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| <span class="mdl-layout-title toc">Table Of Contents</span> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul> |
| <li class="toctree-l2"><a class="reference internal" href="../../../../../tutorials/getting-started/index.html">Getting Started</a><ul> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/packages/gluon/index.html">Gluon</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/gluon/text/index.html">Text Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/training/trainer.html">Trainer</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../../tutorials/packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/packages/legacy/index.html">Legacy</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/legacy/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/legacy/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/legacy/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/legacy/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/legacy/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/legacy/ndarray/sparse/index.html">Tutorials</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../../tutorials/packages/legacy/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../../../../tutorials/packages/legacy/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/packages/np/index.html">What is NP on MXNet</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/np/cheat-sheet.html">The NP on MXNet cheat sheet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/np/np-vs-numpy.html">Differences between NP on MXNet and NumPy</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/packages/onnx/index.html">ONNX</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../../api/np/routines.linalg.html">Linear algebra (<code class="xref py py-mod docutils literal notranslate"><span class="pre">numpy.linalg</span></code>)</a><ul> |
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| <li class="toctree-l1"><a class="reference internal" href="../../../../../tutorials/index.html">Python Tutorials</a><ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/index.html">Crash Course</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/0-introduction.html">Introduction</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-nparray.html">Step 1: Manipulate data with NP on MXNet</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-create-nn.html">Step 2: Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Step 3: Automatic differentiation with autograd</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-components.html">Step 4: Necessary components that are not in the network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html">Step 5: <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html#Using-your-own-data-with-custom-Datasets">Using your own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders">New in MXNet 2.0: faster C++ backend dataloaders</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-train-nn.html">Step 6: Train a Neural Network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/7-use-gpus.html">Step 7: Load and Run a NN using GPU</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/gluon_migration_guide.html">Gluon2.0: Migration Guide</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/getting-started/logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/packages/autograd/index.html">Automatic Differentiation</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/gluon/image/index.html">Image Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/loss/loss.html">Loss functions</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
| </ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
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| |
| <h1>Source code for mxnet.gluon.model_zoo.vision.resnet</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># coding: utf-8</span> |
| <span class="c1"># pylint: disable= arguments-differ</span> |
| <span class="sd">"""ResNets, implemented in Gluon."""</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'ResNetV1'</span><span class="p">,</span> <span class="s1">'ResNetV2'</span><span class="p">,</span> |
| <span class="s1">'BasicBlockV1'</span><span class="p">,</span> <span class="s1">'BasicBlockV2'</span><span class="p">,</span> |
| <span class="s1">'BottleneckV1'</span><span class="p">,</span> <span class="s1">'BottleneckV2'</span><span class="p">,</span> |
| <span class="s1">'resnet18_v1'</span><span class="p">,</span> <span class="s1">'resnet34_v1'</span><span class="p">,</span> <span class="s1">'resnet50_v1'</span><span class="p">,</span> <span class="s1">'resnet101_v1'</span><span class="p">,</span> <span class="s1">'resnet152_v1'</span><span class="p">,</span> |
| <span class="s1">'resnet18_v2'</span><span class="p">,</span> <span class="s1">'resnet34_v2'</span><span class="p">,</span> <span class="s1">'resnet50_v2'</span><span class="p">,</span> <span class="s1">'resnet101_v2'</span><span class="p">,</span> <span class="s1">'resnet152_v2'</span><span class="p">,</span> |
| <span class="s1">'get_resnet'</span><span class="p">]</span> |
| |
| <span class="kn">import</span> <span class="nn">os</span> |
| |
| <span class="kn">from</span> <span class="nn">....device</span> <span class="kn">import</span> <span class="n">cpu</span> |
| <span class="kn">from</span> <span class="nn">...block</span> <span class="kn">import</span> <span class="n">HybridBlock</span> |
| <span class="kn">from</span> <span class="nn">...</span> <span class="kn">import</span> <span class="n">nn</span> |
| <span class="kn">from</span> <span class="nn">....</span> <span class="kn">import</span> <span class="n">base</span> |
| <span class="kn">from</span> <span class="nn">....</span> <span class="n">util</span> <span class="kn">import</span> <span class="nn">use_np</span><span class="o">,</span> <span class="nn">wrap_ctx_to_device_func</span> |
| <span class="kn">from</span> <span class="nn">....</span> <span class="kn">import</span> <span class="n">npx</span> |
| |
| <span class="c1"># Helpers</span> |
| <span class="k">def</span> <span class="nf">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> |
| <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">)</span> |
| |
| |
| <span class="c1"># Blocks</span> |
