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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.detection</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=unused-import, too-many-lines</span> |
| <span class="sd">"""Read images and perform augmentations for object detection."""</span> |
| |
| |
| <span class="kn">import</span> <span class="nn">json</span> |
| <span class="kn">import</span> <span class="nn">logging</span> |
| <span class="kn">import</span> <span class="nn">random</span> |
| <span class="kn">import</span> <span class="nn">warnings</span> |
| |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| |
| <span class="kn">from</span> <span class="nn">..base</span> <span class="kn">import</span> <span class="n">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._internal</span> <span class="kn">import</span> <span class="n">_cvcopyMakeBorder</span> <span class="k">as</span> <span class="n">copyMakeBorder</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">.image</span> <span class="kn">import</span> <span class="n">RandomOrderAug</span><span class="p">,</span> <span class="n">ColorJitterAug</span><span class="p">,</span> <span class="n">LightingAug</span><span class="p">,</span> <span class="n">ColorNormalizeAug</span> |
| <span class="kn">from</span> <span class="nn">.image</span> <span class="kn">import</span> <span class="n">ResizeAug</span><span class="p">,</span> <span class="n">ForceResizeAug</span><span class="p">,</span> <span class="n">CastAug</span><span class="p">,</span> <span class="n">HueJitterAug</span><span class="p">,</span> <span class="n">RandomGrayAug</span> |
| <span class="kn">from</span> <span class="nn">.image</span> <span class="kn">import</span> <span class="n">fixed_crop</span><span class="p">,</span> <span class="n">ImageIter</span><span class="p">,</span> <span class="n">Augmenter</span> |
| <span class="kn">from</span> <span class="nn">..util</span> <span class="kn">import</span> <span class="n">is_np_array</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> |
| |
| |
| <div class="viewcode-block" id="DetAugmenter"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.DetAugmenter">[docs]</a><span class="k">class</span> <span class="nc">DetAugmenter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Detection base augmenter"""</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="DetAugmenter.dumps"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.DetAugmenter.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="n">label</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="DetBorrowAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.DetBorrowAug">[docs]</a><span class="k">class</span> <span class="nc">DetBorrowAug</span><span class="p">(</span><span class="n">DetAugmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Borrow standard augmenter from image classification.</span> |
| <span class="sd"> Which is good once you know label won't be affected after this augmenter.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> augmenter : mx.image.Augmenter</span> |
| <span class="sd"> The borrowed standard augmenter which has no effect on label</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">augmenter</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">augmenter</span><span class="p">,</span> <span class="n">Augmenter</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'Borrowing from invalid Augmenter'</span><span class="p">)</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">DetBorrowAug</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">augmenter</span><span class="o">=</span><span class="n">augmenter</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="n">augmenter</span> <span class="o">=</span> <span class="n">augmenter</span> |
| |
| <div class="viewcode-block" id="DetBorrowAug.dumps"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.DetBorrowAug.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 one 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="bp">self</span><span class="o">.</span><span class="n">augmenter</span><span class="o">.</span><span class="n">dumps</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="n">label</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter implementation body"""</span> |
| <span class="n">src</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">augmenter</span><span class="p">(</span><span class="n">src</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="DetRandomSelectAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.DetRandomSelectAug">[docs]</a><span class="k">class</span> <span class="nc">DetRandomSelectAug</span><span class="p">(</span><span class="n">DetAugmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Randomly select one augmenter to apply, with chance to skip all.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> aug_list : list of DetAugmenter</span> |
| <span class="sd"> The random selection will be applied to one of the augmenters</span> |
| <span class="sd"> skip_prob : float</span> |
| <span class="sd"> The probability to skip all augmenters and return input directly</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">aug_list</span><span class="p">,</span> <span class="n">skip_prob</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">DetRandomSelectAug</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">skip_prob</span><span class="o">=</span><span class="n">skip_prob</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">aug_list</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span> |
| <span class="n">aug_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">aug_list</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">aug</span> <span class="ow">in</span> <span class="n">aug_list</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">aug</span><span class="p">,</span> <span class="n">DetAugmenter</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Allow DetAugmenter in list only'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">aug_list</span><span class="p">:</span> |
| <span class="n">skip_prob</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># disabled</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">aug_list</span> <span class="o">=</span> <span class="n">aug_list</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">skip_prob</span> <span class="o">=</span> <span class="n">skip_prob</span> |
| |
| <div class="viewcode-block" id="DetRandomSelectAug.dumps"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.DetRandomSelectAug.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 default."""</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">aug_list</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="n">label</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter implementation 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">skip_prob</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> |
| <span class="k">else</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">aug_list</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">aug_list</span><span class="p">[</span><span class="mi">0</span><span class="p">](</span><span class="n">src</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="DetHorizontalFlipAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.DetHorizontalFlipAug">[docs]</a><span class="k">class</span> <span class="nc">DetHorizontalFlipAug</span><span class="p">(</span><span class="n">DetAugmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Random horizontal flipping.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> p : float</span> |
| <span class="sd"> chance [0, 1] to flip</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">DetHorizontalFlipAug</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="n">label</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter implementation"""</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="bp">self</span><span class="o">.</span><span class="n">_flip_label</span><span class="p">(</span><span class="n">label</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_flip_label</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Helper function to flip label."""</span> |
| <span class="n">tmp</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="n">label</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> |
| <span class="n">label</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="n">label</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> |
| <span class="n">label</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span></div> |
| |
| |
| <div class="viewcode-block" id="DetRandomCropAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.DetRandomCropAug">[docs]</a><span class="k">class</span> <span class="nc">DetRandomCropAug</span><span class="p">(</span><span class="n">DetAugmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Random cropping with constraints</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> min_object_covered : float, default=0.1</span> |
| <span class="sd"> The cropped area of the image must contain at least this fraction of</span> |
| <span class="sd"> any bounding box supplied. The value of this parameter should be non-negative.</span> |
| <span class="sd"> In the case of 0, the cropped area does not need to overlap any of the</span> |
| <span class="sd"> bounding boxes supplied.</span> |
| <span class="sd"> min_eject_coverage : float, default=0.3</span> |
| <span class="sd"> The minimum coverage of cropped sample w.r.t its original size. With this</span> |
| <span class="sd"> constraint, objects that have marginal area after crop will be discarded.</span> |
| <span class="sd"> aspect_ratio_range : tuple of floats, default=(0.75, 1.33)</span> |
| <span class="sd"> The cropped area of the image must have an aspect ratio = width / height</span> |
| <span class="sd"> within this range.</span> |
| <span class="sd"> area_range : tuple of floats, default=(0.05, 1.0)</span> |
| <span class="sd"> The cropped area of the image must contain a fraction of the supplied</span> |
| <span class="sd"> image within in this range.</span> |
| <span class="sd"> max_attempts : int, default=50</span> |
| <span class="sd"> Number of attempts at generating a cropped/padded region of the image of the</span> |
| <span class="sd"> specified constraints. After max_attempts failures, return the original image.</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">min_object_covered</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">aspect_ratio_range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="mf">1.33</span><span class="p">),</span> |
