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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/np/index.html">mxnet.np</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.indexing.html">Indexing</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.meshgrid.html">mxnet.np.meshgrid</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tril.html">mxnet.np.tril</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.leaky_relu.html">mxnet.npx.leaky_relu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/npx/generated/mxnet.npx.multibox_detection.html">mxnet.npx.multibox_detection</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| |
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| <nav class="mdl-navigation"> |
| <ul> |
| <li class="toctree-l1"><a class="reference internal" href="../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/packages/index.html">Packages</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/optimizer/index.html">Optimizers</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/viz/index.html">Visualization</a><ul> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/compression/index.html">Compression</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/gradient_compression">Gradient Compression</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/performance/backend/index.html">Accelerated Backend Tools</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/index.html">oneDNN</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization.html">oneDNN Quantization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/tvm.html">Use TVM</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/profiler.html">Profiling MXNet Models</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/deploy/index.html">Deployment</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/export/index.html">Export</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/export/onnx.html">Exporting to ONNX format</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/export_network.html">Export Gluon CV Models</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html">Save / Load Parameters</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/inference/index.html">Inference</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/cpp.html">Deploy into C++</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/inference/image_classification_jetson.html">Image Classication using pretrained ResNet-50 model on Jetson module</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/index.html">Run on AWS</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_ec2.html">Run on an EC2 Instance</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/use_sagemaker.html">Run on Amazon SageMaker</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/deploy/run-on-aws/cloud.html">MXNet on the Cloud</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../../tutorials/extend/index.html">Extend</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/extend/customop.html">Custom Numpy Operators</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/new_op">New Operator Creation</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/add_op_in_backend">New Operator in MXNet Backend</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/faq/using_rtc">Using RTC for CUDA kernels</a></li> |
| </ul> |
| </li> |
| </ul> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../api/index.html">Python API</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../api/np/index.html">mxnet.np</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/arrays.html">Array objects</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.ndarray.html">The N-dimensional array (<code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>)</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/arrays.indexing.html">Indexing</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../api/np/routines.html">Routines</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-creation.html">Array creation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.eye.html">mxnet.np.eye</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.empty.html">mxnet.np.empty</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.full.html">mxnet.np.full</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.identity.html">mxnet.np.identity</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones.html">mxnet.np.ones</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ones_like.html">mxnet.np.ones_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros.html">mxnet.np.zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.zeros_like.html">mxnet.np.zeros_like</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array.html">mxnet.np.array</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copy.html">mxnet.np.copy</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linspace.html">mxnet.np.linspace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logspace.html">mxnet.np.logspace</a></li> |
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| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.transpose.html">mxnet.np.transpose</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rollaxis.html">mxnet.np.rollaxis</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expand_dims.html">mxnet.np.expand_dims</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_to.html">mxnet.np.broadcast_to</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_1d.html">mxnet.np.atleast_1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_2d.html">mxnet.np.atleast_2d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.atleast_3d.html">mxnet.np.atleast_3d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.concatenate.html">mxnet.np.concatenate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.stack.html">mxnet.np.stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vstack.html">mxnet.np.vstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.column_stack.html">mxnet.np.column_stack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hstack.html">mxnet.np.hstack</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dsplit.html">mxnet.np.dsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tile.html">mxnet.np.tile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.insert.html">mxnet.np.insert</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reshape.html">mxnet.np.reshape</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flip.html">mxnet.np.flip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumsum.html">mxnet.np.cumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanprod.html">mxnet.np.nanprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nansum.html">mxnet.np.nansum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cumprod.html">mxnet.np.cumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumprod.html">mxnet.np.nancumprod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nancumsum.html">mxnet.np.nancumsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.diff.html">mxnet.np.diff</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ediff1d.html">mxnet.np.ediff1d</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cross.html">mxnet.np.cross</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trapz.html">mxnet.np.trapz</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.exp.html">mxnet.np.exp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.expm1.html">mxnet.np.expm1</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log.html">mxnet.np.log</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log10.html">mxnet.np.log10</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log2.html">mxnet.np.log2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.log1p.html">mxnet.np.log1p</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.logaddexp.html">mxnet.np.logaddexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.i0.html">mxnet.np.i0</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ldexp.html">mxnet.np.ldexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.signbit.html">mxnet.np.signbit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.copysign.html">mxnet.np.copysign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.frexp.html">mxnet.np.frexp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.spacing.html">mxnet.np.spacing</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lcm.html">mxnet.np.lcm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.gcd.html">mxnet.np.gcd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.add.html">mxnet.np.add</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.reciprocal.html">mxnet.np.reciprocal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.negative.html">mxnet.np.negative</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divide.html">mxnet.np.divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.power.html">mxnet.np.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.subtract.html">mxnet.np.subtract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mod.html">mxnet.np.mod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.multiply.html">mxnet.np.multiply</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.true_divide.html">mxnet.np.true_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.remainder.html">mxnet.np.remainder</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.positive.html">mxnet.np.positive</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.float_power.html">mxnet.np.float_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmod.html">mxnet.np.fmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.modf.html">mxnet.np.modf</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.divmod.html">mxnet.np.divmod</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor_divide.html">mxnet.np.floor_divide</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.clip.html">mxnet.np.clip</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sqrt.html">mxnet.np.sqrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cbrt.html">mxnet.np.cbrt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.square.html">mxnet.np.square</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.absolute.html">mxnet.np.absolute</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sign.html">mxnet.np.sign</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.maximum.html">mxnet.np.maximum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.minimum.html">mxnet.np.minimum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fabs.html">mxnet.np.fabs</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.heaviside.html">mxnet.np.heaviside</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmax.html">mxnet.np.fmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fmin.html">mxnet.np.fmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nan_to_num.html">mxnet.np.nan_to_num</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.interp.html">mxnet.np.interp</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/random/index.html">np.random</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.choice.html">mxnet.np.random.choice</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.shuffle.html">mxnet.np.random.shuffle</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.normal.html">mxnet.np.random.normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.uniform.html">mxnet.np.random.uniform</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rand.html">mxnet.np.random.rand</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.randint.html">mxnet.np.random.randint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.beta.html">mxnet.np.random.beta</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.chisquare.html">mxnet.np.random.chisquare</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.exponential.html">mxnet.np.random.exponential</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.f.html">mxnet.np.random.f</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gamma.html">mxnet.np.random.gamma</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.gumbel.html">mxnet.np.random.gumbel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.laplace.html">mxnet.np.random.laplace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.logistic.html">mxnet.np.random.logistic</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.lognormal.html">mxnet.np.random.lognormal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multinomial.html">mxnet.np.random.multinomial</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.multivariate_normal.html">mxnet.np.random.multivariate_normal</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.pareto.html">mxnet.np.random.pareto</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.power.html">mxnet.np.random.power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.rayleigh.html">mxnet.np.random.rayleigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/random/generated/mxnet.np.random.weibull.html">mxnet.np.random.weibull</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.sort.html">Sorting, searching, and counting</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.sort.html">mxnet.np.ndarray.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sort.html">mxnet.np.sort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.lexsort.html">mxnet.np.lexsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argsort.html">mxnet.np.argsort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.msort.html">mxnet.np.msort</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.partition.html">mxnet.np.partition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argpartition.html">mxnet.np.argpartition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmax.html">mxnet.np.argmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argmin.html">mxnet.np.argmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmax.html">mxnet.np.nanargmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanargmin.html">mxnet.np.nanargmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.argwhere.html">mxnet.np.argwhere</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nonzero.html">mxnet.np.nonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flatnonzero.html">mxnet.np.flatnonzero</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.where.html">mxnet.np.where</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.searchsorted.html">mxnet.np.searchsorted</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.extract.html">mxnet.np.extract</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.count_nonzero.html">mxnet.np.count_nonzero</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.statistics.html">Statistics</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.min.html">mxnet.np.min</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.max.html">mxnet.np.max</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amin.html">mxnet.np.amin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.amax.html">mxnet.np.amax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmin.html">mxnet.np.nanmin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmax.html">mxnet.np.nanmax</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ptp.html">mxnet.np.ptp</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.percentile.html">mxnet.np.percentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanpercentile.html">mxnet.np.nanpercentile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.quantile.html">mxnet.np.quantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanquantile.html">mxnet.np.nanquantile</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.mean.html">mxnet.np.mean</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.std.html">mxnet.np.std</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.var.html">mxnet.np.var</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.median.html">mxnet.np.median</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.average.html">mxnet.np.average</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanmedian.html">mxnet.np.nanmedian</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanstd.html">mxnet.np.nanstd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.nanvar.html">mxnet.np.nanvar</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.corrcoef.html">mxnet.np.corrcoef</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.correlate.html">mxnet.np.correlate</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cov.html">mxnet.np.cov</a></li> |
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| |
| <h1>Source code for mxnet.image.image</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"># pylint: disable=no-member, too-many-lines, redefined-builtin, protected-access, unused-import, invalid-name</span> |
| <span class="c1"># pylint: disable=too-many-arguments, too-many-locals, no-name-in-module, too-many-branches, too-many-statements</span> |
| <span class="sd">"""Read individual image files and perform augmentations."""</span> |
| |
| |
| <span class="kn">import</span> <span class="nn">sys</span> |
| <span class="kn">import</span> <span class="nn">os</span> |
| <span class="kn">import</span> <span class="nn">random</span> |
| <span class="kn">import</span> <span class="nn">logging</span> |
| <span class="kn">import</span> <span class="nn">json</span> |
| <span class="kn">import</span> <span class="nn">warnings</span> |
| |
| <span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span> |
| |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">numpy</span> <span class="k">as</span> <span class="n">_mx_np</span> <span class="c1"># pylint: disable=reimported</span> |
| |
| |
| <span class="k">try</span><span class="p">:</span> |
| <span class="kn">import</span> <span class="nn">cv2</span> |
| <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> |
| <span class="n">cv2</span> <span class="o">=</span> <span class="kc">None</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">..</span> <span class="kn">import</span> <span class="n">ndarray</span> <span class="k">as</span> <span class="n">nd</span> |
