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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </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> |
| </li> |
| <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> |
| </li> |
| <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> |
| </li> |
| <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> |
| </ul> |
| </li> |
| <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> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.leaky_relu.html">mxnet.npx.leaky_relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_detection.html">mxnet.npx.multibox_detection</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| |
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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> |
| </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> |
| </li> |
| <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> |
| </li> |
| <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> |
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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> |
| <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> |
| <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.gluon.block</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="c1"># coding: utf-8</span> |
| <span class="c1"># pylint: disable= arguments-differ, too-many-lines, reimported</span> |
| <span class="sd">"""Base container class for all neural network models."""</span> |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Block'</span><span class="p">,</span> <span class="s1">'HybridBlock'</span><span class="p">,</span> <span class="s1">'SymbolBlock'</span><span class="p">]</span> |
| |
| <span class="kn">import</span> <span class="nn">enum</span> |
| <span class="kn">import</span> <span class="nn">ctypes</span> |
| <span class="kn">import</span> <span class="nn">copy</span> |
| <span class="kn">import</span> <span class="nn">warnings</span> |
| <span class="kn">import</span> <span class="nn">weakref</span> |
| <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span><span class="p">,</span> <span class="n">defaultdict</span> |
| <span class="kn">import</span> <span class="nn">contextlib</span> |
| <span class="kn">import</span> <span class="nn">contextvars</span> |
| |
| <span class="kn">import</span> <span class="nn">re</span> |
| <span class="kn">import</span> <span class="nn">json</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">mx_real_t</span><span class="p">,</span> <span class="n">MXNetError</span><span class="p">,</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">SymbolHandle</span><span class="p">,</span> <span class="n">py_str</span><span class="p">,</span> <span class="n">check_call</span><span class="p">,</span> <span class="n">_LIB</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">ndarray</span><span class="p">,</span> <span class="n">initializer</span><span class="p">,</span> <span class="n">autograd</span><span class="p">,</span> <span class="n">_deferred_compute</span> <span class="k">as</span> <span class="n">dc</span><span class="p">,</span> <span class="n">name</span> <span class="k">as</span> <span class="n">_name</span><span class="p">,</span> \ |
| <span class="n">profiler</span> <span class="k">as</span> <span class="n">_profiler</span><span class="p">,</span> <span class="n">device</span> <span class="k">as</span> <span class="n">_device</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">..symbol</span> <span class="kn">import</span> <span class="n">Symbol</span><span class="p">,</span> <span class="n">fromjson</span> |
| <span class="kn">from</span> <span class="nn">..ndarray</span> <span class="kn">import</span> <span class="n">NDArray</span><span class="p">,</span> <span class="n">get_dtype_name</span> |
| <span class="kn">from</span> <span class="nn">.parameter</span> <span class="kn">import</span> <span class="n">Parameter</span><span class="p">,</span> <span class="n">DeferredInitializationError</span> |
| <span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">_indent</span><span class="p">,</span> <span class="n">_brief_print_list</span><span class="p">,</span> <span class="n">HookHandle</span><span class="p">,</span> <span class="n">shape_is_known</span> |
| <span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">_check_same_symbol_type</span><span class="p">,</span> <span class="n">_check_all_np_ndarrays</span><span class="p">,</span> <span class="n">_check_block_input_np_ndarrays</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">numpy_extension</span> <span class="k">as</span> <span class="n">_mx_npx</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">numpy</span> <span class="k">as</span> <span class="n">_mx_np</span><span class="p">,</span> <span class="n">ndarray</span> <span class="k">as</span> <span class="n">nd</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="n">util</span> <span class="kn">import</span> <span class="nn">is_np_array</span><span class="o">,</span> <span class="nn">np_shape</span><span class="o">,</span> <span class="nn">np_array</span><span class="o">,</span> <span class="nn">wrap_ctx_to_device_func</span> |
| |
| |
| <span class="n">_naming_counter</span> <span class="o">=</span> <span class="n">contextvars</span><span class="o">.</span><span class="n">ContextVar</span><span class="p">(</span><span class="s1">'namecounter'</span><span class="p">)</span> |
| <span class="n">_prefix</span> <span class="o">=</span> <span class="n">contextvars</span><span class="o">.</span><span class="n">ContextVar</span><span class="p">(</span><span class="s1">'prefix'</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="s1">''</span><span class="p">)</span> |
| |
| |
| <span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span> |
| <span class="k">def</span> <span class="nf">_block_scope</span><span class="p">(</span><span class="n">block</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Append the classname of the current Block to the symbolic and memory profiler name scopes."""</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">block</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> |
| <span class="n">counter</span> <span class="o">=</span> <span class="n">_naming_counter</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">counter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">count</span> <span class="o">=</span> <span class="n">counter</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> |
| <span class="n">counter</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span> <span class="o">+</span> <span class="mi">1</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">name</span><span class="si">}{</span><span class="n">count</span><span class="si">}</span><span class="s1">'</span> |
| <span class="n">counter_token</span> <span class="o">=</span> <span class="n">_naming_counter</span><span class="o">.</span><span class="n">set</span><span class="p">({})</span> |
| <span class="n">prefix_token</span> <span class="o">=</span> <span class="n">_prefix</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">_prefix</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> <span class="o">+</span> <span class="n">name</span> <span class="o">+</span> <span class="s1">'_'</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">_name</span><span class="o">.</span><span class="n">Prefix</span><span class="p">(</span><span class="n">_prefix</span><span class="o">.</span><span class="n">get</span><span class="p">()):</span> |
| <span class="k">with</span> <span class="n">_profiler</span><span class="o">.</span><span class="n">scope</span><span class="p">(</span><span class="n">name</span> <span class="o">+</span> <span class="s1">':'</span><span class="p">):</span> |
| <span class="k">yield</span> |
| <span class="n">_naming_counter</span><span class="o">.</span><span class="n">reset</span><span class="p">(</span><span class="n">counter_token</span><span class="p">)</span> |
| <span class="n">_prefix</span><span class="o">.</span><span class="n">reset</span><span class="p">(</span><span class="n">prefix_token</span><span class="p">)</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_gather_type_device_info</span><span class="p">(</span><span class="n">args</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Analyze the elements inside the nested args object and find:</span> |
| <span class="sd"> - If there exists ndarray</span> |
| <span class="sd"> - If there exists symbol</span> |
| <span class="sd"> - All devices appearing in args</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> args : list or NDArray or Symbol</span> |
| <span class="sd"> Could be a nested architecture.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> has_symbol : bool</span> |
| <span class="sd"> Whether the elements in args contains symbols</span> |
| <span class="sd"> has_ndarray : bool</span> |
| <span class="sd"> Whether the elements in args contains ndarrays</span> |
| <span class="sd"> device_set : set of mxnet.device.Device</span> |
| <span class="sd"> Contains all possible devices of the inner ndarrays in args. Can be empty if there is no</span> |
| <span class="sd"> ndarray inside args.</span> |
| <span class="sd"> first_device : mxnet.device.Device or None</span> |
| <span class="sd"> Device of the first appeared NDArray (for backward-compatibility)</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">return</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="p">{</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">},</span> <span class="n">args</span><span class="o">.</span><span class="n">device</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">Symbol</span><span class="p">):</span> |
| <span class="k">return</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="nb">set</span><span class="p">(),</span> <span class="kc">None</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</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">has_symbol</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="n">has_ndarray</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="n">device_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> |
| <span class="n">first_device</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span> |
| <span class="n">ele_has_sym</span><span class="p">,</span> <span class="n">ele_has_nd</span><span class="p">,</span> <span class="n">ele_device_set</span><span class="p">,</span> <span class="n">ele_first_device</span> <span class="o">=</span>\ |
| <span class="n">_gather_type_device_info</span><span class="p">(</span><span class="n">ele</span><span class="p">)</span> |
| <span class="n">has_symbol</span> <span class="o">=</span> <span class="n">has_symbol</span> <span class="ow">or</span> <span class="n">ele_has_sym</span> |
| <span class="n">has_ndarray</span> <span class="o">=</span> <span class="n">has_ndarray</span> <span class="ow">or</span> <span class="n">ele_has_nd</span> |
| <span class="k">if</span> <span class="n">first_device</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">ele_first_device</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">first_device</span> <span class="o">=</span> <span class="n">ele_first_device</span> |
| <span class="n">device_set</span> <span class="o">=</span> <span class="n">device_set</span> <span class="o">|</span> <span class="n">ele_device_set</span> |
| <span class="k">if</span> <span class="n">has_symbol</span> <span class="ow">and</span> <span class="n">has_ndarray</span><span class="p">:</span> |
| <span class="k">break</span> |
| <span class="k">return</span> <span class="n">has_symbol</span><span class="p">,</span> <span class="n">has_ndarray</span><span class="p">,</span> <span class="n">device_set</span><span class="p">,</span> <span class="n">first_device</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="kc">False</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="nb">set</span><span class="p">(),</span> <span class="kc">None</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_flatten</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">inout_str</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Parse the arguments into a flattened list + an additional format array.</span> |
| <span class="sd"> The format array stores the structure of the original arguments to help reconstruct the inputs.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> args : NDArray, Symbol, or (nested) list of Symbol or NDArray</span> |
| <span class="sd"> We allow None inside the args.</span> |
| <span class="sd"> inout_str : str</span> |
| <span class="sd"> The name of the HybridBlock</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> flat : list of Symbol or NDArray</span> |
| <span class="sd"> The flatten version of the input args.</span> |
| <span class="sd"> fmts : (nested) list of ints</span> |
| <span class="sd"> Stores the format information of the original structured args.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">args</span><span class="p">],</span> <span class="nb">int</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">Symbol</span><span class="p">):</span> |
| <span class="n">length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">())</span> |
| <span class="n">length</span> <span class="o">=</span> <span class="n">length</span> <span class="k">if</span> <span class="n">length</span> <span class="o">></span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">0</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">args</span><span class="p">],</span> <span class="nb">int</span><span class="p">(</span><span class="n">length</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">args</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">[</span><span class="kc">None</span><span class="p">],</span> <span class="nb">int</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="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</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="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"When hybridized, the input of HybridBlock </span><span class="si">{}</span><span class="s2">"</span> |
| <span class="s2">" must be (nested) list of Symbol"</span> |
| <span class="s2">" or NDArray, "</span> |
| <span class="s2">"but got </span><span class="si">{}</span><span class="s2"> of type </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">inout_str</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">args</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">args</span><span class="p">))))</span> |
| <span class="n">flat</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">fmts</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span> |
| <span class="n">arg</span><span class="p">,</span> <span class="n">fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">inout_str</span><span class="p">)</span> |
| <span class="n">flat</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">arg</span><span class="p">)</span> |
| <span class="n">fmts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fmt</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">flat</span><span class="p">,</span> <span class="n">fmts</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_regroup</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">fmt</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Reconstruct the structured arguments based on the flattened version.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> args : NDArray, Symbol, or (nested) list of Symbol or NDArray</span> |
| <span class="sd"> We allow None inside the args.</span> |
| <span class="sd"> fmt : (nested) list of ints</span> |
| <span class="sd"> Stores the format information of the original structured args.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> ret : NDArray, Symbol, or (nested) list of Symbol or NDArray</span> |
| |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="nf">_merger</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">fmt</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Recursive call to merge the arguments"""</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fmt</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">fmt</span> <span class="o"><</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Unsupported encoded format </span><span class="si">{}</span><span class="s2">."</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fmt</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">fmt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</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="k">if</span> <span class="n">fmt</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'We do not support passing types that are not None'</span> |
| <span class="s1">' when the initial HybridBlock has received NoneType and'</span> |
| <span class="s1">' has been hybridized.'</span> |
| <span class="s1">' Received arg = </span><span class="si">{}</span><span class="s1">, fmt = </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">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">fmt</span><span class="p">))</span> |
| <span class="k">return</span> <span class="kc">None</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="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">args</span><span class="p">[:</span><span class="n">fmt</span><span class="p">],</span> <span class="n">args</span><span class="p">[</span><span class="n">fmt</span><span class="p">:]</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</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="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"When hybridized, the output of HybridBlock must be (nested)"</span> |
| <span class="s2">" list of Symbol or NDArray, "</span> |
| <span class="s2">"but got </span><span class="si">{}</span><span class="s2"> of type </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">args</span><span class="p">)))</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">fmt</span><span class="p">:</span> |
| <span class="n">res</span><span class="p">,</span> <span class="n">args</span> <span class="o">=</span> <span class="n">_merger</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> |
| <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">res</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">ret</span><span class="p">,</span> <span class="n">args</span> |
| <span class="k">return</span> <span class="n">_merger</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">fmt</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> |
| |
| |
| <div class="viewcode-block" id="Block"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block">[docs]</a><span class="k">class</span> <span class="nc">Block</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""Base class for all neural network layers and models. Your models should</span> |
| <span class="sd"> subclass this class.</span> |
| |
| <span class="sd"> :py:class:`Block` can be nested recursively in a tree structure. You can create and</span> |
| <span class="sd"> assign child :py:class:`Block` as regular attributes::</span> |
| |
| <span class="sd"> import mxnet as mx</span> |
| <span class="sd"> from mxnet.gluon import Block, nn</span> |
| |
| <span class="sd"> class Model(Block):</span> |
| <span class="sd"> def __init__(self, **kwargs):</span> |
| <span class="sd"> super(Model, self).__init__(**kwargs)</span> |
| <span class="sd"> self.dense0 = nn.Dense(20)</span> |
| <span class="sd"> self.dense1 = nn.Dense(20)</span> |
| |
| <span class="sd"> def forward(self, x):</span> |
| <span class="sd"> x = mx.npx.relu(self.dense0(x))</span> |
| <span class="sd"> return mx.npx.relu(self.dense1(x))</span> |
| |
| <span class="sd"> model = Model()</span> |
| <span class="sd"> model.initialize(device=mx.cpu(0))</span> |
| <span class="sd"> model(mx.np.zeros((10, 10), device=mx.cpu(0)))</span> |
| |
| |
| <span class="sd"> Child :py:class:`Block` assigned this way will be registered and :py:meth:`collect_params`</span> |
| <span class="sd"> will collect their Parameters recursively. You can also manually register</span> |
| <span class="sd"> child blocks with :py:meth:`register_child`.</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="bp">self</span><span class="o">.</span><span class="n">_children</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(</span><span class="se">\n</span><span class="si">{modstr}</span><span class="se">\n</span><span class="s1">)'</span> |
| <span class="n">modstr</span> <span class="o">=</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">' (</span><span class="si">{key}</span><span class="s1">): </span><span class="si">{block}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="n">key</span><span class="p">,</span> |
| <span class="n">block</span><span class="o">=</span><span class="n">_indent</span><span class="p">(</span><span class="n">block</span><span class="o">.</span><span class="fm">__repr__</span><span class="p">(),</span> <span class="mi">2</span><span class="p">))</span> |
| <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">block</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</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">block</span><span class="p">,</span> <span class="n">Block</span><span class="p">)])</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">modstr</span><span class="o">=</span><span class="n">modstr</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__setattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Registers parameters."""</span> |
| |
| <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span> |
| <span class="n">existing</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">existing</span><span class="p">,</span> <span class="p">(</span><span class="n">Parameter</span><span class="p">,</span> <span class="n">Block</span><span class="p">))</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">existing</span><span class="p">)):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'Changing attribute type for </span><span class="si">{name}</span><span class="s1"> from </span><span class="si">{type1}</span><span class="s1"> to </span><span class="si">{type2}</span><span class="s1">'</span> \ |
| <span class="s1">'is not allowed.'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> |
| <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">type1</span><span class="o">=</span><span class="nb">type</span><span class="p">(</span><span class="n">existing</span><span class="p">),</span> <span class="n">type2</span><span class="o">=</span><span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">)))</span> |
| |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">Block</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">register_child</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">Parameter</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span> |
| |
| <span class="nb">super</span><span class="p">(</span><span class="n">Block</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__setattr__</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="k">def</span> <span class="nf">_check_container_with_block</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">children</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> |
