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<span class="mdl-layout-title toc">Table Of Contents</span>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li>
<li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li>
</ul>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li>
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<li class="toctree-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>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
</ul>
</li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/viz/index.html">Visualization</a><ul>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li>
<li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li>
<li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/index.html">Intel MKL-DNN</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html#Improving-accuracy-with-Intel®-Neural-Compressor">Improving accuracy with Intel® Neural Compressor</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/mkldnn/mkldnn_readme.html">Install MXNet with MKL-DNN</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tensorrt/index.html">TensorRT</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/tensorrt/tensorrt.html">Optimizing Deep Learning Computation Graphs with TensorRT</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li>
<li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li>
</ul>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/image-augmentation.html">Image Augmentation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
</ul>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li>
</ul>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li>
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<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li>
<li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li>
</ul>
</li>
<li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
</ul>
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</ul>
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<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
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<h1>Source code for mxnet.module.python_module</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"># &quot;License&quot;); 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"># &quot;AS IS&quot; 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"># pylint: disable=too-many-instance-attributes, too-many-arguments, unnecessary-pass</span>
<span class="sd">&quot;&quot;&quot;Provide some handy classes for user to implement a simple computation module</span>
<span class="sd">in Python easily.</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">from</span> <span class="nn">.base_module</span> <span class="kn">import</span> <span class="n">BaseModule</span>
<span class="kn">from</span> <span class="nn">..initializer</span> <span class="kn">import</span> <span class="n">Uniform</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">ndarray</span> <span class="k">as</span> <span class="n">nd</span>
<div class="viewcode-block" id="PythonModule"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.PythonModule">[docs]</a><span class="k">class</span> <span class="nc">PythonModule</span><span class="p">(</span><span class="n">BaseModule</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;A convenient module class that implements many of the module APIs as</span>
<span class="sd"> empty functions.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data_names : list of str</span>
<span class="sd"> Names of the data expected by the module.</span>
<span class="sd"> label_names : list of str</span>
<span class="sd"> Names of the labels expected by the module. Could be ``None`` if the</span>
<span class="sd"> module does not need labels.</span>
<span class="sd"> output_names : list of str</span>
<span class="sd"> Names of the outputs.</span>
<span class="sd"> &quot;&quot;&quot;</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">data_names</span><span class="p">,</span> <span class="n">label_names</span><span class="p">,</span> <span class="n">output_names</span><span class="p">,</span> <span class="n">logger</span><span class="o">=</span><span class="n">logging</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">PythonModule</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="n">logger</span><span class="o">=</span><span class="n">logger</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data_names</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
<span class="n">data_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">data_names</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">label_names</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
<span class="n">label_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">label_names</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_data_names</span> <span class="o">=</span> <span class="n">data_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_label_names</span> <span class="o">=</span> <span class="n">label_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_output_names</span> <span class="o">=</span> <span class="n">output_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_output_shapes</span> <span class="o">=</span> <span class="kc">None</span>
<span class="c1">################################################################################</span>
<span class="c1"># Symbol information</span>
<span class="c1">################################################################################</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">data_names</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;A list of names for data required by this module.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_names</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">output_names</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;A list of names for the outputs of this module.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_output_names</span>
<span class="c1">################################################################################</span>
<span class="c1"># Input/Output information</span>
<span class="c1">################################################################################</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">data_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;A list of (name, shape) pairs specifying the data inputs to this module.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">label_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;A list of (name, shape) pairs specifying the label inputs to this module.</span>
<span class="sd"> If this module does not accept labels -- either it is a module without loss</span>
<span class="sd"> function, or it is not bound for training, then this should return an empty</span>
<span class="sd"> list ``[]```.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">output_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;A list of (name, shape) pairs specifying the outputs of this module.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_output_shapes</span>