| <span class="kn">from</span> <span class="nn">..ndarray</span> <span class="kn">import</span> <span class="n">_internal</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">io</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">recordio</span> |
| <span class="kn">from</span> <span class="nn">..</span> <span class="n">util</span> <span class="kn">import</span> <span class="nn">is_np_array</span> |
| <span class="kn">from</span> <span class="nn">..ndarray.numpy</span> <span class="kn">import</span> <span class="n">_internal</span> <span class="k">as</span> <span class="n">_npi</span> |
| |
| |
| <div class="viewcode-block" id="imread"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.imread">[docs]</a><span class="k">def</span> <span class="nf">imread</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Read and decode an image to an NDArray.</span> |
| |
| <span class="sd"> .. note:: `imread` uses OpenCV (not the CV2 Python library).</span> |
| <span class="sd"> MXNet must have been built with USE_OPENCV=1 for `imdecode` to work.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> filename : str</span> |
| <span class="sd"> Name of the image file to be loaded.</span> |
| <span class="sd"> flag : {0, 1}, default 1</span> |
| <span class="sd"> 1 for three channel color output. 0 for grayscale output.</span> |
| <span class="sd"> to_rgb : bool, default True</span> |
| <span class="sd"> True for RGB formatted output (MXNet default).</span> |
| <span class="sd"> False for BGR formatted output (OpenCV default).</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> Output buffer. Use `None` for automatic allocation.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An `NDArray` containing the image.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> mx.img.imread("flower.jpg")</span> |
| <span class="sd"> <NDArray 224x224x3 @cpu(0)></span> |
| |
| <span class="sd"> Set `flag` parameter to 0 to get grayscale output</span> |
| |
| <span class="sd"> >>> mx.img.imread("flower.jpg", flag=0)</span> |
| <span class="sd"> <NDArray 224x224x1 @cpu(0)></span> |
| |
| <span class="sd"> Set `to_rgb` parameter to 0 to get output in OpenCV format (BGR)</span> |
| |
| <span class="sd"> >>> mx.img.imread("flower.jpg", to_rgb=0)</span> |
| <span class="sd"> <NDArray 224x224x3 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="n">read_fn</span> <span class="o">=</span> <span class="n">_npi</span><span class="o">.</span><span class="n">cvimread</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">read_fn</span> <span class="o">=</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_cvimread</span> |
| <span class="k">return</span> <span class="n">read_fn</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="imresize"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.imresize">[docs]</a><span class="k">def</span> <span class="nf">imresize</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Resize image with OpenCV.</span> |
| |
| <span class="sd"> .. note:: `imresize` uses OpenCV (not the CV2 Python library). MXNet must have been built</span> |
| <span class="sd"> with USE_OPENCV=1 for `imresize` to work.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> src : NDArray</span> |
| <span class="sd"> source image</span> |
| <span class="sd"> w : int, required</span> |
| <span class="sd"> Width of resized image.</span> |
| <span class="sd"> h : int, required</span> |
| <span class="sd"> Height of resized image.</span> |
| <span class="sd"> interp : int, optional, default=1</span> |
| <span class="sd"> Interpolation method (default=cv2.INTER_LINEAR).</span> |
| <span class="sd"> Possible values:</span> |
| <span class="sd"> 0: Nearest Neighbors Interpolation.</span> |
| <span class="sd"> 1: Bilinear interpolation.</span> |
| <span class="sd"> 2: Bicubic interpolation over 4x4 pixel neighborhood.</span> |
| <span class="sd"> 3: Area-based (resampling using pixel area relation). It may be a</span> |
| <span class="sd"> preferred method for image decimation, as it gives moire-free</span> |
| <span class="sd"> results. But when the image is zoomed, it is similar to the Nearest</span> |
| <span class="sd"> Neighbors method. (used by default).</span> |
| <span class="sd"> 4: Lanczos interpolation over 8x8 pixel neighborhood.</span> |
| <span class="sd"> 9: Cubic for enlarge, area for shrink, bilinear for others</span> |
| <span class="sd"> 10: Random select from interpolation method metioned above.</span> |
| <span class="sd"> Note:</span> |
| <span class="sd"> When shrinking an image, it will generally look best with AREA-based</span> |
| <span class="sd"> interpolation, whereas, when enlarging an image, it will generally look best</span> |
| <span class="sd"> with Bicubic (slow) or Bilinear (faster but still looks OK).</span> |
| <span class="sd"> More details can be found in the documentation of OpenCV, please refer to</span> |
| <span class="sd"> http://docs.opencv.org/master/da/d54/group__imgproc__transform.html.</span> |
| |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> The output NDArray to hold the result.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> out : NDArray or list of NDArrays</span> |
| <span class="sd"> The output of this function.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> with open("flower.jpeg", 'rb') as fp:</span> |
| <span class="sd"> ... str_image = fp.read()</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> >>> image = mx.img.imdecode(str_image)</span> |
| <span class="sd"> >>> image</span> |
| <span class="sd"> <NDArray 2321x3482x3 @cpu(0)></span> |
| <span class="sd"> >>> new_image = mx.img.resize(image, 240, 360)</span> |
| <span class="sd"> >>> new_image</span> |
| <span class="sd"> <NDArray 240x360x3 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="n">resize_fn</span> <span class="o">=</span> <span class="n">_npi</span><span class="o">.</span><span class="n">cvimresize</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_cvimresize</span> |
| <span class="k">return</span> <span class="n">resize_fn</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="imdecode"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.imdecode">[docs]</a><span class="k">def</span> <span class="nf">imdecode</span><span class="p">(</span><span class="n">buf</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Decode an image to an NDArray.</span> |
| |
| <span class="sd"> .. note:: `imdecode` uses OpenCV (not the CV2 Python library).</span> |
| <span class="sd"> MXNet must have been built with USE_OPENCV=1 for `imdecode` to work.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> buf : str/bytes/bytearray or numpy.ndarray</span> |
| <span class="sd"> Binary image data as string or numpy ndarray.</span> |
| <span class="sd"> flag : int, optional, default=1</span> |
| <span class="sd"> 1 for three channel color output. 0 for grayscale output.</span> |
| <span class="sd"> to_rgb : int, optional, default=1</span> |
| <span class="sd"> 1 for RGB formatted output (MXNet default). 0 for BGR formatted output (OpenCV default).</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> Output buffer. Use `None` for automatic allocation.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An `NDArray` containing the image.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> with open("flower.jpg", 'rb') as fp:</span> |
| <span class="sd"> ... str_image = fp.read()</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> >>> image = mx.img.imdecode(str_image)</span> |
| <span class="sd"> >>> image</span> |
| <span class="sd"> <NDArray 224x224x3 @cpu(0)></span> |
| |
| <span class="sd"> Set `flag` parameter to 0 to get grayscale output</span> |
| |
| <span class="sd"> >>> with open("flower.jpg", 'rb') as fp:</span> |
| <span class="sd"> ... str_image = fp.read()</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> >>> image = mx.img.imdecode(str_image, flag=0)</span> |
| <span class="sd"> >>> image</span> |
| <span class="sd"> <NDArray 224x224x1 @cpu(0)></span> |
| |
| <span class="sd"> Set `to_rgb` parameter to 0 to get output in OpenCV format (BGR)</span> |
| |
| <span class="sd"> >>> with open("flower.jpg", 'rb') as fp:</span> |
| <span class="sd"> ... str_image = fp.read()</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> >>> image = mx.img.imdecode(str_image, to_rgb=0)</span> |
| <span class="sd"> >>> image</span> |
| <span class="sd"> <NDArray 224x224x3 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">buf</span><span class="p">,</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">buf</span><span class="p">,</span> <span class="p">(</span><span class="nb">bytes</span><span class="p">,</span> <span class="nb">bytearray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'buf must be of type bytes, bytearray or numpy.ndarray,'</span> |
| <span class="s1">'if you would like to input type str, please convert to bytes'</span><span class="p">)</span> |
| <span class="n">array_fn</span> <span class="o">=</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">array</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span> |
| <span class="n">buf</span> <span class="o">=</span> <span class="n">array_fn</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">buf</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span> |
| |
| <span class="n">cvimdecode</span> <span class="o">=</span> <span class="n">_npi</span><span class="o">.</span><span class="n">cvimdecode</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_cvimdecode</span> |
| <span class="k">return</span> <span class="n">cvimdecode</span><span class="p">(</span><span class="n">buf</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="scale_down"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.scale_down">[docs]</a><span class="k">def</span> <span class="nf">scale_down</span><span class="p">(</span><span class="n">src_size</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Scales down crop size if it's larger than image size.</span> |
| |
| <span class="sd"> If width/height of the crop is larger than the width/height of the image,</span> |
| <span class="sd"> sets the width/height to the width/height of the image.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> src_size : tuple of int</span> |
| <span class="sd"> Size of the image in (width, height) format.</span> |
| <span class="sd"> size : tuple of int</span> |
| <span class="sd"> Size of the crop in (width, height) format.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> tuple of int</span> |
| <span class="sd"> A tuple containing the scaled crop size in (width, height) format.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> src_size = (640,480)</span> |
| <span class="sd"> >>> size = (720,120)</span> |
| <span class="sd"> >>> new_size = mx.img.scale_down(src_size, size)</span> |
| <span class="sd"> >>> new_size</span> |
| <span class="sd"> (640,106)</span> |
| <span class="sd"> """</span> |
| <span class="n">w</span><span class="p">,</span> <span class="n">h</span> <span class="o">=</span> <span class="n">size</span> |
| <span class="n">sw</span><span class="p">,</span> <span class="n">sh</span> <span class="o">=</span> <span class="n">src_size</span> |
| <span class="k">if</span> <span class="n">sh</span> <span class="o"><</span> <span class="n">h</span><span class="p">:</span> |
| <span class="n">w</span><span class="p">,</span> <span class="n">h</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">w</span> <span class="o">*</span> <span class="n">sh</span><span class="p">)</span> <span class="o">/</span> <span class="n">h</span><span class="p">,</span> <span class="n">sh</span> |
| <span class="k">if</span> <span class="n">sw</span> <span class="o"><</span> <span class="n">w</span><span class="p">:</span> |
| <span class="n">w</span><span class="p">,</span> <span class="n">h</span> <span class="o">=</span> <span class="n">sw</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">h</span> <span class="o">*</span> <span class="n">sw</span><span class="p">)</span> <span class="o">/</span> <span class="n">w</span> |
| <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">w</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">h</span><span class="p">)</span></div> |
| |
| |
| <span class="k">def</span> <span class="nf">copyMakeBorder</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">top</span><span class="p">,</span> <span class="n">bot</span><span class="p">,</span> <span class="n">left</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Pad image border with OpenCV.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> src : NDArray</span> |
| <span class="sd"> source image</span> |
| <span class="sd"> top : int, required</span> |
| <span class="sd"> Top margin.</span> |
| <span class="sd"> bot : int, required</span> |
| <span class="sd"> Bottom margin.</span> |
| <span class="sd"> left : int, required</span> |
| <span class="sd"> Left margin.</span> |
| <span class="sd"> right : int, required</span> |
| <span class="sd"> Right margin.</span> |
| <span class="sd"> type : int, optional, default='0'</span> |
| <span class="sd"> Filling type (default=cv2.BORDER_CONSTANT).</span> |
| <span class="sd"> 0 - cv2.BORDER_CONSTANT - Adds a constant colored border.</span> |
| <span class="sd"> 1 - cv2.BORDER_REFLECT - Border will be mirror reflection of the</span> |
| <span class="sd"> border elements, like this : fedcba|abcdefgh|hgfedcb</span> |
| <span class="sd"> 2 - cv2.BORDER_REFLECT_101 or cv.BORDER_DEFAULT - Same as above,</span> |
| <span class="sd"> but with a slight change, like this : gfedcb|abcdefgh|gfedcba</span> |
| <span class="sd"> 3 - cv2.BORDER_REPLICATE - Last element is replicated throughout,</span> |
| <span class="sd"> like this: aaaaaa|abcdefgh|hhhhhhh</span> |
| <span class="sd"> 4 - cv2.BORDER_WRAP - it will look like this : cdefgh|abcdefgh|abcdefg</span> |
| <span class="sd"> value : double, optional, default=0</span> |
| <span class="sd"> (Deprecated! Use ``values`` instead.) Fill with single value.</span> |
| <span class="sd"> values : tuple of <double>, optional, default=[]</span> |
| <span class="sd"> Fill with value(RGB[A] or gray), up to 4 channels.</span> |
| |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> The output NDArray to hold the result.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> out : NDArray or list of NDArrays</span> |
| <span class="sd"> The output of this function.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> with open("flower.jpeg", 'rb') as fp:</span> |
| <span class="sd"> ... str_image = fp.read()</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> >>> image = mx.img.imdecode(str_image)</span> |
| <span class="sd"> >>> image</span> |
| <span class="sd"> <NDArray 2321x3482x3 @cpu(0)></span> |
| <span class="sd"> >>> new_image = mx_border = mx.image.copyMakeBorder(mx_img, 1, 2, 3, 4, type=0)</span> |
| <span class="sd"> >>> new_image</span> |
| <span class="sd"> <NDArray 2324x3489x3 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_cvcopyMakeBorder</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">top</span><span class="p">,</span> <span class="n">bot</span><span class="p">,</span> <span class="n">left</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_get_interp_method</span><span class="p">(</span><span class="n">interp</span><span class="p">,</span> <span class="n">sizes</span><span class="o">=</span><span class="p">()):</span> |
| <span class="w"> </span><span class="sd">"""Get the interpolation method for resize functions.</span> |
| <span class="sd"> The major purpose of this function is to wrap a random interp method selection</span> |
| <span class="sd"> and a auto-estimation method.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> interp : int</span> |
| <span class="sd"> interpolation method for all resizing operations</span> |
| |
| <span class="sd"> Possible values:</span> |
| <span class="sd"> 0: Nearest Neighbors Interpolation.</span> |
| <span class="sd"> 1: Bilinear interpolation.</span> |
| <span class="sd"> 2: Bicubic interpolation over 4x4 pixel neighborhood.</span> |
| <span class="sd"> 3: Area-based (resampling using pixel area relation). It may be a</span> |
| <span class="sd"> preferred method for image decimation, as it gives moire-free</span> |
| <span class="sd"> results. But when the image is zoomed, it is similar to the Nearest</span> |
| <span class="sd"> Neighbors method. (used by default).</span> |
| <span class="sd"> 4: Lanczos interpolation over 8x8 pixel neighborhood.</span> |
| <span class="sd"> 9: Cubic for enlarge, area for shrink, bilinear for others</span> |
| <span class="sd"> 10: Random select from interpolation method metioned above.</span> |
| <span class="sd"> Note:</span> |
| <span class="sd"> When shrinking an image, it will generally look best with AREA-based</span> |
| <span class="sd"> interpolation, whereas, when enlarging an image, it will generally look best</span> |
| <span class="sd"> with Bicubic (slow) or Bilinear (faster but still looks OK).</span> |
| <span class="sd"> More details can be found in the documentation of OpenCV, please refer to</span> |
| <span class="sd"> http://docs.opencv.org/master/da/d54/group__imgproc__transform.html.</span> |
| <span class="sd"> sizes : tuple of int</span> |
| <span class="sd"> (old_height, old_width, new_height, new_width), if None provided, auto(9)</span> |
| <span class="sd"> will return Area(2) anyway.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> int</span> |
| <span class="sd"> interp method from 0 to 4</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">interp</span> <span class="o">==</span> <span class="mi">9</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">sizes</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">sizes</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span> |
| <span class="n">oh</span><span class="p">,</span> <span class="n">ow</span><span class="p">,</span> <span class="n">nh</span><span class="p">,</span> <span class="n">nw</span> <span class="o">=</span> <span class="n">sizes</span> |
