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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <ul> |
| <li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <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> |
| </li> |
| </ul> |
| </li> |
| <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-l3"><a class="reference internal" href="../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
| </ul> |
| </li> |
| </ul> |
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| <li class="toctree-l1"><a class="reference internal" href="../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../api/np/index.html">mxnet.np</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/arrays.indexing.html">Indexing</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.meshgrid.html">mxnet.np.meshgrid</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.tril.html">mxnet.np.tril</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanprod.html">mxnet.np.nanprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cumprod.html">mxnet.np.cumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nancumprod.html">mxnet.np.nancumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cross.html">mxnet.np.cross</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.exp.html">mxnet.np.exp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.log.html">mxnet.np.log</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.log10.html">mxnet.np.log10</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.log2.html">mxnet.np.log2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.log1p.html">mxnet.np.log1p</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.logaddexp.html">mxnet.np.logaddexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.i0.html">mxnet.np.i0</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ldexp.html">mxnet.np.ldexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.signbit.html">mxnet.np.signbit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.copysign.html">mxnet.np.copysign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.lcm.html">mxnet.np.lcm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.gcd.html">mxnet.np.gcd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.add.html">mxnet.np.add</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.reciprocal.html">mxnet.np.reciprocal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.negative.html">mxnet.np.negative</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.divide.html">mxnet.np.divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.power.html">mxnet.np.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.subtract.html">mxnet.np.subtract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.mod.html">mxnet.np.mod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.positive.html">mxnet.np.positive</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.float_power.html">mxnet.np.float_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fmod.html">mxnet.np.fmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.divmod.html">mxnet.np.divmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.floor_divide.html">mxnet.np.floor_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.absolute.html">mxnet.np.absolute</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fabs.html">mxnet.np.fabs</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.heaviside.html">mxnet.np.heaviside</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fmax.html">mxnet.np.fmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fmin.html">mxnet.np.fmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nan_to_num.html">mxnet.np.nan_to_num</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.interp.html">mxnet.np.interp</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/random/index.html">np.random</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.choice.html">mxnet.np.random.choice</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.shuffle.html">mxnet.np.random.shuffle</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.normal.html">mxnet.np.random.normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.uniform.html">mxnet.np.random.uniform</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.rand.html">mxnet.np.random.rand</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.randint.html">mxnet.np.random.randint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.beta.html">mxnet.np.random.beta</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.exponential.html">mxnet.np.random.exponential</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.f.html">mxnet.np.random.f</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.gamma.html">mxnet.np.random.gamma</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.gumbel.html">mxnet.np.random.gumbel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.laplace.html">mxnet.np.random.laplace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.logistic.html">mxnet.np.random.logistic</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.lognormal.html">mxnet.np.random.lognormal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.multinomial.html">mxnet.np.random.multinomial</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.multivariate_normal.html">mxnet.np.random.multivariate_normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.pareto.html">mxnet.np.random.pareto</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.power.html">mxnet.np.random.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.rayleigh.html">mxnet.np.random.rayleigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.weibull.html">mxnet.np.random.weibull</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ndarray.sort.html">mxnet.np.ndarray.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sort.html">mxnet.np.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.lexsort.html">mxnet.np.lexsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.argsort.html">mxnet.np.argsort</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanargmax.html">mxnet.np.nanargmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanargmin.html">mxnet.np.nanargmin</a></li> |
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| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.statistics.html">Statistics</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanmax.html">mxnet.np.nanmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ptp.html">mxnet.np.ptp</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanquantile.html">mxnet.np.nanquantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.average.html">mxnet.np.average</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanmedian.html">mxnet.np.nanmedian</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanstd.html">mxnet.np.nanstd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanvar.html">mxnet.np.nanvar</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.corrcoef.html">mxnet.np.corrcoef</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.correlate.html">mxnet.np.correlate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cov.html">mxnet.np.cov</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.histogram.html">mxnet.np.histogram</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.histogram2d.html">mxnet.np.histogram2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.histogramdd.html">mxnet.np.histogramdd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.bincount.html">mxnet.np.bincount</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.histogram_bin_edges.html">mxnet.np.histogram_bin_edges</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.digitize.html">mxnet.np.digitize</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../api/npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.reset_np.html">mxnet.npx.reset_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.cpu.html">mxnet.npx.cpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.cpu_pinned.html">mxnet.npx.cpu_pinned</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.current_device.html">mxnet.npx.current_device</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.num_gpus.html">mxnet.npx.num_gpus</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.convolution.html">mxnet.npx.convolution</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.fully_connected.html">mxnet.npx.fully_connected</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../api/npx/generated/mxnet.npx.leaky_relu.html">mxnet.npx.leaky_relu</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <nav class="mdl-navigation"> |
| <ul> |
| <li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </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> |
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| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
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| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
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| <li class="toctree-l2"><a class="reference internal" href="../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../tutorials/extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
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| <li class="toctree-l1"><a class="reference internal" href="../../api/index.html">Python API</a><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/arrays.indexing.html">Indexing</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
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| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanprod.html">mxnet.np.nanprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cumprod.html">mxnet.np.cumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nancumprod.html">mxnet.np.nancumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cross.html">mxnet.np.cross</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.exp.html">mxnet.np.exp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.log.html">mxnet.np.log</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.log10.html">mxnet.np.log10</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.log2.html">mxnet.np.log2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.log1p.html">mxnet.np.log1p</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.logaddexp.html">mxnet.np.logaddexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.i0.html">mxnet.np.i0</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ldexp.html">mxnet.np.ldexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.signbit.html">mxnet.np.signbit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.copysign.html">mxnet.np.copysign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.lcm.html">mxnet.np.lcm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.gcd.html">mxnet.np.gcd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.add.html">mxnet.np.add</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.reciprocal.html">mxnet.np.reciprocal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.negative.html">mxnet.np.negative</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.divide.html">mxnet.np.divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.power.html">mxnet.np.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.subtract.html">mxnet.np.subtract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.mod.html">mxnet.np.mod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.positive.html">mxnet.np.positive</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.float_power.html">mxnet.np.float_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fmod.html">mxnet.np.fmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.divmod.html">mxnet.np.divmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.floor_divide.html">mxnet.np.floor_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.absolute.html">mxnet.np.absolute</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fabs.html">mxnet.np.fabs</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.heaviside.html">mxnet.np.heaviside</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fmax.html">mxnet.np.fmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.fmin.html">mxnet.np.fmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nan_to_num.html">mxnet.np.nan_to_num</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.interp.html">mxnet.np.interp</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/random/index.html">np.random</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.choice.html">mxnet.np.random.choice</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.shuffle.html">mxnet.np.random.shuffle</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.normal.html">mxnet.np.random.normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.uniform.html">mxnet.np.random.uniform</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.rand.html">mxnet.np.random.rand</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.randint.html">mxnet.np.random.randint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.beta.html">mxnet.np.random.beta</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.exponential.html">mxnet.np.random.exponential</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.f.html">mxnet.np.random.f</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.gamma.html">mxnet.np.random.gamma</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.gumbel.html">mxnet.np.random.gumbel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.laplace.html">mxnet.np.random.laplace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.logistic.html">mxnet.np.random.logistic</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.lognormal.html">mxnet.np.random.lognormal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.multinomial.html">mxnet.np.random.multinomial</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.multivariate_normal.html">mxnet.np.random.multivariate_normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.pareto.html">mxnet.np.random.pareto</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.power.html">mxnet.np.random.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.rayleigh.html">mxnet.np.random.rayleigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/random/generated/mxnet.np.random.weibull.html">mxnet.np.random.weibull</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ndarray.sort.html">mxnet.np.ndarray.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.sort.html">mxnet.np.