| <span class="k">def</span> <span class="nf">_find_unregistered_block_in_container</span><span class="p">(</span><span class="n">data</span><span class="p">):</span> |
| <span class="c1"># Find whether a nested container structure contains Blocks</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</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="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">_find_unregistered_block_in_container</span><span class="p">(</span><span class="n">ele</span><span class="p">):</span> |
| <span class="k">return</span> <span class="kc">True</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">_find_unregistered_block_in_container</span><span class="p">(</span><span class="n">v</span><span class="p">):</span> |
| <span class="k">return</span> <span class="kc">True</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">Block</span><span class="p">):</span> |
| <span class="k">return</span> <span class="ow">not</span> <span class="n">data</span> <span class="ow">in</span> <span class="p">(</span><span class="n">c</span><span class="p">()</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">children</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="kc">False</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="bp">self</span><span class="o">.</span><span class="vm">__dict__</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="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="nb">dict</span><span class="p">))</span> <span class="ow">and</span> <span class="ow">not</span> <span class="p">(</span><span class="n">k</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'__'</span><span class="p">)</span> <span class="ow">or</span> <span class="n">k</span> <span class="o">==</span> <span class="s1">'_children'</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">_find_unregistered_block_in_container</span><span class="p">(</span><span class="n">v</span><span class="p">):</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'"</span><span class="si">{name}</span><span class="s1">" is an unregistered container with Blocks. '</span> |
| <span class="s1">'Note that Blocks inside the list, tuple or dict will not be '</span> |
| <span class="s1">'registered automatically. Make sure to register them using '</span> |
| <span class="s1">'register_child() or switching to '</span> |
| <span class="s1">'nn.Sequential/nn.HybridSequential instead. '</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">+</span> <span class="s2">"."</span> <span class="o">+</span> <span class="n">k</span><span class="p">),</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_alias</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns this :py:class:`Block`'s parameter dictionary (does not include its</span> |
| <span class="sd"> children's parameters)."""</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span> |
| |
| <div class="viewcode-block" id="Block.collect_params"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.collect_params">[docs]</a> <span class="k">def</span> <span class="nf">collect_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">select</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns a :py:class:`Dict` containing this :py:class:`Block` and all of its</span> |
| <span class="sd"> children's Parameters(default), also can returns the select :py:class:`Dict`</span> |
| <span class="sd"> which match some given regular expressions.</span> |
| |
| <span class="sd"> For example, collect the specified parameters in ['conv1.weight', 'conv1.bias', 'fc.weight',</span> |
| <span class="sd"> 'fc.bias']::</span> |
| |
| <span class="sd"> model.collect_params('conv1.weight|conv1.bias|fc.weight|fc.bias')</span> |
| |
| <span class="sd"> or collect all parameters whose names end with 'weight' or 'bias', this can be done</span> |
| <span class="sd"> using regular expressions::</span> |
| |
| <span class="sd"> model.collect_params('.*weight|.*bias')</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> select : str</span> |
| <span class="sd"> regular expressions</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> The selected :py:class:`Dict`</span> |
| <span class="sd"> """</span> |
| <span class="c1"># We need to check here because blocks inside containers are not supported.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_check_container_with_block</span><span class="p">()</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_collect_params_with_prefix</span><span class="p">(</span><span class="n">select</span><span class="o">=</span><span class="n">select</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="nf">_collect_params_with_prefix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="n">select</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">prefix</span><span class="p">:</span> |
| <span class="n">prefix</span> <span class="o">+=</span> <span class="s1">'.'</span> |
| <span class="k">if</span> <span class="n">select</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="n">prefix</span> <span class="o">+</span> <span class="n">key</span> <span class="p">:</span> <span class="n">val</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="bp">self</span><span class="o">.</span><span class="n">_reg_params</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">pattern</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">select</span><span class="p">)</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="n">prefix</span> <span class="o">+</span> <span class="n">key</span> <span class="p">:</span> <span class="n">val</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="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">pattern</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="n">prefix</span> <span class="o">+</span> <span class="n">key</span><span class="p">)}</span> |
| |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">child</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">ret</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">child</span><span class="p">()</span><span class="o">.</span><span class="n">_collect_params_with_prefix</span><span class="p">(</span><span class="n">prefix</span> <span class="o">+</span> <span class="n">name</span><span class="p">,</span> <span class="n">select</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">ret</span> |
| |
| <div class="viewcode-block" id="Block.save_parameters"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.save_parameters">[docs]</a> <span class="k">def</span> <span class="nf">save_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">,</span> <span class="n">deduplicate</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Save parameters to file.</span> |
| |
| <span class="sd"> Saved parameters can only be loaded with `load_parameters`. Note that this</span> |
| <span class="sd"> method only saves parameters, not model structure. If you want to save</span> |
| <span class="sd"> model structures, please use :py:meth:`HybridBlock.export`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> filename : str</span> |
| <span class="sd"> Path to file.</span> |
| <span class="sd"> deduplicate : bool, default False</span> |
| <span class="sd"> If True, save shared parameters only once. Otherwise, if a Block</span> |
| <span class="sd"> contains multiple sub-blocks that share parameters, each of the</span> |
| <span class="sd"> shared parameters will be separately saved for every sub-block.</span> |
| |
| <span class="sd"> References</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> `Saving and Loading Gluon Models \</span> |
| <span class="sd"> <https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html>`_</span> |
| <span class="sd"> """</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_collect_params_with_prefix</span><span class="p">()</span> |
| |
| <span class="k">if</span> <span class="n">deduplicate</span><span class="p">:</span> |
| <span class="c1"># Shared parameters are stored only a single time as of MXNet 1.6.</span> |
| <span class="c1"># Shared parameters are registered under multiple prefixes returned by</span> |
| <span class="c1"># _collect_params_with_prefix. We select a single one and only store</span> |
| <span class="c1"># it. In load_parameters it is sufficient for a shared parameter to</span> |
| <span class="c1"># only set it for a single prefix.</span> |
| <span class="n">reverse_params</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">k</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">params</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">k</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">reverse_params</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| |
| <span class="n">arg_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">val</span><span class="o">.</span><span class="n">_reduce</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">params</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="n">_mx_npx</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="o">**</span><span class="n">arg_dict</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">ndarray</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">arg_dict</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Block.load_parameters"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.load_parameters">[docs]</a> <span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">load_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_missing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">ignore_extra</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">cast_dtype</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dtype_source</span><span class="o">=</span><span class="s1">'current'</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Load parameters from file previously saved by `save_parameters`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> filename : str</span> |
| <span class="sd"> Path to parameter file.</span> |
| <span class="sd"> device : Device or list of Device, default cpu()</span> |
| <span class="sd"> Device(s) to initialize loaded parameters on.</span> |
| <span class="sd"> allow_missing : bool, default False</span> |
| <span class="sd"> Whether to silently skip loading parameters not represents in the file.</span> |
| <span class="sd"> ignore_extra : bool, default False</span> |
| <span class="sd"> Whether to silently ignore parameters from the file that are not</span> |
| <span class="sd"> present in this Block.</span> |
| <span class="sd"> cast_dtype : bool, default False</span> |
| <span class="sd"> Cast the data type of the NDArray loaded from the checkpoint to the dtype</span> |
| <span class="sd"> provided by the Parameter if any.</span> |
| <span class="sd"> dtype_source : str, default 'current'</span> |
| <span class="sd"> must be in {'current', 'saved'}</span> |
| <span class="sd"> Only valid if cast_dtype=True, specify the source of the dtype for casting</span> |
| <span class="sd"> the parameters</span> |
| <span class="sd"> References</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> `Saving and Loading Gluon Models \</span> |
| <span class="sd"> <https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html>`_</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="c1"># failure may happen when loading parameters saved as NDArrays within</span> |
| <span class="c1"># NumPy semantics. Check the failure type and recover from it if it happens.</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">loaded</span> <span class="o">=</span> <span class="n">_mx_npx</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span> |
| <span class="k">except</span> <span class="n">MXNetError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span> |
| <span class="n">err_msg</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">)</span> |
| <span class="k">if</span> <span class="s1">'is_np_shape'</span> <span class="ow">in</span> <span class="n">err_msg</span><span class="p">:</span> |
| <span class="c1"># Loading failure due to parameters saved without numpy semantics.</span> |
| <span class="c1"># Temporarily disable numpy semantics and load parameters. After it's</span> |
| <span class="c1"># done, resume the numpy semantics. This is fine because the cases</span> |
| <span class="c1"># numpy ndarray covers is a superset of the legacy ndarray's.</span> |
| <span class="k">with</span> <span class="n">np_array</span><span class="p">(</span><span class="kc">False</span><span class="p">):</span> |
| <span class="k">with</span> <span class="n">np_shape</span><span class="p">(</span><span class="kc">False</span><span class="p">):</span> |
| <span class="n">loaded_nds</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">loaded_nds</span><span class="p">,</span> <span class="nb">dict</span><span class="p">),</span>\ |
| <span class="s1">'expecting a dict type, got </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">loaded_nds</span><span class="p">)))</span> |
| <span class="n">loaded</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">loaded_nds</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">loaded_nds</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="n">err_msg</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">loaded</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">loaded</span><span class="p">:</span> |
| <span class="k">return</span> |
| <span class="n">full_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'params'</span><span class="p">:</span> <span class="n">loaded</span><span class="p">,</span> <span class="s1">'filename'</span><span class="p">:</span> <span class="n">filename</span><span class="p">}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">load_dict</span><span class="p">(</span><span class="n">full_dict</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">allow_missing</span><span class="p">,</span> <span class="n">ignore_extra</span><span class="p">,</span> <span class="n">cast_dtype</span><span class="p">,</span> <span class="n">dtype_source</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Block.load_dict"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.load_dict">[docs]</a> <span class="k">def</span> <span class="nf">load_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">param_dict</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_missing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">ignore_extra</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">cast_dtype</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dtype_source</span><span class="o">=</span><span class="s2">"current"</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Load parameters from dict</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> param_dict : dict</span> |
| <span class="sd"> Dictionary containing model parameters</span> |
| <span class="sd"> device : Device, optional</span> |
| <span class="sd"> Device context on which the memory is allocated. Default is</span> |
| <span class="sd"> `mxnet.device.current_device()`.</span> |
| <span class="sd"> allow_missing : bool, default False</span> |
| <span class="sd"> Whether to silently skip loading parameters not represented in the file.</span> |
| <span class="sd"> ignore_extra : bool, default False</span> |
| <span class="sd"> Whether to silently ignore parameters from the file that are not</span> |
| <span class="sd"> present in this dict.</span> |
| <span class="sd"> cast_dtype : bool, default False</span> |
| <span class="sd"> Cast the data type of the NDArray loaded from the checkpoint to the dtype</span> |
| <span class="sd"> provided by the Parameter if any</span> |
| <span class="sd"> dtype_source : str, default 'current'</span> |
| <span class="sd"> must be in {'current', 'saved'}</span> |
| <span class="sd"> Only valid if cast_dtype=True, specify the source of the dtype for casting</span> |
| <span class="sd"> the parameters</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param_dict</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'filename'</span><span class="p">),</span> <span class="nb">str</span><span class="p">):</span> |
| <span class="c1"># pass from load_parameters</span> |
| <span class="n">filename</span> <span class="o">=</span> <span class="n">param_dict</span><span class="p">[</span><span class="s1">'filename'</span><span class="p">]</span> |
| <span class="n">param_dict</span> <span class="o">=</span> <span class="n">param_dict</span><span class="p">[</span><span class="s1">'params'</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">filename</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span> |
| <span class="n">error_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"file: </span><span class="si">{</span><span class="n">filename</span><span class="si">}</span><span class="s2">"</span> <span class="k">if</span> <span class="n">filename</span> <span class="k">else</span> <span class="s2">"param_dict"</span> |
| <span class="n">loaded</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">[</span><span class="mi">4</span><span class="p">:]</span> <span class="k">if</span> <span class="n">k</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'arg:'</span><span class="p">)</span> <span class="ow">or</span> <span class="n">k</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'aux:'</span><span class="p">)</span> <span class="k">else</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="n">param_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">allow_missing</span><span class="p">:</span> |
| <span class="n">params_inv</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</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">params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">params_inv</span><span class="p">[</span><span class="n">v</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">assert</span> <span class="nb">any</span><span class="p">(</span><span class="n">p</span> <span class="ow">in</span> <span class="n">loaded</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">params_inv</span><span class="p">[</span><span class="n">param</span><span class="p">]),</span> \ |
| <span class="sa">f</span><span class="s2">"Parameter '</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">' is missing in '</span><span class="si">{</span><span class="n">error_str</span><span class="si">}</span><span class="s2">', which contains parameters: </span><span class="si">{</span><span class="n">_brief_print_list</span><span class="p">(</span><span class="n">loaded</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">}</span><span class="s2">. "</span> \ |
| <span class="s2">"Set allow_missing=True to ignore missing parameters."</span> |
| |
| <span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">_device</span><span class="o">.</span><span class="n">current_device</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">loaded</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">ignore_extra</span> <span class="ow">and</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">params</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">"Parameter '</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">' loaded from '</span><span class="si">{</span><span class="n">error_str</span><span class="si">}</span><span class="s2">' is not present in Dict, "</span> \ |
| <span class="sa">f</span><span class="s2">"which contains parameters </span><span class="si">{</span><span class="n">_brief_print_list</span><span class="p">(</span><span class="n">params</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">}</span><span class="s2">. Set ignore_extra=True to ignore. "</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">params</span><span class="p">:</span> |
| <span class="n">param</span> <span class="o">=</span> <span class="n">loaded</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param</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">param</span> <span class="o">=</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">param</span><span class="p">)</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">param</span><span class="p">)</span> |
| <span class="n">params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">_load_init</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">cast_dtype</span><span class="o">=</span><span class="n">cast_dtype</span><span class="p">,</span> <span class="n">dtype_source</span><span class="o">=</span><span class="n">dtype_source</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Block.register_child"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.register_child">[docs]</a> <span class="k">def</span> <span class="nf">register_child</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Registers block as a child of self. :py:class:`Block` s assigned to self as</span> |
| <span class="sd"> attributes will be registered automatically."""</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">weakref</span><span class="o">.</span><span class="n">ref</span><span class="p">(</span><span class="n">block</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Block.register_forward_pre_hook"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.register_forward_pre_hook">[docs]</a> <span class="k">def</span> <span class="nf">register_forward_pre_hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hook</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Registers a forward pre-hook on the block.</span> |
| |
| <span class="sd"> The hook function is called immediately before :func:`forward`.</span> |
| <span class="sd"> It should not modify the input or output.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> hook : callable</span> |
| <span class="sd"> The forward hook function of form `hook(block, input) -> None`.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :class:`mxnet.gluon.utils.HookHandle`</span> |
| <span class="sd"> """</span> |
| <span class="n">handle</span> <span class="o">=</span> <span class="n">HookHandle</span><span class="p">()</span> |
| <span class="n">handle</span><span class="o">.</span><span class="n">attach</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span><span class="p">,</span> <span class="n">hook</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">handle</span></div> |
| |
| <div class="viewcode-block" id="Block.register_forward_hook"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.register_forward_hook">[docs]</a> <span class="k">def</span> <span class="nf">register_forward_hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hook</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Registers a forward hook on the block.</span> |
| |
| <span class="sd"> The hook function is called immediately after :func:`forward`.</span> |
| <span class="sd"> It should not modify the input or output.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> hook : callable</span> |
