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<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>
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<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li>
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<li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li>
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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-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/2-nn.html">Create a neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/4-train.html">Train the neural network</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/5-predict.html">Predict with a pre-trained model</a></li>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/getting-started/crash-course/6-use_gpus.html">Use GPUs</a></li>
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<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 external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</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/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</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>
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<li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li>
<li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li>
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<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>
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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>
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<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>
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<li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/training/normalization/index.html">Normalization Blocks</a></li>
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<li class="toctree-l3"><a class="reference internal" href="../../../../../tutorials/packages/ndarray/index.html">NDArray</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li>
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<h1>Source code for mxnet.gluon.contrib.nn.basic_layers</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"># &quot;License&quot;); 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"># &quot;AS IS&quot; 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">&quot;&quot;&quot;Custom neural network layers in model_zoo.&quot;&quot;&quot;</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Concurrent&#39;</span><span class="p">,</span> <span class="s1">&#39;HybridConcurrent&#39;</span><span class="p">,</span> <span class="s1">&#39;Identity&#39;</span><span class="p">,</span> <span class="s1">&#39;SparseEmbedding&#39;</span><span class="p">,</span>
<span class="s1">&#39;SyncBatchNorm&#39;</span><span class="p">,</span> <span class="s1">&#39;PixelShuffle1D&#39;</span><span class="p">,</span> <span class="s1">&#39;PixelShuffle2D&#39;</span><span class="p">,</span>
<span class="s1">&#39;PixelShuffle3D&#39;</span><span class="p">]</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">....</span> <span class="kn">import</span> <span class="n">nd</span><span class="p">,</span> <span class="n">context</span>
<span class="kn">from</span> <span class="nn">...block</span> <span class="kn">import</span> <span class="n">HybridBlock</span><span class="p">,</span> <span class="n">Block</span>
<span class="kn">from</span> <span class="nn">...nn</span> <span class="kn">import</span> <span class="n">Sequential</span><span class="p">,</span> <span class="n">HybridSequential</span><span class="p">,</span> <span class="n">BatchNorm</span>
<div class="viewcode-block" id="Concurrent"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.Concurrent">[docs]</a><span class="k">class</span> <span class="nc">Concurrent</span><span class="p">(</span><span class="n">Sequential</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Lays `Block` s concurrently.</span>
<span class="sd"> This block feeds its input to all children blocks, and</span>
<span class="sd"> produce the output by concatenating all the children blocks&#39; outputs</span>
<span class="sd"> on the specified axis.</span>
<span class="sd"> Example::</span>
<span class="sd"> net = Concurrent()</span>
<span class="sd"> # use net&#39;s name_scope to give children blocks appropriate names.</span>
<span class="sd"> with net.name_scope():</span>
<span class="sd"> net.add(nn.Dense(10, activation=&#39;relu&#39;))</span>
<span class="sd"> net.add(nn.Dense(20))</span>
<span class="sd"> net.add(Identity())</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> axis : int, default -1</span>
<span class="sd"> The axis on which to concatenate the outputs.</span>
<span class="sd"> &quot;&quot;&quot;</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">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Concurrent</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="n">prefix</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="o">=</span> <span class="n">axis</span>
<div class="viewcode-block" id="Concurrent.forward"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.Concurrent.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">out</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
<span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="o">*</span><span class="n">out</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span>
<span class="k">return</span> <span class="n">out</span></div></div>
