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| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/gluon/data/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> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/gluon/loss/index.html">Losses</a><ul> |
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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/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-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/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../../../../tutorials/packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
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| <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> |
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| <li class="toctree-l5"><a class="reference internal" href="../../../../../tutorials/packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <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-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-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/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
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| <h1>Source code for mxnet.gluon.contrib.estimator.estimator</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># coding: utf-8</span> |
| <span class="c1"># pylint: disable=wildcard-import, unused-variable</span> |
| <span class="sd">"""Gluon Estimator"""</span> |
| |
| <span class="kn">import</span> <span class="nn">copy</span> |
| <span class="kn">import</span> <span class="nn">logging</span> |
| <span class="kn">import</span> <span class="nn">sys</span> |
| <span class="kn">import</span> <span class="nn">warnings</span> |
| |
| <span class="kn">from</span> <span class="nn">.event_handler</span> <span class="kn">import</span> <span class="n">MetricHandler</span><span class="p">,</span> <span class="n">ValidationHandler</span><span class="p">,</span> <span class="n">LoggingHandler</span><span class="p">,</span> <span class="n">StoppingHandler</span><span class="p">,</span> <span class="n">GradientUpdateHandler</span> |
| <span class="kn">from</span> <span class="nn">.event_handler</span> <span class="kn">import</span> <span class="n">TrainBegin</span><span class="p">,</span> <span class="n">EpochBegin</span><span class="p">,</span> <span class="n">BatchBegin</span><span class="p">,</span> <span class="n">BatchEnd</span><span class="p">,</span> <span class="n">EpochEnd</span><span class="p">,</span> <span class="n">TrainEnd</span> |
| <span class="kn">from</span> <span class="nn">.event_handler</span> <span class="kn">import</span> <span class="n">_check_event_handlers</span> |
| <span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">_check_metrics</span><span class="p">,</span> <span class="n">_suggest_metric_for_loss</span><span class="p">,</span> <span class="n">_check_handler_metric_ref</span> |
| <span class="kn">from</span> <span class="nn">...data</span> <span class="kn">import</span> <span class="n">DataLoader</span> |
| <span class="kn">from</span> <span class="nn">...loss</span> <span class="kn">import</span> <span class="n">Loss</span> <span class="k">as</span> <span class="n">gluon_loss</span> |
| <span class="kn">from</span> <span class="nn">...trainer</span> <span class="kn">import</span> <span class="n">Trainer</span> |
| <span class="kn">from</span> <span class="nn">...utils</span> <span class="kn">import</span> <span class="n">split_and_load</span> |
| <span class="kn">from</span> <span class="nn">....context</span> <span class="kn">import</span> <span class="n">Context</span><span class="p">,</span> <span class="n">cpu</span><span class="p">,</span> <span class="n">gpu</span><span class="p">,</span> <span class="n">num_gpus</span> |
| <span class="kn">from</span> <span class="nn">....metric</span> <span class="kn">import</span> <span class="n">Loss</span> <span class="k">as</span> <span class="n">metric_loss</span> |
| <span class="kn">from</span> <span class="nn">.batch_processor</span> <span class="kn">import</span> <span class="n">BatchProcessor</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Estimator'</span><span class="p">]</span> |
| |
| |
| <div class="viewcode-block" id="Estimator"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.estimator.Estimator">[docs]</a><span class="k">class</span> <span class="nc">Estimator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span> |
| <span class="sd">"""Estimator Class for easy model training</span> |
| |
| <span class="sd"> :py:class:`Estimator` can be used to facilitate the training & validation process</span> |
| |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> net : gluon.Block</span> |
| <span class="sd"> The model used for training.</span> |
| <span class="sd"> loss : gluon.loss.Loss</span> |
| <span class="sd"> Loss (objective) function to calculate during training.</span> |
| <span class="sd"> train_metrics : EvalMetric or list of EvalMetric</span> |
| <span class="sd"> Training metrics for evaluating models on training dataset.</span> |
| <span class="sd"> val_metrics : EvalMetric or list of EvalMetric</span> |
| <span class="sd"> Validation metrics for evaluating models on validation dataset.</span> |
| <span class="sd"> initializer : Initializer</span> |
| <span class="sd"> Initializer to initialize the network.</span> |
| <span class="sd"> trainer : Trainer</span> |
| <span class="sd"> Trainer to apply optimizer on network parameters.</span> |
| <span class="sd"> context : Context or list of Context</span> |
| <span class="sd"> Device(s) to run the training on.</span> |
| <span class="sd"> val_net : gluon.Block</span> |
| <span class="sd"> The model used for validation. The validation model does not necessarily belong to</span> |
