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<h1>Source code for mxnet.module.module</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"># pylint: disable=too-many-instance-attributes, too-many-arguments, protected-access, too-many-branches</span>
<span class="c1"># pylint: disable=too-many-public-methods</span>
<span class="sd">"""A `Module` implement the `BaseModule` API by wrapping a `Symbol` and one or</span>
<span class="sd">more `Executor` for data parallelization.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="k">import</span> <span class="n">context</span> <span class="k">as</span> <span class="n">ctx</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="k">import</span> <span class="n">optimizer</span> <span class="k">as</span> <span class="n">opt</span>
<span class="kn">from</span> <span class="nn">.executor_group</span> <span class="k">import</span> <span class="n">DataParallelExecutorGroup</span>
<span class="kn">from</span> <span class="nn">..model</span> <span class="k">import</span> <span class="n">_create_kvstore</span><span class="p">,</span> <span class="n">_initialize_kvstore</span><span class="p">,</span> <span class="n">_update_params</span><span class="p">,</span> <span class="n">_update_params_on_kvstore</span>
<span class="kn">from</span> <span class="nn">..model</span> <span class="k">import</span> <span class="n">load_checkpoint</span>
<span class="kn">from</span> <span class="nn">..initializer</span> <span class="k">import</span> <span class="n">Uniform</span><span class="p">,</span> <span class="n">InitDesc</span>
<span class="kn">from</span> <span class="nn">..io</span> <span class="k">import</span> <span class="n">DataDesc</span>
<span class="kn">from</span> <span class="nn">..ndarray</span> <span class="k">import</span> <span class="n">zeros</span>
<span class="kn">from</span> <span class="nn">.base_module</span> <span class="k">import</span> <span class="n">BaseModule</span><span class="p">,</span> <span class="n">_check_input_names</span><span class="p">,</span> <span class="n">_parse_data_desc</span>
<div class="viewcode-block" id="Module"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module">[docs]</a><span class="k">class</span> <span class="nc">Module</span><span class="p">(</span><span class="n">BaseModule</span><span class="p">):</span>
<span class="sd">"""Module is a basic module that wrap a `Symbol`. It is functionally the same</span>
<span class="sd"> as the `FeedForward` model, except under the module API.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> symbol : Symbol</span>
<span class="sd"> data_names : list of str</span>
<span class="sd"> Defaults to `('data')` for a typical model used in image classification.</span>
<span class="sd"> label_names : list of str</span>
<span class="sd"> Defaults to `('softmax_label')` for a typical model used in image</span>
<span class="sd"> classification.</span>
<span class="sd"> logger : Logger</span>
<span class="sd"> Defaults to `logging`.</span>
<span class="sd"> context : Context or list of Context</span>
<span class="sd"> Defaults to ``mx.cpu()``.</span>
<span class="sd"> work_load_list : list of number</span>
<span class="sd"> Default ``None``, indicating uniform workload.</span>
<span class="sd"> fixed_param_names: list of str</span>
<span class="sd"> Default ``None``, indicating no network parameters are fixed.</span>
<span class="sd"> state_names : list of str</span>
<span class="sd"> states are similar to data and label, but not provided by data iterator.</span>
<span class="sd"> Instead they are initialized to 0 and can be set by `set_states()`.</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">data_names</span><span class="o">=</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,),</span> <span class="n">label_names</span><span class="o">=</span><span class="p">(</span><span class="s1">'softmax_label'</span><span class="p">,),</span>
<span class="n">logger</span><span class="o">=</span><span class="n">logging</span><span class="p">,</span> <span class="n">context</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">work_load_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">fixed_param_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">state_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Module</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">logger</span><span class="o">=</span><span class="n">logger</span><span class="p">)</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">ctx</span><span class="o">.</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="bp">self</span><span class="o">.</span><span class="n">_context</span> <span class="o">=</span> <span class="n">context</span>
<span class="k">if</span> <span class="n">work_load_list</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">work_load_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_context</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">work_load_list</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_context</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_work_load_list</span> <span class="o">=</span> <span class="n">work_load_list</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span> <span class="o">=</span> <span class="n">symbol</span>
<span class="n">data_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">data_names</span><span class="p">)</span> <span class="k">if</span> <span class="n">data_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
<span class="n">label_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">label_names</span><span class="p">)</span> <span class="k">if</span> <span class="n">label_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
<span class="n">state_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">state_names</span><span class="p">)</span> <span class="k">if</span> <span class="n">state_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
<span class="n">fixed_param_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">fixed_param_names</span><span class="p">)</span> <span class="k">if</span> <span class="n">fixed_param_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
<span class="n">_check_input_names</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">data_names</span><span class="p">,</span> <span class="s2">"data"</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">_check_input_names</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">label_names</span><span class="p">,</span> <span class="s2">"label"</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
<span class="n">_check_input_names</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">state_names</span><span class="p">,</span> <span class="s2">"state"</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">_check_input_names</span><span class="p">(</span><span class="n">symbol</span><span class="p">,</span> <span class="n">fixed_param_names</span><span class="p">,</span> <span class="s2">"fixed_param"</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">arg_names</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">list_arguments</span><span class="p">()</span>
