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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current"> |
| <li class="toctree-l2"><a class="reference internal" href="../../getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../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="../../getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../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="../../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="../../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="../../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="../../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="../../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="../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../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 current"><a class="reference internal" href="../index.html">Packages</a><ul class="current"> |
| <li class="toctree-l3"><a class="reference internal" href="../autograd/index.html">Automatic Differentiation</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/nn.html">Layers and Blocks</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../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="../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="../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="../gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../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="../gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../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="../legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
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| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
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| <li class="toctree-l3 current"><a class="reference internal" href="index.html">ONNX</a><ul class="current"> |
| <li class="toctree-l4 current"><a class="current reference internal" href="#">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="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> |
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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-l2"><a class="reference internal" href="../../performance/index.html">Performance</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../performance/compression/index.html">Compression</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../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="../../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="../../performance/backend/amp.html">Using AMP: Automatic Mixed Precision</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arange.html">mxnet.np.arange</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.array-manipulation.html">Array manipulation routines</a><ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ravel.html">mxnet.np.ravel</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.flatten.html">mxnet.np.ndarray.flatten</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.swapaxes.html">mxnet.np.swapaxes</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.T.html">mxnet.np.ndarray.T</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.moveaxis.html">mxnet.np.moveaxis</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.squeeze.html">mxnet.np.squeeze</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.broadcast_arrays.html">mxnet.np.broadcast_arrays</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dstack.html">mxnet.np.dstack</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.split.html">mxnet.np.split</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hsplit.html">mxnet.np.hsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vsplit.html">mxnet.np.vsplit</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.array_split.html">mxnet.np.array_split</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.repeat.html">mxnet.np.repeat</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unique.html">mxnet.np.unique</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.delete.html">mxnet.np.delete</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.append.html">mxnet.np.append</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.resize.html">mxnet.np.resize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trim_zeros.html">mxnet.np.trim_zeros</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.roll.html">mxnet.np.roll</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rot90.html">mxnet.np.rot90</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fliplr.html">mxnet.np.fliplr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.flipud.html">mxnet.np.flipud</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.set_printoptions.html">mxnet.np.set_printoptions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.inner.html">mxnet.np.inner</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.outer.html">mxnet.np.outer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rad2deg.html">mxnet.np.rad2deg</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.unwrap.html">mxnet.np.unwrap</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sinh.html">mxnet.np.sinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cosh.html">mxnet.np.cosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tanh.html">mxnet.np.tanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsinh.html">mxnet.np.arcsinh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccosh.html">mxnet.np.arccosh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctanh.html">mxnet.np.arctanh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.rint.html">mxnet.np.rint</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.fix.html">mxnet.np.fix</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.floor.html">mxnet.np.floor</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ceil.html">mxnet.np.ceil</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trunc.html">mxnet.np.trunc</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.around.html">mxnet.np.around</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.round_.html">mxnet.np.round_</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sum.html">mxnet.np.sum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.prod.html">mxnet.np.prod</a></li> |
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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 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current"> |
