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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> |
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| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
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| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
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| <li class="toctree-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> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li> |
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| <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> |
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| <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> |
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| <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> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.lexsort.html">mxnet.np.lexsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.argsort.html">mxnet.np.argsort</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanargmax.html">mxnet.np.nanargmax</a></li> |
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| <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> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.nanmax.html">mxnet.np.nanmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.ptp.html">mxnet.np.ptp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.percentile.html">mxnet.np.percentile</a></li> |
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| <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> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.histogram.html">mxnet.np.histogram</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.histogram2d.html">mxnet.np.histogram2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.histogramdd.html">mxnet.np.histogramdd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.bincount.html">mxnet.np.bincount</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.histogram_bin_edges.html">mxnet.np.histogram_bin_edges</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../api/np/generated/mxnet.np.digitize.html">mxnet.np.digitize</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../api/npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.reset_np.html">mxnet.npx.reset_np</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.cpu.html">mxnet.npx.cpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.cpu_pinned.html">mxnet.npx.cpu_pinned</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.current_device.html">mxnet.npx.current_device</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.num_gpus.html">mxnet.npx.num_gpus</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.convolution.html">mxnet.npx.convolution</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.fully_connected.html">mxnet.npx.fully_connected</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../api/npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <ul> |
| <li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
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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/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> |
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| <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> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../api/np/routines.io.html">Input and output</a><ul> |
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| <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> |
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| <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.nn.conv_layers</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</span> |
| <span class="sd">"""Convolutional neural network layers."""</span> |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Conv1D'</span><span class="p">,</span> <span class="s1">'Conv2D'</span><span class="p">,</span> <span class="s1">'Conv3D'</span><span class="p">,</span> |
| <span class="s1">'Conv1DTranspose'</span><span class="p">,</span> <span class="s1">'Conv2DTranspose'</span><span class="p">,</span> <span class="s1">'Conv3DTranspose'</span><span class="p">,</span> |
| <span class="s1">'MaxPool1D'</span><span class="p">,</span> <span class="s1">'MaxPool2D'</span><span class="p">,</span> <span class="s1">'MaxPool3D'</span><span class="p">,</span> |
| <span class="s1">'AvgPool1D'</span><span class="p">,</span> <span class="s1">'AvgPool2D'</span><span class="p">,</span> <span class="s1">'AvgPool3D'</span><span class="p">,</span> |
| <span class="s1">'GlobalMaxPool1D'</span><span class="p">,</span> <span class="s1">'GlobalMaxPool2D'</span><span class="p">,</span> <span class="s1">'GlobalMaxPool3D'</span><span class="p">,</span> |
| <span class="s1">'GlobalAvgPool1D'</span><span class="p">,</span> <span class="s1">'GlobalAvgPool2D'</span><span class="p">,</span> <span class="s1">'GlobalAvgPool3D'</span><span class="p">,</span> |
| <span class="s1">'ReflectionPad2D'</span><span class="p">,</span> <span class="s1">'DeformableConvolution'</span><span class="p">,</span> <span class="s1">'ModulatedDeformableConvolution'</span><span class="p">,</span> |
| <span class="s1">'PixelShuffle1D'</span><span class="p">,</span> <span class="s1">'PixelShuffle2D'</span><span class="p">,</span> <span class="s1">'PixelShuffle3D'</span><span class="p">]</span> |
| |
| <span class="kn">from</span> <span class="nn">..block</span> <span class="kn">import</span> <span class="n">HybridBlock</span> |
| <span class="kn">from</span> <span class="nn">..parameter</span> <span class="kn">import</span> <span class="n">Parameter</span> |
| <span class="kn">from</span> <span class="nn">...</span> <span class="kn">import</span> <span class="n">np</span><span class="p">,</span> <span class="n">npx</span> |
| <span class="kn">from</span> <span class="nn">...base</span> <span class="kn">import</span> <span class="n">numeric_types</span> |
| <span class="kn">from</span> <span class="nn">.activations</span> <span class="kn">import</span> <span class="n">Activation</span> |
| <span class="kn">from</span> <span class="nn">...util</span> <span class="kn">import</span> <span class="n">use_np</span> |
| |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">_Conv</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Abstract nD convolution layer (private, used as implementation base).</span> |
| |
| <span class="sd"> This layer creates a convolution kernel that is convolved</span> |
| <span class="sd"> with the layer input to produce a tensor of outputs.</span> |
| <span class="sd"> If `use_bias` is `True`, a bias vector is created and added to the outputs.</span> |
| <span class="sd"> Finally, if `activation` is not `None`,</span> |
| <span class="sd"> it is applied to the outputs as well.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> channels : int</span> |
| <span class="sd"> The dimensionality of the output space</span> |
| <span class="sd"> i.e. the number of output channels in the convolution.</span> |
| <span class="sd"> kernel_size : int or tuple/list of n ints</span> |
| <span class="sd"> Specifies the dimensions of the convolution window.</span> |
| <span class="sd"> strides: int or tuple/list of n ints,</span> |
| <span class="sd"> Specifies the strides of the convolution.</span> |
| <span class="sd"> padding : int or tuple/list of n ints,</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly zero-padded</span> |
| <span class="sd"> on both sides for padding number of points</span> |
| <span class="sd"> dilation: int or tuple/list of n ints,</span> |
| <span class="sd"> Specifies the dilation rate to use for dilated convolution.</span> |
| <span class="sd"> groups : int</span> |
| <span class="sd"> Controls the connections between inputs and outputs.</span> |
| <span class="sd"> At groups=1, all inputs are convolved to all outputs.</span> |
| <span class="sd"> At groups=2, the operation becomes equivalent to having two convolution</span> |
| <span class="sd"> layers side by side, each seeing half the input channels, and producing</span> |
| <span class="sd"> half the output channels, and both subsequently concatenated.</span> |
| <span class="sd"> layout : str,</span> |
| <span class="sd"> Dimension ordering of data and weight. Can be 'NCW', 'NWC', 'NCHW',</span> |
| <span class="sd"> 'NHWC', 'NCDHW', 'NDHWC', etc. 'N', 'C', 'H', 'W', 'D' stands for</span> |
| <span class="sd"> batch, channel, height, width and depth dimensions respectively.</span> |
| <span class="sd"> Convolution is performed over 'D', 'H', and 'W' dimensions.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> The number of input channels to this layer. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and `in_channels` will be inferred from the shape of input data.</span> |
| <span class="sd"> activation : str</span> |
| <span class="sd"> Activation function to use. See :func:`~mxnet.npx.activation`.</span> |
| <span class="sd"> If you don't specify anything, no activation is applied</span> |
| <span class="sd"> (ie. "linear" activation: `a(x) = x`).</span> |
| <span class="sd"> use_bias: bool</span> |
| <span class="sd"> Whether the layer uses a bias vector.</span> |
| <span class="sd"> weight_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the `weight` weights matrix.</span> |
| <span class="sd"> bias_initializer: str or `Initializer`</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span> |
| <span class="n">groups</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">op_name</span><span class="o">=</span><span class="s1">'convolution'</span><span class="p">,</span> <span class="n">adj</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">_Conv</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_channels</span> <span class="o">=</span> <span class="n">channels</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_in_channels</span> <span class="o">=</span> <span class="n">in_channels</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span> <span class="o">=</span> <span class="n">kernel_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_layout</span> <span class="o">=</span> <span class="n">layout</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> <span class="o">=</span> <span class="n">groups</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">strides</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">strides</span> <span class="o">=</span> <span class="p">(</span><span class="n">strides</span><span class="p">,)</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">padding</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">padding</span> <span class="o">=</span> <span class="p">(</span><span class="n">padding</span><span class="p">,)</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dilation</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">dilation</span> <span class="o">=</span> <span class="p">(</span><span class="n">dilation</span><span class="p">,)</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_op_name</span> <span class="o">=</span> <span class="n">op_name</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span> <span class="o">=</span> <span class="p">{</span> |
| <span class="s1">'kernel'</span><span class="p">:</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="s1">'stride'</span><span class="p">:</span> <span class="n">strides</span><span class="p">,</span> <span class="s1">'dilate'</span><span class="p">:</span> <span class="n">dilation</span><span class="p">,</span> |
| <span class="s1">'pad'</span><span class="p">:</span> <span class="n">padding</span><span class="p">,</span> <span class="s1">'num_filter'</span><span class="p">:</span> <span class="n">channels</span><span class="p">,</span> <span class="s1">'num_group'</span><span class="p">:</span> <span class="n">groups</span><span class="p">,</span> |
| <span class="s1">'no_bias'</span><span class="p">:</span> <span class="ow">not</span> <span class="n">use_bias</span><span class="p">,</span> <span class="s1">'layout'</span><span class="p">:</span> <span class="n">layout</span><span class="p">}</span> |
| <span class="k">if</span> <span class="n">adj</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">_kwargs</span><span class="p">[</span><span class="s1">'adj'</span><span class="p">]</span> <span class="o">=</span> <span class="n">adj</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_infer</span><span class="p">(),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">weight_initializer</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="k">if</span> <span class="n">use_bias</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'bias'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">channels</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">bias_initializer</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="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="k">if</span> <span class="n">activation</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">act</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="n">activation</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <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="n">device</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">device</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">npx</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op_name</span><span class="p">)(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">npx</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op_name</span><span class="p">)(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">(</span><span class="n">act</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">act</span> |
| |
| <span class="k">def</span> <span class="nf">pre_infer</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Pre-infer the shape of weight parameter based on kernel size, group size and channels</span> |
| <span class="sd"> """</span> |
| <span class="n">wshape</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op_name</span> <span class="o">==</span> <span class="s2">"convolution"</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">_kernel_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'N'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_channels</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'W'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</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">_kernel_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'N'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_channels</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'H'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'W'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">"kernel_size must be 1, 2 or 3"</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'N'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_channels</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'D'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'H'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'W'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op_name</span> <span class="o">==</span> <span class="s2">"deconvolution"</span><span class="p">,</span> \ |
| <span class="s2">"Only support operator name with convolution and deconvolution"</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">_kernel_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_channels</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'W'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">0</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">_kernel_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_channels</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'H'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'W'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</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">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">"kernel_size must be 1, 2 or 3"</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_channels</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'D'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'H'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'W'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> |
| <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">wshape</span><span class="p">)</span> |
| |
| <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="n">x</span><span class="p">):</span> |
| <span class="n">dshape1</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)]</span> |
| <span class="n">wshape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op_name</span> <span class="o">==</span> <span class="s2">"convolution"</span><span class="p">:</span> |
| <span class="n">wshape_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">wshape</span><span class="p">)</span> |
| <span class="n">wshape_list</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)]</span> <span class="o">=</span> <span class="n">dshape1</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_op_name</span> <span class="o">==</span> <span class="s2">"deconvolution"</span><span class="p">,</span> \ |
| <span class="s2">"Only support operator name with convolution and deconvolution"</span> |
| <span class="n">wshape_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">wshape</span><span class="p">)</span> |
| <span class="n">wshape_list</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'N'</span><span class="p">)]</span> <span class="o">=</span> <span class="n">dshape1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">wshape_list</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="s1">'conv'</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="si">{mapping}</span><span class="s1">, kernel_size=</span><span class="si">{kernel}</span><span class="s1">, stride=</span><span class="si">{stride}</span><span class="s1">'</span> |