| <div class="viewcode-block" id="BasicBlockV1"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.BasicBlockV1">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">BasicBlockV1</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""BasicBlock V1 from `"Deep Residual Learning for Image Recognition"</span> |
| <span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</span> |
| <span class="sd"> This is used for ResNet V1 for 18, 34 layers.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> channels : int</span> |
| <span class="sd"> Number of output channels.</span> |
| <span class="sd"> stride : int</span> |
| <span class="sd"> Stride size.</span> |
| <span class="sd"> downsample : bool, default False</span> |
| <span class="sd"> Whether to downsample the input.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> Number of input channels. Default is 0, to infer from the graph.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">downsample</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">BasicBlockV1</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">body</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="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">())</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="s1">'relu'</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">channels</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">())</span> |
| <span class="k">if</span> <span class="n">downsample</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">downsample</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="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> |
| <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">())</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <div class="viewcode-block" id="BasicBlockV1.forward"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.BasicBlockV1.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">residual</span> <span class="o">=</span> <span class="n">x</span> |
| |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">body</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">:</span> |
| <span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">(</span><span class="n">residual</span><span class="p">)</span> |
| |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="n">residual</span><span class="o">+</span><span class="n">x</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">x</span></div></div> |
| |
| |
| <div class="viewcode-block" id="BottleneckV1"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.BottleneckV1">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">BottleneckV1</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Bottleneck V1 from `"Deep Residual Learning for Image Recognition"</span> |
| <span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</span> |
| <span class="sd"> This is used for ResNet V1 for 50, 101, 152 layers.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> channels : int</span> |
| <span class="sd"> Number of output channels.</span> |
| <span class="sd"> stride : int</span> |
| <span class="sd"> Stride size.</span> |
| <span class="sd"> downsample : bool, default False</span> |
| <span class="sd"> Whether to downsample the input.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> Number of input channels. Default is 0, to infer from the graph.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">downsample</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">BottleneckV1</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">body</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="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="o">//</span><span class="mi">4</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">stride</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">())</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="s1">'relu'</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="o">//</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">channels</span><span class="o">//</span><span class="mi">4</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">())</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="s1">'relu'</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">body</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">())</span> |
| <span class="k">if</span> <span class="n">downsample</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">downsample</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="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> |
| <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">())</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <div class="viewcode-block" id="BottleneckV1.forward"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.BottleneckV1.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">residual</span> <span class="o">=</span> <span class="n">x</span> |
| |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">body</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">:</span> |
| <span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">(</span><span class="n">residual</span><span class="p">)</span> |
| |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">residual</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">x</span></div></div> |
| |
| |
| <div class="viewcode-block" id="BasicBlockV2"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.BasicBlockV2">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">BasicBlockV2</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""BasicBlock V2 from</span> |
| <span class="sd"> `"Identity Mappings in Deep Residual Networks"</span> |
| <span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</span> |
| <span class="sd"> This is used for ResNet V2 for 18, 34 layers.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> channels : int</span> |
| <span class="sd"> Number of output channels.</span> |
| <span class="sd"> stride : int</span> |
| <span class="sd"> Stride size.</span> |
| <span class="sd"> downsample : bool, default False</span> |
