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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.test_utils</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">"""Tools for testing."""</span> |
| <span class="c1"># pylint: disable=too-many-lines</span> |
| <span class="kn">import</span> <span class="nn">time</span> |
| <span class="kn">import</span> <span class="nn">gzip</span> |
| <span class="kn">import</span> <span class="nn">struct</span> |
| <span class="kn">import</span> <span class="nn">traceback</span> |
| <span class="kn">import</span> <span class="nn">numbers</span> |
| <span class="kn">import</span> <span class="nn">sys</span> |
| <span class="kn">import</span> <span class="nn">os</span> |
| <span class="kn">import</span> <span class="nn">platform</span> |
| <span class="kn">import</span> <span class="nn">errno</span> |
| <span class="kn">import</span> <span class="nn">logging</span> |
| <span class="kn">import</span> <span class="nn">bz2</span> |
| <span class="kn">import</span> <span class="nn">zipfile</span> |
| <span class="kn">import</span> <span class="nn">json</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">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| <span class="kn">import</span> <span class="nn">numpy.testing</span> <span class="k">as</span> <span class="nn">npt</span> |
| <span class="kn">import</span> <span class="nn">numpy.random</span> <span class="k">as</span> <span class="nn">rnd</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">ss</span> |
| <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> |
| <span class="n">ss</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="kn">import</span> <span class="nn">requests</span> |
| <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> |
| <span class="c1"># in rare cases requests may be not installed</span> |
| <span class="k">pass</span> |
| <span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="kn">from</span> <span class="nn">.device</span> <span class="kn">import</span> <span class="n">current_device</span> |
| <span class="kn">from</span> <span class="nn">.ndarray.ndarray</span> <span class="kn">import</span> <span class="n">_STORAGE_TYPE_STR_TO_ID</span><span class="p">,</span> <span class="n">get_dtype_name</span> |
| <span class="kn">from</span> <span class="nn">.symbol</span> <span class="kn">import</span> <span class="n">Symbol</span> |
| <span class="kn">from</span> <span class="nn">.symbol.numpy</span> <span class="kn">import</span> <span class="n">_Symbol</span> <span class="k">as</span> <span class="n">np_symbol</span> |
| <span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="n">use_np</span><span class="p">,</span> <span class="n">use_np_default_dtype</span><span class="p">,</span> <span class="n">getenv</span><span class="p">,</span> <span class="n">setenv</span> <span class="c1"># pylint: disable=unused-import</span> |
| <span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="n">get_max_supported_compute_capability</span><span class="p">,</span> <span class="n">get_rtc_compile_opts</span> <span class="c1"># pylint: disable=unused-import</span> |
| <span class="kn">from</span> <span class="nn">.runtime</span> <span class="kn">import</span> <span class="n">Features</span> |
| <span class="kn">from</span> <span class="nn">.numpy_extension</span> <span class="kn">import</span> <span class="n">get_cuda_compute_capability</span> |
| |
| |
| <div class="viewcode-block" id="default_device"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.default_device">[docs]</a><span class="k">def</span> <span class="nf">default_device</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Get default device for regression test."""</span> |
| <span class="c1"># _TODO: get device from environment variable to support</span> |
| <span class="c1"># testing with GPUs</span> |
| <span class="k">return</span> <span class="n">current_device</span><span class="p">()</span></div> |
| |
| |
| <div class="viewcode-block" id="set_default_device"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.set_default_device">[docs]</a><span class="k">def</span> <span class="nf">set_default_device</span><span class="p">(</span><span class="n">device</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Set default device."""</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">_current</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">device</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="default_dtype"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.default_dtype">[docs]</a><span class="k">def</span> <span class="nf">default_dtype</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Get default data type for regression test."""</span> |
| <span class="c1"># _TODO: get default dtype from environment variable</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span></div> |
| |
| <div class="viewcode-block" id="default_rtols"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.default_rtols">[docs]</a><span class="k">def</span> <span class="nf">default_rtols</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Get default relative tolerances for data comparisons involving each data type."""</span> |
| <span class="k">return</span> <span class="p">{</span><span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mf">1e-2</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mf">1e-4</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mf">1e-5</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">bool</span><span class="p">):</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mi">0</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mi">0</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mi">0</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint32</span><span class="p">):</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mi">0</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint64</span><span class="p">):</span> <span class="mi">0</span><span class="p">}</span></div> |
| |
| <div class="viewcode-block" id="default_atols"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.default_atols">[docs]</a><span class="k">def</span> <span class="nf">default_atols</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Get default absolute tolerances for data comparisons involving each data type."""</span> |
| <span class="k">return</span> <span class="p">{</span><span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mf">1e-1</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mf">1e-3</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mf">1e-20</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">bool</span><span class="p">):</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mi">0</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mi">0</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mi">0</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint32</span><span class="p">):</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mi">0</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint64</span><span class="p">):</span> <span class="mi">0</span><span class="p">}</span></div> |
| |
| <div class="viewcode-block" id="default_numeric_eps"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.default_numeric_eps">[docs]</a><span class="k">def</span> <span class="nf">default_numeric_eps</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Get default epsilon for finite difference gradient calculations with data type."""</span> |
| <span class="c1"># prefer a power-of-two eps, since no bits are dropped when serving as an input delta</span> |
| <span class="k">return</span> <span class="p">{</span><span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mf">1.0</span> <span class="o">/</span> <span class="mi">2</span><span class="o">**</span><span class="mi">6</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mf">1.0</span> <span class="o">/</span> <span class="mi">2</span><span class="o">**</span><span class="mi">9</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="mf">1.0</span> <span class="o">/</span> <span class="mi">2</span><span class="o">**</span><span class="mi">14</span><span class="p">}</span></div> |
| |
| |
| <div class="viewcode-block" id="effective_dtype"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.effective_dtype">[docs]</a><span class="k">def</span> <span class="nf">effective_dtype</span><span class="p">(</span><span class="n">dat</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Return the most appropriate dtype for determining the tolerance used in dat comparisons</span> |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dat : np.ndarray or mx.nd.array or mx.np.ndarray</span> |
| <span class="sd"> """</span> |
| <span class="c1"># On arch 80 gpus or later, a float32-io gemm or conv op will trim the mantissa of</span> |
| <span class="c1"># data inputs to be of comparable precision to a float16, so float16 becomes the</span> |
| <span class="c1"># 'effective dtype' for tolerance tests involving such op outputs.</span> |
| |
| <span class="c1"># Is TF32 enabled in the device (the default on arch 80 GPUs)</span> |
| <span class="k">def</span> <span class="nf">is_TF32_enabled</span><span class="p">(</span><span class="n">device</span><span class="p">):</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</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="ow">and</span> |
| <span class="n">get_cuda_compute_capability</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="o">>=</span> <span class="mi">80</span> <span class="ow">and</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'NVIDIA_TF32_OVERRIDE'</span><span class="p">)</span> <span class="o">!=</span> <span class="s1">'0'</span><span class="p">)</span> |
| <span class="k">except</span><span class="p">:</span> <span class="c1"># pylint: disable=bare-except</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| |
| <span class="n">device</span> <span class="o">=</span> <span class="n">dat</span><span class="o">.</span><span class="n">device</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">dat</span><span class="p">,</span> <span class="s1">'device'</span><span class="p">)</span> <span class="k">else</span> <span class="kc">None</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">dat</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="ow">and</span> <span class="n">is_TF32_enabled</span><span class="p">(</span><span class="n">device</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</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="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">dtype</span></div> |
| |
| |
| <div class="viewcode-block" id="get_tolerance"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.get_tolerance">[docs]</a><span class="k">def</span> <span class="nf">get_tolerance</span><span class="p">(</span><span class="n">dat</span><span class="p">,</span> <span class="n">tol</span><span class="p">,</span> <span class="n">default_tol</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Return the tolerance to be used for dat comparisons based on the given tol, datatype and device.</span> |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dat : np.ndarray or mx.nd.array or mx.np.ndarray</span> |
| <span class="sd"> tol : float, or a dict of dtype->float</span> |
| <span class="sd"> default_tol : default dict of dtype->float for all types</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tol</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Number</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">tol</span> |
| |
| <span class="c1"># If the caller has supplied a tol dict, use that if it has an entry for dtype,</span> |
| <span class="c1"># else use the supplied default tol dict.</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">effective_dtype</span><span class="p">(</span><span class="n">dat</span><span class="p">)</span> |
| <span class="n">tol</span> <span class="o">=</span> <span class="p">{}</span> <span class="k">if</span> <span class="n">tol</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">tol</span> |
| <span class="k">return</span> <span class="n">tol</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="n">default_tol</span><span class="p">[</span><span class="n">dtype</span><span class="p">])</span></div> |
| |
| |
| <div class="viewcode-block" id="get_tols"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.get_tols">[docs]</a><span class="k">def</span> <span class="nf">get_tols</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""For comparing two datasets 'x' and 'y', what relative and absolute tolerances should be used."""</span> |
| <span class="c1"># Tolerance analysis needs 'dtype' of 'x' and 'y', so convert numbers to numpy scalars as needed</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Number</span><span class="p">):</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Number</span><span class="p">):</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> |
| |
| <span class="c1"># If tols are not specified, use the largest default tol for 'x' and 'y' based on their ctx and dtype.</span> |
| <span class="n">rtol</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">get_tolerance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">default_rtols</span><span class="p">()),</span> |
| <span class="n">get_tolerance</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">default_rtols</span><span class="p">()))</span> |
| <span class="n">atol</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">get_tolerance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">atol</span><span class="p">,</span> <span class="n">default_atols</span><span class="p">()),</span> |
| <span class="n">get_tolerance</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">atol</span><span class="p">,</span> <span class="n">default_atols</span><span class="p">()))</span> |
| |
| <span class="k">return</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span></div> |
| |
| |
| <div class="viewcode-block" id="get_atol"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.get_atol">[docs]</a><span class="k">def</span> <span class="nf">get_atol</span><span class="p">(</span><span class="n">atol</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="n">np</span><span class="o">.</span><span class="n">dtype</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="w"> </span><span class="sd">"""Get default numerical threshold for regression test."""</span> |
| <span class="k">return</span> <span class="n">default_atols</span><span class="p">()[</span><span class="n">dtype</span><span class="p">]</span> <span class="k">if</span> <span class="n">atol</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">atol</span></div> |
| |
| <div class="viewcode-block" id="get_rtol"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.get_rtol">[docs]</a><span class="k">def</span> <span class="nf">get_rtol</span><span class="p">(</span><span class="n">rtol</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="n">np</span><span class="o">.</span><span class="n">dtype</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="w"> </span><span class="sd">"""Get default numerical threshold for regression test."""</span> |
| <span class="k">return</span> <span class="n">default_rtols</span><span class="p">()[</span><span class="n">dtype</span><span class="p">]</span> <span class="k">if</span> <span class="n">rtol</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">rtol</span></div> |
| |
| <div class="viewcode-block" id="get_etol"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.get_etol">[docs]</a><span class="k">def</span> <span class="nf">get_etol</span><span class="p">(</span><span class="n">etol</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Get default numerical threshold for regression test."""</span> |
| <span class="c1"># _TODO: get from env variable, different threshold might</span> |
| <span class="c1"># be needed for different device and dtype</span> |
| <span class="k">return</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">etol</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">etol</span></div> |
| |
| <div class="viewcode-block" id="random_arrays"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.random_arrays">[docs]</a><span class="k">def</span> <span class="nf">random_arrays</span><span class="p">(</span><span class="o">*</span><span class="n">shapes</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Generate some random numpy arrays."""</span> |
| <span class="n">arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">default_dtype</span><span class="p">())</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">s</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">default_dtype</span><span class="p">())</span> |
| <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">shapes</span><span class="p">]</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">arrays</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">arrays</span></div> |
| |
| |
| <div class="viewcode-block" id="random_uniform_arrays"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.random_uniform_arrays">[docs]</a><span class="k">def</span> <span class="nf">random_uniform_arrays</span><span class="p">(</span><span class="o">*</span><span class="n">shapes</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">"""Generate some random numpy arrays."""</span> |
| <span class="n">low</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">'low'</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span> |
| <span class="n">high</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">'high'</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span> |
| <span class="n">dtype</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">'dtype'</span><span class="p">,</span> <span class="n">default_dtype</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">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'Got unexpected argument/s : '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span> |
| <span class="n">arrays</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">s</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">shapes</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">arrays</span></div> |
| |
| |
| <div class="viewcode-block" id="random_sample"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.random_sample">[docs]</a><span class="k">def</span> <span class="nf">random_sample</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Return a k length list of the elements chosen from the population sequence."""</span> |
| <span class="k">assert</span> <span class="mi">0</span> <span class="o"><=</span> <span class="n">k</span> <span class="o"><=</span> <span class="nb">len</span><span class="p">(</span><span class="n">population</span><span class="p">)</span> |
| <span class="n">population_copy</span> <span class="o">=</span> <span class="n">population</span><span class="p">[:]</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">population_copy</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">population_copy</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">k</span><span class="p">]</span></div> |
| |
| |
| <span class="k">def</span> <span class="nf">_sorted_items</span><span class="p">(</span><span class="n">d</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Return (key, value) pairs of dict 'd' in a deterministic order (sorted by key)."""</span> |
| <span class="k">return</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_sorted_dict</span><span class="p">(</span><span class="n">d</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Return ordered dictionary containing items ordered by their keys."""</span> |
| <span class="k">return</span> <span class="n">OrderedDict</span><span class="p">(</span><span class="n">_sorted_items</span><span class="p">(</span><span class="n">d</span><span class="p">))</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_validate_csr_generation_inputs</span><span class="p">(</span><span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">,</span> <span class="n">density</span><span class="p">,</span> |
| <span class="n">distribution</span><span class="o">=</span><span class="s2">"uniform"</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Validates inputs for csr generation helper functions</span> |
| <span class="sd"> """</span> |
| <span class="n">total_nnz</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">num_rows</span> <span class="o">*</span> <span class="n">num_cols</span> <span class="o">*</span> <span class="n">density</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">density</span> <span class="o"><</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">density</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">"density has to be between 0 and 1"</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">num_rows</span> <span class="o"><=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">num_cols</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"num_rows or num_cols should be greater than 0"</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">distribution</span> <span class="o">==</span> <span class="s2">"powerlaw"</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">total_nnz</span> <span class="o"><</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">num_rows</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"not supported for this density: </span><span class="si">{</span><span class="n">density</span><span class="si">}</span><span class="s2">"</span> |
| <span class="sa">f</span><span class="s2">" for this shape (</span><span class="si">{</span><span class="n">num_rows</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">num_cols</span><span class="si">}</span><span class="s2">)"</span> |
| <span class="s2">" Please keep :"</span> |
| <span class="s2">" num_rows * num_cols * density >= 2 * num_rows"</span><span class="p">)</span> |
| |
| |
| <div class="viewcode-block" id="shuffle_csr_column_indices"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.shuffle_csr_column_indices">[docs]</a><span class="k">def</span> <span class="nf">shuffle_csr_column_indices</span><span class="p">(</span><span class="n">csr</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Shuffle CSR column indices per row</span> |
| <span class="sd"> This allows validation of unordered column indices, which is not a requirement</span> |
| <span class="sd"> for a valid CSR matrix</span> |
| <span class="sd"> """</span> |
| <span class="n">row_count</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">csr</span><span class="o">.</span><span class="n">indptr</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">row_count</span><span class="p">):</span> |
| <span class="n">start_index</span> <span class="o">=</span> <span class="n">csr</span><span class="o">.</span><span class="n">indptr</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="n">end_index</span> <span class="o">=</span> <span class="n">csr</span><span class="o">.</span><span class="n">indptr</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> |
| <span class="n">sublist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">csr</span><span class="o">.</span><span class="n">indices</span><span class="p">[</span><span class="n">start_index</span> <span class="p">:</span> <span class="n">end_index</span><span class="p">])</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">sublist</span><span class="p">)</span> |
| <span class="n">csr</span><span class="o">.</span><span class="n">indices</span><span class="p">[</span><span class="n">start_index</span> <span class="p">:</span> <span class="n">end_index</span><span class="p">]</span> <span class="o">=</span> <span class="n">sublist</span></div> |
| |
| |
| <span class="k">def</span> <span class="nf">_get_uniform_dataset_csr</span><span class="p">(</span><span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="mf">0.1</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="n">data_init</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">shuffle_csr_indices</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns CSRNDArray with uniform distribution</span> |
| <span class="sd"> This generates a csr matrix with totalnnz unique randomly chosen numbers</span> |
| <span class="sd"> from num_rows*num_cols and arranges them in the 2d array in the</span> |
| <span class="sd"> following way:</span> |
| <span class="sd"> row_index = (random_number_generated / num_rows)</span> |
| <span class="sd"> col_index = random_number_generated - row_index * num_cols</span> |
| <span class="sd"> """</span> |
| <span class="n">_validate_csr_generation_inputs</span><span class="p">(</span><span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">,</span> <span class="n">density</span><span class="p">,</span> |
| <span class="n">distribution</span><span class="o">=</span><span class="s2">"uniform"</span><span class="p">)</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span> <span class="k">as</span> <span class="n">spsp</span> |
| <span class="n">csr</span> <span class="o">=</span> <span class="n">spsp</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">,</span> <span class="n">density</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="nb">format</span><span class="o">=</span><span class="s2">"csr"</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">data_init</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">csr</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">data_init</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">shuffle_csr_indices</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span> |
| <span class="n">shuffle_csr_column_indices</span><span class="p">(</span><span class="n">csr</span><span class="p">)</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">((</span><span class="n">csr</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">csr</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span> <span class="n">csr</span><span class="o">.</span><span class="n">indptr</span><span class="p">),</span> |
| <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</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">except</span> <span class="ne">ImportError</span><span class="p">:</span> |
| <span class="k">assert</span><span class="p">(</span><span class="n">data_init</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">),</span> \ |
| <span class="s2">"data_init option is not supported when scipy is absent"</span> |
| <span class="k">assert</span><span class="p">(</span><span class="ow">not</span> <span class="n">shuffle_csr_indices</span><span class="p">),</span> \ |
| <span class="s2">"shuffle_csr_indices option is not supported when scipy is absent"</span> |
| <span class="c1"># scipy not available. try to generate one from a dense array</span> |
| <span class="n">dns</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</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="n">masked_dns</span> <span class="o">=</span> <span class="n">dns</span> <span class="o">*</span> <span class="p">(</span><span class="n">dns</span> <span class="o"><</span> <span class="n">density</span><span class="p">)</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">masked_dns</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s1">'csr'</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">result</span> |
| |
| <span class="k">def</span> <span class="nf">_get_powerlaw_dataset_csr</span><span class="p">(</span><span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="mf">0.1</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">"""Returns CSRNDArray with powerlaw distribution</span> |
| <span class="sd"> with exponentially increasing number of non zeros in each row.</span> |
| <span class="sd"> Not supported for cases where total_nnz < 2*num_rows. This is because</span> |
| <span class="sd"> the algorithm first tries to ensure that there are rows with no zeros by</span> |
| <span class="sd"> putting non zeros at beginning of each row.</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">_validate_csr_generation_inputs</span><span class="p">(</span><span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</span><span class="p">,</span> <span class="n">density</span><span class="p">,</span> |
| <span class="n">distribution</span><span class="o">=</span><span class="s2">"powerlaw"</span><span class="p">)</span> |
| |
| <span class="n">total_nnz</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">num_rows</span> <span class="o">*</span> <span class="n">num_cols</span> <span class="o">*</span> <span class="n">density</span><span class="p">)</span> |
| |
| <span class="n">unused_nnz</span> <span class="o">=</span> <span class="n">total_nnz</span> |
| <span class="n">output_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">num_rows</span><span class="p">,</span> <span class="n">num_cols</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="c1"># Start with ones on each row so that no row is empty</span> |
| <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_rows</span><span class="p">):</span> |
| <span class="n">output_arr</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">rnd</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mf">0.001</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="n">unused_nnz</span> <span class="o">=</span> <span class="n">unused_nnz</span> <span class="o">-</span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="n">unused_nnz</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">output_arr</span><span class="p">)</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s2">"csr"</span><span class="p">)</span> |
| |
| <span class="c1"># Populate rest of matrix with 2^i items in ith row.</span> |
| <span class="c1"># if we have used all total nnz return the sparse matrix</span> |
| <span class="c1"># else if we reached max column size then fill up full columns until we use all nnz</span> |
| <span class="n">col_max</span> <span class="o">=</span> <span class="mi">2</span> |
| <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_rows</span><span class="p">):</span> |
| <span class="n">col_limit</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">num_cols</span><span class="p">,</span> <span class="n">col_max</span><span class="p">)</span> |
| <span class="c1"># In case col_limit reached assign same value to all elements, which is much faster</span> |
| <span class="k">if</span> <span class="n">col_limit</span> <span class="o">==</span> <span class="n">num_cols</span> <span class="ow">and</span> <span class="n">unused_nnz</span> <span class="o">></span> <span class="n">col_limit</span><span class="p">:</span> |
| <span class="n">output_arr</span><span class="p">[</span><span class="n">row</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">rnd</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mf">0.001</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="n">unused_nnz</span> <span class="o">=</span> <span class="n">unused_nnz</span> <span class="o">-</span> <span class="n">col_limit</span> <span class="o">+</span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="n">unused_nnz</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">output_arr</span><span class="p">)</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s2">"csr"</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">continue</span> |
| <span class="k">for</span> <span class="n">col_index</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">col_limit</span><span class="p">):</span> |
| <span class="n">output_arr</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col_index</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">rnd</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mf">0.001</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="n">unused_nnz</span> <span class="o">=</span> <span class="n">unused_nnz</span> <span class="o">-</span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="n">unused_nnz</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">output_arr</span><span class="p">)</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s2">"csr"</span><span class="p">)</span> |
