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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>
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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-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/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>
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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-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</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-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</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>
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 <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>
 <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-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>
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 <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>
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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/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>
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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>
 <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li>
 <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/index.html">Tutorials</a><ul>
 <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>
 <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li>
 <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-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 internal" href="../../../../tutorials/packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</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-l3"><a class="reference internal" href="../../../../tutorials/packages/viz/index.html">Visualization</a><ul>
 <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/visualize_graph">Visualize networks</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="../../../../tutorials/performance/index.html">Performance</a><ul>
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 <li class="toctree-l4"><a class="reference external" href="https://gluon-cv.mxnet.io/build/examples_deployment/int8_inference.html">GluonCV with Quantized Models</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/performance/backend/mkldnn/mkldnn_quantization.html">Quantize with MKL-DNN backend</a></li>
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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/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-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/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>
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 <li class="toctree-l4"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/index.html">Data Tutorials</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/data_augmentation.html">Image Augmentation</a></li>
 <li class="toctree-l5"><a class="reference internal" href="../../../../tutorials/packages/gluon/data/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/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/transformer.html">Machine Translation with Transformer</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-l4"><a class="reference internal" href="../../../../tutorials/packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li>
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   <h1>Source code for mxnet.gluon.data.dataloader</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=ungrouped-imports</span>
 <span class="sd">&quot;&quot;&quot;Dataset generator.&quot;&quot;&quot;</span>
 <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;DataLoader&#39;</span><span class="p">]</span>

 <span class="kn">import</span> <span class="nn">pickle</span>
 <span class="kn">import</span> <span class="nn">io</span>
 <span class="kn">import</span> <span class="nn">sys</span>
 <span class="kn">import</span> <span class="nn">signal</span>
 <span class="kn">import</span> <span class="nn">multiprocessing</span>
 <span class="kn">import</span> <span class="nn">multiprocessing.queues</span>
 <span class="kn">from</span> <span class="nn">multiprocessing.reduction</span> <span class="kn">import</span> <span class="n">ForkingPickler</span>
 <span class="kn">from</span> <span class="nn">multiprocessing.pool</span> <span class="kn">import</span> <span class="n">ThreadPool</span>
 <span class="kn">import</span> <span class="nn">threading</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

 <span class="k">try</span><span class="p">:</span>
     <span class="kn">import</span> <span class="nn">multiprocessing.resource_sharer</span>
 <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
     <span class="k">pass</span>

 <span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">sampler</span> <span class="k">as</span> <span class="n">_sampler</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">...util</span> <span class="kn">import</span> <span class="n">is_np_shape</span><span class="p">,</span> <span class="n">is_np_array</span><span class="p">,</span> <span class="n">set_np</span>
 <span class="kn">from</span> <span class="nn">...</span> <span class="kn">import</span> <span class="n">numpy</span> <span class="k">as</span> <span class="n">_mx_np</span>  <span class="c1"># pylint: disable=reimported</span>

 <span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">platform</span> <span class="o">==</span> <span class="s1">&#39;darwin&#39;</span> <span class="ow">or</span> <span class="n">sys</span><span class="o">.</span><span class="n">platform</span> <span class="o">==</span> <span class="s1">&#39;win32&#39;</span><span class="p">:</span>
     <span class="k">def</span> <span class="nf">rebuild_ndarray</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Rebuild ndarray from pickled shared memory&quot;&quot;&quot;</span>
         <span class="c1"># pylint: disable=no-value-for-parameter</span>
         <span class="k">return</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">(</span><span class="n">nd</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">_new_from_shared_mem</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">))</span>

     <span class="k">def</span> <span class="nf">reduce_ndarray</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Reduce ndarray to shared memory handle&quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="n">rebuild_ndarray</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">_to_shared_mem</span><span class="p">()</span>
 <span class="k">else</span><span class="p">:</span>
     <span class="k">def</span> <span class="nf">rebuild_ndarray</span><span class="p">(</span><span class="n">pid</span><span class="p">,</span> <span class="n">fd</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Rebuild ndarray from pickled shared memory&quot;&quot;&quot;</span>
         <span class="c1"># pylint: disable=no-value-for-parameter</span>
         <span class="n">fd</span> <span class="o">=</span> <span class="n">fd</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
         <span class="k">return</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">(</span><span class="n">nd</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">_new_from_shared_mem</span><span class="p">(</span><span class="n">pid</span><span class="p">,</span> <span class="n">fd</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">))</span>

     <span class="k">def</span> <span class="nf">reduce_ndarray</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Reduce ndarray to shared memory handle&quot;&quot;&quot;</span>
         <span class="c1"># keep a local ref before duplicating fd</span>
         <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">Context</span><span class="p">(</span><span class="s1">&#39;cpu_shared&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
         <span class="n">pid</span><span class="p">,</span> <span class="n">fd</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">_to_shared_mem</span><span class="p">()</span>
         <span class="n">fd</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">reduction</span><span class="o">.</span><span class="n">DupFd</span><span class="p">(</span><span class="n">fd</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">rebuild_ndarray</span><span class="p">,</span> <span class="p">(</span><span class="n">pid</span><span class="p">,</span> <span class="n">fd</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>

 <span class="n">ForkingPickler</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">,</span> <span class="n">reduce_ndarray</span><span class="p">)</span>

 <span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">platform</span> <span class="o">==</span> <span class="s1">&#39;darwin&#39;</span> <span class="ow">or</span> <span class="n">sys</span><span class="o">.</span><span class="n">platform</span> <span class="o">==</span> <span class="s1">&#39;win32&#39;</span><span class="p">:</span>
     <span class="k">def</span> <span class="nf">rebuild_np_ndarray</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Rebuild ndarray from pickled shared memory&quot;&quot;&quot;</span>
         <span class="c1"># pylint: disable=no-value-for-parameter</span>
         <span class="k">return</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">(</span><span class="n">nd</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">_new_from_shared_mem</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">))</span>