<span class="n">input_names</span> <span class="o">=</span> <span class="n">data_names</span> <span class="o">+</span> <span class="n">label_names</span> <span class="o">+</span> <span class="n">state_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_param_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">arg_names</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">input_names</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_fixed_param_names</span> <span class="o">=</span> <span class="n">fixed_param_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_aux_names</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">list_auxiliary_states</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_data_names</span> <span class="o">=</span> <span class="n">data_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_label_names</span> <span class="o">=</span> <span class="n">label_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_state_names</span> <span class="o">=</span> <span class="n">state_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_output_names</span> <span class="o">=</span> <span class="n">symbol</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_arg_params</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_aux_params</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_params_dirty</span> <span class="o">=</span> <span class="kc">False</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_updater</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_preload_opt_states</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span> <span class="o">=</span> <span class="kc">None</span>
<span class="nd">@staticmethod</span>
<div class="viewcode-block" id="Module.load"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.load">[docs]</a> <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">load_optimizer_states</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Creates a model from previously saved checkpoint.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> prefix : str</span>
<span class="sd"> path prefix of saved model files. You should have</span>
<span class="sd"> "prefix-symbol.json", "prefix-xxxx.params", and</span>
<span class="sd"> optionally "prefix-xxxx.states", where xxxx is the</span>
<span class="sd"> epoch number.</span>
<span class="sd"> epoch : int</span>
<span class="sd"> epoch to load.</span>
<span class="sd"> load_optimizer_states : bool</span>
<span class="sd"> whether to load optimizer states. Checkpoint needs</span>
<span class="sd"> to have been made with save_optimizer_states=True.</span>
<span class="sd"> data_names : list of str</span>
<span class="sd"> Default is `('data')` for a typical model used in image classification.</span>
<span class="sd"> label_names : list of str</span>
<span class="sd"> Default is `('softmax_label')` for a typical model used in image</span>
<span class="sd"> classification.</span>
<span class="sd"> logger : Logger</span>
<span class="sd"> Default is `logging`.</span>
<span class="sd"> context : Context or list of Context</span>
<span class="sd"> Default is ``cpu()``.</span>
<span class="sd"> work_load_list : list of number</span>
<span class="sd"> Default ``None``, indicating uniform workload.</span>
<span class="sd"> fixed_param_names: list of str</span>
<span class="sd"> Default ``None``, indicating no network parameters are fixed.</span>
<span class="sd"> """</span>
<span class="n">sym</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">auxs</span> <span class="o">=</span> <span class="n">load_checkpoint</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
<span class="n">mod</span> <span class="o">=</span> <span class="n">Module</span><span class="p">(</span><span class="n">symbol</span><span class="o">=</span><span class="n">sym</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">mod</span><span class="o">.</span><span class="n">_arg_params</span> <span class="o">=</span> <span class="n">args</span>
<span class="n">mod</span><span class="o">.</span><span class="n">_aux_params</span> <span class="o">=</span> <span class="n">auxs</span>
<span class="n">mod</span><span class="o">.</span><span class="n">params_initialized</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">if</span> <span class="n">load_optimizer_states</span><span class="p">:</span>
<span class="n">mod</span><span class="o">.</span><span class="n">_preload_opt_states</span> <span class="o">=</span> <span class="s1">'</span><span class="si">%s</span><span class="s1">-</span><span class="si">%04d</span><span class="s1">.states'</span><span class="o">%</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
<span class="k">return</span> <span class="n">mod</span></div>
<div class="viewcode-block" id="Module.save_checkpoint"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.save_checkpoint">[docs]</a> <span class="k">def</span> <span class="nf">save_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">save_optimizer_states</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">"""Saves current progress to checkpoint.</span>
<span class="sd"> Use `mx.callback.module_checkpoint` as `epoch_end_callback` to save during training.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> prefix : str</span>
<span class="sd"> The file prefix to checkpoint to.</span>
<span class="sd"> epoch : int</span>
<span class="sd"> The current epoch number.</span>
<span class="sd"> save_optimizer_states : bool</span>
<span class="sd"> Whether to save optimizer states to continue training.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'</span><span class="si">%s</span><span class="s1">-symbol.json'</span><span class="o">%</span><span class="n">prefix</span><span class="p">)</span>
<span class="n">param_name</span> <span class="o">=</span> <span class="s1">'</span><span class="si">%s</span><span class="s1">-</span><span class="si">%04d</span><span class="s1">.params'</span> <span class="o">%</span> <span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">save_params</span><span class="p">(</span><span class="n">param_name</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">'Saved checkpoint to </span><span class="se">\"</span><span class="si">%s</span><span class="se">\"</span><span class="s1">'</span><span class="p">,</span> <span class="n">param_name</span><span class="p">)</span>
<span class="k">if</span> <span class="n">save_optimizer_states</span><span class="p">:</span>
<span class="n">state_name</span> <span class="o">=</span> <span class="s1">'</span><span class="si">%s</span><span class="s1">-</span><span class="si">%04d</span><span class="s1">.states'</span> <span class="o">%</span> <span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">epoch</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">save_optimizer_states</span><span class="p">(</span><span class="n">state_name</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">'Saved optimizer state to </span><span class="se">\"</span><span class="si">%s</span><span class="se">\"</span><span class="s1">'</span><span class="p">,</span> <span class="n">state_name</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_reset_bind</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Internal function to reset binded state."""</span>