| <span class="n">area_range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span> <span class="n">min_eject_coverage</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">max_attempts</span><span class="o">=</span><span class="mi">50</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">aspect_ratio_range</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">aspect_ratio_range</span><span class="p">,</span> <span class="n">numeric_types</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 fixed aspect ratio: </span><span class="si">%s</span><span class="s1"> in DetRandomCropAug'</span><span class="p">,</span> |
| <span class="nb">str</span><span class="p">(</span><span class="n">aspect_ratio_range</span><span class="p">))</span> |
| <span class="n">aspect_ratio_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">aspect_ratio_range</span><span class="p">,</span> <span class="n">aspect_ratio_range</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">area_range</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">area_range</span><span class="p">,</span> <span class="n">numeric_types</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 fixed area range: </span><span class="si">%s</span><span class="s1"> in DetRandomCropAug'</span><span class="p">,</span> <span class="n">area_range</span><span class="p">)</span> |
| <span class="n">area_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">area_range</span><span class="p">,</span> <span class="n">area_range</span><span class="p">)</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">DetRandomCropAug</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">min_object_covered</span><span class="o">=</span><span class="n">min_object_covered</span><span class="p">,</span> |
| <span class="n">aspect_ratio_range</span><span class="o">=</span><span class="n">aspect_ratio_range</span><span class="p">,</span> |
| <span class="n">area_range</span><span class="o">=</span><span class="n">area_range</span><span class="p">,</span> |
| <span class="n">min_eject_coverage</span><span class="o">=</span><span class="n">min_eject_coverage</span><span class="p">,</span> |
| <span class="n">max_attempts</span><span class="o">=</span><span class="n">max_attempts</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">min_object_covered</span> <span class="o">=</span> <span class="n">min_object_covered</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">min_eject_coverage</span> <span class="o">=</span> <span class="n">min_eject_coverage</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">max_attempts</span> <span class="o">=</span> <span class="n">max_attempts</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">aspect_ratio_range</span> <span class="o">=</span> <span class="n">aspect_ratio_range</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">area_range</span> <span class="o">=</span> <span class="n">area_range</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">enabled</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">if</span> <span class="p">(</span><span class="n">area_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o"><=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">area_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">></span> <span class="n">area_range</span><span class="p">[</span><span class="mi">1</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">'Skip DetRandomCropAug due to invalid area_range: </span><span class="si">%s</span><span class="s1">'</span><span class="p">,</span> <span class="n">area_range</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="p">(</span><span class="n">aspect_ratio_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">></span> <span class="n">aspect_ratio_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">or</span> <span class="n">aspect_ratio_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">):</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'Skip DetRandomCropAug due to invalid aspect_ratio_range: </span><span class="si">%s</span><span class="s1">'</span><span class="p">,</span> |
| <span class="n">aspect_ratio_range</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">enabled</span> <span class="o">=</span> <span class="kc">True</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="n">label</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter implementation body"""</span> |
| <span class="n">crop</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_random_crop_proposal</span><span class="p">(</span><span class="n">label</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="k">if</span> <span class="n">crop</span><span class="p">:</span> |
| <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="n">crop</span> |
| <span class="n">src</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">x</span><span class="p">,</span> <span class="n">y</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="kc">None</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_calculate_areas</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Calculate areas for multiple labels"""</span> |
| <span class="n">heights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">label</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">-</span> <span class="n">label</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span> |
| <span class="n">widths</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">label</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="n">label</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span> |
| <span class="k">return</span> <span class="n">heights</span> <span class="o">*</span> <span class="n">widths</span> |
| |
| |
| <span class="k">def</span> <span class="nf">_intersect</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">xmin</span><span class="p">,</span> <span class="n">ymin</span><span class="p">,</span> <span class="n">xmax</span><span class="p">,</span> <span class="n">ymax</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Calculate intersect areas, normalized."""</span> |
| <span class="n">left</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">label</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">xmin</span><span class="p">)</span> |
| <span class="n">right</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">label</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">xmax</span><span class="p">)</span> |
| <span class="n">top</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">label</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">ymin</span><span class="p">)</span> |
| <span class="n">bot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">label</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">ymax</span><span class="p">)</span> |
| <span class="n">invalid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logical_or</span><span class="p">(</span><span class="n">left</span> <span class="o">>=</span> <span class="n">right</span><span class="p">,</span> <span class="n">top</span> <span class="o">>=</span> <span class="n">bot</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">label</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="n">out</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">left</span> |
| <span class="n">out</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">top</span> |
| <span class="n">out</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">right</span> |
| <span class="n">out</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">bot</span> |
| <span class="n">out</span><span class="p">[</span><span class="n">invalid</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">return</span> <span class="n">out</span> |
| |
| <span class="k">def</span> <span class="nf">_check_satisfy_constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">xmin</span><span class="p">,</span> <span class="n">ymin</span><span class="p">,</span> <span class="n">xmax</span><span class="p">,</span> <span class="n">ymax</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Check if constrains are satisfied"""</span> |
| <span class="k">if</span> <span class="p">(</span><span class="n">xmax</span> <span class="o">-</span> <span class="n">xmin</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">ymax</span> <span class="o">-</span> <span class="n">ymin</span><span class="p">)</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="k">return</span> <span class="kc">False</span> <span class="c1"># only 1 pixel</span> |
| <span class="n">x1</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">xmin</span><span class="p">)</span> <span class="o">/</span> <span class="n">width</span> |
| <span class="n">y1</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">ymin</span><span class="p">)</span> <span class="o">/</span> <span class="n">height</span> |
| <span class="n">x2</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">xmax</span><span class="p">)</span> <span class="o">/</span> <span class="n">width</span> |
| <span class="n">y2</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">ymax</span><span class="p">)</span> <span class="o">/</span> <span class="n">height</span> |
| <span class="n">object_areas</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_calculate_areas</span><span class="p">(</span><span class="n">label</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:])</span> |
| <span class="n">valid_objects</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">object_areas</span> <span class="o">*</span> <span class="n">width</span> <span class="o">*</span> <span class="n">height</span> <span class="o">></span> <span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">valid_objects</span><span class="o">.</span><span class="n">size</span> <span class="o"><</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| <span class="n">intersects</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_intersect</span><span class="p">(</span><span class="n">label</span><span class="p">[</span><span class="n">valid_objects</span><span class="p">,</span> <span class="mi">1</span><span class="p">:],</span> <span class="n">x1</span><span class="p">,</span> <span class="n">y1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="n">y2</span><span class="p">)</span> |
