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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> |
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| <li class="toctree-l4"><a class="reference internal" href="../../np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
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| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.num_gpus.html">mxnet.npx.num_gpus</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.fully_connected.html">mxnet.npx.fully_connected</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <nav class="mdl-navigation"> |
| <ul class="current"> |
| <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> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
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| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../tutorials/performance/compression/int8.html">Deploy with int-8</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_readme.html">Install MXNet with oneDNN</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../tutorials/performance/backend/dnnl/dnnl_quantization_inc.html">Improving accuracy with Intel® Neural Compressor</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../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="../../np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
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| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l2"><a class="reference internal" href="../../npx/index.html">NPX: NumPy Neural Network Extension</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.set_np.html">mxnet.npx.set_np</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu.html">mxnet.npx.gpu</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.gpu_memory_info.html">mxnet.npx.gpu_memory_info</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.activation.html">mxnet.npx.activation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.batch_norm.html">mxnet.npx.batch_norm</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.dropout.html">mxnet.npx.dropout</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.embedding.html">mxnet.npx.embedding</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.layer_norm.html">mxnet.npx.layer_norm</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.pooling.html">mxnet.npx.pooling</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../npx/generated/mxnet.npx.rnn.html">mxnet.npx.rnn</a></li> |
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| |
| <div class="section" id="module-mxnet.io"> |
| <span id="mxnet-io"></span><h1>mxnet.io<a class="headerlink" href="#module-mxnet.io" title="Permalink to this headline">¶</a></h1> |
| <p>Data iterators for common data formats and utility functions.</p> |
| <p><strong>Functions</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.CSVIter" title="mxnet.io.CSVIter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CSVIter</span></code></a>(*args, **kwargs)</p></td> |
| <td><p>b”Returns the CSV file iterator.nnIn this function, the <cite>data_shape</cite> parameter is used to set the shape of each line of the input data.nIf a row in an input file is <cite>1,2,3,4,5,6`</cite> and <cite>data_shape</cite> is (3,2), that rownwill be reshaped, yielding the array [[1,2],[3,4],[5,6]] of shape (3,2).nnBy default, the <cite>CSVIter</cite> has <cite>round_batch</cite> parameter set to <code class="docutils literal notranslate"><span class="pre">True</span></code>. So, if <cite>batch_size</cite>nis 3 and there are 4 total rows in CSV file, 2 more examplesnare consumed at the first round. If <cite>reset</cite> function is called after first round,nthe call is ignored and remaining examples are returned in the second round.nnIf one wants all the instances in the second round after calling <cite>reset</cite>, make surento set <cite>round_batch</cite> to False.nnIf <code class="docutils literal notranslate"><span class="pre">data_csv</span> <span class="pre">=</span> <span class="pre">'data/'</span></code> is set, then all the files in this directory will be read.nn``reset()`` is expected to be called only after a complete pass of data.nnBy default, the CSVIter parses all entries in the data file as float32 data type,nif <cite>dtype</cite> argument is set to be ‘int32’ or ‘int64’ then CSVIter will parse all entries in the filenas int32 or int64 data type accordingly.nnExamples::nn // Contents of CSV file <code class="docutils literal notranslate"><span class="pre">data/data.csv</span></code>.n 1,2,3n 2,3,4n 3,4,5n 4,5,6nn // Creates a <cite>CSVIter</cite> with <cite>batch_size`=2 and default `round_batch`=True.n CSVIter = mx.io.CSVIter(data_csv = ‘data/data.csv’, data_shape = (3,),n batch_size = 2)nn // Two batches read from the above iterator are as follows:n [[ 1. 2. 3.]n [ 2. 3. 4.]]n [[ 3. 4. 5.]n [ 4. 5. 6.]]nn // Creates a `CSVIter</cite> with default <cite>round_batch</cite> set to True.n CSVIter = mx.io.CSVIter(data_csv = ‘data/data.csv’, data_shape = (3,),n batch_size = 3)nn // Two batches read from the above iterator in the first pass are as follows:n [[1. 2. 3.]n [2. 3. 4.]n [3. 4. 5.]]nn [[4. 5. 6.]n [1. 2. 3.]n [2. 3. 4.]]nn // Now, <cite>reset</cite> method is called.n CSVIter.reset()nn // Batch read from the above iterator in the second pass is as follows:n [[ 3. 4. 5.]n [ 4. 5. 6.]n [ 1. 2. 3.]]nn // Creates a <cite>CSVIter</cite> with <cite>round_batch`=False.n CSVIter = mx.io.CSVIter(data_csv = ‘data/data.csv’, data_shape = (3,),n batch_size = 3, round_batch=False)nn // Contents of two batches read from the above iterator in both passes, after callingn // `reset</cite> method before second pass, is as follows:n [[1. 2. 3.]n [2. 3. 4.]n [3. 4. 5.]]nn [[4. 5. 6.]n [2. 3. 4.]n [3. 4. 5.]]nn // Creates a ‘CSVIter’ with <cite>dtype`=’int32’n CSVIter = mx.io.CSVIter(data_csv = ‘data/data.csv’, data_shape = (3,),n batch_size = 3, round_batch=False, dtype=’int32’)nn // Contents of two batches read from the above iterator in both passes, after callingn // `reset</cite> method before second pass, is as follows:n [[1 2 3]n [2 3 4]n [3 4 5]]nn [[4 5 6]n [2 3 4]n [3 4 5]]nnnnDefined in /work/mxnet/src/io/iter_csv.cc:L307”</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.ImageDetRecordIter" title="mxnet.io.ImageDetRecordIter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ImageDetRecordIter</span></code></a>(*args, **kwargs)</p></td> |
| <td><p>b’Create iterator for image detection dataset packed in recordio.’</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.ImageRecordInt8Iter" title="mxnet.io.ImageRecordInt8Iter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ImageRecordInt8Iter</span></code></a>(*args, **kwargs)</p></td> |
| <td><p>b”Iterating on image RecordIO filesnn.. note:: <code class="docutils literal notranslate"><span class="pre">ImageRecordInt8Iter</span></code> is deprecated. Use ImageRecordIter(dtype=’int8’) instead.nnThis iterator is identical to <code class="docutils literal notranslate"><span class="pre">ImageRecordIter</span></code> except for using <code class="docutils literal notranslate"><span class="pre">int8</span></code> asnthe data type instead of <code class="docutils literal notranslate"><span class="pre">float</span></code>.nnnnDefined in /work/mxnet/src/io/iter_image_recordio_2.cc:L947”</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.ImageRecordIter" title="mxnet.io.ImageRecordIter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ImageRecordIter</span></code></a>(*args, **kwargs)</p></td> |
| <td><p>b’Iterates on image RecordIO filesnnReads batches of images from .rec RecordIO files. One can use <code class="docutils literal notranslate"><span class="pre">im2rec.py</span></code> tooln(in tools/) to pack raw image files into RecordIO files. This iterator is lessnflexible to customization but is fast and has lot of language bindings. Toniterate over raw images directly use <code class="docutils literal notranslate"><span class="pre">ImageIter</span></code> instead (in Python).nnExample::nn data_iter = mx.io.ImageRecordIter(n path_imgrec=”./sample.rec”, # The target record file.n data_shape=(3, 227, 227), # Output data shape; 227x227 region will be cropped from the original image.n batch_size=4, # Number of items per batch.n resize=256 # Resize the shorter edge to 256 before cropping.n # You can specify more augmentation options. Use help(mx.io.ImageRecordIter) to see all the options.n )n # You can now use the data_iter to access batches of images.n batch = data_iter.next() # first batch.n images = batch.data[0] # This will contain 4 (=batch_size) images each of 3x227x227.n # process the imagesn …n data_iter.reset() # To restart the iterator from the beginning.nnnnDefined in /work/mxnet/src/io/iter_image_recordio_2.cc:L914’</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.ImageRecordIter_v1" title="mxnet.io.ImageRecordIter_v1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ImageRecordIter_v1</span></code></a>(*args, **kwargs)</p></td> |
| <td><p>b’Iterating on image RecordIO filesnn.. note::nn <code class="docutils literal notranslate"><span class="pre">ImageRecordIter_v1</span></code> is deprecated. Use <code class="docutils literal notranslate"><span class="pre">ImageRecordIter</span></code> instead.nnnRead images batches from RecordIO files with a rich of data augmentationnoptions.nnOne can use <code class="docutils literal notranslate"><span class="pre">tools/im2rec.py</span></code> to pack individual image files into RecordIOnfiles.nnnnDefined in /work/mxnet/src/io/iter_image_recordio.cc:L354’</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.ImageRecordUInt8Iter" title="mxnet.io.ImageRecordUInt8Iter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ImageRecordUInt8Iter</span></code></a>(*args, **kwargs)</p></td> |
| <td><p>b”Iterating on image RecordIO filesnn.. note:: ImageRecordUInt8Iter is deprecated. Use ImageRecordIter(dtype=’uint8’) instead.nnThis iterator is identical to <code class="docutils literal notranslate"><span class="pre">ImageRecordIter</span></code> except for using <code class="docutils literal notranslate"><span class="pre">uint8</span></code> asnthe data type instead of <code class="docutils literal notranslate"><span class="pre">float</span></code>.nnnnDefined in /work/mxnet/src/io/iter_image_recordio_2.cc:L931”</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.ImageRecordUInt8Iter_v1" title="mxnet.io.ImageRecordUInt8Iter_v1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ImageRecordUInt8Iter_v1</span></code></a>(*args, **kwargs)</p></td> |
| <td><p>b’Iterating on image RecordIO filesnn.. note::nn <code class="docutils literal notranslate"><span class="pre">ImageRecordUInt8Iter_v1</span></code> is deprecated. Use <code class="docutils literal notranslate"><span class="pre">ImageRecordUInt8Iter</span></code> instead.nnThis iterator is identical to <code class="docutils literal notranslate"><span class="pre">ImageRecordIter</span></code> except for using <code class="docutils literal notranslate"><span class="pre">uint8</span></code> asnthe data type instead of <code class="docutils literal notranslate"><span class="pre">float</span></code>.nnnnDefined in /work/mxnet/src/io/iter_image_recordio.cc:L377’</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.LibSVMIter" title="mxnet.io.LibSVMIter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LibSVMIter</span></code></a>(*args, **kwargs)</p></td> |
| <td><p>b”Returns the LibSVM iterator which returns data with <cite>csr</cite>nstorage type. This iterator is experimental and should be used with care.nnThe input data is stored in a format similar to LibSVM file format, except that the <strong>indicesnare expected to be zero-based instead of one-based, and the column indices for each row arenexpected to be sorted in ascending order</strong>. Details of the LibSVM format are availablen`here. <<a class="reference external" href="https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/">https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/</a>>`_nnnThe <cite>data_shape</cite> parameter is used to set the shape of each line of the data.nThe dimension of both <cite>data_shape</cite> and <cite>label_shape</cite> are expected to be 1.nnThe <cite>data_libsvm</cite> parameter is used to set the path input LibSVM file.nWhen it is set to a directory, all the files in the directory will be read.nnWhen <cite>label_libsvm</cite> is set to <code class="docutils literal notranslate"><span class="pre">NULL</span></code>, both data and label are read from the file specifiednby <cite>data_libsvm</cite>. In this case, the data is stored in <cite>csr</cite> storage type, while the label is a 1Dndense array.nnThe <cite>LibSVMIter</cite> only support <cite>round_batch</cite> parameter set to <code class="docutils literal notranslate"><span class="pre">True</span></code>. Therefore, if <cite>batch_size</cite>nis 3 and there are 4 total rows in libsvm file, 2 more examples are consumed at the first round.nnWhen <cite>num_parts</cite> and <cite>part_index</cite> are provided, the data is split into <cite>num_parts</cite> partitions,nand the iterator only reads the <cite>part_index</cite>-th partition. However, the partitions are notnguaranteed to be even.nn``reset()`` is expected to be called only after a complete pass of data.nnExample::nn # Contents of libsvm file <code class="docutils literal notranslate"><span class="pre">data.t</span></code>.n 1.0 0:0.5 2:1.2n -2.0n -3.0 0:0.6 1:2.4 2:1.2n 4 2:-1.2nn # Creates a <cite>LibSVMIter</cite> with <cite>batch_size`=3.n >>> data_iter = mx.io.LibSVMIter(data_libsvm = ‘data.t’, data_shape = (3,), batch_size = 3)n # The data of the first batch is stored in csr storage typen >>> batch = data_iter.next()n >>> csr = batch.data[0]n <CSRNDArray 3x3 @cpu(0)>n >>> csr.asnumpy()n [[ 0.5 0. 1.2 ]n [ 0. 0. 0. ]n [ 0.6 2.4 1.2]]n # The label of first batchn >>> label = batch.label[0]n >>> labeln [ 1. -2. -3.]n <NDArray 3 @cpu(0)>nn >>> second_batch = data_iter.next()n # The data of the second batchn >>> second_batch.data[0].asnumpy()n [[ 0. 0. -1.2 ]n [ 0.5 0. 1.2 ]n [ 0. 0. 0. ]]n # The label of the second batchn >>> second_batch.label[0].asnumpy()n [ 4. 1. -2.]nn >>> data_iter.reset()n # To restart the iterator for the second pass of the datannWhen `label_libsvm</cite> is set to the path to another LibSVM file,ndata is read from <cite>data_libsvm</cite> and label from <cite>label_libsvm</cite>.nIn this case, both data and label are stored in the csr format.nIf the label column in the <cite>data_libsvm</cite> file is ignored.nnExample::nn # Contents of libsvm file <code class="docutils literal notranslate"><span class="pre">label.t</span></code>n 1.0n -2.0 0:0.125n -3.0 2:1.2n 4 1:1.0 2:-1.2nn # Creates a <cite>LibSVMIter</cite> with specified label filen >>> data_iter = mx.io.LibSVMIter(data_libsvm = ‘data.t’, data_shape = (3,),n label_libsvm = ‘label.t’, label_shape = (3,), batch_size = 3)nn # Both data and label are in csr storage typen >>> batch = data_iter.next()n >>> csr_data = batch.data[0]n <CSRNDArray 3x3 @cpu(0)>n >>> csr_data.asnumpy()n [[ 0.5 0. 1.2 ]n [ 0. 0. 0. ]n [ 0.6 2.4 1.2 ]]n >>> csr_label = batch.label[0]n <CSRNDArray 3x3 @cpu(0)>n >>> csr_label.asnumpy()n [[ 0. 0. 0. ]n [ 0.125 0. 0. ]n [ 0. 0. 1.2 ]]nnnnDefined in /work/mxnet/src/io/iter_libsvm.cc:L299”</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.MNISTIter" title="mxnet.io.MNISTIter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MNISTIter</span></code></a>(*args, **kwargs)</p></td> |
| <td><p>b’Iterating on the MNIST dataset.nnDefined in /work/mxnet/src/io/iter_mnist.cc:L258’</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Classes</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.DataBatch" title="mxnet.io.DataBatch"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataBatch</span></code></a>(data[, label, pad, index, …])</p></td> |
| <td><p>A data batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.DataDesc" title="mxnet.io.DataDesc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataDesc</span></code></a></p></td> |
| <td><p>DataDesc is used to store name, shape, type and layout information of the data or the label.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.DataIter" title="mxnet.io.DataIter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataIter</span></code></a>([batch_size])</p></td> |
| <td><p>The base class for an MXNet data iterator.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.MXDataIter" title="mxnet.io.MXDataIter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MXDataIter</span></code></a>(handle[, data_name, label_name])</p></td> |
| <td><p>A python wrapper a C++ data iterator.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.NDArrayIter" title="mxnet.io.NDArrayIter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NDArrayIter</span></code></a>(data[, label, batch_size, …])</p></td> |
| <td><p>Returns an iterator for <code class="docutils literal notranslate"><span class="pre">mx.nd.NDArray</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>, <code class="docutils literal notranslate"><span class="pre">h5py.Dataset</span></code> <code class="docutils literal notranslate"><span class="pre">mx.nd.sparse.CSRNDArray</span></code> or <code class="docutils literal notranslate"><span class="pre">scipy.sparse.csr_matrix</span></code>.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.PrefetchingIter" title="mxnet.io.PrefetchingIter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PrefetchingIter</span></code></a>(iters[, rename_data, …])</p></td> |
| <td><p>Performs pre-fetch for other data iterators.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.ResizeIter" title="mxnet.io.ResizeIter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ResizeIter</span></code></a>(data_iter, size[, reset_internal])</p></td> |
| <td><p>Resize a data iterator to a given number of batches.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="function"> |
| <dt id="mxnet.io.CSVIter"> |