| <span class="n">col_max</span> <span class="o">=</span> <span class="n">col_max</span> <span class="o">*</span> <span class="mi">2</span> |
| |
| <span class="k">if</span> <span class="n">unused_nnz</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"not supported for this density: </span><span class="si">{</span><span class="n">density</span><span class="si">}</span><span class="s2">"</span> |
| <span class="sa">f</span><span class="s2">" for this shape (</span><span class="si">{</span><span class="n">num_rows</span><span class="si">}</span><span class="s2">,</span><span class="si">{</span><span class="n">num_cols</span><span class="si">}</span><span class="s2">)"</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">output_arr</span><span class="p">)</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s2">"csr"</span><span class="p">)</span> |
| |
| |
| <div class="viewcode-block" id="assign_each"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.assign_each">[docs]</a><span class="k">def</span> <span class="nf">assign_each</span><span class="p">(</span><span class="n">the_input</span><span class="p">,</span> <span class="n">function</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Return ndarray composed of passing each array value through some function"""</span> |
| <span class="k">if</span> <span class="n">function</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">the_input</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">it_input</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nditer</span><span class="p">(</span><span class="n">the_input</span><span class="p">,</span> <span class="n">flags</span><span class="o">=</span><span class="p">[</span><span class="s1">'f_index'</span><span class="p">])</span> |
| |
| <span class="n">output</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">the_input</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> |
| <span class="n">it_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nditer</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">flags</span><span class="o">=</span><span class="p">[</span><span class="s1">'f_index'</span><span class="p">],</span> <span class="n">op_flags</span><span class="o">=</span><span class="p">[</span><span class="s1">'writeonly'</span><span class="p">])</span> |
| |
| <span class="k">while</span> <span class="ow">not</span> <span class="n">it_input</span><span class="o">.</span><span class="n">finished</span><span class="p">:</span> |
| <span class="n">val_input</span> <span class="o">=</span> <span class="n">it_input</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">it_out</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">function</span><span class="p">(</span><span class="n">val_input</span><span class="p">)</span> |
| <span class="n">it_input</span><span class="o">.</span><span class="n">iternext</span><span class="p">()</span> |
| <span class="n">it_out</span><span class="o">.</span><span class="n">iternext</span><span class="p">()</span> |
| |
| <span class="k">return</span> <span class="n">output</span></div> |
| |
| <div class="viewcode-block" id="assign_each2"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.assign_each2">[docs]</a><span class="k">def</span> <span class="nf">assign_each2</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">,</span> <span class="n">function</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Return ndarray composed of passing two array values through some function"""</span> |
| <span class="k">if</span> <span class="n">function</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">input1</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">input1</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">input2</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">it_input1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nditer</span><span class="p">(</span><span class="n">input1</span><span class="p">,</span> <span class="n">flags</span><span class="o">=</span><span class="p">[</span><span class="s1">'f_index'</span><span class="p">])</span> |
| <span class="n">it_input2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nditer</span><span class="p">(</span><span class="n">input2</span><span class="p">,</span> <span class="n">flags</span><span class="o">=</span><span class="p">[</span><span class="s1">'f_index'</span><span class="p">])</span> |
| |
| <span class="n">output</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">input1</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> |
| <span class="n">it_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nditer</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">flags</span><span class="o">=</span><span class="p">[</span><span class="s1">'f_index'</span><span class="p">],</span> <span class="n">op_flags</span><span class="o">=</span><span class="p">[</span><span class="s1">'writeonly'</span><span class="p">])</span> |
| |
| <span class="k">while</span> <span class="ow">not</span> <span class="n">it_input1</span><span class="o">.</span><span class="n">finished</span><span class="p">:</span> |
| <span class="n">val_input1</span> <span class="o">=</span> <span class="n">it_input1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">val_input2</span> <span class="o">=</span> <span class="n">it_input2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">it_out</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">function</span><span class="p">(</span><span class="n">val_input1</span><span class="p">,</span> <span class="n">val_input2</span><span class="p">)</span> |
| <span class="n">it_input1</span><span class="o">.</span><span class="n">iternext</span><span class="p">()</span> |
| <span class="n">it_input2</span><span class="o">.</span><span class="n">iternext</span><span class="p">()</span> |
| <span class="n">it_out</span><span class="o">.</span><span class="n">iternext</span><span class="p">()</span> |
| |
| <span class="k">return</span> <span class="n">output</span></div> |
| |
| <span class="k">def</span> <span class="nf">create_2d_np_tensor</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">):</span> |
| <span class="n">inp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">rows</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="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">inp</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">inp</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">columns</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">inp</span> |
| |
| <span class="c1"># For testing Large Tensors having total size > 2^32 elements</span> |
| <span class="k">def</span> <span class="nf">create_2d_tensor</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">):</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">rows</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="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">columns</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">b</span> |
| |
| <span class="c1"># For testing Large Vectors having total size > 2^32 elements</span> |
| <span class="k">def</span> <span class="nf">create_vector</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">):</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">size</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">return</span> <span class="n">a</span> |
| |
| <div class="viewcode-block" id="rand_sparse_ndarray"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.rand_sparse_ndarray">[docs]</a><span class="k">def</span> <span class="nf">rand_sparse_ndarray</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">stype</span><span class="p">,</span> <span class="n">density</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="n">distribution</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">data_init</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">rsp_indices</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">modifier_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">shuffle_csr_indices</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Generate a random sparse ndarray. Returns the ndarray, value(np) and indices(np)</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> shape: list or tuple</span> |
| <span class="sd"> stype: str</span> |
| <span class="sd"> valid values: "csr" or "row_sparse"</span> |
| <span class="sd"> density: float, optional</span> |
| <span class="sd"> should be between 0 and 1</span> |
| <span class="sd"> distribution: str, optional</span> |
| <span class="sd"> valid values: "uniform" or "powerlaw"</span> |
| <span class="sd"> dtype: numpy.dtype, optional</span> |
| <span class="sd"> default value is None</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> Result of type CSRNDArray or RowSparseNDArray</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> Below is an example of the powerlaw distribution with csr as the stype.</span> |
| <span class="sd"> It calculates the nnz using the shape and density.</span> |
| <span class="sd"> It fills up the ndarray with exponentially increasing number of elements.</span> |
| <span class="sd"> If there are enough unused_nnzs, n+1th row will have twice more nnzs compared to nth row.</span> |
| <span class="sd"> else, remaining unused_nnzs will be used in n+1th row</span> |
| <span class="sd"> If number of cols is too small and we have already reached column size it will fill up</span> |
| <span class="sd"> all following columns in all followings rows until we reach the required density.</span> |
| |
| <span class="sd"> >>> csr_arr, _ = rand_sparse_ndarray(shape=(5, 16), stype="csr",</span> |
| <span class="sd"> density=0.50, distribution="powerlaw")</span> |
| <span class="sd"> >>> indptr = csr_arr.indptr.asnumpy()</span> |
| <span class="sd"> >>> indices = csr_arr.indices.asnumpy()</span> |
| <span class="sd"> >>> data = csr_arr.data.asnumpy()</span> |
| <span class="sd"> >>> row2nnz = len(data[indptr[1]:indptr[2]])</span> |
| <span class="sd"> >>> row3nnz = len(data[indptr[2]:indptr[3]])</span> |
| <span class="sd"> >>> assert(row3nnz == 2*row2nnz)</span> |
| <span class="sd"> >>> row4nnz = len(data[indptr[3]:indptr[4]])</span> |
| <span class="sd"> >>> assert(row4nnz == 2*row3nnz)</span> |
| |
| <span class="sd"> """</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">ctx</span> <span class="k">if</span> <span class="n">ctx</span> <span class="k">else</span> <span class="n">default_device</span><span class="p">()</span> |
| <span class="n">density</span> <span class="o">=</span> <span class="n">rnd</span><span class="o">.</span><span class="n">rand</span><span class="p">()</span> <span class="k">if</span> <span class="n">density</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">density</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">default_dtype</span><span class="p">()</span> <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">dtype</span> |
| <span class="n">distribution</span> <span class="o">=</span> <span class="s2">"uniform"</span> <span class="k">if</span> <span class="n">distribution</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">distribution</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="o">==</span> <span class="s1">'row_sparse'</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="p">(</span><span class="n">distribution</span> <span class="o">==</span> <span class="s2">"uniform"</span><span class="p">),</span> \ |
| <span class="sa">f</span><span class="s2">"Distribution </span><span class="si">{</span><span class="n">distribution</span><span class="si">}</span><span class="s2"> not supported for row_sparse"</span> |
| <span class="c1"># sample index</span> |
| <span class="k">if</span> <span class="n">rsp_indices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">indices</span> <span class="o">=</span> <span class="n">rsp_indices</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span> <span class="o"><=</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">idx_sample</span> <span class="o">=</span> <span class="n">rnd</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">idx_sample</span> <span class="o"><</span> <span class="n">density</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">indices</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="s1">'row_sparse'</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="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">result</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</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="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([]))</span> |
| <span class="c1"># generate random values</span> |
| <span class="n">val</span> <span class="o">=</span> <span class="n">rnd</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">indices</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">*</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> |
| |
| <span class="c1"># Allow caller to override or adjust random values</span> |
| <span class="k">if</span> <span class="n">data_init</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">val</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">data_init</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">modifier_func</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">val</span> <span class="o">=</span> <span class="n">assign_each</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">modifier_func</span><span class="p">)</span> |
| |
| <span class="n">arr</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">row_sparse_array</span><span class="p">((</span><span class="n">val</span><span class="p">,</span> <span class="n">indices</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">arr</span><span class="p">,</span> <span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">indices</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">stype</span> <span class="o">==</span> <span class="s1">'csr'</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> |
| <span class="k">if</span> <span class="n">distribution</span> <span class="o">==</span> <span class="s2">"uniform"</span><span class="p">:</span> |
| <span class="n">csr</span> <span class="o">=</span> <span class="n">_get_uniform_dataset_csr</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">density</span><span class="p">,</span> |
| <span class="n">data_init</span><span class="o">=</span><span class="n">data_init</span><span class="p">,</span> |
| <span class="n">shuffle_csr_indices</span><span class="o">=</span><span class="n">shuffle_csr_indices</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="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">csr</span><span class="p">,</span> <span class="p">(</span><span class="n">csr</span><span class="o">.</span><span class="n">indptr</span><span class="p">,</span> <span class="n">csr</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span> <span class="n">csr</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">distribution</span> <span class="o">==</span> <span class="s2">"powerlaw"</span><span class="p">:</span> |
| <span class="n">csr</span> <span class="o">=</span> <span class="n">_get_powerlaw_dataset_csr</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">density</span><span class="o">=</span><span class="n">density</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="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">csr</span><span class="p">,</span> <span class="p">(</span><span class="n">csr</span><span class="o">.</span><span class="n">indptr</span><span class="p">,</span> <span class="n">csr</span><span class="o">.</span><span class="n">indices</span><span class="p">,</span> <span class="n">csr</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span><span class="p">(</span><span class="kc">False</span><span class="p">),</span> <span class="sa">f</span><span class="s2">"Distribution not supported: </span><span class="si">{</span><span class="n">distribution</span><span class="si">}</span><span class="s2">"</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span><span class="p">(</span><span class="kc">False</span><span class="p">),</span> <span class="s2">"unknown storage type"</span> |
| <span class="k">return</span> <span class="kc">False</span></div> |
| |
| <div class="viewcode-block" id="rand_ndarray"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.rand_ndarray">[docs]</a><span class="k">def</span> <span class="nf">rand_ndarray</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="s1">'default'</span><span class="p">,</span> <span class="n">density</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="n">modifier_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">shuffle_csr_indices</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">distribution</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Generate a random sparse ndarray. Returns the generated ndarray."""</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">ctx</span> <span class="k">if</span> <span class="n">ctx</span> <span class="k">else</span> <span class="n">default_device</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="n">arr</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">random_arrays</span><span class="p">(</span><span class="n">shape</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="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">arr</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">rand_sparse_ndarray</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">stype</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="n">density</span><span class="p">,</span> |
| <span class="n">modifier_func</span><span class="o">=</span><span class="n">modifier_func</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="n">shuffle_csr_indices</span><span class="o">=</span><span class="n">shuffle_csr_indices</span><span class="p">,</span> |
| <span class="n">distribution</span><span class="o">=</span><span class="n">distribution</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">arr</span></div> |
| |
| |
| <div class="viewcode-block" id="create_sparse_array"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.create_sparse_array">[docs]</a><span class="k">def</span> <span class="nf">create_sparse_array</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">stype</span><span class="p">,</span> <span class="n">data_init</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">rsp_indices</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="n">modifier_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="mf">.5</span><span class="p">,</span> |
| <span class="n">shuffle_csr_indices</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Create a sparse array, For Rsp, assure indices are in a canonical format"""</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="o">==</span> <span class="s1">'row_sparse'</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">rsp_indices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">arr_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">rsp_indices</span><span class="p">)</span> |
| <span class="n">arr_indices</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">arr_indices</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="n">arr_data</span><span class="p">,</span> <span class="p">(</span><span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="o">=</span> <span class="n">rand_sparse_ndarray</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">stype</span><span class="p">,</span> |
| <span class="n">density</span><span class="o">=</span><span class="n">density</span><span class="p">,</span> |
| <span class="n">data_init</span><span class="o">=</span><span class="n">data_init</span><span class="p">,</span> |
| <span class="n">rsp_indices</span><span class="o">=</span><span class="n">arr_indices</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="n">modifier_func</span><span class="o">=</span><span class="n">modifier_func</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">stype</span> <span class="o">==</span> <span class="s1">'csr'</span><span class="p">:</span> |
| <span class="n">arr_data</span><span class="p">,</span> <span class="p">(</span><span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="o">=</span> <span class="n">rand_sparse_ndarray</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> |
| <span class="n">stype</span><span class="p">,</span> |
| <span class="n">density</span><span class="o">=</span><span class="n">density</span><span class="p">,</span> |
| <span class="n">data_init</span><span class="o">=</span><span class="n">data_init</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="n">modifier_func</span><span class="o">=</span><span class="n">modifier_func</span><span class="p">,</span> |
| <span class="n">shuffle_csr_indices</span><span class="o">=</span><span class="n">shuffle_csr_indices</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">msg</span> <span class="o">=</span> <span class="s2">"Unknown storage type: "</span> <span class="o">+</span> <span class="n">stype</span> |
| <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">arr_data</span></div> |
| |
| |
| <div class="viewcode-block" id="create_sparse_array_zd"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.create_sparse_array_zd">[docs]</a><span class="k">def</span> <span class="nf">create_sparse_array_zd</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">stype</span><span class="p">,</span> <span class="n">density</span><span class="p">,</span> <span class="n">data_init</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">rsp_indices</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="n">modifier_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">shuffle_csr_indices</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Create sparse array, using only rsp_indices to determine density"""</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="o">==</span> <span class="s1">'row_sparse'</span><span class="p">:</span> |
| <span class="n">density</span> <span class="o">=</span> <span class="mf">0.0</span> |
| <span class="k">if</span> <span class="n">rsp_indices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">rsp_indices</span><span class="p">)</span> <span class="o"><=</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">create_sparse_array</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">stype</span><span class="p">,</span> |
| <span class="n">data_init</span><span class="o">=</span><span class="n">data_init</span><span class="p">,</span> |
| <span class="n">rsp_indices</span><span class="o">=</span><span class="n">rsp_indices</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="n">modifier_func</span><span class="o">=</span><span class="n">modifier_func</span><span class="p">,</span> |
| <span class="n">density</span><span class="o">=</span><span class="n">density</span><span class="p">,</span> |
| <span class="n">shuffle_csr_indices</span><span class="o">=</span><span class="n">shuffle_csr_indices</span><span class="p">)</span></div> |
| |
| |
| <span class="k">def</span> <span class="nf">rand_shape_2d</span><span class="p">(</span><span class="n">dim0</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">dim1</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">allow_zero_size</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="n">low</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">allow_zero_size</span> <span class="k">else</span> <span class="mi">1</span> |
| <span class="k">return</span> <span class="n">rnd</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">dim0</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">rnd</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">dim1</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> |
| |
| |
| <span class="k">def</span> <span class="nf">rand_shape_3d</span><span class="p">(</span><span class="n">dim0</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">dim1</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">dim2</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">allow_zero_size</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="n">low</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">allow_zero_size</span> <span class="k">else</span> <span class="mi">1</span> |
| <span class="k">return</span> <span class="n">rnd</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">dim0</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">rnd</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">dim1</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">rnd</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">dim2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> |
| |
| |
| <span class="k">def</span> <span class="nf">rand_shape_nd</span><span class="p">(</span><span class="n">num_dim</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">allow_zero_size</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="n">low</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">allow_zero_size</span> <span class="k">else</span> <span class="mi">1</span> |
| <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">rnd</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">dim</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">num_dim</span><span class="p">))</span> |
| |
| |
| <span class="k">def</span> <span class="nf">rand_coord_2d</span><span class="p">(</span><span class="n">x_low</span><span class="p">,</span> <span class="n">x_high</span><span class="p">,</span> <span class="n">y_low</span><span class="p">,</span> <span class="n">y_high</span><span class="p">):</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">x_low</span><span class="p">,</span> <span class="n">x_high</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">y_low</span><span class="p">,</span> <span class="n">y_high</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> |
| |
| |
| <div class="viewcode-block" id="np_reduce"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.np_reduce">[docs]</a><span class="k">def</span> <span class="nf">np_reduce</span><span class="p">(</span><span class="n">dat</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="n">keepdims</span><span class="p">,</span> <span class="n">numpy_reduce_func</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Compatible reduce for old version of NumPy.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dat : np.ndarray</span> |
| <span class="sd"> Same as NumPy.</span> |
| |
| <span class="sd"> axis : None or int or list-like</span> |
| <span class="sd"> Same as NumPy.</span> |
| |
| <span class="sd"> keepdims : bool</span> |
| <span class="sd"> Same as NumPy.</span> |
| |
| <span class="sd"> numpy_reduce_func : function</span> |
| <span class="sd"> A NumPy reducing function like ``np.sum`` or ``np.max``.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="n">axis</span> <span class="o">=</span> <span class="p">[</span><span class="n">axis</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">axis</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span> <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dat</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">dat</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">axis</span><span class="p">)):</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">numpy_reduce_func</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">i</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">keepdims</span><span class="p">:</span> |
| <span class="n">keepdims_shape</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">dat</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">axis</span><span class="p">:</span> |
| <span class="n">keepdims_shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">keepdims_shape</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">ret</span></div> |
| |
| |
| <span class="k">def</span> <span class="nf">_find_max_violation</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Finds and returns the location of maximum violation."""</span> |
| <span class="c1"># 'smart' absdiff that considers inf's as equals (to match np.allclose)</span> |
| <span class="n">absdiff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">),</span> <span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">a</span><span class="o">-</span><span class="n">b</span><span class="p">))</span> |
| <span class="n">tol</span> <span class="o">=</span> <span class="n">atol</span> <span class="o">+</span> <span class="n">rtol</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> |
| <span class="n">violation</span> <span class="o">=</span> <span class="n">absdiff</span><span class="o">/</span><span class="p">(</span><span class="n">tol</span><span class="o">+</span><span class="mf">1e-20</span><span class="p">)</span> |
| <span class="n">loc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">violation</span><span class="p">)</span> |
| <span class="n">idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unravel_index</span><span class="p">(</span><span class="n">loc</span><span class="p">,</span> <span class="n">violation</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">idx</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">violation</span><span class="p">)</span> |
| |
| |
| <div class="viewcode-block" id="same"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.same">[docs]</a><span class="k">def</span> <span class="nf">same</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Test if two NumPy arrays are the same.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> a : np.ndarray</span> |
| <span class="sd"> b : np.ndarray</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span></div> |
| |
| |
| <span class="k">def</span> <span class="nf">checkShapes</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">b</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="n">msg</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">build_err_msg</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">],</span> |
| <span class="n">err_msg</span><span class="o">=</span><span class="s2">"a.shape = </span><span class="si">{}</span><span class="s2"> and b.shape = </span><span class="si">{}</span><span class="s2"> are not equal"</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">a</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span> |
| <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span> |
| |
| |
| <div class="viewcode-block" id="almost_equal"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.almost_equal">[docs]</a><span class="k">def</span> <span class="nf">almost_equal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">equal_nan</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">use_broadcast</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Test if two numpy arrays are almost equal."""</span> |
| <span class="c1"># pylint: disable=unexpected-keyword-arg</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">use_broadcast</span><span class="p">:</span> |
| <span class="n">checkShapes</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="n">get_rtol</span><span class="p">(</span><span class="n">rtol</span><span class="p">),</span> <span class="n">atol</span><span class="o">=</span><span class="n">get_atol</span><span class="p">(</span><span class="n">atol</span><span class="p">),</span> <span class="n">equal_nan</span><span class="o">=</span><span class="n">equal_nan</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable=unexpected-keyword-arg</span> |
| |