| <li class="toctree-l2"><a class="reference internal" href="../../getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../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="../../getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../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="../../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="../../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="../../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="../../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="../../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="../../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="../../getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../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 current"><a class="reference internal" href="../index.html">Packages</a><ul class="current"> |
| <li class="toctree-l3"><a class="reference internal" href="../autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../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="../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="../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="../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="../gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../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="../gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../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="../legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.io.html">Input and output</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.genfromtxt.html">mxnet.np.genfromtxt</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.ndarray.tolist.html">mxnet.np.ndarray.tolist</a></li> |
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| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.dot.html">mxnet.np.dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.vdot.html">mxnet.np.vdot</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tensordot.html">mxnet.np.tensordot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.einsum.html">mxnet.np.einsum</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.multi_dot.html">mxnet.np.linalg.multi_dot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.matmul.html">mxnet.np.matmul</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_power.html">mxnet.np.linalg.matrix_power</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.kron.html">mxnet.np.kron</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.svd.html">mxnet.np.linalg.svd</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cholesky.html">mxnet.np.linalg.cholesky</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.qr.html">mxnet.np.linalg.qr</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eig.html">mxnet.np.linalg.eig</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigh.html">mxnet.np.linalg.eigh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvals.html">mxnet.np.linalg.eigvals</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.eigvalsh.html">mxnet.np.linalg.eigvalsh</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.norm.html">mxnet.np.linalg.norm</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.trace.html">mxnet.np.trace</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.cond.html">mxnet.np.linalg.cond</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.det.html">mxnet.np.linalg.det</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.matrix_rank.html">mxnet.np.linalg.matrix_rank</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.slogdet.html">mxnet.np.linalg.slogdet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.solve.html">mxnet.np.linalg.solve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorsolve.html">mxnet.np.linalg.tensorsolve</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.lstsq.html">mxnet.np.linalg.lstsq</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.inv.html">mxnet.np.linalg.inv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.pinv.html">mxnet.np.linalg.pinv</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.linalg.tensorinv.html">mxnet.np.linalg.tensorinv</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../api/np/routines.math.html">Mathematical functions</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.sin.html">mxnet.np.sin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.cos.html">mxnet.np.cos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.tan.html">mxnet.np.tan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arcsin.html">mxnet.np.arcsin</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arccos.html">mxnet.np.arccos</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan.html">mxnet.np.arctan</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.degrees.html">mxnet.np.degrees</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.radians.html">mxnet.np.radians</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.hypot.html">mxnet.np.hypot</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.arctan2.html">mxnet.np.arctan2</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../api/np/generated/mxnet.np.deg2rad.html">mxnet.np.deg2rad</a></li> |
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| <!--- Licensed to the Apache Software Foundation (ASF) under one --><!--- or more contributor license agreements. See the NOTICE file --><!--- distributed with this work for additional information --><!--- regarding copyright ownership. The ASF licenses this file --><!--- to you under the Apache License, Version 2.0 (the --><!--- "License"); you may not use this file except in compliance --><!--- with the License. You may obtain a copy of the License at --><!--- http://www.apache.org/licenses/LICENSE-2.0 --><!--- Unless required by applicable law or agreed to in writing, --><!--- software distributed under the License is distributed on an --><!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --><!--- KIND, either express or implied. See the License for the --><!--- specific language governing permissions and limitations --><!--- under the License. --><div class="section" id="Fine-tuning-an-ONNX-model"> |
| <h1>Fine-tuning an ONNX model<a class="headerlink" href="#Fine-tuning-an-ONNX-model" title="Permalink to this headline">¶</a></h1> |