| <span class="sd"> the same model class as the training model. But the two models typically share the</span> |
| <span class="sd"> same architecture. Therefore the validation model can reuse parameters of the</span> |
| <span class="sd"> training model.</span> |
| |
| <span class="sd"> The code example of consruction of val_net sharing the same network parameters as</span> |
| <span class="sd"> the training net is given below:</span> |
| |
| <span class="sd"> >>> net = _get_train_network()</span> |
| <span class="sd"> >>> val_net = _get_test_network(params=net.collect_params())</span> |
| <span class="sd"> >>> net.initialize(ctx=ctx)</span> |
| <span class="sd"> >>> est = Estimator(net, loss, val_net=val_net)</span> |
| |
| <span class="sd"> Proper namespace match is required for weight sharing between two networks. Most networks</span> |
| <span class="sd"> inheriting :py:class:`Block` can share their parameters correctly. An exception is</span> |
| <span class="sd"> Sequential networks that Block scope must be specified for correct weight sharing. For</span> |
| <span class="sd"> the naming in mxnet Gluon API, please refer to the site</span> |
| <span class="sd"> (https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/blocks/naming.html)</span> |
| <span class="sd"> for future information.</span> |
| <span class="sd"> val_loss : gluon.loss.loss</span> |
| <span class="sd"> Loss (objective) function to calculate during validation. If set val_loss</span> |
| <span class="sd"> None, it will use the same loss function as self.loss</span> |
| <span class="sd"> batch_processor: BatchProcessor</span> |
| <span class="sd"> BatchProcessor provides customized fit_batch() and evaluate_batch() methods</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">logger</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="sd">"""logging.Logger object associated with the Estimator.</span> |
| |
| <span class="sd"> The logger is used for all logs generated by this estimator and its</span> |
| <span class="sd"> handlers. A new logging.Logger is created during Estimator construction and</span> |
| <span class="sd"> configured to write all logs with level logging.INFO or higher to</span> |
| <span class="sd"> sys.stdout.</span> |
| |
| <span class="sd"> You can modify the logging settings using the standard Python methods. For</span> |
| <span class="sd"> example, to save logs to a file in addition to printing them to stdout</span> |
| <span class="sd"> output, you can attach a logging.FileHandler to the logger.</span> |
| |
| <span class="sd"> >>> est = Estimator(net, loss)</span> |
| <span class="sd"> >>> import logging</span> |
| <span class="sd"> >>> est.logger.addHandler(logging.FileHandler(filename))</span> |
| |
| <span class="sd"> """</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">net</span><span class="p">,</span> |
| <span class="n">loss</span><span class="p">,</span> |
| <span class="n">train_metrics</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">val_metrics</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">initializer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">trainer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">context</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">val_net</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">val_loss</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">batch_processor</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">net</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_loss</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_train_metrics</span> <span class="o">=</span> <span class="n">_check_metrics</span><span class="p">(</span><span class="n">train_metrics</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_val_metrics</span> <span class="o">=</span> <span class="n">_check_metrics</span><span class="p">(</span><span class="n">val_metrics</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_add_default_training_metrics</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_add_validation_metrics</span><span class="p">()</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">val_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> |
| <span class="k">if</span> <span class="n">val_loss</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">val_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_loss</span><span class="p">(</span><span class="n">val_loss</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">val_net</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span> |
| <span class="k">if</span> <span class="n">val_net</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">val_net</span> <span class="o">=</span> <span class="n">val_net</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">Logger</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'Estimator'</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">addHandler</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">StreamHandler</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="p">))</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">context</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_context</span><span class="p">(</span><span class="n">context</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_initialize</span><span class="p">(</span><span class="n">initializer</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">trainer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_trainer</span><span class="p">(</span><span class="n">trainer</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">batch_processor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_batch_processor</span><span class="p">(</span><span class="n">batch_processor</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_check_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loss</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="n">loss</span><span class="p">,</span> <span class="n">gluon_loss</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"loss must be a Loss, "</span> |