| <span class="n">len_kernel_size</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">_kwargs</span><span class="p">[</span><span class="s1">'kernel'</span><span class="p">])</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">[</span><span class="s1">'pad'</span><span class="p">]</span> <span class="o">!=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,)</span> <span class="o">*</span> <span class="n">len_kernel_size</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', padding=</span><span class="si">{pad}</span><span class="s1">'</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">[</span><span class="s1">'dilate'</span><span class="p">]</span> <span class="o">!=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">*</span> <span class="n">len_kernel_size</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', dilation=</span><span class="si">{dilate}</span><span class="s1">'</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="s1">'out_pad'</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">out_pad</span> <span class="o">!=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,)</span> <span class="o">*</span> <span class="n">len_kernel_size</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', output_padding=</span><span class="si">{out_pad}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">out_pad</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">out_pad</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">[</span><span class="s1">'num_group'</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', groups=</span><span class="si">{num_group}</span><span class="s1">'</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', bias=False'</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">)</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">')'</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">shape</span> |
| <span class="k">if</span> <span class="s1">'Transpose'</span> <span class="ow">in</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">mapping</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{1}</span><span class="s1"> -> </span><span class="si">{0}</span><span class="s1">'</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">mapping</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{0}</span><span class="s1"> -> </span><span class="si">{1}</span><span class="s1">'</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">mapping</span><span class="o">=</span><span class="n">mapping</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">)</span> |
| |
| |
| <div class="viewcode-block" id="Conv1D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Conv1D">[docs]</a><span class="k">class</span> <span class="nc">Conv1D</span><span class="p">(</span><span class="n">_Conv</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""1D convolution layer (e.g. temporal convolution).</span> |
| |
| <span class="sd"> This layer creates a convolution kernel that is convolved</span> |
| <span class="sd"> with the layer input over a single spatial (or temporal) dimension</span> |
| <span class="sd"> to produce a tensor of outputs.</span> |
| <span class="sd"> If `use_bias` is True, a bias vector is created and added to the outputs.</span> |
| <span class="sd"> Finally, if `activation` is not `None`,</span> |
| <span class="sd"> it is applied to the outputs as well.</span> |
| |
| <span class="sd"> If `in_channels` is not specified, `Parameter` initialization will be</span> |
| <span class="sd"> deferred to the first time `forward` is called and `in_channels` will be</span> |
| <span class="sd"> inferred from the shape of input data.</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> channels : int</span> |
| <span class="sd"> The dimensionality of the output space, i.e. the number of output</span> |
| <span class="sd"> channels (filters) in the convolution.</span> |
| <span class="sd"> kernel_size :int or tuple/list of 1 int</span> |
| <span class="sd"> Specifies the dimensions of the convolution window.</span> |
| <span class="sd"> strides : int or tuple/list of 1 int,</span> |
| <span class="sd"> Specify the strides of the convolution.</span> |
| <span class="sd"> padding : int or a tuple/list of 1 int,</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly zero-padded</span> |
| <span class="sd"> on both sides for padding number of points</span> |
| <span class="sd"> dilation : int or tuple/list of 1 int</span> |
| <span class="sd"> Specifies the dilation rate to use for dilated convolution.</span> |
| <span class="sd"> groups : int</span> |
| <span class="sd"> Controls the connections between inputs and outputs.</span> |
| <span class="sd"> At groups=1, all inputs are convolved to all outputs.</span> |
| <span class="sd"> At groups=2, the operation becomes equivalent to having two conv</span> |
| <span class="sd"> layers side by side, each seeing half the input channels, and producing</span> |
| <span class="sd"> half the output channels, and both subsequently concatenated.</span> |
| <span class="sd"> layout: str, default 'NCW'</span> |
| <span class="sd"> Dimension ordering of data and weight. Only supports 'NCW' layout for now.</span> |
| <span class="sd"> 'N', 'C', 'W' stands for batch, channel, and width (time) dimensions</span> |
| <span class="sd"> respectively. Convolution is applied on the 'W' dimension.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> The number of input channels to this layer. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and `in_channels` will be inferred from the shape of input data.</span> |
| <span class="sd"> activation : str</span> |
| <span class="sd"> Activation function to use. See :func:`~mxnet.npx.activation`.</span> |
| <span class="sd"> If you don't specify anything, no activation is applied</span> |
| <span class="sd"> (ie. "linear" activation: `a(x) = x`).</span> |
| <span class="sd"> use_bias : bool</span> |
| <span class="sd"> Whether the layer uses a bias vector.</span> |
| <span class="sd"> weight_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the `weight` weights matrix.</span> |
| <span class="sd"> bias_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 3D input tensor with shape `(batch_size, in_channels, width)`</span> |
| <span class="sd"> when `layout` is `NCW`. For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 3D output tensor with shape `(batch_size, channels, out_width)`</span> |
| <span class="sd"> when `layout` is `NCW`. out_width is calculated as::</span> |
| |
| <span class="sd"> out_width = floor((width+2*padding-dilation*(kernel_size-1)-1)/stride)+1</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> |
| <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCW'</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="o">==</span> <span class="s1">'NCW'</span><span class="p">,</span> <span class="s2">"Only supports 'NCW' layout for now"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">kernel_size</span><span class="p">,)</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"kernel_size must be a number or a list of 1 ints"</span> |
| <span class="n">op_name</span> <span class="o">=</span> <span class="s1">'convolution'</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Conv1D</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">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="p">,</span> <span class="n">activation</span><span class="p">,</span> <span class="n">use_bias</span><span class="p">,</span> <span class="n">weight_initializer</span><span class="p">,</span> <span class="n">bias_initializer</span><span class="p">,</span> |
| <span class="n">op_name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="Conv2D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Conv2D">[docs]</a><span class="k">class</span> <span class="nc">Conv2D</span><span class="p">(</span><span class="n">_Conv</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""2D convolution layer (e.g. spatial convolution over images).</span> |
| |
| <span class="sd"> This layer creates a convolution kernel that is convolved</span> |
| <span class="sd"> with the layer input to produce a tensor of</span> |
| <span class="sd"> outputs. If `use_bias` is True,</span> |
| <span class="sd"> a bias vector is created and added to the outputs. Finally, if</span> |
| <span class="sd"> `activation` is not `None`, it is applied to the outputs as well.</span> |
| |
| <span class="sd"> If `in_channels` is not specified, `Parameter` initialization will be</span> |
| <span class="sd"> deferred to the first time `forward` is called and `in_channels` will be</span> |
| <span class="sd"> inferred from the shape of input data.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> channels : int</span> |
| <span class="sd"> The dimensionality of the output space, i.e. the number of output</span> |
| <span class="sd"> channels (filters) in the convolution.</span> |
| <span class="sd"> kernel_size :int or tuple/list of 2 int</span> |
| <span class="sd"> Specifies the dimensions of the convolution window.</span> |
| <span class="sd"> strides : int or tuple/list of 2 int,</span> |
| <span class="sd"> Specify the strides of the convolution.</span> |
| <span class="sd"> padding : int or a tuple/list of 2 int,</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly zero-padded</span> |
| <span class="sd"> on both sides for padding number of points</span> |
| <span class="sd"> dilation : int or tuple/list of 2 int</span> |
| <span class="sd"> Specifies the dilation rate to use for dilated convolution.</span> |
| <span class="sd"> groups : int</span> |
| <span class="sd"> Controls the connections between inputs and outputs.</span> |
| <span class="sd"> At groups=1, all inputs are convolved to all outputs.</span> |
| <span class="sd"> At groups=2, the operation becomes equivalent to having two conv</span> |
| <span class="sd"> layers side by side, each seeing half the input channels, and producing</span> |
| <span class="sd"> half the output channels, and both subsequently concatenated.</span> |
| <span class="sd"> layout : str, default 'NCHW'</span> |
| <span class="sd"> Dimension ordering of data and weight. Only supports 'NCHW' and 'NHWC'</span> |
| <span class="sd"> layout for now. 'N', 'C', 'H', 'W' stands for batch, channel, height,</span> |
| <span class="sd"> and width dimensions respectively. Convolution is applied on the 'H' and</span> |
| <span class="sd"> 'W' dimensions.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> The number of input channels to this layer. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and `in_channels` will be inferred from the shape of input data.</span> |
| <span class="sd"> activation : str</span> |
| <span class="sd"> Activation function to use. See :func:`~mxnet.npx.activation`.</span> |
| <span class="sd"> If you don't specify anything, no activation is applied</span> |
| <span class="sd"> (ie. "linear" activation: `a(x) = x`).</span> |
| <span class="sd"> use_bias : bool</span> |
| <span class="sd"> Whether the layer uses a bias vector.</span> |
| <span class="sd"> weight_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the `weight` weights matrix.</span> |
| <span class="sd"> bias_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 4D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, height, width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 4D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, out_height, out_width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> out_height and out_width are calculated as::</span> |
| |
| <span class="sd"> out_height = floor((height+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0])+1</span> |
| <span class="sd"> out_width = floor((width+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1])+1</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</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="n">dilation</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCHW'</span><span class="p">,</span> |
| <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCHW'</span><span class="p">,</span> <span class="s1">'NHWC'</span><span class="p">),</span> <span class="s2">"Only supports 'NCHW' and 'NHWC' layout for now"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">kernel_size</span><span class="p">,)</span><span class="o">*</span><span class="mi">2</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"kernel_size must be a number or a list of 2 ints"</span> |
| <span class="n">op_name</span> <span class="o">=</span> <span class="s1">'convolution'</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Conv2D</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">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="p">,</span> <span class="n">activation</span><span class="p">,</span> <span class="n">use_bias</span><span class="p">,</span> <span class="n">weight_initializer</span><span class="p">,</span> <span class="n">bias_initializer</span><span class="p">,</span> |
| <span class="n">op_name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="Conv3D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Conv3D">[docs]</a><span class="k">class</span> <span class="nc">Conv3D</span><span class="p">(</span><span class="n">_Conv</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""3D convolution layer (e.g. spatial convolution over volumes).</span> |
| |
| <span class="sd"> This layer creates a convolution kernel that is convolved</span> |
| <span class="sd"> with the layer input to produce a tensor of</span> |
| <span class="sd"> outputs. If `use_bias` is `True`,</span> |
| <span class="sd"> a bias vector is created and added to the outputs. Finally, if</span> |
| <span class="sd"> `activation` is not `None`, it is applied to the outputs as well.</span> |
| |
| <span class="sd"> If `in_channels` is not specified, `Parameter` initialization will be</span> |
| <span class="sd"> deferred to the first time `forward` is called and `in_channels` will be</span> |
| <span class="sd"> inferred from the shape of input data.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> channels : int</span> |
| <span class="sd"> The dimensionality of the output space, i.e. the number of output</span> |
| <span class="sd"> channels (filters) in the convolution.</span> |
| <span class="sd"> kernel_size :int or tuple/list of 3 int</span> |
| <span class="sd"> Specifies the dimensions of the convolution window.</span> |
| <span class="sd"> strides : int or tuple/list of 3 int,</span> |
| <span class="sd"> Specify the strides of the convolution.</span> |
| <span class="sd"> padding : int or a tuple/list of 3 int,</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly zero-padded</span> |
| <span class="sd"> on both sides for padding number of points</span> |
| <span class="sd"> dilation : int or tuple/list of 3 int</span> |
| <span class="sd"> Specifies the dilation rate to use for dilated convolution.</span> |
| <span class="sd"> groups : int</span> |
| <span class="sd"> Controls the connections between inputs and outputs.</span> |
| <span class="sd"> At groups=1, all inputs are convolved to all outputs.</span> |
| <span class="sd"> At groups=2, the operation becomes equivalent to having two conv</span> |
| <span class="sd"> layers side by side, each seeing half the input channels, and producing</span> |
| <span class="sd"> half the output channels, and both subsequently concatenated.</span> |
| <span class="sd"> layout : str, default 'NCDHW'</span> |
| <span class="sd"> Dimension ordering of data and weight. Only supports 'NCDHW' and 'NDHWC'</span> |
| <span class="sd"> layout for now. 'N', 'C', 'H', 'W', 'D' stands for batch, channel, height,</span> |
| <span class="sd"> width and depth dimensions respectively. Convolution is applied on the 'D',</span> |
| <span class="sd"> 'H' and 'W' dimensions.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> The number of input channels to this layer. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and `in_channels` will be inferred from the shape of input data.</span> |
| <span class="sd"> activation : str</span> |
| <span class="sd"> Activation function to use. See :func:`~mxnet.npx.activation`.</span> |
| <span class="sd"> If you don't specify anything, no activation is applied</span> |
| <span class="sd"> (ie. "linear" activation: `a(x) = x`).</span> |
| <span class="sd"> use_bias : bool</span> |
| <span class="sd"> Whether the layer uses a bias vector.</span> |
| <span class="sd"> weight_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the `weight` weights matrix.</span> |
| <span class="sd"> bias_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 5D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, depth, height, width)` when `layout` is `NCDHW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 5D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, out_depth, out_height, out_width)` when `layout` is `NCDHW`.</span> |
| <span class="sd"> out_depth, out_height and out_width are calculated as::</span> |
| |
| <span class="sd"> out_depth = floor((depth+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0])+1</span> |
| <span class="sd"> out_height = floor((height+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1])+1</span> |
| <span class="sd"> out_width = floor((width+2*padding[2]-dilation[2]*(kernel_size[2]-1)-1)/stride[2])+1</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</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="mi">0</span><span class="p">),</span> |
| <span class="n">dilation</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCDHW'</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCDHW'</span><span class="p">,</span> <span class="s1">'NDHWC'</span><span class="p">),</span> <span class="s2">"Only supports 'NCDHW' and 'NDHWC' layout for now"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">kernel_size</span><span class="p">,)</span><span class="o">*</span><span class="mi">3</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">"kernel_size must be a number or a list of 3 ints"</span> |