<div class="viewcode-block" id="HybridConcurrent"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.HybridConcurrent">[docs]</a><span class="k">class</span> <span class="nc">HybridConcurrent</span><span class="p">(</span><span class="n">HybridSequential</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Lays `HybridBlock` s concurrently.</span>
<span class="sd"> This block feeds its input to all children blocks, and</span>
<span class="sd"> produce the output by concatenating all the children blocks&#39; outputs</span>
<span class="sd"> on the specified axis.</span>
<span class="sd"> Example::</span>
<span class="sd"> net = HybridConcurrent()</span>
<span class="sd"> # use net&#39;s name_scope to give children blocks appropriate names.</span>
<span class="sd"> with net.name_scope():</span>
<span class="sd"> net.add(nn.Dense(10, activation=&#39;relu&#39;))</span>
<span class="sd"> net.add(nn.Dense(20))</span>
<span class="sd"> net.add(Identity())</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> axis : int, default -1</span>
<span class="sd"> The axis on which to concatenate the outputs.</span>
<span class="sd"> &quot;&quot;&quot;</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">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HybridConcurrent</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="n">prefix</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="o">=</span> <span class="n">axis</span>
<div class="viewcode-block" id="HybridConcurrent.hybrid_forward"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.HybridConcurrent.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_children</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
<span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="o">*</span><span class="n">out</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">)</span>
<span class="k">return</span> <span class="n">out</span></div></div>
<div class="viewcode-block" id="Identity"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.Identity">[docs]</a><span class="k">class</span> <span class="nc">Identity</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Block that passes through the input directly.</span>
<span class="sd"> This block can be used in conjunction with HybridConcurrent</span>
<span class="sd"> block for residual connection.</span>
<span class="sd"> Example::</span>
<span class="sd"> net = HybridConcurrent()</span>
<span class="sd"> # use net&#39;s name_scope to give child Blocks appropriate names.</span>
<span class="sd"> with net.name_scope():</span>
<span class="sd"> net.add(nn.Dense(10, activation=&#39;relu&#39;))</span>
<span class="sd"> net.add(nn.Dense(20))</span>
<span class="sd"> net.add(Identity())</span>
<span class="sd"> &quot;&quot;&quot;</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">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Identity</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="n">prefix</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">)</span>
<div class="viewcode-block" id="Identity.hybrid_forward"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.Identity.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</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="SparseEmbedding"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.SparseEmbedding">[docs]</a><span class="k">class</span> <span class="nc">SparseEmbedding</span><span class="p">(</span><span class="n">Block</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Turns non-negative integers (indexes/tokens) into dense vectors</span>
<span class="sd"> of fixed size. eg. [4, 20] -&gt; [[0.25, 0.1], [0.6, -0.2]]</span>
<span class="sd"> This SparseBlock is designed for distributed training with extremely large</span>
<span class="sd"> input dimension. Both weight and gradient w.r.t. weight are `RowSparseNDArray`.</span>
<span class="sd"> Note: if `sparse_grad` is set to True, the gradient w.r.t weight will be</span>
<span class="sd"> sparse. Only a subset of optimizers support sparse gradients, including SGD, AdaGrad</span>
<span class="sd"> and Adam. By default lazy updates is turned on, which may perform differently</span>
<span class="sd"> from standard updates. For more details, please check the Optimization API at:</span>
<span class="sd"> https://mxnet.incubator.apache.org/api/python/optimization/optimization.html</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> input_dim : int</span>
<span class="sd"> Size of the vocabulary, i.e. maximum integer index + 1.</span>
<span class="sd"> output_dim : int</span>
<span class="sd"> Dimension of the dense embedding.</span>
<span class="sd"> dtype : str or np.dtype, default &#39;float32&#39;</span>
<span class="sd"> Data type of output embeddings.</span>
<span class="sd"> weight_initializer : Initializer</span>
<span class="sd"> Initializer for the `embeddings` matrix.</span>
<span class="sd"> Inputs:</span>
<span class="sd"> - **data**: (N-1)-D tensor with shape: `(x1, x2, ..., xN-1)`.</span>
<span class="sd"> Output:</span>
<span class="sd"> - **out**: N-D tensor with shape: `(x1, x2, ..., xN-1, output_dim)`.</span>
<span class="sd"> &quot;&quot;&quot;</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">input_dim</span><span class="p">,</span> <span class="n">output_dim</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">,</span>
<span class="n">weight_initializer</span><span class="o">=</span><span class="kc">None</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">SparseEmbedding</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">_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;input_dim&#39;</span><span class="p">:</span> <span class="n">input_dim</span><span class="p">,</span> <span class="s1">&#39;output_dim&#39;</span><span class="p">:</span> <span class="n">output_dim</span><span class="p">,</span>