| <span class="s2">"refer to gluon.loss.Loss:</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">loss</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">loss</span> |
| |
| <span class="k">def</span> <span class="nf">_check_context</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">):</span> |
| <span class="c1"># infer available context</span> |
| <span class="n">gpus</span> <span class="o">=</span> <span class="n">num_gpus</span><span class="p">()</span> |
| <span class="n">available_gpus</span> <span class="o">=</span> <span class="p">[</span><span class="n">gpu</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="nb">range</span><span class="p">(</span><span class="n">gpus</span><span class="p">)]</span> |
| |
| <span class="k">if</span> <span class="n">context</span><span class="p">:</span> |
| <span class="c1"># check context values, only accept Context or a list of Context</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">context</span><span class="p">,</span> <span class="n">Context</span><span class="p">):</span> |
| <span class="n">context</span> <span class="o">=</span> <span class="p">[</span><span class="n">context</span><span class="p">]</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">context</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">([</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">Context</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">context</span><span class="p">]):</span> |
| <span class="n">context</span> <span class="o">=</span> <span class="n">context</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"context must be a Context or a list of Context, "</span> |
| <span class="s2">"for example mx.cpu() or [mx.gpu(0), mx.gpu(1)], "</span> |
| <span class="s2">"refer to mxnet.Context:</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">context</span><span class="p">))</span> |
| <span class="k">for</span> <span class="n">ctx</span> <span class="ow">in</span> <span class="n">context</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">ctx</span> <span class="ow">in</span> <span class="n">available_gpus</span> <span class="ow">or</span> <span class="nb">str</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">),</span> \ |
| <span class="s2">"</span><span class="si">%s</span><span class="s2"> is not available, please make sure "</span> \ |
| <span class="s2">"your context is in one of: mx.cpu(), </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> \ |
| <span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="s2">", "</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">ctx</span><span class="p">)</span> <span class="k">for</span> <span class="n">ctx</span> <span class="ow">in</span> <span class="n">available_gpus</span><span class="p">]))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="c1"># provide default context</span> |
| <span class="k">if</span> <span class="n">gpus</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="c1"># only use 1 GPU by default</span> |
| <span class="k">if</span> <span class="n">gpus</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"You have multiple GPUs, gpu(0) will be used by default."</span> |
| <span class="s2">"To utilize all your GPUs, specify context as a list of gpus, "</span> |
| <span class="s2">"e.g. context=[mx.gpu(0), mx.gpu(1)] "</span><span class="p">)</span> |
| <span class="n">context</span> <span class="o">=</span> <span class="p">[</span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">context</span> <span class="o">=</span> <span class="p">[</span><span class="n">cpu</span><span class="p">()]</span> |
| <span class="k">return</span> <span class="n">context</span> |
| |
| <span class="k">def</span> <span class="nf">_check_batch_processor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_processor</span><span class="p">):</span> |
| <span class="c1"># check whether the batch processor contains fit_batch() and evaluate_batch() methods</span> |
| <span class="k">if</span> <span class="n">batch_processor</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">model_fit</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">batch_processor</span><span class="p">,</span> <span class="s1">'fit_batch'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="n">model_evaluate</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">batch_processor</span><span class="p">,</span> <span class="s1">'evaluate_batch'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">callable</span><span class="p">(</span><span class="n">model_fit</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">callable</span><span class="p">(</span><span class="n">model_evaluate</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Customized Batch Processor must contain fit_batch()'</span> |
| <span class="s1">' and evaluate_batch() methods'</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">batch_processor</span> <span class="o">=</span> <span class="n">BatchProcessor</span><span class="p">()</span> |
| <span class="k">return</span> <span class="n">batch_processor</span> |
| |
| <span class="k">def</span> <span class="nf">_initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initializer</span><span class="p">):</span> |
| <span class="c1"># initialize the network</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_initialized</span><span class="p">():</span> |
| <span class="c1"># net is partially or not initialized,</span> |
| <span class="c1"># initialize with user specified initializer</span> |
| <span class="c1"># if initializer is None, default initializer will be used</span> |
| <span class="c1"># do not re-init layers already initialized</span> |