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.lexsort.html">mxnet.np.lexsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.argsort.html">mxnet.np.argsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.msort.html">mxnet.np.msort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.partition.html">mxnet.np.partition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.argpartition.html">mxnet.np.argpartition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanargmax.html">mxnet.np.nanargmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanargmin.html">mxnet.np.nanargmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.argwhere.html">mxnet.np.argwhere</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nonzero.html">mxnet.np.nonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.flatnonzero.html">mxnet.np.flatnonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.where.html">mxnet.np.where</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.searchsorted.html">mxnet.np.searchsorted</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.extract.html">mxnet.np.extract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.count_nonzero.html">mxnet.np.count_nonzero</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../api/np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.amin.html">mxnet.np.amin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.amax.html">mxnet.np.amax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanmin.html">mxnet.np.nanmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanmax.html">mxnet.np.nanmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.ptp.html">mxnet.np.ptp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.percentile.html">mxnet.np.percentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanpercentile.html">mxnet.np.nanpercentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.quantile.html">mxnet.np.quantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanquantile.html">mxnet.np.nanquantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.average.html">mxnet.np.average</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanmedian.html">mxnet.np.nanmedian</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanstd.html">mxnet.np.nanstd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.nanvar.html">mxnet.np.nanvar</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.corrcoef.html">mxnet.np.corrcoef</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.correlate.html">mxnet.np.correlate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../api/np/generated/mxnet.np.cov.html">mxnet.np.cov</a></li> |
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| |
| <h1>Source code for mxnet.util</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| <span class="sd">"""general utility functions"""</span> |
| |
| <span class="kn">import</span> <span class="nn">ctypes</span> |
| <span class="kn">import</span> <span class="nn">functools</span> |
| <span class="kn">import</span> <span class="nn">inspect</span> |
| <span class="kn">import</span> <span class="nn">threading</span> |
| <span class="kn">import</span> <span class="nn">tempfile</span> |
| <span class="kn">import</span> <span class="nn">platform</span> |
| <span class="kn">from</span> <span class="nn">contextlib</span> <span class="kn">import</span> <span class="n">contextmanager</span> |
| |
| <span class="kn">from</span> <span class="nn">struct</span> <span class="kn">import</span> <span class="n">calcsize</span> |
| <span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="p">(</span><span class="n">_LIB</span><span class="p">,</span> <span class="n">check_call</span><span class="p">,</span> <span class="n">c_str</span><span class="p">,</span> <span class="n">py_str</span><span class="p">,</span> |
| <span class="n">numeric_types</span><span class="p">,</span> <span class="n">integer_types</span><span class="p">,</span> <span class="n">long</span><span class="p">,</span> |
| <span class="n">_MAX_VALUE_64_BIT_UNSIGNED_</span><span class="p">,</span> |
| <span class="n">_MAX_VALUE_64_BIT_SIGNED_</span><span class="p">,</span> |
| <span class="n">_MAX_VALUE_FLOAT32_REPRESENT_</span><span class="p">)</span> |
| |
| |
| <span class="n">_np_ufunc_default_kwargs</span> <span class="o">=</span> <span class="p">{</span> |
| <span class="s1">'where'</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> |
| <span class="s1">'casting'</span><span class="p">:</span> <span class="s1">'same_kind'</span><span class="p">,</span> |
| <span class="s1">'order'</span><span class="p">:</span> <span class="s1">'K'</span><span class="p">,</span> |
| <span class="s1">'dtype'</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="s1">'subok'</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> |
| <span class="p">}</span> |
| |
| <span class="n">_set_np_shape_logged</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="n">_set_np_array_logged</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="n">_set_np_default_dtype_logged</span> <span class="o">=</span> <span class="kc">False</span> |
| |
| |
| <span class="k">def</span> <span class="nf">get_gpu_count</span><span class="p">():</span> |
| <span class="n">size</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXGetGPUCount</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">size</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">size</span><span class="o">.</span><span class="n">value</span> |
| |
| |
| <span class="k">def</span> <span class="nf">get_gpu_memory</span><span class="p">(</span><span class="n">gpu_dev_id</span><span class="p">):</span> |
| <span class="n">free_mem</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint64</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="n">total_mem</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint64</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXGetGPUMemoryInformation64</span><span class="p">(</span><span class="n">gpu_dev_id</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">free_mem</span><span class="p">),</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">total_mem</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">free_mem</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">total_mem</span><span class="o">.</span><span class="n">value</span> |
| |
| |
| <div class="viewcode-block" id="set_np_shape"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.set_np_shape">[docs]</a><span class="k">def</span> <span class="nf">set_np_shape</span><span class="p">(</span><span class="n">active</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Turns on/off NumPy shape semantics, in which `()` represents the shape of scalar tensors,</span> |
| <span class="sd"> and tuples with `0` elements, for example, `(0,)`, `(1, 0, 2)`, represent the shapes</span> |
| <span class="sd"> of zero-size tensors. This is turned off by default for keeping backward compatibility.</span> |
| |
| <span class="sd"> Please note that this is designed as an infrastructure for the incoming</span> |
| <span class="sd"> MXNet-NumPy operators. Legacy operators registered in the modules</span> |
| <span class="sd"> `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts</span> |
| <span class="sd"> in NumPy within this semantics.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> active : bool</span> |
| <span class="sd"> Indicates whether to turn on/off NumPy shape semantics.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> A bool value indicating the previous state of NumPy shape semantics.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> import mxnet as mx</span> |
| <span class="sd"> >>> prev_state = mx.set_np_shape(True)</span> |
| <span class="sd"> >>> print(prev_state)</span> |
| <span class="sd"> False</span> |
| <span class="sd"> >>> print(mx.is_np_shape())</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">global</span> <span class="n">_set_np_shape_logged</span> |
| <span class="k">if</span> <span class="n">active</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">_set_np_shape_logged</span><span class="p">:</span> |
| <span class="kn">import</span> <span class="nn">logging</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'NumPy-shape semantics has been activated in your code. '</span> |
| <span class="s1">'This is required for creating and manipulating scalar and zero-size '</span> |
| <span class="s1">'tensors, which were not supported in MXNet before, as in the official '</span> |
| <span class="s1">'NumPy library. Please DO NOT manually deactivate this semantics while '</span> |
| <span class="s1">'using `mxnet.numpy` and `mxnet.numpy_extension` modules.'</span><span class="p">)</span> |
| <span class="n">_set_np_shape_logged</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">elif</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Deactivating NumPy shape semantics while NumPy array semantics is still'</span> |
| <span class="s1">' active is not allowed. Please consider calling `npx.reset_np()` to'</span> |
| <span class="s1">' deactivate both of them.'</span><span class="p">)</span> |
| <span class="n">prev</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXSetIsNumpyShape</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">active</span><span class="p">),</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">prev</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="nb">bool</span><span class="p">(</span><span class="n">prev</span><span class="o">.</span><span class="n">value</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="is_np_shape"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.is_np_shape">[docs]</a><span class="k">def</span> <span class="nf">is_np_shape</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Checks whether the NumPy shape semantics is currently turned on.</span> |
| <span class="sd"> In NumPy shape semantics, `()` represents the shape of scalar tensors,</span> |
| <span class="sd"> and tuples with `0` elements, for example, `(0,)`, `(1, 0, 2)`, represent</span> |
| <span class="sd"> the shapes of zero-size tensors. This is turned off by default for keeping</span> |
| <span class="sd"> backward compatibility.</span> |
| |
| <span class="sd"> In the NumPy shape semantics, `-1` indicates an unknown size. For example,</span> |
| <span class="sd"> `(-1, 2, 2)` means that the size of the first dimension is unknown. Its size</span> |
| <span class="sd"> may be inferred during shape inference.</span> |
| |
| <span class="sd"> Please note that this is designed as an infrastructure for the incoming</span> |
| <span class="sd"> MXNet-NumPy operators. Legacy operators registered in the modules</span> |
| <span class="sd"> `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts</span> |
| <span class="sd"> in NumPy within this semantics.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> A bool value indicating whether the NumPy shape semantics is currently on.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> import mxnet as mx</span> |
| <span class="sd"> >>> prev_state = mx.set_np_shape(True)</span> |
| <span class="sd"> >>> print(prev_state)</span> |
| <span class="sd"> False</span> |
| <span class="sd"> >>> print(mx.is_np_shape())</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="n">curr</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_bool</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXIsNumpyShape</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">curr</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">curr</span><span class="o">.</span><span class="n">value</span></div> |
| |
| |
| <span class="k">class</span> <span class="nc">_NumpyShapeScope</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Scope for managing NumPy shape semantics.</span> |
| <span class="sd"> In NumPy shape semantics, `()` represents the shape of scalar tensors,</span> |
| <span class="sd"> and tuples with `0` elements, for example, `(0,)`, `(1, 0, 2)`, represent</span> |
| <span class="sd"> the shapes of zero-size tensors.</span> |
| |
| <span class="sd"> Do not use this class directly. Use `np_shape(active)` instead.</span> |
| |
| <span class="sd"> Example::</span> |
| |
| <span class="sd"> with _NumpyShapeScope(True):</span> |
| <span class="sd"> y = model(x)</span> |
| <span class="sd"> backward([y])</span> |
| |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">is_np_shape</span><span class="p">):</span> <span class="c1">#pylint: disable=redefined-outer-name</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_enter_is_np_shape</span> <span class="o">=</span> <span class="n">is_np_shape</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_prev_is_np_shape</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_enter_is_np_shape</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_prev_is_np_shape</span> <span class="o">=</span> <span class="n">set_np_shape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_enter_is_np_shape</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ptype</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">trace</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_enter_is_np_shape</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prev_is_np_shape</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_enter_is_np_shape</span><span class="p">:</span> |
| <span class="n">set_np_shape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_prev_is_np_shape</span><span class="p">)</span> |
| |
| |
| <div class="viewcode-block" id="np_shape"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.np_shape">[docs]</a><span class="k">def</span> <span class="nf">np_shape</span><span class="p">(</span><span class="n">active</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns an activated/deactivated NumPy shape scope to be used in 'with' statement</span> |
| <span class="sd"> and captures code that needs the NumPy shape semantics, i.e. support of scalar and</span> |
| <span class="sd"> zero-size tensors.</span> |
| |
| <span class="sd"> Please note that this is designed as an infrastructure for the incoming</span> |
| <span class="sd"> MXNet-NumPy operators. Legacy operators registered in the modules</span> |
| <span class="sd"> `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts</span> |
| <span class="sd"> in NumPy even within this scope.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> active : bool</span> |
| <span class="sd"> Indicates whether to activate NumPy-shape semantics.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> _NumpyShapeScope</span> |
| <span class="sd"> A scope object for wrapping the code w/ or w/o NumPy-shape semantics.</span> |
| |
| <span class="sd"> Example::</span> |
| |
| <span class="sd"> with mx.np_shape(active=True):</span> |
| <span class="sd"> # A scalar tensor's shape is `()`, whose `ndim` is `0`.</span> |
| <span class="sd"> scalar = mx.nd.ones(shape=())</span> |
| <span class="sd"> assert scalar.shape == ()</span> |