<span class="c1">################################################################################</span>
<span class="c1"># Parameters of a module</span>
<span class="c1">################################################################################</span>
<div class="viewcode-block" id="PythonModule.get_params"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.PythonModule.get_params">[docs]</a> <span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Gets parameters, those are potentially copies of the actual parameters used</span>
<span class="sd"> to do computation on the device. Subclass should override this method if contains</span>
<span class="sd"> parameters.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> ``({}, {})``, a pair of empty dict.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="p">(</span><span class="nb">dict</span><span class="p">(),</span> <span class="nb">dict</span><span class="p">())</span></div>
<div class="viewcode-block" id="PythonModule.init_params"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.PythonModule.init_params">[docs]</a> <span class="k">def</span> <span class="nf">init_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initializer</span><span class="o">=</span><span class="n">Uniform</span><span class="p">(</span><span class="mf">0.01</span><span class="p">),</span> <span class="n">arg_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">allow_missing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_init</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">allow_extra</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Initializes the parameters and auxiliary states. By default this function</span>
<span class="sd"> does nothing. Subclass should override this method if contains parameters.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> initializer : Initializer</span>
<span class="sd"> Called to initialize parameters if needed.</span>
<span class="sd"> arg_params : dict</span>
<span class="sd"> If not ``None``, should be a dictionary of existing `arg_params`. Initialization</span>
<span class="sd"> will be copied from that.</span>
<span class="sd"> aux_params : dict</span>
<span class="sd"> If not ``None``, should be a dictionary of existing `aux_params`. Initialization</span>
<span class="sd"> will be copied from that.</span>
<span class="sd"> allow_missing : bool</span>
<span class="sd"> If ``True``, params could contain missing values, and the initializer will be</span>
<span class="sd"> called to fill those missing params.</span>
<span class="sd"> force_init : bool</span>
<span class="sd"> If ``True``, will force re-initialize even if already initialized.</span>
<span class="sd"> allow_extra : boolean, optional</span>
<span class="sd"> Whether allow extra parameters that are not needed by symbol.</span>
<span class="sd"> If this is True, no error will be thrown when arg_params or aux_params</span>
<span class="sd"> contain extra parameters that is not needed by the executor.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">pass</span></div>
<div class="viewcode-block" id="PythonModule.update"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.PythonModule.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Updates parameters according to the installed optimizer and the gradients computed</span>
<span class="sd"> in the previous forward-backward batch. Currently we do nothing here. Subclass should</span>
<span class="sd"> override this method if contains parameters.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">pass</span></div>
<div class="viewcode-block" id="PythonModule.update_metric"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.PythonModule.update_metric">[docs]</a> <span class="k">def</span> <span class="nf">update_metric</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eval_metric</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">pre_sliced</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Evaluates and accumulates evaluation metric on outputs of the last forward computation.</span>
<span class="sd"> Subclass should override this method if needed.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> eval_metric : EvalMetric</span>
<span class="sd"> labels : list of NDArray</span>
<span class="sd"> Typically ``data_batch.label``.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="c1"># since we do not need labels, we are probably not a module with a loss</span>
<span class="c1"># function or predictions, so just ignore this call</span>
<span class="k">return</span>
<span class="k">if</span> <span class="n">pre_sliced</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;PythonModule does not support presliced labels&quot;</span><span class="p">)</span>
<span class="c1"># by default we expect our outputs are some scores that could be evaluated</span>
<span class="n">eval_metric</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">())</span></div>
<span class="c1">################################################################################</span>
<span class="c1"># module setup</span>
<span class="c1">################################################################################</span>
<div class="viewcode-block" id="PythonModule.bind"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.PythonModule.bind">[docs]</a> <span class="k">def</span> <span class="nf">bind</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_shapes</span><span class="p">,</span> <span class="n">label_shapes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">for_training</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">inputs_need_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_rebind</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">shared_module</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">grad_req</span><span class="o">=</span><span class="s1">&#39;write&#39;</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Binds the symbols to construct executors. This is necessary before one</span>
<span class="sd"> can perform computation with the module.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data_shapes : list of (str, tuple)</span>
<span class="sd"> Typically is ``data_iter.provide_data``.</span>
<span class="sd"> label_shapes : list of (str, tuple)</span>
<span class="sd"> Typically is ``data_iter.provide_label``.</span>
<span class="sd"> for_training : bool</span>
<span class="sd"> Default is ``True``. Whether the executors should be bind for training.</span>
<span class="sd"> inputs_need_grad : bool</span>
<span class="sd"> Default is ``False``. Whether the gradients to the input data need to be computed.</span>
<span class="sd"> Typically this is not needed. But this might be needed when implementing composition</span>
<span class="sd"> of modules.</span>
<span class="sd"> force_rebind : bool</span>