| <span class="k">if</span> <span class="n">nh</span> <span class="o">></span> <span class="n">oh</span> <span class="ow">and</span> <span class="n">nw</span> <span class="o">></span> <span class="n">ow</span><span class="p">:</span> |
| <span class="k">return</span> <span class="mi">2</span> |
| <span class="k">elif</span> <span class="n">nh</span> <span class="o"><</span> <span class="n">oh</span> <span class="ow">and</span> <span class="n">nw</span> <span class="o"><</span> <span class="n">ow</span><span class="p">:</span> |
| <span class="k">return</span> <span class="mi">3</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="mi">1</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="mi">2</span> |
| <span class="k">if</span> <span class="n">interp</span> <span class="o">==</span> <span class="mi">10</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">interp</span> <span class="ow">not</span> <span class="ow">in</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">2</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="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Unknown interp method </span><span class="si">{</span><span class="n">interp</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">interp</span> |
| |
| |
| <div class="viewcode-block" id="resize_short"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.resize_short">[docs]</a><span class="k">def</span> <span class="nf">resize_short</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Resizes shorter edge to size.</span> |
| |
| <span class="sd"> .. note:: `resize_short` uses OpenCV (not the CV2 Python library).</span> |
| <span class="sd"> MXNet must have been built with OpenCV for `resize_short` to work.</span> |
| |
| <span class="sd"> Resizes the original image by setting the shorter edge to size</span> |
| <span class="sd"> and setting the longer edge accordingly.</span> |
| <span class="sd"> Resizing function is called from OpenCV.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> src : NDArray</span> |
| <span class="sd"> The original image.</span> |
| <span class="sd"> size : int</span> |
| <span class="sd"> The length to be set for the shorter edge.</span> |
| <span class="sd"> interp : int, optional, default=2</span> |
| <span class="sd"> Interpolation method used for resizing the image.</span> |
| <span class="sd"> Possible values:</span> |
| <span class="sd"> 0: Nearest Neighbors Interpolation.</span> |
| <span class="sd"> 1: Bilinear interpolation.</span> |
| <span class="sd"> 2: Bicubic interpolation over 4x4 pixel neighborhood.</span> |
| <span class="sd"> 3: Area-based (resampling using pixel area relation). It may be a</span> |
| <span class="sd"> preferred method for image decimation, as it gives moire-free</span> |
| <span class="sd"> results. But when the image is zoomed, it is similar to the Nearest</span> |
| <span class="sd"> Neighbors method. (used by default).</span> |
| <span class="sd"> 4: Lanczos interpolation over 8x8 pixel neighborhood.</span> |
| <span class="sd"> 9: Cubic for enlarge, area for shrink, bilinear for others</span> |
| <span class="sd"> 10: Random select from interpolation method metioned above.</span> |
| <span class="sd"> Note:</span> |
| <span class="sd"> When shrinking an image, it will generally look best with AREA-based</span> |
| <span class="sd"> interpolation, whereas, when enlarging an image, it will generally look best</span> |
| <span class="sd"> with Bicubic (slow) or Bilinear (faster but still looks OK).</span> |
| <span class="sd"> More details can be found in the documentation of OpenCV, please refer to</span> |
| <span class="sd"> http://docs.opencv.org/master/da/d54/group__imgproc__transform.html.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An 'NDArray' containing the resized image.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> with open("flower.jpeg", 'rb') as fp:</span> |
| <span class="sd"> ... str_image = fp.read()</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> >>> image = mx.img.imdecode(str_image)</span> |
| <span class="sd"> >>> image</span> |
| <span class="sd"> <NDArray 2321x3482x3 @cpu(0)></span> |
| <span class="sd"> >>> size = 640</span> |
| <span class="sd"> >>> new_image = mx.img.resize_short(image, size)</span> |
| <span class="sd"> >>> new_image</span> |
| <span class="sd"> <NDArray 2321x3482x3 @cpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">src</span><span class="o">.</span><span class="n">shape</span> |
| <span class="k">if</span> <span class="n">h</span> <span class="o">></span> <span class="n">w</span><span class="p">:</span> |
| <span class="n">new_h</span><span class="p">,</span> <span class="n">new_w</span> <span class="o">=</span> <span class="n">size</span> <span class="o">*</span> <span class="n">h</span> <span class="o">//</span> <span class="n">w</span><span class="p">,</span> <span class="n">size</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">new_h</span><span class="p">,</span> <span class="n">new_w</span> <span class="o">=</span> <span class="n">size</span><span class="p">,</span> <span class="n">size</span> <span class="o">*</span> <span class="n">w</span> <span class="o">//</span> <span class="n">h</span> |
| <span class="k">return</span> <span class="n">imresize</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">new_w</span><span class="p">,</span> <span class="n">new_h</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="n">_get_interp_method</span><span class="p">(</span><span class="n">interp</span><span class="p">,</span> <span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">new_h</span><span class="p">,</span> <span class="n">new_w</span><span class="p">)))</span></div> |
| |
| |
| <div class="viewcode-block" id="fixed_crop"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.fixed_crop">[docs]</a><span class="k">def</span> <span class="nf">fixed_crop</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Crop src at fixed location, and (optionally) resize it to size.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> src : NDArray</span> |
| <span class="sd"> Input image</span> |
| <span class="sd"> x0 : int</span> |
| <span class="sd"> Left boundary of the cropping area</span> |
| <span class="sd"> y0 : int</span> |
| <span class="sd"> Top boundary of the cropping area</span> |
| <span class="sd"> w : int</span> |
| <span class="sd"> Width of the cropping area</span> |
| <span class="sd"> h : int</span> |
| <span class="sd"> Height of the cropping area</span> |
| <span class="sd"> size : tuple of (w, h)</span> |
| <span class="sd"> Optional, resize to new size after cropping</span> |
| <span class="sd"> interp : int, optional, default=2</span> |
| <span class="sd"> Interpolation method. See resize_short for details.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An `NDArray` containing the cropped image.</span> |
| <span class="sd"> """</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">src</span><span class="p">[</span><span class="n">y0</span><span class="p">:</span><span class="n">y0</span><span class="o">+</span><span class="n">h</span><span class="p">,</span> <span class="n">x0</span><span class="p">:</span><span class="n">x0</span><span class="o">+</span><span class="n">w</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">)</span> <span class="o">!=</span> <span class="n">size</span><span class="p">:</span> |
| <span class="n">sizes</span> <span class="o">=</span> <span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">imresize</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="o">*</span><span class="n">size</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="n">_get_interp_method</span><span class="p">(</span><span class="n">interp</span><span class="p">,</span> <span class="n">sizes</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">out</span></div> |
| |
| |
| <div class="viewcode-block" id="random_crop"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.random_crop">[docs]</a><span class="k">def</span> <span class="nf">random_crop</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Randomly crop `src` with `size` (width, height).</span> |
| <span class="sd"> Upsample result if `src` is smaller than `size`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> src: Source image `NDArray`</span> |
| <span class="sd"> size: Size of the crop formatted as (width, height). If the `size` is larger</span> |
| <span class="sd"> than the image, then the source image is upsampled to `size` and returned.</span> |
| <span class="sd"> interp: int, optional, default=2</span> |
| <span class="sd"> Interpolation method. See resize_short for details.</span> |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An `NDArray` containing the cropped image.</span> |
| <span class="sd"> Tuple</span> |
| <span class="sd"> A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the</span> |
| <span class="sd"> original image and (width, height) are the dimensions of the cropped image.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> im = mx.nd.array(cv2.imread("flower.jpg"))</span> |
| <span class="sd"> >>> cropped_im, rect = mx.image.random_crop(im, (100, 100))</span> |
| <span class="sd"> >>> print cropped_im</span> |
| <span class="sd"> <NDArray 100x100x1 @cpu(0)></span> |
| <span class="sd"> >>> print rect</span> |
| <span class="sd"> (20, 21, 100, 100)</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">src</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">new_w</span><span class="p">,</span> <span class="n">new_h</span> <span class="o">=</span> <span class="n">scale_down</span><span class="p">((</span><span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span> <span class="n">size</span><span class="p">)</span> |
| |
| <span class="n">x0</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">w</span> <span class="o">-</span> <span class="n">new_w</span><span class="p">)</span> |
| <span class="n">y0</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">h</span> <span class="o">-</span> <span class="n">new_h</span><span class="p">)</span> |
| |
| <span class="n">out</span> <span class="o">=</span> <span class="n">fixed_crop</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">,</span> <span class="n">new_w</span><span class="p">,</span> <span class="n">new_h</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">interp</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">out</span><span class="p">,</span> <span class="p">(</span><span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">,</span> <span class="n">new_w</span><span class="p">,</span> <span class="n">new_h</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="center_crop"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.center_crop">[docs]</a><span class="k">def</span> <span class="nf">center_crop</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Crops the image `src` to the given `size` by trimming on all four</span> |
| <span class="sd"> sides and preserving the center of the image. Upsamples if `src` is smaller</span> |
| <span class="sd"> than `size`.</span> |
| |
| <span class="sd"> .. note:: This requires MXNet to be compiled with USE_OPENCV.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> src : NDArray</span> |
| <span class="sd"> Binary source image data.</span> |
| <span class="sd"> size : list or tuple of int</span> |
| <span class="sd"> The desired output image size.</span> |
| <span class="sd"> interp : int, optional, default=2</span> |
| <span class="sd"> Interpolation method. See resize_short for details.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The cropped image.</span> |
| <span class="sd"> Tuple</span> |
| <span class="sd"> (x, y, width, height) where x, y are the positions of the crop in the</span> |
| <span class="sd"> original image and width, height the dimensions of the crop.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> with open("flower.jpg", 'rb') as fp:</span> |
| <span class="sd"> ... str_image = fp.read()</span> |
| <span class="sd"> ...</span> |
| <span class="sd"> >>> image = mx.image.imdecode(str_image)</span> |
| <span class="sd"> >>> image</span> |
| <span class="sd"> <NDArray 2321x3482x3 @cpu(0)></span> |
| <span class="sd"> >>> cropped_image, (x, y, width, height) = mx.image.center_crop(image, (1000, 500))</span> |
| <span class="sd"> >>> cropped_image</span> |
| <span class="sd"> <NDArray 500x1000x3 @cpu(0)></span> |
| <span class="sd"> >>> x, y, width, height</span> |
| <span class="sd"> (1241, 910, 1000, 500)</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">src</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">new_w</span><span class="p">,</span> <span class="n">new_h</span> <span class="o">=</span> <span class="n">scale_down</span><span class="p">((</span><span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span> <span class="n">size</span><span class="p">)</span> |
| |
| <span class="n">x0</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">w</span> <span class="o">-</span> <span class="n">new_w</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="n">y0</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">h</span> <span class="o">-</span> <span class="n">new_h</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> |
| |
| <span class="n">out</span> <span class="o">=</span> <span class="n">fixed_crop</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">,</span> <span class="n">new_w</span><span class="p">,</span> <span class="n">new_h</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">interp</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">out</span><span class="p">,</span> <span class="p">(</span><span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">,</span> <span class="n">new_w</span><span class="p">,</span> <span class="n">new_h</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="color_normalize"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.color_normalize">[docs]</a><span class="k">def</span> <span class="nf">color_normalize</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Normalize src with mean and std.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> src : NDArray</span> |
| <span class="sd"> Input image</span> |
| <span class="sd"> mean : NDArray</span> |
| <span class="sd"> RGB mean to be subtracted</span> |
| <span class="sd"> std : NDArray</span> |
| <span class="sd"> RGB standard deviation to be divided</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An `NDArray` containing the normalized image.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">mean</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">src</span> <span class="o">-=</span> <span class="n">mean</span> |
| <span class="k">if</span> <span class="n">std</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">src</span> <span class="o">/=</span> <span class="n">std</span> |
| <span class="k">return</span> <span class="n">src</span></div> |
| |
| |
| <div class="viewcode-block" id="random_size_crop"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.random_size_crop">[docs]</a><span class="k">def</span> <span class="nf">random_size_crop</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">area</span><span class="p">,</span> <span class="n">ratio</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Randomly crop src with size. Randomize area and aspect ratio.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> src : NDArray</span> |
| <span class="sd"> Input image</span> |
| <span class="sd"> size : tuple of (int, int)</span> |
| <span class="sd"> Size of the crop formatted as (width, height).</span> |
| <span class="sd"> area : float in (0, 1] or tuple of (float, float)</span> |
| <span class="sd"> If tuple, minimum area and maximum area to be maintained after cropping</span> |
| <span class="sd"> If float, minimum area to be maintained after cropping, maximum area is set to 1.0</span> |
| <span class="sd"> ratio : tuple of (float, float)</span> |
| <span class="sd"> Aspect ratio range as (min_aspect_ratio, max_aspect_ratio)</span> |
| <span class="sd"> interp: int, optional, default=2</span> |
| <span class="sd"> Interpolation method. See resize_short for details.</span> |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An `NDArray` containing the cropped image.</span> |
| <span class="sd"> Tuple</span> |
| <span class="sd"> A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the</span> |
| <span class="sd"> original image and (width, height) are the dimensions of the cropped image.</span> |
| |
| <span class="sd"> """</span> |
| <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">src</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">src_area</span> <span class="o">=</span> <span class="n">h</span> <span class="o">*</span> <span class="n">w</span> |
| |
| <span class="k">if</span> <span class="s1">'min_area'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'`min_area` is deprecated. Please use `area` instead.'</span><span class="p">,</span> |
| <span class="ne">DeprecationWarning</span><span class="p">)</span> |
| <span class="n">area</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'min_area'</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="p">,</span> <span class="s2">"unexpected keyword arguments for `random_size_crop`."</span> |
| |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">area</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">area</span> <span class="o">=</span> <span class="p">(</span><span class="n">area</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span> |
| <span class="n">target_area</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">area</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">area</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="n">src_area</span> |
| <span class="n">log_ratio</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">ratio</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">ratio</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span> |
| <span class="n">new_ratio</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">*</span><span class="n">log_ratio</span><span class="p">))</span> |
| |