| <div class="viewcode-block" id="locationError"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.locationError">[docs]</a><span class="k">def</span> <span class="nf">locationError</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">names</span><span class="p">,</span> <span class="n">maxError</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Create element mismatch comment</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> a, b : compared np.ndarray's</span> |
| <span class="sd"> index : tuple of coordinate arrays</span> |
| <span class="sd"> Location of violation</span> |
| <span class="sd"> names : tuple of names</span> |
| <span class="sd"> The names of compared arrays.</span> |
| <span class="sd"> maxError: boolean, optional</span> |
| <span class="sd"> Flag indicating that maximum error is reporting.</span> |
| <span class="sd"> """</span> |
| <span class="n">maximum</span> <span class="o">=</span> <span class="s2">"maximum "</span> <span class="k">if</span> <span class="n">maxError</span> <span class="k">else</span> <span class="s2">""</span> |
| <span class="k">return</span> <span class="sa">f</span><span class="s2">"Location of </span><span class="si">{</span><span class="n">maximum</span><span class="si">}</span><span class="s2"> error: </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">index</span><span class="p">)</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">=</span><span class="si">{</span><span class="n">a</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="si">:</span><span class="s2">.8f</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">names</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">=</span><span class="si">{</span><span class="n">b</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="si">:</span><span class="s2">.8f</span><span class="si">}</span><span class="s2">"</span></div> |
| <div class="viewcode-block" id="assert_almost_equal"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.assert_almost_equal">[docs]</a><span class="k">def</span> <span class="nf">assert_almost_equal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">),</span> <span class="n">equal_nan</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">use_broadcast</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">mismatches</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)):</span> |
| <span class="w"> </span><span class="sd">"""Test that two numpy arrays are almost equal. Raise exception message if not.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> a : np.ndarray or mx.nd.array</span> |
| <span class="sd"> b : np.ndarray or mx.nd.array</span> |
| <span class="sd"> rtol : None or float or dict of dtype -> float</span> |
| <span class="sd"> The relative threshold. Default threshold will be used if set to ``None``.</span> |
| <span class="sd"> atol : None or float or dict of dtype -> float</span> |
| <span class="sd"> The absolute threshold. Default threshold will be used if set to ``None``.</span> |
| <span class="sd"> names : tuple of names, optional</span> |
| <span class="sd"> The names used in error message when an exception occurs</span> |
| <span class="sd"> equal_nan : boolean, optional</span> |
| <span class="sd"> The flag determining how to treat NAN values in comparison</span> |
| <span class="sd"> mismatches : tuple of mismatches</span> |
| <span class="sd"> Maximum number of mismatches to be printed (mismatches[0]) and determine (mismatches[1])</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">use_broadcast</span><span class="p">:</span> |
| <span class="n">checkShapes</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> |
| |
| <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span> <span class="o">=</span> <span class="n">get_tols</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">use_np_allclose</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</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="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">b</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">if</span> <span class="ow">not</span> <span class="n">use_np_allclose</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="nb">hasattr</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="s1">'ctx'</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="s1">'ctx'</span><span class="p">)</span> <span class="ow">and</span> <span class="n">a</span><span class="o">.</span><span class="n">device</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">device</span> <span class="ow">and</span> <span class="n">a</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">b</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span> |
| <span class="n">use_np_allclose</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| |
| <span class="k">if</span> <span class="n">use_np_allclose</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="s1">'dtype'</span><span class="p">)</span> <span class="ow">and</span> <span class="n">a</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">bool_</span> <span class="ow">and</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="s1">'dtype'</span><span class="p">)</span> <span class="ow">and</span> <span class="n">b</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">bool_</span><span class="p">:</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_equal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> |
| <span class="k">return</span> |
| <span class="k">if</span> <span class="n">almost_equal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">,</span> <span class="n">equal_nan</span><span class="o">=</span><span class="n">equal_nan</span><span class="p">):</span> |
| <span class="k">return</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">,</span> <span class="n">equal_nan</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">output</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">return</span> |
| |
| <span class="n">a</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| |
| <span class="n">index</span><span class="p">,</span> <span class="n">rel</span> <span class="o">=</span> <span class="n">_find_max_violation</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">index</span> <span class="o">!=</span> <span class="p">():</span> |
| <span class="c1"># a, b are the numpy arrays</span> |
| <span class="n">indexErr</span> <span class="o">=</span> <span class="n">index</span> |
| <span class="n">relErr</span> <span class="o">=</span> <span class="n">rel</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">*** Maximum errors for vector of size </span><span class="si">{}</span><span class="s1">: rtol=</span><span class="si">{}</span><span class="s1">, atol=</span><span class="si">{}</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">))</span> |
| <span class="n">aTmp</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="n">bTmp</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="n">i</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="k">while</span> <span class="n">i</span> <span class="o"><=</span> <span class="n">a</span><span class="o">.</span><span class="n">size</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">i</span> <span class="o"><=</span> <span class="n">mismatches</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">3d</span><span class="si">}</span><span class="s2">: Error </span><span class="si">{</span><span class="n">rel</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">locationError</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="w"> </span><span class="n">b</span><span class="p">,</span><span class="w"> </span><span class="n">index</span><span class="p">,</span><span class="w"> </span><span class="n">names</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| |
| <span class="n">aTmp</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">bTmp</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">if</span> <span class="n">almost_equal</span><span class="p">(</span><span class="n">aTmp</span><span class="p">,</span> <span class="n">bTmp</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">,</span> <span class="n">equal_nan</span><span class="o">=</span><span class="n">equal_nan</span><span class="p">):</span> |
| <span class="k">break</span> |
| |
| <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="n">i</span> <span class="o"><=</span> <span class="n">mismatches</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">or</span> <span class="n">mismatches</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">index</span><span class="p">,</span> <span class="n">rel</span> <span class="o">=</span> <span class="n">_find_max_violation</span><span class="p">(</span><span class="n">aTmp</span><span class="p">,</span> <span class="n">bTmp</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">break</span> |
| |
| <span class="n">mismatchDegree</span> <span class="o">=</span> <span class="s2">"at least "</span> <span class="k">if</span> <span class="n">mismatches</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">></span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">></span> <span class="n">mismatches</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">else</span> <span class="s2">""</span> |
| <span class="n">errMsg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"Error </span><span class="si">{</span><span class="n">relErr</span><span class="si">}</span><span class="s2"> exceeds tolerance rtol=</span><span class="si">{</span><span class="n">rtol</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">, atol=</span><span class="si">{</span><span class="n">atol</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2"> "</span> \ |
| <span class="sa">f</span><span class="s2">"(mismatch </span><span class="si">{</span><span class="n">mismatchDegree</span><span class="si">}{</span><span class="mi">100</span><span class="o">*</span><span class="n">i</span><span class="o">/</span><span class="n">a</span><span class="o">.</span><span class="n">size</span><span class="si">}</span><span class="s2">%).</span><span class="se">\n</span><span class="s2">"</span> \ |
| <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">locationError</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="w"> </span><span class="n">b</span><span class="p">,</span><span class="w"> </span><span class="n">indexErr</span><span class="p">,</span><span class="w"> </span><span class="n">names</span><span class="p">,</span><span class="w"> </span><span class="n">maxError</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">errMsg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"Error </span><span class="si">{</span><span class="n">rel</span><span class="si">}</span><span class="s2"> exceeds tolerance rtol=</span><span class="si">{</span><span class="n">rtol</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">, atol=</span><span class="si">{</span><span class="n">atol</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">.</span><span class="se">\n</span><span class="s2">"</span> |
| |
| <span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">threshold</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">suppress</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">msg</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">build_err_msg</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">],</span> <span class="n">err_msg</span><span class="o">=</span><span class="n">errMsg</span><span class="p">)</span> |
| |
| <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span></div> |
| |
| |
| <span class="k">def</span> <span class="nf">assert_allclose</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="mf">1e-07</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">equal_nan</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">,</span> <span class="n">equal_nan</span><span class="o">=</span><span class="n">equal_nan</span><span class="p">)</span> |
| |
| |
| <div class="viewcode-block" id="assert_almost_equal_with_err"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.assert_almost_equal_with_err">[docs]</a><span class="k">def</span> <span class="nf">assert_almost_equal_with_err</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">etol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">),</span> <span class="n">equal_nan</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">mismatches</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)):</span> |
| <span class="w"> </span><span class="sd">"""Test that two numpy arrays are almost equal within given error rate. Raise exception message if not.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> a : np.ndarray</span> |
| <span class="sd"> b : np.ndarray</span> |
| <span class="sd"> rtol : None or float or dict of dtype -> float</span> |
| <span class="sd"> The relative threshold. Default threshold will be used if set to ``None``.</span> |
| <span class="sd"> atol : None or float or dict of dtype -> float</span> |
| <span class="sd"> The absolute threshold. Default threshold will be used if set to ``None``.</span> |
| <span class="sd"> etol : None or float</span> |
| <span class="sd"> The error rate threshold. If etol is float, return true if error_rate < etol even if</span> |
| <span class="sd"> any error is found.</span> |
| <span class="sd"> names : tuple of names, optional</span> |
| <span class="sd"> The names used in error message when an exception occurs</span> |
| <span class="sd"> equal_nan : boolean, optional</span> |
| <span class="sd"> The flag determining how to treat NAN values in comparison</span> |
| <span class="sd"> mismatches : tuple of mismatches</span> |
| <span class="sd"> Maximum number of mismatches to be printed (mismatches[0]) and determine (mismatches[1])</span> |
| <span class="sd"> """</span> |
| <span class="n">etol</span> <span class="o">=</span> <span class="n">get_etol</span><span class="p">(</span><span class="n">etol</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">etol</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span> <span class="o">=</span> <span class="n">get_tols</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">equals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">)</span> |
| <span class="n">err</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">equals</span><span class="p">)</span> <span class="o">/</span> <span class="n">equals</span><span class="o">.</span><span class="n">size</span> |
| <span class="k">if</span> <span class="n">err</span> <span class="o">></span> <span class="n">etol</span><span class="p">:</span> |
| <span class="n">index</span><span class="p">,</span> <span class="n">rel</span> <span class="o">=</span> <span class="n">_find_max_violation</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">)</span> |
| <span class="n">indexErr</span> <span class="o">=</span> <span class="n">index</span> |
| <span class="n">relErr</span> <span class="o">=</span> <span class="n">rel</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">*** Maximum errors for vector of size </span><span class="si">{}</span><span class="s1">: rtol=</span><span class="si">{}</span><span class="s1">, atol=</span><span class="si">{}</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">))</span> |
| <span class="n">aTmp</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="n">bTmp</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="n">i</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="k">while</span> <span class="n">i</span> <span class="o"><=</span> <span class="n">a</span><span class="o">.</span><span class="n">size</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">i</span> <span class="o"><=</span> <span class="n">mismatches</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">i</span><span class="si">:</span><span class="s2">3d</span><span class="si">}</span><span class="s2">: Error </span><span class="si">{</span><span class="n">rel</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">locationError</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="w"> </span><span class="n">b</span><span class="p">,</span><span class="w"> </span><span class="n">index</span><span class="p">,</span><span class="w"> </span><span class="n">names</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| |
| <span class="n">aTmp</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">bTmp</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">if</span> <span class="n">almost_equal</span><span class="p">(</span><span class="n">aTmp</span><span class="p">,</span> <span class="n">bTmp</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">,</span> <span class="n">equal_nan</span><span class="o">=</span><span class="n">equal_nan</span><span class="p">):</span> |
| <span class="k">break</span> |
| |
| <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="n">i</span> <span class="o"><=</span> <span class="n">mismatches</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">or</span> <span class="n">mismatches</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">index</span><span class="p">,</span> <span class="n">rel</span> <span class="o">=</span> <span class="n">_find_max_violation</span><span class="p">(</span><span class="n">aTmp</span><span class="p">,</span> <span class="n">bTmp</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">break</span> |
| |
| <span class="n">mismatchDegree</span> <span class="o">=</span> <span class="s2">"at least "</span> <span class="k">if</span> <span class="n">mismatches</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">></span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">></span> <span class="n">mismatches</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">else</span> <span class="s2">""</span> |
| <span class="n">errMsg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"Error </span><span class="si">{</span><span class="n">relErr</span><span class="si">}</span><span class="s2"> exceeds tolerance rtol=</span><span class="si">{</span><span class="n">rtol</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2">, atol=</span><span class="si">{</span><span class="n">atol</span><span class="si">:</span><span class="s2">e</span><span class="si">}</span><span class="s2"> "</span> \ |
| <span class="sa">f</span><span class="s2">"(mismatch </span><span class="si">{</span><span class="n">mismatchDegree</span><span class="si">}{</span><span class="mi">100</span><span class="o">*</span><span class="n">i</span><span class="o">/</span><span class="n">a</span><span class="o">.</span><span class="n">size</span><span class="si">}</span><span class="s2">%).</span><span class="se">\n</span><span class="s2">"</span> \ |
| <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">locationError</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="w"> </span><span class="n">b</span><span class="p">,</span><span class="w"> </span><span class="n">indexErr</span><span class="p">,</span><span class="w"> </span><span class="n">names</span><span class="p">,</span><span class="w"> </span><span class="n">maxError</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">threshold</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">suppress</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">msg</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">build_err_msg</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">],</span> <span class="n">err_msg</span><span class="o">=</span><span class="n">errMsg</span><span class="p">)</span> |
| <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">,</span> <span class="n">equal_nan</span><span class="o">=</span><span class="n">equal_nan</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="assert_almost_equal_ignore_nan"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.assert_almost_equal_ignore_nan">[docs]</a><span class="k">def</span> <span class="nf">assert_almost_equal_ignore_nan</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="p">(</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">)):</span> |
| <span class="w"> </span><span class="sd">"""Test that two NumPy arrays are almost equal (ignoring NaN in either array).</span> |
| <span class="sd"> Combines a relative and absolute measure of approximate eqality.</span> |
| <span class="sd"> If either the relative or absolute check passes, the arrays are considered equal.</span> |
| <span class="sd"> Including an absolute check resolves issues with the relative check where all</span> |
| <span class="sd"> array values are close to zero.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> a : np.ndarray</span> |
| <span class="sd"> b : np.ndarray</span> |
| <span class="sd"> rtol : None or float</span> |
| <span class="sd"> The relative threshold. Default threshold will be used if set to ``None``.</span> |
| <span class="sd"> atol : None or float</span> |
| <span class="sd"> The absolute threshold. Default threshold will be used if set to ``None``.</span> |
| <span class="sd"> """</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> |
| <span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> |
| <span class="n">nan_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_or</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">a</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">b</span><span class="p">))</span> |
| <span class="n">a</span><span class="p">[</span><span class="n">nan_mask</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">b</span><span class="p">[</span><span class="n">nan_mask</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">,</span> <span class="n">names</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="assert_exception"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.assert_exception">[docs]</a><span class="k">def</span> <span class="nf">assert_exception</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">exception_type</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">"""Test that function f will throw an exception of type given by `exception_type`"""</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">f</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">assert</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> |
| <span class="k">except</span> <span class="n">exception_type</span><span class="p">:</span> |
| <span class="k">return</span></div> |
| |
| |
| <span class="k">def</span> <span class="nf">_parse_location</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">location</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">default_dtype</span><span class="p">()):</span> |
| <span class="w"> </span><span class="sd">"""Parses the given location to a ordered dictionary.</span> |
| |
| <span class="sd"> Arguments of the provided op `sym` are used as dictionary keys</span> |
| <span class="sd"> and elements of `location` are used as values.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sym : Symbol</span> |
| <span class="sd"> Symbol containing op</span> |
| <span class="sd"> location : list or tuple or dict</span> |
| <span class="sd"> Argument values location</span> |
| |
| <span class="sd"> - if type is list or tuple of `np.ndarray`</span> |
| <span class="sd"> inner elements are arrays correspoding to</span> |
| <span class="sd"> ``sym.list_arguments()``.</span> |
| <span class="sd"> - if type is dict of str -> `np.ndarray`</span> |
| <span class="sd"> maps the name of arguments to the corresponding `np.ndarray`.</span> |
| <span class="sd"> *In either case, value of all the arguments must be provided.*</span> |
| <span class="sd"> ctx : Device</span> |
| <span class="sd"> Device context.</span> |
| <span class="sd"> dtype: "asnumpy" or np.float16 or np.float32 or np.float64</span> |
| <span class="sd"> If dtype is "asnumpy" then the mx.nd.array created will have the same</span> |
| <span class="sd"> type as th numpy array from which it is copied.</span> |
| <span class="sd"> Otherwise, dtype is the explicit datatype for all mx.nd.array objects</span> |
| <span class="sd"> created in this function.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> dict</span> |
| <span class="sd"> Dictionary with `sym` arguments as keys and `location` elements as</span> |
| <span class="sd"> values.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> a = mx.symbol.Variable('a')</span> |
| <span class="sd"> >>> b = mx.symbol.Variable('b')</span> |
| <span class="sd"> >>> l1 = np.ndarray([2,3])</span> |
| <span class="sd"> >>> l2 = np.ndarray([3,4])</span> |
| <span class="sd"> >>> _parse_location(a * b, [l1, l2], None)</span> |
| <span class="sd"> {'a': <NDArray 2x3 @cpu(0)>, 'b': <NDArray 3x4 @cpu(0)>}</span> |
| <span class="sd"> >>> _parse_location(a * b, {'a': l1, 'b': l2}, None)</span> |
| <span class="sd"> {'a': <NDArray 2x3 @cpu(0)>, 'b': <NDArray 3x4 @cpu(0)>}</span> |
| <span class="sd"> >>> _parse_location(a * b, {'a': l1}, None)</span> |
| <span class="sd"> ValueError: Symbol arguments and keys of the given location do not match.</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">location</span><span class="p">,</span> <span class="p">(</span><span class="nb">dict</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="k">assert</span> <span class="n">dtype</span> <span class="o">==</span> <span class="s2">"asnumpy"</span> <span class="ow">or</span> <span class="n">dtype</span> <span class="ow">in</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="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">location</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">location</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">!=</span> <span class="nb">set</span><span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Symbol arguments and keys of the given location do not match."</span> |
| <span class="sa">f</span><span class="s2">"symbol args:</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()))</span><span class="si">}</span><span class="s2">, location.keys():</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">location</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">location</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</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">zip</span><span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">(),</span> <span class="n">location</span><span class="p">)}</span> |
| <span class="n">location</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">v</span><span class="o">.</span><span class="n">dtype</span> <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="s2">"asnumpy"</span> <span class="k">else</span> <span class="n">dtype</span><span class="p">)</span> \ |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</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">else</span> <span class="n">v</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="n">location</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| <span class="k">return</span> <span class="n">_sorted_dict</span><span class="p">(</span><span class="n">location</span><span class="p">)</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_parse_aux_states</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">aux_states</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">default_dtype</span><span class="p">()):</span> |
| <span class="w"> </span><span class="sd">"""Parses the given auxiliary states to a dictionary.</span> |
| |
| <span class="sd"> Auxiliary states of the provided op `sym` are used as dictionary</span> |
| <span class="sd"> keys and elements of `aux_states` are used as values.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sym : Symbol</span> |
| <span class="sd"> Symbol containing op</span> |
| <span class="sd"> aux_states : None or list or dict</span> |
| <span class="sd"> Aux states</span> |
| |
| <span class="sd"> - if type is list or tuple of `np.ndarray`</span> |
| <span class="sd"> inner elements are arrays correspoding to</span> |
| <span class="sd"> ``sym.list_auxiliary_states()``.</span> |
| <span class="sd"> - if type is dict of str -> `np.ndarray`</span> |
| <span class="sd"> maps the name of arguments to the corresponding `np.ndarray`.</span> |
| <span class="sd"> *In either case, all aux states of `sym` must be provided.*</span> |
| <span class="sd"> ctx : Device</span> |
| <span class="sd"> Device context.</span> |
| <span class="sd"> dtype: "asnumpy" or np.float16 or np.float32 or np.float64</span> |
| <span class="sd"> If dtype is "asnumpy" then the mx.nd.array created will have the same</span> |
| <span class="sd"> type as th numpy array from which it is copied.</span> |
| <span class="sd"> Otherwise, dtype is the explicit datatype for all mx.nd.array objects</span> |
| <span class="sd"> created in this function.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> dict</span> |
| <span class="sd"> Dictionary with `sym` aux states as keys and `aux_states` elements</span> |
| <span class="sd"> as values.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> data = mx.symbol.Variable('data')</span> |
| <span class="sd"> >>> weight = mx.sym.Variable(name='fc1_weight')</span> |
| <span class="sd"> >>> fc1 = mx.symbol.FullyConnected(data = data, weight=weight, name='fc1', num_hidden=128)</span> |
| <span class="sd"> >>> fc2 = mx.symbol.BatchNorm(fc1, name='batchnorm0')</span> |
| <span class="sd"> >>> mean_states = np.ones(3)</span> |
| <span class="sd"> >>> var_states = np.ones(3)</span> |
| <span class="sd"> >>> _parse_aux_states(fc2, [mean_states, var_states], None)</span> |
| <span class="sd"> {'batchnorm0_moving_var': <NDArray 3 @cpu(0)>, 'batchnorm0_moving_mean': <NDArray 3 @cpu(0)>}</span> |
| <span class="sd"> >>> _parse_aux_states(fc2, {'batchnorm0_moving_var': mean_states,</span> |
| <span class="sd"> ... 'batchnorm0_moving_mean': var_states}, None)</span> |
| <span class="sd"> {'batchnorm0_moving_var': <NDArray 3 @cpu(0)>, 'batchnorm0_moving_mean': <NDArray 3 @cpu(0)>}</span> |
| <span class="sd"> >>> _parse_aux_states(fc2, {'batchnorm0_moving_var': mean_states}, None)</span> |
| <span class="sd"> ValueError: Symbol aux_states names and given aux_states do not match.</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="n">dtype</span> <span class="o">==</span> <span class="s2">"asnumpy"</span> <span class="ow">or</span> <span class="n">dtype</span> <span class="ow">in</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="k">if</span> <span class="n">aux_states</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="nb">isinstance</span><span class="p">(</span><span class="n">aux_states</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">aux_states</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">!=</span> <span class="nb">set</span><span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">()):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Symbol aux_states names and given aux_states do not match."</span> |
| <span class="sa">f</span><span class="s2">"symbol aux_names:</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">()))</span><span class="si">}</span><span class="s2">, aux_states.keys:</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">aux_states</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">aux_states</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">aux_names</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">()</span> |
| <span class="n">aux_states</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span><span class="n">v</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">zip</span><span class="p">(</span><span class="n">aux_names</span><span class="p">,</span> <span class="n">aux_states</span><span class="p">)}</span> |
| <span class="n">aux_states</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">v</span><span class="o">.</span><span class="n">dtype</span> <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="s2">"asnumpy"</span> <span class="k">else</span> <span class="n">dtype</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="n">aux_states</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| <span class="k">return</span> <span class="n">aux_states</span> |
| |