| <span class="n">coverages</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_calculate_areas</span><span class="p">(</span><span class="n">intersects</span><span class="p">)</span> <span class="o">/</span> <span class="n">object_areas</span><span class="p">[</span><span class="n">valid_objects</span><span class="p">]</span> |
| <span class="n">coverages</span> <span class="o">=</span> <span class="n">coverages</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">coverages</span> <span class="o">></span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]]</span> |
| <span class="k">return</span> <span class="n">coverages</span><span class="o">.</span><span class="n">size</span> <span class="o">></span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">amin</span><span class="p">(</span><span class="n">coverages</span><span class="p">)</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">min_object_covered</span> |
| |
| <span class="k">def</span> <span class="nf">_update_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">crop_box</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Convert labels according to crop box"""</span> |
| <span class="n">xmin</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">crop_box</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="n">width</span> |
| <span class="n">ymin</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">crop_box</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="n">height</span> |
| <span class="n">w</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">crop_box</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span> <span class="o">/</span> <span class="n">width</span> |
| <span class="n">h</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">crop_box</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span> <span class="o">/</span> <span class="n">height</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="n">out</span><span class="p">[:,</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="o">-=</span> <span class="n">xmin</span> |
| <span class="n">out</span><span class="p">[:,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)]</span> <span class="o">-=</span> <span class="n">ymin</span> |
| <span class="n">out</span><span class="p">[:,</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="o">/=</span> <span class="n">w</span> |
| <span class="n">out</span><span class="p">[:,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)]</span> <span class="o">/=</span> <span class="n">h</span> |
| <span class="n">out</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">out</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">5</span><span class="p">])</span> |
| <span class="n">out</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">out</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:</span><span class="mi">5</span><span class="p">])</span> |
| <span class="n">coverage</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_calculate_areas</span><span class="p">(</span><span class="n">out</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:])</span> <span class="o">*</span> <span class="n">w</span> <span class="o">*</span> <span class="n">h</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_calculate_areas</span><span class="p">(</span><span class="n">label</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:])</span> |
| <span class="n">valid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">out</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">></span> <span class="n">out</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">out</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">></span> <span class="n">out</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">])</span> |
| <span class="n">valid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">valid</span><span class="p">,</span> <span class="n">coverage</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">min_eject_coverage</span><span class="p">)</span> |
| <span class="n">valid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">valid</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">valid</span><span class="o">.</span><span class="n">size</span> <span class="o"><</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">return</span> <span class="kc">None</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">out</span><span class="p">[</span><span class="n">valid</span><span class="p">,</span> <span class="p">:]</span> |
| <span class="k">return</span> <span class="n">out</span> |
| |
| <span class="k">def</span> <span class="nf">_random_crop_proposal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Propose cropping areas"""</span> |
| <span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">sqrt</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">enabled</span> <span class="ow">or</span> <span class="n">height</span> <span class="o"><=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">width</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">()</span> |
| <span class="n">min_area</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">area_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">height</span> <span class="o">*</span> <span class="n">width</span> |
| <span class="n">max_area</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">area_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">height</span> <span class="o">*</span> <span class="n">width</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="bp">self</span><span class="o">.</span><span class="n">max_attempts</span><span class="p">):</span> |
| <span class="n">ratio</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">aspect_ratio_range</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">ratio</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">continue</span> |
| <span class="n">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">sqrt</span><span class="p">(</span><span class="n">min_area</span> <span class="o">/</span> <span class="n">ratio</span><span class="p">)))</span> |
| <span class="n">max_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">sqrt</span><span class="p">(</span><span class="n">max_area</span> <span class="o">/</span> <span class="n">ratio</span><span class="p">)))</span> |
| <span class="k">if</span> <span class="nb">round</span><span class="p">(</span><span class="n">max_h</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">)</span> <span class="o">></span> <span class="n">width</span><span class="p">:</span> |
| <span class="c1"># find smallest max_h satifying round(max_h * ratio) <= width</span> |
| <span class="n">max_h</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">width</span> <span class="o">+</span> <span class="mf">0.4999999</span><span class="p">)</span> <span class="o">/</span> <span class="n">ratio</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">max_h</span> <span class="o">></span> <span class="n">height</span><span class="p">:</span> |
| <span class="n">max_h</span> <span class="o">=</span> <span class="n">height</span> |
| <span class="k">if</span> <span class="n">h</span> <span class="o">></span> <span class="n">max_h</span><span class="p">:</span> |
| <span class="n">h</span> <span class="o">=</span> <span class="n">max_h</span> |
| <span class="k">if</span> <span class="n">h</span> <span class="o"><</span> <span class="n">max_h</span><span class="p">:</span> |
| <span class="c1"># generate random h in range [h, max_h]</span> |
| <span class="n">h</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">max_h</span><span class="p">)</span> |
| <span class="n">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">h</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">))</span> |
| <span class="k">assert</span> <span class="n">w</span> <span class="o"><=</span> <span class="n">width</span> |
| |
| <span class="c1"># trying to fix rounding problems</span> |
| <span class="n">area</span> <span class="o">=</span> <span class="n">w</span> <span class="o">*</span> <span class="n">h</span> |
| <span class="k">if</span> <span class="n">area</span> <span class="o"><</span> <span class="n">min_area</span><span class="p">:</span> |
| <span class="n">h</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="n">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">h</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">))</span> |
| <span class="n">area</span> <span class="o">=</span> <span class="n">w</span> <span class="o">*</span> <span class="n">h</span> |
| <span class="k">if</span> <span class="n">area</span> <span class="o">></span> <span class="n">max_area</span><span class="p">:</span> |
| <span class="n">h</span> <span class="o">-=</span> <span class="mi">1</span> |
| <span class="n">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">h</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">))</span> |
| <span class="n">area</span> <span class="o">=</span> <span class="n">w</span> <span class="o">*</span> <span class="n">h</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">min_area</span> <span class="o"><=</span> <span class="n">area</span> <span class="o"><=</span> <span class="n">max_area</span> <span class="ow">and</span> <span class="mi">0</span> <span class="o"><=</span> <span class="n">w</span> <span class="o"><=</span> <span class="n">width</span> <span class="ow">and</span> <span class="mi">0</span> <span class="o"><=</span> <span class="n">h</span> <span class="o"><=</span> <span class="n">height</span><span class="p">):</span> |
| <span class="k">continue</span> |
| |
| <span class="n">y</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="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">height</span> <span class="o">-</span> <span class="n">h</span><span class="p">))</span> |
| <span class="n">x</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="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">width</span> <span class="o">-</span> <span class="n">w</span><span class="p">))</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_satisfy_constraints</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">x</span> <span class="o">+</span> <span class="n">w</span><span class="p">,</span> <span class="n">y</span> <span class="o">+</span> <span class="n">h</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">):</span> |
| <span class="n">new_label</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_labels</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">new_label</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">new_label</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">()</span></div> |