| <span class="sd"> Whether to downsample the input.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> Number of input channels. Default is 0, to infer from the graph.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">downsample</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">BasicBlockV2</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">channels</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">downsample</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <div class="viewcode-block" id="BasicBlockV2.forward"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.BasicBlockV2.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">residual</span> <span class="o">=</span> <span class="n">x</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">:</span> |
| <span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">residual</span></div></div> |
| |
| |
| <div class="viewcode-block" id="BottleneckV2"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.BottleneckV2">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">BottleneckV2</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""Bottleneck V2 from</span> |
| <span class="sd"> `"Identity Mappings in Deep Residual Networks"</span> |
| <span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</span> |
| <span class="sd"> This is used for ResNet V2 for 50, 101, 152 layers.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> channels : int</span> |
| <span class="sd"> Number of output channels.</span> |
| <span class="sd"> stride : int</span> |
| <span class="sd"> Stride size.</span> |
| <span class="sd"> downsample : bool, default False</span> |
| <span class="sd"> Whether to downsample the input.</span> |
| <span class="sd"> in_channels : int, default 0</span> |
| <span class="sd"> Number of input channels. Default is 0, to infer from the graph.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">downsample</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">BottleneckV2</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="o">//</span><span class="mi">4</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="o">//</span><span class="mi">4</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">channels</span><span class="o">//</span><span class="mi">4</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">bn3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">downsample</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> |
| <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <div class="viewcode-block" id="BottleneckV2.forward"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.BottleneckV2.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">residual</span> <span class="o">=</span> <span class="n">x</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">:</span> |
| <span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">npx</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">act_type</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">residual</span></div></div> |
| |
| |
| <span class="c1"># Nets</span> |
| <div class="viewcode-block" id="ResNetV1"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.ResNetV1">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">ResNetV1</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""ResNet V1 model from</span> |
| <span class="sd"> `"Deep Residual Learning for Image Recognition"</span> |
| <span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> block : gluon.HybridBlock</span> |
| <span class="sd"> Class for the residual block. Options are BasicBlockV1, BottleneckV1.</span> |
| <span class="sd"> layers : list of int</span> |
| <span class="sd"> Numbers of layers in each block</span> |
| <span class="sd"> channels : list of int</span> |
| <span class="sd"> Numbers of channels in each block. Length should be one larger than layers list.</span> |
| <span class="sd"> classes : int, default 1000</span> |
| <span class="sd"> Number of classification classes.</span> |
| <span class="sd"> thumbnail : bool, default False</span> |
| <span class="sd"> Enable thumbnail.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">thumbnail</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">ResNetV1</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">layers</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">channels</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</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="k">if</span> <span class="n">thumbnail</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">())</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="s1">'relu'</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> |
| |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">num_layer</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">layers</span><span class="p">):</span> |
| <span class="n">stride</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">2</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="n">num_layer</span><span class="p">,</span> <span class="n">channels</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> |
| <span class="n">stride</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">GlobalAvgPool2D</span><span class="p">())</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">output</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">classes</span><span class="p">,</span> <span class="n">in_units</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> |
| |
| <span class="k">def</span> <span class="nf">_make_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="n">layer</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">layer</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">channels</span> <span class="o">!=</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">))</span> |
| <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">layers</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span> |
| <span class="n">layer</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">layer</span> |
| |
| <div class="viewcode-block" id="ResNetV1.forward"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.ResNetV1.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| |
| <span class="k">return</span> <span class="n">x</span></div></div> |
| |
| |
| <div class="viewcode-block" id="ResNetV2"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.ResNetV2">[docs]</a><span class="nd">@use_np</span> |
| <span class="k">class</span> <span class="nc">ResNetV2</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""ResNet V2 model from</span> |