| <code class="sig-name descname">CSVIter</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.CSVIter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>b”Returns the CSV file iterator.nnIn this function, the <cite>data_shape</cite> parameter is used to set the shape of each line of the input data.nIf a row in an input file is <cite>1,2,3,4,5,6`</cite> and <cite>data_shape</cite> is (3,2), that rownwill be reshaped, yielding the array [[1,2],[3,4],[5,6]] of shape (3,2).nnBy default, the <cite>CSVIter</cite> has <cite>round_batch</cite> parameter set to <code class="docutils literal notranslate"><span class="pre">True</span></code>. So, if <cite>batch_size</cite>nis 3 and there are 4 total rows in CSV file, 2 more examplesnare consumed at the first round. If <cite>reset</cite> function is called after first round,nthe call is ignored and remaining examples are returned in the second round.nnIf one wants all the instances in the second round after calling <cite>reset</cite>, make surento set <cite>round_batch</cite> to False.nnIf <code class="docutils literal notranslate"><span class="pre">data_csv</span> <span class="pre">=</span> <span class="pre">'data/'</span></code> is set, then all the files in this directory will be read.nn``reset()`` is expected to be called only after a complete pass of data.nnBy default, the CSVIter parses all entries in the data file as float32 data type,nif <cite>dtype</cite> argument is set to be ‘int32’ or ‘int64’ then CSVIter will parse all entries in the filenas int32 or int64 data type accordingly.nnExamples::nn // Contents of CSV file <code class="docutils literal notranslate"><span class="pre">data/data.csv</span></code>.n 1,2,3n 2,3,4n 3,4,5n 4,5,6nn // Creates a <cite>CSVIter</cite> with <cite>batch_size`=2 and default `round_batch`=True.n CSVIter = mx.io.CSVIter(data_csv = ‘data/data.csv’, data_shape = (3,),n batch_size = 2)nn // Two batches read from the above iterator are as follows:n [[ 1. 2. 3.]n [ 2. 3. 4.]]n [[ 3. 4. 5.]n [ 4. 5. 6.]]nn // Creates a `CSVIter</cite> with default <cite>round_batch</cite> set to True.n CSVIter = mx.io.CSVIter(data_csv = ‘data/data.csv’, data_shape = (3,),n batch_size = 3)nn // Two batches read from the above iterator in the first pass are as follows:n [[1. 2. 3.]n [2. 3. 4.]n [3. 4. 5.]]nn [[4. 5. 6.]n [1. 2. 3.]n [2. 3. 4.]]nn // Now, <cite>reset</cite> method is called.n CSVIter.reset()nn // Batch read from the above iterator in the second pass is as follows:n [[ 3. 4. 5.]n [ 4. 5. 6.]n [ 1. 2. 3.]]nn // Creates a <cite>CSVIter</cite> with <cite>round_batch`=False.n CSVIter = mx.io.CSVIter(data_csv = ‘data/data.csv’, data_shape = (3,),n batch_size = 3, round_batch=False)nn // Contents of two batches read from the above iterator in both passes, after callingn // `reset</cite> method before second pass, is as follows:n [[1. 2. 3.]n [2. 3. 4.]n [3. 4. 5.]]nn [[4. 5. 6.]n [2. 3. 4.]n [3. 4. 5.]]nn // Creates a ‘CSVIter’ with <cite>dtype`=’int32’n CSVIter = mx.io.CSVIter(data_csv = ‘data/data.csv’, data_shape = (3,),n batch_size = 3, round_batch=False, dtype=’int32’)nn // Contents of two batches read from the above iterator in both passes, after callingn // `reset</cite> method before second pass, is as follows:n [[1 2 3]n [2 3 4]n [3 4 5]]nn [[4 5 6]n [2 3 4]n [3 4 5]]nnnnDefined in /work/mxnet/src/io/iter_csv.cc:L307”</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data_csv</strong> (<em>string</em><em>, </em><em>required</em>) – The input CSV file or a directory path.</p></li> |
| <li><p><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>required</em>) – The shape of one example.</p></li> |
| <li><p><strong>label_csv</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='NULL'</em>) – The input CSV file or a directory path. If NULL, all labels will be returned as 0.</p></li> |
| <li><p><strong>label_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>1</em><em>]</em>) – The shape of one label.</p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>required</em>) – Batch size.</p></li> |
| <li><p><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Whether to use round robin to handle overflow batch or not.</p></li> |
| <li><p><strong>prefetch_buffer</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</p></li> |
| <li><p><strong>ctx</strong> (<em>{'cpu'</em><em>, </em><em>'cpu_pinned'</em><em>, </em><em>'gpu'}</em><em>,</em><em>optional</em><em>, </em><em>default='gpu'</em>) – Context data loader optimized for. Note that it only indicates the optimization strategy for devices, by no means the prefetcher will load data to GPUs. If ctx is ‘cpu_pinned’ and device_id is not -1, it will use cpu_pinned(device_id) as ctx</p></li> |
| <li><p><strong>device_id</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – The default device id for context. -1 indicate it’s on default device</p></li> |
| <li><p><strong>dtype</strong> (<em>{None</em><em>, </em><em>'bfloat16'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'int64'</em><em>, </em><em>'int8'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal notranslate"><span class="pre">None</span></code> means no change.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result iterator.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.io.MXDataIter" title="mxnet.io.MXDataIter">MXDataIter</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.io.DataBatch"> |
| <em class="property">class </em><code class="sig-name descname">DataBatch</code><span class="sig-paren">(</span><em class="sig-param">data</em>, <em class="sig-param">label=None</em>, <em class="sig-param">pad=None</em>, <em class="sig-param">index=None</em>, <em class="sig-param">bucket_key=None</em>, <em class="sig-param">provide_data=None</em>, <em class="sig-param">provide_label=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#DataBatch"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.DataBatch" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
| <p>A data batch.</p> |
| <p>MXNet’s data iterator returns a batch of data for each <cite>next</cite> call. |
| This data contains <cite>batch_size</cite> number of examples.</p> |
| <p>If the input data consists of images, then shape of these images depend on |
| the <cite>layout</cite> attribute of <cite>DataDesc</cite> object in <cite>provide_data</cite> parameter.</p> |
| <p>If <cite>layout</cite> is set to ‘NCHW’ then, images should be stored in a 4-D matrix |
| of shape <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">num_channel,</span> <span class="pre">height,</span> <span class="pre">width)</span></code>. |
| If <cite>layout</cite> is set to ‘NHWC’ then, images should be stored in a 4-D matrix |
| of shape <code class="docutils literal notranslate"><span class="pre">(batch_size,</span> <span class="pre">height,</span> <span class="pre">width,</span> <span class="pre">num_channel)</span></code>. |
| The channels are often in RGB order.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (list of <cite>NDArray</cite>, each array containing <cite>batch_size</cite> examples.) – A list of input data.</p></li> |
| <li><p><strong>label</strong> (list of <cite>NDArray</cite>, each array often containing a 1-dimensional array. optional) – A list of input labels.</p></li> |
| <li><p><strong>pad</strong> (<em>int</em><em>, </em><em>optional</em>) – The number of examples padded at the end of a batch. It is used when the |
| total number of examples read is not divisible by the <cite>batch_size</cite>. |
| These extra padded examples are ignored in prediction.</p></li> |
| <li><p><strong>index</strong> (<em>numpy.array</em><em>, </em><em>optional</em>) – The example indices in this batch.</p></li> |
| <li><p><strong>bucket_key</strong> (<em>int</em><em>, </em><em>optional</em>) – The bucket key, used for bucketing module.</p></li> |
| <li><p><strong>provide_data</strong> (list of <cite>DataDesc</cite>, optional) – A list of <cite>DataDesc</cite> objects. <cite>DataDesc</cite> is used to store |
| name, shape, type and layout information of the data. |
| The <em>i</em>-th element describes the name and shape of <code class="docutils literal notranslate"><span class="pre">data[i]</span></code>.</p></li> |
| <li><p><strong>provide_label</strong> (list of <cite>DataDesc</cite>, optional) – A list of <cite>DataDesc</cite> objects. <cite>DataDesc</cite> is used to store |
| name, shape, type and layout information of the label. |
| The <em>i</em>-th element describes the name and shape of <code class="docutils literal notranslate"><span class="pre">label[i]</span></code>.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.io.DataDesc"> |
| <em class="property">class </em><code class="sig-name descname">DataDesc</code><a class="reference internal" href="../../../_modules/mxnet/io/io.html#DataDesc"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.DataDesc" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.io.io.DataDesc</span></code></p> |
| <p>DataDesc is used to store name, shape, type and layout |
| information of the data or the label.</p> |
| <p>The <cite>layout</cite> describes how the axes in <cite>shape</cite> should be interpreted, |
| for example for image data setting <cite>layout=NCHW</cite> indicates |
| that the first axis is number of examples in the batch(N), |
| C is number of channels, H is the height and W is the width of the image.</p> |
| <p>For sequential data, by default <cite>layout</cite> is set to <code class="docutils literal notranslate"><span class="pre">NTC</span></code>, where |
| N is number of examples in the batch, T the temporal axis representing time |
| and C is the number of channels.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>cls</strong> (<a class="reference internal" href="#mxnet.io.DataDesc" title="mxnet.io.DataDesc"><em>DataDesc</em></a>) – The class.</p></li> |
| <li><p><strong>name</strong> (<em>str</em>) – Data name.</p></li> |
| <li><p><strong>shape</strong> (<em>tuple of int</em>) – Data shape.</p></li> |
| <li><p><strong>dtype</strong> (<em>np.dtype</em><em>, </em><em>optional</em>) – Data type.</p></li> |
| <li><p><strong>layout</strong> (<em>str</em><em>, </em><em>optional</em>) – Data layout.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.DataDesc.get_batch_axis" title="mxnet.io.DataDesc.get_batch_axis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_batch_axis</span></code></a>(layout)</p></td> |
| <td><p>Get the dimension that corresponds to the batch size.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.DataDesc.get_list" title="mxnet.io.DataDesc.get_list"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_list</span></code></a>(shapes, types)</p></td> |
| <td><p>Get DataDesc list from attribute lists.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <dl class="method"> |
| <dt id="mxnet.io.DataDesc.get_batch_axis"> |
| <em class="property">static </em><code class="sig-name descname">get_batch_axis</code><span class="sig-paren">(</span><em class="sig-param">layout</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#DataDesc.get_batch_axis"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.DataDesc.get_batch_axis" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get the dimension that corresponds to the batch size.</p> |
| <p>When data parallelism is used, the data will be automatically split and |
| concatenated along the batch-size dimension. Axis can be -1, which means |
| the whole array will be copied for each data-parallelism device.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>layout</strong> (<em>str</em>) – layout string. For example, “NCHW”.</p> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>An axis indicating the batch_size dimension.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p>int</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.DataDesc.get_list"> |
| <em class="property">static </em><code class="sig-name descname">get_list</code><span class="sig-paren">(</span><em class="sig-param">shapes</em>, <em class="sig-param">types</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#DataDesc.get_list"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.DataDesc.get_list" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get DataDesc list from attribute lists.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>shapes</strong> (<em>a tuple of</em><em> (</em><em>name</em><em>, </em><em>shape</em><em>)</em>) – </p></li> |
| <li><p><strong>types</strong> (<em>a tuple of</em><em> (</em><em>name</em><em>, </em><em>np.dtype</em><em>)</em>) – </p></li> |
| </ul> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.io.DataIter"> |
| <em class="property">class </em><code class="sig-name descname">DataIter</code><span class="sig-paren">(</span><em class="sig-param">batch_size=0</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#DataIter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.DataIter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
| <p>The base class for an MXNet data iterator.</p> |
| <p>All I/O in MXNet is handled by specializations of this class. Data iterators |
| in MXNet are similar to standard-iterators in Python. On each call to <cite>next</cite> |
| they return a <cite>DataBatch</cite> which represents the next batch of data. When |
| there is no more data to return, it raises a <cite>StopIteration</cite> exception.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>optional</em>) – The batch size, namely the number of items in the batch.</p> |
| </dd> |
| </dl> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.DataIter.getdata" title="mxnet.io.DataIter.getdata"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getdata</span></code></a>()</p></td> |
| <td><p>Get data of current batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.DataIter.getindex" title="mxnet.io.DataIter.getindex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getindex</span></code></a>()</p></td> |
| <td><p>Get index of the current batch.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.DataIter.getlabel" title="mxnet.io.DataIter.getlabel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getlabel</span></code></a>()</p></td> |
| <td><p>Get label of the current batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.DataIter.getpad" title="mxnet.io.DataIter.getpad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getpad</span></code></a>()</p></td> |
| <td><p>Get the number of padding examples in the current batch.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.DataIter.iter_next" title="mxnet.io.DataIter.iter_next"><code class="xref py py-obj docutils literal notranslate"><span class="pre">iter_next</span></code></a>()</p></td> |
| <td><p>Move to the next batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.DataIter.next" title="mxnet.io.DataIter.next"><code class="xref py py-obj docutils literal notranslate"><span class="pre">next</span></code></a>()</p></td> |
| <td><p>Get next data batch from iterator.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.DataIter.reset" title="mxnet.io.DataIter.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset the iterator to the begin of the data.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="#mxnet.io.NDArrayIter" title="mxnet.io.NDArrayIter"><code class="xref py py-class docutils literal notranslate"><span class="pre">NDArrayIter</span></code></a></dt><dd><p>Data-iterator for MXNet NDArray or numpy-ndarray objects.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.io.CSVIter" title="mxnet.io.CSVIter"><code class="xref py py-class docutils literal notranslate"><span class="pre">CSVIter</span></code></a></dt><dd><p>Data-iterator for csv data.</p> |
| </dd> |
| <dt><a class="reference internal" href="#mxnet.io.LibSVMIter" title="mxnet.io.LibSVMIter"><code class="xref py py-class docutils literal notranslate"><span class="pre">LibSVMIter</span></code></a></dt><dd><p>Data-iterator for libsvm data.</p> |
| </dd> |
| <dt><code class="xref py py-class docutils literal notranslate"><span class="pre">ImageIter</span></code></dt><dd><p>Data-iterator for images.</p> |
| </dd> |
| </dl> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.io.DataIter.getdata"> |
| <code class="sig-name descname">getdata</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#DataIter.getdata"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.DataIter.getdata" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get data of current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>The data of the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>list of NDArray</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.DataIter.getindex"> |
| <code class="sig-name descname">getindex</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#DataIter.getindex"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.DataIter.getindex" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get index of the current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><strong>index</strong> – The indices of examples in the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>numpy.array</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.DataIter.getlabel"> |
| <code class="sig-name descname">getlabel</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#DataIter.getlabel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.DataIter.getlabel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get label of the current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>The label of the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>list of NDArray</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.DataIter.getpad"> |
| <code class="sig-name descname">getpad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#DataIter.getpad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.DataIter.getpad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get the number of padding examples in the current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>Number of padding examples in the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>int</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.DataIter.iter_next"> |
| <code class="sig-name descname">iter_next</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#DataIter.iter_next"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.DataIter.iter_next" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Move to the next batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>Whether the move is successful.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>boolean</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.DataIter.next"> |
| <code class="sig-name descname">next</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#DataIter.next"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.DataIter.next" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get next data batch from iterator.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>The data of next batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p><a class="reference internal" href="#mxnet.io.DataBatch" title="mxnet.io.DataBatch">DataBatch</a></p> |
| </dd> |
| <dt class="field-odd">Raises</dt> |
| <dd class="field-odd"><p><strong>StopIteration</strong> – If the end of the data is reached.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.DataIter.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#DataIter.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.DataIter.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset the iterator to the begin of the data.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.io.ImageDetRecordIter"> |
| <code class="sig-name descname">ImageDetRecordIter</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.ImageDetRecordIter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>b’Create iterator for image detection dataset packed in recordio.’</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path_imglist</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Dataset Param: Path to image list.</p></li> |
| <li><p><strong>path_imgrec</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='./data/imgrec.rec'</em>) – Dataset Param: Path to image record file.</p></li> |
| <li><p><strong>aug_seq</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='det_aug_default'</em>) – Augmentation Param: the augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters. Make sure you don’t use normal augmenters for detection tasks.</p></li> |