| <span class="sd"> The forward hook function of form `hook(block, input, output) -> None`.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> :class:`mxnet.gluon.utils.HookHandle`</span> |
| <span class="sd"> """</span> |
| <span class="n">handle</span> <span class="o">=</span> <span class="n">HookHandle</span><span class="p">()</span> |
| <span class="n">handle</span><span class="o">.</span><span class="n">attach</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span><span class="p">,</span> <span class="n">hook</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">handle</span></div> |
| |
| <div class="viewcode-block" id="Block.apply"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.apply">[docs]</a> <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fn</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Applies ``fn`` recursively to every child block as well as self.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> fn : callable</span> |
| <span class="sd"> Function to be applied to each submodule, of form `fn(block)`.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> this block</span> |
| <span class="sd"> """</span> |
| <span class="k">for</span> <span class="n">cld</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">cld</span><span class="p">()</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">fn</span><span class="p">)</span> |
| <span class="n">fn</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> |
| |
| <div class="viewcode-block" id="Block.initialize"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.initialize">[docs]</a> <span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="n">initializer</span><span class="o">.</span><span class="n">Uniform</span><span class="p">(),</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">force_reinit</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Initializes :py:class:`Parameter` s of this :py:class:`Block` and its children.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> init : Initializer</span> |
| <span class="sd"> Global default Initializer to be used when :py:meth:`Parameter.init` is ``None``.</span> |
| <span class="sd"> Otherwise, :py:meth:`Parameter.init` takes precedence.</span> |
| <span class="sd"> device : Device or list of Device</span> |
| <span class="sd"> Keeps a copy of Parameters on one or many device(s).</span> |
| <span class="sd"> verbose : bool, default False</span> |
| <span class="sd"> Whether to verbosely print out details on initialization.</span> |
| <span class="sd"> force_reinit : bool, default False</span> |
| <span class="sd"> Whether to force re-initialization if parameter is already initialized.</span> |
| <span class="sd"> """</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span> |
| <span class="n">init</span><span class="o">.</span><span class="n">set_verbosity</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">v</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">init</span><span class="p">,</span> <span class="n">force_reinit</span><span class="o">=</span><span class="n">force_reinit</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Block.save"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.save">[docs]</a> <span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefix</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Save the model architecture and parameters to load again later</span> |
| |
| <span class="sd"> Saves the model architecture as a nested dictionary where each Block</span> |
| <span class="sd"> in the model is a dictionary and its children are sub-dictionaries.</span> |
| |
| <span class="sd"> Each Block is uniquely identified by Block class name and a unique ID.</span> |
| <span class="sd"> We save each Block's parameter UUID to restore later in order to match</span> |
| <span class="sd"> the saved parameters.</span> |
| |
| <span class="sd"> Recursively traverses a Block's children in order (since its an</span> |
| <span class="sd"> OrderedDict) and uses the unique ID to denote that specific Block.</span> |
| |
| <span class="sd"> Assumes that the model is created in an identical order every time.</span> |
| <span class="sd"> If the model is not able to be recreated deterministically do not</span> |
| <span class="sd"> use this set of APIs to save/load your model.</span> |
| |
| <span class="sd"> For HybridBlocks, the cached_graph is saved (Symbol & inputs) if</span> |
| <span class="sd"> it has already been hybridized.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> prefix : str</span> |
| <span class="sd"> The prefix to use in filenames for saving this model:</span> |
| <span class="sd"> <prefix>-model.json and <prefix>-model.params</span> |
| <span class="sd"> """</span> |
| <span class="c1"># create empty model structure</span> |
| <span class="n">model</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="k">def</span> <span class="nf">_save_cached_graphs</span><span class="p">(</span><span class="n">blk</span><span class="p">,</span> <span class="n">structure</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="c1"># create new entry for this block</span> |
| <span class="n">mdl</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="c1"># encode unique name based on block type and ID</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">blk</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> |
| <span class="n">structure</span><span class="p">[</span><span class="n">name</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">index</span><span class="p">)]</span> <span class="o">=</span> <span class="n">mdl</span> |
| <span class="n">index</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">blk</span><span class="p">,</span> <span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">blk</span><span class="o">.</span><span class="n">_cached_graph</span><span class="p">:</span> |
| <span class="c1"># save in/out formats</span> |
| <span class="n">mdl</span><span class="p">[</span><span class="s1">'in_format'</span><span class="p">]</span> <span class="o">=</span> <span class="n">blk</span><span class="o">.</span><span class="n">_in_format</span> |
| <span class="n">mdl</span><span class="p">[</span><span class="s1">'out_format'</span><span class="p">]</span> <span class="o">=</span> <span class="n">blk</span><span class="o">.</span><span class="n">_out_format</span> |
| <span class="c1"># save cached graph & input symbols</span> |
| <span class="n">syms</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="n">blk</span><span class="o">.</span><span class="n">_cached_graph</span> |
| <span class="n">mdl_syms</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">sym</span> <span class="ow">in</span> <span class="n">syms</span><span class="p">:</span> |
| <span class="n">mdl_syms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">tojson</span><span class="p">())</span> |
| <span class="n">mdl</span><span class="p">[</span><span class="s1">'inputs'</span><span class="p">]</span> <span class="o">=</span> <span class="n">mdl_syms</span> |
| <span class="n">mdl</span><span class="p">[</span><span class="s1">'symbol'</span><span class="p">]</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">tojson</span><span class="p">()</span> |
| <span class="n">mdl</span><span class="p">[</span><span class="s1">'hybridized'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">mdl</span><span class="p">[</span><span class="s1">'hybridized'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="c1"># save param uuids</span> |
| <span class="n">pmap</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="n">mdl</span><span class="p">[</span><span class="s1">'params'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pmap</span> |
| <span class="n">pnames</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">blk</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> |
| <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">pnames</span><span class="p">:</span> |
| <span class="n">param</span> <span class="o">=</span> <span class="n">blk</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> |
| <span class="n">pmap</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">_uuid</span> |
| <span class="c1"># recursively save children</span> |
| <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">blk</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">index</span> <span class="o">=</span> <span class="n">_save_cached_graphs</span><span class="p">(</span><span class="n">child</span><span class="p">(),</span> <span class="n">mdl</span><span class="p">,</span> <span class="n">index</span><span class="p">)</span> |
| <span class="c1"># return latest index (ie. block count)</span> |
| <span class="k">return</span> <span class="n">index</span> |
| |
| <span class="c1"># save top-level block</span> |
| <span class="n">_save_cached_graphs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span> |
| <span class="c1"># save model</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'-model.json'</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span> |
| <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">fp</span><span class="p">)</span> |
| <span class="c1"># save params</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">save_parameters</span><span class="p">(</span><span class="s1">'MyModel-model.params'</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Block.load"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.load">[docs]</a> <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefix</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Load a model saved using the `save` API</span> |
| |
| <span class="sd"> Reconfigures a model using the saved configuration. This function</span> |
| <span class="sd"> does not regenerate the model architecture. It resets each Block's</span> |
| <span class="sd"> parameter UUIDs as they were when saved in order to match the names of the</span> |
| <span class="sd"> saved parameters.</span> |
| |
| <span class="sd"> This function assumes the Blocks in the model were created in the same</span> |
| <span class="sd"> order they were when the model was saved. This is because each Block is</span> |
| <span class="sd"> uniquely identified by Block class name and a unique ID in order (since</span> |
| <span class="sd"> its an OrderedDict) and uses the unique ID to denote that specific Block.</span> |
| |
| <span class="sd"> Assumes that the model is created in an identical order every time.</span> |
| <span class="sd"> If the model is not able to be recreated deterministically do not</span> |
| <span class="sd"> use this set of APIs to save/load your model.</span> |
| |
| <span class="sd"> For HybridBlocks, the cached_graph (Symbol & inputs) and settings are</span> |
| <span class="sd"> restored if it had been hybridized before saving.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> prefix : str</span> |
| <span class="sd"> The prefix to use in filenames for loading this model:</span> |
| <span class="sd"> <prefix>-model.json and <prefix>-model.params</span> |
| <span class="sd"> """</span> |
| <span class="c1"># load model json from file</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">prefix</span><span class="o">+</span><span class="s1">'-model.json'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fp</span><span class="p">:</span> |
| <span class="n">model</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_load_cached_graphs</span><span class="p">(</span><span class="n">blk</span><span class="p">,</span> <span class="n">structure</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="c1"># get block name</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">blk</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> |
| <span class="c1"># lookup previous encoded name based on block type and ID</span> |
| <span class="n">mdl</span> <span class="o">=</span> <span class="n">structure</span><span class="p">[</span><span class="n">name</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">index</span><span class="p">)]</span> |
| <span class="n">index</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">blk</span><span class="p">,</span> <span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">mdl</span><span class="p">[</span><span class="s1">'hybridized'</span><span class="p">]:</span> |
| <span class="c1"># restore in/out formats</span> |
| <span class="n">blk</span><span class="o">.</span><span class="n">_in_format</span> <span class="o">=</span> <span class="n">mdl</span><span class="p">[</span><span class="s1">'in_format'</span><span class="p">]</span> |
| <span class="n">blk</span><span class="o">.</span><span class="n">_out_format</span> <span class="o">=</span> <span class="n">mdl</span><span class="p">[</span><span class="s1">'out_format'</span><span class="p">]</span> |
| <span class="c1"># get saved symbol</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">fromjson</span><span class="p">(</span><span class="n">mdl</span><span class="p">[</span><span class="s1">'symbol'</span><span class="p">])</span> |
| <span class="n">syms</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="c1"># recreate inputs for this symbol</span> |
| <span class="k">for</span> <span class="n">inp</span> <span class="ow">in</span> <span class="n">mdl</span><span class="p">[</span><span class="s1">'inputs'</span><span class="p">]:</span> |
| <span class="n">syms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fromjson</span><span class="p">(</span><span class="n">inp</span><span class="p">))</span> |
| <span class="c1"># reset cached_graph and active status</span> |
| <span class="n">blk</span><span class="o">.</span><span class="n">_cached_graph</span> <span class="o">=</span> <span class="p">(</span><span class="n">syms</span><span class="p">,</span> <span class="n">out</span><span class="p">)</span> |
| <span class="n">blk</span><span class="o">.</span><span class="n">_active</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="c1"># reload param uuids</span> |
| <span class="n">pmap</span> <span class="o">=</span> <span class="n">mdl</span><span class="p">[</span><span class="s1">'params'</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">p</span><span class="p">,</span> <span class="n">uuid</span> <span class="ow">in</span> <span class="n">pmap</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">param</span> <span class="o">=</span> <span class="n">blk</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> |
| <span class="n">param</span><span class="o">.</span><span class="n">_uuid</span> <span class="o">=</span> <span class="n">uuid</span> |
| <span class="c1"># recursively reload children</span> |
| <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">blk</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">index</span> <span class="o">=</span> <span class="n">_load_cached_graphs</span><span class="p">(</span><span class="n">child</span><span class="p">(),</span> <span class="n">mdl</span><span class="p">,</span> <span class="n">index</span><span class="p">)</span> |
| <span class="c1"># return latest index (ie. block count)</span> |
| <span class="k">return</span> <span class="n">index</span> |
| |
| <span class="c1"># load top-level block</span> |
| <span class="n">_load_cached_graphs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span> |
| <span class="c1"># load params</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">load_parameters</span><span class="p">(</span><span class="s1">'MyModel-model.params'</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Block.hybridize"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.hybridize">[docs]</a> <span class="k">def</span> <span class="nf">hybridize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">active</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">""" Please refer description of HybridBlock hybridize().</span> |
| <span class="sd"> """</span> |
| <span class="k">for</span> <span class="n">cld</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">cld</span><span class="p">()</span><span class="o">.</span><span class="n">hybridize</span><span class="p">(</span><span class="n">active</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Block.cast"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.cast">[docs]</a> <span class="k">def</span> <span class="nf">cast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Cast this Block to use another data type.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dtype : str or numpy.dtype</span> |
| <span class="sd"> The new data type.</span> |
| <span class="sd"> """</span> |
| <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">child</span><span class="p">()</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">param</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Block.zero_grad"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.zero_grad">[docs]</a> <span class="k">def</span> <span class="nf">zero_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Sets all Parameters' gradient buffer to 0."""</span> |
| <span class="c1"># collect gradient arrays for each device</span> |
| <span class="n">arrays</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">grad_req</span> <span class="o">==</span> <span class="s1">'null'</span> <span class="ow">or</span> <span class="n">p</span><span class="o">.</span><span class="n">_grad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">continue</span> |
| <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="n">p</span><span class="o">.</span><span class="n">list_grad</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">g</span><span class="o">.</span><span class="n">stype</span> <span class="o">==</span> <span class="s1">'row_sparse'</span><span class="p">:</span> |
| <span class="n">ndarray</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">g</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">g</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="n">arrays</span><span class="p">[</span><span class="n">g</span><span class="o">.</span><span class="n">device</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">g</span><span class="o">.</span><span class="n">as_nd_ndarray</span><span class="p">())</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">arrays</span><span class="p">[</span><span class="n">g</span><span class="o">.</span><span class="n">device</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">g</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">0</span><span class="p">:</span> |
| <span class="k">return</span> |
| |
| <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">ndarray</span><span class="o">.</span><span class="n">reset_arrays</span><span class="p">(</span><span class="o">*</span><span class="n">arr</span><span class="p">,</span> <span class="n">num_arrays</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">arr</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="Block.reset_device"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.reset_device">[docs]</a> <span class="k">def</span> <span class="nf">reset_device</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Re-assign all Parameters to other devices.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> device : Device or list of Device, default :py:meth:`device.current_device()`.</span> |
| <span class="sd"> Assign Parameter to given device. If device is a list of Device, a</span> |
| <span class="sd"> copy will be made for each device.</span> |
| <span class="sd"> """</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">i</span><span class="o">.</span><span class="n">reset_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Block.reset_ctx"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.reset_ctx">[docs]</a> <span class="k">def</span> <span class="nf">reset_ctx</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ctx</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""This function has been deprecated. Please refer to ``Block.reset_device``."""</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'Block.reset_ctx has been renamed to'</span> |
| <span class="s1">' Block.reset_device'</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset_device</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Block.setattr"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.setattr">[docs]</a> <span class="k">def</span> <span class="nf">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Set an attribute to a new value for all Parameters.</span> |
| |
| <span class="sd"> For example, set grad_req to null if you don't need gradient w.r.t a</span> |
| <span class="sd"> model's Parameters::</span> |
| |
| <span class="sd"> model.setattr('grad_req', 'null')</span> |
| |
| <span class="sd"> or change the learning rate multiplier::</span> |
| |
| <span class="sd"> model.setattr('lr_mult', 0.5)</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> name : str</span> |
| <span class="sd"> Name of the attribute.</span> |
| <span class="sd"> value : valid type for attribute name</span> |
| <span class="sd"> The new value for the attribute.</span> |
| <span class="sd"> """</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Block.share_parameters"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.share_parameters">[docs]</a> <span class="k">def</span> <span class="nf">share_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shared</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Share parameters recursively inside the model.</span> |
| |
| <span class="sd"> For example, if you want ``dense1`` to share ``dense0``'s weights, you can do::</span> |
| |
| <span class="sd"> dense0 = nn.Dense(20)</span> |
| <span class="sd"> dense1 = nn.Dense(20)</span> |
| <span class="sd"> dense1.share_parameters(dense0.collect_params())</span> |
| |
| <span class="sd"> which equals to</span> |
| <span class="sd"> dense1.weight = dense0.weight</span> |
| <span class="sd"> dense1.bias = dense0.bias</span> |
| |
| <span class="sd"> Note that unlike the `load_parameters` or `load_dict` functions,</span> |
| <span class="sd"> `share_parameters` results in the `Parameter` object being shared (or</span> |
| <span class="sd"> tied) between the models, whereas `load_parameters` or `load_dict` only</span> |
| <span class="sd"> set the value of the data dictionary of a model. If you call</span> |
| <span class="sd"> `load_parameters` or `load_dict` after `share_parameters`, the loaded</span> |
| <span class="sd"> value will be reflected in all networks that use the shared (or tied)</span> |
| <span class="sd"> `Parameter` object.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> shared : Dict</span> |
| <span class="sd"> Dict of the shared parameters.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> this block</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">shared</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">shared</span><span class="p">,</span> <span class="p">(</span><span class="nb">dict</span><span class="p">,</span> <span class="n">OrderedDict</span><span class="p">)):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"'shared' should be in type of Dict. Get type </span><span class="si">{}</span><span class="s2">!"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">shared</span><span class="p">)))</span> |