| |
| |
| <div class="viewcode-block" id="DetRandomPadAug"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.DetRandomPadAug">[docs]</a><span class="k">class</span> <span class="nc">DetRandomPadAug</span><span class="p">(</span><span class="n">DetAugmenter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Random padding augmenter.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> aspect_ratio_range : tuple of floats, default=(0.75, 1.33)</span> |
| <span class="sd"> The padded area of the image must have an aspect ratio = width / height</span> |
| <span class="sd"> within this range.</span> |
| <span class="sd"> area_range : tuple of floats, default=(1.0, 3.0)</span> |
| <span class="sd"> The padded area of the image must be larger than the original area</span> |
| <span class="sd"> max_attempts : int, default=50</span> |
| <span class="sd"> Number of attempts at generating a padded region of the image of the</span> |
| <span class="sd"> specified constraints. After max_attempts failures, return the original image.</span> |
| <span class="sd"> pad_val: float or tuple of float, default=(128, 128, 128)</span> |
| <span class="sd"> pixel value to be filled when padding is enabled.</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">aspect_ratio_range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="mf">1.33</span><span class="p">),</span> <span class="n">area_range</span><span class="o">=</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">),</span> |
| <span class="n">max_attempts</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">pad_val</span><span class="o">=</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</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">pad_val</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pad_val</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">)</span> |
| <span class="n">pad_val</span> <span class="o">=</span> <span class="p">(</span><span class="n">pad_val</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">aspect_ratio_range</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">aspect_ratio_range</span><span class="p">,</span> <span class="n">numeric_types</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 fixed aspect ratio: </span><span class="si">%s</span><span class="s1"> in DetRandomPadAug'</span><span class="p">,</span> |
| <span class="nb">str</span><span class="p">(</span><span class="n">aspect_ratio_range</span><span class="p">))</span> |
| <span class="n">aspect_ratio_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">aspect_ratio_range</span><span class="p">,</span> <span class="n">aspect_ratio_range</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">area_range</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">area_range</span><span class="p">,</span> <span class="n">numeric_types</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 fixed area range: </span><span class="si">%s</span><span class="s1"> in DetRandomPadAug'</span><span class="p">,</span> <span class="n">area_range</span><span class="p">)</span> |
| <span class="n">area_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">area_range</span><span class="p">,</span> <span class="n">area_range</span><span class="p">)</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">DetRandomPadAug</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">aspect_ratio_range</span><span class="o">=</span><span class="n">aspect_ratio_range</span><span class="p">,</span> |
| <span class="n">area_range</span><span class="o">=</span><span class="n">area_range</span><span class="p">,</span> <span class="n">max_attempts</span><span class="o">=</span><span class="n">max_attempts</span><span class="p">,</span> |
| <span class="n">pad_val</span><span class="o">=</span><span class="n">pad_val</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">pad_val</span> <span class="o">=</span> <span class="n">pad_val</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">aspect_ratio_range</span> <span class="o">=</span> <span class="n">aspect_ratio_range</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">area_range</span> <span class="o">=</span> <span class="n">area_range</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">max_attempts</span> <span class="o">=</span> <span class="n">max_attempts</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">enabled</span> <span class="o">=</span> <span class="kc">False</span> |
| <span class="k">if</span> <span class="p">(</span><span class="n">area_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o"><=</span> <span class="mf">1.0</span> <span class="ow">or</span> <span class="n">area_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">></span> <span class="n">area_range</span><span class="p">[</span><span class="mi">1</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">'Skip DetRandomPadAug due to invalid parameters: </span><span class="si">%s</span><span class="s1">'</span><span class="p">,</span> <span class="n">area_range</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="p">(</span><span class="n">aspect_ratio_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o"><=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">aspect_ratio_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">></span> <span class="n">aspect_ratio_range</span><span class="p">[</span><span class="mi">1</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">'Skip DetRandomPadAug due to invalid aspect_ratio_range: </span><span class="si">%s</span><span class="s1">'</span><span class="p">,</span> |
| <span class="n">aspect_ratio_range</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">enabled</span> <span class="o">=</span> <span class="kc">True</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="n">label</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Augmenter body"""</span> |
| <span class="n">height</span><span class="p">,</span> <span class="n">width</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">pad</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_random_pad_proposal</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">pad</span><span class="p">:</span> |
| <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="n">pad</span> |
| <span class="n">src</span> <span class="o">=</span> <span class="n">copyMakeBorder</span><span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">h</span><span class="o">-</span><span class="n">y</span><span class="o">-</span><span class="n">height</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">w</span><span class="o">-</span><span class="n">x</span><span class="o">-</span><span class="n">width</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">values</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pad_val</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">src</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_update_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">pad_box</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Update label according to padding region"""</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="n">out</span><span class="p">[:,</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="o">=</span> <span class="p">(</span><span class="n">out</span><span class="p">[:,</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="o">*</span> <span class="n">width</span> <span class="o">+</span> <span class="n">pad_box</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="n">pad_box</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> |
| <span class="n">out</span><span class="p">[:,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)]</span> <span class="o">=</span> <span class="p">(</span><span class="n">out</span><span class="p">[:,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)]</span> <span class="o">*</span> <span class="n">height</span> <span class="o">+</span> <span class="n">pad_box</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="n">pad_box</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> |
| <span class="k">return</span> <span class="n">out</span> |
| |
| <span class="k">def</span> <span class="nf">_random_pad_proposal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Generate random padding region"""</span> |
| <span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">sqrt</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">enabled</span> <span class="ow">or</span> <span class="n">height</span> <span class="o"><=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">width</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">()</span> |
| <span class="n">min_area</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">area_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">height</span> <span class="o">*</span> <span class="n">width</span> |
| <span class="n">max_area</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">area_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">height</span> <span class="o">*</span> <span class="n">width</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="bp">self</span><span class="o">.</span><span class="n">max_attempts</span><span class="p">):</span> |
| <span class="n">ratio</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">aspect_ratio_range</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">ratio</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">continue</span> |
| <span class="n">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">sqrt</span><span class="p">(</span><span class="n">min_area</span> <span class="o">/</span> <span class="n">ratio</span><span class="p">)))</span> |
| <span class="n">max_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">sqrt</span><span class="p">(</span><span class="n">max_area</span> <span class="o">/</span> <span class="n">ratio</span><span class="p">)))</span> |
| <span class="k">if</span> <span class="nb">round</span><span class="p">(</span><span class="n">h</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">)</span> <span class="o"><</span> <span class="n">width</span><span class="p">:</span> |
| <span class="n">h</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">width</span> <span class="o">+</span> <span class="mf">0.499999</span><span class="p">)</span> <span class="o">/</span> <span class="n">ratio</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">h</span> <span class="o"><</span> <span class="n">height</span><span class="p">:</span> |
| <span class="n">h</span> <span class="o">=</span> <span class="n">height</span> |