| |
| <div class="viewcode-block" id="numeric_grad"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.numeric_grad">[docs]</a><span class="k">def</span> <span class="nf">numeric_grad</span><span class="p">(</span><span class="n">executor</span><span class="p">,</span> <span class="n">location</span><span class="p">,</span> <span class="n">aux_states</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> |
| <span class="n">use_forward_train</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="n">default_dtype</span><span class="p">()):</span> |
| <span class="w"> </span><span class="sd">"""Calculates a numeric gradient via finite difference method.</span> |
| |
| <span class="sd"> Class based on Theano's `theano.gradient.numeric_grad` [1]</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> executor : Executor</span> |
| <span class="sd"> Executor that computes the forward pass.</span> |
| <span class="sd"> location : list of numpy.ndarray or dict of str to numpy.ndarray</span> |
| <span class="sd"> Argument values used as location to compute gradient</span> |
| <span class="sd"> Maps the name of arguments to the corresponding numpy.ndarray.</span> |
| <span class="sd"> Value of all the arguments must be provided.</span> |
| <span class="sd"> aux_states : None or list of numpy.ndarray or dict of str to numpy.ndarray, optional</span> |
| <span class="sd"> Auxiliary states values used as location to compute gradient</span> |
| <span class="sd"> Maps the name of aux_states to the corresponding numpy.ndarray.</span> |
| <span class="sd"> Value of all the auxiliary arguments must be provided.</span> |
| <span class="sd"> eps : float, optional</span> |
| <span class="sd"> Epsilon for the finite-difference method.</span> |
| <span class="sd"> use_forward_train : bool, optional</span> |
| <span class="sd"> Whether to use `is_train=True` in testing.</span> |
| <span class="sd"> dtype: np.float16 or np.float32 or np.float64</span> |
| <span class="sd"> Datatype for mx.nd.array.</span> |
| |
| <span class="sd"> References</span> |
| <span class="sd"> ---------</span> |
| <span class="sd"> ..[1] https://github.com/Theano/Theano/blob/master/theano/gradient.py</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="nf">as_stype</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">stype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">cast_storage</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">var</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="n">stype</span><span class="o">=</span><span class="n">stype</span><span class="p">)</span> |
| |
| <span class="k">assert</span> <span class="n">dtype</span> <span class="ow">in</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="n">approx_grads</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</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">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">location</span><span class="o">.</span><span class="n">items</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="n">location</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">stype</span> <span class="o">=</span> <span class="n">executor</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">stype</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="n">executor</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">k</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">as_stype</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">stype</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">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">location</span><span class="p">:</span> |
| <span class="n">location</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">order</span><span class="o">=</span><span class="s1">'C'</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="n">location</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">v</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">kind</span> <span class="o">!=</span> <span class="s1">'f'</span><span class="p">:</span> |
| <span class="k">continue</span> |
| <span class="n">stype</span> <span class="o">=</span> <span class="n">executor</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">stype</span> |
| <span class="n">old_value</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">))):</span> |
| <span class="c1"># inplace update</span> |
| <span class="n">v</span><span class="o">.</span><span class="n">ravel</span><span class="p">()[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="n">eps</span><span class="o">/</span><span class="mf">2.0</span> |
| <span class="n">executor</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">k</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">as_stype</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">stype</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">if</span> <span class="n">aux_states</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">aux_states</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">executor</span><span class="o">.</span><span class="n">aux_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">val</span> |
| <span class="n">executor</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="n">use_forward_train</span><span class="p">)</span> |
| <span class="n">f_peps</span> <span class="o">=</span> <span class="n">executor</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| |
| <span class="n">v</span><span class="o">.</span><span class="n">ravel</span><span class="p">()[</span><span class="n">i</span><span class="p">]</span> <span class="o">-=</span> <span class="n">eps</span> |
| <span class="n">executor</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">k</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">as_stype</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">stype</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">if</span> <span class="n">aux_states</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">aux_states</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">adstype</span> <span class="o">=</span> <span class="n">executor</span><span class="o">.</span><span class="n">aux_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">stype</span> |
| <span class="n">executor</span><span class="o">.</span><span class="n">aux_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">as_stype</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">adstype</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="n">executor</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="n">use_forward_train</span><span class="p">)</span> |
| <span class="n">f_neps</span> <span class="o">=</span> <span class="n">executor</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| |
| <span class="n">approx_grad</span> <span class="o">=</span> <span class="p">(</span><span class="n">f_peps</span> <span class="o">-</span> <span class="n">f_neps</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="n">eps</span> |
| <span class="n">approx_grads</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">ravel</span><span class="p">()[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">approx_grad</span> |
| <span class="n">v</span><span class="o">.</span><span class="n">ravel</span><span class="p">()[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">old_value</span><span class="o">.</span><span class="n">ravel</span><span class="p">()[</span><span class="n">i</span><span class="p">]</span> |
| <span class="c1"># copy back the original value</span> |
| <span class="n">executor</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">k</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">as_stype</span><span class="p">(</span><span class="n">old_value</span><span class="p">,</span> <span class="n">stype</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">return</span> <span class="n">approx_grads</span></div> |
| |
| <div class="viewcode-block" id="check_numeric_gradient"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.check_numeric_gradient">[docs]</a><span class="k">def</span> <span class="nf">check_numeric_gradient</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">location</span><span class="p">,</span> <span class="n">aux_states</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">numeric_eps</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">atol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">grad_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_forward_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">grad_stype_dict</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="n">default_dtype</span><span class="p">()):</span> |
| <span class="w"> </span><span class="sd">"""Verify an operation by checking backward pass via finite difference method.</span> |
| |
| <span class="sd"> Based on Theano's `theano.gradient.verify_grad` [1]</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sym : Symbol</span> |
| <span class="sd"> Symbol containing op to test</span> |
| <span class="sd"> location : list or tuple or dict</span> |
| <span class="sd"> Argument values used as location to compute gradient</span> |
| |
| <span class="sd"> - if type is list of numpy.ndarray, \</span> |
| <span class="sd"> inner elements should have the same order as mxnet.sym.list_arguments().</span> |
| |
| <span class="sd"> - if type is dict of str -> numpy.ndarray, \</span> |
| <span class="sd"> maps the name of arguments to the corresponding numpy.ndarray.</span> |
| |
| <span class="sd"> *In either case, value of all the arguments must be provided.*</span> |
| <span class="sd"> aux_states : list or tuple or dict, optional</span> |
| <span class="sd"> The auxiliary states required when generating the executor for the symbol.</span> |
| <span class="sd"> numeric_eps : float, optional</span> |
| <span class="sd"> Delta for the finite difference method that approximates the gradient.</span> |
| <span class="sd"> rtol : None or float</span> |
| <span class="sd"> The relative threshold. Default threshold will be used if set to ``None``.</span> |
| <span class="sd"> atol : None or float</span> |
| <span class="sd"> The absolute threshold. Default threshold will be used if set to ``None``.</span> |
| <span class="sd"> grad_nodes : None or list or tuple or dict, optional</span> |
| <span class="sd"> Names of the nodes to check gradient on</span> |
| <span class="sd"> use_forward_train : bool</span> |
| <span class="sd"> Whether to use is_train=True when computing the finite-difference.</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Check the gradient computation on the specified device.</span> |
| <span class="sd"> grad_stype_dict : dict of str->str, optional</span> |
| <span class="sd"> Storage type dictionary for gradient ndarrays.</span> |
| <span class="sd"> dtype: np.float16 or np.float32 or np.float64</span> |
| <span class="sd"> Datatype for mx.nd.array.</span> |
| |
| <span class="sd"> References</span> |
| <span class="sd"> ---------</span> |
| <span class="sd"> [1] https://github.com/Theano/Theano/blob/master/theano/gradient.py</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="n">dtype</span> <span class="ow">in</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="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">default_device</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="nf">random_projection</span><span class="p">(</span><span class="n">shape</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Get a random weight matrix with not too small elements</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> shape : list or tuple</span> |
| <span class="sd"> """</span> |
| <span class="c1"># random_projection should not have elements too small,</span> |
| <span class="c1"># otherwise too much precision is lost in numerical gradient</span> |
| <span class="n">plain</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.1</span> |
| <span class="k">return</span> <span class="n">plain</span> |
| |
| <span class="n">location</span> <span class="o">=</span> <span class="n">_parse_location</span><span class="p">(</span><span class="n">sym</span><span class="o">=</span><span class="n">sym</span><span class="p">,</span> <span class="n">location</span><span class="o">=</span><span class="n">location</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</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="n">location_npy</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span><span class="n">v</span><span class="o">.</span><span class="n">asnumpy</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="n">location</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| <span class="n">aux_states</span> <span class="o">=</span> <span class="n">_parse_aux_states</span><span class="p">(</span><span class="n">sym</span><span class="o">=</span><span class="n">sym</span><span class="p">,</span> <span class="n">aux_states</span><span class="o">=</span><span class="n">aux_states</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</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">if</span> <span class="n">aux_states</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">aux_states_npy</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">asnumpy</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="n">aux_states</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">aux_states_npy</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="n">grad_nodes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">grad_nodes</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()</span> |
| <span class="n">grad_req</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="s1">'write'</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">grad_nodes</span><span class="p">}</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">grad_nodes</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">grad_nodes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">grad_nodes</span><span class="p">)</span> |
| <span class="n">grad_req</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="s1">'write'</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">grad_nodes</span><span class="p">}</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">grad_nodes</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="n">grad_req</span> <span class="o">=</span> <span class="n">grad_nodes</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="n">grad_nodes</span> <span class="o">=</span> <span class="n">grad_nodes</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span> |
| |
| <span class="n">input_shape</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">shape</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="n">location</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| <span class="n">_</span><span class="p">,</span> <span class="n">out_shape</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">infer_shape</span><span class="p">(</span><span class="o">**</span><span class="n">input_shape</span><span class="p">)</span> |
| <span class="n">proj</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="s2">"__random_proj"</span><span class="p">)</span> |
| <span class="n">is_np_sym</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">np_symbol</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">is_np_sym</span><span class="p">:</span> <span class="c1"># convert to np symbol for using element-wise multiplication</span> |
| <span class="n">proj</span> <span class="o">=</span> <span class="n">proj</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">sym</span> <span class="o">*</span> <span class="n">proj</span> |
| <span class="k">if</span> <span class="n">is_np_sym</span><span class="p">:</span> <span class="c1"># convert to classic symbol so that make_loss can be used</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">as_nd_ndarray</span><span class="p">()</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">make_loss</span><span class="p">(</span><span class="n">out</span><span class="p">)</span> |
| |
| <span class="n">location</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">location</span><span class="o">.</span><span class="n">items</span><span class="p">())</span> <span class="o">+</span> |
| <span class="p">[(</span><span class="s2">"__random_proj"</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">random_projection</span><span class="p">(</span><span class="n">out_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> |
| <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</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="n">args_grad_npy</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">k</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">location</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span> |
| <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">grad_nodes</span><span class="p">]</span> |
| <span class="o">+</span> <span class="p">[(</span><span class="s2">"__random_proj"</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">out_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))])</span> |
| |
| <span class="n">args_grad</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</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">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">args_grad_npy</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| <span class="k">if</span> <span class="n">grad_stype_dict</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">grad_stype_dict</span><span class="p">,</span> <span class="nb">dict</span><span class="p">),</span> <span class="s2">"grad_stype_dict must be a dict"</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="n">grad_stype_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">args_grad</span> <span class="ow">and</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">_STORAGE_TYPE_STR_TO_ID</span> <span class="ow">and</span> <span class="n">v</span> <span class="o">!=</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="c1"># create an uninitialized sparse ndarray for executor</span> |
| <span class="c1"># if the symbolic grad is expected to be zero, it should not be initialized at all</span> |
| <span class="n">args_grad</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">args_grad</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">args_grad</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> |
| <span class="n">args_grad</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> |
| |
| <span class="n">grad_req</span><span class="p">[</span><span class="s2">"__random_proj"</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'write'</span> |
| <span class="n">executor</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">_bind</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="n">grad_req</span><span class="p">,</span> |
| <span class="n">args</span><span class="o">=</span><span class="n">location</span><span class="p">,</span> <span class="n">args_grad</span><span class="o">=</span><span class="n">args_grad</span><span class="p">,</span> <span class="n">aux_states</span><span class="o">=</span><span class="n">aux_states</span><span class="p">)</span> |
| |
| <span class="n">inps</span> <span class="o">=</span> <span class="n">executor</span><span class="o">.</span><span class="n">arg_arrays</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">inps</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">location</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Executor arg_arrays and and location len do not match."</span> |
| <span class="sa">f</span><span class="s2">"Got </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">inps</span><span class="p">)</span><span class="si">}</span><span class="s2"> inputs and </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">location</span><span class="p">)</span><span class="si">}</span><span class="s2"> locations"</span><span class="p">)</span> |
| |
| <span class="n">executor</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">executor</span><span class="o">.</span><span class="n">outputs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> |
| |
| <span class="n">eps</span> <span class="o">=</span> <span class="n">get_tolerance</span><span class="p">(</span><span class="n">executor</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">numeric_eps</span><span class="p">,</span> <span class="n">default_numeric_eps</span><span class="p">())</span> |
| <span class="c1"># cannot use finite differences with small eps without high precision</span> |
| <span class="k">if</span> <span class="n">dtype</span> <span class="ow">in</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">float16</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">eps</span> <span class="o">>=</span> <span class="mf">1e-5</span> |
| |
| <span class="n">executor</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> |
| <span class="n">symbolic_grads</span> <span class="o">=</span> <span class="n">executor</span><span class="o">.</span><span class="n">grad_dict</span> |
| |
| <span class="n">numeric_gradients</span> <span class="o">=</span> <span class="n">numeric_grad</span><span class="p">(</span> |
| <span class="n">executor</span><span class="p">,</span> <span class="n">location_npy</span><span class="p">,</span> <span class="n">aux_states_npy</span><span class="p">,</span> |
| <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">,</span> <span class="n">use_forward_train</span><span class="o">=</span><span class="n">use_forward_train</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">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">grad_nodes</span><span class="p">:</span> |
| <span class="n">fd_grad</span> <span class="o">=</span> <span class="n">numeric_gradients</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="n">orig_grad</span> <span class="o">=</span> <span class="n">args_grad_npy</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="n">sym_grad</span> <span class="o">=</span> <span class="n">symbolic_grads</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">grad_req</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'write'</span><span class="p">:</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">fd_grad</span><span class="p">,</span> <span class="n">sym_grad</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">,</span> |
| <span class="p">(</span><span class="sa">f</span><span class="s2">"NUMERICAL_</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"BACKWARD_</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">))</span> |
| <span class="k">elif</span> <span class="n">grad_req</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'add'</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sym_grad</span><span class="p">,</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">sym_grad</span> <span class="o">=</span> <span class="n">sym_grad</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">fd_grad</span><span class="p">,</span> <span class="n">sym_grad</span> <span class="o">-</span> <span class="n">orig_grad</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">,</span> |
| <span class="p">(</span><span class="sa">f</span><span class="s2">"NUMERICAL_</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"BACKWARD_</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">))</span> |
| <span class="k">elif</span> <span class="n">grad_req</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'null'</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">sym_grad</span> <span class="ow">is</span> <span class="kc">None</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Invalid grad_req </span><span class="si">{</span><span class="n">grad_req</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="si">}</span><span class="s2"> for argument </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="check_symbolic_forward"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.check_symbolic_forward">[docs]</a><span class="k">def</span> <span class="nf">check_symbolic_forward</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">location</span><span class="p">,</span> <span class="n">expected</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">aux_states</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">equal_nan</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="n">default_dtype</span><span class="p">()):</span> |
| <span class="w"> </span><span class="sd">"""Compares a symbol's forward results with the expected ones.</span> |
| <span class="sd"> Prints error messages if the forward results are not the same as the expected ones.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ---------</span> |
| <span class="sd"> sym : Symbol</span> |
| <span class="sd"> output symbol</span> |
| <span class="sd"> location : list of np.ndarray or dict of str to np.ndarray</span> |
| <span class="sd"> The evaluation point</span> |
| |
| <span class="sd"> - if type is list of np.ndarray</span> |
| <span class="sd"> Contains all the numpy arrays corresponding to `sym.list_arguments()`.</span> |
| <span class="sd"> - if type is dict of str to np.ndarray</span> |
| <span class="sd"> Contains the mapping between argument names and their values.</span> |
| <span class="sd"> expected : list of np.ndarray or dict of str to np.ndarray</span> |
| <span class="sd"> The expected output value</span> |
| |
| <span class="sd"> - if type is list of np.ndarray</span> |
| <span class="sd"> Contains arrays corresponding to exe.outputs.</span> |
| <span class="sd"> - if type is dict of str to np.ndarray</span> |
| <span class="sd"> Contains mapping between sym.list_output() and exe.outputs.</span> |
| <span class="sd"> rtol : None or float</span> |
| <span class="sd"> The relative threshold. Default threshold will be used if set to ``None``.</span> |
| <span class="sd"> atol : None or float</span> |
| <span class="sd"> The absolute threshold. Default threshold will be used if set to ``None``.</span> |
| <span class="sd"> aux_states : list of np.ndarray of dict, optional</span> |
| <span class="sd"> - if type is list of np.ndarray</span> |
| <span class="sd"> Contains all the NumPy arrays corresponding to sym.list_auxiliary_states</span> |
| <span class="sd"> - if type is dict of str to np.ndarray</span> |
| <span class="sd"> Contains the mapping between names of auxiliary states and their values.</span> |
| <span class="sd"> device : Device, optional</span> |
| <span class="sd"> running context</span> |
| <span class="sd"> dtype: "asnumpy" or np.float16 or np.float32 or np.float64</span> |
| <span class="sd"> If dtype is "asnumpy" then the mx.nd.array created will have the same</span> |
| <span class="sd"> type as th numpy array from which it is copied.</span> |
| <span class="sd"> Otherwise, dtype is the explicit datatype for all mx.nd.array objects</span> |
| <span class="sd"> created in this function.</span> |
| |
| <span class="sd"> equal_nan: Boolean</span> |
| <span class="sd"> if True, `nan` is a valid value for checking equivalency (ie `nan` == `nan`)</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> shape = (2, 2)</span> |
| <span class="sd"> >>> lhs = mx.symbol.Variable('lhs')</span> |
| <span class="sd"> >>> rhs = mx.symbol.Variable('rhs')</span> |
| <span class="sd"> >>> sym_dot = mx.symbol.dot(lhs, rhs)</span> |
| <span class="sd"> >>> mat1 = np.array([[1, 2], [3, 4]])</span> |
| <span class="sd"> >>> mat2 = np.array([[5, 6], [7, 8]])</span> |
| <span class="sd"> >>> ret_expected = np.array([[19, 22], [43, 50]])</span> |
| <span class="sd"> >>> check_symbolic_forward(sym_dot, [mat1, mat2], [ret_expected])</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="n">dtype</span> <span class="o">==</span> <span class="s2">"asnumpy"</span> <span class="ow">or</span> <span class="n">dtype</span> <span class="ow">in</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="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">default_device</span><span class="p">()</span> |
| |
| <span class="n">location</span> <span class="o">=</span> <span class="n">_parse_location</span><span class="p">(</span><span class="n">sym</span><span class="o">=</span><span class="n">sym</span><span class="p">,</span> <span class="n">location</span><span class="o">=</span><span class="n">location</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</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="n">aux_states</span> <span class="o">=</span> <span class="n">_parse_aux_states</span><span class="p">(</span><span class="n">sym</span><span class="o">=</span><span class="n">sym</span><span class="p">,</span> <span class="n">aux_states</span><span class="o">=</span><span class="n">aux_states</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</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">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">expected</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="n">expected</span> <span class="o">=</span> <span class="p">[</span><span class="n">expected</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sym</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()]</span> |
| <span class="n">args_grad_data</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">v</span><span class="o">.</span><span class="n">dtype</span> <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="s2">"asnumpy"</span> <span class="k">else</span> <span class="n">dtype</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="n">location</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| |
| <span class="n">executor</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">_bind</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="n">location</span><span class="p">,</span> <span class="n">args_grad</span><span class="o">=</span><span class="n">args_grad_data</span><span class="p">,</span> <span class="n">aux_states</span><span class="o">=</span><span class="n">aux_states</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">executor</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">g</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">g</span><span class="p">[()]</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">g</span><span class="p">[:]</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| <span class="n">executor</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">executor</span><span class="o">.</span><span class="n">outputs</span> |
| <span class="k">for</span> <span class="n">output_name</span><span class="p">,</span> <span class="n">expect</span><span class="p">,</span> <span class="n">output</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">(),</span> <span class="n">expected</span><span class="p">,</span> <span class="n">outputs</span><span class="p">):</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">expect</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">,</span> |
| <span class="p">(</span><span class="sa">f</span><span class="s2">"EXPECTED_</span><span class="si">{</span><span class="n">output_name</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"FORWARD_</span><span class="si">{</span><span class="n">output_name</span><span class="si">}</span><span class="s2">"</span><span class="p">),</span> |
| <span class="n">equal_nan</span><span class="o">=</span><span class="n">equal_nan</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">executor</span><span class="o">.</span><span class="n">outputs</span></div> |
| |
| <div class="viewcode-block" id="check_symbolic_backward"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.check_symbolic_backward">[docs]</a><span class="k">def</span> <span class="nf">check_symbolic_backward</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">location</span><span class="p">,</span> <span class="n">out_grads</span><span class="p">,</span> <span class="n">expected</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">aux_states</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'write'</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">grad_stypes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">equal_nan</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="n">default_dtype</span><span class="p">()):</span> |
