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<h1>Source code for mxnet.callback</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="sd">"""Callback functions that can be used to track various status during epoch."""</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">from</span> <span class="nn">.model</span> <span class="kn">import</span> <span class="n">save_checkpoint</span>
<div class="viewcode-block" id="module_checkpoint"><a class="viewcode-back" href="../../api/python/callback/callback.html#mxnet.callback.module_checkpoint">[docs]</a><span class="k">def</span> <span class="nf">module_checkpoint</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">period</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_optimizer_states</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
<span class="sd">"""Callback to checkpoint Module to prefix every epoch.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> mod : subclass of BaseModule</span>
<span class="sd"> The module to checkpoint.</span>
<span class="sd"> prefix : str</span>
<span class="sd"> The file prefix for this checkpoint.</span>
<span class="sd"> period : int</span>
<span class="sd"> How many epochs to wait before checkpointing. Defaults to 1.</span>
<span class="sd"> save_optimizer_states : bool</span>
<span class="sd"> Indicates whether or not to save optimizer states for continued training.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> callback : function</span>
<span class="sd"> The callback function that can be passed as iter_end_callback to fit.</span>
<span class="sd"> """</span>
<span class="n">period</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">period</span><span class="p">))</span>
<span class="c1"># pylint: disable=unused-argument</span>
<span class="k">def</span> <span class="nf">_callback</span><span class="p">(</span><span class="n">iter_no</span><span class="p">,</span> <span class="n">sym</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">arg</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">aux</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""The checkpoint function."""</span>
<span class="k">if</span> <span class="p">(</span><span class="n">iter_no</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">period</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">mod</span><span class="o">.</span><span class="n">save_checkpoint</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">iter_no</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">save_optimizer_states</span><span class="p">)</span>
<span class="k">return</span> <span class="n">_callback</span></div>
<div class="viewcode-block" id="do_checkpoint"><a class="viewcode-back" href="../../api/python/callback/callback.html#mxnet.callback.do_checkpoint">[docs]</a><span class="k">def</span> <span class="nf">do_checkpoint</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">period</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="sd">"""A callback that saves a model checkpoint every few epochs.</span>
<span class="sd"> Each checkpoint is made up of a couple of binary files: a model description file and a</span>
<span class="sd"> parameters (weights and biases) file. The model description file is named</span>
<span class="sd"> `prefix`--symbol.json and the parameters file is named `prefix`-`epoch_number`.params</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> prefix : str</span>
<span class="sd"> Prefix for the checkpoint filenames.</span>
<span class="sd"> period : int, optional</span>
<span class="sd"> Interval (number of epochs) between checkpoints. Default `period` is 1.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> callback : function</span>
<span class="sd"> A callback function that can be passed as `epoch_end_callback` to fit.</span>
<span class="sd"> Example</span>
<span class="sd"> -------</span>
<span class="sd"> >>> module.fit(iterator, num_epoch=n_epoch,</span>
<span class="sd"> ... epoch_end_callback = mx.callback.do_checkpoint("mymodel", 1))</span>
<span class="sd"> Start training with [cpu(0)]</span>
<span class="sd"> Epoch[0] Resetting Data Iterator</span>
<span class="sd"> Epoch[0] Time cost=0.100</span>
<span class="sd"> Saved checkpoint to "mymodel-0001.params"</span>
<span class="sd"> Epoch[1] Resetting Data Iterator</span>
<span class="sd"> Epoch[1] Time cost=0.060</span>
<span class="sd"> Saved checkpoint to "mymodel-0002.params"</span>
<span class="sd"> """</span>
<span class="n">period</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">period</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">_callback</span><span class="p">(</span><span class="n">iter_no</span><span class="p">,</span> <span class="n">sym</span><span class="p">,</span> <span class="n">arg</span><span class="p">,</span> <span class="n">aux</span><span class="p">):</span>
<span class="sd">"""The checkpoint function."""</span>
<span class="k">if</span> <span class="p">(</span><span class="n">iter_no</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">period</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">save_checkpoint</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">iter_no</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">sym</span><span class="p">,</span> <span class="n">arg</span><span class="p">,</span> <span class="n">aux</span><span class="p">)</span>
<span class="k">return</span> <span class="n">_callback</span></div>
<div class="viewcode-block" id="log_train_metric"><a class="viewcode-back" href="../../api/python/callback/callback.html#mxnet.callback.log_train_metric">[docs]</a><span class="k">def</span> <span class="nf">log_train_metric</span><span class="p">(</span><span class="n">period</span><span class="p">,</span> <span class="n">auto_reset</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
<span class="sd">"""Callback to log the training evaluation result every period.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> period : int</span>