| <span class="k">if</span> <span class="n">initializer</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">init</span><span class="o">=</span><span class="n">initializer</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">context</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">net</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">ctx</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">initializer</span><span class="p">:</span> |
| <span class="c1"># net is fully initialized, and user passed not None initializer</span> |
| <span class="c1"># do not force reinitialize, give warning</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"Network already fully initialized, skipping initialization. "</span> |
| <span class="s2">"You don't need to pass initializer if you already "</span> |
| <span class="s2">"initialized your net. "</span> |
| <span class="s2">"You can use net.initialize(init=your_initializer, force_reinit=True)"</span> |
| <span class="s2">"to force re-initialize."</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_check_trainer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">trainer</span><span class="p">):</span> |
| <span class="c1"># handle trainer</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">trainer</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"No trainer specified, default SGD optimizer "</span> |
| <span class="s2">"with learning rate 0.001 is used."</span><span class="p">)</span> |
| <span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">(),</span> |
| <span class="s1">'sgd'</span><span class="p">,</span> <span class="p">{</span><span class="s1">'learning_rate'</span><span class="p">:</span> <span class="mf">0.001</span><span class="p">})</span> |
| <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">trainer</span><span class="p">,</span> <span class="n">Trainer</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Trainer must be a Gluon Trainer instance, refer to "</span> |
| <span class="s2">"gluon.Trainer:</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">trainer</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">trainer</span> |
| |
| <span class="k">def</span> <span class="nf">_is_initialized</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">param_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">collect_params</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">param_dict</span><span class="p">:</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">param_dict</span><span class="p">[</span><span class="n">param</span><span class="p">]</span><span class="o">.</span><span class="n">list_ctx</span><span class="p">()</span> |
| <span class="k">except</span> <span class="ne">RuntimeError</span><span class="p">:</span> |
| <span class="k">return</span> <span class="kc">False</span> |
| <span class="k">return</span> <span class="kc">True</span> |
| |
| <span class="k">def</span> <span class="nf">_get_data_and_label</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">batch_axis</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">split_and_load</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ctx_list</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">batch_axis</span><span class="o">=</span><span class="n">batch_axis</span><span class="p">)</span> |
| <span class="n">label</span> <span class="o">=</span> <span class="n">split_and_load</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">ctx_list</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">batch_axis</span><span class="o">=</span><span class="n">batch_axis</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">data</span><span class="p">,</span> <span class="n">label</span> |
| |
| <span class="k">def</span> <span class="nf">_add_default_training_metrics</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_metrics</span><span class="p">:</span> |
| <span class="n">suggested_metric</span> <span class="o">=</span> <span class="n">_suggest_metric_for_loss</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">suggested_metric</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_train_metrics</span> <span class="o">=</span> <span class="p">[</span><span class="n">suggested_metric</span><span class="p">]</span> |
| <span class="n">loss_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">rstrip</span><span class="p">(</span><span class="s1">'1234567890'</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_train_metrics</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">metric_loss</span><span class="p">(</span><span class="n">loss_name</span><span class="p">))</span> |
| |
| <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_metrics</span><span class="p">:</span> |
| <span class="c1"># add training prefix to the metric name</span> |
| <span class="c1"># it is useful for event handlers to distinguish them from validation metrics</span> |
| <span class="n">metric</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'training '</span> <span class="o">+</span> <span class="n">metric</span><span class="o">.</span><span class="n">name</span> |
| |
| <span class="k">def</span> <span class="nf">_add_validation_metrics</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_metrics</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_val_metrics</span> <span class="o">=</span> <span class="p">[</span><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">metric</span><span class="p">)</span> <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_metrics</span><span class="p">]</span> |
| |
| <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_val_metrics</span><span class="p">:</span> |
| <span class="c1"># add validation prefix to the metric name</span> |
| <span class="c1"># it is useful for event handlers to distinguish them from training metrics</span> |