| <p>Fine-tuning is a common practice in Transfer Learning. One can take advantage of the pre-trained weights of a network, and use them as an initializer for their own task. Indeed, quite often it is difficult to gather a dataset large enough that it would allow training from scratch deep and complex networks such as ResNet152 or VGG16. For example in an image classification task, using a network trained on a large dataset like ImageNet gives a good base from which the weights can be slightly |
| updated, or fine-tuned, to predict accurately the new classes. We will see in this tutorial that this can be achieved even with a relatively small number of new training examples.</p> |
| <p><a class="reference external" href="https://github.com/onnx/onnx">Open Neural Network Exchange (ONNX)</a> provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.</p> |
| <p>In this tutorial we will:</p> |
| <ul class="simple"> |
| <li><p>learn how to pick a specific layer from a pre-trained .onnx model file</p></li> |
| <li><p>learn how to load this model in Gluon and fine-tune it on a different dataset</p></li> |
| </ul> |
| <div class="section" id="Pre-requisite"> |
| <h2>Pre-requisite<a class="headerlink" href="#Pre-requisite" title="Permalink to this headline">¶</a></h2> |
| <p>To run the tutorial you will need to have installed the following python modules: - <a class="reference external" href="https://mxnet.apache.org/get_started">MXNet > 1.1.0</a> - <a class="reference external" href="https://github.com/onnx/onnx">onnx</a> - matplotlib</p> |
| <p>We recommend that you have first followed this tutorial: - <a class="reference internal" href="inference_on_onnx_model.html"><span class="doc">Inference using an ONNX model on MXNet Gluon</span></a></p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">import</span> <span class="nn">json</span> |
| <span class="kn">import</span> <span class="nn">logging</span> |
| <span class="kn">import</span> <span class="nn">multiprocessing</span> |
| <span class="kn">import</span> <span class="nn">os</span> |
| <span class="kn">import</span> <span class="nn">tarfile</span> |
| |
| <span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">)</span> |
| |
| <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> |
| <span class="kn">import</span> <span class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span> |
| <span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">gluon</span><span class="p">,</span> <span class="n">np</span><span class="p">,</span> <span class="n">npx</span><span class="p">,</span> <span class="n">autograd</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.data.vision.datasets</span> <span class="kn">import</span> <span class="n">ImageFolderDataset</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.data</span> <span class="kn">import</span> <span class="n">DataLoader</span> |
| <span class="kn">import</span> <span class="nn">mxnet.contrib.onnx</span> <span class="k">as</span> <span class="nn">onnx_mxnet</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">onp</span> |
| |
| <span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="Downloading-supporting-files"> |
| <h3>Downloading supporting files<a class="headerlink" href="#Downloading-supporting-files" title="Permalink to this headline">¶</a></h3> |
| <p>These are images and a vizualisation script:</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">image_folder</span> <span class="o">=</span> <span class="s2">"images"</span> |
| <span class="n">utils_file</span> <span class="o">=</span> <span class="s2">"utils.py"</span> <span class="c1"># contain utils function to plot nice visualization</span> |
| <span class="n">images</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'wrench.jpg'</span><span class="p">,</span> <span class="s1">'dolphin.jpg'</span><span class="p">,</span> <span class="s1">'lotus.jpg'</span><span class="p">]</span> |
| <span class="n">base_url</span> <span class="o">=</span> <span class="s2">"https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/</span><span class="si">{}</span><span class="s2">?raw=true"</span> |
| |
| |
| <span class="k">for</span> <span class="n">image</span> <span class="ow">in</span> <span class="n">images</span><span class="p">:</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">test_utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">base_url</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">image_folder</span><span class="p">,</span> <span class="n">image</span><span class="p">)),</span> <span class="n">fname</span><span class="o">=</span><span class="n">image</span><span class="p">,</span><span class="n">dirname</span><span class="o">=</span><span class="n">image_folder</span><span class="p">)</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">test_utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">base_url</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">utils_file</span><span class="p">),</span> <span class="n">fname</span><span class="o">=</span><span class="n">utils_file</span><span class="p">)</span> |
| |
| <span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="o">*</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Downloading-a-model-from-the-ONNX-model-zoo"> |
| <h2>Downloading a model from the ONNX model zoo<a class="headerlink" href="#Downloading-a-model-from-the-ONNX-model-zoo" title="Permalink to this headline">¶</a></h2> |
| <p>We download a pre-trained model, in our case the <a class="reference external" href="https://arxiv.org/abs/1409.4842">GoogleNet</a> model, trained on <a class="reference external" href="http://www.image-net.org/">ImageNet</a> from the <a class="reference external" href="https://github.com/onnx/models">ONNX model zoo</a>. The model comes packaged in an archive <code class="docutils literal notranslate"><span class="pre">tar.gz</span></code> file containing an <code class="docutils literal notranslate"><span class="pre">model.onnx</span></code> model file.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">base_url</span> <span class="o">=</span> <span class="s2">"https://s3.amazonaws.com/download.onnx/models/opset_3/"</span> |
| <span class="n">current_model</span> <span class="o">=</span> <span class="s2">"bvlc_googlenet"</span> |
| <span class="n">model_folder</span> <span class="o">=</span> <span class="s2">"model"</span> |
| <span class="n">archive_file</span> <span class="o">=</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">.tar.gz"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">current_model</span><span class="p">)</span> |
| <span class="n">archive_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_folder</span><span class="p">,</span> <span class="n">archive_file</span><span class="p">)</span> |