| <span class="n">new_w</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">target_area</span> <span class="o">*</span> <span class="n">new_ratio</span><span class="p">)))</span> |
| <span class="n">new_h</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">target_area</span> <span class="o">/</span> <span class="n">new_ratio</span><span class="p">)))</span> |
| |
| <span class="k">if</span> <span class="n">new_w</span> <span class="o"><=</span> <span class="n">w</span> <span class="ow">and</span> <span class="n">new_h</span> <span class="o"><=</span> <span class="n">h</span><span class="p">:</span> |
| <span class="n">x0</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">w</span> <span class="o">-</span> <span class="n">new_w</span><span class="p">)</span> |
| <span class="n">y0</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">h</span> <span class="o">-</span> <span class="n">new_h</span><span class="p">)</span> |
| |
| <span class="n">out</span> <span class="o">=</span> <span class="n">fixed_crop</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">,</span> <span class="n">new_w</span><span class="p">,</span> <span class="n">new_h</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">interp</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">out</span><span class="p">,</span> <span class="p">(</span><span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">,</span> <span class="n">new_w</span><span class="p">,</span> <span class="n">new_h</span><span class="p">)</span> |
| |
| <span class="c1"># fall back to center_crop</span> |
| <span class="k">return</span> <span class="n">center_crop</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">interp</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="imrotate"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.imrotate">[docs]</a><span class="k">def</span> <span class="nf">imrotate</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">rotation_degrees</span><span class="p">,</span> <span class="n">zoom_in</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">zoom_out</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Rotates the input image(s) of a specific rotation degree.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> src : NDArray</span> |
| <span class="sd"> Input image (format CHW) or batch of images (format NCHW),</span> |
| <span class="sd"> in both case is required a float32 data type.</span> |
| <span class="sd"> rotation_degrees: scalar or NDArray</span> |
| <span class="sd"> Wanted rotation in degrees. In case of `src` being a single image</span> |
| <span class="sd"> a scalar is needed, otherwise a mono-dimensional vector of angles</span> |
| <span class="sd"> or a scalar.</span> |
| <span class="sd"> zoom_in: bool</span> |
| <span class="sd"> If True input image(s) will be zoomed in a way so that no padding</span> |
| <span class="sd"> will be shown in the output result.</span> |
| <span class="sd"> zoom_out: bool</span> |
| <span class="sd"> If True input image(s) will be zoomed in a way so that the whole</span> |
| <span class="sd"> original image will be contained in the output result.</span> |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An `NDArray` containing the rotated image(s).</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">zoom_in</span> <span class="ow">and</span> <span class="n">zoom_out</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"`zoom_in` and `zoom_out` cannot be both True"</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">src</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"Only `float32` images are supported by this function"</span><span class="p">)</span> |
| <span class="c1"># handles the case in which a single image is passed to this function</span> |
| <span class="n">expanded</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">if</span> <span class="n">src</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span> |
| <span class="n">expanded</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="n">src</span> <span class="o">=</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">src</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rotation_degrees</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">"When a single image is passed the rotation angle is "</span> |
| <span class="s2">"required to be a scalar."</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">src</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">4</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Only 3D and 4D are supported by this function"</span><span class="p">)</span> |
| |
| <span class="c1"># when a scalar is passed we wrap it into an array</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rotation_degrees</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span> |
| <span class="n">rotation_degrees</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">rotation_degrees</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">src</span><span class="p">),</span> |
| <span class="n">ctx</span><span class="o">=</span><span class="n">src</span><span class="o">.</span><span class="n">ctx</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">src</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">rotation_degrees</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> |
| <span class="s2">"The number of images must be equal to the number of rotation angles"</span> |
| <span class="p">)</span> |
| |
| <span class="n">rotation_degrees</span> <span class="o">=</span> <span class="n">rotation_degrees</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">src</span><span class="o">.</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="n">rotation_rad</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">rotation_degrees</span> <span class="o">/</span> <span class="mi">180</span> |
| <span class="c1"># reshape the rotations angle in order to be broadcasted</span> |
| <span class="c1"># over the `src` tensor</span> |
| <span class="n">rotation_rad</span> <span class="o">=</span> <span class="n">rotation_rad</span><span class="o">.</span><span class="n">expand_dims</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="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> |
| <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">src</span><span class="o">.</span><span class="n">shape</span> |
| |
| <span class="c1"># Generate a grid centered at the center of the image</span> |
| <span class="n">hscale</span> <span class="o">=</span> <span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">h</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="n">wscale</span> <span class="o">=</span> <span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">w</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="n">h_matrix</span> <span class="o">=</span> <span class="p">(</span> |
| <span class="n">nd</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">src</span><span class="o">.</span><span class="n">ctx</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float32'</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">w</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="o">-</span> <span class="n">hscale</span> |
| <span class="p">)</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="n">w_matrix</span> <span class="o">=</span> <span class="p">(</span> |
| <span class="n">nd</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">src</span><span class="o">.</span><span class="n">ctx</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float32'</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">w</span><span class="p">),</span> <span class="n">h</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-</span> <span class="n">wscale</span> |
| <span class="p">)</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="c1"># perform rotation on the grid</span> |
| <span class="n">c_alpha</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">rotation_rad</span><span class="p">)</span> |
| <span class="n">s_alpha</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">rotation_rad</span><span class="p">)</span> |
| <span class="n">w_matrix_rot</span> <span class="o">=</span> <span class="n">w_matrix</span> <span class="o">*</span> <span class="n">c_alpha</span> <span class="o">-</span> <span class="n">h_matrix</span> <span class="o">*</span> <span class="n">s_alpha</span> |
| <span class="n">h_matrix_rot</span> <span class="o">=</span> <span class="n">w_matrix</span> <span class="o">*</span> <span class="n">s_alpha</span> <span class="o">+</span> <span class="n">h_matrix</span> <span class="o">*</span> <span class="n">c_alpha</span> |
| <span class="c1"># NOTE: grid normalization must be performed after the rotation</span> |
| <span class="c1"># to keep the aspec ratio</span> |
| <span class="n">w_matrix_rot</span> <span class="o">=</span> <span class="n">w_matrix_rot</span> <span class="o">/</span> <span class="n">wscale</span> |
| <span class="n">h_matrix_rot</span> <span class="o">=</span> <span class="n">h_matrix_rot</span> <span class="o">/</span> <span class="n">hscale</span> |
| |
| <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">h</span><span class="p">],</span> <span class="n">ctx</span><span class="o">=</span><span class="n">src</span><span class="o">.</span><span class="n">ctx</span><span class="p">),</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">w</span><span class="p">],</span> <span class="n">ctx</span><span class="o">=</span><span class="n">src</span><span class="o">.</span><span class="n">ctx</span><span class="p">)</span> |
| <span class="c1"># compute the scale factor in case `zoom_in` or `zoom_out` are True</span> |
| <span class="k">if</span> <span class="n">zoom_in</span> <span class="ow">or</span> <span class="n">zoom_out</span><span class="p">:</span> |
| <span class="n">rho_corner</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">h</span> <span class="o">*</span> <span class="n">h</span> <span class="o">+</span> <span class="n">w</span> <span class="o">*</span> <span class="n">w</span><span class="p">)</span> |
| <span class="n">ang_corner</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">arctan</span><span class="p">(</span><span class="n">h</span> <span class="o">/</span> <span class="n">w</span><span class="p">)</span> |
| <span class="n">corner1_x_pos</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">rho_corner</span> <span class="o">*</span> <span class="n">nd</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">ang_corner</span> <span class="o">+</span> <span class="n">nd</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">rotation_rad</span><span class="p">)))</span> |
| <span class="n">corner1_y_pos</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">rho_corner</span> <span class="o">*</span> <span class="n">nd</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">ang_corner</span> <span class="o">+</span> <span class="n">nd</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">rotation_rad</span><span class="p">)))</span> |
| <span class="n">corner2_x_pos</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">rho_corner</span> <span class="o">*</span> <span class="n">nd</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">ang_corner</span> <span class="o">-</span> <span class="n">nd</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">rotation_rad</span><span class="p">)))</span> |
| <span class="n">corner2_y_pos</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">rho_corner</span> <span class="o">*</span> <span class="n">nd</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">ang_corner</span> <span class="o">-</span> <span class="n">nd</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">rotation_rad</span><span class="p">)))</span> |
| <span class="n">max_x</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">corner1_x_pos</span><span class="p">,</span> <span class="n">corner2_x_pos</span><span class="p">)</span> |
| <span class="n">max_y</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">corner1_y_pos</span><span class="p">,</span> <span class="n">corner2_y_pos</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">zoom_out</span><span class="p">:</span> |
| <span class="n">scale_x</span> <span class="o">=</span> <span class="n">max_x</span> <span class="o">/</span> <span class="n">w</span> |
| <span class="n">scale_y</span> <span class="o">=</span> <span class="n">max_y</span> <span class="o">/</span> <span class="n">h</span> |
| <span class="n">globalscale</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">scale_x</span><span class="p">,</span> <span class="n">scale_y</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">scale_x</span> <span class="o">=</span> <span class="n">w</span> <span class="o">/</span> <span class="n">max_x</span> |
| <span class="n">scale_y</span> <span class="o">=</span> <span class="n">h</span> <span class="o">/</span> <span class="n">max_y</span> |
| <span class="n">globalscale</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">scale_x</span><span class="p">,</span> <span class="n">scale_y</span><span class="p">)</span> |
| <span class="n">globalscale</span> <span class="o">=</span> <span class="n">globalscale</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">globalscale</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="n">grid</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">w_matrix_rot</span><span class="o">.</span><span class="n">expand_dims</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">h_matrix_rot</span><span class="o">.</span><span class="n">expand_dims</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">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="n">grid</span> <span class="o">=</span> <span class="n">grid</span> <span class="o">*</span> <span class="n">globalscale</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="n">src</span> <span class="o">=</span> <span class="n">src</span><span class="o">.</span><span class="n">as_nd_ndarray</span><span class="p">()</span> |
| <span class="n">rot_img</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">BilinearSampler</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">grid</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="n">rot_img</span> <span class="o">=</span> <span class="n">rot_img</span><span class="o">.</span><span class="n">as_np_ndarray</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">expanded</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">rot_img</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">rot_img</span></div> |
| |
| |
| <div class="viewcode-block" id="random_rotate"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.random_rotate">[docs]</a><span class="k">def</span> <span class="nf">random_rotate</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">angle_limits</span><span class="p">,</span> <span class="n">zoom_in</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">zoom_out</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Random rotates `src` by an angle included in angle limits.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> src : NDArray</span> |
| <span class="sd"> Input image (format CHW) or batch of images (format NCHW),</span> |
| <span class="sd"> in both case is required a float32 data type.</span> |
| <span class="sd"> angle_limits: tuple</span> |
| <span class="sd"> Tuple of 2 elements containing the upper and lower limit</span> |
| <span class="sd"> for rotation angles in degree.</span> |
| <span class="sd"> zoom_in: bool</span> |
| <span class="sd"> If True input image(s) will be zoomed in a way so that no padding</span> |
| <span class="sd"> will be shown in the output result.</span> |
| <span class="sd"> zoom_out: bool</span> |
| <span class="sd"> If True input image(s) will be zoomed in a way so that the whole</span> |
| <span class="sd"> original image will be contained in the output result.</span> |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An `NDArray` containing the rotated image(s).</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">src</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span> |
| <span class="n">rotation_degrees</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">*</span><span class="n">angle_limits</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">n</span> <span class="o">=</span> <span class="n">src</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">rotation_degrees</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span> |
| <span class="o">*</span><span class="n">angle_limits</span><span class="p">,</span> |
| <span class="n">size</span><span class="o">=</span><span class="n">n</span> |
| <span class="p">))</span> |
| <span class="k">return</span> <span class="n">imrotate</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">rotation_degrees</span><span class="p">,</span> |
| <span class="n">zoom_in</span><span class="o">=</span><span class="n">zoom_in</span><span class="p">,</span> <span class="n">zoom_out</span><span class="o">=</span><span class="n">zoom_out</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="Augmenter"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.Augmenter">[docs]</a><span class="k">class</span> <span class="nc">Augmenter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Image Augmenter base class"""</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="o">**</span><span class="n">kwargs</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="n">kwargs</span> |
| <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">v</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span> |
| <span class="n">v</span> <span class="o">=</span> <span class="n">v</span><span class="o">.</span><span class="n">tolist</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="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span> |
| |
| <div class="viewcode-block" id="Augmenter.dumps"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.Augmenter.dumps">[docs]</a> <span class="k">def</span> <span class="nf">dumps</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Saves the Augmenter to string</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> str</span> |