| |
| <span class="sd"> # If NumPy shape semantics is enabled, 0 in a shape means that</span> |
| <span class="sd"> # dimension contains zero elements.</span> |
| <span class="sd"> data = mx.sym.var("data", shape=(0, 2, 3))</span> |
| <span class="sd"> ret = mx.sym.sin(data)</span> |
| <span class="sd"> arg_shapes, out_shapes, _ = ret.infer_shape()</span> |
| <span class="sd"> assert arg_shapes[0] == (0, 2, 3)</span> |
| <span class="sd"> assert out_shapes[0] == (0, 2, 3)</span> |
| |
| <span class="sd"> # -1 means unknown shape dimension size in the new NumPy shape definition</span> |
| <span class="sd"> data = mx.sym.var("data", shape=(-1, 2, 3))</span> |
| <span class="sd"> ret = mx.sym.sin(data)</span> |
| <span class="sd"> arg_shapes, out_shapes, _ = ret.infer_shape_partial()</span> |
| <span class="sd"> assert arg_shapes[0] == (-1, 2, 3)</span> |
| <span class="sd"> assert out_shapes[0] == (-1, 2, 3)</span> |
| |
| <span class="sd"> # When a shape is completely unknown when NumPy shape semantics is on, it is</span> |
| <span class="sd"> # represented as `None` in Python.</span> |
| <span class="sd"> data = mx.sym.var("data")</span> |
| <span class="sd"> ret = mx.sym.sin(data)</span> |
| <span class="sd"> arg_shapes, out_shapes, _ = ret.infer_shape_partial()</span> |
| <span class="sd"> assert arg_shapes[0] is None</span> |
| <span class="sd"> assert out_shapes[0] is None</span> |
| |
| <span class="sd"> with mx.np_shape(active=False):</span> |
| <span class="sd"> # 0 means unknown shape dimension size in the legacy shape definition.</span> |
| <span class="sd"> data = mx.sym.var("data", shape=(0, 2, 3))</span> |
| <span class="sd"> ret = mx.sym.sin(data)</span> |
| <span class="sd"> arg_shapes, out_shapes, _ = ret.infer_shape_partial()</span> |
| <span class="sd"> assert arg_shapes[0] == (0, 2, 3)</span> |
| <span class="sd"> assert out_shapes[0] == (0, 2, 3)</span> |
| |
| <span class="sd"> # When a shape is completely unknown in the legacy mode (default), its ndim is</span> |
| <span class="sd"> # equal to 0 and it is represented as `()` in Python.</span> |
| <span class="sd"> data = mx.sym.var("data")</span> |
| <span class="sd"> ret = mx.sym.sin(data)</span> |
| <span class="sd"> arg_shapes, out_shapes, _ = ret.infer_shape_partial()</span> |
| <span class="sd"> assert arg_shapes[0] == ()</span> |
| <span class="sd"> assert out_shapes[0] == ()</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_NumpyShapeScope</span><span class="p">(</span><span class="n">active</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="use_np_shape"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.use_np_shape">[docs]</a><span class="k">def</span> <span class="nf">use_np_shape</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A decorator wrapping a function or class with activated NumPy-shape semantics.</span> |
| <span class="sd"> When `func` is a function, this ensures that the execution of the function is scoped with NumPy</span> |
| <span class="sd"> shape semantics, such as the support for zero-dim and zero size tensors. When</span> |
| <span class="sd"> `func` is a class, it ensures that all the methods, static functions, and properties</span> |
| <span class="sd"> of the class are executed with the NumPy shape semantics.</span> |
| |
| <span class="sd"> .. code-block:: python</span> |
| |
| <span class="sd"> import mxnet as mx</span> |
| <span class="sd"> @mx.use_np_shape</span> |
| <span class="sd"> def scalar_one():</span> |
| <span class="sd"> return mx.nd.ones(())</span> |
| <span class="sd"> print(scalar_one())</span> |
| |
| <span class="sd"> @np.use_np_shape</span> |
| <span class="sd"> class ScalarTensor(object):</span> |
| <span class="sd"> def __init__(self, val=None):</span> |
| <span class="sd"> if val is None:</span> |
| <span class="sd"> val = ScalarTensor.random().value</span> |
| <span class="sd"> self._scalar = mx.nd.ones(()) * val</span> |
| |
| <span class="sd"> def __repr__(self):</span> |
| <span class="sd"> print("Is __repr__ in np_shape semantics? {}!".format(str(np.is_np_shape())))</span> |
| <span class="sd"> return str(self._scalar.asnumpy())</span> |
| |
| <span class="sd"> @staticmethod</span> |
| <span class="sd"> def random():</span> |
| <span class="sd"> val = mx.nd.random.uniform().asnumpy().item()</span> |
| <span class="sd"> return ScalarTensor(val)</span> |
| |
| <span class="sd"> @property</span> |
| <span class="sd"> def value(self):</span> |
| <span class="sd"> print("Is value property in np_shape semantics? {}!".format(str(np.is_np_shape())))</span> |
| <span class="sd"> return self._scalar.asnumpy().item()</span> |
| |
| <span class="sd"> print("Is global scope of np_shape activated? {}!".format(str(np.is_np_shape())))</span> |
| <span class="sd"> scalar_tensor = ScalarTensor()</span> |
| <span class="sd"> print(scalar_tensor)</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> func : a user-provided callable function or class to be scoped by the NumPy-shape semantics.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> Function or class</span> |
| <span class="sd"> A function or class wrapped in the NumPy-shape scope.</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">if</span> <span class="n">inspect</span><span class="o">.</span><span class="n">isclass</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">inspect</span><span class="o">.</span><span class="n">getmembers</span><span class="p">(</span> |
| <span class="n">func</span><span class="p">,</span> |
| <span class="n">predicate</span><span class="o">=</span> |
| <span class="k">lambda</span> <span class="n">f</span><span class="p">:</span> <span class="n">inspect</span><span class="o">.</span><span class="n">isfunction</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="ow">or</span> <span class="n">inspect</span><span class="o">.</span><span class="n">ismethod</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="nb">property</span><span class="p">)):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">method</span><span class="p">,</span> <span class="nb">property</span><span class="p">):</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="nb">property</span><span class="p">(</span><span class="n">use_np_shape</span><span class="p">(</span><span class="n">method</span><span class="o">.</span><span class="fm">__get__</span><span class="p">),</span> |
| <span class="n">method</span><span class="o">.</span><span class="fm">__set__</span><span class="p">,</span> |
| <span class="n">method</span><span class="o">.</span><span class="fm">__delattr__</span><span class="p">,</span> |
| <span class="n">method</span><span class="o">.</span><span class="vm">__doc__</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">use_np_shape</span><span class="p">(</span><span class="n">method</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">func</span> |
| <span class="k">elif</span> <span class="n">callable</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">_with_np_shape</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">with</span> <span class="n">np_shape</span><span class="p">(</span><span class="n">active</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">_with_np_shape</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'use_np_shape can only decorate classes and callable objects, '</span> |
| <span class="s1">'while received a </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">func</span><span class="p">))))</span></div> |
| |
| |
| <span class="k">def</span> <span class="nf">_sanity_check_params</span><span class="p">(</span><span class="n">func_name</span><span class="p">,</span> <span class="n">unsupported_params</span><span class="p">,</span> <span class="n">param_dict</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">param_name</span> <span class="ow">in</span> <span class="n">unsupported_params</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">param_name</span> <span class="ow">in</span> <span class="n">param_dict</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"function </span><span class="si">{}</span><span class="s2"> does not support parameter </span><span class="si">{}</span><span class="s2">"</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">func_name</span><span class="p">,</span> <span class="n">param_name</span><span class="p">))</span> |
| |
| |
| <div class="viewcode-block" id="set_module"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.set_module">[docs]</a><span class="k">def</span> <span class="nf">set_module</span><span class="p">(</span><span class="n">module</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Decorator for overriding __module__ on a function or class.</span> |
| |
| <span class="sd"> Example usage::</span> |
| |
| <span class="sd"> @set_module('mxnet.numpy')</span> |
| <span class="sd"> def example():</span> |
| <span class="sd"> pass</span> |
| |
| <span class="sd"> assert example.__module__ == 'numpy'</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="nf">decorator</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">module</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">func</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">=</span> <span class="n">module</span> |
| <span class="k">return</span> <span class="n">func</span> |
| <span class="k">return</span> <span class="n">decorator</span></div> |
| |
| |
| <span class="k">class</span> <span class="nc">_NumpyArrayScope</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Scope for managing NumPy array creation. This is often used</span> |
| <span class="sd"> with `is_np_array=True` in initializer to enforce array creation</span> |
| <span class="sd"> as type `mxnet.numpy.ndarray`, instead of `mx.nd.NDArray` in Gluon.</span> |
| |
| <span class="sd"> Do not use this class directly. Use `np_array(active)` instead.</span> |
| <span class="sd"> """</span> |
| <span class="n">_current</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">local</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">is_np_array</span><span class="p">):</span> <span class="c1"># pylint: disable=redefined-outer-name</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_old_scope</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_is_np_array</span> <span class="o">=</span> <span class="n">is_np_array</span> |
| |
| <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">_NumpyArrayScope</span><span class="o">.</span><span class="n">_current</span><span class="p">,</span> <span class="s2">"value"</span><span class="p">):</span> |
| <span class="n">_NumpyArrayScope</span><span class="o">.</span><span class="n">_current</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="n">_NumpyArrayScope</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_old_scope</span> <span class="o">=</span> <span class="n">_NumpyArrayScope</span><span class="o">.</span><span class="n">_current</span><span class="o">.</span><span class="n">value</span> |
| <span class="n">_NumpyArrayScope</span><span class="o">.</span><span class="n">_current</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="bp">self</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| |
| <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ptype</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">trace</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_old_scope</span> |
| <span class="n">_NumpyArrayScope</span><span class="o">.</span><span class="n">_current</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_old_scope</span> |
| |
| |
| <div class="viewcode-block" id="np_array"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.np_array">[docs]</a><span class="k">def</span> <span class="nf">np_array</span><span class="p">(</span><span class="n">active</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns an activated/deactivated NumPy-array scope to be used in 'with' statement</span> |
| <span class="sd"> and captures code that needs the NumPy-array semantics.</span> |
| |
| <span class="sd"> Currently, this is used in Gluon to enforce array creation in `Block`s as type</span> |
| <span class="sd"> `mxnet.numpy.ndarray`, instead of `mx.nd.NDArray`.</span> |
| |
| <span class="sd"> It is recommended to use the decorator `use_np_array` to decorate the classes</span> |
| <span class="sd"> that need this semantics, instead of using this function in a `with` statement</span> |
| <span class="sd"> unless you know exactly what has been scoped by this semantics.</span> |
| |
| <span class="sd"> Please note that this is designed as an infrastructure for the incoming</span> |
| <span class="sd"> MXNet-NumPy operators. Legacy operators registered in the modules</span> |
| <span class="sd"> `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts</span> |
| <span class="sd"> in NumPy even within this scope.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> active : bool</span> |
| <span class="sd"> Indicates whether to activate NumPy-array semantics.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> _NumpyShapeScope</span> |
| <span class="sd"> A scope object for wrapping the code w/ or w/o NumPy-shape semantics.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_NumpyArrayScope</span><span class="p">(</span><span class="n">active</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="is_np_array"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.is_np_array">[docs]</a><span class="k">def</span> <span class="nf">is_np_array</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Checks whether the NumPy-array semantics is currently turned on.</span> |
| <span class="sd"> This is currently used in Gluon for checking whether an array of type `mxnet.numpy.ndarray`</span> |
| <span class="sd"> or `mx.nd.NDArray` should be created. For example, at the time when a parameter</span> |
| <span class="sd"> is created in a `Block`, an `mxnet.numpy.ndarray` is created if this returns true; else</span> |
| <span class="sd"> an `mx.nd.NDArray` is created.</span> |
| |
| <span class="sd"> Normally, users are not recommended to use this API directly unless you known exactly</span> |
| <span class="sd"> what is going on under the hood.</span> |
| |
| <span class="sd"> Please note that this is designed as an infrastructure for the incoming</span> |
| <span class="sd"> MXNet-NumPy operators. Legacy operators registered in the modules</span> |
| <span class="sd"> `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts</span> |
| <span class="sd"> in NumPy within this semantics.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> A bool value indicating whether the NumPy-array semantics is currently on.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_NumpyArrayScope</span><span class="o">.</span><span class="n">_current</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">_is_np_array</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span> |