<span class="s1">&#39;dtype&#39;</span><span class="p">:</span> <span class="n">dtype</span><span class="p">,</span> <span class="s1">&#39;sparse_grad&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">}</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;weight&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">input_dim</span><span class="p">,</span> <span class="n">output_dim</span><span class="p">),</span>
<span class="n">init</span><span class="o">=</span><span class="n">weight_initializer</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
<span class="n">grad_stype</span><span class="o">=</span><span class="s1">&#39;row_sparse&#39;</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="s1">&#39;row_sparse&#39;</span><span class="p">)</span>
<div class="viewcode-block" id="SparseEmbedding.forward"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.SparseEmbedding.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">weight</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">row_sparse_data</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="n">nd</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;fwd&#39;</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">)</span></div>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{block_name}</span><span class="s1">(</span><span class="si">{input_dim}</span><span class="s1"> -&gt; </span><span class="si">{output_dim}</span><span class="s1">, </span><span class="si">{dtype}</span><span class="s1">)&#39;</span>
<span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">block_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span>
<span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="SyncBatchNorm"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.SyncBatchNorm">[docs]</a><span class="k">class</span> <span class="nc">SyncBatchNorm</span><span class="p">(</span><span class="n">BatchNorm</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Cross-GPU Synchronized Batch normalization (SyncBN)</span>
<span class="sd"> Standard BN [1]_ implementation only normalize the data within each device.</span>
<span class="sd"> SyncBN normalizes the input within the whole mini-batch.</span>
<span class="sd"> We follow the implementation described in the paper [2]_.</span>
<span class="sd"> Note: Current implementation of SyncBN does not support FP16 training.</span>
<span class="sd"> For FP16 inference, use standard nn.BatchNorm instead of SyncBN.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> in_channels : int, default 0</span>
<span class="sd"> Number of channels (feature maps) in input data. If not specified,</span>
<span class="sd"> initialization will be deferred to the first time `forward` is called</span>
<span class="sd"> and `in_channels` will be inferred from the shape of input data.</span>
<span class="sd"> num_devices : int, default number of visible GPUs</span>
<span class="sd"> momentum: float, default 0.9</span>
<span class="sd"> Momentum for the moving average.</span>
<span class="sd"> epsilon: float, default 1e-5</span>
<span class="sd"> Small float added to variance to avoid dividing by zero.</span>
<span class="sd"> center: bool, default True</span>
<span class="sd"> If True, add offset of `beta` to normalized tensor.</span>
<span class="sd"> If False, `beta` is ignored.</span>
<span class="sd"> scale: bool, default True</span>
<span class="sd"> If True, multiply by `gamma`. If False, `gamma` is not used.</span>
<span class="sd"> When the next layer is linear (also e.g. `nn.relu`),</span>
<span class="sd"> this can be disabled since the scaling</span>
<span class="sd"> will be done by the next layer.</span>
<span class="sd"> use_global_stats: bool, default False</span>
<span class="sd"> If True, use global moving statistics instead of local batch-norm. This will force</span>
<span class="sd"> change batch-norm into a scale shift operator.</span>
<span class="sd"> If False, use local batch-norm.</span>
<span class="sd"> beta_initializer: str or `Initializer`, default &#39;zeros&#39;</span>
<span class="sd"> Initializer for the beta weight.</span>
<span class="sd"> gamma_initializer: str or `Initializer`, default &#39;ones&#39;</span>
<span class="sd"> Initializer for the gamma weight.</span>
<span class="sd"> running_mean_initializer: str or `Initializer`, default &#39;zeros&#39;</span>
<span class="sd"> Initializer for the running mean.</span>
<span class="sd"> running_variance_initializer: str or `Initializer`, default &#39;ones&#39;</span>
<span class="sd"> Initializer for the running variance.</span>
<span class="sd"> Inputs:</span>
<span class="sd"> - **data**: input tensor with arbitrary shape.</span>
<span class="sd"> Outputs:</span>
<span class="sd"> - **out**: output tensor with the same shape as `data`.</span>
<span class="sd"> Reference:</span>
<span class="sd"> .. [1] Ioffe, Sergey, and Christian Szegedy. &quot;Batch normalization: Accelerating \</span>
<span class="sd"> deep network training by reducing internal covariate shift.&quot; *ICML 2015*</span>
<span class="sd"> .. [2] Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, \</span>
<span class="sd"> Ambrish Tyagi, and Amit Agrawal. &quot;Context Encoding for Semantic Segmentation.&quot; *CVPR 2018*</span>
<span class="sd"> &quot;&quot;&quot;</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">in_channels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_devices</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span>
