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| <h1>Source code for mxnet.module.sequential_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-arguments, too-many-locals, too-many-instance-attributes</span> |
| <span class="sd">"""`SequentialModule` is a container module that chains a number of modules together."""</span> |
| |
| <span class="kn">import</span> <span class="nn">logging</span> |
| <span class="kn">import</span> <span class="nn">copy</span> |
| |
| <span class="kn">from</span> <span class="nn">..initializer</span> <span class="kn">import</span> <span class="n">Uniform</span> |
| |
| <span class="kn">from</span> <span class="nn">.base_module</span> <span class="kn">import</span> <span class="n">BaseModule</span> |
| |
| <div class="viewcode-block" id="SequentialModule"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.SequentialModule">[docs]</a><span class="k">class</span> <span class="nc">SequentialModule</span><span class="p">(</span><span class="n">BaseModule</span><span class="p">):</span> |
| <span class="sd">"""A SequentialModule is a container module that can chain multiple modules together.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> Building a computation graph with this kind of imperative container is less</span> |
| <span class="sd"> flexible and less efficient than the symbolic graph. So, this should be only used as a</span> |
| <span class="sd"> handy utility.</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">META_TAKE_LABELS</span> <span class="o">=</span> <span class="s1">'take_labels'</span> |
| <span class="n">META_AUTO_WIRING</span> <span class="o">=</span> <span class="s1">'auto_wiring'</span> |
| |
| <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">logger</span><span class="o">=</span><span class="n">logging</span><span class="p">):</span> |
| <span class="nb">super</span><span class="p">(</span><span class="n">SequentialModule</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="bp">self</span><span class="o">.</span><span class="n">_modules</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_metas</span> <span class="o">=</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="bp">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_data_shapes</span> <span class="o">=</span> <span class="bp">None</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_meta_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([</span><span class="nb">getattr</span><span class="p">(</span><span class="n">SequentialModule</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">SequentialModule</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'META_'</span><span class="p">)])</span> |
| |
| <div class="viewcode-block" id="SequentialModule.add"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.SequentialModule.add">[docs]</a> <span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">module</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="sd">"""Adds a module to the chain.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> module : BaseModule</span> |
| <span class="sd"> The new module to add.</span> |
| <span class="sd"> kwargs : **keywords</span> |
| <span class="sd"> All the keyword arguments are saved as meta information</span> |
| <span class="sd"> for the added module. The currently known meta includes</span> |
| |
| <span class="sd"> - `take_labels`: indicating whether the module expect to</span> |
| <span class="sd"> take labels when doing computation. Note any module in</span> |
| <span class="sd"> the chain can take labels (not necessarily only the top</span> |
| <span class="sd"> most one), and they all take the same labels passed</span> |
| <span class="sd"> from the original data batch for the `SequentialModule`.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> self</span> |
| <span class="sd"> This function returns `self` to allow us to easily chain a</span> |
| <span class="sd"> series of `add` calls.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> # An example of addinging two modules to a chain.</span> |
| <span class="sd"> >>> seq_mod = mx.mod.SequentialModule()</span> |
| <span class="sd"> >>> seq_mod.add(mod1)</span> |
| <span class="sd"> >>> seq_mod.add(mod2)</span> |
| <span class="sd"> """</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">module</span><span class="p">)</span> |
| |
| <span class="c1"># a sanity check to avoid typo</span> |
| <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_meta_keys</span><span class="p">,</span> <span class="p">(</span><span class="s1">'Unknown meta "</span><span class="si">%s</span><span class="s1">", a typo?'</span> <span class="o">%</span> <span class="n">key</span><span class="p">)</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">_metas</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> |
| |
| <span class="c1"># after adding new modules, we are reset back to raw states, needs</span> |
| <span class="c1"># to bind, init_params, etc.</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="o">=</span> <span class="bp">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">params_initialized</span> <span class="o">=</span> <span class="bp">False</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">optimizer_initialized</span> <span class="o">=</span> <span class="bp">False</span> |
| |
| <span class="k">return</span> <span class="bp">self</span> <span class="c1"># for easier chaining</span></div> |
| |
| <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">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data_names</span> |
| <span class="k">return</span> <span class="p">[]</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">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">output_names</span> |
| <span class="k">return</span> <span class="p">[]</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"> list</span> |
| <span class="sd"> A list of `(name, shape)` pairs. The data shapes of the first module</span> |
| <span class="sd"> is the data shape of a `SequentialModule`.</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">_modules</span><span class="p">[</span><span class="mi">0</span><span class="p">]</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"> list</span> |
| <span class="sd"> A list of `(name, shape)` pairs. 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"> list</span> |
| <span class="sd"> A list of `(name, shape)` pairs. The output shapes of the last</span> |