<span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="o">=</span> <span class="kc">False</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span> <span class="o">=</span> <span class="kc">None</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">data_names</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""A list of names for data required by this module."""</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_names</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">label_names</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""A list of names for labels required by this module."""</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label_names</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">output_names</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""A list of names for the outputs of this module."""</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_output_names</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">data_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Gets data shapes.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> A list of `(name, shape)` pairs.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">label_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Gets label shapes.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> A list of `(name, shape)` pairs.</span>
<span class="sd"> The return value could be ``None`` if</span>
<span class="sd"> the module does not need labels, or if the module is not bound for</span>
<span class="sd"> training (in this case, label information is not available).</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">output_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Gets output shapes.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> A list of `(name, shape)` pairs.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">get_output_shapes</span><span class="p">()</span>
<div class="viewcode-block" id="Module.get_params"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.get_params">[docs]</a> <span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Gets current parameters.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> `(arg_params, aux_params)`</span>
<span class="sd"> A pair of dictionaries each mapping parameter names to NDArray values.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params_dirty</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_sync_params_from_devices</span><span class="p">()</span>
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_arg_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_params</span><span class="p">)</span></div>
<div class="viewcode-block" id="Module.init_params"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.init_params">[docs]</a> <span class="k">def</span> <span class="nf">init_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">initializer</span><span class="o">=</span><span class="n">Uniform</span><span class="p">(</span><span class="mf">0.01</span><span class="p">),</span> <span class="n">arg_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">allow_missing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_init</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">allow_extra</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">"""Initializes the parameters and auxiliary states.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> initializer : Initializer</span>
<span class="sd"> Called to initialize parameters if needed.</span>
<span class="sd"> arg_params : dict</span>
<span class="sd"> If not ``None``, should be a dictionary of existing arg_params. Initialization</span>
<span class="sd"> will be copied from that.</span>
<span class="sd"> aux_params : dict</span>
<span class="sd"> If not ``None``, should be a dictionary of existing aux_params. Initialization</span>
<span class="sd"> will be copied from that.</span>
<span class="sd"> allow_missing : bool</span>
<span class="sd"> If ``True``, params could contain missing values, and the initializer will be</span>
<span class="sd"> called to fill those missing params.</span>
<span class="sd"> force_init : bool</span>
<span class="sd"> If ``True``, will force re-initialize even if already initialized.</span>
<span class="sd"> allow_extra : boolean, optional</span>
<span class="sd"> Whether allow extra parameters that are not needed by symbol.</span>
<span class="sd"> If this is True, no error will be thrown when arg_params or aux_params</span>
<span class="sd"> contain extra parameters that is not needed by the executor.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">force_init</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">"Parameters already initialized and force_init=False. "</span>
<span class="s2">"init_params call ignored."</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="k">return</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span><span class="p">,</span> <span class="s1">'call bind before initializing the parameters'</span>
<span class="k">def</span> <span class="nf">_impl</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">cache</span><span class="p">):</span>
<span class="sd">"""Internal helper for parameter initialization"""</span>
<span class="k">if</span> <span class="n">cache</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">cache</span><span class="p">:</span>
<span class="n">cache_arr</span> <span class="o">=</span> <span class="n">cache</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
<span class="c1"># just in case the cached array is just the target itself</span>
<span class="k">if</span> <span class="n">cache_arr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">arr</span><span class="p">:</span>
<span class="n">cache_arr</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">allow_missing</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"</span><span class="si">%s</span><span class="s2"> is not presented"</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span>
<span class="k">if</span> <span class="n">initializer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">initializer</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">arr</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">initializer</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">arr</span><span class="p">)</span>
<span class="n">attrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="o">.</span><span class="n">attr_dict</span><span class="p">()</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">arr</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_arg_params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">desc</span> <span class="o">=</span> <span class="n">InitDesc</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="kc">None</span><span class="p">))</span>
<span class="n">_impl</span><span class="p">(</span><span class="n">desc</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">)</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">arr</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">desc</span> <span class="o">=</span> <span class="n">InitDesc</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">attrs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="kc">None</span><span class="p">))</span>
<span class="n">_impl</span><span class="p">(</span><span class="n">desc</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span> <span class="o">=</span> <span class="kc">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_params_dirty</span> <span class="o">=</span> <span class="kc">False</span>
<span class="c1"># copy the initialized parameters to devices</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_arg_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_params</span><span class="p">,</span>
<span class="n">allow_extra</span><span class="o">=</span><span class="n">allow_extra</span><span class="p">)</span></div>