<span class="n">center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_global_stats</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">beta_initializer</span><span class="o">=</span><span class="s1">&#39;zeros&#39;</span><span class="p">,</span>
<span class="n">gamma_initializer</span><span class="o">=</span><span class="s1">&#39;ones&#39;</span><span class="p">,</span> <span class="n">running_mean_initializer</span><span class="o">=</span><span class="s1">&#39;zeros&#39;</span><span class="p">,</span>
<span class="n">running_variance_initializer</span><span class="o">=</span><span class="s1">&#39;ones&#39;</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">SyncBatchNorm</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
<span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">momentum</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="n">epsilon</span><span class="p">,</span>
<span class="n">center</span><span class="o">=</span><span class="n">center</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">,</span>
<span class="n">use_global_stats</span><span class="o">=</span><span class="n">use_global_stats</span><span class="p">,</span>
<span class="n">beta_initializer</span><span class="o">=</span><span class="n">beta_initializer</span><span class="p">,</span>
<span class="n">gamma_initializer</span><span class="o">=</span><span class="n">gamma_initializer</span><span class="p">,</span>
<span class="n">running_mean_initializer</span><span class="o">=</span><span class="n">running_mean_initializer</span><span class="p">,</span>
<span class="n">running_variance_initializer</span><span class="o">=</span><span class="n">running_variance_initializer</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="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">num_devices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_num_devices</span><span class="p">()</span> <span class="k">if</span> <span class="n">num_devices</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">num_devices</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;eps&#39;</span><span class="p">:</span> <span class="n">epsilon</span><span class="p">,</span> <span class="s1">&#39;momentum&#39;</span><span class="p">:</span> <span class="n">momentum</span><span class="p">,</span>
<span class="s1">&#39;fix_gamma&#39;</span><span class="p">:</span> <span class="ow">not</span> <span class="n">scale</span><span class="p">,</span> <span class="s1">&#39;use_global_stats&#39;</span><span class="p">:</span> <span class="n">use_global_stats</span><span class="p">,</span>
<span class="s1">&#39;ndev&#39;</span><span class="p">:</span> <span class="n">num_devices</span><span class="p">,</span> <span class="s1">&#39;key&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">prefix</span><span class="p">}</span>
<span class="k">def</span> <span class="nf">_get_num_devices</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Caution using SyncBatchNorm: &quot;</span>
<span class="s2">&quot;if not using all the GPUs, please mannually set num_devices&quot;</span><span class="p">,</span>
<span class="ne">UserWarning</span><span class="p">)</span>
<span class="n">num_devices</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">num_gpus</span><span class="p">()</span>
<span class="n">num_devices</span> <span class="o">=</span> <span class="n">num_devices</span> <span class="k">if</span> <span class="n">num_devices</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">num_devices</span>
<div class="viewcode-block" id="SyncBatchNorm.hybrid_forward"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.SyncBatchNorm.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">gamma</span><span class="p">,</span> <span class="n">beta</span><span class="p">,</span> <span class="n">running_mean</span><span class="p">,</span> <span class="n">running_var</span><span class="p">):</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">contrib</span><span class="o">.</span><span class="n">SyncBatchNorm</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">gamma</span><span class="p">,</span> <span class="n">beta</span><span class="p">,</span> <span class="n">running_mean</span><span class="p">,</span> <span class="n">running_var</span><span class="p">,</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;fwd&#39;</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">_kwargs</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="PixelShuffle1D"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.PixelShuffle1D">[docs]</a><span class="k">class</span> <span class="nc">PixelShuffle1D</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Pixel-shuffle layer for upsampling in 1 dimension.</span>
<span class="sd"> Pixel-shuffling is the operation of taking groups of values along</span>
<span class="sd"> the *channel* dimension and regrouping them into blocks of pixels</span>
<span class="sd"> along the ``W`` dimension, thereby effectively multiplying that dimension</span>
<span class="sd"> by a constant factor in size.</span>
<span class="sd"> For example, a feature map of shape :math:`(fC, W)` is reshaped</span>
<span class="sd"> into :math:`(C, fW)` by forming little value groups of size :math:`f`</span>
<span class="sd"> and arranging them in a grid of size :math:`W`.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> factor : int or 1-tuple of int</span>
<span class="sd"> Upsampling factor, applied to the ``W`` dimension.</span>
<span class="sd"> Inputs:</span>
<span class="sd"> - **data**: Tensor of shape ``(N, f*C, W)``.</span>
<span class="sd"> Outputs:</span>