| <span class="n">_NumpyArrayScope</span><span class="o">.</span><span class="n">_current</span><span class="p">,</span> <span class="s2">"value"</span><span class="p">)</span> <span class="k">else</span> <span class="kc">False</span></div> |
| |
| |
| <div class="viewcode-block" id="use_np_array"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.use_np_array">[docs]</a><span class="k">def</span> <span class="nf">use_np_array</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A decorator wrapping Gluon `Block`s and all its methods, properties, and static functions</span> |
| <span class="sd"> with the semantics of NumPy-array, which means that where ndarrays are created,</span> |
| <span class="sd"> `mxnet.numpy.ndarray`s should be created, instead of legacy ndarrays of type `mx.nd.NDArray`.</span> |
| <span class="sd"> For example, at the time when a parameter is created in a `Block`, an `mxnet.numpy.ndarray`</span> |
| <span class="sd"> is created if it's decorated with this decorator.</span> |
| |
| <span class="sd"> .. code-block:: python</span> |
| |
| <span class="sd"> import mxnet as mx</span> |
| <span class="sd"> from mxnet import gluon, nd, np</span> |
| <span class="sd"> from mxnet.gluon import Parameter</span> |
| |
| <span class="sd"> class TestHybridBlock1(gluon.HybridBlock):</span> |
| <span class="sd"> def __init__(self):</span> |
| <span class="sd"> super(TestHybridBlock1, self).__init__()</span> |
| <span class="sd"> self.w = Parameter('w', shape=(2, 2))</span> |
| |
| <span class="sd"> def forward(self, x):</span> |
| <span class="sd"> return nd.dot(x, self.w.data())</span> |
| |
| <span class="sd"> x = mx.nd.ones((2, 2))</span> |
| <span class="sd"> net1 = TestHybridBlock1()</span> |
| <span class="sd"> net1.initialize()</span> |
| <span class="sd"> out = net1.forward(x)</span> |
| <span class="sd"> for _, v in net1.collect_params().items():</span> |
| <span class="sd"> assert type(v.data()) is mx.nd.NDArray</span> |
| <span class="sd"> assert type(out) is mx.nd.NDArray</span> |
| |
| <span class="sd"> @mx.util.use_np_array</span> |
| <span class="sd"> class TestHybridBlock2(gluon.HybridBlock):</span> |
| <span class="sd"> def __init__(self):</span> |
| <span class="sd"> super(TestHybridBlock2, self).__init__()</span> |
| <span class="sd"> self.w = Parameter('w', shape=(2, 2))</span> |
| |
| <span class="sd"> def forward(self, x):</span> |
| <span class="sd"> return np.dot(x, self.w.data())</span> |
| |
| <span class="sd"> x = np.ones((2, 2))</span> |
| <span class="sd"> net2 = TestHybridBlock2()</span> |
| <span class="sd"> net2.initialize()</span> |
| <span class="sd"> out = net2.forward(x)</span> |
| <span class="sd"> for _, v in net2.collect_params().items():</span> |
| <span class="sd"> print(type(v.data()))</span> |
| <span class="sd"> assert type(v.data()) is np.ndarray</span> |
| <span class="sd"> assert type(out) is np.ndarray</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> func : a user-provided callable function or class to be scoped by the NumPy-array semantics.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> Function or class</span> |
| <span class="sd"> A function or class wrapped in the NumPy-array scope.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">inspect</span><span class="o">.</span><span class="n">isclass</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">inspect</span><span class="o">.</span><span class="n">getmembers</span><span class="p">(</span> |
| <span class="n">func</span><span class="p">,</span> |
| <span class="n">predicate</span><span class="o">=</span> |
| <span class="k">lambda</span> <span class="n">f</span><span class="p">:</span> <span class="n">inspect</span><span class="o">.</span><span class="n">isfunction</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="ow">or</span> <span class="n">inspect</span><span class="o">.</span><span class="n">ismethod</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="nb">property</span><span class="p">)):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">method</span><span class="p">,</span> <span class="nb">property</span><span class="p">):</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="nb">property</span><span class="p">(</span><span class="n">use_np_array</span><span class="p">(</span><span class="n">method</span><span class="o">.</span><span class="fm">__get__</span><span class="p">),</span> |
| <span class="n">method</span><span class="o">.</span><span class="fm">__set__</span><span class="p">,</span> |
| <span class="n">method</span><span class="o">.</span><span class="fm">__delattr__</span><span class="p">,</span> |
| <span class="n">method</span><span class="o">.</span><span class="vm">__doc__</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">use_np_array</span><span class="p">(</span><span class="n">method</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">func</span> |
| <span class="k">elif</span> <span class="n">callable</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">_with_np_array</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">with</span> <span class="n">np_array</span><span class="p">(</span><span class="n">active</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">_with_np_array</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'use_np_array can only decorate classes and callable objects, '</span> |
| <span class="s1">'while received a </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">func</span><span class="p">))))</span></div> |
| |
| |
| <div class="viewcode-block" id="use_np"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.use_np">[docs]</a><span class="k">def</span> <span class="nf">use_np</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A convenience decorator for wrapping user provided functions and classes in the scope of</span> |
| <span class="sd"> both NumPy-shape and NumPy-array semantics, which means that ``(1)`` empty tuples ``()`` and</span> |
| <span class="sd"> tuples with zeros, such as ``(0, 1)``, ``(1, 0, 2)``, will be treated as scalar tensors' shapes and</span> |
| <span class="sd"> zero-size tensors' shapes in shape inference functions of operators, instead of as unknown</span> |
| <span class="sd"> in legacy mode; (2) ndarrays of type :class:`mxnet.numpy.ndarray` should be created instead of</span> |
| <span class="sd"> :class:`mx.nd.NDArray`.</span> |
| |
| <span class="sd"> .. code-block:: python</span> |
| |
| <span class="sd"> import mxnet as mx</span> |
| <span class="sd"> from mxnet import gluon, nd, np</span> |
| <span class="sd"> from mxnet.gluon import Parameter</span> |
| |
| <span class="sd"> class TestHybridBlock1(gluon.HybridBlock):</span> |
| <span class="sd"> def __init__(self):</span> |
| <span class="sd"> super(TestHybridBlock1, self).__init__()</span> |
| <span class="sd"> self.w = Parameter('w', shape=(2, 2))</span> |
| |
| <span class="sd"> def forward(self, x):</span> |
| <span class="sd"> return nd.dot(x, self.w.data()) + nd.ones((1,))</span> |
| |
| <span class="sd"> x = mx.nd.ones((2, 2))</span> |
| <span class="sd"> net1 = TestHybridBlock1()</span> |
| <span class="sd"> net1.initialize()</span> |
| <span class="sd"> out = net1.forward(x)</span> |
| <span class="sd"> for _, v in net1.collect_params().items():</span> |
| <span class="sd"> assert type(v.data()) is mx.nd.NDArray</span> |
| <span class="sd"> assert type(out) is mx.nd.NDArray</span> |
| |
| <span class="sd"> @mx.util.use_np</span> |
| <span class="sd"> class TestHybridBlock2(gluon.HybridBlock):</span> |
| <span class="sd"> def __init__(self):</span> |
| <span class="sd"> super(TestHybridBlock2, self).__init__()</span> |
| <span class="sd"> self.w = Parameter('w', shape=(2, 2))</span> |
| |
| <span class="sd"> def forward(self, x):</span> |
| <span class="sd"> return np.dot(x, self.w.data()) + np.ones(())</span> |
| |
| <span class="sd"> x = np.ones((2, 2))</span> |
| <span class="sd"> net2 = TestHybridBlock2()</span> |
| <span class="sd"> net2.initialize()</span> |
| <span class="sd"> out = net2.forward(x)</span> |
| <span class="sd"> for _, v in net2.collect_params().items():</span> |
| <span class="sd"> print(type(v.data()))</span> |
| <span class="sd"> assert type(v.data()) is np.ndarray</span> |
| <span class="sd"> assert type(out) is np.ndarray</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> func : a user-provided callable function or class to be scoped by the</span> |
| <span class="sd"> NumPy-shape and NumPy-array semantics.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> Function or class</span> |
| <span class="sd"> A function or class wrapped in the Numpy-shape and NumPy-array scope.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">use_np_shape</span><span class="p">(</span><span class="n">use_np_array</span><span class="p">(</span><span class="n">func</span><span class="p">))</span></div> |
| |
| |
| <div class="viewcode-block" id="np_ufunc_legal_option"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.np_ufunc_legal_option">[docs]</a><span class="k">def</span> <span class="nf">np_ufunc_legal_option</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Checking if ufunc arguments are legal inputs</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> key : string</span> |
| <span class="sd"> the key of the ufunc argument.</span> |
| <span class="sd"> value : string</span> |
| <span class="sd"> the value of the ufunc argument.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> legal : boolean</span> |
| <span class="sd"> Whether or not the argument is a legal one. True when the key is one of the ufunc</span> |
| <span class="sd"> arguments and value is an allowed value. False when the key is not one of the ufunc</span> |
| <span class="sd"> arugments or the value is not an allowed value even when the key is a legal one.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">'where'</span><span class="p">:</span> |
| <span class="k">return</span> <span class="kc">True</span> |
| <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">'casting'</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">value</span> <span class="ow">in</span> <span class="nb">set</span><span class="p">([</span><span class="s1">'no'</span><span class="p">,</span> <span class="s1">'equiv'</span><span class="p">,</span> <span class="s1">'safe'</span><span class="p">,</span> <span class="s1">'same_kind'</span><span class="p">,</span> <span class="s1">'unsafe'</span><span class="p">]))</span> |
| <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">'order'</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> |
| <span class="k">return</span> <span class="kc">True</span> |
| <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">'dtype'</span><span class="p">:</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">_np</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">value</span> <span class="ow">in</span> <span class="nb">set</span><span class="p">([</span><span class="n">_np</span><span class="o">.</span><span class="n">int8</span><span class="p">,</span> <span class="n">_np</span><span class="o">.</span><span class="n">uint8</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="n">_np</span><span class="o">.</span><span class="n">int64</span><span class="p">,</span> |
| <span class="n">_np</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span> <span class="n">_np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">_np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> |
| <span class="s1">'int8'</span><span class="p">,</span> <span class="s1">'uint8'</span><span class="p">,</span> <span class="s1">'int32'</span><span class="p">,</span> <span class="s1">'int64'</span><span class="p">,</span> |
| <span class="s1">'float16'</span><span class="p">,</span> <span class="s1">'float32'</span><span class="p">,</span> <span class="s1">'float64'</span><span class="p">]))</span> |
| <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">'subok'</span><span class="p">:</span> |
| <span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">bool</span><span class="p">)</span> |
| <span class="k">return</span> <span class="kc">False</span></div> |
| |
| |
| <div class="viewcode-block" id="wrap_np_unary_func"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.wrap_np_unary_func">[docs]</a><span class="k">def</span> <span class="nf">wrap_np_unary_func</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A convenience decorator for wrapping numpy-compatible unary ufuncs to provide uniform</span> |
| <span class="sd"> error handling.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> func : a numpy-compatible unary function to be wrapped for better error handling.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> Function</span> |
| <span class="sd"> A function wrapped with proper error handling.</span> |
| <span class="sd"> """</span> |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">_wrap_np_unary_func</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="c1"># if argument is not in the set of ufunc arguments</span> |
| <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">_np_ufunc_default_kwargs</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2"> is an invalid keyword to function </span><span class="se">\'</span><span class="si">{}</span><span class="se">\'</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">func</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span> |
| <span class="c1"># if argument is one of the ufunc arguments, but not with the default value</span> |
| <span class="k">if</span> <span class="n">value</span> <span class="o">!=</span> <span class="n">_np_ufunc_default_kwargs</span><span class="p">[</span><span class="n">key</span><span class="p">]:</span> |
| <span class="c1"># if the provided value of the argument is a legal option, raise NotImplementedError</span> |
| <span class="k">if</span> <span class="n">np_ufunc_legal_option</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2">=</span><span class="si">{}</span><span class="s2"> is not implemented yet for operator </span><span class="si">{}</span><span class="s2">"</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">value</span><span class="p">),</span> <span class="n">func</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span> |