| <span class="n">url</span> <span class="o">=</span> <span class="s2">"</span><span class="si">{}{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">base_url</span><span class="p">,</span> <span class="n">archive_file</span><span class="p">)</span> |
| <span class="n">onnx_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_folder</span><span class="p">,</span> <span class="n">current_model</span><span class="p">,</span> <span class="s1">'model.onnx'</span><span class="p">)</span> |
| |
| <span class="c1"># Download the zipped model</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">test_utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">dirname</span> <span class="o">=</span> <span class="n">model_folder</span><span class="p">)</span> |
| |
| <span class="c1"># Extract the model</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_folder</span><span class="p">,</span> <span class="n">current_model</span><span class="p">)):</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Extracting </span><span class="si">{}</span><span class="s1"> in </span><span class="si">{}</span><span class="s1">...'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">archive_path</span><span class="p">,</span> <span class="n">model_folder</span><span class="p">))</span> |
| <span class="n">tar</span> <span class="o">=</span> <span class="n">tarfile</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">archive_path</span><span class="p">,</span> <span class="s2">"r:gz"</span><span class="p">)</span> |
| <span class="n">tar</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="n">model_folder</span><span class="p">)</span> |
| <span class="n">tar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Model extracted.'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Downloading-the-Caltech101-dataset"> |
| <h2>Downloading the Caltech101 dataset<a class="headerlink" href="#Downloading-the-Caltech101-dataset" title="Permalink to this headline">¶</a></h2> |
| <p>The <a class="reference external" href="https://data.caltech.edu/records/20086">Caltech101 dataset</a> is made of pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images.</p> |
| <p><em>L. Fei-Fei, R. Fergus and P. Perona. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. IEEE. CVPR 2004, Workshop on Generative-Model Based Vision. 2004</em></p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">data_folder</span> <span class="o">=</span> <span class="s2">"data"</span> |
| <span class="n">dataset_name</span> <span class="o">=</span> <span class="s2">"101_ObjectCategories"</span> |
| <span class="n">archive_file</span> <span class="o">=</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">.tar.gz"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">)</span> |
| <span class="n">archive_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_folder</span><span class="p">,</span> <span class="n">archive_file</span><span class="p">)</span> |
| <span class="n">data_url</span> <span class="o">=</span> <span class="s2">"https://s3.us-east-2.amazonaws.com/mxnet-public/"</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">archive_path</span><span class="p">):</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">test_utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="s2">"</span><span class="si">{}{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data_url</span><span class="p">,</span> <span class="n">archive_file</span><span class="p">),</span> <span class="n">dirname</span> <span class="o">=</span> <span class="n">data_folder</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Extracting </span><span class="si">{}</span><span class="s1"> in </span><span class="si">{}</span><span class="s1">...'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">archive_file</span><span class="p">,</span> <span class="n">data_folder</span><span class="p">))</span> |
| <span class="n">tar</span> <span class="o">=</span> <span class="n">tarfile</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">archive_path</span><span class="p">,</span> <span class="s2">"r:gz"</span><span class="p">)</span> |
| <span class="n">tar</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="n">data_folder</span><span class="p">)</span> |
| <span class="n">tar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Data extracted.'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">training_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_folder</span><span class="p">,</span> <span class="n">dataset_name</span><span class="p">)</span> |
| <span class="n">testing_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_folder</span><span class="p">,</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">_test"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="Load-the-data-using-an-ImageFolderDataset-and-a-DataLoader"> |
| <h3>Load the data using an ImageFolderDataset and a DataLoader<a class="headerlink" href="#Load-the-data-using-an-ImageFolderDataset-and-a-DataLoader" title="Permalink to this headline">¶</a></h3> |
| <p>We need to transform the images to a format accepted by the network</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">EDGE</span> <span class="o">=</span> <span class="mi">224</span> |
| <span class="n">SIZE</span> <span class="o">=</span> <span class="p">(</span><span class="n">EDGE</span><span class="p">,</span> <span class="n">EDGE</span><span class="p">)</span> |
| <span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">32</span> |
| <span class="n">NUM_WORKERS</span> <span class="o">=</span> <span class="mi">6</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>We transform the dataset images using the following operations: - resize the shorter edge to 224, the longer edge will be greater or equal to 224 - center and crop an area of size (224,224) - transpose the channels to be (3,224,224)</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">label</span><span class="p">):</span> |
| <span class="n">resized</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">resize_short</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">EDGE</span><span class="p">)</span> |
| <span class="n">cropped</span><span class="p">,</span> <span class="n">crop_info</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">center_crop</span><span class="p">(</span><span class="n">resized</span><span class="p">,</span> <span class="n">SIZE</span><span class="p">)</span> |