| <span class="sd"> JSON formatted string that describes the Augmenter.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</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="o">.</span><span class="n">lower</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">])</span></div> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Abstract implementation body"""</span> |
| <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"Must override implementation."</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="SequentialAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.SequentialAug">[docs]</a><span class="k">class</span> <span class="nc">SequentialAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Composing a sequential augmenter list.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> ts : list of augmenters</span> |
| <span class="sd"> A series of augmenters to be applied in sequential order.</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">ts</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">SequentialAug</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">ts</span> <span class="o">=</span> <span class="n">ts</span> |
| |
| <div class="viewcode-block" id="SequentialAug.dumps"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.SequentialAug.dumps">[docs]</a> <span class="k">def</span> <span class="nf">dumps</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Override the default to avoid duplicate dump."""</span> |
| <span class="k">return</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="o">.</span><span class="n">lower</span><span class="p">(),</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">dumps</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ts</span><span class="p">]]</span></div> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="k">for</span> <span class="n">aug</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ts</span><span class="p">:</span> |
| <span class="n">src</span> <span class="o">=</span> <span class="n">aug</span><span class="p">(</span><span class="n">src</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">src</span></div> |
| |
| |
| <div class="viewcode-block" id="ResizeAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ResizeAug">[docs]</a><span class="k">class</span> <span class="nc">ResizeAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Make resize shorter edge to size augmenter.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> size : int</span> |
| <span class="sd"> The length to be set for the shorter edge.</span> |
| <span class="sd"> interp : int, optional, default=2</span> |
| <span class="sd"> Interpolation method. See resize_short for details.</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">size</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">ResizeAug</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">size</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="n">interp</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="n">size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">interp</span> <span class="o">=</span> <span class="n">interp</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="k">return</span> <span class="n">resize_short</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">interp</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="ForceResizeAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ForceResizeAug">[docs]</a><span class="k">class</span> <span class="nc">ForceResizeAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Force resize to size regardless of aspect ratio</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> size : tuple of (int, int)</span> |
| <span class="sd"> The desired size as in (width, height)</span> |
| <span class="sd"> interp : int, optional, default=2</span> |
| <span class="sd"> Interpolation method. See resize_short for details.</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">size</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">ForceResizeAug</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">size</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="n">interp</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="n">size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">interp</span> <span class="o">=</span> <span class="n">interp</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="n">sizes</span> <span class="o">=</span> <span class="p">(</span><span class="n">src</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">src</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">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">size</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="k">return</span> <span class="n">imresize</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="n">_get_interp_method</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interp</span><span class="p">,</span> <span class="n">sizes</span><span class="p">))</span></div> |
| |
| |
| <div class="viewcode-block" id="RandomCropAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.RandomCropAug">[docs]</a><span class="k">class</span> <span class="nc">RandomCropAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Make random crop augmenter</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> size : int</span> |
| <span class="sd"> The length to be set for the shorter edge.</span> |
| <span class="sd"> interp : int, optional, default=2</span> |
| <span class="sd"> Interpolation method. See resize_short for details.</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">size</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">RandomCropAug</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">size</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="n">interp</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="n">size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">interp</span> <span class="o">=</span> <span class="n">interp</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="k">return</span> <span class="n">random_crop</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">interp</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span></div> |
| |
| |
| <div class="viewcode-block" id="RandomSizedCropAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.RandomSizedCropAug">[docs]</a><span class="k">class</span> <span class="nc">RandomSizedCropAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Make random crop with random resizing and random aspect ratio jitter augmenter.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> size : tuple of (int, int)</span> |
| <span class="sd"> Size of the crop formatted as (width, height).</span> |
| <span class="sd"> area : float in (0, 1] or tuple of (float, float)</span> |
| <span class="sd"> If tuple, minimum area and maximum area to be maintained after cropping</span> |
| <span class="sd"> If float, minimum area to be maintained after cropping, maximum area is set to 1.0</span> |
| <span class="sd"> ratio : tuple of (float, float)</span> |
| <span class="sd"> Aspect ratio range as (min_aspect_ratio, max_aspect_ratio)</span> |
| <span class="sd"> interp: int, optional, default=2</span> |
| <span class="sd"> Interpolation method. See resize_short for details.</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">size</span><span class="p">,</span> <span class="n">area</span><span class="p">,</span> <span class="n">ratio</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="mi">2</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">RandomSizedCropAug</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">size</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">area</span><span class="o">=</span><span class="n">area</span><span class="p">,</span> |
| <span class="n">ratio</span><span class="o">=</span><span class="n">ratio</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="n">interp</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="n">size</span> |
| <span class="k">if</span> <span class="s1">'min_area'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'`min_area` is deprecated. Please use `area` instead.'</span><span class="p">,</span> |
| <span class="ne">DeprecationWarning</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">area</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'min_area'</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">area</span> <span class="o">=</span> <span class="n">area</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">ratio</span> <span class="o">=</span> <span class="n">ratio</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">interp</span> <span class="o">=</span> <span class="n">interp</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="p">,</span> <span class="s2">"unexpected keyword arguments for `RandomSizedCropAug`."</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="k">return</span> <span class="n">random_size_crop</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">area</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ratio</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">interp</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span></div> |
| |
| |
| <div class="viewcode-block" id="CenterCropAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.CenterCropAug">[docs]</a><span class="k">class</span> <span class="nc">CenterCropAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Make center crop augmenter.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> size : list or tuple of int</span> |
| <span class="sd"> The desired output image size.</span> |
| <span class="sd"> interp : int, optional, default=2</span> |
| <span class="sd"> Interpolation method. See resize_short for details.</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">size</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">CenterCropAug</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">size</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">interp</span><span class="o">=</span><span class="n">interp</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="n">size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">interp</span> <span class="o">=</span> <span class="n">interp</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="k">return</span> <span class="n">center_crop</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">interp</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span></div> |
| |
| |
| <div class="viewcode-block" id="RandomOrderAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.RandomOrderAug">[docs]</a><span class="k">class</span> <span class="nc">RandomOrderAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Apply list of augmenters in random order</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> ts : list of augmenters</span> |
| <span class="sd"> A series of augmenters to be applied in random order</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">ts</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">RandomOrderAug</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">ts</span> <span class="o">=</span> <span class="n">ts</span> |
| |
| <div class="viewcode-block" id="RandomOrderAug.dumps"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.RandomOrderAug.dumps">[docs]</a> <span class="k">def</span> <span class="nf">dumps</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Override the default to avoid duplicate dump."""</span> |
| <span class="k">return</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="o">.</span><span class="n">lower</span><span class="p">(),</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">dumps</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ts</span><span class="p">]]</span></div> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ts</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ts</span><span class="p">:</span> |
| <span class="n">src</span> <span class="o">=</span> <span class="n">t</span><span class="p">(</span><span class="n">src</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">src</span></div> |
| |
| |
| <div class="viewcode-block" id="BrightnessJitterAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.BrightnessJitterAug">[docs]</a><span class="k">class</span> <span class="nc">BrightnessJitterAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Random brightness jitter augmentation.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> brightness : float</span> |
| <span class="sd"> The brightness jitter ratio range, [0, 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">brightness</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">BrightnessJitterAug</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">brightness</span><span class="o">=</span><span class="n">brightness</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">brightness</span> <span class="o">=</span> <span class="n">brightness</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="n">alpha</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">+</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">brightness</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">brightness</span><span class="p">)</span> |
| <span class="n">src</span> <span class="o">*=</span> <span class="n">alpha</span> |
| <span class="k">return</span> <span class="n">src</span></div> |
| |
| |
| <div class="viewcode-block" id="ContrastJitterAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ContrastJitterAug">[docs]</a><span class="k">class</span> <span class="nc">ContrastJitterAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Random contrast jitter augmentation.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> contrast : float</span> |
| <span class="sd"> The contrast jitter ratio range, [0, 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">contrast</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">ContrastJitterAug</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">contrast</span><span class="o">=</span><span class="n">contrast</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">contrast</span> <span class="o">=</span> <span class="n">contrast</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">coef</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[[</span><span class="mf">0.299</span><span class="p">,</span> <span class="mf">0.587</span><span class="p">,</span> <span class="mf">0.114</span><span class="p">]]])</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="n">alpha</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">+</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">contrast</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">contrast</span><span class="p">)</span> |
| <span class="n">gray</span> <span class="o">=</span> <span class="n">src</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">coef</span> |
| <span class="n">gray</span> <span class="o">=</span> <span class="p">(</span><span class="mf">3.0</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">/</span> <span class="n">gray</span><span class="o">.</span><span class="n">size</span><span class="p">)</span> <span class="o">*</span> <span class="n">nd</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span> |
| <span class="n">src</span> <span class="o">*=</span> <span class="n">alpha</span> |
| <span class="n">src</span> <span class="o">+=</span> <span class="n">gray</span> |
| <span class="k">return</span> <span class="n">src</span></div> |
| |
| |
| <div class="viewcode-block" id="SaturationJitterAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.SaturationJitterAug">[docs]</a><span class="k">class</span> <span class="nc">SaturationJitterAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Random saturation jitter augmentation.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> saturation : float</span> |
| <span class="sd"> The saturation jitter ratio range, [0, 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">saturation</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">SaturationJitterAug</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">saturation</span><span class="o">=</span><span class="n">saturation</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">saturation</span> <span class="o">=</span> <span class="n">saturation</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">coef</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[[</span><span class="mf">0.299</span><span class="p">,</span> <span class="mf">0.587</span><span class="p">,</span> <span class="mf">0.114</span><span class="p">]]])</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="n">alpha</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">+</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">saturation</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">saturation</span><span class="p">)</span> |
| <span class="n">gray</span> <span class="o">=</span> <span class="n">src</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">coef</span> |
| <span class="n">gray</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">gray</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| <span class="n">gray</span> <span class="o">*=</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> |
| <span class="n">src</span> <span class="o">*=</span> <span class="n">alpha</span> |
| <span class="n">src</span> <span class="o">+=</span> <span class="n">gray</span> |
| <span class="k">return</span> <span class="n">src</span></div> |
| |
| |
| <div class="viewcode-block" id="HueJitterAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.HueJitterAug">[docs]</a><span class="k">class</span> <span class="nc">HueJitterAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Random hue jitter augmentation.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> hue : float</span> |