| <span class="k">if</span> <span class="s1">'training'</span> <span class="ow">in</span> <span class="n">metric</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> |
| <span class="n">metric</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'training'</span><span class="p">,</span> <span class="s1">'validation'</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">metric</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'validation '</span> <span class="o">+</span> <span class="n">metric</span><span class="o">.</span><span class="n">name</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">train_metrics</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="n">_train_metrics</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">val_metrics</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="n">_val_metrics</span> |
| |
| <div class="viewcode-block" id="Estimator.evaluate"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.estimator.Estimator.evaluate">[docs]</a> <span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> |
| <span class="n">val_data</span><span class="p">,</span> |
| <span class="n">batch_axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="n">event_handlers</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""Evaluate model on validation data.</span> |
| |
| <span class="sd"> This function calls :py:func:`evaluate_batch` on each of the batches from the</span> |
| <span class="sd"> validation data loader. Thus, for custom use cases, it's possible to inherit the</span> |
| <span class="sd"> estimator class and override :py:func:`evaluate_batch`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> val_data : DataLoader</span> |
| <span class="sd"> Validation data loader with data and labels.</span> |
| <span class="sd"> batch_axis : int, default 0</span> |
| <span class="sd"> Batch axis to split the validation data into devices.</span> |
| <span class="sd"> event_handlers : EventHandler or list of EventHandler</span> |
| <span class="sd"> List of :py:class:`EventHandlers` to apply during validation. Besides</span> |
| <span class="sd"> event handlers specified here, a default MetricHandler and a LoggingHandler</span> |
| <span class="sd"> will be added if not specified explicitly.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">val_data</span><span class="p">,</span> <span class="n">DataLoader</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Estimator only support input as Gluon DataLoader. Alternatively, you "</span> |
| <span class="s2">"can transform your DataIter or any NDArray into Gluon DataLoader. "</span> |
| <span class="s2">"Refer to gluon.data.DataLoader"</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_metrics</span><span class="p">:</span> |
| <span class="n">metric</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span> |
| <span class="n">estimator_ref</span> <span class="o">=</span> <span class="bp">self</span> |
| |
| <span class="n">event_handlers</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_default_validation_handlers</span><span class="p">(</span><span class="n">event_handlers</span><span class="p">)</span> |
| |
| <span class="n">_</span><span class="p">,</span> <span class="n">epoch_begin</span><span class="p">,</span> <span class="n">batch_begin</span><span class="p">,</span> <span class="n">batch_end</span><span class="p">,</span> \ |
| <span class="n">epoch_end</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_categorize_handlers</span><span class="p">(</span><span class="n">event_handlers</span><span class="p">)</span> |
| |
| <span class="n">estimator_ref</span> <span class="o">=</span> <span class="bp">self</span> |
| |
| <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">epoch_begin</span><span class="p">:</span> |
| <span class="n">handler</span><span class="o">.</span><span class="n">epoch_begin</span><span class="p">(</span><span class="n">estimator_ref</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">val_data</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">batch_begin</span><span class="p">:</span> |
| <span class="n">handler</span><span class="o">.</span><span class="n">batch_begin</span><span class="p">(</span><span class="n">estimator_ref</span><span class="p">,</span> <span class="n">batch</span><span class="o">=</span><span class="n">batch</span><span class="p">)</span> |
| |
| <span class="n">_</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span> <span class="n">loss</span> <span class="o">=</span> \ |
| <span class="bp">self</span><span class="o">.</span><span class="n">batch_processor</span><span class="o">.</span><span class="n">evaluate_batch</span><span class="p">(</span><span class="n">estimator_ref</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> |
| <span class="n">batch_axis</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">batch_end</span><span class="p">:</span> |
| <span class="n">handler</span><span class="o">.</span><span class="n">batch_end</span><span class="p">(</span><span class="n">estimator_ref</span><span class="p">,</span> <span class="n">batch</span><span class="o">=</span><span class="n">batch</span><span class="p">,</span> <span class="n">pred</span><span class="o">=</span><span class="n">pred</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">loss</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">epoch_end</span><span class="p">:</span> |
| <span class="n">handler</span><span class="o">.</span><span class="n">epoch_end</span><span class="p">(</span><span class="n">estimator_ref</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="Estimator.fit"><a class="viewcode-back" href="../../../../../api/gluon/contrib/index.html#mxnet.gluon.contrib.estimator.Estimator.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> |