| <span class="n">transposed</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">cropped</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">transposed</span><span class="p">,</span> <span class="n">label</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>The train and test dataset are created automatically by passing the root of each folder. The labels are built using the sub-folders names as label.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">train_root</span> |
| <span class="n">__label1</span> |
| <span class="n">____image1</span> |
| <span class="n">____image2</span> |
| <span class="n">__label2</span> |
| <span class="n">____image3</span> |
| <span class="n">____image4</span> |
| </pre></div> |
| </div> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">dataset_train</span> <span class="o">=</span> <span class="n">ImageFolderDataset</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="n">training_path</span><span class="p">)</span> |
| <span class="n">dataset_test</span> <span class="o">=</span> <span class="n">ImageFolderDataset</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="n">testing_path</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>We use several worker processes, which means the dataloading and pre-processing is going to be distributed across multiple processes. This will help preventing our GPU from starving and waiting for the data to be copied across</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">dataloader_train</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset_train</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">transform</span><span class="p">,</span> <span class="n">lazy</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">BATCH_SIZE</span><span class="p">,</span> <span class="n">last_batch</span><span class="o">=</span><span class="s1">'rollover'</span><span class="p">,</span> |
| <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">NUM_WORKERS</span><span class="p">)</span> |
| <span class="n">dataloader_test</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset_test</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">transform</span><span class="p">,</span> <span class="n">lazy</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">BATCH_SIZE</span><span class="p">,</span> <span class="n">last_batch</span><span class="o">=</span><span class="s1">'rollover'</span><span class="p">,</span> |
| <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="n">NUM_WORKERS</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Train dataset: </span><span class="si">{}</span><span class="s2"> images, Test dataset: </span><span class="si">{}</span><span class="s2"> images"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset_train</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataset_test</span><span class="p">)))</span> |
| </pre></div> |
| </div> |
| </div> |
| <p><code class="docutils literal notranslate"><span class="pre">Train</span> <span class="pre">dataset:</span> <span class="pre">6996</span> <span class="pre">images,</span> <span class="pre">Test</span> <span class="pre">dataset:</span> <span class="pre">1681</span> <span class="pre">images</span></code></p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">categories</span> <span class="o">=</span> <span class="n">dataset_train</span><span class="o">.</span><span class="n">synsets</span> |
| <span class="n">NUM_CLASSES</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">categories</span><span class="p">)</span> |
| <span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">32</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Let’s plot the 1000th image to test the dataset</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">N</span> <span class="o">=</span> <span class="mi">1000</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">((</span><span class="n">transform</span><span class="p">(</span><span class="n">dataset_train</span><span class="p">[</span><span class="n">N</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">))))</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="n">categories</span><span class="p">[</span><span class="n">dataset_train</span><span class="p">[</span><span class="n">N</span><span class="p">][</span><span class="mi">1</span><span class="p">]])</span> |
| </pre></div> |
| </div> |
| </div> |
| <p><code class="docutils literal notranslate"><span class="pre">Motorbikes</span></code></p> |
| <p><img alt="onnx motorbike" src="https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/onnx/motorbike.png?raw=true" /></p> |
| </div> |
| </div> |
| <div class="section" id="Fine-Tuning-the-ONNX-model"> |
| <h2>Fine-Tuning the ONNX model<a class="headerlink" href="#Fine-Tuning-the-ONNX-model" title="Permalink to this headline">¶</a></h2> |
| <div class="section" id="Getting-the-last-layer"> |
| <h3>Getting the last layer<a class="headerlink" href="#Getting-the-last-layer" title="Permalink to this headline">¶</a></h3> |
| <p>Load the ONNX model</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">sym</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span> <span class="o">=</span> <span class="n">onnx_mxnet</span><span class="o">.</span><span class="n">import_model</span><span class="p">(</span><span class="n">onnx_path</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>This function get the output of a given layer</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">def</span> <span class="nf">get_layer_output</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">,</span> <span class="n">layer_name</span><span class="p">):</span> |
| <span class="n">all_layers</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">get_internals</span><span class="p">()</span> |
| <span class="n">net</span> <span class="o">=</span> <span class="n">all_layers</span><span class="p">[</span><span class="n">layer_name</span><span class="o">+</span><span class="s1">'_output'</span><span class="p">]</span> |
| <span class="n">net</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">net</span><span class="p">)</span> |
| <span class="n">new_args</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">({</span><span class="n">k</span><span class="p">:</span><span class="n">arg_params</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">arg_params</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">net</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()})</span> |