<span class="sd"> Default is ``False``. This function does nothing if the executors are already</span>
<span class="sd"> bound. But with this ``True``, the executors will be forced to rebind.</span>
<span class="sd"> shared_module : Module</span>
<span class="sd"> Default is ``None``. This is used in bucketing. When not ``None``, the shared module</span>
<span class="sd"> essentially corresponds to a different bucket -- a module with different symbol</span>
<span class="sd"> but with the same sets of parameters (e.g. unrolled RNNs with different lengths).</span>
<span class="sd"> grad_req : str, list of str, dict of str to str</span>
<span class="sd"> Requirement for gradient accumulation. Can be &#39;write&#39;, &#39;add&#39;, or &#39;null&#39;</span>
<span class="sd"> (default to &#39;write&#39;).</span>
<span class="sd"> Can be specified globally (str) or for each argument (list, dict).</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">force_rebind</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s1">&#39;Already bound, ignoring bind()&#39;</span><span class="p">)</span>
<span class="k">return</span>
<span class="k">assert</span> <span class="n">grad_req</span> <span class="o">==</span> <span class="s1">&#39;write&#39;</span><span class="p">,</span> <span class="s2">&quot;Python module only support write gradient&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">for_training</span> <span class="o">=</span> <span class="n">for_training</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inputs_need_grad</span> <span class="o">=</span> <span class="n">inputs_need_grad</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">data_shapes</span><span class="p">)</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">_data_names</span><span class="p">)</span>
<span class="k">assert</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data_shapes</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span> <span class="o">=</span> <span class="n">data_shapes</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span> <span class="o">=</span> <span class="n">label_shapes</span>
<span class="k">if</span> <span class="n">label_shapes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_label_names</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">label_shapes</span><span class="p">)</span>
<span class="k">assert</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">label_shapes</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_output_shapes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_output_shapes</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">_compute_output_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;The subclass should implement this method to compute the shape of</span>
<span class="sd"> outputs. This method can assume that the ``data_shapes`` and ``label_shapes``</span>
<span class="sd"> are already initialized.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
<div class="viewcode-block" id="PythonModule.init_optimizer"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.PythonModule.init_optimizer">[docs]</a> <span class="k">def</span> <span class="nf">init_optimizer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kvstore</span><span class="o">=</span><span class="s1">&#39;local&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;sgd&#39;</span><span class="p">,</span>
<span class="n">optimizer_params</span><span class="o">=</span><span class="p">((</span><span class="s1">&#39;learning_rate&#39;</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">),),</span> <span class="n">force_init</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Installs and initializes optimizers. By default we do nothing. Subclass should</span>
<span class="sd"> override this method if needed.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> kvstore : str or KVStore</span>
<span class="sd"> Default `&#39;local&#39;`.</span>
<span class="sd"> optimizer : str or Optimizer</span>
<span class="sd"> Default `&#39;sgd&#39;`</span>
<span class="sd"> optimizer_params : dict</span>
<span class="sd"> Default `((&#39;learning_rate&#39;, 0.01),)`. The default value is not a dictionary,</span>
<span class="sd"> just to avoid pylint warning of dangerous default values.</span>
<span class="sd"> force_init : bool</span>
<span class="sd"> Default `False`, indicating whether we should force re-initializing the</span>
<span class="sd"> optimizer in the case an optimizer is already installed.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">pass</span></div></div>
<div class="viewcode-block" id="PythonLossModule"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.PythonLossModule">[docs]</a><span class="k">class</span> <span class="nc">PythonLossModule</span><span class="p">(</span><span class="n">PythonModule</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;A convenient module class that implements many of the module APIs as</span>
<span class="sd"> empty functions.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> name : str</span>
<span class="sd"> Names of the module. The outputs will be named `[name + &#39;_output&#39;]`.</span>
<span class="sd"> data_names : list of str</span>
<span class="sd"> Defaults to ``[&#39;data&#39;]``. Names of the data expected by this module.</span>
<span class="sd"> Should be a list of only one name.</span>
<span class="sd"> label_names : list of str</span>
<span class="sd"> Default ``[&#39;softmax_label&#39;]``. Names of the labels expected by the module.</span>
<span class="sd"> Should be a list of only one name.</span>
<span class="sd"> grad_func : function</span>
<span class="sd"> Optional. If not ``None``, should be a function that takes `scores`</span>
<span class="sd"> and `labels`, both of type `NDArray`, and return the gradients with</span>
<span class="sd"> respect to the scores according to this loss function. The return</span>
<span class="sd"> value could be a numpy array or an `NDArray`.</span>
<span class="sd"> &quot;&quot;&quot;</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">name</span><span class="o">=</span><span class="s1">&#39;pyloss&#39;</span><span class="p">,</span> <span class="n">data_names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">,),</span> <span class="n">label_names</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;softmax_label&#39;</span><span class="p">,),</span>
<span class="n">logger</span><span class="o">=</span><span class="n">logging</span><span class="p">,</span> <span class="n">grad_func</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">PythonLossModule</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="n">data_names</span><span class="p">,</span> <span class="n">label_names</span><span class="p">,</span>