| <span class="sd"> `"Identity Mappings in Deep Residual Networks"</span> |
| <span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> block : gluon.HybridBlock</span> |
| <span class="sd"> Class for the residual block. Options are BasicBlockV1, BottleneckV1.</span> |
| <span class="sd"> layers : list of int</span> |
| <span class="sd"> Numbers of layers in each block</span> |
| <span class="sd"> channels : list of int</span> |
| <span class="sd"> Numbers of channels in each block. Length should be one larger than layers list.</span> |
| <span class="sd"> classes : int, default 1000</span> |
| <span class="sd"> Number of classification classes.</span> |
| <span class="sd"> thumbnail : bool, default False</span> |
| <span class="sd"> Enable thumbnail.</span> |
| <span class="sd"> """</span> |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">thumbnail</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">ResNetV2</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">layers</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">channels</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</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="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">(</span><span class="n">scale</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">thumbnail</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_conv3x3</span><span class="p">(</span><span class="n">channels</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">())</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="s1">'relu'</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> |
| |
| <span class="n">in_channels</span> <span class="o">=</span> <span class="n">channels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">num_layer</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">layers</span><span class="p">):</span> |
| <span class="n">stride</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">2</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">block</span><span class="p">,</span> <span class="n">num_layer</span><span class="p">,</span> <span class="n">channels</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">],</span> |
| <span class="n">stride</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">))</span> |
| <span class="n">in_channels</span> <span class="o">=</span> <span class="n">channels</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span><span class="p">())</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Activation</span><span class="p">(</span><span class="s1">'relu'</span><span class="p">))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">GlobalAvgPool2D</span><span class="p">())</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">())</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">output</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">classes</span><span class="p">,</span> <span class="n">in_units</span><span class="o">=</span><span class="n">in_channels</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_make_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="n">layer</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">layer</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">channels</span> <span class="o">!=</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">))</span> |
| <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">layers</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span> |
| <span class="n">layer</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="n">channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">layer</span> |
| |
| <div class="viewcode-block" id="ResNetV2.forward"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.ResNetV2.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">x</span></div></div> |
| |
| |
| <span class="c1"># Specification</span> |
| <span class="n">resnet_spec</span> <span class="o">=</span> <span class="p">{</span><span class="mi">18</span><span class="p">:</span> <span class="p">(</span><span class="s1">'basic_block'</span><span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">]),</span> |
| <span class="mi">34</span><span class="p">:</span> <span class="p">(</span><span class="s1">'basic_block'</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">]),</span> |
| <span class="mi">50</span><span class="p">:</span> <span class="p">(</span><span class="s1">'bottle_neck'</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">2048</span><span class="p">]),</span> |
| <span class="mi">101</span><span class="p">:</span> <span class="p">(</span><span class="s1">'bottle_neck'</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">23</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">2048</span><span class="p">]),</span> |
| <span class="mi">152</span><span class="p">:</span> <span class="p">(</span><span class="s1">'bottle_neck'</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">36</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">2048</span><span class="p">])}</span> |
| |
| <span class="n">resnet_net_versions</span> <span class="o">=</span> <span class="p">[</span><span class="n">ResNetV1</span><span class="p">,</span> <span class="n">ResNetV2</span><span class="p">]</span> |
| <span class="n">resnet_block_versions</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">'basic_block'</span><span class="p">:</span> <span class="n">BasicBlockV1</span><span class="p">,</span> <span class="s1">'bottle_neck'</span><span class="p">:</span> <span class="n">BottleneckV1</span><span class="p">},</span> |
| <span class="p">{</span><span class="s1">'basic_block'</span><span class="p">:</span> <span class="n">BasicBlockV2</span><span class="p">,</span> <span class="s1">'bottle_neck'</span><span class="p">:</span> <span class="n">BottleneckV2</span><span class="p">}]</span> |
| |
| |
| <span class="c1"># Constructor</span> |
| <div class="viewcode-block" id="get_resnet"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.get_resnet">[docs]</a><span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">get_resnet</span><span class="p">(</span><span class="n">version</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cpu</span><span class="p">(),</span> |