     <span class="k">def</span> <span class="nf">reduce_np_ndarray</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Reduce ndarray to shared memory handle&quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="n">rebuild_np_ndarray</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">_to_shared_mem</span><span class="p">()</span>
 <span class="k">else</span><span class="p">:</span>
     <span class="k">def</span> <span class="nf">rebuild_np_ndarray</span><span class="p">(</span><span class="n">pid</span><span class="p">,</span> <span class="n">fd</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Rebuild ndarray from pickled shared memory&quot;&quot;&quot;</span>
         <span class="c1"># pylint: disable=no-value-for-parameter</span>
         <span class="n">fd</span> <span class="o">=</span> <span class="n">fd</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
         <span class="k">return</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">(</span><span class="n">nd</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="n">_new_from_shared_mem</span><span class="p">(</span><span class="n">pid</span><span class="p">,</span> <span class="n">fd</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">))</span>

     <span class="k">def</span> <span class="nf">reduce_np_ndarray</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Reduce ndarray to shared memory handle&quot;&quot;&quot;</span>
         <span class="c1"># keep a local ref before duplicating fd</span>
         <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">Context</span><span class="p">(</span><span class="s1">&#39;cpu_shared&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
         <span class="n">pid</span><span class="p">,</span> <span class="n">fd</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">_to_shared_mem</span><span class="p">()</span>
         <span class="n">fd</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">reduction</span><span class="o">.</span><span class="n">DupFd</span><span class="p">(</span><span class="n">fd</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">rebuild_np_ndarray</span><span class="p">,</span> <span class="p">(</span><span class="n">pid</span><span class="p">,</span> <span class="n">fd</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>

 <span class="n">ForkingPickler</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="n">_mx_np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">reduce_np_ndarray</span><span class="p">)</span>


 <span class="k">class</span> <span class="nc">ConnectionWrapper</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Connection wrapper for multiprocessing that supports sending</span>
 <span class="sd">    NDArray via shared memory.&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">conn</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_conn</span> <span class="o">=</span> <span class="n">conn</span>

     <span class="k">def</span> <span class="nf">send</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">obj</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Send object&quot;&quot;&quot;</span>
         <span class="n">buf</span> <span class="o">=</span> <span class="n">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">()</span>
         <span class="n">ForkingPickler</span><span class="p">(</span><span class="n">buf</span><span class="p">,</span> <span class="n">pickle</span><span class="o">.</span><span class="n">HIGHEST_PROTOCOL</span><span class="p">)</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">send_bytes</span><span class="p">(</span><span class="n">buf</span><span class="o">.</span><span class="n">getvalue</span><span class="p">())</span>

     <span class="k">def</span> <span class="nf">recv</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Receive object&quot;&quot;&quot;</span>
         <span class="n">buf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">recv_bytes</span><span class="p">()</span>
         <span class="k">return</span> <span class="n">pickle</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">buf</span><span class="p">)</span>

     <span class="k">def</span> <span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Emmulate conn&quot;&quot;&quot;</span>
         <span class="n">attr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_conn&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
         <span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">attr</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>


 <span class="k">class</span> <span class="nc">Queue</span><span class="p">(</span><span class="n">multiprocessing</span><span class="o">.</span><span class="n">queues</span><span class="o">.</span><span class="n">Queue</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Wrapper for multiprocessing queue that dumps NDArray with shared memory.&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="o">*</span><span class="n">args</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="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">multiprocessing</span><span class="o">.</span><span class="n">get_context</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">_reader</span> <span class="o">=</span> <span class="n">ConnectionWrapper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_reader</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_writer</span> <span class="o">=</span> <span class="n">ConnectionWrapper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_writer</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_send</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_writer</span><span class="o">.</span><span class="n">send</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_recv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reader</span><span class="o">.</span><span class="n">recv</span>


 <span class="k">class</span> <span class="nc">SimpleQueue</span><span class="p">(</span><span class="n">multiprocessing</span><span class="o">.</span><span class="n">queues</span><span class="o">.</span><span class="n">SimpleQueue</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Wrapper for multiprocessing SimpleQueue that dumps NDArray with shared memory.</span>
 <span class="sd">       SimpleQueue don&#39;t use threading internally.</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="o">*</span><span class="n">args</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="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">multiprocessing</span><span class="o">.</span><span class="n">get_context</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">_reader</span> <span class="o">=</span> <span class="n">ConnectionWrapper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_reader</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_writer</span> <span class="o">=</span> <span class="n">ConnectionWrapper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_writer</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_send</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_writer</span><span class="o">.</span><span class="n">send</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_recv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reader</span><span class="o">.</span><span class="n">recv</span>


 <span class="k">def</span> <span class="nf">default_batchify_fn</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Collate data into batch.&quot;&quot;&quot;</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">nd</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="p">)</span>
     <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">):</span>
         <span class="n">data</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="p">)</span>
         <span class="k">return</span> <span class="p">[</span><span class="n">default_batchify_fn</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
         <span class="n">array_fn</span> <span class="o">=</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">array</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span>
         <span class="k">return</span> <span class="n">array_fn</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>


 <span class="k">def</span> <span class="nf">default_mp_batchify_fn</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Collate data into batch. Use shared memory for stacking.&quot;&quot;&quot;</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span>
         <span class="n">empty_fn</span> <span class="o">=</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">empty</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">nd</span><span class="o">.</span><span class="n">empty</span>
         <span class="n">out</span> <span class="o">=</span> <span class="n">empty_fn</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),)</span> <span class="o">+</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
                        <span class="n">ctx</span><span class="o">=</span><span class="n">context</span><span class="o">.</span><span class="n">Context</span><span class="p">(</span><span class="s1">&#39;cpu_shared&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
         <span class="k">if</span> <span class="n">is_np_array</span><span class="p">():</span>
             <span class="k">return</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">nd</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span>
     <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">):</span>
         <span class="n">data</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="p">)</span>
         <span class="k">return</span> <span class="p">[</span><span class="n">default_mp_batchify_fn</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
         <span class="n">array_fn</span> <span class="o">=</span> <span class="n">_mx_np</span><span class="o">.</span><span class="n">array</span> <span class="k">if</span> <span class="n">is_np_array</span><span class="p">()</span> <span class="k">else</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span>
         <span class="k">return</span> <span class="n">array_fn</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
                         <span class="n">ctx</span><span class="o">=</span><span class="n">context</span><span class="o">.</span><span class="n">Context</span><span class="p">(</span><span class="s1">&#39;cpu_shared&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>


 <span class="k">def</span> <span class="nf">_as_in_context</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ctx</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Move data into new context.&quot;&quot;&quot;</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">nd</span><span class="o">.</span><span class="n">NDArray</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span>
     <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
         <span class="k">return</span> <span class="p">[</span><span class="n">_as_in_context</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
     <span class="k">return</span> <span class="n">data</span>