| <span class="c1"># otherwise raise TypeError with not understood error message</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2">=</span><span class="si">{}</span><span class="s2"> not understood for operator </span><span class="si">{}</span><span class="s2">"</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">func</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">_wrap_np_unary_func</span></div> |
| |
| |
| <div class="viewcode-block" id="wrap_np_binary_func"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.wrap_np_binary_func">[docs]</a><span class="k">def</span> <span class="nf">wrap_np_binary_func</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A convenience decorator for wrapping numpy-compatible binary ufuncs to provide uniform</span> |
| <span class="sd"> error handling.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> func : a numpy-compatible binary function to be wrapped for better error handling.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> Function</span> |
| <span class="sd"> A function wrapped with proper error handling.</span> |
| <span class="sd"> """</span> |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">_wrap_np_binary_func</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="c1"># if argument is not in the set of ufunc arguments</span> |
| <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">_np_ufunc_default_kwargs</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2"> is an invalid keyword to function </span><span class="se">\'</span><span class="si">{}</span><span class="se">\'</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">func</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span> |
| <span class="c1"># if argument is one of the ufunc arguments, but not with the default value</span> |
| <span class="k">if</span> <span class="n">value</span> <span class="o">!=</span> <span class="n">_np_ufunc_default_kwargs</span><span class="p">[</span><span class="n">key</span><span class="p">]:</span> |
| <span class="c1"># if the provided value of the argument is a legal option, raise NotImplementedError</span> |
| <span class="k">if</span> <span class="n">np_ufunc_legal_option</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2">=</span><span class="si">{}</span><span class="s2"> is not implemented yet"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">value</span><span class="p">)))</span> |
| <span class="c1"># otherwise raise TypeError with not understood error message</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> not understood"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">_wrap_np_binary_func</span></div> |
| |
| <div class="viewcode-block" id="wrap_data_api_statical_func"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.wrap_data_api_statical_func">[docs]</a><span class="k">def</span> <span class="nf">wrap_data_api_statical_func</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> A convenience decorator for wrapping data apis standardized statical functions to provide</span> |
| <span class="sd"> context keyward backward compatibility</span> |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> func : a numpy-compatible array statical function to be wrapped for context keyward change.</span> |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> Function</span> |
| <span class="sd"> A function wrapped with context keyward changes.</span> |
| <span class="sd"> """</span> |
| |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">_wrap_api_creation_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">correction</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'ddof'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">correction</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'correction'</span><span class="p">]</span> <span class="o">=</span> <span class="n">correction</span> |
| <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">_wrap_api_creation_func</span></div> |
| |
| <div class="viewcode-block" id="wrap_data_api_linalg_func"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.wrap_data_api_linalg_func">[docs]</a><span class="k">def</span> <span class="nf">wrap_data_api_linalg_func</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> A convenience decorator for wrapping data apis standardized linalg functions to provide</span> |
| <span class="sd"> context keyward backward compatibility</span> |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> func : a numpy-compatible array linalg function to be wrapped for context keyward change.</span> |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> Function</span> |
| <span class="sd"> A function wrapped with context keyward changes.</span> |
| <span class="sd"> """</span> |
| |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">_wrap_linalg_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">upper</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'UPLO'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="n">rcond</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'rcond'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="n">tol</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'tol'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">upper</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">upper</span> <span class="o">==</span> <span class="s1">'U'</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'upper'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'upper'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">if</span> <span class="n">rcond</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'rtol'</span><span class="p">]</span> <span class="o">=</span> <span class="n">rcond</span> |
| <span class="k">if</span> <span class="n">tol</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'rtol'</span><span class="p">]</span> <span class="o">=</span> <span class="n">tol</span> |
| <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">_wrap_linalg_func</span></div> |
| |
| |
| <div class="viewcode-block" id="wrap_sort_functions"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.wrap_sort_functions">[docs]</a><span class="k">def</span> <span class="nf">wrap_sort_functions</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A convenience decorator for wrapping sort functions</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> func : a numpy-compatible array creation function to be wrapped for parameter keyword change.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> Function</span> |
| <span class="sd"> A function wrapped with changed keywords.</span> |
| <span class="sd"> """</span> |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">_wrap_sort_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">kind</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'kind'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'order'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">kind</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'stable'</span><span class="p">]</span> <span class="o">=</span> <span class="n">kind</span> <span class="o">==</span> <span class="s1">'stable'</span> |
| <span class="k">if</span> <span class="n">order</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"order not supported here"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">_wrap_sort_func</span></div> |
| |
| |
| <div class="viewcode-block" id="wrap_ctx_to_device_func"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.wrap_ctx_to_device_func">[docs]</a><span class="k">def</span> <span class="nf">wrap_ctx_to_device_func</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A convenience decorator for converting ctx to device keyward backward compatibility</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> func : a function to be wrapped for context keyward change.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> Function</span> |
| <span class="sd"> A function wrapped with context keyward changes.</span> |
| <span class="sd"> """</span> |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">_wrap_func_with_ctx</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'ctx'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">kwargs</span><span class="p">[</span><span class="s1">'device'</span><span class="p">]</span> <span class="o">=</span> <span class="n">device</span> |
| <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">_wrap_func_with_ctx</span></div> |
| |
| |
| <span class="c1"># pylint: disable=exec-used</span> |
| <div class="viewcode-block" id="numpy_fallback"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.numpy_fallback">[docs]</a><span class="k">def</span> <span class="nf">numpy_fallback</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""decorator for falling back to offical numpy for a specific function"""</span> |
| <span class="k">def</span> <span class="nf">get_device</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">new_device</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">new_device</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">new_device</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">new_device</span> <span class="o">=</span> <span class="n">device</span> |
| <span class="k">assert</span> <span class="n">device</span> <span class="o">==</span> <span class="n">new_device</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"inconsistent device </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">device</span><span class="p">)</span><span class="si">}</span><span class="s2"> and </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">new_device</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span> |
| <span class="k">return</span> <span class="n">device</span> |
| |
| <span class="k">def</span> <span class="nf">_as_official_np_array</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="nb">object</span><span class="p">,</span> <span class="s1">'asnumpy'</span><span class="p">):</span> |
| <span class="k">return</span> <span class="nb">object</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">(),</span> <span class="nb">object</span><span class="o">.</span><span class="n">device</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">object</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span> |
| <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="nb">object</span><span class="p">:</span> |
| <span class="n">new_arr</span><span class="p">,</span> <span class="n">new_device</span> <span class="o">=</span> <span class="n">_as_official_np_array</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">get_device</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">new_device</span><span class="p">)</span> |
| <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_arr</span><span class="p">)</span> |
| <span class="k">return</span> <span class="nb">object</span><span class="o">.</span><span class="vm">__class__</span><span class="p">(</span><span class="n">tmp</span><span class="p">),</span> <span class="n">device</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">object</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="n">tmp</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">object</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">new_v</span><span class="p">,</span> <span class="n">new_device</span> <span class="o">=</span> <span class="n">_as_official_np_array</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">get_device</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">new_device</span><span class="p">)</span> |
| <span class="n">tmp</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_v</span> |
| <span class="k">return</span> <span class="n">tmp</span><span class="p">,</span> <span class="n">device</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="nb">object</span><span class="p">,</span> <span class="kc">None</span> |
| |
| <span class="kn">from</span> <span class="nn">.ndarray</span> <span class="kn">import</span> <span class="n">from_numpy</span> |
| <span class="kn">from</span> <span class="nn">.numpy</span> <span class="kn">import</span> <span class="n">array</span> |
| <span class="kn">from</span> <span class="nn">.device</span> <span class="kn">import</span> <span class="n">current_device</span> |
| <span class="k">def</span> <span class="nf">_as_mx_np_array</span><span class="p">(</span><span class="nb">object</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">current_device</span><span class="p">()):</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">_np</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">object</span><span class="p">,</span> <span class="n">_np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">from_numpy</span><span class="p">(</span><span class="nb">object</span><span class="p">)</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">array</span><span class="p">(</span><span class="nb">object</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">ret</span> <span class="k">if</span> <span class="p">(</span><span class="s1">'cpu'</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">device</span><span class="p">))</span> <span class="k">else</span> <span class="n">ret</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">))</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">object</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span> |
| <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">_as_mx_np_array</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span> <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="nb">object</span><span class="p">]</span> |
| <span class="k">return</span> <span class="nb">object</span><span class="o">.</span><span class="vm">__class__</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">object</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span><span class="n">_as_mx_np_array</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">object</span><span class="p">}</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="nb">object</span> |
| |
| <span class="kn">import</span> <span class="nn">re</span> |
| <span class="n">func_name</span> <span class="o">=</span> <span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> |
| <span class="n">func_doc</span> <span class="o">=</span> <span class="n">func</span><span class="o">.</span><span class="vm">__doc__</span> |
| <span class="n">func_source</span> <span class="o">=</span> <span class="n">inspect</span><span class="o">.</span><span class="n">getsource</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="n">func_source</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="sa">r</span><span class="s1">'np\.'</span><span class="p">,</span> <span class="s1">'onp.'</span><span class="p">,</span> <span class="n">func_source</span><span class="p">)</span> |