| <span class="n">op_name</span> <span class="o">=</span> <span class="s1">'convolution'</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Conv3D</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">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="p">,</span> <span class="n">activation</span><span class="p">,</span> <span class="n">use_bias</span><span class="p">,</span> <span class="n">weight_initializer</span><span class="p">,</span> <span class="n">bias_initializer</span><span class="p">,</span> |
| <span class="n">op_name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="Conv1DTranspose"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Conv1DTranspose">[docs]</a><span class="k">class</span> <span class="nc">Conv1DTranspose</span><span class="p">(</span><span class="n">_Conv</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Transposed 1D convolution layer (sometimes called Deconvolution).</span> |
| |
| <span class="sd"> The need for transposed convolutions generally arises</span> |
| <span class="sd"> from the desire to use a transformation going in the opposite direction</span> |
| <span class="sd"> of a normal convolution, i.e., from something that has the shape of the</span> |
| <span class="sd"> output of some convolution to something that has the shape of its input</span> |
| <span class="sd"> while maintaining a connectivity pattern that is compatible with</span> |
| <span class="sd"> said convolution.</span> |
| |
| <span class="sd"> If `in_channels` is not specified, `Parameter` initialization will be</span> |
| <span class="sd"> deferred to the first time `forward` is called and `in_channels` will be</span> |
| <span class="sd"> inferred from the shape of input data.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> channels : int</span> |
| <span class="sd"> The dimensionality of the output space, i.e. the number of output</span> |
| <span class="sd"> channels (filters) in the convolution.</span> |
| <span class="sd"> kernel_size :int or tuple/list of 1 int</span> |
| <span class="sd"> Specifies the dimensions of the convolution window.</span> |
| <span class="sd"> strides : int or tuple/list of 1 int</span> |
| <span class="sd"> Specify the strides of the convolution.</span> |
| <span class="sd"> padding : int or a tuple/list of 1 int,</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly zero-padded</span> |
| <span class="sd"> on both sides for padding number of points</span> |
| <span class="sd"> output_padding: int or a tuple/list of 1 int</span> |
| <span class="sd"> Controls the amount of implicit zero-paddings on both sides of the</span> |
| <span class="sd"> output for output_padding number of points for each dimension.</span> |
| <span class="sd"> dilation : int or tuple/list of 1 int</span> |
| <span class="sd"> Controls the spacing between the kernel points; also known as the</span> |
| <span class="sd"> a trous algorithm</span> |
| <span class="sd"> groups : int</span> |
| <span class="sd"> Controls the connections between inputs and outputs.</span> |
| <span class="sd"> At groups=1, all inputs are convolved to all outputs.</span> |
| <span class="sd"> At groups=2, the operation becomes equivalent to having two conv</span> |
| <span class="sd"> layers side by side, each seeing half the input channels, and producing</span> |
| <span class="sd"> half the output channels, and both subsequently concatenated.</span> |
| <span class="sd"> layout : str, default 'NCW'</span> |
| <span class="sd"> Dimension ordering of data and weight. Only supports 'NCW' layout for now.</span> |
| <span class="sd"> 'N', 'C', 'W' stands for batch, channel, and width (time) dimensions</span> |
| <span class="sd"> respectively. Convolution is applied on the 'W' dimension.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> The number of input channels to this layer. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and `in_channels` will be inferred from the shape of input data.</span> |
| <span class="sd"> activation : str</span> |
| <span class="sd"> Activation function to use. See :func:`~mxnet.npx.activation`.</span> |
| <span class="sd"> If you don't specify anything, no activation is applied</span> |
| <span class="sd"> (ie. "linear" activation: `a(x) = x`).</span> |
| <span class="sd"> use_bias : bool</span> |
| <span class="sd"> Whether the layer uses a bias vector.</span> |
| <span class="sd"> weight_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the `weight` weights matrix.</span> |
| <span class="sd"> bias_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 3D input tensor with shape `(batch_size, in_channels, width)`</span> |
| <span class="sd"> when `layout` is `NCW`. For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 3D output tensor with shape `(batch_size, channels, out_width)`</span> |
| <span class="sd"> when `layout` is `NCW`. out_width is calculated as::</span> |
| |
| <span class="sd"> out_width = (width-1)*strides-2*padding+kernel_size+output_padding</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">output_padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCW'</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="o">==</span> <span class="s1">'NCW'</span><span class="p">,</span> <span class="s2">"Only supports 'NCW' layout for now"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">kernel_size</span><span class="p">,)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output_padding</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">output_padding</span> <span class="o">=</span> <span class="p">(</span><span class="n">output_padding</span><span class="p">,)</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"kernel_size must be a number or a list of 1 ints"</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">output_padding</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"output_padding must be a number or a list of 1 ints"</span> |
| <span class="n">op_name</span> <span class="o">=</span> <span class="s1">'deconvolution'</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Conv1DTranspose</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">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="p">,</span> <span class="n">activation</span><span class="p">,</span> <span class="n">use_bias</span><span class="p">,</span> <span class="n">weight_initializer</span><span class="p">,</span> |
| <span class="n">bias_initializer</span><span class="p">,</span> <span class="n">op_name</span><span class="o">=</span><span class="n">op_name</span><span class="p">,</span> <span class="n">adj</span><span class="o">=</span><span class="n">output_padding</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">outpad</span> <span class="o">=</span> <span class="n">output_padding</span></div> |
| |
| |
| <div class="viewcode-block" id="Conv2DTranspose"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Conv2DTranspose">[docs]</a><span class="k">class</span> <span class="nc">Conv2DTranspose</span><span class="p">(</span><span class="n">_Conv</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Transposed 2D convolution layer (sometimes called Deconvolution).</span> |
| |
| <span class="sd"> The need for transposed convolutions generally arises</span> |
| <span class="sd"> from the desire to use a transformation going in the opposite direction</span> |
| <span class="sd"> of a normal convolution, i.e., from something that has the shape of the</span> |
| <span class="sd"> output of some convolution to something that has the shape of its input</span> |
| <span class="sd"> while maintaining a connectivity pattern that is compatible with</span> |
| <span class="sd"> said convolution.</span> |
| |
| <span class="sd"> If `in_channels` is not specified, `Parameter` initialization will be</span> |
| <span class="sd"> deferred to the first time `forward` is called and `in_channels` will be</span> |
| <span class="sd"> inferred from the shape of input data.</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> channels : int</span> |
| <span class="sd"> The dimensionality of the output space, i.e. the number of output</span> |
| <span class="sd"> channels (filters) in the convolution.</span> |
| <span class="sd"> kernel_size :int or tuple/list of 2 int</span> |
| <span class="sd"> Specifies the dimensions of the convolution window.</span> |
| <span class="sd"> strides : int or tuple/list of 2 int</span> |
| <span class="sd"> Specify the strides of the convolution.</span> |
| <span class="sd"> padding : int or a tuple/list of 2 int,</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly zero-padded</span> |
| <span class="sd"> on both sides for padding number of points</span> |
| <span class="sd"> output_padding: int or a tuple/list of 2 int</span> |
| <span class="sd"> Controls the amount of implicit zero-paddings on both sides of the</span> |
| <span class="sd"> output for output_padding number of points for each dimension.</span> |
| <span class="sd"> dilation : int or tuple/list of 2 int</span> |
| <span class="sd"> Controls the spacing between the kernel points; also known as the</span> |
| <span class="sd"> a trous algorithm</span> |
| <span class="sd"> groups : int</span> |
| <span class="sd"> Controls the connections between inputs and outputs.</span> |
| <span class="sd"> At groups=1, all inputs are convolved to all outputs.</span> |
| <span class="sd"> At groups=2, the operation becomes equivalent to having two conv</span> |
| <span class="sd"> layers side by side, each seeing half the input channels, and producing</span> |
| <span class="sd"> half the output channels, and both subsequently concatenated.</span> |
| <span class="sd"> layout : str, default 'NCHW'</span> |
| <span class="sd"> Dimension ordering of data and weight. Only supports 'NCHW' and 'NHWC'</span> |
| <span class="sd"> layout for now. 'N', 'C', 'H', 'W' stands for batch, channel, height,</span> |
| <span class="sd"> and width dimensions respectively. Convolution is applied on the 'H' and</span> |
| <span class="sd"> 'W' dimensions.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> The number of input channels to this layer. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and `in_channels` will be inferred from the shape of input data.</span> |
| <span class="sd"> activation : str</span> |
| <span class="sd"> Activation function to use. See :func:`~mxnet.npx.activation`.</span> |
| <span class="sd"> If you don't specify anything, no activation is applied</span> |
| <span class="sd"> (ie. "linear" activation: `a(x) = x`).</span> |
| <span class="sd"> use_bias : bool</span> |
| <span class="sd"> Whether the layer uses a bias vector.</span> |
| <span class="sd"> weight_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the `weight` weights matrix.</span> |
| <span class="sd"> bias_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 4D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, height, width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 4D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, out_height, out_width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> out_height and out_width are calculated as::</span> |
| |
| <span class="sd"> out_height = (height-1)*strides[0]-2*padding[0]+kernel_size[0]+output_padding[0]</span> |
| <span class="sd"> out_width = (width-1)*strides[1]-2*padding[1]+kernel_size[1]+output_padding[1]</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</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="n">output_padding</span><span class="o">=</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="n">dilation</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCHW'</span><span class="p">,</span> |
| <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCHW'</span><span class="p">,</span> <span class="s1">'NHWC'</span><span class="p">),</span> <span class="s2">"Only supports 'NCHW' and 'NHWC' layout for now"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">kernel_size</span><span class="p">,)</span><span class="o">*</span><span class="mi">2</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output_padding</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">output_padding</span> <span class="o">=</span> <span class="p">(</span><span class="n">output_padding</span><span class="p">,)</span><span class="o">*</span><span class="mi">2</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"kernel_size must be a number or a list of 2 ints"</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">output_padding</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"output_padding must be a number or a list of 2 ints"</span> |
| <span class="n">op_name</span> <span class="o">=</span> <span class="s1">'deconvolution'</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Conv2DTranspose</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">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="p">,</span> <span class="n">activation</span><span class="p">,</span> <span class="n">use_bias</span><span class="p">,</span> <span class="n">weight_initializer</span><span class="p">,</span> |
| <span class="n">bias_initializer</span><span class="p">,</span> <span class="n">op_name</span><span class="o">=</span><span class="n">op_name</span><span class="p">,</span> <span class="n">adj</span><span class="o">=</span><span class="n">output_padding</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">outpad</span> <span class="o">=</span> <span class="n">output_padding</span></div> |
| |
| |
| <div class="viewcode-block" id="Conv3DTranspose"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.Conv3DTranspose">[docs]</a><span class="k">class</span> <span class="nc">Conv3DTranspose</span><span class="p">(</span><span class="n">_Conv</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Transposed 3D convolution layer (sometimes called Deconvolution).</span> |
| |
| <span class="sd"> The need for transposed convolutions generally arises</span> |
| <span class="sd"> from the desire to use a transformation going in the opposite direction</span> |
| <span class="sd"> of a normal convolution, i.e., from something that has the shape of the</span> |
| <span class="sd"> output of some convolution to something that has the shape of its input</span> |
| <span class="sd"> while maintaining a connectivity pattern that is compatible with</span> |
| <span class="sd"> said convolution.</span> |
| |
| <span class="sd"> If `in_channels` is not specified, `Parameter` initialization will be</span> |
| <span class="sd"> deferred to the first time `forward` is called and `in_channels` will be</span> |
| <span class="sd"> inferred from the shape of input data.</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> channels : int</span> |
| <span class="sd"> The dimensionality of the output space, i.e. the number of output</span> |
| <span class="sd"> channels (filters) in the convolution.</span> |
| <span class="sd"> kernel_size :int or tuple/list of 3 int</span> |
| <span class="sd"> Specifies the dimensions of the convolution window.</span> |
| <span class="sd"> strides : int or tuple/list of 3 int</span> |
| <span class="sd"> Specify the strides of the convolution.</span> |
| <span class="sd"> padding : int or a tuple/list of 3 int,</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly zero-padded</span> |
| <span class="sd"> on both sides for padding number of points</span> |
| <span class="sd"> output_padding: int or a tuple/list of 3 int</span> |
| <span class="sd"> Controls the amount of implicit zero-paddings on both sides of the</span> |
| <span class="sd"> output for output_padding number of points for each dimension.</span> |
| <span class="sd"> dilation : int or tuple/list of 3 int</span> |
| <span class="sd"> Controls the spacing between the kernel points; also known as the</span> |
| <span class="sd"> a trous algorithm.</span> |
| <span class="sd"> groups : int</span> |
| <span class="sd"> Controls the connections between inputs and outputs.</span> |
| <span class="sd"> At groups=1, all inputs are convolved to all outputs.</span> |
| <span class="sd"> At groups=2, the operation becomes equivalent to having two conv</span> |
| <span class="sd"> layers side by side, each seeing half the input channels, and producing</span> |
| <span class="sd"> half the output channels, and both subsequently concatenated.</span> |
| <span class="sd"> layout : str, default 'NCDHW'</span> |
| <span class="sd"> Dimension ordering of data and weight. Only supports 'NCDHW' and 'NDHWC'</span> |
| <span class="sd"> layout for now. 'N', 'C', 'H', 'W', 'D' stands for batch, channel, height,</span> |
| <span class="sd"> width and depth dimensions respectively. Convolution is applied on the 'D',</span> |
| <span class="sd"> 'H' and 'W' dimensions.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> The number of input channels to this layer. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and `in_channels` will be inferred from the shape of input data.</span> |
| <span class="sd"> activation : str</span> |
| <span class="sd"> Activation function to use. See :func:`~mxnet.npx.activation`.</span> |
| <span class="sd"> If you don't specify anything, no activation is applied</span> |
| <span class="sd"> (ie. "linear" activation: `a(x) = x`).</span> |
| <span class="sd"> use_bias : bool</span> |
| <span class="sd"> Whether the layer uses a bias vector.</span> |
| <span class="sd"> weight_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the `weight` weights matrix.</span> |
| <span class="sd"> bias_initializer : str or `Initializer`</span> |
| <span class="sd"> Initializer for the bias vector.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 5D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, depth, height, width)` when `layout` is `NCDHW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 5D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, out_depth, out_height, out_width)` when `layout` is `NCDHW`.</span> |
| <span class="sd"> out_depth, out_height and out_width are calculated as::</span> |
| |
| <span class="sd"> out_depth = (depth-1)*strides[0]-2*padding[0]+kernel_size[0]+output_padding[0]</span> |
| <span class="sd"> out_height = (height-1)*strides[1]-2*padding[1]+kernel_size[1]+output_padding[1]</span> |