 <span class="k">def</span> <span class="nf">worker_loop_v1</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">key_queue</span><span class="p">,</span> <span class="n">data_queue</span><span class="p">,</span> <span class="n">batchify_fn</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Worker loop for multiprocessing DataLoader.&quot;&quot;&quot;</span>
     <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
         <span class="n">idx</span><span class="p">,</span> <span class="n">samples</span> <span class="o">=</span> <span class="n">key_queue</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
         <span class="k">if</span> <span class="n">idx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">break</span>
         <span class="n">batch</span> <span class="o">=</span> <span class="n">batchify_fn</span><span class="p">([</span><span class="n">dataset</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">samples</span><span class="p">])</span>
         <span class="n">data_queue</span><span class="o">.</span><span class="n">put</span><span class="p">((</span><span class="n">idx</span><span class="p">,</span> <span class="n">batch</span><span class="p">))</span>

 <span class="k">def</span> <span class="nf">fetcher_loop_v1</span><span class="p">(</span><span class="n">data_queue</span><span class="p">,</span> <span class="n">data_buffer</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                     <span class="n">pin_device_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">data_buffer_lock</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Fetcher loop for fetching data from queue and put in reorder dict.&quot;&quot;&quot;</span>
     <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
         <span class="n">idx</span><span class="p">,</span> <span class="n">batch</span> <span class="o">=</span> <span class="n">data_queue</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
         <span class="k">if</span> <span class="n">idx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">break</span>
         <span class="k">if</span> <span class="n">pin_memory</span><span class="p">:</span>
             <span class="n">batch</span> <span class="o">=</span> <span class="n">_as_in_context</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">context</span><span class="o">.</span><span class="n">cpu_pinned</span><span class="p">(</span><span class="n">pin_device_id</span><span class="p">))</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">batch</span> <span class="o">=</span> <span class="n">_as_in_context</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">context</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span>
         <span class="k">if</span> <span class="n">data_buffer_lock</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">with</span> <span class="n">data_buffer_lock</span><span class="p">:</span>
                 <span class="n">data_buffer</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">batch</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">data_buffer</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">batch</span>


 <span class="k">class</span> <span class="nc">_MultiWorkerIterV1</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Internal multi-worker iterator for DataLoader.&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">num_workers</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">batchify_fn</span><span class="p">,</span> <span class="n">batch_sampler</span><span class="p">,</span>
                  <span class="n">pin_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">pin_device_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">worker_fn</span><span class="o">=</span><span class="n">worker_loop_v1</span><span class="p">):</span>
         <span class="k">assert</span> <span class="n">num_workers</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;_MultiWorkerIter is not for </span><span class="si">{}</span><span class="s2"> workers&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_workers</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_num_workers</span> <span class="o">=</span> <span class="n">num_workers</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span> <span class="o">=</span> <span class="n">dataset</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span> <span class="o">=</span> <span class="n">batchify_fn</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span> <span class="o">=</span> <span class="n">batch_sampler</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_key_queue</span> <span class="o">=</span> <span class="n">Queue</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_data_queue</span> <span class="o">=</span> <span class="n">SimpleQueue</span><span class="p">()</span>

         <span class="bp">self</span><span class="o">.</span><span class="n">_data_buffer</span> <span class="o">=</span> <span class="p">{}</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_data_buffer_lock</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Lock</span><span class="p">()</span>

         <span class="bp">self</span><span class="o">.</span><span class="n">_rcvd_idx</span> <span class="o">=</span> <span class="mi">0</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_sent_idx</span> <span class="o">=</span> <span class="mi">0</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_iter</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_shutdown</span> <span class="o">=</span> <span class="kc">False</span>

         <span class="n">workers</span> <span class="o">=</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="bp">self</span><span class="o">.</span><span class="n">_num_workers</span><span class="p">):</span>
             <span class="n">worker</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">Process</span><span class="p">(</span>
                 <span class="n">target</span><span class="o">=</span><span class="n">worker_fn</span><span class="p">,</span>
                 <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_key_queue</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_queue</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span><span class="p">))</span>
             <span class="n">worker</span><span class="o">.</span><span class="n">daemon</span> <span class="o">=</span> <span class="kc">True</span>
             <span class="n">worker</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
             <span class="n">workers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">worker</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_workers</span> <span class="o">=</span> <span class="n">workers</span>

         <span class="bp">self</span><span class="o">.</span><span class="n">_fetcher</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Thread</span><span class="p">(</span>
             <span class="n">target</span><span class="o">=</span><span class="n">fetcher_loop_v1</span><span class="p">,</span>
             <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data_queue</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_buffer</span><span class="p">,</span> <span class="n">pin_memory</span><span class="p">,</span>
                   <span class="n">pin_device_id</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_buffer_lock</span><span class="p">))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_fetcher</span><span class="o">.</span><span class="n">daemon</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_fetcher</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>

         <span class="c1"># pre-fetch</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="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_workers</span><span class="p">):</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_push_next</span><span class="p">()</span>

     <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span><span class="p">)</span>

     <span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>

     <span class="k">def</span> <span class="nf">_push_next</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Assign next batch workload to workers.&quot;&quot;&quot;</span>
         <span class="n">r</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_iter</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">r</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">return</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_key_queue</span><span class="o">.</span><span class="n">put</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">_sent_idx</span><span class="p">,</span> <span class="n">r</span><span class="p">))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_sent_idx</span> <span class="o">+=</span> <span class="mi">1</span>

     <span class="k">def</span> <span class="fm">__next__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shutdown</span><span class="p">,</span> <span class="s2">&quot;call __next__ after shutdown is forbidden&quot;</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rcvd_idx</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sent_idx</span><span class="p">:</span>
             <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_buffer</span><span class="p">,</span> <span class="s2">&quot;Data buffer should be empty at this moment&quot;</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
             <span class="k">raise</span> <span class="ne">StopIteration</span>

         <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rcvd_idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_buffer</span><span class="p">:</span>
                 <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_buffer_lock</span><span class="p">:</span>
                     <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_buffer</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_rcvd_idx</span><span class="p">)</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_rcvd_idx</span> <span class="o">+=</span> <span class="mi">1</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_push_next</span><span class="p">()</span>
                 <span class="k">return</span> <span class="n">batch</span>