| <span class="n">new_aux</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">({</span><span class="n">k</span><span class="p">:</span><span class="n">aux_params</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">aux_params</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">net</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()})</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">new_args</span><span class="p">,</span> <span class="n">new_aux</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Here we print the different layers of the network to make it easier to pick the right one</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">sym</span><span class="o">.</span><span class="n">get_internals</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <p><code class="docutils literal notranslate"><span class="pre"><Symbol</span> <span class="pre">group</span> <span class="pre">[data_0,</span> <span class="pre">pad0,</span> <span class="pre">conv1/7x7_s2_w_0,</span> <span class="pre">conv1/7x7_s2_b_0,</span> <span class="pre">convolution0,</span> <span class="pre">relu0,</span> <span class="pre">pad1,</span> <span class="pre">pooling0,</span> <span class="pre">lrn0,</span> <span class="pre">pad2,</span> <span class="pre">conv2/3x3_reduce_w_0,</span> <span class="pre">conv2/3x3_reduce_b_0,</span> <span class="pre">convolution1,</span> <span class="pre">relu1,</span> <span class="pre">pad3,</span> <span class="pre">conv2/3x3_w_0,</span> <span class="pre">conv2/3x3_b_0,</span> <span class="pre">convolution2,</span> <span class="pre">relu2,</span> <span class="pre">lrn1,</span> <span class="pre">pad4,</span> <span class="pre">pooling1,</span> <span class="pre">pad5,</span> <span class="pre">inception_3a/1x1_w_0,</span> <span class="pre">inception_3a/1x1_b_0,</span> <span class="pre">convolution3,</span> <span class="pre">relu3,</span> <span class="pre">pad6,</span> <span class="pre">.................................................................................inception_5b/pool_proj_b_0,</span> <span class="pre">convolution56,</span> <span class="pre">relu56,</span> <span class="pre">concat8,</span> <span class="pre">pad70,</span> <span class="pre">pooling13,</span> <span class="pre">dropout0,</span> <span class="pre">flatten0,</span> <span class="pre">loss3/classifier_w_0,</span> <span class="pre">linalg_gemm20,</span> <span class="pre">loss3/classifier_b_0,</span> <span class="pre">_mulscalar0,</span> <span class="pre">broadcast_add0,</span> <span class="pre">softmax0]></span></code></p> |
| <p>We get the network until the output of the <code class="docutils literal notranslate"><span class="pre">flatten0</span></code> layer</p> |
| <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">new_sym</span><span class="p">,</span> <span class="n">new_arg_params</span><span class="p">,</span> <span class="n">new_aux_params</span> <span class="o">=</span> <span class="n">get_layer_output</span><span class="p">(</span><span class="n">sym</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">,</span> <span class="s1">'flatten0'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="Fine-tuning-in-gluon"> |
| <h3>Fine-tuning in gluon<a class="headerlink" href="#Fine-tuning-in-gluon" title="Permalink to this headline">¶</a></h3> |
| <p>We can now take advantage of the features and pattern detection knowledge that our network learnt training on ImageNet, and apply that to the new Caltech101 dataset.</p> |
| <p>We pick a device, fine-tuning on CPU will be <strong>WAY</strong> slower.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">device</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">gpu</span><span class="p">()</span> <span class="k">if</span> <span class="n">mx</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>We create a symbol block that is going to hold all our pre-trained layers, and assign the weights of the different pre-trained layers to the newly created SymbolBlock</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">import</span> <span class="nn">warnings</span> |
| <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">():</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">simplefilter</span><span class="p">(</span><span class="s2">"ignore"</span><span class="p">)</span> |
| <span class="n">pre_trained</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">SymbolBlock</span><span class="p">(</span><span class="n">outputs</span><span class="o">=</span><span class="n">new_sym</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">sym</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s1">'data_0'</span><span class="p">))</span> |
| <span class="n">net_params</span> <span class="o">=</span> <span class="n">pre_trained</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">new_arg_params</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">net_params</span><span class="p">:</span> |
| <span class="n">net_params</span><span class="p">[</span><span class="n">param</span><span class="p">]</span><span class="o">.</span><span class="n">_load_init</span><span class="p">(</span><span class="n">new_arg_params</span><span class="p">[</span><span class="n">param</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">new_aux_params</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">net_params</span><span class="p">:</span> |
| <span class="n">net_params</span><span class="p">[</span><span class="n">param</span><span class="p">]</span><span class="o">.</span><span class="n">_load_init</span><span class="p">(</span><span class="n">new_aux_params</span><span class="p">[</span><span class="n">param</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| |
| </pre></div> |
| </div> |
| </div> |
| <p>We create the new dense layer with the right new number of classes (101) and initialize the weights</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">dense_layer</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="n">NUM_CLASSES</span><span class="p">)</span> |
| <span class="n">dense_layer</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">Xavier</span><span class="p">(</span><span class="n">magnitude</span><span class="o">=</span><span class="mf">2.24</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>We add the SymbolBlock and the new dense layer to a HybridSequential network</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">HybridSequential</span><span class="p">()</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">pre_trained</span><span class="p">)</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">dense_layer</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Loss"> |