| <span class="n">shared_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">shared</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_shared_parameters</span><span class="p">(</span><span class="n">shared</span><span class="p">,</span> <span class="n">shared_set</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">shared_set</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">shared_set</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"Parameter name </span><span class="si">{}</span><span class="s2"> is not in the current model!"</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="k">return</span> <span class="bp">self</span></div> |
| |
| <span class="k">def</span> <span class="nf">_shared_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shared</span><span class="p">,</span> <span class="n">shared_set</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s2">""</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">prefix</span><span class="p">:</span> |
| <span class="n">prefix</span> <span class="o">+=</span> <span class="s1">'.'</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">:</span> |
| <span class="n">key</span> <span class="o">=</span> <span class="n">prefix</span> <span class="o">+</span> <span class="n">name</span> |
| <span class="k">if</span> <span class="n">shared</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">shared</span><span class="p">[</span><span class="n">key</span><span class="p">])</span> |
| <span class="n">shared_set</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">child</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="n">child</span><span class="p">()</span><span class="o">.</span><span class="n">_shared_parameters</span><span class="p">(</span><span class="n">shared</span><span class="p">,</span> <span class="n">shared_set</span><span class="p">,</span> <span class="n">prefix</span> <span class="o">+</span> <span class="n">name</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</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">"""Calls forward. Only accepts positional arguments."""</span> |
| <span class="k">for</span> <span class="n">hook</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">)</span> |
| |
| <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">hook</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">out</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">_mx_npx</span><span class="o">.</span><span class="n">is_np_array</span><span class="p">():</span> |
| <span class="n">_check_all_np_ndarrays</span><span class="p">(</span><span class="n">out</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">out</span> |
| |
| <div class="viewcode-block" id="Block.forward"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</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">"""Overrides to implement forward computation using :py:class:`NDArray`. Only</span> |
| <span class="sd"> accepts positional arguments.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> *args : list of NDArray</span> |
| <span class="sd"> Input tensors.</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= invalid-name</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span></div> |
| |
| <div class="viewcode-block" id="Block.register_op_hook"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.register_op_hook">[docs]</a> <span class="k">def</span> <span class="nf">register_op_hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callback</span><span class="p">,</span> <span class="n">monitor_all</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Install callback monitor.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> callback : function</span> |
| <span class="sd"> Function called to inspect the values of the intermediate outputs</span> |
| <span class="sd"> of blocks after hybridization. It takes 3 parameters:</span> |
| <span class="sd"> name of the tensor being inspected (str)</span> |
| <span class="sd"> name of the operator producing or consuming that tensor (str)</span> |
| <span class="sd"> tensor being inspected (NDArray).</span> |
| <span class="sd"> monitor_all : bool, default False</span> |
| <span class="sd"> If True, monitor both input and output, otherwise monitor output only.</span> |
| <span class="sd"> """</span> |
| <span class="k">for</span> <span class="n">cld</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">cld</span><span class="p">()</span><span class="o">.</span><span class="n">register_op_hook</span><span class="p">(</span><span class="n">callback</span><span class="p">,</span> <span class="n">monitor_all</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Block.summary"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.Block.summary">[docs]</a> <span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">inputs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Print the summary of the model's output and parameters.</span> |
| |
| <span class="sd"> The network must have been initialized, and must not have been hybridized.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> inputs : object</span> |
| <span class="sd"> Any input that the model supports. For any tensor in the input, only</span> |
| <span class="sd"> :class:`mxnet.ndarray.NDArray` is supported.</span> |
| <span class="sd"> """</span> |
| <span class="n">summary</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span> |
| <span class="n">seen</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> |
| <span class="n">hooks</span> <span class="o">=</span> <span class="p">[]</span> |
| |
| <span class="k">def</span> <span class="nf">_get_shape_str</span><span class="p">(</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">def</span> <span class="nf">flatten</span><span class="p">(</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</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="k">return</span> <span class="p">[</span><span class="n">args</span><span class="p">],</span> <span class="nb">int</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="n">flat</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">fmts</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span> |
| <span class="n">arg</span><span class="p">,</span> <span class="n">fmt</span> <span class="o">=</span> <span class="n">flatten</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> |
| <span class="n">flat</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">arg</span><span class="p">)</span> |
| <span class="n">fmts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fmt</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">flat</span><span class="p">,</span> <span class="n">fmts</span> |
| |
| <span class="k">def</span> <span class="nf">regroup</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">fmt</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fmt</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">fmt</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</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="k">return</span> <span class="n">args</span><span class="p">[:</span><span class="n">fmt</span><span class="p">],</span> <span class="n">args</span><span class="p">[</span><span class="n">fmt</span><span class="p">:]</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">fmt</span><span class="p">:</span> |
| <span class="n">res</span><span class="p">,</span> <span class="n">args</span> <span class="o">=</span> <span class="n">regroup</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> |
| <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">res</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">ret</span><span class="p">,</span> <span class="n">args</span> |
| |
| <span class="n">flat_args</span><span class="p">,</span> <span class="n">fmts</span> <span class="o">=</span> <span class="n">flatten</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> |
| <span class="n">flat_arg_shapes</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">shape</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">ndarray</span><span class="o">.</span><span class="n">NDArray</span><span class="p">)</span> <span class="k">else</span> <span class="n">x</span> |
| <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">flat_args</span><span class="p">]</span> |
| <span class="n">shapes</span> <span class="o">=</span> <span class="n">regroup</span><span class="p">(</span><span class="n">flat_arg_shapes</span><span class="p">,</span> <span class="n">fmts</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">shapes</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="n">shape_str</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">shapes</span><span class="p">)[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">shape_str</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">shapes</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">shape_str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'L'</span><span class="p">,</span> <span class="s1">''</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_register_summary_hook</span><span class="p">(</span><span class="n">block</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="n">HybridBlock</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">block</span><span class="o">.</span><span class="n">_active</span><span class="p">,</span> \ |
| <span class="s1">'"</span><span class="si">{}</span><span class="s1">" must not be hybridized to print summary.'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">block</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">_summary_hook</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">outputs</span><span class="p">):</span> |
| <span class="n">class_name</span> <span class="o">=</span> <span class="n">block</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> |
| <span class="n">block_idx</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">summary</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> |
| |
| <span class="n">m_key</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">class_name</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">block_idx</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s1">'</span> |
| <span class="n">summary</span><span class="p">[</span><span class="n">m_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span> |
| <span class="n">summary</span><span class="p">[</span><span class="n">m_key</span><span class="p">][</span><span class="s1">'output_shape'</span><span class="p">]</span> <span class="o">=</span> <span class="n">_get_shape_str</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> |
| |
| <span class="n">params</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">summary</span><span class="p">[</span><span class="n">m_key</span><span class="p">][</span><span class="s1">'trainable'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">summary</span><span class="p">[</span><span class="n">m_key</span><span class="p">][</span><span class="s1">'shared'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">block</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">params</span> <span class="o">+=</span> <span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="p">()</span><span class="o">.</span><span class="n">size</span> |
| <span class="n">summary</span><span class="p">[</span><span class="n">m_key</span><span class="p">][</span><span class="s1">'trainable'</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">grad_req</span> <span class="o">==</span> <span class="s1">'null'</span> <span class="k">else</span> <span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="p">()</span><span class="o">.</span><span class="n">size</span> |
| <span class="k">if</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">seen</span><span class="p">:</span> |
| <span class="n">summary</span><span class="p">[</span><span class="n">m_key</span><span class="p">][</span><span class="s1">'shared'</span><span class="p">]</span> <span class="o">+=</span> <span class="n">p</span><span class="o">.</span><span class="n">data</span><span class="p">()</span><span class="o">.</span><span class="n">size</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">seen</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> |
| <span class="n">summary</span><span class="p">[</span><span class="n">m_key</span><span class="p">][</span><span class="s1">'n_params'</span><span class="p">]</span> <span class="o">=</span> <span class="n">params</span> |
| |
| <span class="kn">from</span> <span class="nn">.nn.basic_layers</span> <span class="kn">import</span> <span class="n">Sequential</span><span class="p">,</span> <span class="n">HybridSequential</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="p">(</span><span class="n">Sequential</span><span class="p">,</span> <span class="n">HybridSequential</span><span class="p">)):</span> |
| <span class="n">hooks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="o">.</span><span class="n">register_forward_hook</span><span class="p">(</span><span class="n">_summary_hook</span><span class="p">))</span> |
| |
| <span class="n">summary</span><span class="p">[</span><span class="s1">'Input'</span><span class="p">]</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span> |
| <span class="n">summary</span><span class="p">[</span><span class="s1">'Input'</span><span class="p">][</span><span class="s1">'output_shape'</span><span class="p">]</span> <span class="o">=</span> <span class="n">_get_shape_str</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> |
| <span class="n">summary</span><span class="p">[</span><span class="s1">'Input'</span><span class="p">][</span><span class="s1">'n_params'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">summary</span><span class="p">[</span><span class="s1">'Input'</span><span class="p">][</span><span class="s1">'trainable'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">summary</span><span class="p">[</span><span class="s1">'Input'</span><span class="p">][</span><span class="s1">'shared'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span> |
| |
| <span class="k">try</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">_register_summary_hook</span><span class="p">)</span> |
| <span class="bp">self</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span> |
| |
| <span class="n">line_format</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{:>20}</span><span class="s1"> </span><span class="si">{:>42}</span><span class="s1"> </span><span class="si">{:>15}</span><span class="s1">'</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'-'</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">line_format</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">'Layer (type)'</span><span class="p">,</span> <span class="s1">'Output Shape'</span><span class="p">,</span> <span class="s1">'Param #'</span><span class="p">))</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'='</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span> |
| <span class="n">total_params</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">trainable_params</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">shared_params</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">summary</span><span class="p">:</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">line_format</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> |
| <span class="nb">str</span><span class="p">(</span><span class="n">summary</span><span class="p">[</span><span class="n">layer</span><span class="p">][</span><span class="s1">'output_shape'</span><span class="p">]),</span> |
| <span class="n">summary</span><span class="p">[</span><span class="n">layer</span><span class="p">][</span><span class="s1">'n_params'</span><span class="p">]))</span> |
| <span class="n">total_params</span> <span class="o">+=</span> <span class="n">summary</span><span class="p">[</span><span class="n">layer</span><span class="p">][</span><span class="s1">'n_params'</span><span class="p">]</span> |
| <span class="n">trainable_params</span> <span class="o">+=</span> <span class="n">summary</span><span class="p">[</span><span class="n">layer</span><span class="p">][</span><span class="s1">'trainable'</span><span class="p">]</span> |
| <span class="n">shared_params</span> <span class="o">+=</span> <span class="n">summary</span><span class="p">[</span><span class="n">layer</span><span class="p">][</span><span class="s1">'shared'</span><span class="p">]</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'='</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Parameters in forward computation graph, duplicate included'</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">' Total params: '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">total_params</span><span class="p">))</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">' Trainable params: '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">trainable_params</span><span class="p">))</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">' Non-trainable params: '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">total_params</span> <span class="o">-</span> <span class="n">trainable_params</span><span class="p">))</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Shared params in forward computation graph: '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">shared_params</span><span class="p">))</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Unique parameters in model: '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">total_params</span> <span class="o">-</span> <span class="n">shared_params</span><span class="p">))</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'-'</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span> |
| <span class="k">finally</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <span class="n">hooks</span><span class="p">:</span> |
| <span class="n">h</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span></div></div> |
| |
| |
| <div class="viewcode-block" id="HybridBlock"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.HybridBlock">[docs]</a><span class="k">class</span> <span class="nc">HybridBlock</span><span class="p">(</span><span class="n">Block</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""`HybridBlock` supports forwarding with both Symbol and NDArray.</span> |
| |
| <span class="sd"> `HybridBlock` is similar to `Block`, with a few differences::</span> |
| |
| <span class="sd"> import mxnet as mx</span> |
| <span class="sd"> from mxnet.gluon import HybridBlock, nn</span> |
| |
| <span class="sd"> class Model(HybridBlock):</span> |
| <span class="sd"> def __init__(self, **kwargs):</span> |
| <span class="sd"> super(Model, self).__init__(**kwargs)</span> |
| <span class="sd"> self.dense0 = nn.Dense(20)</span> |
| <span class="sd"> self.dense1 = nn.Dense(20)</span> |
| |
| <span class="sd"> def forward(self, x):</span> |
| <span class="sd"> x = mx.npx.relu(self.dense0(x))</span> |
| <span class="sd"> return mx.npx.relu(self.dense1(x))</span> |
| |
| <span class="sd"> model = Model()</span> |
| <span class="sd"> model.initialize(device=mx.cpu(0))</span> |
| <span class="sd"> model.hybridize()</span> |
| <span class="sd"> model(mx.np.zeros((10, 10), device=mx.cpu(0)))</span> |
| |
| <span class="sd"> Forward computation in :py:class:`HybridBlock` must be static to work with :py:class:`Symbol` s,</span> |
| <span class="sd"> i.e. you cannot call :py:meth:`NDArray.asnumpy`, :py:attr:`NDArray.shape`,</span> |
| <span class="sd"> :py:attr:`NDArray.dtype`, `NDArray` indexing (`x[i]`) etc on tensors.</span> |
| <span class="sd"> Also, you cannot use branching or loop logic that bases on non-constant</span> |
| <span class="sd"> expressions like random numbers or intermediate results, since they change</span> |
| <span class="sd"> the graph structure for each iteration.</span> |
| |
| <span class="sd"> Before activating with :py:meth:`hybridize()`, :py:class:`HybridBlock` works just like normal</span> |
| <span class="sd"> :py:class:`Block`. After activation, :py:class:`HybridBlock` will create a symbolic graph</span> |
| <span class="sd"> representing the forward computation and cache it. On subsequent forwards,</span> |
| <span class="sd"> the cached graph will be used instead of :py:meth:`forward`.</span> |
| |
| <span class="sd"> Please see references for detailed tutorial.</span> |
| |
| <span class="sd"> References</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> `Hybridize - A Hybrid of Imperative and Symbolic Programming</span> |
| <span class="sd"> <https://mxnet.apache.org/versions/master/api/python/docs/tutorials/packages/gluon/blocks/hybridize.html>`_</span> |
| <span class="sd"> """</span> |
| <span class="k">class</span> <span class="nc">OptConstraint</span><span class="p">:</span> |
| <span class="k">class</span> <span class="nc">Flag</span><span class="p">(</span><span class="n">enum</span><span class="o">.</span><span class="n">Flag</span><span class="p">):</span> |
| <span class="n">DisableAMP</span> <span class="o">=</span> <span class="n">enum</span><span class="o">.</span><span class="n">auto</span><span class="p">()</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flag</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">flag</span> <span class="o">=</span> <span class="n">flag</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">enter_state</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">enter_state</span> <span class="o">=</span> <span class="n">HybridBlock</span><span class="o">.</span><span class="n">OptConstraint</span><span class="o">.</span><span class="n">Flag</span><span class="p">(</span><span class="n">get_optimization_constraints</span><span class="p">())</span> |
| <span class="n">target_state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">enter_state</span> <span class="o">|</span> <span class="bp">self</span><span class="o">.</span><span class="n">flag</span> |
| <span class="n">set_optimization_constraints</span><span class="p">(</span><span class="n">target_state</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ptype</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">trace</span><span class="p">):</span> |
| <span class="n">set_optimization_constraints</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">enter_state</span><span class="p">)</span> |
| |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">disable_all</span><span class="p">():</span> |
| <span class="n">opt_flag</span> <span class="o">=</span> <span class="n">HybridBlock</span><span class="o">.</span><span class="n">OptConstraint</span><span class="o">.</span><span class="n">Flag</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">flag</span> <span class="ow">in</span> <span class="n">HybridBlock</span><span class="o">.</span><span class="n">OptConstraint</span><span class="o">.</span><span class="n">Flag</span><span class="p">:</span> |
| <span class="n">opt_flag</span> <span class="o">|=</span> <span class="n">flag</span> |
| |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">disable_amp</span><span class="p">():</span> |