| <li><p><strong>label_width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Dataset Param: How many labels for an image, -1 for variable label size.</p></li> |
| <li><p><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>required</em>) – Dataset Param: Shape of each instance generated by the DataIter.</p></li> |
| <li><p><strong>preprocess_threads</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='4'</em>) – Backend Param: Number of thread to do preprocessing.</p></li> |
| <li><p><strong>verbose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Auxiliary Param: Whether to output parser information.</p></li> |
| <li><p><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – partition the data into multiple parts</p></li> |
| <li><p><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – the index of the part will read</p></li> |
| <li><p><strong>shuffle_chunk_size</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – the size(MB) of the shuffle chunk, used with shuffle=True, it can enable global shuffling</p></li> |
| <li><p><strong>shuffle_chunk_seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – the seed for chunk shuffling</p></li> |
| <li><p><strong>label_pad_width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – pad output label width if set larger than 0, -1 for auto estimate</p></li> |
| <li><p><strong>label_pad_value</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=-1</em>) – label padding value if enabled</p></li> |
| <li><p><strong>shuffle</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Whether to shuffle data.</p></li> |
| <li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Augmentation Param: Random Seed.</p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>required</em>) – Batch size.</p></li> |
| <li><p><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Whether to use round robin to handle overflow batch or not.</p></li> |
| <li><p><strong>prefetch_buffer</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</p></li> |
| <li><p><strong>ctx</strong> (<em>{'cpu'</em><em>, </em><em>'cpu_pinned'</em><em>, </em><em>'gpu'}</em><em>,</em><em>optional</em><em>, </em><em>default='gpu'</em>) – Context data loader optimized for. Note that it only indicates the optimization strategy for devices, by no means the prefetcher will load data to GPUs. If ctx is ‘cpu_pinned’ and device_id is not -1, it will use cpu_pinned(device_id) as ctx</p></li> |
| <li><p><strong>device_id</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – The default device id for context. -1 indicate it’s on default device</p></li> |
| <li><p><strong>dtype</strong> (<em>{None</em><em>, </em><em>'bfloat16'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'int64'</em><em>, </em><em>'int8'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal notranslate"><span class="pre">None</span></code> means no change.</p></li> |
| <li><p><strong>resize</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Augmentation Param: scale shorter edge to size before applying other augmentations, -1 to disable.</p></li> |
| <li><p><strong>rand_crop_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability of random cropping, <= 0 to disable</p></li> |
| <li><p><strong>min_crop_scales</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>0</em><em>]</em>) – Augmentation Param: Min crop scales.</p></li> |
| <li><p><strong>max_crop_scales</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>1</em><em>]</em>) – Augmentation Param: Max crop scales.</p></li> |
| <li><p><strong>min_crop_aspect_ratios</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>1</em><em>]</em>) – Augmentation Param: Min crop aspect ratios.</p></li> |
| <li><p><strong>max_crop_aspect_ratios</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>1</em><em>]</em>) – Augmentation Param: Max crop aspect ratios.</p></li> |
| <li><p><strong>min_crop_overlaps</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>0</em><em>]</em>) – Augmentation Param: Minimum crop IOU between crop_box and ground-truths.</p></li> |
| <li><p><strong>max_crop_overlaps</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>1</em><em>]</em>) – Augmentation Param: Maximum crop IOU between crop_box and ground-truth.</p></li> |
| <li><p><strong>min_crop_sample_coverages</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>0</em><em>]</em>) – Augmentation Param: Minimum ratio of intersect/crop_area between crop box and ground-truths.</p></li> |
| <li><p><strong>max_crop_sample_coverages</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>1</em><em>]</em>) – Augmentation Param: Maximum ratio of intersect/crop_area between crop box and ground-truths.</p></li> |
| <li><p><strong>min_crop_object_coverages</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>0</em><em>]</em>) – Augmentation Param: Minimum ratio of intersect/gt_area between crop box and ground-truths.</p></li> |
| <li><p><strong>max_crop_object_coverages</strong> (<em>tuple of <float></em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>1</em><em>]</em>) – Augmentation Param: Maximum ratio of intersect/gt_area between crop box and ground-truths.</p></li> |
| <li><p><strong>num_crop_sampler</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Augmentation Param: Number of crop samplers.</p></li> |
| <li><p><strong>crop_emit_mode</strong> (<em>{'center'</em><em>, </em><em>'overlap'}</em><em>,</em><em>optional</em><em>, </em><em>default='center'</em>) – Augmentation Param: Emition mode for invalid ground-truths after crop. center: emit if centroid of object is out of crop region; overlap: emit if overlap is less than emit_overlap_thresh.</p></li> |
| <li><p><strong>emit_overlap_thresh</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0.300000012</em>) – Augmentation Param: Emit overlap thresh for emit mode overlap only.</p></li> |
| <li><p><strong>max_crop_trials</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>25</em><em>]</em>) – Augmentation Param: Skip cropping if fail crop trail count exceeds this number.</p></li> |
| <li><p><strong>rand_pad_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability for random padding.</p></li> |
| <li><p><strong>max_pad_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Augmentation Param: Maximum padding scale.</p></li> |
| <li><p><strong>max_random_hue</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Augmentation Param: Maximum random value of H channel in HSL color space.</p></li> |
| <li><p><strong>random_hue_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability to apply random hue.</p></li> |
| <li><p><strong>max_random_saturation</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Augmentation Param: Maximum random value of S channel in HSL color space.</p></li> |
| <li><p><strong>random_saturation_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability to apply random saturation.</p></li> |
| <li><p><strong>max_random_illumination</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Augmentation Param: Maximum random value of L channel in HSL color space.</p></li> |
| <li><p><strong>random_illumination_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability to apply random illumination.</p></li> |
| <li><p><strong>max_random_contrast</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Maximum random value of delta contrast.</p></li> |
| <li><p><strong>random_contrast_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability to apply random contrast.</p></li> |
| <li><p><strong>rand_mirror_prob</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Probability to apply horizontal flip aka. mirror.</p></li> |
| <li><p><strong>fill_value</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='127'</em>) – Augmentation Param: Filled color value while padding.</p></li> |
| <li><p><strong>inter_method</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Augmentation Param: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</p></li> |
| <li><p><strong>resize_mode</strong> (<em>{'fit'</em><em>, </em><em>'force'</em><em>, </em><em>'shrink'}</em><em>,</em><em>optional</em><em>, </em><em>default='force'</em>) – Augmentation Param: How image data fit in data_shape. force: force reshape to data_shape regardless of aspect ratio; shrink: ensure each side fit in data_shape, preserve aspect ratio; fit: fit image to data_shape, preserve ratio, will upscale if applicable.</p></li> |
| <li><p><strong>mean_img</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Augmentation Param: Mean Image to be subtracted.</p></li> |
| <li><p><strong>mean_r</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Mean value on R channel.</p></li> |
| <li><p><strong>mean_g</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Mean value on G channel.</p></li> |
| <li><p><strong>mean_b</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Mean value on B channel.</p></li> |
| <li><p><strong>mean_a</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Mean value on Alpha channel.</p></li> |
| <li><p><strong>std_r</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Standard deviation on R channel.</p></li> |
| <li><p><strong>std_g</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Standard deviation on G channel.</p></li> |
| <li><p><strong>std_b</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Standard deviation on B channel.</p></li> |
| <li><p><strong>std_a</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Standard deviation on Alpha channel.</p></li> |
| <li><p><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Augmentation Param: Scale in color space.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result iterator.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.io.MXDataIter" title="mxnet.io.MXDataIter">MXDataIter</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.io.ImageRecordInt8Iter"> |
| <code class="sig-name descname">ImageRecordInt8Iter</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.ImageRecordInt8Iter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>b”Iterating on image RecordIO filesnn.. note:: <code class="docutils literal notranslate"><span class="pre">ImageRecordInt8Iter</span></code> is deprecated. Use ImageRecordIter(dtype=’int8’) instead.nnThis iterator is identical to <code class="docutils literal notranslate"><span class="pre">ImageRecordIter</span></code> except for using <code class="docutils literal notranslate"><span class="pre">int8</span></code> asnthe data type instead of <code class="docutils literal notranslate"><span class="pre">float</span></code>.nnnnDefined in /work/mxnet/src/io/iter_image_recordio_2.cc:L947”</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path_imglist</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of record> <one or more labels> <relative path from root folder>.</p></li> |
| <li><p><strong>path_imgrec</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.</p></li> |
| <li><p><strong>path_imgidx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO index (.idx) file. Created with tools/im2rec.py.</p></li> |
| <li><p><strong>aug_seq</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='aug_default'</em>) – The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.</p></li> |
| <li><p><strong>label_width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The number of labels per image.</p></li> |
| <li><p><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>required</em>) – The shape of one output image in (channels, height, width) format.</p></li> |
| <li><p><strong>preprocess_threads</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='4'</em>) – The number of threads to do preprocessing.</p></li> |
| <li><p><strong>verbose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – If or not output verbose information.</p></li> |
| <li><p><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Virtually partition the data into these many parts.</p></li> |
| <li><p><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The <em>i</em>-th virtual partition to be read.</p></li> |
| <li><p><strong>device_id</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The device id used to create context for internal NDArray. Setting device_id to -1 will create Context::CPU(0). Setting device_id to valid positive device id will create Context::CPUPinned(device_id). Default is 0.</p></li> |
| <li><p><strong>shuffle_chunk_size</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The data shuffle buffer size in MB. Only valid if shuffle is true.</p></li> |
| <li><p><strong>shuffle_chunk_seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed for shuffling</p></li> |
| <li><p><strong>seed_aug</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – Random seed for augmentations.</p></li> |
| <li><p><strong>shuffle</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to shuffle data randomly or not.</p></li> |
| <li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed.</p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>required</em>) – Batch size.</p></li> |
| <li><p><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Whether to use round robin to handle overflow batch or not.</p></li> |
| <li><p><strong>prefetch_buffer</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</p></li> |
| <li><p><strong>ctx</strong> (<em>{'cpu'</em><em>, </em><em>'cpu_pinned'</em><em>, </em><em>'gpu'}</em><em>,</em><em>optional</em><em>, </em><em>default='gpu'</em>) – Context data loader optimized for. Note that it only indicates the optimization strategy for devices, by no means the prefetcher will load data to GPUs. If ctx is ‘cpu_pinned’ and device_id is not -1, it will use cpu_pinned(device_id) as ctx</p></li> |
| <li><p><strong>dtype</strong> (<em>{None</em><em>, </em><em>'bfloat16'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'int64'</em><em>, </em><em>'int8'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal notranslate"><span class="pre">None</span></code> means no change.</p></li> |
| <li><p><strong>resize</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Down scale the shorter edge to a new size before applying other augmentations.</p></li> |
| <li><p><strong>rand_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – If or not randomly crop the image</p></li> |
| <li><p><strong>random_resized_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – If or not perform random resized cropping on the image, as a standard preprocessing for resnet training on ImageNet data.</p></li> |
| <li><p><strong>max_rotate_angle</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Rotate by a random degree in <code class="docutils literal notranslate"><span class="pre">[-v,</span> <span class="pre">v]</span></code></p></li> |
| <li><p><strong>max_aspect_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the aspect (namely width/height) to a random value. If min_aspect_ratio is None then the aspect ratio ins sampled from [1 - max_aspect_ratio, 1 + max_aspect_ratio], else it is in <code class="docutils literal notranslate"><span class="pre">[min_aspect_ratio,</span> <span class="pre">max_aspect_ratio]</span></code></p></li> |
| <li><p><strong>min_aspect_ratio</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Change the aspect (namely width/height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_aspect_ratio,</span> <span class="pre">max_aspect_ratio]</span></code></p></li> |
| <li><p><strong>max_shear_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Apply a shear transformation (namely <code class="docutils literal notranslate"><span class="pre">(x,y)->(x+my,y)</span></code>) with <code class="docutils literal notranslate"><span class="pre">m</span></code> randomly chose from <code class="docutils literal notranslate"><span class="pre">[-max_shear_ratio,</span> <span class="pre">max_shear_ratio]</span></code></p></li> |
| <li><p><strong>max_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal notranslate"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size].``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>min_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal notranslate"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size].``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>max_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal notranslate"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal notranslate"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal notranslate"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>min_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal notranslate"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal notranslate"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal notranslate"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>max_random_area</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Change the area (namely width * height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_random_area,</span> <span class="pre">max_random_area]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is False.</p></li> |
| <li><p><strong>min_random_area</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Change the area (namely width * height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_random_area,</span> <span class="pre">max_random_area]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is False.</p></li> |
| <li><p><strong>max_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e+10</em>) – Set the maximal width and height after all resize and rotate argumentation are applied</p></li> |
| <li><p><strong>min_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Set the minimal width and height after all resize and rotate argumentation are applied</p></li> |
| <li><p><strong>brightness</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-brightness,</span> <span class="pre">brightness]</span></code> to the brightness of image.</p></li> |
| <li><p><strong>contrast</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-contrast,</span> <span class="pre">contrast]</span></code> to the contrast of image.</p></li> |
| <li><p><strong>saturation</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-saturation,</span> <span class="pre">saturation]</span></code> to the saturation of image.</p></li> |
| <li><p><strong>pca_noise</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add PCA based noise to the image.</p></li> |
| <li><p><strong>random_h</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_h,</span> <span class="pre">random_h]</span></code> to the H channel in HSL color space.</p></li> |
| <li><p><strong>random_s</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_s,</span> <span class="pre">random_s]</span></code> to the S channel in HSL color space.</p></li> |
| <li><p><strong>random_l</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_l,</span> <span class="pre">random_l]</span></code> to the L channel in HSL color space.</p></li> |
| <li><p><strong>rotate</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Rotate by an angle. If set, it overwrites the <code class="docutils literal notranslate"><span class="pre">max_rotate_angle</span></code> option.</p></li> |