| <span class="sd"> The hue jitter ratio range, [0, 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">hue</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">HueJitterAug</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">hue</span><span class="o">=</span><span class="n">hue</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">hue</span> <span class="o">=</span> <span class="n">hue</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">tyiq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.299</span><span class="p">,</span> <span class="mf">0.587</span><span class="p">,</span> <span class="mf">0.114</span><span class="p">],</span> |
| <span class="p">[</span><span class="mf">0.596</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.274</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.321</span><span class="p">],</span> |
| <span class="p">[</span><span class="mf">0.211</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.523</span><span class="p">,</span> <span class="mf">0.311</span><span class="p">]])</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">ityiq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.956</span><span class="p">,</span> <span class="mf">0.621</span><span class="p">],</span> |
| <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.272</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.647</span><span class="p">],</span> |
| <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.107</span><span class="p">,</span> <span class="mf">1.705</span><span class="p">]])</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body.</span> |
| <span class="sd"> Using approximate linear transfomation described in:</span> |
| <span class="sd"> https://beesbuzz.biz/code/hsv_color_transforms.php</span> |
| <span class="sd"> """</span> |
| <span class="n">alpha</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">hue</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hue</span><span class="p">)</span> |
| <span class="n">u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> |
| <span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> |
| <span class="n">bt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> |
| <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="o">-</span><span class="n">w</span><span class="p">],</span> |
| <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">u</span><span class="p">]])</span> |
| <span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ityiq</span><span class="p">,</span> <span class="n">bt</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">tyiq</span><span class="p">)</span><span class="o">.</span><span class="n">T</span> |
| <span class="n">src</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">t</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">src</span></div> |
| |
| |
| <div class="viewcode-block" id="ColorJitterAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ColorJitterAug">[docs]</a><span class="k">class</span> <span class="nc">ColorJitterAug</span><span class="p">(</span><span class="n">RandomOrderAug</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Apply random brightness, contrast and saturation jitter in random order.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> brightness : float</span> |
| <span class="sd"> The brightness jitter ratio range, [0, 1]</span> |
| <span class="sd"> contrast : float</span> |
| <span class="sd"> The contrast jitter ratio range, [0, 1]</span> |
| <span class="sd"> saturation : float</span> |
| <span class="sd"> The saturation jitter ratio range, [0, 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">brightness</span><span class="p">,</span> <span class="n">contrast</span><span class="p">,</span> <span class="n">saturation</span><span class="p">):</span> |
| <span class="n">ts</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">if</span> <span class="n">brightness</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">ts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">BrightnessJitterAug</span><span class="p">(</span><span class="n">brightness</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">contrast</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">ts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ContrastJitterAug</span><span class="p">(</span><span class="n">contrast</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">saturation</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">ts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">SaturationJitterAug</span><span class="p">(</span><span class="n">saturation</span><span class="p">))</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">ColorJitterAug</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">ts</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="LightingAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.LightingAug">[docs]</a><span class="k">class</span> <span class="nc">LightingAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Add PCA based noise.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> alphastd : float</span> |
| <span class="sd"> Noise level</span> |
| <span class="sd"> eigval : 3x1 np.array</span> |
| <span class="sd"> Eigen values</span> |
| <span class="sd"> eigvec : 3x3 np.array</span> |
| <span class="sd"> Eigen vectors</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">alphastd</span><span class="p">,</span> <span class="n">eigval</span><span class="p">,</span> <span class="n">eigvec</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">LightingAug</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">alphastd</span><span class="o">=</span><span class="n">alphastd</span><span class="p">,</span> <span class="n">eigval</span><span class="o">=</span><span class="n">eigval</span><span class="p">,</span> <span class="n">eigvec</span><span class="o">=</span><span class="n">eigvec</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">alphastd</span> <span class="o">=</span> <span class="n">alphastd</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">eigval</span> <span class="o">=</span> <span class="n">eigval</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">eigvec</span> <span class="o">=</span> <span class="n">eigvec</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="n">alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alphastd</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,))</span> |
| <span class="n">rgb</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">eigvec</span> <span class="o">*</span> <span class="n">alpha</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eigval</span><span class="p">)</span> |
| <span class="n">src</span> <span class="o">+=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">rgb</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">src</span></div> |
| |
| |
| <div class="viewcode-block" id="ColorNormalizeAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ColorNormalizeAug">[docs]</a><span class="k">class</span> <span class="nc">ColorNormalizeAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Mean and std normalization.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> mean : NDArray</span> |
| <span class="sd"> RGB mean to be subtracted</span> |
| <span class="sd"> std : NDArray</span> |
| <span class="sd"> RGB standard deviation to be divided</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">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">ColorNormalizeAug</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">mean</span><span class="o">=</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="n">std</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">mean</span> <span class="o">=</span> <span class="n">mean</span> <span class="k">if</span> <span class="n">mean</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">)</span> <span class="k">else</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mean</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">std</span> <span class="o">=</span> <span class="n">std</span> <span class="k">if</span> <span class="n">std</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">std</span><span class="p">,</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">)</span> <span class="k">else</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">std</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="k">return</span> <span class="n">color_normalize</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">std</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="RandomGrayAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.RandomGrayAug">[docs]</a><span class="k">class</span> <span class="nc">RandomGrayAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Randomly convert to gray image.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> p : float</span> |
| <span class="sd"> Probability to convert to grayscale</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">p</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">RandomGrayAug</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">p</span><span class="o">=</span><span class="n">p</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">p</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">mat</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.21</span><span class="p">,</span> <span class="mf">0.21</span><span class="p">,</span> <span class="mf">0.21</span><span class="p">],</span> |
| <span class="p">[</span><span class="mf">0.72</span><span class="p">,</span> <span class="mf">0.72</span><span class="p">,</span> <span class="mf">0.72</span><span class="p">],</span> |
| <span class="p">[</span><span class="mf">0.07</span><span class="p">,</span> <span class="mf">0.07</span><span class="p">,</span> <span class="mf">0.07</span><span class="p">]])</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="k">if</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">:</span> |
| <span class="n">src</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mat</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">src</span></div> |
| |
| |
| <div class="viewcode-block" id="HorizontalFlipAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.HorizontalFlipAug">[docs]</a><span class="k">class</span> <span class="nc">HorizontalFlipAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Random horizontal flip.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> p : float</span> |
| <span class="sd"> Probability to flip image horizontally</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">p</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">HorizontalFlipAug</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">p</span><span class="o">=</span><span class="n">p</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">p</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="k">if</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">:</span> |
| <span class="n">src</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">src</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="k">return</span> <span class="n">src</span></div> |
| |
| |
| <div class="viewcode-block" id="CastAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.CastAug">[docs]</a><span class="k">class</span> <span class="nc">CastAug</span><span class="p">(</span><span class="n">Augmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Cast to float32"""</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">typ</span><span class="o">=</span><span class="s1">'float32'</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">CastAug</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="nb">type</span><span class="o">=</span><span class="n">typ</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">typ</span> <span class="o">=</span> <span class="n">typ</span> |
| |
| <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="n">src</span> <span class="o">=</span> <span class="n">src</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">typ</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">src</span></div> |
| |
| |
| <div class="viewcode-block" id="CreateAugmenter"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.CreateAugmenter">[docs]</a><span class="k">def</span> <span class="nf">CreateAugmenter</span><span class="p">(</span><span class="n">data_shape</span><span class="p">,</span> <span class="n">resize</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">rand_crop</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">rand_resize</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">rand_mirror</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">mean</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">brightness</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">contrast</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">saturation</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="n">pca_noise</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">rand_gray</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">inter_method</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Creates an augmenter list.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> data_shape : tuple of int</span> |
| <span class="sd"> Shape for output data</span> |
| <span class="sd"> resize : int</span> |
| <span class="sd"> Resize shorter edge if larger than 0 at the begining</span> |
| <span class="sd"> rand_crop : bool</span> |
| <span class="sd"> Whether to enable random cropping other than center crop</span> |
| <span class="sd"> rand_resize : bool</span> |
| <span class="sd"> Whether to enable random sized cropping, require rand_crop to be enabled</span> |
| <span class="sd"> rand_gray : float</span> |
| <span class="sd"> [0, 1], probability to convert to grayscale for all channels, the number</span> |
| <span class="sd"> of channels will not be reduced to 1</span> |
| <span class="sd"> rand_mirror : bool</span> |
| <span class="sd"> Whether to apply horizontal flip to image with probability 0.5</span> |
| <span class="sd"> mean : np.ndarray or None</span> |
| <span class="sd"> Mean pixel values for [r, g, b]</span> |
| <span class="sd"> std : np.ndarray or None</span> |
| <span class="sd"> Standard deviations for [r, g, b]</span> |
| <span class="sd"> brightness : float</span> |
| <span class="sd"> Brightness jittering range (percent)</span> |
| <span class="sd"> contrast : float</span> |
| <span class="sd"> Contrast jittering range (percent)</span> |
| <span class="sd"> saturation : float</span> |
| <span class="sd"> Saturation jittering range (percent)</span> |
| <span class="sd"> hue : float</span> |
| <span class="sd"> Hue jittering range (percent)</span> |
| <span class="sd"> pca_noise : float</span> |
| <span class="sd"> Pca noise level (percent)</span> |
| <span class="sd"> inter_method : int, default=2(Area-based)</span> |
| <span class="sd"> Interpolation method for all resizing operations</span> |
| |
| <span class="sd"> Possible values:</span> |
| <span class="sd"> 0: Nearest Neighbors Interpolation.</span> |
| <span class="sd"> 1: Bilinear interpolation.</span> |
| <span class="sd"> 2: Bicubic interpolation over 4x4 pixel neighborhood.</span> |
| <span class="sd"> 3: Area-based (resampling using pixel area relation). It may be a</span> |
| <span class="sd"> preferred method for image decimation, as it gives moire-free</span> |
| <span class="sd"> results. But when the image is zoomed, it is similar to the Nearest</span> |
| <span class="sd"> Neighbors method. (used by default).</span> |
| <span class="sd"> 4: Lanczos interpolation over 8x8 pixel neighborhood.</span> |
| <span class="sd"> 9: Cubic for enlarge, area for shrink, bilinear for others</span> |
| <span class="sd"> 10: Random select from interpolation method metioned above.</span> |
| <span class="sd"> Note:</span> |
| <span class="sd"> When shrinking an image, it will generally look best with AREA-based</span> |
| <span class="sd"> interpolation, whereas, when enlarging an image, it will generally look best</span> |
| <span class="sd"> with Bicubic (slow) or Bilinear (faster but still looks OK).</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # An example of creating multiple augmenters</span> |
| <span class="sd"> >>> augs = mx.image.CreateAugmenter(data_shape=(3, 300, 300), rand_mirror=True,</span> |
| <span class="sd"> ... mean=True, brightness=0.125, contrast=0.125, rand_gray=0.05,</span> |
| <span class="sd"> ... saturation=0.125, pca_noise=0.05, inter_method=10)</span> |
| <span class="sd"> >>> # dump the details</span> |
| <span class="sd"> >>> for aug in augs:</span> |
| <span class="sd"> ... aug.dumps()</span> |
| <span class="sd"> """</span> |
| <span class="n">auglist</span> <span class="o">=</span> <span class="p">[]</span> |
| |
| <span class="k">if</span> <span class="n">resize</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">auglist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ResizeAug</span><span class="p">(</span><span class="n">resize</span><span class="p">,</span> <span class="n">inter_method</span><span class="p">))</span> |
| |
| <span class="n">crop_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">data_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">data_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> |
| <span class="k">if</span> <span class="n">rand_resize</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">rand_crop</span> |
| <span class="n">auglist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">RandomSizedCropAug</span><span class="p">(</span><span class="n">crop_size</span><span class="p">,</span> <span class="mf">0.08</span><span class="p">,</span> <span class="p">(</span><span class="mf">3.0</span> <span class="o">/</span> <span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span> <span class="o">/</span> <span class="mf">3.0</span><span class="p">),</span> <span class="n">inter_method</span><span class="p">))</span> |
| <span class="k">elif</span> <span class="n">rand_crop</span><span class="p">:</span> |
| <span class="n">auglist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">RandomCropAug</span><span class="p">(</span><span class="n">crop_size</span><span class="p">,</span> <span class="n">inter_method</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">auglist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">CenterCropAug</span><span class="p">(</span><span class="n">crop_size</span><span class="p">,</span> <span class="n">inter_method</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="n">rand_mirror</span><span class="p">:</span> |