| <span class="n">val_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">epochs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">event_handlers</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">batches</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> |
| <span class="n">batch_axis</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> |
| <span class="sd">"""Trains the model with a given :py:class:`DataLoader` for a specified</span> |
| <span class="sd"> number of epochs or batches. The batch size is inferred from the</span> |
| <span class="sd"> data loader's batch_size.</span> |
| |
| <span class="sd"> This function calls :py:func:`fit_batch` on each of the batches from the</span> |
| <span class="sd"> training data loader. Thus, for custom use cases, it's possible to inherit the</span> |
| <span class="sd"> estimator class and override :py:func:`fit_batch`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> train_data : DataLoader</span> |
| <span class="sd"> Training data loader with data and labels.</span> |
| <span class="sd"> val_data : DataLoader, default None</span> |
| <span class="sd"> Validation data loader with data and labels.</span> |
| <span class="sd"> epochs : int, default None</span> |
| <span class="sd"> Number of epochs to iterate on the training data.</span> |
| <span class="sd"> You can only specify one and only one type of iteration(epochs or batches).</span> |
| <span class="sd"> event_handlers : EventHandler or list of EventHandler</span> |
| <span class="sd"> List of :py:class:`EventHandlers` to apply during training. Besides</span> |
| <span class="sd"> the event handlers specified here, a StoppingHandler,</span> |
| <span class="sd"> LoggingHandler and MetricHandler will be added by default if not</span> |
| <span class="sd"> yet specified manually. If validation data is provided, a</span> |
| <span class="sd"> ValidationHandler is also added if not already specified.</span> |
| <span class="sd"> batches : int, default None</span> |
| <span class="sd"> Number of batches to iterate on the training data.</span> |
| <span class="sd"> You can only specify one and only one type of iteration(epochs or batches).</span> |
| <span class="sd"> batch_axis : int, default 0</span> |
| <span class="sd"> Batch axis to split the training data into devices.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="n">DataLoader</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Estimator only support input as Gluon DataLoader. Alternatively, you "</span> |
| <span class="s2">"can transform your DataIter or any NDArray into Gluon DataLoader. "</span> |
| <span class="s2">"Refer to gluon.data.dataloader"</span><span class="p">)</span> |
| |
| <span class="c1"># must specify one and only one of epochs or batches</span> |
| <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="n">epochs</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="ow">not</span> <span class="n">batches</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> |
| <span class="s2">"Fit only support exactly one type of iteration, "</span> |
| <span class="s2">"train by number of epochs or number of batches."</span> |
| <span class="s2">"Please specify one and only one of: epochs or batches."</span><span class="p">)</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">max_epoch</span> <span class="o">=</span> <span class="n">epochs</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">max_batch</span> <span class="o">=</span> <span class="n">batches</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">batch_axis</span> <span class="o">=</span> <span class="n">batch_axis</span> |
| |
| <span class="c1"># provide default handlers</span> |
| <span class="n">event_handlers</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_default_handlers</span><span class="p">(</span><span class="n">val_data</span><span class="p">,</span> <span class="n">event_handlers</span><span class="p">)</span> |
| |
| <span class="n">train_begin</span><span class="p">,</span> <span class="n">epoch_begin</span><span class="p">,</span> <span class="n">batch_begin</span><span class="p">,</span> \ |
| <span class="n">batch_end</span><span class="p">,</span> <span class="n">epoch_end</span><span class="p">,</span> <span class="n">train_end</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_categorize_handlers</span><span class="p">(</span><span class="n">event_handlers</span><span class="p">)</span> |
| |
| <span class="c1"># pass a reference to all event handlers</span> |
| <span class="n">estimator_ref</span> <span class="o">=</span> <span class="bp">self</span> |
| <span class="c1"># training begin</span> |
| <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">train_begin</span><span class="p">:</span> |
| <span class="n">handler</span><span class="o">.</span><span class="n">train_begin</span><span class="p">(</span><span class="n">estimator_ref</span><span class="p">)</span> |
| |
| <span class="k">while</span> <span class="kc">True</span><span class="p">:</span> |
| <span class="c1"># epoch begin</span> |
| <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">epoch_begin</span><span class="p">:</span> |
| <span class="n">handler</span><span class="o">.</span><span class="n">epoch_begin</span><span class="p">(</span><span class="n">estimator_ref</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_data</span><span class="p">):</span> |
| <span class="c1"># batch begin</span> |
| <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">batch_begin</span><span class="p">:</span> |
| <span class="n">handler</span><span class="o">.</span><span class="n">batch_begin</span><span class="p">(</span><span class="n">estimator_ref</span><span class="p">,</span> <span class="n">batch</span><span class="o">=</span><span class="n">batch</span><span class="p">)</span> |
| |