| <li><p><strong>fill_value</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='255'</em>) – Set the padding pixels value to <code class="docutils literal notranslate"><span class="pre">fill_value</span></code>.</p></li> |
| <li><p><strong>inter_method</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</p></li> |
| <li><p><strong>pad</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Change size from <code class="docutils literal notranslate"><span class="pre">[width,</span> <span class="pre">height]</span></code> into <code class="docutils literal notranslate"><span class="pre">[pad</span> <span class="pre">+</span> <span class="pre">width</span> <span class="pre">+</span> <span class="pre">pad,</span> <span class="pre">pad</span> <span class="pre">+</span> <span class="pre">height</span> <span class="pre">+</span> <span class="pre">pad]</span></code> by padding pixes</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result iterator.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.io.MXDataIter" title="mxnet.io.MXDataIter">MXDataIter</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.io.ImageRecordIter"> |
| <code class="sig-name descname">ImageRecordIter</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.ImageRecordIter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>b’Iterates on image RecordIO filesnnReads batches of images from .rec RecordIO files. One can use <code class="docutils literal notranslate"><span class="pre">im2rec.py</span></code> tooln(in tools/) to pack raw image files into RecordIO files. This iterator is lessnflexible to customization but is fast and has lot of language bindings. Toniterate over raw images directly use <code class="docutils literal notranslate"><span class="pre">ImageIter</span></code> instead (in Python).nnExample::nn data_iter = mx.io.ImageRecordIter(n path_imgrec=”./sample.rec”, # The target record file.n data_shape=(3, 227, 227), # Output data shape; 227x227 region will be cropped from the original image.n batch_size=4, # Number of items per batch.n resize=256 # Resize the shorter edge to 256 before cropping.n # You can specify more augmentation options. Use help(mx.io.ImageRecordIter) to see all the options.n )n # You can now use the data_iter to access batches of images.n batch = data_iter.next() # first batch.n images = batch.data[0] # This will contain 4 (=batch_size) images each of 3x227x227.n # process the imagesn …n data_iter.reset() # To restart the iterator from the beginning.nnnnDefined in /work/mxnet/src/io/iter_image_recordio_2.cc:L914’</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path_imglist</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of record> <one or more labels> <relative path from root folder>.</p></li> |
| <li><p><strong>path_imgrec</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.</p></li> |
| <li><p><strong>path_imgidx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO index (.idx) file. Created with tools/im2rec.py.</p></li> |
| <li><p><strong>aug_seq</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='aug_default'</em>) – The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.</p></li> |
| <li><p><strong>label_width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The number of labels per image.</p></li> |
| <li><p><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>required</em>) – The shape of one output image in (channels, height, width) format.</p></li> |
| <li><p><strong>preprocess_threads</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='4'</em>) – The number of threads to do preprocessing.</p></li> |
| <li><p><strong>verbose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – If or not output verbose information.</p></li> |
| <li><p><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Virtually partition the data into these many parts.</p></li> |
| <li><p><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The <em>i</em>-th virtual partition to be read.</p></li> |
| <li><p><strong>device_id</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The device id used to create context for internal NDArray. Setting device_id to -1 will create Context::CPU(0). Setting device_id to valid positive device id will create Context::CPUPinned(device_id). Default is 0.</p></li> |
| <li><p><strong>shuffle_chunk_size</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The data shuffle buffer size in MB. Only valid if shuffle is true.</p></li> |
| <li><p><strong>shuffle_chunk_seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed for shuffling</p></li> |
| <li><p><strong>seed_aug</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – Random seed for augmentations.</p></li> |
| <li><p><strong>shuffle</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to shuffle data randomly or not.</p></li> |
| <li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed.</p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>required</em>) – Batch size.</p></li> |
| <li><p><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Whether to use round robin to handle overflow batch or not.</p></li> |
| <li><p><strong>prefetch_buffer</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</p></li> |
| <li><p><strong>ctx</strong> (<em>{'cpu'</em><em>, </em><em>'cpu_pinned'</em><em>, </em><em>'gpu'}</em><em>,</em><em>optional</em><em>, </em><em>default='gpu'</em>) – Context data loader optimized for. Note that it only indicates the optimization strategy for devices, by no means the prefetcher will load data to GPUs. If ctx is ‘cpu_pinned’ and device_id is not -1, it will use cpu_pinned(device_id) as ctx</p></li> |
| <li><p><strong>dtype</strong> (<em>{None</em><em>, </em><em>'bfloat16'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'int64'</em><em>, </em><em>'int8'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal notranslate"><span class="pre">None</span></code> means no change.</p></li> |
| <li><p><strong>resize</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Down scale the shorter edge to a new size before applying other augmentations.</p></li> |
| <li><p><strong>rand_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – If or not randomly crop the image</p></li> |
| <li><p><strong>random_resized_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – If or not perform random resized cropping on the image, as a standard preprocessing for resnet training on ImageNet data.</p></li> |
| <li><p><strong>max_rotate_angle</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Rotate by a random degree in <code class="docutils literal notranslate"><span class="pre">[-v,</span> <span class="pre">v]</span></code></p></li> |
| <li><p><strong>max_aspect_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the aspect (namely width/height) to a random value. If min_aspect_ratio is None then the aspect ratio ins sampled from [1 - max_aspect_ratio, 1 + max_aspect_ratio], else it is in <code class="docutils literal notranslate"><span class="pre">[min_aspect_ratio,</span> <span class="pre">max_aspect_ratio]</span></code></p></li> |
| <li><p><strong>min_aspect_ratio</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Change the aspect (namely width/height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_aspect_ratio,</span> <span class="pre">max_aspect_ratio]</span></code></p></li> |
| <li><p><strong>max_shear_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Apply a shear transformation (namely <code class="docutils literal notranslate"><span class="pre">(x,y)->(x+my,y)</span></code>) with <code class="docutils literal notranslate"><span class="pre">m</span></code> randomly chose from <code class="docutils literal notranslate"><span class="pre">[-max_shear_ratio,</span> <span class="pre">max_shear_ratio]</span></code></p></li> |
| <li><p><strong>max_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal notranslate"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size].``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>min_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal notranslate"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size].``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>max_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal notranslate"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal notranslate"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal notranslate"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>min_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal notranslate"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal notranslate"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal notranslate"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>max_random_area</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Change the area (namely width * height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_random_area,</span> <span class="pre">max_random_area]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is False.</p></li> |
| <li><p><strong>min_random_area</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Change the area (namely width * height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_random_area,</span> <span class="pre">max_random_area]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is False.</p></li> |
| <li><p><strong>max_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e+10</em>) – Set the maximal width and height after all resize and rotate argumentation are applied</p></li> |
| <li><p><strong>min_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Set the minimal width and height after all resize and rotate argumentation are applied</p></li> |
| <li><p><strong>brightness</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-brightness,</span> <span class="pre">brightness]</span></code> to the brightness of image.</p></li> |
| <li><p><strong>contrast</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-contrast,</span> <span class="pre">contrast]</span></code> to the contrast of image.</p></li> |
| <li><p><strong>saturation</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-saturation,</span> <span class="pre">saturation]</span></code> to the saturation of image.</p></li> |
| <li><p><strong>pca_noise</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add PCA based noise to the image.</p></li> |
| <li><p><strong>random_h</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_h,</span> <span class="pre">random_h]</span></code> to the H channel in HSL color space.</p></li> |
| <li><p><strong>random_s</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_s,</span> <span class="pre">random_s]</span></code> to the S channel in HSL color space.</p></li> |
| <li><p><strong>random_l</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_l,</span> <span class="pre">random_l]</span></code> to the L channel in HSL color space.</p></li> |
| <li><p><strong>rotate</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Rotate by an angle. If set, it overwrites the <code class="docutils literal notranslate"><span class="pre">max_rotate_angle</span></code> option.</p></li> |
| <li><p><strong>fill_value</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='255'</em>) – Set the padding pixels value to <code class="docutils literal notranslate"><span class="pre">fill_value</span></code>.</p></li> |
| <li><p><strong>inter_method</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</p></li> |
| <li><p><strong>pad</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Change size from <code class="docutils literal notranslate"><span class="pre">[width,</span> <span class="pre">height]</span></code> into <code class="docutils literal notranslate"><span class="pre">[pad</span> <span class="pre">+</span> <span class="pre">width</span> <span class="pre">+</span> <span class="pre">pad,</span> <span class="pre">pad</span> <span class="pre">+</span> <span class="pre">height</span> <span class="pre">+</span> <span class="pre">pad]</span></code> by padding pixes</p></li> |
| <li><p><strong>mirror</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to mirror the image or not. If true, images are flipped along the horizontal axis.</p></li> |
| <li><p><strong>rand_mirror</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to randomly mirror images or not. If true, 50% of the images will be randomly mirrored (flipped along the horizontal axis)</p></li> |
| <li><p><strong>mean_img</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Filename of the mean image.</p></li> |
| <li><p><strong>mean_r</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the R channel</p></li> |
| <li><p><strong>mean_g</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the G channel</p></li> |
| <li><p><strong>mean_b</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the B channel</p></li> |
| <li><p><strong>mean_a</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the alpha channel</p></li> |
| <li><p><strong>std_r</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Augmentation Param: Standard deviation on R channel.</p></li> |
| <li><p><strong>std_g</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Augmentation Param: Standard deviation on G channel.</p></li> |
| <li><p><strong>std_b</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Augmentation Param: Standard deviation on B channel.</p></li> |
| <li><p><strong>std_a</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Augmentation Param: Standard deviation on Alpha channel.</p></li> |
| <li><p><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Multiply the image with a scale value.</p></li> |
| <li><p><strong>max_random_contrast</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the contrast with a value randomly chosen from <code class="docutils literal notranslate"><span class="pre">[-max_random_contrast,</span> <span class="pre">max_random_contrast]</span></code></p></li> |
| <li><p><strong>max_random_illumination</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the illumination with a value randomly chosen from <code class="docutils literal notranslate"><span class="pre">[-max_random_illumination,</span> <span class="pre">max_random_illumination]</span></code></p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result iterator.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.io.MXDataIter" title="mxnet.io.MXDataIter">MXDataIter</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.io.ImageRecordIter_v1"> |
| <code class="sig-name descname">ImageRecordIter_v1</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.ImageRecordIter_v1" title="Permalink to this definition">¶</a></dt> |
| <dd><p>b’Iterating on image RecordIO filesnn.. note::nn <code class="docutils literal notranslate"><span class="pre">ImageRecordIter_v1</span></code> is deprecated. Use <code class="docutils literal notranslate"><span class="pre">ImageRecordIter</span></code> instead.nnnRead images batches from RecordIO files with a rich of data augmentationnoptions.nnOne can use <code class="docutils literal notranslate"><span class="pre">tools/im2rec.py</span></code> to pack individual image files into RecordIOnfiles.nnnnDefined in /work/mxnet/src/io/iter_image_recordio.cc:L354’</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path_imglist</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of record> <one or more labels> <relative path from root folder>.</p></li> |
| <li><p><strong>path_imgrec</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.</p></li> |
| <li><p><strong>path_imgidx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO index (.idx) file. Created with tools/im2rec.py.</p></li> |
| <li><p><strong>aug_seq</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='aug_default'</em>) – The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.</p></li> |
| <li><p><strong>label_width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The number of labels per image.</p></li> |
| <li><p><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>required</em>) – The shape of one output image in (channels, height, width) format.</p></li> |
| <li><p><strong>preprocess_threads</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='4'</em>) – The number of threads to do preprocessing.</p></li> |
| <li><p><strong>verbose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – If or not output verbose information.</p></li> |
| <li><p><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Virtually partition the data into these many parts.</p></li> |
| <li><p><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The <em>i</em>-th virtual partition to be read.</p></li> |
| <li><p><strong>device_id</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The device id used to create context for internal NDArray. Setting device_id to -1 will create Context::CPU(0). Setting device_id to valid positive device id will create Context::CPUPinned(device_id). Default is 0.</p></li> |
| <li><p><strong>shuffle_chunk_size</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The data shuffle buffer size in MB. Only valid if shuffle is true.</p></li> |
| <li><p><strong>shuffle_chunk_seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed for shuffling</p></li> |
| <li><p><strong>seed_aug</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – Random seed for augmentations.</p></li> |
| <li><p><strong>shuffle</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to shuffle data randomly or not.</p></li> |
| <li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed.</p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>required</em>) – Batch size.</p></li> |
| <li><p><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Whether to use round robin to handle overflow batch or not.</p></li> |
| <li><p><strong>prefetch_buffer</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</p></li> |
| <li><p><strong>ctx</strong> (<em>{'cpu'</em><em>, </em><em>'cpu_pinned'</em><em>, </em><em>'gpu'}</em><em>,</em><em>optional</em><em>, </em><em>default='gpu'</em>) – Context data loader optimized for. Note that it only indicates the optimization strategy for devices, by no means the prefetcher will load data to GPUs. If ctx is ‘cpu_pinned’ and device_id is not -1, it will use cpu_pinned(device_id) as ctx</p></li> |
| <li><p><strong>dtype</strong> (<em>{None</em><em>, </em><em>'bfloat16'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'int64'</em><em>, </em><em>'int8'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal notranslate"><span class="pre">None</span></code> means no change.</p></li> |
| <li><p><strong>resize</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Down scale the shorter edge to a new size before applying other augmentations.</p></li> |
| <li><p><strong>rand_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – If or not randomly crop the image</p></li> |
| <li><p><strong>random_resized_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – If or not perform random resized cropping on the image, as a standard preprocessing for resnet training on ImageNet data.</p></li> |