| <span class="sd"> out_width = (width-1)*strides[2]-2*padding[2]+kernel_size[2]+output_padding[2]</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</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="mi">0</span><span class="p">),</span> |
| <span class="n">output_padding</span><span class="o">=</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="mi">0</span><span class="p">),</span> <span class="n">dilation</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCDHW'</span><span class="p">,</span> |
| <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCDHW'</span><span class="p">,</span> <span class="s1">'NDHWC'</span><span class="p">),</span> <span class="s2">"Only supports 'NCDHW' and 'NDHWC' layout for now"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">kernel_size</span><span class="p">,)</span><span class="o">*</span><span class="mi">3</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output_padding</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">output_padding</span> <span class="o">=</span> <span class="p">(</span><span class="n">output_padding</span><span class="p">,)</span><span class="o">*</span><span class="mi">3</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">"kernel_size must be a number or a list of 3 ints"</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">output_padding</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">"output_padding must be a number or a list of 3 ints"</span> |
| <span class="n">op_name</span> <span class="o">=</span> <span class="s1">'deconvolution'</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">Conv3DTranspose</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">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="p">,</span> <span class="n">activation</span><span class="p">,</span> <span class="n">use_bias</span><span class="p">,</span> <span class="n">weight_initializer</span><span class="p">,</span> <span class="n">bias_initializer</span><span class="p">,</span> |
| <span class="n">op_name</span><span class="o">=</span><span class="n">op_name</span><span class="p">,</span> <span class="n">adj</span><span class="o">=</span><span class="n">output_padding</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">outpad</span> <span class="o">=</span> <span class="n">output_padding</span></div> |
| |
| |
| <span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">_Pooling</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Abstract class for different pooling layers."""</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">pool_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="p">,</span> <span class="n">global_pool</span><span class="p">,</span> |
| <span class="n">pool_type</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="n">count_include_pad</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="nb">super</span><span class="p">(</span><span class="n">_Pooling</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">strides</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">strides</span> <span class="o">=</span> <span class="n">pool_size</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">strides</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">strides</span> <span class="o">=</span> <span class="p">(</span><span class="n">strides</span><span class="p">,)</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">pool_size</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">padding</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">padding</span> <span class="o">=</span> <span class="p">(</span><span class="n">padding</span><span class="p">,)</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">pool_size</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span> <span class="o">=</span> <span class="p">{</span> |
| <span class="s1">'kernel'</span><span class="p">:</span> <span class="n">pool_size</span><span class="p">,</span> <span class="s1">'stride'</span><span class="p">:</span> <span class="n">strides</span><span class="p">,</span> <span class="s1">'pad'</span><span class="p">:</span> <span class="n">padding</span><span class="p">,</span> |
| <span class="s1">'global_pool'</span><span class="p">:</span> <span class="n">global_pool</span><span class="p">,</span> <span class="s1">'pool_type'</span><span class="p">:</span> <span class="n">pool_type</span><span class="p">,</span> |
| <span class="s1">'layout'</span><span class="p">:</span> <span class="n">layout</span><span class="p">,</span> |
| <span class="s1">'pooling_convention'</span><span class="p">:</span> <span class="s1">'full'</span> <span class="k">if</span> <span class="n">ceil_mode</span> <span class="k">else</span> <span class="s1">'valid'</span><span class="p">}</span> |
| <span class="k">if</span> <span class="n">count_include_pad</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">_kwargs</span><span class="p">[</span><span class="s1">'count_include_pad'</span><span class="p">]</span> <span class="o">=</span> <span class="n">count_include_pad</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="s1">'pool'</span> |
| |
| <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="k">return</span> <span class="n">npx</span><span class="o">.</span><span class="n">pooling</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</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">(size=</span><span class="si">{kernel}</span><span class="s1">, stride=</span><span class="si">{stride}</span><span class="s1">, padding=</span><span class="si">{pad}</span><span class="s1">, ceil_mode=</span><span class="si">{ceil_mode}</span><span class="s1">'</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', global_pool=</span><span class="si">{global_pool}</span><span class="s1">, pool_type=</span><span class="si">{pool_type}</span><span class="s1">, layout=</span><span class="si">{layout}</span><span class="s1">)'</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">ceil_mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">[</span><span class="s1">'pooling_convention'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'full'</span><span class="p">,</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">)</span> |
| |
| |
| <div class="viewcode-block" id="MaxPool1D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.MaxPool1D">[docs]</a><span class="k">class</span> <span class="nc">MaxPool1D</span><span class="p">(</span><span class="n">_Pooling</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Max pooling operation for one dimensional data.</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pool_size: int</span> |
| <span class="sd"> Size of the max pooling windows.</span> |
| <span class="sd"> strides: int, or None</span> |
| <span class="sd"> Factor by which to downscale. E.g. 2 will halve the input size.</span> |
| <span class="sd"> If `None`, it will default to `pool_size`.</span> |
| <span class="sd"> padding: int</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly</span> |
| <span class="sd"> zero-padded on both sides for padding number of points.</span> |
| <span class="sd"> layout : str, default 'NCW'</span> |
| <span class="sd"> Dimension ordering of data and out ('NCW' or 'NWC').</span> |
| <span class="sd"> 'N', 'C', 'W' stands for batch, channel, and width (time) dimensions</span> |
| <span class="sd"> respectively. Pooling is applied on the W dimension.</span> |
| <span class="sd"> ceil_mode : bool, default False</span> |
| <span class="sd"> When `True`, will use ceil instead of floor to compute the output shape.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 3D input tensor with shape `(batch_size, in_channels, width)`</span> |
| <span class="sd"> when `layout` is `NCW`. For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 3D output tensor with shape `(batch_size, channels, out_width)`</span> |
| <span class="sd"> when `layout` is `NCW`. out_width is calculated as::</span> |
| |
| <span class="sd"> out_width = floor((width+2*padding-pool_size)/strides)+1</span> |
| |
| <span class="sd"> When `ceil_mode` is `True`, ceil will be used instead of floor in this</span> |
| <span class="sd"> equation.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCW'</span><span class="p">,</span> |
| <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCW'</span><span class="p">,</span> <span class="s1">'NWC'</span><span class="p">),</span>\ |
| <span class="s2">"Only NCW and NWC layouts are valid for 1D Pooling"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pool_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">pool_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">pool_size</span><span class="p">,)</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">pool_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"pool_size must be a number or a list of 1 ints"</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">MaxPool1D</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">pool_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">'max'</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="MaxPool2D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.MaxPool2D">[docs]</a><span class="k">class</span> <span class="nc">MaxPool2D</span><span class="p">(</span><span class="n">_Pooling</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Max pooling operation for two dimensional (spatial) data.</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pool_size: int or list/tuple of 2 ints,</span> |
| <span class="sd"> Size of the max pooling windows.</span> |
| <span class="sd"> strides: int, list/tuple of 2 ints, or None.</span> |
| <span class="sd"> Factor by which to downscale. E.g. 2 will halve the input size.</span> |
| <span class="sd"> If `None`, it will default to `pool_size`.</span> |
| <span class="sd"> padding: int or list/tuple of 2 ints,</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly</span> |
| <span class="sd"> zero-padded on both sides for padding number of points.</span> |
| <span class="sd"> layout : str, default 'NCHW'</span> |
| <span class="sd"> Dimension ordering of data and out ('NCHW' or 'NHWC').</span> |
| <span class="sd"> 'N', 'C', 'H', 'W' stands for batch, channel, height, and width</span> |
| <span class="sd"> dimensions respectively. padding is applied on 'H' and 'W' dimension.</span> |
| <span class="sd"> ceil_mode : bool, default False</span> |
| <span class="sd"> When `True`, will use ceil instead of floor to compute the output shape.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 4D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, height, width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 4D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, out_height, out_width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> out_height and out_width are calculated as::</span> |
| |
| <span class="sd"> out_height = floor((height+2*padding[0]-pool_size[0])/strides[0])+1</span> |
| <span class="sd"> out_width = floor((width+2*padding[1]-pool_size[1])/strides[1])+1</span> |
| |
| <span class="sd"> When `ceil_mode` is `True`, ceil will be used instead of floor in this</span> |
| <span class="sd"> equation.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCHW'</span><span class="p">,</span> |
| <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCHW'</span><span class="p">,</span> <span class="s1">'NHWC'</span><span class="p">),</span>\ |
| <span class="s2">"Only NCHW and NHWC layouts are valid for 2D Pooling"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pool_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">pool_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">pool_size</span><span class="p">,)</span><span class="o">*</span><span class="mi">2</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">pool_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"pool_size must be a number or a list of 2 ints"</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">MaxPool2D</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">pool_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">'max'</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="MaxPool3D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.MaxPool3D">[docs]</a><span class="k">class</span> <span class="nc">MaxPool3D</span><span class="p">(</span><span class="n">_Pooling</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Max pooling operation for 3D data (spatial or spatio-temporal).</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pool_size: int or list/tuple of 3 ints,</span> |
| <span class="sd"> Size of the max pooling windows.</span> |
| <span class="sd"> strides: int, list/tuple of 3 ints, or None.</span> |
| <span class="sd"> Factor by which to downscale. E.g. 2 will halve the input size.</span> |
| <span class="sd"> If `None`, it will default to `pool_size`.</span> |
| <span class="sd"> padding: int or list/tuple of 3 ints,</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly</span> |
| <span class="sd"> zero-padded on both sides for padding number of points.</span> |
| <span class="sd"> layout : str, default 'NCDHW'</span> |
| <span class="sd"> Dimension ordering of data and out ('NCDHW' or 'NDHWC').</span> |
| <span class="sd"> 'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and</span> |
| <span class="sd"> depth dimensions respectively. padding is applied on 'D', 'H' and 'W'</span> |
| <span class="sd"> dimension.</span> |
| <span class="sd"> ceil_mode : bool, default False</span> |
| <span class="sd"> When `True`, will use ceil instead of floor to compute the output shape.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 5D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, depth, height, width)` when `layout` is `NCW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 5D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, out_depth, out_height, out_width)` when `layout` is `NCDHW`.</span> |
| <span class="sd"> out_depth, out_height and out_width are calculated as::</span> |
| |
| <span class="sd"> out_depth = floor((depth+2*padding[0]-pool_size[0])/strides[0])+1</span> |
| <span class="sd"> out_height = floor((height+2*padding[1]-pool_size[1])/strides[1])+1</span> |
| <span class="sd"> out_width = floor((width+2*padding[2]-pool_size[2])/strides[2])+1</span> |
| |
| <span class="sd"> When `ceil_mode` is `True`, ceil will be used instead of floor in this</span> |
| <span class="sd"> equation.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCDHW'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCDHW'</span><span class="p">,</span> <span class="s1">'NDHWC'</span><span class="p">),</span>\ |
| <span class="s2">"Only NCDHW and NDHWC layouts are valid for 3D Pooling"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pool_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">pool_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">pool_size</span><span class="p">,)</span><span class="o">*</span><span class="mi">3</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">pool_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">"pool_size must be a number or a list of 3 ints"</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">MaxPool3D</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">pool_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">'max'</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="AvgPool1D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.AvgPool1D">[docs]</a><span class="k">class</span> <span class="nc">AvgPool1D</span><span class="p">(</span><span class="n">_Pooling</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Average pooling operation for temporal data.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pool_size: int</span> |
| <span class="sd"> Size of the average pooling windows.</span> |
| <span class="sd"> strides: int, or None</span> |
| <span class="sd"> Factor by which to downscale. E.g. 2 will halve the input size.</span> |
| <span class="sd"> If `None`, it will default to `pool_size`.</span> |
| <span class="sd"> padding: int</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly</span> |
| <span class="sd"> zero-padded on both sides for padding number of points.</span> |
| <span class="sd"> layout : str, default 'NCW'</span> |
| <span class="sd"> Dimension ordering of data and out ('NCW' or 'NWC').</span> |
| <span class="sd"> 'N', 'C', 'W' stands for batch, channel, and width (time) dimensions</span> |
| <span class="sd"> respectively. padding is applied on 'W' dimension.</span> |
| <span class="sd"> ceil_mode : bool, default False</span> |
| <span class="sd"> When `True`, will use ceil instead of floor to compute the output shape.</span> |
| <span class="sd"> count_include_pad : bool, default True</span> |
| <span class="sd"> When 'False', will exclude padding elements when computing the average value.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 3D input tensor with shape `(batch_size, in_channels, width)`</span> |
| <span class="sd"> when `layout` is `NCW`. For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 3D output tensor with shape `(batch_size, channels, out_width)`</span> |
| <span class="sd"> when `layout` is `NCW`. out_width is calculated as::</span> |
| |
| <span class="sd"> out_width = floor((width+2*padding-pool_size)/strides)+1</span> |
| |
| <span class="sd"> When `ceil_mode` is `True`, ceil will be used instead of floor in this</span> |
| <span class="sd"> equation.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCW'</span><span class="p">,</span> |
| <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">count_include_pad</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="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCW'</span><span class="p">,</span> <span class="s1">'NWC'</span><span class="p">),</span>\ |