| <span class="n">_</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span> <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_processor</span><span class="o">.</span><span class="n">fit_batch</span><span class="p">(</span><span class="n">estimator_ref</span><span class="p">,</span> |
| <span class="n">batch</span><span class="p">,</span> <span class="n">batch_axis</span><span class="p">)</span> |
| <span class="c1"># batch end</span> |
| |
| <span class="n">batch_end_result</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">batch_end</span><span class="p">:</span> |
| <span class="n">batch_end_result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">handler</span><span class="o">.</span><span class="n">batch_end</span><span class="p">(</span><span class="n">estimator_ref</span><span class="p">,</span> <span class="n">batch</span><span class="o">=</span><span class="n">batch</span><span class="p">,</span> |
| <span class="n">pred</span><span class="o">=</span><span class="n">pred</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">loss</span><span class="p">))</span> |
| <span class="c1"># if any handler signaled to stop</span> |
| <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">batch_end_result</span><span class="p">):</span> |
| <span class="k">break</span> |
| |
| <span class="c1"># epoch end</span> |
| <span class="n">epoch_end_result</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">epoch_end</span><span class="p">:</span> |
| <span class="n">epoch_end_result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">handler</span><span class="o">.</span><span class="n">epoch_end</span><span class="p">(</span><span class="n">estimator_ref</span><span class="p">))</span> |
| <span class="c1"># if any handler signaled to stop</span> |
| <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">epoch_end_result</span><span class="p">):</span> |
| <span class="k">break</span> |
| |
| <span class="c1"># train end</span> |
| <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">train_end</span><span class="p">:</span> |
| <span class="n">handler</span><span class="o">.</span><span class="n">train_end</span><span class="p">(</span><span class="n">estimator_ref</span><span class="p">)</span></div> |
| |
| <span class="k">def</span> <span class="nf">_prepare_default_handlers</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val_data</span><span class="p">,</span> <span class="n">event_handlers</span><span class="p">):</span> |
| <span class="n">event_handlers</span> <span class="o">=</span> <span class="n">_check_event_handlers</span><span class="p">(</span><span class="n">event_handlers</span><span class="p">)</span> |
| <span class="n">added_default_handlers</span> <span class="o">=</span> <span class="p">[]</span> |
| |
| <span class="c1"># no need to add to default handler check as StoppingHandler does not use metrics</span> |
| <span class="n">added_default_handlers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">StoppingHandler</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_epoch</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_batch</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">GradientUpdateHandler</span><span class="p">)</span> <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">event_handlers</span><span class="p">):</span> |
| <span class="n">added_default_handlers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">GradientUpdateHandler</span><span class="p">())</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">MetricHandler</span><span class="p">)</span> <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">event_handlers</span><span class="p">):</span> |
| <span class="n">added_default_handlers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">MetricHandler</span><span class="p">(</span><span class="n">metrics</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">train_metrics</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">ValidationHandler</span><span class="p">)</span> <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">event_handlers</span><span class="p">):</span> |
| <span class="c1"># no validation handler</span> |
| <span class="k">if</span> <span class="n">val_data</span><span class="p">:</span> |
| <span class="c1"># add default validation handler if validation data found</span> |
| <span class="n">added_default_handlers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ValidationHandler</span><span class="p">(</span><span class="n">val_data</span><span class="o">=</span><span class="n">val_data</span><span class="p">,</span> |
| <span class="n">eval_fn</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluate</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">LoggingHandler</span><span class="p">)</span> <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">event_handlers</span><span class="p">):</span> |
| <span class="n">added_default_handlers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">LoggingHandler</span><span class="p">(</span><span class="n">metrics</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">train_metrics</span><span class="p">))</span> |
| |
| <span class="c1"># if there is a mix of user defined event handlers and default event handlers</span> |
| <span class="c1"># they should have the same set of metrics</span> |
| <span class="n">mixing_handlers</span> <span class="o">=</span> <span class="n">event_handlers</span> <span class="ow">and</span> <span class="n">added_default_handlers</span> |
| |
| <span class="n">event_handlers</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">added_default_handlers</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">mixing_handlers</span><span class="p">:</span> |
| <span class="c1"># check if all handlers have the same set of references to metrics</span> |