| <span class="n">root</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">base</span><span class="o">.</span><span class="n">data_dir</span><span class="p">(),</span> <span class="s1">'models'</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""ResNet V1 model from `"Deep Residual Learning for Image Recognition"</span> |
| <span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</span> |
| <span class="sd"> ResNet V2 model from `"Identity Mappings in Deep Residual Networks"</span> |
| <span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> version : int</span> |
| <span class="sd"> Version of ResNet. Options are 1, 2.</span> |
| <span class="sd"> num_layers : int</span> |
| <span class="sd"> Numbers of layers. Options are 18, 34, 50, 101, 152.</span> |
| <span class="sd"> pretrained : bool, default False</span> |
| <span class="sd"> Whether to load the pretrained weights for model.</span> |
| <span class="sd"> device : Device, default CPU</span> |
| <span class="sd"> The device in which to load the pretrained weights.</span> |
| <span class="sd"> root : str, default $MXNET_HOME/models</span> |
| <span class="sd"> Location for keeping the model parameters.</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="n">num_layers</span> <span class="ow">in</span> <span class="n">resnet_spec</span><span class="p">,</span> \ |
| <span class="sa">f</span><span class="s2">"Invalid number of layers: </span><span class="si">{</span><span class="n">num_layers</span><span class="si">}</span><span class="s2">. Options are </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">resnet_spec</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">}</span><span class="s2">"</span> |
| <span class="n">block_type</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">channels</span> <span class="o">=</span> <span class="n">resnet_spec</span><span class="p">[</span><span class="n">num_layers</span><span class="p">]</span> |
| <span class="k">assert</span> <span class="n">version</span> <span class="o">>=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">version</span> <span class="o"><=</span> <span class="mi">2</span><span class="p">,</span> \ |
| <span class="sa">f</span><span class="s2">"Invalid resnet version: </span><span class="si">{</span><span class="n">version</span><span class="si">}</span><span class="s2">. Options are 1 and 2."</span> |
| <span class="n">resnet_class</span> <span class="o">=</span> <span class="n">resnet_net_versions</span><span class="p">[</span><span class="n">version</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">block_class</span> <span class="o">=</span> <span class="n">resnet_block_versions</span><span class="p">[</span><span class="n">version</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="n">block_type</span><span class="p">]</span> |
| <span class="n">net</span> <span class="o">=</span> <span class="n">resnet_class</span><span class="p">(</span><span class="n">block_class</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">pretrained</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">..model_store</span> <span class="kn">import</span> <span class="n">get_model_file</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">load_parameters</span><span class="p">(</span><span class="n">get_model_file</span><span class="p">(</span><span class="sa">f</span><span class="s1">'resnet</span><span class="si">{</span><span class="n">num_layers</span><span class="si">}</span><span class="s1">_v</span><span class="si">{</span><span class="n">version</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> |
| <span class="n">root</span><span class="o">=</span><span class="n">root</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">return</span> <span class="n">net</span></div> |
| |
| <div class="viewcode-block" id="resnet18_v1"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.resnet18_v1">[docs]</a><span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">resnet18_v1</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""ResNet-18 V1 model from `"Deep Residual Learning for Image Recognition"</span> |
| <span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pretrained : bool, default False</span> |
| <span class="sd"> Whether to load the pretrained weights for model.</span> |
| <span class="sd"> device : Device, default CPU</span> |
| <span class="sd"> The device in which to load the pretrained weights.</span> |
| <span class="sd"> root : str, default '$MXNET_HOME/models'</span> |
| <span class="sd"> Location for keeping the model parameters.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">18</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="resnet34_v1"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.resnet34_v1">[docs]</a><span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">resnet34_v1</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""ResNet-34 V1 model from `"Deep Residual Learning for Image Recognition"</span> |
| <span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pretrained : bool, default False</span> |
| <span class="sd"> Whether to load the pretrained weights for model.</span> |
| <span class="sd"> device : Device, default CPU</span> |
| <span class="sd"> The device in which to load the pretrained weights.</span> |
| <span class="sd"> root : str, default '$MXNET_HOME/models'</span> |
| <span class="sd"> Location for keeping the model parameters.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">34</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="resnet50_v1"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.resnet50_v1">[docs]</a><span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">resnet50_v1</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""ResNet-50 V1 model from `"Deep Residual Learning for Image Recognition"</span> |
| <span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pretrained : bool, default False</span> |
| <span class="sd"> Whether to load the pretrained weights for model.</span> |
| <span class="sd"> device : Device, default CPU</span> |
| <span class="sd"> The device in which to load the pretrained weights.</span> |
| <span class="sd"> root : str, default '$MXNET_HOME/models'</span> |