| <span class="s2">"Only NCW and NWC layouts are valid for 1D Pooling"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pool_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">pool_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">pool_size</span><span class="p">,)</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">pool_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"pool_size must be a number or a list of 1 ints"</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">AvgPool1D</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">pool_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">'avg'</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="n">count_include_pad</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="AvgPool2D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.AvgPool2D">[docs]</a><span class="k">class</span> <span class="nc">AvgPool2D</span><span class="p">(</span><span class="n">_Pooling</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Average pooling operation for spatial data.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pool_size: int or list/tuple of 2 ints,</span> |
| <span class="sd"> Size of the average pooling windows.</span> |
| <span class="sd"> strides: int, list/tuple of 2 ints, or None.</span> |
| <span class="sd"> Factor by which to downscale. E.g. 2 will halve the input size.</span> |
| <span class="sd"> If `None`, it will default to `pool_size`.</span> |
| <span class="sd"> padding: int or list/tuple of 2 ints,</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly</span> |
| <span class="sd"> zero-padded on both sides for padding number of points.</span> |
| <span class="sd"> layout : str, default 'NCHW'</span> |
| <span class="sd"> Dimension ordering of data and out ('NCHW' or 'NHWC').</span> |
| <span class="sd"> 'N', 'C', 'H', 'W' stands for batch, channel, height, and width</span> |
| <span class="sd"> dimensions respectively. padding is applied on 'H' and 'W' dimension.</span> |
| <span class="sd"> ceil_mode : bool, default False</span> |
| <span class="sd"> When True, will use ceil instead of floor to compute the output shape.</span> |
| <span class="sd"> count_include_pad : bool, default True</span> |
| <span class="sd"> When 'False', will exclude padding elements when computing the average value.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 4D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, height, width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 4D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, out_height, out_width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> out_height and out_width are calculated as::</span> |
| |
| <span class="sd"> out_height = floor((height+2*padding[0]-pool_size[0])/strides[0])+1</span> |
| <span class="sd"> out_width = floor((width+2*padding[1]-pool_size[1])/strides[1])+1</span> |
| |
| <span class="sd"> When `ceil_mode` is `True`, ceil will be used instead of floor in this</span> |
| <span class="sd"> equation.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCHW'</span><span class="p">,</span> <span class="n">count_include_pad</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="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCHW'</span><span class="p">,</span> <span class="s1">'NHWC'</span><span class="p">),</span>\ |
| <span class="s2">"Only NCHW and NHWC layouts are valid for 2D Pooling"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pool_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">pool_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">pool_size</span><span class="p">,)</span><span class="o">*</span><span class="mi">2</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">pool_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"pool_size must be a number or a list of 2 ints"</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">AvgPool2D</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">pool_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">'avg'</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="n">count_include_pad</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="AvgPool3D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.AvgPool3D">[docs]</a><span class="k">class</span> <span class="nc">AvgPool3D</span><span class="p">(</span><span class="n">_Pooling</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Average pooling operation for 3D data (spatial or spatio-temporal).</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pool_size: int or list/tuple of 3 ints,</span> |
| <span class="sd"> Size of the average pooling windows.</span> |
| <span class="sd"> strides: int, list/tuple of 3 ints, or None.</span> |
| <span class="sd"> Factor by which to downscale. E.g. 2 will halve the input size.</span> |
| <span class="sd"> If `None`, it will default to `pool_size`.</span> |
| <span class="sd"> padding: int or list/tuple of 3 ints,</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly</span> |
| <span class="sd"> zero-padded on both sides for padding number of points.</span> |
| <span class="sd"> layout : str, default 'NCDHW'</span> |
| <span class="sd"> Dimension ordering of data and out ('NCDHW' or 'NDHWC').</span> |
| <span class="sd"> 'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and</span> |
| <span class="sd"> depth dimensions respectively. padding is applied on 'D', 'H' and 'W'</span> |
| <span class="sd"> dimension.</span> |
| <span class="sd"> ceil_mode : bool, default False</span> |
| <span class="sd"> When True, will use ceil instead of floor to compute the output shape.</span> |
| <span class="sd"> count_include_pad : bool, default True</span> |
| <span class="sd"> When 'False', will exclude padding elements when computing the average value.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 5D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, depth, height, width)` when `layout` is `NCDHW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 5D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, out_depth, out_height, out_width)` when `layout` is `NCDHW`.</span> |
| <span class="sd"> out_depth, out_height and out_width are calculated as::</span> |
| |
| <span class="sd"> out_depth = floor((depth+2*padding[0]-pool_size[0])/strides[0])+1</span> |
| <span class="sd"> out_height = floor((height+2*padding[1]-pool_size[1])/strides[1])+1</span> |
| <span class="sd"> out_width = floor((width+2*padding[2]-pool_size[2])/strides[2])+1</span> |
| |
| <span class="sd"> When `ceil_mode` is `True,` ceil will be used instead of floor in this</span> |
| <span class="sd"> equation.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCDHW'</span><span class="p">,</span> <span class="n">count_include_pad</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="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCDHW'</span><span class="p">,</span> <span class="s1">'NDHWC'</span><span class="p">),</span>\ |
| <span class="s2">"Only NCDHW and NDHWC layouts are valid for 3D Pooling"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pool_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">pool_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">pool_size</span><span class="p">,)</span><span class="o">*</span><span class="mi">3</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">pool_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">"pool_size must be a number or a list of 3 ints"</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">AvgPool3D</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">pool_size</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="s1">'avg'</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="n">count_include_pad</span><span class="p">,</span> |
| <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="GlobalMaxPool1D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.GlobalMaxPool1D">[docs]</a><span class="k">class</span> <span class="nc">GlobalMaxPool1D</span><span class="p">(</span><span class="n">_Pooling</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Gloabl max pooling operation for one dimensional (temporal) data.</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> layout : str, default 'NCW'</span> |
| <span class="sd"> Dimension ordering of data and out ('NCW' or 'NWC').</span> |
| <span class="sd"> 'N', 'C', 'W' stands for batch, channel, and width (time) dimensions</span> |
| <span class="sd"> respectively. Pooling is applied on the W dimension.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 3D input tensor with shape `(batch_size, in_channels, width)`</span> |
| <span class="sd"> when `layout` is `NCW`. For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 3D output tensor with shape `(batch_size, channels, 1)`</span> |
| <span class="sd"> when `layout` is `NCW`.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCW'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCW'</span><span class="p">,</span> <span class="s1">'NWC'</span><span class="p">),</span>\ |
| <span class="s2">"Only NCW and NWC layouts are valid for 1D Pooling"</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">GlobalMaxPool1D</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="p">(</span><span class="mi">1</span><span class="p">,),</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">'max'</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="GlobalMaxPool2D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.GlobalMaxPool2D">[docs]</a><span class="k">class</span> <span class="nc">GlobalMaxPool2D</span><span class="p">(</span><span class="n">_Pooling</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Global max pooling operation for two dimensional (spatial) data.</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> layout : str, default 'NCHW'</span> |
| <span class="sd"> Dimension ordering of data and out ('NCHW' or 'NHWC').</span> |
| <span class="sd"> 'N', 'C', 'H', 'W' stands for batch, channel, height, and width</span> |
| <span class="sd"> dimensions respectively. padding is applied on 'H' and 'W' dimension.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 4D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, height, width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 4D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, 1, 1)` when `layout` is `NCHW`.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCHW'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCHW'</span><span class="p">,</span> <span class="s1">'NHWC'</span><span class="p">),</span>\ |
| <span class="s2">"Only NCHW and NHWC layouts are valid for 2D Pooling"</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">GlobalMaxPool2D</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="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">'max'</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="GlobalMaxPool3D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.GlobalMaxPool3D">[docs]</a><span class="k">class</span> <span class="nc">GlobalMaxPool3D</span><span class="p">(</span><span class="n">_Pooling</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Global max pooling operation for 3D data (spatial or spatio-temporal).</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> layout : str, default 'NCDHW'</span> |
| <span class="sd"> Dimension ordering of data and out ('NCDHW' or 'NDHWC').</span> |
| <span class="sd"> 'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and</span> |
| <span class="sd"> depth dimensions respectively. padding is applied on 'D', 'H' and 'W'</span> |
| <span class="sd"> dimension.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 5D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, depth, height, width)` when `layout` is `NCW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 5D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, 1, 1, 1)` when `layout` is `NCDHW`.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCDHW'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCDHW'</span><span class="p">,</span> <span class="s1">'NDHWC'</span><span class="p">),</span>\ |
| <span class="s2">"Only NCDHW and NDHWC layouts are valid for 3D Pooling"</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">GlobalMaxPool3D</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="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">'max'</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="GlobalAvgPool1D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.GlobalAvgPool1D">[docs]</a><span class="k">class</span> <span class="nc">GlobalAvgPool1D</span><span class="p">(</span><span class="n">_Pooling</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Global average pooling operation for temporal data.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> layout : str, default 'NCW'</span> |
| <span class="sd"> Dimension ordering of data and out ('NCW' or 'NWC').</span> |
| <span class="sd"> 'N', 'C', 'W' stands for batch, channel, and width (time) dimensions</span> |
| <span class="sd"> respectively. padding is applied on 'W' dimension.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 3D input tensor with shape `(batch_size, in_channels, width)`</span> |
| <span class="sd"> when `layout` is `NCW`. For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 3D output tensor with shape `(batch_size, channels, 1)`.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCW'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCW'</span><span class="p">,</span> <span class="s1">'NWC'</span><span class="p">),</span>\ |
| <span class="s2">"Only NCW and NWC layouts are valid for 1D Pooling"</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">GlobalAvgPool1D</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="p">(</span><span class="mi">1</span><span class="p">,),</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">'avg'</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="GlobalAvgPool2D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.GlobalAvgPool2D">[docs]</a><span class="k">class</span> <span class="nc">GlobalAvgPool2D</span><span class="p">(</span><span class="n">_Pooling</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Global average pooling operation for spatial data.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> layout : str, default 'NCHW'</span> |
| <span class="sd"> Dimension ordering of data and out ('NCHW' or 'NHWC').</span> |
| <span class="sd"> 'N', 'C', 'H', 'W' stands for batch, channel, height, and width</span> |
| <span class="sd"> dimensions respectively.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 4D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, height, width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 4D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, 1, 1)` when `layout` is `NCHW`.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCHW'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCHW'</span><span class="p">,</span> <span class="s1">'NHWC'</span><span class="p">),</span>\ |
| <span class="s2">"Only NCHW and NHWC layouts are valid for 2D Pooling"</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">GlobalAvgPool2D</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="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">'avg'</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="GlobalAvgPool3D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.GlobalAvgPool3D">[docs]</a><span class="k">class</span> <span class="nc">GlobalAvgPool3D</span><span class="p">(</span><span class="n">_Pooling</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Global average pooling operation for 3D data (spatial or spatio-temporal).</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> layout : str, default 'NCDHW'</span> |
| <span class="sd"> Dimension ordering of data and out ('NCDHW' or 'NDHWC').</span> |
| <span class="sd"> 'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and</span> |
| <span class="sd"> depth dimensions respectively. padding is applied on 'D', 'H' and 'W'</span> |
| <span class="sd"> dimension.</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 5D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, depth, height, width)` when `layout` is `NCDHW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 5D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, 1, 1, 1)` when `layout` is `NCDHW`.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCDHW'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCDHW'</span><span class="p">,</span> <span class="s1">'NDHWC'</span><span class="p">),</span>\ |
| <span class="s2">"Only NCDHW and NDHWC layouts are valid for 3D Pooling"</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">GlobalAvgPool3D</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="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">'avg'</span><span class="p">,</span> <span class="n">layout</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="ReflectionPad2D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.ReflectionPad2D">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">ReflectionPad2D</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Pads the input tensor using the reflection of the input boundary.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> padding: int</span> |
| <span class="sd"> An integer padding size</span> |
| |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: input tensor with the shape :math:`(N, C, H_{in}, W_{in})`.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: output tensor with the shape :math:`(N, C, H_{out}, W_{out})`, where</span> |
| |
| <span class="sd"> .. math::</span> |
| |
| <span class="sd"> H_{out} = H_{in} + 2 \cdot padding</span> |
| |
| <span class="sd"> W_{out} = W_{in} + 2 \cdot padding</span> |
| |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> m = nn.ReflectionPad2D(3)</span> |
| <span class="sd"> >>> input = mx.np.random.normal(size=(16, 3, 224, 224))</span> |
| <span class="sd"> >>> output = m(input)</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">ReflectionPad2D</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="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">padding</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">padding</span> <span class="o">=</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="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">padding</span><span class="p">)</span> |