| <span class="sd"> module is the output shape of a `SequentialModule`.</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">_modules</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">output_shapes</span> |
| |
| <div class="viewcode-block" id="SequentialModule.get_params"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.SequentialModule.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. This</span> |
| <span class="sd"> is a merged dictionary of all the parameters in the modules.</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">arg_params</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span> |
| <span class="n">aux_params</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span> |
| |
| <span class="k">for</span> <span class="n">module</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">:</span> |
| <span class="n">arg</span><span class="p">,</span> <span class="n">aux</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span> |
| <span class="n">arg_params</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">arg</span><span class="p">)</span> |
| <span class="n">aux_params</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">aux</span><span class="p">)</span> |
| |
| <span class="k">return</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></div> |
| |
| <div class="viewcode-block" id="SequentialModule.init_params"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.SequentialModule.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="bp">None</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> |
| <span class="n">allow_missing</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">force_init</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">allow_extra</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span> |
| <span class="sd">"""Initializes parameters.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> initializer : Initializer</span> |
| <span class="sd"> arg_params : dict</span> |
| <span class="sd"> Default ``None``. Existing parameters. This has higher priority</span> |
| <span class="sd"> than `initializer`.</span> |
| <span class="sd"> aux_params : dict</span> |
| <span class="sd"> Default ``None``. Existing auxiliary states. This has higher priority</span> |
| <span class="sd"> than `initializer`.</span> |
| <span class="sd"> allow_missing : bool</span> |
| <span class="sd"> Allow missing values in `arg_params` and `aux_params` (if not ``None``).</span> |
| <span class="sd"> In this case, missing values will be filled with `initializer`.</span> |
| <span class="sd"> force_init : bool</span> |
| <span class="sd"> Default ``False``.</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="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">for</span> <span class="n">module</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">:</span> |
| <span class="n">module</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="n">initializer</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="c1"># make sure we do not have duplicated parameter names</span> |
| <span class="k">def</span> <span class="nf">_check_name</span><span class="p">(</span><span class="n">known_names</span><span class="p">,</span> <span class="n">new_names</span><span class="p">,</span> <span class="n">modules</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span> |
| <span class="sd">"""Internal function to help checking duplicated names."""</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">new_names</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">known_names</span><span class="p">,</span> <span class="s2">"Duplicated parameter names: "</span> <span class="o">+</span> \ |
| <span class="p">(</span><span class="s1">'name "</span><span class="si">%s</span><span class="s1">" in layer </span><span class="si">%d</span><span class="s1"> (</span><span class="si">%s</span><span class="s1">) is already '</span> <span class="o">%</span> <span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">modules</span><span class="p">[</span><span class="n">i</span><span class="p">])))</span> <span class="o">+</span> \ |
| <span class="p">(</span><span class="s1">'used in layer </span><span class="si">%d</span><span class="s1"> (</span><span class="si">%s</span><span class="s1">).'</span> <span class="o">%</span> <span class="p">(</span><span class="n">known_names</span><span class="p">[</span><span class="n">name</span><span class="p">],</span> |
| <span class="nb">type</span><span class="p">(</span><span class="n">modules</span><span class="p">[</span><span class="n">known_names</span><span class="p">[</span><span class="n">name</span><span class="p">]])))</span> |
| <span class="n">known_names</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span> |
| |
| <span class="n">arg_names</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span> |
| <span class="n">aux_names</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span> |
| <span class="k">for</span> <span class="n">i_layer</span><span class="p">,</span> <span class="n">module</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">_modules</span><span class="p">):</span> |
| <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span> |
| <span class="n">_check_name</span><span class="p">(</span><span class="n">arg_names</span><span class="p">,</span> <span class="n">arg_params</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">,</span> <span class="n">i_layer</span><span class="p">)</span> |
| <span class="n">_check_name</span><span class="p">(</span><span class="n">aux_names</span><span class="p">,</span> <span class="n">aux_params</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">,</span> <span class="n">i_layer</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="bp">True</span></div> |
| |
| <div class="viewcode-block" id="SequentialModule.bind"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.SequentialModule.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="bp">None</span><span class="p">,</span> <span class="n">for_training</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> |
| <span class="n">inputs_need_grad</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">force_rebind</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">shared_module</span><span class="o">=</span><span class="bp">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 bind 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``. Currently shared module is not supported for `SequentialModule`.</span> |
| <span class="sd"> grad_req : str, list of str, dict of str to str</span> |
| <span class="sd"> Requirement for gradient accumulation. Can be 'write', 'add', or 'null'</span> |