| <span class="n">auglist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">HorizontalFlipAug</span><span class="p">(</span><span class="mf">0.5</span><span class="p">))</span> |
| |
| <span class="n">auglist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">CastAug</span><span class="p">())</span> |
| |
| <span class="k">if</span> <span class="n">brightness</span> <span class="ow">or</span> <span class="n">contrast</span> <span class="ow">or</span> <span class="n">saturation</span><span class="p">:</span> |
| <span class="n">auglist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ColorJitterAug</span><span class="p">(</span><span class="n">brightness</span><span class="p">,</span> <span class="n">contrast</span><span class="p">,</span> <span class="n">saturation</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="n">hue</span><span class="p">:</span> |
| <span class="n">auglist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">HueJitterAug</span><span class="p">(</span><span class="n">hue</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="n">pca_noise</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">eigval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">55.46</span><span class="p">,</span> <span class="mf">4.794</span><span class="p">,</span> <span class="mf">1.148</span><span class="p">])</span> |
| <span class="n">eigvec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="o">-</span><span class="mf">0.5675</span><span class="p">,</span> <span class="mf">0.7192</span><span class="p">,</span> <span class="mf">0.4009</span><span class="p">],</span> |
| <span class="p">[</span><span class="o">-</span><span class="mf">0.5808</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0045</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.8140</span><span class="p">],</span> |
| <span class="p">[</span><span class="o">-</span><span class="mf">0.5836</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.6948</span><span class="p">,</span> <span class="mf">0.4203</span><span class="p">]])</span> |
| <span class="n">auglist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">LightingAug</span><span class="p">(</span><span class="n">pca_noise</span><span class="p">,</span> <span class="n">eigval</span><span class="p">,</span> <span class="n">eigvec</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="n">rand_gray</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">auglist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">RandomGrayAug</span><span class="p">(</span><span class="n">rand_gray</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="n">mean</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span> |
| <span class="n">mean</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">123.68</span><span class="p">,</span> <span class="mf">116.28</span><span class="p">,</span> <span class="mf">103.53</span><span class="p">])</span> |
| <span class="k">elif</span> <span class="n">mean</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">))</span> <span class="ow">and</span> <span class="n">mean</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</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="k">if</span> <span class="n">std</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span> |
| <span class="n">std</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">58.395</span><span class="p">,</span> <span class="mf">57.12</span><span class="p">,</span> <span class="mf">57.375</span><span class="p">])</span> |
| <span class="k">elif</span> <span class="n">std</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">std</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">))</span> <span class="ow">and</span> <span class="n">std</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</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="k">if</span> <span class="n">mean</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">std</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">auglist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ColorNormalizeAug</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">))</span> |
| |
| <span class="k">return</span> <span class="n">auglist</span></div> |
| |
| |
| <div class="viewcode-block" id="ImageIter"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageIter">[docs]</a><span class="k">class</span> <span class="nc">ImageIter</span><span class="p">(</span><span class="n">io</span><span class="o">.</span><span class="n">DataIter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Image data iterator with a large number of augmentation choices.</span> |
| <span class="sd"> This iterator supports reading from both .rec files and raw image files.</span> |
| |
| <span class="sd"> To load input images from .rec files, use `path_imgrec` parameter and to load from raw image</span> |
| <span class="sd"> files, use `path_imglist` and `path_root` parameters.</span> |
| |
| <span class="sd"> To use data partition (for distributed training) or shuffling, specify `path_imgidx` parameter.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> batch_size : int</span> |
| <span class="sd"> Number of examples per batch.</span> |
| <span class="sd"> data_shape : tuple</span> |
| <span class="sd"> Data shape in (channels, height, width) format.</span> |
| <span class="sd"> For now, only RGB image with 3 channels is supported.</span> |
| <span class="sd"> label_width : int, optional</span> |
| <span class="sd"> Number of labels per example. The default label width is 1.</span> |
| <span class="sd"> path_imgrec : str</span> |
| <span class="sd"> Path to image record file (.rec).</span> |
| <span class="sd"> Created with tools/im2rec.py or bin/im2rec.</span> |
| <span class="sd"> path_imglist : str</span> |
| <span class="sd"> Path to image list (.lst).</span> |
| <span class="sd"> Created with tools/im2rec.py or with custom script.</span> |
| <span class="sd"> Format: Tab separated record of index, one or more labels and relative_path_from_root.</span> |
| <span class="sd"> imglist: list</span> |
| <span class="sd"> A list of images with the label(s).</span> |
| <span class="sd"> Each item is a list [imagelabel: float or list of float, imgpath].</span> |
| <span class="sd"> path_root : str</span> |
| <span class="sd"> Root folder of image files.</span> |
| <span class="sd"> path_imgidx : str</span> |
| <span class="sd"> Path to image index file. Needed for partition and shuffling when using .rec source.</span> |
| <span class="sd"> shuffle : bool</span> |
| <span class="sd"> Whether to shuffle all images at the start of each iteration or not.</span> |
| <span class="sd"> Can be slow for HDD.</span> |
| <span class="sd"> part_index : int</span> |
| <span class="sd"> Partition index.</span> |
| <span class="sd"> num_parts : int</span> |
| <span class="sd"> Total number of partitions.</span> |
| <span class="sd"> data_name : str</span> |
| <span class="sd"> Data name for provided symbols.</span> |
| <span class="sd"> label_name : str</span> |
| <span class="sd"> Label name for provided symbols.</span> |
| <span class="sd"> dtype : str</span> |
| <span class="sd"> Label data type. Default: float32. Other options: int32, int64, float64</span> |
| <span class="sd"> last_batch_handle : str, optional</span> |
| <span class="sd"> How to handle the last batch.</span> |
| <span class="sd"> This parameter can be 'pad'(default), 'discard' or 'roll_over'.</span> |
| <span class="sd"> If 'pad', the last batch will be padded with data starting from the begining</span> |
| <span class="sd"> If 'discard', the last batch will be discarded</span> |
| <span class="sd"> If 'roll_over', the remaining elements will be rolled over to the next iteration</span> |
| <span class="sd"> kwargs : ...</span> |
| <span class="sd"> More arguments for creating augmenter. See mx.image.CreateAugmenter.</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">batch_size</span><span class="p">,</span> <span class="n">data_shape</span><span class="p">,</span> <span class="n">label_width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> |
| <span class="n">path_imgrec</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">path_imglist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">path_root</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">path_imgidx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">part_index</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_parts</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">aug_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">imglist</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">data_name</span><span class="o">=</span><span class="s1">'data'</span><span class="p">,</span> <span class="n">label_name</span><span class="o">=</span><span class="s1">'softmax_label'</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float32'</span><span class="p">,</span> |
| <span class="n">last_batch_handle</span><span class="o">=</span><span class="s1">'pad'</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">ImageIter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> |
| <span class="k">assert</span> <span class="n">path_imgrec</span> <span class="ow">or</span> <span class="n">path_imglist</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">imglist</span><span class="p">,</span> <span class="nb">list</span><span class="p">))</span> |
| <span class="k">assert</span> <span class="n">dtype</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'int32'</span><span class="p">,</span> <span class="s1">'float32'</span><span class="p">,</span> <span class="s1">'int64'</span><span class="p">,</span> <span class="s1">'float64'</span><span class="p">],</span> <span class="n">dtype</span> <span class="o">+</span> <span class="s1">' label not supported'</span> |
| <span class="n">num_threads</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'MXNET_CPU_WORKER_NTHREADS'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'Using </span><span class="si">%s</span><span class="s1"> threads for decoding...'</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">num_threads</span><span class="p">))</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'Set enviroment variable MXNET_CPU_WORKER_NTHREADS to a'</span> |
| <span class="s1">' larger number to use more threads.'</span><span class="p">)</span> |
| <span class="n">class_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="k">if</span> <span class="n">path_imgrec</span><span class="p">:</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'</span><span class="si">%s</span><span class="s1">: loading recordio </span><span class="si">%s</span><span class="s1">...'</span><span class="p">,</span> |
| <span class="n">class_name</span><span class="p">,</span> <span class="n">path_imgrec</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">path_imgidx</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">imgrec</span> <span class="o">=</span> <span class="n">recordio</span><span class="o">.</span><span class="n">MXIndexedRecordIO</span><span class="p">(</span><span class="n">path_imgidx</span><span class="p">,</span> <span class="n">path_imgrec</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">imgidx</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">imgrec</span><span class="o">.</span><span class="n">keys</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">imgrec</span> <span class="o">=</span> <span class="n">recordio</span><span class="o">.</span><span class="n">MXRecordIO</span><span class="p">(</span><span class="n">path_imgrec</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">imgidx</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">imgrec</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="n">array_fn</span> <span class="o">=</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">array</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span> |
| <span class="k">if</span> <span class="n">path_imglist</span><span class="p">:</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'</span><span class="si">%s</span><span class="s1">: loading image list </span><span class="si">%s</span><span class="s1">...'</span><span class="p">,</span> <span class="n">class_name</span><span class="p">,</span> <span class="n">path_imglist</span><span class="p">)</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path_imglist</span><span class="p">)</span> <span class="k">as</span> <span class="n">fin</span><span class="p">:</span> |
| <span class="n">imglist</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="n">imgkeys</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">iter</span><span class="p">(</span><span class="n">fin</span><span class="o">.</span><span class="n">readline</span><span class="p">,</span> <span class="s1">''</span><span class="p">):</span> |
| <span class="n">line</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'</span><span class="se">\t</span><span class="s1">'</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">array_fn</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="n">key</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="n">imglist</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">line</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> |
| <span class="n">imgkeys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">imglist</span> <span class="o">=</span> <span class="n">imglist</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">imglist</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'</span><span class="si">%s</span><span class="s1">: loading image list...'</span><span class="p">,</span> <span class="n">class_name</span><span class="p">)</span> |
| <span class="n">result</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="n">imgkeys</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">index</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">imglist</span><span class="p">:</span> |
| <span class="n">key</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> |
| <span class="n">index</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> <span class="o">></span> <span class="mi">2</span><span class="p">:</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">array_fn</span><span class="p">(</span><span class="n">img</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">img</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">array_fn</span><span class="p">([</span><span class="n">img</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">array_fn</span><span class="p">(</span><span class="n">img</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">img</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> |
| <span class="n">imgkeys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">key</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">imglist</span> <span class="o">=</span> <span class="n">result</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">imglist</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">path_root</span> <span class="o">=</span> <span class="n">path_root</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">check_data_shape</span><span class="p">(</span><span class="n">data_shape</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">provide_data</span> <span class="o">=</span> <span class="p">[(</span><span class="n">data_name</span><span class="p">,</span> <span class="p">(</span><span class="n">batch_size</span><span class="p">,)</span> <span class="o">+</span> <span class="n">data_shape</span><span class="p">)]</span> |
| <span class="k">if</span> <span class="n">label_width</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">provide_label</span> <span class="o">=</span> <span class="p">[(</span><span class="n">label_name</span><span class="p">,</span> <span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">label_width</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">provide_label</span> <span class="o">=</span> <span class="p">[(</span><span class="n">label_name</span><span class="p">,</span> <span class="p">(</span><span class="n">batch_size</span><span class="p">,))]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">data_shape</span> <span class="o">=</span> <span class="n">data_shape</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">label_width</span> <span class="o">=</span> <span class="n">label_width</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">shuffle</span> <span class="o">=</span> <span class="n">shuffle</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">imgrec</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="o">=</span> <span class="n">imgkeys</span> |
| <span class="k">elif</span> <span class="n">shuffle</span> <span class="ow">or</span> <span class="n">num_parts</span> <span class="o">></span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">path_imgidx</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">imgidx</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">imgidx</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="k">if</span> <span class="n">num_parts</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">part_index</span> <span class="o"><</span> <span class="n">num_parts</span> |
| <span class="n">N</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">seq</span><span class="p">)</span> |