<div class="viewcode-block" id="Module.set_params"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.set_params">[docs]</a> <span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">,</span> <span class="n">allow_missing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_init</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">allow_extra</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">"""Assigns parameter and aux state values.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> arg_params : dict</span>
<span class="sd"> Dictionary of name to `NDArray`.</span>
<span class="sd"> aux_params : dict</span>
<span class="sd"> Dictionary of name to `NDArray`.</span>
<span class="sd"> allow_missing : bool</span>
<span class="sd"> If ``True``, params could contain missing values, and the initializer will be</span>
<span class="sd"> called to fill those missing params.</span>
<span class="sd"> force_init : bool</span>
<span class="sd"> If ``True``, will force re-initialize even if already initialized.</span>
<span class="sd"> allow_extra : boolean, optional</span>
<span class="sd"> Whether allow extra parameters that are not needed by symbol.</span>
<span class="sd"> If this is True, no error will be thrown when arg_params or aux_params</span>
<span class="sd"> contain extra parameters that is not needed by the executor.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> >>> # An example of setting module parameters.</span>
<span class="sd"> >>> sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, n_epoch_load)</span>
<span class="sd"> >>> mod.set_params(arg_params=arg_params, aux_params=aux_params)</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">allow_missing</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">init_params</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">arg_params</span><span class="o">=</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="n">aux_params</span><span class="p">,</span>
<span class="n">allow_missing</span><span class="o">=</span><span class="n">allow_missing</span><span class="p">,</span> <span class="n">force_init</span><span class="o">=</span><span class="n">force_init</span><span class="p">,</span>
<span class="n">allow_extra</span><span class="o">=</span><span class="n">allow_extra</span><span class="p">)</span>
<span class="k">return</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">force_init</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">"Parameters already initialized and force_init=False. "</span>
<span class="s2">"set_params call ignored."</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">,</span> <span class="n">allow_extra</span><span class="o">=</span><span class="n">allow_extra</span><span class="p">)</span>
<span class="c1"># because we didn't update self._arg_params, they are dirty now.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_params_dirty</span> <span class="o">=</span> <span class="kc">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span> <span class="o">=</span> <span class="kc">True</span></div>
<div class="viewcode-block" id="Module.bind"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.bind">[docs]</a> <span class="k">def</span> <span class="nf">bind</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_shapes</span><span class="p">,</span> <span class="n">label_shapes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">for_training</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">inputs_need_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">force_rebind</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">shared_module</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">grad_req</span><span class="o">=</span><span class="s1">'write'</span><span class="p">):</span>
<span class="sd">"""Binds the symbols to construct executors. This is necessary before one</span>
<span class="sd"> can perform computation with the module.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data_shapes : list of (str, tuple)</span>
<span class="sd"> Typically is ``data_iter.provide_data``.</span>
<span class="sd"> label_shapes : list of (str, tuple)</span>
<span class="sd"> Typically is ``data_iter.provide_label``.</span>
<span class="sd"> for_training : bool</span>
<span class="sd"> Default is ``True``. Whether the executors should be bound for training.</span>
<span class="sd"> inputs_need_grad : bool</span>
<span class="sd"> Default is ``False``. Whether the gradients to the input data need to be computed.</span>
<span class="sd"> Typically this is not needed. But this might be needed when implementing composition</span>
<span class="sd"> of modules.</span>
<span class="sd"> force_rebind : bool</span>
<span class="sd"> Default is ``False``. This function does nothing if the executors are already</span>
<span class="sd"> bound. But with this ``True``, the executors will be forced to rebind.</span>
<span class="sd"> shared_module : Module</span>
<span class="sd"> Default is ``None``. This is used in bucketing. When not ``None``, the shared module</span>
<span class="sd"> essentially corresponds to a different bucket -- a module with different symbol</span>
<span class="sd"> but with the same sets of parameters (e.g. unrolled RNNs with different lengths).</span>
<span class="sd"> """</span>
<span class="c1"># force rebinding is typically used when one want to switch from</span>
<span class="c1"># training to prediction phase.</span>
<span class="k">if</span> <span class="n">force_rebind</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_reset_bind</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</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">warning</span><span class="p">(</span><span class="s1">'Already bound, ignoring bind()'</span><span class="p">)</span>
<span class="k">return</span>
<span class="bp">self</span><span class="o">.</span><span class="n">for_training</span> <span class="o">=</span> <span class="n">for_training</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inputs_need_grad</span> <span class="o">=</span> <span class="n">inputs_need_grad</span>
<span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="o">=</span> <span class="kc">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_grad_req</span> <span class="o">=</span> <span class="n">grad_req</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">for_training</span><span class="p">:</span>
<span class="k">assert</span> <span class="ow">not</span> <span class="n">inputs_need_grad</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">pass</span>
<span class="c1"># this is not True, as some module might not contains a loss function</span>
<span class="c1"># that consumes the labels</span>
<span class="c1"># assert label_shapes is not None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span> <span class="o">=</span> <span class="n">_parse_data_desc</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data_names</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">,</span> <span class="n">data_shapes</span><span class="p">,</span> <span class="n">label_shapes</span><span class="p">)</span>
<span class="k">if</span> <span class="n">shared_module</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">shared_module</span><span class="p">,</span> <span class="n">Module</span><span class="p">)</span> <span class="ow">and</span> \
<span class="n">shared_module</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="n">shared_module</span><span class="o">.</span><span class="n">params_initialized</span>