| <span class="k">return</span> <span class="n">HybridBlock</span><span class="o">.</span><span class="n">OptConstraint</span><span class="p">(</span><span class="n">HybridBlock</span><span class="o">.</span><span class="n">OptConstraint</span><span class="o">.</span><span class="n">Flag</span><span class="o">.</span><span class="n">DisableAMP</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">HybridBlock</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="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">"hybrid_forward"</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">,</span> <span class="p">(</span> |
| <span class="s2">"'forward' instead of 'hybrid_forward' interface needs to be used starting from Gluon2.0."</span> |
| <span class="s2">"Please follow MXNet2.0 Migration Guide to use new APIs."</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span> <span class="o">=</span> <span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_out_format</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_called_infer_shape_already</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_active</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_flags</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_callback</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_all</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_backend</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_backend_opts</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_partition_if_dynamic</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_first_forward</span> <span class="o">=</span> <span class="kc">True</span> |
| |
| <span class="k">def</span> <span class="fm">__setattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Registers parameters."""</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__setattr__</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="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_active</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"Currently the model has been hybridized. Automatically deactivate the hybridization </span><span class="se">\</span> |
| <span class="s2"> when changing the children blocks."</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_active</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_clear_cached_op</span><span class="p">()</span> |
| |
| <span class="nd">@staticmethod</span> |
| <span class="k">def</span> <span class="nf">generate_arg_names</span><span class="p">(</span><span class="n">arg_num</span><span class="p">):</span> |
| <span class="k">return</span> <span class="p">[</span><span class="s1">'data'</span><span class="p">]</span> <span class="k">if</span> <span class="n">arg_num</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="p">[</span><span class="s1">'data</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">i</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">arg_num</span><span class="p">)]</span> |
| |
| <span class="k">def</span> <span class="nf">_get_graph</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span><span class="p">:</span> |
| <span class="n">flatten_args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="s2">"input"</span><span class="p">)</span> |
| <span class="n">flatten_args</span> <span class="o">=</span> <span class="p">[</span><span class="n">ele</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span> <span class="k">if</span> <span class="n">ele</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">flatten_args</span><span class="p">]</span> |
| <span class="n">real_args</span> <span class="o">=</span> <span class="p">[</span><span class="n">ele</span> <span class="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">flatten_args</span> <span class="k">if</span> <span class="n">ele</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">len</span><span class="p">(</span><span class="n">real_args</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">ValueError</span><span class="p">(</span><span class="s1">'All args are None and we do not support such a case.'</span> |
| <span class="s1">' Received args=</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">args</span><span class="p">))</span> |
| <span class="n">arg_names</span> <span class="o">=</span> <span class="n">HybridBlock</span><span class="o">.</span><span class="n">generate_arg_names</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">real_args</span><span class="p">))</span> |
| <span class="n">symbol_inputs</span> <span class="o">=</span> <span class="p">[</span> |
| <span class="n">symbol</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">name</span><span class="p">)</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg</span><span class="p">,</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="k">else</span> <span class="n">symbol</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">arg</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">real_args</span><span class="p">,</span> <span class="n">arg_names</span><span class="p">)</span> |
| <span class="p">]</span> |
| <span class="n">dc</span><span class="o">.</span><span class="n">set_variable</span><span class="p">(</span><span class="n">real_args</span><span class="p">,</span> <span class="n">symbol_inputs</span><span class="p">)</span> |
| <span class="n">args</span> <span class="o">=</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">flatten_args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span><span class="p">)</span> |
| <span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">pause</span><span class="p">(),</span> <span class="n">dc</span><span class="o">.</span><span class="n">context</span><span class="p">():</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| <span class="n">flatten_out</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_out_format</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="s2">"output"</span><span class="p">)</span> |
| <span class="n">symbol_outputs</span> <span class="o">=</span> <span class="n">dc</span><span class="o">.</span><span class="n">get_symbol</span><span class="p">(</span><span class="n">flatten_out</span><span class="p">,</span> <span class="n">sym_cls</span><span class="o">=</span><span class="nb">type</span><span class="p">(</span><span class="n">symbol_inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span> |
| <span class="n">dc</span><span class="o">.</span><span class="n">clear</span><span class="p">(</span><span class="n">flatten_out</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span> <span class="o">=</span> <span class="n">symbol_inputs</span><span class="p">,</span> <span class="n">symbol_outputs</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span> |
| |
| <span class="k">def</span> <span class="nf">_build_cache</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">update_graph</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="n">data</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_graph</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| <span class="n">data_names</span> <span class="o">=</span> <span class="p">{</span><span class="n">data</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data</span><span class="p">)}</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="n">p</span><span class="o">.</span><span class="n">var</span><span class="p">()</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="p">()}</span> |
| <span class="n">param_serialization_names</span> <span class="o">=</span> <span class="p">{</span><span class="n">p</span><span class="o">.</span><span class="n">var</span><span class="p">()</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">n</span> <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</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="n">param_names</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">params</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> |
| <span class="n">input_names</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">list_inputs</span><span class="p">()</span> |
| <span class="n">expected_names</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">input_names</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">expected_names</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">param_names</span> <span class="ow">or</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">data_names</span><span class="p">,</span> \ |
| <span class="sa">f</span><span class="s2">"Unknown input to HybridBlock: </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">"</span> |
| |
| <span class="n">used_data_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">data_names</span> <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">expected_names</span><span class="p">]</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">used_data_names</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data_names</span><span class="p">):</span> |
| <span class="n">unused</span> <span class="o">=</span> <span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">-th'</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">data_names</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">expected_names</span><span class="p">])</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The </span><span class="si">{</span><span class="n">unused</span><span class="si">}</span><span class="s2"> input to HybridBlock is not used by "</span> |
| <span class="s2">"any computation. Is this intended?"</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span> |
| |
| <span class="n">used_param_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">param_names</span> <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">expected_names</span><span class="p">]</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">used_param_names</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">param_names</span><span class="p">):</span> |
| <span class="n">unused</span> <span class="o">=</span> <span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">param_names</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">used_param_names</span><span class="p">)))</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Parameter </span><span class="si">{</span><span class="n">unused</span><span class="si">}</span><span class="s2"> is not used by any computation. "</span> |
| <span class="s2">"Is this intended?"</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span> |
| |
| <span class="n">args</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="s2">"input"</span><span class="p">)</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">input_names</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">params</span><span class="p">:</span> |
| <span class="n">params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">()</span> |
| <span class="k">except</span> <span class="n">DeferredInitializationError</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_deferred_infer_shape</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">input_names</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">params</span><span class="p">:</span> |
| <span class="n">params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">_finish_deferred_init</span><span class="p">()</span> |
| |
| <span class="n">arg_dict</span><span class="p">,</span> <span class="n">aux_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(),</span> <span class="nb">dict</span><span class="p">()</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backend</span><span class="p">:</span> |
| <span class="c1"># set device for inputs</span> |
| <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">device_set</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_gather_type_device_info</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">args</span><span class="p">))</span> |
| <span class="n">device</span> <span class="o">=</span> <span class="n">device_set</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">device_set</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="kc">None</span> |
| <span class="c1"># get list of params in the order of out.list_arguments</span> |
| <span class="n">input_shapes</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">out</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">data_names</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="ow">and</span> <span class="n">data_names</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="n">data_names</span><span class="p">[</span><span class="n">name</span><span class="p">]],</span> <span class="n">NDArray</span><span class="p">):</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">args</span><span class="p">[</span><span class="n">data_names</span><span class="p">[</span><span class="n">name</span><span class="p">]]</span> |
| <span class="k">elif</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="n">data_names</span><span class="p">[</span><span class="n">name</span><span class="p">]],</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">)</span> <span class="ow">and</span> |
| <span class="s1">'__shape__'</span> <span class="ow">in</span> <span class="n">args</span><span class="p">[</span><span class="n">data_names</span><span class="p">[</span><span class="n">name</span><span class="p">]]</span><span class="o">.</span><span class="n">list_attr</span><span class="p">()):</span> |
| <span class="n">shape_str</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="n">data_names</span><span class="p">[</span><span class="n">name</span><span class="p">]]</span><span class="o">.</span><span class="n">list_attr</span><span class="p">()[</span><span class="s1">'__shape__'</span><span class="p">]</span> |
| <span class="n">input_shapes</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="n">shape_str</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s1">'()'</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">','</span><span class="p">)))</span> |
| <span class="k">elif</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">params</span><span class="p">:</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">params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">()</span> |
| |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">out</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">data_names</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="ow">and</span> <span class="n">data_names</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="n">data_names</span><span class="p">[</span><span class="n">name</span><span class="p">]],</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">aux_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="n">data_names</span><span class="p">[</span><span class="n">name</span><span class="p">]]</span> |
| <span class="k">elif</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="n">data_names</span><span class="p">[</span><span class="n">name</span><span class="p">]],</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">)</span> <span class="ow">and</span> |
| <span class="s1">'__shape__'</span> <span class="ow">in</span> <span class="n">args</span><span class="p">[</span><span class="n">data_names</span><span class="p">[</span><span class="n">name</span><span class="p">]]</span><span class="o">.</span><span class="n">list_attr</span><span class="p">()):</span> |
| <span class="n">shape_str</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="n">data_names</span><span class="p">[</span><span class="n">name</span><span class="p">]]</span><span class="o">.</span><span class="n">list_attr</span><span class="p">()[</span><span class="s1">'__shape__'</span><span class="p">]</span> |
| <span class="n">input_shapes</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="n">shape_str</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s1">'()'</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">','</span><span class="p">)))</span> |
| <span class="k">elif</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">params</span><span class="p">:</span> |
| <span class="n">aux_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">()</span> |
| |
| <span class="c1"># Partition the graph</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">optimize_for</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backend</span><span class="p">,</span> <span class="n">arg_dict</span><span class="p">,</span> <span class="n">aux_dict</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">input_shapes</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_backend_opts</span><span class="p">)</span> |
| |
| <span class="c1">#update cached graph with partitioned graph</span> |
| <span class="k">if</span> <span class="n">update_graph</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span> <span class="o">=</span> <span class="n">data</span><span class="p">,</span> <span class="n">out</span> |
| |
| <span class="n">input_names</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">list_inputs</span><span class="p">()</span> |
| <span class="n">data_indices</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">param_indices</span> <span class="o">=</span> <span class="p">[]</span> |
| |
| <span class="c1"># In the default case, _cached_ops_args contains all the parameters from params (the sets are identical)</span> |
| <span class="c1"># In the case of Partition API optimized graph _cached_ops_args might contain some parameters from params,</span> |
| <span class="c1"># might contain some new parameters created during optimization and added to `arg_dict/aux_dict`,</span> |
| <span class="c1"># and might not contain some parameters that were deleted during optimization.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op_args</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">input_names</span><span class="p">):</span> |
| <span class="n">triple</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">data_names</span><span class="p">:</span> |
| <span class="n">data_indices</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="n">triple</span> <span class="o">=</span> <span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">data_names</span><span class="p">[</span><span class="n">name</span><span class="p">])</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">param_indices</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">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">params</span><span class="p">:</span> |
| <span class="n">param</span> <span class="o">=</span> <span class="n">params</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="n">serialization_name</span> <span class="o">=</span> <span class="n">param_serialization_names</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="c1"># HybridBlock.export</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># The param is missing from the original params dictionary, which means the param must have</span> |
| <span class="c1"># been added by the Partition API backend</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">arg_dict</span> <span class="ow">or</span> <span class="n">name</span><span class="p">:</span> |
| <span class="n">param_data</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="k">elif</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">aux_dict</span><span class="p">:</span> |
| <span class="n">param_data</span> <span class="o">=</span> <span class="n">aux_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">'A parameter was added to the graph during optimization but it was not '</span> |
| <span class="s1">'added to the parameter dicts.</span><span class="se">\n</span><span class="s1">'</span> |
| <span class="s1">'Please check the backend.'</span><span class="p">)</span> |
| |
| <span class="n">param</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">param_data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="n">param</span><span class="o">.</span><span class="n">_var_name</span> <span class="o">=</span> <span class="n">name</span> |
| <span class="n">serialization_name</span> <span class="o">=</span> <span class="n">name</span> <span class="c1"># HybridBlock.export</span> |
| <span class="n">param</span><span class="o">.</span><span class="n">_load_init</span><span class="p">(</span><span class="n">param_data</span><span class="p">,</span> <span class="n">param_data</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">triple</span> <span class="o">=</span> <span class="p">(</span><span class="kc">False</span><span class="p">,</span> <span class="n">serialization_name</span><span class="p">,</span> <span class="n">param</span><span class="p">)</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op_args</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">triple</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">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_flags</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">):</span> |
| <span class="n">kv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_flags</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">kv</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'data_indices'</span><span class="p">,</span> <span class="s1">'param_indices'</span><span class="p">]:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_flags</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">kv</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_flags</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">'data_indices'</span><span class="p">,</span> <span class="n">data_indices</span><span class="p">),</span> <span class="p">(</span><span class="s1">'param_indices'</span><span class="p">,</span> <span class="n">param_indices</span><span class="p">)]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_flags</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">CachedOp</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_flags</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_deferred_infer_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">infer_shape</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</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">error_msg</span> <span class="o">=</span> <span class="s2">"Deferred initialization failed because shape"</span>\ |
| <span class="s2">" cannot be inferred. </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">e</span><span class="p">)</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">error_msg</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_call_cached_op</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_build_cache</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_first_forward</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_partition_if_dynamic</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_first_forward</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="c1"># partition static shape ops if the graph contains any dynamic shape op</span> |
| <span class="n">_</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span> |