| <span class="n">func_source</span> <span class="o">=</span> <span class="n">func_source</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]</span> |
| <span class="n">indentation</span> <span class="o">=</span> <span class="n">func_source</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'def'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">indentation</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"should wrap a function"</span><span class="p">)</span> |
| <span class="n">stripped</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">func_source</span><span class="p">:</span> |
| <span class="n">stripped</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="n">indentation</span><span class="p">:])</span> |
| <span class="n">stripped</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="s1">' import numpy as onp'</span><span class="p">)</span> |
| <span class="n">func_source</span> <span class="o">=</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">stripped</span><span class="p">)</span> |
| <span class="n">local</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="n">exec</span><span class="p">(</span><span class="n">func_source</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">local</span><span class="p">)</span> |
| <span class="n">func</span> <span class="o">=</span> <span class="n">local</span><span class="p">[</span><span class="n">func_name</span><span class="p">]</span> |
| <span class="n">func</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">func_doc</span> |
| |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">_fallback_to_official_np</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="c1"># for every ndarray input, fallback</span> |
| <span class="n">new_args</span><span class="p">,</span> <span class="n">device0</span> <span class="o">=</span> <span class="n">_as_official_np_array</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> |
| <span class="n">new_kwargs</span><span class="p">,</span> <span class="n">device1</span> <span class="o">=</span> <span class="n">_as_official_np_array</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">get_device</span><span class="p">(</span><span class="n">device0</span><span class="p">,</span> <span class="n">device1</span><span class="p">)</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">new_args</span><span class="p">,</span> <span class="o">**</span><span class="n">new_kwargs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">ret</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Only functions with return values are allowed to use this decorator"</span><span class="p">)</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">_as_mx_np_array</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">ret</span> |
| |
| <span class="k">return</span> <span class="n">_fallback_to_official_np</span></div> |
| <span class="c1"># pylint: enable=exec-used</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_set_np_array</span><span class="p">(</span><span class="n">active</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Turns on/off NumPy array semantics for the current thread in which `mxnet.numpy.ndarray`</span> |
| <span class="sd"> is expected to be created, instead of the legacy `mx.nd.NDArray`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ---------</span> |
| <span class="sd"> active : bool</span> |
| <span class="sd"> A boolean value indicating whether the NumPy-array semantics should be turned on or off.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> A bool value indicating the previous state of NumPy array semantics.</span> |
| <span class="sd"> """</span> |
| <span class="k">global</span> <span class="n">_set_np_array_logged</span> |
| <span class="k">if</span> <span class="n">active</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">_set_np_array_logged</span><span class="p">:</span> |
| <span class="kn">import</span> <span class="nn">logging</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'NumPy array semantics has been activated in your code. This allows you'</span> |
| <span class="s1">' to use operators from MXNet NumPy and NumPy Extension modules as well'</span> |
| <span class="s1">' as MXNet NumPy `ndarray`s.'</span><span class="p">)</span> |
| <span class="n">_set_np_array_logged</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="n">cur_state</span> <span class="o">=</span> <span class="n">is_np_array</span><span class="p">()</span> |
| <span class="n">_NumpyArrayScope</span><span class="o">.</span><span class="n">_current</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="n">_NumpyArrayScope</span><span class="p">(</span><span class="n">active</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">cur_state</span> |
| |
| |
| <div class="viewcode-block" id="set_np"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.set_np">[docs]</a><span class="k">def</span> <span class="nf">set_np</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Setting NumPy shape and array semantics at the same time.</span> |
| <span class="sd"> It is required to keep NumPy shape semantics active while activating NumPy array semantics.</span> |
| <span class="sd"> Deactivating NumPy shape semantics while NumPy array semantics is still active is not allowed.</span> |
| <span class="sd"> It is highly recommended to set these two flags to `True` at the same time to fully enable</span> |
| <span class="sd"> NumPy-like behaviors. Please refer to the Examples section for a better understanding.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> shape : bool</span> |
| <span class="sd"> A boolean value indicating whether the NumPy-shape semantics should be turned on or off.</span> |
| <span class="sd"> When this flag is set to `True`, zero-size and zero-dim shapes are all valid shapes in</span> |
| <span class="sd"> shape inference process, instead of treated as unknown shapes in legacy mode.</span> |
| <span class="sd"> array : bool</span> |
| <span class="sd"> A boolean value indicating whether the NumPy-array semantics should be turned on or off.</span> |
| <span class="sd"> When this flag is set to `True`, it enables Gluon code flow to use or generate `mxnet.numpy.ndarray`s</span> |
| <span class="sd"> instead of `mxnet.ndarray.NDArray`. For example, a `Block` would create parameters of type</span> |
| <span class="sd"> `mxnet.numpy.ndarray`.</span> |
| <span class="sd"> dtype : bool</span> |
| <span class="sd"> A boolean value indicating whether the NumPy-dtype semantics should be turned on or off.</span> |
| <span class="sd"> When this flag is set to `True`, default dtype is float64.</span> |
| <span class="sd"> When this flag is set to `False`, default dtype is float32.</span> |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import mxnet as mx</span> |
| |
| <span class="sd"> Creating zero-dim ndarray in legacy mode would fail at shape inference.</span> |
| |
| <span class="sd"> >>> mx.nd.ones(shape=())</span> |
| <span class="sd"> mxnet.base.MXNetError: Operator _ones inferring shapes failed.</span> |
| |
| <span class="sd"> >>> mx.nd.ones(shape=(2, 0, 3))</span> |
| <span class="sd"> mxnet.base.MXNetError: Operator _ones inferring shapes failed.</span> |
| |
| <span class="sd"> In legacy mode, Gluon layers would create parameters and outputs of type `mx.nd.NDArray`.</span> |
| |
| <span class="sd"> >>> from mxnet.gluon import nn</span> |
| <span class="sd"> >>> dense = nn.Dense(2)</span> |
| <span class="sd"> >>> dense.initialize()</span> |
| <span class="sd"> >>> dense(mx.nd.ones(shape=(3, 2)))</span> |
| <span class="sd"> [[0.01983214 0.07832371]</span> |
| <span class="sd"> [0.01983214 0.07832371]</span> |
| <span class="sd"> [0.01983214 0.07832371]]</span> |
| <span class="sd"> <NDArray 3x2 @cpu(0)></span> |
| |
| <span class="sd"> >>> [p.data() for p in dense.collect_params().values()]</span> |
| <span class="sd"> [</span> |
| <span class="sd"> [[0.0068339 0.01299825]</span> |
| <span class="sd"> [0.0301265 0.04819721]]</span> |
| <span class="sd"> <NDArray 2x2 @cpu(0)>,</span> |
| <span class="sd"> [0. 0.]</span> |
| <span class="sd"> <NDArray 2 @cpu(0)>]</span> |
| |
| <span class="sd"> When the `shape` flag is `True`, both shape inferences are successful.</span> |
| |
| <span class="sd"> >>> from mxnet import np, npx</span> |
| <span class="sd"> >>> npx.set_np() # this is required to activate NumPy-like behaviors</span> |
| |
| <span class="sd"> >>> np.ones(shape=())</span> |
| <span class="sd"> array(1.)</span> |
| <span class="sd"> >>> np.ones(shape=(2, 0, 3))</span> |
| <span class="sd"> array([], shape=(2, 0, 3))</span> |
| |
| <span class="sd"> When the `array` flag is `True`, Gluon layers would create parameters and outputs of type `mx.np.ndarray`.</span> |
| |
| <span class="sd"> >>> dense = nn.Dense(2)</span> |
| <span class="sd"> >>> dense.initialize()</span> |
| <span class="sd"> >>> dense(np.ones(shape=(3, 2)))</span> |
| <span class="sd"> array([[0.01983214, 0.07832371],</span> |
| <span class="sd"> [0.01983214, 0.07832371],</span> |
| <span class="sd"> [0.01983214, 0.07832371]])</span> |
| |
| <span class="sd"> >>> [p.data() for p in dense.collect_params().values()]</span> |
| <span class="sd"> [array([[0.0068339 , 0.01299825],</span> |
| <span class="sd"> [0.0301265 , 0.04819721]]), array([0., 0.])]</span> |
| |
| <span class="sd"> >>> npx.set_np(dtype=True)</span> |
| <span class="sd"> >>> np.ones(shape=()).dtype</span> |
| <span class="sd"> dtype('float64')</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">shape</span> <span class="ow">and</span> <span class="n">array</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'NumPy Shape semantics is required in using NumPy array semantics.'</span><span class="p">)</span> |
| <span class="n">_set_np_array</span><span class="p">(</span><span class="n">array</span><span class="p">)</span> |
| <span class="n">set_np_shape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> |
| <span class="n">set_np_default_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="reset_np"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.reset_np">[docs]</a><span class="k">def</span> <span class="nf">reset_np</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Deactivate NumPy shape and array and deafult dtype semantics at the same time."""</span> |
| <span class="n">set_np</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div> |
| |
| |
| <span class="n">_CUDA_SUCCESS</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| |
| <div class="viewcode-block" id="get_cuda_compute_capability"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.get_cuda_compute_capability">[docs]</a><span class="k">def</span> <span class="nf">get_cuda_compute_capability</span><span class="p">(</span><span class="n">device</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns the cuda compute capability of the input `device`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> device : Device</span> |
| <span class="sd"> GPU context whose corresponding cuda compute capability is to be retrieved.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> cuda_compute_capability : int</span> |
| <span class="sd"> CUDA compute capability. For example, it returns 70 for CUDA arch equal to `sm_70`.</span> |
| |
| <span class="sd"> References</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549#file-cuda_check-py</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">device</span><span class="o">.</span><span class="n">device_type</span> <span class="o">!=</span> <span class="s1">'gpu'</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Expecting a gpu context to get cuda compute capability, '</span> |
| <span class="s1">'while received device </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">device</span><span class="p">)))</span> |
| |
| <span class="n">libnames</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'libcuda.so'</span><span class="p">,</span> <span class="s1">'libcuda.dylib'</span><span class="p">,</span> <span class="s1">'nvcuda.dll'</span><span class="p">,</span> <span class="s1">'cuda.dll'</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">libname</span> <span class="ow">in</span> <span class="n">libnames</span><span class="p">:</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">cuda</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">CDLL</span><span class="p">(</span><span class="n">libname</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">OSError</span><span class="p">:</span> |
| <span class="k">continue</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">break</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">OSError</span><span class="p">(</span><span class="s2">"could not load any of: "</span> <span class="o">+</span> <span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">libnames</span><span class="p">))</span> |
| |
| <span class="c1"># Some constants taken from cuda.h</span> |
| |
| <span class="n">cc_major</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">cc_minor</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">cuda_device</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">error_str</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">()</span> |
| |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">cuda</span><span class="o">.</span><span class="n">cuInit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">ret</span> <span class="o">!=</span> <span class="n">_CUDA_SUCCESS</span><span class="p">:</span> |
| <span class="n">cuda</span><span class="o">.</span><span class="n">cuGetErrorString</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">error_str</span><span class="p">))</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">'cuInit failed with erro code </span><span class="si">{}</span><span class="s1">: </span><span class="si">{}</span><span class="s1">'</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">error_str</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">decode</span><span class="p">()))</span> |
| |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">cuda</span><span class="o">.</span><span class="n">cuDeviceGet</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">cuda_device</span><span class="p">),</span> <span class="n">device</span><span class="o">.</span><span class="n">device_id</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">ret</span> <span class="o">!=</span> <span class="n">_CUDA_SUCCESS</span><span class="p">:</span> |