| <h3>Loss<a class="headerlink" href="#Loss" title="Permalink to this headline">¶</a></h3> |
| <p>Softmax cross entropy for multi-class classification</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">softmax_cross_entropy</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropyLoss</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Trainer"> |
| <h3>Trainer<a class="headerlink" href="#Trainer" title="Permalink to this headline">¶</a></h3> |
| <p>Initialize trainer with common training parameters</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">LEARNING_RATE</span> <span class="o">=</span> <span class="mf">0.0005</span> |
| <span class="n">WDECAY</span> <span class="o">=</span> <span class="mf">0.00001</span> |
| <span class="n">MOMENTUM</span> <span class="o">=</span> <span class="mf">0.9</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>The trainer will retrain and fine-tune the entire network. If we use <code class="docutils literal notranslate"><span class="pre">dense_layer</span></code> instead of <code class="docutils literal notranslate"><span class="pre">net</span></code> in the cell below, the gradient updates would only be applied to the new last dense layer. Essentially we would be using the pre-trained network as a featurizer.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">trainer</span> <span class="o">=</span> <span class="n">gluon</span><span class="o">.</span><span class="n">Trainer</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(),</span> <span class="s1">'sgd'</span><span class="p">,</span> |
| <span class="p">{</span><span class="s1">'learning_rate'</span><span class="p">:</span> <span class="n">LEARNING_RATE</span><span class="p">,</span> |
| <span class="s1">'wd'</span><span class="p">:</span><span class="n">WDECAY</span><span class="p">,</span> |
| <span class="s1">'momentum'</span><span class="p">:</span><span class="n">MOMENTUM</span><span class="p">})</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Evaluation-loop"> |
| <h3>Evaluation loop<a class="headerlink" href="#Evaluation-loop" title="Permalink to this headline">¶</a></h3> |
| <p>We measure the accuracy in a non-blocking way, using <code class="docutils literal notranslate"><span class="pre">np.array</span></code> to take care of the parallelisation that MXNet and Gluon offers.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span> <span class="k">def</span> <span class="nf">evaluate_accuracy_gluon</span><span class="p">(</span><span class="n">data_iterator</span><span class="p">,</span> <span class="n">net</span><span class="p">):</span> |
| <span class="n">num_instance</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">sum_metric</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</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="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data_iterator</span><span class="p">):</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
| <span class="n">prediction</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span> |
| <span class="n">num_instance</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">prediction</span><span class="p">)</span> |
| <span class="n">sum_metric</span> <span class="o">+=</span> <span class="p">(</span><span class="n">prediction</span><span class="o">==</span><span class="n">label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> |
| <span class="n">accuracy</span> <span class="o">=</span> <span class="p">(</span><span class="n">sum_metric</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">/</span><span class="n">num_instance</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">accuracy</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="o">%%</span><span class="n">time</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Untrained network Test Accuracy: </span><span class="si">{0:.4f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">evaluate_accuracy_gluon</span><span class="p">(</span><span class="n">dataloader_test</span><span class="p">,</span> <span class="n">net</span><span class="p">)))</span> |
| </pre></div> |
| </div> |
| </div> |
| <p><code class="docutils literal notranslate"><span class="pre">Untrained</span> <span class="pre">network</span> <span class="pre">Test</span> <span class="pre">Accuracy:</span> <span class="pre">0.0192</span></code></p> |
| </div> |
| <div class="section" id="Training-loop"> |
| <h3>Training loop<a class="headerlink" href="#Training-loop" title="Permalink to this headline">¶</a></h3> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">val_accuracy</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</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="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader_train</span><span class="p">):</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">i</span><span class="o">%</span><span class="mi">20</span><span class="o">==</span><span class="mi">0</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">></span><span class="mi">0</span><span class="p">:</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Batch [</span><span class="si">{0}</span><span class="s1">] loss: </span><span class="si">{1:.4f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">loss</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()))</span> |
| |
| <span class="k">with</span> <span class="n">autograd</span><span class="o">.</span><span class="n">record</span><span class="p">():</span> |
| <span class="n">output</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
| <span class="n">loss</span> <span class="o">=</span> <span class="n">softmax_cross_entropy</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span> |
| <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> |
| <span class="n">trainer</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| |
| <span class="n">npx</span><span class="o">.</span><span class="n">waitall</span><span class="p">()</span> <span class="c1"># wait at the end of the epoch</span> |
| <span class="n">new_val_accuracy</span> <span class="o">=</span> <span class="n">evaluate_accuracy_gluon</span><span class="p">(</span><span class="n">dataloader_test</span><span class="p">,</span> <span class="n">net</span><span class="p">)</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s2">"Epoch [</span><span class="si">{0}</span><span class="s2">] Test Accuracy </span><span class="si">{1:.4f}</span><span class="s2"> "</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">new_val_accuracy</span><span class="p">))</span> |