| <span class="n">C</span> <span class="o">=</span> <span class="n">N</span> <span class="o">//</span> <span class="n">num_parts</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">seq</span><span class="p">[</span><span class="n">part_index</span> <span class="o">*</span> <span class="n">C</span><span class="p">:(</span><span class="n">part_index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">C</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">aug_list</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">auglist</span> <span class="o">=</span> <span class="n">CreateAugmenter</span><span class="p">(</span><span class="n">data_shape</span><span class="p">,</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="bp">self</span><span class="o">.</span><span class="n">auglist</span> <span class="o">=</span> <span class="n">aug_list</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">cur</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_allow_read</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">last_batch_handle</span> <span class="o">=</span> <span class="n">last_batch_handle</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">num_image</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">seq</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_data</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_label</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_idx</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| |
| <div class="viewcode-block" id="ImageIter.reset"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageIter.reset">[docs]</a> <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Resets the iterator to the beginning of the data."""</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">shuffle</span><span class="p">:</span> |
| <span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">seq</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_batch_handle</span> <span class="o">!=</span> <span class="s1">'roll_over'</span> <span class="ow">or</span> \ |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">imgrec</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">imgrec</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">cur</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_allow_read</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_allow_read</span> <span class="o">=</span> <span class="kc">True</span></div> |
| |
| <div class="viewcode-block" id="ImageIter.hard_reset"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageIter.hard_reset">[docs]</a> <span class="k">def</span> <span class="nf">hard_reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Resets the iterator and ignore roll over data"""</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">shuffle</span><span class="p">:</span> |
| <span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">seq</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">imgrec</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">imgrec</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">cur</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_allow_read</span> <span class="o">=</span> <span class="kc">True</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_data</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_label</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_idx</span> <span class="o">=</span> <span class="kc">None</span></div> |
| |
| <div class="viewcode-block" id="ImageIter.next_sample"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageIter.next_sample">[docs]</a> <span class="k">def</span> <span class="nf">next_sample</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Helper function for reading in next sample."""</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_allow_read</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">StopIteration</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cur</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_image</span><span class="p">:</span> |
| <span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">seq</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">cur</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_batch_handle</span> <span class="o">!=</span> <span class="s1">'discard'</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">cur</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">raise</span> <span class="ne">StopIteration</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">cur</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">imgrec</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">imgrec</span><span class="o">.</span><span class="n">read_idx</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span> |
| <span class="n">header</span><span class="p">,</span> <span class="n">img</span> <span class="o">=</span> <span class="n">recordio</span><span class="o">.</span><span class="n">unpack</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">imglist</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">header</span><span class="o">.</span><span class="n">label</span><span class="p">,</span> <span class="n">img</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">imglist</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">img</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">label</span><span class="p">,</span> <span class="n">fname</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">imglist</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">label</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">read_image</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">imgrec</span><span class="o">.</span><span class="n">read</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">s</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_batch_handle</span> <span class="o">!=</span> <span class="s1">'discard'</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">imgrec</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| <span class="k">raise</span> <span class="ne">StopIteration</span> |
| <span class="n">header</span><span class="p">,</span> <span class="n">img</span> <span class="o">=</span> <span class="n">recordio</span><span class="o">.</span><span class="n">unpack</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">header</span><span class="o">.</span><span class="n">label</span><span class="p">,</span> <span class="n">img</span></div> |
| |
| <span class="k">def</span> <span class="nf">_batchify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_data</span><span class="p">,</span> <span class="n">batch_label</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Helper function for batchifying data"""</span> |
| <span class="n">i</span> <span class="o">=</span> <span class="n">start</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="k">while</span> <span class="n">i</span> <span class="o"><</span> <span class="n">batch_size</span><span class="p">:</span> |
| <span class="n">label</span><span class="p">,</span> <span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">next_sample</span><span class="p">()</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">imdecode</span><span class="p">(</span><span class="n">s</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">check_valid_image</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">RuntimeError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span> |
| <span class="n">logging</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s1">'Invalid image, skipping: </span><span class="si">%s</span><span class="s1">'</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">))</span> |
| <span class="k">continue</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">augmentation_transform</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">i</span> <span class="o"><</span> <span class="n">batch_size</span><span class="p">,</span> <span class="s1">'Batch size must be multiples of augmenter output length'</span> |
| <span class="n">batch_data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">postprocess_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
| <span class="n">batch_label</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">label</span> |
| <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="k">except</span> <span class="ne">StopIteration</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">i</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">StopIteration</span> |
| <span class="k">return</span> <span class="n">i</span> |
| |
| <div class="viewcode-block" id="ImageIter.next"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageIter.next">[docs]</a> <span class="k">def</span> <span class="nf">next</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Returns the next batch of data."""</span> |
| <span class="n">batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> |
| <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_shape</span> |
| <span class="c1"># if last batch data is rolled over</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="c1"># check both the data and label have values</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache_label</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">"_cache_label didn't have values"</span> |
| <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache_idx</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">"_cache_idx didn't have values"</span> |
| <span class="n">batch_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache_data</span> |
| <span class="n">batch_label</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache_label</span> |
| <span class="n">i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache_idx</span> |
| <span class="c1"># clear the cache data</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="n">zeros_fn</span> <span class="o">=</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">zeros</span> |
| <span class="n">empty_fn</span> <span class="o">=</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">empty</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">zeros_fn</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">zeros</span> |
| <span class="n">empty_fn</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">empty</span> |
| <span class="n">batch_data</span> <span class="o">=</span> <span class="n">zeros_fn</span><span class="p">((</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span> |
| <span class="n">batch_label</span> <span class="o">=</span> <span class="n">empty_fn</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">provide_label</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="n">i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batchify</span><span class="p">(</span><span class="n">batch_data</span><span class="p">,</span> <span class="n">batch_label</span><span class="p">)</span> |
| <span class="c1"># calculate the padding</span> |
| <span class="n">pad</span> <span class="o">=</span> <span class="n">batch_size</span> <span class="o">-</span> <span class="n">i</span> |
| <span class="c1"># handle padding for the last batch</span> |
| <span class="k">if</span> <span class="n">pad</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_batch_handle</span> <span class="o">==</span> <span class="s1">'discard'</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">StopIteration</span> |
| <span class="c1"># if the option is 'roll_over', throw StopIteration and cache the data</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_batch_handle</span> <span class="o">==</span> <span class="s1">'roll_over'</span> <span class="ow">and</span> \ |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_data</span> <span class="o">=</span> <span class="n">batch_data</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_label</span> <span class="o">=</span> <span class="n">batch_label</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_idx</span> <span class="o">=</span> <span class="n">i</span> |
| <span class="k">raise</span> <span class="ne">StopIteration</span> |
| |
| <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batchify</span><span class="p">(</span><span class="n">batch_data</span><span class="p">,</span> <span class="n">batch_label</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_batch_handle</span> <span class="o">==</span> <span class="s1">'pad'</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_allow_read</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_data</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_label</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_cache_idx</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="k">return</span> <span class="n">io</span><span class="o">.</span><span class="n">DataBatch</span><span class="p">([</span><span class="n">batch_data</span><span class="p">],</span> <span class="p">[</span><span class="n">batch_label</span><span class="p">],</span> <span class="n">pad</span><span class="o">=</span><span class="n">pad</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="ImageIter.check_data_shape"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageIter.check_data_shape">[docs]</a> <span class="k">def</span> <span class="nf">check_data_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_shape</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Checks if the input data shape is valid"""</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">len</span><span class="p">(</span><span class="n">data_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'data_shape should have length 3, with dimensions CxHxW'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">data_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'This iterator expects inputs to have 3 channels.'</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="ImageIter.check_valid_image"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageIter.check_valid_image">[docs]</a> <span class="k">def</span> <span class="nf">check_valid_image</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Checks if the input data is valid"""</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">'Data shape is wrong'</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="ImageIter.imdecode"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageIter.imdecode">[docs]</a> <span class="k">def</span> <span class="nf">imdecode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">s</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Decodes a string or byte string to an NDArray.</span> |
| <span class="sd"> See mx.img.imdecode for more details."""</span> |
| <span class="k">def</span> <span class="nf">locate</span><span class="p">():</span> |
| <span class="w"> </span><span class="sd">"""Locate the image file/index if decode fails."""</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">seq</span><span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">cur</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_image</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">idx</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cur</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_image</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">imglist</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">_</span><span class="p">,</span> <span class="n">fname</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">imglist</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> |
| <span class="n">msg</span> <span class="o">=</span> <span class="s2">"filename: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">msg</span> <span class="o">=</span> <span class="s2">"index: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span> |
| <span class="k">return</span> <span class="s2">"Broken image "</span> <span class="o">+</span> <span class="n">msg</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">imdecode</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2">, </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">locate</span><span class="p">(),</span> <span class="n">e</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">img</span></div> |
| |
| <div class="viewcode-block" id="ImageIter.read_image"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageIter.read_image">[docs]</a> <span class="k">def</span> <span class="nf">read_image</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fname</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Reads an input image `fname` and returns the decoded raw bytes.</span> |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> dataIter.read_image('Face.jpg') # returns decoded raw bytes.</span> |
| <span class="sd"> """</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">path_root</span><span class="p">,</span> <span class="n">fname</span><span class="p">),</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fin</span><span class="p">:</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">fin</span><span class="o">.</span><span class="n">read</span><span class="p">()</span> |
| <span class="k">return</span> <span class="n">img</span></div> |
| |
| <div class="viewcode-block" id="ImageIter.augmentation_transform"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageIter.augmentation_transform">[docs]</a> <span class="k">def</span> <span class="nf">augmentation_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Transforms input data with specified augmentation."""</span> |
| <span class="k">for</span> <span class="n">aug</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">auglist</span><span class="p">:</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">aug</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">data</span></div> |
| |
| <div class="viewcode-block" id="ImageIter.postprocess_data"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageIter.postprocess_data">[docs]</a> <span class="k">def</span> <span class="nf">postprocess_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">datum</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Final postprocessing step before image is loaded into the batch."""</span> |
| <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span> |
| <span class="k">return</span> <span class="n">datum</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</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="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">nd</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">axes</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span></div></div> |
| </pre></div> |
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