with proper error handling.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../../autograd/index.html#mxnet.autograd.Function" title="mxnet.autograd.Function">Function</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.util.wrap_np_unary_func"> |
| <code class="sig-prename descclassname">mxnet.util.</code><code class="sig-name descname">wrap_np_unary_func</code><span class="sig-paren">(</span><em class="sig-param">func</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/util.html#wrap_np_unary_func"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.util.wrap_np_unary_func" title="Permalink to this definition">¶</a></dt> |
| <dd><p>A convenience decorator for wrapping numpy-compatible unary ufuncs to provide uniform |
| error handling.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>func</strong> (<em>a numpy-compatible unary function to be wrapped for better error handling.</em>) – </p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>A function wrapped with proper error handling.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="../../autograd/index.html#mxnet.autograd.Function" title="mxnet.autograd.Function">Function</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </div> |
| |
| |
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