     <span class="k">def</span> <span class="nf">next</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__next__</span><span class="p">()</span>

     <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span>

     <span class="k">def</span> <span class="nf">shutdown</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Shutdown internal workers by pushing terminate signals.&quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shutdown</span><span class="p">:</span>
             <span class="c1"># send shutdown signal to the fetcher and join data queue first</span>
             <span class="c1"># Remark:   loop_fetcher need to be joined prior to the workers.</span>
             <span class="c1">#           otherwise, the fetcher may fail at getting data</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_data_queue</span><span class="o">.</span><span class="n">put</span><span class="p">((</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">))</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_fetcher</span><span class="o">.</span><span class="n">join</span><span class="p">()</span>
             <span class="c1"># send shutdown signal to all worker processes</span>
             <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_workers</span><span class="p">):</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_key_queue</span><span class="o">.</span><span class="n">put</span><span class="p">((</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">))</span>
             <span class="c1"># force shut down any alive worker processes</span>
             <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_workers</span><span class="p">:</span>
                 <span class="k">if</span> <span class="n">w</span><span class="o">.</span><span class="n">is_alive</span><span class="p">():</span>
                     <span class="n">w</span><span class="o">.</span><span class="n">terminate</span><span class="p">()</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_shutdown</span> <span class="o">=</span> <span class="kc">True</span>


 <span class="k">class</span> <span class="nc">DataLoaderV1</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Loads data from a dataset and returns mini-batches of data.</span>

 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
 <span class="sd">    dataset : Dataset</span>
 <span class="sd">        Source dataset. Note that numpy and mxnet arrays can be directly used</span>
 <span class="sd">        as a Dataset.</span>
 <span class="sd">    batch_size : int</span>
 <span class="sd">        Size of mini-batch.</span>
 <span class="sd">    shuffle : bool</span>
 <span class="sd">        Whether to shuffle the samples.</span>
 <span class="sd">    sampler : Sampler</span>
 <span class="sd">        The sampler to use. Either specify sampler or shuffle, not both.</span>
 <span class="sd">    last_batch : {&#39;keep&#39;, &#39;discard&#39;, &#39;rollover&#39;}</span>
 <span class="sd">        How to handle the last batch if batch_size does not evenly divide</span>
 <span class="sd">        `len(dataset)`.</span>

 <span class="sd">        keep - A batch with less samples than previous batches is returned.</span>
 <span class="sd">        discard - The last batch is discarded if its incomplete.</span>
 <span class="sd">        rollover - The remaining samples are rolled over to the next epoch.</span>
 <span class="sd">    batch_sampler : Sampler</span>
 <span class="sd">        A sampler that returns mini-batches. Do not specify batch_size,</span>
 <span class="sd">        shuffle, sampler, and last_batch if batch_sampler is specified.</span>
 <span class="sd">    batchify_fn : callable</span>
 <span class="sd">        Callback function to allow users to specify how to merge samples</span>
 <span class="sd">        into a batch. Defaults to `default_batchify_fn`::</span>

 <span class="sd">            def default_batchify_fn(data):</span>
 <span class="sd">                if isinstance(data[0], nd.NDArray):</span>
 <span class="sd">                    return nd.stack(*data)</span>
 <span class="sd">                elif isinstance(data[0], tuple):</span>
 <span class="sd">                    data = zip(*data)</span>
 <span class="sd">                    return [default_batchify_fn(i) for i in data]</span>
 <span class="sd">                else:</span>
 <span class="sd">                    data = np.asarray(data)</span>
 <span class="sd">                    return nd.array(data, dtype=data.dtype)</span>

 <span class="sd">    num_workers : int, default 0</span>
 <span class="sd">        The number of multiprocessing workers to use for data preprocessing.</span>
 <span class="sd">    pin_memory : boolean, default False</span>
 <span class="sd">        If ``True``, the dataloader will copy NDArrays into pinned memory</span>
 <span class="sd">        before returning them. Copying from CPU pinned memory to GPU is faster</span>
 <span class="sd">        than from normal CPU memory.</span>
 <span class="sd">    pin_device_id : int, default 0</span>
 <span class="sd">        The device id to use for allocating pinned memory if pin_memory is ``True``</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">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">last_batch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_sampler</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batchify_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">pin_device_id</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span> <span class="o">=</span> <span class="n">dataset</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_pin_memory</span> <span class="o">=</span> <span class="n">pin_memory</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_pin_device_id</span> <span class="o">=</span> <span class="n">pin_device_id</span>

         <span class="k">if</span> <span class="n">batch_sampler</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">batch_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;batch_size must be specified unless &quot;</span> \
                                  <span class="s2">&quot;batch_sampler is specified&quot;</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">sampler</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="k">if</span> <span class="n">shuffle</span><span class="p">:</span>
                     <span class="n">sampler</span> <span class="o">=</span> <span class="n">_sampler</span><span class="o">.</span><span class="n">RandomSampler</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">))</span>
                 <span class="k">else</span><span class="p">:</span>
                     <span class="n">sampler</span> <span class="o">=</span> <span class="n">_sampler</span><span class="o">.</span><span class="n">SequentialSampler</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">))</span>
             <span class="k">elif</span> <span class="n">shuffle</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;shuffle must not be specified if sampler is specified&quot;</span><span class="p">)</span>

             <span class="n">batch_sampler</span> <span class="o">=</span> <span class="n">_sampler</span><span class="o">.</span><span class="n">BatchSampler</span><span class="p">(</span>
                 <span class="n">sampler</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">last_batch</span> <span class="k">if</span> <span class="n">last_batch</span> <span class="k">else</span> <span class="s1">&#39;keep&#39;</span><span class="p">)</span>
         <span class="k">elif</span> <span class="n">batch_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">shuffle</span> <span class="ow">or</span> <span class="n">sampler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> \
                 <span class="n">last_batch</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;batch_size, shuffle, sampler and last_batch must &quot;</span> \
                              <span class="s2">&quot;not be specified if batch_sampler is specified.&quot;</span><span class="p">)</span>

         <span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span> <span class="o">=</span> <span class="n">batch_sampler</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_num_workers</span> <span class="o">=</span> <span class="n">num_workers</span> <span class="k">if</span> <span class="n">num_workers</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
         <span class="k">if</span> <span class="n">batchify_fn</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">num_workers</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span> <span class="o">=</span> <span class="n">default_mp_batchify_fn</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span> <span class="o">=</span> <span class="n">default_batchify_fn</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span> <span class="o">=</span> <span class="n">batchify_fn</span>