<span class="sd"> The number of batch to log the training evaluation metric.</span>
<span class="sd"> auto_reset : bool</span>
<span class="sd"> Reset the metric after each log.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> callback : function</span>
<span class="sd"> The callback function that can be passed as iter_epoch_callback to fit.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">_callback</span><span class="p">(</span><span class="n">param</span><span class="p">):</span>
<span class="sd">"""The checkpoint function."""</span>
<span class="k">if</span> <span class="n">param</span><span class="o">.</span><span class="n">nbatch</span> <span class="o">%</span> <span class="n">period</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">param</span><span class="o">.</span><span class="n">eval_metric</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">name_value</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">eval_metric</span><span class="o">.</span><span class="n">get_name_value</span><span class="p">()</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">name_value</span><span class="p">:</span>
<span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'Iter[</span><span class="si">%d</span><span class="s1">] Batch[</span><span class="si">%d</span><span class="s1">] Train-</span><span class="si">%s</span><span class="s1">=</span><span class="si">%f</span><span class="s1">'</span><span class="p">,</span>
<span class="n">param</span><span class="o">.</span><span class="n">epoch</span><span class="p">,</span> <span class="n">param</span><span class="o">.</span><span class="n">nbatch</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
<span class="k">if</span> <span class="n">auto_reset</span><span class="p">:</span>
<span class="n">param</span><span class="o">.</span><span class="n">eval_metric</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="k">return</span> <span class="n">_callback</span></div>
<div class="viewcode-block" id="Speedometer"><a class="viewcode-back" href="../../api/python/callback/callback.html#mxnet.callback.Speedometer">[docs]</a><span class="k">class</span> <span class="nc">Speedometer</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">"""Logs training speed and evaluation metrics periodically.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> batch_size: int</span>
<span class="sd"> Batch size of data.</span>
<span class="sd"> frequent: int</span>
<span class="sd"> Specifies how frequently training speed and evaluation metrics</span>
<span class="sd"> must be logged. Default behavior is to log once every 50 batches.</span>
<span class="sd"> auto_reset : bool</span>
<span class="sd"> Reset the evaluation metrics after each log.</span>
<span class="sd"> Example</span>
<span class="sd"> -------</span>
<span class="sd"> >>> # Print training speed and evaluation metrics every ten batches. Batch size is one.</span>
<span class="sd"> >>> module.fit(iterator, num_epoch=n_epoch,</span>
<span class="sd"> ... batch_end_callback=mx.callback.Speedometer(1, 10))</span>
<span class="sd"> Epoch[0] Batch [10] Speed: 1910.41 samples/sec Train-accuracy=0.200000</span>
<span class="sd"> Epoch[0] Batch [20] Speed: 1764.83 samples/sec Train-accuracy=0.400000</span>
<span class="sd"> Epoch[0] Batch [30] Speed: 1740.59 samples/sec Train-accuracy=0.500000</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">batch_size</span><span class="p">,</span> <span class="n">frequent</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">auto_reset</span><span class="o">=</span><span class="bp">True</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
<span class="bp">self</span><span class="o">.</span><span class="n">frequent</span> <span class="o">=</span> <span class="n">frequent</span>
<span class="bp">self</span><span class="o">.</span><span class="n">init</span> <span class="o">=</span> <span class="bp">False</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tic</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">last_count</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">auto_reset</span> <span class="o">=</span> <span class="n">auto_reset</span>
<div class="viewcode-block" id="Speedometer.__call__"><a class="viewcode-back" href="../../api/python/callback/callback.html#mxnet.callback.Speedometer.__call__">[docs]</a> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">param</span><span class="p">):</span>
<span class="sd">"""Callback to Show speed."""</span>
<span class="n">count</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">nbatch</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_count</span> <span class="o">></span> <span class="n">count</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">init</span> <span class="o">=</span> <span class="bp">False</span>
<span class="bp">self</span><span class="o">.</span><span class="n">last_count</span> <span class="o">=</span> <span class="n">count</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">init</span><span class="p">:</span>
<span class="k">if</span> <span class="n">count</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequent</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">speed</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequent</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">/</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">tic</span><span class="p">)</span>
<span class="k">if</span> <span class="n">param</span><span class="o">.</span><span class="n">eval_metric</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
<span class="n">name_value</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">eval_metric</span><span class="o">.</span><span class="n">get_name_value</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">auto_reset</span><span class="p">:</span>
<span class="n">param</span><span class="o">.</span><span class="n">eval_metric</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