| <span class="k">if</span> <span class="n">h</span> <span class="o">></span> <span class="n">max_h</span><span class="p">:</span> |
| <span class="n">h</span> <span class="o">=</span> <span class="n">max_h</span> |
| <span class="k">if</span> <span class="n">h</span> <span class="o"><</span> <span class="n">max_h</span><span class="p">:</span> |
| <span class="n">h</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">max_h</span><span class="p">)</span> |
| <span class="n">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">h</span> <span class="o">*</span> <span class="n">ratio</span><span class="p">))</span> |
| <span class="k">if</span> <span class="p">(</span><span class="n">h</span> <span class="o">-</span> <span class="n">height</span><span class="p">)</span> <span class="o"><</span> <span class="mi">2</span> <span class="ow">or</span> <span class="p">(</span><span class="n">w</span> <span class="o">-</span> <span class="n">width</span><span class="p">)</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="k">continue</span> <span class="c1"># marginal padding is not helpful</span> |
| |
| <span class="n">y</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="nb">max</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">height</span><span class="p">))</span> |
| <span class="n">x</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="nb">max</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">width</span><span class="p">))</span> |
| <span class="n">new_label</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_labels</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">),</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">new_label</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">()</span></div> |
| |
| |
| <div class="viewcode-block" id="CreateMultiRandCropAugmenter"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.CreateMultiRandCropAugmenter">[docs]</a><span class="k">def</span> <span class="nf">CreateMultiRandCropAugmenter</span><span class="p">(</span><span class="n">min_object_covered</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">aspect_ratio_range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="mf">1.33</span><span class="p">),</span> |
| <span class="n">area_range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span> <span class="n">min_eject_coverage</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> |
| <span class="n">max_attempts</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">skip_prob</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Helper function to create multiple random crop augmenters.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> min_object_covered : float or list of float, default=0.1</span> |
| <span class="sd"> The cropped area of the image must contain at least this fraction of</span> |
| <span class="sd"> any bounding box supplied. The value of this parameter should be non-negative.</span> |
| <span class="sd"> In the case of 0, the cropped area does not need to overlap any of the</span> |
| <span class="sd"> bounding boxes supplied.</span> |
| <span class="sd"> min_eject_coverage : float or list of float, default=0.3</span> |
| <span class="sd"> The minimum coverage of cropped sample w.r.t its original size. With this</span> |
| <span class="sd"> constraint, objects that have marginal area after crop will be discarded.</span> |
| <span class="sd"> aspect_ratio_range : tuple of floats or list of tuple of floats, default=(0.75, 1.33)</span> |
| <span class="sd"> The cropped area of the image must have an aspect ratio = width / height</span> |
| <span class="sd"> within this range.</span> |
| <span class="sd"> area_range : tuple of floats or list of tuple of floats, default=(0.05, 1.0)</span> |
| <span class="sd"> The cropped area of the image must contain a fraction of the supplied</span> |
| <span class="sd"> image within in this range.</span> |
| <span class="sd"> max_attempts : int or list of int, default=50</span> |
| <span class="sd"> Number of attempts at generating a cropped/padded region of the image of the</span> |
| <span class="sd"> specified constraints. After max_attempts failures, return the original image.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # An example of creating multiple random crop augmenters</span> |
| <span class="sd"> >>> min_object_covered = [0.1, 0.3, 0.5, 0.7, 0.9] # use 5 augmenters</span> |
| <span class="sd"> >>> aspect_ratio_range = (0.75, 1.33) # use same range for all augmenters</span> |
| <span class="sd"> >>> area_range = [(0.1, 1.0), (0.2, 1.0), (0.2, 1.0), (0.3, 0.9), (0.5, 1.0)]</span> |
| <span class="sd"> >>> min_eject_coverage = 0.3</span> |
| <span class="sd"> >>> max_attempts = 50</span> |
| <span class="sd"> >>> aug = mx.image.det.CreateMultiRandCropAugmenter(min_object_covered=min_object_covered,</span> |
| <span class="sd"> aspect_ratio_range=aspect_ratio_range, area_range=area_range,</span> |
| <span class="sd"> min_eject_coverage=min_eject_coverage, max_attempts=max_attempts,</span> |
| <span class="sd"> skip_prob=0)</span> |
| <span class="sd"> >>> aug.dumps() # show some details</span> |
| |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="nf">align_parameters</span><span class="p">(</span><span class="n">params</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Align parameters as pairs"""</span> |
| <span class="n">out_params</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">num</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">params</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="n">p</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span><span class="p">]</span> |
| <span class="n">out_params</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> |
| <span class="n">num</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="p">))</span> |
| <span class="c1"># align for each param</span> |
| <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">out_params</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o">!=</span> <span class="n">num</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> |
| <span class="n">out_params</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">p</span> <span class="o">*</span> <span class="n">num</span> |
| <span class="k">return</span> <span class="n">out_params</span> |
| |
| <span class="n">aligned_params</span> <span class="o">=</span> <span class="n">align_parameters</span><span class="p">([</span><span class="n">min_object_covered</span><span class="p">,</span> <span class="n">aspect_ratio_range</span><span class="p">,</span> <span class="n">area_range</span><span class="p">,</span> |
| <span class="n">min_eject_coverage</span><span class="p">,</span> <span class="n">max_attempts</span><span class="p">])</span> |
| <span class="n">augs</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">moc</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">ar</span><span class="p">,</span> <span class="n">mec</span><span class="p">,</span> <span class="n">ma</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">aligned_params</span><span class="p">):</span> |
| <span class="n">augs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">DetRandomCropAug</span><span class="p">(</span><span class="n">min_object_covered</span><span class="o">=</span><span class="n">moc</span><span class="p">,</span> <span class="n">aspect_ratio_range</span><span class="o">=</span><span class="n">arr</span><span class="p">,</span> |
| <span class="n">area_range</span><span class="o">=</span><span class="n">ar</span><span class="p">,</span> <span class="n">min_eject_coverage</span><span class="o">=</span><span class="n">mec</span><span class="p">,</span> <span class="n">max_attempts</span><span class="o">=</span><span class="n">ma</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">DetRandomSelectAug</span><span class="p">(</span><span class="n">augs</span><span class="p">,</span> <span class="n">skip_prob</span><span class="o">=</span><span class="n">skip_prob</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="CreateDetAugmenter"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.CreateDetAugmenter">[docs]</a><span class="k">def</span> <span class="nf">CreateDetAugmenter</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="mi">0</span><span class="p">,</span> <span class="n">rand_pad</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">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">pca_noise</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">inter_method</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">min_object_covered</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> |
| <span class="n">aspect_ratio_range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="mf">1.33</span><span class="p">),</span> <span class="n">area_range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">),</span> |
| <span class="n">min_eject_coverage</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">max_attempts</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">pad_val</span><span class="o">=</span><span class="p">(</span><span class="mi">127</span><span class="p">,</span> <span class="mi">127</span><span class="p">,</span> <span class="mi">127</span><span class="p">)):</span> |
| <span class="w"> </span><span class="sd">"""Create augmenters for detection.</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 : float</span> |
| <span class="sd"> [0, 1], probability to apply random cropping</span> |
| <span class="sd"> rand_pad : float</span> |
| <span class="sd"> [0, 1], probability to apply random padding</span> |
| <span class="sd"> rand_gray : float</span> |
| <span class="sd"> [0, 1], probability to convert to grayscale for all channels</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: 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"> 3: Bicubic interpolation over 4x4 pixel neighborhood.</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"> min_object_covered : float</span> |
| <span class="sd"> The cropped area of the image must contain at least this fraction of</span> |
| <span class="sd"> any bounding box supplied. The value of this parameter should be non-negative.</span> |
| <span class="sd"> In the case of 0, the cropped area does not need to overlap any of the</span> |
| <span class="sd"> bounding boxes supplied.</span> |
| <span class="sd"> min_eject_coverage : float</span> |
| <span class="sd"> The minimum coverage of cropped sample w.r.t its original size. With this</span> |