<span class="n">shared_group</span> <span class="o">=</span> <span class="n">shared_module</span><span class="o">.</span><span class="n">_exec_group</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">shared_group</span><span class="o">.</span><span class="n">execs</span><span class="p">)</span> <span class="o">>=</span> <span class="nb">len</span><span class="p">(</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="n">shared_group</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span> <span class="o">=</span> <span class="n">DataParallelExecutorGroup</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_symbol</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_context</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_work_load_list</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param_names</span><span class="p">,</span>
<span class="n">for_training</span><span class="p">,</span> <span class="n">inputs_need_grad</span><span class="p">,</span>
<span class="n">shared_group</span><span class="p">,</span> <span class="n">logger</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="p">,</span>
<span class="n">fixed_param_names</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_fixed_param_names</span><span class="p">,</span>
<span class="n">grad_req</span><span class="o">=</span><span class="n">grad_req</span><span class="p">,</span>
<span class="n">state_names</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_state_names</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_total_exec_bytes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">_total_exec_bytes</span>
<span class="k">if</span> <span class="n">shared_module</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">params_initialized</span> <span class="o">=</span> <span class="kc">True</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_arg_params</span> <span class="o">=</span> <span class="n">shared_module</span><span class="o">.</span><span class="n">_arg_params</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_aux_params</span> <span class="o">=</span> <span class="n">shared_module</span><span class="o">.</span><span class="n">_aux_params</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span><span class="p">:</span>
<span class="c1"># if the parameters are already initialized, we are re-binding</span>
<span class="c1"># so automatically copy the already initialized params</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_arg_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_params</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_arg_params</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_params</span> <span class="ow">is</span> <span class="kc">None</span>
<span class="n">param_arrays</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">stype</span><span class="o">=</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">stype</span><span class="p">)</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">param_arrays</span>
<span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_arg_params</span> <span class="o">=</span> <span class="p">{</span><span class="n">name</span><span class="p">:</span><span class="n">arr</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">arr</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_param_names</span><span class="p">,</span> <span class="n">param_arrays</span><span class="p">)}</span>
<span class="n">aux_arrays</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">zeros</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">aux_arrays</span>
<span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_aux_params</span> <span class="o">=</span> <span class="p">{</span><span class="n">name</span><span class="p">:</span><span class="n">arr</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">arr</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_aux_names</span><span class="p">,</span> <span class="n">aux_arrays</span><span class="p">)}</span>
<span class="k">if</span> <span class="n">shared_module</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">shared_module</span><span class="o">.</span><span class="n">optimizer_initialized</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">borrow_optimizer</span><span class="p">(</span><span class="n">shared_module</span><span class="p">)</span></div>
<div class="viewcode-block" id="Module.reshape"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.reshape">[docs]</a> <span class="k">def</span> <span class="nf">reshape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_shapes</span><span class="p">,</span> <span class="n">label_shapes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""Reshapes the module for new input shapes.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data_shapes : list of (str, tuple)</span>
<span class="sd"> Typically is ``data_iter.provide_data``.</span>
<span class="sd"> label_shapes : list of (str, tuple)</span>
<span class="sd"> Typically is ``data_iter.provide_label``.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span> <span class="o">=</span> <span class="n">_parse_data_desc</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data_names</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">,</span> <span class="n">data_shapes</span><span class="p">,</span> <span class="n">label_shapes</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span><span class="p">)</span></div>
<div class="viewcode-block" id="Module.init_optimizer"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.init_optimizer">[docs]</a> <span class="k">def</span> <span class="nf">init_optimizer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kvstore</span><span class="o">=</span><span class="s1">'local'</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">'sgd'</span><span class="p">,</span>
<span class="n">optimizer_params</span><span class="o">=</span><span class="p">((</span><span class="s1">'learning_rate'</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">),),</span> <span class="n">force_init</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">"""Installs and initializes optimizers.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> kvstore : str or KVStore</span>
<span class="sd"> Default `'local'`.</span>
<span class="sd"> optimizer : str or Optimizer</span>
<span class="sd"> Default `'sgd'`</span>
<span class="sd"> optimizer_params : dict</span>
<span class="sd"> Default `(('learning_rate', 0.01),)`. The default value is not a dictionary,</span>
<span class="sd"> just to avoid pylint warning of dangerous default values.</span>
<span class="sd"> force_init : bool</span>
<span class="sd"> Default ``False``, indicating whether we should force re-initializing the</span>
<span class="sd"> optimizer in the case an optimizer is already installed.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer_initialized</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">force_init</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">warning</span><span class="p">(</span><span class="s1">'optimizer already initialized, ignoring...'</span><span class="p">)</span>
<span class="k">return</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_params_dirty</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_sync_params_from_devices</span><span class="p">()</span>
<span class="p">(</span><span class="n">kvstore</span><span class="p">,</span> <span class="n">update_on_kvstore</span><span class="p">)</span> <span class="o">=</span> \