| <li><p><strong>max_rotate_angle</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Rotate by a random degree in <code class="docutils literal notranslate"><span class="pre">[-v,</span> <span class="pre">v]</span></code></p></li> |
| <li><p><strong>max_aspect_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the aspect (namely width/height) to a random value. If min_aspect_ratio is None then the aspect ratio ins sampled from [1 - max_aspect_ratio, 1 + max_aspect_ratio], else it is in <code class="docutils literal notranslate"><span class="pre">[min_aspect_ratio,</span> <span class="pre">max_aspect_ratio]</span></code></p></li> |
| <li><p><strong>min_aspect_ratio</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Change the aspect (namely width/height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_aspect_ratio,</span> <span class="pre">max_aspect_ratio]</span></code></p></li> |
| <li><p><strong>max_shear_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Apply a shear transformation (namely <code class="docutils literal notranslate"><span class="pre">(x,y)->(x+my,y)</span></code>) with <code class="docutils literal notranslate"><span class="pre">m</span></code> randomly chose from <code class="docutils literal notranslate"><span class="pre">[-max_shear_ratio,</span> <span class="pre">max_shear_ratio]</span></code></p></li> |
| <li><p><strong>max_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal notranslate"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size].``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>min_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal notranslate"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size].``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>max_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal notranslate"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal notranslate"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal notranslate"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>min_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal notranslate"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal notranslate"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal notranslate"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>max_random_area</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Change the area (namely width * height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_random_area,</span> <span class="pre">max_random_area]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is False.</p></li> |
| <li><p><strong>min_random_area</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Change the area (namely width * height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_random_area,</span> <span class="pre">max_random_area]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is False.</p></li> |
| <li><p><strong>max_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e+10</em>) – Set the maximal width and height after all resize and rotate argumentation are applied</p></li> |
| <li><p><strong>min_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Set the minimal width and height after all resize and rotate argumentation are applied</p></li> |
| <li><p><strong>brightness</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-brightness,</span> <span class="pre">brightness]</span></code> to the brightness of image.</p></li> |
| <li><p><strong>contrast</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-contrast,</span> <span class="pre">contrast]</span></code> to the contrast of image.</p></li> |
| <li><p><strong>saturation</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-saturation,</span> <span class="pre">saturation]</span></code> to the saturation of image.</p></li> |
| <li><p><strong>pca_noise</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add PCA based noise to the image.</p></li> |
| <li><p><strong>random_h</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_h,</span> <span class="pre">random_h]</span></code> to the H channel in HSL color space.</p></li> |
| <li><p><strong>random_s</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_s,</span> <span class="pre">random_s]</span></code> to the S channel in HSL color space.</p></li> |
| <li><p><strong>random_l</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_l,</span> <span class="pre">random_l]</span></code> to the L channel in HSL color space.</p></li> |
| <li><p><strong>rotate</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Rotate by an angle. If set, it overwrites the <code class="docutils literal notranslate"><span class="pre">max_rotate_angle</span></code> option.</p></li> |
| <li><p><strong>fill_value</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='255'</em>) – Set the padding pixels value to <code class="docutils literal notranslate"><span class="pre">fill_value</span></code>.</p></li> |
| <li><p><strong>inter_method</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</p></li> |
| <li><p><strong>pad</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Change size from <code class="docutils literal notranslate"><span class="pre">[width,</span> <span class="pre">height]</span></code> into <code class="docutils literal notranslate"><span class="pre">[pad</span> <span class="pre">+</span> <span class="pre">width</span> <span class="pre">+</span> <span class="pre">pad,</span> <span class="pre">pad</span> <span class="pre">+</span> <span class="pre">height</span> <span class="pre">+</span> <span class="pre">pad]</span></code> by padding pixes</p></li> |
| <li><p><strong>mirror</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to mirror the image or not. If true, images are flipped along the horizontal axis.</p></li> |
| <li><p><strong>rand_mirror</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to randomly mirror images or not. If true, 50% of the images will be randomly mirrored (flipped along the horizontal axis)</p></li> |
| <li><p><strong>mean_img</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Filename of the mean image.</p></li> |
| <li><p><strong>mean_r</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the R channel</p></li> |
| <li><p><strong>mean_g</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the G channel</p></li> |
| <li><p><strong>mean_b</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the B channel</p></li> |
| <li><p><strong>mean_a</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The mean value to be subtracted on the alpha channel</p></li> |
| <li><p><strong>std_r</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Augmentation Param: Standard deviation on R channel.</p></li> |
| <li><p><strong>std_g</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Augmentation Param: Standard deviation on G channel.</p></li> |
| <li><p><strong>std_b</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Augmentation Param: Standard deviation on B channel.</p></li> |
| <li><p><strong>std_a</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Augmentation Param: Standard deviation on Alpha channel.</p></li> |
| <li><p><strong>scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Multiply the image with a scale value.</p></li> |
| <li><p><strong>max_random_contrast</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the contrast with a value randomly chosen from <code class="docutils literal notranslate"><span class="pre">[-max_random_contrast,</span> <span class="pre">max_random_contrast]</span></code></p></li> |
| <li><p><strong>max_random_illumination</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the illumination with a value randomly chosen from <code class="docutils literal notranslate"><span class="pre">[-max_random_illumination,</span> <span class="pre">max_random_illumination]</span></code></p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result iterator.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.io.MXDataIter" title="mxnet.io.MXDataIter">MXDataIter</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.io.ImageRecordUInt8Iter"> |
| <code class="sig-name descname">ImageRecordUInt8Iter</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.ImageRecordUInt8Iter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>b”Iterating on image RecordIO filesnn.. note:: ImageRecordUInt8Iter is deprecated. Use ImageRecordIter(dtype=’uint8’) instead.nnThis iterator is identical to <code class="docutils literal notranslate"><span class="pre">ImageRecordIter</span></code> except for using <code class="docutils literal notranslate"><span class="pre">uint8</span></code> asnthe data type instead of <code class="docutils literal notranslate"><span class="pre">float</span></code>.nnnnDefined in /work/mxnet/src/io/iter_image_recordio_2.cc:L931”</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path_imglist</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of record> <one or more labels> <relative path from root folder>.</p></li> |
| <li><p><strong>path_imgrec</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.</p></li> |
| <li><p><strong>path_imgidx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO index (.idx) file. Created with tools/im2rec.py.</p></li> |
| <li><p><strong>aug_seq</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='aug_default'</em>) – The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.</p></li> |
| <li><p><strong>label_width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The number of labels per image.</p></li> |
| <li><p><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>required</em>) – The shape of one output image in (channels, height, width) format.</p></li> |
| <li><p><strong>preprocess_threads</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='4'</em>) – The number of threads to do preprocessing.</p></li> |
| <li><p><strong>verbose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – If or not output verbose information.</p></li> |
| <li><p><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Virtually partition the data into these many parts.</p></li> |
| <li><p><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The <em>i</em>-th virtual partition to be read.</p></li> |
| <li><p><strong>device_id</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The device id used to create context for internal NDArray. Setting device_id to -1 will create Context::CPU(0). Setting device_id to valid positive device id will create Context::CPUPinned(device_id). Default is 0.</p></li> |
| <li><p><strong>shuffle_chunk_size</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The data shuffle buffer size in MB. Only valid if shuffle is true.</p></li> |
| <li><p><strong>shuffle_chunk_seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed for shuffling</p></li> |
| <li><p><strong>seed_aug</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – Random seed for augmentations.</p></li> |
| <li><p><strong>shuffle</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to shuffle data randomly or not.</p></li> |
| <li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed.</p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>required</em>) – Batch size.</p></li> |
| <li><p><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Whether to use round robin to handle overflow batch or not.</p></li> |
| <li><p><strong>prefetch_buffer</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</p></li> |
| <li><p><strong>ctx</strong> (<em>{'cpu'</em><em>, </em><em>'cpu_pinned'</em><em>, </em><em>'gpu'}</em><em>,</em><em>optional</em><em>, </em><em>default='gpu'</em>) – Context data loader optimized for. Note that it only indicates the optimization strategy for devices, by no means the prefetcher will load data to GPUs. If ctx is ‘cpu_pinned’ and device_id is not -1, it will use cpu_pinned(device_id) as ctx</p></li> |
| <li><p><strong>dtype</strong> (<em>{None</em><em>, </em><em>'bfloat16'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'int64'</em><em>, </em><em>'int8'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal notranslate"><span class="pre">None</span></code> means no change.</p></li> |
| <li><p><strong>resize</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Down scale the shorter edge to a new size before applying other augmentations.</p></li> |
| <li><p><strong>rand_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – If or not randomly crop the image</p></li> |
| <li><p><strong>random_resized_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – If or not perform random resized cropping on the image, as a standard preprocessing for resnet training on ImageNet data.</p></li> |
| <li><p><strong>max_rotate_angle</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Rotate by a random degree in <code class="docutils literal notranslate"><span class="pre">[-v,</span> <span class="pre">v]</span></code></p></li> |
| <li><p><strong>max_aspect_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the aspect (namely width/height) to a random value. If min_aspect_ratio is None then the aspect ratio ins sampled from [1 - max_aspect_ratio, 1 + max_aspect_ratio], else it is in <code class="docutils literal notranslate"><span class="pre">[min_aspect_ratio,</span> <span class="pre">max_aspect_ratio]</span></code></p></li> |
| <li><p><strong>min_aspect_ratio</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Change the aspect (namely width/height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_aspect_ratio,</span> <span class="pre">max_aspect_ratio]</span></code></p></li> |
| <li><p><strong>max_shear_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Apply a shear transformation (namely <code class="docutils literal notranslate"><span class="pre">(x,y)->(x+my,y)</span></code>) with <code class="docutils literal notranslate"><span class="pre">m</span></code> randomly chose from <code class="docutils literal notranslate"><span class="pre">[-max_shear_ratio,</span> <span class="pre">max_shear_ratio]</span></code></p></li> |
| <li><p><strong>max_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal notranslate"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size].``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>min_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal notranslate"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size].``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>max_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal notranslate"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal notranslate"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal notranslate"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>min_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal notranslate"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal notranslate"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal notranslate"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>max_random_area</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Change the area (namely width * height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_random_area,</span> <span class="pre">max_random_area]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is False.</p></li> |
| <li><p><strong>min_random_area</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Change the area (namely width * height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_random_area,</span> <span class="pre">max_random_area]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is False.</p></li> |
| <li><p><strong>max_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e+10</em>) – Set the maximal width and height after all resize and rotate argumentation are applied</p></li> |
| <li><p><strong>min_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Set the minimal width and height after all resize and rotate argumentation are applied</p></li> |
| <li><p><strong>brightness</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-brightness,</span> <span class="pre">brightness]</span></code> to the brightness of image.</p></li> |
| <li><p><strong>contrast</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-contrast,</span> <span class="pre">contrast]</span></code> to the contrast of image.</p></li> |
| <li><p><strong>saturation</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-saturation,</span> <span class="pre">saturation]</span></code> to the saturation of image.</p></li> |
| <li><p><strong>pca_noise</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add PCA based noise to the image.</p></li> |
| <li><p><strong>random_h</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_h,</span> <span class="pre">random_h]</span></code> to the H channel in HSL color space.</p></li> |
| <li><p><strong>random_s</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_s,</span> <span class="pre">random_s]</span></code> to the S channel in HSL color space.</p></li> |
| <li><p><strong>random_l</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_l,</span> <span class="pre">random_l]</span></code> to the L channel in HSL color space.</p></li> |
| <li><p><strong>rotate</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Rotate by an angle. If set, it overwrites the <code class="docutils literal notranslate"><span class="pre">max_rotate_angle</span></code> option.</p></li> |
| <li><p><strong>fill_value</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='255'</em>) – Set the padding pixels value to <code class="docutils literal notranslate"><span class="pre">fill_value</span></code>.</p></li> |
| <li><p><strong>inter_method</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</p></li> |
| <li><p><strong>pad</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Change size from <code class="docutils literal notranslate"><span class="pre">[width,</span> <span class="pre">height]</span></code> into <code class="docutils literal notranslate"><span class="pre">[pad</span> <span class="pre">+</span> <span class="pre">width</span> <span class="pre">+</span> <span class="pre">pad,</span> <span class="pre">pad</span> <span class="pre">+</span> <span class="pre">height</span> <span class="pre">+</span> <span class="pre">pad]</span></code> by padding pixes</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result iterator.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.io.MXDataIter" title="mxnet.io.MXDataIter">MXDataIter</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.io.ImageRecordUInt8Iter_v1"> |
| <code class="sig-name descname">ImageRecordUInt8Iter_v1</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.ImageRecordUInt8Iter_v1" title="Permalink to this definition">¶</a></dt> |
| <dd><p>b’Iterating on image RecordIO filesnn.. note::nn <code class="docutils literal notranslate"><span class="pre">ImageRecordUInt8Iter_v1</span></code> is deprecated. Use <code class="docutils literal notranslate"><span class="pre">ImageRecordUInt8Iter</span></code> instead.nnThis iterator is identical to <code class="docutils literal notranslate"><span class="pre">ImageRecordIter</span></code> except for using <code class="docutils literal notranslate"><span class="pre">uint8</span></code> asnthe data type instead of <code class="docutils literal notranslate"><span class="pre">float</span></code>.nnnnDefined in /work/mxnet/src/io/iter_image_recordio.cc:L377’</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>path_imglist</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image list (.lst) file. Generally created with tools/im2rec.py. Format (Tab separated): <index of record> <one or more labels> <relative path from root folder>.</p></li> |