| <span class="k">assert</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">padding</span><span class="p">)</span> <span class="o">==</span> <span class="mi">8</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_padding</span> <span class="o">=</span> <span class="n">padding</span> |
| |
| <div class="viewcode-block" id="ReflectionPad2D.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.ReflectionPad2D.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="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Use pad operator in numpy extension module,</span> |
| <span class="sd"> which has backward support for reflect mode</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">npx</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'reflect'</span><span class="p">,</span> <span class="n">pad_width</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_padding</span><span class="p">)</span></div></div> |
| |
| |
| <div class="viewcode-block" id="DeformableConvolution"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.DeformableConvolution">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">DeformableConvolution</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""2-D Deformable Convolution v_1 (Dai, 2017).</span> |
| <span class="sd"> Normal Convolution uses sampling points in a regular grid, while the sampling</span> |
| <span class="sd"> points of Deformablem Convolution can be offset. The offset is learned with a</span> |
| <span class="sd"> separate convolution layer during the training. Both the convolution layer for</span> |
| <span class="sd"> generating the output features and the offsets are included in this gluon layer.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> channels : int,</span> |
| <span class="sd"> The dimensionality of the output space</span> |
| <span class="sd"> i.e. the number of output channels in the convolution.</span> |
| <span class="sd"> kernel_size : int or tuple/list of 2 ints, (Default value = (1,1))</span> |
| <span class="sd"> Specifies the dimensions of the convolution window.</span> |
| <span class="sd"> strides : int or tuple/list of 2 ints, (Default value = (1,1))</span> |
| <span class="sd"> Specifies the strides of the convolution.</span> |
| <span class="sd"> padding : int or tuple/list of 2 ints, (Default value = (0,0))</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly zero-padded</span> |
| <span class="sd"> on both sides for padding number of points.</span> |
| <span class="sd"> dilation : int or tuple/list of 2 ints, (Default value = (1,1))</span> |
| <span class="sd"> Specifies the dilation rate to use for dilated convolution.</span> |
| <span class="sd"> groups : int, (Default value = 1)</span> |
| <span class="sd"> Controls the connections between inputs and outputs.</span> |
| <span class="sd"> At groups=1, all inputs are convolved to all outputs.</span> |
| <span class="sd"> At groups=2, the operation becomes equivalent to having two convolution</span> |
| <span class="sd"> layers side by side, each seeing half the input channels, and producing</span> |
| <span class="sd"> half the output channels, and both subsequently concatenated.</span> |
| <span class="sd"> num_deformable_group : int, (Default value = 1)</span> |
| <span class="sd"> Number of deformable group partitions.</span> |
| <span class="sd"> layout : str, (Default value = NCHW)</span> |
| <span class="sd"> Dimension ordering of data and weight. Can be 'NCW', 'NWC', 'NCHW',</span> |
| <span class="sd"> 'NHWC', 'NCDHW', 'NDHWC', etc. 'N', 'C', 'H', 'W', 'D' stands for</span> |
| <span class="sd"> batch, channel, height, width and depth dimensions respectively.</span> |
| <span class="sd"> Convolution is performed over 'D', 'H', and 'W' dimensions.</span> |
| <span class="sd"> use_bias : bool, (Default value = True)</span> |
| <span class="sd"> Whether the layer for generating the output features uses a bias vector.</span> |
| <span class="sd"> in_channels : int, (Default value = 0)</span> |
| <span class="sd"> The number of input channels to this layer. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and input channels will be inferred from the shape of input data.</span> |
| <span class="sd"> activation : str, (Default value = None)</span> |
| <span class="sd"> Activation function to use. See :func:`~mxnet.npx.activation`.</span> |
| <span class="sd"> If you don't specify anything, no activation is applied</span> |
| <span class="sd"> (ie. "linear" activation: `a(x) = x`).</span> |
| <span class="sd"> weight_initializer : str or `Initializer`, (Default value = None)</span> |
| <span class="sd"> Initializer for the `weight` weights matrix for the convolution layer</span> |
| <span class="sd"> for generating the output features.</span> |
| <span class="sd"> bias_initializer : str or `Initializer`, (Default value = zeros)</span> |
| <span class="sd"> Initializer for the bias vector for the convolution layer</span> |
| <span class="sd"> for generating the output features.</span> |
| <span class="sd"> offset_weight_initializer : str or `Initializer`, (Default value = zeros)</span> |
| <span class="sd"> Initializer for the `weight` weights matrix for the convolution layer</span> |
| <span class="sd"> for generating the offset.</span> |
| <span class="sd"> offset_bias_initializer : str or `Initializer`, (Default value = zeros),</span> |
| <span class="sd"> Initializer for the bias vector for the convolution layer</span> |
| <span class="sd"> for generating the offset.</span> |
| <span class="sd"> offset_use_bias: bool, (Default value = True)</span> |
| <span class="sd"> Whether the layer for generating the offset uses a bias vector.</span> |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 4D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, height, width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 4D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, out_height, out_width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> out_height and out_width are calculated as::</span> |
| |
| <span class="sd"> out_height = floor((height+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0])+1</span> |
| <span class="sd"> out_width = floor((width+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1])+1</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</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="n">dilation</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> |
| <span class="n">num_deformable_group</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCHW'</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">offset_weight_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">offset_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">offset_use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">op_name</span><span class="o">=</span><span class="s1">'DeformableConvolution'</span><span class="p">,</span> <span class="n">adj</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">DeformableConvolution</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_channels</span> <span class="o">=</span> <span class="n">channels</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_in_channels</span> <span class="o">=</span> <span class="n">in_channels</span> |
| |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCHW'</span><span class="p">,</span> <span class="s1">'NHWC'</span><span class="p">),</span> <span class="s2">"Only supports 'NCHW' and 'NHWC' layout for now"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">kernel_size</span><span class="p">,)</span> <span class="o">*</span> <span class="mi">2</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">strides</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">strides</span> <span class="o">=</span> <span class="p">(</span><span class="n">strides</span><span class="p">,)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">padding</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">padding</span> <span class="o">=</span> <span class="p">(</span><span class="n">padding</span><span class="p">,)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dilation</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">dilation</span> <span class="o">=</span> <span class="p">(</span><span class="n">dilation</span><span class="p">,)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_op_name</span> <span class="o">=</span> <span class="n">op_name</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span> <span class="o">=</span> <span class="n">kernel_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_layout</span> <span class="o">=</span> <span class="n">layout</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> <span class="o">=</span> <span class="n">groups</span> |
| |
| <span class="n">offset_channels</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">kernel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">num_deformable_group</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_offset_channels</span> <span class="o">=</span> <span class="n">offset_channels</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_offset</span> <span class="o">=</span> <span class="p">{</span> |
| <span class="s1">'kernel'</span><span class="p">:</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="s1">'stride'</span><span class="p">:</span> <span class="n">strides</span><span class="p">,</span> <span class="s1">'dilate'</span><span class="p">:</span> <span class="n">dilation</span><span class="p">,</span> |
| <span class="s1">'pad'</span><span class="p">:</span> <span class="n">padding</span><span class="p">,</span> <span class="s1">'num_filter'</span><span class="p">:</span> <span class="n">offset_channels</span><span class="p">,</span> <span class="s1">'num_group'</span><span class="p">:</span> <span class="n">groups</span><span class="p">,</span> |
| <span class="s1">'no_bias'</span><span class="p">:</span> <span class="ow">not</span> <span class="n">offset_use_bias</span><span class="p">,</span> <span class="s1">'layout'</span><span class="p">:</span> <span class="n">layout</span><span class="p">}</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_deformable_conv</span> <span class="o">=</span> <span class="p">{</span> |
| <span class="s1">'kernel'</span><span class="p">:</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="s1">'stride'</span><span class="p">:</span> <span class="n">strides</span><span class="p">,</span> <span class="s1">'dilate'</span><span class="p">:</span> <span class="n">dilation</span><span class="p">,</span> |
| <span class="s1">'pad'</span><span class="p">:</span> <span class="n">padding</span><span class="p">,</span> <span class="s1">'num_filter'</span><span class="p">:</span> <span class="n">channels</span><span class="p">,</span> <span class="s1">'num_group'</span><span class="p">:</span> <span class="n">groups</span><span class="p">,</span> |
| <span class="s1">'num_deformable_group'</span><span class="p">:</span> <span class="n">num_deformable_group</span><span class="p">,</span> |
| <span class="s1">'no_bias'</span><span class="p">:</span> <span class="ow">not</span> <span class="n">use_bias</span><span class="p">,</span> <span class="s1">'layout'</span><span class="p">:</span> <span class="n">layout</span><span class="p">}</span> |
| |
| <span class="k">if</span> <span class="n">adj</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_offset</span><span class="p">[</span><span class="s1">'adj'</span><span class="p">]</span> <span class="o">=</span> <span class="n">adj</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_deformable_conv</span><span class="p">[</span><span class="s1">'adj'</span><span class="p">]</span> <span class="o">=</span> <span class="n">adj</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">offset_weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'offset_weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_infer_offset_weight</span><span class="p">(),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">offset_weight_initializer</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="k">if</span> <span class="n">offset_use_bias</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">offset_bias</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'offset_bias'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">offset_channels</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">offset_bias_initializer</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="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">offset_bias</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'deformable_conv_weight'</span><span class="p">,</span> |
| <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_infer_weight</span><span class="p">(),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">weight_initializer</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="k">if</span> <span class="n">use_bias</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_bias</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'deformable_conv_bias'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">channels</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">bias_initializer</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="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_bias</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="k">if</span> <span class="n">activation</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="n">activation</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <div class="viewcode-block" id="DeformableConvolution.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.DeformableConvolution.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="n">device</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">device</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset_bias</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">offset</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">convolution</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset_weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">cudnn_off</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_offset</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">offset</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">convolution</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset_weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset_bias</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="n">cudnn_off</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_offset</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_bias</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">deformable_convolution</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="n">offset</span><span class="p">,</span> |
| <span class="n">weight</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="n">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_deformable_conv</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">deformable_convolution</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="n">offset</span><span class="p">,</span> |
| <span class="n">weight</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="n">bias</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_bias</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">,</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_deformable_conv</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">:</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">(</span><span class="n">act</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">act</span></div> |
| |
| |
| <div class="viewcode-block" id="DeformableConvolution.pre_infer_offset_weight"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.DeformableConvolution.pre_infer_offset_weight">[docs]</a> <span class="k">def</span> <span class="nf">pre_infer_offset_weight</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Pre-infer the shape of offsite weight parameter based on kernel size,</span> |
| <span class="sd"> group size and offset channels</span> |
| <span class="sd"> """</span> |
| <span class="n">wshape</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'N'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_offset_channels</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'H'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'W'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">wshape</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="DeformableConvolution.pre_infer_weight"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.DeformableConvolution.pre_infer_weight">[docs]</a> <span class="k">def</span> <span class="nf">pre_infer_weight</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Pre-infer the shape of weight parameter based on kernel size, group size and channels</span> |
| <span class="sd"> """</span> |
| <span class="n">wshape</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'N'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_channels</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'H'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'W'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">wshape</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="DeformableConvolution.infer_shape"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.DeformableConvolution.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="n">x</span><span class="p">):</span> |
| <span class="n">dshape1</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)]</span> |
| <span class="n">wshape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_weight</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">wshape_offset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset_weight</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">wshape_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">wshape</span><span class="p">)</span> |
| <span class="n">wshape_offset_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">wshape_offset</span><span class="p">)</span> |