| <span class="sd"> Location for keeping the model parameters.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="resnet101_v1"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.resnet101_v1">[docs]</a><span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">resnet101_v1</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""ResNet-101 V1 model from `"Deep Residual Learning for Image Recognition"</span> |
| <span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pretrained : bool, default False</span> |
| <span class="sd"> Whether to load the pretrained weights for model.</span> |
| <span class="sd"> device : Device, default CPU</span> |
| <span class="sd"> The device in which to load the pretrained weights.</span> |
| <span class="sd"> root : str, default '$MXNET_HOME/models'</span> |
| <span class="sd"> Location for keeping the model parameters.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">101</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="resnet152_v1"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.resnet152_v1">[docs]</a><span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">resnet152_v1</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""ResNet-152 V1 model from `"Deep Residual Learning for Image Recognition"</span> |
| <span class="sd"> <http://arxiv.org/abs/1512.03385>`_ paper.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pretrained : bool, default False</span> |
| <span class="sd"> Whether to load the pretrained weights for model.</span> |
| <span class="sd"> device : Device, default CPU</span> |
| <span class="sd"> The device in which to load the pretrained weights.</span> |
| <span class="sd"> root : str, default '$MXNET_HOME/models'</span> |
| <span class="sd"> Location for keeping the model parameters.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">152</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="resnet18_v2"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.resnet18_v2">[docs]</a><span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">resnet18_v2</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""ResNet-18 V2 model from `"Identity Mappings in Deep Residual Networks"</span> |
| <span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pretrained : bool, default False</span> |
| <span class="sd"> Whether to load the pretrained weights for model.</span> |
| <span class="sd"> device : Device, default CPU</span> |
| <span class="sd"> The device in which to load the pretrained weights.</span> |
| <span class="sd"> root : str, default '$MXNET_HOME/models'</span> |
| <span class="sd"> Location for keeping the model parameters.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">18</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="resnet34_v2"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.resnet34_v2">[docs]</a><span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">resnet34_v2</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""ResNet-34 V2 model from `"Identity Mappings in Deep Residual Networks"</span> |
| <span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pretrained : bool, default False</span> |
| <span class="sd"> Whether to load the pretrained weights for model.</span> |
| <span class="sd"> device : Device, default CPU</span> |
| <span class="sd"> The device in which to load the pretrained weights.</span> |
| <span class="sd"> root : str, default '$MXNET_HOME/models'</span> |
| <span class="sd"> Location for keeping the model parameters.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">34</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="resnet50_v2"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.resnet50_v2">[docs]</a><span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">resnet50_v2</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""ResNet-50 V2 model from `"Identity Mappings in Deep Residual Networks"</span> |
| <span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pretrained : bool, default False</span> |
| <span class="sd"> Whether to load the pretrained weights for model.</span> |
| <span class="sd"> device : Device, default CPU</span> |
| <span class="sd"> The device in which to load the pretrained weights.</span> |
| <span class="sd"> root : str, default '$MXNET_HOME/models'</span> |
| <span class="sd"> Location for keeping the model parameters.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="resnet101_v2"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.resnet101_v2">[docs]</a><span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">resnet101_v2</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""ResNet-101 V2 model from `"Identity Mappings in Deep Residual Networks"</span> |
| <span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pretrained : bool, default False</span> |
| <span class="sd"> Whether to load the pretrained weights for model.</span> |
| <span class="sd"> device : Device, default CPU</span> |
| <span class="sd"> The device in which to load the pretrained weights.</span> |
| <span class="sd"> root : str, default '$MXNET_HOME/models'</span> |
| <span class="sd"> Location for keeping the model parameters.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">101</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="resnet152_v2"><a class="viewcode-back" href="../../../../../api/gluon/model_zoo/index.html#mxnet.gluon.model_zoo.vision.resnet152_v2">[docs]</a><span class="nd">@wrap_ctx_to_device_func</span> |
| <span class="k">def</span> <span class="nf">resnet152_v2</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="w"> </span><span class="sa">r</span><span class="sd">"""ResNet-152 V2 model from `"Identity Mappings in Deep Residual Networks"</span> |
| <span class="sd"> <https://arxiv.org/abs/1603.05027>`_ paper.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> pretrained : bool, default False</span> |
| <span class="sd"> Whether to load the pretrained weights for model.</span> |
| <span class="sd"> device : Device, default CPU</span> |
| <span class="sd"> The device in which to load the pretrained weights.</span> |
| <span class="sd"> root : str, default '$MXNET_HOME/models'</span> |
| <span class="sd"> Location for keeping the model parameters.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">get_resnet</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">152</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| </pre></div> |
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