| <li><p><strong>path_imgrec</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO (.rec) file or a directory path. Created with tools/im2rec.py.</p></li> |
| <li><p><strong>path_imgidx</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default=''</em>) – Path to the image RecordIO index (.idx) file. Created with tools/im2rec.py.</p></li> |
| <li><p><strong>aug_seq</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='aug_default'</em>) – The augmenter names to represent sequence of augmenters to be applied, seperated by comma. Additional keyword parameters will be seen by these augmenters.</p></li> |
| <li><p><strong>label_width</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The number of labels per image.</p></li> |
| <li><p><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>required</em>) – The shape of one output image in (channels, height, width) format.</p></li> |
| <li><p><strong>preprocess_threads</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='4'</em>) – The number of threads to do preprocessing.</p></li> |
| <li><p><strong>verbose</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – If or not output verbose information.</p></li> |
| <li><p><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – Virtually partition the data into these many parts.</p></li> |
| <li><p><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The <em>i</em>-th virtual partition to be read.</p></li> |
| <li><p><strong>device_id</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The device id used to create context for internal NDArray. Setting device_id to -1 will create Context::CPU(0). Setting device_id to valid positive device id will create Context::CPUPinned(device_id). Default is 0.</p></li> |
| <li><p><strong>shuffle_chunk_size</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – The data shuffle buffer size in MB. Only valid if shuffle is true.</p></li> |
| <li><p><strong>shuffle_chunk_seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed for shuffling</p></li> |
| <li><p><strong>seed_aug</strong> (<em>int</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default='None'</em>) – Random seed for augmentations.</p></li> |
| <li><p><strong>shuffle</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Whether to shuffle data randomly or not.</p></li> |
| <li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – The random seed.</p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>required</em>) – Batch size.</p></li> |
| <li><p><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Whether to use round robin to handle overflow batch or not.</p></li> |
| <li><p><strong>prefetch_buffer</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</p></li> |
| <li><p><strong>ctx</strong> (<em>{'cpu'</em><em>, </em><em>'cpu_pinned'</em><em>, </em><em>'gpu'}</em><em>,</em><em>optional</em><em>, </em><em>default='gpu'</em>) – Context data loader optimized for. Note that it only indicates the optimization strategy for devices, by no means the prefetcher will load data to GPUs. If ctx is ‘cpu_pinned’ and device_id is not -1, it will use cpu_pinned(device_id) as ctx</p></li> |
| <li><p><strong>dtype</strong> (<em>{None</em><em>, </em><em>'bfloat16'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'int64'</em><em>, </em><em>'int8'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal notranslate"><span class="pre">None</span></code> means no change.</p></li> |
| <li><p><strong>resize</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Down scale the shorter edge to a new size before applying other augmentations.</p></li> |
| <li><p><strong>rand_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – If or not randomly crop the image</p></li> |
| <li><p><strong>random_resized_crop</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – If or not perform random resized cropping on the image, as a standard preprocessing for resnet training on ImageNet data.</p></li> |
| <li><p><strong>max_rotate_angle</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Rotate by a random degree in <code class="docutils literal notranslate"><span class="pre">[-v,</span> <span class="pre">v]</span></code></p></li> |
| <li><p><strong>max_aspect_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Change the aspect (namely width/height) to a random value. If min_aspect_ratio is None then the aspect ratio ins sampled from [1 - max_aspect_ratio, 1 + max_aspect_ratio], else it is in <code class="docutils literal notranslate"><span class="pre">[min_aspect_ratio,</span> <span class="pre">max_aspect_ratio]</span></code></p></li> |
| <li><p><strong>min_aspect_ratio</strong> (<em>float</em><em> or </em><em>None</em><em>, </em><em>optional</em><em>, </em><em>default=None</em>) – Change the aspect (namely width/height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_aspect_ratio,</span> <span class="pre">max_aspect_ratio]</span></code></p></li> |
| <li><p><strong>max_shear_ratio</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Apply a shear transformation (namely <code class="docutils literal notranslate"><span class="pre">(x,y)->(x+my,y)</span></code>) with <code class="docutils literal notranslate"><span class="pre">m</span></code> randomly chose from <code class="docutils literal notranslate"><span class="pre">[-max_shear_ratio,</span> <span class="pre">max_shear_ratio]</span></code></p></li> |
| <li><p><strong>max_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal notranslate"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size].``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>min_crop_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Crop both width and height into a random size in <code class="docutils literal notranslate"><span class="pre">[min_crop_size,</span> <span class="pre">max_crop_size].``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>max_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal notranslate"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal notranslate"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal notranslate"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>min_random_scale</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Resize into <code class="docutils literal notranslate"><span class="pre">[width*s,</span> <span class="pre">height*s]</span></code> with <code class="docutils literal notranslate"><span class="pre">s</span></code> randomly chosen from <code class="docutils literal notranslate"><span class="pre">[min_random_scale,</span> <span class="pre">max_random_scale]``Ignored</span> <span class="pre">if</span> <span class="pre">``random_resized_crop</span></code> is True.</p></li> |
| <li><p><strong>max_random_area</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Change the area (namely width * height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_random_area,</span> <span class="pre">max_random_area]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is False.</p></li> |
| <li><p><strong>min_random_area</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Change the area (namely width * height) to a random value in <code class="docutils literal notranslate"><span class="pre">[min_random_area,</span> <span class="pre">max_random_area]</span></code>. Ignored if <code class="docutils literal notranslate"><span class="pre">random_resized_crop</span></code> is False.</p></li> |
| <li><p><strong>max_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=1e+10</em>) – Set the maximal width and height after all resize and rotate argumentation are applied</p></li> |
| <li><p><strong>min_img_size</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Set the minimal width and height after all resize and rotate argumentation are applied</p></li> |
| <li><p><strong>brightness</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-brightness,</span> <span class="pre">brightness]</span></code> to the brightness of image.</p></li> |
| <li><p><strong>contrast</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-contrast,</span> <span class="pre">contrast]</span></code> to the contrast of image.</p></li> |
| <li><p><strong>saturation</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-saturation,</span> <span class="pre">saturation]</span></code> to the saturation of image.</p></li> |
| <li><p><strong>pca_noise</strong> (<em>float</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Add PCA based noise to the image.</p></li> |
| <li><p><strong>random_h</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_h,</span> <span class="pre">random_h]</span></code> to the H channel in HSL color space.</p></li> |
| <li><p><strong>random_s</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_s,</span> <span class="pre">random_s]</span></code> to the S channel in HSL color space.</p></li> |
| <li><p><strong>random_l</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Add a random value in <code class="docutils literal notranslate"><span class="pre">[-random_l,</span> <span class="pre">random_l]</span></code> to the L channel in HSL color space.</p></li> |
| <li><p><strong>rotate</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – Rotate by an angle. If set, it overwrites the <code class="docutils literal notranslate"><span class="pre">max_rotate_angle</span></code> option.</p></li> |
| <li><p><strong>fill_value</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='255'</em>) – Set the padding pixels value to <code class="docutils literal notranslate"><span class="pre">fill_value</span></code>.</p></li> |
| <li><p><strong>inter_method</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – The interpolation method: 0-NN 1-bilinear 2-cubic 3-area 4-lanczos4 9-auto 10-rand.</p></li> |
| <li><p><strong>pad</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Change size from <code class="docutils literal notranslate"><span class="pre">[width,</span> <span class="pre">height]</span></code> into <code class="docutils literal notranslate"><span class="pre">[pad</span> <span class="pre">+</span> <span class="pre">width</span> <span class="pre">+</span> <span class="pre">pad,</span> <span class="pre">pad</span> <span class="pre">+</span> <span class="pre">height</span> <span class="pre">+</span> <span class="pre">pad]</span></code> by padding pixes</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result iterator.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.io.MXDataIter" title="mxnet.io.MXDataIter">MXDataIter</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.io.LibSVMIter"> |
| <code class="sig-name descname">LibSVMIter</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.LibSVMIter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>b”Returns the LibSVM iterator which returns data with <cite>csr</cite>nstorage type. This iterator is experimental and should be used with care.nnThe input data is stored in a format similar to LibSVM file format, except that the <strong>indicesnare expected to be zero-based instead of one-based, and the column indices for each row arenexpected to be sorted in ascending order</strong>. Details of the LibSVM format are availablen`here. <<a class="reference external" href="https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/">https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/</a>>`_nnnThe <cite>data_shape</cite> parameter is used to set the shape of each line of the data.nThe dimension of both <cite>data_shape</cite> and <cite>label_shape</cite> are expected to be 1.nnThe <cite>data_libsvm</cite> parameter is used to set the path input LibSVM file.nWhen it is set to a directory, all the files in the directory will be read.nnWhen <cite>label_libsvm</cite> is set to <code class="docutils literal notranslate"><span class="pre">NULL</span></code>, both data and label are read from the file specifiednby <cite>data_libsvm</cite>. In this case, the data is stored in <cite>csr</cite> storage type, while the label is a 1Dndense array.nnThe <cite>LibSVMIter</cite> only support <cite>round_batch</cite> parameter set to <code class="docutils literal notranslate"><span class="pre">True</span></code>. Therefore, if <cite>batch_size</cite>nis 3 and there are 4 total rows in libsvm file, 2 more examples are consumed at the first round.nnWhen <cite>num_parts</cite> and <cite>part_index</cite> are provided, the data is split into <cite>num_parts</cite> partitions,nand the iterator only reads the <cite>part_index</cite>-th partition. However, the partitions are notnguaranteed to be even.nn``reset()`` is expected to be called only after a complete pass of data.nnExample::nn # Contents of libsvm file <code class="docutils literal notranslate"><span class="pre">data.t</span></code>.n 1.0 0:0.5 2:1.2n -2.0n -3.0 0:0.6 1:2.4 2:1.2n 4 2:-1.2nn # Creates a <cite>LibSVMIter</cite> with <cite>batch_size`=3.n >>> data_iter = mx.io.LibSVMIter(data_libsvm = ‘data.t’, data_shape = (3,), batch_size = 3)n # The data of the first batch is stored in csr storage typen >>> batch = data_iter.next()n >>> csr = batch.data[0]n <CSRNDArray 3x3 @cpu(0)>n >>> csr.asnumpy()n [[ 0.5 0. 1.2 ]n [ 0. 0. 0. ]n [ 0.6 2.4 1.2]]n # The label of first batchn >>> label = batch.label[0]n >>> labeln [ 1. -2. -3.]n <NDArray 3 @cpu(0)>nn >>> second_batch = data_iter.next()n # The data of the second batchn >>> second_batch.data[0].asnumpy()n [[ 0. 0. -1.2 ]n [ 0.5 0. 1.2 ]n [ 0. 0. 0. ]]n # The label of the second batchn >>> second_batch.label[0].asnumpy()n [ 4. 1. -2.]nn >>> data_iter.reset()n # To restart the iterator for the second pass of the datannWhen `label_libsvm</cite> is set to the path to another LibSVM file,ndata is read from <cite>data_libsvm</cite> and label from <cite>label_libsvm</cite>.nIn this case, both data and label are stored in the csr format.nIf the label column in the <cite>data_libsvm</cite> file is ignored.nnExample::nn # Contents of libsvm file <code class="docutils literal notranslate"><span class="pre">label.t</span></code>n 1.0n -2.0 0:0.125n -3.0 2:1.2n 4 1:1.0 2:-1.2nn # Creates a <cite>LibSVMIter</cite> with specified label filen >>> data_iter = mx.io.LibSVMIter(data_libsvm = ‘data.t’, data_shape = (3,),n label_libsvm = ‘label.t’, label_shape = (3,), batch_size = 3)nn # Both data and label are in csr storage typen >>> batch = data_iter.next()n >>> csr_data = batch.data[0]n <CSRNDArray 3x3 @cpu(0)>n >>> csr_data.asnumpy()n [[ 0.5 0. 1.2 ]n [ 0. 0. 0. ]n [ 0.6 2.4 1.2 ]]n >>> csr_label = batch.label[0]n <CSRNDArray 3x3 @cpu(0)>n >>> csr_label.asnumpy()n [[ 0. 0. 0. ]n [ 0.125 0. 0. ]n [ 0. 0. 1.2 ]]nnnnDefined in /work/mxnet/src/io/iter_libsvm.cc:L299”</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data_libsvm</strong> (<em>string</em><em>, </em><em>required</em>) – The input zero-base indexed LibSVM data file or a directory path.</p></li> |
| <li><p><strong>data_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>required</em>) – The shape of one example.</p></li> |
| <li><p><strong>label_libsvm</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='NULL'</em>) – The input LibSVM label file or a directory path. If NULL, all labels will be read from <code class="docutils literal notranslate"><span class="pre">data_libsvm</span></code>.</p></li> |
| <li><p><strong>label_shape</strong> (<em>Shape</em><em>(</em><em>tuple</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=</em><em>[</em><em>1</em><em>]</em>) – The shape of one label.</p></li> |
| <li><p><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – partition the data into multiple parts</p></li> |
| <li><p><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – the index of the part will read</p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>required</em>) – Batch size.</p></li> |
| <li><p><strong>round_batch</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Whether to use round robin to handle overflow batch or not.</p></li> |
| <li><p><strong>prefetch_buffer</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</p></li> |
| <li><p><strong>ctx</strong> (<em>{'cpu'</em><em>, </em><em>'cpu_pinned'</em><em>, </em><em>'gpu'}</em><em>,</em><em>optional</em><em>, </em><em>default='gpu'</em>) – Context data loader optimized for. Note that it only indicates the optimization strategy for devices, by no means the prefetcher will load data to GPUs. If ctx is ‘cpu_pinned’ and device_id is not -1, it will use cpu_pinned(device_id) as ctx</p></li> |
| <li><p><strong>device_id</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – The default device id for context. -1 indicate it’s on default device</p></li> |
| <li><p><strong>dtype</strong> (<em>{None</em><em>, </em><em>'bfloat16'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'int64'</em><em>, </em><em>'int8'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal notranslate"><span class="pre">None</span></code> means no change.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result iterator.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.io.MXDataIter" title="mxnet.io.MXDataIter">MXDataIter</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="function"> |
| <dt id="mxnet.io.MNISTIter"> |
| <code class="sig-name descname">MNISTIter</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#mxnet.io.MNISTIter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>b’Iterating on the MNIST dataset.nnDefined in /work/mxnet/src/io/iter_mnist.cc:L258’</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>image</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='./train-images-idx3-ubyte'</em>) – Dataset Param: Mnist image path.</p></li> |
| <li><p><strong>label</strong> (<em>string</em><em>, </em><em>optional</em><em>, </em><em>default='./train-labels-idx1-ubyte'</em>) – Dataset Param: Mnist label path.</p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='128'</em>) – Batch Param: Batch Size.</p></li> |
| <li><p><strong>shuffle</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=1</em>) – Augmentation Param: Whether to shuffle data.</p></li> |
| <li><p><strong>flat</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Augmentation Param: Whether to flat the data into 1D.</p></li> |
| <li><p><strong>seed</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – Augmentation Param: Random Seed.</p></li> |
| <li><p><strong>silent</strong> (<em>boolean</em><em>, </em><em>optional</em><em>, </em><em>default=0</em>) – Auxiliary Param: Whether to print out data info.</p></li> |
| <li><p><strong>num_parts</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='1'</em>) – partition the data into multiple parts</p></li> |
| <li><p><strong>part_index</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='0'</em>) – the index of the part will read</p></li> |
| <li><p><strong>prefetch_buffer</strong> (<em>long</em><em> (</em><em>non-negative</em><em>)</em><em>, </em><em>optional</em><em>, </em><em>default=4</em>) – Maximum number of batches to prefetch.</p></li> |