| <span class="w"> </span><span class="sd">"""Compares a symbol's backward results with the expected ones.</span> |
| <span class="sd"> Prints error messages if the backward results are not the same as the expected results.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ---------</span> |
| <span class="sd"> sym : Symbol</span> |
| <span class="sd"> output symbol</span> |
| <span class="sd"> location : list of np.ndarray or dict of str to np.ndarray</span> |
| <span class="sd"> The evaluation point</span> |
| |
| <span class="sd"> - if type is list of np.ndarray</span> |
| <span class="sd"> Contains all the NumPy arrays corresponding to ``mx.sym.list_arguments``.</span> |
| <span class="sd"> - if type is dict of str to np.ndarray</span> |
| <span class="sd"> Contains the mapping between argument names and their values.</span> |
| <span class="sd"> out_grads : None or list of np.ndarray or dict of str to np.ndarray</span> |
| <span class="sd"> NumPys arrays corresponding to sym.outputs for incomming gradient.</span> |
| |
| <span class="sd"> - if type is list of np.ndarray</span> |
| <span class="sd"> Contains arrays corresponding to ``exe.outputs``.</span> |
| <span class="sd"> - if type is dict of str to np.ndarray</span> |
| <span class="sd"> contains mapping between mxnet.sym.list_output() and Executor.outputs</span> |
| <span class="sd"> expected : list of np.ndarray or dict of str to np.ndarray</span> |
| <span class="sd"> expected gradient values</span> |
| |
| <span class="sd"> - if type is list of np.ndarray</span> |
| <span class="sd"> Contains arrays corresponding to exe.grad_arrays</span> |
| <span class="sd"> - if type is dict of str to np.ndarray</span> |
| <span class="sd"> Contains mapping between ``sym.list_arguments()`` and exe.outputs.</span> |
| <span class="sd"> rtol : None or float</span> |
| <span class="sd"> The relative threshold. Default threshold will be used if set to ``None``.</span> |
| <span class="sd"> atol : None or float</span> |
| <span class="sd"> The absolute threshold. Default threshold will be used if set to ``None``.</span> |
| <span class="sd"> aux_states : list of np.ndarray or dict of str to np.ndarray</span> |
| <span class="sd"> grad_req : str or list of str or dict of str to str, optional</span> |
| <span class="sd"> Gradient requirements. 'write', 'add' or 'null'.</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Running context.</span> |
| <span class="sd"> grad_stypes: dict of str->str</span> |
| <span class="sd"> dictionary of mapping argument name to stype for the gradient</span> |
| <span class="sd"> equal_nan: Boolean</span> |
| <span class="sd"> if True, `nan` is a valid value for checking equivalency (ie `nan` == `nan`)</span> |
| <span class="sd"> dtype: np.float16 or np.float32 or np.float64</span> |
| <span class="sd"> Datatype for mx.nd.array.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> lhs = mx.symbol.Variable('lhs')</span> |
| <span class="sd"> >>> rhs = mx.symbol.Variable('rhs')</span> |
| <span class="sd"> >>> sym_add = mx.symbol.elemwise_add(lhs, rhs)</span> |
| <span class="sd"> >>> mat1 = np.array([[1, 2], [3, 4]])</span> |
| <span class="sd"> >>> mat2 = np.array([[5, 6], [7, 8]])</span> |
| <span class="sd"> >>> grad1 = mx.nd.zeros(shape)</span> |
| <span class="sd"> >>> grad2 = mx.nd.zeros(shape)</span> |
| <span class="sd"> >>> exec_add = sym_add._bind(default_device(), args={'lhs': mat1, 'rhs': mat2},</span> |
| <span class="sd"> ... args_grad={'lhs': grad1, 'rhs': grad2}, grad_req={'lhs': 'write', 'rhs': 'write'})</span> |
| <span class="sd"> >>> exec_add.forward(is_train=True)</span> |
| <span class="sd"> >>> ograd = mx.nd.ones(shape)</span> |
| <span class="sd"> >>> grad_expected = ograd.copy().asnumpy()</span> |
| <span class="sd"> >>> check_symbolic_backward(sym_add, [mat1, mat2], [ograd], [grad_expected, grad_expected])</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="n">dtype</span> <span class="o">==</span> <span class="s1">'asnumpy'</span> <span class="ow">or</span> <span class="n">dtype</span> <span class="ow">in</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="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">default_device</span><span class="p">()</span> |
| |
| <span class="n">location</span> <span class="o">=</span> <span class="n">_parse_location</span><span class="p">(</span><span class="n">sym</span><span class="o">=</span><span class="n">sym</span><span class="p">,</span> <span class="n">location</span><span class="o">=</span><span class="n">location</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</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="n">aux_states</span> <span class="o">=</span> <span class="n">_parse_aux_states</span><span class="p">(</span><span class="n">sym</span><span class="o">=</span><span class="n">sym</span><span class="p">,</span> <span class="n">aux_states</span><span class="o">=</span><span class="n">aux_states</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</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">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">expected</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">expected</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span><span class="n">v</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">zip</span><span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">(),</span> <span class="n">expected</span><span class="p">)}</span> |
| |
| <span class="c1"># Dirty the output buffer deterministically, for reproducibility.</span> |
| <span class="n">args_grad_npy</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">v</span><span class="o">.</span><span class="n">shape</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="n">_sorted_items</span><span class="p">(</span><span class="n">expected</span><span class="p">)}</span> |
| <span class="n">args_grad_data</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="n">args_grad_npy</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">nd</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">expected</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="s2">"asnumpy"</span> <span class="k">else</span> <span class="n">dtype</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">grad_stypes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">grad_stypes</span><span class="p">:</span> |
| <span class="n">stype</span> <span class="o">=</span> <span class="n">grad_stypes</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">stype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">stype</span> <span class="o">!=</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">create_sparse_array</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">stype</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">nd</span> |
| <span class="n">args_grad_data</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">out</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">args_grad_data</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">nd</span> |
| |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">grad_req</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> |
| <span class="n">grad_req</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span><span class="n">grad_req</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sym</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()}</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">grad_req</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">grad_req</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span><span class="n">v</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">zip</span><span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">(),</span> <span class="n">grad_req</span><span class="p">)}</span> |
| |
| <span class="n">executor</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">_bind</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="n">location</span><span class="p">,</span> <span class="n">args_grad</span><span class="o">=</span><span class="n">args_grad_data</span><span class="p">,</span> |
| <span class="n">aux_states</span><span class="o">=</span><span class="n">aux_states</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="n">grad_req</span><span class="p">)</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">executor</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">out_grads</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span> |
| <span class="n">outg</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">arr</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">out_grads</span><span class="p">):</span> |
| <span class="n">stype</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">stype</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arr</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="n">dtype</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">dtype</span> <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="s2">"asnumpy"</span> <span class="k">else</span> <span class="n">dtype</span> |
| <span class="n">outg</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</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="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="n">stype</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">outg</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="n">stype</span><span class="p">))</span> |
| <span class="n">out_grads</span> <span class="o">=</span> <span class="n">outg</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">out_grads</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="n">outg</span> <span class="o">=</span> <span class="nb">dict</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="n">out_grads</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</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="n">dtype</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">dtype</span> <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="s2">"asnumpy"</span> <span class="k">else</span> <span class="n">dtype</span> |
| <span class="n">outg</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</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="n">outg</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span> |
| <span class="n">out_grads</span> <span class="o">=</span> <span class="n">outg</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">out_grads</span> <span class="ow">is</span> <span class="kc">None</span> |
| <span class="n">executor</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">out_grads</span><span class="p">)</span> |
| |
| <span class="n">grads</span> <span class="o">=</span> <span class="n">args_grad_data</span> |
| |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">expected</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">grad_req</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'write'</span><span class="p">:</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">expected</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> <span class="n">grads</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">,</span> |
| <span class="p">(</span><span class="sa">f</span><span class="s2">"EXPECTED_</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"BACKWARD_</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">),</span> |
| <span class="n">equal_nan</span><span class="o">=</span><span class="n">equal_nan</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">grad_req</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'add'</span><span class="p">:</span> |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">grads</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">grads</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">)</span> <span class="k">else</span> <span class="n">grads</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">expected</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> <span class="n">grad</span> <span class="o">-</span> <span class="n">args_grad_npy</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> |
| <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">,</span> <span class="p">(</span><span class="sa">f</span><span class="s2">"EXPECTED_</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"BACKWARD_</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">),</span> |
| <span class="n">equal_nan</span><span class="o">=</span><span class="n">equal_nan</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">grad_req</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'null'</span><span class="p">:</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">args_grad_npy</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> <span class="n">grads</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> |
| <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">,</span> <span class="p">(</span><span class="sa">f</span><span class="s2">"EXPECTED_</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"BACKWARD_</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">),</span> |
| <span class="n">equal_nan</span><span class="o">=</span><span class="n">equal_nan</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Invalid grad_req </span><span class="si">{</span><span class="n">grad_req</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="si">}</span><span class="s2"> for argument </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">args_grad_data</span></div> |
| |
| <div class="viewcode-block" id="check_speed"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.check_speed">[docs]</a><span class="k">def</span> <span class="nf">check_speed</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">location</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">N</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">typ</span><span class="o">=</span><span class="s2">"whole"</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">"""Check the running speed of a symbol.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sym : Symbol</span> |
| <span class="sd"> Symbol to run the speed test.</span> |
| <span class="sd"> location : none or dict of str to np.ndarray</span> |
| <span class="sd"> Location to evaluate the inner executor.</span> |
| <span class="sd"> ctx : Context</span> |
| <span class="sd"> Running context.</span> |
| <span class="sd"> N : int, optional</span> |
| <span class="sd"> Repeat times.</span> |
| <span class="sd"> grad_req : None or str or list of str or dict of str to str, optional</span> |
| <span class="sd"> Gradient requirements.</span> |
| <span class="sd"> typ : str, optional</span> |
| <span class="sd"> "whole" or "forward"</span> |
| |
| <span class="sd"> - "whole"</span> |
| <span class="sd"> Test the forward_backward speed.</span> |
| <span class="sd"> - "forward"</span> |
| <span class="sd"> Only test the forward speed.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">default_device</span><span class="p">()</span> |
| |
| <span class="k">if</span> <span class="n">grad_req</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">grad_req</span> <span class="o">=</span> <span class="s1">'write'</span> |
| <span class="k">if</span> <span class="n">location</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">exe</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">_simple_bind</span><span class="p">(</span><span class="n">grad_req</span><span class="o">=</span><span class="n">grad_req</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="n">location</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">arr</span> <span class="ow">in</span> |
| <span class="n">exe</span><span class="o">.</span><span class="n">arg_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">location</span><span class="p">,</span> <span class="nb">dict</span><span class="p">),</span> <span class="sa">f</span><span class="s1">'Expect dict, get "location"=</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">location</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span> |
| <span class="n">exe</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">_simple_bind</span><span class="p">(</span><span class="n">grad_req</span><span class="o">=</span><span class="n">grad_req</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> |
| <span class="o">**</span><span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">shape</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="n">location</span><span class="o">.</span><span class="n">items</span><span class="p">()})</span> |
| |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">iarr</span> <span class="ow">in</span> <span class="n">location</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">exe</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">iarr</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">exe</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">typ</span> <span class="o">==</span> <span class="s2">"whole"</span><span class="p">:</span> |
| <span class="c1"># Warm up</span> |
| <span class="n">exe</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">exe</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">out_grads</span><span class="o">=</span><span class="n">exe</span><span class="o">.</span><span class="n">outputs</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">exe</span><span class="o">.</span><span class="n">outputs</span><span class="p">:</span> |
| <span class="n">output</span><span class="o">.</span><span class="n">wait_to_read</span><span class="p">()</span> |
| <span class="c1"># Test forward + backward</span> |
| <span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span> |
| <span class="n">exe</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">exe</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">out_grads</span><span class="o">=</span><span class="n">exe</span><span class="o">.</span><span class="n">outputs</span><span class="p">)</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">waitall</span><span class="p">()</span> |
| <span class="n">toc</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| <span class="n">forward_backward_time</span> <span class="o">=</span> <span class="p">(</span><span class="n">toc</span> <span class="o">-</span> <span class="n">tic</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">N</span> |
| <span class="k">return</span> <span class="n">forward_backward_time</span> |
| <span class="k">elif</span> <span class="n">typ</span> <span class="o">==</span> <span class="s2">"forward"</span><span class="p">:</span> |
| <span class="c1"># Warm up</span> |
| <span class="n">exe</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">exe</span><span class="o">.</span><span class="n">outputs</span><span class="p">:</span> |
| <span class="n">output</span><span class="o">.</span><span class="n">wait_to_read</span><span class="p">()</span> |
| |
| <span class="c1"># Test forward only</span> |
| <span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span><span class="p">):</span> |
| <span class="n">exe</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">waitall</span><span class="p">()</span> |
| <span class="n">toc</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> |
| <span class="n">forward_time</span> <span class="o">=</span> <span class="p">(</span><span class="n">toc</span> <span class="o">-</span> <span class="n">tic</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">N</span> |
| <span class="k">return</span> <span class="n">forward_time</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'typ can only be "whole" or "forward".'</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="check_consistency"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.check_consistency">[docs]</a><span class="k">def</span> <span class="nf">check_consistency</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">ctx_list</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'write'</span><span class="p">,</span> |
| <span class="n">arg_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">raise_on_err</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ground_truth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">equal_nan</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">use_uniform</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">rand_type</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Check symbol gives the same output for different running context</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> sym : Symbol or list of Symbols</span> |
| <span class="sd"> Symbol(s) to run the consistency test.</span> |
| <span class="sd"> ctx_list : list</span> |
| <span class="sd"> Running context. See example for more detail.</span> |
| <span class="sd"> scale : float, optional</span> |
| <span class="sd"> Standard deviation of the inner normal distribution. Used in initialization.</span> |
| <span class="sd"> grad_req : str or list of str or dict of str to str</span> |
| <span class="sd"> Gradient requirement.</span> |
| <span class="sd"> arg_params : dict of input name -> input data</span> |
| <span class="sd"> data to use for non-aux inputs</span> |
| <span class="sd"> aux_params : dict of input name -> input data</span> |
| <span class="sd"> data to use for aux inputs</span> |
| <span class="sd"> rtol : float or dictionary dtype->float, optional</span> |
| <span class="sd"> The relative error tolerance.</span> |
| <span class="sd"> atol : float or dictionary dtype->float, optional</span> |
| <span class="sd"> The absolute error tolerance.</span> |
| <span class="sd"> raise_on_err : bool, optional, defaults to True</span> |
| <span class="sd"> Should an error raise an exception (or just output exception message)</span> |
| <span class="sd"> ground_truth : dict of output name -> data, optional</span> |
| <span class="sd"> Provided ideal result to be compared against</span> |
| <span class="sd"> equal_nan : bool, optional, defaults to False</span> |
| <span class="sd"> Should nans be treated as equal in the comparison</span> |
| <span class="sd"> use_uniform: bool</span> |
| <span class="sd"> Optional, When flag set to true,</span> |
| <span class="sd"> random input data generated follows uniform distribution,</span> |
| <span class="sd"> not normal distribution</span> |
| <span class="sd"> rand_type: np.dtype</span> |
| <span class="sd"> casts the randomly generated data to this type</span> |
| <span class="sd"> Optional, when input data is passed via arg_params,</span> |
| <span class="sd"> defaults to np.float64 (numpy float default)</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # create the symbol</span> |
| <span class="sd"> >>> sym = mx.sym.Convolution(num_filter=3, kernel=(3,3), name='conv')</span> |
| <span class="sd"> >>> # initialize the running context</span> |
| <span class="sd"> >>> ctx_list =\</span> |
| <span class="sd">[{'ctx': mx.gpu(0), 'conv_data': (2, 2, 10, 10), 'type_dict': {'conv_data': np.float64}},\</span> |
| <span class="sd"> {'ctx': mx.gpu(0), 'conv_data': (2, 2, 10, 10), 'type_dict': {'conv_data': np.float32}},\</span> |
| <span class="sd"> {'ctx': mx.gpu(0), 'conv_data': (2, 2, 10, 10), 'type_dict': {'conv_data': np.float16}},\</span> |
| <span class="sd"> {'ctx': mx.cpu(0), 'conv_data': (2, 2, 10, 10), 'type_dict': {'conv_data': np.float64}},\</span> |
| <span class="sd"> {'ctx': mx.cpu(0), 'conv_data': (2, 2, 10, 10), 'type_dict': {'conv_data': np.float32}}]</span> |
| <span class="sd"> >>> check_consistency(sym, ctx_list)</span> |
| <span class="sd"> >>> sym = mx.sym.Concat(name='concat', num_args=2)</span> |
| <span class="sd"> >>> ctx_list = \</span> |
| <span class="sd">[{'ctx': mx.gpu(0), 'concat_arg1': (2, 10), 'concat_arg0': (2, 10),\</span> |
| <span class="sd"> 'type_dict': {'concat_arg0': np.float64, 'concat_arg1': np.float64}},\</span> |
| <span class="sd"> {'ctx': mx.gpu(0), 'concat_arg1': (2, 10), 'concat_arg0': (2, 10),\</span> |
| <span class="sd"> 'type_dict': {'concat_arg0': np.float32, 'concat_arg1': np.float32}},\</span> |
| <span class="sd"> {'ctx': mx.gpu(0), 'concat_arg1': (2, 10), 'concat_arg0': (2, 10),\</span> |
| <span class="sd"> 'type_dict': {'concat_arg0': np.float16, 'concat_arg1': np.float16}},\</span> |
| <span class="sd"> {'ctx': mx.cpu(0), 'concat_arg1': (2, 10), 'concat_arg0': (2, 10),\</span> |
| <span class="sd"> 'type_dict': {'concat_arg0': np.float64, 'concat_arg1': np.float64}},\</span> |
| <span class="sd"> {'ctx': mx.cpu(0), 'concat_arg1': (2, 10), 'concat_arg0': (2, 10),\</span> |
| <span class="sd"> 'type_dict': {'concat_arg0': np.float32, 'concat_arg1': np.float32}}]</span> |
| <span class="sd"> >>> check_consistency(sym, ctx_list)</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">ctx_list</span><span class="p">)</span> <span class="o">></span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">Symbol</span><span class="p">):</span> |
| <span class="n">sym</span> <span class="o">=</span> <span class="p">[</span><span class="n">sym</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">ctx_list</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">sym</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">ctx_list</span><span class="p">)</span> |
| |
| <span class="n">output_names</span> <span class="o">=</span> <span class="n">sym</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()</span> |
| <span class="n">arg_names</span> <span class="o">=</span> <span class="n">sym</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()</span> |
| <span class="n">exe_list</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">ctx</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">ctx_list</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">s</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()</span> <span class="o">==</span> <span class="n">arg_names</span> |
| <span class="k">assert</span> <span class="n">s</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()</span> <span class="o">==</span> <span class="n">output_names</span> |
| <span class="n">exe_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">_simple_bind</span><span class="p">(</span><span class="n">grad_req</span><span class="o">=</span><span class="n">grad_req</span><span class="p">,</span> <span class="o">**</span><span class="n">ctx</span><span class="p">))</span> |
| |
| <span class="n">arg_params</span> <span class="o">=</span> <span class="p">{}</span> <span class="k">if</span> <span class="n">arg_params</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">arg_params</span> |
| <span class="n">aux_params</span> <span class="o">=</span> <span class="p">{}</span> <span class="k">if</span> <span class="n">aux_params</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">aux_params</span> |
| |
| <span class="c1"># returns the least precise of two dtypes</span> |
| <span class="k">def</span> <span class="nf">smaller_dtype</span><span class="p">(</span><span class="n">dt1</span><span class="p">,</span> <span class="n">dt2</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">dt1</span> <span class="k">if</span> <span class="n">dt2</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">dt1</span><span class="p">)</span><span class="o">.</span><span class="n">itemsize</span> <span class="o"><</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">dt2</span><span class="p">)</span><span class="o">.</span><span class="n">itemsize</span> <span class="k">else</span> <span class="n">dt2</span> |
| |
| <span class="c1"># It's important to assign random inputs in a deterministic order, for reproducibility.</span> |
| <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">_sorted_items</span><span class="p">(</span><span class="n">exe_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">arg_dict</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">n</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">arg_params</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">use_uniform</span><span class="p">:</span> |
| <span class="n">arg_params</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=-</span><span class="mf">0.92</span> <span class="o">*</span> <span class="n">scale</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mf">0.92</span> <span class="o">*</span> <span class="n">scale</span><span class="p">,</span> |
| <span class="n">size</span><span class="o">=</span><span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">rand_type</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">arg_params</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> |
| <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">rand_type</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">exe_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">aux_dict</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">n</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">aux_params</span><span class="p">:</span> |
| <span class="n">aux_params</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">for</span> <span class="n">exe</span> <span class="ow">in</span> <span class="n">exe_list</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">exe</span><span class="o">.</span><span class="n">arg_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">arr</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">arg_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">exe</span><span class="o">.</span><span class="n">aux_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">arr</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">aux_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="c1"># We need to initialize the gradient arrays if it's add.</span> |
| <span class="k">if</span> <span class="p">(</span><span class="n">grad_req</span> <span class="o">==</span> <span class="s2">"add"</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">exe</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">:</span> |
| <span class="n">arr</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">arr</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| |
| <span class="c1"># test</span> |
| <span class="k">for</span> <span class="n">exe</span> <span class="ow">in</span> <span class="n">exe_list</span><span class="p">:</span> |
| <span class="n">exe</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| |
| <span class="n">dtypes</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">exe</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="k">for</span> <span class="n">exe</span> <span class="ow">in</span> <span class="n">exe_list</span><span class="p">]</span> |
| <span class="c1"># Select the ground truth as the first model having the highest precision output[0]</span> |
| <span class="n">gt_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">dtypes</span><span class="p">)</span> |
| <span class="n">gt</span> <span class="o">=</span> <span class="n">ground_truth</span> |
| <span class="k">if</span> <span class="n">gt</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">gt</span> <span class="o">=</span> <span class="n">exe_list</span><span class="p">[</span><span class="n">gt_idx</span><span class="p">]</span><span class="o">.</span><span class="n">output_dict</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">exe</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">exe_list</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">gt_idx</span><span class="p">:</span> |
| <span class="k">continue</span> |
| |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">arr</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">output_names</span><span class="p">,</span> <span class="n">exe</span><span class="o">.</span><span class="n">outputs</span><span class="p">):</span> |