| <span class="n">wshape_list</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)]</span> <span class="o">=</span> <span class="n">dshape1</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="n">wshape_offset_list</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)]</span> <span class="o">=</span> <span class="n">dshape1</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_weight</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">wshape_list</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">offset_weight</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">wshape_offset_list</span><span class="p">)</span></div> |
| |
| <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="s1">'deformable_conv'</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="si">{mapping}</span><span class="s1">, kernel_size=</span><span class="si">{kernel}</span><span class="s1">, stride=</span><span class="si">{stride}</span><span class="s1">'</span> |
| <span class="n">len_kernel_size</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">_kwargs_deformable_conv</span><span class="p">[</span><span class="s1">'kernel'</span><span class="p">])</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_deformable_conv</span><span class="p">[</span><span class="s1">'pad'</span><span class="p">]</span> <span class="o">!=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,)</span> <span class="o">*</span> <span class="n">len_kernel_size</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', padding=</span><span class="si">{pad}</span><span class="s1">'</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_deformable_conv</span><span class="p">[</span><span class="s1">'dilate'</span><span class="p">]</span> <span class="o">!=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">*</span> <span class="n">len_kernel_size</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', dilation=</span><span class="si">{dilate}</span><span class="s1">'</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="s1">'out_pad'</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">out_pad</span> <span class="o">!=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,)</span> <span class="o">*</span> <span class="n">len_kernel_size</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', output_padding=</span><span class="si">{out_pad}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">out_pad</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">out_pad</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_deformable_conv</span><span class="p">[</span><span class="s1">'num_group'</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', groups=</span><span class="si">{num_group}</span><span class="s1">'</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_bias</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', bias=False'</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">', </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">)</span> |
| <span class="n">s</span> <span class="o">+=</span> <span class="s1">')'</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_weight</span><span class="o">.</span><span class="n">shape</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">mapping</span><span class="o">=</span><span class="s1">'</span><span class="si">{0}</span><span class="s1"> -> </span><span class="si">{1}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_deformable_conv</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="ModulatedDeformableConvolution"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.ModulatedDeformableConvolution">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">ModulatedDeformableConvolution</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""2-D Deformable Convolution v2 (Dai, 2018).</span> |
| |
| <span class="sd"> The modulated deformable convolution operation is described in https://arxiv.org/abs/1811.11168</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> channels : int,</span> |
| <span class="sd"> The dimensionality of the output space</span> |
| <span class="sd"> i.e. the number of output channels in the convolution.</span> |
| <span class="sd"> kernel_size : int or tuple/list of 2 ints, (Default value = (1,1))</span> |
| <span class="sd"> Specifies the dimensions of the convolution window.</span> |
| <span class="sd"> strides : int or tuple/list of 2 ints, (Default value = (1,1))</span> |
| <span class="sd"> Specifies the strides of the convolution.</span> |
| <span class="sd"> padding : int or tuple/list of 2 ints, (Default value = (0,0))</span> |
| <span class="sd"> If padding is non-zero, then the input is implicitly zero-padded</span> |
| <span class="sd"> on both sides for padding number of points.</span> |
| <span class="sd"> dilation : int or tuple/list of 2 ints, (Default value = (1,1))</span> |
| <span class="sd"> Specifies the dilation rate to use for dilated convolution.</span> |
| <span class="sd"> groups : int, (Default value = 1)</span> |
| <span class="sd"> Controls the connections between inputs and outputs.</span> |
| <span class="sd"> At groups=1, all inputs are convolved to all outputs.</span> |
| <span class="sd"> At groups=2, the operation becomes equivalent to having two convolution</span> |
| <span class="sd"> layers side by side, each seeing half the input channels, and producing</span> |
| <span class="sd"> half the output channels, and both subsequently concatenated.</span> |
| <span class="sd"> num_deformable_group : int, (Default value = 1)</span> |
| <span class="sd"> Number of deformable group partitions.</span> |
| <span class="sd"> layout : str, (Default value = NCHW)</span> |
| <span class="sd"> Dimension ordering of data and weight. Can be 'NCW', 'NWC', 'NCHW',</span> |
| <span class="sd"> 'NHWC', 'NCDHW', 'NDHWC', etc. 'N', 'C', 'H', 'W', 'D' stands for</span> |
| <span class="sd"> batch, channel, height, width and depth dimensions respectively.</span> |
| <span class="sd"> Convolution is performed over 'D', 'H', and 'W' dimensions.</span> |
| <span class="sd"> use_bias : bool, (Default value = True)</span> |
| <span class="sd"> Whether the layer for generating the output features uses a bias vector.</span> |
| <span class="sd"> in_channels : int, (Default value = 0)</span> |
| <span class="sd"> The number of input channels to this layer. If not specified,</span> |
| <span class="sd"> initialization will be deferred to the first time `forward` is called</span> |
| <span class="sd"> and input channels will be inferred from the shape of input data.</span> |
| <span class="sd"> activation : str, (Default value = None)</span> |
| <span class="sd"> Activation function to use. See :func:`~mxnet.ndarray.Activation`.</span> |
| <span class="sd"> If you don't specify anything, no activation is applied</span> |
| <span class="sd"> (ie. "linear" activation: `a(x) = x`).</span> |
| <span class="sd"> weight_initializer : str or `Initializer`, (Default value = None)</span> |
| <span class="sd"> Initializer for the `weight` weights matrix for the convolution layer</span> |
| <span class="sd"> for generating the output features.</span> |
| <span class="sd"> bias_initializer : str or `Initializer`, (Default value = zeros)</span> |
| <span class="sd"> Initializer for the bias vector for the convolution layer</span> |
| <span class="sd"> for generating the output features.</span> |
| <span class="sd"> offset_weight_initializer : str or `Initializer`, (Default value = zeros)</span> |
| <span class="sd"> Initializer for the `weight` weights matrix for the convolution layer</span> |
| <span class="sd"> for generating the offset.</span> |
| <span class="sd"> offset_bias_initializer : str or `Initializer`, (Default value = zeros),</span> |
| <span class="sd"> Initializer for the bias vector for the convolution layer</span> |
| <span class="sd"> for generating the offset.</span> |
| <span class="sd"> offset_use_bias: bool, (Default value = True)</span> |
| <span class="sd"> Whether the layer for generating the offset uses a bias vector.</span> |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: 4D input tensor with shape</span> |
| <span class="sd"> `(batch_size, in_channels, height, width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> For other layouts shape is permuted accordingly.</span> |
| |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: 4D output tensor with shape</span> |
| <span class="sd"> `(batch_size, channels, out_height, out_width)` when `layout` is `NCHW`.</span> |
| <span class="sd"> out_height and out_width are calculated as::</span> |
| |
| <span class="sd"> out_height = floor((height+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0])+1</span> |
| <span class="sd"> out_width = floor((width+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1])+1</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</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="n">dilation</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> |
| <span class="n">num_deformable_group</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">'NCHW'</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">weight_initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> |
| <span class="n">offset_weight_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">offset_bias_initializer</span><span class="o">=</span><span class="s1">'zeros'</span><span class="p">,</span> <span class="n">offset_use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">op_name</span><span class="o">=</span><span class="s1">'ModulatedDeformableConvolution'</span><span class="p">,</span> <span class="n">adj</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">ModulatedDeformableConvolution</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_channels</span> <span class="o">=</span> <span class="n">channels</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_in_channels</span> <span class="o">=</span> <span class="n">in_channels</span> |
| |
| <span class="k">assert</span> <span class="n">layout</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'NCHW'</span><span class="p">,</span> <span class="s1">'NHWC'</span><span class="p">),</span> <span class="s2">"Only supports 'NCHW' and 'NHWC' layout for now"</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">kernel_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">kernel_size</span><span class="p">,)</span> <span class="o">*</span> <span class="mi">2</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">strides</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">strides</span> <span class="o">=</span> <span class="p">(</span><span class="n">strides</span><span class="p">,)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">padding</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">padding</span> <span class="o">=</span> <span class="p">(</span><span class="n">padding</span><span class="p">,)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dilation</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">dilation</span> <span class="o">=</span> <span class="p">(</span><span class="n">dilation</span><span class="p">,)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_op_name</span> <span class="o">=</span> <span class="n">op_name</span> |
| |
| <span class="n">offset_channels</span> <span class="o">=</span> <span class="n">num_deformable_group</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">*</span> <span class="n">kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">kernel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">offset_split_index</span> <span class="o">=</span> <span class="n">num_deformable_group</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">kernel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_layout</span> <span class="o">=</span> <span class="n">layout</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> <span class="o">=</span> <span class="n">groups</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_offset_channels</span> <span class="o">=</span> <span class="n">offset_channels</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span> <span class="o">=</span> <span class="n">kernel_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_offset</span> <span class="o">=</span> <span class="p">{</span> |
| <span class="s1">'kernel'</span><span class="p">:</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="s1">'stride'</span><span class="p">:</span> <span class="n">strides</span><span class="p">,</span> <span class="s1">'dilate'</span><span class="p">:</span> <span class="n">dilation</span><span class="p">,</span> |
| <span class="s1">'pad'</span><span class="p">:</span> <span class="n">padding</span><span class="p">,</span> <span class="s1">'num_filter'</span><span class="p">:</span> <span class="n">offset_channels</span><span class="p">,</span> <span class="s1">'num_group'</span><span class="p">:</span> <span class="n">groups</span><span class="p">,</span> |
| <span class="s1">'no_bias'</span><span class="p">:</span> <span class="ow">not</span> <span class="n">offset_use_bias</span><span class="p">,</span> <span class="s1">'layout'</span><span class="p">:</span> <span class="n">layout</span><span class="p">}</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_deformable_conv</span> <span class="o">=</span> <span class="p">{</span> |
| <span class="s1">'kernel'</span><span class="p">:</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="s1">'stride'</span><span class="p">:</span> <span class="n">strides</span><span class="p">,</span> <span class="s1">'dilate'</span><span class="p">:</span> <span class="n">dilation</span><span class="p">,</span> |
| <span class="s1">'pad'</span><span class="p">:</span> <span class="n">padding</span><span class="p">,</span> <span class="s1">'num_filter'</span><span class="p">:</span> <span class="n">channels</span><span class="p">,</span> <span class="s1">'num_group'</span><span class="p">:</span> <span class="n">groups</span><span class="p">,</span> |
| <span class="s1">'num_deformable_group'</span><span class="p">:</span> <span class="n">num_deformable_group</span><span class="p">,</span> |
| <span class="s1">'no_bias'</span><span class="p">:</span> <span class="ow">not</span> <span class="n">use_bias</span><span class="p">,</span> <span class="s1">'layout'</span><span class="p">:</span> <span class="n">layout</span><span class="p">}</span> |
| |
| <span class="k">if</span> <span class="n">adj</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_offset</span><span class="p">[</span><span class="s1">'adj'</span><span class="p">]</span> <span class="o">=</span> <span class="n">adj</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_deformable_conv</span><span class="p">[</span><span class="s1">'adj'</span><span class="p">]</span> <span class="o">=</span> <span class="n">adj</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'deformable_conv_weight'</span><span class="p">,</span> |
| <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_infer_weight</span><span class="p">(),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">weight_initializer</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="k">if</span> <span class="n">use_bias</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_bias</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'deformable_conv_bias'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">channels</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">bias_initializer</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="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_bias</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">offset_weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'offset_weight'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_infer_offset_weight</span><span class="p">(),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">offset_weight_initializer</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="k">if</span> <span class="n">offset_use_bias</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">offset_bias</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="s1">'offset_bias'</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">offset_channels</span><span class="p">,),</span> |
| <span class="n">init</span><span class="o">=</span><span class="n">offset_bias_initializer</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="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">offset_bias</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="k">if</span> <span class="n">activation</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="n">activation</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <div class="viewcode-block" id="ModulatedDeformableConvolution.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.ModulatedDeformableConvolution.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="n">device</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">device</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset_bias</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">offset</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">convolution</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset_weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="n">cudnn_off</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_offset</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">offset</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">convolution</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset_weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">offset_bias</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">cudnn_off</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_offset</span><span class="p">)</span> |
| |
| <span class="n">offset_t</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">slice_axis</span><span class="p">(</span><span class="n">offset</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">begin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">offset_split_index</span><span class="p">)</span> |