| <li><p><strong>ctx</strong> (<em>{'cpu'</em><em>, </em><em>'cpu_pinned'</em><em>, </em><em>'gpu'}</em><em>,</em><em>optional</em><em>, </em><em>default='gpu'</em>) – Context data loader optimized for. Note that it only indicates the optimization strategy for devices, by no means the prefetcher will load data to GPUs. If ctx is ‘cpu_pinned’ and device_id is not -1, it will use cpu_pinned(device_id) as ctx</p></li> |
| <li><p><strong>device_id</strong> (<em>int</em><em>, </em><em>optional</em><em>, </em><em>default='-1'</em>) – The default device id for context. -1 indicate it’s on default device</p></li> |
| <li><p><strong>dtype</strong> (<em>{None</em><em>, </em><em>'bfloat16'</em><em>, </em><em>'float16'</em><em>, </em><em>'float32'</em><em>, </em><em>'float64'</em><em>, </em><em>'int32'</em><em>, </em><em>'int64'</em><em>, </em><em>'int8'</em><em>, </em><em>'uint8'}</em><em>,</em><em>optional</em><em>, </em><em>default='None'</em>) – Output data type. <code class="docutils literal notranslate"><span class="pre">None</span></code> means no change.</p></li> |
| </ul> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><p>The result iterator.</p> |
| </dd> |
| <dt class="field-odd">Return type</dt> |
| <dd class="field-odd"><p><a class="reference internal" href="#mxnet.io.MXDataIter" title="mxnet.io.MXDataIter">MXDataIter</a></p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.io.MXDataIter"> |
| <em class="property">class </em><code class="sig-name descname">MXDataIter</code><span class="sig-paren">(</span><em class="sig-param">handle</em>, <em class="sig-param">data_name='data'</em>, <em class="sig-param">label_name='softmax_label'</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#MXDataIter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.MXDataIter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.io.io.DataIter</span></code></p> |
| <p>A python wrapper a C++ data iterator.</p> |
| <p>This iterator is the Python wrapper to all native C++ data iterators, such |
| as <cite>CSVIter</cite>, <cite>ImageRecordIter</cite>, <cite>MNISTIter</cite>, etc. When initializing |
| <cite>CSVIter</cite> for example, you will get an <cite>MXDataIter</cite> instance to use in your |
| Python code. Calls to <cite>next</cite>, <cite>reset</cite>, etc will be delegated to the |
| underlying C++ data iterators.</p> |
| <p>Usually you don’t need to interact with <cite>MXDataIter</cite> directly unless you are |
| implementing your own data iterators in C++. To do that, please refer to |
| examples under the <cite>src/io</cite> folder.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>handle</strong> (<em>DataIterHandle</em><em>, </em><em>required</em>) – The handle to the underlying C++ Data Iterator.</p></li> |
| <li><p><strong>data_name</strong> (<em>str</em><em>, </em><em>optional</em>) – Data name. Default to “data”.</p></li> |
| <li><p><strong>label_name</strong> (<em>str</em><em>, </em><em>optional</em>) – Label name. Default to “softmax_label”.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.MXDataIter.getdata" title="mxnet.io.MXDataIter.getdata"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getdata</span></code></a>()</p></td> |
| <td><p>Get data of current batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.MXDataIter.getindex" title="mxnet.io.MXDataIter.getindex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getindex</span></code></a>()</p></td> |
| <td><p>Get index of the current batch.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.MXDataIter.getlabel" title="mxnet.io.MXDataIter.getlabel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getlabel</span></code></a>()</p></td> |
| <td><p>Get label of the current batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.MXDataIter.getpad" title="mxnet.io.MXDataIter.getpad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getpad</span></code></a>()</p></td> |
| <td><p>Get the number of padding examples in the current batch.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.MXDataIter.iter_next" title="mxnet.io.MXDataIter.iter_next"><code class="xref py py-obj docutils literal notranslate"><span class="pre">iter_next</span></code></a>()</p></td> |
| <td><p>Move to the next batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.MXDataIter.next" title="mxnet.io.MXDataIter.next"><code class="xref py py-obj docutils literal notranslate"><span class="pre">next</span></code></a>()</p></td> |
| <td><p>Get next data batch from iterator.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.MXDataIter.reset" title="mxnet.io.MXDataIter.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset the iterator to the begin of the data.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <p><code class="xref py py-class docutils literal notranslate"><span class="pre">src</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">e.g.</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None</span></code></p> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.io.MXDataIter.getdata"> |
| <code class="sig-name descname">getdata</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#MXDataIter.getdata"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.MXDataIter.getdata" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get data of current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>The data of the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>list of NDArray</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.MXDataIter.getindex"> |
| <code class="sig-name descname">getindex</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#MXDataIter.getindex"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.MXDataIter.getindex" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get index of the current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><strong>index</strong> – The indices of examples in the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>numpy.array</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.MXDataIter.getlabel"> |
| <code class="sig-name descname">getlabel</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#MXDataIter.getlabel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.MXDataIter.getlabel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get label of the current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>The label of the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>list of NDArray</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.MXDataIter.getpad"> |
| <code class="sig-name descname">getpad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#MXDataIter.getpad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.MXDataIter.getpad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get the number of padding examples in the current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>Number of padding examples in the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>int</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.MXDataIter.iter_next"> |
| <code class="sig-name descname">iter_next</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#MXDataIter.iter_next"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.MXDataIter.iter_next" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Move to the next batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>Whether the move is successful.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>boolean</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.MXDataIter.next"> |
| <code class="sig-name descname">next</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#MXDataIter.next"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.MXDataIter.next" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get next data batch from iterator.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>The data of next batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p><a class="reference internal" href="#mxnet.io.DataBatch" title="mxnet.io.DataBatch">DataBatch</a></p> |
| </dd> |
| <dt class="field-odd">Raises</dt> |
| <dd class="field-odd"><p><strong>StopIteration</strong> – If the end of the data is reached.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.MXDataIter.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#MXDataIter.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.MXDataIter.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset the iterator to the begin of the data.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.io.NDArrayIter"> |
| <em class="property">class </em><code class="sig-name descname">NDArrayIter</code><span class="sig-paren">(</span><em class="sig-param">data</em>, <em class="sig-param">label=None</em>, <em class="sig-param">batch_size=1</em>, <em class="sig-param">shuffle=False</em>, <em class="sig-param">last_batch_handle='pad'</em>, <em class="sig-param">data_name='data'</em>, <em class="sig-param">label_name='softmax_label'</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#NDArrayIter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.NDArrayIter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.io.io.DataIter</span></code></p> |
| <p>Returns an iterator for <code class="docutils literal notranslate"><span class="pre">mx.nd.NDArray</span></code>, <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>, <code class="docutils literal notranslate"><span class="pre">h5py.Dataset</span></code> |
| <code class="docutils literal notranslate"><span class="pre">mx.nd.sparse.CSRNDArray</span></code> or <code class="docutils literal notranslate"><span class="pre">scipy.sparse.csr_matrix</span></code>.</p> |
| <p class="rubric">Examples</p> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.NDArrayIter.getdata" title="mxnet.io.NDArrayIter.getdata"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getdata</span></code></a>()</p></td> |
| <td><p>Get data.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.NDArrayIter.getlabel" title="mxnet.io.NDArrayIter.getlabel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getlabel</span></code></a>()</p></td> |
| <td><p>Get label.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.NDArrayIter.getpad" title="mxnet.io.NDArrayIter.getpad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getpad</span></code></a>()</p></td> |
| <td><p>Get pad value of DataBatch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.NDArrayIter.hard_reset" title="mxnet.io.NDArrayIter.hard_reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hard_reset</span></code></a>()</p></td> |
| <td><p>Ignore roll over data and set to start.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.NDArrayIter.iter_next" title="mxnet.io.NDArrayIter.iter_next"><code class="xref py py-obj docutils literal notranslate"><span class="pre">iter_next</span></code></a>()</p></td> |
| <td><p>Increments the coursor by batch_size for next batch and check current cursor if it exceed the number of data points.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.NDArrayIter.next" title="mxnet.io.NDArrayIter.next"><code class="xref py py-obj docutils literal notranslate"><span class="pre">next</span></code></a>()</p></td> |
| <td><p>Returns the next batch of data.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.NDArrayIter.reset" title="mxnet.io.NDArrayIter.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Resets the iterator to the beginning of the data.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p><strong>Attributes</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.NDArrayIter.provide_data" title="mxnet.io.NDArrayIter.provide_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">provide_data</span></code></a></p></td> |
| <td><p>The name and shape of data provided by this iterator.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.NDArrayIter.provide_label" title="mxnet.io.NDArrayIter.provide_label"><code class="xref py py-obj docutils literal notranslate"><span class="pre">provide_label</span></code></a></p></td> |
| <td><p>The name and shape of label provided by this iterator.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">40</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="n">last_batch_handle</span><span class="o">=</span><span class="s1">'discard'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">dataiter</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="nb">print</span> <span class="n">batch</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="gp">... </span> <span class="n">batch</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> |
| <span class="gp">...</span> |
| <span class="go">[[[ 36. 37.]</span> |
| <span class="go"> [ 38. 39.]]</span> |
| <span class="go"> [[ 16. 17.]</span> |
| <span class="go"> [ 18. 19.]]</span> |
| <span class="go"> [[ 12. 13.]</span> |
| <span class="go"> [ 14. 15.]]]</span> |
| <span class="go">(3L, 2L, 2L)</span> |
| <span class="go">[[[ 32. 33.]</span> |
| <span class="go"> [ 34. 35.]]</span> |
| <span class="go"> [[ 4. 5.]</span> |
| <span class="go"> [ 6. 7.]]</span> |
| <span class="go"> [[ 24. 25.]</span> |
| <span class="go"> [ 26. 27.]]]</span> |
| <span class="go">(3L, 2L, 2L)</span> |
| <span class="go">[[[ 8. 9.]</span> |
| <span class="go"> [ 10. 11.]]</span> |
| <span class="go"> [[ 20. 21.]</span> |
| <span class="go"> [ 22. 23.]]</span> |
| <span class="go"> [[ 28. 29.]</span> |
| <span class="go"> [ 30. 31.]]]</span> |
| <span class="go">(3L, 2L, 2L)</span> |
| <span class="gp">>>> </span><span class="n">dataiter</span><span class="o">.</span><span class="n">provide_data</span> <span class="c1"># Returns a list of `DataDesc`</span> |
| <span class="go">[DataDesc[data,(3, 2L, 2L),<type 'numpy.float32'>,NCHW]]</span> |
| <span class="gp">>>> </span><span class="n">dataiter</span><span class="o">.</span><span class="n">provide_label</span> <span class="c1"># Returns a list of `DataDesc`</span> |
| <span class="go">[DataDesc[softmax_label,(3, 1L),<type 'numpy.float32'>,NCHW]]</span> |
| </pre></div> |
| </div> |
| <p>In the above example, data is shuffled as <cite>shuffle</cite> parameter is set to <cite>True</cite> |
| and remaining examples are discarded as <cite>last_batch_handle</cite> parameter is set to <cite>discard</cite>.</p> |
| <p>Usage of <cite>last_batch_handle</cite> parameter:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="kc">True</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="gp">>>> </span><span class="n">batchidx</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="gp">>>> </span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">dataiter</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="n">batchidx</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="gp">...</span> |
| <span class="gp">>>> </span><span class="n">batchidx</span> <span class="c1"># Padding added after the examples read are over. So, 10/3+1 batches are created.</span> |
| <span class="go">4</span> |
| <span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="n">last_batch_handle</span><span class="o">=</span><span class="s1">'discard'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">batchidx</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="gp">>>> </span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">dataiter</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="n">batchidx</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="gp">...</span> |
| <span class="gp">>>> </span><span class="n">batchidx</span> <span class="c1"># Remaining examples are discarded. So, 10/3 batches are created.</span> |
| <span class="go">3</span> |
| <span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">last_batch_handle</span><span class="o">=</span><span class="s1">'roll_over'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">batchidx</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="gp">>>> </span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">dataiter</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="n">batchidx</span> <span class="o">+=</span> <span class="mi">1</span> |
| <span class="gp">...</span> |
| <span class="gp">>>> </span><span class="n">batchidx</span> <span class="c1"># Remaining examples are rolled over to the next iteration.</span> |
| <span class="go">3</span> |
| <span class="gp">>>> </span><span class="n">dataiter</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">dataiter</span><span class="o">.</span><span class="n">next</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="go">[[[ 36. 37.]</span> |
| <span class="go"> [ 38. 39.]]</span> |
| <span class="go"> [[ 0. 1.]</span> |
| <span class="go"> [ 2. 3.]]</span> |
| <span class="go"> [[ 4. 5.]</span> |
| <span class="go"> [ 6. 7.]]]</span> |
| <span class="go">(3L, 2L, 2L)</span> |
| </pre></div> |
| </div> |
| <p><cite>NDArrayIter</cite> also supports multiple input and labels.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'data1'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)),</span> <span class="s1">'data2'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))}</span> |
| <span class="gp">>>> </span><span class="n">label</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'label1'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span> <span class="s1">'label2'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">1</span><span class="p">))}</span> |
| <span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</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="mi">3</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="n">last_batch_handle</span><span class="o">=</span><span class="s1">'discard'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p><cite>NDArrayIter</cite> also supports <code class="docutils literal notranslate"><span class="pre">mx.nd.sparse.CSRNDArray</span></code> |
| with <cite>last_batch_handle</cite> set to <cite>discard</cite>.</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">csr_data</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">40</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span><span class="mi">4</span><span class="p">)))</span><span class="o">.</span><span class="n">tostype</span><span class="p">(</span><span class="s1">'csr'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> |
| <span class="gp">>>> </span><span class="n">dataiter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">csr_data</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">last_batch_handle</span><span class="o">=</span><span class="s1">'discard'</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="p">[</span><span class="n">batch</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">dataiter</span><span class="p">]</span> |
| <span class="go">[</span> |
| <span class="go"><CSRNDArray 3x4 @cpu(0)>,</span> |
| <span class="go"><CSRNDArray 3x4 @cpu(0)>,</span> |
| <span class="go"><CSRNDArray 3x4 @cpu(0)>]</span> |
| </pre></div> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data</strong> (<em>array</em><em> or </em><em>list of array</em><em> or </em><em>dict of string to array</em>) – The input data.</p></li> |
| <li><p><strong>label</strong> (<em>array</em><em> or </em><em>list of array</em><em> or </em><em>dict of string to array</em><em>, </em><em>optional</em>) – The input label.</p></li> |