| <span class="n">gtarr</span> <span class="o">=</span> <span class="n">gt</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">gtarr</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">,</span> <span class="n">equal_nan</span><span class="o">=</span><span class="n">equal_nan</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">AssertionError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span> |
| <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Predict Err: ctx </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1"> vs ctx </span><span class="si">{</span><span class="n">gt_idx</span><span class="si">}</span><span class="s1"> at </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span> |
| <span class="n">traceback</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">raise_on_err</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="n">e</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">))</span> |
| |
| <span class="c1"># train</span> |
| <span class="k">if</span> <span class="n">grad_req</span> <span class="o">!=</span> <span class="s1">'null'</span><span class="p">:</span> |
| <span class="c1"># Perform forward()</span> |
| <span class="k">for</span> <span class="n">exe</span> <span class="ow">in</span> <span class="n">exe_list</span><span class="p">:</span> |
| <span class="n">exe</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">is_train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="c1"># Use the first executor's output data, cast to the least precise dtype,</span> |
| <span class="c1"># as the gradient data to pass to all executor's backward() call.</span> |
| <span class="n">least_precise_dtype</span> <span class="o">=</span> <span class="p">[</span><span class="n">out</span><span class="o">.</span><span class="n">dtype</span> <span class="k">for</span> <span class="n">out</span> <span class="ow">in</span> <span class="n">exe_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">outputs</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">exe</span> <span class="ow">in</span> <span class="n">exe_list</span><span class="p">:</span> |
| <span class="n">least_precise_dtype</span> <span class="o">=</span> <span class="p">[</span><span class="n">smaller_dtype</span><span class="p">(</span><span class="n">out1</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span> \ |
| <span class="k">for</span> <span class="p">(</span><span class="n">out1</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">exe</span><span class="o">.</span><span class="n">outputs</span><span class="p">,</span> <span class="n">least_precise_dtype</span><span class="p">)]</span> |
| <span class="n">golden_data_np</span> <span class="o">=</span> <span class="p">[</span><span class="n">out</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dt</span><span class="p">)</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> \ |
| <span class="k">for</span> <span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">exe_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">outputs</span><span class="p">,</span> <span class="n">least_precise_dtype</span><span class="p">)]</span> |
| <span class="c1"># Perform backward()</span> |
| <span class="k">for</span> <span class="n">exe</span> <span class="ow">in</span> <span class="n">exe_list</span><span class="p">:</span> |
| <span class="n">out_grads</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">golden_np</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">exe</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">out</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">stype</span><span class="p">)</span> |
| <span class="k">for</span> <span class="p">(</span><span class="n">golden_np</span><span class="p">,</span> <span class="n">out</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">golden_data_np</span><span class="p">,</span> <span class="n">exe</span><span class="o">.</span><span class="n">outputs</span><span class="p">)]</span> |
| <span class="n">exe</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">out_grads</span><span class="p">)</span> |
| |
| <span class="n">gt</span> <span class="o">=</span> <span class="n">ground_truth</span> |
| <span class="k">if</span> <span class="n">gt</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">gt</span> <span class="o">=</span> <span class="n">exe_list</span><span class="p">[</span><span class="n">gt_idx</span><span class="p">]</span><span class="o">.</span><span class="n">output_dict</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">grad_req</span> <span class="o">!=</span> <span class="s1">'null'</span><span class="p">:</span> |
| <span class="n">gt</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">exe_list</span><span class="p">[</span><span class="n">gt_idx</span><span class="p">]</span><span class="o">.</span><span class="n">grad_dict</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">exe</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">exe_list</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">gt_idx</span><span class="p">:</span> |
| <span class="k">continue</span> |
| |
| <span class="n">curr</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="n">output_names</span> <span class="o">+</span> <span class="n">arg_names</span><span class="p">,</span> <span class="n">exe</span><span class="o">.</span><span class="n">outputs</span> <span class="o">+</span> <span class="n">exe</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">curr</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">gt</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">arr</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="n">name</span> |
| <span class="k">continue</span> |
| |
| <span class="n">gtarr</span> <span class="o">=</span> <span class="n">gt</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">rt</span><span class="p">,</span> <span class="n">at</span> <span class="o">=</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span> |
| <span class="c1"># If the primary data i/o type is float16, then the tolerance used when</span> |
| <span class="c1"># comparing a float32 input gradient (e.g. batchnorm gamma) should be float16.</span> |
| <span class="n">smaller_arr_dtype</span> <span class="o">=</span> <span class="n">smaller_dtype</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtypes</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> |
| <span class="n">smaller_gt_dtype</span> <span class="o">=</span> <span class="n">smaller_dtype</span><span class="p">(</span><span class="n">gtarr</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtypes</span><span class="p">[</span><span class="n">gt_idx</span><span class="p">])</span> |
| <span class="k">if</span> <span class="n">smaller_arr_dtype</span> <span class="o">!=</span> <span class="n">arr</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">or</span> \ |
| <span class="n">smaller_gt_dtype</span> <span class="o">!=</span> <span class="n">gtarr</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span> |
| <span class="n">rt</span><span class="p">,</span> <span class="n">at</span> <span class="o">=</span> <span class="n">get_tols</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">smaller_arr_dtype</span><span class="p">),</span> |
| <span class="n">gtarr</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">smaller_gt_dtype</span><span class="p">),</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">)</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">gtarr</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="n">rt</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">at</span><span class="p">,</span> <span class="n">equal_nan</span><span class="o">=</span><span class="n">equal_nan</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">AssertionError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Train Err: </span><span class="si">{}</span><span class="s1"> </span><span class="si">{}</span><span class="s1"> ctx </span><span class="si">{}</span><span class="s1"> vs </span><span class="si">{}</span><span class="s1"> </span><span class="si">{}</span><span class="s1"> ctx </span><span class="si">{}</span><span class="s1"> at </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">get_dtype_name</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="n">arr</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> |
| <span class="n">get_dtype_name</span><span class="p">(</span><span class="n">gtarr</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="n">gtarr</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">gt_idx</span><span class="p">,</span> <span class="n">name</span><span class="p">))</span> |
| <span class="n">traceback</span><span class="o">.</span><span class="n">print_exc</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">raise_on_err</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="n">e</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">))</span> |
| |
| <span class="k">return</span> <span class="n">gt</span></div> |
| |
| <div class="viewcode-block" id="list_gpus"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.list_gpus">[docs]</a><span class="k">def</span> <span class="nf">list_gpus</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Return a list of GPUs</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> list of int:</span> |
| <span class="sd"> If there are n GPUs, then return a list [0,1,...,n-1]. Otherwise returns</span> |
| <span class="sd"> [].</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="nb">range</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">get_gpu_count</span><span class="p">())</span></div> |
| |
| <div class="viewcode-block" id="download"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.download">[docs]</a><span class="k">def</span> <span class="nf">download</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">fname</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dirname</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">retries</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Download an given URL</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| |
| <span class="sd"> url : str</span> |
| <span class="sd"> URL to download</span> |
| <span class="sd"> fname : str, optional</span> |
| <span class="sd"> filename of the downloaded file. If None, then will guess a filename</span> |
| <span class="sd"> from url.</span> |
| <span class="sd"> dirname : str, optional</span> |
| <span class="sd"> output directory name. If None, then guess from fname or use the current</span> |
| <span class="sd"> directory</span> |
| <span class="sd"> overwrite : bool, optional</span> |
| <span class="sd"> Default is false, which means skipping download if the local file</span> |
| <span class="sd"> exists. If true, then download the url to overwrite the local file if</span> |
| <span class="sd"> exists.</span> |
| <span class="sd"> retries : integer, default 5</span> |
| <span class="sd"> The number of times to attempt the download in case of failure or non 200 return codes</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> str</span> |
| <span class="sd"> The filename of the downloaded file</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">assert</span> <span class="n">retries</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"Number of retries should be at least 0"</span> |
| |
| <span class="k">if</span> <span class="n">fname</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">fname</span> <span class="o">=</span> <span class="n">url</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'/'</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> |
| |
| <span class="k">if</span> <span class="n">dirname</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">dirname</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">fname</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dirname</span><span class="p">,</span> <span class="n">fname</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">dirname</span> <span class="o">!=</span> <span class="s2">""</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">dirname</span><span class="p">):</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'create directory </span><span class="si">%s</span><span class="s1">'</span><span class="p">,</span> <span class="n">dirname</span><span class="p">)</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">dirname</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">OSError</span> <span class="k">as</span> <span class="n">exc</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">exc</span><span class="o">.</span><span class="n">errno</span> <span class="o">!=</span> <span class="n">errno</span><span class="o">.</span><span class="n">EEXIST</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">OSError</span><span class="p">(</span><span class="s1">'failed to create '</span> <span class="o">+</span> <span class="n">dirname</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">overwrite</span> <span class="ow">and</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">fname</span><span class="p">):</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"</span><span class="si">%s</span><span class="s2"> exists, skipping download"</span><span class="p">,</span> <span class="n">fname</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">fname</span> |
| |
| <span class="k">while</span> <span class="n">retries</span><span class="o">+</span><span class="mi">1</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="c1"># Disable pyling too broad Exception</span> |
| <span class="c1"># pylint: disable=W0703</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">r</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">stream</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">r</span><span class="o">.</span><span class="n">status_code</span> <span class="o">==</span> <span class="mi">200</span><span class="p">,</span> <span class="sa">f</span><span class="s2">"failed to open </span><span class="si">{</span><span class="n">url</span><span class="si">}</span><span class="s2">"</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">chunk</span> <span class="ow">in</span> <span class="n">r</span><span class="o">.</span><span class="n">iter_content</span><span class="p">(</span><span class="n">chunk_size</span><span class="o">=</span><span class="mi">1024</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">chunk</span><span class="p">:</span> <span class="c1"># filter out keep-alive new chunks</span> |
| <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">chunk</span><span class="p">)</span> |
| <span class="k">break</span> |
| <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span> |
| <span class="n">retries</span> <span class="o">-=</span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="n">retries</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="n">e</span> |
| |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"download failed, retrying, </span><span class="si">{}</span><span class="s2"> attempt</span><span class="si">{}</span><span class="s2"> left"</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">retries</span><span class="p">,</span> <span class="s1">'s'</span> <span class="k">if</span> <span class="n">retries</span> <span class="o">></span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">''</span><span class="p">))</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"downloaded </span><span class="si">%s</span><span class="s2"> into </span><span class="si">%s</span><span class="s2"> successfully"</span><span class="p">,</span> <span class="n">url</span><span class="p">,</span> <span class="n">fname</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">fname</span></div> |
| |
| |
| <div class="viewcode-block" id="get_mnist"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.get_mnist">[docs]</a><span class="k">def</span> <span class="nf">get_mnist</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="s1">'data'</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Download and load the MNIST dataset</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> path : str</span> |
| <span class="sd"> Path in which to save the files.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> dict</span> |
| <span class="sd"> A dict containing the data.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="nf">read_data</span><span class="p">(</span><span class="n">label_url</span><span class="p">,</span> <span class="n">image_url</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">label_url</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="n">path</span><span class="p">))</span> <span class="k">as</span> <span class="n">flbl</span><span class="p">:</span> |
| <span class="n">struct</span><span class="o">.</span><span class="n">unpack</span><span class="p">(</span><span class="s2">">II"</span><span class="p">,</span> <span class="n">flbl</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="mi">8</span><span class="p">))</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">flbl</span><span class="o">.</span><span class="n">read</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">image_url</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="n">path</span><span class="p">),</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fimg</span><span class="p">:</span> |
| <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">struct</span><span class="o">.</span><span class="n">unpack</span><span class="p">(</span><span class="s2">">IIII"</span><span class="p">,</span> <span class="n">fimg</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="mi">16</span><span class="p">))</span> |
| <span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">fimg</span><span class="o">.</span><span class="n">read</span><span class="p">(),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">label</span><span class="p">),</span> <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">)</span> |
| <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">image</span><span class="p">)</span> |
| |
| <span class="c1"># changed to mxnet.io for more stable hosting</span> |
| <span class="n">url_path</span> <span class="o">=</span> <span class="s1">'https://repo.mxnet.io/gluon/dataset/mnist/'</span> |
| <span class="p">(</span><span class="n">train_lbl</span><span class="p">,</span> <span class="n">train_img</span><span class="p">)</span> <span class="o">=</span> <span class="n">read_data</span><span class="p">(</span> |
| <span class="n">url_path</span><span class="o">+</span><span class="s1">'train-labels-idx1-ubyte.gz'</span><span class="p">,</span> <span class="n">url_path</span><span class="o">+</span><span class="s1">'train-images-idx3-ubyte.gz'</span><span class="p">)</span> |
| <span class="p">(</span><span class="n">test_lbl</span><span class="p">,</span> <span class="n">test_img</span><span class="p">)</span> <span class="o">=</span> <span class="n">read_data</span><span class="p">(</span> |
| <span class="n">url_path</span><span class="o">+</span><span class="s1">'t10k-labels-idx1-ubyte.gz'</span><span class="p">,</span> <span class="n">url_path</span><span class="o">+</span><span class="s1">'t10k-images-idx3-ubyte.gz'</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">{</span><span class="s1">'train_data'</span><span class="p">:</span><span class="n">train_img</span><span class="p">,</span> <span class="s1">'train_label'</span><span class="p">:</span><span class="n">train_lbl</span><span class="p">,</span> |
| <span class="s1">'test_data'</span><span class="p">:</span><span class="n">test_img</span><span class="p">,</span> <span class="s1">'test_label'</span><span class="p">:</span><span class="n">test_lbl</span><span class="p">}</span></div> |
| |
| <div class="viewcode-block" id="get_mnist_ubyte"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.get_mnist_ubyte">[docs]</a><span class="k">def</span> <span class="nf">get_mnist_ubyte</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="s1">'data'</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Downloads ubyte version of the MNIST dataset into a directory in the current directory</span> |
| <span class="sd"> with the name `data` and extracts all files in the zip archive to this directory.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> |
| <span class="n">files</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'train-images-idx3-ubyte'</span><span class="p">,</span> <span class="s1">'train-labels-idx1-ubyte'</span><span class="p">,</span> |
| <span class="s1">'t10k-images-idx3-ubyte'</span><span class="p">,</span> <span class="s1">'t10k-labels-idx1-ubyte'</span><span class="p">]</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">f</span><span class="p">))</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files</span><span class="p">):</span> |
| <span class="n">get_mnist</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files</span><span class="p">:</span> |
| <span class="n">ubyte_file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span> |
| <span class="n">zip_file_path</span> <span class="o">=</span> <span class="n">ubyte_file_path</span> <span class="o">+</span> <span class="s1">'.gz'</span> |
| <span class="k">with</span> <span class="n">gzip</span><span class="o">.</span><span class="n">GzipFile</span><span class="p">(</span><span class="n">zip_file_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">zf</span><span class="p">:</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">ubyte_file_path</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">ubyte_file</span><span class="p">:</span> |
| <span class="n">ubyte_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">zf</span><span class="o">.</span><span class="n">read</span><span class="p">())</span></div> |
| |
| <div class="viewcode-block" id="get_cifar10"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.get_cifar10">[docs]</a><span class="k">def</span> <span class="nf">get_cifar10</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="s1">'data'</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Downloads CIFAR10 dataset into a directory in the current directory with the name `data`,</span> |
| <span class="sd"> and then extracts all files into the directory `data/cifar`.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> |
| <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'cifar'</span><span class="p">,</span> <span class="s1">'train.rec'</span><span class="p">)))</span> <span class="ow">or</span> \ |
| <span class="p">(</span><span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'cifar'</span><span class="p">,</span> <span class="s1">'test.rec'</span><span class="p">)))</span> <span class="ow">or</span> \ |
| <span class="p">(</span><span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'cifar'</span><span class="p">,</span> <span class="s1">'train.lst'</span><span class="p">)))</span> <span class="ow">or</span> \ |
| <span class="p">(</span><span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'cifar'</span><span class="p">,</span> <span class="s1">'test.lst'</span><span class="p">))):</span> |
| <span class="n">url</span> <span class="o">=</span> <span class="s1">'https://repo.mxnet.io/gluon/dataset/cifar10/cifar10-b9ac2870.zip'</span> |
| <span class="n">sha1</span> <span class="o">=</span> <span class="s1">'b9ac287012f2dad9dfb49d8271c39ecdd7db376c'</span> |
| <span class="n">zip_file_path</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gluon</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="n">path</span><span class="p">,</span> <span class="n">sha1_hash</span><span class="o">=</span><span class="n">sha1</span><span class="p">,</span> |
| <span class="n">verify_ssl</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">zip_file_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">zf</span><span class="p">:</span> |
| <span class="n">zf</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="n">path</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="get_mnist_iterator"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.get_mnist_iterator">[docs]</a><span class="k">def</span> <span class="nf">get_mnist_iterator</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">input_shape</span><span class="p">,</span> <span class="n">num_parts</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">part_index</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="s1">'data'</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns training and validation iterators for MNIST dataset</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">get_mnist_ubyte</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> |
| <span class="n">flat</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">input_shape</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">3</span> |
| |
| <span class="n">train_dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">MNISTIter</span><span class="p">(</span> |
| <span class="n">image</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">"train-images-idx3-ubyte"</span><span class="p">),</span> |
| <span class="n">label</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">"train-labels-idx1-ubyte"</span><span class="p">),</span> |
| <span class="n">input_shape</span><span class="o">=</span><span class="n">input_shape</span><span class="p">,</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> |
| <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">flat</span><span class="o">=</span><span class="n">flat</span><span class="p">,</span> |
| <span class="n">num_parts</span><span class="o">=</span><span class="n">num_parts</span><span class="p">,</span> |
| <span class="n">part_index</span><span class="o">=</span><span class="n">part_index</span><span class="p">)</span> |
| |
| <span class="n">val_dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">MNISTIter</span><span class="p">(</span> |
| <span class="n">image</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">"t10k-images-idx3-ubyte"</span><span class="p">),</span> |
| <span class="n">label</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">"t10k-labels-idx1-ubyte"</span><span class="p">),</span> |
| <span class="n">input_shape</span><span class="o">=</span><span class="n">input_shape</span><span class="p">,</span> |
| <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> |
| <span class="n">flat</span><span class="o">=</span><span class="n">flat</span><span class="p">,</span> |
| <span class="n">num_parts</span><span class="o">=</span><span class="n">num_parts</span><span class="p">,</span> |
| <span class="n">part_index</span><span class="o">=</span><span class="n">part_index</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="p">(</span><span class="n">train_dataiter</span><span class="p">,</span> <span class="n">val_dataiter</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="get_bz2_data"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.get_bz2_data">[docs]</a><span class="k">def</span> <span class="nf">get_bz2_data</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">data_name</span><span class="p">,</span> <span class="n">url</span><span class="p">,</span> <span class="n">data_origin_name</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Download and extract bz2 data.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| |
| <span class="sd"> data_dir : str</span> |
| <span class="sd"> Absolute or relative path of the directory name to store bz2 files</span> |
| <span class="sd"> data_name : str</span> |
| <span class="sd"> Name of the output file in which bz2 contents will be extracted</span> |
| <span class="sd"> url : str</span> |
| <span class="sd"> URL to download data from</span> |
| <span class="sd"> data_origin_name : str</span> |
| <span class="sd"> Name of the downloaded b2 file</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> get_bz2_data("data_dir", "kdda.t",</span> |
| <span class="sd"> "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/kdda.t.bz2",</span> |
| <span class="sd"> "kdda.t.bz2")</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">data_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">data_name</span><span class="p">)</span> |
| <span class="n">data_origin_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">data_origin_name</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">data_name</span><span class="p">):</span> |
| <span class="n">download</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">fname</span><span class="o">=</span><span class="n">data_origin_name</span><span class="p">,</span> <span class="n">dirname</span><span class="o">=</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">bz_file</span> <span class="o">=</span> <span class="n">bz2</span><span class="o">.</span><span class="n">BZ2File</span><span class="p">(</span><span class="n">data_origin_name</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">data_name</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">bz_file</span><span class="p">:</span> |
| <span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">line</span><span class="p">)</span> |
| <span class="n">bz_file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">data_origin_name</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="same_array"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.same_array">[docs]</a><span class="k">def</span> <span class="nf">same_array</span><span class="p">(</span><span class="n">array1</span><span class="p">,</span> <span class="n">array2</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Check whether two NDArrays sharing the same memory block</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| |
| <span class="sd"> array1 : NDArray</span> |
| <span class="sd"> First NDArray to be checked</span> |
| <span class="sd"> array2 : NDArray</span> |
| <span class="sd"> Second NDArray to be checked</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> bool</span> |
| <span class="sd"> Whether two NDArrays share the same memory</span> |
| <span class="sd"> """</span> |
| <span class="n">array1</span><span class="p">[:]</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">same</span><span class="p">(</span><span class="n">array1</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">(),</span> <span class="n">array2</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()):</span> |
| <span class="n">array1</span><span class="p">[:]</span> <span class="o">-=</span> <span class="mi">1</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| <span class="n">array1</span><span class="p">[:]</span> <span class="o">-=</span> <span class="mi">1</span> |
| <span class="k">return</span> <span class="n">same</span><span class="p">(</span><span class="n">array1</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">(),</span> <span class="n">array2</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span></div> |
| |
| |
| <div class="viewcode-block" id="discard_stderr"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.discard_stderr">[docs]</a><span class="nd">@contextmanager</span> |
| <span class="k">def</span> <span class="nf">discard_stderr</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Discards error output of a routine if invoked as:</span> |
| |
| <span class="sd"> with discard_stderr():</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> """</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">devnull</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span> <span class="k">as</span> <span class="n">bit_bucket</span><span class="p">:</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">stderr_fileno</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">stderr</span><span class="o">.</span><span class="n">fileno</span><span class="p">()</span> |