<span class="n">msg</span> <span class="o">=</span> <span class="s1">'Epoch[</span><span class="si">%d</span><span class="s1">] Batch [</span><span class="si">%d</span><span class="s1">]</span><span class="se">\t</span><span class="s1">Speed: </span><span class="si">%.2f</span><span class="s1"> samples/sec'</span>
<span class="n">msg</span> <span class="o">+=</span> <span class="s1">'</span><span class="se">\t</span><span class="si">%s</span><span class="s1">=</span><span class="si">%f</span><span class="s1">'</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">name_value</span><span class="p">)</span>
<span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">msg</span><span class="p">,</span> <span class="n">param</span><span class="o">.</span><span class="n">epoch</span><span class="p">,</span> <span class="n">count</span><span class="p">,</span> <span class="n">speed</span><span class="p">,</span> <span class="o">*</span><span class="nb">sum</span><span class="p">(</span><span class="n">name_value</span><span class="p">,</span> <span class="p">()))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">"Iter[</span><span class="si">%d</span><span class="s2">] Batch [</span><span class="si">%d</span><span class="s2">]</span><span class="se">\t</span><span class="s2">Speed: </span><span class="si">%.2f</span><span class="s2"> samples/sec"</span><span class="p">,</span>
<span class="n">param</span><span class="o">.</span><span class="n">epoch</span><span class="p">,</span> <span class="n">count</span><span class="p">,</span> <span class="n">speed</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</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">init</span> <span class="o">=</span> <span class="bp">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span></div></div>
<div class="viewcode-block" id="ProgressBar"><a class="viewcode-back" href="../../api/python/callback/callback.html#mxnet.callback.ProgressBar">[docs]</a><span class="k">class</span> <span class="nc">ProgressBar</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">"""Displays a progress bar, indicating the percentage of batches processed within each epoch.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> total: int</span>
<span class="sd"> total number of batches per epoch</span>
<span class="sd"> length: int</span>
<span class="sd"> number of chars to define maximum length of progress bar</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> progress_bar = mx.callback.ProgressBar(total=2)</span>
<span class="sd"> >>> mod.fit(data, num_epoch=5, batch_end_callback=progress_bar)</span>
<span class="sd"> [========--------] 50.0%</span>
<span class="sd"> [================] 100.0%</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">total</span><span class="p">,</span> <span class="n">length</span><span class="o">=</span><span class="mi">80</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bar_len</span> <span class="o">=</span> <span class="n">length</span>
<span class="bp">self</span><span class="o">.</span><span class="n">total</span> <span class="o">=</span> <span class="n">total</span>
<div class="viewcode-block" id="ProgressBar.__call__"><a class="viewcode-back" href="../../api/python/callback/callback.html#mxnet.callback.ProgressBar.__call__">[docs]</a> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">param</span><span class="p">):</span>
<span class="sd">"""Callback to Show progress bar."""</span>
<span class="n">count</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">nbatch</span>
<span class="n">filled_len</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bar_len</span> <span class="o">*</span> <span class="n">count</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">total</span><span class="p">)))</span>
<span class="n">percents</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="mf">100.0</span> <span class="o">*</span> <span class="n">count</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">total</span><span class="p">))</span>
<span class="n">prog_bar</span> <span class="o">=</span> <span class="s1">'='</span> <span class="o">*</span> <span class="n">filled_len</span> <span class="o">+</span> <span class="s1">'-'</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bar_len</span> <span class="o">-</span> <span class="n">filled_len</span><span class="p">)</span>
<span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'[</span><span class="si">%s</span><span class="s1">] </span><span class="si">%s%s</span><span class="se">\r</span><span class="s1">'</span><span class="p">,</span> <span class="n">prog_bar</span><span class="p">,</span> <span class="n">percents</span><span class="p">,</span> <span class="s1">'%'</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="LogValidationMetricsCallback"><a class="viewcode-back" href="../../api/python/callback/callback.html#mxnet.callback.LogValidationMetricsCallback">[docs]</a><span class="k">class</span> <span class="nc">LogValidationMetricsCallback</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">"""Just logs the eval metrics at the end of an epoch."""</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">param</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">param</span><span class="o">.</span><span class="n">eval_metric</span><span class="p">:</span>
<span class="k">return</span>
<span class="n">name_value</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">eval_metric</span><span class="o">.</span><span class="n">get_name_value</span><span class="p">()</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">name_value</span><span class="p">:</span>
<span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'Epoch[</span><span class="si">%d</span><span class="s1">] Validation-</span><span class="si">%s</span><span class="s1">=</span><span class="si">%f</span><span class="s1">'</span><span class="p">,</span> <span class="n">param</span><span class="o">.</span><span class="n">epoch</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span></div>
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