| <li><p><strong>batch_size</strong> (<em>int</em>) – Batch size of data.</p></li> |
| <li><p><strong>shuffle</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to shuffle the data. |
| Only supported if no h5py.Dataset inputs are used.</p></li> |
| <li><p><strong>last_batch_handle</strong> (<em>str</em><em>, </em><em>optional</em>) – How to handle the last batch. This parameter can be ‘pad’, ‘discard’ or |
| ‘roll_over’. |
| If ‘pad’, the last batch will be padded with data starting from the begining |
| If ‘discard’, the last batch will be discarded |
| If ‘roll_over’, the remaining elements will be rolled over to the next iteration and |
| note that it is intended for training and can cause problems if used for prediction.</p></li> |
| <li><p><strong>data_name</strong> (<em>str</em><em>, </em><em>optional</em>) – The data name.</p></li> |
| <li><p><strong>label_name</strong> (<em>str</em><em>, </em><em>optional</em>) – The label name.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <dl class="method"> |
| <dt id="mxnet.io.NDArrayIter.getdata"> |
| <code class="sig-name descname">getdata</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#NDArrayIter.getdata"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.NDArrayIter.getdata" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get data.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.NDArrayIter.getlabel"> |
| <code class="sig-name descname">getlabel</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#NDArrayIter.getlabel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.NDArrayIter.getlabel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get label.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.NDArrayIter.getpad"> |
| <code class="sig-name descname">getpad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#NDArrayIter.getpad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.NDArrayIter.getpad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get pad value of DataBatch.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.NDArrayIter.hard_reset"> |
| <code class="sig-name descname">hard_reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#NDArrayIter.hard_reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.NDArrayIter.hard_reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Ignore roll over data and set to start.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.NDArrayIter.iter_next"> |
| <code class="sig-name descname">iter_next</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#NDArrayIter.iter_next"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.NDArrayIter.iter_next" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Increments the coursor by batch_size for next batch |
| and check current cursor if it exceed the number of data points.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.NDArrayIter.next"> |
| <code class="sig-name descname">next</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#NDArrayIter.next"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.NDArrayIter.next" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the next batch of data.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.NDArrayIter.provide_data"> |
| <em class="property">property </em><code class="sig-name descname">provide_data</code><a class="headerlink" href="#mxnet.io.NDArrayIter.provide_data" title="Permalink to this definition">¶</a></dt> |
| <dd><p>The name and shape of data provided by this iterator.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.NDArrayIter.provide_label"> |
| <em class="property">property </em><code class="sig-name descname">provide_label</code><a class="headerlink" href="#mxnet.io.NDArrayIter.provide_label" title="Permalink to this definition">¶</a></dt> |
| <dd><p>The name and shape of label provided by this iterator.</p> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.NDArrayIter.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#NDArrayIter.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.NDArrayIter.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Resets the iterator to the beginning of the data.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.io.PrefetchingIter"> |
| <em class="property">class </em><code class="sig-name descname">PrefetchingIter</code><span class="sig-paren">(</span><em class="sig-param">iters</em>, <em class="sig-param">rename_data=None</em>, <em class="sig-param">rename_label=None</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#PrefetchingIter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.PrefetchingIter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.io.io.DataIter</span></code></p> |
| <p>Performs pre-fetch for other data iterators.</p> |
| <p>This iterator will create another thread to perform <code class="docutils literal notranslate"><span class="pre">iter_next</span></code> and then |
| store the data in memory. It potentially accelerates the data read, at the |
| cost of more memory usage.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>iters</strong> (<a class="reference internal" href="#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a><em> or </em><em>list of DataIter</em>) – The data iterators to be pre-fetched.</p></li> |
| <li><p><strong>rename_data</strong> (<em>None</em><em> or </em><em>list of dict</em>) – The <em>i</em>-th element is a renaming map for the <em>i</em>-th iter, in the form of |
| {‘original_name’ : ‘new_name’}. Should have one entry for each entry |
| in iter[i].provide_data.</p></li> |
| <li><p><strong>rename_label</strong> (<em>None</em><em> or </em><em>list of dict</em>) – Similar to <code class="docutils literal notranslate"><span class="pre">rename_data</span></code>.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.PrefetchingIter.getdata" title="mxnet.io.PrefetchingIter.getdata"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getdata</span></code></a>()</p></td> |
| <td><p>Get data of current batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.PrefetchingIter.getindex" title="mxnet.io.PrefetchingIter.getindex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getindex</span></code></a>()</p></td> |
| <td><p>Get index of the current batch.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.PrefetchingIter.getlabel" title="mxnet.io.PrefetchingIter.getlabel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getlabel</span></code></a>()</p></td> |
| <td><p>Get label of the current batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.PrefetchingIter.getpad" title="mxnet.io.PrefetchingIter.getpad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getpad</span></code></a>()</p></td> |
| <td><p>Get the number of padding examples in the current batch.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.PrefetchingIter.iter_next" title="mxnet.io.PrefetchingIter.iter_next"><code class="xref py py-obj docutils literal notranslate"><span class="pre">iter_next</span></code></a>()</p></td> |
| <td><p>Move to the next batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.PrefetchingIter.next" title="mxnet.io.PrefetchingIter.next"><code class="xref py py-obj docutils literal notranslate"><span class="pre">next</span></code></a>()</p></td> |
| <td><p>Get next data batch from iterator.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.PrefetchingIter.reset" title="mxnet.io.PrefetchingIter.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset the iterator to the begin of the data.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">iter1</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">({</span><span class="s1">'data'</span><span class="p">:</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span><span class="mi">10</span><span class="p">))},</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">25</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">iter2</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">({</span><span class="s1">'data'</span><span class="p">:</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span><span class="mi">10</span><span class="p">))},</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">25</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">piter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">PrefetchingIter</span><span class="p">([</span><span class="n">iter1</span><span class="p">,</span> <span class="n">iter2</span><span class="p">],</span> |
| <span class="gp">... </span> <span class="n">rename_data</span><span class="o">=</span><span class="p">[{</span><span class="s1">'data'</span><span class="p">:</span> <span class="s1">'data_1'</span><span class="p">},</span> <span class="p">{</span><span class="s1">'data'</span><span class="p">:</span> <span class="s1">'data_2'</span><span class="p">}])</span> |
| <span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">piter</span><span class="o">.</span><span class="n">provide_data</span><span class="p">)</span> |
| <span class="go">[DataDesc[data_1,(25, 10L),<type 'numpy.float32'>,NCHW],</span> |
| <span class="go"> DataDesc[data_2,(25, 10L),<type 'numpy.float32'>,NCHW]]</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.io.PrefetchingIter.getdata"> |
| <code class="sig-name descname">getdata</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#PrefetchingIter.getdata"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.PrefetchingIter.getdata" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get data of current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>The data of the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>list of NDArray</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.PrefetchingIter.getindex"> |
| <code class="sig-name descname">getindex</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#PrefetchingIter.getindex"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.PrefetchingIter.getindex" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get index of the current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><strong>index</strong> – The indices of examples in the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>numpy.array</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.PrefetchingIter.getlabel"> |
| <code class="sig-name descname">getlabel</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#PrefetchingIter.getlabel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.PrefetchingIter.getlabel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get label of the current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>The label of the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>list of NDArray</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.PrefetchingIter.getpad"> |
| <code class="sig-name descname">getpad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#PrefetchingIter.getpad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.PrefetchingIter.getpad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get the number of padding examples in the current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>Number of padding examples in the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>int</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.PrefetchingIter.iter_next"> |
| <code class="sig-name descname">iter_next</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#PrefetchingIter.iter_next"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.PrefetchingIter.iter_next" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Move to the next batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>Whether the move is successful.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>boolean</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.PrefetchingIter.next"> |
| <code class="sig-name descname">next</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#PrefetchingIter.next"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.PrefetchingIter.next" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get next data batch from iterator.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>The data of next batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p><a class="reference internal" href="#mxnet.io.DataBatch" title="mxnet.io.DataBatch">DataBatch</a></p> |
| </dd> |
| <dt class="field-odd">Raises</dt> |
| <dd class="field-odd"><p><strong>StopIteration</strong> – If the end of the data is reached.</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.PrefetchingIter.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#PrefetchingIter.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.PrefetchingIter.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset the iterator to the begin of the data.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| <dl class="class"> |
| <dt id="mxnet.io.ResizeIter"> |
| <em class="property">class </em><code class="sig-name descname">ResizeIter</code><span class="sig-paren">(</span><em class="sig-param">data_iter</em>, <em class="sig-param">size</em>, <em class="sig-param">reset_internal=True</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#ResizeIter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.ResizeIter" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">mxnet.io.io.DataIter</span></code></p> |
| <p>Resize a data iterator to a given number of batches.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><ul class="simple"> |
| <li><p><strong>data_iter</strong> (<a class="reference internal" href="#mxnet.io.DataIter" title="mxnet.io.DataIter"><em>DataIter</em></a>) – The data iterator to be resized.</p></li> |
| <li><p><strong>size</strong> (<em>int</em>) – The number of batches per epoch to resize to.</p></li> |
| <li><p><strong>reset_internal</strong> (<em>bool</em>) – Whether to reset internal iterator on ResizeIter.reset.</p></li> |
| </ul> |
| </dd> |
| </dl> |
| <p><strong>Methods</strong></p> |
| <table class="longtable docutils align-default"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.ResizeIter.getdata" title="mxnet.io.ResizeIter.getdata"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getdata</span></code></a>()</p></td> |
| <td><p>Get data of current batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.ResizeIter.getindex" title="mxnet.io.ResizeIter.getindex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getindex</span></code></a>()</p></td> |
| <td><p>Get index of the current batch.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.ResizeIter.getlabel" title="mxnet.io.ResizeIter.getlabel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getlabel</span></code></a>()</p></td> |
| <td><p>Get label of the current batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.ResizeIter.getpad" title="mxnet.io.ResizeIter.getpad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getpad</span></code></a>()</p></td> |
| <td><p>Get the number of padding examples in the current batch.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#mxnet.io.ResizeIter.iter_next" title="mxnet.io.ResizeIter.iter_next"><code class="xref py py-obj docutils literal notranslate"><span class="pre">iter_next</span></code></a>()</p></td> |
| <td><p>Move to the next batch.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#mxnet.io.ResizeIter.reset" title="mxnet.io.ResizeIter.reset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset</span></code></a>()</p></td> |
| <td><p>Reset the iterator to the begin of the data.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">nd_iter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">NDArrayIter</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span><span class="mi">10</span><span class="p">)),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">25</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">resize_iter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">ResizeIter</span><span class="p">(</span><span class="n">nd_iter</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">resize_iter</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> |
| <span class="go">[<NDArray 25x10 @cpu(0)>]</span> |
| <span class="go">[<NDArray 25x10 @cpu(0)>]</span> |
| </pre></div> |
| </div> |
| <dl class="method"> |
| <dt id="mxnet.io.ResizeIter.getdata"> |
| <code class="sig-name descname">getdata</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#ResizeIter.getdata"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.ResizeIter.getdata" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get data of current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>The data of the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>list of NDArray</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.ResizeIter.getindex"> |
| <code class="sig-name descname">getindex</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#ResizeIter.getindex"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.ResizeIter.getindex" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get index of the current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p><strong>index</strong> – The indices of examples in the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>numpy.array</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.ResizeIter.getlabel"> |
| <code class="sig-name descname">getlabel</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#ResizeIter.getlabel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.ResizeIter.getlabel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get label of the current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>The label of the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>list of NDArray</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.ResizeIter.getpad"> |
| <code class="sig-name descname">getpad</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#ResizeIter.getpad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.ResizeIter.getpad" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Get the number of padding examples in the current batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>Number of padding examples in the current batch.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>int</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.ResizeIter.iter_next"> |
| <code class="sig-name descname">iter_next</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#ResizeIter.iter_next"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.ResizeIter.iter_next" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Move to the next batch.</p> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><p>Whether the move is successful.</p> |
| </dd> |
| <dt class="field-even">Return type</dt> |
| <dd class="field-even"><p>boolean</p> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="method"> |
| <dt id="mxnet.io.ResizeIter.reset"> |
| <code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/io/io.html#ResizeIter.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.io.ResizeIter.reset" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Reset the iterator to the begin of the data.</p> |
| </dd></dl> |
| |
| </dd></dl> |
| |
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