| <span class="n">is_dynamic</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">has_dynamic_shape_op</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">is_dynamic</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_backend</span> <span class="o">=</span> <span class="s1">'static_shape'</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_backend_opts</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="bp">self</span><span class="o">.</span><span class="n">_flags</span><span class="p">}</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_build_cache</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">update_graph</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op</span><span class="p">,</span> <span class="s2">"Gluon failed to build the cache. "</span> \ |
| <span class="s2">"This should never happen. "</span> \ |
| <span class="s2">"Please submit an issue on Github"</span> \ |
| <span class="s2">" https://github.com/apache/mxnet."</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_callback</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op</span><span class="o">.</span><span class="n">_register_op_hook</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_callback</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_all</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_flags</span><span class="p">)</span> <span class="o">>=</span> <span class="mi">2</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_flags</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_flags</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"register_op_hook is experimental when static_alloc=True / static_shape=True "</span> |
| <span class="s2">" and may not work correctly"</span><span class="p">)</span> |
| |
| <span class="n">args</span><span class="p">,</span> <span class="n">fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="s2">"input"</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">fmt</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span><span class="p">:</span> |
| <span class="c1"># Do not raise in the case that the fmt or stored_fmt ends with None and</span> |
| <span class="c1"># We are relying on the default values.</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span><span class="p">)</span> <span class="o">></span> <span class="nb">len</span><span class="p">(</span><span class="n">fmt</span><span class="p">):</span> |
| <span class="n">valid</span> <span class="o">=</span> <span class="nb">all</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</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="nb">len</span><span class="p">(</span><span class="n">fmt</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span><span class="p">))])</span> |
| <span class="n">valid</span> <span class="o">=</span> <span class="n">valid</span> <span class="ow">and</span> <span class="p">(</span><span class="n">fmt</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">fmt</span><span class="p">)])</span> |
| <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span><span class="p">)</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">fmt</span><span class="p">):</span> |
| <span class="n">valid</span> <span class="o">=</span> <span class="nb">all</span><span class="p">([</span><span class="n">fmt</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</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="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">fmt</span><span class="p">))])</span> |
| <span class="n">valid</span> <span class="o">=</span> <span class="n">valid</span> <span class="ow">and</span> <span class="p">(</span><span class="n">fmt</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span><span class="p">)]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">valid</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">valid</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The argument structure of HybridBlock does not match"</span> |
| <span class="s2">" the cached version. Stored format = </span><span class="si">{}</span><span class="s2">, input format = </span><span class="si">{}</span><span class="s2">"</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fmt</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span><span class="p">))</span> |
| |
| <span class="n">args_without_none</span> <span class="o">=</span> <span class="p">[</span><span class="n">ele</span> <span class="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">args</span> <span class="k">if</span> <span class="n">ele</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">]</span> |
| <span class="n">cargs</span> <span class="o">=</span> <span class="p">[</span><span class="n">args_without_none</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">if</span> <span class="n">is_arg</span> <span class="k">else</span> <span class="n">i</span><span class="o">.</span><span class="n">data</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">is_arg</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op_args</span><span class="p">]</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op</span><span class="p">(</span><span class="o">*</span><span class="n">cargs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="p">[</span><span class="n">out</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">_regroup</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_out_format</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="HybridBlock.optimize_for"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.HybridBlock.optimize_for">[docs]</a> <span class="k">def</span> <span class="nf">optimize_for</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">clear</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">partition_if_dynamic</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">static_alloc</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">static_shape</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">inline_limit</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> |
| <span class="n">forward_bulk_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">backward_bulk_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Partitions the current HybridBlock and optimizes it for a given backend</span> |
| <span class="sd"> without executing a forward pass. Modifies the HybridBlock in-place.</span> |
| |
| <span class="sd"> Immediately partitions a HybridBlock using the specified backend. Combines</span> |
| <span class="sd"> the work done in the hybridize API with part of the work done in the forward</span> |
| <span class="sd"> pass without calling the CachedOp. Can be used in place of hybridize,</span> |
| <span class="sd"> afterwards `export` can be called or inference can be run. See README.md in</span> |
| <span class="sd"> example/extensions/lib_subgraph/README.md for more details.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> # partition and then export to file</span> |
| <span class="sd"> block.optimize_for(x, backend='myPart')</span> |
| <span class="sd"> block.export('partitioned')</span> |
| |
| <span class="sd"> # partition and then run inference</span> |
| <span class="sd"> block.optimize_for(x, backend='myPart')</span> |
| <span class="sd"> block(x)</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> x : NDArray</span> |
| <span class="sd"> first input to model</span> |
| <span class="sd"> *args : NDArray</span> |
| <span class="sd"> other inputs to model</span> |
| <span class="sd"> backend : str</span> |
| <span class="sd"> The name of backend, as registered in `SubgraphBackendRegistry`, default None</span> |
| <span class="sd"> backend_opts : dict of user-specified options to pass to the backend for partitioning, optional</span> |
| <span class="sd"> Passed on to `PrePartition` and `PostPartition` functions of `SubgraphProperty`</span> |
| <span class="sd"> clear : bool, default False</span> |
| <span class="sd"> clears any previous optimizations</span> |
| <span class="sd"> partition_if_dynamic : bool, default False</span> |
| <span class="sd"> whether to partition the graph when dynamic shape op exists</span> |
| <span class="sd"> static_alloc : bool, default False</span> |
| <span class="sd"> Statically allocate memory to improve speed. Memory usage may increase.</span> |
| <span class="sd"> static_shape : bool, default False</span> |
| <span class="sd"> Optimize for invariant input shapes between iterations. Must also</span> |
| <span class="sd"> set static_alloc to True. Change of input shapes is still allowed</span> |
| <span class="sd"> but slower.</span> |
| <span class="sd"> inline_limit : optional int, default 2</span> |
| <span class="sd"> Maximum number of operators that can be inlined.</span> |
| <span class="sd"> forward_bulk_size : optional int, default None</span> |
| <span class="sd"> Segment size of bulk execution during forward pass.</span> |
| <span class="sd"> backward_bulk_size : optional int, default None</span> |
| <span class="sd"> Segment size of bulk execution during backward pass.</span> |
| <span class="sd"> **kwargs: The backend options, optional</span> |
| <span class="sd"> Passed on to `PrePartition` and `PostPartition` functions of `SubgraphProperty`</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_backend</span> <span class="o">=</span> <span class="n">backend</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="bp">self</span><span class="o">.</span><span class="n">_backend_opts</span> <span class="o">=</span> <span class="n">kwargs</span> |
| |
| <span class="k">if</span> <span class="n">clear</span> <span class="ow">or</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_active</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">hybridize</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">partition_if_dynamic</span><span class="p">,</span> <span class="n">static_alloc</span><span class="p">,</span> <span class="n">static_shape</span><span class="p">,</span> |
| <span class="n">inline_limit</span><span class="p">,</span> <span class="n">forward_bulk_size</span><span class="p">,</span> <span class="n">backward_bulk_size</span><span class="p">)</span> |
| |
| <span class="c1"># do part of forward API call</span> |
| <span class="n">has_symbol</span><span class="p">,</span> <span class="n">has_ndarray</span><span class="p">,</span> <span class="n">device_set</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_gather_type_device_info</span><span class="p">([</span><span class="n">x</span><span class="p">]</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">args</span><span class="p">))</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">has_symbol</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">has_ndarray</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'In HybridBlock, there must be one NDArray or one Symbol in the input.'</span> |
| <span class="s1">' Please check the type of the args.</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">device_set</span><span class="p">)</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="s1">'Found multiple devices in the input, '</span> |
| <span class="s1">'After hybridized, the HybridBlock only supports one input '</span> |
| <span class="s1">'device. You can print the ele.device in the '</span> |
| <span class="s1">'input arguments to inspect their devices. '</span> |
| <span class="s1">'Find all devices = </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">device_set</span><span class="p">))</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_build_cache</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op</span><span class="p">,</span> <span class="s2">"Gluon failed to build the cache. "</span> \ |
| <span class="s2">"This should never happen. "</span> \ |
| <span class="s2">"Please submit an issue on Github"</span> \ |
| <span class="s2">" https://github.com/apache/mxnet."</span> |
| <span class="c1"># do not actually call the cached_op</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_first_forward</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="c1"># clear the backend</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_backend</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_backend_opts</span> <span class="o">=</span> <span class="p">{}</span></div> |
| |
| <span class="k">def</span> <span class="nf">_clear_cached_op</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span> <span class="o">=</span> <span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_first_forward</span> <span class="o">=</span> <span class="kc">True</span> |
| |
| <div class="viewcode-block" id="HybridBlock.register_child"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.HybridBlock.register_child">[docs]</a> <span class="k">def</span> <span class="nf">register_child</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> |
| <span class="s2">"Children of HybridBlock must also be HybridBlock, "</span> \ |
| <span class="sa">f</span><span class="s2">"but </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">block</span><span class="p">)</span><span class="si">}</span><span class="s2"> has type </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">block</span><span class="p">))</span><span class="si">}</span><span class="s2">. If you are using Sequential, "</span> \ |
| <span class="s2">"please try HybridSequential instead."</span><span class="p">)</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">register_child</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_active</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"Currently the model has been hybridized. Automatically deactivate the hybridization </span><span class="se">\</span> |
| <span class="s2"> when adding new children block."</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_active</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_clear_cached_op</span><span class="p">()</span></div> |
| |
| <div class="viewcode-block" id="HybridBlock.hybridize"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.HybridBlock.hybridize">[docs]</a> <span class="k">def</span> <span class="nf">hybridize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">active</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">partition_if_dynamic</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">static_alloc</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">static_shape</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">inline_limit</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> |
| <span class="n">forward_bulk_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">backward_bulk_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Activates or deactivates :py:class:`HybridBlock` s recursively. Has no effect on</span> |
| <span class="sd"> non-hybrid children.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> active : bool, default True</span> |
| <span class="sd"> Whether to turn hybrid on or off.</span> |
| <span class="sd"> partition_if_dynamic : bool, default False</span> |
| <span class="sd"> whether to partition the graph when dynamic shape op exists</span> |
| <span class="sd"> static_alloc : bool, default False</span> |
| <span class="sd"> Statically allocate memory to improve speed. Memory usage may increase.</span> |
| <span class="sd"> static_shape : bool, default False</span> |
| <span class="sd"> Optimize for invariant input shapes between iterations. Must also</span> |
| <span class="sd"> set static_alloc to True. Change of input shapes is still allowed</span> |
| <span class="sd"> but slower.</span> |
| <span class="sd"> inline_limit : optional int, default 2</span> |
| <span class="sd"> Maximum number of operators that can be inlined.</span> |
| <span class="sd"> forward_bulk_size : optional int, default None</span> |
| <span class="sd"> Segment size of bulk execution during forward pass.</span> |
| <span class="sd"> backward_bulk_size : optional int, default None</span> |
| <span class="sd"> Segment size of bulk execution during backward pass.</span> |
| <span class="sd"> """</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_active</span> <span class="o">=</span> <span class="n">active</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_partition_if_dynamic</span> <span class="o">=</span> <span class="n">partition_if_dynamic</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_flags</span> <span class="o">=</span> <span class="p">[(</span><span class="s2">"static_alloc"</span><span class="p">,</span> <span class="n">static_alloc</span><span class="p">),</span> <span class="p">(</span><span class="s2">"static_shape"</span><span class="p">,</span> <span class="n">static_shape</span><span class="p">),</span> |
| <span class="p">(</span><span class="s2">"inline_limit"</span><span class="p">,</span> <span class="n">inline_limit</span><span class="p">)]</span> |
| <span class="k">if</span> <span class="n">forward_bulk_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_flags</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="s2">"forward_bulk_size"</span><span class="p">,</span> <span class="n">forward_bulk_size</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">backward_bulk_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_flags</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="s2">"backward_bulk_size"</span><span class="p">,</span> <span class="n">backward_bulk_size</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_clear_cached_op</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">active</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'"</span><span class="si">{block}</span><span class="s1">" is being hybridized while still having forward hook/pre-hook. '</span> |
| <span class="s1">'If "</span><span class="si">{block}</span><span class="s1">" is a child of HybridBlock, the hooks will not take effect.'</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">block</span><span class="o">=</span><span class="bp">self</span><span class="p">))</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">hybridize</span><span class="p">(</span><span class="n">active</span><span class="p">,</span> |
| <span class="n">static_alloc</span><span class="o">=</span><span class="n">static_alloc</span><span class="p">,</span> |
| <span class="n">static_shape</span><span class="o">=</span><span class="n">static_shape</span><span class="p">,</span> |
| <span class="n">inline_limit</span><span class="o">=</span><span class="n">inline_limit</span><span class="p">,</span> |
| <span class="n">forward_bulk_size</span><span class="o">=</span><span class="n">forward_bulk_size</span><span class="p">,</span> |
| <span class="n">backward_bulk_size</span><span class="o">=</span><span class="n">backward_bulk_size</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="HybridBlock.cast"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.HybridBlock.cast">[docs]</a> <span class="k">def</span> <span class="nf">cast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_active</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"Currently the model has been hybridized. Automatically deactivate the hybridization </span><span class="se">\</span> |
| <span class="s2"> when cast the block to use another data type."</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_active</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_clear_cached_op</span><span class="p">()</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="nf">_infer_attrs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">infer_fn</span><span class="p">,</span> <span class="n">attr</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">"""Generic infer attributes."""</span> |
| <span class="n">inputs</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_graph</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| <span class="n">args</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="s2">"input"</span><span class="p">)</span> |
| <span class="n">args_without_none</span> <span class="o">=</span> <span class="p">[</span><span class="n">ele</span> <span class="k">for</span> <span class="n">ele</span> <span class="ow">in</span> <span class="n">args</span> <span class="k">if</span> <span class="n">ele</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">]</span> |
| <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">(</span><span class="n">record</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">w</span><span class="p">:</span> |
| <span class="n">arg_attrs</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">aux_attrs</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">infer_fn</span><span class="p">)(</span> |
| <span class="o">**</span><span class="p">{</span><span class="n">i</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="n">attr</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">args_without_none</span><span class="p">)})</span> |
| <span class="k">if</span> <span class="n">arg_attrs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">w</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">message</span><span class="p">)</span> |
| <span class="n">sdict</span> <span class="o">=</span> <span class="p">{</span><span class="n">i</span><span class="p">:</span> <span class="n">j</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">(),</span> <span class="n">arg_attrs</span><span class="p">)}</span> |
| <span class="n">sdict</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">name</span> <span class="p">:</span> <span class="n">attr</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">attr</span> <span class="ow">in</span> \ |
| <span class="nb">zip</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">(),</span> <span class="n">aux_attrs</span><span class="p">)})</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">attr</span><span class="p">,</span> <span class="n">sdict</span><span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">var</span><span class="p">()</span><span class="o">.</span><span class="n">name</span><span class="p">])</span> |
| |
| <div class="viewcode-block" id="HybridBlock.infer_shape"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.HybridBlock.infer_shape">[docs]</a> <span class="k">def</span> <span class="nf">infer_shape</span><span class="p">(</span><span class="bp">self</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">"""Infers shape of Parameters from inputs."""</span> |
| <span class="c1"># pylint: disable=unused-argument</span> |
| <span class="c1"># In Gluon 2, users must implement infer_shape, if any deferred</span> |
| <span class="c1"># initialized parameters are associated with the HybridBlock</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="o">.</span><span class="n">values</span><span class="p">()</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">shape_is_known</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">shape</span><span class="p">)]</span> |
| <span class="k">if</span> <span class="n">params</span><span class="p">:</span> |
| <span class="n">params_str</span> <span class="o">=</span> <span class="s2">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2"> (</span><span class="si">{}</span><span class="s2">)"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">p</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">params</span><span class="p">)</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span> |