<span class="sd"> - **out**: Tensor of shape ``(N, C, W*f)``.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; pxshuf = PixelShuffle1D(2)</span>
<span class="sd"> &gt;&gt;&gt; x = mx.nd.zeros((1, 8, 3))</span>
<span class="sd"> &gt;&gt;&gt; pxshuf(x).shape</span>
<span class="sd"> (1, 4, 6)</span>
<span class="sd"> &quot;&quot;&quot;</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">factor</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">PixelShuffle1D</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_factor</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">factor</span><span class="p">)</span>
<div class="viewcode-block" id="PixelShuffle1D.hybrid_forward"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.PixelShuffle1D.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Perform pixel-shuffling on the input.&quot;&quot;&quot;</span>
<span class="n">f</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_factor</span>
<span class="c1"># (N, C*f, W)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="c1"># (N, C, f, W)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> <span class="c1"># (N, C, W, f)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">))</span> <span class="c1"># (N, C, W*f)</span>
<span class="k">return</span> <span class="n">x</span></div>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">(</span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_factor</span><span class="p">)</span></div>
<div class="viewcode-block" id="PixelShuffle2D"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.PixelShuffle2D">[docs]</a><span class="k">class</span> <span class="nc">PixelShuffle2D</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Pixel-shuffle layer for upsampling in 2 dimensions.</span>
<span class="sd"> Pixel-shuffling is the operation of taking groups of values along</span>
<span class="sd"> the *channel* dimension and regrouping them into blocks of pixels</span>
<span class="sd"> along the ``H`` and ``W`` dimensions, thereby effectively multiplying</span>
<span class="sd"> those dimensions by a constant factor in size.</span>
<span class="sd"> For example, a feature map of shape :math:`(f^2 C, H, W)` is reshaped</span>
<span class="sd"> into :math:`(C, fH, fW)` by forming little :math:`f \times f` blocks</span>
<span class="sd"> of pixels and arranging them in an :math:`H \times W` grid.</span>
<span class="sd"> Pixel-shuffling together with regular convolution is an alternative,</span>
<span class="sd"> learnable way of upsampling an image by arbitrary factors. It is reported</span>
<span class="sd"> to help overcome checkerboard artifacts that are common in upsampling with</span>
<span class="sd"> transposed convolutions (also called deconvolutions). See the paper</span>
<span class="sd"> `Real-Time Single Image and Video Super-Resolution Using an Efficient</span>
<span class="sd"> Sub-Pixel Convolutional Neural Network &lt;https://arxiv.org/abs/1609.05158&gt;`_</span>
<span class="sd"> for further details.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> factor : int or 2-tuple of int</span>
<span class="sd"> Upsampling factors, applied to the ``H`` and ``W`` dimensions,</span>
<span class="sd"> in that order.</span>
<span class="sd"> Inputs:</span>
<span class="sd"> - **data**: Tensor of shape ``(N, f1*f2*C, H, W)``.</span>
<span class="sd"> Outputs:</span>
<span class="sd"> - **out**: Tensor of shape ``(N, C, H*f1, W*f2)``.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; pxshuf = PixelShuffle2D((2, 3))</span>
<span class="sd"> &gt;&gt;&gt; x = mx.nd.zeros((1, 12, 3, 5))</span>
<span class="sd"> &gt;&gt;&gt; pxshuf(x).shape</span>
<span class="sd"> (1, 2, 6, 15)</span>
<span class="sd"> &quot;&quot;&quot;</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">factor</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">PixelShuffle2D</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="k">try</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_factors</span> <span class="o">=</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">factor</span><span class="p">),)</span> <span class="o">*</span> <span class="mi">2</span>
<span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_factors</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">fac</span><span class="p">)</span> <span class="k">for</span> <span class="n">fac</span> <span class="ow">in</span> <span class="n">factor</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_factors</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;wrong length </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_factors</span><span class="p">))</span>
<div class="viewcode-block" id="PixelShuffle2D.hybrid_forward"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.PixelShuffle2D.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Perform pixel-shuffling on the input.&quot;&quot;&quot;</span>
<span class="n">f1</span><span class="p">,</span> <span class="n">f2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_factors</span>
<span class="c1"># (N, f1*f2*C, H, W)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">f1</span> <span class="o">*</span> <span class="n">f2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="c1"># (N, C, f1*f2, H, W)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="c1"># (N, C, f1, f2, H, W)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="c1"># (N, C, H, f1, W, f2)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">))</span> <span class="c1"># (N, C, H*f1, W*f2)</span>
<span class="k">return</span> <span class="n">x</span></div>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">(</span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_factors</span><span class="p">)</span></div>