| <span class="n">mask</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">slice_axis</span><span class="p">(</span><span class="n">offset</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">begin</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">offset_split_index</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> |
| <span class="n">mask</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_bias</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">modulated_deformable_convolution</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="n">offset_t</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> |
| <span class="n">weight</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="n">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_deformable_conv</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">modulated_deformable_convolution</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="n">offset_t</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span> |
| <span class="n">weight</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_weight</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> |
| <span class="n">bias</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_bias</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s1">'fwd'</span><span class="p">,</span> |
| <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs_deformable_conv</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">:</span> |
| <span class="n">act</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">(</span><span class="n">act</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">act</span></div> |
| |
| <div class="viewcode-block" id="ModulatedDeformableConvolution.pre_infer_offset_weight"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.ModulatedDeformableConvolution.pre_infer_offset_weight">[docs]</a> <span class="k">def</span> <span class="nf">pre_infer_offset_weight</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Pre-infer the shape of offsite weight parameter based on kernel size,</span> |
| <span class="sd"> group size and offset channels</span> |
| <span class="sd"> """</span> |
| <span class="n">wshape</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'N'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_offset_channels</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'H'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'W'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">wshape</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="ModulatedDeformableConvolution.pre_infer_weight"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.ModulatedDeformableConvolution.pre_infer_weight">[docs]</a> <span class="k">def</span> <span class="nf">pre_infer_weight</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Pre-infer the shape of weight parameter based on kernel size, group size and channels</span> |
| <span class="sd"> """</span> |
| <span class="n">wshape</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'N'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_channels</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'H'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'W'</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kernel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">wshape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">wshape</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="ModulatedDeformableConvolution.infer_shape"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.ModulatedDeformableConvolution.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="n">x</span><span class="p">):</span> |
| <span class="n">dshape1</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)]</span> |
| <span class="n">wshape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_weight</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">wshape_offset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">offset_weight</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">wshape_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">wshape</span><span class="p">)</span> |
| <span class="n">wshape_offset_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">wshape_offset</span><span class="p">)</span> |
| <span class="n">wshape_list</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)]</span> <span class="o">=</span> <span class="n">dshape1</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="n">wshape_offset_list</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_layout</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'C'</span><span class="p">)]</span> <span class="o">=</span> <span class="n">dshape1</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">_groups</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">deformable_conv_weight</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">wshape_list</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">offset_weight</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">wshape_offset_list</span><span class="p">)</span></div> |
| |
| <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="s1">'modulated_deformable_conv'</span></div> |
| |
| |
| <div class="viewcode-block" id="PixelShuffle1D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.PixelShuffle1D">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">PixelShuffle1D</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Pixel-shuffle layer for upsampling in 1 dimension.</span> |
| |
| <span class="sd"> Pixel-shuffling is the operation of taking groups of values along</span> |
| <span class="sd"> the *channel* dimension and regrouping them into blocks of pixels</span> |
| <span class="sd"> along the ``W`` dimension, thereby effectively multiplying that dimension</span> |
| <span class="sd"> by a constant factor in size.</span> |
| |
| <span class="sd"> For example, a feature map of shape :math:`(fC, W)` is reshaped</span> |
| <span class="sd"> into :math:`(C, fW)` by forming little value groups of size :math:`f`</span> |
| <span class="sd"> and arranging them in a grid of size :math:`W`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> factor : int or 1-tuple of int</span> |
| <span class="sd"> Upsampling factor, applied to the ``W`` dimension.</span> |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: Tensor of shape ``(N, f*C, W)``.</span> |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: Tensor of shape ``(N, C, W*f)``.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> pxshuf = PixelShuffle1D(2)</span> |
| <span class="sd"> >>> x = mx.np.zeros((1, 8, 3))</span> |
| <span class="sd"> >>> pxshuf(x).shape</span> |
| <span class="sd"> (1, 4, 6)</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">factor</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">PixelShuffle1D</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_factor</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">factor</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="PixelShuffle1D.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.PixelShuffle1D.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="w"> </span><span class="sd">"""Perform pixel-shuffling on the input."""</span> |
| <span class="n">f</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_factor</span> <span class="c1"># (N, C*f, W)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">))</span> <span class="c1"># (N, C, f, W)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> <span class="c1"># (N, C, W, f)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">))</span> <span class="c1"># (N, C, W*f)</span> |
| <span class="k">return</span> <span class="n">x</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="k">return</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="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="bp">self</span><span class="o">.</span><span class="n">_factor</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="PixelShuffle2D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.PixelShuffle2D">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">PixelShuffle2D</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Pixel-shuffle layer for upsampling in 2 dimensions.</span> |
| |
| <span class="sd"> Pixel-shuffling is the operation of taking groups of values along</span> |
| <span class="sd"> the *channel* dimension and regrouping them into blocks of pixels</span> |
| <span class="sd"> along the ``H`` and ``W`` dimensions, thereby effectively multiplying</span> |
| <span class="sd"> those dimensions by a constant factor in size.</span> |
| |
| <span class="sd"> For example, a feature map of shape :math:`(f^2 C, H, W)` is reshaped</span> |
| <span class="sd"> into :math:`(C, fH, fW)` by forming little :math:`f \times f` blocks</span> |
| <span class="sd"> of pixels and arranging them in an :math:`H \times W` grid.</span> |
| |
| <span class="sd"> Pixel-shuffling together with regular convolution is an alternative,</span> |
| <span class="sd"> learnable way of upsampling an image by arbitrary factors. It is reported</span> |
| <span class="sd"> to help overcome checkerboard artifacts that are common in upsampling with</span> |
| <span class="sd"> transposed convolutions (also called deconvolutions). See the paper</span> |
| <span class="sd"> `Real-Time Single Image and Video Super-Resolution Using an Efficient</span> |
| <span class="sd"> Sub-Pixel Convolutional Neural Network <https://arxiv.org/abs/1609.05158>`_</span> |
| <span class="sd"> for further details.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> factor : int or 2-tuple of int</span> |
| <span class="sd"> Upsampling factors, applied to the ``H`` and ``W`` dimensions,</span> |
| <span class="sd"> in that order.</span> |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: Tensor of shape ``(N, f1*f2*C, H, W)``.</span> |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: Tensor of shape ``(N, C, H*f1, W*f2)``.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> pxshuf = PixelShuffle2D((2, 3))</span> |
| <span class="sd"> >>> x = mx.np.zeros((1, 12, 3, 5))</span> |
| <span class="sd"> >>> pxshuf(x).shape</span> |
| <span class="sd"> (1, 2, 6, 15)</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">factor</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">PixelShuffle2D</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">try</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_factors</span> <span class="o">=</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">factor</span><span class="p">),)</span> <span class="o">*</span> <span class="mi">2</span> |
| <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_factors</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">fac</span><span class="p">)</span> <span class="k">for</span> <span class="n">fac</span> <span class="ow">in</span> <span class="n">factor</span><span class="p">)</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">_factors</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"wrong length </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">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_factors</span><span class="p">))</span> |
| |
| <div class="viewcode-block" id="PixelShuffle2D.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.PixelShuffle2D.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="w"> </span><span class="sd">"""Perform pixel-shuffling on the input."""</span> |
| <span class="n">f1</span><span class="p">,</span> <span class="n">f2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_factors</span> |
| <span class="c1"># (N, f1*f2*C, H, W)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">f1</span> <span class="o">*</span> <span class="n">f2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">))</span> <span class="c1"># (N, C, f1*f2, H, W)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">))</span> <span class="c1"># (N, C, f1, f2, H, W)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="c1"># (N, C, H, f1, W, f2)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">))</span> <span class="c1"># (N, C, H*f1, W*f2)</span> |
| <span class="k">return</span> <span class="n">x</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="k">return</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="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="bp">self</span><span class="o">.</span><span class="n">_factors</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="PixelShuffle3D"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.PixelShuffle3D">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">PixelShuffle3D</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Pixel-shuffle layer for upsampling in 3 dimensions.</span> |
| |
| <span class="sd"> Pixel-shuffling (or voxel-shuffling in 3D) is the operation of taking</span> |
| <span class="sd"> groups of values along the *channel* dimension and regrouping them into</span> |
| <span class="sd"> blocks of voxels along the ``D``, ``H`` and ``W`` dimensions, thereby</span> |
| <span class="sd"> effectively multiplying those dimensions by a constant factor in size.</span> |
| |
| <span class="sd"> For example, a feature map of shape :math:`(f^3 C, D, H, W)` is reshaped</span> |
| <span class="sd"> into :math:`(C, fD, fH, fW)` by forming little :math:`f \times f \times f`</span> |
| <span class="sd"> blocks of voxels and arranging them in a :math:`D \times H \times W` grid.</span> |
| |
| <span class="sd"> Pixel-shuffling together with regular convolution is an alternative,</span> |
| <span class="sd"> learnable way of upsampling an image by arbitrary factors. It is reported</span> |
| <span class="sd"> to help overcome checkerboard artifacts that are common in upsampling with</span> |
| <span class="sd"> transposed convolutions (also called deconvolutions). See the paper</span> |
| <span class="sd"> `Real-Time Single Image and Video Super-Resolution Using an Efficient</span> |
| <span class="sd"> Sub-Pixel Convolutional Neural Network <https://arxiv.org/abs/1609.05158>`_</span> |
| <span class="sd"> for further details.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> factor : int or 3-tuple of int</span> |
| <span class="sd"> Upsampling factors, applied to the ``D``, ``H`` and ``W``</span> |
| <span class="sd"> dimensions, in that order.</span> |
| |
| <span class="sd"> Inputs:</span> |
| <span class="sd"> - **data**: Tensor of shape ``(N, f1*f2*f3*C, D, H, W)``.</span> |
| <span class="sd"> Outputs:</span> |
| <span class="sd"> - **out**: Tensor of shape ``(N, C, D*f1, H*f2, W*f3)``.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> pxshuf = PixelShuffle3D((2, 3, 4))</span> |
| <span class="sd"> >>> x = mx.np.zeros((1, 48, 3, 5, 7))</span> |
| <span class="sd"> >>> pxshuf(x).shape</span> |
| <span class="sd"> (1, 2, 6, 15, 28)</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">factor</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">PixelShuffle3D</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">try</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_factors</span> <span class="o">=</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">factor</span><span class="p">),)</span> <span class="o">*</span> <span class="mi">3</span> |
| <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_factors</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">fac</span><span class="p">)</span> <span class="k">for</span> <span class="n">fac</span> <span class="ow">in</span> <span class="n">factor</span><span class="p">)</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">_factors</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">"wrong length </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">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_factors</span><span class="p">))</span> |
| |
| <div class="viewcode-block" id="PixelShuffle3D.forward"><a class="viewcode-back" href="../../../../api/gluon/nn/index.html#mxnet.gluon.nn.PixelShuffle3D.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="w"> </span><span class="sd">"""Perform pixel-shuffling on the input."""</span> |
| <span class="c1"># `transpose` doesn't support 8D, need other implementation</span> |
| <span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="p">,</span> <span class="n">f3</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_factors</span> |
| <span class="c1"># (N, C*f1*f2*f3, D, H, W)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">f1</span> <span class="o">*</span> <span class="n">f2</span> <span class="o">*</span> <span class="n">f3</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">))</span> <span class="c1"># (N, C, f1*f2*f3, D, H, W)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> <span class="c1"># (N, C, D, f1*f2*f3, H, W)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="o">*</span><span class="n">f3</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">))</span> <span class="c1"># (N, C, D, f1, f2*f3, H, W)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">))</span> <span class="c1"># (N, C, D*f1, f2*f3, H, W)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span> <span class="c1"># (N, C, D*f1, H, f2*f3, W)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="n">f2</span><span class="p">,</span> <span class="n">f3</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">))</span> <span class="c1"># (N, C, D*f1, H, f2, f3, W)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">))</span> <span class="c1"># (N, C, D*f1, H*f2, f3, W)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="c1"># (N, C, D*f1, H*f2, W, f3)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">))</span> <span class="c1"># (N, C, D*f1, H*f2, W*f3)</span> |
| <span class="k">return</span> <span class="n">x</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="k">return</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="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="bp">self</span><span class="o">.</span><span class="n">_factors</span><span class="p">)</span></div> |
| </pre></div> |
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