<span class="n">_create_kvstore</span><span class="p">(</span><span class="n">kvstore</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_context</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_arg_params</span><span class="p">)</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">batch_size</span>
<span class="k">if</span> <span class="n">kvstore</span> <span class="ow">and</span> <span class="s1">'dist'</span> <span class="ow">in</span> <span class="n">kvstore</span><span class="o">.</span><span class="n">type</span> <span class="ow">and</span> <span class="s1">'_sync'</span> <span class="ow">in</span> <span class="n">kvstore</span><span class="o">.</span><span class="n">type</span><span class="p">:</span>
<span class="n">batch_size</span> <span class="o">*=</span> <span class="n">kvstore</span><span class="o">.</span><span class="n">num_workers</span>
<span class="n">rescale_grad</span> <span class="o">=</span> <span class="mf">1.0</span><span class="o">/</span><span class="n">batch_size</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">idx2name</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">if</span> <span class="n">update_on_kvstore</span><span class="p">:</span>
<span class="n">idx2name</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">param_names</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_context</span><span class="p">)):</span>
<span class="n">idx2name</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">i</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_context</span><span class="p">)</span><span class="o">+</span><span class="n">k</span><span class="p">:</span> <span class="n">n</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">param_names</span><span class="p">)})</span>
<span class="n">optimizer_params</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">optimizer_params</span><span class="p">)</span>
<span class="k">if</span> <span class="s1">'rescale_grad'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">optimizer_params</span><span class="p">:</span>
<span class="n">optimizer_params</span><span class="p">[</span><span class="s1">'rescale_grad'</span><span class="p">]</span> <span class="o">=</span> <span class="n">rescale_grad</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span>
<span class="n">sym</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">symbol</span><span class="p">,</span> <span class="n">param_idx2name</span><span class="o">=</span><span class="n">idx2name</span><span class="p">,</span>
<span class="o">**</span><span class="n">optimizer_params</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">opt</span><span class="o">.</span><span class="n">Optimizer</span><span class="p">)</span>
<span class="k">if</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">rescale_grad</span> <span class="o">!=</span> <span class="n">rescale_grad</span><span class="p">:</span>
<span class="c1">#pylint: disable=no-member</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
<span class="s2">"Optimizer created manually outside Module but rescale_grad "</span> <span class="o">+</span>
<span class="s2">"is not normalized to 1.0/batch_size/num_workers (</span><span class="si">%s</span><span class="s2"> vs. </span><span class="si">%s</span><span class="s2">). "</span><span class="o">%</span><span class="p">(</span>
<span class="n">optimizer</span><span class="o">.</span><span class="n">rescale_grad</span><span class="p">,</span> <span class="n">rescale_grad</span><span class="p">)</span> <span class="o">+</span>
<span class="s2">"Is this intended?"</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span> <span class="o">=</span> <span class="n">optimizer</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span> <span class="o">=</span> <span class="n">kvstore</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span> <span class="o">=</span> <span class="n">update_on_kvstore</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_updater</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">kvstore</span><span class="p">:</span>
<span class="c1"># copy initialized local parameters to kvstore</span>
<span class="n">_initialize_kvstore</span><span class="p">(</span><span class="n">kvstore</span><span class="o">=</span><span class="n">kvstore</span><span class="p">,</span>
<span class="n">param_arrays</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">param_arrays</span><span class="p">,</span>
<span class="n">arg_params</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_arg_params</span><span class="p">,</span>
<span class="n">param_names</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_param_names</span><span class="p">,</span>
<span class="n">update_on_kvstore</span><span class="o">=</span><span class="n">update_on_kvstore</span><span class="p">)</span>
<span class="k">if</span> <span class="n">update_on_kvstore</span><span class="p">:</span>
<span class="n">kvstore</span><span class="o">.</span><span class="n">set_optimizer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</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">_updater</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">get_updater</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optimizer_initialized</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_preload_opt_states</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">load_optimizer_states</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_preload_opt_states</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_preload_opt_states</span> <span class="o">=</span> <span class="kc">None</span></div>
<div class="viewcode-block" id="Module.borrow_optimizer"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.borrow_optimizer">[docs]</a> <span class="k">def</span> <span class="nf">borrow_optimizer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shared_module</span><span class="p">):</span>
<span class="sd">"""Borrows optimizer from a shared module. Used in bucketing, where exactly the same</span>
<span class="sd"> optimizer (esp. kvstore) is used.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> shared_module : Module</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">shared_module</span><span class="o">.</span><span class="n">optimizer_initialized</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_optimizer</span> <span class="o">=</span> <span class="n">shared_module</span><span class="o">.</span><span class="n">_optimizer</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span> <span class="o">=</span> <span class="n">shared_module</span><span class="o">.</span><span class="n">_kvstore</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span> <span class="o">=</span> <span class="n">shared_module</span><span class="o">.</span><span class="n">_update_on_kvstore</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_updater</span> <span class="o">=</span> <span class="n">shared_module</span><span class="o">.</span><span class="n">_updater</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optimizer_initialized</span> <span class="o">=</span> <span class="kc">True</span></div>
<div class="viewcode-block" id="Module.forward"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_batch</span><span class="p">,</span> <span class="n">is_train</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""Forward computation. It supports data batches with different shapes, such as</span>
<span class="sd"> different batch sizes or different image sizes.</span>
<span class="sd"> If reshaping of data batch relates to modification of symbol or module, such as</span>