     <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_workers</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
             <span class="k">def</span> <span class="nf">same_process_iter</span><span class="p">():</span>
                 <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span><span class="p">:</span>
                     <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">])</span>
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pin_memory</span><span class="p">:</span>
                         <span class="n">ret</span> <span class="o">=</span> <span class="n">_as_in_context</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">context</span><span class="o">.</span><span class="n">cpu_pinned</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pin_device_id</span><span class="p">))</span>
                     <span class="k">yield</span> <span class="n">ret</span>
             <span class="k">return</span> <span class="n">same_process_iter</span><span class="p">()</span>

         <span class="c1"># multi-worker</span>
         <span class="k">return</span> <span class="n">_MultiWorkerIterV1</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_workers</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span><span class="p">,</span>
                                   <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span><span class="p">,</span>
                                   <span class="bp">self</span><span class="o">.</span><span class="n">_pin_memory</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pin_device_id</span><span class="p">)</span>

     <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span><span class="p">)</span>


 <span class="k">def</span> <span class="nf">_thread_worker_initializer</span><span class="p">(</span><span class="n">active_shape</span><span class="p">,</span> <span class="n">active_array</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Initializer for ThreadPool.&quot;&quot;&quot;</span>
     <span class="n">set_np</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">active_shape</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">active_array</span><span class="p">)</span>


 <span class="n">_worker_dataset</span> <span class="o">=</span> <span class="kc">None</span>
 <span class="k">def</span> <span class="nf">_worker_initializer</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">active_shape</span><span class="p">,</span> <span class="n">active_array</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Initialier for processing pool.&quot;&quot;&quot;</span>
     <span class="c1"># global dataset is per-process based and only available in worker processes</span>
     <span class="c1"># this is only necessary to handle MXIndexedRecordIO because otherwise dataset</span>
     <span class="c1"># can be passed as argument</span>
     <span class="k">global</span> <span class="n">_worker_dataset</span>
     <span class="n">_worker_dataset</span> <span class="o">=</span> <span class="n">dataset</span>
     <span class="n">set_np</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">active_shape</span><span class="p">,</span> <span class="n">array</span><span class="o">=</span><span class="n">active_array</span><span class="p">)</span>

 <span class="k">def</span> <span class="nf">_worker_fn</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">batchify_fn</span><span class="p">,</span> <span class="n">dataset</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Function for processing data in worker process.&quot;&quot;&quot;</span>
     <span class="c1"># pylint: disable=unused-argument</span>
     <span class="c1"># it is required that each worker process has to fork a new MXIndexedRecordIO handle</span>
     <span class="c1"># preserving dataset as global variable can save tons of overhead and is safe in new process</span>
     <span class="k">global</span> <span class="n">_worker_dataset</span>
     <span class="n">batch</span> <span class="o">=</span> <span class="n">batchify_fn</span><span class="p">([</span><span class="n">_worker_dataset</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">samples</span><span class="p">])</span>
     <span class="n">buf</span> <span class="o">=</span> <span class="n">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">()</span>
     <span class="n">ForkingPickler</span><span class="p">(</span><span class="n">buf</span><span class="p">,</span> <span class="n">pickle</span><span class="o">.</span><span class="n">HIGHEST_PROTOCOL</span><span class="p">)</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">buf</span><span class="o">.</span><span class="n">getvalue</span><span class="p">()</span>

 <span class="k">def</span> <span class="nf">_thread_worker_fn</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">batchify_fn</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Threadpool worker function for processing data.&quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="n">batchify_fn</span><span class="p">([</span><span class="n">dataset</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">samples</span><span class="p">])</span>

 <span class="k">class</span> <span class="nc">_MultiWorkerIter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Internal multi-worker iterator for DataLoader.&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">worker_pool</span><span class="p">,</span> <span class="n">batchify_fn</span><span class="p">,</span> <span class="n">batch_sampler</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                  <span class="n">pin_device_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">worker_fn</span><span class="o">=</span><span class="n">_worker_fn</span><span class="p">,</span> <span class="n">prefetch</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dataset</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">data_loader</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=</span><span class="mi">120</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_worker_pool</span> <span class="o">=</span> <span class="n">worker_pool</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span> <span class="o">=</span> <span class="n">batchify_fn</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span> <span class="o">=</span> <span class="n">batch_sampler</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_data_buffer</span> <span class="o">=</span> <span class="p">{}</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_rcvd_idx</span> <span class="o">=</span> <span class="mi">0</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_sent_idx</span> <span class="o">=</span> <span class="mi">0</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_iter</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_worker_fn</span> <span class="o">=</span> <span class="n">worker_fn</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_pin_memory</span> <span class="o">=</span> <span class="n">pin_memory</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_pin_device_id</span> <span class="o">=</span> <span class="n">pin_device_id</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span> <span class="o">=</span> <span class="n">dataset</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_data_loader</span> <span class="o">=</span> <span class="n">data_loader</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_timeout</span> <span class="o">=</span> <span class="n">timeout</span>
         <span class="c1"># pre-fetch</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">prefetch</span><span class="p">):</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_push_next</span><span class="p">()</span>

     <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span><span class="p">)</span>

     <span class="k">def</span> <span class="nf">_push_next</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Assign next batch workload to workers.&quot;&quot;&quot;</span>
         <span class="n">r</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_iter</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">r</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">return</span>
         <span class="n">async_ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_worker_pool</span><span class="o">.</span><span class="n">apply_async</span><span class="p">(</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_worker_fn</span><span class="p">,</span> <span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span><span class="p">))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_data_buffer</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_sent_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">async_ret</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_sent_idx</span> <span class="o">+=</span> <span class="mi">1</span>

     <span class="k">def</span> <span class="fm">__next__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_push_next</span><span class="p">()</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rcvd_idx</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sent_idx</span><span class="p">:</span>
             <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_buffer</span><span class="p">,</span> <span class="s2">&quot;Data buffer should be empty at this moment&quot;</span>
             <span class="k">raise</span> <span class="ne">StopIteration</span>