| <span class="sd"> constraint, objects that have marginal area after crop will be discarded.</span> |
| <span class="sd"> aspect_ratio_range : tuple of floats</span> |
| <span class="sd"> The cropped area of the image must have an aspect ratio = width / height</span> |
| <span class="sd"> within this range.</span> |
| <span class="sd"> area_range : tuple of floats</span> |
| <span class="sd"> The cropped area of the image must contain a fraction of the supplied</span> |
| <span class="sd"> image within in this range.</span> |
| <span class="sd"> max_attempts : int</span> |
| <span class="sd"> Number of attempts at generating a cropped/padded region of the image of the</span> |
| <span class="sd"> specified constraints. After max_attempts failures, return the original image.</span> |
| <span class="sd"> pad_val: float</span> |
| <span class="sd"> Pixel value to be filled when padding is enabled. pad_val will automatically</span> |
| <span class="sd"> be subtracted by mean and divided by std if applicable.</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.CreateDetAugmenter(data_shape=(3, 300, 300), rand_crop=0.5,</span> |
| <span class="sd"> ... rand_pad=0.5, rand_mirror=True, mean=True, brightness=0.125, contrast=0.125,</span> |
| <span class="sd"> ... saturation=0.125, pca_noise=0.05, inter_method=10, min_object_covered=[0.3, 0.5, 0.9],</span> |
| <span class="sd"> ... area_range=(0.3, 3.0))</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">DetBorrowAug</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="k">if</span> <span class="n">rand_crop</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">crop_augs</span> <span class="o">=</span> <span class="n">CreateMultiRandCropAugmenter</span><span class="p">(</span><span class="n">min_object_covered</span><span class="p">,</span> <span class="n">aspect_ratio_range</span><span class="p">,</span> |
| <span class="n">area_range</span><span class="p">,</span> <span class="n">min_eject_coverage</span><span class="p">,</span> |
| <span class="n">max_attempts</span><span class="p">,</span> <span class="n">skip_prob</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</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">crop_augs</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">rand_mirror</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">DetHorizontalFlipAug</span><span class="p">(</span><span class="mf">0.5</span><span class="p">))</span> |
| |
| <span class="c1"># apply random padding as late as possible to save computation</span> |
| <span class="k">if</span> <span class="n">rand_pad</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">pad_aug</span> <span class="o">=</span> <span class="n">DetRandomPadAug</span><span class="p">(</span><span class="n">aspect_ratio_range</span><span class="p">,</span> |
| <span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">area_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">max_attempts</span><span class="p">,</span> <span class="n">pad_val</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">DetRandomSelectAug</span><span class="p">([</span><span class="n">pad_aug</span><span class="p">],</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">rand_pad</span><span class="p">))</span> |
| |
| <span class="c1"># force resize</span> |
| <span class="n">auglist</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">DetBorrowAug</span><span class="p">(</span><span class="n">ForceResizeAug</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="n">inter_method</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">DetBorrowAug</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">DetBorrowAug</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">DetBorrowAug</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">DetBorrowAug</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">DetBorrowAug</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">np</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="n">np</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">np</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="n">np</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">DetBorrowAug</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="ImageDetIter"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageDetIter">[docs]</a><span class="k">class</span> <span class="nc">ImageDetIter</span><span class="p">(</span><span class="n">ImageIter</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Image iterator with a large number of augmentation choices for detection.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> aug_list : list or None</span> |
| <span class="sd"> Augmenter list for generating distorted images</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"> 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"> Name for detection labels</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.CreateDetAugmenter.</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">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">'label'</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">ImageDetIter</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">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">data_shape</span><span class="o">=</span><span class="n">data_shape</span><span class="p">,</span> |
| <span class="n">path_imgrec</span><span class="o">=</span><span class="n">path_imgrec</span><span class="p">,</span> <span class="n">path_imglist</span><span class="o">=</span><span class="n">path_imglist</span><span class="p">,</span> |
| <span class="n">path_root</span><span class="o">=</span><span class="n">path_root</span><span class="p">,</span> <span class="n">path_imgidx</span><span class="o">=</span><span class="n">path_imgidx</span><span class="p">,</span> |
| <span class="n">shuffle</span><span class="o">=</span><span class="n">shuffle</span><span class="p">,</span> <span class="n">part_index</span><span class="o">=</span><span class="n">part_index</span><span class="p">,</span> |
| <span class="n">num_parts</span><span class="o">=</span><span class="n">num_parts</span><span class="p">,</span> <span class="n">aug_list</span><span class="o">=</span><span class="p">[],</span> <span class="n">imglist</span><span class="o">=</span><span class="n">imglist</span><span class="p">,</span> |
| <span class="n">data_name</span><span class="o">=</span><span class="n">data_name</span><span class="p">,</span> <span class="n">label_name</span><span class="o">=</span><span class="n">label_name</span><span class="p">,</span> |
| <span class="n">last_batch_handle</span><span class="o">=</span><span class="n">last_batch_handle</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">CreateDetAugmenter</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="c1"># went through all labels to get the proper label shape</span> |
| <span class="n">label_shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_estimate_label_shape</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="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">label_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">label_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">label_shape</span> <span class="o">=</span> <span class="n">label_shape</span> |
| |
| <span class="k">def</span> <span class="nf">_check_valid_label</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Validate label and its shape."""</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span> <span class="ow">or</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o"><</span> <span class="mi">5</span><span class="p">:</span> |
| <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"Label with shape (1+, 5+) required, </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">label</span><span class="p">)</span><span class="si">}</span><span class="s2"> received."</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span> |
| <span class="n">valid_label</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">label</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">label</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">></span> <span class="n">label</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> |
| <span class="n">label</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">></span> <span class="n">label</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="k">if</span> <span class="n">valid_label</span><span class="o">.</span><span class="n">size</span> <span class="o"><</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">'Invalid label occurs.'</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_estimate_label_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Helper function to estimate label shape"""</span> |
| <span class="n">max_count</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="kc">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="k">while</span> <span class="kc">True</span><span class="p">:</span> |
| <span class="n">label</span><span class="p">,</span> <span class="n">_</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">label</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parse_label</span><span class="p">(</span><span class="n">label</span><span class="p">)</span> |
| <span class="n">max_count</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_count</span><span class="p">,</span> <span class="n">label</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="k">except</span> <span class="ne">StopIteration</span><span class="p">:</span> |
| <span class="k">pass</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">max_count</span><span class="p">,</span> <span class="n">label</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="k">if</span> <span class="n">label</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="mi">5</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_parse_label</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Helper function to parse object detection label.</span> |
| |
| <span class="sd"> Format for raw label:</span> |
| <span class="sd"> n \t k \t ... \t [id \t xmin\t ymin \t xmax \t ymax \t ...] \t [repeat]</span> |
| <span class="sd"> where n is the width of header, 2 or larger</span> |
| <span class="sd"> k is the width of each object annotation, can be arbitrary, at least 5</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">label</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">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="n">raw</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span> |