| <span class="n">known_metrics</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_metrics</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_metrics</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">event_handlers</span><span class="p">:</span> |
| <span class="n">_check_handler_metric_ref</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">known_metrics</span><span class="p">)</span> |
| |
| <span class="n">event_handlers</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">handler</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="s1">'priority'</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">event_handlers</span> |
| |
| <span class="k">def</span> <span class="nf">_prepare_default_validation_handlers</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">event_handlers</span><span class="p">):</span> |
| <span class="n">event_handlers</span> <span class="o">=</span> <span class="n">_check_event_handlers</span><span class="p">(</span><span class="n">event_handlers</span><span class="p">)</span> |
| <span class="n">added_default_handlers</span> <span class="o">=</span> <span class="p">[]</span> |
| |
| <span class="c1"># add default logging handler and metric handler for validation</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">MetricHandler</span><span class="p">)</span> <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">event_handlers</span><span class="p">):</span> |
| <span class="n">added_default_handlers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">MetricHandler</span><span class="p">(</span><span class="n">metrics</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">val_metrics</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">LoggingHandler</span><span class="p">)</span> <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">event_handlers</span><span class="p">):</span> |
| <span class="n">added_default_handlers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">LoggingHandler</span><span class="p">(</span><span class="n">metrics</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">val_metrics</span><span class="p">))</span> |
| |
| <span class="n">mixing_handlers</span> <span class="o">=</span> <span class="n">event_handlers</span> <span class="ow">and</span> <span class="n">added_default_handlers</span> |
| <span class="n">event_handlers</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">added_default_handlers</span><span class="p">)</span> |
| |
| <span class="c1"># check if all handlers refer to well-defined validation metrics</span> |
| <span class="k">if</span> <span class="n">mixing_handlers</span><span class="p">:</span> |
| <span class="n">known_metrics</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">val_metrics</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">event_handlers</span><span class="p">:</span> |
| <span class="n">_check_handler_metric_ref</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">known_metrics</span><span class="p">)</span> |
| |
| <span class="n">event_handlers</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">handler</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="s1">'priority'</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">event_handlers</span> |
| |
| <span class="k">def</span> <span class="nf">_categorize_handlers</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">event_handlers</span><span class="p">):</span> |
| <span class="sd">"""</span> |
| <span class="sd"> categorize handlers into 6 event lists to avoid calling empty methods</span> |
| <span class="sd"> for example, only event handlers with train_begin method</span> |
| <span class="sd"> implemented will be called at train begin</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">train_begin</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">epoch_begin</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">batch_begin</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">batch_end</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">epoch_end</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">train_end</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">handler</span> <span class="ow">in</span> <span class="n">event_handlers</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">TrainBegin</span><span class="p">):</span> |
| <span class="n">train_begin</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">handler</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">EpochBegin</span><span class="p">):</span> |
| <span class="n">epoch_begin</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">handler</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">BatchBegin</span><span class="p">):</span> |
| <span class="n">batch_begin</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">handler</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">BatchEnd</span><span class="p">):</span> |
| <span class="n">batch_end</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">handler</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">EpochEnd</span><span class="p">):</span> |
| <span class="n">epoch_end</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">handler</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">handler</span><span class="p">,</span> <span class="n">TrainEnd</span><span class="p">):</span> |
| <span class="n">train_end</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">handler</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">train_begin</span><span class="p">,</span> <span class="n">epoch_begin</span><span class="p">,</span> <span class="n">batch_begin</span><span class="p">,</span> <span class="n">batch_end</span><span class="p">,</span> <span class="n">epoch_end</span><span class="p">,</span> <span class="n">train_end</span></div> |
| </pre></div> |
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