| <span class="s2">"</span><span class="si">{name}</span><span class="s2"> has parameters with unknown shape. You need to either specify the shape "</span> |
| <span class="s2">"in __init__ or implement </span><span class="si">{name}</span><span class="s2">.infer_shape to set the parameter shapes "</span> |
| <span class="s2">"based on the first input. Parameters with unknown shapes are </span><span class="si">{params}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> |
| <span class="n">name</span><span class="o">=</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params_str</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="HybridBlock.infer_type"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.HybridBlock.infer_type">[docs]</a> <span class="k">def</span> <span class="nf">infer_type</span><span class="p">(</span><span class="bp">self</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">"""Infers data type of Parameters from inputs."""</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_infer_attrs</span><span class="p">(</span><span class="s1">'infer_type'</span><span class="p">,</span> <span class="s1">'dtype'</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="HybridBlock.export"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.HybridBlock.export">[docs]</a> <span class="k">def</span> <span class="nf">export</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">epoch</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">remove_amp_cast</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Export HybridBlock to json format that can be loaded by</span> |
| <span class="sd"> `gluon.SymbolBlock.imports` or the C++ interface.</span> |
| |
| <span class="sd"> .. note:: When there are only one input, it will have name `data`. When there</span> |
| <span class="sd"> Are more than one inputs, they will be named as `data0`, `data1`, etc.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> path : str or None</span> |
| <span class="sd"> Path to save model. Two files `path-symbol.json` and `path-xxxx.params`</span> |
| <span class="sd"> will be created, where xxxx is the 4 digits epoch number.</span> |
| <span class="sd"> If None, do not export to file but return Python Symbol object and</span> |
| <span class="sd"> corresponding dictionary of parameters.</span> |
| <span class="sd"> epoch : int</span> |
| <span class="sd"> Epoch number of saved model.</span> |
| <span class="sd"> remove_amp_cast : bool, optional</span> |
| <span class="sd"> Whether to remove the amp_cast and amp_multicast operators, before saving the model.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> symbol_filename : str</span> |
| <span class="sd"> Filename to which model symbols were saved, including `path` prefix.</span> |
| <span class="sd"> params_filename : str</span> |
| <span class="sd"> Filename to which model parameters were saved, including `path` prefix.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span> |
| <span class="s2">"Please first call block.hybridize() and then run forward with "</span> |
| <span class="s2">"this block at least once before calling export."</span><span class="p">)</span> |
| <span class="n">sym</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> |
| |
| <span class="c1"># Deduplicate params (shared parameters use the same input symbol)</span> |
| <span class="n">reverse_params</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">k</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="bp">self</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="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">k</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">reverse_params</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| |
| <span class="c1"># In export we have global information on the structure of the graph</span> |
| <span class="c1"># can rename the symbol inputs to human-readable, deterministic names.</span> |
| <span class="c1"># That's not true in general, which is why internally random unique identifiers are used.</span> |
| <span class="n">rename_map</span> <span class="o">=</span> <span class="p">{</span><span class="n">param</span><span class="o">.</span><span class="n">var</span><span class="p">()</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">name</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> |
| <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">sym</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">():</span> |
| <span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">rename_map</span><span class="p">:</span> |
| <span class="n">var</span><span class="o">.</span><span class="n">_set_attr</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">rename_map</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">])</span> |
| |
| <span class="n">path_string</span> <span class="o">=</span> <span class="n">path</span> <span class="k">if</span> <span class="n">path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="s2">""</span> |
| <span class="n">sym_filename</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">path_string</span><span class="si">}</span><span class="s1">-symbol.json'</span> |
| <span class="k">if</span> <span class="n">path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">sym</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sym_filename</span><span class="p">,</span> <span class="n">remove_amp_cast</span><span class="o">=</span><span class="n">remove_amp_cast</span><span class="p">)</span> |
| |
| <span class="n">arg_names</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="n">aux_names</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="n">arg_dict</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="k">for</span> <span class="n">is_arg</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op_args</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">is_arg</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">arg_names</span><span class="p">:</span> |
| <span class="n">arg_dict</span><span class="p">[</span><span class="s1">'arg:</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">name</span><span class="p">)]</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">_reduce</span><span class="p">()</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">aux_names</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'Parameter "</span><span class="si">{name}</span><span class="s1">" is not found in the graph. '</span> |
| <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">),</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">arg_dict</span><span class="p">[</span><span class="sa">f</span><span class="s1">'aux:</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s1">'</span><span class="p">]</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">_reduce</span><span class="p">()</span> |
| <span class="n">params_filename</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">path_string</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">epoch</span><span class="si">:</span><span class="s1">04d</span><span class="si">}</span><span class="s1">.params'</span> |
| |
| <span class="k">if</span> <span class="n">path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="n">_mx_npx</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="n">params_filename</span><span class="p">,</span> <span class="o">**</span><span class="n">arg_dict</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">ndarray</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">params_filename</span><span class="p">,</span> <span class="n">arg_dict</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">sym_filename</span><span class="p">,</span> <span class="n">params_filename</span> <span class="k">if</span> <span class="n">arg_dict</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">remove_amp_cast</span><span class="p">:</span> |
| <span class="n">handle</span> <span class="o">=</span> <span class="n">SymbolHandle</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXSymbolRemoveAmpCast</span><span class="p">(</span><span class="n">sym</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">handle</span><span class="p">)))</span> |
| <span class="n">sym</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">sym</span><span class="p">)(</span><span class="n">handle</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">sym</span><span class="p">,</span> <span class="n">arg_dict</span></div> |
| |
| <div class="viewcode-block" id="HybridBlock.register_op_hook"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.HybridBlock.register_op_hook">[docs]</a> <span class="k">def</span> <span class="nf">register_op_hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callback</span><span class="p">,</span> <span class="n">monitor_all</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Install op hook for block recursively.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> callback : function</span> |
| <span class="sd"> Function called to inspect the values of the intermediate outputs</span> |
| <span class="sd"> of blocks after hybridization. It takes 3 parameters:</span> |
| <span class="sd"> name of the tensor being inspected (str)</span> |
| <span class="sd"> name of the operator producing or consuming that tensor (str)</span> |
| <span class="sd"> tensor being inspected (NDArray).</span> |
| <span class="sd"> monitor_all : bool, default False</span> |
| <span class="sd"> If True, monitor both input and output, otherwise monitor output only.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="nf">c_callback</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">op_name</span><span class="p">,</span> <span class="n">array</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""wrapper for user callback"""</span> |
| <span class="n">array</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">NDArrayHandle</span><span class="p">)</span> |
| <span class="n">array</span> <span class="o">=</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">writable</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">name</span> <span class="o">=</span> <span class="n">py_str</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> |
| <span class="n">op_name</span> <span class="o">=</span> <span class="n">py_str</span><span class="p">(</span><span class="n">op_name</span><span class="p">)</span> |
| <span class="n">callback</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">op_name</span><span class="p">,</span> <span class="n">array</span><span class="p">)</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_callback</span> <span class="o">=</span> <span class="n">c_callback</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_monitor_all</span> <span class="o">=</span> <span class="n">monitor_all</span> |
| <span class="k">for</span> <span class="n">cld</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">cld</span><span class="p">()</span><span class="o">.</span><span class="n">_callback</span> <span class="o">=</span> <span class="n">c_callback</span> |
| <span class="n">cld</span><span class="p">()</span><span class="o">.</span><span class="n">_monitor_all</span> <span class="o">=</span> <span class="n">monitor_all</span></div> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="n">_check_block_input_np_ndarrays</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">])</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">forward</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">HybridBlock</span><span class="o">.</span><span class="n">forward</span><span class="p">,</span> <span class="p">(</span> |
| <span class="s1">'Must define </span><span class="si">{name}</span><span class="s1">.forward. '</span> |
| <span class="s1">'Defining </span><span class="si">{name}</span><span class="s1">.hybrid_forward is deprecated.'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span> |
| |
| <span class="n">_</span><span class="p">,</span> <span class="n">has_ndarray</span><span class="p">,</span> <span class="n">device_set</span><span class="p">,</span> <span class="n">first_device</span> <span class="o">=</span> <span class="n">_gather_type_device_info</span><span class="p">([</span><span class="n">x</span><span class="p">]</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">args</span><span class="p">))</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">has_ndarray</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'In HybridBlock, there must be one NDArray in the input.'</span> |
| <span class="s1">' Please check the type of the args.</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_active</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">dc</span><span class="o">.</span><span class="n">is_deferred_compute</span><span class="p">():</span> |
| <span class="c1"># Do not call CachedOp if not hybridized or inside deferred compute mode.</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">device_set</span><span class="p">)</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="s1">'Find multiple devices in the input, '</span> |
| <span class="s1">'After hybridized, the HybridBlock only supports one input '</span> |
| <span class="s1">'device. You can print the ele.device in the '</span> |
| <span class="s1">'input arguments to inspect their devices. '</span> |
| <span class="s1">'Find all devices = </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">device_set</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_called_infer_shape_already</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">infer_shape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">p</span><span class="o">.</span><span class="n">_finish_deferred_init</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_called_infer_shape_already</span> <span class="o">=</span> <span class="kc">True</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_active</span><span class="p">:</span> |
| <span class="c1"># Normal imperative computation of forward()</span> |
| <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">dc</span><span class="o">.</span><span class="n">is_deferred_compute</span><span class="p">():</span> |
| <span class="c1"># Deferred compute is already enabled. This typically means that the current</span> |
| <span class="c1"># HybridBlock is a child block of a HybridBlock that has been hybridized.</span> |
| <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| |
| <span class="k">with</span> <span class="n">first_device</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_cached_op</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="HybridBlock.forward"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.HybridBlock.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</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">"""Overrides the forward computation. Arguments must be</span> |
| <span class="sd"> :py:class:`mxnet.numpy.ndarray`."""</span> |
| |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span></div> |
| |
| <div class="viewcode-block" id="HybridBlock.reset_device"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.HybridBlock.reset_device">[docs]</a> <span class="k">def</span> <span class="nf">reset_device</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Re-assign all Parameters to other devices. If the Block is hybridized, it will reset the _cached_op_args.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> device : Device or list of Device, default :py:meth:`device.current_device()`.</span> |
| <span class="sd"> Assign Parameter to given device. If device is a list of Device, a</span> |
| <span class="sd"> copy will be made for each device.</span> |
| <span class="sd"> """</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_op_args</span><span class="p">:</span> |
| <span class="c1"># resetting parameters creating by the partitioning backend</span> |
| <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">params</span><span class="p">:</span> |
| <span class="n">p</span><span class="o">.</span><span class="n">reset_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">p</span><span class="o">.</span><span class="n">reset_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="HybridBlock.reset_ctx"><a class="viewcode-back" href="../../../api/gluon/nn/index.html#mxnet.gluon.loss.HybridBlock.reset_ctx">[docs]</a> <span class="k">def</span> <span class="nf">reset_ctx</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ctx</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""This function has been deprecated. Please refer to ``HybridBlock.reset_device``."""</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'HybridBlock.reset_ctx has been renamed to'</span> |
| <span class="s1">' HybridBlock.reset_device'</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset_device</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span></div></div> |
| |
| |
| <div class="viewcode-block" id="SymbolBlock"><a class="viewcode-back" href="../../../api/gluon/symbol_block.html#mxnet.gluon.loss.SymbolBlock">[docs]</a><span class="k">class</span> <span class="nc">SymbolBlock</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Construct block from symbol. This is useful for using pre-trained models</span> |
| <span class="sd"> as feature extractors. For example, you may want to extract the output</span> |
| <span class="sd"> from fc2 layer in AlexNet.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> outputs : Symbol or list of Symbol</span> |
| <span class="sd"> The desired output for SymbolBlock.</span> |
| <span class="sd"> inputs : Symbol or list of Symbol</span> |
| <span class="sd"> The Variables in output's argument that should be used as inputs.</span> |
| <span class="sd"> params : dict</span> |
| <span class="sd"> Parameter dictionary for arguments and auxililary states of outputs</span> |
| <span class="sd"> that are not inputs.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # To extract the feature from fc1 and fc2 layers of AlexNet:</span> |
| <span class="sd"> >>> alexnet = gluon.model_zoo.vision.alexnet(pretrained=True, device=mx.cpu())</span> |
| <span class="sd"> >>> inputs = mx.sym.var('data')</span> |
| <span class="sd"> >>> out = alexnet(inputs)</span> |
| <span class="sd"> >>> internals = out.get_internals()</span> |
| <span class="sd"> >>> print(internals.list_outputs())</span> |
| <span class="sd"> ['data', ..., 'features_9_act_fwd_output', ..., 'features_11_act_fwd_output', ...]</span> |
| <span class="sd"> >>> outputs = [internals['features_9_act_fwd_output'],</span> |
| <span class="sd"> internals['features_11_act_fwd_output']]</span> |
| <span class="sd"> >>> # Create SymbolBlock that shares parameters with alexnet</span> |
| <span class="sd"> >>> feat_model = gluon.SymbolBlock(outputs, inputs, params=alexnet.collect_params())</span> |
| <span class="sd"> >>> x = mx.nd.random.normal(shape=(16, 3, 224, 224))</span> |
| <span class="sd"> >>> print(feat_model(x))</span> |
| <span class="sd"> """</span> |
| <div class="viewcode-block" id="SymbolBlock.imports"><a class="viewcode-back" href="../../../api/gluon/symbol_block.html#mxnet.gluon.loss.SymbolBlock.imports">[docs]</a> <span class="nd">@staticmethod</span> |
| <span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">imports</span><span class="p">(</span><span class="n">symbol_file</span><span class="p">,</span> <span class="n">input_names</span><span class="p">,</span> <span class="n">param_file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">allow_missing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">ignore_extra</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Import model previously saved by `gluon.HybridBlock.export`</span> |
| <span class="sd"> as a `gluon.SymbolBlock` for use in Gluon.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> symbol_file : str</span> |
| <span class="sd"> Path to symbol file.</span> |
| <span class="sd"> input_names : list of str</span> |
| <span class="sd"> List of input variable names</span> |
| <span class="sd"> param_file : str, optional</span> |
| <span class="sd"> Path to parameter file.</span> |
| <span class="sd"> device : Device, default None</span> |
| <span class="sd"> The device to initialize `gluon.SymbolBlock` on.</span> |
| <span class="sd"> allow_missing : bool, default False</span> |
| <span class="sd"> Whether to silently skip loading parameters not represents in the file.</span> |
| <span class="sd"> ignore_extra : bool, default False</span> |
| <span class="sd"> Whether to silently ignore parameters from the file that are not</span> |
| <span class="sd"> present in this Block.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> gluon.SymbolBlock</span> |
| <span class="sd"> `gluon.SymbolBlock` loaded from symbol and parameter files.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> net1 = gluon.model_zoo.vision.resnet18_v1(pretrained=True)</span> |
| <span class="sd"> >>> net1.hybridize()</span> |
| <span class="sd"> >>> x = mx.nd.random.normal(shape=(1, 3, 32, 32))</span> |
| <span class="sd"> >>> out1 = net1(x)</span> |
| <span class="sd"> >>> net1.export('net1', epoch=1)</span> |
| <span class="sd"> >>></span> |
| <span class="sd"> >>> net2 = gluon.SymbolBlock.imports(</span> |
| <span class="sd"> ... 'net1-symbol.json', ['data'], 'net1-0001.params')</span> |
| <span class="sd"> >>> out2 = net2(x)</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="n">sym</span> <span class="o">=</span> <span class="n">np_symbol</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">symbol_file</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">sym</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">symbol_file</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_names</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> |
| <span class="n">input_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_names</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">param_file</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="c1"># Get a valid type inference by using fp32</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">symbol</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">mx_real_t</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">input_names</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># Do not specify type, rely on saved params type instead</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">symbol</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">symbol</span><span class="o">.</span><span class="n">var</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="ow">in</span> <span class="n">input_names</span><span class="p">]</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">SymbolBlock</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">param_file</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ret</span><span class="o">.</span><span class="n">load_parameters</span><span class="p">(</span><span class="n">param_file</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">allow_missing</span><span class="p">,</span> <span class="n">ignore_extra</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">'saved'</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">ret</span></div> |