| <span class="k">if</span> <span class="n">raw</span><span class="o">.</span><span class="n">size</span> <span class="o"><</span> <span class="mi">7</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"Label shape is invalid: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">raw</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span> |
| <span class="n">header_width</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">raw</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="n">obj_width</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">raw</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> |
| <span class="k">if</span> <span class="p">(</span><span class="n">raw</span><span class="o">.</span><span class="n">size</span> <span class="o">-</span> <span class="n">header_width</span><span class="p">)</span> <span class="o">%</span> <span class="n">obj_width</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"Label shape </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">raw</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="si">}</span><span class="s2"> inconsistent with annotation width </span><span class="si">{</span><span class="n">obj_width</span><span class="si">}</span><span class="s2">."</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">raw</span><span class="p">[</span><span class="n">header_width</span><span class="p">:],</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">obj_width</span><span class="p">))</span> |
| <span class="c1"># remove bad ground-truths</span> |
| <span class="n">valid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">out</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">></span> <span class="n">out</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">out</span><span class="p">[:,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">></span> <span class="n">out</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="k">if</span> <span class="n">valid</span><span class="o">.</span><span class="n">size</span> <span class="o"><</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">'Encounter sample with no valid label.'</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">out</span><span class="p">[</span><span class="n">valid</span><span class="p">,</span> <span class="p">:]</span> |
| |
| <div class="viewcode-block" id="ImageDetIter.reshape"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageDetIter.reshape">[docs]</a> <span class="k">def</span> <span class="nf">reshape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">label_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Reshape iterator for data_shape or label_shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> data_shape : tuple or None</span> |
| <span class="sd"> Reshape the data_shape to the new shape if not None</span> |
| <span class="sd"> label_shape : tuple or None</span> |
| <span class="sd"> Reshape label shape to new shape if not None</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">data_shape</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">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="bp">self</span><span class="o">.</span><span class="n">provide_data</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</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="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="k">if</span> <span class="n">label_shape</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">check_label_shape</span><span class="p">(</span><span class="n">label_shape</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="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">0</span><span class="p">],</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,)</span> <span class="o">+</span> <span class="n">label_shape</span><span class="p">)]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">label_shape</span> <span class="o">=</span> <span class="n">label_shape</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">"""Override the 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="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">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="n">label</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parse_label</span><span class="p">(</span><span class="n">label</span><span class="p">)</span> |
| <span class="n">data</span><span class="p">,</span> <span class="n">label</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="n">label</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_check_valid_label</span><span class="p">(</span><span class="n">label</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="k">for</span> <span class="n">datum</span> <span class="ow">in</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">datum</span><span class="p">)</span> |
| <span class="n">num_object</span> <span class="o">=</span> <span class="n">label</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">batch_label</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">:</span><span class="n">num_object</span><span class="p">]</span> <span class="o">=</span> <span class="n">array_fn</span><span class="p">(</span><span class="n">label</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">num_object</span> <span class="o"><</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">shape</span><span class="p">[</span><span class="mi">0</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="n">num_object</span><span class="p">:]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</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="ImageDetIter.next"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageDetIter.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">"""Override the function for returning next batch."""</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="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">batch_label</span><span class="p">[:]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</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="ImageDetIter.augmentation_transform"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageDetIter.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="n">label</span><span class="p">):</span> <span class="c1"># pylint: disable=arguments-differ</span> |
| <span class="w"> </span><span class="sd">"""Override Transforms input data with specified augmentations."""</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="p">,</span> <span class="n">label</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="n">label</span><span class="p">)</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="ImageDetIter.check_label_shape"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageDetIter.check_label_shape">[docs]</a> <span class="k">def</span> <span class="nf">check_label_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">label_shape</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Checks if the new label 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">label_shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'label_shape should have length 2'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">label_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span> |
| <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'Attempts to reduce label count from </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">label_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1"> to </span><span class="si">{</span><span class="n">label_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">, not allowed.'</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">label_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</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="mi">2</span><span class="p">]:</span> |
| <span class="n">msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'label_shape object width inconsistent: </span><span class="si">{</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="mi">2</span><span class="p">]</span><span class="si">}</span><span class="s1"> vs </span><span class="si">{</span><span class="n">label_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s1">.'</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="ImageDetIter.draw_next"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageDetIter.draw_next">[docs]</a> <span class="k">def</span> <span class="nf">draw_next</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">thickness</span><span class="o">=</span><span class="mi">2</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">clip</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> |
| <span class="n">waitKey</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">window_name</span><span class="o">=</span><span class="s1">'draw_next'</span><span class="p">,</span> <span class="n">id2labels</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Display next image with bounding boxes drawn.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> color : tuple</span> |
| <span class="sd"> Bounding box color in RGB, use None for random color</span> |
| <span class="sd"> thickness : int</span> |
| <span class="sd"> Bounding box border thickness</span> |
| <span class="sd"> mean : True or numpy.ndarray</span> |
| <span class="sd"> Compensate for the mean to have better visual effect</span> |
| <span class="sd"> std : True or numpy.ndarray</span> |
| <span class="sd"> Revert standard deviations</span> |
| <span class="sd"> clip : bool</span> |
| <span class="sd"> If true, clip to [0, 255] for better visual effect</span> |
| <span class="sd"> waitKey : None or int</span> |
| <span class="sd"> Hold the window for waitKey milliseconds if set, skip ploting if None</span> |
| <span class="sd"> window_name : str</span> |
| <span class="sd"> Plot window name if waitKey is set.</span> |
| <span class="sd"> id2labels : dict</span> |
| <span class="sd"> Mapping of labels id to labels name.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> numpy.ndarray</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # use draw_next to get images with bounding boxes drawn</span> |
| <span class="sd"> >>> iterator = mx.image.ImageDetIter(1, (3, 600, 600), path_imgrec='train.rec')</span> |
| <span class="sd"> >>> for image in iterator.draw_next(waitKey=None):</span> |
| <span class="sd"> ... # display image</span> |
| <span class="sd"> >>> # or let draw_next display using cv2 module</span> |
| <span class="sd"> >>> for image in iterator.draw_next(waitKey=0, window_name='disp'):</span> |