         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rcvd_idx</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sent_idx</span><span class="p">,</span> <span class="s2">&quot;rcvd_idx must be smaller than sent_idx&quot;</span>
         <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rcvd_idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_buffer</span><span class="p">,</span> <span class="s2">&quot;fatal error with _push_next, rcvd_idx missing&quot;</span>
         <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_buffer</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_rcvd_idx</span><span class="p">)</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">batch</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">ret</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_timeout</span><span class="p">))</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="n">batch</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_timeout</span><span class="p">)</span>
             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pin_memory</span><span class="p">:</span>
                 <span class="n">batch</span> <span class="o">=</span> <span class="n">_as_in_context</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">context</span><span class="o">.</span><span class="n">cpu_pinned</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pin_device_id</span><span class="p">))</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_rcvd_idx</span> <span class="o">+=</span> <span class="mi">1</span>
             <span class="k">return</span> <span class="n">batch</span>
         <span class="k">except</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">TimeoutError</span><span class="p">:</span>
             <span class="n">msg</span> <span class="o">=</span> <span class="s1">&#39;&#39;&#39;Worker timed out after </span><span class="si">{}</span><span class="s1"> seconds. This might be caused by </span><span class="se">\n</span><span class="s1"></span>
 <span class="s1">            - Slow transform. Please increase timeout to allow slower data loading in each worker.</span>
 <span class="s1">            &#39;&#39;&#39;</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="n">_timeout</span><span class="p">)</span>
             <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_worker_pool</span><span class="p">,</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">pool</span><span class="o">.</span><span class="n">ThreadPool</span><span class="p">):</span>
                 <span class="n">msg</span> <span class="o">+=</span> <span class="s1">&#39;&#39;&#39;- Insufficient shared_memory if `timeout` is large enough.</span>
 <span class="s1">            Please consider reduce `num_workers` or increase shared_memory in system.</span>
 <span class="s1">            &#39;&#39;&#39;</span>
             <span class="nb">print</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
             <span class="k">raise</span>
         <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_worker_pool</span><span class="o">.</span><span class="n">terminate</span><span class="p">()</span>
             <span class="k">raise</span>

     <span class="k">def</span> <span class="nf">next</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__next__</span><span class="p">()</span>

     <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span>


 <div class="viewcode-block" id="DataLoader"><a class="viewcode-back" href="../../../../api/gluon/data/index.html#mxnet.gluon.data.DataLoader">[docs]</a><span class="k">class</span> <span class="nc">DataLoader</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Loads data from a dataset and returns mini-batches of data.</span>

 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
 <span class="sd">    dataset : Dataset</span>
 <span class="sd">        Source dataset. Note that numpy and mxnet arrays can be directly used</span>
 <span class="sd">        as a Dataset.</span>
 <span class="sd">    batch_size : int</span>
 <span class="sd">        Size of mini-batch.</span>
 <span class="sd">    shuffle : bool</span>
 <span class="sd">        Whether to shuffle the samples.</span>
 <span class="sd">    sampler : Sampler</span>
 <span class="sd">        The sampler to use. Either specify sampler or shuffle, not both.</span>
 <span class="sd">    last_batch : {&#39;keep&#39;, &#39;discard&#39;, &#39;rollover&#39;}</span>
 <span class="sd">        How to handle the last batch if batch_size does not evenly divide</span>
 <span class="sd">        `len(dataset)`.</span>

 <span class="sd">        keep - A batch with less samples than previous batches is returned.</span>
 <span class="sd">        discard - The last batch is discarded if its incomplete.</span>
 <span class="sd">        rollover - The remaining samples are rolled over to the next epoch.</span>
 <span class="sd">    batch_sampler : Sampler</span>
 <span class="sd">        A sampler that returns mini-batches. Do not specify batch_size,</span>
 <span class="sd">        shuffle, sampler, and last_batch if batch_sampler is specified.</span>
 <span class="sd">    batchify_fn : callable</span>
 <span class="sd">        Callback function to allow users to specify how to merge samples</span>
 <span class="sd">        into a batch. Defaults to `default_batchify_fn`::</span>

 <span class="sd">            def default_batchify_fn(data):</span>
 <span class="sd">                if isinstance(data[0], nd.NDArray):</span>
 <span class="sd">                    return nd.stack(*data)</span>
 <span class="sd">                elif isinstance(data[0], tuple):</span>
 <span class="sd">                    data = zip(*data)</span>
 <span class="sd">                    return [default_batchify_fn(i) for i in data]</span>
 <span class="sd">                else:</span>
 <span class="sd">                    data = np.asarray(data)</span>
 <span class="sd">                    return nd.array(data, dtype=data.dtype)</span>

 <span class="sd">    num_workers : int, default 0</span>
 <span class="sd">        The number of multiprocessing workers to use for data preprocessing.</span>
 <span class="sd">    pin_memory : boolean, default False</span>
 <span class="sd">        If ``True``, the dataloader will copy NDArrays into pinned memory</span>
 <span class="sd">        before returning them. Copying from CPU pinned memory to GPU is faster</span>
 <span class="sd">        than from normal CPU memory.</span>
 <span class="sd">    pin_device_id : int, default 0</span>
 <span class="sd">        The device id to use for allocating pinned memory if pin_memory is ``True``</span>
 <span class="sd">    prefetch : int, default is `num_workers * 2`</span>
 <span class="sd">        The number of prefetching batches only works if `num_workers` &gt; 0.</span>
 <span class="sd">        If `prefetch` &gt; 0, it allow worker process to prefetch certain batches before</span>
 <span class="sd">        acquiring data from iterators.</span>
 <span class="sd">        Note that using large prefetching batch will provide smoother bootstrapping performance,</span>
 <span class="sd">        but will consume more shared_memory. Using smaller number may forfeit the purpose of using</span>
 <span class="sd">        multiple worker processes, try reduce `num_workers` in this case.</span>
 <span class="sd">        By default it defaults to `num_workers * 2`.</span>
 <span class="sd">    thread_pool : bool, default False</span>
 <span class="sd">        If ``True``, use threading pool instead of multiprocessing pool. Using threadpool</span>
 <span class="sd">        can avoid shared memory usage. If `DataLoader` is more IO bounded or GIL is not a killing</span>
 <span class="sd">        problem, threadpool version may achieve better performance than multiprocessing.</span>
 <span class="sd">    timeout : int, default is 120</span>
 <span class="sd">        The timeout in seconds for each worker to fetch a batch data. Only modify this number</span>
 <span class="sd">        unless you are experiencing timeout and you know it&#39;s due to slow data loading.</span>
 <span class="sd">        Sometimes full `shared_memory` will cause all workers to hang and causes timeout. In these</span>
 <span class="sd">        cases please reduce `num_workers` or increase system `shared_memory` size instead.</span>
 <span class="sd">    auto_reload : bool, default is True</span>
 <span class="sd">        control whether prefetch data after a batch is ended.</span>