| |
| <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{name}</span><span class="s1">(</span><span class="se">\n</span><span class="si">{modstr}</span><span class="se">\n</span><span class="s1">)'</span> |
| <span class="n">modstr</span> <span class="o">=</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">'</span><span class="si">{block}</span><span class="s1"> : </span><span class="si">{numinputs}</span><span class="s1"> -> </span><span class="si">{numoutputs}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">block</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> |
| <span class="n">numinputs</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> |
| <span class="n">numoutputs</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span> |
| <span class="n">list_outputs</span><span class="p">()))])</span> |
| <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">modstr</span><span class="o">=</span><span class="n">modstr</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">SymbolBlock</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="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Symbol</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">())</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">outputs</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="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">outputs</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">syms</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="s2">"input"</span><span class="p">)</span> |
| <span class="n">out</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_out_format</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="s2">"output"</span><span class="p">)</span> |
| <span class="n">input_names</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">syms</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">get_internals</span><span class="p">()</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">())</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> \ |
| <span class="sa">f</span><span class="s2">"Input symbols must be variable, but </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="si">}</span><span class="s2"> is an output of operators"</span> |
| <span class="n">input_names</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> |
| |
| <span class="c1"># check if any symbol is row_sparse</span> |
| <span class="n">row_sparse_storage</span> <span class="o">=</span> <span class="n">ndarray</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">_STORAGE_TYPE_STR_TO_ID</span><span class="p">[</span><span class="s1">'row_sparse'</span><span class="p">]</span> |
| |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">out</span><span class="p">:</span> |
| <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">i</span><span class="o">.</span><span class="n">get_internals</span><span class="p">():</span> |
| <span class="k">assert</span><span class="p">(</span><span class="n">j</span><span class="o">.</span><span class="n">attr</span><span class="p">(</span><span class="s2">"__storage_type__"</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">str</span><span class="p">(</span><span class="n">row_sparse_storage</span><span class="p">)),</span> \ |
| <span class="sa">f</span><span class="s2">"SymbolBlock doesn't support Parameter '</span><span class="si">{</span><span class="n">j</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">' because its storage "</span> \ |
| <span class="s2">"type is 'row_sparse'."</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">out</span><span class="p">)</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">Group</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">_check_same_symbol_type</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">out</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| |
| <span class="c1"># Infer type of parameters. Without this, every parameter will be created with</span> |
| <span class="c1"># default type i.e., fp32</span> |
| <span class="n">arg_params</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()</span> |
| <span class="n">aux_params</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">()</span> |
| |
| <span class="n">arg_types</span><span class="p">,</span> <span class="n">aux_types</span> <span class="o">=</span> <span class="n">_infer_param_types</span><span class="p">(</span><span class="n">syms</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">params</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="n">unused_params</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">params</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">arg_params</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">aux_params</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">unused_params</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">ValueError</span><span class="p">(</span><span class="s1">'</span><span class="si">{}</span><span class="s1"> params are unused by the model.'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">unused_params</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span> <span class="o">=</span> <span class="n">params</span> |
| |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">arg</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">arg_params</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">arg</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">[</span><span class="n">arg</span><span class="p">]</span><span class="o">.</span><span class="n">_check_and_setattr</span><span class="p">(</span><span class="n">allow_deferred_init</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">arg_types</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">[</span><span class="n">arg</span><span class="p">]</span><span class="o">.</span><span class="n">_var</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">[</span><span class="n">arg</span><span class="p">]</span><span class="o">.</span><span class="n">_var_name</span> <span class="o">=</span> <span class="n">arg</span> |
| <span class="k">elif</span> <span class="n">arg</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">input_names</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">[</span><span class="n">arg</span><span class="p">]</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">arg</span><span class="p">,</span> <span class="n">allow_deferred_init</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">arg_types</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">[</span><span class="n">arg</span><span class="p">]</span><span class="o">.</span><span class="n">_var_name</span> <span class="o">=</span> <span class="n">arg</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">aux</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">aux_params</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">aux</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">[</span><span class="n">aux</span><span class="p">]</span><span class="o">.</span><span class="n">_check_and_setattr</span><span class="p">(</span><span class="n">grad_req</span><span class="o">=</span><span class="s1">'null'</span><span class="p">,</span> <span class="n">allow_deferred_init</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">aux_types</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">[</span><span class="n">aux</span><span class="p">]</span><span class="o">.</span><span class="n">_var</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">[</span><span class="n">aux</span><span class="p">]</span><span class="o">.</span><span class="n">_var_name</span> <span class="o">=</span> <span class="n">aux</span> |
| <span class="k">elif</span> <span class="n">aux</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">input_names</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">[</span><span class="n">aux</span><span class="p">]</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">aux</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'null'</span><span class="p">,</span> |
| <span class="n">allow_deferred_init</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">aux_types</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_reg_params</span><span class="p">[</span><span class="n">aux</span><span class="p">]</span><span class="o">.</span><span class="n">_var_name</span> <span class="o">=</span> <span class="n">aux</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span> <span class="o">=</span> <span class="n">syms</span><span class="p">,</span> <span class="n">out</span> |
| |
| <div class="viewcode-block" id="SymbolBlock.infer_shape"><a class="viewcode-back" href="../../../api/gluon/symbol_block.html#mxnet.gluon.loss.SymbolBlock.infer_shape">[docs]</a> <span class="k">def</span> <span class="nf">infer_shape</span><span class="p">(</span><span class="bp">self</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">"""Infers shape of Parameters from inputs."""</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_infer_attrs</span><span class="p">(</span><span class="s1">'infer_shape'</span><span class="p">,</span> <span class="s1">'shape'</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</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">"""Calls forward. Only accepts positional arguments."""</span> |
| <span class="k">for</span> <span class="n">hook</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">])</span> |
| |
| <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">hook</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> |
| <span class="n">hook</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">],</span> <span class="n">out</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">out</span> |
| |
| <div class="viewcode-block" id="SymbolBlock.forward"><a class="viewcode-back" href="../../../api/gluon/symbol_block.html#mxnet.gluon.loss.SymbolBlock.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">dc</span><span class="o">.</span><span class="n">is_deferred_compute</span><span class="p">():</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">'Calling a SymbolBlock from within HybridBlock '</span> |
| <span class="s1">'is not yet supported in Gluon 2.'</span><span class="p">)</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">NDArray</span><span class="p">):</span> |
| <span class="k">with</span> <span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_cached_op</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> |
| |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">Symbol</span><span class="p">),</span> \ |
| <span class="s2">"HybridBlock requires the first argument to forward be either "</span> \ |
| <span class="sa">f</span><span class="s2">"Symbol or NDArray, but got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span> |
| <span class="n">args</span><span class="p">,</span> <span class="n">in_fmt</span> <span class="o">=</span> <span class="n">_flatten</span><span class="p">([</span><span class="n">x</span><span class="p">]</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">args</span><span class="p">),</span> <span class="s2">"input"</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">in_fmt</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_in_format</span><span class="p">,</span> <span class="s2">"Invalid input format"</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> |
| <span class="n">ret</span><span class="o">.</span><span class="n">_compose</span><span class="p">(</span><span class="o">**</span><span class="p">{</span><span class="n">k</span><span class="o">.</span><span class="n">name</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="bp">self</span><span class="o">.</span><span class="n">_cached_graph</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="k">return</span> <span class="n">_regroup</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">ret</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_out_format</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="nf">_clear_cached_op</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">SymbolBlock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">_clear_cached_op</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span> <span class="o">=</span> <span class="n">tmp</span> |
| |
| <div class="viewcode-block" id="SymbolBlock.cast"><a class="viewcode-back" href="../../../api/gluon/symbol_block.html#mxnet.gluon.loss.SymbolBlock.cast">[docs]</a> <span class="k">def</span> <span class="nf">cast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_clear_cached_op</span><span class="p">()</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">SymbolBlock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">get_dtype_name</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> <span class="o">==</span> <span class="s1">'float16'</span><span class="p">:</span> |
| <span class="c1"># correct BatchNorm types back to float32 due to its special requirement</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cached_graph</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">params_list</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">get_internals</span><span class="p">()</span><span class="o">.</span><span class="n">list_inputs</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">params_list</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'running_var'</span><span class="p">):</span> |
| <span class="n">prefix</span> <span class="o">=</span> <span class="n">node</span><span class="p">[:</span><span class="o">-</span><span class="mi">11</span><span class="p">]</span> |
| <span class="n">sibs</span> <span class="o">=</span> <span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'running_mean'</span><span class="p">,</span> <span class="s1">'gamma'</span><span class="p">,</span> <span class="s1">'beta'</span><span class="p">)]</span> |
| <span class="n">is_bn</span> <span class="o">=</span> <span class="nb">all</span><span class="p">(</span><span class="n">p</span> <span class="ow">in</span> <span class="n">params_list</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">sibs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">is_bn</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">node</span><span class="p">)</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="s1">'float32'</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">sib</span> <span class="ow">in</span> <span class="n">sibs</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">sib</span><span class="p">)</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="s1">'float32'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'moving_var'</span><span class="p">):</span> |
| <span class="c1"># another convention used</span> |
| <span class="n">prefix</span> <span class="o">=</span> <span class="n">node</span><span class="p">[:</span><span class="o">-</span><span class="mi">10</span><span class="p">]</span> |
| <span class="n">sibs</span> <span class="o">=</span> <span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'moving_mean'</span><span class="p">,</span> <span class="s1">'gamma'</span><span class="p">,</span> <span class="s1">'beta'</span><span class="p">)]</span> |
| <span class="n">is_bn</span> <span class="o">=</span> <span class="nb">all</span><span class="p">(</span><span class="n">p</span> <span class="ow">in</span> <span class="n">params_list</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">sibs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">is_bn</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">node</span><span class="p">)</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="s1">'float32'</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">sib</span> <span class="ow">in</span> <span class="n">sibs</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">sib</span><span class="p">)</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="s1">'float32'</span><span class="p">)</span></div></div> |
| |
| <span class="k">def</span> <span class="nf">_infer_param_types</span><span class="p">(</span><span class="n">in_params</span><span class="p">,</span> <span class="n">out_params</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">,</span> <span class="n">default_dtype</span><span class="o">=</span><span class="n">mx_real_t</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Utility function that helps in inferring DType of args and auxs params</span> |
| <span class="sd"> from given input param.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> in_params: List of Symbol</span> |
| <span class="sd"> List of input symbol variables.</span> |
| <span class="sd"> out_params: Symbol</span> |
| <span class="sd"> Output symbol variable.</span> |
| <span class="sd"> arg_params: List of Str</span> |
| <span class="sd"> List of names of argument parametrs.</span> |
| <span class="sd"> aux_params: List of Str</span> |
| <span class="sd"> List of names of auxiliary parameters.</span> |
| <span class="sd"> default_dtype: numpy.dtype or str, default 'float32'</span> |
| <span class="sd"> Default data type for arg_params and aux_params, if unable to infer the type.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> arg_types: List of numpy.dtype</span> |
| <span class="sd"> List of arg_params type. Order is same as arg_params.</span> |
| <span class="sd"> Defaults to 'float32', if unable to infer type.</span> |
| <span class="sd"> aux_types: List of numpy.dtype</span> |
| <span class="sd"> List of aux_params type. Order is same as aux_params.</span> |
| <span class="sd"> Defaults to 'float32', if unable to infer type.</span> |
| <span class="sd"> """</span> |
| <span class="n">arg_types</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="n">aux_types</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="c1"># Get Input symbol details. This will be used to infer types of</span> |
| <span class="c1"># other parameters.</span> |
| <span class="n">input_sym_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">in_param</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">in_param</span> <span class="ow">in</span> <span class="n">in_params</span><span class="p">]</span> |
| |
| <span class="c1"># Try to infer input types. If not successful, we will set default dtype.</span> |
| <span class="c1"># If successful, we will try to infer other params in the graph.</span> |
| <span class="n">input_sym_arg_types</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">can_infer_input_type</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="k">for</span> <span class="n">in_param</span> <span class="ow">in</span> <span class="n">in_params</span><span class="p">:</span> |
| <span class="n">input_sym_arg_type</span> <span class="o">=</span> <span class="n">in_param</span><span class="o">.</span><span class="n">infer_type</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">input_sym_arg_type</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">input_sym_arg_type</span><span class="p">)</span> <span class="o"><</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">can_infer_input_type</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">break</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">input_sym_arg_types</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">in_param</span><span class="o">.</span><span class="n">infer_type</span><span class="p">()[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> |
| |
| <span class="c1"># Try to infer types of other parameters.</span> |
| <span class="k">if</span> <span class="n">can_infer_input_type</span><span class="p">:</span> |
| <span class="n">params</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">input_sym_names</span><span class="p">,</span> <span class="n">input_sym_arg_types</span><span class="p">)}</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">arg_types</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">aux_types</span> <span class="o">=</span> <span class="n">out_params</span><span class="o">.</span><span class="n">infer_type</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span> |
| <span class="k">except</span> <span class="n">MXNetError</span><span class="p">:</span> |
| <span class="c1"># Cannot infer type with current input</span> |
| <span class="n">arg_types</span><span class="p">,</span> <span class="n">aux_types</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span> |
| |
| <span class="k">if</span> <span class="n">arg_types</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">arg_types</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">arg_params</span><span class="p">):</span> |
| <span class="n">arg_types</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="n">arg_params</span><span class="p">:</span> |
| <span class="n">arg_types</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">default_dtype</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">aux_types</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">aux_types</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">aux_params</span><span class="p">):</span> |
| <span class="n">aux_types</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="n">aux_params</span><span class="p">:</span> |
| <span class="n">aux_types</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">default_dtype</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="p">(</span><span class="n">arg_types</span><span class="p">,</span> <span class="n">aux_types</span><span class="p">)</span> |
| |
| |
| <span class="k">def</span> <span class="nf">set_optimization_constraints</span><span class="p">(</span><span class="n">state</span><span class="p">):</span> |
| <span class="n">prev_state</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXSetOptimizationConstraints</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint</span><span class="p">(</span><span class="n">state</span><span class="o">.</span><span class="n">value</span><span class="p">),</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">prev_state</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">HybridBlock</span><span class="o">.</span><span class="n">OptConstraint</span><span class="o">.</span><span class="n">Flag</span><span class="p">(</span><span class="n">prev_state</span><span class="o">.</span><span class="n">value</span><span class="p">)</span> |
| |
| |
| <span class="k">def</span> <span class="nf">get_optimization_constraints</span><span class="p">():</span> |
| <span class="n">curr</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXGetOptimizationConstraints</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">curr</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">HybridBlock</span><span class="o">.</span><span class="n">OptConstraint</span><span class="o">.</span><span class="n">Flag</span><span class="p">(</span><span class="n">curr</span><span class="o">.</span><span class="n">value</span><span class="p">)</span> |
| </pre></div> |
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