| <span class="n">old_stderr</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">dup</span><span class="p">(</span><span class="n">stderr_fileno</span><span class="p">)</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">dup2</span><span class="p">(</span><span class="n">bit_bucket</span><span class="o">.</span><span class="n">fileno</span><span class="p">(),</span> <span class="n">stderr_fileno</span><span class="p">)</span> |
| <span class="k">yield</span> |
| <span class="k">finally</span><span class="p">:</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">dup2</span><span class="p">(</span><span class="n">old_stderr</span><span class="p">,</span> <span class="n">stderr_fileno</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span> |
| <span class="c1"># On some systems is stderr not a file descriptor but actually a virtual pipeline</span> |
| <span class="c1"># that can not be copied</span> |
| <span class="k">yield</span></div> |
| |
| |
| <div class="viewcode-block" id="DummyIter"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.DummyIter">[docs]</a><span class="k">class</span> <span class="nc">DummyIter</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">DataIter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""A dummy iterator that always returns the same batch of data</span> |
| <span class="sd"> (the first data batch of the real data iter). This is usually used for speed testing.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> real_iter: mx.io.DataIter</span> |
| <span class="sd"> The real data iterator where the first batch of data comes from</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">real_iter</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">DummyIter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">real_iter</span> <span class="o">=</span> <span class="n">real_iter</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">provide_data</span> <span class="o">=</span> <span class="n">real_iter</span><span class="o">.</span><span class="n">provide_data</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">provide_label</span> <span class="o">=</span> <span class="n">real_iter</span><span class="o">.</span><span class="n">provide_label</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">real_iter</span><span class="o">.</span><span class="n">batch_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">the_batch</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">real_iter</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| |
| <div class="viewcode-block" id="DummyIter.next"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.DummyIter.next">[docs]</a> <span class="k">def</span> <span class="nf">next</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Get a data batch from iterator. The first data batch of real iter is always returned.</span> |
| <span class="sd"> StopIteration will never be raised.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> DataBatch</span> |
| <span class="sd"> The data of next batch.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">the_batch</span></div></div> |
| |
| <div class="viewcode-block" id="gen_buckets_probs_with_ppf"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.gen_buckets_probs_with_ppf">[docs]</a><span class="k">def</span> <span class="nf">gen_buckets_probs_with_ppf</span><span class="p">(</span><span class="n">ppf</span><span class="p">,</span> <span class="n">nbuckets</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Generate the buckets and probabilities for chi_square test when the ppf (Quantile function)</span> |
| <span class="sd"> is specified.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> ppf : function</span> |
| <span class="sd"> The Quantile function that takes a probability and maps it back to a value.</span> |
| <span class="sd"> It's the inverse of the cdf function</span> |
| <span class="sd"> nbuckets : int</span> |
| <span class="sd"> size of the buckets</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> buckets : list of tuple</span> |
| <span class="sd"> The generated buckets</span> |
| <span class="sd"> probs : list</span> |
| <span class="sd"> The generate probabilities</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="n">nbuckets</span> <span class="o">></span> <span class="mi">0</span> |
| <span class="n">probs</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">nbuckets</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nbuckets</span><span class="p">)]</span> |
| <span class="n">buckets</span> <span class="o">=</span> <span class="p">[(</span><span class="n">ppf</span><span class="p">(</span><span class="n">i</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">nbuckets</span><span class="p">)),</span> <span class="n">ppf</span><span class="p">((</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">nbuckets</span><span class="p">)))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nbuckets</span><span class="p">)]</span> |
| <span class="k">return</span> <span class="n">buckets</span><span class="p">,</span> <span class="n">probs</span></div> |
| |
| <div class="viewcode-block" id="mean_check"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.mean_check">[docs]</a><span class="k">def</span> <span class="nf">mean_check</span><span class="p">(</span><span class="n">generator</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">nsamples</span><span class="o">=</span><span class="mi">1000000</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Test the generator by matching the mean.</span> |
| |
| <span class="sd"> We test the sample mean by checking if it falls inside the range</span> |
| <span class="sd"> (mu - 3 * sigma / sqrt(n), mu + 3 * sigma / sqrt(n))</span> |
| |
| <span class="sd"> References::</span> |
| |
| <span class="sd"> @incollection{goucher2009beautiful,</span> |
| <span class="sd"> title={Beautiful Testing: Leading Professionals Reveal How They Improve Software},</span> |
| <span class="sd"> author={Goucher, Adam and Riley, Tim},</span> |
| <span class="sd"> year={2009},</span> |
| <span class="sd"> chapter=10</span> |
| <span class="sd"> }</span> |
| |
| <span class="sd"> Examples::</span> |
| |
| <span class="sd"> generator = lambda x: np.random.normal(0, 1.0, size=x)</span> |
| <span class="sd"> mean_check_ret = mean_check(generator, 0, 1.0)</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> generator : function</span> |
| <span class="sd"> The generator function. It's expected to generate N i.i.d samples by calling generator(N).</span> |
| <span class="sd"> mu : float</span> |
| <span class="sd"> sigma : float</span> |
| <span class="sd"> nsamples : int</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> ret : bool</span> |
| <span class="sd"> Whether the mean test succeeds</span> |
| <span class="sd"> """</span> |
| <span class="n">samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">generator</span><span class="p">(</span><span class="n">nsamples</span><span class="p">))</span> |
| <span class="n">sample_mean</span> <span class="o">=</span> <span class="n">samples</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="p">(</span><span class="n">sample_mean</span> <span class="o">></span> <span class="n">mu</span> <span class="o">-</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">sigma</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">nsamples</span><span class="p">))</span> <span class="ow">and</span>\ |
| <span class="p">(</span><span class="n">sample_mean</span> <span class="o"><</span> <span class="n">mu</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">sigma</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">nsamples</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">ret</span></div> |
| |
| <div class="viewcode-block" id="get_im2rec_path"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.get_im2rec_path">[docs]</a><span class="k">def</span> <span class="nf">get_im2rec_path</span><span class="p">(</span><span class="n">home_env</span><span class="o">=</span><span class="s2">"MXNET_HOME"</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Get path to the im2rec.py tool</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| |
| <span class="sd"> home_env : str</span> |
| <span class="sd"> Env variable that holds the path to the MXNET folder</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> str</span> |
| <span class="sd"> The path to im2rec.py</span> |
| <span class="sd"> """</span> |
| <span class="c1"># Check first if the path to MXNET is passed as an env variable</span> |
| <span class="k">if</span> <span class="n">home_env</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">:</span> |
| <span class="n">mxnet_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="n">home_env</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># Else use currently imported mxnet as reference</span> |
| <span class="n">mxnet_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="vm">__file__</span><span class="p">)</span> |
| <span class="c1"># If MXNet was installed through pip, the location of im2rec.py</span> |
| <span class="n">im2rec_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">mxnet_path</span><span class="p">,</span> <span class="s1">'tools'</span><span class="p">,</span> <span class="s1">'im2rec.py'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">im2rec_path</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">im2rec_path</span> |
| <span class="c1"># If MXNet has been built locally</span> |
| <span class="n">im2rec_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">mxnet_path</span><span class="p">,</span> <span class="s1">'..'</span><span class="p">,</span> <span class="s1">'..'</span><span class="p">,</span> <span class="s1">'tools'</span><span class="p">,</span> <span class="s1">'im2rec.py'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">im2rec_path</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">im2rec_path</span> |
| <span class="k">raise</span> <span class="ne">IOError</span><span class="p">(</span><span class="s1">'Could not find path to tools/im2rec.py'</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="var_check"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.var_check">[docs]</a><span class="k">def</span> <span class="nf">var_check</span><span class="p">(</span><span class="n">generator</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">nsamples</span><span class="o">=</span><span class="mi">1000000</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Test the generator by matching the variance.</span> |
| <span class="sd"> It will need a large number of samples and is not recommended to use</span> |
| |
| <span class="sd"> We test the sample variance by checking if it falls inside the range</span> |
| <span class="sd"> (sigma^2 - 3 * sqrt(2 * sigma^4 / (n-1)), sigma^2 + 3 * sqrt(2 * sigma^4 / (n-1)))</span> |
| |
| <span class="sd"> References::</span> |
| |
| <span class="sd"> @incollection{goucher2009beautiful,</span> |
| <span class="sd"> title={Beautiful Testing: Leading Professionals Reveal How They Improve Software},</span> |
| <span class="sd"> author={Goucher, Adam and Riley, Tim},</span> |
| <span class="sd"> year={2009},</span> |
| <span class="sd"> chapter=10</span> |
| <span class="sd"> }</span> |
| |
| <span class="sd"> Examples::</span> |
| |
| <span class="sd"> generator = lambda x: np.random.normal(0, 1.0, size=x)</span> |
| <span class="sd"> var_check_ret = var_check(generator, 0, 1.0)</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> generator : function</span> |
| <span class="sd"> The generator function. It's expected to generate N i.i.d samples by calling generator(N).</span> |
| <span class="sd"> sigma : float</span> |
| <span class="sd"> nsamples : int</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> ret : bool</span> |
| <span class="sd"> Whether the variance test succeeds</span> |
| <span class="sd"> """</span> |
| <span class="n">samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">generator</span><span class="p">(</span><span class="n">nsamples</span><span class="p">))</span> |
| <span class="n">sample_var</span> <span class="o">=</span> <span class="n">samples</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">ddof</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="p">(</span><span class="n">sample_var</span> <span class="o">></span> <span class="n">sigma</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">-</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">sigma</span> <span class="o">**</span> <span class="mi">4</span> <span class="o">/</span> <span class="p">(</span><span class="n">nsamples</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)))</span> <span class="ow">and</span>\ |
| <span class="p">(</span><span class="n">sample_var</span> <span class="o"><</span> <span class="n">sigma</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">sigma</span> <span class="o">**</span> <span class="mi">4</span> <span class="o">/</span> <span class="p">(</span><span class="n">nsamples</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">ret</span></div> |
| |
| <div class="viewcode-block" id="chi_square_check"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.chi_square_check">[docs]</a><span class="k">def</span> <span class="nf">chi_square_check</span><span class="p">(</span><span class="n">generator</span><span class="p">,</span> <span class="n">buckets</span><span class="p">,</span> <span class="n">probs</span><span class="p">,</span> <span class="n">nsamples</span><span class="o">=</span><span class="mi">1000000</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Run the chi-square test for the generator. The generator can be both continuous and discrete.</span> |
| |
| <span class="sd"> If the generator is continuous, the buckets should contain tuples of (range_min, range_max) \</span> |
| <span class="sd"> and the probs should be the corresponding ideal probability within the specific ranges. \</span> |
| <span class="sd"> Otherwise, the buckets should contain all the possible values generated over the discrete distribution and the \</span> |
| <span class="sd"> probs should be groud-truth probability.</span> |
| |
| <span class="sd"> Usually the user is required to specify the probs parameter.</span> |
| |
| <span class="sd"> After obtaining the p value, we could further use the standard p > 0.05 (alpha) threshold to get \</span> |
| <span class="sd"> the final result.</span> |
| |
| <span class="sd"> Examples::</span> |
| |
| <span class="sd"> buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.norm.ppf(x, 0, 1), 5)</span> |
| <span class="sd"> generator = lambda x: np.random.normal(0, 1.0, size=x)</span> |
| <span class="sd"> p = chi_square_check(generator=generator, buckets=buckets, probs=probs)</span> |
| <span class="sd"> assert(p > 0.05)</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> generator: function</span> |
| <span class="sd"> A function that is assumed to generate i.i.d samples from a specific distribution.</span> |
| <span class="sd"> generator(N) should generate N random samples.</span> |
| <span class="sd"> buckets: list of tuple or list of number</span> |
| <span class="sd"> The buckets to run the chi-square the test. Make sure that the buckets cover</span> |
| <span class="sd"> the whole range of the distribution. Also, the buckets must be in ascending order and have</span> |
| <span class="sd"> no intersection</span> |
| <span class="sd"> probs: list or tuple</span> |
| <span class="sd"> The ground-truth probability of the random value fall in a specific bucket.</span> |
| <span class="sd"> nsamples:int</span> |
| <span class="sd"> The number of samples to generate for the testing</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> p : float</span> |
| <span class="sd"> p value that the generator has the expected distribution.</span> |
| <span class="sd"> A higher value indicates a larger confidence</span> |
| <span class="sd"> obs_freq : list</span> |
| <span class="sd"> Observed frequency of buckets</span> |
| <span class="sd"> expected_freq : list</span> |
| <span class="sd"> The expected (ground-truth) frequency of the buckets</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">ss</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">"scipy is not available."</span> |
| <span class="s2">" Please check if the scipy python bindings are installed."</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">buckets</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> |
| <span class="n">samples</span> <span class="o">=</span> <span class="n">generator</span><span class="p">(</span><span class="n">nsamples</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">buckets</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">buckets</span><span class="p">[</span><span class="mi">0</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="c1"># Check whether the buckets are valid and fill them into a npy array</span> |
| <span class="n">continuous_dist</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="n">buckets_npy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">buckets</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">buckets</span><span class="p">):</span> |
| <span class="k">assert</span><span class="p">(</span><span class="n">buckets</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o"><=</span> <span class="n">buckets</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span> |
| <span class="k">if</span> <span class="n">i</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">buckets</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">assert</span><span class="p">(</span><span class="n">buckets</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o"><=</span> <span class="n">buckets</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> |
| <span class="n">buckets_npy</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">buckets</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">buckets_npy</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">buckets</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">continuous_dist</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="n">expected_freq</span> <span class="o">=</span> <span class="p">(</span><span class="n">nsamples</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">probs</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">continuous_dist</span><span class="p">:</span> |
| <span class="n">sample_bucket_ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="n">buckets_npy</span><span class="p">,</span> <span class="n">samples</span><span class="p">,</span> <span class="n">side</span><span class="o">=</span><span class="s1">'right'</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">sample_bucket_ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">continuous_dist</span><span class="p">:</span> |
| <span class="n">sample_bucket_ids</span> <span class="o">=</span> <span class="n">sample_bucket_ids</span> <span class="o">//</span> <span class="mi">2</span> |
| <span class="n">obs_freq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">buckets</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">buckets</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">continuous_dist</span><span class="p">:</span> |
| <span class="n">obs_freq</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">sample_bucket_ids</span> <span class="o">==</span> <span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">obs_freq</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">sample_bucket_ids</span> <span class="o">==</span> <span class="n">buckets</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| <span class="n">_</span><span class="p">,</span> <span class="n">p</span> <span class="o">=</span> <span class="n">ss</span><span class="o">.</span><span class="n">chisquare</span><span class="p">(</span><span class="n">f_obs</span><span class="o">=</span><span class="n">obs_freq</span><span class="p">,</span> <span class="n">f_exp</span><span class="o">=</span><span class="n">expected_freq</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">p</span><span class="p">,</span> <span class="n">obs_freq</span><span class="p">,</span> <span class="n">expected_freq</span></div> |
| |
| <div class="viewcode-block" id="verify_generator"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.verify_generator">[docs]</a><span class="k">def</span> <span class="nf">verify_generator</span><span class="p">(</span><span class="n">generator</span><span class="p">,</span> <span class="n">buckets</span><span class="p">,</span> <span class="n">probs</span><span class="p">,</span> <span class="n">nsamples</span><span class="o">=</span><span class="mi">1000000</span><span class="p">,</span> <span class="n">nrepeat</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">success_rate</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.05</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Verify whether the generator is correct using chi-square testing.</span> |
| |
| <span class="sd"> The test is repeated for "nrepeat" times and we check if the success rate is</span> |
| <span class="sd"> above the threshold (25% by default).</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> generator: function</span> |
| <span class="sd"> A function that is assumed to generate i.i.d samples from a specific distribution.</span> |
| <span class="sd"> generator(N) should generate N random samples.</span> |
| <span class="sd"> buckets: list of tuple or list of number</span> |
| <span class="sd"> The buckets to run the chi-square the test. Make sure that the buckets cover</span> |
| <span class="sd"> the whole range of the distribution. Also, the buckets must be in ascending order and</span> |
| <span class="sd"> have no intersection</span> |
| <span class="sd"> probs: list or tuple</span> |
| <span class="sd"> The ground-truth probability of the random value fall in a specific bucket.</span> |
| <span class="sd"> nsamples: int</span> |
| <span class="sd"> The number of samples to generate for the testing</span> |
| <span class="sd"> nrepeat: int</span> |
| <span class="sd"> The times to repeat the test</span> |
| <span class="sd"> success_rate: float</span> |
| <span class="sd"> The desired success rate</span> |
| <span class="sd"> alpha: float</span> |
| <span class="sd"> The desired threshold for type-I error i.e. when a true null hypothesis is rejected</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> cs_ret_l: list</span> |
| <span class="sd"> The p values of the chi-square test.</span> |
| <span class="sd"> """</span> |
| <span class="n">cs_ret_l</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">obs_freq_l</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">expected_freq_l</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nrepeat</span><span class="p">):</span> |
| <span class="n">cs_ret</span><span class="p">,</span> <span class="n">obs_freq</span><span class="p">,</span> <span class="n">expected_freq</span> <span class="o">=</span> <span class="n">chi_square_check</span><span class="p">(</span><span class="n">generator</span><span class="o">=</span><span class="n">generator</span><span class="p">,</span> <span class="n">buckets</span><span class="o">=</span><span class="n">buckets</span><span class="p">,</span> |
| <span class="n">probs</span><span class="o">=</span><span class="n">probs</span><span class="p">,</span> <span class="n">nsamples</span><span class="o">=</span><span class="n">nsamples</span><span class="p">)</span> |
| <span class="n">cs_ret_l</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cs_ret</span><span class="p">)</span> |
| <span class="n">obs_freq_l</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">obs_freq</span><span class="p">)</span> |
| <span class="n">expected_freq_l</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">expected_freq</span><span class="p">)</span> |
| <span class="n">success_num</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">cs_ret_l</span><span class="p">)</span> <span class="o">></span> <span class="n">alpha</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">success_num</span> <span class="o"><</span> <span class="n">nrepeat</span> <span class="o">*</span> <span class="n">success_rate</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Generator test fails, Chi-square p=</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">cs_ret_l</span><span class="p">)</span><span class="si">}</span><span class="s2">, "</span> |
| <span class="sa">f</span><span class="s2">"obs_freq=</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">obs_freq_l</span><span class="p">)</span><span class="si">}</span><span class="s2">, expected_freq=</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">expected_freq_l</span><span class="p">)</span><span class="si">}</span><span class="s2">."</span> |
| <span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">buckets=</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">buckets</span><span class="p">)</span><span class="si">}</span><span class="s2">, probs=</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">cs_ret_l</span></div> |
| |
| |
| <div class="viewcode-block" id="compare_ndarray_tuple"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.compare_ndarray_tuple">[docs]</a><span class="k">def</span> <span class="nf">compare_ndarray_tuple</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Compare ndarray tuple."""</span> |
| <span class="k">if</span> <span class="n">t1</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">t2</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> |
| |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">s1</span><span class="p">,</span> <span class="n">s2</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">):</span> |
| <span class="n">compare_ndarray_tuple</span><span class="p">(</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">,</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">t1</span><span class="p">,</span> <span class="n">t2</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="compare_optimizer"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.compare_optimizer">[docs]</a><span class="k">def</span> <span class="nf">compare_optimizer</span><span class="p">(</span><span class="n">opt1</span><span class="p">,</span> <span class="n">opt2</span><span class="p">,</span> <span class="n">shapes</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">w_stype</span><span class="o">=</span><span class="s1">'default'</span><span class="p">,</span> <span class="n">g_stype</span><span class="o">=</span><span class="s1">'default'</span><span class="p">,</span> |
| <span class="n">rtol</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">compare_states</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Compare opt1 and opt2."""</span> |
| |
| <span class="n">w1_list</span><span class="p">,</span> <span class="n">w2_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span> |
| <span class="n">g1_list</span><span class="p">,</span> <span class="n">g2_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span> |
| <span class="n">s1_list</span><span class="p">,</span> <span class="n">s2_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">shape</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">shapes</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">w_stype</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="n">w2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">default_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="n">w1</span> <span class="o">=</span> <span class="n">w2</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">default_device</span><span class="p">())</span> |
| <span class="k">elif</span> <span class="n">w_stype</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">,</span> <span class="s1">'csr'</span><span class="p">):</span> |
| <span class="n">w2</span> <span class="o">=</span> <span class="n">rand_ndarray</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">w_stype</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="mi">1</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="n">w1</span> <span class="o">=</span> <span class="n">w2</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">default_device</span><span class="p">())</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s1">'default'</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"type not supported yet"</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">g_stype</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="n">g2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">default_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="n">g1</span> <span class="o">=</span> <span class="n">g2</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">default_device</span><span class="p">())</span> |
| <span class="k">elif</span> <span class="n">g_stype</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">,</span> <span class="s1">'csr'</span><span class="p">):</span> |
| <span class="n">g2</span> <span class="o">=</span> <span class="n">rand_ndarray</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">g_stype</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="n">g1</span> <span class="o">=</span> <span class="n">g2</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">default_device</span><span class="p">())</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s1">'default'</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"type not supported yet"</span><span class="p">)</span> |
| <span class="n">s1</span> <span class="o">=</span> <span class="n">opt1</span><span class="o">.</span><span class="n">create_state_multi_precision</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">w1</span><span class="p">)</span> |
| <span class="n">s2</span> <span class="o">=</span> <span class="n">opt2</span><span class="o">.</span><span class="n">create_state_multi_precision</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">w2</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">compare_states</span><span class="p">:</span> |
| <span class="n">compare_ndarray_tuple</span><span class="p">(</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">)</span> |
| |
| <span class="n">w1_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">w1</span><span class="p">)</span> |
| <span class="n">w2_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">w2</span><span class="p">)</span> |
| <span class="n">g1_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">g1</span><span class="p">)</span> |
| <span class="n">g2_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">g2</span><span class="p">)</span> |
| <span class="n">s1_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s1</span><span class="p">)</span> |
| <span class="n">s2_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s2</span><span class="p">)</span> |
| |
| <span class="n">indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">shapes</span><span class="p">)))</span> |
| <span class="n">opt1</span><span class="o">.</span><span class="n">update_multi_precision</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">w1_list</span><span class="p">,</span> <span class="n">g1_list</span><span class="p">,</span> <span class="n">s1_list</span><span class="p">)</span> |
| <span class="n">opt2</span><span class="o">.</span><span class="n">update_multi_precision</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">w2_list</span><span class="p">,</span> <span class="n">g2_list</span><span class="p">,</span> <span class="n">s2_list</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">compare_states</span><span class="p">:</span> |
| <span class="n">compare_ndarray_tuple</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">s1_list</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">s2_list</span><span class="p">),</span> <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">)</span> |