| <span class="n">cuda</span><span class="o">.</span><span class="n">cuGetErrorString</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">error_str</span><span class="p">))</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">'cuDeviceGet failed with error code </span><span class="si">{}</span><span class="s1">: </span><span class="si">{}</span><span class="s1">'</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">error_str</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">decode</span><span class="p">()))</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">cuda</span><span class="o">.</span><span class="n">cuDeviceComputeCapability</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">cc_major</span><span class="p">),</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">cc_minor</span><span class="p">),</span> <span class="n">cuda_device</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">ret</span> <span class="o">!=</span> <span class="n">_CUDA_SUCCESS</span><span class="p">:</span> |
| <span class="n">cuda</span><span class="o">.</span><span class="n">cuGetErrorString</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">error_str</span><span class="p">))</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">'cuDeviceComputeCapability failed with error code </span><span class="si">{}</span><span class="s1">: </span><span class="si">{}</span><span class="s1">'</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">error_str</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">decode</span><span class="p">()))</span> |
| <span class="k">return</span> <span class="n">cc_major</span><span class="o">.</span><span class="n">value</span> <span class="o">*</span> <span class="mi">10</span> <span class="o">+</span> <span class="n">cc_minor</span><span class="o">.</span><span class="n">value</span></div> |
| |
| |
| <div class="viewcode-block" id="default_array"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.default_array">[docs]</a><span class="k">def</span> <span class="nf">default_array</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Creates an array from any object exposing the default(nd or np) array interface.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> source_array : array_like</span> |
| <span class="sd"> An object exposing the array interface, an object whose `__array__`</span> |
| <span class="sd"> method returns an array, or any (nested) sequence.</span> |
| <span class="sd"> device : Device, optional</span> |
| <span class="sd"> Device context (default is the current default context).</span> |
| <span class="sd"> dtype : str or numpy.dtype, optional</span> |
| <span class="sd"> The data type of the output array. The default dtype is ``source_array.dtype``</span> |
| <span class="sd"> if `source_array` is an `NDArray`, `float32` otherwise.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An `NDArray`(nd or np) with the same contents as the `source_array`.</span> |
| <span class="sd"> """</span> |
| <span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">nd</span> <span class="k">as</span> <span class="n">_mx_nd</span> |
| <span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">np</span> <span class="k">as</span> <span class="n">_mx_np</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="k">return</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">_mx_nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span></div> |
| |
| <span class="k">class</span> <span class="nc">_NumpyDefaultDtypeScope</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Scope for managing NumPy default dtype semantics.</span> |
| <span class="sd"> In NumPy default dtype semantics, default dtype is 'float64',</span> |
| <span class="sd"> i.e. np.array([1, 2, 3]).dtype = np.float64</span> |
| <span class="sd"> Original default dtype without this semantic is 'float32'.</span> |
| |
| <span class="sd"> Do not use this class directly. Use `np_shape(active)` instead.</span> |
| |
| <span class="sd"> Example::</span> |
| |
| <span class="sd"> with _NumpyDefaultDtypeScope(True):</span> |
| <span class="sd"> y = model(x)</span> |
| <span class="sd"> backward([y])</span> |
| |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">is_np_default_dtype</span><span class="p">):</span> <span class="c1">#pylint: disable=redefined-outer-name</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_enter_is_np_default_dtype</span> <span class="o">=</span> <span class="n">is_np_default_dtype</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_prev_is_np_default_dtype</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_enter_is_np_default_dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_prev_is_np_default_dtype</span> <span class="o">=</span> <span class="n">set_np_default_dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_enter_is_np_default_dtype</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ptype</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">trace</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_enter_is_np_default_dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span>\ |
| <span class="bp">self</span><span class="o">.</span><span class="n">_prev_is_np_default_dtype</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_enter_is_np_default_dtype</span><span class="p">:</span> |
| <span class="n">set_np_default_dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_prev_is_np_default_dtype</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="np_default_dtype"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.np_default_dtype">[docs]</a><span class="k">def</span> <span class="nf">np_default_dtype</span><span class="p">(</span><span class="n">active</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns an activated/deactivated NumPy-default_dtype scope to be used in 'with' statement</span> |
| <span class="sd"> and captures code that needs the NumPy default dtype semantics. i.e. default dtype is float64.</span> |
| |
| <span class="sd"> Please note that this is designed as an infrastructure for the incoming</span> |
| <span class="sd"> MXNet-NumPy operators. Legacy operators registered in the modules</span> |
| <span class="sd"> `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts</span> |
| <span class="sd"> in NumPy even within this scope.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> active : bool</span> |
| <span class="sd"> Indicates whether to activate NumPy default dtype semantics.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> _NumpyDefaultDtypeScope</span> |
| <span class="sd"> A scope object for wrapping the code w/ or w/o NumPy-default_dtype semantics.</span> |
| |
| <span class="sd"> Example::</span> |
| |
| <span class="sd"> with mx.np_default_dtype(active=True):</span> |
| <span class="sd"> # Default Dtype is 'float64', consistent with offical NumPy behavior.</span> |
| <span class="sd"> arr = mx.np.array([1, 2, 3])</span> |
| <span class="sd"> assert arr.dtype == 'float64'</span> |
| |
| <span class="sd"> with mx.np_default_dtype(active=False):</span> |
| <span class="sd"> # Default Dtype is 'float32' in the legacy default dtype definition.</span> |
| <span class="sd"> arr = mx.np.array([1, 2, 3])</span> |
| <span class="sd"> assert arr.dtype == 'float32'</span> |
| |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_NumpyDefaultDtypeScope</span><span class="p">(</span><span class="n">active</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="use_np_default_dtype"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.use_np_default_dtype">[docs]</a><span class="k">def</span> <span class="nf">use_np_default_dtype</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A decorator wrapping a function or class with activated NumPy-default_dtype semantics.</span> |
| <span class="sd"> When `func` is a function, this ensures that the execution of the function is scoped with NumPy</span> |
| <span class="sd"> default dtype semantics, with the support for float64 as default dtype.</span> |
| <span class="sd"> When`func` is a class, it ensures that all the methods, static functions, and properties</span> |
| <span class="sd"> of the class are executed with the NumPy-default_dtype semantics.</span> |
| |
| <span class="sd"> .. code-block:: python</span> |
| |
| <span class="sd"> import mxnet as mx</span> |
| <span class="sd"> @mx.use_np_default_dtype</span> |
| <span class="sd"> def float64_one():</span> |
| <span class="sd"> return mx.nd.ones(()).dtype</span> |
| <span class="sd"> print(float64_one())</span> |
| |
| <span class="sd"> @np.use_np_default_dtype</span> |
| <span class="sd"> class Float64Tensor(object):</span> |
| <span class="sd"> def __init__(self, data=None):</span> |
| <span class="sd"> if data is None:</span> |
| <span class="sd"> data = Float64Tensor.random().data</span> |
| <span class="sd"> self._data = data</span> |
| |
| <span class="sd"> def __repr__(self):</span> |
| <span class="sd"> print("Is __repr__ in np_default_dtype semantics? {}!".format(str(np.is_np_deafult_dtype())))</span> |
| <span class="sd"> return str(self._data.asnumpy())</span> |
| |
| <span class="sd"> @staticmethod</span> |
| <span class="sd"> def random():</span> |
| <span class="sd"> data = mx.nd.random.uniform(shape=(2,2))</span> |
| <span class="sd"> return ScalarTensor(data)</span> |
| |
| <span class="sd"> @property</span> |
| <span class="sd"> def value(self):</span> |
| <span class="sd"> print("Is value property in np_dafault_dtype semantics? {}!".format(str(np.is_np_default_dtype())))</span> |
| <span class="sd"> return self._data.asnumpy()</span> |
| |
| <span class="sd"> print("Is global scope of np_default_dtype activated? {}!".format(str(np.is_np_default_dtype())))</span> |
| <span class="sd"> float64_tensor = Float64Tensor()</span> |
| <span class="sd"> print(float64_tensor)</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> func : a user-provided callable function or class to be scoped by the NumPy-default_dtype semantics.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> Function or class</span> |
| <span class="sd"> A function or class wrapped in the NumPy-default_dtype scope.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">inspect</span><span class="o">.</span><span class="n">isclass</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">inspect</span><span class="o">.</span><span class="n">getmembers</span><span class="p">(</span> |
| <span class="n">func</span><span class="p">,</span> |
| <span class="n">predicate</span><span class="o">=</span> |
| <span class="k">lambda</span> <span class="n">f</span><span class="p">:</span> <span class="n">inspect</span><span class="o">.</span><span class="n">isfunction</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="ow">or</span> <span class="n">inspect</span><span class="o">.</span><span class="n">ismethod</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="nb">property</span><span class="p">)):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">method</span><span class="p">,</span> <span class="nb">property</span><span class="p">):</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="nb">property</span><span class="p">(</span><span class="n">use_np_default_dtype</span><span class="p">(</span><span class="n">method</span><span class="o">.</span><span class="fm">__get__</span><span class="p">),</span> |
| <span class="n">method</span><span class="o">.</span><span class="fm">__set__</span><span class="p">,</span> |
| <span class="n">method</span><span class="o">.</span><span class="fm">__delattr__</span><span class="p">,</span> |
| <span class="n">method</span><span class="o">.</span><span class="vm">__doc__</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">use_np_default_dtype</span><span class="p">(</span><span class="n">method</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">func</span> |
| <span class="k">elif</span> <span class="n">callable</span><span class="p">(</span><span class="n">func</span><span class="p">):</span> |
| <span class="nd">@functools</span><span class="o">.</span><span class="n">wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">_with_np_default_dtype</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">with</span> <span class="n">np_default_dtype</span><span class="p">(</span><span class="n">active</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">_with_np_default_dtype</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'use_np_default_dtype can only decorate classes and callable objects, '</span> |
| <span class="s1">'while received a </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">func</span><span class="p">))))</span></div> |
| |
| <div class="viewcode-block" id="is_np_default_dtype"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.is_np_default_dtype">[docs]</a><span class="k">def</span> <span class="nf">is_np_default_dtype</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Checks whether the NumPy default dtype semantics is currently turned on.</span> |
| <span class="sd"> In NumPy default dtype semantics, default dtype is float64.</span> |
| |
| <span class="sd"> Please note that this is designed as an infrastructure for the incoming</span> |
| <span class="sd"> MXNet-NumPy operators. Legacy operators registered in the modules</span> |
| <span class="sd"> `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts</span> |
| <span class="sd"> in NumPy even within this scope.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> A bool value indicating whether the NumPy default dtype semantics is currently on.</span> |
| |
| <span class="sd"> See Also</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> set_np_default_dtype : Set default dtype equals to offical numpy</span> |
| <span class="sd"> set_np : npx.set_np(dtype=True) has equal performance to npx.set_np_default_dtype(True)</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> import mxnet as mx</span> |
| <span class="sd"> >>> from mxnet import npx</span> |
| <span class="sd"> >>> prev_state = npx.set_np_default_dtype(True)</span> |
| <span class="sd"> >>> print(prev_state)</span> |
| <span class="sd"> False</span> |
| <span class="sd"> >>> print(npx.is_np_default_dtype())</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="n">curr</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_bool</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXIsNumpyDefaultDtype</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">curr</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">curr</span><span class="o">.</span><span class="n">value</span></div> |
| |
| <div class="viewcode-block" id="set_np_default_dtype"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.set_np_default_dtype">[docs]</a><span class="k">def</span> <span class="nf">set_np_default_dtype</span><span class="p">(</span><span class="n">is_np_default_dtype</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> <span class="c1"># pylint: disable=redefined-outer-name</span> |