| <span class="sd"> (default to 'write').</span> |
| <span class="sd"> Can be specified globally (str) or for each argument (list, dict).</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">force_rebind</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="k">if</span> <span class="n">inputs_need_grad</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">for_training</span> <span class="ow">is</span> <span class="bp">True</span> |
| <span class="k">assert</span> <span class="n">shared_module</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">,</span> <span class="s1">'Shared module is not supported'</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">,</span> <span class="s1">'Attempting to bind an empty SequentialModule'</span> |
| |
| <span class="bp">self</span><span class="o">.</span><span class="n">binded</span> <span class="o">=</span> <span class="bp">True</span> |
| |
| <span class="c1"># the same label shapes are used for all chained modules</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span> <span class="o">=</span> <span class="n">label_shapes</span> |
| |
| <span class="n">my_data_shapes</span> <span class="o">=</span> <span class="n">data_shapes</span> |
| <span class="n">anybody_ever_needs_label</span> <span class="o">=</span> <span class="bp">False</span> |
| <span class="k">for</span> <span class="n">i_layer</span><span class="p">,</span> <span class="n">module</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">_modules</span><span class="p">):</span> |
| <span class="n">meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_metas</span><span class="p">[</span><span class="n">i_layer</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">SequentialModule</span><span class="o">.</span><span class="n">META_TAKE_LABELS</span> <span class="ow">in</span> <span class="n">meta</span> <span class="ow">and</span> \ |
| <span class="n">meta</span><span class="p">[</span><span class="n">SequentialModule</span><span class="o">.</span><span class="n">META_TAKE_LABELS</span><span class="p">]:</span> |
| <span class="n">my_label_shapes</span> <span class="o">=</span> <span class="n">label_shapes</span> |
| <span class="n">anybody_ever_needs_label</span> <span class="o">=</span> <span class="bp">True</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">my_label_shapes</span> <span class="o">=</span> <span class="bp">None</span> |
| |
| <span class="n">my_inputs_need_grad</span> <span class="o">=</span> <span class="nb">bool</span><span class="p">(</span><span class="n">inputs_need_grad</span> <span class="ow">or</span> |
| <span class="p">(</span><span class="n">for_training</span> <span class="ow">and</span> <span class="n">i_layer</span> <span class="o">></span> <span class="mi">0</span><span class="p">))</span> |
| |
| <span class="k">if</span> <span class="n">meta</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">SequentialModule</span><span class="o">.</span><span class="n">META_AUTO_WIRING</span><span class="p">,</span> <span class="bp">False</span><span class="p">):</span> |
| <span class="n">data_names</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">data_names</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">data_names</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">my_data_shapes</span><span class="p">)</span> |
| <span class="n">my_data_shapes</span> <span class="o">=</span> <span class="p">[(</span><span class="n">new_name</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">new_name</span><span class="p">,</span> <span class="p">(</span><span class="n">_</span><span class="p">,</span> <span class="n">shape</span><span class="p">))</span> |
| <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">data_names</span><span class="p">,</span> <span class="n">my_data_shapes</span><span class="p">)]</span> |
| |
| <span class="n">module</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">data_shapes</span><span class="o">=</span><span class="n">my_data_shapes</span><span class="p">,</span> <span class="n">label_shapes</span><span class="o">=</span><span class="n">my_label_shapes</span><span class="p">,</span> |
| <span class="n">for_training</span><span class="o">=</span><span class="n">for_training</span><span class="p">,</span> <span class="n">inputs_need_grad</span><span class="o">=</span><span class="n">my_inputs_need_grad</span><span class="p">,</span> |
| <span class="n">force_rebind</span><span class="o">=</span><span class="n">force_rebind</span><span class="p">,</span> <span class="n">shared_module</span><span class="o">=</span><span class="bp">None</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="c1"># the output of the previous module is the data of the next module</span> |
| <span class="n">my_data_shapes</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">output_shapes</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">anybody_ever_needs_label</span><span class="p">:</span> |
| <span class="c1"># then I do not need label either</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_label_shapes</span> <span class="o">=</span> <span class="bp">None</span></div> |
| |
| <div class="viewcode-block" id="SequentialModule.init_optimizer"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.SequentialModule.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="bp">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">for</span> <span class="n">module</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">:</span> |
| <span class="n">module</span><span class="o">.</span><span class="n">init_optimizer</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">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> |
| <span class="n">optimizer_params</span><span class="o">=</span><span class="n">optimizer_params</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="bp">self</span><span class="o">.</span><span class="n">optimizer_initialized</span> <span class="o">=</span> <span class="bp">True</span></div> |
| |
| <div class="viewcode-block" id="SequentialModule.forward"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.SequentialModule.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="bp">None</span><span class="p">):</span> |
| <span class="sd">"""Forward computation.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> data_batch : DataBatch</span> |
| <span class="sd"> is_train : bool</span> |
| <span class="sd"> Default is ``None``, in which case `is_train` is take as ``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="c1"># make a shallow copy, just to maintain necessary properties (if any) like</span> |