| <span class="n">compare_ndarray_tuple</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">w1_list</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">w2_list</span><span class="p">),</span> <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="compare_optimizer_noise_seeded"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.compare_optimizer_noise_seeded">[docs]</a><span class="k">def</span> <span class="nf">compare_optimizer_noise_seeded</span><span class="p">(</span><span class="n">opt1</span><span class="p">,</span> <span class="n">opt2</span><span class="p">,</span> <span class="n">shapes</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">noise_seed</span><span class="p">,</span> |
| <span class="n">w_stype</span><span class="o">=</span><span class="s1">'default'</span><span class="p">,</span> <span class="n">g_stype</span><span class="o">=</span><span class="s1">'default'</span><span class="p">,</span> |
| <span class="n">rtol</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span> <span class="n">compare_states</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Compare opt1 and opt2 with the added functionality that the seed for generating random noise</span> |
| <span class="sd"> in the SGLD optimizer update is set so that the same noise is used in opt1 and opt2.</span> |
| |
| <span class="sd"> """</span> |
| <span class="n">w1_list</span><span class="p">,</span> <span class="n">w2_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span> |
| <span class="n">g1_list</span><span class="p">,</span> <span class="n">g2_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span> |
| <span class="n">s1_list</span><span class="p">,</span> <span class="n">s2_list</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">shape</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">shapes</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">w_stype</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="n">w2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">default_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="n">w1</span> <span class="o">=</span> <span class="n">w2</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">default_device</span><span class="p">())</span> |
| <span class="k">elif</span> <span class="n">w_stype</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">,</span> <span class="s1">'csr'</span><span class="p">):</span> |
| <span class="n">w2</span> <span class="o">=</span> <span class="n">rand_ndarray</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">w_stype</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="mi">1</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="n">w1</span> <span class="o">=</span> <span class="n">w2</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">default_device</span><span class="p">())</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s1">'default'</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"type not supported yet"</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">g_stype</span> <span class="o">==</span> <span class="s1">'default'</span><span class="p">:</span> |
| <span class="n">g2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">default_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="n">g1</span> <span class="o">=</span> <span class="n">g2</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">default_device</span><span class="p">())</span> |
| <span class="k">elif</span> <span class="n">g_stype</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'row_sparse'</span><span class="p">,</span> <span class="s1">'csr'</span><span class="p">):</span> |
| <span class="n">g2</span> <span class="o">=</span> <span class="n">rand_ndarray</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">g_stype</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="n">g1</span> <span class="o">=</span> <span class="n">g2</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">default_device</span><span class="p">())</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s1">'default'</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"type not supported yet"</span><span class="p">)</span> |
| <span class="n">s1</span> <span class="o">=</span> <span class="n">opt1</span><span class="o">.</span><span class="n">create_state_multi_precision</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">w1</span><span class="p">)</span> |
| <span class="n">s2</span> <span class="o">=</span> <span class="n">opt2</span><span class="o">.</span><span class="n">create_state_multi_precision</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">w2</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">compare_states</span><span class="p">:</span> |
| <span class="n">compare_ndarray_tuple</span><span class="p">(</span><span class="n">s1</span><span class="p">,</span> <span class="n">s2</span><span class="p">)</span> |
| |
| <span class="n">w1_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">w1</span><span class="p">)</span> |
| <span class="n">w2_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">w2</span><span class="p">)</span> |
| <span class="n">g1_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">g1</span><span class="p">)</span> |
| <span class="n">g2_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">g2</span><span class="p">)</span> |
| <span class="n">s1_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s1</span><span class="p">)</span> |
| <span class="n">s2_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s2</span><span class="p">)</span> |
| |
| <span class="n">indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">shapes</span><span class="p">)))</span> |
| <span class="c1"># set seed for Gaussian noise replication</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">noise_seed</span><span class="p">)</span> |
| <span class="n">opt1</span><span class="o">.</span><span class="n">update_multi_precision</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">w1_list</span><span class="p">,</span> <span class="n">g1_list</span><span class="p">,</span> <span class="n">s1_list</span><span class="p">)</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">noise_seed</span><span class="p">)</span> |
| <span class="n">opt2</span><span class="o">.</span><span class="n">update_multi_precision</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">w2_list</span><span class="p">,</span> <span class="n">g2_list</span><span class="p">,</span> <span class="n">s2_list</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">compare_states</span><span class="p">:</span> |
| <span class="n">compare_ndarray_tuple</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">s1_list</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">s2_list</span><span class="p">),</span> <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">)</span> |
| <span class="n">compare_ndarray_tuple</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">w1_list</span><span class="p">),</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">w2_list</span><span class="p">),</span> <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="same_symbol_structure"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.same_symbol_structure">[docs]</a><span class="k">def</span> <span class="nf">same_symbol_structure</span><span class="p">(</span><span class="n">sym1</span><span class="p">,</span> <span class="n">sym2</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Compare two symbols to check if they have the same computation graph structure.</span> |
| <span class="sd"> Returns true if operator corresponding to a particular node id is same in both</span> |
| <span class="sd"> symbols for all nodes</span> |
| <span class="sd"> """</span> |
| <span class="n">conf</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">sym1</span><span class="o">.</span><span class="n">tojson</span><span class="p">())</span> |
| <span class="n">nodes</span> <span class="o">=</span> <span class="n">conf</span><span class="p">[</span><span class="s2">"nodes"</span><span class="p">]</span> |
| <span class="n">conf2</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">sym2</span><span class="o">.</span><span class="n">tojson</span><span class="p">())</span> |
| <span class="n">nodes2</span> <span class="o">=</span> <span class="n">conf2</span><span class="p">[</span><span class="s2">"nodes"</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">node1</span><span class="p">,</span> <span class="n">node2</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">nodes</span><span class="p">,</span> <span class="n">nodes2</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">node1</span><span class="p">[</span><span class="s2">"op"</span><span class="p">]</span> <span class="o">!=</span> <span class="n">node2</span><span class="p">[</span><span class="s2">"op"</span><span class="p">]:</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| <span class="k">return</span> <span class="kc">True</span></div> |
| |
| |
| <div class="viewcode-block" id="environment"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.environment">[docs]</a><span class="nd">@contextmanager</span> |
| <span class="k">def</span> <span class="nf">environment</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Environment variable setter and unsetter via `with` idiom.</span> |
| |
| <span class="sd"> Takes a specification of env var names and desired values and adds those</span> |
| <span class="sd"> settings to the environment in advance of running the body of the `with`</span> |
| <span class="sd"> statement. The original environment state is restored afterwards, even</span> |
| <span class="sd"> if exceptions are raised in the `with` body.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> args:</span> |
| <span class="sd"> if 2 args are passed:</span> |
| <span class="sd"> name, desired_value strings of the single env var to update, or</span> |
| <span class="sd"> if 1 arg is passed:</span> |
| <span class="sd"> a dict of name:desired_value for env var's to update</span> |
| |
| <span class="sd"> """</span> |
| |
| <span class="c1"># On Linux, env var changes made through python's os.environ are seen</span> |
| <span class="c1"># by the backend. On Windows though, the C runtime gets a snapshot</span> |
| <span class="c1"># of the environment that cannot be altered by os.environ. Here we</span> |
| <span class="c1"># check, using a wrapped version of the backend's getenv(), that</span> |
| <span class="c1"># the desired env var value is seen by the backend, and otherwise use</span> |
| <span class="c1"># a wrapped setenv() to establish that value in the backend.</span> |
| |
| <span class="c1"># Also on Windows, a set env var can never have the value '', since</span> |
| <span class="c1"># the command 'set FOO= ' is used to unset the variable. Perhaps</span> |
| <span class="c1"># as a result, the wrapped dmlc::GetEnv() routine returns the same</span> |
| <span class="c1"># value for unset variables and those set to ''. As a result, we</span> |
| <span class="c1"># ignore discrepancy.</span> |
| <span class="k">def</span> <span class="nf">validate_backend_setting</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="n">can_use_setenv</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="n">backend_value</span> <span class="o">=</span> <span class="n">getenv</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">value</span> <span class="o">==</span> <span class="n">backend_value</span> <span class="ow">or</span> \ |
| <span class="n">value</span> <span class="o">==</span> <span class="s1">''</span> <span class="ow">and</span> <span class="n">backend_value</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</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">return</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">can_use_setenv</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">'Could not set env var </span><span class="si">{}</span><span class="s1">=</span><span class="si">{}</span><span class="s1"> within C Runtime'</span><span class="o">.</span><span class="n">format</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="n">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="n">validate_backend_setting</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="n">can_use_setenv</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| |
| <span class="c1"># Core routine to alter environment from a dict of env_var_name, env_var_value pairs</span> |
| <span class="k">def</span> <span class="nf">set_environ</span><span class="p">(</span><span class="n">env_var_dict</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">env_var_name</span><span class="p">,</span> <span class="n">env_var_value</span> <span class="ow">in</span> <span class="n">env_var_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">env_var_value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">env_var_name</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="n">env_var_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">env_var_value</span> |
| <span class="n">validate_backend_setting</span><span class="p">(</span><span class="n">env_var_name</span><span class="p">,</span> <span class="n">env_var_value</span><span class="p">)</span> |
| |
| <span class="c1"># Create env_var name:value dict from the two calling methods of this routine</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="n">env_vars</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">'Expecting one dict arg or two args: env var name and value'</span> |
| <span class="n">env_vars</span> <span class="o">=</span> <span class="p">{</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span> <span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">]}</span> |
| |
| <span class="c1"># Take a snapshot of the existing environment variable state</span> |
| <span class="c1"># for those variables to be changed. get() return None for unset keys.</span> |
| <span class="n">snapshot</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">env_vars</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span> |
| |
| <span class="c1"># Alter the environment per the env_vars dict</span> |
| <span class="n">set_environ</span><span class="p">(</span><span class="n">env_vars</span><span class="p">)</span> |
| |
| <span class="c1"># Now run the wrapped code</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="k">yield</span> |
| <span class="k">finally</span><span class="p">:</span> |
| <span class="c1"># the backend engines may still be referencing the changed env var state</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">waitall</span><span class="p">()</span> |
| <span class="c1"># reinstate original env_var state per the snapshot taken earlier</span> |
| <span class="n">set_environ</span><span class="p">(</span><span class="n">snapshot</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="collapse_sum_like"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.collapse_sum_like">[docs]</a><span class="k">def</span> <span class="nf">collapse_sum_like</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">shape</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Given `a` as a numpy ndarray, perform reduce_sum on `a` over the axes that do not</span> |
| <span class="sd"> exist in `shape`. Note that an ndarray with `shape` must be broadcastable to `a`.</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">>=</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">a</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">a</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="n">axes</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">ndim_diff</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ndim_diff</span><span class="p">):</span> |
| <span class="n">axes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">shape</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">s</span> <span class="o">!=</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="n">ndim_diff</span><span class="p">]:</span> |
| <span class="k">assert</span> <span class="n">s</span> <span class="o">==</span> <span class="mi">1</span> |
| <span class="n">axes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="n">ndim_diff</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">axes</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="is_cd_run"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.is_cd_run">[docs]</a><span class="k">def</span> <span class="nf">is_cd_run</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Checks if the test is running as part of a Continuous Delivery run"""</span> |
| <span class="k">return</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"CD_JOB"</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">==</span> <span class="s2">"1"</span></div> |
| |
| |
| <span class="n">_features</span> <span class="o">=</span> <span class="n">Features</span><span class="p">()</span> |
| |
| |
| <div class="viewcode-block" id="has_tvm_ops"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.has_tvm_ops">[docs]</a><span class="k">def</span> <span class="nf">has_tvm_ops</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Returns True if MXNet is compiled with TVM generated operators. If current ctx</span> |
| <span class="sd"> is GPU, it only returns True for CUDA compute capability > 52 where FP16 is supported.</span> |
| <span class="sd"> """</span> |
| <span class="n">built_with_tvm_op</span> <span class="o">=</span> <span class="n">_features</span><span class="o">.</span><span class="n">is_enabled</span><span class="p">(</span><span class="s2">"TVM_OP"</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="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">try</span><span class="p">:</span> |
| <span class="n">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="k">except</span><span class="p">:</span> <span class="c1"># pylint: disable=bare-except</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Failed to get CUDA compute capability for context </span><span class="si">{}</span><span class="s1">. The operators '</span> |
| <span class="s1">'built with USE_TVM_OP=1 will not be run in unit tests.'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">device</span><span class="p">))</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Cuda arch compute capability: sm_</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">cc</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">built_with_tvm_op</span> <span class="ow">and</span> <span class="n">cc</span> <span class="o">>=</span> <span class="mi">53</span> |
| <span class="k">return</span> <span class="n">built_with_tvm_op</span></div> |
| |
| |
| <div class="viewcode-block" id="is_op_runnable"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.is_op_runnable">[docs]</a><span class="k">def</span> <span class="nf">is_op_runnable</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Returns True for all CPU tests. Returns True for GPU tests that are either of the following.</span> |
| <span class="sd"> 1. Built with USE_TVM_OP=0.</span> |
| <span class="sd"> 2. Built with USE_TVM_OP=1, but with compute capability >= 53.</span> |
| <span class="sd"> """</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">current_device</span><span class="p">()</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">if</span> <span class="ow">not</span> <span class="n">_features</span><span class="o">.</span><span class="n">is_enabled</span><span class="p">(</span><span class="s2">"TVM_OP"</span><span class="p">):</span> |
| <span class="k">return</span> <span class="kc">True</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">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="k">except</span><span class="p">:</span> <span class="c1"># pylint: disable=bare-except</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Failed to get CUDA compute capability for context </span><span class="si">{}</span><span class="s1">. The operators '</span> |
| <span class="s1">'built with USE_TVM_OP=1 will not be run in unit tests.'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">device</span><span class="p">))</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Cuda arch compute capability: sm_</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">cc</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">cc</span> <span class="o">>=</span> <span class="mi">53</span> |
| <span class="k">return</span> <span class="kc">True</span></div> |
| |
| |
| <div class="viewcode-block" id="check_gluon_hybridize_consistency"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.check_gluon_hybridize_consistency">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">def</span> <span class="nf">check_gluon_hybridize_consistency</span><span class="p">(</span><span class="n">net_builder</span><span class="p">,</span> <span class="n">data_l</span><span class="p">,</span> <span class="n">numpy_func</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">test_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">rtol</span><span class="o">=</span><span class="mf">1E-4</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1E-4</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Check whether a HybridBlock has consistent output when hybridized or not hybridized</span> |
| |
| <span class="sd"> The network should not contain any random number generators.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> net_builder : function</span> |
| <span class="sd"> The builder of the HybridBlock that we are going to check the consistency.</span> |
| <span class="sd"> Inside the implementation, we will call net_builder() to construct the hybrid block.</span> |
| <span class="sd"> Also, the net_builder will need to support specifying the params</span> |
| <span class="sd"> data_l : list of mx.np.ndarray</span> |
| <span class="sd"> List of input ndarrays.</span> |
| <span class="sd"> numpy_func : function, optional</span> |
| <span class="sd"> The ground truth numpy function that has the same functionality as net_builder().</span> |
| <span class="sd"> Default None.</span> |
| <span class="sd"> test_grad : bool, optional</span> |
| <span class="sd"> Whether to test the consistency of the gradient. Default True.</span> |
| <span class="sd"> rtol : float, optional</span> |
| <span class="sd"> The relative error tolerance, default 1E-4. Default 1E-4.</span> |
| <span class="sd"> atol : float, optional</span> |
| <span class="sd"> The absolute error tolerance, default 1E-4. Default 1E-4.</span> |
| <span class="sd"> """</span> |
| <span class="n">saved_out_np</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="n">saved_grad_np_l</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="n">params_init</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="n">use_autograd_flags</span> <span class="o">=</span> <span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">]</span> <span class="k">if</span> <span class="n">test_grad</span> <span class="k">else</span> <span class="p">[</span><span class="kc">False</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">hybridize</span> <span class="ow">in</span> <span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">]:</span> |
| <span class="k">for</span> <span class="n">use_autograd</span> <span class="ow">in</span> <span class="n">use_autograd_flags</span><span class="p">:</span> |
| <span class="n">net</span> <span class="o">=</span> <span class="n">net_builder</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">params_init</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">initialize</span><span class="p">()</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">load_dict</span><span class="p">(</span><span class="n">params_init</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">hybridize</span><span class="p">:</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">hybridize</span><span class="p">()</span> |
| <span class="n">in_data_l</span> <span class="o">=</span> <span class="p">[</span><span class="n">ele</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">data_l</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">use_autograd</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">in_data_l</span><span class="p">:</span> |
| <span class="n">ele</span><span class="o">.</span><span class="n">attach_grad</span><span class="p">()</span> |
| <span class="k">with</span> <span class="n">mx</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="o">*</span><span class="n">in_data_l</span><span class="p">)</span> |
| <span class="n">out</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">out</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="o">*</span><span class="n">in_data_l</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">params_init</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="c1"># Deferred initialization finished</span> |
| <span class="n">params_init</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span><span class="o">.</span><span class="n">data</span><span class="p">()</span><span class="o">.</span><span class="n">asnumpy</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="n">net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| <span class="k">if</span> <span class="n">saved_out_np</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">saved_out_np</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># Check for correctness</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">(),</span> <span class="n">saved_out_np</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">use_autograd</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">saved_grad_np_l</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">saved_grad_np_l</span> <span class="o">=</span> <span class="p">[</span><span class="n">ele</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">in_data_l</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># Check for correctness</span> |
| <span class="k">for</span> <span class="n">data</span><span class="p">,</span> <span class="n">saved_grad_np</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">in_data_l</span><span class="p">,</span> <span class="n">saved_grad_np_l</span><span class="p">):</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">(),</span> <span class="n">saved_grad_np</span><span class="p">,</span> |
| <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">numpy_func</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">numpy_out</span> <span class="o">=</span> <span class="n">numpy_func</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">ele</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">data_l</span><span class="p">])</span> |
| <span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">saved_out_np</span><span class="p">,</span> <span class="n">numpy_out</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="new_matrix_with_real_eigvals_2d"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.new_matrix_with_real_eigvals_2d">[docs]</a><span class="k">def</span> <span class="nf">new_matrix_with_real_eigvals_2d</span><span class="p">(</span><span class="n">n</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Generate a well-conditioned matrix with small real eigenvalues."""</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span> |
| <span class="n">q</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> |
| <span class="k">while</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">D</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span> |
| <span class="n">I</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> |
| <span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="p">,))</span> |
| <span class="n">v</span> <span class="o">=</span> <span class="n">v</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">v_T</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span> |
| <span class="n">U</span> <span class="o">=</span> <span class="n">I</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">v_T</span><span class="p">)</span> |
| <span class="n">q</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">U</span><span class="p">,</span> <span class="n">D</span><span class="p">)</span> |
| <span class="k">if</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">cond</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="o"><</span> <span class="mi">3</span><span class="p">):</span> |
| <span class="k">break</span> |
| <span class="n">D</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mf">10.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span> |
| <span class="n">q_inv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">q</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">q_inv</span><span class="p">,</span> <span class="n">D</span><span class="p">),</span> <span class="n">q</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="new_matrix_with_real_eigvals_nd"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.new_matrix_with_real_eigvals_nd">[docs]</a><span class="k">def</span> <span class="nf">new_matrix_with_real_eigvals_nd</span><span class="p">(</span><span class="n">shape</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Generate well-conditioned matrices with small real eigenvalues."""</span> |
| <span class="n">n</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]))</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="o">></span> <span class="mi">2</span> <span class="k">else</span> <span class="mi">1</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">new_matrix_with_real_eigvals_2d</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="new_orthonormal_matrix_2d"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.new_orthonormal_matrix_2d">[docs]</a><span class="k">def</span> <span class="nf">new_orthonormal_matrix_2d</span><span class="p">(</span><span class="n">n</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Generate a orthonormal matrix."""</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span> |
| <span class="n">x_trans</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">T</span> |
| <span class="n">sym_mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">x_trans</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">qr</span><span class="p">(</span><span class="n">sym_mat</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span></div> |
| |
| |
| <div class="viewcode-block" id="new_sym_matrix_with_real_eigvals_2d"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.new_sym_matrix_with_real_eigvals_2d">[docs]</a><span class="k">def</span> <span class="nf">new_sym_matrix_with_real_eigvals_2d</span><span class="p">(</span><span class="n">n</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Generate a sym matrix with real eigenvalues."""</span> |
| <span class="n">q</span> <span class="o">=</span> <span class="n">new_orthonormal_matrix_2d</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> |
| <span class="n">D</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mf">10.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">q</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">D</span><span class="p">),</span> <span class="n">q</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="new_sym_matrix_with_real_eigvals_nd"><a class="viewcode-back" href="../../api/test_utils/index.html#mxnet.test_utils.new_sym_matrix_with_real_eigvals_nd">[docs]</a><span class="k">def</span> <span class="nf">new_sym_matrix_with_real_eigvals_nd</span><span class="p">(</span><span class="n">shape</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Generate sym matrices with real eigenvalues."""</span> |
| <span class="n">n</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]))</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="o">></span> <span class="mi">2</span> <span class="k">else</span> <span class="mi">1</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">new_sym_matrix_with_real_eigvals_2d</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span></div> |
| </pre></div> |
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