 <span class="sd">    Example:</span>
 <span class="sd">    &gt;&gt;&gt; from mxnet.gluon.data import DataLoader, ArrayDataset</span>
 <span class="sd">    &gt;&gt;&gt; train_data = ArrayDataset([i for i in range(10)],[9-i for i in range(10)])</span>
 <span class="sd">    &gt;&gt;&gt; def transform_train(sample):</span>
 <span class="sd">    ...   if sample == 0 : print(&#39;(pre)fetching data here&#39;)</span>
 <span class="sd">    ...   return sample</span>
 <span class="sd">    ...</span>
 <span class="sd">    &gt;&gt;&gt; train_iter = DataLoader(train_data.transform_first(transform_train),</span>
 <span class="sd">    ...                         auto_reload=False, batch_size=1,num_workers=1)</span>
 <span class="sd">    &gt;&gt;&gt; # no prefetch is performed, the prefetch &amp; autoload start after</span>
 <span class="sd">    &gt;&gt;&gt; # train_iter.__iter__() is called.</span>
 <span class="sd">    &gt;&gt;&gt; for i in train_iter:pass</span>
 <span class="sd">    (pre)fetching data here</span>
 <span class="sd">    &gt;&gt;&gt; train_iter = DataLoader(train_data.transform_first(transform_train),</span>
 <span class="sd">    ...                         batch_size=1,num_workers=1)</span>
 <span class="sd">    (pre)fetching data here</span>
 <span class="sd">    &gt;&gt;&gt; it = iter(train_iter) # nothing is generated since lazy-evaluation occurs</span>
 <span class="sd">    &gt;&gt;&gt; it2 = iter(train_iter)</span>
 <span class="sd">    &gt;&gt;&gt; it3 = iter(train_iter)</span>
 <span class="sd">    &gt;&gt;&gt; it4 = iter(train_iter)</span>
 <span class="sd">    &gt;&gt;&gt; _ = next(it2) # the first iter we are using is the prefetched iter.</span>
 <span class="sd">    &gt;&gt;&gt; _ = next(it) # since the prefetched iter is consumed, we have to fetch data for `it`.</span>
 <span class="sd">    (pre)fetching data here</span>
 <span class="sd">    &gt;&gt;&gt; _ = [None for _ in it3]</span>
 <span class="sd">    (pre)fetching data here</span>
 <span class="sd">    (pre)fetching data here</span>
 <span class="sd">    &gt;&gt;&gt; # Here, 2 prefetches are triggered, one is fetching the first batch of `it3` and</span>
 <span class="sd">    &gt;&gt;&gt; # another is when `it3` yield its last item, a prefetch is automatically performed.</span>
 <span class="sd">    &gt;&gt;&gt; _ = [None for _ in it]</span>
 <span class="sd">    &gt;&gt;&gt; # no prefetch is happened since train_loader has already prefetch data.</span>
 <span class="sd">    &gt;&gt;&gt; _ = next(it4)</span>
 <span class="sd">    &gt;&gt;&gt; # since the prefetch is performed, it4 become the prefetched iter.</span>
 <span class="sd">    &gt;&gt;&gt;</span>
 <span class="sd">    &gt;&gt;&gt; test_data = ArrayDataset([i for i in range(10)],[9-i for i in range(10)])</span>
 <span class="sd">    &gt;&gt;&gt; test_iter = DataLoader(test_data, batch_size=1,num_workers=1)</span>
 <span class="sd">    &gt;&gt;&gt; for epoch in range(200):</span>
 <span class="sd">    ...   # there is almost no difference between it and the default DataLoader</span>
 <span class="sd">    ...   for data, label in train_iter:</span>
 <span class="sd">    ...     # training...</span>
 <span class="sd">    ...   for data, label in test_iter:</span>
 <span class="sd">    ...     # testing...</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">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">last_batch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_sampler</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batchify_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                  <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">pin_device_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
                  <span class="n">prefetch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">thread_pool</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=</span><span class="mi">120</span><span class="p">,</span> <span class="n">auto_reload</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">_dataset</span> <span class="o">=</span> <span class="n">dataset</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_pin_memory</span> <span class="o">=</span> <span class="n">pin_memory</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_pin_device_id</span> <span class="o">=</span> <span class="n">pin_device_id</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_thread_pool</span> <span class="o">=</span> <span class="n">thread_pool</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_timeout</span> <span class="o">=</span> <span class="n">timeout</span>
         <span class="k">assert</span> <span class="n">timeout</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;timeout must be positive, given </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="n">timeout</span><span class="p">)</span>

         <span class="k">if</span> <span class="n">batch_sampler</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">batch_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;batch_size must be specified unless &quot;</span> \
                                  <span class="s2">&quot;batch_sampler is specified&quot;</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">sampler</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="k">if</span> <span class="n">shuffle</span><span class="p">:</span>
                     <span class="n">sampler</span> <span class="o">=</span> <span class="n">_sampler</span><span class="o">.</span><span class="n">RandomSampler</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">))</span>
                 <span class="k">else</span><span class="p">:</span>
                     <span class="n">sampler</span> <span class="o">=</span> <span class="n">_sampler</span><span class="o">.</span><span class="n">SequentialSampler</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">))</span>
             <span class="k">elif</span> <span class="n">shuffle</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;shuffle must not be specified if sampler is specified&quot;</span><span class="p">)</span>

             <span class="n">batch_sampler</span> <span class="o">=</span> <span class="n">_sampler</span><span class="o">.</span><span class="n">BatchSampler</span><span class="p">(</span>
                 <span class="n">sampler</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">last_batch</span> <span class="k">if</span> <span class="n">last_batch</span> <span class="k">else</span> <span class="s1">&#39;keep&#39;</span><span class="p">)</span>
         <span class="k">elif</span> <span class="n">batch_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">shuffle</span> <span class="ow">or</span> <span class="n">sampler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> \
                 <span class="n">last_batch</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;batch_size, shuffle, sampler and last_batch must &quot;</span> \
                              <span class="s2">&quot;not be specified if batch_sampler is specified.&quot;</span><span class="p">)</span>