| <span class="c1"># bucket_key, pad, etc.</span> |
| <span class="n">data_batch</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">data_batch</span><span class="p">)</span> |
| |
| <span class="k">for</span> <span class="n">i_layer</span><span class="p">,</span> <span class="n">module</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">_modules</span><span class="p">):</span> |
| <span class="n">module</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="o">=</span><span class="n">is_train</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="n">i_layer</span><span class="o">+</span><span class="mi">1</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">_modules</span><span class="p">):</span> |
| <span class="c1"># the last layer, do not need to do the followings</span> |
| <span class="k">break</span> |
| |
| <span class="n">data_batch</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">get_outputs</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="s1">'provide_data'</span><span class="p">):</span> |
| <span class="c1"># need to update this, in case the internal module is using bucketing</span> |
| <span class="c1"># or whatever</span> |
| <span class="n">data_names</span> <span class="o">=</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="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">output_shapes</span><span class="p">]</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">data_names</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">data_batch</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> |
| <span class="n">data_batch</span><span class="o">.</span><span class="n">provide_data</span> <span class="o">=</span> <span class="p">[(</span><span class="n">name</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> |
| <span class="nb">zip</span><span class="p">(</span><span class="n">data_names</span><span class="p">,</span> <span class="n">data_batch</span><span class="o">.</span><span class="n">data</span><span class="p">)]</span></div> |
| |
| <div class="viewcode-block" id="SequentialModule.backward"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.SequentialModule.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="bp">None</span><span class="p">):</span> |
| <span class="sd">"""Backward computation."""</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">for</span> <span class="n">i_layer</span><span class="p">,</span> <span class="n">module</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</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">_modules</span><span class="p">)),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">))):</span> |
| <span class="n">module</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> |
| <span class="k">if</span> <span class="n">i_layer</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">break</span> |
| |
| <span class="n">out_grads</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="n">get_input_grads</span><span class="p">()</span></div> |
| |
| <div class="viewcode-block" id="SequentialModule.update"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.SequentialModule.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 installed optimizer and the gradient computed</span> |
| <span class="sd"> in the previous forward-backward cycle.</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="k">for</span> <span class="n">module</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">:</span> |
| <span class="n">module</span><span class="o">.</span><span class="n">update</span><span class="p">()</span></div> |
| |
| <div class="viewcode-block" id="SequentialModule.get_outputs"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.SequentialModule.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="bp">True</span><span class="p">):</span> |
| <span class="sd">"""Gets outputs from a previous forward computation.</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"> If `merge_multi_context` is ``True``, it is like ``[out1,</span> |
| <span class="sd"> out2]``. Otherwise, it is like ``[[out1_dev1, out1_dev2], [out2_dev1,</span> |
| <span class="sd"> out2_dev2]]``. All the output elements are numpy 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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</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="SequentialModule.get_input_grads"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.SequentialModule.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="bp">True</span><span class="p">):</span> |
| <span class="sd">"""Gets the gradients with respect to the inputs of the module.</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 NDArrays or list of list of NDArrays</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"> """</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">_modules</span><span class="p">[</span><span class="mi">0</span><span class="p">]</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="SequentialModule.update_metric"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.SequentialModule.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"> 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="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">for</span> <span class="n">meta</span><span class="p">,</span> <span class="n">module</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">_metas</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">SequentialModule</span><span class="o">.</span><span class="n">META_TAKE_LABELS</span> <span class="ow">in</span> <span class="n">meta</span> <span class="ow">and</span> \ |
| <span class="n">meta</span><span class="p">[</span><span class="n">SequentialModule</span><span class="o">.</span><span class="n">META_TAKE_LABELS</span><span class="p">]:</span> |
| <span class="n">module</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> |
| |
| <div class="viewcode-block" id="SequentialModule.install_monitor"><a class="viewcode-back" href="../../../api/python/module/module.html#mxnet.module.SequentialModule.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="k">for</span> <span class="n">module</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">:</span> |
| <span class="n">module</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> |
| </pre></div> |
| </div> |
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| Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. |
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