<span class="sd"> changing image layout ordering or switching from training to predicting, module</span>
<span class="sd"> rebinding is required.</span>
<span class="sd"> See Also</span>
<span class="sd"> ----------</span>
<span class="sd"> :meth:`BaseModule.forward`.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data_batch : DataBatch</span>
<span class="sd"> Could be anything with similar API implemented.</span>
<span class="sd"> is_train : bool</span>
<span class="sd"> Default is ``None``, which means ``is_train`` takes the value of ``self.for_training``.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
<span class="n">curr_data_shapes</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span><span class="p">)</span>
<span class="n">new_data_shapes</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
<span class="k">if</span> <span class="n">curr_data_shapes</span> <span class="o">!=</span> <span class="n">new_data_shapes</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">data_batch</span><span class="p">,</span> <span class="s2">"provide_data"</span><span class="p">)</span> <span class="ow">and</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">provide_data</span><span class="p">:</span>
<span class="n">new_dshape</span> <span class="o">=</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">provide_data</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">new_dshape</span> <span class="o">=</span> <span class="p">[</span><span class="n">DataDesc</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">i</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">i</span><span class="o">.</span><span class="n">layout</span><span class="p">)</span> \
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">shape</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span><span class="p">,</span> <span class="n">new_data_shapes</span><span class="p">)]</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">data_batch</span><span class="p">,</span> <span class="s2">"provide_label"</span><span class="p">)</span> <span class="ow">and</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">provide_label</span><span class="p">:</span>
<span class="n">new_lshape</span> <span class="o">=</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">provide_label</span>
<span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">data_batch</span><span class="p">,</span> <span class="s2">"label"</span><span class="p">)</span> <span class="ow">and</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">label</span><span class="p">:</span>
<span class="n">new_lshape</span> <span class="o">=</span> <span class="p">[</span><span class="n">DataDesc</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">j</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">i</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">i</span><span class="o">.</span><span class="n">layout</span><span class="p">)</span> \
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span><span class="p">,</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">label</span><span class="p">)]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">new_lshape</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">new_dshape</span><span class="p">,</span> <span class="n">new_lshape</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">data_batch</span><span class="p">,</span> <span class="n">is_train</span><span class="p">)</span></div>
<div class="viewcode-block" id="Module.backward"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.backward">[docs]</a> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out_grads</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""Backward computation.</span>
<span class="sd"> See Also</span>
<span class="sd"> ----------</span>
<span class="sd"> :meth:`BaseModule.backward`.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> out_grads : NDArray or list of NDArray, optional</span>
<span class="sd"> Gradient on the outputs to be propagated back.</span>
<span class="sd"> This parameter is only needed when bind is called</span>
<span class="sd"> on outputs that are not a loss function.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">out_grads</span><span class="o">=</span><span class="n">out_grads</span><span class="p">)</span></div>
<div class="viewcode-block" id="Module.update"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Updates parameters according to the installed optimizer and the gradients computed</span>
<span class="sd"> in the previous forward-backward batch.</span>
<span class="sd"> See Also</span>
<span class="sd"> ----------</span>
<span class="sd"> :meth:`BaseModule.update`.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer_initialized</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_params_dirty</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span><span class="p">:</span>
<span class="n">_update_params_on_kvstore</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">param_arrays</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">param_names</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">_update_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">param_arrays</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">grad_arrays</span><span class="p">,</span>
<span class="n">updater</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_updater</span><span class="p">,</span>
<span class="n">num_device</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_context</span><span class="p">),</span>
<span class="n">kvstore</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="p">,</span>
<span class="n">param_names</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">param_names</span><span class="p">)</span></div>
<div class="viewcode-block" id="Module.get_outputs"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.get_outputs">[docs]</a> <span class="k">def</span> <span class="nf">get_outputs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">merge_multi_context</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">"""Gets outputs of the previous forward computation.</span>
<span class="sd"> If ``merge_multi_context`` is ``True``, it is like ``[out1, out2]``. Otherwise, it</span>
<span class="sd"> is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output</span>
<span class="sd"> elements are `NDArray`. When `merge_multi_context` is `False`, those `NDArray`</span>
<span class="sd"> might live on different devices.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> merge_multi_context : bool</span>
<span class="sd"> Default is ``True``. In the case when data-parallelism is used, the outputs</span>
<span class="sd"> will be collected from multiple devices. A ``True`` value indicate that we</span>
<span class="sd"> should merge the collected results so that they look like from a single</span>
<span class="sd"> executor.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> list of NDArray or list of list of NDArray</span>
<span class="sd"> Output.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">(</span><span class="n">merge_multi_context</span><span class="o">=</span><span class="n">merge_multi_context</span><span class="p">)</span></div>
<div class="viewcode-block" id="Module.get_input_grads"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.get_input_grads">[docs]</a> <span class="k">def</span> <span class="nf">get_input_grads</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">merge_multi_context</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">"""Gets the gradients with respect to the inputs of the module.</span>