         <span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span> <span class="o">=</span> <span class="n">batch_sampler</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_num_workers</span> <span class="o">=</span> <span class="n">num_workers</span> <span class="k">if</span> <span class="n">num_workers</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_worker_pool</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_prefetch</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">prefetch</span><span class="p">)</span> <span class="k">if</span> <span class="n">prefetch</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_workers</span><span class="p">)</span>
         <span class="n">nd</span><span class="o">.</span><span class="n">waitall</span><span class="p">()</span>
         <span class="kn">import</span> <span class="nn">gc</span>
         <span class="n">gc</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
         <span class="n">nd</span><span class="o">.</span><span class="n">waitall</span><span class="p">()</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_workers</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_thread_pool</span><span class="p">:</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_worker_pool</span> <span class="o">=</span> <span class="n">ThreadPool</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_num_workers</span><span class="p">,</span>
                                                <span class="n">initializer</span><span class="o">=</span><span class="n">_thread_worker_initializer</span><span class="p">,</span>
                                                <span class="n">initargs</span><span class="o">=</span><span class="p">(</span><span class="n">is_np_shape</span><span class="p">(),</span> <span class="n">is_np_array</span><span class="p">()))</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="c1"># set ignore keyboard interupt signal before forking processes</span>
                 <span class="n">original_sigint_handler</span> <span class="o">=</span> <span class="n">signal</span><span class="o">.</span><span class="n">signal</span><span class="p">(</span><span class="n">signal</span><span class="o">.</span><span class="n">SIGINT</span><span class="p">,</span> <span class="n">signal</span><span class="o">.</span><span class="n">SIG_IGN</span><span class="p">)</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_worker_pool</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">Pool</span><span class="p">(</span>
                     <span class="bp">self</span><span class="o">.</span><span class="n">_num_workers</span><span class="p">,</span> <span class="n">initializer</span><span class="o">=</span><span class="n">_worker_initializer</span><span class="p">,</span>
                     <span class="n">initargs</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span><span class="p">,</span> <span class="n">is_np_shape</span><span class="p">(),</span> <span class="n">is_np_array</span><span class="p">()])</span>
                 <span class="c1"># resume keyboard interupt signal in main process</span>
                 <span class="n">signal</span><span class="o">.</span><span class="n">signal</span><span class="p">(</span><span class="n">signal</span><span class="o">.</span><span class="n">SIGINT</span><span class="p">,</span> <span class="n">original_sigint_handler</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">batchify_fn</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">num_workers</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span> <span class="o">=</span> <span class="n">default_mp_batchify_fn</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span> <span class="o">=</span> <span class="n">default_batchify_fn</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span> <span class="o">=</span> <span class="n">batchify_fn</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">auto_reload</span> <span class="o">=</span> <span class="n">auto_reload</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">auto_reload</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">refresh</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">clean</span><span class="p">()</span> <span class="c1"># ensure self._iter exists.</span>

     <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_iter</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">refresh</span><span class="p">()</span>
         <span class="n">t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_iter</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_iter</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># ensure a single iter would not using twice.</span>
         <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">t</span><span class="p">:</span>
             <span class="k">yield</span> <span class="n">item</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_iter</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">auto_reload</span><span class="p">:</span>
             <span class="c1"># ensure we do not waste any exist iter by mistake</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">refresh</span><span class="p">()</span>

     <span class="k">def</span> <span class="nf">_prefetch_iter</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_workers</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
             <span class="k">def</span> <span class="nf">same_process_iter</span><span class="p">():</span>
                 <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span><span class="p">:</span>
                     <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">])</span>
                     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pin_memory</span><span class="p">:</span>
                         <span class="n">ret</span> <span class="o">=</span> <span class="n">_as_in_context</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">context</span><span class="o">.</span><span class="n">cpu_pinned</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pin_device_id</span><span class="p">))</span>
                     <span class="k">yield</span> <span class="n">ret</span>
             <span class="k">return</span> <span class="n">same_process_iter</span><span class="p">()</span>

         <span class="c1"># multi-worker</span>
         <span class="k">return</span> <span class="n">_MultiWorkerIter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_worker_pool</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batchify_fn</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span><span class="p">,</span>
                                 <span class="n">pin_memory</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_pin_memory</span><span class="p">,</span> <span class="n">pin_device_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_pin_device_id</span><span class="p">,</span>
                                 <span class="n">worker_fn</span><span class="o">=</span><span class="n">_thread_worker_fn</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_thread_pool</span> <span class="k">else</span> <span class="n">_worker_fn</span><span class="p">,</span>
                                 <span class="n">prefetch</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_prefetch</span><span class="p">,</span>
                                 <span class="n">dataset</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_dataset</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_thread_pool</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
                                 <span class="n">data_loader</span><span class="o">=</span><span class="bp">self</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_timeout</span><span class="p">)</span>

     <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_sampler</span><span class="p">)</span>

     <span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_worker_pool</span><span class="p">:</span>
             <span class="c1"># manually terminate due to a bug that pool is not automatically terminated</span>
             <span class="c1"># https://bugs.python.org/issue34172</span>
             <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_worker_pool</span><span class="p">,</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">pool</span><span class="o">.</span><span class="n">Pool</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_worker_pool</span><span class="o">.</span><span class="n">terminate</span><span class="p">()</span>

 <div class="viewcode-block" id="DataLoader.refresh"><a class="viewcode-back" href="../../../../api/gluon/data/index.html#mxnet.gluon.data.DataLoader.refresh">[docs]</a>    <span class="k">def</span> <span class="nf">refresh</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Refresh its iter, fetch data again from its dataset&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_iter</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prefetch_iter</span><span class="p">()</span></div>

 <div class="viewcode-block" id="DataLoader.clean"><a class="viewcode-back" href="../../../../api/gluon/data/index.html#mxnet.gluon.data.DataLoader.clean">[docs]</a>    <span class="k">def</span> <span class="nf">clean</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Remove its prefetched iter, the prefetch step will start after call its __iter__()&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_iter</span> <span class="o">=</span> <span class="kc">None</span></div></div>
 </pre></div>

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