<span class="p">[</span><span class="n">name</span> <span class="o">+</span> <span class="s1">&#39;_output&#39;</span><span class="p">],</span> <span class="n">logger</span><span class="o">=</span><span class="n">logger</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">=</span> <span class="n">name</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">data_names</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">label_names</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_scores</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_labels</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_scores_grad</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">grad_func</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">callable</span><span class="p">(</span><span class="n">grad_func</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad_func</span> <span class="o">=</span> <span class="n">grad_func</span>
<span class="k">def</span> <span class="nf">_compute_output_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Computes the shapes of outputs. As a loss module with outputs, we simply</span>
<span class="sd"> output whatever we receive as inputs (i.e. the scores).</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">_name</span> <span class="o">+</span> <span class="s1">&#39;_output&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">])]</span>
<div class="viewcode-block" id="PythonLossModule.forward"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.PythonLossModule.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">data_batch</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Forward computation. Here we do nothing but to keep a reference to</span>
<span class="sd"> the scores and the labels so that we can do backward computation.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data_batch : DataBatch</span>
<span class="sd"> Could be anything with similar API implemented.</span>
<span class="sd"> is_train : bool</span>
<span class="sd"> Default is ``None``, which means `is_train` takes the value of ``self.for_training``.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_scores</span> <span class="o">=</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="n">is_train</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">is_train</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">for_training</span>
<span class="k">if</span> <span class="n">is_train</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_labels</span> <span class="o">=</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">label</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span></div>
<div class="viewcode-block" id="PythonLossModule.get_outputs"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.PythonLossModule.get_outputs">[docs]</a> <span class="k">def</span> <span class="nf">get_outputs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">merge_multi_context</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Gets outputs of the previous forward computation. As a output loss module,</span>
<span class="sd"> we treat the inputs to this module as scores, and simply return them.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> merge_multi_context : bool</span>
<span class="sd"> Should always be ``True``, because we do not use multiple contexts for computing.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">merge_multi_context</span> <span class="ow">is</span> <span class="kc">True</span>
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_scores</span><span class="p">]</span></div>
<div class="viewcode-block" id="PythonLossModule.backward"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.PythonLossModule.backward">[docs]</a> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out_grads</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Backward computation.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> out_grads : NDArray or list of NDArray, optional</span>
<span class="sd"> Gradient on the outputs to be propagated back.</span>
<span class="sd"> This parameter is only needed when bind is called</span>
<span class="sd"> on outputs that are not a loss function.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">out_grads</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;For a loss module, out_grads should be None&#39;</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">for_training</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_backward_impl</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">_backward_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Actual implementation of the backward computation. The computation</span>
<span class="sd"> should take ``self._scores`` and ``self._labels`` and then compute the</span>
<span class="sd"> gradients with respect to the scores, store it as an `NDArray` in</span>
<span class="sd"> ``self._scores_grad``.</span>
<span class="sd"> Instead of defining a subclass and overriding this function,</span>
<span class="sd"> a more convenient way is to pass in a `grad_func` when constructing</span>
<span class="sd"> the module object. Then it will be called to compute the gradients.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_func</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">grad</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_grad_func</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_scores</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_labels</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">grad</span><span class="p">,</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span>
<span class="n">grad</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">grad</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_scores_grad</span> <span class="o">=</span> <span class="n">grad</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
<div class="viewcode-block" id="PythonLossModule.get_input_grads"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.PythonLossModule.get_input_grads">[docs]</a> <span class="k">def</span> <span class="nf">get_input_grads</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">merge_multi_context</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Gets the gradients to the inputs, computed in the previous backward computation.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> merge_multi_context : bool</span>
<span class="sd"> Should always be ``True`` because we do not use multiple context for computation.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">merge_multi_context</span> <span class="ow">is</span> <span class="kc">True</span>
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_scores_grad</span><span class="p">]</span></div>
<div class="viewcode-block" id="PythonLossModule.install_monitor"><a class="viewcode-back" href="../../../api/module/index.html#mxnet.module.PythonLossModule.install_monitor">[docs]</a> <span class="k">def</span> <span class="nf">install_monitor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mon</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Installs monitor on all executors.&quot;&quot;&quot;</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div></div>
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