<span class="sd"> If ``merge_multi_context`` is ``True``, it is like ``[grad1, grad2]``. Otherwise, it</span>
<span class="sd"> is like ``[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]``. All the output</span>
<span class="sd"> elements are `NDArray`.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> merge_multi_context : bool</span>
<span class="sd"> Default is ``True``. In the case when data-parallelism is used, the outputs</span>
<span class="sd"> will be collected from multiple devices. A ``True`` value indicate that we</span>
<span class="sd"> should merge the collected results so that they look like from a single</span>
<span class="sd"> executor.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> list of NDArray or list of list of NDArray</span>
<span class="sd"> Input gradients</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">inputs_need_grad</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">get_input_grads</span><span class="p">(</span><span class="n">merge_multi_context</span><span class="o">=</span><span class="n">merge_multi_context</span><span class="p">)</span></div>
<div class="viewcode-block" id="Module.get_states"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.get_states">[docs]</a> <span class="k">def</span> <span class="nf">get_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">merge_multi_context</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="sd">"""Gets states from all devices.</span>
<span class="sd"> If `merge_multi_context` is ``True``, it is like ``[out1, out2]``. Otherwise, it</span>
<span class="sd"> is like ``[[out1_dev1, out1_dev2], [out2_dev1, out2_dev2]]``. All the output</span>
<span class="sd"> elements are `NDArray`.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> merge_multi_context : bool</span>
<span class="sd"> Default is ``True``. In the case when data-parallelism is used, the states</span>
<span class="sd"> will be collected from multiple devices. A ``True`` value indicate that we</span>
<span class="sd"> should merge the collected results so that they look like from a single</span>
<span class="sd"> executor.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> list of NDArray or list of list of NDArray</span>
<span class="sd"> States</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">get_states</span><span class="p">(</span><span class="n">merge_multi_context</span><span class="o">=</span><span class="n">merge_multi_context</span><span class="p">)</span></div>
<div class="viewcode-block" id="Module.set_states"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.set_states">[docs]</a> <span class="k">def</span> <span class="nf">set_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">states</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""Sets value for states. Only one of the states &amp; value can be specified.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> states : list of list of NDArrays</span>
<span class="sd"> source states arrays formatted like ``[[state1_dev1, state1_dev2],</span>
<span class="sd"> [state2_dev1, state2_dev2]]``.</span>
<span class="sd"> value : number</span>
<span class="sd"> a single scalar value for all state arrays.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">set_states</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="Module.update_metric"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.update_metric">[docs]</a> <span class="k">def</span> <span class="nf">update_metric</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eval_metric</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="sd">"""Evaluates and accumulates evaluation metric on outputs of the last forward computation.</span>
<span class="sd"> See Also</span>
<span class="sd"> ----------</span>
<span class="sd"> :meth:`BaseModule.update_metric`.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> eval_metric : EvalMetric</span>
<span class="sd"> labels : list of NDArray</span>
<span class="sd"> Typically ``data_batch.label``.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">update_metric</span><span class="p">(</span><span class="n">eval_metric</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_sync_params_from_devices</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""Synchronizes parameters from devices to CPU. This function should be called after</span>
<span class="sd"> calling `update` that updates the parameters on the devices, before one can read the</span>
<span class="sd"> latest parameters from ``self._arg_params`` and ``self._aux_params``.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">get_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_arg_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_aux_params</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_params_dirty</span> <span class="o">=</span> <span class="kc">False</span>
<div class="viewcode-block" id="Module.save_optimizer_states"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.save_optimizer_states">[docs]</a> <span class="k">def</span> <span class="nf">save_optimizer_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fname</span><span class="p">):</span>
<span class="sd">"""Saves optimizer (updater) state to a file.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> fname : str</span>
<span class="sd"> Path to output states file.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer_initialized</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="o">.</span><span class="n">save_optimizer_states</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span>
<span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_updater</span><span class="o">.</span><span class="n">get_states</span><span class="p">())</span></div>
<div class="viewcode-block" id="Module.load_optimizer_states"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.load_optimizer_states">[docs]</a> <span class="k">def</span> <span class="nf">load_optimizer_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fname</span><span class="p">):</span>
<span class="sd">"""Loads optimizer (updater) state from a file.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> fname : str</span>
<span class="sd"> Path to input states file.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer_initialized</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_update_on_kvstore</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_kvstore</span><span class="o">.</span><span class="n">load_optimizer_states</span><span class="p">(</span><span class="n">fname</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">_updater</span><span class="o">.</span><span class="n">set_states</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">())</span></div>
<div class="viewcode-block" id="Module.install_monitor"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.Module.install_monitor">[docs]</a> <span class="k">def</span> <span class="nf">install_monitor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mon</span><span class="p">):</span>
<span class="sd">"""Installs monitor on all executors. """</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_exec_group</span><span class="o">.</span><span class="n">install_monitor</span><span class="p">(</span><span class="n">mon</span><span class="p">)</span></div></div>
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