| |
| <span class="c1"># We perform early-stopping regularization, to prevent the model from overfitting</span> |
| <span class="k">if</span> <span class="n">val_accuracy</span> <span class="o">></span> <span class="n">new_val_accuracy</span><span class="p">:</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'Validation accuracy is decreasing, stopping training'</span><span class="p">)</span> |
| <span class="k">break</span> |
| <span class="n">val_accuracy</span> <span class="o">=</span> <span class="n">new_val_accuracy</span> |
| </pre></div> |
| </div> |
| </div> |
| <p><code class="docutils literal notranslate"><span class="pre">Epoch</span> <span class="pre">4,</span> <span class="pre">Test</span> <span class="pre">Accuracy</span> <span class="pre">0.8942307829856873</span></code></p> |
| </div> |
| </div> |
| <div class="section" id="Testing"> |
| <h2>Testing<a class="headerlink" href="#Testing" title="Permalink to this headline">¶</a></h2> |
| <p>In the previous tutorial, we saw that the network trained on ImageNet couldn’t classify correctly <code class="docutils literal notranslate"><span class="pre">wrench</span></code>, <code class="docutils literal notranslate"><span class="pre">dolphin</span></code>, <code class="docutils literal notranslate"><span class="pre">lotus</span></code> because these are not categories of the ImageNet dataset.</p> |
| <p>Let’s see if our network fine-tuned on Caltech101 is up for the task:</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Number of predictions to show</span> |
| <span class="n">TOP_P</span> <span class="o">=</span> <span class="mi">3</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Convert img to format expected by the network</span> |
| <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="n">img</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">np</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">expand_dims</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="c1"># Load and transform the test images</span> |
| <span class="n">caltech101_images_test</span> <span class="o">=</span> <span class="p">[</span><span class="n">plt</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">image_folder</span><span class="p">,</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">img</span><span class="p">)))</span> <span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">images</span><span class="p">]</span> |
| <span class="n">caltech101_images_transformed</span> <span class="o">=</span> <span class="p">[</span><span class="n">transform</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> <span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">caltech101_images_test</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Helper function to run batches of data</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">def</span> <span class="nf">run_batch</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span> |
| <span class="n">results</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span> |
| <span class="n">outputs</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> |
| <span class="n">results</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">o</span> <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">outputs</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()])</span> |
| <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">results</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">result</span> <span class="o">=</span> <span class="n">run_batch</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">caltech101_images_transformed</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">plot_predictions</span><span class="p">(</span><span class="n">caltech101_images_test</span><span class="p">,</span> <span class="n">result</span><span class="p">,</span> <span class="n">categories</span><span class="p">,</span> <span class="n">TOP_P</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p><img alt="onnx caltech101 correct" src="https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/onnx/caltech101_correct.png?raw=true" /></p> |
| <p><strong>Great!</strong> The network classified these images correctly after being fine-tuned on a dataset that contains images of <code class="docutils literal notranslate"><span class="pre">wrench</span></code>, <code class="docutils literal notranslate"><span class="pre">dolphin</span></code> and <code class="docutils literal notranslate"><span class="pre">lotus</span></code></p> |
| </div> |
| </div> |
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| <div class="localtoc"> |
| <p class="caption"> |
| <span class="caption-text">Table Of Contents</span> |
| </p> |
| <ul> |
| <li><a class="reference internal" href="#">Fine-tuning an ONNX model</a><ul> |
| <li><a class="reference internal" href="#Pre-requisite">Pre-requisite</a><ul> |
| <li><a class="reference internal" href="#Downloading-supporting-files">Downloading supporting files</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#Downloading-a-model-from-the-ONNX-model-zoo">Downloading a model from the ONNX model zoo</a></li> |
| <li><a class="reference internal" href="#Downloading-the-Caltech101-dataset">Downloading the Caltech101 dataset</a><ul> |
| <li><a class="reference internal" href="#Load-the-data-using-an-ImageFolderDataset-and-a-DataLoader">Load the data using an ImageFolderDataset and a DataLoader</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#Fine-Tuning-the-ONNX-model">Fine-Tuning the ONNX model</a><ul> |
| <li><a class="reference internal" href="#Getting-the-last-layer">Getting the last layer</a></li> |
| <li><a class="reference internal" href="#Fine-tuning-in-gluon">Fine-tuning in gluon</a></li> |
| <li><a class="reference internal" href="#Loss">Loss</a></li> |
| <li><a class="reference internal" href="#Trainer">Trainer</a></li> |
| <li><a class="reference internal" href="#Evaluation-loop">Evaluation loop</a></li> |
| <li><a class="reference internal" href="#Training-loop">Training loop</a></li> |
| </ul> |
| </li> |
| <li><a class="reference internal" href="#Testing">Testing</a></li> |
| </ul> |
| </li> |
| </ul> |
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| <div>ONNX</div> |
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| <div>Running inference on MXNet/Gluon from an ONNX model</div> |
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