<div class="viewcode-block" id="PixelShuffle3D"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.PixelShuffle3D">[docs]</a><span class="k">class</span> <span class="nc">PixelShuffle3D</span><span class="p">(</span><span class="n">HybridBlock</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">&quot;&quot;&quot;Pixel-shuffle layer for upsampling in 3 dimensions.</span>
<span class="sd"> Pixel-shuffling (or voxel-shuffling in 3D) is the operation of taking</span>
<span class="sd"> groups of values along the *channel* dimension and regrouping them into</span>
<span class="sd"> blocks of voxels along the ``D``, ``H`` and ``W`` dimensions, thereby</span>
<span class="sd"> effectively multiplying those dimensions by a constant factor in size.</span>
<span class="sd"> For example, a feature map of shape :math:`(f^3 C, D, H, W)` is reshaped</span>
<span class="sd"> into :math:`(C, fD, fH, fW)` by forming little :math:`f \times f \times f`</span>
<span class="sd"> blocks of voxels and arranging them in a :math:`D \times H \times W` grid.</span>
<span class="sd"> Pixel-shuffling together with regular convolution is an alternative,</span>
<span class="sd"> learnable way of upsampling an image by arbitrary factors. It is reported</span>
<span class="sd"> to help overcome checkerboard artifacts that are common in upsampling with</span>
<span class="sd"> transposed convolutions (also called deconvolutions). See the paper</span>
<span class="sd"> `Real-Time Single Image and Video Super-Resolution Using an Efficient</span>
<span class="sd"> Sub-Pixel Convolutional Neural Network &lt;https://arxiv.org/abs/1609.05158&gt;`_</span>
<span class="sd"> for further details.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> factor : int or 3-tuple of int</span>
<span class="sd"> Upsampling factors, applied to the ``D``, ``H`` and ``W``</span>
<span class="sd"> dimensions, in that order.</span>
<span class="sd"> Inputs:</span>
<span class="sd"> - **data**: Tensor of shape ``(N, f1*f2*f3*C, D, H, W)``.</span>
<span class="sd"> Outputs:</span>
<span class="sd"> - **out**: Tensor of shape ``(N, C, D*f1, H*f2, W*f3)``.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; pxshuf = PixelShuffle3D((2, 3, 4))</span>
<span class="sd"> &gt;&gt;&gt; x = mx.nd.zeros((1, 48, 3, 5, 7))</span>
<span class="sd"> &gt;&gt;&gt; pxshuf(x).shape</span>
<span class="sd"> (1, 2, 6, 15, 28)</span>
<span class="sd"> &quot;&quot;&quot;</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">factor</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">PixelShuffle3D</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="k">try</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_factors</span> <span class="o">=</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">factor</span><span class="p">),)</span> <span class="o">*</span> <span class="mi">3</span>
<span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_factors</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">fac</span><span class="p">)</span> <span class="k">for</span> <span class="n">fac</span> <span class="ow">in</span> <span class="n">factor</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_factors</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;wrong length </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_factors</span><span class="p">))</span>
<div class="viewcode-block" id="PixelShuffle3D.hybrid_forward"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.nn.PixelShuffle3D.hybrid_forward">[docs]</a> <span class="k">def</span> <span class="nf">hybrid_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">F</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Perform pixel-shuffling on the input.&quot;&quot;&quot;</span>
<span class="c1"># `transpose` doesn&#39;t support 8D, need other implementation</span>
<span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="p">,</span> <span class="n">f3</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_factors</span>
<span class="c1"># (N, C*f1*f2*f3, D, H, W)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">f1</span> <span class="o">*</span> <span class="n">f2</span> <span class="o">*</span> <span class="n">f3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="c1"># (N, C, f1*f2*f3, D, H, W)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">x</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="c1"># (N, C, D, f1*f2*f3, H, W)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="o">*</span><span class="n">f3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="c1"># (N, C, D, f1, f2*f3, H, W)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="c1"># (N, C, D*f1, f2*f3, H, W)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">x</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="c1"># (N, C, D*f1, H, f2*f3, W)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="n">f2</span><span class="p">,</span> <span class="n">f3</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="c1"># (N, C, D*f1, H, f2, f3, W)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="c1"># (N, C, D*f1, H*f2, f3, W)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="c1"># (N, C, D*f1, H*f2, W, f3)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">))</span> <span class="c1"># (N, C, D*f1, H*f2, W*f3)</span>
<span class="k">return</span> <span class="n">x</span></div>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">(</span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_factors</span><span class="p">)</span></div>
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