| <span class="w"> </span><span class="sd">"""Turns on/off NumPy default dtype semantics, because mxnet.numpy.ndarray use</span> |
| <span class="sd"> 32 bit data storage as default (e.g. float32 and int 32) while offical NumPy use</span> |
| <span class="sd"> 64 bit data storage as default (e.g. float64 and int64).</span> |
| <span class="sd"> This is turned off by default for keeping backward compatibility.</span> |
| |
| <span class="sd"> Please note that this is designed as an infrastructure for the incoming</span> |
| <span class="sd"> MXNet-NumPy operators. Legacy operators registered in the modules</span> |
| <span class="sd"> `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts</span> |
| <span class="sd"> in NumPy within this semantics.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> active : bool</span> |
| <span class="sd"> Indicates whether to turn on/off NumPy default dtype semantics.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> A bool value indicating the previous state of NumPy default dtype semantics.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> import mxnet as mx</span> |
| <span class="sd"> >>> from mxnet import npx</span> |
| <span class="sd"> >>> prev_state = npx.set_np_default_dtype(True)</span> |
| <span class="sd"> >>> print(prev_state)</span> |
| <span class="sd"> False</span> |
| <span class="sd"> >>> print(npx.is_np_default_dtype())</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| <span class="k">global</span> <span class="n">_set_np_default_dtype_logged</span> |
| <span class="k">if</span> <span class="n">is_np_default_dtype</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">_set_np_default_dtype_logged</span><span class="p">:</span> |
| <span class="kn">import</span> <span class="nn">logging</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'NumPy array default dtype has been changed from flaot32 to float64 in your code.'</span><span class="p">)</span> |
| <span class="n">_set_np_default_dtype_logged</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="n">prev</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_bool</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXSetIsNumpyDefaultDtype</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_bool</span><span class="p">(</span><span class="n">is_np_default_dtype</span><span class="p">),</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">prev</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">prev</span><span class="o">.</span><span class="n">value</span></div> |
| |
| |
| <div class="viewcode-block" id="getenv"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.getenv">[docs]</a><span class="k">def</span> <span class="nf">getenv</span><span class="p">(</span><span class="n">name</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Get the setting of an environment variable from the C Runtime.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : string type</span> |
| <span class="sd"> The environment variable name</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> value : string</span> |
| <span class="sd"> The value of the environment variable, or None if not set</span> |
| <span class="sd"> """</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXGetEnv</span><span class="p">(</span><span class="n">c_str</span><span class="p">(</span><span class="n">name</span><span class="p">),</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">ret</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">ret</span><span class="o">.</span><span class="n">value</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">py_str</span><span class="p">(</span><span class="n">ret</span><span class="o">.</span><span class="n">value</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="setenv"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.setenv">[docs]</a><span class="k">def</span> <span class="nf">setenv</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Set an environment variable in the C Runtime.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : string type</span> |
| <span class="sd"> The environment variable name</span> |
| <span class="sd"> value : string type</span> |
| <span class="sd"> The desired value to set the environment value to</span> |
| <span class="sd"> """</span> |
| <span class="n">passed_value</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">value</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">c_str</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXSetEnv</span><span class="p">(</span><span class="n">c_str</span><span class="p">(</span><span class="n">name</span><span class="p">),</span> <span class="n">passed_value</span><span class="p">))</span></div> |
| |
| |
| <div class="viewcode-block" id="get_max_supported_compute_capability"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.get_max_supported_compute_capability">[docs]</a><span class="k">def</span> <span class="nf">get_max_supported_compute_capability</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Get the maximum compute capability (SM arch) supported by the nvrtc compiler</span> |
| <span class="sd"> """</span> |
| <span class="n">max_supported_cc</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXGetMaxSupportedArch</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">max_supported_cc</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">max_supported_cc</span><span class="o">.</span><span class="n">value</span></div> |
| |
| |
| <div class="viewcode-block" id="get_rtc_compile_opts"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.get_rtc_compile_opts">[docs]</a><span class="k">def</span> <span class="nf">get_rtc_compile_opts</span><span class="p">(</span><span class="n">device</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Get the compile ops suitable for the context, given the toolkit/driver config</span> |
| <span class="sd"> """</span> |
| <span class="n">device_cc</span> <span class="o">=</span> <span class="n">get_cuda_compute_capability</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">max_supported_cc</span> <span class="o">=</span> <span class="n">get_max_supported_compute_capability</span><span class="p">()</span> |
| |
| <span class="c1"># CUDA toolkits starting with 11.1 (first to support arch 86) can compile directly to SASS</span> |
| <span class="n">can_compile_to_SASS</span> <span class="o">=</span> <span class="n">max_supported_cc</span> <span class="o">>=</span> <span class="mi">86</span> |
| <span class="n">should_compile_to_SASS</span> <span class="o">=</span> <span class="n">can_compile_to_SASS</span> <span class="ow">and</span> \ |
| <span class="n">device_cc</span> <span class="o"><=</span> <span class="n">max_supported_cc</span> |
| <span class="n">device_cc_as_used</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">device_cc</span><span class="p">,</span> <span class="n">max_supported_cc</span><span class="p">)</span> |
| <span class="n">arch_opt</span> <span class="o">=</span> <span class="s2">"--gpu-architecture=</span><span class="si">{}</span><span class="s2">_</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">"sm"</span> <span class="k">if</span> <span class="n">should_compile_to_SASS</span> <span class="k">else</span> <span class="s2">"compute"</span><span class="p">,</span> |
| <span class="n">device_cc_as_used</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">arch_opt</span><span class="p">]</span></div> |
| |
| <div class="viewcode-block" id="set_flush_denorms"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.set_flush_denorms">[docs]</a><span class="k">def</span> <span class="nf">set_flush_denorms</span><span class="p">(</span><span class="n">value</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Change floating-point calculations on CPU when dealing with denormalized values.</span> |
| <span class="sd"> This is only applicable to architectures which supports flush-to-zero.</span> |
| <span class="sd"> Denormalized values are positive and negative values that are very close to 0</span> |
| <span class="sd"> (exponent is the smallest possible value).</span> |
| <span class="sd"> Flushing denormalized values to 0 can speedup calculations if such values occurs,</span> |
| <span class="sd"> but if fulfilling whole IEEE 754 standard is required this option should be disabled.</span> |
| <span class="sd"> Flushing denormalized values is enabled in MXNet by default.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> value : bool</span> |
| <span class="sd"> State of flush-to-zero and denormals-are-zero in MXCSR register</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> prev_state : bool</span> |
| <span class="sd"> Previous state of flush-to-zero in MXCSR register</span> |
| <span class="sd"> """</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_bool</span><span class="p">()</span> |
| <span class="n">passed_value</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_bool</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXSetFlushDenorms</span><span class="p">(</span><span class="n">passed_value</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">ret</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">ret</span><span class="o">.</span><span class="n">value</span></div> |
| |
| |
| <div class="viewcode-block" id="dtype_from_number"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.dtype_from_number">[docs]</a><span class="k">def</span> <span class="nf">dtype_from_number</span><span class="p">(</span><span class="n">number</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Get the data type from the given int or float number</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">number</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">),</span>\ |
| <span class="s2">"The input number should be either int for float types"</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">_np</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">number</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="n">long</span><span class="p">)):</span> |
| <span class="k">if</span> <span class="n">number</span> <span class="o">></span> <span class="n">_MAX_VALUE_64_BIT_UNSIGNED_</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">OverflowError</span><span class="p">(</span><span class="s2">"Integer out of bounds"</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">number</span> <span class="o">></span> <span class="n">_MAX_VALUE_64_BIT_SIGNED_</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">_np</span><span class="o">.</span><span class="n">uint64</span> |
| <span class="k">elif</span> <span class="n">calcsize</span><span class="p">(</span><span class="s2">"P"</span><span class="p">)</span> <span class="o">==</span> <span class="mi">8</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">_np</span><span class="o">.</span><span class="n">int64</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">_np</span><span class="o">.</span><span class="n">int32</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">number</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">number</span><span class="p">)</span> <span class="o">></span> <span class="n">_MAX_VALUE_FLOAT32_REPRESENT_</span> <span class="ow">or</span> \ |
| <span class="p">((</span><span class="ow">not</span> <span class="n">_np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">number</span><span class="p">))</span> <span class="ow">and</span> \ |
| <span class="p">(</span><span class="n">_np</span><span class="o">.</span><span class="n">float32</span><span class="p">(</span><span class="n">number</span><span class="p">)</span> <span class="o">==</span> <span class="nb">int</span><span class="p">(</span><span class="n">number</span><span class="p">))</span> <span class="ow">and</span> \ |
| <span class="p">(</span><span class="n">number</span> <span class="o">!=</span> <span class="nb">int</span><span class="p">(</span><span class="n">number</span><span class="p">))):</span> |
| <span class="k">return</span> <span class="n">_np</span><span class="o">.</span><span class="n">float64</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">_np</span><span class="o">.</span><span class="n">float64</span> <span class="k">if</span> <span class="n">is_np_default_dtype</span><span class="p">()</span> <span class="k">else</span> <span class="n">_np</span><span class="o">.</span><span class="n">float32</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">number</span><span class="p">,</span> <span class="n">_np</span><span class="o">.</span><span class="n">generic</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">number</span><span class="o">.</span><span class="n">dtype</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'type </span><span class="si">{}</span><span class="s1"> not supported'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">number</span><span class="p">))))</span></div> |
| |
| <span class="c1"># This is a wrapping of tempfile.TemporaryDirectory(), known to have cleanup issues on Windows.</span> |
| <span class="c1"># The problem is partially handled as of Python 3.10 by the adding of a 'ignore_cleanup_errors'</span> |
| <span class="c1"># parameter. Once MXNet's Python version is forced to be >= 3.10, a simplification of this</span> |
| <span class="c1"># function to use 'ignore_cleanup_errors' would be possible. Until the fundamental Windows</span> |
| <span class="c1"># issues are resolved, best to use this routine instead of tempfile.TemporaryDirectory().</span> |
| <div class="viewcode-block" id="TemporaryDirectory"><a class="viewcode-back" href="../../api/util/index.html#mxnet.util.TemporaryDirectory">[docs]</a><span class="nd">@contextmanager</span> |
| <span class="k">def</span> <span class="nf">TemporaryDirectory</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A context wrapper of tempfile.TemporaryDirectory() that ignores cleanup errors on Windows.</span> |
| <span class="sd"> """</span> |
| <span class="nb">dir</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">TemporaryDirectory</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="k">yield</span> <span class="nb">dir</span><span class="o">.</span><span class="n">name</span> |
| <span class="k">finally</span><span class="p">:</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="nb">dir</span><span class="o">.</span><span class="n">cleanup</span><span class="p">()</span> |
| <span class="k">except</span> <span class="ne">PermissionError</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">!=</span> <span class="s1">'Windows'</span><span class="p">:</span> |
| <span class="k">raise</span></div> |
| </pre></div> |
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