| <span class="sd"> ... pass</span> |
| <span class="sd"> """</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="k">as</span> <span class="n">e</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">'Unable to import cv2, skip drawing: </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">return</span> |
| <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="k">while</span> <span class="kc">True</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="n">label</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parse_label</span><span class="p">(</span><span class="n">label</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">count</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="n">data</span><span class="p">,</span> <span class="n">label</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="n">label</span><span class="p">)</span> |
| <span class="n">image</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| |
| <span class="c1"># revert color_normalize</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">np</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="n">np</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">std</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">image</span> <span class="o">*=</span> <span class="n">std</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">np</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="n">np</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">mean</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">image</span> <span class="o">+=</span> <span class="n">mean</span> |
| |
| <span class="c1"># swap RGB</span> |
| <span class="n">image</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span> <span class="o">=</span> <span class="n">image</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> |
| <span class="k">if</span> <span class="n">clip</span><span class="p">:</span> |
| <span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</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">minimum</span><span class="p">(</span><span class="mi">255</span><span class="p">,</span> <span class="n">image</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">color</span><span class="p">:</span> |
| <span class="n">color</span> <span class="o">=</span> <span class="n">color</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span> |
| <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">shape</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">label</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">x1</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">width</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">x1</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">continue</span> |
| <span class="n">y1</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">height</span><span class="p">)</span> |
| <span class="n">x2</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="n">width</span><span class="p">)</span> |
| <span class="n">y2</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">4</span><span class="p">]</span> <span class="o">*</span> <span class="n">height</span><span class="p">)</span> |
| <span class="n">bc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">*</span> <span class="mi">255</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">color</span> <span class="k">else</span> <span class="n">color</span> |
| <span class="n">cv2</span><span class="o">.</span><span class="n">rectangle</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y1</span><span class="p">),</span> <span class="p">(</span><span class="n">x2</span><span class="p">,</span> <span class="n">y2</span><span class="p">),</span> <span class="n">bc</span><span class="p">,</span> <span class="n">thickness</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">id2labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">cls_id</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> |
| <span class="k">if</span> <span class="n">cls_id</span> <span class="ow">in</span> <span class="n">id2labels</span><span class="p">:</span> |
| <span class="n">cls_name</span> <span class="o">=</span> <span class="n">id2labels</span><span class="p">[</span><span class="n">cls_id</span><span class="p">]</span> |
| <span class="n">text</span> <span class="o">=</span> <span class="s2">"</span><span class="si">{:s}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cls_name</span><span class="p">)</span> |
| <span class="n">font</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">FONT_HERSHEY_SIMPLEX</span> |
| <span class="n">font_scale</span> <span class="o">=</span> <span class="mf">0.5</span> |
| <span class="n">text_height</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">getTextSize</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">font</span><span class="p">,</span> <span class="n">font_scale</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="n">tc</span> <span class="o">=</span> <span class="p">(</span><span class="mi">255</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">255</span><span class="p">)</span> |
| <span class="n">tpos</span> <span class="o">=</span> <span class="p">(</span><span class="n">x1</span> <span class="o">+</span> <span class="mi">5</span><span class="p">,</span> <span class="n">y1</span> <span class="o">+</span> <span class="n">text_height</span> <span class="o">+</span> <span class="mi">5</span><span class="p">)</span> |
| <span class="n">cv2</span><span class="o">.</span><span class="n">putText</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">text</span><span class="p">,</span> <span class="n">tpos</span><span class="p">,</span> <span class="n">font</span><span class="p">,</span> <span class="n">font_scale</span><span class="p">,</span> <span class="n">tc</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">waitKey</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">cv2</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">window_name</span><span class="p">,</span> <span class="n">image</span><span class="p">)</span> |
| <span class="n">cv2</span><span class="o">.</span><span class="n">waitKey</span><span class="p">(</span><span class="n">waitKey</span><span class="p">)</span> |
| <span class="k">yield</span> <span class="n">image</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">count</span><span class="p">:</span> |
| <span class="k">return</span></div> |
| |
| <div class="viewcode-block" id="ImageDetIter.sync_label_shape"><a class="viewcode-back" href="../../../api/legacy/image/index.html#mxnet.image.ImageDetIter.sync_label_shape">[docs]</a> <span class="k">def</span> <span class="nf">sync_label_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">it</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""Synchronize label shape with the input iterator. This is useful when</span> |
| <span class="sd"> train/validation iterators have different label padding.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> it : ImageDetIter</span> |
| <span class="sd"> The other iterator to synchronize</span> |
| <span class="sd"> verbose : bool</span> |
| <span class="sd"> Print verbose log if true</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> ImageDetIter</span> |
| <span class="sd"> The synchronized other iterator, the internal label shape is updated as well.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> train_iter = mx.image.ImageDetIter(32, (3, 300, 300), path_imgrec='train.rec')</span> |
| <span class="sd"> >>> val_iter = mx.image.ImageDetIter(32, (3, 300, 300), path.imgrec='val.rec')</span> |
| <span class="sd"> >>> train_iter.label_shape</span> |
| <span class="sd"> (30, 6)</span> |
| <span class="sd"> >>> val_iter.label_shape</span> |
| <span class="sd"> (25, 6)</span> |
| <span class="sd"> >>> val_iter = train_iter.sync_label_shape(val_iter, verbose=False)</span> |
| <span class="sd"> >>> train_iter.label_shape</span> |
| <span class="sd"> (30, 6)</span> |
| <span class="sd"> >>> val_iter.label_shape</span> |
| <span class="sd"> (30, 6)</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">it</span><span class="p">,</span> <span class="n">ImageDetIter</span><span class="p">),</span> <span class="s1">'Synchronize with invalid iterator.'</span> |
| <span class="n">train_label_shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_shape</span> |
| <span class="n">val_label_shape</span> <span class="o">=</span> <span class="n">it</span><span class="o">.</span><span class="n">label_shape</span> |
| <span class="k">assert</span> <span class="n">train_label_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">val_label_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">"object width mismatch."</span> |
| <span class="n">max_count</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">train_label_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">val_label_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="k">if</span> <span class="n">max_count</span> <span class="o">></span> <span class="n">train_label_shape</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">reshape</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="p">(</span><span class="n">max_count</span><span class="p">,</span> <span class="n">train_label_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span> |
| <span class="k">if</span> <span class="n">max_count</span> <span class="o">></span> <span class="n">val_label_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span> |
| <span class="n">it</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="p">(</span><span class="n">max_count</span><span class="p">,</span> <span class="n">val_label_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span> |
| <span class="k">if</span> <span class="n">verbose</span> <span class="ow">and</span> <span class="n">max_count</span> <span class="o">></span> <span class="nb">min</span><span class="p">(</span><span class="n">train_label_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">val_label_shape</span><span class="p">[</span><span class="mi">0</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">'Resized label_shape to (</span><span class="si">%d</span><span class="s1">, </span><span class="si">%d</span><span class="s1">).'</span><span class="p">,</span> <span class="n">max_count</span><span class="p">,</span> <span class="n">train_label_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> |
| <span class="k">return</span> <span class="n">it</span></div></div> |
| </pre></div> |
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